Pub Date : 2023-01-31DOI: 10.22452/mjcs.vol36no1.5
Saud Al-Amri, S. Hamid, Nurul Fazmidar Mohd Noor (Corresponding Author), Abdullah Gani
With the rapid evolution of interactive technology, the popularity of mobile augmented reality (MAR) as a learning aid has continued to grow. However, several studies have revealed that research on the impact of AR in the educational domain is both insufficient and in an early phase. More studies are required to evaluate the effectiveness of utilizing MAR in this domain. The purpose of this study was to measure the effect of a mobile training course designed using MAR on trainees’ motivation. We reviewed the associated concepts, highlighted the importance and effectiveness of MAR and explained the benefits and challenges of employing MAR in the educational domain. This study drew on John Keller’s motivational model components and emphasized the significance of intrinsic motivation. We used a quantitative approach and designed a mobile training course that uses MAR to train government employees in Oman. A total of 32 employees were randomly divided into an experimental group and a control group. The experimental group used the designed application, and the control group took a training course online via computers. A motivational survey was conducted, and SPSS statistical software was used for data analysis. The results revealed that there was a significant difference in the mean motivation value for the experimental group: the trainees from the experimental group were more motivated than those from the control group. This study confirms that learners are motivated to participate in mobile training courses designed using MAR, which can contribute to the development of human resources in various domains.
{"title":"UTILIZATION OF MOBILE AUGMENTED REALITY IN A COURSE CONTENT: AN IMPACT STUDY","authors":"Saud Al-Amri, S. Hamid, Nurul Fazmidar Mohd Noor (Corresponding Author), Abdullah Gani","doi":"10.22452/mjcs.vol36no1.5","DOIUrl":"https://doi.org/10.22452/mjcs.vol36no1.5","url":null,"abstract":"With the rapid evolution of interactive technology, the popularity of mobile augmented reality (MAR) as a learning aid has continued to grow. However, several studies have revealed that research on the impact of AR in the educational domain is both insufficient and in an early phase. More studies are required to evaluate the effectiveness of utilizing MAR in this domain. The purpose of this study was to measure the effect of a mobile training course designed using MAR on trainees’ motivation. We reviewed the associated concepts, highlighted the importance and effectiveness of MAR and explained the benefits and challenges of employing MAR in the educational domain. This study drew on John Keller’s motivational model components and emphasized the significance of intrinsic motivation. We used a quantitative approach and designed a mobile training course that uses MAR to train government employees in Oman. A total of 32 employees were randomly divided into an experimental group and a control group. The experimental group used the designed application, and the control group took a training course online via computers. A motivational survey was conducted, and SPSS statistical software was used for data analysis. The results revealed that there was a significant difference in the mean motivation value for the experimental group: the trainees from the experimental group were more motivated than those from the control group. This study confirms that learners are motivated to participate in mobile training courses designed using MAR, which can contribute to the development of human resources in various domains.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42522130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.22452/mjcs.vol36no1.4
F. T. Koay, Choo Jun Tan (Corresponding Author), S. Teh, P. C. Teoh, H. Low
Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.
{"title":"SUPPORTING DECISION MAKING WITH AN ARIZ-BASED MODEL FOR SMART MANUFACTURING","authors":"F. T. Koay, Choo Jun Tan (Corresponding Author), S. Teh, P. C. Teoh, H. Low","doi":"10.22452/mjcs.vol36no1.4","DOIUrl":"https://doi.org/10.22452/mjcs.vol36no1.4","url":null,"abstract":"Smart manufacturing has transformed the way decisions are made. By accelerating the delivery of data to the various decision points, more rapid decision-making processes can be realized. A generic Decision Support System (DSS) utilizes an efficient technique, which integrates the algorithm for inventive problem solving (ARIZ) and supervised machine learning into a model for supporting various automated decision making processes. The proposed model is to examine the theoretical framework of ARIZ by devising an ARIZ-based DSS model. It incorporates supervised ML algorithms to assist decision making processes. Three case studies from the manufacturing sector are evaluated. The results indicate the capability of the proposed DSS in achieving a high accuracy rate and, at the same time reducing the time and resources required for decision making. Our study has simplified the data processing and extraction processes through an automated ARIZ-based DSS model; therefore enabling a non-technical user the opportunity to harvest the vast knowledge from the collected data for efficient decision making.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45203459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.22452/mjcs.vol36no1.3
Muthukumar B, Murugan S, Bharathi B (Corresponding Author)
The objective of this work is to classify the mind mapping decisions “like”, “dislike” and “neutral” in Epilepsy patients by applying the concepts of rough sets. An effective rough set-based classification of mental status in epilepsy patients has been computed using the features such as meditation, familiarity, theta, attention, appreciation, beta, mental effort, delta, alpha and gamma. The significance of features is considered as conditional attributes and the expected mood is represented as decision attributes. To analyze the impact of the features, the cardinality and rough set-based approximation are computed. Grey Relational Analysis (GRA) algorithm is applied for classification of patient decision is either like or dislike or neutral. The experimental results on classification of mind mapping of epilepsy patients using rough set-based approximation yields 95% accuracy.
