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Smart Water Ball Launcher: An Arduino-Based Fire Extinguishing System

Smart Water Ball Launcher: An Arduino-Based Fire Extinguishi...

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Intelligent data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Govindu L V N Sridhar M. Janakiram M. Vishnu Ram Sai G. Surya Narayana Reddy Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The proposed work offers the production of an Arduino controlled Fire-RoboCar equipped with a water ball launcher. In a world dominated by human-machine interaction in industry, the efficiency of any task to be done is promoted by this proposed work and delivers solutions to needs like fighting fires through the provision of a productive method. At a time when increased pollution and the expense of fossil fuel are becoming very high, this alternative is environment-friendly. The Fire-RoboCar is a moving robot that can accomplish fire extinguishing tasking in an industrial complex, proving that the system may easily transport water to desired locations. This proposed work advances robotic vehicles' capabilities and provides innovative solutions to modern industrial challenges.

关键词： Service robots Weapons Scalability Water conservation Water pollution Sensors Safety Sustainable development Wireless fidelity Remote control

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6G Mobile Network Latency Mobile Edge computing Cloud Architecture

6G Mobile Network Latency Mobile Edge Computing Cloud Archit...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Preeti Naval V Pushparajesh Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Communication refers to the process by which one individual conveys information to another. Telecommunication and the instruments are made available by technological gadgets, which is now feasible. 5G mobile network standards are being used at present. It is thought that 6th-generation mobile network standards will address several user needs. 6G will be universally available by 2030. In a cloud computing setup, a specific hardware or geographic location is not required. The main aim of this method is to offer computing resources whenever they are needed. By fusing the two, mobile cloud computing enables more flexible and personalized solutions. Machine learning is finding its way into a wide variety of devices. With the use of AI., mobile network services (MNS) may improve the reliability and personalization of user experiences in areas like fraud detection, mobile transaction security, network operation monitoring, and management. A world without traffic accidents, high-tec, specialized healthcare, and criminal activity would be a reality if 6G mobile network services integrated cloud computing with artificial intelligence. The most important requirements for 6G standards are high data rate connection per user and the ability to manage huge record numbers. Research and development are underway for the next-generation mobile network. There are several intriguing aspects of this age. The most pressing problem that requires solving using suitable forensic methods is security. High-performance computers are needed to improve customer service. Two situations are created by combining technological, organizational, and cloud-hosted application-based research approaches in a high-performance computing setting: monitoring performance via remote desktop connection and real-time stream processing. This article seeks to narrow the designated worldwide spectators with a wide variety of empirical chances to allocate resources for trustworthy and defensible on-demand services.

关键词： 6G mobile communication Cloud computing High performance computing Forensics Machine learning Communications technology Fraud Security Standards Monitoring

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Optimizing Household Energy Consumption: A Web-Based Tool for Real-Time Carbon Footprint Calculation and Sustainable Benchmarking

Optimizing Household Energy Consumption: A Web-Based Tool fo...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Harsh Raja Jayaditya Kishore J. Kalaivani V. Angayarkanni P. Rama Department Of Computing Technologies Faculty Of Engineering And Technology Srm Institute Of Science And Technology Chengalpattu District Tamil Nadu India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

This is an innovative project from an energy-saving perspective. This project ensures that users become in control of their energy consumption and able to take an actively reducing their carbon footprints. It provides users with an interface to input details about household appliances and usage patterns, through which a correct calculation of the carbon footprint could be done. Compared against standard norms, it would help the users identify certain areas where improvement is possible and adopt more sustainable use of energy. Apart from personalized energy assessments, it has now become the central source that connects users to local schemes and initiatives on sustainable energy, including information on resources and programs into waste energy and carbon emission reduction. This integration, therefore, promotes more engagement and involvement within the community as users are nudged to join in the efforts that are being made for the goal of energy conservation. The platform educates its users about energy consumption and aids them in making green choices in data; however, with a holistic approach in carbon footprint management and education pertaining to sustainable long-term behavior, the process takes place through integrated accurate energy tracking, fine recommendations, and community-driven action.

关键词： Energy consumption Energy conservation Green products Real-time systems Sustainable development Carbon footprint Standards Research and development Meteorology Resilience

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Leveraging Sequential Deep Learning Models for Robust Compiler Bug Detection in Software Logs

Leveraging Sequential Deep Learning Models for Robust Compil...

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Intelligent data Communication technologies and Internet of Things (IDCIoT), international conference on

作者： Sahil Mishra Utkarsh Tiwari Kavitha C. R. Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Compiler bugs critically impact the correctness of software applications, making their detection and resolution vital for improving software quality. This paper proposes a novel approach leveraging sequential deep learning models, including Long short-term memory, Gated recurrent unit, Recurrent neural network, and a stacked ensemble model, to enhance compiler bug identification. By combining the strengths of these architectures, the proposed method improves predictive accuracy and generalization. To ensure model interpretability, we employ LIME (Local Interpretable Model-agnostic Explanations) to identify the features influencing bug detection decisions. Experimental results demonstrate that the stacked ensemble outperforms individual models in terms of precision, recall, Fl-score, and ROC-AUC. This work advances the state of compiler log analysis and contributes significantly to software quality assurance by integrating robust deep learning techniques with explainable AI.

