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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Kumar, Abhishek Sah, Bashant Kumar Mehrotra, Tushar Rajput, Gaurav Kumar Sharda School of Engineering & Technology Sharda University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India College of Computing Sciences & It Teerthanker Mahaveer University Moradabad India

ISBN: (纸本)9798350323917

The double-spending problem in blockchain technology is a significant challenge that threatens the integrity and trustworthiness of decentralized systems. This problem occurs when a user attempts to spend the same cryptocurrency unit twice, leading to a situation where the blockchain network must decide which transaction to accept and which to reject. One of the most urgent problems with blockchain technology is the issue of double spending, as it undermines the fundamental principles of trust and transparency that underlie decentralized systems. Various factors can contribute to the double-spending problem, including network latency, malicious actors, and the consensus mechanism used to validate transactions. This study investigates the many approaches put out to solve the double-spending issue in blockchain technology. The proof-of-work consensus mechanism, which necessitates network users to carry out difficult calculations in order to validate transactions, is one of the most popular alternatives. The proof-of-stake consensus technique is an additional remedy, which relies on participants staking their cryptocurrency units to validate transactions. While both mechanisms have their advantages and disadvantages, they are not foolproof and can be vulnerable to attacks. Emerging technologies, like multi-party computation and zero-knowledge proofs, are being investigated in addition to current solutions to the double-spending issue. Overall, this paper highlights the critical nature of the double-spending problem in blockchain technology and evaluates the existing and emerging solutions to the issue. © 2023 IEEE.

关键词： Blockchain

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Study of the impact of analytical modelling based on electric heating loads and differential equations for power regulation 5

Study of the impact of analytical modelling based on electri...

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5th International Academic Exchange Conference on Science and technology Innovation, IAECST 2023

作者： Huang, Changquan Zhang, Renlong Zhu, Yukun Wang, Chenxi Liu, Henan Fu, Jiaxian Yango University School of College of Artificial Intelligence Fujian China University of Jinan Business School Shandong China Liaocheng University School of Computing Shandong China Lanzhou University of Technology Materials Science and Engineering Gansu China Harbin University of Science and Technology School of College of Rongcheng Harbin China School of Electrical and Information Engineering Hunan Institute of Engineering Hunan China

ISBN: (纸本)9798350357738

In this study, an equivalent model of room temperature variation is established, and the switching state function, thermal power function and time variables are introduced to construct an analytical model of electric heating for a typical user, which in turn obtains the dynamic evolution of the steady state solution. Specifically, due to the large specific heat capacity of the wall, its temperature changes are small;the room temperature and heating power show periodic changes, while the power and thermal resistance are positively correlated to the room temperature fluctuation, and the heat capacity is generally negatively correlated. When the outdoor temperature reaches 15°C, the power consumption and cost reach the highest point. We also constructed an analytical model for power regulation based on differential equations, taking into account the thermal inertia of the building and the model temperature constraints, and found that the initial upward and downward adjustments had smoother changes in the available durations. When the time reaches 15 hours, the down-regulation durations show a decreasing trend, while the indoor temperatures rapidly approach the wall temperatures until they reach relative equilibrium. Especially noteworthy is that, when the outdoor temperature is lower, the power up-regulation is larger in order to keep the temperature within this range because the que value is set between 18°C and 22°C;conversely, the power up-regulation is smaller when the outdoor temperature is higher. It provides useful insights for the future development of green, efficient and intelligent electric heating systems. © 2023 IEEE.

