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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Ramprasad, S.R. Rampriya, R. Poongodai, A. Govindharaj, I. Vimal Raja, R. Yazhinian, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Madanapalle India Ck College of Engineering and Technology Department of Computer Science and Engineering Cuddalore India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510022

Irreversible vision loss, generally insidious in start and lacking apparent symptoms, is usually attributed to glaucoma. Detecting glaucoma in its early stages is crucial since it may reduce disease progression. Conventional diagnostic approaches, depending on manual assessments, are notably prone to mistakes. Hence, automated glaucoma analysis acquires vital relevance for precise and prompt detection. Moreover, medical picture databases frequently display asymmetries, creating a problem. To solve these challenges, this paper presents a unique framework employing generative adversarial networks (GANs) to generate images, thus addressing dataset imbalances. Specifically., in the context of fundus images, standard approaches like image-to-image translation are applied to build synthetic fundus images and associated vascular networks, attempting to boost overall image quality and capture finer details. Gaussian filtering is originally done to pre-process the raw dataset, eliminating unwanted noise. Subsequently, GANs are applied for dataset balancing, providing synthetic images that boost classification accuracy. Optic cup segmentation is conducted using the Enhanced Level Set Algorithm. Finally, Pretrained MobileNetV2 permits accurate classification of glaucomatous images into normal and pathological categories. Experimental results illustrate the efficacy of our suggested framework, obtaining an accuracy of 98.9%, exceeding existing techniques. © 2024 IEEE.

关键词： Generative adversarial networks

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A COMPARATIVE EXPLORATION OF ML CLASSIFIERS IN RECOGNITION OF OVARIAN CANCER

A COMPARATIVE EXPLORATION OF ML CLASSIFIERS IN RECOGNITION O...

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Sharma, Siddarth Nachappa, M.N. Kapse, Vinod Mansiram Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India Department of Electronics and Communication Engineering Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350355338

Ovaries are an essential aspect of a woman's reproductive system. The synthesis of female sex hormones and female gametes gives these little glands great significance. These small glandular structures, shaped like almonds and devoid of ducts, exist on either side of the uterus, connected by the ovarian ligament. Ovarian cancer can develop for several causes, but experts have identified a variety of distinct ways to categorize the disease. Predicting the onset of ovarian cancer at an early stage has the potential to save many lives by slowing the disease's progression. The use of CAD (computer-aided diagnosis) systems is a noninvasive procedure for early detection of ovarian cancer that can spare patients the stress and inconvenience of a biopsy. Different methods are discussed in this review study for categorizing ovarian cancer tumors. We conducted an analysis of field data from Mass Spectrometry (MS) in order to develop a computer-assisted method. By using machine learning approaches, we were able to distinguish between malignant and benign cancerous cells by classifying the data into different groups. We conducted a comparison analysis to examine several approaches on different datasets and operational settings in order to determine which worked best. Our comparison analyses clearly show that the Multilayer Perceptron (MLP) is the best option for these kinds of detection tasks, outperforming other models in terms of accuracy, sensitivity, specificity, and errors. © 2024 IEEE.

关键词： Noninvasive medical procedures

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Low Parameters UNet for Energy-Efficient Cloud Detection

Low Parameters UNet for Energy-Efficient Cloud Detection

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Chen, Chun-Fu Lee, Pei-Jun Bui, Trong-An The Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan The Institute of Aerospace and System Engineering National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9798331521165

Applying cloud detection algorithms to edge devices using semantic segmentation poses significant challenges due to the high computational complexity and power consumption. This paper proposes a parameter reduction method that combines linear channel pruning and adaptive pruning techniques to effectively compress the large UNet model. Linear channel pruning efficiently reduces model redundancy, while adaptive pruning further enhances the model's sparsity. This approach not only maintains performance comparable to the original UNet in terms of Intersection over Union (IoU) and other key metrics, but also significantly reduces the model's parameter count, thereby lowering DRAM access power consumption. Experimental results show that the first and second stages of parameter reduction decreased the model's parameter count by 98.5% and 70.3%, respectively, while reducing memory access by 67.5% and 65%, respectively. © 2025 IEEE.

关键词： Semantic Segmentation

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Data Pushing Framework in NDN-Enabled IoT

IEEE Networking Letters

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IEEE Networking Letters 2022年第1期4卷 16-19页

作者： Wang, Xiaonan Lu, Yimin Wang, Zhanyu Li, Feng Department of Computer Science and Engineering Changshu Institute of Technology Changshu215500 China

The Named Data Networking (NDN) is a novel communication mechanism that may assist in enhancing efficiency of data delivery in the Internet of Things (IoT). In this letter, we propose a data pushing framework in NDN-enabled IoT to reduce data pushing costs and failures caused by provider and consumer mobility. The experimental data justifies its advantages. © 2019 IEEE.

