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Advancing Diabetic Retinopathy Research with a Dataset from Aizawl region of Mizoram 3

Advancing Diabetic Retinopathy Research with a Dataset from ...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Vanlalnunpuia, C. Lalhmingliana Ambeth Kumar, V.D. Kumar Khan, Ajoy Vanlalawmpuia, R. Khiangte, David Lalmuanpuia Lalchhanhima, R. Mizoram University Dept. of Computer Engineering Aizawl India Mizoram University Dept. of Information Technology Aizawl India Eye Care Hospital Aizawl India

ISBN: (纸本)9798331523893

This study presents a high-resolution Diabetic Retinopathy dataset collected from Eye Care Hospital in Aizawl, Mizoram, highlighting its importance for advancing DR detection in underrepresented populations. The dataset includes 495 retinal images captured using an OCT RS 330 device, with quality maintained through a preprocessing pipeline. Images were filtered for clarity using blur metrics and enhanced with adaptive histogram equalization to emphasize diagnostically relevant features. A baseline deep learning pipeline for multiclass DR classification achieved training, validation, and test accuracies of 50%, 49%, and 56%, respectively, reflecting the dataset's complexity. Advanced augmentations like MixUp and CutMix were applied, setting a benchmark for future research. This work underscores the dataset's unique contributions by providing a foundation for robust diagnostic models. This study supports inclusive and practical AI applications in ophthalmology. © 2025 IEEE.

关键词： Transillumination

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Privacy-Preserving Attribute-Based Access control with Non-Monotonic Access Structure 7

Privacy-Preserving Attribute-Based Access Control with Non-M...

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7th Cyber Security in Networking Conference, CSNet 2023

作者： Ashouri-Talouki, Maede Kahani, Nafiseh Barati, Masoud University of Isfahan Dept. of Information Technology Iran Carleton University Systems and Computer Engineering Canada School of Information Technology Carleton University Canada

ISBN: (纸本)9798350342871

Attribute-Based Encryption (ABE) with non-monotonic access policies provides fine-grained access control for widespread applications like Cloud-assisted HealthIoT systems. In this context, multi-authority ABE with untrusted authorities eliminates the need for a trusted authority, but ensuring user's identity and attributes-set privacy against these authorities remains a significant challenge. This paper proposes a new, efficient multi-authority ABE approach that preserves user's identity privacy and attributes-set privacy, and is secure against collusion attack. Also, the proposed approach provides non-monotonic access policies, which supports positive and negative constraints using NOT operation as well as AND and OR operations. © 2023 IEEE.

关键词： Access control

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Indonesia's Cultural Heritages Preservation: Knowing Wayang More Deeply

Indonesia's Cultural Heritages Preservation: Knowing Wayang ...

引用

2023 International Conference on Sustaining Heritage: Innovative and Digital Approaches, ICSH 2023

作者： Tiwari, Raj Gaurang Rout, Saroja Kumar Kumar, Anurag Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9798350398281

Culture is an invaluable heritage of our ancestors. Culture is also a national identity that makes us have different characteristics from other countries. One of Indonesia's cultural heritage that is starting to fade and must be preserved is wayang. As the next generation, we are obliged to introduce wayang to future generations and also to the international community. In this modern era, one effective way is to create wayang recognition technology. In this paper, we take this opportunity as an effort to help preserve Indonesian culture which will be applied with technology. A deep learning-based model has been developed to help us to get to know wayang more deeply starting from its type, area of origin, description, and other information by using the pictures of wayang and then classifying them. Results show that InceptionV3 performs in a much better way to classify wayang types. © 2023 IEEE.

关键词： Computer vision

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Unraveling Genetic Complexity: Fine-tuning Machine Learning Models for DNA Sequence Analysis with K-mer Size 5

Unraveling Genetic Complexity: Fine-tuning Machine Learning ...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Lukman Adnyana, I Made Anggar Dwi Purnama, Fajar Latumakulita, Luther Alexander Rosalia, Ayang Armelita Pawana, I Wayan Adi Juliawan Indonesia University of Education Dept. of Marine Information System Bandung Indonesia Udayana University Dept. of Electrical Engineering Denpasar Indonesia Udayana University Dept. of Information Technology Denpasar Indonesia Sam Ratulangi University Dept. of Information System Manado Indonesia

ISBN: (纸本)9798350369489

In the realm of genetic analysis, the utilization of machine learning models holds great potential for unraveling the intricate complexities of DNA sequence data. This study aims to address the pressing need for advanced DNA sequence analysis methods by leveraging K-mer representations and fine-tuning machine learning algorithms to achieve optimal performance. By elucidating the reasons behind the study and highlighting the significance of these approaches, this research contributes to the broader understanding of DNA sequence intricacies. © 2023 IEEE.