关键词： Temperature distribution

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Traffic Sign Detection Using YOLOv8

Traffic Sign Detection Using YOLOv8

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Rahul Kumar Aniket Gupta Rajeswari D Department of Data Science and Business Systems School of computing College of Engineering and Technology

Traffic signals and other signs like parking., stop signs., etc. have become very crucial in autonomous and s elf-driving cars as it helps the smart system to comply with the basic traffic rules along with that it helps navigate routes based on the signs thus enabling a more secure driving experience for the drivers. There have been a lot of new algorithms that have emerged in the past recent years regarding this. In this paper., this research has used the new YOLOv8 object detection system to help us detect traffic signs as it is much fas ter and more precis e than its previous iterations. To improve the algorithm., this paper has used a dataset comprising photos of traffic signs taken at different angles and different light intensities. This system can predict the traffic signs with 93% accuracy.

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Deep Learning Driven - Pest Classification and Sustainable Agriculture 3

Deep Learning Driven - Pest Classification and Sustainable A...

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

作者： Kumar, M. Sirish Usha, R. Reddy, B. Rama Kantha Ramachandra, S. Prakash, P. Jaya Swetha, B. Mohan Babu University School of Computing Dept. of Data Science India Madanapalle Institute of Technology and Science Madanapalle India Sri Venkateswara College of Engineering Tirupati India Mother Theresa Institute of Engineering and Technology Palamaner India Sri Venkateswara College of Engineering Department of It Tirupati India Mohan Babu University Dept. of Master of Computer Applications India

ISBN: (纸本)9798331509675

This study introduces a system designed to identify pests in crops and classify them as either beneficial or harmful. The project begins by providing a comprehensive overview of various pest identification methods, analyzing their strengths and limitations. To address the problem, a categorization approach is proposed that incorporates convolutional neural networks (CNN) to build the pest identification and classification model. The model was trained using a dataset containing images of different pest species. Extensive testing was conducted on a large dataset, and the system's performance was validated against multiple traditional classification models. The proposed framework achieved a classification accuracy of 90%, outperforming conventional methods. This research highlights agriculture's vital role in the economy and underscores the potential of advanced technologies in supporting sustainable farming practices. © 2025 IEEE.

关键词： Agriculture Sustainability Classification Accuracy Classification Model Convolutional Neural Network (CNN) Pest Classification

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Crop Recommendation with BiLSTM-MERNN Algorithm for Precision Agriculture 1

Crop Recommendation with BiLSTM-MERNN Algorithm for Precisio...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Sreemathy, J. Prasath, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India SRM Institute of Science and Technology Department of Networking and Communications School of Computing Tamilnadu Chennai India

ISBN: (纸本)9798350336009

The term 'precision agriculture' (PA) refers to the application of high-tech sensors and analysis tools to increase agricultural output and assist with management decisions. Deep learning, also referred as DL, has lately emerged as a potential remedy for information assertions and CPU vision difficulties. It offers a lot of promise and can be applied to agriculture just like it does to other industries. With this goal in mind, this study makes a case for establishing a crop recommendation system dependent on DL. This location is responsible for collecting a great amount of historical data on agricultural productivity, climatic conditions, and the preprocessing of said data. After that, crop recommendations are generated using an algorithm known as the Bidirectional LSTM-Modified Elman Recurrent Neural Network (BiLSTM-MERNN). A crop dataset is used to test how well the proposed method performs compared to other methods already in use, demonstrating the method's applicability. © 2023 IEEE.

关键词： Crops

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Cloud-Based Road Safety for Real-Time Vehicle Rash Driving Alerts with Random Forest Algorithm 3

Cloud-Based Road Safety for Real-Time Vehicle Rash Driving A...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Presitha Aarthi, M. Reddy, Chilukala Mahender Anbarasi, A. Mohankumar, N. Ishwarya, M.V. Murugan, S. Shree Sathyam College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Salem India Gitam University Department of Computer Science and Engineering Telangana Hyderabad India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India B.V. Raju Institute of Technology Department of Electronics and Communication Engineering Telangana Hyderabad India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

