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COMPLEMENTARY DUALS—BOTH FIXED POINTS (III)—

Bulletin of the Kyushu Institute of Technology - Pure and Applied Mathematics 2024年第71期2024卷 13-42页

作者： Iwamoto, Seiichi Kimura, Yutaka Fujita, Toshiharu Kyushu University Fukuoka813-0012 Japan Department of Management Science and Engineering Faculty of Systems Science and Technology Akita Prefectural University Akita Yurihonjo015-0055 Japan Department of Basic Sciences Faculty of Engineering Kyushu Institute of Technology Tobata Kitakyushu804-8550 Japan

We consider a quadratic minimization problem with both fixed endpoints and its associated maximization problem from a viewpoint of complementarity. We show that a complementary identity with an elementary inequality generates a pair of (n-1)-variable minimization problem (primal) and (n + 1)-variable maximization one (dual). The identity produces an equality condition, which is a linear system of 2n-equation on 2n-variable. The condition splits into a pair of linear systems of (n-1)-equation on (n - 1)-variable and of (n + 1)-equation on (n + 1)-variable. The former solves the primal, while the latter does the dual. Both the optimal solutions are characterized by the Fibonacci sequence. The solutions are also given through dynamic programming. © 2024, Kyushu Institute of Technology. All rights reserved.

关键词： Dynamic programming

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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The Impact of TV White Space (TVWS) and 5G Networks on Bimodal Learning for Master's Programs in engineering science at the Higher Institute of Technology, University of Bangui

The Impact of TV White Space (TVWS) and 5G Networks on Bimod...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Ndassimba, Edgard Zoh-Ponguele, Nathan Kossingou, Ghislain Mervyl Saint-Juste Faculty of Science University of Bangui Department of Computer Engineering Bangui Central African Republic

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512965

In this study, we presente digital solution for training master's students in engineering sciences, using a bimodal learning approach at the Higher Institute of Technology of Bangui's University. This proposal addresses the lack of master's-level programs in DevOps and Network Telecommunications, as well as the insufficient expertise of local educators in Information and Communication Technology (ICT). It also aims to strengthen the instruction of science, technology, engineering, and mathematics (STEM). The solution is based on remote learning platforms hosted in the public cloud, engaging students and teachers in a bimodal teaching model that delivers simultaneous instruction to both inperson and remote students via a high-speed TV White Space (TVWS) and 5G network. The expected impact is the training of competent engineers capable of successfully carrying out projects in the domain of information and communication technology, thereby contributing to the development of the Central African Republic. © 2025 IEEE.

关键词： Students

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Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis

Machine Learning Techniques for Diabetes Prediction: A Compa...

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2023 International Conference on Sustainable Computing and Smart systems, ICSCSS 2023

作者： Mythili, J. Surendhar, T. Suryaprakash, P. Suresh Kumar, K. Faculty of Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India Computer Science and Engineering K S Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode India

ISBN: (纸本)9798350333602

As the prevalence of diabetes mellitus rises, more families are being impacted. Most diabetics don't know much about their health situation or the risks they face before getting a diagnosis. A unique data mining-based method was suggested for the prediction of type 2 diabetes mellitus (T2DM) in this study. The accuracy and robustness of the prediction model are the primary priorities, ensuring flexibility so that it may be used to other datasets. The model is made up of two parts: the enhanced Logistic Regression with Multilayer Perception (LRMLP) and Naïve Bayes (NB) method with a set of preprocessing techniques. To compare the findings with those of other studies, the Diabetes Dataset for Pima Indians was used with the Environment for Knowledge Analysis toolkit. According to the findings, the model's prediction accuracy is higher than that of earlier research. Additionally, this approach guarantees that the dataset quality is enough. The system was applied to two more diabetes datasets in order to assess its effectiveness further. Both research findings show strong performance. The model is shown to be helpful for managing diabetic health in a realistic manner as a result. For experimental analysis, data from the University of California, Irvine's (UCI) machine learning repository's Pima Indian Diabetes Database (PIDD) is used. These classification algorithms' performance is evaluated using a variety of statistical metrics, including sensitivity (recall), precision, F-score, and accuracy. The measurement of correctly and erroneously classified cases is called accuracy. According to the testing findings, LR-MLP outscored other classification algorithms and provides the maximum accuracy of 96.3%. © 2023 IEEE.

关键词： Forecasting

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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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Detection of Artery/Vein in Retinal Images Using CNN and GCN for Diagnosis of Hypertensive Retinopathy 4th

Detection of Artery/Vein in Retinal Images Using CNN and GCN...

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4th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2023

作者： Al Sariera, Esra’a Mahmoud Jamil Padma, M.C. Al Sariera, Thamer Mitib Department of Computer Science and Engineering PES College of Engineering Mandya University of Mysore Mysore India Department of Computer Science and Information Systems Amman Arab University Amman Jordan

ISBN: (纸本)9789819965854

A progressive disease of the retina known as hypertensive retinopathy (HR) is linked to both high blood pressure and diabetes mellitus. The severity and persistence of high blood pressure are directly connected with the development of HR. The results of the HR include constricted arterioles, retinal hemorrhage, macular edema, and cotton-like patches as symptoms of eye pathological problems. In this paper, a novel strategy that combines convolutional neural network (CNN) and graph convolutional network (GCN) is proposed to improve the accuracy of categorizing retinal blood vessels and classification of HR phases. The process is extracting vessel features from the spatial domain and representing them using graphs. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diagnosis

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Comparison of Q&A Classification Using BiLSTM Based on Closed Domain Knowledge 12

Comparison of Q&A Classification Using BiLSTM Based on Close...

