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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera Zafar Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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2023 International Research Conference on Smart computing and Systems engineering, SCSE 2023

作者： Arunoda, D.P.H. Walpola, S.R. Piumira, S.M.I. Athukorala, A.D.M.P. Thilakarathna, Thusithanjana Chandrasiri, Sanjeevi Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350341454

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy. © 2023 IEEE.

关键词： Students

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Development of Pattern-Based Test Case Catalog to Increase Reuse and Variety in software Testing

Development of Pattern-Based Test Case Catalog to Increase R...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Alwan Kemal Dana Sulistiyo Kusumo Rosa Reska Riskiana Software Engineering Department School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

In software development, the quality control process is a critical element that aims to oversee and verify product quality. Poor quality control processes are frequently a key factor in software project failures. software testing is key to identifying defects, but designing an effective test suite requires a huge effort that can affect cost, effectiveness, maintenance time, and development schedules. The Directorate of the Center for Excellence in Information Technology (PUTI) as one of the IT support units within Telkom University, is currently facing the same challenges in managing the BaSO application, which is an application for making official letters online. This application requires further testing to ensure that each function runs properly in various browser and device environments. In this case, the reuse and pattern approach was proposed as a solution, and based on this approach, a test case catalog was created. To assess the design system's impact and effectiveness, an experimental study was conducted for the PUTI QA team to test the BaSO application. The experimental results show that the use of a test case catalog during the test case development process can increase the reusability of test cases, with an average precision value of about 50%. In addition, the obtained test case variation value also increased by 35.5%. The findings highlight the significant impact of using a test case catalog, thus contributing to a more effective software testing process.

关键词： software testing Schedules Process control Quality control Feature extraction software Product design Quality assessment Pattern recognition software development management

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A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (Extended Abstract) 39

A Survey on Modern Deep Neural Network for Traffic Predictio...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Tedjopurnomo, David Alexander Bao, Zhifeng Zheng, Baihua Choudhury, Farhana Murtaza Qin, A.K. Rmit University School of Computing Technologies Australia Singapore Management University School of Computing and Information Systems Singapore The University of Melbourne School of Computing and Information Systems Australia Swinburne University of Technology Department of Computer Science and Software Engineering Australia

ISBN: (纸本)9798350322279

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate this issue is through traffic prediction. This research field has evolved greatly ever since its inception in the late 70s. Recently, deep neural network models have gained popularity thanks to its predictive power, but despite this, literature surveys of such methods are rare;making it difficult to ascertain the progress of this research field. In this work, we address this issue by presenting an up-to-date survey of deep neural network for traffic prediction. We provide detailed explanations of popular deep neural network architectures used in the traffic flow prediction literatures, categorize and describe the literatures themselves, present an overview of the commonalities and differences among different works, and finally provide a discussion regarding the challenges and future directions for this field. © 2023 IEEE.

关键词： Deep neural networks

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EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educational Support

EduMAS: A Novel LLM-Powered Multi-Agent Framework for Educat...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Li, Qiaomu Xie, Ying Chakravarty, Sumit Lee, Dabae Kennesaw State University College of Computing and Software Engineering MariettaGA30060 United States Kennesaw State University Department of Instructional Technology MariettaGA30060 United States

ISBN: (纸本)9798350362480

In general, educational support with Large Language Models (LLMs) faces challenges in knowledge organization, expertise integration, and contextual adaptation. So, we present EduMAS, a novel multi-agent framework that coordinates specialized agents with graph-based knowledge navigation. Our framework introduces three key innovations: (1) Specialized Agents that provide expertise in different learning aspects to solve decomposed subtasks professionally;(2) Graph Navigator for graph-based knowledge extraction and selection to improve the quality of responses;(3) The Emotional Awareness mechanism for better contextual adaptation. Through comprehensive experiments on college-level physics education and evaluated by six state-of-the-art LLMs, EduMAS demonstrates significant improvements over the baseline model in complex concept integration, cross-disciplinary understanding, and theory-to-application translation. Ablation studies further validate the contribution of each framework component, Specialized Agents and Graph Navigator play important roles in performance improvement. Our work provides strong support for LLM-powered multi-agent system in AI-assisted education. © 2024 IEEE.

关键词： Multi agent systems

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Enhancing Early Childhood Learning using Image Processing for Sinhala Medium Education 5

Enhancing Early Childhood Learning using Image Processing fo...

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5th International Conference on Advancements in computing, ICAC 2023

作者： Saranga, R.G.V. Jayasinghe, A.H.N. Divyantha, U.A.S. De Silva, S.W.L.P. Swarnakantha, N.H.P. Ravi Supunya Kumari, P. K. Suriya Sri Lanka Institute of Information Technology Faculty of Computing Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

The study aims to develop a mobile application for young children to learn Sinhala letters, shapes, colors, and storytelling incorporating machine learning models to evaluate and enhance educational activities. With the rise of online education during the COVID-19 pandemic, the familiarity of children with mobile devices provides an opportunity to create an engaging and educational experience. The application will teach Sinhala letters using object images, allowing children to upload their own images for feedback. It also includes a feature for children to practice writing letters and analyze their progress. Also, the application introduces colors and shapes in Sinhala, encouraging children to draw and track their improvement. Additionally, the application aims to generate stories in Sinhala to improve children's creativity and thinking knowledge. This research addresses a critical gap in existing Sinhala learning applications by integrating machine learning for activity assessment, promising to significantly impact and improve early language education for children. © 2023 IEEE.