关键词： DNA sequences

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An Innovative Approach to Virtual Private Networks for Enhancing Digital Security and Accessibility 6

An Innovative Approach to Virtual Private Networks for Enhan...

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6th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2024

作者： Ozdenizci Kose, Busra Coskun, Vedat Coskun, Arslan Yaya, Senol Dept. of Management Information Systems Gebze Technical University Kocaeli Turkey Dept. of Sofware Engineering Istanbul Atlas University Istanbul Turkey TURCOM Technology Istanbul Turkey

ISBN: (纸本)9798350394634

In today’s digital landscape, Virtual Private Networks (VPNs) are critical tools that ensure secure and private online interactions. They are crucial for protecting both individuals and enterprises from cyber threats while facilitating access to geographically restricted content. This study aims to investigate the trcVPN system, which proposes an innovative approach to VPN functionality designed to meet the complex demands of today's digital environment. trcVPN addresses the limitations of traditional VPN technologies by introducing advanced features that enhance security, user experience, and adaptability. The system integrates cutting-edge encryption protocols and dynamic connectivity options to ensure robust digital security and improved accessibility, thereby supporting various cybersecurity needs. Additionally, the designed trcVPNPrice module, a key innovation within the system, leverages geographic pricing differentials to offer users significant cost savings on a variety of online services, from airline tickets to hotel bookings. This study details the architectural and functional advancements of trcVPN that collectively enhance both the security and economic efficiency of internet usage. This comprehensive analysis underscores the importance of evolving VPN solutions to adapt to the global, interconnected landscape, where user needs and security challenges continuously evolve. © 2024 IEEE.

关键词： Network security

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Real-time Human Movement Recognition and Interaction in Virtual Fitness using Image Recognition and Motion Analysis 12

Real-time Human Movement Recognition and Interaction in Virt...

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12th International Conference on Awareness Science and technology, iCAST 2023

作者： Yeh, ChunHsiu Shen, Wei-Cheng Ma, Chi-Wei Yeh, Qiu-Tong Kuo, Chung-Wei Chen, Jong-Shin Dept. of Information Engineering Computer Science Feng Chia University Taichung Taiwan CollegeofComputerScience Jimei University Xiamen China Dept. of Information Communication Engineering ChaoYang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This research focuses on the application of image recognition, motion detection, and artificial intelligence techniques to achieve real-time identification and analysis of human movements in a dynamic manner. The primary goal is to establish a seamless association between captured human movements and corresponding actions performed by virtual avatars. The implementation involves the utilization of image processing methodologies from python's OpenCV and pygame libraries. MediaPipe technology is employed to capture users' images and facilitate accurate detection of specific movement patterns. By calculating the angles between users' limbs and torso, the correctness of certain movements can be assessed. The research is practically developed and applied to a simulated fitness system, where an interactive exercise game platform is devised. The system is characterized by its engaging user interface, enhancing the ease and enjoyment of fitness activities. The seamless integration of interactive virtual avatars and real users within physical activities serves to stimulate greater participation, particularly among the elderly. The amalgamation of image recognition and motion detection technologies presents opportunities for combining fitness and entertainment in diverse life scenarios, offering an enhanced experience of physical exercise and enjoyment. © 2023 IEEE.

关键词： Image recognition

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future Communication Paradigm 22

Enhancing 6G-Satellite Network Integration with Artificial I...

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22nd International Learning and technology Conference, L and T 2025

作者： Salah, Ibrahim Hussein, Aziza I. Wageh Lotfy, Mostafa Mabrook, Mohamed Mourad Almaaqal University College of Engineering Control and Computer Department Basrah61001 Iraq Effat University College of Engineering Energy and Technology Research Center Saudi Arabia Faculty of Technology and Education Beni-Suef University Dept. of Process Control Technology Beni-Suef Egypt Beni-Suef University Faculty of Navigation Science & Space Technology Space Communication Dept. Beni-Suef Egypt

ISBN: (纸本)9798331532215

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future. © 2025 IEEE.

