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4th International Conference on Advancements in Computing, ICAC 2022

作者： Weerasinghe, D.N.H. Thiwanka, K.A.T. Jayasith, H.B.C. Onella Natalie, P.A.D. Samantha Rajapaksha, U.U. Karunasena, Anuradha Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

The great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system's stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most computer science-related students get assessed in UML modelling, diagram plagiarism isn't considered in most exams since it's a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage. © 2022 IEEE.

关键词： Unified Modeling Language

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Voice Enabled Intelligent Programming Assistant 4

Voice Enabled Intelligent Programming Assistant

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Wataketiya, Ravindu Chandrasiri, Navinda Kithsiri, Ramesh Malwatta, Hirush Nadeeshani, Madhuka Siriwardana, Samanthi Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

In modern era where software development is of vital importance, software developers are challenged with conditions like Repetitive Strain Injury (RSI) which hinders their ability to work effectively. Furthermore, people with difficulties with using their hands also find it challenging to program in the traditional manner. As a solution, coding with one's voice has been experimented with, but current solutions lack interactivity and are harder to use and setup leaving much room for improvement in this domain. In this research work, by using input classifier models with accuracies over 90%, intent classifiers with accuracies over 70%, code parsing and various human computer interaction techniques, we developed a conversationally interactive, programming language agnostic, easy to setup and easy to use Voice Coding Assistant. This will potentially help a global audience of programmers to achieve their goals and improve productivity and lead a healthier life. We have named the system thus developed, "Venic". © 2022 IEEE.

关键词： Human computer interaction

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Deep Learning Based Classification of Diabetic Risk Using LSTM-RNN Method

Deep Learning Based Classification of Diabetic Risk Using LS...

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International Conference on Artificial Intelligence and Smart Environment, ICAISE 2024

作者： Arularasan, A.N. Nithisha, J. Khadirkumar, N. Jothi, M. Farhaoui, Yousef Gopalakrishnan, S. Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Vandalur Tamilnadu Chennai 600 048 India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamilnadu Chennai India Department of Computer Science and Engineering Maha Barathi Engineering College Tamil Nadu Chinnasalem 606201 India Department of Computer Science and Business Systems PSNA College of Engineering and Technology Tamilnadu Dindigul 624622 India T-IDMS Department of Computer Science Faculty of Sciences and Techniques Errachidia Moulay Ismail University of Meknes Meknes Morocco Department of Information Technology Hindustan Institute of Technology and Science Tamil Nadu Kelambakkam 603103 India

ISBN: (纸本)9783031909207

Diabetes is one of the most significant non-communicable diseases and can cause serious complications in individuals such as heart disease, kidney failure and blindness. Disease identification and staging at an early and specific level is of outmost importance in order to avoid further deterioration of the patient’s health. This research aims at developing a more complex classification method for diabetes prediction through a two-stage model. The initial process involves using the Support Vector Machine to select the features from the dataset to help boost the accuracy of the model. In the second phase, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is used to classify the dataset, thus enabling capturing temporal pattern in data related to diabetes. One of the major strengths of LSTM-RNN for electricity load forecasting is its ability to handle missing data and irregular time series data as it produces better predictive results as compared to other models. Our method shows superior values in the measures of correctness, specificity, capacity, and stability in terms of data perturbation taking into consideration of non-linearity inherent in the diabetes dataset. The study points to the possibility of the combination of SVM and LSTM-RNN to enhance the diagnosis and treatment of diabetes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Diabetes Prediction Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) Support Vector Machine (SVM) Temporal Data

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一种比较学者学术影响力的新方法（英文）

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Journal of Data and information science 2025年第2期 40-60+187页

作者： Anand Bihari Sudhakar Tripathi Akshay Deepak P.Mohan Kumar Department of Computational Intelligence School of computer Science and Engineering Vellore Institute of Technology Department of Information Technology Rajkiya Engineering College Department of Computer Science and Engineering National Institute of Technology Patna Department of Database Systems School of computer Science and Engineering Vellore Institute of Technology

