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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Thomopoulos, George A. Kiortsi, Panagiota Sigalas, Damianos Dumi Kosmopoulos, Dimitris Igglezakis, Ioannis Fidas, Christos University of Patras Department of Electrical and Computer Engineering Greece University of Thessaloniki Faculty of Law Aristotle Greece University of Patras Department of Computer Engineering and Informatics Greece

ISBN: (纸本)9798350368833

The phishing problem poses a significant threat in modern information systems, putting both individuals and businesses at risk of financial and professional harm. Owing to social media's rapid development and widespread appeal, deception of this sort is hurting millions of people and growing more dangerous. The current work, as part of the AILA (Artificial Intelligence-driven Framework and Legal Advice Tools for Phishing Prevention and Mitigation in Information Systems) project, aims to specify and validate an AI-driven multifactor (human, technology and legal) anti-phishing data model, with the implementation of focus group studies. The findings assist to provide human, technology, and legislative user model endpoints that will be identified and discussed for explicit and implicit user modeling, which will guide the development of the corresponding AI-driven user modeling and profiling mechanisms. To this end a Large Language System is planned to be employed. © 2024 IEEE.

关键词： Phishing

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Semi-Automated Video Archiving Using Object Detection, Tracking and Ontological Annotation 4

Semi-Automated Video Archiving Using Object Detection, Track...

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4th International Conference on Artificial Intelligence, Robotics and Control, AIRC 2023

作者： Ayman, Shehab Eldeen Hussein, Walid Karam, Omar H. The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt The British University in Egypt Faculty of Informatics and Computer Science Department of Computer Science Cairo Egypt

ISBN: (纸本)9798350348248

In light of this unmistakable exponential data expansion, visual media archiving must be rethought. Human generated meta-data might not be sufficient for efficient data retrieval. Object detection and object recognition enable the generation of computer-generated meta data that improve the storage and retrieval of data. We present a semi-automated approach for preserving video footage that combines object identification and ontologies. This significantly cuts down on search time, data scrubbing by providing only the relevant information making for more efficient queries. © 2023 IEEE.

关键词： computer vision

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A MLP-Mixer and mixture of expert model for remaining useful life prediction of lithium-ion batteries

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Frontiers of computer Science 2024年第5期18卷 1-10页

作者： Lingling ZHAO Shitao SONG Pengyan WANG Chunyu WANG Junjie WANG Maozu GUO Faculty of Computing Harbin Institute of TechnologyHarbin 150001China School of Electrical Engineering Liaoning University of TechnologyJinzhou 121004China School of Computer Science Northeast Electric Power UniversityJilin 132000China Department of Medical Informatics School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjing 211166China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing 100044China

Accurately predicting the Remaining Useful Life(RUL)of lithium-ion batteries is crucial for battery management *** learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series ***,the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be *** address this challenge,this paper proposes a novel deep learning model,the MLP-Mixer and Mixture of Expert(MMMe)model,for RUL *** MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level ***,we devise an ensemble predictor based on a Mixture-of-Experts(MoE)architecture to generate reliable RUL *** experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods,providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation *** code and dataset are available at the website of github.

关键词： lithium-ion battery remaining useful life deep learning MLP-Mixer mixture-of-experts

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Navigating online learning challenges and opportunities: Insights from small group of lecturers during pandemic 7

Navigating online learning challenges and opportunities: Ins...

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7th Information Systems International Conference, ISICO 2023

作者： Faza, Ahmad Santoso, Harry Budi Hadi Putra, Panca O. Universitas Multimeia Nusantara Faculty of Engineering and Informatics Tangerang15810 Indonesia Universitas Indonesia Faculty of Computer Science Depok16424 Indonesia

The implementation of e-learning platforms in higher education offers opportunities to enhance education quality and increase access to flexible learning. However, challenges arise, including limited guidance for students, high dropout rates, and obstacles faced by instructors. This qualitative case study explores the challenges and opportunities of online learning from instructors' perspectives. Data was collected through a survey administered to 23 instructors, and thematic analysis was conducted to identify the themes of challenges and opportunities. Findings reveal challenges in interaction, practical session delivery, monitoring, assessment, and identity verification. Institutional support challenges include insufficient training and socialization. Individual Challenges encompass digital skills, health concerns, and fatigue. Technological challenges involve availability, integration, capacity, and network-related issues. Despite these challenges, online learning provides opportunities for time effectiveness, flexibility, personal development, innovative teaching methods, and collaboration with external experts. © 2023 The Authors. Published by Elsevier B.V.

