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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Putra, Adima Mahardika Zaini, Ahmad Pramunanto, Eko Institut Teknologi Sepuluh Nopember Faculty of Intelligent Electrical and Informatics Technology Department of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Sleepiness is a condition when the level of human consciousness decreases. Sleepiness is not easy to measure externally. If this is allowed just like that, it would be very dangerous if we were doing activities that requires full control of consciousness such as activities driving. This set of tools and methods for detecting drowsiness has been developed. However, in its implementation the intrusive method uses this tool less practical. In addition, the sleep detection method uses video images as well experiencing problems due to the potential for data loss in the form of facial features. To get this data, you must use a near infrared camera which has a low resolution and lacks detail. Therefore it is necessary an algorithm that is able to detect facial features to the maximum for classifying a person's sleepiness scale. To achieve the goal, a program will be created that functions to perform the extraction facial features and eye features. The program will function to detect number of frames containing facial features and performing condition classification eyes closed or open which will then be saved in a 'csv' file to be processed. Furthermore, the data will be carried out in the training process using 1D CNN architecture. The results of the training process that has been carried out The previous model is the model that will be used in performing the scale classification drowsiness. There are 6 experimental scenarios to get the best results. From all the results that have been obtained, it can be concluded that the best model results are the result of the training process with epoch values by 30 and the addition of synthetic data. Accuracy value obtained of 89% and the loss value of 34%. © 2022 IEEE.

关键词： Classification (of information)

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Reassessing Glove Embeddings in Deep Learning: A Comparative Study with Classical ML Approaches 15

Reassessing Glove Embeddings in Deep Learning: A Comparative...

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15th International Conference on Emerging Ubiquitous Systems and Pervasive Networks / 14th International Conference on Current and Future Trends of Information and Communication Technologies in Healthcare, EUSPN/ICTH 2024

作者： Labd, Zakia Bahassine, Said Housni, Khalid Aadi, Fatima Zahrae Aithamou Laboratory of research in informatics LatRI Department of Computer Science Faculty of Sciences Ibn Tofail University Kenitra Morocco Laboratory of Artificial Intelligence and Complex Systems Engineering Department of Computer Science National Higher School of Arts and Crafts Hassan II University Casablanca Morocco

The importance of text classification algorithms has increased due to the growing availability of large-scale data. This has led to a greater demand for efficient classification techniques and encoding algorithms. Word embedding techniques, like Glove, have shown significant success in encoding semantic relationships between words. This research paper aims to reassess the effectiveness of Glove embeddings coupled with deep learning algorithms. The impact of Glove embedding on two widely used deep learning models: Recurrent Neural Networks (RNN) and Recurrent Convolutional Neural Networks (RCNN) is analyzed. The results highlight the impact of Glove embeddings on deep learning models, showcasing significant performance enhancements in some cases while having minimal effects in others. By examining the impact of Glove embeddings on traditional ML algorithms in a previous study, valuable context for understanding the performance differences between the two approaches is obtained. © 2024 The Authors. Published by Elsevier B.V.

关键词： Convolutional neural networks

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Developing an Anti -Phishing Large Language Model: A Focus Group Study on Human, Technological, and Legal Factors

Developing an Anti -Phishing Large Language Model: A Focus G...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

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

ISBN: (数字)9798350368833

ISBN: (纸本)9798350368840

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.

关键词： Analytical models Data privacy Law Social networking (online) Phishing Prevention and mitigation Data models User experience Security Information systems

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Identification Important Nodes in Diffusion Process by User Experience on Social Network 28

Identification Important Nodes in Diffusion Process by User ...

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28th International computer Conference, computer Society of Iran, CSICC 2023

作者： Kazemzadeh, Farzaneh Safaei, Ali Asghar Mirzarezaee, Mitra Islamic Azad University Department of Computer Engineering Science and Research Branch Tehran Iran Tarbiat Modares University Faculty of Medical Sciences Department of Medical Informatics Tehran Iran

ISBN: (纸本)9798350338195

The influence maximization problem (IMP) has been proposed in social networks. Nowadays, it is considered an important and practical problem due to the earnings potential by identifying a set of influential nodes, and therefore, it has been attracted by many researchers. This problem seeks to identify a set with K nodes among the social network nodes to maximize the influence and diffusion of information in that community. Algorithms proposed by other researchers have many shortcomings in terms of accuracy and run time of the algorithm. Hence, this article aimed to find the best, most accurate, and fastest solution to the *** article presented the UXM algorithm and used the User Experience criterion for the first time to solve this problem. At first, taking into account the reach club phenomenon and using criteria such as clustering coefficient, degree and also using user experience, nodes with more influence have been selected as the primary candidate set. Then, according to the component nodes, K final influential nodes have been selected. In this way, it could identify the set of nodes as accurately as possible with high efficiency in the shortest possible time. The evaluation of this algorithm and its comparison with other algorithms indicated excellent results in terms of run time and accuracy in selecting the set of nodes by the proposed algorithm. © 2023 IEEE.

