Great knowledge and experience on microbiology are required for accurate bacteria identification.Automation of bacteria identification is required because there might be a shortage of skilled microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
Extraction of spots in dna microarrays using genetic algorithmsipij
DNA microarray technology is an eminent tool for genomic studies. Accurate extraction of spots is a
crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
This approach provides reliable results for identification of even low intensity spots and elimination of
spurious spots.
IRJET- Image Classification using Deep Learning Neural Networks for Brain...IRJET Journal
This document discusses using a convolutional neural network (CNN) to classify brain tumor MRI images. It begins with an introduction to brain tumors and MRI as a diagnostic tool. It then reviews related work applying deep learning to medical image classification tasks. The proposed CNN model contains convolutional and max pooling layers for feature extraction, and fully connected layers for classification. The model is trained on a dataset of 253 MRI brain images from Kaggle to classify images as containing a tumor or being tumor-free. Experimental results show the CNN achieving 98.5% accuracy in classification, demonstrating the feasibility of the approach.
IRJET- Crop Leaf Disease Diagnosis using Convolutional Neural NetworkIRJET Journal
This document describes a system for diagnosing crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, rice, wheat, sugarcane, and grapes. It uses a MobileNet model and CNN architecture trained on datasets of images of healthy and diseased leaves. The system achieves 97.33% accuracy in diagnosing diseases in grape leaves. It aims to help farmers detect diseases early and determine the appropriate pesticides.
IRJET - Identification of Malarial Parasites using Deep LearningIRJET Journal
This document presents a method for identifying malarial parasites using deep learning. The traditional method of manually examining stained blood slides under a microscope is time-consuming and relies on expert availability. The proposed method uses image processing to automate diagnosis and provide quicker, more accurate results. Images of blood samples are preprocessed, segmented, and features are extracted for classification using deep learning models like convolutional neural networks and support vector machines. This can help detect the presence of malarial parasites in blood more sensitively and accurately than manual examination alone.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
This document reviews the use of deep learning techniques for brain tumor analysis. It begins with an introduction to brain tumors and the importance of image-based analysis. It then discusses how deep learning methods like convolutional neural networks have achieved state-of-the-art performance in segmenting, classifying, and predicting survival from brain tumor MRI scans. The review presents a taxonomy of research on applying deep learning to brain tumor analysis and discusses challenges and opportunities in the field.
A Novel Leaf-fragment Dataset and ResNet for Small-scale Image AnalysisA. Hasib Uddin
This document presents research on using deep learning for plant identification through leaf vein patterns. The researchers collected over 80,000 leaf images of 4 plant species and 2 cotyledon types. They extracted the green color channel and center cropped the images. Various image sizes were then generated for analysis. A ResNet model was used for cotyledon and species classification and compared to ResNet-152. The model achieved good performance on the small dataset. Future work could include adding more image data and exploring other color channels.
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
Extraction of spots in dna microarrays using genetic algorithmsipij
DNA microarray technology is an eminent tool for genomic studies. Accurate extraction of spots is a
crucial issue since biological interpretations depend on it. The image analysis starts with the formation of
grid, which is a laborious process requiring human intervention. This paper presents a method for optimal
search of the spots using genetic algorithm without formation of grid. The information of every spot is
extracted by obtaining a pixel belonging to that spot. The method developed selects pixels of high intensity
in the image, thereby spot is recognized. The objective function, which is implemented, helps in identifying
the exact pixel. The algorithm is applied to different sizes of sub images and features of the spots are
obtained. It is found that there is a tradeoff between accuracy in the number of spots identified and time
required for processing the image. Segmentation process is independent of shape, size and location of the
spots. Background estimation is one step process as both foreground and complete spot are realized.
Coding of the proposed algorithm is developed in MATLAB-7 and applied to cDNA microarray images.
This approach provides reliable results for identification of even low intensity spots and elimination of
spurious spots.
IRJET- Image Classification using Deep Learning Neural Networks for Brain...IRJET Journal
This document discusses using a convolutional neural network (CNN) to classify brain tumor MRI images. It begins with an introduction to brain tumors and MRI as a diagnostic tool. It then reviews related work applying deep learning to medical image classification tasks. The proposed CNN model contains convolutional and max pooling layers for feature extraction, and fully connected layers for classification. The model is trained on a dataset of 253 MRI brain images from Kaggle to classify images as containing a tumor or being tumor-free. Experimental results show the CNN achieving 98.5% accuracy in classification, demonstrating the feasibility of the approach.
IRJET- Crop Leaf Disease Diagnosis using Convolutional Neural NetworkIRJET Journal
This document describes a system for diagnosing crop leaf diseases using convolutional neural networks. The system can identify diseases in five major crops: corn, rice, wheat, sugarcane, and grapes. It uses a MobileNet model and CNN architecture trained on datasets of images of healthy and diseased leaves. The system achieves 97.33% accuracy in diagnosing diseases in grape leaves. It aims to help farmers detect diseases early and determine the appropriate pesticides.
IRJET - Identification of Malarial Parasites using Deep LearningIRJET Journal
This document presents a method for identifying malarial parasites using deep learning. The traditional method of manually examining stained blood slides under a microscope is time-consuming and relies on expert availability. The proposed method uses image processing to automate diagnosis and provide quicker, more accurate results. Images of blood samples are preprocessed, segmented, and features are extracted for classification using deep learning models like convolutional neural networks and support vector machines. This can help detect the presence of malarial parasites in blood more sensitively and accurately than manual examination alone.
Detecting malaria using a deep convolutional neural networkYusuf Brima
Experiment with Deep Residual Convolutional Neural Network to classify microscopic blood cell images (Uninfected, Parasitized)
Utiling ResNet,Deep Residual Learning for Image Recognition (He et al, 2015) architecture.
Uses Keras with a Tensorflow backend.
This document reviews the use of deep learning techniques for brain tumor analysis. It begins with an introduction to brain tumors and the importance of image-based analysis. It then discusses how deep learning methods like convolutional neural networks have achieved state-of-the-art performance in segmenting, classifying, and predicting survival from brain tumor MRI scans. The review presents a taxonomy of research on applying deep learning to brain tumor analysis and discusses challenges and opportunities in the field.
A Novel Leaf-fragment Dataset and ResNet for Small-scale Image AnalysisA. Hasib Uddin
This document presents research on using deep learning for plant identification through leaf vein patterns. The researchers collected over 80,000 leaf images of 4 plant species and 2 cotyledon types. They extracted the green color channel and center cropped the images. Various image sizes were then generated for analysis. A ResNet model was used for cotyledon and species classification and compared to ResNet-152. The model achieved good performance on the small dataset. Future work could include adding more image data and exploring other color channels.
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
IRJET - Automating the Identification of Forest Animals and Alerting in Case ...IRJET Journal
This document describes a proposed system to automatically identify and monitor forest animals using deep learning and computer vision techniques. The system would collect images using cameras traps and use a convolutional neural network (CNN) to identify animals in the images. It would be trained on a dataset of 1500 images across 5 animal categories. If the system identifies an animal encroaching on villages, it would trigger an alert to notify the forest department. The system aims to automate time-consuming manual animal identification tasks and provide alerts about potential human-animal conflicts. It could help conservation efforts by monitoring wildlife populations over time more efficiently.
Pattern recognition using video surveillance for wildlife applicationsprjpublications
This document summarizes a research paper that proposes a wildlife monitoring system using video surveillance and pattern recognition. The system uses motion detection to capture images when movement is detected. A pattern recognition module then analyzes the images using Histogram of Oriented Gradients (HOG) to distinguish between harmful and harmless animals. If a harmful animal is identified, the system notifies authorities of the animal type and location using GSM and GPS modules. The researchers tested the system using a database of animal images and found that HOG provided accurate classification of tigers and other animals.
INSPECTION OF PROFILED FRP COMPOSITE STRUCTURES BY MICROWAVE NDEjmicro
Fiber reinforced polymer (FRP) composites are employed in various applications of aerospace and defence industry. FRP composites are preferred as major structural parts due to their high stiffness strength and light weight.Non-destructive evaluation (NDE) plays an important role in assessing the quality and health monitoring of FRP composite structures during their manufacturing and in-service period.Different NDE techniques, such as ultrasonics, thermography, X-ray radiography, etc are employed for evaluating the quality of the composite structures.Microwave non-destructive evaluation (MWNDE) is an emerging NDE technique for characterizing and inspecting dielectric structures. Microwave NDE finds application in the areas of dielectric material characterization, determining thickness variation, defect detection and bond quality inspection.Inspection of profiled FRP composite structures by near-field reflection microwave NDE technique is presented in this paper. Application of Microwave NDE for bond quality inspection of FRP composite structures and thickness variation of composite structures is discussed. Results of inspected profiled composite structures by swept frequency reflection microwave NDE technique in the frequency range of X-band and Ku-band respectively are presented
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
This study developed a new quantitative method for mass spectrometry imaging (MSI) using matrix-assisted laser desorption/ionization (MALDI). Liver tissue samples from rats administered varying doses of olanzapine were analyzed by both MALDI-MSI and liquid chromatography-tandem mass spectrometry (LC/MS/MS) to determine drug concentrations. A linear correlation between MSI response and LC/MS/MS concentrations was obtained, allowing MSI data to be quantitated based on a conversion factor. This new method provides a way to quantitatively interpret MSI data in terms of drug concentrations and could help advance MSI for applications in drug development and safety assessment.