关键词： Satellite communication systems

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Leveraging Network Vulnerability Detection using Improved Import Vector Machine and Cuckoo Search based Grey Wolf Optimizer 1

Leveraging Network Vulnerability Detection using Improved Im...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Preethi, P. Vasudevan, I. Saravanan, S. Krishna Prakash, R. Devendhiran, A. Kongunadu College of Engineering and Technology Dept. of Computer Science and Engineering Trichy India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Dept. of Computer Science and Engineering Chennai India Kl University Dept. of Computer Science and Engineering Vijayawada India Sona College of Technology Dept. of Information Technology Salem India Vivekanandha College of Engineering for Women Dept. of Computer Science and Technology Namakkal India

ISBN: (纸本)9798350328042

In the current interconnected and susceptible digital environment, the demand for reliable network attack prediction systems has reached a critical level. This research paper presents a novel approach for network intrusion detection utilizing the Improved Import Vector Machine (IVM) classification algorithm combined with feature selection using Cuckoo Search based Grey Wolf Optimization (CS-GWO). The aim is to improve the accuracy and effectiveness of intrusion detection systems by selecting the most relevant features and employing a powerful classification algorithm. The proposed methodology begins with data preprocessing, ensuring the dataset is appropriately cleaned and normalized. Subsequently, the CS-GWO algorithm is applied for feature selection, leveraging the strengths of both Cuckoo Search and Grey Wolf Optimization. This hybrid approach efficiently explores the search space to identify the optimal feature subset that contributes to accurate intrusion detection. Once the feature selection process is completed, the IVM classification algorithm is employed to classify network instances into intrusion or normal categories. The IVM algorithm is chosen for its ability to handle high-dimensional data and its robustness in dealing with imbalanced datasets commonly encountered in network intrusion detection. To evaluate the performance of the proposed approach, extensive experiments are conducted using benchmark datasets. Various evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of the system in detecting network intrusions. The results demonstrate that the combination of IVM classification and CS-GWO feature selection significantly improves the accuracy and efficiency of network intrusion detection. The selected features obtained through CS-GWO contribute to enhanced classification performance, enabling the system to accurately identify and respond to potential intrusions in real-time scenarios. © 2023 IE

关键词： Network security

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Evaluation of Machine Learning Algorithms for Heart Disease Prediction in Healthcare

Evaluation of Machine Learning Algorithms for Heart Disease ...

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2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Prasher, Shikha Nelson, Leema Hariharan, Shanmugasundaram Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9798350309980

Cardiovascular disease is most significant of the leading causes of mortality in the modern society. An important concern in clinical data processing is the diagnosis of heart disease. The enormous amount of information collected by the healthcare field has been established as helpful for using machine learning (ML) to help with judgement and prediction. In this work, four supervised machine learning techniques such as K-nearest neighbour (KNN), Multi-layer perceptron Decision Tree (DT) and REPTree have been used for predicting heart disease. The developed classifier model is tested with 'Heart disease Prediction' obtained from Kaggle Repository. The performance metrics used to evaluate the classifier models are accuracy, precision, recall and f1-score. The existing four machine learning classifiers has been evaluated to forecast cardiac diseases and obtained maximum accuracy of 99.7% for KNN classifier. © 2023 IEEE.

关键词： Decision trees

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Comparison of Track Management Strategies in Automotive Track-to-Track Fusion Algorithms

Comparison of Track Management Strategies in Automotive Trac...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： László Lindenmaier Szilárd Aradi Tamás Bécsi Balázs Fekete Dept. of Control for Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary Dept. of Control Engineering and Information Technology Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

Advanced driver assistance and automated driving systems rely on an enhanced perception, which requires a reliable fusion of heterogeneous data from multiple sensors. Track-to-track fusion is a commonly used architecture in automotive perception systems. However, track management is still under-discussed among other modules of track-to-track fusion, i.e., association and state fusion, despite its significant impact on the fidelity of the fused data. Track management is responsible for maintaining the fused track list by handling appearing and disappearing objects and, more importantly, determining if a sensor track was generated by a real target or false detections. In this paper, we compare different track management strategies using simulated and real data from a radar-camera sensor cluster, analyzing their false alarm filtering effectiveness.

关键词： Target tracking Sensor fusion Radar tracking Sensor systems Reliability Informatics Machine intelligence Intelligent sensors Automotive engineering Vehicles

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