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2023 International Conference on Informatics, Multimedia, Cyber and Information systems, ICIMCIS 2023

作者： Arista, Artika Shuib, Liyana Ismail, Maizatul Akmar Universitas Pembangunan Nasional Veteran Jakarta Information Systems Faculty of Computer Science Jakarta Indonesia Faculty of Computer Science and Information Technology Universiti Malaya Department of Information Systems Kuala Lumpur Malaysia

ISBN: (纸本)9798350307306

ChatGPT is a useful tool for teaching and learning in the context of education. Despite ChatGPT's growing popularity across several fields, little is known about how it is accepted and used, particularly in the context of higher education. ChatGPT holds a lot of potential to be a helpful tool for both students and lecturers. However, in higher education, the introduction of ChatGPT has raised serious questions about academic integrity. Thus, this research focuses on the overview of chatGPT in Higher Education in Indonesia and Malaysia. This study is conducted using the literature review method. According to this research result, this tool can help students and instructors with academic writing, language instruction, and learning in class in Higher Education in Indonesia. Higher Education in Malaysia is also raising optimism that this technology will speed up the transformation of higher education. Even though there are positive and negative effects of ChatGPT. Future research will need to conduct more case studies and interviews to obtain an in-depth comprehension of the application of ChatGPT in learning especially in higher education. © 2023 IEEE.

关键词： Students

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Beyond Standard Methods: A Novel Image Encryption Scheme Combining Base64, Key Hashing, and Fragmentation for Robust Protection 1

Beyond Standard Methods: A Novel Image Encryption Scheme Com...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Venkatesh, P. Ganeshbabu, K. Asvant, S. Kowshik, T. K.S.Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Namakkal India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

The growing reliance on digital images in various domains necessitates robust protection against unauthorized access and manipulation. This research proposes a novel image encryption scheme combining Base64 encoding, key hashing, and key splitting techniques to achieve enhanced security and resilience. Firstly, the image data is converted to a Base64 string, rendering it unintelligible in its raw form. Subsequently, a secret key undergoes a hashing function to generate a fixed-length digest, ensuring security through controlled encryption. This hashed key is then fragmented into multiple image sub-shares, further obfuscating its presence and hindering potential key extraction attempts. The encryption process leverages the combination of Base64 encoded data, and the derived key to scramble the image information into a cipher text. This cipher text, along with the fragmented key sub-shares, constitutes the encrypted output. Decryption involves reassembling the key fragments, applying the inverse hashing function, and utilizing the reconstructed key to reverse the encryption process, ultimately revealing the original image. This proposed scheme offers several advantages compared to the existing methods. Combining Base64 encoding with key hashing and splitting, provides multi-layered protection against unauthorized access and potential brute-force attacks. Additionally, the key fragmentation introduces spatial redundancy, enhancing resilience against partial key loss or corruption. Through rigorous security analysis and performance evaluation, the proposed scheme's robustness is demonstrated against various attacks while maintaining efficient encryption and decryption processes. This novel approach holds promising potential for securing sensitive image data in diverse applications across various fields. © 2024 IEEE.

关键词： Cryptography

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Thermal Model Validation of a Power Electronics Module for Digital Twin Application

Thermal Model Validation of a Power Electronics Module for D...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Zoller, Svenja Cabre-Piqueras, Claudia Schilder, Boris Filba-Martinez, Alber Acker, Lukas Frankfurt University of Applied Science Faculty of Computer Science and Engineering Frankfurt Germany Power Systems Dept. Barcelona Spain

ISBN: (纸本)9798350364446

Thermal stress is a critical failure factor for power electronics components. Hence, when developing a precise digital twin for a power system, it is imperative that thermal models provide a reliable portrayal of the component physics. In that regard, manufacturers offer simplified thermal models that enable designers and users to estimate its behaviour. Nevertheless, the accuracy of these models remains unknown, thus compromising the overall certainty of the digital twin. Therefore, it is of interest to discern whether these simplified models are sufficient for the precision required by the digital twin, and to assess the need of developing a more accurate model. Hence, this paper focuses on validating the theoretical thermal model of a power electronics switch module setup by using the deviation of the module mean time to failure (MTTF) as the acceptance parameter. To achieve this, a novel experimental validation methodology is proposed to retrieve an experimental thermal model and compare it to the theoretical one. This validation is performed considering the discrete thermal impedances and introducing the study of the coupling effects within the module. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Metaheuristic Optimization of Time Series Models for Predicting Networks Traffic

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computers, Materials & Continua 2023年第4期75卷 427-442页

作者： Reem Alkanhel El-Sayed M.El-kenawy D.L.Elsheweikh Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh 11671Saudi Arabia

