Since there are provisions of many attributes that are not possible or difficult to follow by networks conventionally, mobile ad-hoc networks are extensively deployed. This application starts through the defense sectors, the sensory node presents in the hostile territories down to the gadgets for congestion communication in traffic by general transportation when travelling for adequate provision of infrastructure during disaster recovery. As a lot of importance related to (mobile ad hoc network) MANET application, one important factor in ad-hoc networks is security. Using image processing for securing MANET is the area of focus of this research. Therefore, in this article, the security threats are assessed and representative proposals are summarized in ad-hoc network’s context. The study reviewed the current situation of the art for original to security provision called mobile ad hoc network for wireless networking. The threats to security are recognized while the present solution is observed. The study additionally summarized education erudite, talks on general issues and future instructions are recognized. Also, in this study, the forecast weighted clustering algorithm (FWCA) is employed as a cluster head over weighted clustering algorithm (WCA) is examined as quality in cluster-based routing, service is highly significant with MANET.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
This document summarizes a case study on implementing predictive maintenance processes in a mechatronic industry using machine learning algorithms. A company installed sensors on a cutting machine to monitor blade status in real-time. A software platform was developed to analyze sensor data using k-Means clustering and LSTM algorithms to predict blade break conditions. The platform classified risk maps and predicted alert levels based on recent variable values. This approach aimed to optimize maintenance and reduce machine downtime for customers.
IRJET- Confidential Data Access through Deep Learning Iris BiometricsIRJET Journal
This document describes a study that explores using iris recognition and deep learning as a biometric authentication method for sensitive mobile transactions. The proposed system uses a deep neural network classifier and edge detection with adaptive contour segmentation to identify individuals from iris images. It authenticates website access through MATLAB. The system is said to enhance security compared to existing methods by fusing information from iris and surrounding eye region features. Evaluation shows it reduces computation time and improves specificity, sensitivity and accuracy compared to region-based segmentation alone.
IRJET-MText Extraction from Images using Convolutional Neural NetworkIRJET Journal
The document describes a text extraction model that uses convolutional neural networks (CNNs) to detect and recognize text in images. It discusses pre-processing techniques like binarization and filtering used to improve accuracy. A CNN based on ResNet18 architecture is used for text recognition, trained with CTC loss to handle variable-length text. Keywords can be searched for in extracted text and highlighted. The system allows browsing images, extracting text, searching text, and storing extracted text in an editable document format. While current technology can extract text from simple backgrounds, this model aims to handle more complex real-world images.
Artificial Neural Network (ANN) is a fast-growing method which has been used in different
industries during recent years. The main idea for creating ANN which is a subset of artificial
intelligence is to provide a simple model of human brain in order to solve complex scientific and
industrial problems. ANNs are high-value and low-cost tools in modelling, simulation, control,
condition monitoring, sensor validation and fault diagnosis of different systems. It have high
flexibility and robustness in modeling, simulating and diagnosing the behavior of rotating machines
even in the presence of inaccurate input data. They can provide high computational speed for
complicated tasks that require rapid response such as real-time processing of several simultaneous
signals. ANNs can also be used to improve efficiency and productivity of energy in rotating
equipment
Device to evaluate cleanliness of fiber optic connectors using image processi...IJECEIAES
This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.
Mining knowledge graphs to map heterogeneous relations between the internet o...IJECEIAES
Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to objectoriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
DIP Using Image Encryption and XOR Operation Affine Transformiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
PREDICTIVE MAINTENANCE AND ENGINEERED PROCESSES IN MECHATRONIC INDUSTRY: AN I...ijaia
This document summarizes a case study on implementing predictive maintenance processes in a mechatronic industry using machine learning algorithms. A company installed sensors on a cutting machine to monitor blade status in real-time. A software platform was developed to analyze sensor data using k-Means clustering and LSTM algorithms to predict blade break conditions. The platform classified risk maps and predicted alert levels based on recent variable values. This approach aimed to optimize maintenance and reduce machine downtime for customers.
IRJET- Confidential Data Access through Deep Learning Iris BiometricsIRJET Journal
This document describes a study that explores using iris recognition and deep learning as a biometric authentication method for sensitive mobile transactions. The proposed system uses a deep neural network classifier and edge detection with adaptive contour segmentation to identify individuals from iris images. It authenticates website access through MATLAB. The system is said to enhance security compared to existing methods by fusing information from iris and surrounding eye region features. Evaluation shows it reduces computation time and improves specificity, sensitivity and accuracy compared to region-based segmentation alone.
IRJET-MText Extraction from Images using Convolutional Neural NetworkIRJET Journal
The document describes a text extraction model that uses convolutional neural networks (CNNs) to detect and recognize text in images. It discusses pre-processing techniques like binarization and filtering used to improve accuracy. A CNN based on ResNet18 architecture is used for text recognition, trained with CTC loss to handle variable-length text. Keywords can be searched for in extracted text and highlighted. The system allows browsing images, extracting text, searching text, and storing extracted text in an editable document format. While current technology can extract text from simple backgrounds, this model aims to handle more complex real-world images.
Artificial Neural Network (ANN) is a fast-growing method which has been used in different
industries during recent years. The main idea for creating ANN which is a subset of artificial
intelligence is to provide a simple model of human brain in order to solve complex scientific and
industrial problems. ANNs are high-value and low-cost tools in modelling, simulation, control,
condition monitoring, sensor validation and fault diagnosis of different systems. It have high
flexibility and robustness in modeling, simulating and diagnosing the behavior of rotating machines
even in the presence of inaccurate input data. They can provide high computational speed for
complicated tasks that require rapid response such as real-time processing of several simultaneous
signals. ANNs can also be used to improve efficiency and productivity of energy in rotating
equipment
Device to evaluate cleanliness of fiber optic connectors using image processi...IJECEIAES
This work proposes a portable, handheld electronic device, which measures the cleanliness in fiber optic connectors via digital image processing and artificial neural networks. Its purpose is to reduce the evaluation subjectivity in visual inspection done by human experts. Although devices with this purpose already exist, they tend to be cost-prohibitive and do not take advantage of neither image processing nor artificial intelligence to improve their results. The device consists of an optical microscope for fiber optic connector analysis, a digital camera adapter, a reduced-board computer, an image processing algorithm, a neural network algorithm and an LCD screen for equipment operation and results visualization. The image processing algorithm applies grayscale histogram equalization, Gaussian filtering, Canny filtering, Hough transform, region of interest segmentation and obtaining radiometric descriptors as inputs to the neural network. Validation consisted of comparing the results by the proposed device with those obtained by agreeing human experts via visual inspection. Results yield an average Cohen's Kappa of 0.926, which implies a very satisfactory performance by the proposed device.
Mining knowledge graphs to map heterogeneous relations between the internet o...IJECEIAES
Patterns for the internet of things (IoT) which represent proven solutions used to solve design problems in the IoT are numerous. Similar to objectoriented design patterns, these IoT patterns contain multiple mutual heterogeneous relationships. However, these pattern relationships are hidden and virtually unidentified in most documents. In this paper, we use machine learning techniques to automatically mine knowledge graphs to map these relationships between several IoT patterns. The end result is a semantic knowledge graph database which outlines patterns as vertices and their relations as edges. We have identified four main relationships between the IoT patterns-a pattern is similar to another pattern if it addresses the same use case problem, a large-scale pattern uses a small- scale pattern in a lower level layer, a large pattern is composed of multiple smaller scale patterns underneath it, and patterns complement and combine with each other to resolve a given use case problem. Our results show some promising prospects towards the use of machine learning techniques to generate an automated repository to organise the IoT patterns, which are usually extracted at various levels of abstraction and granularity.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
DIP Using Image Encryption and XOR Operation Affine Transformiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
IRJET- Proposed Approach for Layout & Handwritten Character Recognization in OCRIRJET Journal
This document proposes an approach for layout analysis and handwritten character recognition in optical character recognition systems. It discusses challenges in recognizing text in documents containing both printed and handwritten text, as well as non-text elements like images. The proposed approach uses document segmentation techniques like connected component analysis and block segmentation to separate text and non-text regions, and further separates printed and handwritten text regions. It aims to apply these techniques to handwritten bills to allow segmentation and analysis of the bills in a user-friendly way for small business owners. The key steps involve preprocessing, text/non-text segmentation, layer separation for printed/handwritten text, and block segmentation using a run-length smearing algorithm and white space detection.
