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International Conference on computer Vision and Internet of Things 2023 (ICCVIoT'23)

作者： R. Sujatha D. Helen U. Hemamalini V. Divya A. Priya Dharshini School of Information Technology & Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Applications Faculty of Science and Humanities SRM Institute of Science and Technology Kattankulathur Chennai India Department of Information Technology VELS Institute of Science technology and Advanced Studies Pallavaram Chennai India

According to the Global Cancer Observatory's 2018 estimations, gastrointestinal cancer is the third most common cause of cancer deaths and the fifth most common cause of being diagnosed with cancer worldwide. Diagnosis is crucial, and gastroscopy is used to detect stomach cancer early to enhance patient survival. This study proposed a Deep Learning based computer-Aided Diagnostics (CADx) method to identify gastroscopy disease. The dataset is trained with an adversarial training technique. The proposed approach uses the deep convolution neural network based on VGG16, VGG19, and InceptionV3. The dataset is trained with an adversarial training technique. The Inception V3 has the best accuracy among them, with an accuracy in the training of 94.44% and a validation accuracy of 84.75%, with a minor loss compared to VGG16 and VGG19 models.

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An Evaluation of User Experience in a Virtual Semiconductor Laboratory Utilizing HMDs during Covid-19 Pandemic

An Evaluation of User Experience in a Virtual Semiconductor ...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Ahmed Jamah Ahmed Alnagrat Abdalslam S Imhmed Mohamed Abubaker Almintisir Abubaker Akeel Rizalafande Che Ismail Ababaker Mohamed Ibrahim Noureldeen Syed Zulkarnain Syed Idrus Department of Computer Science and Information Technology Higher Institute of Science and Technology Wadi al-Shati Wadi al-Shati Libya Faculty of Information Technology Aljufra University Aljufra Libya Department of Mathematics and Computer Faculty of Education Wadi Alshatti University Wadi Alshatti Libya Faculty of Electronic Engineering Technology (FTKEN) Universiti Malaysia Perlis (UniMAP) Arau Malaysia Department of Communication Faculty of Business & Communication (FPK) Universiti Malaysia Perlis (UniMAP) Arau Malaysia

Virtual reality (VR) technology has been increasingly adopted for creating engaging educational environments, such as virtual laboratories. This study evaluated the user experience of a virtual semiconductor laboratory environment, utilizing Reverb G2 HMD VR headsets, through a user experience questionnaire (UEQ-S) and a psychometric analysis of its 26 items. Results showed that most participants found the virtual laboratories enjoyable, understandable, creative, easy to learn, and valuable. The psychometric analysis revealed relatively consistent and reliable construct related to user experience. The Kruskal-Wallis H test demonstrated that participants perceived the VR headsets to be easier to use when they found the virtual laboratories more enjoyable. However, there were no significant differences in user experience scores based on preferred VR headset, and user experience did not predict headset preference. These findings suggest that VR technology presents a promising solution for addressing practical challenges in education and fostering student engagement, although further research is needed to optimize the user experience and enhance student motivation.

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Tensor-Based Factorial Hidden Markov Model for Cyber-Physical-Social Services

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IEEE Transactions on Services Computing 2025年

作者： Lu, Zhixing Yang, Laurence T. Azman, Azreen Zhang, Shunli Zhou, Fang Hainan University School of Computer Science and Technology Hainan China Universiti Putra Malaysia Faculty of Computer Science and Information Technology Serdang Malaysia Zhengzhou University School of Computer and Artificial Intelligence School of Software Engineering Zhengzhou China Huazhong University of Science and Technology School of Computer Science and Technology Huazhong China St. Francis Xavier University Department of Computer Science Canada

With the rapid development and widespread application of information, computer, and communication technologies, Cyber-Physical-Social Systems (CPSS) have gained increasing importance and attention. To enable intelligent applications and provide better services for CPSS users, efficient data analytical models are crucial. This paper presents a novel data analytic framework for CPSS services. First, a Tensor-Based Factorial Hidden Markov Model (T-FHMM) is introduced to comprehensively analyze multi-user activity features, enhancing CPSS activity analytics. A tensor-based Forward-Backward algorithm is then designed for T-FHMM to efficiently perform evaluation tasks using multiple probabilistic computing micro-services. Additionally, a tensor-based Baum-Welch algorithm is developed to accurately learn model parameters via parameter optimization micro-services. Furthermore, a tensor-based Viterbi algorithm is implemented with specific micro-services to improve prediction tasks. Finally, the comprehensive performance of the proposed model and algorithms is validated on three open datasets through self-comparison and other-comparison. Experimental results demonstrate that the proposed method outperforms compared methods in terms of accuracy, precision, recall, and F1-score. © 2008-2012 IEEE.

