Pub Date : 2022-07-27DOI: 10.22452/mjcs.vol35no3.2
A. Anandhan, M. Ismail, Liyana Shuib
Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.
{"title":"EXPERT RECOMMENDATION THROUGH TAG RELATIONSHIP IN COMMUNITY QUESTION ANSWERING","authors":"A. Anandhan, M. Ismail, Liyana Shuib","doi":"10.22452/mjcs.vol35no3.2","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no3.2","url":null,"abstract":"Community Question Answering (CQA) services are technical discussion forums websites on social media that serve as a platform for users to interact mainly via question and answer. However, users of this platform have posed dissatisfaction over the slow response and the preference for user domains due to the overwhelming information in CQA websites. Numerous past studies focusing on expert recommendation are solely based on the information available from websites where they rarely account for the preference of users’ domain knowledge. This condition prompts the need to identify experts for the questions posted on community-based websites. Thus, this study attempts to identify ranking experts’ derived from the tag relationship among users in the CQA websites to construct user profiles where their interests are realized in the form of tags. Experts are considered users who post high-quality answers and are often recommended by the system based on their previous posts and associated tags. These associations further describe tags that often co-occur in posts and the significant domains of user interest. The current study further explores this relationship by adopting the “Tag Relationship Expert Recommendation (TRER)” method where Questions Answer (QA) Space is utilized as a dataset to identify users with similar interests and subsequently rank experts based on the tag-tag relationship for user’s question. The results show that the TRER method outperforms existing baseline methods by effectively improving the performance of relevant domain experts in CQA, thereby facilitating the expert recommendation process in answering questions posted by technical and academic professionals.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46191688","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 : 2022-07-27DOI: 10.22452/mjcs.vol35no3.1
B. Khan, R. F. Olanrewaju, M. A. Morshidi, R. N. Mir, M. L. Mat Kiah, Abdul Mobeen Khan
With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.
{"title":"EVOLUTION AND ANALYSIS OF SECURED HASH ALGORITHM (SHA) FAMILY","authors":"B. Khan, R. F. Olanrewaju, M. A. Morshidi, R. N. Mir, M. L. Mat Kiah, Abdul Mobeen Khan","doi":"10.22452/mjcs.vol35no3.1","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no3.1","url":null,"abstract":"With the rapid advancement of technologies and proliferation of intelligent devices, connecting to the internet challenges have grown manifold, such as ensuring communication security and keeping user credentials secret. Data integrity and user privacy have become crucial concerns in any ecosystem of advanced and interconnected communications. Cryptographic hash functions have been extensively employed to ensure data integrity in insecure environments. Hash functions are also combined with digital signatures to offer identity verification mechanisms and non-repudiation services. The federal organization National Institute of Standards and Technology (NIST) established the SHA to provide security and optimal performance over some time. The most well-known hashing standards are SHA-1, SHA-2, and SHA-3. This paper discusses the background of hashing, followed by elaborating on the evolution of the SHA family. The main goal is to present a comparative analysis of these hashing standards and focus on their security strength, performance and limitations against common attacks. The complete assessment was carried out using statistical analysis, performance analysis and extensive fault analysis over a defined test environment. The study outcome showcases the issues of SHA-1 besides exploring the security benefits of all the dominant variants of SHA-2 and SHA-3. The study also concludes that SHA-3 is the best option to mitigate novice intruders while allowing better performance cost-effectively.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42560590","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 : 2022-04-29DOI: 10.22452/mjcs.vol35no2.2
T. Sariki, G. Kumar
In this era of information overload, Recommender Systems have become increasingly important to assist internet users in finding the right choice from umpteen numbers of choices. Especially, in the case of book recommender systems, suggesting an appropriate book by considering user preferences can increase the number of book readers in turn having an aftereffect on the users’ lifestyle by reducing stress, stimulating imagination, improving vocabulary, and making readers smarter. The majority of book recommender systems in the literature have used Collaborative Filtering (CF) and Content-Based Filtering (CBF) methods. Even though CBF methods have shown better performance than CF methods, they are mostly confined to shallow linguistic features. The present work proposed an aggrandized framework having three concurrent modules to improve the recommendation process. NER module extracts the Named Entities from the entire book content which are the key semantic units in providing clues on the possible choices of reading other related books. The Visual feature extraction module analyzes the book front cover to detect objects and text on the cover as well as the description of the cover which can bestow a clue for the genre of that book. The Stylometry module enhances the feature set used in the literature to analyze the author’s literary style for identifying similar authors to the present author of the book. These three modules conjointly improved the overall recommendation accuracy by 18% over the baseline CBF method that indicates the effectiveness of the present framework.