Filter Based Approach for Genomic Feature Set Selection (FBA-GFS)IJCSEA Journal
Feature selection is an effective method used in text categorization for sorting a set of documents into certain number of predefined categories. It is an important method for improving the efficiency and accuracy of text categorization algorithms by removing irredundant terms from the corpus. Genome contains the total amount of genetic information in the chromosomes of an organism, including its genes and DNA sequences. In this paper a Clustering technique called Hierarchical Techniques is used tocategories the Features from the Genome documents. A framework is proposed for Genomic Feature set Selection. A Filter based Feature Selection Method like
2 statistics, CHIR statistics are used to select the Feature set. The Selected Feature set is verified by using F-measure and it is biologically validated for Biological relevance using the BLAST tool.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
Image Mining from Gel Diagrams in Biomedical PublicationsTobias Kuhn
(CC Attribution License does not apply to included third-party material on slides 5 and 17; see the paper for the references: http://www.tkuhn.ch/pub/kuhn2012smbm.pdf )
This document discusses various techniques for clustering microarray data, including one-way, two-way, co-clustering, and biclustering techniques. It provides examples of methods such as gene shaving, COSA, coupled two-way clustering, spectral bi-clustering, and SAMBA. While many novel clustering methods have been developed, the document notes that few are widely used in practice and there has been little work evaluating the performance of different techniques. Development of clustering methods for microarray data continues.
Molecular and Cellular Biologist_Tanima MallikTanima Mallik
Tanima Mallik has extensive experience in molecular biology, microbiology, and biochemistry. She holds two Master's degrees and has worked on several research projects investigating topics like the mechanism of ATP synthase and inducing apoptosis in lung cancer cells. Her skills include techniques like single molecule studies, PCR, chromatography, and microscopy. She has taught at the university level and is proficient in planning and executing research projects.
User verification systems that use a single source of biometric information are not sufficient to meet today’s high security requirements for applications. This is because these systems have to contend with noisy data, intra-class variations, spoof attack and non-universality. Therefore, there is need for employing multiple sources of biometric information to provide better recognition performance as compared to the systems based on single trait. This paper is an overview of different categories of multibiometric systems, information fusion in multibiometric systems, and approaches to feature fusion at feature selection phase.
Francisco Javier Pedraza III is pursuing a PhD in Physics from the University of Texas at San Antonio with an expected graduation date of May 2017. His dissertation focuses on the synthesis and characterization of biocompatible and multifunctional nanoparticles and their application in medical diagnostics and therapy. He has over 4 years of experience as a graduate research assistant developing novel lanthanide-based nanomaterials and studying their viability in medicine through cytotoxicity measurements and two-photon imaging. He maintains a 3.85 GPA and has received multiple honors and fellowships including an MBRS-RISE Ph.D. scholarship and supply grant.
This document summarizes the applications of seed image analysis in seed science research. It discusses how image analysis can be used for varietal identification, characterization, and germination testing. Seed image analysis involves acquiring digital images of seeds and using computer programs to extract quantitative data on seed characteristics like size, shape, color and texture. This data can then be used to automatically classify and identify seed varieties. The document outlines several studies that achieved 98% or higher accuracy in classifying different wheat and bean varieties using seed image analysis. It also discusses how the technique can be applied to testing seed germination and distinguishing new varieties for plant breeding programs.
This guide outlines the business benefits of facebook and is an elementary guide for recruitment businesses contemplating setting up a facebook business page. It can also be used as a reference guide for established pages, acting as a checklist to reaffirm best practices
Plant Monitoring using Image Processing, Raspberry PI & IOTIRJET Journal
This document describes a plant monitoring system using image processing, a Raspberry Pi, and the Internet of Things. The system uses image processing techniques like segmentation, feature extraction and classification on images of plant leaves to detect diseases. Sensors connected to an Arduino board such as a humidity sensor, gas sensors and a light sensor are used to monitor environmental conditions. The Arduino and Raspberry Pi are connected to allow the sensors data to be sent to the Raspberry Pi. The Raspberry Pi then sends notifications about the plant health and environmental conditions to smartphones. This allows remote monitoring of farm conditions.
Implementation of Malaria Parasite Detection System Using Image Processingijtsrd
Malaria is a critical disease for which the instant detection is essential so as to control it. Microscopes are used to detect the disease and pathologists use the manual technique because of which there is several chance of incorrect detection being made regarding the disease. If the incorrect detection is made then the disease can turn into more difficult situation. So the study relating to the computerized detection is done in this paper that will facilitate in instant detection of the disease to some level. An image processing scheme is capable to enhance outcome of malaria parasite cell detection. In image processing image consistency is very essential to acquire correct result. Therefore to increase the correctness of the malaria detection system, we proposed new image processing based system which includes two algorithms. One is Haar wavelet algorithm for image transformation and other is K nearest neighbor algorithm for image classification. This system helps to reduce time as well as offer the better accuracy to detect Malaria to some degree. Kanchan N. Poharkar | Dr. S. A. Ladhake"Implementation of Malaria Parasite Detection System Using Image Processing" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd12798.pdf http://www.ijtsrd.com/computer-science/other/12798/implementation-of-malaria-parasite-detection-system-using-image-processing/kanchan-n-poharkar
This paper proposes the development of a software that performs the pre-diagnosis of malignant melanoma, spincellular carcinoma and basal-cell carcinoma. The software is divided into five modules, these being: digital imaging, analysis and processing, storage, feature extraction and classification by means of an Artificial Neural Network (ANN). The results shown the performance of the software for two different combination of activation functions in the network. With the use of spectroscopic techniques for the acquisition of images and the combination of non-linear and linear activation functions in the ANN, the software shows an effectiveness greater than 80%, concluding that it can be an effective tool as an aid in the diagnosis of cancer of skin.
IRJET - Automating the Identification of Forest Animals and Alerting in Case ...IRJET Journal
This document describes a proposed system to automatically identify and monitor forest animals using deep learning and computer vision techniques. The system would collect images using cameras traps and use a convolutional neural network (CNN) to identify animals in the images. It would be trained on a dataset of 1500 images across 5 animal categories. If the system identifies an animal encroaching on villages, it would trigger an alert to notify the forest department. The system aims to automate time-consuming manual animal identification tasks and provide alerts about potential human-animal conflicts. It could help conservation efforts by monitoring wildlife populations over time more efficiently.
Pattern recognition using video surveillance for wildlife applicationsprjpublications
This document summarizes a research paper that proposes a wildlife monitoring system using video surveillance and pattern recognition. The system uses motion detection to capture images when movement is detected. A pattern recognition module then analyzes the images using Histogram of Oriented Gradients (HOG) to distinguish between harmful and harmless animals. If a harmful animal is identified, the system notifies authorities of the animal type and location using GSM and GPS modules. The researchers tested the system using a database of animal images and found that HOG provided accurate classification of tigers and other animals.
INSPECTION OF PROFILED FRP COMPOSITE STRUCTURES BY MICROWAVE NDEjmicro
Fiber reinforced polymer (FRP) composites are employed in various applications of aerospace and defence industry. FRP composites are preferred as major structural parts due to their high stiffness strength and light weight.Non-destructive evaluation (NDE) plays an important role in assessing the quality and health monitoring of FRP composite structures during their manufacturing and in-service period.Different NDE techniques, such as ultrasonics, thermography, X-ray radiography, etc are employed for evaluating the quality of the composite structures.Microwave non-destructive evaluation (MWNDE) is an emerging NDE technique for characterizing and inspecting dielectric structures. Microwave NDE finds application in the areas of dielectric material characterization, determining thickness variation, defect detection and bond quality inspection.Inspection of profiled FRP composite structures by near-field reflection microwave NDE technique is presented in this paper. Application of Microwave NDE for bond quality inspection of FRP composite structures and thickness variation of composite structures is discussed. Results of inspected profiled composite structures by swept frequency reflection microwave NDE technique in the frequency range of X-band and Ku-band respectively are presented
Malaria is a serious disease for which the immediate diagnosis is required in order to control it otherwise it leads to death. Microscopes are used to detect the disease and pathologists use the manual method due to which there is a lot of possibility of false detection. This project removes the human error while detecting the malarial parasites in blood sample using image processing. A general framework to perform detection of malarial parasite, which includes image preprocessing, extracting infected blood cells, morphological operation and highlighting the infected cells is described. This methodology may serve as a rapid diagnostic tool for malaria, even where the expert in microscopic analysis may not be available.