The cloud-based road safety technology introduced in this research improves real-time vehicle behavior monitoring and warns for rash driving. It uses cloud computing to gather and interpret data from onboard car sensors and other traffic monitoring equipment. Powerful machine learning technology the Random Forest (RF) algorithm analyses this data intelligently. The RF algorithm is trained on a varied array of driving metrics to detect rash driving tendencies. Real-time data streams may be processed efficiently and scalable on the cloud, enabling speedy decision-making for rash driving detection. A network sends alerts to authorities and nearby vehicles. The system is adaptable to different road conditions, resistant to sensor data noise, and able to enhance rash driving detection accuracy via model updates. Cloud architecture makes the system available worldwide. With its focus on real-time notifications utilizing the RF algorithm, this cloud-based road safety system may reduce the hazards of reckless driving and avoid accidents. © 2024 IEEE.

关键词： Cloud computing architecture

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Intelligent IoT-Driven Advanced Predictive Maintenance System for Industrial Applications

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SN Computer Science 2025年第2期6卷 1-24页

作者： Dhinakaran, D. Edwin Raja, S. Velselvi, R. Purushotham, N. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India Department of Computer Science and Engineering P.S.R Engineering College Sivakasi India Department of CSE School of Computing Mohan Babu University Tirupati India

Predictive Maintenance (PdM) aims to ensure the continuous operation of high-risk industrial systems. This challenge is especially critical in environments where equipment failure can cause major financial losses and disrupt operations. Traditional PdM approaches, while fairly effective, often fall short in accurately predicting failures due to their reliance on simple statistical methods and predefined rules. The proposed work introduces an Intelligent IoT-Driven Advanced Predictive Maintenance System (APdM) that addresses these limitations. By integrating advanced machine learning techniques, including supervised and reinforcement learning, with IoT technologies to enhance predictive accuracy as well as the operational efficiency. The system leverages Explainable AI (XAI) to ensure transparency in decision-making and employs federated learning for distributed model training, preserving data privacy across multiple edge devices. The proposed APdM system was evaluated using the Kaggle "Air Compressor Predictive Maintenance" Dataset, which provided extensive sensor data from heavy vehicle operations. Comparative analysis against four prevailing PdM approaches—Traditional PdM, Decision Support System-PdM, Genetic Algorithm-based PdM, and AI-based PdM—demonstrated the superior performance of our system. The results show that the APdM system, powered by intelligent IoT integration, diminishes the Mean Error Percentage (MEP) by up to 38.4% compared to traditional methods and decreases the Mean Absolute Error (MAE) by 36.7% relative to DSS-PdM. Additionally, the system achieves a 78% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) over GA-PdM, underscoring its robust performance in real-world industrial scenarios. These outcomes validate the effectiveness of the proposed APdM system, marking a significant improvement in the field of predictive maintenance through the intelligent integration of IoT technologies. © The Author(s), under exclusive licence to S

关键词： Explainable AI IoT Machine learning Predictive maintenance Supervised and reinforcement learning

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The Gm System for the use of AGMR System 4

The Gm System for the use of AGMR System

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4th IEEE International Conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Verma, Aditya Al-Jawahry, Hassan M. Alsailawi, H.A. Kirubanantham, P. Sherineliyas Saadoun, Osama Nazim Manalmoradkarim Zaidan, Doaa Talib Department of Cse Graphic Era Hill University Uttarakhand Dehradun248007 India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq University of Kerbala College of Medicine Department of Chemistry and Biochemistry Kerbala56001 Iraq Srm Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Kattankulathur SRM Nagar Chennai India Hindustan Institute of Technology and Science Department of Computer Applications India Al-kunooze University College Law Department Basra Iraq National University of Science and Technology College of Pharmacy DhiQar64001 Iraq Kut University College Department of Laser and Optical Electronics Engineering Al Kut Iraq