{"title":"AN AGGRANDIZED FRAMEWORK FOR ENRICHING BOOK RECOMMENDATION SYSTEM","authors":"T. Sariki, G. Kumar","doi":"10.22452/mjcs.vol35no2.2","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no2.2","url":null,"abstract":"In this era of information overload, Recommender Systems have become increasingly important to assist internet users in finding the right choice from umpteen numbers of choices. Especially, in the case of book recommender systems, suggesting an appropriate book by considering user preferences can increase the number of book readers in turn having an aftereffect on the users’ lifestyle by reducing stress, stimulating imagination, improving vocabulary, and making readers smarter. The majority of book recommender systems in the literature have used Collaborative Filtering (CF) and Content-Based Filtering (CBF) methods. Even though CBF methods have shown better performance than CF methods, they are mostly confined to shallow linguistic features. The present work proposed an aggrandized framework having three concurrent modules to improve the recommendation process. NER module extracts the Named Entities from the entire book content which are the key semantic units in providing clues on the possible choices of reading other related books. The Visual feature extraction module analyzes the book front cover to detect objects and text on the cover as well as the description of the cover which can bestow a clue for the genre of that book. The Stylometry module enhances the feature set used in the literature to analyze the author’s literary style for identifying similar authors to the present author of the book. These three modules conjointly improved the overall recommendation accuracy by 18% over the baseline CBF method that indicates the effectiveness of the present framework.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49202714","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 : 2022-04-29DOI: 10.22452/mjcs.vol35no2.5
B. H. Nayef, Siti Norul Huda Sheikh Abdullah, R. Sulaiman, Z. Alyasseri
Machine learning (ML) techniques are part of artificial intelligence. ML involves imitating human behavior in solving different problems, such as object detection, text handwriting recognition, and image classification. Several techniques can be used in machine learning, such as Neural Networks (NN). The expansion in information technology enables researchers to collect large amounts of various data types. The challenging issue is to uncover neural network parameters suitable for object detection problems. Therefore, this paper presents a literature review of the latest proposed and developed components in neural network techniques to cope with different sizes and data types. A brief discussion is also introduced to demonstrate the different types of neural network parameters, such as activation functions, loss functions, and regularization methods. Moreover, this paper also uncovers parameter optimization methods and hyperparameters of the model, such as weight, the learning rate, and the number of iterations. From the literature, it is notable that choosing the activation function, loss function, number of neural network layers, and data size is the major factor affecting NN performance. Additionally, utilizing deep learning NN resulted in a significant improvement in model performance for a variety of issues, which became the researcher's attention.
{"title":"VARIANTS OF NEURAL NETWORKS: A REVIEW","authors":"B. H. Nayef, Siti Norul Huda Sheikh Abdullah, R. Sulaiman, Z. Alyasseri","doi":"10.22452/mjcs.vol35no2.5","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no2.5","url":null,"abstract":"Machine learning (ML) techniques are part of artificial intelligence. ML involves imitating human behavior in solving different problems, such as object detection, text handwriting recognition, and image classification. Several techniques can be used in machine learning, such as Neural Networks (NN). The expansion in information technology enables researchers to collect large amounts of various data types. The challenging issue is to uncover neural network parameters suitable for object detection problems. Therefore, this paper presents a literature review of the latest proposed and developed components in neural network techniques to cope with different sizes and data types. A brief discussion is also introduced to demonstrate the different types of neural network parameters, such as activation functions, loss functions, and regularization methods. Moreover, this paper also uncovers parameter optimization methods and hyperparameters of the model, such as weight, the learning rate, and the number of iterations. From the literature, it is notable that choosing the activation function, loss function, number of neural network layers, and data size is the major factor affecting NN performance. Additionally, utilizing deep learning NN resulted in a significant improvement in model performance for a variety of issues, which became the researcher's attention.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49637658","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 : 2022-04-29DOI: 10.22452/mjcs.vol35no2.1
Myat Noe Win, Sri Devi Ravana, Liyana Shuib