Top Cited Article in Informatics Engineering Research: October 2020ieijjournal
Informatics is rapidly developing field. The study of informatics involves human-computer interaction and how an interface can be built to maximize user-efficiency. Due to the growth in IT, individuals and organizations increasingly process information digitally. This has led to the study of informatics to solve privacy, security, healthcare, education, poverty, and challenges in our environment. The Informatics Engineering, an International Journal (IEIJ) is a open access peer-reviewed journal that publishes articles which contribute new results in all areas of Informatics. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on the human use of computing fields such as communication, mathematics, multimedia, and human-computer interaction design and establishing new collaborations in these areas.
This study developed a new quantitative method for mass spectrometry imaging (MSI) using matrix-assisted laser desorption/ionization (MALDI). Liver tissue samples from rats administered varying doses of olanzapine were analyzed by both MALDI-MSI and liquid chromatography-tandem mass spectrometry (LC/MS/MS) to determine drug concentrations. A linear correlation between MSI response and LC/MS/MS concentrations was obtained, allowing MSI data to be quantitated based on a conversion factor. This new method provides a way to quantitatively interpret MSI data in terms of drug concentrations and could help advance MSI for applications in drug development and safety assessment.
Filter Based Approach for Genomic Feature Set Selection (FBA-GFS)IJCSEA Journal
Feature selection is an effective method used in text categorization for sorting a set of documents into certain number of predefined categories. It is an important method for improving the efficiency and accuracy of text categorization algorithms by removing irredundant terms from the corpus. Genome contains the total amount of genetic information in the chromosomes of an organism, including its genes and DNA sequences. In this paper a Clustering technique called Hierarchical Techniques is used tocategories the Features from the Genome documents. A framework is proposed for Genomic Feature set Selection. A Filter based Feature Selection Method like
2 statistics, CHIR statistics are used to select the Feature set. The Selected Feature set is verified by using F-measure and it is biologically validated for Biological relevance using the BLAST tool.
A Novel Approach for Tomato Diseases Classification Based on Deep Convolution...Mohammad Shakirul islam
This document summarizes Mohammad Shakirul Islam's research paper on classifying tomato plant diseases using deep convolutional neural networks. The paper includes sections on motivation, literature review, proposed methodology, results discussion, and future work. The proposed methodology uses a dataset of 3000 images across 6 tomato disease classes. A convolutional neural network model with 5 convolution layers, 5 max pooling layers, and 2 dense layers is trained on 80% of the data and tested on the remaining 20% for classification performance. Results show the model achieved high training and validation accuracy for identifying different tomato diseases.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
Image Mining from Gel Diagrams in Biomedical PublicationsTobias Kuhn
(CC Attribution License does not apply to included third-party material on slides 5 and 17; see the paper for the references: http://www.tkuhn.ch/pub/kuhn2012smbm.pdf )
This document discusses various techniques for clustering microarray data, including one-way, two-way, co-clustering, and biclustering techniques. It provides examples of methods such as gene shaving, COSA, coupled two-way clustering, spectral bi-clustering, and SAMBA. While many novel clustering methods have been developed, the document notes that few are widely used in practice and there has been little work evaluating the performance of different techniques. Development of clustering methods for microarray data continues.
Molecular and Cellular Biologist_Tanima MallikTanima Mallik
Tanima Mallik has extensive experience in molecular biology, microbiology, and biochemistry. She holds two Master's degrees and has worked on several research projects investigating topics like the mechanism of ATP synthase and inducing apoptosis in lung cancer cells. Her skills include techniques like single molecule studies, PCR, chromatography, and microscopy. She has taught at the university level and is proficient in planning and executing research projects.
User verification systems that use a single source of biometric information are not sufficient to meet today’s high security requirements for applications. This is because these systems have to contend with noisy data, intra-class variations, spoof attack and non-universality. Therefore, there is need for employing multiple sources of biometric information to provide better recognition performance as compared to the systems based on single trait. This paper is an overview of different categories of multibiometric systems, information fusion in multibiometric systems, and approaches to feature fusion at feature selection phase.
Francisco Javier Pedraza III is pursuing a PhD in Physics from the University of Texas at San Antonio with an expected graduation date of May 2017. His dissertation focuses on the synthesis and characterization of biocompatible and multifunctional nanoparticles and their application in medical diagnostics and therapy. He has over 4 years of experience as a graduate research assistant developing novel lanthanide-based nanomaterials and studying their viability in medicine through cytotoxicity measurements and two-photon imaging. He maintains a 3.85 GPA and has received multiple honors and fellowships including an MBRS-RISE Ph.D. scholarship and supply grant.
This document summarizes the applications of seed image analysis in seed science research. It discusses how image analysis can be used for varietal identification, characterization, and germination testing. Seed image analysis involves acquiring digital images of seeds and using computer programs to extract quantitative data on seed characteristics like size, shape, color and texture. This data can then be used to automatically classify and identify seed varieties. The document outlines several studies that achieved 98% or higher accuracy in classifying different wheat and bean varieties using seed image analysis. It also discusses how the technique can be applied to testing seed germination and distinguishing new varieties for plant breeding programs.
This guide outlines the business benefits of facebook and is an elementary guide for recruitment businesses contemplating setting up a facebook business page. It can also be used as a reference guide for established pages, acting as a checklist to reaffirm best practices
TCI 2015 The Construction of The Cluster of Information Technology and Commun...TCI Network
The ICT industry in Baja California, Mexico employs over 74,000 people and contributes 11% to the state's GDP. Previous cluster initiatives like IT@baja and CENI2T helped build the institutional foundations. The current initiative, BIT Center, is a collaboration between the Mexican Chamber of Electronics, the state university UABC, and local government to create a business incubator for ICT companies. Currently utilizing 48% of its capacity, BIT Center aims to replicate its model in Ensenada to support over 100 professionals and further develop the ICT cluster in Baja California.
TCI 2015 Go International - 10 Year Experience on Practical WorkTCI Network
The document discusses the ICT Cluster Bern's strategy for internationalization support over the past 10 years. It describes implementing economic excursions for knowledge sharing and business initiation, building an international ICT network, initiating international joint projects, and import/export actions. Key activities included organizing annual economic excursions to different countries and regions, facilitating cross-border projects to foster collaboration and trust between members, and developing the cluster and cluster manager's international reputation over the long term. The strategy aims to provide international opportunities for members and strengthen the ICT sector through global connections.
TCIOceania14 Economic & social impacts of clustersTCI Network
Clusters provide economic and social benefits to member businesses. Firms in clusters experience higher value added growth, profitability growth, and wages compared to non-cluster firms. Clusters also increase innovation performance, integration into supply chains, and average wages. Cluster participation boosts the probability of innovation and research collaboration. Strong clusters drive regional employment growth and new industry emergence through connections between related industries.
TCI 2014 North American Manufacturing Revival-A Tale of Two CountriesTCI Network
The document discusses the automotive manufacturing industries in North America, focusing on the United States and Mexico. It notes that US production is forecast to continue growing due to factors like lower fuel prices. Mexico's industry is also growing, with projections to produce over 4 million vehicles annually by 2020. The document outlines Magna's significant presence in both countries, with over 10,000 employees in Michigan and over 23,000 in Mexico across 30 facilities. Magna has invested $300 million in Mexico recently and helped strengthen the local automotive sector and supply chain.
Oketec Co. is a South Korean company established in 2000 that specializes in automation equipment. The document provides an overview of Oketec's history and certifications, main business areas of production automation, key R&D projects participated in or led relating to automated clutches, and the commercialization potential for its fuel-efficient automated clutch technology, particularly in the Chinese market. The automated clutch allows for precision clutch control and flexible driving to reduce fuel consumption by 10-25% for commercial vehicles.
Sungmoon Co., Ltd is a Korean company established in 2009 specializing in ship engine parts and marine environmental systems. They joined the KICOX industrial cluster project to diversify from ship blocks into higher value products like LNG engine components. The project provided over KRW400 million in subsidies for R&D and prototyping, supporting revenue growth of 3660% from 2009-2014 and employment growth of 1675%. Sungmoon now holds patents related to double-pipe technologies and aims to reach KRW30 billion in revenue by 2020 by expanding partnerships and into new business areas like offshore systems.
TCI 2015 How the Blue Mountains Creative Industries Cluster is Driving Jobs a...TCI Network
The document discusses how the Blue Mountains Creative Industries Cluster in regional Australia is driving jobs and growth in screen production. It focuses on how the cluster, located in the Blue Mountains region, is shaping the creative economy through supporting screen production industries. The document was presented by Kelly Blainey from Blue Mountains Economic Enterprise Australia and provides information on their work developing the local screen production cluster and creative industries.
TCI 2014 Mexico and Latin American EconomiesTCI Network
This document discusses strategic economic sectors in Mexico and Latin America. It identifies transportation equipment, electronics, plastics, tourism, food/beverages, chemicals, and metals as Mexico's key sectors. The top sectors across Latin America are energy, automotive, electronics, metals, agribusiness, and chemicals. Common strategic sectors between Mexico and Latin America include automotive, electronics, chemicals, metals, and agribusiness. The document also presents an initiative called Icluster that aims to strengthen economic cooperation between regions in Mexico and the US through focusing on strategic clusters like electronics, chemicals, logistics, and others.