{"title":"CLASSIFICATION OF GENDER BASED FOCUS MAPPING FOR EPILEPSY PATIENTS USING ROUGH SETS","authors":"Muthukumar B, Murugan S, Bharathi B (Corresponding Author)","doi":"10.22452/mjcs.vol36no1.3","DOIUrl":"https://doi.org/10.22452/mjcs.vol36no1.3","url":null,"abstract":"The objective of this work is to classify the mind mapping decisions “like”, “dislike” and “neutral” in Epilepsy patients by applying the concepts of rough sets. An effective rough set-based classification of mental status in epilepsy patients has been computed using the features such as meditation, familiarity, theta, attention, appreciation, beta, mental effort, delta, alpha and gamma. The significance of features is considered as conditional attributes and the expected mood is represented as decision attributes. To analyze the impact of the features, the cardinality and rough set-based approximation are computed. Grey Relational Analysis (GRA) algorithm is applied for classification of patient decision is either like or dislike or neutral. The experimental results on classification of mind mapping of epilepsy patients using rough set-based approximation yields 95% accuracy.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.22452/mjcs.vol36no1.2
Senthil kumar.Arunachalam (Corresponding Author), S. Devaraj, Bhavani Sridharan
Glaucoma and Stargardt’s, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt’s disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona–Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt’s disease with retinal fundus images. The DNP-MDMSMSIC method comprises diverse types of layers that support to identify early detection of disease with improved accuracy and less time. Process as explained; initially, numerous qualified retinal images are given as input to the input layer. These input images are transmitted further to the hidden layer 1 to perform image pre-processing. In DNP-MDMSMSIC, Space-Variant Perona–Malik Diffusive Image Preprocessing is carried out to decrease the noise from input image without removing contents like edges, lines, etc., for image interpretation with a higher peak signal-to-noise ratio. This preprocessed image is further processed in the hidden layer 2 where the feature extraction process is performed to extract features like color, texture, and intensity with a higher degree of accuracy. Based on the extracted features, an input feature image gets segmented in hidden layer 3. Mean Shift Mode Seeking Segmentation algorithm is employed to segment the pixels in image space with corresponding feature space points. Then the segmented images are given to the output layer to perform retinal fundus image classification using Bregman Divergence Function. During the image classification, the distance between two segmented regions (i.e., testing image region of particular class and training image region) with convex is measured. In this way, the retinal fundus images get classified with higher accuracy. Experimental evaluation is performed by considering the metrics such as peak signal-to-noise, disease detection accuracy, disease detection time, and error rate corresponding to the number of retina fundus images and image size. DNP-MDMSMSIC method is designed to detect Glaucoma and Stargardt’s disease at an earlier stage with higher accuracy by 8% and less time by 20% with aid of ACRIMA database.