关键词： Solvents

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Exploratory Study on User Experience (UX) of Public E-Procurement System in Malaysia: A Qualitative Approach 9

Exploratory Study on User Experience (UX) of Public E-Procur...

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9th International HCI and UX Conference in Indonesia, CHIuXiD 2023

作者： Mior Ibrahim, Emma Nuraihan Kusaini, Amiruddin Ismail, Juhaida Abu Hashim, Anis Hasliza Misron, Mudiana Mokhsin College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Selangor Malaysia Mathematics and Computer Science University of Twente Hallenweg 15 Faculty of Electrical Engineering Netherlands

ISBN: (纸本)9798350304084

The public e-Procurement system in Malaysia is a crucial component of the e-Government services. Its primary function is to provide a platform for government agencies to efficiently procure goods and services from business users. The user experience (UX) of this e-Procurement system has emerged as a pivotal factor in the success of the broader e-Government digital transformation. Ensuring that the digitalization of the public e-Procurement system aligns with its objectives and delivers a top-tier Government Electronic Procurement System is imperative. This research aims to gain a comprehensive understanding of the UX issues and challenges faced by stakeholders when using the public e-Procurement system. The study employs a qualitative approach, delving into the perspectives and insights of e-Procurement system users. Data from interview sessions involving nine (9) participants have been meticulously interpreted, coded, and subjected to thematic analysis, revealing five distinct dimensions of issues and challenges: usability, performance, operational, quality, and user satisfaction. To address these identified concerns, we proposed several recommendations. Enhancements in system user interface (UI), navigational aspects, system features, performance, and operational aspects are essential to ensure sustained quality and user satisfaction within the e-Procurement system. The study also explores avenues for future work to further refine and augment these improvements. © 2023 IEEE.

关键词： User interfaces

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Diabetic Foot Ulcer Detection on Mobile Platforms through Thermal Imaging and Deep Learning

Diabetic Foot Ulcer Detection on Mobile Platforms through Th...

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2023 International Conference on Smart-Green Technology in Electrical and Information Systems, ICSGTEIS 2023

作者： Arnia, Fitri Saddami, Khairun Muharar, Rusdha Dwi Pratiwi, Dea Ananda Nurdin, Yudha Universitas Syiah Kuala Department of Electrical and Computer Engineering Indonesia Universitas Syiah Kuala Telematics Research Center Indonesia FernUniversität in Hagen Faculty of Mathematics and Informatics Germany

ISBN: (纸本)9798350382822

Diabetes mellitus, causing substantial global mortality with millions affected in 2019, necessitates early detection methods to aid Diabetic Foot Ulcer (DFU) patients. We proposed a mobile application for early DFU identification using a Convolutional Neural Network (CNN) classifier. The application embedded a fine-tuned MobileNetV2 model specifically adapted for DFUs, trained on a plantar thermogram dataset through transfer learning. We evaluate the model's classification performance pre- and post-embedded into the application. Results reveal that the pre-embedded model achieved 90% accuracy, 100% specificity, and 80% sensitivity. After embedding, the model retained 85% accuracy, 80% specificity, and 90% sensitivity. Notably, a slight accuracy decrease post-embedded is likely attributed to the normalization of the input image. This study emphasizes that the interplay between model performance and mobile embedded might influence the model's performance. It underscores the meticulous consideration of mobile platform-specific processes crucial for accurate and effective early DFU detection. © 2023 IEEE.

关键词： Infrared imaging

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Green Measurements for Software Product Based on Sustainability Dimensions

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computer Systems Science & engineering 2022年第4期41卷 271-288页

作者： Komeil Raisian Jamaiah Yahaya Aziz Deraman Department of Computer Isfahan(Khorasgan)BranchIslamic Azad UniversityIsfahanIran Faculty of Information Science and Technology Universiti Kebangsaan MalaysiaBangi43600SelangorMalaysia Faculty of Ocean Technology Engineering&Informatics Universiti Malaysia Terengganu21030TerengganuMalaysia

