Pub Date : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.6
N. Kumar, V. Gandhi
This paper presents a hybrid isolated power system modeling and Load Frequency Controller (LFC) for different penetration levels of wind power generation. The system considered here consists of a conventional diesel generator, wind generator and energy storage system for a LFC. The purpose of the considered system is to suppress the frequency deviations (balancing the real power generation and demand). An inertia control is used to obtain the best possible gain of the wind system and Fuzzy Logic Control (FLC) is used to control the frequency of the hybrid system. The results are compared with different control techniques. The dynamic performance of the system is studied at different cases by changing the wind penetration level and also by increasing the load disturbances.
{"title":"LOAD FREQUENCY CONTROL FOR AN ISOLATED HYBRID POWER SYSTEM WITH HYBRID CONTROL TECHNIQUE AND COMPARATIVE ANALYSIS WITH DIFFERENT CONTROL TECHNIQUES","authors":"N. Kumar, V. Gandhi","doi":"10.22452/mjcs.sp2020no1.6","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.6","url":null,"abstract":"This paper presents a hybrid isolated power system modeling and Load Frequency Controller (LFC) for different penetration levels of wind power generation. The system considered here consists of a conventional diesel generator, wind generator and energy storage system for a LFC. The purpose of the considered system is to suppress the frequency deviations (balancing the real power generation and demand). An inertia control is used to obtain the best possible gain of the wind system and Fuzzy Logic Control (FLC) is used to control the frequency of the hybrid system. The results are compared with different control techniques. The dynamic performance of the system is studied at different cases by changing the wind penetration level and also by increasing the load disturbances.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47939773","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.9
R. Jayaraman, V. Subramaniyaswamy, Logesh Ravi
Accurate group movie recommendation systems are a need in society today. We find that people tend to watch movies in groups rather than by themselves. However, the groups of people that tend to watch movies together are very diverse. In the existing methods, the characteristics of individual users are simply aggregated to obtain the group’s attributes and most of the time useful data is not utilized. This can be improved upon by ensuring the utilization of all the data that we are presented with from the scenario. The method proposed in this paper is termed integrated as we weighed in the individual traits of each user in the group when predicting the group’s rating for a movie. We used the concept of Hesitant Fuzzy Sets (HFS) in order to keep track of the characteristics of each of the users individually. The method we proposed in this paper employs Matrix Factorisation (MF) based Collaborative Filtering (CF) along with hesitant fuzzy sets. The way we performed MF based CF for a group is that we found the factors first and then formed the groups. The ratings were then predicted for these groups. The groups we have considered are of three sizes - 3 users, 5 users, and 10 users.
{"title":"AN ADAPTIVE HESITANT FUZZY SETS BASED GROUP RECOMMENDATION SYSTEM","authors":"R. Jayaraman, V. Subramaniyaswamy, Logesh Ravi","doi":"10.22452/mjcs.sp2020no1.9","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.9","url":null,"abstract":"Accurate group movie recommendation systems are a need in society today. We find that people tend to watch movies in groups rather than by themselves. However, the groups of people that tend to watch movies together are very diverse. In the existing methods, the characteristics of individual users are simply aggregated to obtain the group’s attributes and most of the time useful data is not utilized. This can be improved upon by ensuring the utilization of all the data that we are presented with from the scenario. The method proposed in this paper is termed integrated as we weighed in the individual traits of each user in the group when predicting the group’s rating for a movie. We used the concept of Hesitant Fuzzy Sets (HFS) in order to keep track of the characteristics of each of the users individually. The method we proposed in this paper employs Matrix Factorisation (MF) based Collaborative Filtering (CF) along with hesitant fuzzy sets. The way we performed MF based CF for a group is that we found the factors first and then formed the groups. The ratings were then predicted for these groups. The groups we have considered are of three sizes - 3 users, 5 users, and 10 users.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45479162","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.7
R. Tomar, Hanumat G. Sastry, M. Prateek
The present research work proposes a novel approach for efficient information dissemination in Vehicular Networks using V2I based communication. This approach makes use of the V2I communication to establish the connectivity between the cloud server and the vehicle. The approach is focused on finding the utility of existing IoT protocols in the Vehicular Networks. The foundation of this work relies in the (Message Queue Telemetry Transport Protocol) MQTT protocol, IoT and IaaS. The paper presents the background technologies used in the work and propose the approach. The proposed approach has been experimentally tested and found significant results.
