Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively.
Histogram Equalization for Improving Quality of Low-Resolution Ultrasonograph...TELKOMNIKA JOURNAL
A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety. The Abstract should be informative and completely self-explanatory, provide a clear statement of the problem, the proposed approach or solution, and point out major findings and conclusions. The Abstract should be 100 to 150 words in length. The abstract should be written in the past tense. Standard nomenclature should be used and abbreviations should be avoided. No literature should be cited. The keyword list provides the opportunity to add keywords, used by the indexing and abstracting services, in addition to those already present in the title. Judicious use of keywords may increase the ease with which interested parties can locate our article.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process In this approach, the process and knowledge of digital image processing to diagnose exudates
from images of retina is applied. An automated method to detect and localize the presence of exudates and Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils is proposed. First, the image is segmented using colour K-means Clustering algorithm. The segmented image along with Optic Disc (OD) is chosen. To Classify these segmented region, features based on colour and texture are extracted. The selected feature vector are then classified into exudates and nonexudates using a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,
which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using a clinical reference standard, images with exudates were detected with 96% success rate. This method appears promising as it can detect the very small areas of exudates.
MICCS: A Novel Framework for Medical Image Compression Using Compressive Sens...IJECEIAES
The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-ofInterest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.
Detection of Retinal pigmentosa in paediatric agejagan477830
In order to register the user who wants to use the programme, the project Detection of Retinal Pigmentosa in Paediatric Age Patients combines deep learning with MySQL.
1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
Histogram Equalization for Improving Quality of Low-Resolution Ultrasonograph...TELKOMNIKA JOURNAL
A well-prepared abstract enables the reader to identify the basic content of a document quickly and accurately, to determine its relevance to their interests, and thus to decide whether to read the document in its entirety. The Abstract should be informative and completely self-explanatory, provide a clear statement of the problem, the proposed approach or solution, and point out major findings and conclusions. The Abstract should be 100 to 150 words in length. The abstract should be written in the past tense. Standard nomenclature should be used and abbreviations should be avoided. No literature should be cited. The keyword list provides the opportunity to add keywords, used by the indexing and abstracting services, in addition to those already present in the title. Judicious use of keywords may increase the ease with which interested parties can locate our article.
Enhancement of Medical Images using Histogram Based Hybrid TechniqueINFOGAIN PUBLICATION
Digital Image Processing is very important area of research. A number of techniques are available for image enhancement of gray scale images as well as color images. They work very efficiently for enhancement of the gray scale as well as color images. Important techniques namely Histogram Equalization, BBHE, RSWHE, RSWHE (recursion=2, gamma=No), AGCWD (Recursion=0, gamma=0) have been used quite frequently for image enhancement. But there are some shortcomings of the present techniques. The major shortcoming is that while enhancement, the brightness of the image deteriorates quite a lot. So there was need for some technique for image enhancement so that while enhancement was done, the brightness of the images does not go down. To remove this shortcoming, a new hybrid technique namely RESWHE+AGCWD (recursion=2, gamma=0 or 1) was proposed. The results of the proposed technique were compared with the existing techniques. In the present methodology, the brightness did not decrease during image enhancement. So the results and the technique was validated and accepted. The parameters via PSNR, MSE, AMBE etc. are taken for performance evaluation and validation of the proposed technique against the existing techniques which results in better outperform.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
An Efficient Integrated Approach for the Detection of Exudates and Diabetic M...acijjournal
Diabetic Retinopathy (DR) is a major cause of blindness. Exudates are one of the primary signs of diabetic retinopathy which is a main cause of blindness that could be prevented with an early screening process In this approach, the process and knowledge of digital image processing to diagnose exudates
from images of retina is applied. An automated method to detect and localize the presence of exudates and Maculopathy from low-contrast digital images of Retinopathy patient’s with non-dilated pupils is proposed. First, the image is segmented using colour K-means Clustering algorithm. The segmented image along with Optic Disc (OD) is chosen. To Classify these segmented region, features based on colour and texture are extracted. The selected feature vector are then classified into exudates and nonexudates using a Support Vector Machine (SVM) Classifier. Also the detection of Diabetic Maculopathy,
which is the severe stage of Diabetic Retinopathy is performed using Morphological Operation. Using a clinical reference standard, images with exudates were detected with 96% success rate. This method appears promising as it can detect the very small areas of exudates.
MICCS: A Novel Framework for Medical Image Compression Using Compressive Sens...IJECEIAES
The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-ofInterest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio.
Detection of Retinal pigmentosa in paediatric agejagan477830
In order to register the user who wants to use the programme, the project Detection of Retinal Pigmentosa in Paediatric Age Patients combines deep learning with MySQL.
1) The document discusses various medical image fusion techniques including pixel level, feature level, and decision level fusion.
2) It proposes a novel pixel level fusion method called Iterative Block Level Principal Component Averaging fusion that divides images into blocks and calculates principal components for each block.
3) Experimental results on fusing noise free and noise filtered MR images show that the proposed method performs well in terms of average mutual information and structural similarity compared to other algorithms.
Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segm...IJECEIAES
This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.
Diabetic retinopathy is one of the leading complication of diabetes and also one of the leading preventable blindness. Early diagnosis and treatment may prevent such condition or in other words, annoyance of the disease may be overcome. The fundus images produced by automated fundus camera are often noisy making it difficult for doctors to precisely detect the abnormalities in fundus images. In the present paper, we propose to use vessel extraction of Retinal image enhancement and implemented in Raspberry Pi board using opencv library for faster execution and cost effective processing unit which helps during mass screening of diabetic retinopathy. The effectiveness of the proposed techniques is evaluated using different metrics and Micro-aneurysms. Finally, a considerable improvement in the enhancement of the Diabetic Retinopathy images is achieved.
The Forward-Backward Time-Stepping (FBTS) had proven its potential to reconstruct images of buried objects in inhomogeneous medium with useful quantitative information about its size, shape, and locality. The Total Variation regularization method was incorporated with the FBTS algorithm to deal with the ill-posedness or ill-conditionedness of the inverse problem. The effectiveness of the proposed technique is confirmed by numerical simulations. The numerical method was carried out on simple object detection through FBTS with and without TV regularization method. The detection and reconstruction of relative permittivity and conductivity of the simple object have shown an improvement as TV regularization method applied whereas it smoothed the vibrations of the images and gave a better estimation of the image’s boundaries.
Effective segmentation of sclera, iris and pupil in noisy eye imagesTELKOMNIKA JOURNAL
In today’s sensitive environment, for personal authentication, iris recognition is the most attentive
technique among the various biometric technologies. One of the key steps in the iris recognition system is
the accurate iris segmentation from its surrounding noises including pupil and sclera of a captured
eye-image. In our proposed method, initially input image is preprocessed by using bilateral filtering.
After the preprocessing of images contour based features such as, brightness, color and texture features
are extracted. Then entropy is measured based on the extracted contour based features to effectively
distinguishing the data in the images. Finally, the convolution neural network (CNN) is used for
the effective sclera, iris and pupil parts segmentations based on the entropy measure. The proposed
results are analyzed to demonstrate the better performance of the proposed segmentation method than
the existing methods.
IRJET- A Novel Algorithm for Detection of Papilledema in Luminosity and C...IRJET Journal
This document presents a novel algorithm for detecting papilledema in retinal images using luminosity and contrast enhancement. The algorithm first enhances poor quality retinal images by applying gamma correction to increase luminosity and contrast limited adaptive histogram equalization (CLAHE) to boost contrast. It then uses particle swarm optimization to locate the optic disc, which is the brightest region in the retina. By measuring the size of the located optic disc and comparing it to a threshold, the algorithm can detect if papilledema, which is swelling of the optic disc, is present. If the optic disc size is above the threshold, papilledema is detected.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image enhancement in palmprint recognition: a novel approach for improved bio...IJECEIAES
Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8 × 7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
Literature Review on Single Image Super Resolutionijtsrd
In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low-resolution (LR) images as input and maps them as output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification. Shalini Dubey | Prof. Pankaj Sahu | Prof. Surya Bazal"Literature Review on Single Image Super Resolution" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18339.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18339/literature-review-on-single-image-super-resolution/shalini-dubey
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Improving the iterative back projection estimation through Lorentzian sharp i...IJECEIAES
This document summarizes a study that proposed an enhancement technique for the iterative back projection (IBP) super resolution estimation method. The study aimed to improve the IBP method by using a Lorentzian error function with a sharp infinite symmetrical filter (SISEF) to provide edge enhancement. The IBP method suffers from jaggy and ringing artifacts due to the iterative reconstruction process and lack of edge guidance during back projection. The proposed method combines IBP with the Lorentzian SISEF filter to produce a higher resolution output image with finer edge details while increasing robustness to noise and reducing ringing artifacts. The SISEF filter provides precise edge information to guide the back projection process, and the Lorentzian error norm suppresses
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
This document presents a study that uses pre-trained convolutional neural networks (CNNs) as feature extractors for blur detection in digital breast tomosynthesis (DBT) images. Specifically, it examines ResNet18, ResNet50, AlexNet, VGG16 and InceptionV3 CNNs connected to a support vector machine (SVM) classifier to label DBT images as blurry or not blurry. The CNN-SVM combinations are evaluated based on accuracy, receiver-operating characteristic curves, area under the curve, and execution time. The results found that InceptionV3 achieved the best accuracy of 0.9961 and area under the curve of 0.9961, while AlexNet had the shortest processing time. The study aims to
Image Contrast Enhancement Approach using Differential Evolution and Particle...IRJET Journal
This document presents a method for enhancing the contrast of gray-scale images using differential evolution optimization. It proposes using a parameterized intensity transformation function to modify pixel gray levels, with the goal of maximizing image contrast. The differential evolution algorithm is used to optimize the parameters of the transformation function. Experimental results applying this method are compared to other contrast enhancement techniques like histogram equalization and particle swarm optimization. The document provides background on image enhancement techniques, a literature review of previous work applying evolutionary algorithms like particle swarm optimization to image enhancement, and details of the proposed differential evolution approach, including the transformation function and fitness function used to evaluate contrast.
