Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.
{"title":"Learning Behavior Analysis to Identify Learner's Learning Style based on Machine Learning Techniques","authors":"Zohra Mehenaoui, Y. Lafifi, Layachi Zemmouri","doi":"10.3897/jucs.81518","DOIUrl":"https://doi.org/10.3897/jucs.81518","url":null,"abstract":"Learning styles cover various attributes related to the attitude and the learning behavior of individuals. Research and educational theories confirm that considering learning styles in distance learning environments can improve academic performance and learner satisfaction. The traditional approach to identify learning styles is based on asking students to fill out a questionnaire. This approach is considerably less accurate due to the learners’ lack of awareness of their own preferences. Furthermore, learners’ learning styles are defined only once. In this study, we propose an automatic approach to identify learners’ learning styles based on patterns of learning behavior with respect to Felder and Silverman Learning Style Model (FSLSM), in an online learning environment. Patterns of behavior were analysed based on a data-driven approach. This approach exploits different Machine Learning (ML) techniques to detect the learning styles of learners. To validate our proposals, experiments were carried out in a higher education institution with 73 students enrolled in online courses on the ADLS (Automatic Detection of Learning Styles) system that we implemented. A 9 runs cross-validation was used to evaluate the selected ML techniques. Detection accuracy, recall, precision, and F measure were observed. The findings show the possibility of detecting learning styles automatically based on learning behavior with high performances. Different levels of accuracy were found for the different dimensions of FSLSM. However, Support Vector Machines (SVM) have exhibited great ability in predicting learning styles for all dimensions of FSLSM with an accuracy average of 88%.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"103 1","pages":"1193-1220"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78251579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Requirements Engineering is one of the fundamental activities in the software development process and is oriented toward what should be produced. One of the development team’s most common problems is a lack of communication regarding an understanding of the discourse domain and how to integrate and process excessive information originating from different sources. This may lead to errors of omission and the consequent production of incomplete and inconsistent artifacts, which will have a direct effect on the quality of the software. The use of machine learning techniques helps the development team produce successful software on the basis of the acquisition of knowledge and human experience with which to understand the domain of the application. This paper, therefore, presents a proposal for a new methodological process oriented toward the construction of a vocabulary concerning the application domain. The authors propose to do this by employing Natural Language Processing (NLP), ontologies and heuristics that will lead to the production of a Lexicon that is common to analysts and customers, both of whom will understand the universe of discourse, thus mitigating problems of completeness. This objective has been achieved by carrying out a Systematic Literature Review of the artificial intelligence techniques employed in the requirements engineering process, which led to the discovery that 41.37% use NLP, while 55.71% apply ontologies such as semantic reasoners which help solve the problem of language ambiguity, the structures in specifications or the identification of key concepts with which to establish traceability links. However, the review also showed that the problems regarding the comprehension and completeness of requirements problems have yet to be resolved.