Traffic prediction of wireless networks attracted many researchersand practitioners during the past decades. However, wireless traffic frequentlyexhibits strong nonlinearities and complicated patterns, which makes it challengingto be predicted accurately. Many of the existing approaches forpredicting wireless network traffic are unable to produce accurate predictionsbecause they lack the ability to describe the dynamic spatial-temporalcorrelations of wireless network traffic data. In this paper, we proposed anovel meta-heuristic optimization approach based on fitness grey wolf anddipper throated optimization algorithms for boosting the prediction accuracyof traffic volume. The proposed algorithm is employed to optimize the hyperparametersof long short-term memory (LSTM) network as an efficient timeseries modeling approach which is widely used in sequence prediction *** prove the superiority of the proposed algorithm, four other optimizationalgorithms were employed to optimize LSTM, and the results were *** evaluation results confirmed the effectiveness of the proposed approachin predicting the traffic of wireless networks accurately. On the other hand,a statistical analysis is performed to emphasize the stability of the proposedapproach.

关键词： Network traffic soft computing LSTM metaheuristic optimization

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An Ontology-Based Customer Relationship Management Model for Educational Institutions Using Service-Oriented Architecture 3rd

An Ontology-Based Customer Relationship Management Model for...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： Samih, Islam Abdelsalam, Mohammed Moawad, Ibrahim Fathy Business Information Systems Department Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Abbassia Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University New Mansoura City Egypt

ISBN: (纸本)9789819947638

Increasing satisfaction of customers is the most important goal for any institution. Therefore, customer relationship management (CRM) is founded for this purpose to keep track of the interaction between the organization and their customers to satisfy them and to guarantee their loyalty. In this paper, we introduce a layered architecture model for customer relationship management in Education domain. This model integrates educational domain ontology and service-oriented architecture to improve the business processes and hence to increase the customer satisfaction in the educational institutions. The model design is presented in detail for a huge Egyptian educational institution called the Education City. The paper presents a workflow design for customer relationship management describing the interaction among stakeholders to achieve the different business processes in the Education City. As well as, a detailed ontology design for the Education City is presented to describe and to visualize the different scenarios for the services provided by the Education City. To achieve flexibility and reusability, the model was built based on service-oriented architecture for the possibility of reusing the model in more than one field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Reusability

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Enhancing Self-regulated Learning Through an Emotional Chatbot: A Conceptual Framework 9th

Enhancing Self-regulated Learning Through an Emotional Chatb...

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9th International Conference on Smart City Applications, SCA 2024

作者： El Mourabit, Imane Jai Andaloussi, Said Ouchetto, Ouail Miyara, Mounia Computer Science and Systems Laboratory Department of Mathematics and Computer Sciences Faculty of Sciences Ain Chock Hassan II University of Casablanca Casablanca Morocco

ISBN: (纸本)9783031886522

Artificial Intelligence (AI) chatbots play an important role in modernizing education, particularly e-learning platforms by acting as intelligent tools to address issues such as disengagement and dropout rates. AI chatbots are also used to captivate students through engaging and enjoyable learning activities. These chatbots can enhance the pedagogical concept of Self-Regulated Learning (SRL) by providing personalized support and feedback to students. This work explores how AI chatbots might increase students’ SRL skills and foster successful learning outcomes in e-learning environments. Our proposed conceptual framework aims to link the AI chatbots (technological side) and self-regulated learning (pedagogical side) using the power of academic emotions. In the forethought phase of SRL, our AI chatbot will guide students in setting short-term goals while detecting their initial motivation and then monitor students in the following phases of the SRL process. The framework highlights how AI chatbots using academic emotions can be used to support learning needs and will serve as a roadmap for future theory development and empirical investigation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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A New Approach for Quantum Phase Estimation Based Algorithms for Machine Learning 8th

A New Approach for Quantum Phase Estimation Based Algorithm...

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8th International Conference on Smart City Applications, SCA 2023

作者： Ouedrhiri, Oumayma Banouar, Oumayma El Hadaj, Salah Raghay, Said Department of Computer Science and Mathematics University of Quebec at Trois-Riviéres Trois-Riviéres Canada Faculty of Sciences and Techniques Laboratory of Computer Science Engineering and Systems Marrakesh Morocco

ISBN: (纸本)9783031543753

One of the greatest developments in computer science is undoubtedly quantum computing. It has demonstrated to give various benefits over the classical algorithms, particularly in the significant reduction of processing time, due to the parallelism and entanglement properties. One of the most crucial quantum computing algorithms is quantum phase estimation (QPE). It is called the eigenvalue finding module for unitary operators. It has helped to solve the order finding and the factoring problem, and to calculate the eigenvalues of unitary matrices and quantum sampling methods. In this paper, we study recent improved versions for the QPE procedure, their advantages and experimentation. We also propose a new approach for QPE based algorithms for machine learning (ML). These algorithms are the Harrow-Hassidim-Lloyd (HHL) algorithm for solving linear systems, the quantum singular value thresholding (QSVT) algorithm for matrix completion in recommender systems, and the quantum principal components analysis (QPCA) for data visualization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Machine learning