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
A Novel Framework For Preprocessing Of Breast Ultra Sound Images By Combining...IRJET Journal
The document presents a novel framework for preprocessing breast ultrasound images that combines non-local means filtering and morphological operations. Non-local means filtering is used to reduce speckle noise, which is a significant issue for ultrasound images. Then morphological techniques are applied to enhance the noise-reduced images. The framework achieves peak signal-to-noise ratios of 60-80 decibels when tested on real breast ultrasound images. It provides an effective method for preprocessing ultrasound images to reduce noise and improve image quality.
An Automatic ROI of The Fundus Photography IJECEIAES
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Social media marketing (SMM) is a form of digital marketing that utilizes social media platforms to promote products, services, or brands. The goal of social media marketing is to connect with the target audience, build brand awareness, increase website traffic, and drive engagement and conversions. Here are some key aspects of social media marketing:
Strategy Development:
Identify your target audience: Understand the demographics, interests, and online behavior of your target audience.
Set clear goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your social media campaigns.
Choose the right platforms: Select social media platforms that align with your target audience and business objectives.
Content Creation:
Create engaging content: Develop content that resonates with your audience, such as images, videos, infographics, and text posts.
Maintain consistency: Establish a consistent posting schedule to keep your audience engaged and informed.
Use a variety of content types: Experiment with different content formats to keep your social media presence diverse and interesting.
Audience Engagement:
Respond to comments and messages: Engage with your audience by responding to comments, messages, and mentions in a timely manner.
Encourage user-generated content: Encourage your followers to create and share content related to your brand.
Run contests and giveaways: Organize contests or giveaways to boost engagement and attract new followers.
Paid Advertising:
Utilize paid social media advertising: Platforms like Facebook, Instagram, Twitter, and LinkedIn offer advertising options to reach a larger audience.
Targeted advertising: Use advanced targeting options to reach specific demographics, interests, and behaviors.
Analytics and Monitoring:
Use analytics tools: Monitor the performance of your social media campaigns using analytics tools provided by the platforms or third-party tools.
Adjust strategies based on data: Analyze the data and adjust your strategies to optimize performance and achieve better results.
Influencer Marketing:
Collaborate with influencers: Partner with influencers who align with your brand to reach a wider audience and build credibility.
Leverage user trust: Influencers can help establish trust with their followers, leading to increased brand credibility.
Social Media Trends:
Stay updated: Keep track of emerging trends in social media marketing and adapt your strategies accordingly.
Experiment with new features: Platforms regularly introduce new features; experiment with these features to stay ahead of the curve.
Remember that effective social media marketing requires a consistent and strategic approach. Regularly assess your performance, listen to your audience, and adjust your strategies to meet your goals.
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human ...CSCJournals
In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
This document discusses a face recognition system that aims to improve verification rates under varying lighting conditions. It proposes a framework that combines image normalization, feature extraction, and subspace representation. Each stage increases resistance to illumination variations. The framework achieves significant improvements over other methods, with a verification rate of 88.1% at a 0.1% false acceptance rate. Key components of the system include preprocessing techniques like integral normalized gradient images, feature extraction methods like local binary patterns and Gabor wavelets, and classification using score fusion based on log-likelihood ratios of classifier outputs.
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
More Related Content
Similar to Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images
Hybrid Multilevel Thresholding and Improved Harmony Search Algorithm for Segm...IJECEIAES
This paper proposes a new method for image segmentation is hybrid multilevel thresholding and improved harmony search algorithm. Improved harmony search algorithm which is a method for finding vector solutions by increasing its accuracy. The proposed method looks for a random candidate solution, then its quality is evaluated through the Otsu objective function. Furthermore, the operator continues to evolve the solution candidate circuit until the optimal solution is found. The dataset used in this study is the retina dataset, tongue, lenna, baboon, and cameraman. The experimental results show that this method produces the high performance as seen from peak signal-to-noise ratio analysis (PNSR). The PNSR result for retinal image averaged 40.342 dB while for the average tongue image 35.340 dB. For lenna, baboon and cameramen produce an average of 33.781 dB, 33.499 dB, and 34.869 dB. Furthermore, the process of object recognition and identification is expected to use this method to produce a high degree of accuracy.
Diabetic retinopathy is one of the leading complication of diabetes and also one of the leading preventable blindness. Early diagnosis and treatment may prevent such condition or in other words, annoyance of the disease may be overcome. The fundus images produced by automated fundus camera are often noisy making it difficult for doctors to precisely detect the abnormalities in fundus images. In the present paper, we propose to use vessel extraction of Retinal image enhancement and implemented in Raspberry Pi board using opencv library for faster execution and cost effective processing unit which helps during mass screening of diabetic retinopathy. The effectiveness of the proposed techniques is evaluated using different metrics and Micro-aneurysms. Finally, a considerable improvement in the enhancement of the Diabetic Retinopathy images is achieved.
The Forward-Backward Time-Stepping (FBTS) had proven its potential to reconstruct images of buried objects in inhomogeneous medium with useful quantitative information about its size, shape, and locality. The Total Variation regularization method was incorporated with the FBTS algorithm to deal with the ill-posedness or ill-conditionedness of the inverse problem. The effectiveness of the proposed technique is confirmed by numerical simulations. The numerical method was carried out on simple object detection through FBTS with and without TV regularization method. The detection and reconstruction of relative permittivity and conductivity of the simple object have shown an improvement as TV regularization method applied whereas it smoothed the vibrations of the images and gave a better estimation of the image’s boundaries.
Effective segmentation of sclera, iris and pupil in noisy eye imagesTELKOMNIKA JOURNAL
In today’s sensitive environment, for personal authentication, iris recognition is the most attentive
technique among the various biometric technologies. One of the key steps in the iris recognition system is
the accurate iris segmentation from its surrounding noises including pupil and sclera of a captured
eye-image. In our proposed method, initially input image is preprocessed by using bilateral filtering.
After the preprocessing of images contour based features such as, brightness, color and texture features
are extracted. Then entropy is measured based on the extracted contour based features to effectively
distinguishing the data in the images. Finally, the convolution neural network (CNN) is used for
the effective sclera, iris and pupil parts segmentations based on the entropy measure. The proposed
results are analyzed to demonstrate the better performance of the proposed segmentation method than
the existing methods.
IRJET- A Novel Algorithm for Detection of Papilledema in Luminosity and C...IRJET Journal
This document presents a novel algorithm for detecting papilledema in retinal images using luminosity and contrast enhancement. The algorithm first enhances poor quality retinal images by applying gamma correction to increase luminosity and contrast limited adaptive histogram equalization (CLAHE) to boost contrast. It then uses particle swarm optimization to locate the optic disc, which is the brightest region in the retina. By measuring the size of the located optic disc and comparing it to a threshold, the algorithm can detect if papilledema, which is swelling of the optic disc, is present. If the optic disc size is above the threshold, papilledema is detected.
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Journals
Abstract Diabetic retinopathy is the common cause of blindness. This paper presents the mathematical morphology method to detect and eliminate the optic disc (OD) and the blood vessels. Detection of optic disc and the blood vessels are the necessary steps in the detection of diabetic retinopathy because the blood vessels and the optic disc are the normal features of the retinal image. And also, the optic disc and the exudates are the brightest portion of the image. Detection of optic disc and the blood vessels can help the ophthalmologists to detect the diseases earlier and faster. Optic disc and the blood vessels are detected and eliminated by using mathematical morphology methods such as closing, filling, morphological reconstruction and Otsu algorithm. The objective of this paper is to detect the normal features of the image. By using the result, the ophthalmologists can detect the diseases easily. Keywords: Blood vessels, Diabetic retinopathy, mathematical morphology, Otsu algorithm, optic disc (OD)
Automatic detection of optic disc and blood vessels from retinal images using...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Image enhancement in palmprint recognition: a novel approach for improved bio...IJECEIAES
Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8 × 7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.
Literature Review on Single Image Super Resolutionijtsrd
In this paper, a detailed survey study on single image super-resolution (SR) has been presented, which aims at recovering a high-resolution (HR) image from a given low-resolution (LR) one. It is always the research emphasis because of the requirement of higher definition video displaying, such as the new generation of Ultra High Definition (UHD) TVs. Super-resolution (SR) is a popular topic of image processing that focuses on the enhancement of image resolution. In general, SR takes one or several low-resolution (LR) images as input and maps them as output images with high resolution (HR), which has been widely applied in remote sensing, medical imaging, biometric identification. Shalini Dubey | Prof. Pankaj Sahu | Prof. Surya Bazal"Literature Review on Single Image Super Resolution" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18339.pdf http://www.ijtsrd.com/engineering/electronics-and-communication-engineering/18339/literature-review-on-single-image-super-resolution/shalini-dubey
A deep learning approach based on stochastic gradient descent and least absol...nooriasukmaningtyas
More than eighty-five to ninety percentage of the diabetic patients are affected with diabetic retinopathy (DR) which is an eye disorder that leads to blindness. The computational techniques can support to detect the DR by using the retinal images. However, it is hard to measure the DR with the raw retinal image. This paper proposes an effective method for identification of DR from the retinal images. In this research work, initially the Weiner filter is used for preprocessing the raw retinal image. Then the preprocessed image is segmented using fuzzy c-mean technique. Then from the segmented image, the features are extracted using grey level co-occurrence matrix (GLCM). After extracting the fundus image, the feature selection is performed stochastic gradient descent, and least absolute shrinkage and selection operator (LASSO) for accurate identification during the classification process. Then the inception v3-convolutional neural network (IV3-CNN) model is used in the classification process to classify the image as DR image or non-DR image. By applying the proposed method, the classification performance of IV3-CNN model in identifying DR is studied. Using the proposed method, the DR is identified with the accuracy of about 95%, and the processed retinal image is identified as mild DR.
