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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Kavya, Dyapa Sindhuja, Nerella Srija, Anugu Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

Plant disease detection systems may face challenges in accurately distinguishing between various diseases, especially when symptoms overlap or environmental factors introduce ambiguity, impacting the system's reliability. The agricultural innovation described involves an integrated system for early plant disease detection and targeted pesticide recommendations. It utilizes preprocessing, feature extraction, and advanced classification algorithms. Data from IoT sensors and high-resolution imaging devices are collected to monitor crop conditions and remove image noise using Gaussian smoothing. Feature extraction, particularly the Histogram Oriented Gradients method, is employed to identify disease symptoms and environmental parameters. Advanced machine learning algorithms, including Convolutional Neural Networks (CNN), are used to classify diseases and recommend pesticides based on disease type, severity, crop, and environmental conditions. This innovation enhances timely disease identification and promotes more precise and sustainable pesticide use, leading to increased agricultural productivity and reduced environmental impact. MATLAB is used for research evaluation, showing 7% superior performance compared to existing methodologies. © 2023 IEEE.

关键词： Agricultural Innovation Convolutional neural network Gaussian smoothing Histogram oriented gradients pesticides Plant disease

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Investigating the Utility of Data Mining for Automated Credit Scoring 15

Investigating the Utility of Data Mining for Automated Credi...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Yadav, Dhyan Garima Soni, Rudransh Anandhasilambarasan, D. Sasirekha, R. Prema, S. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Karnataka Bangalore India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore641021 India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai127 India Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India

ISBN: (纸本)9798350370249

Data mining is a powerful device for automatic credit scoring. This paper will look into the application of statistics mining for automated credit score scoring by discussing its advantages and downsides. The form may even look at cutting-edge research in the location and discuss the demanding situations that information mining techniques pose while used for automatic credit score scoring. Similarly, the paper will provide pointers on how statistics mining techniques can improve the effectiveness of computerized credit score scoring. Subsequently, a precis of the paper's conclusions and implications for future studies could be supplied. © 2024 IEEE.

关键词： Conclusions Implications Improve Information Scoring

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Real Time Fire Detection and Alert Triggering System

Real Time Fire Detection and Alert Triggering System

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Balakrishnan, D. Mariappan, Umasree Kumar, Netapalli Nithish Nagaprudhvi, Kothuru Premchand, Muthyam Vijay, Gopala Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Krishnakoil India Sri Vidya College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Virudhunagar India

ISBN: (纸本)9798350315455

The real-time fire detection and triggering system presented in this study harnesses camera images for swift and reliable fire detection, leveraging a multi-stage approach. The system's preprocessing phase involves image enhancement and noise reduction to optimize image quality, while the feature extraction step extracts relevant information from the images to improve discrimination between fire and non-fire regions. Classification is carried out using a machine learning model, like Convolutional Neural Networks (CNNs), to discern potential fire events in the images. Upon detecting a fire, the system triggers immediate alarms or safety protocols, thus enhancing fire prevention and response mechanisms in real-time scenarios. The overall implementation of the research work is done in the python environment from which it is proved that the proposed methodology attains better performance than the existing research methodology. The proposed work attains better performance than the existing research methodologies in terms of 7% increased accuracy. And in terms of precision, recall and f-measure proposed method attains 6%, 4% improvement than the existing research methodologies. © 2023 IEEE.

关键词： alert triggering system convolutional neural network feature extraction Fire detection image quality

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AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction 40

AdapTraj: A Multi-Source Domain Generalization Framework for...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Qian, Tangwen Chen, Yile Cong, Gao Xu, Yongjun Wang, Fei Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Multi-agent trajectory prediction, as a critical task in modeling complex interactions of objects in dynamic systems, has attracted significant research attention in recent years. Despite the promising advances, existing studies all follow the assumption that data distribution observed during model learning matches that encountered in real-world deployments. However, this assumption often does not hold in practice, as inherent distribution shifts might exist in the mobility patterns for deploy-ment environments, thus leading to poor domain generalization and performance degradation. Consequently, it is appealing to leverage trajectories from multiple source domains to mitigate such discrepancies for multi-agent trajectory prediction task. However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer;(2) inadequate modeling for external factors. To address these issues, we propose a new causal formulation to explicitly model four types of features: domain-invariant and domain-specific features for both the focal agent and neighboring agents. Building upon the new formulation, we propose AdapTraj, a multi-source domain generalization framework specifically tailored for multi-agent trajectory prediction. AdapTraj serves as a plug-and-play module that is adaptable to a variety of models. Extensive experiments on four datasets with different domains demonstrate that AdapTraj consistently outperforms other baselines by a substantial margin. © 2024 IEEE.