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LLM-Driven Cross-Platform Code Generation for Polyhedral Optimized NPDP Codes 15th

LLM-Driven Cross-Platform Code Generation for Polyhedral Op...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Palkowski, Marek Faculty of Computer Science and Information Systems West Pomeranian University of Technology in Szczecin Zolnierska 49 Szczecin71210 Poland

ISBN: (纸本)9783031856990

This paper explores using large language models (LLM) for automatic and source-to-source programming across various cross-platform languages and libraries, including generating CUDA code for high performance computing (HPC). Our proposed solution aims to demonstrate the capabilities of ChatGPT in generating valid CUDA code based on existing OpenMP code obtained from a polyhedral compilers Traco and Dapt. Specifically, we intend to showcase the model’s proficiency in producing equivalent and valid GPU code. Furthermore, we extend the application of our solution by prompting the AI model to generate similar code for another input benchmark, emphasizing its adaptability and versatility across different scenarios. This approach seeks to highlight the model’s capacity to provide automated and efficient solutions for diverse programming challenges. We chose non-trivial kernels from the non-serial polyadic dynamic programming (NPDP) benchmark with non-uniform loops. The focus is on ensuring code validity and understanding limitations in obtaining valid CUDA code. Additionally, we assess the efficiency and scalability of NVIDIA A100 GPU codes generated by artificial intelligence (AI) models, comparing them with optimized Intel Xeon Gold CPU codes from polyhedral optimizers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Dynamic programming

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Career Center Application as Centralization of Career Development: An Empirical Investigation Using the Unified Theory of Acceptance and Use of Technology (UTAUT) Model 10

Career Center Application as Centralization of Career Develo...

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Pratama, Arista Mukaromah, Siti Idhom, Mohammad Ningtyas, Silvia Ayu Parlika, Rizky Suryantari, Putu Anggi University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Information Systems Surabaya Indonesia University of Pembangunan Nasional "Veteran"Jawa Timur Faculty of Computer Science Department of Informatics Surabaya Indonesia

ISBN: (纸本)9798331521295

The Career Center Application is one of the innovations in the application of technology used to assist in finding and identifying relevant career opportunities according to the interests and competencies of students and graduates of higher education institutions. This study aims to analyze the success factors in the acceptance and use of applications of the Career Center using the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The study used Warp-PLS version 7.0 with 348 respondents using the UPNVJT Career Center application using the UTAUT method. The results of the structural model indicate a significant value of the relationship between the variables processed using SEM/PLS analysis technique using Warp-PLS 7.0. The results of this study show that the effort expectancy variable produces a path coefficient or direct influence of 0.136 on behavioral intention. Social influence has a positive impact on the behavior intention variable, and the facilitating conditions create a direct influence. © 2024 IEEE.

关键词： Employment

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Guardian AI: Synthetic Media Forensics through Multimodal Fusion and Advanced Machine Learning 1

Guardian AI: Synthetic Media Forensics through Multimodal Fu...

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1st IEEE International Conference on Cognitive Robotics and Intelligent systems, ICC - ROBINS 2024

作者： Karthikeyan, K. Swetha, R. Deepanraj, S. Dhandapani, S. K.S.Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Namakkal India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

The burgeoning spread of synthetic media disrupts content verification and threatens online trust. This research proposes Guardian AI, a robust deepfake detection system achieving 93% accuracy by harnessing the synergistic power of facial recognition, image forensics, and machine learning. Guardian AI extracts diverse features from videos: facial recognition models analyze landmarks, expressions, and lip-syncing for inconsistencies;image forensics algorithms detect manipulated pixels, lighting patterns, and compression artifacts;and temporal analysis captures unnatural head movements and frame-to-frame motion discrepancies. These multifaceted features are then fused and fed into a rigorously trained deep learning model on multi-modal datasets of real and deepfake videos. Guardian AI classifies video inputs as real or fake, providing a confidence score for its prediction. By leveraging facial recognition's subtle inconsistency detection, image forensics' manipulation artifact identification, and machine learning's robust multi-cue integration, Guardian AI achieves exceptional accuracy and generalizability, adapting to evolving deepfake creation techniques with its diverse training data. This study signifies a significant contribution to content verification by delivering a high accuracy deepfake detection system, paving the way for a more reliable and trustworthy online environment. © 2024 IEEE.

关键词： Face recognition

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