This document provides 10 tips for making a mobile site user friendly. The tips include limiting navigation layers and options to simplify the user experience, using icons instead of lists for easier recognition, including back buttons for navigation, prominently displaying search functions, ensuring call-to-action buttons are large enough to click, allowing one-click checkout, and limiting menu options to 5-7 items. Following these tips can help speed up the mobile experience and make navigation and tasks simple for users on mobile devices.
TCILatinAmerica16 Desarrollo Territorial “Instrumentos de medición”TCI Network
Este documento describe el desarrollo de instrumentos de medición para evaluar el desarrollo territorial en Chile. Explica que se comenzó con 107 variables agrupadas en dimensiones como capital humano y capital social, y luego se fueron reduciendo a través de criterios de selección y métodos estadísticos hasta llegar a 20 variables clave. El objetivo final es generar un indicador compuesto que permita comparar el desempeño territorial entre comunas y tomar mejores decisiones de política pública.
Strategic alignment is a conviction that is considered extremely important in understanding how organizations can apply their arrangement of information technology (IT) into substantial boosts in achievement. To attain alignment advantage, Information Technology Infrastructure Library (ITIL) prepares a framework of best practice approch for IT Service Management in all countries and Control Objectives for Information and Related Technology (COBIT) is an IT governance framework and aiding toolset that permits managers to stretch the gap between control prerequisites, technical matters and business risks. The purpose of this paper is to recognize how COBIT can complement ITIL to attain Business-IT Alignment.
TCI 2016 Innovation in food clusters in Minas Gerais, BrazilTCI Network
This presentation discusses food clusters in Minas Gerais, Brazil. It provides examples of several successful food clusters in the region, including fresh fruit clusters, vegetable clusters, dairy clusters, bakery clusters, food distribution clusters, and food tech clusters. Two artisan cheese clusters are highlighted: Canastra Artisan Cheese and coffee clusters in the Cerrado Mineiro region. The presentation emphasizes how cluster policies since 2002 have helped food clusters in Minas Gerais increase competitiveness by moving to new business segments, developing advanced strategies, and using new technologies.
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
BACTERIA IDENTIFICATION FROM MICROSCOPIC MORPHOLOGY USING NAÏVE BAYESijcseit
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification
framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from
microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework
comprises two steps. In the first step, the system is trained using a set of microscopic images containing
Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features.
Edge-based descriptors are then extracted from the input images. In the second step, we use theNaïve
Bayes classifier to performprobabilistic inference based on the input descriptors. 64 images for each class
of bacteria were used as the training setand 222 images consisting of the three classes of bacteria and
other random images such as humans and airplanes were used as the test set. There are no images
overlapped between the training set and the test set. The system was found to be able to accurately
discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images
that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a
simple machine learning classifier with a set of simple image-based features can result in high
classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step
automatic bacteria identification approach and motivate us to extend this framework to identify a variety of
other types of bacteria.
Bacteria identification from microscopic morphology using naïve bayesijcseit
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. We propose an automatic bacteria identification
framework that can classify three famous classes of bacteria namely Cocci, Bacilli and Vibrio from
microscopic morphology using the Naïve Bayes classifier. The proposed bacteria identification framework
comprises two steps. In the first step, the system is trained using a set of microscopic images containing
Cocci, Bacilli, and Vibrio. The input images are normalized to emphasize the diameter and shape features.
Edge-based descriptors are then extracted from the input images. In the second step, we use theNaïve
Bayes classifier to performprobabilistic inference based on the input descriptors. 64 images for each class
of bacteria were used as the training setand 222 images consisting of the three classes of bacteria and
other random images such as humans and airplanes were used as the test set. There are no images
overlapped between the training set and the test set. The system was found to be able to accurately
discriminate the three classes of bacteria. Moreover, the system was also found to be able to reject images
that did not belong to any of the three classes of bacteria. The preliminary results demonstrate how a
simple machine learning classifier with a set of simple image-based features can result in high
classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our two-step
automatic bacteria identification approach and motivate us to extend this framework to identify a variety of
other types of bacteria.
Gaussian Multi-Scale Feature Disassociation Screening in Tuberculosiseijceronline
This summary provides the high level information from the document in 3 sentences:
Tuberculosis is a major infectious disease that if left untreated can have high mortality rates, and while treatments exist diagnosis remains a challenge. The document discusses several methods for diagnosing tuberculosis including sputum smear microscopy, skin tests, and newer molecular diagnostic tests, as well as developing an automated method for detecting tuberculosis manifestations in chest radiographs. It proposes extracting the lung region from chest x-rays and then computing texture and shape features to classify the x-rays as normal or abnormal using a binary classifier in order to enable mass screening of large populations.
Intelligent algorithms for cell tracking and image segmentationijcsit
This research develop the managing within network and relationship mechanism in agribusiness
management through serious game. Agribusiness is represented as sand that work together in the market
(sandpile) to maintain networks and relationships. This research apply agent base model for predicting
activity network based on the parameters that exist in the collaboration. The result indicate that average
selling, average buying and market price (CK = 4) are not approach the value of the open market but
precisely coincide with eachother. Total bought and total sold are tend to be high value. This condition
suggests a very tight competition. The average selling, average buying and market price (CK = 0.01) are
approach the value of the open market. Total bought and total sold are not as high as total bought and total
sold, by using CK = 4, this condition shows the competition is not too tight.
Intelligent algorithms for cell tracking and image segmentationijcsit
Sensitive and accurate cell tracking system is important to cell motility studies. Recently, researchers have
developed several methods for detecting and tracking the living cells. To improve the living cells tracking
systems performance and accuracy, we focused on developing a novel technique for image processing. The
algorithm we propose presents novel image segmentation and tracking system technique to incorporate the
advantages of both Topological Alignments and snakes for more accurate tracking approach. The results
demonstrate that the proposed algorithm achieves accurate tracking for detecting and analyzing the
mobility of the living cells. The RMSE between the manual and the computed displacement was less than
12% on average. Where the Active Contour method gave a velocity RMSE of less than 11%, improves to
less than 8% by using the novel Algorithm. We have achieved better tracking and detecting for the cells,
also the ability of the system to improve the low contrast, under and over segmentation which is the most
cell tracking challenge problems and responsible for lacking accuracy in cell tracking techniques.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
A HYBRID METHOD FOR AUTOMATIC COUNTING OF MICROORGANISMS IN MICROSCOPIC IMAGESacijjournal
Microscopic image analysis is an essential process to enable the automatic enumeration and quantitative
analysis of microbial images. There are several system are available for numerating microbial growth.
Some of the existing method may be inefficient to accurately count the overlapped microorganisms.
Therefore, in this paper we proposed an efficient method for automatic segmentation and counting of
microorganisms in microscopic images. This method uses a hybrid approach based on morphological
operation, active contour model and counting by region labelling process. The colony count value obtained
by this proposed method is compared with the manual count and the count value obtained from the existing
method.
Image Forgery Detection Methods- A ReviewIRJET Journal
This document reviews various methods for detecting image forgery. It begins with an introduction to the topic, explaining the need for image forgery detection techniques due to the widespread manipulation of images online. It then categorizes common types of image manipulation and provides a literature review comparing the accuracy and citations of different detection techniques, such as CNN-based methods, transform-domain methods using DCT and DWT, and methods analyzing JPEG compression artifacts. The review finds that CNN-based methods generally achieve the highest accuracy, around 90-100%, but also notes transform-domain and JPEG-based methods can also achieve reasonably high accuracy ranging from 70-100% depending on the technique and testing parameters.
IRJET - Plant Leaf Disease Diagnosis from Color Imagery using Co-Occurrence M...IRJET Journal
This document presents a method for classifying plant leaf diseases from color images using texture and color features extracted from the images along with an artificial neural network classifier. The proposed system first preprocesses the input images, then extracts color features like mean and standard deviation of HSV color space and texture features like energy, contrast, homogeneity and correlation using a gray level co-occurrence matrix. These features are then used to train a backpropagation neural network classifier to automatically classify test images into disease categories. Experimental results show the backpropagation network provides high accuracy for plant disease classification, with 97.2% accuracy on validation data and lower error rates than support vector machines.
IRJET- Plant Leaf Disease Diagnosis from Color Imagery using Co-Occurrence Ma...IRJET Journal
This document presents a method for classifying plant leaf diseases from color images using texture and color features. The proposed system first preprocesses input images, then extracts features like color (mean, standard deviation of HSV channels) and texture (energy, contrast, homogeneity, correlation from GLCM). These features are used to train a backpropagation neural network classifier. The system was tested on images of six plant diseases and showed minimum training error and good classification accuracy. This automated approach could help inexperienced farmers and experts more accurately diagnose plant diseases.