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12th Electrical Power, Electronics, Communications, Controls and Informatics Seminar, EECCIS 2024

作者： Gunawan, Alifi Lazuardi Bachtiar, Fitra Abdurrachman Setiawan, Budi Darma University Malang Faculty of Computer Science Brawijaya Informatics Engineering Department Indonesia

ISBN: (纸本)9798350379914

The faculty of computer science, Universitas Brawijaya (Filkom UB) is committed to providing quality services for the users especially internal and external stakeholders, one of which is through the HaloFilkom service. HaloFilkom services have limitations in terms of time. HaloFilkom services are not available 24 hours due to limited working hours. Questions asked by users are not answered directly. This weakness in the HaloFilkom system can be overcome by using a chatbot system. Chatbot is an interactive system that works with natural human language and can work 24 hours. Thus, the current study explores the basic chatbot model by classifying the Q&A in the closed domain knowledge. The dataset in this research is in the form of pairs of questions and answers regarding various topics at the Filkom UB. The knowledge is preprocessed using text preprocessing which includes case folding, tokenization, padding, and tensorization. One of the chatbot models is a generative model. Creating a generative chatbot model can be done using the Seq2Seq model mechanism which consists of an encoder and decoder. The model created consists of four different architectures, namely a model with an LSTM encoder without attention and with attention and a BiLSTM model encoder without attention and with attention. Hyperparameter tuning was conducted to obtain the best hyperparameter combination. The experiment results show the best hyperparameter combination obtained is hidden size 448, drop out rate 0.5, learning rate 0.001, batch size 64, and teacher force 0. The model with the best loss is obtained with a BiLSTM encoder architecture without an attention mechanism with a train loss of 0.120. The model with the highest BLEU Score was obtained by a model with a BiLSTM encoder architecture without an attention mechanism with a BLEU Score of 0.8587 on the training data. Testing using prompt testing obtained an average BLEU Score of 0.3745 on the BiLSTM encoder without an attention mechanism mo

关键词： Teaching

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Attention-Guided CNN with LSTM for Temporal Gastrointestinal Disease Progression Prediction in Endoscopic Video Sequences 4

Attention-Guided CNN with LSTM for Temporal Gastrointestinal...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Mohamad, Fayazoddin Dhumpati, Raghu Reenadevi, R. Rao, P.S.V. Srinivasa Sutaria, Kamal Kulkarni, Shirish NY United States Bahrain Polytechnic Department of Computer Science & Engineering Bahrain City Bahrain Salem India Joginpally B R Engineering College Department of Computer Science and Engineering Hyderabad India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The detection and tracking of changes in the progression of GI disease using endoscopic video analysis remains difficult due to temporal changes and image complexity. Proper models of prediction are critical in diagnosing individuals at an early stage and offering them treatment. In the work presented in this paper, we introduce novel Attention-Guided Convolutional Neural Network (AG-CNN) combined with LSTM networks to analyze temporal endoscopic video sequences. It uses spatial attention to catch necessary areas and temporal attention to capture assertive illness transformation in video frames. For disease progression prediction the proposed model yield 94.8% accuracy with 93.2% sensitivity and 95.6% specificity. The attention mechanism increased the feature extraction speed by 22 percent when compared to traditional CNN-LSTM classes. Independent validation checks confirmed that the two models produced a prediction concordance of 92.7% based on different patient samples. The overall performance of temporal GI disease progression prediction by the AG-CNN-LSTM framework shows potential usage in clinical diagnosis by providing automated disease progression monitoring and treatment planning. © 2024 IEEE.

关键词： Video analysis

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Preprocessing with Skin Segmentation to Improve Monk Skin Tone (MST) Classification 11

Preprocessing with Skin Segmentation to Improve Monk Skin To...

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11th International Conference on Electrical engineering, computer science and Informatics, EECSI 2024

作者： Alifia, Rizka Vanissa Ayu, Media Anugerah Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350355314

Skin tones come in a diverse range of shades and are often necessary for various computer vision tasks. While skin detection is a well-studied focus, skin tone classification is not. Most works also use the Fitzpatrick Skin Type (FST) scale despite it being developed for skin phototype classification purposes. Due to the lack of a proper skin tone scale, the Monk Skin Tone (MST) was created, which was used to perform skin tone classification using Convolutional Neural Network (CNN) model as well as the Individual Topology Angle (ITA) value threshold method with skin segmentation as a preprocessing for both classification methods. To investigate if skin segmentation can improve classification accuracy, the MST classification was performed using both segmented and unsegmented images involving the IT A value threshold method as well as two CNN models that were trained using different image types. The experimental results showed that the best way to perform MST classification was using a CNN model trained and tested with segmented images, which yielded the highest accuracy score being 0.91, with no signs of over or underfitting. The results also supported that segmentation is necessary, and that it can improve a classification model's accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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