{"title":"DEEP PERONA–MALIK DIFFUSIVE MEAN SHIFT IMAGE CLASSIFICATION FOR EARLY GLAUCOMA AND STARGARDT DISEASE DETECTION","authors":"Senthil kumar.Arunachalam (Corresponding Author), S. Devaraj, Bhavani Sridharan","doi":"10.22452/mjcs.vol36no1.2","DOIUrl":"https://doi.org/10.22452/mjcs.vol36no1.2","url":null,"abstract":"Glaucoma and Stargardt’s, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt’s disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona–Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt’s disease with retinal fundus images. The DNP-MDMSMSIC method comprises diverse types of layers that support to identify early detection of disease with improved accuracy and less time. Process as explained; initially, numerous qualified retinal images are given as input to the input layer. These input images are transmitted further to the hidden layer 1 to perform image pre-processing. In DNP-MDMSMSIC, Space-Variant Perona–Malik Diffusive Image Preprocessing is carried out to decrease the noise from input image without removing contents like edges, lines, etc., for image interpretation with a higher peak signal-to-noise ratio. This preprocessed image is further processed in the hidden layer 2 where the feature extraction process is performed to extract features like color, texture, and intensity with a higher degree of accuracy. Based on the extracted features, an input feature image gets segmented in hidden layer 3. Mean Shift Mode Seeking Segmentation algorithm is employed to segment the pixels in image space with corresponding feature space points. Then the segmented images are given to the output layer to perform retinal fundus image classification using Bregman Divergence Function. During the image classification, the distance between two segmented regions (i.e., testing image region of particular class and training image region) with convex is measured. In this way, the retinal fundus images get classified with higher accuracy. Experimental evaluation is performed by considering the metrics such as peak signal-to-noise, disease detection accuracy, disease detection time, and error rate corresponding to the number of retina fundus images and image size. DNP-MDMSMSIC method is designed to detect Glaucoma and Stargardt’s disease at an earlier stage with higher accuracy by 8% and less time by 20% with aid of ACRIMA database.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46834873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-31DOI: 10.22452/mjcs.vol36no1.1
Vimala Balakrishnan (Corresponding Author), Hii Lee Zing, Eric Guy Claude Laporte
The use of content features, particularly textual and linguistic for fake news detection is under-researched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study investigates a selection of content features such as word bigrams, part of speech distribution etc. to improve fake news detection. We performed a series of experiments on a new dataset gathered during the COVID-19 pandemic using Decision Tree, K-Nearest Neighbor, Logistic Regression, Support Vector Machine and Random Forest. Random Forest yielded the best results, followed closely by Support Vector Machine, across all setups. In general, both the textual and linguistic features were found to improve fake news detection when used separately, however, combining them into a single model did not improve the detection significantly. Differences were also noted between the use of bigrams and part of speech tags. The study shows that textual and linguistic features can be used successfully in detecting fake news using the traditional machine learning approach as opposed to deep learning.
{"title":"COVID-19 INFODEMIC – UNDERSTANDING CONTENT FEATURES IN DETECTING FAKE NEWS USING A MACHINE LEARNING APPROACH","authors":"Vimala Balakrishnan (Corresponding Author), Hii Lee Zing, Eric Guy Claude Laporte","doi":"10.22452/mjcs.vol36no1.1","DOIUrl":"https://doi.org/10.22452/mjcs.vol36no1.1","url":null,"abstract":"The use of content features, particularly textual and linguistic for fake news detection is under-researched, despite empirical evidence showing the features could contribute to differentiating real and fake news. To this end, this study investigates a selection of content features such as word bigrams, part of speech distribution etc. to improve fake news detection. We performed a series of experiments on a new dataset gathered during the COVID-19 pandemic using Decision Tree, K-Nearest Neighbor, Logistic Regression, Support Vector Machine and Random Forest. Random Forest yielded the best results, followed closely by Support Vector Machine, across all setups. In general, both the textual and linguistic features were found to improve fake news detection when used separately, however, combining them into a single model did not improve the detection significantly. Differences were also noted between the use of bigrams and part of speech tags. The study shows that textual and linguistic features can be used successfully in detecting fake news using the traditional machine learning approach as opposed to deep learning.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49069754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.22452/mjcs.sp2022no2.7
Ragaventhiran J, V. P, M. Kodabagi, Syed Thouheed Ahmed, P. Ramadoss, Prisma Megantoro
Malware attacks have grown in prominence in recent years, posing severe security risks and resulting in significant financial losses. The ability to rapidly and reliably classify malware is vital to cybersecurity due to the exponential growth of malware variants. The role of artificial intelligence plays a significant role in cybersecurity industry. Recently, in the field of malware detection deep learning technique seeks more attention than the machine learning techniques due to the complexity of its behavior. Because the deep learning technique performs well than the machine learning techniques in terms of accuracy and it is well suited for large amount of data. The input attribute for the proposed model is windows-based system call sequence which is collected from NT mal detect project. In this work, the unsupervised deep learning technique used for text classification namely LSTM autoencoder and the performance of proposed model compares with existing DL methods such as CNN, RNN and LSTM with the performance parameters of accuracy, precision, recall and F1-measure.