CANCER CLUMPS DETECTION USING IMAGE PROCESSING BASED ON CELL COUNTINGIRJET Journal
This document describes a proposed method for detecting cancer clumps using image processing techniques including cell counting. The method involves preprocessing images using techniques like grayscaling, binarization, and edge detection. Cancer cells are then identified and segmented. Features are extracted from the segmented regions and fed into a deep learning model for classification and counting of cancer cells. The proposed approach aims to automatically detect cancer cells in images as a way to help speed up cancer research and improve accuracy over existing methods. If successfully implemented and refined with feedback, it could open new avenues for cancer cell detection in medical imaging.
A Survey of Convolutional Neural Network Architectures for Deep Learning via ...ijtsrd
Convolutional Neural Network CNN designs can successfully classify, predict and cluster in many artificial intelligence applications. In the health sector, intensive studies continue for disease classification. When the literature in this field is examined, it is seen that the studies are concentrated on the health sector. Thanks to these studies, doctors can make an accurate diagnosis by examining radiological images more consistently. In addition, doctors can save time to do other patient work by using CNN. In this study, related current manuscripts in the health sector were examined. The contributions of these publications to the literature were explained and evaluated. Complementary and contradictory arguments of the presented perspectives were revealed. It has been stated that the current status of the studies carried out and in which direction the future studies should evolve and that they can make an important contribution to the literature. Suggestions have been made for the guidance for future studies. Ahmet Özcan | Mahmut Ünver | Atilla Ergüzen "A Survey of Convolutional Neural Network Architectures for Deep Learning via Health Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49156.pdf Paper URL: https://www.ijtsrd.com/computer-science/artificial-intelligence/49156/a-survey-of-convolutional-neural-network-architectures-for-deep-learning-via-health-images/ahmet-özcan
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,the actual presence of a real legitimate trait in contrast to a fake self - manufactured synthetic or reconstructed sample is a significant problem in biometric authentication,which requires the development of new and efficient protection measures. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens assessment in a fast,user - friendly,and non - intrusive manner,through the use of image quality assessment. The proposed approach presents a very low degree of complexity,which makes it suitable for real - time applications,using 25 general image quality features extracted from one image (i.e.,the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
This document summarizes a research paper on analyzing the nuclear-to-cytoplasmic ratio in cells from microscopic images to detect skin abnormalities. It discusses segmenting nuclei and cytoplasm using techniques like watershed transforms and morphological processing. Marker-controlled watershed segmentation is used to avoid over-segmentation when isolating nuclei. Distance transforms and blob detection help initialize and validate segmented nuclei. Cytoplasm segmentation uses convergence index filtering within constraints of minimum and maximum distance from nuclei. The nuclear-to-cytoplasmic ratio calculated from segmented cells can indicate malignancy and be evaluated alongside a dermatologist's assessment. This automated analysis provides faster and more robust diagnosis of skin conditions than manual examination.
A survey on nuclear to-cytoplasmic ratio analysis using image segmentationeSAT Journals
Abstract In the Bio-medical environment, for the analysis of abnormality detection in the skin including skin cancer in the epidermal layer, the traditional method used is biopsy procedure. The obtained biopsy specimens are processed by various methods viz, various orders of harmonic generations enumerate certain results resulting in the chances of some unexpected infections. Later on to overcome these infections, the followed is optical virtual biopsy procedure with higher harmonics. This yields better results than before. The watershed transform with its gradient works more accurate on the virtual specimens which is observed by microscopically with higher penetration depth. Keywords: - Harmonic generations, Watershed transform, Gradient scale, Morphological processing.
Application for Plant’s Leaf Disease Detection using Deep Learning TechniquesIRJET Journal
This document describes a study that aims to develop a deep learning model for detecting plant leaf diseases. The researchers first review existing literature on plant disease detection techniques. They then outline the architecture of their proposed convolutional neural network model, which involves preprocessing images, extracting features, training the model on labeled image data, and evaluating the trained model on test data. Key steps include converting images to arrays, reshaping and normalizing the data, building a CNN model with convolutional and pooling layers, fitting the model with a portion of the labeled data, and using the remaining data to evaluate the trained model's performance at identifying diseases. The goal is to automatically detect diseases by analyzing images of plant leaves.
This document discusses how nanofabricated structures and microfluidic devices are increasingly being used to study bacteria. Key points include:
1) These tools provide precise spatial and temporal control that has helped answer questions about bacterial growth, chemotaxis, and social behavior.
2) Microstructures can spatially isolate and track individual bacteria or strains, enabling high-throughput analyses of growth, cell fate decisions, and antibiotic resistance at the single cell level.
3) Microfluidic devices using semipermeable barriers allow study of chemical communication between bacteria through metabolic and signaling molecule exchange.
4) Nanofabricated environments with defined geometric features constrain bacterial growth and reveal how populations rapidly adapt to
Detection of Skin Diseases based on Skin lesion imagesIRJET Journal
This document discusses a study on detecting skin diseases from skin lesion images using deep learning algorithms. The researchers collected a dataset of skin images from the International Skin Imaging Collaboration repository. They used two deep neural networks - a convolutional neural network (CNN) and ResNet50 - to classify the images as malignant, benign, or normal. The CNN and ResNet50 models were trained on 80% of the dataset and tested on the remaining 20%. The results showed that the deep learning algorithms could accurately identify different types of skin cancers and diseases from images, which could help in the early detection of skin cancer.
Simulink model for automatic detection and counting of the number of white fl...eSAT Journals
Abstract
The whitefly is a small white insect which feeds from the sap of different variety of plants and causes wide spread destruction. It is
one of the most harmful pests for crops like tomato, cabbage, broccoli etc. The small size of the pest, which is around 2mm in
length, poses a great challenge to detect these pests using image processing technique in the presence of noise, which could be in
the forms of leaf veins, trichomes, water droplets and dust etc. Early detection of pests is required to reduce or prevent the
damage caused by the white-flies. It is also important to count the number of pests as accurately as possible, because based on the
number of pests the amount of pesticide can be determined or appropriate advice can be given to the persons concerned. Manual
counting of white-flies is very tedious and error prone if the number of pests is very high. We can do the counting satisfactorily by
using efficient image processing methods. In this paper a method is proposed to detect and count the number of white-flies using
image processing on Simulink and Matlab software.
Keywords:Image Processing, Simulink, whitefly, count
Similar to Advances in prokaryote classification from microscopic images (20)
NEW Current Issue - CALL FOR PAPERS - Electrical and Computer Engineering An ...ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed open access journal calling for papers on topics related to electrical and computer engineering, including communications, control systems, integrated circuits, power systems, and signal processing. Authors are invited to submit original papers by August 7, 2021 via the journal's online submission system, with notification of acceptance by September 7 and final manuscripts due by September 15 for publication dates determined by the Editor-in-Chief.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering
This work investigates and evaluates the electric energy interruptions to the residential sector resulting from severe power outages. The study results show that this sector will suffer tangible and intangible losses should these outages occur during specific times, seasons, and for prolonged durations. To reduce these power outages and hence mitigate their adverse consequences, the study proposes practical measures that
can be adopted without compromising the consumers’ needs, satisfaction, and convenience.
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The standard grid codes suggested, that the wind generators should stay in connected and reliable active and reactive power should be provided during uncertainties. This paper presents an independent control of Grid Side Converter (GSC) for a doubly fed induction generator (DFIG). A novel GSC controller has
been designed by incorporating a new Enhanced hysteresis comparator (EHC) that utilizes the hysteresis band to produce the suitable switching signal to the GSC to get enhanced controllability during grid unbalance. The EHC produces higher duty-ratio linearity and larger fundamental GSC currents with
lesser harmonics. Thus achieve fast transient response for GSC. All these features are confirmed through
time domain simulation on a 15 KW DFIG Wind Energy Conversion System (WECS).
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed,
open access journal that address the impacts and challenges of Electrical and Computer
Engineering. The journal documents practical and theoretical results which make a fundamental
contribution for the development Electrical and Computer Engineering.
PREPARATION OF POROUS AND RECYCLABLE PVA-TIO2HYBRID HYDROGELecij
Nano TiO2, one of the most effective photocatalysts, has extensive usein fields such as air purification,
sweage treatment, water spitting, reduction of CO2, and solar cells. Nowadays, the most promising method to
recycle nano TiO2during the photocatalysis is to immobilize TiO2onto matrix, such as polyvinyl alcohol
(PVA). However, due to the slow water permeability of PVA after cross-linking, the pollutants could not
contact with nano TiO2photocatalyst in time. To overcome this problem, we dispersed calcium carbonate
particles into a PVA-TiO2 mixture and then filmed the glass. PVA-TiO2-CaCO3 films were obtained by
drying. Through thermal treatment, we obtained the cross-linked PVA-TiO2-CaCO3 films. Finally, the
calcium carbonate in the film was dissolved by hydrochloric acid, and the porous PVA-TiO2 composite
photocatalyst was obtained. The results show the addition of CaCO3 has no obvious effect on PVA
cross-linking and that a large number of cavities have been generated on the surface and inside of porous
PVA-TiO2 hybrid hydrogel film. The size of the holes is about 5-15μm, which is consistent with that of
CaCO3.The photocatalytic rate constant of porous PVA-TiO2 hybrid hydrogel film is 2.49 times higher than
that of nonporous PVA-TiO2 hybrid hydrogel film.