{"title":"A V2I BASED APPROACH TO MULTICAST IN VEHICULAR NETWORKS","authors":"R. Tomar, Hanumat G. Sastry, M. Prateek","doi":"10.22452/mjcs.sp2020no1.7","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.7","url":null,"abstract":"The present research work proposes a novel approach for efficient information dissemination in Vehicular Networks using V2I based communication. This approach makes use of the V2I communication to establish the connectivity between the cloud server and the vehicle. The approach is focused on finding the utility of existing IoT protocols in the Vehicular Networks. The foundation of this work relies in the (Message Queue Telemetry Transport Protocol) MQTT protocol, IoT and IaaS. The paper presents the background technologies used in the work and propose the approach. The proposed approach has been experimentally tested and found significant results.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44889985","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.3
K. Rajesh, V. Ramaswamy, K. Kannan
Cyclone Classification and Prediction models rely on large intensity based on the maximum speed of the wind, along with the classification of intensity. The computational constraints blended with the formation of those intensities, cyclone classification and prediction firmly depreciate the full range of optimal features required for classification and hence accurate representation is less possible. Keeping this point, our study inspects the potential of Spatio-temporal features using a machine learning algorithm as an alternative to the current study of cyclones. This method is called, Spatio-Temporal Kalman Momentum and Two Dimensional Deep Learning (SKM-2DDP) for cyclone classification and prediction. To start with, pre-processing is performed by applying the Kalman Momentum Conservation Filter mechanism based on the design of the Dvorak technique to obtain optimal estimates of state variables and reduce the computational burden involved to remove noise from input cyclone images. With the resultant denoised input cyclone images, Spatio Temporal Feature Extraction is performed. Features obtained from the inherent intrinsic properties of pre-processed cyclone images of several weather conditions result in successful classification. Followed by pre-processing, in this work, features constituting both pixel-wise intensities over time and the directional length are being considered. The dependency of autocorrelation with each pixel’s intensities over time and two temporal features are helped for coarse classification of weather conditions according to their visual effects. Finally, with the inherent intrinsic features extracted, a Two Dimensional Deep Learning model is utilized to foretell the cyclone intensity. Experimental evaluation of the proposed SKM-2DDP method is accomplished using images of cyclone dataset with many factors just as peak signal to noise ratio, prediction accuracy, prediction time and false-positive rate. Our own selves have considered with several cyclone images acquired from two different cyclone image datasets namely OCKHI_DEC2017 and VARDAH_DEC2016.