ISBN: (纸本)9798350360165

Due to its ability to navigate crop fields with ease, mobile robots have become more important in the agricultural industry. This study discusses a number of important topics related to agricultural robot navigation, including mapping, localization, route planning, obstacle detection, and controlled guidance. With laser-based weeding tools installed, the suggested Guiding Manager aims to maximize tracking performance of mobile autonomous robots. Integrating spiral, lateral, and linear speed controls, the system can accommodate a variety of trajectories, including curves and straight lines, that are seen in agricultural settings. To confirm the performance of the controllers under various conditions, such as loose soil, stones, and humidity, a thorough field test is conducted on a tracked mobile platform. With the Lateral Controller, the average angular error is around -0.005 rad, and the average absolute lateral error is approximately 0.09 m. In contrast, the average absolute lateral error and angular inaccuracy displayed by the Spiral Controller are 0.15 m and 0.013 rad, respectively. The system guarantes the safe and accurate implementation of robotic tasks in agriculture, as shown by its ±0.009 rad angular precision and ±0.015 m horizontal accuracy. © 2024 IEEE.

关键词： Agricultural robots

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Advanced Detection of Copy-Move Forgery Using Deep Convolutional Neural Network Features Integrated with Machine Learning-Based Classification Techniques 3rd

Advanced Detection of Copy-Move Forgery Using Deep Convoluti...

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3rd International Conference on Machine Learning, Cloud computing and Intelligent Mining, MLCCIM 2024

作者： Al-khuzaie, Mohammed Y. Chohan, Jasgurpreet Singh Sabah, Hawraa Ali Dey, Protyay Verma, Deeksha Hussein, Laith College of Technical Engineering National University of Science and Technology Dhi Qar Nasiriyah64001 Iraq School of Mechanical Engineering Rayat Bahra University Mohali India NIMS School of Computing Science and Artificial Intelligence NIMS University Rajasthan Rajasthan Jaipur India Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri- Punjab Mohali140307 India College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (纸本)9789819624676

Technological advancements have significantly contributed to the creation and processing of digital images through various image manipulation software available today. Consequently, the need for effective image forgery detection techniques has become paramount to distinguish between authentic and manipulated images. Image forensics is utilized in various fields, including national intelligence agencies, scientific publications, and social networks. One specific type of image manipulation, known as copy-move forgery, involves copying and pasting a portion of an image within the same image, resulting in an altered version of the original. In this study, we introduce a lightweight convolutional neural network (CNN) architecture combined with Support Vector Machine (SVM) classification for copy-move forgery detection (CMFD). This approach uses a similarity measure across feature patches to identify regions containing forgeries. We evaluated the effectiveness of our model using the challenging MICCF600 dataset commonly employed in CMFD research. The proposed model effectively reduces false positives, thereby enhancing pixel-level accuracy and reliability in CMFD applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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SP-DDPT: a simple prescriptive-based domain data preprocessing technique to support multilabel-multicriteria learning with expert information

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International Journal of Computers and Applications 2021年第4期43卷 333-339页

作者： Mehfooza, M. Pattabiraman, V. Department of Information Technology Rajalakshmi Engineering College Chennai India School of Computing Sciences and Engineering VIT University Chennai India School of Computing Sciences and Engineering VIT University Chennai India

computing is a consortium of machine learning techniques which works well and provides a sufficient information to make assessment from the chronological historic dataset. In real today a lot of machine analytic techniques available to explore and to investigate the depth knowledge of the dataset but fails to work with multicriteria. There are domains with multicriteria-based decisions like agricultural, spatial, medical, and etc., These domain dataset requires expert intervention as multicriteria to streamline the knowledge presented from the efficient machine learning techniques. The analysis should extend with prescription in order to make productive actions from the learning. This research work is proposed to support the prescription of dataset with a simple automated framework to express the expert percepted value. The simple prescriptive-based domain data preprocessing technique (SP-DDPT) algorithms were defined to support the universal criteria such as ‘less than,’ ‘more than’ and ‘between.’ The proposed framework was applied to the sample Indian coastal dataset and the expert percepted value was preprocessed and prescribed successfully. © 2018 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Machine learning

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