This survey propose a Novel Joint Data-Hiding and
Compression Scheme (JDHC) for digital images using side match
vector quantization (SMVQ) and image in painting. In this
JDHC scheme image compression and data hiding scheme are
combined into a single module. On the client side, the data should
be hided and compressed in sub codebook such that remaining
block except left and top most of the image. The data hiding and
compression scheme follows raster scanning order i.e. block by
block on row basis. Vector Quantization used with SMVQ and
Image In painting for complex block to control distortion and
error injection. The receiver side process is based on two
methods. First method divide the received image into series of
blocks the receiver achieve hided data and original image
according to the index value in the segmented block. Second
method use edge based harmonic in painting is used to get
original image if any loss in the image.
This document summarizes a student's work on optimizing a networked smart shoe for gait analysis using heuristic algorithms. The student has revised the state transition classification for gait phase detection and is working to complete the genetic algorithm by developing functions for selection, crossover and mutation. The research plan involves finalizing the hardware, improving the software, acquiring data, developing the algorithm, collaborating on the shoe's MR damper, writing publications, and presenting the final project. The future plan is to acquire more data and compare gait detection to visual inspection using optimized thresholds.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
This document compares the performance of three lossless image compression techniques: Run Length Encoding (RLE), Delta encoding, and Huffman encoding. It tests these algorithms on binary, grayscale, and RGB images to evaluate compression ratio, storage savings percentage, and compression time. The results found that Delta encoding achieved the highest compression ratio and storage savings, while Huffman encoding had the fastest compression time. In general, the document evaluates and compares the performance of different lossless image compression algorithms.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
IRJET- Kidney Stone Classification using Deep Neural Networks and Facilitatin...IRJET Journal
The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and treatment recommendations, are sent to the cloud where doctors and patients can access them, allowing automated diagnosis without human intervention.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document summarizes a research paper that developed a deep learning system to detect abnormalities in chest x-rays. The system used a convolutional neural network (CNN) trained on 5000 chest x-ray images labeled as normal or abnormal. The CNN architecture included convolutional and pooling layers. Pre-processing techniques like histogram equalization and data augmentation like image flipping were used. The trained CNN could classify x-rays as normal, abnormal, or detect 14 specific diseases with 1% error rate. While large datasets improve accuracy, they also increase training time. The authors conclude CNN is effective for chest x-ray analysis but note room for improvements like including more diseases and optimized architectures.
IRJET- Automated Student’s Attendance Management using Convolutional Neural N...IRJET Journal
This document describes a proposed system to automate student attendance management using convolutional neural networks and face recognition. The system would take attendance automatically by detecting faces in the classroom and comparing them to a database of student faces. This would make the attendance process more efficient than current manual methods like calling roll numbers or paper sign-ins. The system would use a CNN algorithm and face detection/recognition techniques like PCA to detect and identify student faces during lectures and automatically update attendance records.
Channel encoding system for transmitting image over wireless network IJECEIAES
Various encoding schemes have been introduced till date focusing on an effective image transmission scheme in presence of error-prone artifacts in wireless communication channel. Review of existing schemes of channel encoding systems infer that they are mostly inclined on compression scheme and less over problems of superior retention of signal retention as they lacks an essential consideration of network states. Therefore, the proposed manuscript introduces a cost effective lossless encoding scheme which ensures resilient transmission of different forms of images. Adopting an analytical research methodology, the modeling has been carried out to ensure that a novel series of encoding operation be performed over an image followed by an effective indexing mechanism. The study outcome confirms that proposed system outshines existing encoding schemes in every respect.
Neuro-fuzzy inference system based face recognition using feature extractionTELKOMNIKA JOURNAL
Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.
Mobile learning architecture using fog computing and adaptive data streamingTELKOMNIKA JOURNAL
With the huge development in mobile and network fields, sensor technologies and fog computing help the students for more effective learning, flexible and in and effective manner from anywhere. Using the mobile device for learn encourage the transition to mobile computing (cloud and fog computing) which is led to the ability to design customized system that help student to learn via context aware learning which can be done by set the user preference and use proper methods to show only related manner subject. The presented study works on developing a system of e-learning which has been on the basis of fog computing concepts with deep learning approaches utilized for classification to the data content for accomplishing the context aware learning and use the adaptation of video quality using special equation and the data encrypted and decrypted using 3DES algorithm to ensure the security side of the operation.
A Modern Technique for Unauthorized Human Detection and Intimationijtsrd
"Technological advancements are inevitable and the field of IoT is no exception. The utilization of the technologies in various sectors is highly employed. Even though we use technology in various sectors, the employment of technology for security purposes is very low. The Existing security in various places only CCTV is used for monitoring and recording. Even there are existing security systems where an alert is sent via an email which requires a stable internet connection. It is unlikely that we expect the user to be always connected to an internet source. In the Proposed system, authorized users faces will be trained and stored in a Database. Initially, when an unknown known person enters in the zone the camera module will capture the intruders face. The captured intruder's face will be compared with the trained faces in the database. If the person's face doesn't match, the micro controller will send an alert SMS to the recognized user and also the intruder's captured image will be E mailed to the user. The authorized user should acknowledge the SMS message. If he fails to acknowledge the message within a threshold time limit, an alert call will be made to the concerned user. By this the user gets intimated in real time. Keerthanna G. S | Praveen Kumar P | Vishnu Prasad K | Ms. S. Sri Heera ""A Modern Technique for Unauthorized Human Detection and Intimation"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21659.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21659/a-modern-technique-for-unauthorized-human-detection-and-intimation/keerthanna-g-s"
IRJET- Efficient Auto Annotation for Tag and Image based Searching Over Large...IRJET Journal
This document discusses an efficient auto annotation system for tag and image based searching over large datasets. It proposes an algorithm that incorporates advantages from other algorithms to improve accuracy and performance of image retrieval in a peer-to-peer framework. Key features extracted for image matching include color, shape, and texture. Experimental results on a dataset of 500 images showed the method achieved 92.4% accuracy in retrieving similar images, comparable to other methods. The system allows users to recognize and extract images across peer systems based on image features.
IRJET - Study on the Effects of Increase in the Depth of the Feature Extracto...IRJET Journal
This document discusses a study on the effects of increasing the depth of the feature extractor for recognizing handwritten digits in a convolutional neural network (CNN). Specifically, it analyzes the performance of a CNN model on the Modified National Institute of Standards and Technology (MNIST) dataset with variations in the number of filters used in deeper layers of the proposed model. The study finds that increasing the number of filters in the convolutional layers improves the accuracy of the model for classifying handwritten digits.
Iaetsd a low power and high throughput re-configurable bip for multipurpose a...Iaetsd Iaetsd
This document presents a reconfigurable low power binary image processor for image processing applications. The processor consists of a reconfigurable binary processing module with mixed-grained architecture providing flexibility, efficiency and performance. Line memories are selected for lower power consumption and clock gating technique is used to reduce their power. The processor supports real-time binary image processing operations like morphological transformations and is suitable for applications like object recognition and tracking.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
IRJET - Deep Learning Applications and Frameworks – A ReviewIRJET Journal
This document reviews deep learning applications and frameworks. It begins by defining deep learning and discussing how deep neural networks can be used to automatically identify patterns in large datasets. It then discusses several applications of deep learning, including self-driving cars, news aggregation, natural language processing, virtual assistants, and visual recognition. The document also describes artificial neural networks and deep neural networks. Finally, it reviews several popular deep learning frameworks, including TensorFlow, PyTorch, Keras, Caffe, and Chainer.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
Lightweight digital imaging and communications in medicine image encryption f...TELKOMNIKA JOURNAL
Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for “digital imaging and communications in medicine”. The efficiency of the proposed system is proved in terms of adjacent pixels’ correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.
Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and
other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is
critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The
Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a
user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks
abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is
compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.
IRJET- Proposed Approach for Layout & Handwritten Character Recognization in OCRIRJET Journal
This document proposes an approach for layout analysis and handwritten character recognition in optical character recognition systems. It discusses challenges in recognizing text in documents containing both printed and handwritten text, as well as non-text elements like images. The proposed approach uses document segmentation techniques like connected component analysis and block segmentation to separate text and non-text regions, and further separates printed and handwritten text regions. It aims to apply these techniques to handwritten bills to allow segmentation and analysis of the bills in a user-friendly way for small business owners. The key steps involve preprocessing, text/non-text segmentation, layer separation for printed/handwritten text, and block segmentation using a run-length smearing algorithm and white space detection.
This survey propose a Novel Joint Data-Hiding and
Compression Scheme (JDHC) for digital images using side match
vector quantization (SMVQ) and image in painting. In this
JDHC scheme image compression and data hiding scheme are
combined into a single module. On the client side, the data should
be hided and compressed in sub codebook such that remaining
block except left and top most of the image. The data hiding and
compression scheme follows raster scanning order i.e. block by
block on row basis. Vector Quantization used with SMVQ and
Image In painting for complex block to control distortion and
error injection. The receiver side process is based on two
methods. First method divide the received image into series of
blocks the receiver achieve hided data and original image
according to the index value in the segmented block. Second
method use edge based harmonic in painting is used to get
original image if any loss in the image.
This document summarizes a student's work on optimizing a networked smart shoe for gait analysis using heuristic algorithms. The student has revised the state transition classification for gait phase detection and is working to complete the genetic algorithm by developing functions for selection, crossover and mutation. The research plan involves finalizing the hardware, improving the software, acquiring data, developing the algorithm, collaborating on the shoe's MR damper, writing publications, and presenting the final project. The future plan is to acquire more data and compare gait detection to visual inspection using optimized thresholds.
Efficient mobilenet architecture_as_image_recognitEL Mehdi RAOUHI
1. The document discusses the MobileNet architecture for image recognition on mobile and embedded devices with limited computing resources. MobileNet uses depthwise separable convolutions to reduce computational costs compared to traditional convolutional neural networks.
2. MobileNet splits regular convolutions into depthwise convolutions followed by 1x1 pointwise convolutions. This factorization significantly reduces computations and model size while maintaining accuracy.
3. The document evaluates MobileNet on the Caltech101 dataset using a mobile device. MobileNet achieved 92.4% accuracy while drawing only 2.1 Watts of power, demonstrating its efficiency for resource-constrained environments.
This document compares the performance of three lossless image compression techniques: Run Length Encoding (RLE), Delta encoding, and Huffman encoding. It tests these algorithms on binary, grayscale, and RGB images to evaluate compression ratio, storage savings percentage, and compression time. The results found that Delta encoding achieved the highest compression ratio and storage savings, while Huffman encoding had the fastest compression time. In general, the document evaluates and compares the performance of different lossless image compression algorithms.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document proposes using a convolutional neural network (CNN) to detect abnormalities in chest x-rays. It discusses developing a CNN model with an input of chest x-ray images labeled as normal or abnormal. The model would use techniques like pre-processing, data augmentation, and a network architecture with convolutional and pooling layers to classify images as normal or abnormal. The goal is to build an accurate system for detecting various chest diseases from x-ray images to help doctors with diagnosis.
IRJET- Kidney Stone Classification using Deep Neural Networks and Facilitatin...IRJET Journal
The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and treatment recommendations, are sent to the cloud where doctors and patients can access them, allowing automated diagnosis without human intervention.
IRJET- Chest Abnormality Detection from X-Ray using Deep LearningIRJET Journal
This document summarizes a research paper that developed a deep learning system to detect abnormalities in chest x-rays. The system used a convolutional neural network (CNN) trained on 5000 chest x-ray images labeled as normal or abnormal. The CNN architecture included convolutional and pooling layers. Pre-processing techniques like histogram equalization and data augmentation like image flipping were used. The trained CNN could classify x-rays as normal, abnormal, or detect 14 specific diseases with 1% error rate. While large datasets improve accuracy, they also increase training time. The authors conclude CNN is effective for chest x-ray analysis but note room for improvements like including more diseases and optimized architectures.
IRJET- Automated Student’s Attendance Management using Convolutional Neural N...IRJET Journal
This document describes a proposed system to automate student attendance management using convolutional neural networks and face recognition. The system would take attendance automatically by detecting faces in the classroom and comparing them to a database of student faces. This would make the attendance process more efficient than current manual methods like calling roll numbers or paper sign-ins. The system would use a CNN algorithm and face detection/recognition techniques like PCA to detect and identify student faces during lectures and automatically update attendance records.
Channel encoding system for transmitting image over wireless network IJECEIAES
Various encoding schemes have been introduced till date focusing on an effective image transmission scheme in presence of error-prone artifacts in wireless communication channel. Review of existing schemes of channel encoding systems infer that they are mostly inclined on compression scheme and less over problems of superior retention of signal retention as they lacks an essential consideration of network states. Therefore, the proposed manuscript introduces a cost effective lossless encoding scheme which ensures resilient transmission of different forms of images. Adopting an analytical research methodology, the modeling has been carried out to ensure that a novel series of encoding operation be performed over an image followed by an effective indexing mechanism. The study outcome confirms that proposed system outshines existing encoding schemes in every respect.
Neuro-fuzzy inference system based face recognition using feature extractionTELKOMNIKA JOURNAL
Human face recognition (HFR) is the method of recognizing people in images or videos. There are different HFR methods such as feature-based, eigen-faces, hidden markov model and neural network (NN) based methods. Feature extraction or preprocessing used in first three mentioned methods that associated with the category of the image to recognize. While in the NN method, any type of image can be useful without the requirement to particular data about the type of image, and simultaneously provides superior accuracy. In this paper, HFR system based on neural-fuzzy (NF) has been introduced. In the NN system, backpropagation (BP) algorithm is used to update the weights of the neurons through supervised learning. Two sets of the image have been used for training and testing the network to identify the person. If the test image matches to one of the trained sets of the image, then the system will return recognized. And if the test image does not match to one of the trained sets of the image, then the system will return not recognized. The feature extraction methods used in this paper is Geometric moments and Color feature extraction. The recognition rate of 95.556 % has been achieved. The experimental result illustrations that the association of two techniques that provide better accuracy.
Mobile learning architecture using fog computing and adaptive data streamingTELKOMNIKA JOURNAL
With the huge development in mobile and network fields, sensor technologies and fog computing help the students for more effective learning, flexible and in and effective manner from anywhere. Using the mobile device for learn encourage the transition to mobile computing (cloud and fog computing) which is led to the ability to design customized system that help student to learn via context aware learning which can be done by set the user preference and use proper methods to show only related manner subject. The presented study works on developing a system of e-learning which has been on the basis of fog computing concepts with deep learning approaches utilized for classification to the data content for accomplishing the context aware learning and use the adaptation of video quality using special equation and the data encrypted and decrypted using 3DES algorithm to ensure the security side of the operation.
A Modern Technique for Unauthorized Human Detection and Intimationijtsrd
"Technological advancements are inevitable and the field of IoT is no exception. The utilization of the technologies in various sectors is highly employed. Even though we use technology in various sectors, the employment of technology for security purposes is very low. The Existing security in various places only CCTV is used for monitoring and recording. Even there are existing security systems where an alert is sent via an email which requires a stable internet connection. It is unlikely that we expect the user to be always connected to an internet source. In the Proposed system, authorized users faces will be trained and stored in a Database. Initially, when an unknown known person enters in the zone the camera module will capture the intruders face. The captured intruder's face will be compared with the trained faces in the database. If the person's face doesn't match, the micro controller will send an alert SMS to the recognized user and also the intruder's captured image will be E mailed to the user. The authorized user should acknowledge the SMS message. If he fails to acknowledge the message within a threshold time limit, an alert call will be made to the concerned user. By this the user gets intimated in real time. Keerthanna G. S | Praveen Kumar P | Vishnu Prasad K | Ms. S. Sri Heera ""A Modern Technique for Unauthorized Human Detection and Intimation"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd21659.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/21659/a-modern-technique-for-unauthorized-human-detection-and-intimation/keerthanna-g-s"
IRJET- Efficient Auto Annotation for Tag and Image based Searching Over Large...IRJET Journal
This document discusses an efficient auto annotation system for tag and image based searching over large datasets. It proposes an algorithm that incorporates advantages from other algorithms to improve accuracy and performance of image retrieval in a peer-to-peer framework. Key features extracted for image matching include color, shape, and texture. Experimental results on a dataset of 500 images showed the method achieved 92.4% accuracy in retrieving similar images, comparable to other methods. The system allows users to recognize and extract images across peer systems based on image features.