关键词： E-learning

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Detecting Human Walking Direction Using Wi-Fi Signals

Detecting Human Walking Direction Using Wi-Fi Signals

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International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Ali, Hanan Awad Hassan Seytnazarov, Shinnazar Faculty of Computer Science and Engineering Innopolis University Innopolis420500 Russia Faculty of Computers and Informatics Suez Canal University Ismailia Egypt

ISBN: (纸本)9789819720033

Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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An Effective Hybrid Metaheuristic Algorithm for Solving Global Optimization Algorithms

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Multimedia Tools and Applications 2024年第37期83卷 85103-85138页

作者： Seyyedabbasi, Amir Tareq Tareq, Wadhah Zeyad Bacanin, Nebojsa Computer Engineering Deptartment Faculty of Engineering and Natural Sciences Istinye University Istanbul Turkey Department of Mathematics Saveetha School of Engineering SIMATS Thandalam Tamilnadu India Faculty of Informatics and Computing Singidunum University Belgrade Serbia MEU Research Unit Middle East University Amman Jordan

Recently, the Honey Badger Algorithm (HBA) was proposed as a metaheuristic algorithm. Honey badger hunting behaviour inspired the development of this algorithm. In the exploitation phase, HBA performs poorly and stagnates at the local best solution. On the other hand, the sand cat swarm optimization (SCSO) is a very competitive algorithm compared to other common metaheuristic algorithms since it has outstanding performance in the exploitation phase. Hence, the purpose of this paper is to hybridize HBA with SCSO so that the SCSO can overcome deficiencies of the HBA to improve the quality of the solution. The SCSO can effectively exploit optimal solutions. For the research conducted in this paper, a hybrid metaheuristic algorithm called HBASCSO was developed. The proposed approach was evaluated against challenging CEC benchmark instances taken from CEC2015, CEC2017, and CEC2019 benchmark suites The HBASCSO is also evaluated concerning the original HBA, SCSO, as well as several other recently proposed algorithms. To demonstrate that the proposed method performs significantly better than other competitive algorithms, 30 independent runs of each algorithm were evaluated to determine the best, worst, mean, and standard deviation of fitness functions. In addition, the Wilcoxon rank-sum test is used as a non-parametric comparison, and it has been found that the proposed algorithm outperforms other algorithms. Hence, the HBASCSO achieves an optimum solution that is better than the original algorithms. © The Author(s) 2024.

关键词： Benchmarking

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Hyperparameter Optimization of MobileNet Architecture on Balinese Mask Classification 1

Hyperparameter Optimization of MobileNet Architecture on Bal...

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1st IEEE International Symposium on Consumer Technology, ISCT 2024

作者： Andika, I Gede Dirgayusari, Ayu Manik Arnawan, I Nyoman Wahyu Aristana, Made Dona Yudhi Ardiana, Dewa Putu Institut Bisnis Dan Teknologi Indonesia Computer Systems Engineering Faculty of Technology and Informatics Bali Denpasar Indonesia Institut Bisnis Dan Teknologi Indonesia Infomatics Engineering Faculty of Technology and Informatics Bali Denpasar Indonesia

ISBN: (纸本)9798350365191

This study utilized MobileN et to surpass the accuracy of previous research in the field of Balinese mask image classification. Balinese masks as one of the objects of Balinese cultural heritage must be preserved. Based on the data collected, most of young generation in Bali who has no interest in Balinese masks. The approach in the field of technology is known to have a very effective impact on the younger generation because of its rapid and efficient deployment. To improve accuracy, we analyze hyperparameter combinations such as learning rate, batch size, and dropout. Based on 16 scenarios, we got an accuracy of 98.57% for the combination of learning rate 0.00001, batch size 8, and dropout 40%. This value exceeds previous studies using the VGG16 architecture with an accuracy of 85%. © 2024 IEEE.

关键词： Image classification

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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