Improving the iterative back projection estimation through Lorentzian sharp i...IJECEIAES
This document summarizes a study that proposed an enhancement technique for the iterative back projection (IBP) super resolution estimation method. The study aimed to improve the IBP method by using a Lorentzian error function with a sharp infinite symmetrical filter (SISEF) to provide edge enhancement. The IBP method suffers from jaggy and ringing artifacts due to the iterative reconstruction process and lack of edge guidance during back projection. The proposed method combines IBP with the Lorentzian SISEF filter to produce a higher resolution output image with finer edge details while increasing robustness to noise and reducing ringing artifacts. The SISEF filter provides precise edge information to guide the back projection process, and the Lorentzian error norm suppresses
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
This document presents a study that uses pre-trained convolutional neural networks (CNNs) as feature extractors for blur detection in digital breast tomosynthesis (DBT) images. Specifically, it examines ResNet18, ResNet50, AlexNet, VGG16 and InceptionV3 CNNs connected to a support vector machine (SVM) classifier to label DBT images as blurry or not blurry. The CNN-SVM combinations are evaluated based on accuracy, receiver-operating characteristic curves, area under the curve, and execution time. The results found that InceptionV3 achieved the best accuracy of 0.9961 and area under the curve of 0.9961, while AlexNet had the shortest processing time. The study aims to
Image Contrast Enhancement Approach using Differential Evolution and Particle...IRJET Journal
This document presents a method for enhancing the contrast of gray-scale images using differential evolution optimization. It proposes using a parameterized intensity transformation function to modify pixel gray levels, with the goal of maximizing image contrast. The differential evolution algorithm is used to optimize the parameters of the transformation function. Experimental results applying this method are compared to other contrast enhancement techniques like histogram equalization and particle swarm optimization. The document provides background on image enhancement techniques, a literature review of previous work applying evolutionary algorithms like particle swarm optimization to image enhancement, and details of the proposed differential evolution approach, including the transformation function and fitness function used to evaluate contrast.
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
A Novel Framework For Preprocessing Of Breast Ultra Sound Images By Combining...IRJET Journal
The document presents a novel framework for preprocessing breast ultrasound images that combines non-local means filtering and morphological operations. Non-local means filtering is used to reduce speckle noise, which is a significant issue for ultrasound images. Then morphological techniques are applied to enhance the noise-reduced images. The framework achieves peak signal-to-noise ratios of 60-80 decibels when tested on real breast ultrasound images. It provides an effective method for preprocessing ultrasound images to reduce noise and improve image quality.
An Automatic ROI of The Fundus Photography IJECEIAES
The Region of interest (ROI) of the fundus photography is an important task in medical image processing. It contains a lot of information related to the diagnosis of the retinal disease. So the determination of this ROI is a very influential first step in fundus image processing later. This research proposed a threshold method of segmentation to determine ROI of the fundus photography automatically. Data to be elaborated were the fundus photography’s of 13 patients, captured using Nonmyd7 camera of Kowa Company Ltd in Dr. M. Djamil Hospital, Padang. The results of this processing could determine ROI automatically. The automatic cropping successfully omits as much as possible the non-medical areas shown as dark background, while still maintaining the whole medical areas, comprised the posterior pole of retina captured through the pupil. Thus, this method is helpful in further image processing of posterior areas. We hope that this research will be useful for researchers.
Social media marketing (SMM) is a form of digital marketing that utilizes social media platforms to promote products, services, or brands. The goal of social media marketing is to connect with the target audience, build brand awareness, increase website traffic, and drive engagement and conversions. Here are some key aspects of social media marketing:
Strategy Development:
Identify your target audience: Understand the demographics, interests, and online behavior of your target audience.
Set clear goals: Define specific, measurable, achievable, relevant, and time-bound (SMART) goals for your social media campaigns.
Choose the right platforms: Select social media platforms that align with your target audience and business objectives.
Content Creation:
Create engaging content: Develop content that resonates with your audience, such as images, videos, infographics, and text posts.
Maintain consistency: Establish a consistent posting schedule to keep your audience engaged and informed.
Use a variety of content types: Experiment with different content formats to keep your social media presence diverse and interesting.
Audience Engagement:
Respond to comments and messages: Engage with your audience by responding to comments, messages, and mentions in a timely manner.
Encourage user-generated content: Encourage your followers to create and share content related to your brand.
Run contests and giveaways: Organize contests or giveaways to boost engagement and attract new followers.
Paid Advertising:
Utilize paid social media advertising: Platforms like Facebook, Instagram, Twitter, and LinkedIn offer advertising options to reach a larger audience.
Targeted advertising: Use advanced targeting options to reach specific demographics, interests, and behaviors.
Analytics and Monitoring:
Use analytics tools: Monitor the performance of your social media campaigns using analytics tools provided by the platforms or third-party tools.
Adjust strategies based on data: Analyze the data and adjust your strategies to optimize performance and achieve better results.
Influencer Marketing:
Collaborate with influencers: Partner with influencers who align with your brand to reach a wider audience and build credibility.
Leverage user trust: Influencers can help establish trust with their followers, leading to increased brand credibility.
Social Media Trends:
Stay updated: Keep track of emerging trends in social media marketing and adapt your strategies accordingly.
Experiment with new features: Platforms regularly introduce new features; experiment with these features to stay ahead of the curve.
Remember that effective social media marketing requires a consistent and strategic approach. Regularly assess your performance, listen to your audience, and adjust your strategies to meet your goals.
Fusion of Wavelet Coefficients from Visual and Thermal Face Images for Human ...CSCJournals
In this paper we present a comparative study on fusion of visual and thermal images using different wavelet transformations. Here, coefficients of discrete wavelet transforms from both visual and thermal images are computed separately and combined. Next, inverse discrete wavelet transformation is taken in order to obtain fused face image. Both Haar and Daubechies (db2) wavelet transforms have been used to compare recognition results. For experiments IRIS Thermal/Visual Face Database was used. Experimental results using Haar and Daubechies wavelets show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
This document discusses a face recognition system that aims to improve verification rates under varying lighting conditions. It proposes a framework that combines image normalization, feature extraction, and subspace representation. Each stage increases resistance to illumination variations. The framework achieves significant improvements over other methods, with a verification rate of 88.1% at a 0.1% false acceptance rate. Key components of the system include preprocessing techniques like integral normalized gradient images, feature extraction methods like local binary patterns and Gabor wavelets, and classification using score fusion based on log-likelihood ratios of classifier outputs.
Similar to Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images (20)
Amazon products reviews classification based on machine learning, deep learni...TELKOMNIKA JOURNAL
In recent times, the trend of online shopping through e-commerce stores and websites has grown to a huge extent. Whenever a product is purchased on an e-commerce platform, people leave their reviews about the product. These reviews are very helpful for the store owners and the product’s manufacturers for the betterment of their work process as well as product quality. An automated system is proposed in this work that operates on two datasets D1 and D2 obtained from Amazon. After certain preprocessing steps, N-gram and word embedding-based features are extracted using term frequency-inverse document frequency (TF-IDF), bag of words (BoW) and global vectors (GloVe), and Word2vec, respectively. Four machine learning (ML) models support vector machines (SVM), logistic regression (RF), logistic regression (LR), multinomial Naïve Bayes (MNB), two deep learning (DL) models convolutional neural network (CNN), long-short term memory (LSTM), and standalone bidirectional encoder representations (BERT) are used to classify reviews as either positive or negative. The results obtained by the standard ML, DL models and BERT are evaluated using certain performance evaluation measures. BERT turns out to be the best-performing model in the case of D1 with an accuracy of 90% on features derived by word embedding models while the CNN provides the best accuracy of 97% upon word embedding features in the case of D2. The proposed model shows better overall performance on D2 as compared to D1.
Design, simulation, and analysis of microstrip patch antenna for wireless app...TELKOMNIKA JOURNAL
In this study, a microstrip patch antenna that works at 3.6 GHz was built and tested to see how well it works. In this work, Rogers RT/Duroid 5880 has been used as the substrate material, with a dielectric permittivity of 2.2 and a thickness of 0.3451 mm; it serves as the base for the examined antenna. The computer simulation technology (CST) studio suite is utilized to show the recommended antenna design. The goal of this study was to get a more extensive transmission capacity, a lower voltage standing wave ratio (VSWR), and a lower return loss, but the main goal was to get a higher gain, directivity, and efficiency. After simulation, the return loss, gain, directivity, bandwidth, and efficiency of the supplied antenna are found to be -17.626 dB, 9.671 dBi, 9.924 dBi, 0.2 GHz, and 97.45%, respectively. Besides, the recreation uncovered that the transfer speed side-lobe level at phi was much better than those of the earlier works, at -28.8 dB, respectively. Thus, it makes a solid contender for remote innovation and more robust communication.
Design and simulation an optimal enhanced PI controller for congestion avoida...TELKOMNIKA JOURNAL
This document describes using a snake optimization algorithm to tune the gains of an enhanced proportional-integral controller for congestion avoidance in a TCP/AQM system. The controller aims to maintain a stable and desired queue size without noise or transmission problems. A linearized model of the TCP/AQM system is presented. An enhanced PI controller combining nonlinear gain and original PI gains is proposed. The snake optimization algorithm is then used to tune the parameters of the enhanced PI controller to achieve optimal system performance and response. Simulation results are discussed showing the proposed controller provides a stable and robust behavior for congestion control.