IRJET - Study on the Effects of Increase in the Depth of the Feature Extracto...IRJET Journal
This document discusses a study on the effects of increasing the depth of the feature extractor for recognizing handwritten digits in a convolutional neural network (CNN). Specifically, it analyzes the performance of a CNN model on the Modified National Institute of Standards and Technology (MNIST) dataset with variations in the number of filters used in deeper layers of the proposed model. The study finds that increasing the number of filters in the convolutional layers improves the accuracy of the model for classifying handwritten digits.
Iaetsd a low power and high throughput re-configurable bip for multipurpose a...Iaetsd Iaetsd
This document presents a reconfigurable low power binary image processor for image processing applications. The processor consists of a reconfigurable binary processing module with mixed-grained architecture providing flexibility, efficiency and performance. Line memories are selected for lower power consumption and clock gating technique is used to reduce their power. The processor supports real-time binary image processing operations like morphological transformations and is suitable for applications like object recognition and tracking.
chalenges and apportunity of deep learning for big data analysis fmaru kindeneh
The document discusses challenges and opportunities in analyzing complex data using deep learning. It begins with an introduction to complex data and deep learning. It then provides background on machine learning, different data types, feature engineering, and challenges in deep learning. The problem specification defines complex data and proposes research questions on how deep learning can better handle complex data properties. The method section outlines a literature review and case studies to define complex data and study its impact on deep learning models.
IRJET - Deep Learning Applications and Frameworks – A ReviewIRJET Journal
This document reviews deep learning applications and frameworks. It begins by defining deep learning and discussing how deep neural networks can be used to automatically identify patterns in large datasets. It then discusses several applications of deep learning, including self-driving cars, news aggregation, natural language processing, virtual assistants, and visual recognition. The document also describes artificial neural networks and deep neural networks. Finally, it reviews several popular deep learning frameworks, including TensorFlow, PyTorch, Keras, Caffe, and Chainer.
Application of VLSI In Artificial IntelligenceIOSR Journals
This document discusses the application of VLSI (Very Large Scale Integrated) circuits in artificial intelligence. It begins with a brief history of the development of microelectronics and integrated circuits. It then provides definitions of artificial intelligence and describes how VLSI technology has enabled more powerful computer architectures for AI. The document focuses on how expert systems, which apply reasoning to knowledge bases, have been important early applications of AI to VLSI chip design. It provides examples of expert systems used for tasks like circuit simulation and assisting with VLSI design. In closing, it emphasizes that knowledge-based approaches using rules have advantages for incremental improvements and explaining reasoning.
Lightweight digital imaging and communications in medicine image encryption f...TELKOMNIKA JOURNAL
Diagnosis in healthcare systems relies heavily on the use of medical images. Images such as X-rays, ultrasounds, computed tomography (CT) scans, magnetic resonance imaging (MRIs), and other scans of the brain and other internal organs of patients include private and personal information. However, these images are vulnerable to unauthorized users who unlawfully use them for non-diagnostic reasons due to the lack of security in communication routes and the gaps in the storage systems of hospitals or medical centers. Image encryption is a prominent technique used to protect medical images from unauthorized access in addition to enhancing the security of communication networks. In this paper, researchers offer a lightweight cryptosystem for the secure encryption of medical images that makes use of the present block cipher and a five-dimensional chaotic map. More than 25 images from the open science framework (OSF) public database of patients with coronavirus disease 2019 (COVID-19) were used to evaluate the proposed system. DICOM stands for “digital imaging and communications in medicine”. The efficiency of the proposed system is proved in terms of adjacent pixels’ correlation analysis, National Institute of Standards and Technology (NIST) analysis, mean square error, information entropy, unified average changing intensity, peak-to-signal noise ratio, entropy, and structure similarity index image.
Smart surveillance systems play an important role in security today. The goal of security systems is to protect users against fires, car accidents, and
other forms of violence. The primary function of these systems is to offer security in residential areas. In today’s culture, protecting our homes is
critical. Surveillance, which ranges from private houses to large corporations, is critical in making us feel safe. There are numerous machine learning algorithms for home security systems; however, the deep learning convolutional neural network (CNN) technique outperforms the others. The
Keras, Tensorflow, Cv2, Glob, Imutils, and PIL libraries are used to train and assess the detection method. A web application is used to provide a
user-friendly environment. The flask web framework is used to construct it. The flash-mail, requests, and telegram application programming interface (API) apps are used in the alerting approach. The surveillance system tracks
abnormal activities and uses machine learning to determine if the scenario is normal or not based on the acquired image. After capturing the image, it is
compared with the existing dataset, and the model is trained using normal events. When there is an anomalous event, the model produces an output from which the mean distance for each frame is calculated.
An optimized discrete wavelet transform compression technique for image trans...IJECEIAES
Transferring images in a wireless multimedia sensor network (WMSN) knows a fast development in both research and fields of application. Nevertheless, this area of research faces many problems such as the low quality of the received images after their decompression, the limited number of reconstructed images at the base station, and the high-energy consumption used in the process of compression and decompression. In order to fix these problems, we proposed a compression method based on the classic discrete wavelet transform (DWT). Our method applies the wavelet compression technique multiple times on the same image. As a result, we found that the number of received images is higher than using the classic DWT. In addition, the quality of the received images is much higher compared to the standard DWT. Finally, the energy consumption is lower when we use our technique. Therefore, we can say that our proposed compression technique is more adapted to the WMSN environment.
This document discusses using artificial neural networks and MATLAB 7.10 to develop an efficient system for sorting mechanical spare parts. It involves using wavelet transforms to extract features from images of parts, which are then used to train an artificial neural network. The neural network can accurately recognize parts based on their wavelet features with high efficiency. Simulation results show the system can successfully identify the name of a selected spare part from its image with a graphical output.
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
IRJET- Kidney Stone Classification using Deep Neural Networks and Facilitatin...IRJET Journal
The document discusses classifying kidney stone images using deep neural networks and facilitating diagnosis using IoT. Kidney stone images are acquired and preprocessed by converting to grayscale, enhancing, and segmenting the area of interest. Texture features are extracted using active contour segmentation and classified using a deep neural network model. The results, including stone type and associated symptoms and treatment, are sent to the cloud where doctors and patients can access it, allowing automated diagnosis without human intervention.
Robust Malware Detection using Residual Attention NetworkShamika Ganesan
In this paper, we explore the use of an attention based mechanism known as Residual Attention for malware detection and compare this with existing CNN based methods and conventional Machine Learning algorithms with the help of GIST features. The proposed method outperformed traditional malware detection methods which use Machine Learning and CNN based Deep Learning algorithms, by demonstrating an accuracy of 99.25%.
This paper has been accepted in the International Conference of Consumer Electronics (ICCE 2021).
A hybrid approach for face recognition using a convolutional neural network c...IAESIJAI
Facial recognition technology has been used in many fields such as security,
biometric identification, robotics, video surveillance, health, and commerce
due to its ease of implementation and minimal data processing time.