{"title":"Building an integrated requirements engineering process based on Intelligent Systems and Semantic Reasoning on the basis of a systematic analysis of existing proposals","authors":"Alexandra Corral, L. E. Sanchez, L. Antonelli","doi":"10.3897/jucs.78776","DOIUrl":"https://doi.org/10.3897/jucs.78776","url":null,"abstract":"Requirements Engineering is one of the fundamental activities in the software development process and is oriented toward what should be produced. One of the development team’s most common problems is a lack of communication regarding an understanding of the discourse domain and how to integrate and process excessive information originating from different sources. This may lead to errors of omission and the consequent production of incomplete and inconsistent artifacts, which will have a direct effect on the quality of the software. The use of machine learning techniques helps the development team produce successful software on the basis of the acquisition of knowledge and human experience with which to understand the domain of the application. This paper, therefore, presents a proposal for a new methodological process oriented toward the construction of a vocabulary concerning the application domain. The authors propose to do this by employing Natural Language Processing (NLP), ontologies and heuristics that will lead to the production of a Lexicon that is common to analysts and customers, both of whom will understand the universe of discourse, thus mitigating problems of completeness. This objective has been achieved by carrying out a Systematic Literature Review of the artificial intelligence techniques employed in the requirements engineering process, which led to the discovery that 41.37% use NLP, while 55.71% apply ontologies such as semantic reasoners which help solve the problem of language ambiguity, the structures in specifications or the identification of key concepts with which to establish traceability links. However, the review also showed that the problems regarding the comprehension and completeness of requirements problems have yet to be resolved.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"64 1","pages":"1136-1168"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77275630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatemeh Farnaghi-Zadeh, Mohsen Rahmani, Maryam Amiri
Feature selection plays an important role as a preprocessing step for pattern recognition and machine learning. The goal of feature selection is to determine an optimal subset of relevant features out of a large number of features. The neighborhood discrimination index (NDI) is one of the newest and the most efficient measures to determine distinguishing ability of a feature subset. NDI is computed based on a neighborhood radius (E). Due to the significant impact of E on NDI, selecting an appropriate value of E for each data set might be challenging and very time-consuming. This paper proposes a new approach based on targEt PointS To computE neIgh- borhood relatioNs (EPSTEIN). At first, all the data points are sorted in the descending order of their density. Then, the highest density data points are selected as many as the number of classes. To determine the neighborhood relations, the circles centered on the target points are drawn and the points inside or on the circles are considered to be neighbors. In the next step, the significance of each feature is computed and a greedy algorithm selects appropriate features. The performance of the proposed approach is compared to both the commonest and newest methods of feature selection. The experimental results show that EPSTEIN could select more efficient subsets of features and improve the prediction accuracy of classifiers in comparison to the other state-of-the-art methods such as Correlation-based Feature Selection (CFS), Fast Correlation-Based Filter (FCBF), Heuris- tic Algorithm Based on Neighborhood Discrimination Index (HANDI), Ranking Based Feature Inclusion for Optimal Feature Subset (KNFI), Ranking Based Feature Elimination (KNFE) and Principal Component Analysis and Information Gain (PCA-IG).
{"title":"Feature Selection Using Neighborhood based Entropy","authors":"Fatemeh Farnaghi-Zadeh, Mohsen Rahmani, Maryam Amiri","doi":"10.3897/jucs.79905","DOIUrl":"https://doi.org/10.3897/jucs.79905","url":null,"abstract":"Feature selection plays an important role as a preprocessing step for pattern recognition and machine learning. The goal of feature selection is to determine an optimal subset of relevant features out of a large number of features. The neighborhood discrimination index (NDI) is one of the newest and the most efficient measures to determine distinguishing ability of a feature subset. NDI is computed based on a neighborhood radius (E). Due to the significant impact of E on NDI, selecting an appropriate value of E for each data set might be challenging and very time-consuming. This paper proposes a new approach based on targEt PointS To computE neIgh- borhood relatioNs (EPSTEIN). At first, all the data points are sorted in the descending order of their density. Then, the highest density data points are selected as many as the number of classes. To determine the neighborhood relations, the circles centered on the target points are drawn and the points inside or on the circles are considered to be neighbors. In the next step, the significance of each feature is computed and a greedy algorithm selects appropriate features. The performance of the proposed approach is compared to both the commonest and newest methods of feature selection. The experimental results show that EPSTEIN could select more efficient subsets of features and improve the prediction accuracy of classifiers in comparison to the other state-of-the-art methods such as Correlation-based Feature Selection (CFS), Fast Correlation-Based Filter (FCBF), Heuris- tic Algorithm Based on Neighborhood Discrimination Index (HANDI), Ranking Based Feature Inclusion for Optimal Feature Subset (KNFI), Ranking Based Feature Elimination (KNFE) and Principal Component Analysis and Information Gain (PCA-IG).","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"82 1","pages":"1169-1192"},"PeriodicalIF":0.0,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91242539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.