Due to COVID-19 pandemic, most physical business transactions were pushed online. Online reviews became an excellent source for sentiment analysis to determine a customer's sentiment about a business. This insight is valuable asset for businesses, especially for tourism sector, to be harnessed for business intelligence and craft new marketing strategies. However, traditional sentiment analysis with flat classification and manual aspect categorization technique imposes challenges with non-opinionated reviews and outdated pre-defined aspect categories which limits businesses to filter relevant opinionated reviews and learn new aspects from reviews itself for aspect-based sentiment analysis. Therefore, this paper proposes sentiment attribution analysis with hierarchical classification and automatic aspect categorization to improve the social listening for diligent marketing and recommend potential business optimization to revive the business from surviving to thriving after this pandemic. Hierarchical classification is proposed using hybrid approach. While automatic aspect categorization is constructed with semantic similarity clustering and applied enhanced topic modelling on opinionated reviews. Experimental results on two real-world datasets from two different industries, Airline and Hotel, shows that the sentiment analysis with hierarchical classification outperforms the classification accuracy with a good F1-score compared to baseline papers. Automatic aspect categorization was found to be able to unhide the sentiment of the aspects which was not recognized in manual aspect categorization. Although it is accepted that the effectiveness of aspect-based sentiment analysis on flat classification and manual aspect categorization, none have assessed the effectiveness while using hierarchical classification with a hybrid approach and automatic aspect categorization.
{"title":"SENTIMENT ATTRIBUTION ANALYSIS WITH HIERARCHICAL CLASSIFICATION AND AUTOMATIC ASPECT CATEGORIZATION ON ONLINE USER REVIEWS","authors":"Myat Noe Win, Sri Devi Ravana, Liyana Shuib","doi":"10.22452/mjcs.vol35no2.1","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no2.1","url":null,"abstract":"Due to COVID-19 pandemic, most physical business transactions were pushed online. Online reviews became an excellent source for sentiment analysis to determine a customer's sentiment about a business. This insight is valuable asset for businesses, especially for tourism sector, to be harnessed for business intelligence and craft new marketing strategies. However, traditional sentiment analysis with flat classification and manual aspect categorization technique imposes challenges with non-opinionated reviews and outdated pre-defined aspect categories which limits businesses to filter relevant opinionated reviews and learn new aspects from reviews itself for aspect-based sentiment analysis. Therefore, this paper proposes sentiment attribution analysis with hierarchical classification and automatic aspect categorization to improve the social listening for diligent marketing and recommend potential business optimization to revive the business from surviving to thriving after this pandemic. Hierarchical classification is proposed using hybrid approach. While automatic aspect categorization is constructed with semantic similarity clustering and applied enhanced topic modelling on opinionated reviews. Experimental results on two real-world datasets from two different industries, Airline and Hotel, shows that the sentiment analysis with hierarchical classification outperforms the classification accuracy with a good F1-score compared to baseline papers. Automatic aspect categorization was found to be able to unhide the sentiment of the aspects which was not recognized in manual aspect categorization. Although it is accepted that the effectiveness of aspect-based sentiment analysis on flat classification and manual aspect categorization, none have assessed the effectiveness while using hierarchical classification with a hybrid approach and automatic aspect categorization.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47226893","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 : 2022-04-29DOI: 10.22452/mjcs.vol35no2.4
E. Heidari, A. Movaghar, H. Motameni, Behnam Barzegar
The Internet of Things is a new concept in the world of information and communication technology, in which for each being (whether it be a human, an animal or an object), the possibility of sending and receiving data through communication networks such as the Internet or Intranet is provided. Wireless sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. In this paper, a routing protocol has been proposed and stimulated which is based on the function of the whale optimization algorithm. Clustering is performed through a routing method which is based on energy level, collision reduction, distance between cluster head node and destination, and neighbor energy. Furthermore, the selection of the cluster head node is performed based on the maximum remaining energy, the least distance with other clusters, and energy consumption, where energy consumption for reaching the base station is minimized. By de-creasing the level of cluster head energy from the specified threshold value from among the nodes in the same cluster, a node with an energy level above the threshold would be selected as the new cluster head. Moreover, four conditions (i.e. the shortest route, the leading route, the least distance to the source node, and destination node) are applied for routing. The proposed method was compared to LEACH, EEUC, EECRP, BEAR and CCR algorithms, and the results indicated the superiority of the proposed method to other methods in terms of the number of dead nodes.