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我... 详细信息

研究目的：学术影响力的比较通常依赖于学者的整体科研成果。尽管这类方法简单易行，但如何比较位于不同职业生涯阶段的学者的影响力？很明显，如果一个学者有更丰富的研究经验或开展科研的时间更长，更可能获得较高的影响力。因此，我们不能直接比较位于不同职业生涯阶段的学者的影响力。已有多种文献计量指标尝试考虑时间跨度因素，其中h指数序列和EM/EM'指数序列已经被用于评估和比较学者的科学影响力。h指数序列、EM指数序列和EM'指数序列考虑了学者的年度影响力，并通过指标值及各个年度的分量值对学者进行比较。然而当两位学者在科研年限上存在显著差距时，基于时间序列的文献计量指标难以实现对其学术影响力的有效比较。研究方法：研究提出了一种累积指数计算方法，用于评估学者在其科研生涯某一年龄节点前的科学影响力，并使用89位学者的数据进行了实证测试。研究发现：本研究提出的方法已在89位学者的发表数据上进行了测试，能够清晰地区分两类学者之间的科学影响力差异。此外，该方法还有助于根据研究影响预测未来有潜力的杰出学者。实践意义：本研究提出的方法可被各类权威机构用于比较其学者的科研影响力，资助机构亦可将其用于类似的评估目的。原创性/价值：本研究通过引入一种新的学者科研影响力比较方法，丰富了现有文献体系。研究成果对于构建更加精确且公正的科研评估框架具有重要意义，有助于实现对学术贡献更为公平的评价。

关键词： h指数 EM指数 EM’指数 h指数序列 EM指数序列 EM’指数序列科学影响力比较科研影响力

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Multimedia Fingerprinting Codes Resistant to Linear Attacks and Adversarial Noise

Multimedia Fingerprinting Codes Resistant to Linear Attacks ...

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2023 International Symposium on Networks, computers and Communications, ISNCC 2023

作者： Fernandez, Marcel Kabatiansky, Grigory Kamel, Ibrahim Miao, Ying Rabie, Tamer Farouk The Technical University of Catalonia Department of Network Engineering Spain Center for Next Generation Wireless and IoT Skolkovo Institute of Science and Technology Russia University of Sharjah Electrical and Computer Engineering United Arab Emirates Institute of Engineering Information and Systems University of Tsukuba Japan

ISBN: (纸本)9798350335590

It has recently been shown that there are no multimedia fingerprinting codes that can find all malicious users when they use arbitrary linear attacks plus adversarial noise. It is shown that such codes exist if the complete recovery property is limited to the IPP property, i.e., the property to find at least one malicious user. Moreover, we extend this property to a property that allows us to detect all users whose contribution to the forgery is large enough. Efficient decoding (tracing traitors) algorithms are developed for these codes. © 2023 IEEE.

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Augmented Reality With Authentic GeometryGo App to Help Geometry Learning and Assessments

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IEEE Transactions on Learning Technologies 2023年第5期16卷 769-779页

作者： Hwang, Wu-Yuin Nurtantyana, Rio Purba, Siska Wati Dewi Hariyanti, Uun Suprapto National Dong Hwa University Department of Computer Science and Information Engineering College of Science and Engineering Hualien974 Taiwan National Central University Graduate Institute of Network Learning Technology Taoyuan City320 Taiwan Telkom University School of Computing Bandung40257 Indonesia Universitas Pelita Harapan Department of Master of Educational Technology Kota Tangerang15811 Indonesia Brawijaya University Information Systems Department Kota Malang65145 Indonesia State University Yogyakarta Department of Electronics Engineering Education Yogyakarta55281 Indonesia

We developed an Authentic GeometryGo (AGG) app to help students application of geometry in authentic contexts through authentic measurements and peer assessments. The study included an experimental group (EG) using an AGG app and a control group (CG) using paper-based methods. The results showed that the EG outperformed the CG in terms of estimation. Measuring authentic geometry shapes using the app could foster students' estimation. Furthermore, meaningful peer assessments with visual representations in authentic contexts can facilitate the evaluation of peers' work and improve students' geometry understanding. Therefore, we encourage educators to use augmented reality with appropriate learning activities to sharpen students' measurement quality and visual representations by measuring real geometry shapes and applying mathematics knowledge in authentic contexts. © 2008-2011 IEEE.

