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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Paikrao, Pavan Mishra, Rajiv Kumar Prokysek, Melos Mukherjee, Amrit Pradhan, Jatin Kumar Bhalke, D.G. Wadhwa, Lalit Kumar Electronics and Telecommunication Engineering Department Dr. D.Y.Patil Institute of Technology Pimpri Pune India Department of Electrical Engineering NIT Rourkela India Department of Computer Science University of South Bohemia Czech Republic

ISBN: (纸本)9798331521165

This paper proposes an automated method for detecting and classifying diabetic retinopathy (DR) utilizing ocular image analysis and deep learning. Due to the time-consuming and intrusive nature of traditional DR detection techniques, deep learning algorithms like Convolutional Neural Networks (CNNs) are being investigated for more efficient solutions. Leveraging advancements in deep learning architectures, this research aims to develop a robust model capable of accurately detecting and classifying DR severity levels from retinal images. The study seeks to enhance sensitivity and specificity in DR diagnosis while minimizing manual intervention. By integrating deep learning with eye image analysis, this study aims to advance medical diagnostics in ophthalmology, potentially improving patient outcomes through early detection and personalized treatment planning. © 2025 IEEE.

关键词： Convolutional neural networks

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An Analytical Review of Underwater Image Enhancement Techniques 3

An Analytical Review of Underwater Image Enhancement Techniq...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Gogoi, Kaveri Baruah, Swagat Kumar Konwar, Utkarsh Mahanta, Rajpratim Boruah, Abhijit Sarma, Debajit Kakoty, Nayan M. Department of Computer Science and Engineering DUIET Dibrugarh University Assam Dibrugarh786004 India Department of Electronics and Telecommunication Engineering Barak Valley Engineering College Assam Karimganj788710 India Department of Electronics and Communication Engineering Tezpur University Assam Napaam Tezpur784028 India

Underwater images are integral parts for both terrestrial and aerial environments. Due to the environmental conditions in the deep-sea, poor visibility, haziness, and low contrast causes the distortion of the underwater images and hence image enhancement techniques are significant. Effects of color also plays a major role as refection, refraction, and scattering occurs. The water quality is controlled and influenced by the filtering properties of the water. Researchers proposed Underwater Image Enhancement (UIE) methods to solve these problems. In this paper, analysis has been done for five state of the art Underwater Image Enhancement models namely FUnIEGAN model, U-Shape Transformer model, DeepSeeColor model, Waternet model, and SRCNN model. To improve the quality of the image in terms of noise levels, Peak Signal-to-Noise Ratio (PSNR) is used and to compare images based on characteristics such as luminance, contrast, and structure, Structural Similarity Index (SSIM)is used. Results shows an analytical comparision among the enhancement models, and suggests one among them as superior based on the evaluation metrices. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Image enhancement

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Factors Affecting the Willingness to Share Data in Mobile Health (mHealth) Apps in Indonesia 25

Factors Affecting the Willingness to Share Data in Mobile He...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Rahayu, Anita Adi, Puput Dani Prasetyo Puspitasari, Chasandra Persada, Satria Fadil Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Surabaya60132 Indonesia Research Center for Telecommunication Bandung Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta Indonesia

ISBN: (纸本)9798350378573

This study investigates the factors influencing user willingness to share data in mobile health (m-health) applications in Indonesia, integrating the Technology Acceptance Model, Theory of Planned Behavior, and Privacy Calculus Theory through Structural Equation Modeling (SEM). Data were collected via an online questionnaire from 103 Indonesian active users of m-health platforms, aged 15-55, focusing on applications that provide online consultations and health-related information. The SEM analysis, validated by indices including the Goodness of Fit Index and Root Mean Square Error of Approximation, reveals a strong model fit. The findings highlight that perceived benefits, such as improved health outcomes and personalized advice, are the primary drivers for users' willingness to share data, significantly outweighing concerns over privacy risks. Perceived usefulness and trust in the application also significantly affect data-sharing behavior, while ease of use and social influences have a moderate to minor influence, respectively. These insights are critical for application developers and policymakers in enhancing app utility and user engagement while addressing privacy concerns. The study's limitations include its demographic scope and reliance on self-administered questionnaires, pointing to directions for future research to broaden demographic representation and deepen qualitative insights. © 2024 IEEE.