{"title":"AN UNSUPERVISED MALWARE DETECTION SYSTEM FOR WINDOWS BASED SYSTEM CALL SEQUENCES","authors":"Ragaventhiran J, V. P, M. Kodabagi, Syed Thouheed Ahmed, P. Ramadoss, Prisma Megantoro","doi":"10.22452/mjcs.sp2022no2.7","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no2.7","url":null,"abstract":"Malware attacks have grown in prominence in recent years, posing severe security risks and resulting in significant financial losses. The ability to rapidly and reliably classify malware is vital to cybersecurity due to the exponential growth of malware variants. The role of artificial intelligence plays a significant role in cybersecurity industry. Recently, in the field of malware detection deep learning technique seeks more attention than the machine learning techniques due to the complexity of its behavior. Because the deep learning technique performs well than the machine learning techniques in terms of accuracy and it is well suited for large amount of data. The input attribute for the proposed model is windows-based system call sequence which is collected from NT mal detect project. In this work, the unsupervised deep learning technique used for text classification namely LSTM autoencoder and the performance of proposed model compares with existing DL methods such as CNN, RNN and LSTM with the performance parameters of accuracy, precision, recall and F1-measure.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48577699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.22452/mjcs.sp2022no2.1
Rashmi C, M. Kodabagi
The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.
{"title":"CONNECTING USER PROFILES OF SOCIAL NETWORKS USING PROXIMITY-BASED CLUSTERING","authors":"Rashmi C, M. Kodabagi","doi":"10.22452/mjcs.sp2022no2.1","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no2.1","url":null,"abstract":"The establishment of connections among social network users using their profile information is an important task in social network analysis, which facilitates the development of various technological solutions such as stock market analysis, crime detection, tracking system of fraudulent events, etc. In this work, a proximity-based clustering method for networking LinkedIn profiles is presented. The proposed system computes proximity value between users using various attributes of user profiles. The proximity measures are computed by analyzing unstructured data of user profiles to connect users. The method addresses various issues such as comparison of familiar sentences, finding patterns, and sub-patterns among user profiles, assigning weights on attributes similarity, and computing total similarity which is associated with unstructured data. After computing proximity measures on various attributes of user profiles, the connecting edges between nodes are determined by employing artificial intelligence and a network graph is formed. The method is evaluated on a LinkedIn data-set to form a connected graph. The strength of the proposed methodology lies in the formation of multi-layered network graphs, as it uses various attributes of the user profiles to connect them. The proposed methodology helps various applications like recommendation systems to form network graphs of selected attributes and perform the social network analysis. The method achieves an accuracy of 96%. However, the profiles containing abbreviations of important information are not matched and the system accuracy drops down in such cases.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44673664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.22452/mjcs.sp2022no2.5
Syed Thouheed Ahmed, S. S, Nirmala S. Guptha, Lavanya N L, S. M. Basha, Afifa Salsabil Fathima
Biomedical image processing and decision making is a growing research demand under global pandemic situation. The quality of medical images plays a vital role in streamlining remote diagnosis and processing via telemedicine platform, in providing unambiguous results and decision supports. This paper presents an improved Medical Image Content Quality (MICQ) technique and it aims to enrich the Magnetic Resonance (MR) image content or pixels based on semi supervised clustering technique for the process of deeper analysis and investigation to identify the normal and abnormal portions. The proposed (IMICQ) system is containing three stages namely pre-processing, clustering and validation respectively. In the pre-processing stage, the MICQ divides the MR image into finite number of non-overlapping blocks or vectors with size (2*2). Next stage, the proposed MICQ system iteratively partitions the MR image dataset or vector set into optimum number of highly relative dissimilar clusters based on K-Means clustering technique. In the last stage, the proposed system measures the quality of clustering result which obtained in the previous stage based on Effective Cluster Validation Measure (ECVM). Experimental results show that the MICQ is better suitable to improve MR image content quality for telemedicine platform and to predict the normal and abnormal portions over the image with higher accuracy ratio.