关键词： Deep learning Analytical models Explainable AI Computer bugs Software quality Feature extraction data models Internet of Things data communication Long short term memory

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IoT-Enabled Asthma Trackers for Children with KNN for Improved Breathing Control

IoT-Enabled Asthma Trackers for Children with KNN for Improv...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： P. Sathyanathan K. Balachander Pramod Pandey V. Prasanna Srinivasan T. R. GaneshBabu M. Muthulekshmi Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Maharashtra India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Managing asthma in children is a complex task that needs constant vigilance and personalized treatments to get the best potential control. For better respiratory management, an Internet of Things (IoT)-enabled pediatric asthma tracking system that uses machine learning (ML) methods, particularly for K-nearest neighbors (KNN), is presented. The system's integration of wearable sensors, which continuously monitor vital signs and environmental conditions, provides a comprehensive view of the child's respiratory health. Asthma exacerbation causes, and trends may be discovered by analyzing the obtained data using KNN algorithms. Analysis allows for the generation of real-time warnings and feedback, allowing caregivers to take proactive steps in reducing risks and optimizing treatment procedures. Our IoT-enabled method for improving respiratory control and decreasing asthma-related consequences in children is feasible and effective in pilot research, including children with asthma. Our research highlights the promise of IoT devices and ML algorithms to transform how pediatric asthma is treated by providing tailored, evidence-based treatments that lead to better health outcomes.

关键词： Pediatrics Machine learning algorithms Surveillance Medical services Transforms Nearest neighbor methods Real-time systems Internet of Things Wearable sensors Asthma

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Integrating Blockchain and Artificial Intelligence for Securing Payment processes Against Fraudulent Transactions

Integrating Blockchain and Artificial Intelligence for Secur...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： J V Naga Ramesh Nusrat Hamid Shah Malik Bader Alazzam Divya Nimma V. Selvi A Balakumar Department of CSE Graphic Era Hill University Dehradun India Department of CSE Graphic Era Deemed To Be University Dehradun Uttarakhand India College of Engineering and Computer Science Jazan University Kingdom of Saudi Arabia Faculty of Information Technology Jadara University Irbid Jordan University of Southern Mississippi Data Analyst in UMMC USA Department of Computer Science Mother Teresa Women's University Kodaikanal India Department of ECE K. Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

In recent years, the fast growth of digital payment processes has raised large safety concerns, which include fraud and data breaches, prompting the need for robust safety mechanisms. Existing techniques, which include conventional rule-based systems, regularly fall quick in adaptability and accuracy, leading to multiplied vulnerabilities within the economic zone. This study proposes a unique integration of Blockchain technology with Artificial Intelligence (AI), specially making use of Generative Adversarial Networks (GANs), to enhance the security of monetary transactions. The technique includes growing a decentralized ledger device to make sure transparency and immutability, coupled with a GAN framework for real-time anomaly detection. Then behavior experiments the use of a complete dataset of monetary transactions to validate the effectiveness of the included technique. The consequences imply a considerable improvement in detecting fraudulent activities, accomplishing higher accuracy and decrease false positive rates in comparison to conventional methods. This research no longer most effective addresses the crucial safety gaps found in contemporary monetary transaction systems however additionally demonstrates the potential of integrating Blockchain and AI to create an extra stable monetary environment. The findings recommend that this integrated technique can serve as a scalable solution for enhancing transaction safety, providing valuable insights for both academia and industry practitioners.

关键词： Industries Economics Accuracy data breach Generative adversarial networks Real-time systems Safety Blockchains Fraud Artificial intelligence

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IoT-Based Transfer Learning and Genetic Algorithm Approaches for Smart Environmental Pollution Detection and Management

IoT-Based Transfer Learning and Genetic Algorithm Approaches...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Shafqat Alauddin Sree Vidhya S S. Pragadeeswaran P. Sowjanya Rajashree Bhokare Maaz Allah Khan Department of Chemistry Shibli National College Azamgar Uttar Pradesh India Department of Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi Tamilnadu India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of ECE Malla Reddy Engineering College (Autonomous) Dhulapally Telangana India Department of Electrical Engineering Dr. D. Y. Patil Institute of Technology Pimpri Pune Maharashtra India Department of Engineering &Technology Isabella Thoburn College (Professional Studies) Lucknow Uttar Pradesh India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The identification of environmental pollution remains to be a significant issue, as conventional technologies lack scalability and flexibility to dynamic, real-time situations. This study presents an IoT-enabled hybrid model that combines transfer learning and genetic algorithms to overcome these restrictions. IoT networks enable real-time, extensive data acquisition, whereas transfer learning uses pre-trained models for effective feature extraction and domain adaption. Genetic algorithms enhance hyperparameters and sensor configurations, guaranteeing energy economy and optimal model performance. The suggested CNN-LSTM architecture utilising multi-scale attention attains a training accuracy of 95.6% and an F1 score of 0.96, exceeding the performance of conventional Random Forest and CNN models in accuracy, recall, and precision. The comparative study confirms the model's strength and flexibility, illustrating its capacity for scaled, practical applications. This framework creates a dependable and effective system for intelligent environmental pollution control, providing substantial improvements in detection accuracy and operational efficacy compared to current methods.