Improving the detection of intrusion in vehicular ad-hoc networks with modifi...TELKOMNIKA JOURNAL
Vehicular ad-hoc networks (VANETs) are wireless-equipped vehicles that form networks along the road. The security of this network has been a major challenge. The identity-based cryptosystem (IBC) previously used to secure the networks suffers from membership authentication security features. This paper focuses on improving the detection of intruders in VANETs with a modified identity-based cryptosystem (MIBC). The MIBC is developed using a non-singular elliptic curve with Lagrange interpolation. The public key of vehicles and roadside units on the network are derived from number plates and location identification numbers, respectively. Pseudo-identities are used to mask the real identity of users to preserve their privacy. The membership authentication mechanism ensures that only valid and authenticated members of the network are allowed to join the network. The performance of the MIBC is evaluated using intrusion detection ratio (IDR) and computation time (CT) and then validated with the existing IBC. The result obtained shows that the MIBC recorded an IDR of 99.3% against 94.3% obtained for the existing identity-based cryptosystem (EIBC) for 140 unregistered vehicles attempting to intrude on the network. The MIBC shows lower CT values of 1.17 ms against 1.70 ms for EIBC. The MIBC can be used to improve the security of VANETs.
Conceptual model of internet banking adoption with perceived risk and trust f...TELKOMNIKA JOURNAL
Understanding the primary factors of internet banking (IB) acceptance is critical for both banks and users; nevertheless, our knowledge of the role of users’ perceived risk and trust in IB adoption is limited. As a result, we develop a conceptual model by incorporating perceived risk and trust into the technology acceptance model (TAM) theory toward the IB. The proper research emphasized that the most essential component in explaining IB adoption behavior is behavioral intention to use IB adoption. TAM is helpful for figuring out how elements that affect IB adoption are connected to one another. According to previous literature on IB and the use of such technology in Iraq, one has to choose a theoretical foundation that may justify the acceptance of IB from the customer’s perspective. The conceptual model was therefore constructed using the TAM as a foundation. Furthermore, perceived risk and trust were added to the TAM dimensions as external factors. The key objective of this work was to extend the TAM to construct a conceptual model for IB adoption and to get sufficient theoretical support from the existing literature for the essential elements and their relationships in order to unearth new insights about factors responsible for IB adoption.
Efficient combined fuzzy logic and LMS algorithm for smart antennaTELKOMNIKA JOURNAL
The smart antennas are broadly used in wireless communication. The least mean square (LMS) algorithm is a procedure that is concerned in controlling the smart antenna pattern to accommodate specified requirements such as steering the beam toward the desired signal, in addition to placing the deep nulls in the direction of unwanted signals. The conventional LMS (C-LMS) has some drawbacks like slow convergence speed besides high steady state fluctuation error. To overcome these shortcomings, the present paper adopts an adaptive fuzzy control step size least mean square (FC-LMS) algorithm to adjust its step size. Computer simulation outcomes illustrate that the given model has fast convergence rate as well as low mean square error steady state.
Design and implementation of a LoRa-based system for warning of forest fireTELKOMNIKA JOURNAL
This paper presents the design and implementation of a forest fire monitoring and warning system based on long range (LoRa) technology, a novel ultra-low power consumption and long-range wireless communication technology for remote sensing applications. The proposed system includes a wireless sensor network that records environmental parameters such as temperature, humidity, wind speed, and carbon dioxide (CO2) concentration in the air, as well as taking infrared photos.The data collected at each sensor node will be transmitted to the gateway via LoRa wireless transmission. Data will be collected, processed, and uploaded to a cloud database at the gateway. An Android smartphone application that allows anyone to easily view the recorded data has been developed. When a fire is detected, the system will sound a siren and send a warning message to the responsible personnel, instructing them to take appropriate action. Experiments in Tram Chim Park, Vietnam, have been conducted to verify and evaluate the operation of the system.
Wavelet-based sensing technique in cognitive radio networkTELKOMNIKA JOURNAL
Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased.
A novel compact dual-band bandstop filter with enhanced rejection bandsTELKOMNIKA JOURNAL
In this paper, we present the design of a new wide dual-band bandstop filter (DBBSF) using nonuniform transmission lines. The method used to design this filter is to replace conventional uniform transmission lines with nonuniform lines governed by a truncated Fourier series. Based on how impedances are profiled in the proposed DBBSF structure, the fractional bandwidths of the two 10 dB-down rejection bands are widened to 39.72% and 52.63%, respectively, and the physical size has been reduced compared to that of the filter with the uniform transmission lines. The results of the electromagnetic (EM) simulation support the obtained analytical response and show an improved frequency behavior.
Deep learning approach to DDoS attack with imbalanced data at the application...TELKOMNIKA JOURNAL
A distributed denial of service (DDoS) attack is where one or more computers attack or target a server computer, by flooding internet traffic to the server. As a result, the server cannot be accessed by legitimate users. A result of this attack causes enormous losses for a company because it can reduce the level of user trust, and reduce the company’s reputation to lose customers due to downtime. One of the services at the application layer that can be accessed by users is a web-based lightweight directory access protocol (LDAP) service that can provide safe and easy services to access directory applications. We used a deep learning approach to detect DDoS attacks on the CICDDoS 2019 dataset on a complex computer network at the application layer to get fast and accurate results for dealing with unbalanced data. Based on the results obtained, it is observed that DDoS attack detection using a deep learning approach on imbalanced data performs better when implemented using synthetic minority oversampling technique (SMOTE) method for binary classes. On the other hand, the proposed deep learning approach performs better for detecting DDoS attacks in multiclass when implemented using the adaptive synthetic (ADASYN) method.
The appearance of uncertainties and disturbances often effects the characteristics of either linear or nonlinear systems. Plus, the stabilization process may be deteriorated thus incurring a catastrophic effect to the system performance. As such, this manuscript addresses the concept of matching condition for the systems that are suffering from miss-match uncertainties and exogeneous disturbances. The perturbation towards the system at hand is assumed to be known and unbounded. To reach this outcome, uncertainties and their classifications are reviewed thoroughly. The structural matching condition is proposed and tabulated in the proposition 1. Two types of mathematical expressions are presented to distinguish the system with matched uncertainty and the system with miss-matched uncertainty. Lastly, two-dimensional numerical expressions are provided to practice the proposed proposition. The outcome shows that matching condition has the ability to change the system to a design-friendly model for asymptotic stabilization.
Implementation of FinFET technology based low power 4×4 Wallace tree multipli...TELKOMNIKA JOURNAL
Many systems, including digital signal processors, finite impulse response (FIR) filters, application-specific integrated circuits, and microprocessors, use multipliers. The demand for low power multipliers is gradually rising day by day in the current technological trend. In this study, we describe a 4×4 Wallace multiplier based on a carry select adder (CSA) that uses less power and has a better power delay product than existing multipliers. HSPICE tool at 16 nm technology is used to simulate the results. In comparison to the traditional CSA-based multiplier, which has a power consumption of 1.7 µW and power delay product (PDP) of 57.3 fJ, the results demonstrate that the Wallace multiplier design employing CSA with first zero finding logic (FZF) logic has the lowest power consumption of 1.4 µW and PDP of 27.5 fJ.
Evaluation of the weighted-overlap add model with massive MIMO in a 5G systemTELKOMNIKA JOURNAL
The flaw in 5G orthogonal frequency division multiplexing (OFDM) becomes apparent in high-speed situations. Because the doppler effect causes frequency shifts, the orthogonality of OFDM subcarriers is broken, lowering both their bit error rate (BER) and throughput output. As part of this research, we use a novel design that combines massive multiple input multiple output (MIMO) and weighted overlap and add (WOLA) to improve the performance of 5G systems. To determine which design is superior, throughput and BER are calculated for both the proposed design and OFDM. The results of the improved system show a massive improvement in performance ver the conventional system and significant improvements with massive MIMO, including the best throughput and BER. When compared to conventional systems, the improved system has a throughput that is around 22% higher and the best performance in terms of BER, but it still has around 25% less error than OFDM.
Reflector antenna design in different frequencies using frequency selective s...TELKOMNIKA JOURNAL
In this study, it is aimed to obtain two different asymmetric radiation patterns obtained from antennas in the shape of the cross-section of a parabolic reflector (fan blade type antennas) and antennas with cosecant-square radiation characteristics at two different frequencies from a single antenna. For this purpose, firstly, a fan blade type antenna design will be made, and then the reflective surface of this antenna will be completed to the shape of the reflective surface of the antenna with the cosecant-square radiation characteristic with the frequency selective surface designed to provide the characteristics suitable for the purpose. The frequency selective surface designed and it provides the perfect transmission as possible at 4 GHz operating frequency, while it will act as a band-quenching filter for electromagnetic waves at 5 GHz operating frequency and will be a reflective surface. Thanks to this frequency selective surface to be used as a reflective surface in the antenna, a fan blade type radiation characteristic at 4 GHz operating frequency will be obtained, while a cosecant-square radiation characteristic at 5 GHz operating frequency will be obtained.
Reagentless iron detection in water based on unclad fiber optical sensorTELKOMNIKA JOURNAL
A simple and low-cost fiber based optical sensor for iron detection is demonstrated in this paper. The sensor head consist of an unclad optical fiber with the unclad length of 1 cm and it has a straight structure. Results obtained shows a linear relationship between the output light intensity and iron concentration, illustrating the functionality of this iron optical sensor. Based on the experimental results, the sensitivity and linearity are achieved at 0.0328/ppm and 0.9824 respectively at the wavelength of 690 nm. With the same wavelength, other performance parameters are also studied. Resolution and limit of detection (LOD) are found to be 0.3049 ppm and 0.0755 ppm correspondingly. This iron sensor is advantageous in that it does not require any reagent for detection, enabling it to be simpler and cost-effective in the implementation of the iron sensing.