关键词： Trajectories

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An Improved Imitation Learning Algorithm Based on Markov Chain

An Improved Imitation Learning Algorithm Based on Markov Cha...

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2024 IEEE International Conference on Control science and Systems engineering, ICCSSE 2024

作者： Yu, Jiyang Huang, Dan He, Xiaobao Han, Yong Wang, Jianyu Sun, Kaiwen Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Beijing Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9798331517199

An improved imitation learning algorithm based on Markov chain is proposed, aiming to enhance the performance of autonomous driving in complex environments. Imitation learning learns task execution by observing expert behavior, while Markov chains are used to model state transition processes. The existing imitation learning algorithms have limited ability to handle complex tasks, especially in natural environments. The algorithm proposed in this article improves learning efficiency and autonomous control performance by introducing strategy iteration and reward function design for sudden obstacles. The core idea is to introduce Markov decision processes into traditional imitation learning frameworks to improve learning efficiency and performance. By acquiring a refined strategy, the objective is to mimic the agent's actions within the environment to closely resemble those of the expert. To achieve this, first train an initial strategy using expert examples. Then, use this strategy to generate new examples and update the strategy using these examples. This process iterates continuously until the strategy converges. The experimental results show that on the lunar environment autonomous driving datasets, the accuracy of our algorithm reaches 95% with a mean square error of 0.02, while the accuracy of the comparative algorithm is the highest at 90% with a mean square error of 0.05. This indicates that the improved algorithm can more accurately learn and imitate the driving behavior of experts. In addition, the performance of the algorithm is stable at different iteration times, indicating good convergence. © 2024 IEEE.

关键词： Contrastive Learning

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On the chronological understanding of the homogeneous dielectric barrier discharge

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High Voltage 2023年第6期8卷 1132-1150页

作者： Xinpei Lu Zhi Fang Dong Dai Tao Shao Feng Liu Cheng Zhang Dawei Liu Lanlan Nie Chunqi Jiang State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic EngineeringHuazhong University of Science and TechnologyWuhanChina College of Electrical Engineering and Control Science Nanjing Tech TechnologyNanjingChina School of Electric Power South China University of TechnologyGuangzhouChina Institute of Electrical Engineering Chinese Academy of SciencesBeijingChina Frank Reidy Research Center for Bioelectrics Old Dominion UniversityNorfolkVirginiaUSA Department of Electrical and Computer Engineering Old Dominion UniversityNorfolkVirginiaUSA

Dielectric barrier discharges(DBD)are widely utilised non‐equilibrium atmospheric pressure plasmas with a diverse range of applications,such as material processing,surface treatment,light sources,pollution control,and *** the course of several decades,extensive research has been dedicated to the generation of homogeneous DBD(H‐DBD),focussing on understanding the transition from H‐DBD to filamentary DBD and exploring strategies to create and sustain H‐*** paper first discusses the in-fluence of various parameters on DBD,including gas flow,dielectric material,surface conductivity,and mesh ***,a chronological literature review is presented,highlighting the development of H‐DBD and the associated understanding of its un-derlying *** encompasses the generation of H‐DBD in helium,nitrogen,and ***,the paper provides a brief overview of multiple‐current‐pulse(MCP)behaviours in H‐*** objective of this article is to provide a chronological un-derstanding of homogeneous dielectric barrier discharge(DBD).This understanding will aid in the design of new experiments aimed at better comprehending the mechanisms behind H‐DBD generation and ultimately assist in achieving large‐volume H‐DBD in an air environment.

