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International Conference on computer and Applications (ICCA)

作者： Alaa Hesham Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science.

关键词： Measurement Codes Computational modeling Semantics Natural language generation Focusing Feature extraction Question generation Prompt engineering Programming profession

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A Pairwise Surrogate Model using GNN for Evolutionary Optimization

A Pairwise Surrogate Model using GNN for Evolutionary Optimi...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Gharavian, Vida Rahnamayan, Shahryar Bidgoli, Azam Asilian Makrehchi, Masoud Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Department of Engineering Brock University St. CatharinesON Canada Faculty of Science Wilfrid Laurier University WaterlooON Canada Lab

ISBN: (纸本)9798350337020

Optimization problems widely arise in various science and engineering fields and can be computationally expensive in many real-world applications. Evaluation of the fitness function to assess a candidate solution is the main operation in all optimization procedures which can be heavily compute-intensive. Machine learning-based surrogate models can contribute to learning the specific pattern among the decision variables and objective values to consequently reduce the computation time of fitness evaluation. In this study, we have proposed a novel pairwise surrogate model to identify the superiority between candidate solutions in a pairwise comparison despite the fact that most of the surrogate models try to predict the exact fitness value. The proposed idea can significantly help the optimizer to reach better results in a shorter period of time. It seems comparing two candidate solutions for a greedy selection is much easier than approximating fitness values for both. We demonstrated Graph Neural Network (GNN) for this purpose to be trained on a limited number of pairwise ranks and then utilized to compare a pair of candidate solutions. In order to examine the efficacy of our model, we utilized different well-known single-objective optimization benchmarks in dimensions 10,20, and 30. Moreover, the results of the learning-based evaluation are compared with the results from the real fitness evaluation. The results, assessed in terms of the number of fitness calls and the best-found solution, showed that the proposed method is able to decrease the computing cost of fitness evaluation significantly while we achieve a comparable solution. Our model can be tested with any optimization algorithm which employs a comparison-based mechanism among its candidate solutions. © 2023 IEEE.

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Using Machine Learning to Enhance Personalization in E-Learning

Using Machine Learning to Enhance Personalization in E-Learn...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Palimkar, Sneha S. Kunnathully, Kannan Thota, Srinivasarao Joshi, Meeta Imran Akkalkot, Afsha Al Said, Nidal Coep Technological University Computer Engineering and Technology Maharashtra Pune India Veltech School of Law India Capital One Software Engineering VA United States Marwadi University Faculty of Management Studies India Pune Technological University Computer Science Engineering College of Engineering Maharashtra Pune India Ajman University College of Mass Communication Ajman United Arab Emirates

ISBN: (纸本)9798331508685

Numerous diverse learning materials can be found on e-learning sites. Students in today's e-learning platforms invest a lot of time and energy in locating pertinent learning materials. The student's actual requirements must be taken into account based on a variety of characteristics, including choices, expertise, and learning style. Education must be pertinent to the necessary concept's environment. This study aims to develop an efficient approach for detecting e-learning style and then customizing the e-learning contents to match that style, using machine learning (ML) algorithms to enhance personalization in e-learning. The blended ensemble method combined with the XGBoost meta-learning approach produced the most prominent results for enhancing e-learning style, with an accuracy of 97.6%. Further, the textual material of the e-files is altered using various natural language processing (NLP) approaches. The spaCy NLP-oriented labeled entity identification (LEI) algorithm achieves a 94.2% F1 value and a 0.92 precise match ratio while color-coded textual production of 10 e-files with 790 different phrases. These alterations are intended to suit students' tastes, resulting in an additional personalized and interactive teaching encounter. © 2025 IEEE.

关键词： and Natural language processing Artificial intelligence E-learning Machine learning Students

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Water Quality Monitoring System for Nile Tilapia Farming Using LoRa Technology

Water Quality Monitoring System for Nile Tilapia Farming Usi...

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International Conference on Information Technology (INCIT)

作者： Nawara Chansiri Ponglert Sangkaphet Buppawan Chaleamwong Chaivichit Kaewklom Supawee Makdee Department of Software Engineering Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Thailand Department of Computer Science Faculty of Computer Science Ubon Ratchathani Rajabhat University Ubon Thailand

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

Fish farmers operating at Sirindhorn Dam in Ubon Ratchathani have faced significant challenges, including high mortality rates attributable to unstable weather conditions that compromise water quality. To tackle these issues, the Internet of Things has emerged as a tool to enhance aquaculture management. This research seeks to develop a prototype water quality monitoring system using LoRa technology to facilitate data transmission between sensor nodes in a peer-to-peer configuration. The development and testing of the system demonstrate its ability to accurately monitor key water quality parameters, including dissolved oxygen, pH, and water temperature, while allowing for remote data transmission. This research involved a sample group of five program development experts and 15 general users, with various tools employed for system efficiency assessment. A statistical analysis including the mean and standard deviation was performed to evaluate performance. The findings reveal a high overall efficiency assessment, with an average score of 4.43.