{"title":"PREDICTION OF CYCLONE USING KALMAN SPATIO TEMPORAL AND TWO DIMENSIONAL DEEP LEARNING MODEL","authors":"K. Rajesh, V. Ramaswamy, K. Kannan","doi":"10.22452/mjcs.sp2020no1.3","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.3","url":null,"abstract":"Cyclone Classification and Prediction models rely on large intensity based on the maximum speed of the wind, along with the classification of intensity. The computational constraints blended with the formation of those intensities, cyclone classification and prediction firmly depreciate the full range of optimal features required for classification and hence accurate representation is less possible. Keeping this point, our study inspects the potential of Spatio-temporal features using a machine learning algorithm as an alternative to the current study of cyclones. This method is called, Spatio-Temporal Kalman Momentum and Two Dimensional Deep Learning (SKM-2DDP) for cyclone classification and prediction. To start with, pre-processing is performed by applying the Kalman Momentum Conservation Filter mechanism based on the design of the Dvorak technique to obtain optimal estimates of state variables and reduce the computational burden involved to remove noise from input cyclone images. With the resultant denoised input cyclone images, Spatio Temporal Feature Extraction is performed. Features obtained from the inherent intrinsic properties of pre-processed cyclone images of several weather conditions result in successful classification. Followed by pre-processing, in this work, features constituting both pixel-wise intensities over time and the directional length are being considered. The dependency of autocorrelation with each pixel’s intensities over time and two temporal features are helped for coarse classification of weather conditions according to their visual effects. Finally, with the inherent intrinsic features extracted, a Two Dimensional Deep Learning model is utilized to foretell the cyclone intensity. Experimental evaluation of the proposed SKM-2DDP method is accomplished using images of cyclone dataset with many factors just as peak signal to noise ratio, prediction accuracy, prediction time and false-positive rate. Our own selves have considered with several cyclone images acquired from two different cyclone image datasets namely OCKHI_DEC2017 and VARDAH_DEC2016.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48817325","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.4
M. D, S. N
Electronic payment system plays a vital role in e-commerce and other financial transactions with ever-increasing acceptance of smart device based applications. To ensure secure transactions, various authentication schemes have been proposed in recent times. But existing password and smart card-based traditional e-payment systems have some limitations and also raises security concerns. However, they consume more energy and are not feasible for the e-payment system as it consists of resource constraint devices like mobile devices. Furthermore, it is prone to security issues if the password is guessed or smart card is stolen. Thus to enhance the security and to reduce the computational cost, biometric authentication based payment protocol using elliptic curve cryptography is proposed. Since biometric features are unique and also cannot be stolen or reproduced. The proposed system resists various security attacks like impersonation attack, replay attack, session key agreement, man-in-the-middle attack, and user anonymity. Furthermore, it reduces computational and communication costs when compared to other protocols as it exploits ECC. Thus the proposed authentication protocol is convenient for the electronic payment system. A simulation tool, AVISPA is utilized to verify the security of designed payment protocol and BAN logic for formal security analysis.
{"title":"BIOMETRIC BASED THREE-FACTOR MUTUAL AUTHENTICATION SCHEME FOR ELECTRONIC PAYMENT SYSTEM USING ELLIPTIC CURVE CRYPTOGRAPHY","authors":"M. D, S. N","doi":"10.22452/mjcs.sp2020no1.4","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.4","url":null,"abstract":"Electronic payment system plays a vital role in e-commerce and other financial transactions with ever-increasing acceptance of smart device based applications. To ensure secure transactions, various authentication schemes have been proposed in recent times. But existing password and smart card-based traditional e-payment systems have some limitations and also raises security concerns. However, they consume more energy and are not feasible for the e-payment system as it consists of resource constraint devices like mobile devices. Furthermore, it is prone to security issues if the password is guessed or smart card is stolen. Thus to enhance the security and to reduce the computational cost, biometric authentication based payment protocol using elliptic curve cryptography is proposed. Since biometric features are unique and also cannot be stolen or reproduced. The proposed system resists various security attacks like impersonation attack, replay attack, session key agreement, man-in-the-middle attack, and user anonymity. Furthermore, it reduces computational and communication costs when compared to other protocols as it exploits ECC. Thus the proposed authentication protocol is convenient for the electronic payment system. A simulation tool, AVISPA is utilized to verify the security of designed payment protocol and BAN logic for formal security analysis.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43330060","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.1
Samuel Raj Samuel Raj, M. V. Prakash, T. Prince, K. Shankar, Vijayakumar Varadarajan, Fredi Nonyelu
Web applications are utilized on an extensive scale across the globe and it handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection are procedures that abuse a security defenselessness occurring in the database layer of an application. This research article focuses on website page database security with the help of optimization and encryption methods for Web of Things Environments. Initially, the selected queries in webpage application are injected as per Discrete Bee Colony Optimization (DBCO) procedure. After the Proxy filtering, the injection prevention model is utilized, the injected data with various queries of different special characters are utilized. At long last, the attack gets detected depending on the user query with the assistance of query tree mechanism. Besides, an effective Fully Homomorphic Encryption (FHE) encryption is proposed in the study. From the implementation results, it is to be noted that the proposed method achieved 93.56% security level for the prevented webpage implication-based databases. The effect on the businesses must be comprehended to decrease the risk involved in SQL and DI injection assaults.