{"title":"Towards more trustworthy predictions: A hybrid evidential movie recommender system","authors":"Raoua Abdelkhalek, I. Boukhris, Zied Elouedi","doi":"10.3897/jucs.79777","DOIUrl":"https://doi.org/10.3897/jucs.79777","url":null,"abstract":"Recommender Systems (RSs) are considered as popular tools that have revolutionized the e-commerce and digital marketing. Their main goal is predicting the users’ future preferences and providing accessible and personalized recommendations. However, uncertainty can spread at any level throughout the recommendation process, which may affect the results. In fact, the ratings given by the users are often unreliable. The final provided predictions itself may also be pervaded with uncertainty and doubt. Obviously, the reliability of the predictions cannot be fully certain and trustworthy. For the system to be effective, recommendations must inspire trust in the system and provide reliable and credible recommendations. The user may speculate about the uncertainty pervaded behind the given recommendation. He could tend to a reliable recommendation offering him a global overview about his preferences rather than an inappropriate one that contradicts his activities and objectives. While such imperfection cannot be ignored, traditional RSs are rarely able to deal with the uncertainty spreading around the prediction process, which may affect the credibility, the transparency and the trustworthiness of the current RS. Thus, in this paper, we opt for the uncertain framework of the belief function theory (BFT), which allows us to represent, quantify and manage imperfect evidence. By using the BFT, the users’ preferences and the interactions between the neighbors can be represented under uncertainty. Evidence from different information sources can then be combined leading to more reliable results. The proposed approach is a hybrid evidential movie RS that uses different data sources and delivers a personalized user-interface allowing a global overview of the possible future preferences. This representation would increase the users’ confidence towards the system as well as their satisfaction. Experiments are performed on MovieLens and their additional features provided by the Internet Movie Database (IMDb) and Rotten Tomatoes. The new approach achieves promising results compared to traditional approaches in terms of MAE, NMAE and RMSE. It also reaches interesting Precision, Recall and F-measure values of respectively, 0.782, 0.792 and 0.787.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"15 1","pages":"1003-1029"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82289322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Venilton Falvo Junior, A. Marcolino, Nemésio Freitas Duarte Filho, E. Oliveira, E. Barbosa
The popularity of mobile devices in all social classes has motivated the development of mobile learning (m-learning) applications. The existing applications, even having many benefits and facilities in relation to the teaching-learning process, still presents problems and challenges, es- pecially regarding the development, reuse and architectural standardization. Particularly, there is a growing adoption of the Software Product Line (SPL) concept, in view of research that investigates these gaps. This paradigm enables organizations to explore the similarities and variabilities of their products, increasing the reuse of artifacts and, consequently, reducing costs and development time. In this context, we discuss how systematic reuse can improve the development of solutions in the m-learning domain. Therefore, this work presents the design, development and experimental evaluation of M-SPLearning, an SPL created to enable the systematic production of m-learning applications. Specifically, the conception of M-SPLearning covers from the initial study for an effective domain analysis to the implementation and evaluation of its functional version. In this regard, the products have been experimentally evaluated by industry software developers, pro- viding statistical evidence that the use of our SPL can speed up the time-to-market of m-learning applications, in addition to reducing their respective number of faults.
{"title":"Development and Evaluation of a Software Product Line for M-Learning Applications","authors":"Venilton Falvo Junior, A. Marcolino, Nemésio Freitas Duarte Filho, E. Oliveira, E. Barbosa","doi":"10.3897/jucs.90663","DOIUrl":"https://doi.org/10.3897/jucs.90663","url":null,"abstract":"The popularity of mobile devices in all social classes has motivated the development of mobile learning (m-learning) applications. The existing applications, even having many benefits and facilities in relation to the teaching-learning process, still presents problems and challenges, es- pecially regarding the development, reuse and architectural standardization. Particularly, there is a growing adoption of the Software Product Line (SPL) concept, in view of research that investigates these gaps. This paradigm enables organizations to explore the similarities and variabilities of their products, increasing the reuse of artifacts and, consequently, reducing costs and development time. In this context, we discuss how systematic reuse can improve the development of solutions in the m-learning domain. Therefore, this work presents the design, development and experimental evaluation of M-SPLearning, an SPL created to enable the systematic production of m-learning applications. Specifically, the conception of M-SPLearning covers from the initial study for an effective domain analysis to the implementation and evaluation of its functional version. In this regard, the products have been experimentally evaluated by industry software developers, pro- viding statistical evidence that the use of our SPL can speed up the time-to-market of m-learning applications, in addition to reducing their respective number of faults.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"60 1","pages":"1058-1086"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80544954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.