Impact of CuS counter electrode calcination temperature on quantum dot sensit...TELKOMNIKA JOURNAL
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
In place of the commercial Pt electrode used in quantum sensitized solar cells, the low-cost CuS cathode is created using electrophoresis. High resolution scanning electron microscopy and X-ray diffraction were used to analyze the structure and morphology of structural cubic samples with diameters ranging from 40 nm to 200 nm. The conversion efficiency of solar cells is significantly impacted by the calcination temperatures of cathodes at 100 °C, 120 °C, 150 °C, and 180 °C under vacuum. The fluorine doped tin oxide (FTO)/CuS cathode electrode reached a maximum efficiency of 3.89% when it was calcined at 120 °C. Compared to other temperature combinations, CuS nanoparticles crystallize at 120 °C, which lowers resistance while increasing electron lifetime.
A progressive learning for structural tolerance online sequential extreme lea...TELKOMNIKA JOURNAL
This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extreme learning machine (ELM) and structural tolerance online sequential extreme learning machine (STOS-ELM) that both must retrain the data in a new class data case. The experimental results show that PSTOS-ELM has accuracy and robustness comparable to ELM and STOS-ELM while also can update new class data immediately.
Electroencephalography-based brain-computer interface using neural networksTELKOMNIKA JOURNAL
This study aimed to develop a brain-computer interface that can control an electric wheelchair using electroencephalography (EEG) signals. First, we used the Mind Wave Mobile 2 device to capture raw EEG signals from the surface of the scalp. The signals were transformed into the frequency domain using fast Fourier transform (FFT) and filtered to monitor changes in attention and relaxation. Next, we performed time and frequency domain analyses to identify features for five eye gestures: opened, closed, blink per second, double blink, and lookup. The base state was the opened-eyes gesture, and we compared the features of the remaining four action gestures to the base state to identify potential gestures. We then built a multilayer neural network to classify these features into five signals that control the wheelchair’s movement. Finally, we designed an experimental wheelchair system to test the effectiveness of the proposed approach. The results demonstrate that the EEG classification was highly accurate and computationally efficient. Moreover, the average performance of the brain-controlled wheelchair system was over 75% across different individuals, which suggests the feasibility of this approach.
Adaptive segmentation algorithm based on level set model in medical imagingTELKOMNIKA JOURNAL
For image segmentation, level set models are frequently employed. It offer best solution to overcome the main limitations of deformable parametric models. However, the challenge when applying those models in medical images stills deal with removing blurs in image edges which directly affects the edge indicator function, leads to not adaptively segmenting images and causes a wrong analysis of pathologies wich prevents to conclude a correct diagnosis. To overcome such issues, an effective process is suggested by simultaneously modelling and solving systems’ two-dimensional partial differential equations (PDE). The first PDE equation allows restoration using Euler’s equation similar to an anisotropic smoothing based on a regularized Perona and Malik filter that eliminates noise while preserving edge information in accordance with detected contours in the second equation that segments the image based on the first equation solutions. This approach allows developing a new algorithm which overcome the studied model drawbacks. Results of the proposed method give clear segments that can be applied to any application. Experiments on many medical images in particular blurry images with high information losses, demonstrate that the developed approach produces superior segmentation results in terms of quantity and quality compared to other models already presented in previeous works.
Automatic channel selection using shuffled frog leaping algorithm for EEG bas...TELKOMNIKA JOURNAL
Drug addiction is a complex neurobiological disorder that necessitates comprehensive treatment of both the body and mind. It is categorized as a brain disorder due to its impact on the brain. Various methods such as electroencephalography (EEG), functional magnetic resonance imaging (FMRI), and magnetoencephalography (MEG) can capture brain activities and structures. EEG signals provide valuable insights into neurological disorders, including drug addiction. Accurate classification of drug addiction from EEG signals relies on appropriate features and channel selection. Choosing the right EEG channels is essential to reduce computational costs and mitigate the risk of overfitting associated with using all available channels. To address the challenge of optimal channel selection in addiction detection from EEG signals, this work employs the shuffled frog leaping algorithm (SFLA). SFLA facilitates the selection of appropriate channels, leading to improved accuracy. Wavelet features extracted from the selected input channel signals are then analyzed using various machine learning classifiers to detect addiction. Experimental results indicate that after selecting features from the appropriate channels, classification accuracy significantly increased across all classifiers. Particularly, the multi-layer perceptron (MLP) classifier combined with SFLA demonstrated a remarkable accuracy improvement of 15.78% while reducing time complexity.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images
1. TELKOMNIKA Telecommunication Computing Electronics and Control
Vol. 21, No. 4, August 2023, pp. 846~857
ISSN: 1693-6930, DOI: 10.12928/TELKOMNIKA.v21i4.22251 846
Journal homepage: http://telkomnika.uad.ac.id
Contrast modification for pre-enhancement process in multi-
contrast rubeosis iridis images
Rohana Abdul Karim, Nurul Wahidah Arshad, Yasmin Abdul Wahab
Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang, Pahang, Malaysia
Article Info ABSTRACT
Article history:
Received Nov 16, 2021
Revised Feb 06, 2023
Accepted Feb 16, 2023
Existing researchers for rubeosis iridis disease focused on image
enhancement as a collective group without considering the multi-contrast of
the images. In this paper, the pre-enhancement process was proposed to
improve the quality of iris images for rubeosis iridis disease by separating
the image into three groups; low, medium and high contrast. Increment,
decrement and maintenance of the images’ original contrast were further
operated by noise reduction and multi-contrast manipulation to attain the
best contrast value in each category for increased compatibility prior
subsequent enhancement. As a result, this study proved that there have three
rules for the contrast modification method. Firstly, the histogram
equalization (HE) filter and increasing the image contrast by 50% will
achieve the optimum value for the low contrast category. Experimental
revealed that HE filters successfully increase the luminance value before
undergoing the contrast modification method. Secondly, reducing the 50% of
the image contrast to achieve the optimum value for the high contrast category.
Finally, the image contrast was maintained for the middle contrast category to
optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio
(PSNR) of the outputs were then calculated, yielding an average of 18.25 and
28.87, respectively.
Keywords:
Enhancement
Multi-contrast images
Rubeosis iridis
Suppression
This is an open access article under the CC BY-SA license.
Corresponding Author:
Rohana Abdul Karim
Faculty of Electrical and Electronics Engineering Technology, Universiti Malaysia Pahang
Pekan Campus, 26600 Pekan, Pahang, Malaysia
Email: rohanaak@ump.edu.my
1. INTRODUCTION
Rubeosis iridis is an eyes disease induced from contemporary formation of abnormal blood vessels
via surface of the iris through neovascularization [1] as shown in Figure 1. Normally, this disease is followed
by neovascular glaucoma as it is one of the most common postoperative complications following vitrectomy
for diabetic patient [2]. Typically, the early phase of this disease can be detected through ophthalmological
examinations with aids of technologically-advanced instruments such as the fundus photography [3] and
fluorescein angiography [4]. The screening test involve manual assessment by ophthalmology using high
technology instrument is complicated, costly and time consuming. Therefore, the pre-screening rubeosis
iridis system is highly needed as a tool prior a proper ophthalmologic consultation.
This manual assessment by ophthalmology to examine the rubeosis iridis disease is really
complicated and need more time to get the result. There is a simple, modern and non-intrusive pre-screening
method to detect this disease through iris image processing system. Generally, this approach developed using
several stages include image enhancement process, segmentation, feature extraction and classification [5].
However, this approach required high quality image in order to get the high accuracy results. In addition,
there are various type of camera that can captured the retina image resulting multiresolution iris image.
2. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
847
Nowadays, current smartphone is not only used as a tool to communicate with others but also built in with camera.
Therefore, user can easily use this smart phone camera to capture the iris image and doing the pre-screening of this
disease before getting further ophthalmology consultation. However, the common problems that occured in the
iris image processing system is the quality of image [6]. The image contrast can be low or high depending on
environment factor such as environment, type of camera, surrounding condition and application used [7]. Also,
if the image has poor quality, the amount of information required from the iris image may be reduced.
Besides, image enhancement process is one of the crucial considerations in the iris image processing
system. For last decades, many image enhancement methods have been studied and one of the popular
techniques is enhancement process based on contrast. Basically, the philosophy of the contrast enhancement
process i.e., to enhance the quality of image by enlarging the gray level image. The common of contrast
enhancement process used is based on histogram equalization (HE) method [8]-[11] due to the simplicity and
highly effectiveness. However, the major drawback of this method is losing the originality of image, loss of image
information, over enhancement of brightness as well as the contrast and amplified the noise from the original
image. Many attempts done by researchers to reduce the HE drawback by introducing several methods such as
adaptive histogram equalization (AHE) [12], contrast limited adaptive histogram equalization (CLAHE) [13],
brightness preserving bi-histogram equalization [14], sub-image histogram equalization method [15], recursive
mean separate histogram equalization [16] and bi-histogram equalization [17].
Prior research in rubeosis iridis detection concentrated on image enhancement as a whole, without
taking into account the images’ multi-contrast [18]-[22]. The process leads to over-enhancement or downgrade
the quality of the image. Thus, this study proposed pre-enhancement process with the two well know contrast
enhancement methods which is HE and CLAHE. Basically, the HE method will enhance the entire contrast
image by enhancing the whole image histogram into the uniform histogram [8]. Meanwhile the CLAHE method
modifies the contrast by each of small windows value based on some characteristics and it is applied to improve
the contrast and minimize the noise that presence in the image [23]. Therefore, the main objective of this paper
is to develop an algorithm to modify the contrast image in enhancement process by introducing the contrast
modification technique for the pre-enhancement process, in order to detect rubeosis iridis disease.