Software is a central component in the modern world and vastly affects the environment’s *** demand for energy and resource require-ments is rising when producing hardware and software *** study reveals that many studies focused on green hardware;however,limited efforts were made in the greenness of software *** software products are necessary to solve the issues and problems related to the long-term use of soft-ware,especially from a sustainability *** a proper mechanism for measuring the greenness of a particular software product executed in a speciﬁc environment,the mentioned beneﬁts will not be ***,there are not enough works to address this problem,and the green status of software products is uncertain and *** paper aims to identify the green measurements based on sustainable dimensions in a software *** second objective is to reveal the relationships between the elements and measurements through empirical *** study is conducted in two ***ﬁrst phase is the theoretical phase,where the main components,measurements and practices that inﬂuence the sustainability of a software product are identiﬁ*** second phase is the empirical study that involved 103 respondents in Malaysia investigating current practices of green software in the industrial environment and further identifying the main sustainability dimensions and measurements and their impact on achiev-ing green software *** study has revealed seven green measurements of software product:Productivity,Usability,Cost Reduction,Employee Support,Energy Efﬁciency,Resource Efﬁciency and Tool *** relationships are statistically signiﬁcant,with a signiﬁcance level of less than 0.01(p=0.000).Thus,the hypothesised relationships were all *** contributions of this study revolve around the research perspectives of the measurements to attain a green software product.

关键词： Green software product sustainability dimension empirical study green measurements

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Spatial, Spatiotemporal, and Feature Analysis of Electroencephalogram During Imagined Speech with Visual vs Audio Cues 3

Spatial, Spatiotemporal, and Feature Analysis of Electroence...

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3rd International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2023

作者： Fitriah, Nilam Budikayanti, Astri Zakaria, Hasballah Latifah Erawati Rajab, Tati Universitas Indonesia Faculty of Computer Science Depok Indonesia Universitas Indonesia Department of Neurology Jakarta Indonesia Institut Teknologi Bandung Department of Electrical Engineering and Informatics Bandung Indonesia

ISBN: (纸本)9798350327762

People with speech disorders have difficulties in social life and welfare. To overcome this barrier, researchers have conducted studies to recognize imagined speech from biosignals. The essential biosignals in producing speech are brain signals. This study aimed to determine activated brain regions during the imagined speech by spatial and spatiotemporal analysis to assist feature selection in imagined speech recognition. We recorded the normal subjects' brain signals using electroencephalography (EEG) when they imagined saying a word without vocalization. The protocol ran in two different-time sessions to cover the EEG nonstationary. We utilized visual cues in the first sub-session and audio cues in the second one. One word per sub-session had seven trials, each consisting of a rest state and cue presentation, followed by four repetitions of the imagined speech state, each preceded by a preparation state. We calculated power spectral entropy (PSE) values from the acquired dataset and used them as a feature since they captured signals' activity. The normalized PSE above 0.5 (middle value) was considered as high activity. The results showed that repeated-trial protocol induced brain activity during imagined speech. For visual or audio-cued imagined speech, the selected channels were in the central and left frontal regions, with additional right temporal, left parietal, and left occipital regions for visual-cued imagined speech. The highest PSE was found in the central region. Meanwhile, the lowest PSE was shown in the preparation state, confirming that cue presentation was not affecting imagined speech states. © 2023 IEEE.

关键词： Electroencephalography

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Artificial Intelligence Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The paper proposes “AdaptVR” a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique.

关键词： Training Vibrations Drilling Solid modeling Adaptive learning Adaptation models Force feedback Virtual reality Teeth Dentistry

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A review of federated learning in renewable energy applications:Potential,challenges,and future directions

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Energy and AI 2024年第3期17卷 444-457页

作者： Albin Grataloup Stefan Jonas Angela Meyer Bern University of Applied Sciences School of Engineering and Computer ScienceQuellgasse 21Biel2501Switzerland Universitàdella Svizzera italiana Faculty of InformaticsVia la Santa 1Lugano-Viganello6962Switzerland Delft University of Technology Department of Geoscience and Remote SensingStevinweg 1Delft2628The Netherlands

Federated learning has recently emerged as a privacy-preserving distributed machine learning *** learning enables collaborative training of multiple clients and entire fleets without sharing the involved training *** preserving data privacy,federated learning has the potential to overcome the lack of data sharing in the renewable energy sector which is inhibiting innovation,research and *** paper provides an overview of federated learning in renewable energy *** discuss federated learning algorithms and survey their applications and case studies in renewable energy generation and *** also evaluate the potential and the challenges associated with federated learning applied in power and energy ***,we outline promising future research directions in federated learning for applications in renewable energy.

关键词： Federated learning Renewable energy Dis tributed learning Data heterogeneity Privacy Data sharing Wind energy Solar energy Buildings Review

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