{"title":"Improving Malaria Detection Using L1 Regularization Neural Network","authors":"Ghazala Hcini, Imen Jdey, Hela Ltifi","doi":"10.3897/jucs.81681","DOIUrl":"https://doi.org/10.3897/jucs.81681","url":null,"abstract":"Malaria is a huge public health concern around the world. The conventional method of diagnosing malaria is for qualified technicians to visually examine blood smears for parasite-infected red blood cells under a microscope. This procedure is ineffective. It takes time and requires the expertise of a skilled specialist. The diagnosis is dependent on the individual performing the examination’s experience and understanding. This article offers a new and robust deep learning model for automatically classifying malaria cells as infected or uninfected. This approach is based on a convolutional neural network (CNN). It improved by the regularization method on a publicly available dataset which contains 27, 558 cell images with equal instances of parasitized and uninfected cells from the National Institute of health. The performance of our proposed model is 99.70% of accuracy and 0.0476 loss value.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"41 1","pages":"1087-1107"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84677533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Climate forecasting plays an important role for human life in many areas such as water management, agriculture, natural hazards including drought and flood, tourism, business, and regional investment. Estimating these data is a difficult task as the time series climate parameter values vary monthly and seasonally. Therefore, predicting climate parameters based on learning and artificial intelligence is important to long-term efficient results in these fields. For this purpose, in this study, a time-series based Long Short-Term Memory (LSTM) deep neural network is proposed to predict future climate in Çankırı and Adıyaman cities in Turkey. With the help of this network, the average temperature, relative humidity, and precipitation values, which are known as the most effective climate parameters, have been estimated. An improved Particle Swarm Optimization (PSO) technique is also proposed to optimize input weight values of the LSTM deep network, and reduce the estimation errors. The proposed algorithm is compared with deep models of LSTM variants based on Root Mean Square Error (RMSE), Mean Absolute Deviation (MADE), and Mean Absolute Percentage Error (MAPE) metrics. The proposed adaptive LSTM-PSO and non-adaptive LSTM-PSO models achieved at RMSE 0.98 and 1.05 for temperature, 1.19 and 1.27 for relative humidity, and 4.21 and 4.67 for precipitation estimation, respectively. The RMSE is %7 lower with the proposed adaptive LSTM-PSO method than proposed non-adaptive LSTM-PSO method.