关键词： data sharing mHealth privacy concerns SEM analysis structural equation modeling

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Drone-Based Predictive Structure Analysis: A Review

Drone-Based Predictive Structure Analysis: A Review

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2023 International Conference on Sustainable Emerging Innovations in engineering and Technology, ICSEIET 2023

作者： Patil, Trushant Nandan, Durgesh Sharma, Kanhaiya Parashar, Deepak Department of Robotics and Automation Pune India Department of Electronics and Telecommunication Engg. Pune India Department of Computer Science & Engineering Pune India Faculty of Engineering Pune India

ISBN: (纸本)9798350329186

Drone-based predictive structure analysis is a rapidly growing field that has the potential to revolutionize infrastructure and building inspection. In this review of the research, we give a general overview of how drones could increase the effectiveness and precision of structural analysis, particularly for keeping an eye on difficult-to-reach locations. The usage of drones for 3D mapping, inspection, and monitoring of diverse constructions, such as concrete buildings, bridges, tunnels, and dams. The investigations show that drone-based inspection can produce 3D models and high-resolution photos, allowing for remote monitoring and the early identification of possible problems. The findings show that drones may greatly boost infrastructure management's safety and effectiveness, making them an important tool for predictive structural research. © 2023 IEEE.

关键词： Drones

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Sentiment Analysis in Indonesian Trading using Lexicon-based and Support Vector Machine

Sentiment Analysis in Indonesian Trading using Lexicon-based...

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2023 International Conference on computer science, Information Technology and engineering, ICCoSITE 2023

作者： Oetama, Raymond Sunardi Yanfi, Yanfi Ikhsan Assiddiq Up, Masagus M. Muhamad Isa, Sani Universitas Multimedia Nusantara Information System DepartmentFaculty of Engineering and Informatics Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Many individuals now trade online utilizing trading software in the digital world. Binomo is one of Indonesia's most popular trading platforms. This is because some influencers made several promises to Binomo customers. Since many customers were deceived, this case became quite popular. This study was executed to see how Indonesians felt about the Binomo application after the case went viral. The solution taken was in the form of sentiment analysis because there had been no previous research on sentiment analysis that discussed the Binomo case. The data was scanned using Netlytic tools, a cloud-based text and social network analyzer capable of identifying any talks on social media sites such as Twitter. The sentiment analysis of Binomo trading tweets by using the Multi-Perspective Question Answering lexicon utilized the KNIME tool. But unfortunately, the accuracy of sentiment analysis results is low. Furthermore, the Support Vector Machine technique is also being conducted. The Term Frequency-Inverse Document Frequency method is applied to perform feature extraction whilst the chi-square approach is utilized to identify features that are thought to be useful for inclusion in the classification process and to exclude features that are irrelevant to the target class. The obtained accuracy is 86%. The study proposes that words from the algorithm's outputs can be utilized to improve the quality of sentiment analysis using the lexicon. As an outcome of the algorithm, positive and negative terms are added to the lexicon, increasing the accuracy of sentiment analysis using the new vocabulary from 58.984% to 71.146%. © 2023 IEEE.