{"title":"WEB BASED DATABASE SECURITY IN INTERNET OF THINGS USING FULLY HOMOMORPHIC ENCRYPTION AND DISCRETE BEE COLONY OPTIMIZATION","authors":"Samuel Raj Samuel Raj, M. V. Prakash, T. Prince, K. Shankar, Vijayakumar Varadarajan, Fredi Nonyelu","doi":"10.22452/mjcs.sp2020no1.1","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.1","url":null,"abstract":"Web applications are utilized on an extensive scale across the globe and it handles sensitive individual information of users. Structured Query Language (SQL) Data Inference (DI) and injection are procedures that abuse a security defenselessness occurring in the database layer of an application. This research article focuses on website page database security with the help of optimization and encryption methods for Web of Things Environments. Initially, the selected queries in webpage application are injected as per Discrete Bee Colony Optimization (DBCO) procedure. After the Proxy filtering, the injection prevention model is utilized, the injected data with various queries of different special characters are utilized. At long last, the attack gets detected depending on the user query with the assistance of query tree mechanism. Besides, an effective Fully Homomorphic Encryption (FHE) encryption is proposed in the study. From the implementation results, it is to be noted that the proposed method achieved 93.56% security level for the prevented webpage implication-based databases. The effect on the businesses must be comprehended to decrease the risk involved in SQL and DI injection assaults.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43620407","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 : 2020-11-27DOI: 10.22452/mjcs.sp2020no1.2
Ravi Dharavah, I. J. Raglend
The renewable sources integration with the DC micro grid is playing vital role in the current distribution system for providing the necessary clean power. The available of abundant renewable sources can be integrated through the DC link medium. The intermittent nature of renewable sources and dynamic switching of loads affects the quality of supply and DC link voltage respectively. It may fails to supply the power continuously. Reliability of power supply can be achieved with the use of integration of storage system. Here, the solar photovoltaic (SPV) array is fed with battery storage, fuel cell through the DC link medium. Utilizing SPV array maximum power is tracked using intelligent controller based maximum power point tracking (MPPT) technique. The battery storage with the DC link integration made through the bi-directional DC-DC converter with the help of the battery charge controller. Fuel cell is used as a base source and it’s fed with the DC link system. Finally, the simulation of SPV array, battery storage with fuel cell integration is performed under dynamic switching of loads. The variations in the source and switching of load performance are simulated in the MATLAB/Simulation software.
{"title":"POWER CONDITIONING OF SOLAR PHOTO VOLTAIC, BATTERY WITH FUEL CELL UNDER DYNAMIC SWITCHING OF LOADS","authors":"Ravi Dharavah, I. J. Raglend","doi":"10.22452/mjcs.sp2020no1.2","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.2","url":null,"abstract":"The renewable sources integration with the DC micro grid is playing vital role in the current distribution system for providing the necessary clean power. The available of abundant renewable sources can be integrated through the DC link medium. The intermittent nature of renewable sources and dynamic switching of loads affects the quality of supply and DC link voltage respectively. It may fails to supply the power continuously. Reliability of power supply can be achieved with the use of integration of storage system. Here, the solar photovoltaic (SPV) array is fed with battery storage, fuel cell through the DC link medium. Utilizing SPV array maximum power is tracked using intelligent controller based maximum power point tracking (MPPT) technique. The battery storage with the DC link integration made through the bi-directional DC-DC converter with the help of the battery charge controller. Fuel cell is used as a base source and it’s fed with the DC link system. Finally, the simulation of SPV array, battery storage with fuel cell integration is performed under dynamic switching of loads. The variations in the source and switching of load performance are simulated in the MATLAB/Simulation software.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41764012","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 : 2020-11-26DOI: 10.22452/mjcs.sp2020no1.8
E. Lydia, G. J. Moses, Vijayakumar Varadarajan, Fredi Nonyelu, A. Maseleno, E. Perumal, K. Shankar