关键词： Internet of things

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Advanced Tracking Algorithm for Multiple Dynamic Vehicles using Attention Mechanisms and Kinematic Models 3

Advanced Tracking Algorithm for Multiple Dynamic Vehicles us...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Ravikumar, Kishore Sutaria, Kamal Pathak, Ganesh Pandit Rastogi, Shailesh Chennai Institute of Technology Department of Mechanical Engineering Kundrathur India Parul University Faculty of Engineering and Technology Deparment of Computer Science and Engineering Vadodara India Balaji Institute of Modern Management Pune India Pune India

ISBN: (纸本)9798331508456

Efficient vehicle tracking in urban environments is crucial for intelligent transportation systems but faces challenges such as nonlinear motion, dynamic backgrounds, and frequent occlusions. To address these complexities, we propose the AM-Vehicle-Track system, designed under the tracking-by-detection paradigm. This system integrates the Lightweight Convolutional Block Attention Module (LCBAM), which enhances detection accuracy by refining feature maps through channel-wise and spatial attention, and the Noise-Adaptive Extended Kalman Filter (NSA-EKF), which dynamically adjusts observation noise based on detection confidence to handle nonlinear vehicle motion. Additionally, the Byte data association technique is employed to mitigate issues with low-confidence detections and reduce identity switches. Tested on the challenging VisDrone-MOT dataset, AM-Vehicle-Track achieves a mean object tracking accuracy (MOTA) of 42.2%, an identity F1 score (IDF1) of 51.2%, and processes at 72 frames per second (FPS), outperforming existing state-of-the-art systems. These results highlight its robustness and efficiency in real-time scenarios, making it suitable for intelligent transportation applications. By significantly improving detection accuracy, tracking precision, and scalability, AM-Vehicle-Track establishes a new benchmark in multi-object tracking technologies and demonstrates its potential to enhance the reliability and efficiency of vehicle tracking in complex urban environments. © 2025 IEEE.

关键词： Urban transportation

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Classification Model for Meticulous Presaging Heart disease Results for sarcopenic LRMM and CA calcification in healthy adults using Machine learning 2

Classification Model for Meticulous Presaging Heart disease ...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Aggarwal, Ritu Singh, Gulbir Aggarwal, Eshaan Kumar, Suneet Graphic Era Hill University Department of Computer Science and Engineering Haldwani Campus Uttarakhand India Seth jai Parkash Mukand Lal Institute of Engineering and Technology Department Computer Science and Engineering Haryana Radaur India Mmdu Maharishi Markendeshwar Engineering College Haryana Mullana India Department Computer Science and Engineering Haryana Mullana India

ISBN: (纸本)9798350368413

Sarcopenic obesity, a condition characterized by both muscle loss and excess body fat, is a significant risk factor for cardiovascular disease. This study aims to develop a machine learning model to predict the risk of sarcopenic obesity and heart disease. A dataset comprising 3050 patients aged 66 years and older was obtained from the UC Irvine Machine Learning Repository. The dataset includes relevant attributes such as BMI, protein intake, blood urea nitrogen (BUN), fat percentage, age, complete blood count (CBC), chest pain, sex, and more. Several machine learning algorithms, including Support Vector Machine, Naïve Bayes, Decision Tree, and Logistic Regression, were employed to build the prediction model. The model's performance was evaluated based on its accuracy in identifying individuals at risk of sarcopenic obesity and heart disease. The results of this study demonstrate the potential of machine learning in early disease detection and prevention. By accurately predicting the risk of sarcopenic obesity and heart disease, healthcare providers can implement timely interventions and improve patient outcomes.. © 2024 IEEE.

关键词： Heart

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5G Wireless Communication and IoT: Vision, Applications, and Challenges 2

5G Wireless Communication and IoT: Vision, Applications, and...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhivgade, Anushka Puri, Chetan Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

This article looks at the opportunities, application and challenges of 5G Wireless Communication Technologies and Internet of Things (IoT). A brief history from 1G to 5G is presented and the limitations of 4G and the introduction of 5G as a solution for problems with latency and speed. The paper also describes the 5G IoT concept, the architecture and advanced technologies with focus on different IoT applications such as smart agriculture and smart city. When speaking of aspects such as energy consumption, privacy and security concerns, as well as trade-offs strategies, it underlines how important collaboration is to achieve the benefits that the 5G IoT will bring. The paper itself provides a roadmap for next studies with a purpose of advancing development of the integrated, safe, and efficient wireless. As 5G Wireless Communication and IoT progresses the expectation is that it will enhance and revolutionalize several domains that will offer a scalable connectivity that is faster and more reliable. This join venture is believed to open new value adding opportunities for application in smart city, automobile, medical, industrial, robotic and so on several other fields. The term 5G IoT has also been used with the intention of establishing connections of several devices, very low latency and high degree of data rate for real time analysis and decision making. © 2024 IEEE.

