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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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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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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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ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS) Detection and Prediction System 1

ASHA: Machine Learning-Based Polycystic Ovary Syndrome (PCOS...

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1st International Conference on AIML-Applications for engineering and technology, ICAET 2025

作者： Dixit, Jayati Rai, Anshuman Tandon, Pulak Pandey, Dhiraj JSS Academy of Technical Education Noida Information Technology Department Noida India JSS University Noida Computer Science and Engineering Department Noida India

ISBN: (纸本)9798350355611

About 17.5% of adults worldwide are infertile, which emphasizes the urgent need for cutting-edge reproductive healthcare solutions. Based on statistics from June 2023. According to the World Health Organization (WHO), 8-13% of females on earth who are in their reproductive age have PCOS, and about 70% of cases go undiagnosed. With the integration of PCOS detection, IVF success rate prediction, and a period tracking system, this work offers a comprehensive women's health application. In addition, a large percentage of women suffer from menstruation abnormalities, such as dysmenorrhea (38.1%) and irregular frequency (80.7%). This approach improves early illness detection and fertility planning by using data analysis and machine learning. The techniques used are Random Forest, K-Nearest Neighbor, Logistic Regression, Naive Bayes, Decision Tree and SVM;which provides us better accuracy than previous works. © 2025 IEEE.

关键词： Nearest neighbor search

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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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Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes 2

Deep-Based Hybrid Model For Enhancing Land Region Prediction...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Selvapandian, D. Raja, Laxmi Tharageswari, K. Vm, Senthilkumar Jc, Nikitha Hemalatha, M. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Faculty of Engineering Department of Cyber Security Coimbatore India Mahendra Institute of Technology Department of Information Technology Namakkal India Nandha Arts and Science College Department of Information Technology Erode India

ISBN: (纸本)9798331534950

Although satellite data is not a novel approach to mapping wetlands, its accuracy depends on data quality and categorization algorithms, making it a continuous challenge. The increasing accessibility of satellite data and the development of data modelling technologies, such as big data modelling using machine learning (ML) algorithms, have improved the accuracy of this type of mapping. Our work demonstrated a simple, scalable, and reliable wetland classification using linear Support Vector Machine (l-SVM) and unsupervised k-Nearest Neighbor (k-NN) machine learning approaches. Normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), the two primary inputs were modelled using Landsat optical data. Subsequently, l-SVM was used to supervise k-NN data using training data collected from outside observations. The accuracy test results indicate that there is a considerable reduction in error for both optical indices, and all prediction models have an accuracy of about 99%. © 2024 IEEE.

关键词： Data accuracy

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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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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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Enhanced Source Location Privacy: A Blockchain-based Flamingo Archery Optimization 3

Enhanced Source Location Privacy: A Blockchain-based Flaming...

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

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Harsha, G.K. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Ambedkar Beedi Bangalore560001 India Department of Information Technology Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru560064 India

The SLP(Source Location Privacy) is a critical issue the IoT environment faces, particularly with sensitive data applications. Traditional SLP algorithms fail to protect source node privacy and balance network performance. Very few works tried to address the multiple assets scenario. We have proposed a novel Flamingo Archery Optimization Algorithm (FAO) to choose an optimal path from generated multiple paths. Fractional Flamingo Optimization Algorithm (FFOA) with blockchain routing is proposed with a focus on providing SLP and balanced energy under multiple source and destination scenarios. We simulated the work using the MATLAB simulator. Our evaluation's results demonstrate the benefits of our strategy, exceeding previous approaches with lower energy dissipation, a maximum safety period of 563491.9 m, a network lifetime of 119.819 s, and an energy consumption of 0.123 J. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： MATLAB

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Applying Deep Learning for the Examination of Voice Disorders in the Identification of Parkinson's Disease 1

Applying Deep Learning for the Examination of Voice Disorder...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Gupta, Kushagra Jain, Vedant Andrew, J. Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India

ISBN: (纸本)9798350330915

Parkinson's disease is a degenerative neurological condition marked by difficulties with coordination, tremors, and rigidity in the muscles. Positively, speech recordings show potential as a precocious biomarker for Parkinson's disease, able to identify minute alterations prior to the manifestation of other symptoms. The main objective of this research is to create a machine learning model that can analyse speech recordings and reliably determine whether Parkinson's disease is present. Key questions that the model aims to answer include how well it detects early signs of Parkinson's disease, how well it distinguishes between voice samples from healthy individuals and those from early stages of Parkinson's disease, and whether or not accuracy will vary depending on variables like the length or calibre of voice recordings. The primary goal is to use deep learning methods to analyse voice data to detect Parkinson's disease early on. © 2023 IEEE.

关键词： Deep Learning Extreme Gradient Boosting K-Nearest Neighbour Parkinson's disease Standard Scalar Support vector machines

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