4th International Conference on Electrical Engineering (ELEC 2020)ecij
4th International Conference on Electrical Engineering (ELEC 2020)aims to bring together researchers and practitioners from academia and industry to focus on recent systems and techniques in the broad field of Electrical Engineering. Original research papers, state-of-the-art reviews are invited for publication in all areas of Electrical Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses practical and theoretical results in electrical and computer engineering. Topics of interest include communications, control systems, integrated circuits, power systems, and signal processing. Authors are invited to submit original papers by July 25, 2020 for peer-review and potential publication in September 2020.
4th International Conference on Bioscience & Engineering (BIEN 2020) ecij
The 4th International Conference on Bioscience & Engineering (BIEN 2020) will be held November 28-29, 2020 in Dubai, UAE. The conference will bring together researchers and practitioners from academia and industry to share knowledge on advances in bioscience and engineering. Authors are invited to submit original research papers and reviews by July 26, 2020 on topics including bioengineering, biochemistry, bioinformatics, biomedicine, and more. Selected papers will be published in conference proceedings and special issues of related journals.
Electrical & Computer Engineering: An International Journal (ECIJ)ecij
Scope & Topics
Electrical & Computer Engineering: An International Journal (ECIJ) is a peer-reviewed, open access journal that addresses the impacts and challenges of Electrical and Computer Engineering. The journal documents practical and theoretical results which make a fundamental contribution for the development Electrical and Computer Engineering.
Electrical & Computer Engineering: An International Journal (ECIJ)
ISSN: 2201-5957
https://wireilla.com/engg/ecij/index.html
Paper Submission
Authors are invited to submit papers for this journal through E-mail: ecijjournal@wireilla.com .
Important Dates
•Submission Deadline: March 28, 2020
Contact US
Here's where you can reach us: ecijjournal@wireilla.com
GRID SIDE CONVERTER CONTROL IN DFIG BASED WIND SYSTEM USING ENHANCED HYSTERES...ecij
The document presents a novel control strategy using an Enhanced Hysteresis Controller (EHC) for the Grid Side Converter (GSC) of a DFIG-based wind energy system. The EHC improves upon standard hysteresis control by incorporating the DC link voltage as an input to the integrator, allowing for higher duty ratio linearity, larger fundamental GSC currents with less harmonics. Simulation results on a 15kW DFIG system show the EHC provides fast transient response for the GSC and regulates the DC link voltage with smooth GSC currents and power during grid disturbances like voltage dips. Comparisons to a system without GSC control show significant reductions in oscillations through use of the proposed EHC strategy.
UNION OF GRAVITATIONAL AND ELECTROMAGNETIC FIELDS ON THE BASIS OF NONTRADITIO...ecij
The traditional principle of solving the problem of combining the gravitational and electromagnetic fields is associated with the movement of the transformation of parameters from the electromagnetic to the gravitational field on the basis of Maxwell and Lorentz equations. The proposed non-traditional principle
is associated with the movement of the transformation of parameters from the gravitational to the electromagnetic field, which simplifies the process. Nave principle solving this task by using special physical quantities found by M. Planck in 1900: - Planck’s length, time and mass), the uniqueness of which is that they are obtained on the basis of 3 fundamental physical constants: the velocity c of light in vacuum, the Planck’s constant h and the gravitational constant G, which reduces them to the fundamentals of the Universe. Strict physical regularities were obtained for the based on intercommunication of 3-th
fundamental physical constants c, h and G, that allow to single out wave characteristic νG from G which is identified with the frequency of gravitational field. On this base other wave and substance parameters were strictly defined and their numerical values obtained. It was proved that gravitational field with the given wave parameters can be unified only with electromagnetic field having the same wave parameters that’s why it is possible only on Plank’s level of world creation. The solution of given problems is substantiated by well-known physical laws and conformities and not contradiction to modern knowledge about of material world and the Universe on the whole. It is actual for development of physics and other branches of science and technique.
USING MACHINE LEARNING TO BUILD A SEMI-INTELLIGENT BOT ecij
Nowadays, real-time systems and intelligent systems offer more and more control interface based on voice recognition or human language recognition. Robots and drones will soon be mainly controlled by voice. Other robots will integrate bots to interact with their users, this can be useful both in industry and entertainment. At first, researchers were digging on the side of "ontology reasoning". Given all the technical constraints brought by the treatment of ontologies, an interesting solution has emerged in last years: the construction of a model based on machine learning to connect a human language to a knowledge
base (based for example on RDF). We present in this paper our contribution to build a bot that could be used on real-time systems and drones/robots, using recent machine learning technologies.
MODELING AND SIMULATION OF SOLAR PHOTOVOLTAIC APPLICATION BASED MULTILEVEL IN...ecij
As the solar market is blooming and forecasted to continue this trend in the coming years. The efficiency and reliability of PV based system has always been a contention among researchers. Therefore, multilevel inverters are gaining more assiduity as it has multitude of benefits. It offers high power capability along with low output harmonics. The main disadvantage of MLI is its complexity and requirement of large
number of power devices and passive components. This paper presents a topology that achieves 37.5% reduction in number of passive components and power devices for five-level inverter. This topology is basically based on H-bridge with bi-directional auxiliary switch. This paper includes a stand-alone PV system in which designing and simulation of Boost converter connected with multilevel inverter for ac load is presented. Perturb and observe MPPT algorithm has been implemented to extract maximum power. The premier objective is to obtain Voltage with less harmonic distortion economically. Multicarrier Sinusoidal
PWM techniques have been implemented and analysed for modulation scheme. The Proposed system is simulated n MATLAB/Simulink platform.
Investigation of Interleaved Boost Converter with Voltage multiplier for PV w...ecij
This paper depicts the significance of Interleaved Boost Converter (IBC) with diode-capacitor multiplierwith PV as the input source. Maximum Power Point Tracking (MPPT) was used to obtain maximum power from the PV system. In this, interleaving topology is used to reduce the input current ripple, output voltage ripple, power loss and to suppress the ripple in battery current in the case of Plugin Hybrid Electric Vehicle (PHEV). Moreover, voltage multiplier cells are added in the IBC configuration to reduce the narrow turn-off periods. Two MPPT techniques are compared in this paper: i) Perturb and Observe (P&O) algorithm ii) Fuzzy Logic . The two algorithms are simulated using MATLAB and the comparison of performance parameters like the ripple is done and the results are verified.
A COMPARISON BETWEEN SWARM INTELLIGENCE ALGORITHMS FOR ROUTING PROBLEMSecij
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Advances in prokaryote classification from microscopic images
1. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
DOI : 10.14810/ecij.2014.3202 13
ADVANCES IN PROKARYOTE CLASSIFICATION FROM
MICROSCOPIC IMAGES
Amaleena Mohamad, Noorain A. Jusoh, Zaw Zaw Htike and Shoon Lei Win
Faculty of Engineering, IIUM, Kuala Lumpur, Malaysia
ABSTRACT
Great knowledge and experience on microbiology are required for accurate bacteria identification.
Automation of bacteria identification is required because there might be a shortage of skilled
microbiologists and clinicians at a time of great need. There have been several attempts to perform
automatic background identification. This paper reviews state-of-the-art automatic bacteria identification
techniques. This paper also provides discussion on limitations of state-of-the-art automatic bacteria
identification systems and recommends future direction of automatic bacteria identification.
KEYWORDS
Bacteria Identification, Cocci,Bacilli, Vibrio, Naïve Bayes, Machine Learning
1. INTRODUCTION
Bacteria, which are prokaryotic microorganisms, are the most abundant and simplest organisms in
the world as we know it. Prokaryotes do not possess a nucleus and complex organelles. Because
most prokaryotes range in size less than ten micrometers (µm), microscopes are used to study
bacteria. Bacteria identification is very important in microbiology and pathology as it serves a
basis of understanding diseases. Due to this, various types of methods have been introduced to
classify bacteria in microbiology. Clinicians and microbiologists commonly employ the typing
schemes which are dependent on the phenotypic typing schemes to develop the bacterial
morphology and staining properties of the organism. Vectors, environmental reservoir of
organism and pathogen’s ways of transmission is important for the clinicians. Therefore, it is
extremely essential to perform bacteria classification such that the said information can be
obtained. On the other hand, scientists who are interested in microorganisms’ evolution are
getting more interested in taxonomic techniques which permit the comparison of highly
conserved genes among dissimilar species. Therefore, computerized techniques are required for
this task [21-37].