Bigdata is a challenging field in data processing since the information is retrieved from various search engines through internet. A number of large organizations, that use document clustering,fails in arranging the documents sequentially in their machines. Across the globe, advanced technologyhas contributed to the high speed internet access. But the consequences of useful yet unorganized information in machine files seemto be confused in the retrieval process. Manual ordering of files has its own complications. In this paper, application software like Apache Lucene and Hadoop have taken a lead towards text mining for indexing and parallel implementation of document clustering. In organizations, it identifies the structure of the text data in computer files and its arrangement from files to folders, folders to subfolders, and to higher folders. A deeper analysis of document clustering was performed by considering various efficient algorithms like LSI, SVD and was compared with the newly proposed updated model of Non-Negative Matrix Factorization. The parallel implementation of hadoopdevelopedautomatic clusters for similar documents. MapReduce framework enforced its approach using K-means algorithm for all the incoming documents. The final clusters were automatically organized in folders using Apache Lucene in machines. This model was tested by considering the dataset of Newsgroup20 text documents. Thus this paper determines the implementation of large scale documents using parallel performance of MapReduce and Lucenethat generate automatic arrangement of documents, which reduces the computational time and improves the quick retrieval of documents in any scenario.
{"title":"CLUSTERING AND INDEXING OF MULTIPLE DOCUMENTS USING FEATURE EXTRACTION THROUGH APACHE HADOOP ON BIG DATA","authors":"E. Lydia, G. J. Moses, Vijayakumar Varadarajan, Fredi Nonyelu, A. Maseleno, E. Perumal, K. Shankar","doi":"10.22452/mjcs.sp2020no1.8","DOIUrl":"https://doi.org/10.22452/mjcs.sp2020no1.8","url":null,"abstract":"Bigdata is a challenging field in data processing since the information is retrieved from various search engines through internet. A number of large organizations, that use document clustering,fails in arranging the documents sequentially in their machines. Across the globe, advanced technologyhas contributed to the high speed internet access. But the consequences of useful yet unorganized information in machine files seemto be confused in the retrieval process. Manual ordering of files has its own complications. In this paper, application software like Apache Lucene and Hadoop have taken a lead towards text mining for indexing and parallel implementation of document clustering. In organizations, it identifies the structure of the text data in computer files and its arrangement from files to folders, folders to subfolders, and to higher folders. A deeper analysis of document clustering was performed by considering various efficient algorithms like LSI, SVD and was compared with the newly proposed updated model of Non-Negative Matrix Factorization. The parallel implementation of hadoopdevelopedautomatic clusters for similar documents. MapReduce framework enforced its approach using K-means algorithm for all the incoming documents. The final clusters were automatically organized in folders using Apache Lucene in machines. This model was tested by considering the dataset of Newsgroup20 text documents. Thus this paper determines the implementation of large scale documents using parallel performance of MapReduce and Lucenethat generate automatic arrangement of documents, which reduces the computational time and improves the quick retrieval of documents in any scenario.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44570074","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 : 2020-11-26DOI: 10.22452/MJCS.SP2020NO1.10
C. Prabhu, Tony Jan, M. Prasad, Vijayakumar Varadarajan
Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to the data arising out of IOT devices or sensors, as against cloud server which may be located in data centers (near core Network Centers) located far away from the edge resulting in extreme delays in network transmission and latency, especially when the data is large volume as stream (or ‘Big Data’) arising out of IOT devices or sensors including cameras, etc. Real time response after completing the necessary Analytics on the data generated by IOT devices and sensors becomes critically essential for meeting the real time response requirements of critical applications such as in health care and transportation. What are the relevant techniques for Fog Analytics? In this paper we provide a brief survey of Fog Analytics techniques in stream data analytics, machine learning, deep learning techniques and also game theoretical adversarial learning.