{"title":"REDUCING ENERGY CONSUMPTION IN IOT BY A ROUTING WHALE OPTIMIZATION ALGORITHM","authors":"E. Heidari, A. Movaghar, H. Motameni, Behnam Barzegar","doi":"10.22452/mjcs.vol35no2.4","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no2.4","url":null,"abstract":"The Internet of Things is a new concept in the world of information and communication technology, in which for each being (whether it be a human, an animal or an object), the possibility of sending and receiving data through communication networks such as the Internet or Intranet is provided. Wireless sensors have limited energy resources due to their use of batteries in supplying energy, and since battery replacement in these sensors is not usually feasible, the longevity of wireless sensor networks is limited. Therefore, reducing the energy consumption of the used sensors in IoT networks to increase the network lifetime is one of the crucial challenges and parameters in such networks. In this paper, a routing protocol has been proposed and stimulated which is based on the function of the whale optimization algorithm. Clustering is performed through a routing method which is based on energy level, collision reduction, distance between cluster head node and destination, and neighbor energy. Furthermore, the selection of the cluster head node is performed based on the maximum remaining energy, the least distance with other clusters, and energy consumption, where energy consumption for reaching the base station is minimized. By de-creasing the level of cluster head energy from the specified threshold value from among the nodes in the same cluster, a node with an energy level above the threshold would be selected as the new cluster head. Moreover, four conditions (i.e. the shortest route, the leading route, the least distance to the source node, and destination node) are applied for routing. The proposed method was compared to LEACH, EEUC, EECRP, BEAR and CCR algorithms, and the results indicated the superiority of the proposed method to other methods in terms of the number of dead nodes.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43202198","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 : 2022-04-29DOI: 10.22452/mjcs.vol35no2.3
Hemin Ibrahim, C. Loo
Speech is an effective, quick, and important way for communicating and exchanging complex information between humans. Emotions have always been a part of normal human conversation which makes the speech more attractive. Because of this major role of both speech and emotion, many researchers are inspired by studying Speech Emotion Recognition (SER) which still has plenty of challenges. In this study, we proposed a novel reservoir computing approach with the initialization of random connection weights for the input weight by the truncated normal distribution. Furthermore, Population-Based Training (PBT) is adopted to optimize the hyperparameters of the whole Echo State Network (ESN) model which have a significant impact on the model performance. The proposed model has adopted bidirectional reservoir input to increase the memorization capability, and Sparse Random Projection (SRP) was applied for dimensional reduction as a simple, unsupervised, and low complexity approach. The speaker-independent strategy was employed on EMODB and SAVEE datasets as an acted speech emotion dataset and Aibo as a non-acted dataset. The model achieved 84.8%, 65.95%, and 45.99% unweighted average recalls on the EMODB, SAVEE, and Aibo datasets respectively. The results show that the proposed model outperforms the recent state-of-the-art studies with a cheaper computational cost.