The most basic technique used for classifying bacteria is based on the bacterium's shape and cell
arrangement. The most ordinary shapes of bacteria include rod, cocci (round), and spiral forms.
Cellular arrangements occur singularly, in series, and in groups. Some species have one to
numerous projections called flagella which enable the bacteria to swim and move. Cocci or
coccus for a single cell are round cells, occasionally flattened when being adjacent to each other.
Cocci bacteria can exist individually, in pairs, in groups of four, in chains, in clusters or in cubes
consisting of eight cells. Bacilli are rod-shaped bacteria which also can occur individually, in
pairs, or in chains. Figure 1 shows examples of three famousclasses of bacteria namely: cocci,
vibrio and bacilli.
2. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
14
Figure 1. Three classes of bacteria (a) Cocci (b) Vibrio (c) Bacilli [21]
2. BACTERIA IDENTIFICATION
Bacteria classification plays important role in yielding information for disease control. Bacterial
species are usually sub-grouped to different types and is used for many crucial pathogenic
bacteria such as Salmonellae, E Coli, and Vibriones [1]. H.C. Gram in 1884 discovered the Gram
stain classification remains an important and useful technique until today. This technique
classifies bacteria as either Gram positive or negative based on their morphology and differential
staining properties [2]. Table I shows the general phenotypic classification of bacteria for the
Gram positive [3].
Other types of bacteria classifications that are commonly used are based on the prokaryotes of the
bacteria which include their function and structures such as the slime, capsule, peptidoglycan,
cytoplasmic membrane, flagella, pili, and the secreted products. The main objectives of
phenotypic classification are to generate clusters of strains and establish the hierarchy of bacteria
species as members of different species may share high levels of resemblance [3].
(a)
(b)
(c)
3. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
15
Increasing bacterial adaptation level to human environments proves that the identification of
pathogens in a bacteria species level is unsatisfactory [1].Therefore bacterial type diagnosis is
required, which involves the classification of pathogens below species level. To date, biological
and microbiological data analysis entails an extensive amount of human intervention [1]. The
manual procedures are susceptible to inconsistency and are a tedious and complicated work which
needs abundant correlative data. [3].These procedures also consume a lot of time and energy and
are of great cost [1]. It is therefore important to reduce the amount of human intervention in order
to handle the rising data volume besides trying to achieve adequate level data accuracy. Due to
inefficient manual procedures in classifying bacteria, a considerable amount of research has been
done on automatic bacteria classification using various approaches which include pattern
recognition technique.
Table 1: General Phenotypic Classification of Bacteria
Name Morphology Type of infection
Staphylococci Cocci in
grapelike
clusters
Soft tissue,
bone,
joint, food
poisoning
Streptococci Cocci in pairs,
chains
Skin pharyngitis,
endocarditis,
toxic,
shock
Enterococci Cocci in pairs,
chains
UTI, GI,
catheterrelated,
infections
Bacilli Rods,
sporeforming
Anthrax,
food poisoning,
catheter-related
infections
Clostridia Rods, spore
formers
Tetannus,
diarrhea,
gas gangrene
Pattern recognition which is a field of machine learning is an act of taking in raw data and taking
action based on pattern category [4][5]. The algorithm of pattern recognition normally consists of
image segmentation, feature extraction, and classification [5]. Desired object is first segmented
and the selected features are extracted from the image. Then, the classifier can be trained using
these features as input data. Example of algorithm which can be used in bacteria classification is
shown in figure 1[5].
4. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
16
Figure 2. Algorithm used in bacteria classification technique.
3. IMAGE SEGMENTATION
Image segmentation is the process of dividing an image into multiple regions [6]. In this step,
image is separated into different regions in which each region is almost homogenous making the
union of the two region to be impossible.Image segmentation is very important in pattern
recognition and image processing area as it is widely used in recognizing object and tracking,
face detection, and other computer-vision-related applications [7]. Generally, image segmentation
techniques can be classified into few categories [6].
3.1. Clustering Method
Clustering is a process wherein pixels of same colour, texture, etc is replaced by a cluster. The K-
means algorithm is a type of clustering method used to divide an image into K clusters. The
quality of the algorithm depends on the initial set of clusters and the value of K [6].
3.2. Thresholding Method
Thresholding converts multilevel image into binary image. It compares each pixel value with
some threshold value, T and assign the value of either 0 (background) or 1(foreground) to the
pixel [6].
5. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
17
3.3. Edge detection Method
An edge is a boundary in an image in which significant change occurs in the physical aspect of
the image [2]. Features of edges are commonly chosen as it is fairly easy to be extracted and is
non-susceptible to light and other noise. Edge detector helps to reduce the complexity of
algorithms by cutting down the quantity of input data. Its main purpose is to identify and
locateabtrupt discontinuities .There are many ways to perform edge detection which can be
summarised in the Table 2 [8].
Table 2. Edge Detection Techniques[8]
Operator Advantages Disadvantages
Classical (Sobel, Prewitt,
Kirsch,…)
• Simplicity
• Detection of edges
and their orientations
• Sensitivity to noise
• Inaccurate
Zero Crossing (Laplacian,
Second directional
derivative)
• Detection of edges
and their orientations •
Having fixed
characteristics in all
directions
• Responding to some of
the existing edges
• Sensitivity to noise
Laplacian of Gaussian
(LoG) (Marr-Hildreth)
• Finding the correct
places of edges
• Testing wider area
around the pixel
• Malfunctioning at
corners, curves and
where the gray level
intensity function varies
• Not finding the
orientation of edge
because of using the
Laplacian filter
Gaussian (Canny, Shen-
Castan)
• Using probability for
finding error rate
• Localization and
response
• Improving signal to
noise ratio
• Better detection
especially in noise
conditions
• Complex computations
• false zero crossing
• time consuming
Colored Edge Detectors • Accurate
• more efficient in
object recognition
• Complicated
• complex computations
Bacteria are segmented such that the objects of interest, i.e. the cells, are separated from the
background. Research made by Rusuuvari et al, segmented bacteria by first converting it to grey
scale [9]. Then, adaptive thresholding is applied in which a smoothed version is subtracted from
original image, leaving only details [9].
4.FEATURE EXTRACTION
Since classification is highly dependent on features, it is essential that the features extracted carry
significant information about the studied objects [9]. In bacteria classification, feature extraction
is done to find the morphology of mycobacterium by their shape. Geometry features are used to
6. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
18
measure the perimeter, area, radii, circularity, compactness, eccentricity, and tortuosity of the
samples [10].
At times, bacilli shape is not an adequate indication to be used as a discriminator feature as other
bacteria species and particles share the same morphology. Therefore besides their shape, it is also
essential to consider the color as to improve the discrimination precision [20]. Figure shows the
procedure flow chart of bacilli discrimination [2].
This technique is also based on the new segmentation method followed by an identification
process. The segmentation permits the elimination of a large amount of debris objects, and only
those having a similar bacilli color are preserved. One of the key factors of the existing system is
the analysis and screening of the bacilli shapes [20]. Even if the descriptors used to compute the
cluster centroids are needed for a recognition system, some cases show failures [20]. Because of
that, we used the heuristic information about the shape of bacilli to construct a classification tree
as well as to improve the overall performance of the classification process [11,20].
Figure 3. Bacilli discrimination procedures
5. FEATURE DESCRIPTORS
In image matching, extraction of features is important in providing reliable matching of an image
with different viewpoints. Feature detection in an image aims to depict part of image with
significant or unique information (feature descriptors) [12]. Recent trends of feature detection and
matching which revolves around local features provide an invariant description of image. Local
features can be points, edges or small patches, with intensity, colour and texture being the
common properties of it .Features detected should remained unchanged under different conditions
so that proper image matching can be obtained. Such features are said to be invariant features
[13]. Feature descriptors are important in both training and classification phase. They are
extracted from images, and stored during training phase, while in classification phase, image
query will be matched with all the trained image features, and those which has the maximum
correspondence is considered the best match. Feature descriptor matching can be calculated using
Euclidean, or Mahalanobisor distance ratios [12-13].
Various feature detection algorithms have been proposed in previous research such that a reliable
and robust descriptors can be obtained for image matching purpose. Scale Invariant Feature
Adaptive Color
Thresholding
Color
transformation
Edge detection
+
Morphological
operations
Feature
edge
extraction
+
clustering
Decision
based on
classification
tree
RGB
input
image
Bacilli
Non-Bacilli
luminance
Color validation
7. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
19
Transform (SIFT), and Speeded Up Robust Features(SURF) are the most common descriptors
used in machine vision literature.
5.1. SIFT detector
The SIFT detector consists of four main stages namely, scalespace extrema detection, key point
localization, orientation computation and key point descriptor extraction. During the first stage,
Difference of Gaussians (DoG) is used to identify the potential key points. From the input images,
several Gaussian blurred images are produced and DoG are computed from their neighbours [13].
During the second stage, candidate key points are located by finding extrema in the DoG images.