关键词： Smart agriculture

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Neural Networks for Natural Language Processing

Neural Networks for Natural Language Processing

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2024 Asian Conference on Intelligent Technologies, ACOIT 2024

作者： Deshpande, Sudhindra B. Velrani, K. Sundara Bhagyalakshmi, A. Vani, M. Sowmya Ananthi, S.N. Sahu, Devika Department of Information Science and Engineering KLS Gogte Institute of Technology Udyambag Karnataka Belagavi590006 India Department of Information Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai600062 India Department of Computer Application Mohan Babu University Andhra Pradesh Tirupati517501 India Department of Computer Science and Engineering S.A Engineering College Tamil Nadu Chennai600077 India Department of Computer Science and Engineering Government Engineering College Chhattisgarh Raipur492001 India

ISBN: (纸本)9798350374933

Artificial neural networks have made Natural Language Processing (NLP) a lot different because they can encode language data more efficiently. A lot of different natural language processing tasks, like mood analysis, translation, and text production, have been done very well with these networks are based on the neuronal structure of the human brain. This abstract delves into the fundamental principles of neural networks in natural language processing, with a particular emphasis on notable structures, including the more recent Transformer models, CNNs, and RNNs. The investigation investigates the potential future trajectories, challenges, and applications of improving language comprehension and developing assignments. © 2024 IEEE.

关键词： Natural language processing neural networks neural networks NLP recurrent neural networks RNNs

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Speed Limit Constraint-based Green Vehicle Routing Problem (SL-GVRP) - A New Variant of VRP 3

Speed Limit Constraint-based Green Vehicle Routing Problem (...

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

作者： Paul, P. Victer Jose, Anju Raju, R. Department of Computer Science and Engineering Indian Institute of Information Technology Kerala Kottayam India Deaprtment of Information Technology Sri Manakula Vinayagar Engineering College Ponducherry India

The Vehicle Routing Problem (VRP) represents a renowned and complex challenge that has been extensively studied in the disciplines of computer science and operations research due to its broad applicability in real-world scenarios. Numerous variants of the VRP have been developed to address specific constraints, reflecting its practical significance. This paper introduces a novel variant called the Speed Limit Constraint-based Green Vehicle Routing Problem (SL-GVRP). The primary objective is to determine the optimal route that minimizes travel time by accounting for both distance and maximum speed limits between city pairs. To tackle this problem, an efficient genetic algorithm-based solution is proposed, incorporating three population seeding techniques: nearest neighbour, and the recent ODV-based EV and VV methods. The study evaluates multiple perspectives, including distance-based, time-based, and speed-based approaches. Experimental tests are conducted using instances from the TSPLIB benchmark dataset, with the analysis focusing on the act of different genetic algorithm implementations for solving the SL-GVRP. Performance is evaluated using criteria like computation time, error rate, and mean error rate. © 2025 The Author(s).

关键词： Genetic algorithms

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Exploring the Significance and Paradoxical Nature of the Zero Value in Mathematics Using Artificial Intelligence 4

Exploring the Significance and Paradoxical Nature of the Zer...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Ishwarya, K. Saranya, G. Appathurai, K. Surendran, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Kg College of Arts and Science Department of Computer Technology Coimbatore India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300888

One of the greatest paradoxes in the human mind is the odd number zero. Both everything and nothing are meant by it. It performs an important function in mathematics as the additive identity of integers, real numbers, and a variety of other algebraic structures. Without zero, it would have been difficult for all branches of science - not just mathematics - to come up with definitions that were more exact. The digit 0 serves as a stand-in for other digits in place systems. Here, it creates an interactive learning session to show the existence and importance of '0' to a supernatural that does not utilize zeros in their number system. Three key concepts are used in this study to explain what zero is and what it means. The first idea is based on Apple logic, a fundamental approach to logical reasoning. Understanding gravity includes exploring how the second principle is used in this context. The usage of contemporary technology is the last idea. Without the usage of zero, the logic in all of these concepts will be completely erroneous. These ideas interactively applied artificial intelligence algorithms. © 2023 IEEE.

关键词： Numbering systems

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