关键词： Viterbi algorithm

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Hybrid Machine Learning Classification for Tipple Negative Breast Cancer

Hybrid Machine Learning Classification for Tipple Negative B...

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Electronics, Communications, and Computations (JAC-ECC), International Japan-Africa Conference on

作者： Hend A. Bakr Amira M. Abozaid Ahmed B. Zaky Biomedical Engineering Department Cairo University Giza Egypt Computer Science and Information Technology Programs (CSIT) Egypt Japan University of Science and Technology Molecular Biology division Teaching assistant at Faculty of Science Alexandria University Faculty of Engineering at Shubra Benha University Egypt

This research introduces an innovative approach to dissecting Triple-Negative Breast Cancer (TNBC) by integrating count-based RNA analysis and machine learning techniques. Through the strategic amalgamation of K-means clustering, Convolutional Neural Network (CNN), and Support Vector Machine (SVM), this study transcends conventional classification methods, revealing detailed molecular insights within the complex TNBC landscape. The methodology initiates with count-based RNA analysis for dimensionality reduction, refining molecular classifications through K-means clustering. The incorporation of CNN allows for nuanced feature extraction, capturing intricate genomic relationships, while SVM enhances subtype predictions with precise classification. The addition of Gene Ontology (GO) and pathway analyses enriches our understanding by unraveling the functional implications of identified gene clusters. This synergistic methodology not only advances our comprehension of TNBC subtypes but also establishes a groundwork for targeted therapeutic strategies. The fusion of count-based RNA analysis and machine learning enables a comprehensive and nuanced exploration of TNBC complexity, offering valuable insights for personalized oncology approaches in the era of precision medicine

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CNN-based Labelled Crack Detection for Image Annotation

arXiv

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arXiv 2024年

作者： Ilani, Mohsen Asghari Amini, Leila Karimi, Hossein Kuhshuri, Maryam Shavali School of Mechanical Engineering College of Engineering University of Tehran Tehran Iran Department of Information Systems and Business Analytics College of Business Florida International University MiamiFL33199 United States Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran Department of Electrical Engineering Faculty of Engineering University of Isfahan Isfahan Iran

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Designing a smart bracelet based on arduino for deaf parents to interact with their children 8

Designing a smart bracelet based on arduino for deaf parents...

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8th International Conference on Computing for Sustainable Global Development, INDIACom 2021

作者： Abi Sen, Adnan Ahmed Aljohani, Ayman Ahmad S. Bahbouh, Nour Mahmoud Alhaboob, Omar Islamic University Faculty of Computer and Information Systems Al-Madinah Saudi Arabia Taibah University Faculty of Computer Science and Engineering Al-Madinah Saudi Arabia Granada University Department of Information and Communication Sciences Granada Spain College of Computing and Information Technology Islamic University Al-Madinah Saudi Arabia

ISBN: (纸本)9789380544434

The primary goal of having technology in our world is to make peoples' lives more flexible, comfortable, safer, and sophisticated. The Internet of Things (IoT) and its technologies have been having huge impact in this century in changing many the concepts, services, and methods of dealing and accessing them. In case if a user accessing a service suffers from some health problems or difficulties such as loss or impairment of hearing, then the technology plays even a greater role by helping them to perform their daily tasks in the same way as a healthy person, without any ailment of deficiency, would do. Hearing loss, for example, makes it impossible for parents to respond to the calls of their children. In this research we present an innovative idea for a special bracelet designed mechanism with a smart application to solve the difficult problem. The application listens to children's calls by converting the sound into writing and then classifies the type of sound and then sends an alert to their parents by controlling the bracelet. The bracelet is based on the Arduino controller to vibrate, which enables parents to respond to their children call. A prototype of the proposed bracelet has been built and designed the circuit in addition to building the accompanying application. The proposed solution is characterized by its ease of application, development, and effectiveness, in addition to its low cost compared to other existing tools. © 2021 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth.

关键词： Internet of things

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Support Vector Machine for Multiclass Classification of Redundant Instances

Support Vector Machine for Multiclass Classification of Redu...

