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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Subashini, B. Nijanthan, Nijanthan Madasamy Raja, G. Swetha, Devulapally Sreenivasulu Reddy, L. School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India School of Computing Srmist Department of Data Science and Business Systems Kattankulathur India Paavai Engineering College Tamilnadu Namakkal India Paavai Engineering College Tamilnadu Namakkal India Vaagdevi Engineering College Dept. of Computer Science and Engineering Warangal Dist. Telangana Bolli Kunta506005 India School of Advanced Sciences Kalasalingam Academy of Research and Education Department of Mathematics Tamilnadu Krishnankoil India

ISBN: (纸本)9798331509675

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms. © 2025 IEEE.

关键词： Smart contract

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An Evolutionary Algorithm for the Travelling Sales Man problem

An Evolutionary Algorithm for the Travelling Sales Man probl...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Shaik Mohammad Waseem Jeshwanth N L Lingesh A Matta Jayanth Bhargavi C H Batini Dhanwanth Dept. of Computer Science and Engineering Panimalar Institute of Technology (Anna University) Chennai India Dept. of Artificial Intelligence and Data Science Panimalar Institute of Technology (Anna University) Chennai India Dept. of Computer Science and Engineering Sai Spurthi Institute of Technology India Dept. of AI Engineering Jönköping University Jönköping Sweden

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques.

关键词： Renewable energy sources Costs Evolutionary computation computational efficiency Complexity theory Optimization Tuning Genetic algorithms

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Predicting Lung Cancer with Artificial Neural Network: Analyzing Performance and Feature Importance

Predicting Lung Cancer with Artificial Neural Network: Analy...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Vatsal Mehta Varun M Ashwini Kodipalli Roshan Joy Martis Shridhar B. Devamane Ushasree. A Dept. of AI & Data Science Global Academy of Technology Bangalore Karnataka India Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science School of Computing Mysuru Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Lung cancer is the primary cause of cancer-related deaths globally. This cancer is majorly observed in smokers, increasing air pollution has also begun to affect non-smokers. Research indicates that exposure to polluted air significantly raises the risk of developing lung cancer, posing a major threat to public health. Despite advancements in medical technology, the mortality rate from lung cancer remains high, highlighting the importance of early diagnosis. Machine learning and deep learning offer promising solutions for early and accurate prediction through advanced data analysis. In our study, we utilized a dataset with 26 attributes for discern lung cancer, categorizing cases into three risk levels: high, medium, and low. We applied various algorithms and develop an Artificial Neural Network (ANN), comparing the overall performance with various machine-learning and deep-learning models. Our results show that the ANN achieved a high accuracy of 94.9% using the Adam optimizer, making it an effective model for lung cancer prediction.

关键词： Deep learning Accuracy computational modeling Biological system modeling Lung cancer Mortality Artificial neural networks Predictive models Prediction algorithms Public healthcare

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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Kolla Vivek Gurrala Swetha V T Ram Pavan Kumar M Anusuri Krishna Veni Neti Praveen Masthan Rao Kale Dept. of CSE QIS College of Engineering & Technology (A) Ongole Andhra Pradesh India Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India Dept. of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Dept. of Computer Science & Engineering – Data Science Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Dept. of Computer Science and Design SRKR Engineering College Bhimavaram Andhra Pradesh India Dept. of CSE Pace Institute of Technology and Sciences (A) Ongole Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart cities have emerged as a response to the fast expansion of metropolitan areas; these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions.

关键词： Waste management data privacy Technological innovation Smart cities Scalability data security Medical services Public security Internet of Things Interoperability

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data-Driven Design of Thermoplastic Composites with Tailored Compliance

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Journal of engineering Mechanics 2025年第7期151卷

作者： Upadhyay, Nilay Zawaski, Callie Peco, Christian Reinhart, Wesley F. Dept. of Engineering Science and Mechanics Pennsylvania State Univ. University ParkPA16802 United States Applied Research Laboratory Pennsylvania State Univ. University ParkPA16802 United States Institute for Computational and Data Sciences Pennsylvania State Univ. University ParkPA16802 United States Dept. of Materials Science and Engineering Pennsylvania State Univ. University ParkPA16802 United States Faculty Co-Hire Institute for Computational and Data Sciences Pennsylvania State Univ. University ParkPA16802 United States