关键词： Support vector machines

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A Review of Fintech Management and Security Development 10

A Review of Fintech Management and Security Development

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10th IEEE Information Technology International Seminar, ITIS 2024

作者： Ritonga, Ali Akbar Zuhri Harahap, Syaiful Sooai, Adri Gabriel Nugroho, Aryo Samsir Prasetyo Adi, Puput Dani Universitas Labuhanbatu Faculty of Science and Technology Department of Informatics Management North Sumatera Labuhanbatu Indonesia Universitas Labuhanbatu Faculty of Science and Technology Department of Information System North Sumatera Labuhanbatu Indonesia Universitas Katolik Widya Mandira Faculty of Computer Science Department of Computer Engineering NTT Kupang Indonesia Universitas Narotama Faculty of Computer Science Department of Information System Surabaya Indonesia Universitas Labuhanbatu Faculty of Computer Science Department of Informatics Engineering North Sumatera Labuhanbatu Indonesia Department of Telecommunication Bandung Indonesia

ISBN: (纸本)9798331521295

This research focuses on the review of Fintech and its development on the IoT Platform and also the risks that can be posed to the IoT network used. Finance is the most essential side of several other sectors which in this case often experience many losses, one of the biggest losses is the existence of hackers and theft of money through the Internet platform or Internet Server. This research reviews how the ins and outs of the flow of funds located on servers or online banks. And ways to overcome hackers and bypass data. Comprehensively reviewed in this research review. This research discusses Cryptocurrency Analysis, Bitcoin, and Ethereum Comparison which are some of the methods used in online-based Finance, which is without currency but has the same value even more than currency if the conversion process is carried out at the bank. In this research, it is comprehensively discussed how methods are carried out in the process of securing data, Cryptocurrency, Bitcoin, and Ethereum are comprehensively discussed to get a lot of data and comparisons and the right steps to deal with crime via the internet. The predicted use of IoT in the Fintech sector is 140 billion connected devices, IoT Loans 110 billion connected devices, and IoT insurance with an estimated 90 billion connected devices, in the approaching 2026. © 2024 IEEE.

关键词： Ethereum

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Search Engine for QnA using Distributed Inverted Index System 3

Search Engine for QnA using Distributed Inverted Index Syste...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dey, Snehasis Moharana, Bhimasen De, Utpal Chandra Samant, Tapaswini Behera, Trupti Mayee Banerjee, Shobhan College of Engineering Electronics & Telecommunication Engineering Bhubaneswar India Lovely Professional University Department of Computer Science & Engineering Punjab India Kalinga Institute of Industrial Technology School of Computer Applications Bhubaneswar India Kalinga Institute of Industrial Technology School of Electronics Engineering Bhubaneswar India Birla Institute of Technology and Science Department of Engineering Technology Pilani India

ISBN: (纸本)9798350381931

The internet along with its global uses has also evolved as a platform where various people can pose their questions on certain sites and get them answered by others from the same domain. Certain sites like Quora, Reddit, Yahoo Answers, etc. are sites enabling people to enhance their range over the net to find solutions to various issues or answers to various questions. But this leads to redundancy in data & hence increased memory consumption where different users can pose the same question in different ways. The same thing stands for the answers posted by different users for the same question in various ways. In this paper, we have worked on a search engine system for Question & Answers where given a question, we can find similar questions with the same semantic meaning using Elastic Search which uses an inverted index system & looks at various aspects of the formulated problem statement. © 2024 IEEE.

关键词： Search engines

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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate

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Discover Sustainability 2025年第1期6卷 1-12页

作者： Naik, Bhukya Govardhan Nakkeeran, G. Roy, Dipankar Kiran, G. Uday Gurram, Kalyani Ramanjaneyulu, Gade Venkata Alaneme, George Uwadiegwu Bakare, Mutiu Shola Department of Civil Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle 517325 India Department of Civil Engineering QIS College of Engineering and Technology Andhra Pradesh Ongole 523272 India Department of Civil School of Engineering and Applied Sciences Kampala International University Kampala Uganda Department of Electrical Telecommunication and Computer Engineering Kampala International University Kampala Uganda