However, this technology is influenced by the presence of variations such as
pose, lighting, or occlusion. In this paper, we propose a new approach to
improve the accuracy rate of face recognition in the presence of variation or
occlusion, by combining feature extraction with a histogram of oriented
gradient (HOG), scale invariant feature transform (SIFT), Gabor, and the
Canny contour detector techniques, as well as a convolutional neural
network (CNN) architecture, tested with several combinations of the
activation function used (Softmax and Segmoïd) and the optimization
algorithm used during training (adam, Adamax, RMSprop, and stochastic
gradient descent (SGD)). For this, a preprocessing was performed on two
databases of our database of faces (ORL) and Sheffield faces used, then we
perform a feature extraction operation with the mentioned techniques and
then pass them to our used CNN architecture. The results of our simulations
show a high performance of the SIFT+CNN combination, in the case of the
presence of variations with an accuracy rate up to 100%.
Compact optimized deep learning model for edge: a reviewIJECEIAES
Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices.
IRJET- Deep Learning Techniques for Object DetectionIRJET Journal
The document discusses deep learning techniques for object detection in images. It provides an overview of convolutional neural networks (CNNs), the most popular deep learning approach for computer vision tasks. The document describes the basic architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers. It then discusses several state-of-the-art CNN models for object detection, including ResNet, R-CNN, SSD, and YOLO. The document aims to help newcomers understand the key deep learning techniques and models used for object detection in computer vision.
Telemedicine; use of telecommunication and information technological services, which permits the
communication between the users with convenience and fidelity, as well transmitting medical, images and
health informatics data. Numerous image processing applications like Satellite Imaging, Medical Imaging
and Video has images with too large size or stream size, with a large amount of space or high bandwidth
for communication in its original form. Integrity of the transmitted medical images and the informatics
data, without any compromise in the data is an essential product of telecommunication and information
technology. A colossal need for an adequate compression methodology, in adoption for the compression of
medical images /data, to domicile for various metrics like high bandwidth, resolution factors, storage of the
images/data, the obligation to perpetuate the validity and precision of data for subsequent perceived
diagnosis transactions. This leverages exacting coercions on the restoration error. In this paper we survey
the literature related to the Image Processing Methodologies based on ROI technique/s for Digital Imaging
and Communication for Medicine (DICOM). A scrutiny as such persuades with the several congestions
related to prospective techniques of lossless compression, recommending for a better and a unique image
compression technique.
Dual method cryptography image by two force secure and steganography secret m...TELKOMNIKA JOURNAL
With the go on the evolution of both computer and internet technology, videos, sounds, and scripts are used more and more often. It can be used in sundry techniques in ciphering and data concealing. The objective of this paper is leading to the suggestion of a new method of the combination between encryption and concealment of information so as to make it difficult to identify the transmitted datavia networks. This study has used two force secure (2FS) to encrypt the images, in other words, the SF is frequent twice on the image, to obtain powerful encryption then the concealing of the secret message is done inside the cryptography of the image has been performed using a secret key (cosine curve), and this stego-encryption image has been transformed forthe Internet of things storage in the database in IoT (data flow), when the user needs any information can be access inviaof internet of things (IoTs). The outcome of the proposed system is obtained tobe evaluated through different measures, such aspeak signal noise ratio (PSNR), mean square error (MSE), entropy,correlation coefficient, and histogram. The proposed system is good, efficient, fast, has high security, robustness, and transparency.
An improved robust and secured image steganographic schemeiaemedu
The document summarizes an improved steganographic scheme that embeds secret data in images. It modifies an existing DCT-based scheme by embedding an "embedding map" to indicate the blocks used for concealment. The embedding map is also hidden using DWT coefficients and secured using SURF features. The proposed method aims to overcome limitations in the existing scheme like potential data loss during extraction due to changes in block energy values. Results show the scheme is robust against attacks like noise and compression while maintaining good image quality. However, capacity is still limited as only part of the image can hide the embedding map.
8 of the Must-Read Network & Data Communication Articles Published this weeke...IJCNCJournal
Beamforming for millimetre-wave (mmWave) frequencies has been studied for many years. It is considered as an important enabling technology for communications in these high-frequency ranges and it received a lot of attention in the research community. The special characteristics of the mmWave band made the beamforming problem a challenging one because it depends on many environmental and operational factors. These challenges made any model-based architecture fit only special applications, working scenarios, and specific environment geometry. All these reasons increased the need for more general machine learning based beamforming systems that can work in different environments and conditions. This increased the need for an extended adjustable dataset that can serve as a tool for any machine learning technique to build an efficient beamforming architecture. Deep MIMO dataset has been used in many architectures and designs and has proved its benefits and flexibility to fit in many cases. In this paper, we study the extension of collaborative beamforming that includes many cooperating base stations by studying the impact of User Equipment (UE) speed ranges on the beamforming performance, optimizing the parameters of the neural network architecture of the beamforming design, and suggesting the optimal design that gives the best performance for as a small dataset as possible. Suggested architecture can achieve the same performance achieved before with up to 33% reduction in the dataset size used to train the system which provides a huge reduction in the data collection and processing time.
PERFORMANCE EVALUATION OF CROSS LAYER SECURITY SYSTEM FOR SECURE OPTICAL TRAN...IRJET Journal
This document summarizes a study that evaluated the performance of a cross-layer security system for secure optical image transmission. The system implemented security at both the physical layer using optical CDMA encoding and at the data link layer using AES encryption. At the transmitter, the original image was first AES encrypted and then encoded using an optical orthogonal code before transmission over fiber. At the receiver, the image was successfully recovered only when using the correct decryption and decoding keys. The results demonstrated that this cross-layer approach provided higher security than single layer schemes by making interception or eavesdropping more difficult for unauthorized parties.
Machine learning based augmented reality for improved learning application th...IJECEIAES
Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-ofthe-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience.
Desing on wireless intelligent seneor network on cloud computing system for s...csandit
Sensors on (or attached to) mobile phones can enable attractive sensing applications in
different domains such as environmental monitoring, social networking, healthcare, etc. In this
paper we propose a cloud computing system dedicated on smart home applications. We design
the proposed wireless vision sensor network (WVSN) with its algorithm and hardware
implementation. In WVSN, The partial-vision camera strategy is applied to allocate the
computation task between the sensor node and the central server. Then we propose a high
performance segmentation algorithm. Meanwhile, an efficient binary data compression method
is proposed to cope with the result on labeling information. The proposed algorithm can provide
high precision rate for the smart home applications such as the gesture recognition and
humanoid tracking. To realize the physical system, we implement it on the embedded platform
and the central server with their transmission work
Precaution for Covid-19 based on Mask detection and sensorIRJET Journal
This document describes a system that uses computer vision and sensors to detect if a person is wearing a face mask and monitor their temperature and oxygen levels. The system uses a Raspberry Pi, camera, and sensors. It applies CNN algorithms to detect faces and determine if a mask is present. It also monitors temperature using a temperature sensor and oxygen levels using a pulse sensor. The goal is to help enforce mask-wearing and identify potential COVID-19 cases by their symptoms. It aims to provide an educational platform for learning different machine learning modules in one place and comparing modified user modules to existing ones.
IRJET- A Review on Fake Biometry DetectionIRJET Journal
This document summarizes a review on detecting fake biometrics. It discusses how face recognition technology has advanced but is vulnerable to spoof attacks using fake faces. The paper presents a novel software-based method for detecting fraudulent access attempts across multiple biometric systems like iris and face recognition. Experimental results on public datasets show the proposed method performs competitively compared to other state-of-the-art approaches. It analyzes the general image quality of real biometric samples to distinguish them from fake traits efficiently.
IRJET - Simulation of Colour Image Processing Techniques on VHDLIRJET Journal
This document summarizes research on simulating color image processing techniques using VHDL. It discusses using VHDL to implement thresholding, brightness, and inversion operations on images. The goal is to perform these operations faster than software by taking advantage of the reconfigurability and parallelism of hardware. The paper reviews related work on image processing using FPGAs and proposes simulating the image processing system using a link between MATLAB and VHDL for testing and verification.
Similar to Optimized image processing and clustering to mitigate security threats in mobile ad hoc network (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.