Continued advances in manufacturing processes have rapidly increased the complexity of composite parts that can be manufactured, consequently increasing the number of variables that must be considered during design. Traditional computational methods struggle with time efficiency and complex relations in large design spaces, especially for composite materials with intricate, multivariate, and nonlinear process-structure-property connections. Among computational tools, machine learning methods excel at interpreting complex relationships and efficiently generating designs for target properties. In this study, we systematically built and deployed machine learning models to explore the design space of a layered composite material. We modeled two grades of thermoplastics and generated a data set of the compliance of different layered composite geometries using finite-element simulations. A random forest model utilizing a token-counting featurization scheme was selected based on its exceptional performance. It was used to perform a detailed feature importance analysis and then a series of design tasks. We show that this method can reliably obtain single-objective and multiobjective designs. This work demonstrates the feasibility of a simple data-driven approach to designing composite parts with many design variables and highly nonlinear mechanical behavior. © 2025 American Society of Civil Engineers.

关键词： Structural properties

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CoAPT: Context Attribute words for Prompt Tuning

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Knowledge-Based Systems 2025年 320卷

作者： Lee, Gun An, Subin Baik, Sungyong Lee, Soochahn Dept. of Electronics Engineering Kookmin University Seoul Korea Republic of Dept. of Data Science Dept. of Artificial Intelligence Hanyang University Seoul Korea Republic of

We propose a novel prompt tuning method called CoAPT (Context Attribute words in Prompt Tuning) for few/zero-shot image classification. The core motivation is that attributes are descriptive words with rich information about a given concept. Thus, we aim to enrich text queries of existing prompt tuning methods, improving alignment between text and image embeddings in CLIP embedding space. To do so, CoAPT integrates attribute words as additional prompts within learnable prompt tuning and can be easily incorporated into various existing prompt tuning methods. To facilitate the incorporation of attributes into text embeddings and the alignment with image embeddings, soft prompts are trained together with an additional meta-network that generates input-image-wise feature biases from the concatenated feature encodings of the image–text combined queries. Our experiments demonstrate that CoAPT leads to considerable improvements for existing baseline methods on several few/zero-shot image classification tasks, including base-to-novel generalization, cross-dataset transfer, and domain generalization. Our findings highlight the importance of combining hard and soft prompts and pave the way for future research on the interplay between text and image latent spaces in pre-trained models. © 2025

关键词： Image classification

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Waste Detection in underwater using deep Vision Transformers

Waste Detection in underwater using deep Vision Transformers

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： P K Gupta L. Sai Chandana O. Phani Manoj V. Ruthik Avinash G. Jayavardhan Dept. of Data Science School Of Computing Mohan Babu University Tirupati India Dept. of IT Erstwhile Sree Vidyanikethan Engineering College Mohan Babu University Tirupati India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The detection of waste in underwater environments is a critical task for marine conservation, yet it poses significant challenges due to the complex and dynamic nature of these settings. Factors such as low visibility, varying light conditions, and diverse waste materials make traditional detection methods, like Convolutional Neural Networks (CNNs), less effective. To address these challenges, this research explores the application of Deep Vision Transformers (DVTs) for underwater waste detection. DVTs are particularly well-suited for this task due to their advanced features, including the ability to capture global contextual information and model long-range dependencies within an image. Unlike CNNs, which focus on local features, DVTs utilize self-attention mechanisms to effectively process and prioritize different parts of an image. This leads to robust feature extraction, which is crucial for accurately identifying waste in complex underwater environments. The Project employs techniques such as transfer learning and data augmentation to enhance the performance of DVTs on the limited datasets available for underwater imagery. Fine-tuning the model with labeled underwater images further improves detection accuracy.

关键词： Computer vision Waste materials Transfer learning Marine vegetation Lighting Transformers Feature extraction Water pollution Real-time systems Marine animals

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Healthcare Insurance Fraud Detection Using Machine Learning