The concrete industry has investigated the integration of waste glass (WG) as an alternative for traditional concrete materials in response to growing environmental concerns and the necessity to reduce waste while encouraging recycling. This study explores the use of Waste Glass Coarse Aggregate (WGCA) as a partial replacement for natural coarse aggregates in concrete, assessing its effects on mechanical performance and utilizing machine learning models for predictive analysis. The assessment involved concrete mixtures with replacement levels of 0%, 5%, 10%, and 15% WGCA, focusing on their compressive, tensile, and flexural strengths at both 7 and 28 days of curing. The experimental results demonstrate a gradual decrease in mechanical properties as WGCA content increases, showing a peak reduction of 10% in compressive strength, 13% in tensile strength, and 12% in flexural strength at a 15% replacement level. The findings correspond with suitable structural parameters, strengthening the potential of WGCA in the production of sustainable concrete. The application of predictive modeling through Multiple Linear Regression (MLR) and Support Vector Machine (SVM) revealed notable effectiveness, with MLR attaining a higher R² score of 0.8193, exceeding SVM’s R² of 0.7253 in terms of predictive accuracy. The investigation demonstrates the environmental advantages of WG reuse, serving a significant role in the principles of the circular economy by minimizing landfill waste, preserving natural resources, and lessening greenhouse gas emissions. © The Author(s) 2025.

关键词： Machine learning Mechanical properties Multiple linear regression Natural coarse aggregate Support vector machine Waste glass coarse aggregate

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An Extended Euclidean Algorithm Implementation over Ciphertext Space 2nd

An Extended Euclidean Algorithm Implementation over Ciphert...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Chi, Po-Wen Lin, Yuan-Pang Wang, Ming-Hung Chuang, Yun-Hsin Department of Computer Science and Information Engineering National Taiwan Normal University Taiwan Taipei Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Chiayi Taiwan Engineering Management Graduate Program College of Engineering National Cheng Kung University Taiwan Tainan Taiwan

ISBN: (纸本)9789819777853

Fully homomorphic encryption (FHE) is a useful cryptographic primitive. It makes a third-party manipulate data in the ciphertext space and therefore is a key technology for privacy-preserving outsourcing services. Theoretically, FHE can evaluate any circuits. However, the implementation is not trivial. The reason it lacks lots of basic operations like conditional check and branch control. In this paper, we focus on extended Euclidean algorithm operation over FHE. We introduce the problems we encounter and show how we solve these issues. We implement our design and evaluate its performance on a real computer. Finally, we propose some research directions for our future work. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ciphertext

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Automatic Modulation Classification for NLOS 5G Signals with Deep Learning Approaches 10

Automatic Modulation Classification for NLOS 5G Signals with...

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10th International Conference on Wireless Networks and Mobile Communications, WINCOM 2023

作者： Abdollahi, Meisam Sabzalizadeh, Ramin Javadinia, Samaneh Mashhadi, Sepideh Mehrizi, Sara Sanati Baniasadi, Amirali University of Victoria Department of Electrical & Computer Engineering Victoria Canada Mobile Telecommunication Company of Iran Tehran Iran Iran University of Science and Technology School of Computer Engineering Tehran Iran

ISBN: (纸本)9798350329674

The Automatic Modulation Classification (AMC) technique identifies the modulation scheme used in a received signal automatically without any prior knowledge or manual intervention. When multiple signals with different modulation schemes coexist in wireless communication systems, it's a crucial task. In AMC, features and patterns are typically analyzed to classify signals into one of several predefined modulation schemes based on machine learning algorithms. It has been shown that deep learning (DL) algorithms are effective in AMC. Using deep neural networks for modulation classification is an excellent choice since they can learn complex patterns and hierarchical representations from data. In this paper, first we produce a dataset for non-line-of-sight signals with digital modulation of Binary Phase-shift keying (BPSK), Quadrature Phase Shift Keying (QPSK), and Quadrature Amplitude Modulation (QAM) which are widely used in modern telecommunications to transmit information. We also use three popular DL approaches in many applications for classification and recognition based on DNN, CNN and LSTM for classification. The experimental results show that the CNN approach outperforms the others significantly. © 2023 IEEE.

关键词： Long short-term memory

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