关键词： dielectric barrier pollution

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Multi-party privacy-preserving decision tree training with a privileged party

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science China(Information sciences) 2024年第8期67卷 253-268页

作者： Yiwen TONG Qi FENG Min LUO Debiao HE Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center Qilu University of Technology(Shandong Academy of Sciences)

Currently, a decision tree is the most commonly used data mining algorithm for classification tasks. While a significant number of studies have investigated privacy-preserving decision trees, the methods proposed in these studies often have shortcomings in terms of data privacy breach or efficiency. Additionally,these methods typically only apply to symmetric frameworks, which consist of two or more parties with equal privilege, and are not suitable for asymmetric scenarios where parties have unequal privilege. In this paper,we propose SecureCART, a three-party privacy-preserving decision tree training scheme with a privileged party. We adopt the existing pMPL framework and design novel secure interactive protocols for division,comparison, and asymmetric multiplication. Compared to similar schemes, our division protocol is 93.5–560.4× faster, with the communication overhead reduced by over 90%; further, our multiplication protocol is approximately 1.5× faster, with the communication overhead reduced by around 20%. Our comparison protocol based on function secret sharing maintains good performance when adapted to pMPL. Based on the proposed secure protocols, we implement SecureCART in C++ and analyze its performance using three real-world datasets in both LAN and WAN environments. he experimental results indicate that SecureCART is significantly faster than similar schemes proposed in past studies, and that the loss of accuracy while using SecureCART remains within an acceptable range.

关键词： privacy protection decision trees secure multi-party computation secret sharing privileged party

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Addressing Vaccine Misinformation on Social Media by leveraging Transformers and User Association Dynamics 2

Addressing Vaccine Misinformation on Social Media by leverag...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Rao, Chirag Prabhu, Gautham Manuru Kumar, Ajay Rajendra Gupta, Shourya Shetty, Nisha P. Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Department of Information Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India

Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions. © 2024 Elsevier B.V.. All rights reserved.

关键词： Vaccines

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Exploring Protocol Diversity in In-Vehicle Networks: Simulated Data Revelation

Exploring Protocol Diversity in In-Vehicle Networks: Simulat...

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2024 IEEE International Conference on Vehicular technology and Transportation Systems, ICVTTS 2024

作者： Vanajakshi, J. Renuka, A. Adesh, N.D. Manipal Academy of Higher Education Manipal Institute of Technology Department of Computer Science and Engineering Karnataka Manipal India Manipal Academy of Higher Education Manipal Institute of Technology Department of Information and Communication Technology Karnataka Manipal India

ISBN: (纸本)9798350355512

Modern vehicles heavily rely on various Electronic Control Units (ECUs) to ensure proper operation, safety and efficiency. In-Vehicle Network (IVN) facilitates communication and the flow of information between ECUs. Commonly used IVN protocols include Controller Area Network (CAN), Local Interconnect Network (LIN), FlexRay, Ethernet, Media Oriented System Transport (MOST), and CAN with flexible data transfer (CAN-FD). Understanding the effect of these protocols on data generation, transfer, and ECU behaviour is critical to improving communication and enhancing the safety and security features of automotive communication systems. This work focuses on using the Vector CANoe tool to simulate real-world scenarios and analyse the complexities of IVN protocols. The research aims to improve automotive communication systems by examining the performance of these protocols in virtual environments. The findings from this work offer valuable insights for enhancing in-vehicle communications through simulation and testing. © 2024 IEEE.

关键词： Network security

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A Structured Analysis on IPL 2022 matches by approaching various Data Visualization and Analytics

A Structured Analysis on IPL 2022 matches by approaching var...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Selva Birunda, S. Nagaraj, P. Jency A Jebamani, B. Revathi, B. Muneeswaran, V. Ramco Institute of Technology Department of Artificial Intelligence and Data Science Virudhunagar India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Kalasalingam Academy of Research and Education Department of Electronics and Communication Engineering Virudhunagar India

ISBN: (纸本)9798350348217

The main purpose of making this report is to give the information to the franchise to make their decision on buying the players by bidding in the auction based on their performances. There are many models of reports which describe the IPL seasons. This report's purpose is to give a brief analysis of the IPL 202215th league. In this report, much of the data from the datasets of matches is utilized and analysis is made by using Python packages specifically, Pandas, NumPy, and matplotlib. By making graphs and tables a brief analysis with complete knowledge of an IPL season is given. This analysis describes every good performance and gives good chance to the best players. © 2023 IEEE.

关键词： Data visualization

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