{"title":"RESERVOIR COMPUTING WITH TRUNCATED NORMAL DISTRIBUTION FOR SPEECH EMOTION RECOGNITION","authors":"Hemin Ibrahim, C. Loo","doi":"10.22452/mjcs.vol35no2.3","DOIUrl":"https://doi.org/10.22452/mjcs.vol35no2.3","url":null,"abstract":"Speech is an effective, quick, and important way for communicating and exchanging complex information between humans. Emotions have always been a part of normal human conversation which makes the speech more attractive. Because of this major role of both speech and emotion, many researchers are inspired by studying Speech Emotion Recognition (SER) which still has plenty of challenges. In this study, we proposed a novel reservoir computing approach with the initialization of random connection weights for the input weight by the truncated normal distribution. Furthermore, Population-Based Training (PBT) is adopted to optimize the hyperparameters of the whole Echo State Network (ESN) model which have a significant impact on the model performance. The proposed model has adopted bidirectional reservoir input to increase the memorization capability, and Sparse Random Projection (SRP) was applied for dimensional reduction as a simple, unsupervised, and low complexity approach. The speaker-independent strategy was employed on EMODB and SAVEE datasets as an acted speech emotion dataset and Aibo as a non-acted dataset. The model achieved 84.8%, 65.95%, and 45.99% unweighted average recalls on the EMODB, SAVEE, and Aibo datasets respectively. The results show that the proposed model outperforms the recent state-of-the-art studies with a cheaper computational cost.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48955268","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 : 2022-03-31DOI: 10.22452/mjcs.sp2022no1.2
N. Ambiga, A. Nagarajan
Biometric authentication system uses some technique that measures the physical and biological characteristics of human to identify individuals and thus provide security to a system against fraud or intrusion. Common biometric authentication processes are vulnerable and possibility for imitation. Teeth are an important biological entity that plays a major role in forensic research to identify an individual whom cannot be identified visually. There are different algorithms used in biometric authentication. This paper proposes a unique method to recognize the human teeth by using a combination of Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) to extract significant features and an improved version of Binary Particle Swarm Optimization (BPSO) for feature selection is employed to search the feature vector space in order to obtain optimal feature subset to increase the performance rate. A combination of image pre-processing techniques like background removal, gamma intensity correction and Laplacian of Gaussian (LoG) filter are used to help in correct feature extraction. Using the shift invariance property of DFT, a circular feature extraction technique and the energy compaction property of DCT, a circular sector feature extraction method is presented. Experimental results on IvisionLab/dental-image standard database are shown which exhibit promising performance of the teeth recognition system.
{"title":"DETECTION OF BIO ELEMENTS PRESENT IN HUMAN BIOLOGICAL TISSUE-TOOTH AND ITS USAGE FOR ELEMENT BIOMETRIC AUTHENTICATION","authors":"N. Ambiga, A. Nagarajan","doi":"10.22452/mjcs.sp2022no1.2","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no1.2","url":null,"abstract":"Biometric authentication system uses some technique that measures the physical and biological characteristics of human to identify individuals and thus provide security to a system against fraud or intrusion. Common biometric authentication processes are vulnerable and possibility for imitation. Teeth are an important biological entity that plays a major role in forensic research to identify an individual whom cannot be identified visually. There are different algorithms used in biometric authentication. This paper proposes a unique method to recognize the human teeth by using a combination of Discrete Fourier Transform (DFT) and Discrete Cosine Transform (DCT) to extract significant features and an improved version of Binary Particle Swarm Optimization (BPSO) for feature selection is employed to search the feature vector space in order to obtain optimal feature subset to increase the performance rate. A combination of image pre-processing techniques like background removal, gamma intensity correction and Laplacian of Gaussian (LoG) filter are used to help in correct feature extraction. Using the shift invariance property of DFT, a circular feature extraction technique and the energy compaction property of DCT, a circular sector feature extraction method is presented. Experimental results on IvisionLab/dental-image standard database are shown which exhibit promising performance of the teeth recognition system.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48863490","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 : 2022-03-31DOI: 10.22452/mjcs.sp2022no1.6
Dr. A. Anne Frank Joe, A. Veeramuthu, Dr. K. Ashokkumar
Near InfraRed Spectroscopy (NIRS) based techniques have evolved tremendously and are being perfected over ages to be applied in a wide variety of applications. This study focuses on the selection of optimum classification algorithms, as an automated variety identifier suitable for wheat grains based on the statistical performance indices for the quality analysis and variety classification of wheat grains. NIRS was used to non-destructively determine protein, carbohydrate, ash and moisture content of wheat grains. Structural analysis focuses on the visualization aspect of the wheat grains such as the shape, size (learnt from the length, width, and height), colour and glossiness of the seed coat. In addition to the spectral information, the image derived characteristics are incorporated into the classification models to further enhance the variety identification of 10 varieties of whole wheat samples UP 262, Samba, RR 21, 343, Super sitwa, Punjab, Ankurkedar, Super 303, Pusa 360, PBW 502. Varietal purity of wheat grains is a significant factor to be considered before the milling process. The results clearly reveal that the proposed selective wavelength-based prediction algorithms and selection of limited individual quality parameters, using improved methods to extract these features has aided with the success of classification performed in this work. The proposed novel approach proves that collaborating the selected spectral features and image features further enhances the effectiveness of this work.