Figure 1. Iris with rubeosis iridis disease
2. RESEARCH METHOD
This study was designed using a numerical technique and quantitative analysis. Generally, this study
consists of four stages as illustrated in Figure 2. The details of each stage are outlined.
Figure 2. Block diagram for stages involved in the enhancement process
2.1. Dataset
The dataset was downloaded from publicly Google search engine [24]-[29]. Appropriated inputs
then comprised 38 images of iris with rubeosis iridis disease from various web-based sources with
multiresolution images range from (183×275) up to (1358×2048) pixels. For this study, images with an
obvious abnormal blood vessel appeared were chosen as a dataset. Thus, it will lead to a high-performance
measurement. The dataset was classified into three groups; low, medium, and high resolution. The classification
is based on spatial measurements pixels per inch (𝑝𝑝𝑖), width (𝑤), and height (ℎ). The (1) defines the group
for each of the conditions of the resulted image, 𝐼:
3. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 4, August 2023: 846-857
848
𝑙𝑜𝑤; 𝐼(𝑤, ℎ) = {
300 𝑝𝑝𝑖 < 𝑤 < 500 𝑝𝑝𝑖
𝑎𝑛𝑑
ℎ < 1000 𝑝𝑝𝑖
𝑚𝑒𝑑𝑖𝑢𝑚; 𝐼(𝑤, ℎ) = {
𝑤 < 500 𝑝𝑝𝑖
𝑎𝑛𝑑
500 𝑝𝑝𝑖 ≤ ℎ < 500 𝑝𝑝𝑖
ℎ𝑖𝑔ℎ; 𝐼(𝑤, ℎ) = {
𝑤 > 600 𝑝𝑝𝑖
𝑎𝑛𝑑
ℎ ≥ 1000 𝑝𝑝𝑖
(1)
2.2. Image contrast measurement
Figure 3 depicts the image contrast measuring procedure. The effectiveness of the luminance and
arithmetic mean model was investigated to identify the appropriate model for the dataset. Luminance refers
to the brightness of light emitted or reflected from a surface. The (2) shows a mathematical model for
luminance, 𝑌𝐿:
𝑌𝐿 = 0.299𝑅 + 0.587𝐺 + 0.114𝐵 (2)
Where 𝑅, 𝐺, and 𝐵 denote pixel values at the the respective red, green, and blue channels. Notably,
parameters 𝑅 and 𝐺 outweigh parameter 𝐵 [1] due to the human eye is more sensitive to the green and red
colours. Meanwhile, arithmetic mean model which depicts the average luminance, 𝑌 of all points on a surface
is outlined as in (3):
𝑌 =
(𝑅+𝐺+𝐵)
3
(3)
In addition, qualitative output corresponding judgment of the human eye was validated by employment
of the questionnaire method among ten voluntary respondents who fulfilled the demographical criteria of:
a) Age: between 20 and 24 years old.
b) Health condition: excellent eyesight condition without the hindrance of colour blindness.
This observing procedure is critical for triggering the correct assessment of brightness due to human
eyes are sensitive to the light and colour space. Herewith, scaled responses were requested from each
respondent upon appraising the brightness of virtually displayed images through sole judgment of their naked
eyes. Engaged process concerning independent answering of the questionnaire has been further depicted in
Figure 4. Each responder had one chance to decide for each of the 38 images. Each brightness assessment
category; low, medium, and high was randomly arranged in the image sets. The setting in the room was
controlled by using the same location for all of the respondents and the fluorescent lamp served as the light
source. The display panel was calibrated by setting the brightness to zero. It is useful to avoid any concerns
about the eye’s light adaptation that will decrease the accuracy of judgement.
Figure 3. Flowchart for image contrast measurement
4. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
849
Figure 4. Questionnaire responses from respondents
2.3. Enhancement
Figure 5 shows the entire flowchart used in this stage. Many researchers enhance the contrast
images by applying the HE and CLAHE method without identifying the suitable images needed to apply the
enhancement process. Therefore, this study proposed the pre-enhancement process that includes 2 steps:
classification of the contrast scale category and contrast modification method.
Figure 5. Block diagram for the enhancement process
2.3.1. Identification of the contrast scale category
The iris images were divided into three contrast levels: low, medium, and high. As demonstrated in
Table 1, this classification was based on the Ansel Adam zone system [2]. Using the (1), the luminance
value, 𝑌𝐿, was used to determine the contrast scale value and identify the grey name and contrast category.
Table 1. Contrast category, scale value and gray name by Ansel Adam zone system
Contrast category Contrast scale value Gray name
High 255 Pure white
229−254 Bright white
204−228 Gray
Medium 179−203 Light gray
153−178 Middle light gray
127−152 Middle gray
Low 102−126 Medium dark gray
76−101 Very dark gray
54−75 Dark black
25−53 Near black
0−24 Pure black
5. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 4, August 2023: 846-857
850
2.3.2. Contrast modification method
The best contrast value for the human eye’s is around 60% to 70% from the maximum contrast scale
value (255) [3], which is categorized as medium contrast. Therefore, this step aims to manipulate the contrast
value up to the range of middle contrast scale value from 127−203. In the low contrast group, there are five
grey names: pure dark, near black, dark black, very dark gray, and medium dark gray. All images in the low
contrast category were divided into two groups based on scales: a) 0−101 and b) 102−126 for analysis. These
two groups were formed since the results of the 0−101 contrast modification technique did not show any
increments in contrast value. But, the results of images in the range 102−126 showed an improvement in
contrast value. In the filter step, HE and CLAHE approaches were applied to increase the contrast value for
images on the scale of 0−101. After the enhancement process, all the images on a scale of 0−101 need to
undergo the contrast modification method, which involves increasing the contrast value 10%, 20%, 30%, 40%,
and 50% from the original contrast. The red channel was utilised in this analysis in order to extract the data
region of interest (ROI), which represents the colour of the blood vessels. After that, the contrast modification
method was applied to the images in the middle and high contrast categories. The process is done by increasing
the contrast by 10%, 20%, 30%, 40%, and 50% or decreasing the contrast by 10%, 20%, 30%, 40%, and 50%.
2.4. Performance measurement
The final step is the performance measurement between the enhancement and original images using
mean square error (MSE) and peak signal to noise ratio (PSNR) shown in the (4) and (5), respectively. MSE
is a technique to measure the mean value of error that occurs between the original image and the output image.
While, PSNR represents the ratio between maximum value of contrast (𝑅) and MSE of the output image.
𝑀𝑆𝐸 =
∑ 𝑀.𝑁[𝐼1(𝑀.𝑁)−𝐼2(𝑀.𝑁)]2
𝑀∗𝑁
(4)
Where 𝑀 and 𝑁 are denoted for number of rows and columns respectively. 𝐼1 is the original image and 𝐼2 is
the enhanced image.
𝑃𝑆𝑁𝑅 = 10 𝑙𝑜𝑔10
𝑅2
MSE
(5)
2.5. Validation
The suggested algorithm had been verified using a contrast enhancement assessment dataset that
was available to the public (CEED2016). The dataset was created to evaluate the efficacy of image
enhancement [30]-[32]. The image in the dataset was divided into three categories according to its luminance
value. Synthetic data was generated to increase the number of high contrast datasets by manipulating the
contrast value of the middle contrast images. There are a total of 24 images for validation.
3. RESULTS AND DISCUSSION
3.1. Image contrast measurement results
Table 2 shows the results of a similarity contrast measurement between brightness and arithmetic
mean, which corresponded to the contrast scale value and gray name. The first image (43−264×357), for
example, has a different contrast value between brightness and arithmetic mean, but the range in the middle
gray is identical. Hence, the similarity status is similar. Figure 6 depicted a summary of similarity image
contrast measurement between luminance and arithmetic mean for three groups. Group medium resolution
yielded comparable results between similar and different. While for low and high resolution the status similar
is lower than the status different. Finally, the similarity findings demonstrated that quantitative measurement
is insufficient for presenting the characteristics of the gray image.
Additionally, qualitative measurements as obtained from the visual observation of ten individual
respondents on both luminance and arithmetic calculations have been outlined in Table 3. The qualitative
results indicated that the human eye distinguishes colour could be easily detected using the luminance approach
compared to the arithmetic mean. Therefore, the luminance technique was used for further processing.
3.2. Enhancement results
Table 4 shows the results of 38 iris images based on category, contrast scale, a gray name based on Ansel
Adam zone system, resolution, luminance value before and after the pre-enhancement process. Red text indicates
that it achieved the medium contrast category. Based on the luminance value after contrast modification method
results in Table 4, images in the contrast scale 0−101 (pure black, near black, dark black, and very dark gray) did
not achieve the optimum range scale. In addition, only three iris images that are very dark gray with contrast
increment by 50% meet the medium category. Meanwhile, the resulting contrast modification method in medium
dark gray (102−126) satisfies the medium contrast category after a contrast increment of 50%.
6. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
851
Figure 6. Summary of luminance and arithmetic mean contrast similarity measurement
Table 2. Example result of contras measurement for low resolution image
Low resolution Luminance (contrast value) Gray name Arithmetic (contrast value) Scale Status
43−264×357 141 Middle gray 128 Middle gray Yes
67−239×236 159 Middle light gray 128 Middle gray No
15−208×208 89 Very dark gray 128 Middle gray No
24−196×301 179 Light gray 128 Middle gray No
82−261×400 117 Middle dark gray 128 Middle gray No
94−183×275 73 Dark black 111 Middle dark gray No
3−233×263 2 Middle gray 3 Middle gray Yes
93−232×360 78 Dark black 117 Middle dark gray Yes
6−325×350 58 Dark black 87 Very dark black No
13−189×284 228 Medium dark gray 128 Medium dark gray Yes
h4−185×273 60 Dark black 89 Very dark black No
29−270×388 3 Pure black 6 Pure black Yes
41−342×387 221 Gray 128 Middle gray No
m4−220×229 61 Dark black 92 Very dark black No
47−300×400 0 Pure black 0 Pure black Yes
66−342×400 1 Pure black 2 Pure black Yes
Table 3. Respondents’ observation on contrast measurement
Responder
Low resolution image Medium resolution image High resolution image
Luminance (%) Arithmetic (%) Luminance (%) Arithmetic (%) Luminance (%) Arithmetic (%)
Responder 1 62.50 18.75 41.67 25.00 27.27 9.09
Responder 2 56.25 18.75 41.67 25.00 18.18 0
Responder 3 56.25 12.50 25.00 16.67 18.18 9.09
Responder 4 56.25 18.75 33.33 16.67 9.09 9.09
Responder 5 50.00 6.25 33.33 16.67 18.18 0
Responder 6 56.25 18.75 41.67 25.00 18.18 0
Responder 7 56.25 18.75 41.67 25.00 18.18 0
Responder 8 56.25 18.75 41.67 25.00 9.09 9.09
Responder 9 50.00 6.25 25.00 16.67 9.09 0
Responder 10 43.75 6.25 33.33 16.67 27.27 9.09
Average 54.38% 14.38% 35.83% 20.84% 17.27% 4.55%
Previous results showed that the luminance value after the contrast modification method in the pure
black, near black, dark black, and very dark gray (0−101) categories did not achieve the luminance value in
the middle contrast category. Therefore, this study proposed two well known contrast enhancement methods
which are HE and CLAHE. These methods applied only to all images in pure black, near black, dark black,
and very dark gray categories. Table 5 shows the outcomes after using HE and CLAHE.
Figure 7 shows a boxplot illustrating the further analysis of the results in Table 6. The boxplot
displays the median score for CLAHE compares to the original images are slightly different. Therefore, it’s
not shown a very significant image enhancement to the original images. Besides, the interquartile ranges
between CLAHE and the original have a constant size. These indicate that the dispersion of luminance is
low. Images with a luminance value greater than 100 have a high tendency to improve the contrast from low
to the middle range. Meanwhile, the median score for HE has a significant output compare to the original
images and the dispersion of luminance exceeding 100. Thus, these results showed that the HE method is a
suitable technique compared to the CLAHE method for improving the luminance value in low contrast
categories, particularly for scale values between 0−101.
8. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
853
After that, the iris images after the HE method will go through the contrast modification method to
improve the luminance value. Table 6 shows that the luminance value after contrast modification method in
the (0−101) scale with HE method. From the results, images with the HE method after contrast increment by
30%−50% achieved the optimum contrast scale. HE method enhances the image contrast based on the entire
image histogram with low computational. However, images in the 0−24 scale did not show any improvement
for luminance value. The decrement contrast after HE method did not perform well at this stage due to the
iris images in the (0−101) categories are not suitable for decrement contrast.
Figure 7. Luminance comparision between HE, CLAHE and original images
Table 6. Results of enhancement process following application of HE technique
Scale Resolution
Luminance value
Ori HE
Increment (%)
10 20 30 40 50
76−101 492×749 96 98 108 118 128 138 148
976×2704 81 105 115 125 135 145 155
54−75 571×575 75 115 125 135 145 155 165
183×275 73 100 110 120 130 140 150
220×229 61 227 237 247 257 267 277
1340×2016 61 118 128 138 148 158 168
185×273 60 117 127 137 147 157 167
325×350 58 89 99 109 119 129 139
25−53 393×521 33 152 162 172 182 192 202
0−24 809×1200 20 0 10 20 30 40 50
1358×2048 18 55 65 75 85 95 105
696×1024 10 7 17 27 37 47 57
640×640 9 0 10 20 30 40 50
1080×1080 7 23 33 43 53 63 73
270×388 3 27 37 47 57 67 77
233×263 2 26 36 46 56 66 76
768×1024 2 37 47 57 67 77 87
342×400 1 24 34 44 54 64 74
300×400 0 2 12 22 32 42 52
450×600 0 12 22 32 42 52 62
1096×1280 0 50 60 70 80 90 100
3.3. Simulation results
Finally, the performance measure is calculated based on the MSE and PSNR values. Figure 8 shows
that the MSE and PSNR are low based on the recommendation method. The proposed method yields an average
MSE 18.25 and an average PSNR of 28.87. Low MSE indicates that the image quality is good because an error
in the image became decreasing while the higher PSNR value indicates the better quality of an image. Figure 9
shows a visual output of the image enhancement. The original image is depicted in Figure 9(a), Figure 9(b)
illustrates a contrast reduction of 10%, Figure 9(c) a contrast reduction of 20%, Figure 9(d) a contrast
reduction of 30%, Figure 9(e) a contrast reduction of 40%, and Figure 9(f) a contrast reduction of 50%.
On the other hand, Figure 9(g) presents a contrast increment of 10%, Figure 9(h) demonstrates a contrast
increment of 20%, Figure 9(i) has a contrast increment of 30%, Figure 9(j) has a contrast increment of 40%,
and Figure 9(k) depicts a contrast increment of 50%.
9. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 4, August 2023: 846-857
854
Figure 8. Average MSE and PSNR value
(a) (b) (c)
(d) (e) (f)
(g) (h) (i)
(j) (k)
Figure 9. Iris image for pre-enhancement process with: (a) original image, (b) contrast decrement by 10%,
(c) contrast decrement by 20%, (d) contrast decrement by 30%, (e) contrast decrement by 40%, (f) contrast
decrement by 50% respectively, (g) contrast increment by 10%, (h) contrast increment by 20%, (i) contrast
increment by 30%, (j) contrast increment by 40%, and (k) contrast increment by 50% respectively
10. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
855
3.4. Validation results
Figure 10 illustrates the PSNR validation results for the enhancement of low and high contrast
measurements. The graph has shown a consistence performance with a maximum PSNR is 74.68 and
a minimum PSNR is 63.1. A greater PSNR indicates a good quality performance for the enhancement image.
Meanwhile, the results obtained from the MSE are shown in Figure 11. The average value for low and high
contrast being 0.012 and 0.15 respectively. It indicates that the proposed method produces high-quality images.
Figure 10. PSNR validation
Figure 11. MSE validation
4. CONCLUSION
This study shows that many iris images have poor luminance value due to the low lighting during
the capturing of iris images. The contrast adjustment in the pre-enhancement step is critical to avoid
over-enhancement, which results in the loss of iris image information. In conclusion, a 50% increment of
contrast is suitable for images with luminance values from the scale 102−126. The combination of HE
method with a 50% contrast increment is appropriate for images scale 25−101. Meanwhile, for images in the
middle contrast category, the original contrast is preserved. Finally, images in the high contrast category must
decreasing contrast by 50%. The noise was minimised after the pre-enhancement procedure by contrast
increment or decrement, resulting in low MSE and PSNR values. Besides that, the result shows that the
proposed method was valid for all multiresolution images. In future, human assessment validation of the
contrast category with scale and the gray name is needed to clarify the results.
ACKNOWLEDGEMENTS
We would like to acknowledge funding from Universiti Malaysia Pahang research grant (RDU200323).
11. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 21, No. 4, August 2023: 846-857
856
REFERENCES
[1] D. Pagoulatos and C. Georgakopoulos, “Rubeosis iridis,” Pan African Medical Journal, vol. 28, no. 279, 2017,
doi: 10.11604/pamj.2017.28.279.13717.
[2] X. Liang, Y. Zhang, Y. -P. Li, W. -R. Huang, J. -X. Wang, and X. Li, “Frequency and risk factors for neovascular glaucoma after
vitrectomy in eyes with diabetic retinopathy: An observational study,” Diabetes Therapy, vol. 10, pp. 1801-1809, 2019,
doi: 10.1007/s13300-019-0644-0.
[3] G. R. Slean, A. D. Fu, J. Chen, and A. Kalevar, “Neovascularization of the iris in retinoschisis,” American Journal of
Ophthalmology Case Reports, vol. 7, pp. 99-101, 2017, doi: 10.1016/j.ajoc.2017.06.019.
[4] L. Laatikainen, “Development and classification of rubeosis iridis in diabetic eye disease,” British Journal of Ophthalmology,
vol. 63, no. 3, pp. 150-156, 1979, doi: 10.1136/bjo.63.3.150.
[5] R. Aminah and A. H. Saputro, “Diabetes prediction system based on iridology using machine learning,” in 2019 6th International
Conference on Information Technology, Computer and Electrical Engineering (ICITACEE), 2019, pp. 1-6,
doi: 10.1109/icitacee.2019.8904125.
[6] R. Hassan, S. Kasim, W. A. Z. W. C. Jafery, and Z. A. Shah, “Image enhancement technique at different distance for iris recognition,”
International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 4-2, pp. 1510-1515, 2017,
doi: 10.18517/ijaseit.7.4-2.3392.
[7] R. K. Hapsari, M. I. Utoyo, R. Rulaningtyas, and H. Suprajitno, “Comparison of histogram based image enhancement methods on
iris images,” Journal of Physics: Conference Series, 2020, vol. 1569, doi: 10.1088/1742-6596/1569/2/022002.