Healthcare Insurance Fraud Detection Using Machine Learning

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： Chengamma Chitteti Mopuri Yamuna Mattam Srinath Chakali Govardhan Alavalapati Vignatha Dept. of Data Science Mohan Babu University (Erst While Sree Vidyaniketan Engineering College) Tirupati A.P India Dept. of IT Sree Vidyanikethan Engineering College Tirupati A.P India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Healthcare insurance fraud is a serious problem with huge amount of financial losses and a loss of trust in the healthcare system itself. Traditional methods of detecting fraudulent and inflated claims are increasingly incapable of overcoming the growing sophistication of the fraudster amd struggle to adapt to evolving fraudulent schemes. A framework for detecting fraud in healthcare insurance claims using Machine Learning techniques The data used in this study contains members' details, patients data, medical causes, fees charged, number of claims and the target variable represents if the claim is fraudulent or not. As a model to accomplish this task, we select CatBoost, a potent gradient boosting model with a strong capacity for dealing with categorical data and learning high-level correlations directly on the raw dataset. Isolation Forest, one of such anomaly detection techniques, is employed to find possible outliers and rare fraudulent events, usually not captured by traditional means. They are assessed on real-world data and standard performance metrics: accuracy, precision, recall, and F1-score. Experimental Results demonstrate the promise of machine learning models to improve fraud detection capabilities, reduce loss ratios, increase operational efficiency, and protect the integrity of the healthcare system.

关键词： Analytical models Accuracy computational modeling Insurance Tariffs Medical services Forestry data models Fraud Anomaly detection

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Simulating Mobile Robot Vision: An Analysis of RGB-D Versus RGB-Based Distance Accuracy and CPU Optimization

Simulating Mobile Robot Vision: An Analysis of RGB-D Versus ...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Minseok Kong Jiho Park Daye Lee Nikolaos Kourtzanidis Jungmin So Dept. Computer Science and Engineering Sogang University Seoul South Korea Dept. Artificial Intelligence Dongguk University Seoul South Korea Dept. Data Science Seoul National University Seoul South Korea Cyberworks Robotics Toronto Canada

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

From the perspective of mobile robot vision, to facilitate secure and seamless interactions between the robot and its environment, it is paramount to attain precise and metic-ulous distance estimation between the robot and the detected entities. Moreover, in the absence of GPUs, mobile robots are compelled to rely solely on CPU resources to manage vision-related tasks and other operational imperatives. To address the challenges above, we focused on reducing CPU usage in the vision system and analyzing distance estimation accuracy in two scenarios: leveraging RGB-D and RGB cameras. We executed real-time object detection targeting humans utilizing YOLOv8 Nano, subsequently undertaking a comparative analysis between the distance estimates derived through monocular dept. estimation using a MobileNetV2 model, trained on the KITTI dataset, and the distance measurements obtained from RGB-D cameras. Additionally, we optimized the deep learning models to reduce CPU usage, employing a hardware-specific technique, OpenVINO conversion, and both a hardware-specific and a hardware-agnostic strategy, Post-Training Quantization (PTQ). Consequently, we reduced CPU utilization by approximately 4.62% in the RGB-D scenario and 5.32% in the RGB scenario.

关键词： Accuracy Machine vision dept. measurement Robot vision systems Estimation Object detection Cameras Real-time systems Mobile robots Optimization

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Smoke Level Monitoring and Alerting in Automobiles using IoT

Smoke Level Monitoring and Alerting in Automobiles using IoT

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： M. Vani Pujitha Teja Aravind Kumar Rayalavarapu Lal Basha Shaik Sashank Madhabushi Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India Dept. of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Vehicle emissions significantly exacerbate air pollution, releasing harmful gases such as carbon monoxide (CO), nitrogen dioxide (NO 2 ), and hydrocarbons (HC) from poorly maintained engines and inefficient combustion processes. The study presents an Internet of Things (IoT)-based system to monitor the emissions, which mainly includes key pollutants like CO, NO 2 , and HC in real-time. Strategically positioned gas sensors near the vehicle's exhaust capture emission data, which an Arduino microcontroller processes with high accuracy. The system delivers continuous gas level updates via an LCD display and leverages an IoT framework to ensure seamless integration of sensors, microcontrollers, and mobile devices. This connectivity enables real-time data transmission and remote alerting, enhancing emission management. When pollutant levels surpass predefined safety thresholds, the system triggers immediate alerts through a Global System for Mobile Communications (GSM) module (SIM 900) via SMS to the vehicle owner's mobile, complemented by an audible buzzer. This alert notification encourages timely maintenance, reducing emissions and improving vehicle performance and contributing to cleaner air.

关键词： GSM Microcontrollers Real-time systems Sensor systems Mobile handsets Sensors Safety Nitrogen Gas detectors Monitoring

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