关键词： Temperature sensors Temperature measurement Databases LoRa Water quality Peer-to-peer computing Servers Monitoring Farming Testing

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Triple Key Security Algorithm Against Single Key Attack on Multiple Rounds

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computers, Materials & Continua 2022年第9期72卷 6061-6077页

作者： Muhammad Akram Muhammad Waseem Iqbal Syed Ashraf Ali Muhammad Usman Ashraf Khalid Alsubhi Hani Moaiteq Aljahdali Department of Computer Science Superior University Lahore54000Pakistan Department of Software Engineering Superior University Lahore54000Pakistan Department of Computer Science The Institute of Management Sciences(PAK-AIMS)Lahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Computer Science King Abdulaziz UniversityJeddahSaudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia

In cipher algorithms,the encryption and decryption are based on the same *** are some limitations in cipher algorithms,for example in polyalphabetic substitution cipher the key size must be equal to plaintext otherwise it will be repeated and if the key is known then encryption becomes *** paper aims to improve the said limitations by designing of Triple key security algorithm(TKS)in which the key is modified on polyalphabetic substitution cipher to maintain the size of the key and *** plaintext character is substituted by an alternative *** mode of substitution is transformed cyclically which depends on the current position of the modified *** keys are used in the encryption and decryption process on 8 or 16 rounds with the Exclusively-OR(XOR)of the 1st *** study also identifies a single-key attack on multiple rounds block cipher in mobile communications and applied the proposed technique to prevent the *** utilization of the TKS algorithm,the decryption is illustrated,and security is analyzed in detail with mathematical examples.

关键词： Encryption decryption cipher symmetric key cryptography single key attack

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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Technology acceptance model (TAM) for mobile durian plantation apps(Pro Durian);The analysis for Malaysian farmers 5

Technology acceptance model (TAM) for mobile durian plantati...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Kamarudin, Noraziahtulhidayu Yusoff, Aiman Basari, Abd Samad Hasan Safar, Noor Zuraidin Bin Mohd Hussin, Mohd Hatta Universiti Tun Hussein Onn Multimedia and Signal Processing Focus Group Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Multimedia Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Technology Department of Software Engineering Malaysia Universiti Tun Hussein Onn Faculty of Computer Science and Information Department of Information Security and Web Technology Malaysia Pertubuhan Peladang Senggarang Johor Darul Takzim Malaysia

ISBN: (纸本)9798350337525

During the application development process, it is important for a developer to analyze user requirements. This is crucial to the success of interactive systems and is an essential component of the design of information systems. User requirement analysis aids developers in understanding and analyzing users' needs and technology requirements. Throughout this case study, it is able to forecast the understanding, and technology acceptance, and at the same time improve the quality of work, reducing training and support costing and perennially improve customer or user satisfaction. The method of research selected is questionnaires that focused on collecting the data directly from Malaysian farmers through Google Forms as an online medium. The method was chosen because it is easy to implement and inexpensive due to the current situation of Covid-19. The questionnaire is designed with two sections where;a) includes the participant's demography to analyze information of the intended audience while b)focused on the knowledge of participants about recognition images. Results demonstrate the farmer's opinion and knowledge of the technology of image recognition in the agriculture sector. The quantitative research was undertaken using an online platform due to the Covid-19 pandemic. From the analysis, it is justified that the Technology Acceptance Model (TAM) together with the mobile farming image recognition app is useful in collecting farmers' feedback and positive results have an outcome on the challenges faced by the durian farmers. © 2023 IEEE.

关键词： Image recognition

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An Intelligent Thyroid Diagnosis System Utilizing Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2840-2855页

作者： Sutradhar, Ananda Al Rafi, Mustahsin Ghosh, Pronab Shamrat, F M Javed Mehedi Moniruzzaman, Md. Ahmed, Kawsar Azad, A.K.M. Bui, Francis M. Chen, Li Moni, Mohammad Ali Daffodil International University Department of Computer Science and Engineering Dhaka1216 Bangladesh Lakehead University Department of Computer Science Thunder BayONP7B 5E1 Canada University of Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia Lakehead University Department of Software Engineering Thunder BayONP7B 5E1 Canada University of Saskatchewan Department of Electrical and Computer Engineering SaskatoonSK S7N 5A9 Canada Imam Mohammad Ibn Saud Islamic University Faculty of Science Department of Mathematics and Statistics Riyadh13318 Saudi Arabia Charles Stuart University AI and Cyber Futures Institute BathurstNSW2795 Australia

The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including feature importance (FIS), information gain selections (IGS), and least absolute shrinkage and selection operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as high-risk factors (HRF). Second, the models, including the three stage hybrid classifier (3SHC) and the three stage hybrid artificial neural network (3SHANN), are used as classifiers on the training data set. Third, a local interpretable model-agnostic explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a partial dependence plot (PDP). Finally, the proposed system is validated with extensive experiments, where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector. © 2020 IEEE.

关键词： Neural networks

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A Fused Machine Learning Approach for Intrusion Detection System

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computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

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An Overview of the Approaches for Generating Test Data in the Context of the Quality Assurance of Big Data Applications 17

An Overview of the Approaches for Generating Test Data in th...

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17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023

作者： Staegemann, Daniel Pohl, Matthias Haertel, Christian Daase, Christian Abdallah, Mohammad Turowski, Klaus Otto-von-Guericke University Faculty of Computer Science Magdeburg Research and Competence Cluster Vlba Magdeburg Germany Al-Zaytoonah University of Jordan Department of Software Engineering Amman Jordan

ISBN: (纸本)9798350370911

With society's increasing data production and the corresponding demand for systems that are capable of utilizing them, the big data domain has gained significant importance. However, besides the systems' actual implementation, their testing also needs to be considered. For this, oftentimes, proper test data sets are necessary. This publication discusses several different approaches how these can be provisioned and, further, highlights the respective advantages, disadvantages, and suitable application scenarios. In doing so, researchers and practitioners that are implementing big data applications and need to test them, or who are generally interested in the domain, are supported in their own considerations on how to obtain test data. © 2023 IEEE.

关键词： Big data

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