[8] Sanpachai H. and S. Malisuwan, “A study of image enhancement for iris recognition,” Journal of Industrial and Intelligent
Information, vol. 3, no. 1, pp. 61-64, 2015, doi: 10.12720/jiii.3.1.61-64.
[9] C. Liu, X. Sui, X. Kuang, Y. Liu, G. Gu, and Q. Chen, “Adaptive contrast enhancement for infrared images based on the
neighborhood conditional histogram,” Remote Sensing, vol. 11, no. 11, 2019, doi: 10.3390/rs11111381.
[10] S. Das, T. Gulati, and V. Mittal, “Histogram equalization techniques for contrast enhancement: A review,” International Journal
of Computer Applications, vol. 114, no. 10, pp. 32-36, 2015, doi: 10.5120/20017-2027.
[11] K. G. Suma and V. S. Kumar, “A quantitative analysis of histogram equalization-based methods on fundus images for diabetic
retinopathy detection,” Computational Intelligence and Big Data Analytics, pp. 55-63, 2018, doi: 10.1007/978-981-13-0544-3_5.
[12] F. Mustaghfirin, Erwin, H. K. Putra, U. Yanti, and R. Ricadonna, “The comparison of iris detection using histogram equalization
and adaptive histogram equalization methods,” Journal of Physics: Conference Series, 2019, vol. 1196, doi: 10.1088/1742-
6596/1196/1/012016.
[13] N. B. A. Mustafa, W. M. D. W. Zaki, A. Hussain, and J. C. Hamzah, “Modified curvature-based trigonometric identities for
retinal blood vessel tortuosity measurement in diabetic retinopathy fundus images,” International Journal of Engineering and
Technology, vol. 7, no. 4.11, pp. 133-139, 2018, doi: 10.14419/ijet.v7i4.11.20788.
[14] Q. Cao, Z. Shi, R. Wang, P. Wang and S. Yao, “A brightness-preserving two-dimensional histogram equalization method based on
two-level segmentation,” Multimedia Tools and Applications, vol. 79, pp. 27091-27114, 2020, doi: 10.1007/s11042-020-09265-y.
[15] U. K. Acharya and S. Kumar, “Directed searching optimized mean-exposure based sub-image histogram equalization for grayscale
image enhancement,” Multimedia Tools and Applications, vol. 80, pp. 24005-24025, 2021, doi: 10.1007/s11042-021-10855-7.
[16] S. Yelmanov and Y. Romanyshyn, “Image enhancement in automatic mode by recursive mean-separate contrast stretching,” Data
Stream Mining & Processing, 2020, vol. 1158, pp. 288-306, doi: 10.1007/978-3-030-61656-4_19.
[17] A. Paul, T. Sutradhar, P. Bhattacharya, and S. P. Maity, “Adaptive clip-limit-based bi-histogram equalization algorithm for
infrared image enhancement,” Applied Optics, vol. 59, no. 28, pp. 9032-9041, 2020, doi: 10.1364/AO.395848.
[18] R. A. Karim, N. A. A. A. Mobin, N. W. Arshad, N. F. Zakaria, and M. Z. A. Bakar, “Early rubeosis iridis detection using feature
extraction process,” Lecture Notes in Electrical Engineering, Singapore: Springer, 2020, vol. 632, pp. 379-387, doi: 10.1007/978-
981-15-2317-5_32.
[19] J. F. Banzi and Z. Xue, “An automated tool for non-contact, real time early detection of diabetes by computer vision,”
International Journal of Machine Learning and Computing, vol. 5, no. 3, pp. 225-229, 2015, doi: 10.7763/ijmlc.2015.v5.511.
[20] A. Bansal, R. Agarwal, and R. K. Sharma, “Determining diabetes using iris recognition system,” International Journal of
Diabetes in Developing Countries, vol. 35, pp. 432-438, 2015, doi: 10.1007/s13410-015-0296-1.
[21] N. Padmasini, R. Umamaheswari, R. Kalpana and M. Y. Sikkandar, “Comparative study of iris and retinal images for early
detection of diabetic mellitus,” Journal of Medical Imaging and Health Informatics, vol. 10, no. 2. pp. 316-325, 2020,
doi: 10.1166/jmihi.2020.2973.
[22] P. Samant and R. Agarwal, “Machine learning techniques for medical diagnosis of diabetes using iris images,” Computer Methods
and Programs in Biomedicine, vol. 157, pp. 121-128, 2018, doi: 10.1016/j.cmpb.2018.01.004.
[23] R. Fan, X. Li, S. Lee, T. Li, and H. L. Zhang, “Smart image enhancement using CLAHE based on an F-shift transformation
during decompression,” Electronics, vol. 9, no. 9, 2020, doi: 10.3390/electronics9091374.
[24] M. Oller, C. Esteban, P. Pérez, M. À. Parera, R. Lerma, and S. Llagostera, “Rubeosis iridis as a sign of underlying carotid
stenosis,” Journal of Vascular Surgery, vol. 56, no. 6, pp. 1724-1726, 2012, doi: 10.1016/j.jvs.2012.06.073.
[25] V. S. E. Jeganathan, A. Wirth, and M. P. MacManus, “Ocular risks from orbital and periorbital radiation therapy: A critical review,”
International Journal of Radiation Oncology, Biology, Physics, vol. 79, no. 3, pp. 650-659, 2011, doi: 10.1016/j.ijrobp.2010.09.056.
[26] Iris image dataset (2018). Accessed: Dec. 25, 2018. [Online]. Available:
http://photos1.blogger.com/blogger/3488/1320/1600/DSC07882.jpg
[27] Iris image dataset (2018). Accessed: Dec. 25, 2018. [Online]. Available:
http://www.adeluque.com/images/textos/f5aa26_Rubeosis%20iris.JPG
[28] D. Hannouche and T. H. -Xuan. “Chapter 6 Acute Conjunctivitis.” Ento KeyFastest Otolaryngology & Ophthalmology Insight
Engine. https://entokey.com/acute-conjunctivitis/ (accessed: 25, 2018).
[29] Rubeosis of the iris: Causes (2020). https://www.informacionopticas.com/rubeosis-del-iris/ (accessed: Dec. 25, 2018).
[30] M. A. Qureshi, A. Beghdadi, and M. Deriche, “Towards the design of a consistent image contrast enhancement evaluation
measure,” Signal Processing: Image Communication, vol. 58, pp. 212-227, 2017, doi: 10.1016/j.image.2017.08.004.
[31] M. A. Qureshi, A. Beghdadi, B. Sdiri, M. Deriche, and F. A. -Cheikh, “A comprehensive performance evaluation of objective
quality metrics for contrast enhancement techniques,” in 2016 6th European Workshop on Visual Information Processing
(EUVIP), 2016, pp. 1-5, doi: 10.1109/euvip.2016.7764589.
[32] A. Beghdadi, M. A. Qureshi, and M. Deriche, “A critical look to some contrast enhancement evaluation measures,” in 2015
Colour and Visual Computing Symposium (CVCS), 2015, pp. 1-6, doi: 10.1109/cvcs.2015.7274888.
12. TELKOMNIKA Telecommun Comput El Control
Contrast modification for pre-enhancement process in … (Rohana Abdul Karim)
857
BIOGRAPHIES OF AUTHORS
Rohana Abdul Karim is a Senior Lecturer at Faculty of Electrical & Electronic
Engineering Technology, Universiti Malaysia Pahang (UMP). She received the Bachelor
Degree in Electrical Engineering from Universiti Teknologi Tun Hussein Onn (UTHM) in
2005 and a Master of Computer Science from Universiti Putra Malaysia in 2007. Her PhD
from the National University of Malaysia (UKM) in 2017. Her research interests include
biomedical engineering, computer vision, image processing, pattern recognition, video
analysis and artificial intelligent techniques. Dr. Rohana research’s work has been awarded the
best paper award at International Conference and Exhibition of Women Engineers
(ICEWE11’) and International Conference on Electrical, Control and Computer Engineering
(InECCE2017). Her research work has won several international and local innovation
competitions including the International Invention, Innovation and Technology Exhibition
(ITEX 2010), International Engineering Invention and Innovation Exhibition (i-envex 2011),
BIOMALAYSIA 2011, International Festival of Innovation on Green Technology (i-finog
2018 and 2019). She can be contacted at email: rohanaak@ump.edu.my.
Nurul Wahidah Arshad is a Lecturer at Faculty of Electrical & Electronic
Engineering Technology, Universiti Malaysia Pahang (UMP). She received a B. Eng. (Hons)
degree in Electrical Engineering (Electronics) from Universiti Teknologi Tun Hussein Onn
(UTHM) in 2006, and M.Sc. degree in Communications and Signal Processing from
Newcastle Upon Tyne University, United Kingdom, in 2008. Her research interests include
computer vision, image processing, speech processing, artificial intelligent and engineering
education. Nurul Wahidah research’s work has been won several international and local
innovation competitions including the British Invention Showcase (BIS 2013), International
Invention, Innovation and Technology Exhibition (ITEX 2010, 2013 and 2019), and
International Engineering Invention & Innovation Exhibition (i-envex 2014). She can be
contacted at email: wahidah@ump.edu.my.
Yasmin Abdul Wahab received a B. Eng. (Hons) degree in Electrical
Engineering (Control and Instrumentation), M. Eng. and Ph.D. degrees in Electrical
Engineering from Universiti Teknologi Malaysia (UTM), Johor, Malaysia, in 2008, 2010, and
2017, respectively. In 2010, she joined Universiti Malaysia Pahang (UMP), Pahang, Malaysia,
as a teaching staff member and at present she holds the position of senior lecturer. Her
research interests include process tomography, electrical tomography, sensors technology and
instrumentation, and applied electronics and computer engineering. She can be contacted at
email: yasmin@ump.edu.my.