关键词： Training Adaptation models Pollution Accuracy Scalability Transfer learning Real-time systems Internet of Things Random forests Genetic algorithms

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G-Litter Marine Litter dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration 33

G-Litter Marine Litter Dataset Augmentation with Diffusion M...

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33rd Euromicro international conference on Parallel, Distributed, and Network-Based Processing, PDP 2025

作者： Mellone, Gennaro De Vita, Ciro Giuseppe Nardo, Emanuel Di Coviello, Giuseppe Luccio, Diana Di Aucelli, Pietro Patrizio Ciro Ciaramella, Angelo Montella, Raffaele University of Napoli Parthenope Department of Science and Technology Napoli Italy University of Naples Parthenope Dep. of Science and Technologies Naples Italy Integrated System NEC Laboratories America Inc PrincetonNJ United States

ISBN: (纸本)9798331524937

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset's balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring. © 2025 IEEE.

关键词： Image enhancement

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Using Recurrent Neural Networks to Forecast Climate Change: A Time Series Analysis of Global Temperature Variability

Using Recurrent Neural Networks to Forecast Climate Change: ...

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advances in Computer science, Electrical, Electronics, and Communication technologies (CE2CT), international conference on

作者： Nitasha Rathore Anubha Kuljeet Singh Gaganpreet Kaur Chetna Sharma Anurag Sinha Department of Computer Science and Engineering Bharatiya Vidyapeeth College of Engineering New Delhi KIET Group of Institutions India Department of Computer Science School of Sciences Christ University Delhi NCR Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer science IGNOU New Delhi India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Predicting the upcoming weather instances is very crucial. It depends on different climatic parameters like humidity, pressure, temperature, etc. In this paper, the historical data of the weather in the India area is used for future weather instances of the India for farmers' convenience in terms of the agricultural instance which depends on the weather and, functioning according to that which will restore the *** weather forecasting we have use the machine learning algorithm and probabilistic predictions of the predictive analytics based on soft computing and. NGBoost algorithm and. Linear models of the machine learning for predictive. Comparative weather incidence spaced on the historical data. We have also used, sliding window algorithm of the statistics for predicting the ideology of the concept of different contrasted windows and year. We've also used utility thirds and machine learning algorithm, classified for predicting the weather based on different features The overall implementation in this paper, shows the accuracy which we have gathered from the data set. An implementation of algorithm which ranges between 80% to 90% and the entire algorithm have been compared based on the feature instances. Work can be concluded on the measurement of the algorithm, which we have got after the implementation of Models. Which rely upon the different data features and thus it can be beneficial for preserving the energy and materials in the India agriculture area and forecasting the weather as per day Agricultural conditions.

关键词： Machine learning algorithms Recurrent neural networks Computational modeling Weather forecasting Predictive models Prediction algorithms Classification algorithms Feedforward neural networks Forecasting Meteorology

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Automated Essay Scoring: A Comparative Study of Machine Learning and Deep Learning Approaches

Automated Essay Scoring: A Comparative Study of Machine Lear...

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advances in Electrical, computing, Communication and Sustainable technologies (ICAECT), international conference on

作者： Bhuvi Bansal Jhalak Gupta Manasvi Singh Ritu Rani Garima Jaiswal Arun Sharma Electronics and Communication Engineering Indira Gandhi Delhi Technical University for Women Delhi India Information Technology Indira Gandhi Delhi Technical University for Women Delhi India Electronics and Communication Engineering Bhagwan Parshuram Institute of Technology Delhi India Computer Science Engineering Bennett University Noida

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Automated Essay Scoring (AES) refers to the use of technology to automatically assign scores to essays. It serves as a viable alternative to traditional human grading methods and has become an important tool in high-stakes educational assessments. It has received considerable research attention in academic institutions as it accelerates the process and alleviates the workload on human evaluators. Hence, we studied the Artificial Intelligence and Machine Learning methods that were employed to develop automatic essay scoring systems. We have discussed the limitations in the existing research and explored emerging trends in this area. This study seeks to compare the performance of several common AES frameworks, emphasizing on machine learning with complex language features and deep neural algorithms. Through rigorous testing, the strengths and weaknesses of both traditional and modern models were assessed under varying circumstances and the results were evaluated using the efficiency score which were compared to the grades given by human raters. The results revealed that the neural network model, trained on comprehensive prompt data and employing Natural Language Processing technique for feature extraction, outperformed all the other models. This indicates that it gave a closer alignment with results of the human raters as compared to the conventional Machine Learning models.

关键词： Deep learning Machine learning algorithms Computational modeling Education Artificial neural networks Market research Feature extraction data models Natural language processing Testing

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