关键词： Augmented reality

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Heuristic Search for Nonlinear Substitutions for Cryptographic Applications

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2023年 180卷 288-298页

作者： Kuznetsov, Oleksandr Frontoni, Emanuele Kandiy, Sergey Smirnov, Oleksii Ulianovska, Yuliia Kobylianska, Olena Department of Political Sciences Communication and International Relations University of Macerata Via Crescimbeni 30/32 Macerata62100 Italy Department of Information and Communication Systems Security Faculty of Comupter Science V. N. Karazin Kharkiv National University 4 Svobody Sq Kharkiv61022 Ukraine Department of Information Engineering Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy Cybersecurity & Software Academic Department Central Ukrainian National Technical University 8 University Avenue Kropyvnytskyi25006 Ukraine Department of Computer Science and Software Engineering University of Customs and Finance Vernadskogo Street 2/4 Dnipro49000 Ukraine

Heuristic algorithms are used to solve complex computational problems quickly in various computer applications. Such algorithms use heuristic functions that rank the search alternatives instead of a full enumeration of possible variants. The algorithm selects, at each iteration, an alternative with the best value for the heuristics. In this paper, we investigate the complex computational problem of finding highly nonlinear substitutions (S-boxes) in the space of 8-bit permutations. The generation of S-boxes is an important field of research, since nonlinear substitutions are widely used in various cryptographic applications. For instance, S-boxes in symmetric ciphers are responsible for cryptographic strength to linear, differential, algebraic, and other types of cryptanalysis. We propose new heuristics in the form of a cost function, calculated with the Walsh-Hadamard transform. We use the Hill Climbing Algorithm to find highly nonlinear substitutions. Our experiments demonstrate that the new heuristics give good results – it takes about 80,000 iterations of the algorithm to generate 8-bit S-boxes with a nonlinearity of 104. For the optimized parameters, the number of iterations of the algorithm is comparable to the best known results, which confirms the significance and value of the research. © The Author(s).

关键词： Cost functions

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Reinforcement Learning Driven Smart Charging Algorithms to Enhance Battery Lifespan and Grid Sustainability

Reinforcement Learning Driven Smart Charging Algorithms to E...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Sivakumar, S. Yadav, Mali Ram Bharathi, A. Akila, D. Dineshkumar, P. Banupriya, V. Vel Tech Rangarajan Dr.Sagunthala R And D Institute Of Science And Technology Department Of Electrical And Electronics Engineering Chennai India Lovely Professional University Department Of Physical Education Punjab India Vels Institute Of Science Technology And Advanced Studies Department Of Information Technology Pallavaram India Saveetha College Of Liberal Arts And Sciences Simats Department Of Computer Applications Chennai India Sona College Of Technology Department Of Information Technology Tamil Nadu Salem India M. Kumarasamy College Of Engineering Department Of Computer Science And Business Systems Karur India

ISBN: (纸本)9798350371567

This study investigates the use of RL-driven smart charging algorithms, particularly the Deep Deterministic Policy Gradients (DDPG) model, to maximise grid sustainability and battery lifetime. The current state of this field's study is lacking in real-time adaptation to different settings and in handling the ever-changing nature of charging profiles. By comparison, the suggested approach demonstrates a significant efficiency boost through the use of RL principles, which allow the model to learn and adjust its charging tactics. In specifically, the strategy contributes to a 10% improvement in total grid efficiency while cutting peak demand by 25%, minimising charge-discharge cycles by 20%, and reducing battery degradation rates by 15%. Not only do these measurements show that the suggested method works, but they also show how unique it is in offering a smart charging solution that is both sensitive and adaptive. This research fills in the gaps left by other studies and proposes an optimisation technique that is more dynamic and works in real-time. Its influence on the health of individual batteries and the sustainability of the grid as a whole is noteworthy. © 2024 IEEE.

关键词： Reinforcement learning

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Evaluation of Wireless Sensor Networks Module using IoT Approach 3

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Anand, L. Padmalal, S. Seetha, J. Juliana, R. Naveen Kumar, P.S. Parasa, Gayatri Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattanguluthur India Mangalam College of Engg Department of Computer Science and Engineering Kerala India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India St. Ann's College of Engineering and Technology Department of CSE-DATA Science AP Chirala India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9781665462167

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one. © 2023 IEEE.

关键词： Sensor nodes

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