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Applications of artificial Intelligence in Mechanical Engineering.pdfAtif Razi
Historically, mechanical engineering has relied heavily on human expertise and empirical methods to solve complex problems. With the introduction of computer-aided design (CAD) and finite element analysis (FEA), the field took its first steps towards digitization. These tools allowed engineers to simulate and analyze mechanical systems with greater accuracy and efficiency. However, the sheer volume of data generated by modern engineering systems and the increasing complexity of these systems have necessitated more advanced analytical tools, paving the way for AI.
AI offers the capability to process vast amounts of data, identify patterns, and make predictions with a level of speed and accuracy unattainable by traditional methods. This has profound implications for mechanical engineering, enabling more efficient design processes, predictive maintenance strategies, and optimized manufacturing operations. AI-driven tools can learn from historical data, adapt to new information, and continuously improve their performance, making them invaluable in tackling the multifaceted challenges of modern mechanical engineering.
Build the Next Generation of Apps with the Einstein 1 Platform.
Rejoignez Philippe Ozil pour une session de workshops qui vous guidera à travers les détails de la plateforme Einstein 1, l'importance des données pour la création d'applications d'intelligence artificielle et les différents outils et technologies que Salesforce propose pour vous apporter tous les bénéfices de l'IA.
Determination of Equivalent Circuit parameters and performance characteristic...pvpriya2
Includes the testing of induction motor to draw the circle diagram of induction motor with step wise procedure and calculation for the same. Also explains the working and application of Induction generator
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Impartiality as per ISO /IEC 17025:2017 StandardMuhammadJazib15
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Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELijaia
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%.
Mechanical Engineering on AAI Summer Training Report-003.pdf
Optimized image processing and clustering to mitigate security threats in mobile ad hoc network
1. TELKOMNIKA Telecommunication, Computing, Electronics and Control
Vol. 18, No. 1, February 2020, pp. 476~484
ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018
DOI: 10.12928/TELKOMNIKA.v18i1.13914 476
Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA
Optimized image processing and clustering to
mitigate security threats in mobile ad hoc network
Ibrahim A. Alameri1
, Jawad kadhim mutar2
, Ameer N. Onaizah3
, Iftikhar Ahmed Koondhar4
1
Jaber ibn Hayyan Medical University, Iraq
1
University of Pardubice, Faculty of Economics and Administration, Czech Republic
2
College of Education for Humanity Sciences, Almuthana University, Iraq
3,4
School of Computer Science and Technology, Beijing Institute of Technology, China
3
University of Kufa, Iraq
Article Info ABSTRACT
Article history:
Received Aug 16, 2019
Revised Dec 5, 2019
Accepted Dec 25, 2019
Since there are provisions of many attributes that are not possible or difficult
to follow by networks conventionally, mobile ad-hoc networks are extensively
deployed. This application starts through the defense sectors, the sensory node
presents in the hostile territories down to the gadgets for congestion
communication in traffic by general transportation when travelling for
adequate provision of infrastructure during disaster recovery. As a lot of
importance related to (mobile ad hoc network) MANET application, one
important factor in ad-hoc networks is security. Using image processing for
securing MANET is the area of focus of this research. Therefore, in this article,
the security threats are assessed and representative proposals are summarized
in ad-hoc network’s context. The study reviewed the current situation of
the art for original to security provision called mobile ad hoc network for
wireless networking. The threats to security are recognized while the present
solution is observed. The study additionally summarized education erudite,
talks on general issues and future instructions are recognized. Also, in this
study, the forecast weighted clustering algorithm (FWCA) is employed as
a cluster head over weighted clustering algorithm (WCA) is examined as
quality in cluster-based routing, service is highly significant with MANET.
Keywords:
Image analysis
Image processing
MANET
Security
Weighted clustering algorithm
This is an open access article under the CC BY-SA license.
Corresponding Author:
Ibrahim A. Alameri,
Jaber ibn Hayyan Medical University, Najaf, Iraq.
University of Pardubice, Pardubice, Czeck Republic.
Email: ib.alameri@jmu.edu.iq, ibrahim.alameri@student.upce.cz
1. INTRODUCTION
The The application of computer algorithm to carry out processing of image on digital images is called
digital image processing (DIP) [1]. There are many advantages related to DIP as a field of digital dispensation
or sub-category or analog image processing in excess. An effective deal is provided in broader algorithm range
to be practiced to enter data with easy prevention of “evils” like: signal distortion and build-up noise. DIP may
be developed in the form of multi-dimensional system since definitions of images are over two-dimensions or
more. Images are classified based on their source such as x-ray and visual. The electromagnetic energy range
is the principal source of energy for images; while other sources of energy are: electronic; ultrasonic and
acoustic. The digital image is mapped and sampled as a picture elements or pixels or a grid of dots. Those
digital images are electronically taken snapshots from scene or scanned documents like manuscripts, printed
2. TELKOMNIKA Telecommun Comput El Control
Optimization image processing and clustering to mitigate the security threats in... (Ibrahim A. Alameri)
477
works, photographs and artworks. While the computer generates the visualization, the synthetic images are
used for modelling [2].
A total value such as white, black, shades of colour or grey that are assigned to each pixel is
represented in binary codes as ones and zeros. A computer is used to store the bits or binary digit for each pixel
in a sequence and it is usually called “compressed” as it is being represented mathematically. The computer
read and then interpreted the bits to generate an account of analog to display [3]. The basic steps during
the processing of digital image are: image acquisition, image enhancement, image restoration, colour image
processing, processing of multi-resolution and wavelets, segmentation, description, recognition and
representation of object and morphological processing. Analysis and processing of digital image are applied in
industrial and educational application and in a wide range of artistic [4]. Processing and analysis of soft image
is generally presented in all main platforms of computers. Environmental science, art, medicine and
biotechnology all use image processing.
Therefore, this study proposed a novel method through which security can be provided in all phases.
A trust based multipath routing protocol is used in order to enhance security in the routing phase. As intruders
can monitor and intercept the password, thus, the authentication key transfer in MANET networks via nameless
midway nodes is not suitable to be used. It is imperative to use a strong secure method of key transfer that
hides data of verification keys. Therefore, in this article, the threats to security assess are assessed and
representative proposals are summarized in ad-hoc network’s context. The study reviewed the current situation
of the art for original to security provision called mobile ad hoc network for wireless networking.
2. LITERATURE REVIEW
2.1. Image processing
In a broadest term, an image processing is an umbrella term used for analysing and representing data
in visual form [5]. It is regarded as the manipulation of numeric data present in a digital image in an attempt to
enhance its visual appearance. Satellite photographs can be calibrated, medical images can be clarified and
faded pictures can be enhanced through image processing. Numeric information can also be translated into
visual images by image processing that can be edited, animated, filtered and enhanced in order to show
the association previously not apparent [6]. Analysis of image involves collection of data from digital images
in form of measurements that can be transformed and analysed. An accurate digital substitute for callipers and
rulers is provided by the image analysis.
Images are classified in accordance with their source e.g. X-ray, visual and so on. The electromagnetic
energy spectrum is the principal energy source for images. The ultrasonic, electronic and the acoustic are other
sources of energy. While the visualization is generated by the computer, the synthetic images are used for
modelling. Digital images are electronic snapshots taken from a scanned or scene of documents such as
artwork, printed text, manuscripts and photographs [7]. The digital image is mapped and sampled as a grid of
pixels, picture elements and dots. A tonal value is attached to each pixel i.e. white, black, shades of grey or
color which is represented in binary code as zeros and ones. The binary digits or bits for each pixel are stored
in a sequence by a computer and often minimized to a mathematical representation called compressed. The bits
are then read and interpreted by the computer to produce an analog version for display or printing. In digital
image processing, the fundamental steps include [8]:
− Acquisition of image
− Enhancement of image
− Restoration of image
− Processing of colour image
− Wavelets and multi-resolution processing
− Compression
− Morphological processing
− Segmentation
− Description and representation
2.2. Mobile Ad-Hoc networks
Cloud services can be accessed either by wired network or wireless [9, 10] however MANET is
a wireless network where every device communicate wirelessly [11, 12]. A mobile ad-hoc network (MANET)
is often distinguished as networks with many free and independent nodes, with mobile device composition and
other related pieces of mobile, which place themselves in different categories of setups and the capacity of type
of network, is still under research. MANET is becoming popular more due to its ease of deployment, flexibility
and low cost. Meanwhile to the network must follow a protocol of sophisticated routing in order to achieve
3. ISSN: 1693-6930
TELKOMNIKA Telecommun Comput El Control, Vol. 18, No. 1, February 2020: 476 - 484
478
these benefits. The protocols that were early proposed are not designed to operate when the attackers are
present. Thus, this led to some major challenges in MANETs as explained in the Table 1 [13-20].