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Intelligent Computing and Networking - Proceedings of IC-ICN 2023

作者： Surbakti, Herison Kumar, Athmakuri Naveen Tara, Saikumar Keshta, Ismail khare, Amit Prasad, G.N.R. Faculty of Computing and Digital Technology HELP University Kuala Lumpur Malaysia Senior Software Engineer Glosoft Technologies Pvt Ltd Chennai India Department of ECE BVRIT Hyderabad College of Engineering for Women Hyderabad India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Technology JIET Institute of Design and Technology Rajasthan Jodhpur India Telangana Hyderabad500091 India

ISBN: (纸本)9789819931767

In recent years, support vector machine has become one of the most important classification techniques in pattern recognition, machine learning, and data mining due to its superior classification effect and solid theoretical base. However, its training time will increase dramatically as the number of samples increases, and training will become more sophisticated when dealing with problems involving multiple classifications. A quick training data reduction approach MOIS appropriate for multi-classification tasks is presented as a solution for the aforementioned issues. While eliminating redundant training samples, the boundary samples that play a vital role are chosen in order to considerably reduce training data and the problem of unequal distribution between categories. The experimental results demonstrate that MOIS may maintain or even improve the classification performance of support vector machines while substantially enhancing training efficiency. On the Opt digit dataset, the suggested method improves classification accuracy from 98.94% to 99.05%, while training time is reduced to 15% of the original;in HCL2000, the proposed method improves classification accuracy from 98.94% to 99.05%. When the accuracy rate is marginally increased (from 99.29% to 99.30%) on the first 100 categories dataset, the training time is dramatically reduced to less than 6% of the original. Additionally, MOIS has a high operational efficiency. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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An Efficient Congestion Control Model utilizing IoT wireless sensors in information-Centric Networks 6

An Efficient Congestion Control Model utilizing IoT wireless...

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Joint 6th International Conference on Digital Arts, Media and technology with 4th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunication engineering, ECTI DAMT and NCON 2021

作者： Sukjaimuk, Rungrot Nguyen, Quang N. Sato, Takuro Chiang Rai Rajabhat University School of Computer and Information Technology Chiang Rai Thailand Waseda University Department of Computer Science and Communications Engineering Tokyo Japan Waseda University Faculty of Science and Engineering Tokyo Japan

ISBN: (纸本)9781665415699

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN the proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme the evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks these results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors. © 2021 IEEE.

关键词： Wireless sensor networks

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Blockchain-based loyalty system: feasibility testing

Blockchain-based loyalty system: feasibility testing

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computer Science, information technology and engineering (ICCoSITE), International Conference on

作者： Danh Khoi Nguyen Hoang-Anh Pham Quoc Vinh Nguyen Tuong Nguyen Huynh Trang Hong Son Faculty of Computer Science & Engineering Ho Chi Minh City University of Technology VNU-HCM Ho Chi Minh City Vietnam Faculty of Information Technology Industrial University of Ho Chi Minh City Ho Chi Minh City Vietnam Faculty of Information Technology Hoa Sen University Ho Chi Minh City Vietnam

A loyalty program brings benefits to both companies and customers. In this paper, we consider the use of loyalty program integration in blockchain technology, one of the most promising advanced technologies, where trust is of prime significance and customer identification will no longer require a physical certificate. Based on the essential characteristics of the loyalty program, 4 possible approaches that integrate into a blockchain platform are identified. An analysis through several observations helps to determine the most suitable one to create a loyalty program that benefits all stakeholders. The performance of the proposed system related to the most suitable approach is evaluated according to the criteria that consider the feasibility of practical usability.

关键词： Analytical models System performance Scalability Throughput Real-time systems Blockchains Object recognition

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Exploration of Efficient Neural Network for Path Optimization Method 39

Exploration of Efficient Neural Network for Path Optimizatio...

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39th International Symposium on Lattice Field Theory, LATTICE 2022

作者： Namekawa, Yusuke Kashiwa, Kouji Matsuda, Hidefumi Ohnishi, Akira Takase, Hayato Education and Research Center for Artificial Intelligence and Data Innovation Hiroshima University Hiroshima730-0053 Japan Department of Physics Faculty of Science Kyoto University Kyoto606-8502 Japan Department of Computer Science and Engineering Faculty of Information Engineering Fukuoka Institute of Technology Fukuoka811-0295 Japan Department of Physics Center for Field Theory and Particle Physics Fudan University Shanghai200433 China Yukawa Institute for Theoretical Physics Kyoto University Kyoto606-8502 Japan

We present our attempts to control the sign problem by the path optimization method with an emphasis on the efficiency of the neural network. We found a gauge invariant neural network is successful in the 2-dimensional U(1) gauge theory with a complex coupling. We also investigate the possibility of improvement in the learning process. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).

关键词： Quantum theory

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