{"title":"FOG ANALYTICS - A SURVEY","authors":"C. Prabhu, Tony Jan, M. Prasad, Vijayakumar Varadarajan","doi":"10.22452/MJCS.SP2020NO1.10","DOIUrl":"https://doi.org/10.22452/MJCS.SP2020NO1.10","url":null,"abstract":"Fog computing has emerged as an essential alternative to the cloud. Fog computing is the future as it is nearer to the edge where actually the IOT devices and sensors are located. A Fog Server or Fog Node is located near to the IOT devices, connecting directly (wired or wireless) to them. The Fog Server has a functionality of fast accessibility to the data arising out of IOT devices or sensors, as against cloud server which may be located in data centers (near core Network Centers) located far away from the edge resulting in extreme delays in network transmission and latency, especially when the data is large volume as stream (or ‘Big Data’) arising out of IOT devices or sensors including cameras, etc. Real time response after completing the necessary Analytics on the data generated by IOT devices and sensors becomes critically essential for meeting the real time response requirements of critical applications such as in health care and transportation. What are the relevant techniques for Fog Analytics? In this paper we provide a brief survey of Fog Analytics techniques in stream data analytics, machine learning, deep learning techniques and also game theoretical adversarial learning.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49174813","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 : 2020-10-30DOI: 10.22452/mjcs.vol33no4.6
Raja-Jamilah Raja-Yusof, Zulkifli Mohd Yusoff, Glenn Hardaker
Comprehending text is the aim of reading; however, there is the phenomenon of non-Arabic speakers in Malaysia reading the Qur’an, written in Arabic, without comprehension. Word recognition (WR) theory, through word frequency effect (WFE) and word superiority effect (WSE), are used as a basis to achieve reading comprehension (RC) of the Qur'an. The Eye of Qur'an (EoQu) interface was developed, to visualise word occurrences and word morphology. This is achieved through parallel plot and word segmentation visualization. EoQu can track a user’s personal vocabulary with a presentation of percentage and word position in the Qur’an. Consequently, users know their ability to recognize Arabic words in relation to the whole Qur’an to achieve RC. An experimental study was set up with 90 Malaysian participants, starting with a pre-test, followed by a stratified sampling to divide participants into control and experimental groups (who used EoQu) for the post-test. Results showed evidence of improvement in WR based on scores and time taken to complete the Arabic Word Recognition Test.
{"title":"VISUALIZATION WITH VOCABULARY TRACKING FOR ARABIC WORD RECOGNITION AND READING COMPREHENSION","authors":"Raja-Jamilah Raja-Yusof, Zulkifli Mohd Yusoff, Glenn Hardaker","doi":"10.22452/mjcs.vol33no4.6","DOIUrl":"https://doi.org/10.22452/mjcs.vol33no4.6","url":null,"abstract":"Comprehending text is the aim of reading; however, there is the phenomenon of non-Arabic speakers in Malaysia reading the Qur’an, written in Arabic, without comprehension. Word recognition (WR) theory, through word frequency effect (WFE) and word superiority effect (WSE), are used as a basis to achieve reading comprehension (RC) of the Qur'an. The Eye of Qur'an (EoQu) interface was developed, to visualise word occurrences and word morphology. This is achieved through parallel plot and word segmentation visualization. EoQu can track a user’s personal vocabulary with a presentation of percentage and word position in the Qur’an. Consequently, users know their ability to recognize Arabic words in relation to the whole Qur’an to achieve RC. An experimental study was set up with 90 Malaysian participants, starting with a pre-test, followed by a stratified sampling to divide participants into control and experimental groups (who used EoQu) for the post-test. Results showed evidence of improvement in WR based on scores and time taken to complete the Arabic Word Recognition Test.","PeriodicalId":49894,"journal":{"name":"Malaysian Journal of Computer Science","volume":" ","pages":""},"PeriodicalIF":0.6,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48433311","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}
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