{"title":"Climatological parameters estimation based on artificial intelligence techniques with particle swarm optimization and deep neural networks","authors":"S. Yalçın, Musa Eşit, M. Yuce","doi":"10.3897/jucs.82370","DOIUrl":"https://doi.org/10.3897/jucs.82370","url":null,"abstract":"Climate forecasting plays an important role for human life in many areas such as water management, agriculture, natural hazards including drought and flood, tourism, business, and regional investment. Estimating these data is a difficult task as the time series climate parameter values vary monthly and seasonally. Therefore, predicting climate parameters based on learning and artificial intelligence is important to long-term efficient results in these fields. For this purpose, in this study, a time-series based Long Short-Term Memory (LSTM) deep neural network is proposed to predict future climate in Çankırı and Adıyaman cities in Turkey. With the help of this network, the average temperature, relative humidity, and precipitation values, which are known as the most effective climate parameters, have been estimated. An improved Particle Swarm Optimization (PSO) technique is also proposed to optimize input weight values of the LSTM deep network, and reduce the estimation errors. The proposed algorithm is compared with deep models of LSTM variants based on Root Mean Square Error (RMSE), Mean Absolute Deviation (MADE), and Mean Absolute Percentage Error (MAPE) metrics. The proposed adaptive LSTM-PSO and non-adaptive LSTM-PSO models achieved at RMSE 0.98 and 1.05 for temperature, 1.19 and 1.27 for relative humidity, and 4.21 and 4.67 for precipitation estimation, respectively. The RMSE is %7 lower with the proposed adaptive LSTM-PSO method than proposed non-adaptive LSTM-PSO method.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"28 1","pages":"1108-1133"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77603485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abd Al-Rahman Al-Nounou, O. Al-Khaleel, Fadi Obeidat, M. Al-khaleel
Multiplication is considered one of the most time-consuming and a key operation in wide variety of embedded applications. Speeding up this operation has a significant impact on the overall performance of these applications. A vast number of multiplication approaches are found in the literature where the goal is always to achieve a higher performance. One of these approaches relies on using smaller multiplier blocks which are built based on direct Boolean algebra equations to build large multipliers. In this work, we present a methodology for designing binary multipliers where different sizes customized partial products generation (CPPG) cells are designed and used as smaller building blocks. The sizes of the designed CPPG cells are 2×2, 3×3, 4×4, 5×5, and 6×6. We use these cells to build 8×8, 16×16, 32×32, 64×64, and 128×128 binary multipliers. All of the CPPG cells and the binary multipliers are described using the VHDL language, tested, and implemented using XILINX ISE 14.6 tools targeting different FPGA families. The implementation results show that the best performance is achieved when cell 3×3 is used and Virtex-7 FPGA is targeted. The binary multipliers that are designed using the proposed CPPG cells achieve better performance when compared with the binary multipliers presented in the literature. As an application that utilizes the proposed multiplier, a Multiply-Accumulate (MAC) unit is designed and implemented in Spartan-3E. The implementation results of the MAC unit demonstrate the effectiveness of the proposed multiplier.
乘法运算被认为是各种嵌入式应用程序中最耗时、最关键的运算之一。加速此操作对这些应用程序的整体性能有重大影响。在文献中发现了大量的乘法方法，其目标始终是实现更高的性能。其中一种方法依赖于使用基于直接布尔代数方程的较小乘数块来构建大型乘数。在这项工作中，我们提出了一种设计二进制乘法器的方法，其中不同尺寸的定制部分产品生成(CPPG)单元被设计并用作较小的构建块。设计的CPPG细胞尺寸分别为2×2、3×3、4×4、5×5、6×6。我们使用这些单元格构建8×8、16×16、32×32、64×64和128×128二进制乘数器。所有CPPG单元和二进制乘法器都使用VHDL语言进行描述，并使用针对不同FPGA系列的XILINX ISE 14.6工具进行测试和实现。实现结果表明，当单元为3×3，以Virtex-7 FPGA为目标时，可以获得最佳性能。与文献中提出的二进制乘法器相比，使用所提出的CPPG细胞设计的二进制乘法器具有更好的性能。作为一种利用所提出的乘法器的应用，在Spartan-3E中设计并实现了一个乘法累加(MAC)单元。MAC单元的实现结果证明了所提乘法器的有效性。
{"title":"FPGA Implementation of Fast Binary Multiplication Based on Customized Basic Cells","authors":"Abd Al-Rahman Al-Nounou, O. Al-Khaleel, Fadi Obeidat, M. Al-khaleel","doi":"10.3897/jucs.86282","DOIUrl":"https://doi.org/10.3897/jucs.86282","url":null,"abstract":"Multiplication is considered one of the most time-consuming and a key operation in wide variety of embedded applications. Speeding up this operation has a significant impact on the overall performance of these applications. A vast number of multiplication approaches are found in the literature where the goal is always to achieve a higher performance. One of these approaches relies on using smaller multiplier blocks which are built based on direct Boolean algebra equations to build large multipliers. In this work, we present a methodology for designing binary multipliers where different sizes customized partial products generation (CPPG) cells are designed and used as smaller building blocks. The sizes of the designed CPPG cells are 2×2, 3×3, 4×4, 5×5, and 6×6. We use these cells to build 8×8, 16×16, 32×32, 64×64, and 128×128 binary multipliers. All of the CPPG cells and the binary multipliers are described using the VHDL language, tested, and implemented using XILINX ISE 14.6 tools targeting different FPGA families. The implementation results show that the best performance is achieved when cell 3×3 is used and Virtex-7 FPGA is targeted. The binary multipliers that are designed using the proposed CPPG cells achieve better performance when compared with the binary multipliers presented in the literature. As an application that utilizes the proposed multiplier, a Multiply-Accumulate (MAC) unit is designed and implemented in Spartan-3E. The implementation results of the MAC unit demonstrate the effectiveness of the proposed multiplier.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"50 1","pages":"1030-1057"},"PeriodicalIF":0.0,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90494591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.