{"title":"IMPROVING MEDICAL IMAGE PIXEL QUALITY USING MICQ UNSUPERVISED MACHINE LEARNING TECHNIQUE","authors":"Syed Thouheed Ahmed, S. S, Nirmala S. Guptha, Lavanya N L, S. M. Basha, Afifa Salsabil Fathima","doi":"10.22452/mjcs.sp2022no2.5","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no2.5","url":null,"abstract":"Biomedical image processing and decision making is a growing research demand under global pandemic situation. The quality of medical images plays a vital role in streamlining remote diagnosis and processing via telemedicine platform, in providing unambiguous results and decision supports. This paper presents an improved Medical Image Content Quality (MICQ) technique and it aims to enrich the Magnetic Resonance (MR) image content or pixels based on semi supervised clustering technique for the process of deeper analysis and investigation to identify the normal and abnormal portions. The proposed (IMICQ) system is containing three stages namely pre-processing, clustering and validation respectively. In the pre-processing stage, the MICQ divides the MR image into finite number of non-overlapping blocks or vectors with size (2*2). Next stage, the proposed MICQ system iteratively partitions the MR image dataset or vector set into optimum number of highly relative dissimilar clusters based on K-Means clustering technique. In the last stage, the proposed system measures the quality of clustering result which obtained in the previous stage based on Effective Cluster Validation Measure (ECVM). Experimental results show that the MICQ is better suitable to improve MR image content quality for telemedicine platform and to predict the normal and abnormal portions over the image with higher accuracy ratio.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41566462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.22452/mjcs.sp2022no2.4
Ragaventhiran J, Sindhuja M, Prasath N, M. B., Islabudeen M
A study found that the depression rate is growing at an alarming rate among everyone. Many people who report symptoms of depression mostly have not been diagnosed or underwent treatments for it. If they do not get proper treatments like medication, therapy, guidance or counselling, it would be difficult for them to lead a happy and stress-free lifestyle. India is already on the cusp of a health crisis, and we urgently require a long-term solution to the problem of depression. People are more inclined to open it up to a smart machine than to a human, according to a recent study. Digital interfaces are gaining traction as feasible options for closing the gap and making mental diagnosis and treatment more accessible and inexpensive to everybody. The aim of the project is to develop a chatbot called Therapy Bot using sentiment analysis and cognitive behavioral therapy to predict the mental health status of an individual. Moreover, the chatbot can serve as a good companion to the affected by communicating with friendly manner and help them recover. The chatbot will personalize its responses based on the user's answer to keep the conversation interesting. The chatbots can be used as a complement to treatment or as a kind of interim support while waiting for an appointment. The benefit of such a method is that, rather than reaching a point where a trip to a psychologist is required, an online free version will reach a large number of people, mitigate the negative effects of depression, and contribute to a better of society.
{"title":"MOBILE PHONE RECOMMENDER USING MULTI CRITERIA DECISION MAKING ALGORITHM","authors":"Ragaventhiran J, Sindhuja M, Prasath N, M. B., Islabudeen M","doi":"10.22452/mjcs.sp2022no2.4","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no2.4","url":null,"abstract":"A study found that the depression rate is growing at an alarming rate among everyone. Many people who report symptoms of depression mostly have not been diagnosed or underwent treatments for it. If they do not get proper treatments like medication, therapy, guidance or counselling, it would be difficult for them to lead a happy and stress-free lifestyle. India is already on the cusp of a health crisis, and we urgently require a long-term solution to the problem of depression. People are more inclined to open it up to a smart machine than to a human, according to a recent study. Digital interfaces are gaining traction as feasible options for closing the gap and making mental diagnosis and treatment more accessible and inexpensive to everybody. The aim of the project is to develop a chatbot called Therapy Bot using sentiment analysis and cognitive behavioral therapy to predict the mental health status of an individual. Moreover, the chatbot can serve as a good companion to the affected by communicating with friendly manner and help them recover. The chatbot will personalize its responses based on the user's answer to keep the conversation interesting. The chatbots can be used as a complement to treatment or as a kind of interim support while waiting for an appointment. The benefit of such a method is that, rather than reaching a point where a trip to a psychologist is required, an online free version will reach a large number of people, mitigate the negative effects of depression, and contribute to a better of society.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41438641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-06DOI: 10.22452/mjcs.sp2022no2.3
B. Muthu kumar, J. Ragaventhiran, N. Bhavana, M. Thurai Pandian, M. Islabudeen, A. Sampath
A finger vein authentication system is proposed in this research. Biometrics is the science of determining a person's identity based on physiological or behavioral characteristics. Physical characteristics like fingerprints, a face or a retina, as well as personal characteristics like a signature, are included in these characteristics. Biometric features are significantly more difficult for attackers to replicate or fabricate than traditional methods, and they are extremely rare to lose. Biometric traits are used in the identification system, which increases security and dependability. The technology to verify vein patterns is still relatively new, compared with other human characteristics. The proposed work focuses on developing a contactless sensor to retrieve features from the hand's finger vein pattern using a Deep attribute Representation based Fractional Firefly method (DAR-FFF). Vein pattern identification scans the blood for hemoglobin using an infrared light source. After the participant's palm is placed over the sensing device, an infrared region beam from the device measures the orientation of the arteries. These ultraviolet wavelengths are absorbed by liquid hemoglobin in the vasculature, resulting in dark streaks on the map. The hand's finger has more intricate circulatory pathways and a variety of distinguishing characteristics. Image enhancement, skeletonization, and vein pattern chain code comparison are all processes in this procedure.
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