关键词： Image enhancement

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A Zero-Knowledge Set Membership Proof Scheme Based on the SM2 Algorithm 24

A Zero-Knowledge Set Membership Proof Scheme Based on the SM...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Zhou, Yin Dai, Bingrong Li, Chao School of Computer and Information Engineering Shanghai Polytechnic University My Street Shanghai201209 China Department of Computing Shanghai Development Center of Computer SoftWare Technology Shanghai201102 China

ISBN: (纸本)9798400717147

With the widespread application of blockchain technology, various range proof protocols based on zero-knowledge proofs have been proposed. However, existing range proof protocols suffer from issues such as high communication overhead and computational complexity. Therefore, this paper introduces an efficient and secure Zero-Knowledge Set Membership Proof Protocol (ZSMPP) to address these challenges. Building upon improvements to the proof structure of range proof protocols, the paper integrates the SM2 identity-based digital signature algorithm, effectively avoiding the time-consuming bilinear pairing operations and reducing computational costs. The proposed protocol offers an efficient and secure solution for the given problem. Experimental results demonstrate that, compared to protocols proposed by Bootle, Deng, Mao, and others, the protocol presented in this paper exhibits superior computational efficiency, providing an efficient and secure solution for data security and individual privacy protection in the digital age. © 2024 ACM.

关键词： Authentication

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GenreNet: A Deep Based Approach for Music Genre Classification

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SN Computer Science 2024年第8期5卷 1135页

作者： Bala Ganesh, N. Bhuvaneswari, M.S. Bhagavathi Sankar, K. Ganesh, P. Department of Software and Systems Engineering Vellore Institute of Technology Tamilnadu Vellore India Department of Smart Computing Vellore Institute of Technology Tamilnadu Vellore India Ciena India Haryana Gurgaon India Tata Consultancy Services Tamilnadu Chennai India

Neural network-based Music Genre Classification is a key component in helping users narrow down the selection of songs and listen to music in a certain genre. Audio segmentation has been done as a preprocessing step on the available audio files using the benchmark dataset “GTZAN”. To improve accuracy, audio segmentation divides a 30-s audio recording into ten 3-s audio files. Segments are utilized to create spectrograms, which are then used to extract features. A spectrogram is a visual depiction of a signal’s frequency band. Applying techniques like magphase and Short Time Fourier Transform to the audio data enhances the quality of the spectrograms. Adaptive Moment estimation, Nesterov-accelerated Adaptive Moment estimation (NADAM), Adaptive Moment estimation with Maximum, and Root Mean Squared Propagation are among the optimization techniques used for feature weight optimization to increase the classification rate. NADAM is found to be effective based on performance measures. The proposed GenreNet model is compared with the state-of-art techniques and performance measures such as Accuracy, Precision, Recall, Root Mean Square Error, F1 Score, Loss and Sensitivity have yielded positive results. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Alexnet18 Audio classification CNN MGC Music genre classification Spectrogram SVM

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3-Multi ranked encryption with enhanced security in cloud computing

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Digital Communications and Networks 2023年第2期9卷 313-326页

作者： YeEun Kim Junggab Son Reza M.Parizi Gautam Srivastava Heekuck Oh Major in Bio Artificial Intelligence Dept.of Computer Science and EngineeringHanyang UniversityAnsanRepublic of Korea Department of Computer Science Kennesaw State UniversityMariettaUSA Department of Software Engineering Kennesaw State UniversityMariettaUSA Department of Mathematics and Computer Science Brandon UniversityBrandonCanada Research Center for Interneural Computing China Medical UniversityTaichungTaiwanChina Dept.of Computer Science and Engineering Hanyang UniversityAnsanRepublic of Korea

Searchable Encryption(SE)enables data owners to search remotely stored ciphertexts selectively.A practical model that is closest to real life should be able to handle search queries with multiple keywords and multiple data owners/users,and even return the top-k most relevant search results when *** refer to a model that satisfies all of the conditions a 3-multi ranked search ***,SE schemes that have been proposed to date use fully trusted trapdoor generation centers,and several methods assume a secure connection between the data users and a trapdoor generation *** is,they assume the trapdoor generation center is the only entity that can learn the information regarding queried keywords,but it will never attempt to use it in any other manner than that requested,which is impractical in real *** this study,to enhance the security,we propose a new 3-multi ranked SE scheme that satisfies all conditions without these security *** proposed scheme uses randomized keywords to protect the interested keywords of users from both outside adversaries and the honest-but-curious trapdoor generation center,thereby preventing attackers from determining whether two different queries include the same ***,we develop a method for managing multiple encrypted keywords from every data owner,each encrypted with a different *** evaluation demonstrates that,despite the trade-off overhead that results from the weaker security assumption,the proposed scheme achieves reasonable performance compared to extant schemes,which implies that our scheme is practical and closest to real life.

关键词： Searchable encryption Multi-keyword search Multiple data owners/users Ranked search Enhanced security

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Decoding Diabetes: Unconventional Indicators for Diabetic Diagnosis 10

Decoding Diabetes: Unconventional Indicators for Diabetic Di...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Sangavi, G. Kajanan, T. Methiny, S. Tissera, Wishalya Rajendran, Karthiga Sri Lanka Institute of Information Technology Faculty of Computing Malabe Colombo Sri Lanka Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Colombo Sri Lanka

ISBN: (纸本)9798331532895

Diabetes diagnosis has always involved glucose tests;however, it is possible to recognize diabetic complications by simply looking at skin and nails. This work presents a new approach for identifying diabetes-related complications, including acanthosis nigricans, foot ulcers, and nail infections, utilizing and TensorFlow for deep learning. Both conditions are associated with visual signals and pass through a strictly trained dataset, marked up for maximum accuracy. From the methodologies, including image resizing, data augmentation, and hyperparameter tuning. The findings of accuracy confirm that the platform should register specific characteristics of the problem area and has the essential precision to maintain close attention to possible risks of diabetes and intervene early to reduce the likelihood of patients' deteriorating health. © 2024 IEEE.

关键词： Diagnosis

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