Table 1. Large-scale of challenges in MANETs
Challenges Clarification
1 Independence
In managing different actions of nodes of mobile, there is no centralized management
entity available
2 Dynamic topology
In a random manner, nodes can be connected and mobile dynamically. The connection
of the networks is in variation timely and is in proximities to each other in additional
nodes accordingly.
3 Device Detection
Identification of node relevancies in terms of moves and giving information on the need
for existence of dynamic update lessen the difficulties in automatic selection of optimal
route.
4
Bandwidth
optimization
In terms of capacity, the wired links are greater than the wireless links.
5 Security
Susceptibility of the mobile link to both internal and external intrusion as render of
mobility of node. A big challenge in MANET can be any node that can enter and leave
freely the network and give security communication.
6 Topology Maintenance
One of the major threats among the MANET’s nodes is the information updates of
dynamic links.
7 Network Configuration
The fact that there is dynamism in the infrastructure of MANET is the motive behind
the connection and disconnection of the variable links.
8 Limited Resources
As power and storage capacity are strictly partial, mobile nodes has a reliance on battery
power – a very scarce resource.
9 Scalability
This is whether the network is able to make a provision in the presence of large numbers
of nodes on an acceptable level of service.
10
Limitation in physical
security
Mobility means high risk in security such as shared accessible wireless medium or peer
to peer network agriculture to both malicious attackers and legitimate network users.
There should be consideration for spoofing, service attack denial and eaves dropping
11
Infrastructure-less and
self-operation
Manet is required by self-healing function in order to integrate into blanket of moving
nodes out of range.
12
Poor Transmission of
Quality
This is a wireless communication related problem as a result of many inherent source
of error that lead to degradation of received signal
13 Ad Hoc Addressing Problems related to implementation of standard addressing scheme
Assurance of MANET networks is the major challenge due to its susceptibility to attacks in a mobile
link while the mobility of the nodes renders the network to possessing a highly dynamic topology. External
and internal are the two categories of attacks against routing protocols. The internal attack is a result of
a misconfigured, malicious router, faulty and compromised inside a network domain. A temporary network is
formed by a collection of wireless mobile hosts which forms finally the network in ad-hoc without the need to
include a stand alone infrastructure or centralized administration [6]. Self organization and
self- configuring are the characteristics of the mobile multi-hop ad- hoc network where network structure is
subjected to dynamic changes as a result of node mobility.
In these nodes, channels of random access are utilized by the nodes and thus be incorporated to
participate friendly in the multi- hop forwarding. Working as hosts and routers is what nodes of the network
do and thus transmitting data to or from other nodes in the network. Forwarding the packets in an appropriate
way between the destination and from the source of mobile ad- hoc network requires locating a path by
a routine procedure when infrastructure support is missing as seen in the case of wireless network or when
destination mode is out of the range of a source node transmitting packet. The nodes in these networks
use wireless channel of random access, manifesting it in a good manner to put themselves in multi hop
forwarding as shown in Figure 1. The nodes of networks can be the hosts and the routers data to or from other
nodes in network.
Base station can reach all the mobile nodes within a cell with no routing using broadcast in a common
wireless network. Taking ad hoc networks as example, data can be forwarded by each node to others.
By the way, more challenges will be faced regarding dynamics topology which is known as unpredictable
changes in connectivity. In the current work a novel method have been applied to achieve security in both
phases. First to enhance security in the routing phase by use a trust based multipath routings. Secondly, discover
a secured trustworthy path from a source to a destination with minimal overhead. In previous studies Multiple
node disjoint paths are discovered to enhance the security of the data delivery phase. Furthermore, misbehaving
nodes are detected and exempted from such paths using the trust value of the nodes It’s well known that Sending
confidential data on one path helps attackers to get the whole data easily, whereas sending it in parts on different
disjointed paths increases the confidentiality robustness, as it is almost impossible to obtain all
the parts of a message fragmented and sent on multiple paths existing between the source and the destination.
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Figure 1. Network of a general ad-hoc in-action condition
2.3. System in existence
MANET has been made a popular topic of research with the growth of laptop system and Wi-Fi
networking since the mid-1990s. Evaluations of different security measures are done by majority of academic
papers for providing security against threat to MANET; most protocols are designed to provide
security [21, 22]. Their capabilities are usually connected with all nodes within a few hops of one another
assuming there are varying degrees of mobility within bounded space. Then, there is evaluation of different
protocols according to the measure such as the pocket drop ate, the end-to-end delays, the overhead introduced
by routing protocol and network throughput [23-24].
In order to make password memorable and more secure, graphical passwords are introduced. By using
graphical password, rather than typing alphanumeric characters, the users click on the images. The Pass Points
are new graphical password system and more secure [25]. By digital watermark, authentication of image can
be done [26]. A watermark can be used as a secret image or code encoded into an original image that its function
is to identify both content and image owner. One of the forms of image authentication is
the perpetually use of invisible watermarks. The algorithm of watermark is divided into three categories:
marking algorithm; verification algorithm; and watermark.
The security of the system is improved by the approach of Déjà vu that depends not on recall-based
authentication but on recognition-based. Through the ability to recognize previously seen images, the Déjà vu
authenticates a user [2]. Using image processing and visual cryptography in Secure Authentication is an
algorithm based on image processing and visual cryptography. This applies a way of customer signature
processing and incorporating it into shares subsequently. The bank chose a scheme that determines the total
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number of shares to be produced. Thus, during the creation of two shares, while one is kept by the customer,
the other is stored in the bank of database. During all the deals of the customer, the share is presented. To get
the original signature, the first share is stacked by this share. Therefore, decision is taken using correlation
method whether rejection or acceptance of the customer and output authentication.
3. THE PROPOSED SYSTEM: SECURITY USING IMAGE PROCESSING FOR MANET
In Anytime there is entrance for a user into the mobile ad-hoc network in the nearest future, an image
taken from a user is divided into two: the grey image of the original image of the user will be the first one and
the file with image’s colour pixel value is the other one. The part of the key shall comprise both
the image and the file. There is encryption of the file and grey image with the aid of two keys of various types.
The smallest size of the key in amount will have 128 bits. Then, the encrypted files will be joined and separated
into smaller packets while with the aid from another key, each packet will be encrypted. Before
the image processing, there are two layers of security from this way. Each packet passes via the network. After
receiving packet at the side of handset with the support of first private, there will be separation of encrypted
file for color pixel and. grey image values. After that, there will be decryption with the help of both files and
this will join together to form the image. Small packet size for transmission must always be fixed from this
proposed system to manage a better performance and the receiver side buffer space should be extended to avoid
congestion. This complete image processing is supported under User Datagram Protocol (UDP) which has
higher speed. Nodes When the network is being entered by the user and ready to transfer the secure data with
other nodes in MANET:
− At first, the user captures or selects the input image and then selects the key meant for transmission.
− The user divides the key into two-half.
− The input colour image is divided by the user into: grey images with 256 grey levels and other with
the text files are made up of components of RGB of the colour image.
− Addition of the divided key into grey and text image respectively.
− Then, the encryption of the grey and text image by applying one-time hash algorithm of cryptography.
− Followed by a separate transmission of grey image and text file into the network. This implies if an intruder
gets a file, it would be hard to get a key due to the absence of the FULL key.
− After a separate decryption of the GREY image, by the combination RGB image TEXT files and the grey,
the original image is constructed back.
− Lastly, there is combination of keys in order to have a secure key.