{"title":"Natural Language Enhancement for English Teaching Using Character-Level Recurrent Neural Network with Back Propagation Neural Network based Classification by Deep Learning Architectures","authors":"Zhiling Yang","doi":"10.3897/jucs.94162","DOIUrl":"https://doi.org/10.3897/jucs.94162","url":null,"abstract":"Natural Language Processing (NLP) is an efficient method for enhancing educational outcomes. In educational settings, implementing NLP entails starting the learning process through natural acquisition. English teaching and learning have received increased attention from the relevant education departments as an integral aspect of the new curriculum reform. The environment of English teaching and learning is undergoing extraordinary changes as a result of the constant improvement and extension of teaching level and scale, as well as the growth of Internet information technology. As a result, the current research aims to look into techniques for efficiently using AI (artificial intelligence) apps to teach and learn English from the perspective of university students. This research can measure the levels as well as effectiveness of the employment of AI applications for teaching English based on deep learning techniques. There, the NLP based language enhancement has been carried out using Character-level recurrent neural network with back Propagation neural network (Cha_RNN_BPNN) based classification. With the help of this DL (deep learning) technique, it is possible to use AI methods to assist teachers in analysing and diagnosing students' English learning behaviour, replacing teachers in part to answer students' questions in a timely manner, and automatically grading assignments during the English teaching process. Experimental analysis shows Word Perplexity, Flesch-Kincaid (F-K) Grade Level for Readability, Cosine Similarity for Semantic Coherence, gradient change of NN, validation accuracy, and training accuracy of the proposed technique.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"37 1","pages":"984-1000"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80109939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks.
{"title":"X-Ray Image Authentication Scheme Using SLT and Contourlet Transform for Modern Healthcare System","authors":"Vijay Krishna Pallaw, K. Singh","doi":"10.3897/jucs.94132","DOIUrl":"https://doi.org/10.3897/jucs.94132","url":null,"abstract":"The network’s convenience has created a copyright dilemma for some multimedia works. Nowadays, every healthcare system relies on digital medical images for diagnosis. These medical images are transmitted through communication channels, so there is a risk of tampering and copyright violation. A digital watermarking system can ensure and guarantee that tampering and copyright violation are prevented. This study presents a nonblind digital watermarking approach to X-ray medical images based on Contourlet transform (C.T.) and Slantlet Transform (SLT). Since the two-dimensional signals are represented flexibly by contourlet transforms, the contour plot can be used efficiently to represent curves and smooth contours. At the same time, the SLT has better time-localization & smoothness properties. The maximum energy of an image is conceived in the LL band if SLT transform are employed. Therefore, the LL band is used to entrench the watermark. The additive quantization method has been used to entrench the watermark. The efficiency of our scheme is assessed by different quality parameters and compared with several existing schemes. The results of the experiment show that the proposed scheme performs better and has the ability to resist several attacks.","PeriodicalId":14652,"journal":{"name":"J. Univers. Comput. Sci.","volume":"27 1","pages":"916-929"},"PeriodicalIF":0.0,"publicationDate":"2022-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87194113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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