Spatially unstable key points and low contrast key points are removed while the remaining key
points are localised by interpolating across the DoG images. In the third stage, a principal
orientation is assigned to each key point. Finally, in the fourth phase, a highly distinctive
descriptor are computed for each key point. SIFT descriptors are invariant to rotation, scale,
contrast and partially invariant to other transformations[12-13].
5.2. SURF descriptor
SURF which is commonly known as approximate SIFT generate key points and descriptors very
efficiently by employing integral images and efficient scale space construction. It consists of two
stages namely key point detection and description [13]. Unlike SIFT, SURF does not use
Difference of Gaussian(DoG) to identify keypoint detection. Instead, it uses integral images to
allow fast computation of Gaussian images using box filter. The first stage brings about scale and
location invariance. During the final stage, each detected key point is assigned to a reproducible
orientation and oriented along the orientation as obtained before. The resulting SURF descriptor
is invariant to rotation, scale, and contrast [12-13].
6.CLASSIFICATION
Classification is one of important machine learning areas which collect unprocessed data and
categorizes it into certain classes according to the set of parameters[11]. Classification structure
commonly uses either statistical or syntactic approach depending on the pattern’s statistical
characteristics, assuming a probabilistic system [11]. In recognizing pattern, an extensive range
of algorithms can be applied from the simplest Bayesian classifiers to the much more complex
neural networks [11].
The main task in a classification problem is to decide in which finite dataset category an object
belongs to. A classification task normally involves training and testing datasets which contains
several traits or features such that pattern can be recognized [14]. Supervised and unsupervised
classifiers are two techniques commonly used in image classification. Supervised classifier, also
known as parametric methods require an intensive training phase of the classifier parameters [15].
It involves the act of obtaining the information class of data sets, and classifying the test data by
identifying training data’s best possible match [14]. On the contrary, unsupervised classifier does
not need training and relies directly on data to perform classification[15].It analyses a huge
amount of datasets and split it into a number of classes based on the natural groupings which is
available within datasets [11].
8. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
20
6.1. Bayesian theory
Bayesian learning algorithm which is the most practical learning approach involves the evaluation
of explicit probabilities for hypotheses[11]. Since it does not explicitly manipulate probabilities, it
helps to provide unique perspective in understanding many learning algorithms [11].
6.2. Naive-Bayes Classifier
The Naive Bayes classifier simplifies the assumption that the attribute values are conditionally
independent given target value [11]. It does not support a continuous data, so the independent
variable or variables whose values are continuous values first be divided [16]. The formula to
calculate probability is shown below:
ܲሺܧ|ܪሻ =
[ܲሺܪ|ܧሻX ܲሺܪሻ]
ܲሺܧሻ
where
H is all expected event or hypothesis
E is all events possible
P(H) – probability for event H to happen
P(H|E) – probability for an event H to happen when event E happens
6.3. Decision tree classifier
Decision tree classifier is a classifier which implement an effective hierarchical classifiers. Its tree
structure is easily understandable, besides being able to execute automatic feature selection, and
can help reduce complexity [17].
6.4. KNearest Neighbor classifier
K-nearest neighbor (k-NN) which is said to be the simplest algorithm in machine learning, is a
classifier that classifies objects based on the closest training examples in the feature pace [14]. It
computes the distance from an unknown test pattern to every training pattern and selects the K
nearest training samples to base the classification on [18,26]. It has been implemented on pattern
recognition systems because of its good performance and simple algorithm [27].
Finding k value:
One of the most important things in applying k-nearest neighbour is selecting a suitable
value for k parameter. The apposite value of k generally depends on the dataset. Value
of k which is too small may contribute to over fitting, while overly large k value will
increase computational complexity thus affecting decision making process [14]. To
break ties, it is common to select small and odd value of k, typically 1, 3 or 5 [11].
Finding distance metric:
The performance of k-NN essentially depends on the distance metric used to identify the
nearest neighbours. Study shows that classification accuracy of k-NN can be
significantly improved by selecting a suitable distance metric for the dataset. There are
various types of distance metrics to determine the distance for k-NN classifiers such as
Euclidean distance, city block metric, cosine distance and correlation distance. The
following formulas are used to compute each of the distance metrics [14].
9. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
21
Euclidean distance[14]:
݀ଶ
௦௧ = ሺݔ௦ − ݕ௧ሻሺݔ௦ − ݕ௧ሻ′
City Block Metric[14]:
݀௦௧ = ∑ |
ୀଵ ݔ௦ − ݕ௧ |
Cosine distance[14]:
݀௦௧ = ቆ1 −
ݔ௦ݕ௧
′
ඥሺݔ௦ݔ௦
′ሻሺݕ௧ݕ௧
′ሻ
ቇ
Correlation distance[14]:
݀௦௧ =
ۉ
ۇ1 −
ሺݔ௦ − ̅ݔ௦ሻሺݕ௧ − ݕത௧ሻ′
ටሺݔ௦ݔ௦
′ሻሺݔ௦ݔ௦
′ሻඥሺݕ௧ݕ௧
′ሻሺݕ௧ݕ௧
′ሻی
ۊ
6.5. Neural network
The neural network processes consist of steps which are the learning and testing process. The
back propagation technique is used for neural network training process[10]. It is commonly used
for pattern recognition, image classification, and medical analysis. Training process can obtain
optimum weight; therefore neural network can operate successfully and produce targeted results
Multi layer perception (MLP) is the most familiar neural network model, consisting of succeeding
linear transformations followed by processing with non-linear activation tasks [10,25].
The learning algorithm for multilayer can be conveyed using generalized Delta Rule and gradient
descent since they are having non-linear activation tasks [25]. Radial basis function (RBF) is a
network for approximating functions which consists ofmodeling an input-output mapping as a
linear groupingof radially symmetric tasks. RBFnetwork has rapidtraining and simple system and
does not necessitate a heavy iterative training process asin MLP [10].
General regression neural network estimates any random function involving input and output
vectors, then drawing the function estimated directly from the training data [24]. It is related to
the radial basis function network and is based on a standard statistical procedure called kernel
regression [24]. For the probabilistic neural network, Gaussian distribution is assumed. However,
the assumption of normality does not always be securely justified. When the distribution is not
known and the exact distribution diverges considerably from the assumed one, the conventional
statistical methods normally run into major classification problems thus results in high rate of
misclassification [19].
A research made by Riries (2002) used neural network to classify tuberculosis bacteria in which
result in an excellent output [10]. Among of the severe diseases which can be treated when early
diagnosed are tuberculosis and other mycobacteriosus diseases. Traditionally, in order to identify
tuberculosis bacteria, sputum images is analysed to detect the presence of bacilli. This method
however consume a lot of time and requires specialists to avoid large errors. Therefore, neural
network is used resulting in a mean square error of 0.000368 and zero error classification when a
number of new data is being classified [10]. Figure 4 shows the result of tuberculosis
classification using neural network [20].
10. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
22
Image Geometric Features NN Classification
Result
• Circularity: 0.798063
• Compactness: 15.7461
• Eccentricity: 1.00187
• Tortuosity: 0.295393
TUBERCULUS
BACTERIA
• Circularity: 0.804989
• Compactness: 15.6106
• Eccentricity: 1.00014
• Tortuosity: 0.294254
TUBERCULUS
BACTERIA
•Circularity: 0.801031
• Compactness: 15.6877
• Eccentricity: 1.00237
• Tortuosity: 0.29493
TUBERCULUS
BACTERIA
• Circularity: 0.73098
• Compactness: 17.1911
• Eccentricity: 1.02675
• Tortuosity: 0.303604
NOT
TUBERCULUS
BACTERIA
• Circularity: 0.742655
• Compactness: 16.9209
• Eccentricity: 1.02937
• Tortuosity: 0.305133
NOT
TUBERCULUS
BACTERIA
Figure 4. Result of tuberculosis classification using neural network[10]
7. LIMITATIONS AND CHALLENGES
Traditional techniques of bacterial classification rely on phenotypic identification using gram
staining, biochemical process as well as culture methods. However, these techniques of bacterial
identification have two major problems. They can be used only for organisms which are
cultivated in vitro and show unique biochemical attributes that are not suitable for patterns that
have been used as a characteristic of any known groups of microorganism.In the recent years,
molecular procedures have been proven to be auspicious in defeating the limitations of traditional
phenotypic methods for the detection and classification of bacteria. Real time PCR and
microarrays is also among of the most commonly used molecular methods. However, it is highly
sensitive besides permitting bacteria quant at species level.
8. CONCLUSION
Further research in classification of bacteria using machine learning would be of great help in
microbiological field. It introduces a new and efficient method to categorize bacteria classes to
replace the inefficient old procedure which is tedious and time consuming. This new method
which uses pattern recognition would feasibly help to accurately tell the classes of bacteria which
have caused many types of life threatening diseases and infections. This would directly help to
save lives of millions of people, should the detection of their diseases is done at an early stage.
11. Electrical & Computer Engineering: An International Journal (ECIJ) Volume 3, Number 2, June 2014
23
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