4. RESULTS OF THE EXPERIMENT
The process of taking the head of weighted clustering algorithm is accomplished by
the instantaneous value of weight [24]. The reason why no eligible node can send its weight value because of
the possibility of high traffic, although some nodes are able is to be a cluster head. This leads to
the conclusion that the wrong selection of a cluster head can be taken place [25-27]. Accordingly, a solution
for such an issue can be provided; it is FWCA (Forecast Weight Cluster Algorithm) this alternative takes
the old value on a different side from the current value of the node. The result here leads to an appropriate head
of a cluster. In order to calculate the forecast weight, a mode of computation is employed. This mode is called
EMA: exponential moving average. This is useful in that it does not require the former values of
forecast. [20, 28-30]. Forecasted weight (FW) is defined as:
FW = αWcurrent + (1 − α)FWprevious (1)
α is a smoothing factor; a tunable parameter between zero and one. WCA can be used to calculate the weight
of each node as:
𝑊𝑖 = 𝑤1𝑑𝑖 + 𝑤2𝐷𝑖 + 𝑤3𝑆𝑖 + 𝑤4𝑃𝑖 (2)
where:
d = degree of difference in each node
D = Sum of distance with all neighbours
S = the node’s speed
P = Battery consumed by the battery
w1 +w2 + w3 + w4 = 1.
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The forecast weight (FW) is calculated in our proposed FWCA as:
𝐹𝑊𝑖( 𝑡 + 1) = a ∑ (1 − 𝑎)𝑡−1
𝑘=0
𝑘
𝐹𝑊𝑖( 𝑡 − 𝑘) + (1 − 𝛼) 𝑡𝑊𝑖 (3)
FWi (t+1) = Forecast value for period t + 1 at time, t.
Wi = the actual value at period, t.
FWi (t-k) = Forecast value for period t at time, t – 1
In Figure 2, there are several clusters, s1, s2, s3 respectively at the server nodes of cluster 1, cluster 2
and cluster 3. The forecast weight is calculated with these nodes using weight value of nodes and the game
theory approach is used to decide the cluster head to avoid confliction of having similar weights.
w1 + w2 + w3 + w4 = 1
w1 = 0.7
w1 = 0.2
w1=0.06.
Using the following formula, Tables 2, 3 and 4 [30] present the weight values of nodes for each cluster to
calculate the weight:
Wi = w1di + w2Di + w3Si + w4Pi (4)
Figure 2. Structure of cluster submission
The parameters to calculate weight value was assumed here that as follows:
Mobility of nodes (10km/hr to 30 km/hr)
Distance between nodes (0.1km to 0.9km)
Battery power consumed using the formula to calculate the weight (20 Ampere-hour to 70 Ampere-hour)
Table 2. Cluster no. 1 of nodes
Node ID Weigh Value
1 W1 = 0.7 ∗ 6 + 0.2 ∗ 1.2 + 0.06 ∗ 10 + 0.04 ∗ 30 = 6.24
2 W2 = 0.7 ∗ 4 + 0.2 ∗ 0.35 + 0.06 ∗ 20 + 0.04 ∗ 70 = 6.87
3 W3 = 0.7 ∗ 4 + 0.2 ∗ 0.4 + 0.06 ∗ 25 + 0.04 ∗ 60 = 6.78
4 W4 = 0.7 ∗ 3 + 0.2 ∗ 0.1 + 0.06 ∗ 23.6 + 0.04 ∗ 50 = 6.24
5 W5 = 0.7 ∗ 5 + 0.2 ∗ 0.35 + 0.06 ∗ 15 + 0.04 ∗ 70 = 7.27
6 W6 = 0.7 ∗ 3 + 0.2 ∗ 0.5 + 0.06 ∗ 20 + 0.04 ∗ 70 = 6.50
7 W7 = 0.7 ∗ 4 + 0.2 ∗ 0.45 + 0.06 ∗ 26 + 0.04 ∗ 60 = 6.85
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Table 3. Cluster no. 2 of nodes
Node ID Weight Value
14 W14 = 0.7 ∗ 4 + 0.2 ∗ 0.6 + 0.06 ∗ 15 + 0.04 ∗ 30 = 5.02
6 W6 = 0.7 ∗ 3 + 0.2 ∗ 0.5 + 0.06 ∗ 20 + 0.04 ∗ 70 = 6.50
17 W17 = 0.7 ∗ 3 + 0.2 ∗ 0.9 + 0.06 ∗ 25 + 0.04 ∗ 50 = 7.4
18 W18 = 0.7 ∗ 4 + 0.2 ∗ 0.57 + 0.06 ∗ 20 + 0.04 ∗ 40 = 5.74
19 W19 = 0.7 ∗ 3 + 0.2 ∗ 0.5 + 0.06 ∗ 18 + 0.04 ∗ 60 = 5.02
Table 4. Cluster no. 3 of nodes
Node ID Weight Value
9 W9 = 0.7 ∗ 4 + 0.2 ∗ 0.8 + 0.06 ∗ 10 + 0.04 ∗ 20 = 4.36
3 W3 = 0.7 ∗ 4 + 0.2 ∗ 0.4 + 0.06 ∗ 25 + 0.04 ∗ 60 = 6.78
10 W10 = 0.7 ∗ 3 + 0.2 ∗ 0.9 + 0.06 ∗ 25 + 0.04 ∗ 60 = 6.18
13 W13 = 0.7 ∗ 2 + 0.2 ∗ 0.7 + 0.06 ∗ 30 + 0.04 ∗ 65 = 5.94
15 W15 = 0.7 ∗ 3 + 0.2 ∗ 0.8 + 0.06 ∗ 26 + 0.04 ∗ 60 = 6.22
5. CONCLUSION
For security of networks in MANET, secure key transfer is important. It is difficult to know
the dependable nodes in MANET network without the idealistic concept of the intermediate node identity in
operation. Ad hoc network is a type of networks that do not relay on any infrastructure during establishment.
Where intruders can monitor and intercept the password, authentication key transfer in MANET networks via
nameless midway nodes is not suitable to be used. It is imperative to use a strong secure method of key transfer
that hides data of verification keys. Thus, this proposed system is suitable where key is hidden in
the image from the system that is different from others in order to secure key transfer in MANET networks.
The image then splits into two parts while the parts are therefore encrypted for double level of security. Ability
to develop a doubled level of security of key transfer in the networks of MANET with encrypted secure key
transfer is the primary advantage of the future approach. A cluster head is responsible for routing process in
cluster-based routing protocol and information like cluster links and membership are maintained by this cluster
head in accordance to which what it is possible to dynamically discover the inter-cluster routes. Thus,
A forecasted weighted clustering algorithm is proposed in this study where more eligible and proper nodes are
selected as cluster head as well as introducing server node to reduce per node calculation overhead.
Abbreviations and Acronyms
ID Abbreviations
1 MANET Mobile ad hoc network
2 FWCA Forecast Weighted Clustering Algorithm
3 WCA Weighted Clustering Algorithm
4 DIP Digital Image Processing
5 UDP User Datagram Protocol
6 EMA Exponential Moving Average
7 FW Forecasted Weight
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BIOGRAPHIES OF AUTHORS
Ibrahim Alameri is currently a PhD student at Pardubice universty-Czech Republic.
Additionally, he is working as an assistant lecturer in College of Medicine–Jabir Ibn Hayyan
Medical University. He earned a Master degree in computer science from South Ural State
Universty. Mr. Alameri has eight years of research experience. His research fields are Mobile ad
hoc networks, protocol optimizations and cloud computing.
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Jawad Kadhim Mutar is an assistant lecturer in history department in the educational
college of Humanities in almuthana university. He earned his master in computer science in
osmaniah university.
Ameer N. Onaizah is an assistant lecturer in College of Computer Science and Mathematics
university of Kufa. Currently, he is a PhD student at Beijing Institute of Technology, China
Iftikhar Ahmed Koondhar is currently PhD student at Beijing Institute of Technology, China.
He is working as an assistant professor in the department of computer systems engineering in
Quaid-e-awam University-Pakistan. Mr. Koondhar earned his Master of Engineering degree in
Communication Systems and Networks from Mehran University of Engineering and Technology
- Pakistan. His research interests include computer networks, data mining and high-performance
computing and internet of things.