{"title":"A NOVEL APPROACH TO COMBINE NIR AND IMAGE FEATURES FOR NON-DESTRUCTIVE ASSAY OF INDIAN WHEAT VARIETIES","authors":"Dr. A. Anne Frank Joe, A. Veeramuthu, Dr. K. Ashokkumar","doi":"10.22452/mjcs.sp2022no1.6","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no1.6","url":null,"abstract":"Near InfraRed Spectroscopy (NIRS) based techniques have evolved tremendously and are being perfected over ages to be applied in a wide variety of applications. This study focuses on the selection of optimum classification algorithms, as an automated variety identifier suitable for wheat grains based on the statistical performance indices for the quality analysis and variety classification of wheat grains. NIRS was used to non-destructively determine protein, carbohydrate, ash and moisture content of wheat grains. Structural analysis focuses on the visualization aspect of the wheat grains such as the shape, size (learnt from the length, width, and height), colour and glossiness of the seed coat. In addition to the spectral information, the image derived characteristics are incorporated into the classification models to further enhance the variety identification of 10 varieties of whole wheat samples UP 262, Samba, RR 21, 343, Super sitwa, Punjab, Ankurkedar, Super 303, Pusa 360, PBW 502. Varietal purity of wheat grains is a significant factor to be considered before the milling process. The results clearly reveal that the proposed selective wavelength-based prediction algorithms and selection of limited individual quality parameters, using improved methods to extract these features has aided with the success of classification performed in this work. The proposed novel approach proves that collaborating the selected spectral features and image features further enhances the effectiveness of this work.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44849718","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 : 2022-03-31DOI: 10.22452/mjcs.sp2022no1.3
Arodh Lal Karn, Karamath Ateeq, Sudhakar Sengan, I. V., Logesh Ravi, Dilip Kumar Sharma, S. V.
Deep Learning (DL) in finance is widely regarded as one of the pillars of financial services sectors since it performs crucial functions such as transaction processing and computation, risk assessment, and even behavior prediction. As a subset of data science, DL can learn and develop from their experience, which does not require constant human interference and programming, implying that the technology will improve quickly. By loading an Ensemble Model (EM), a Deep Sequential Learning (DSL)model, and additional upper-layer EM classifier in the correct order, a new “Contained-In-Between (C-I-B)” composite structured DSL model is recommended in this article. In cases like Fraud Detection System (FDS), where the data flow comprises vectors with complex interconnected characteristics, DL models with this structure have proven to be highly efficient. Finally, by utilizing optimized transaction eigenvectors, a NB classifier is trained. This strategy is more effective than most standard approaches in identifying transaction fraud. The proposed model is evaluated for its accuracy, Recall and F-score, and the results show that the model has better performance against its counterparts.
{"title":"B-LSTM-NB BASED COMPOSITE SEQUENCE LEARNING MODEL FOR DETECTING FRAUDULENT FINANCIAL ACTIVITIES","authors":"Arodh Lal Karn, Karamath Ateeq, Sudhakar Sengan, I. V., Logesh Ravi, Dilip Kumar Sharma, S. V.","doi":"10.22452/mjcs.sp2022no1.3","DOIUrl":"https://doi.org/10.22452/mjcs.sp2022no1.3","url":null,"abstract":"Deep Learning (DL) in finance is widely regarded as one of the pillars of financial services sectors since it performs crucial functions such as transaction processing and computation, risk assessment, and even behavior prediction. As a subset of data science, DL can learn and develop from their experience, which does not require constant human interference and programming, implying that the technology will improve quickly. By loading an Ensemble Model (EM), a Deep Sequential Learning (DSL)model, and additional upper-layer EM classifier in the correct order, a new “Contained-In-Between (C-I-B)” composite structured DSL model is recommended in this article. In cases like Fraud Detection System (FDS), where the data flow comprises vectors with complex interconnected characteristics, DL models with this structure have proven to be highly efficient. Finally, by utilizing optimized transaction eigenvectors, a NB classifier is trained. This strategy is more effective than most standard approaches in identifying transaction fraud. The proposed model is evaluated for its accuracy, Recall and F-score, and the results show that the model has better performance against its counterparts.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49359032","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：