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Accident Factors Detection and Prediction for Indian states and district roads 3

Accident Factors Detection and Prediction for Indian states ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Jagadamba, G. Chayashree, G. Kumar, A.V. Senthil Shrinivasacharya, Purohit Siddaganag Institute of Technology Karnataka Tumakuru India Vidyavardhaka Institute of Technology Karnataka Mysuru India Hindusthan College of Arts and Science Tamilnadu Coimbatore India

ISBN: (纸本)9781665462006

The transportation systems are increasing day by day. The increased number of vehicles on the road leads to more accidents. In this situation, priority to road safety is must, and the government has made many attempts to predict and pinpoint accidents zone, roads etc. The usual cause for road accidents is rash driving;however, the accidents are increasing day by day due to other factors. This paper proposes the detection of factors for accidents using the prediction system for Indian states and districts of Karnataka. The proposed system, detected many factors such as seasons, classes of road and road types as the major cause for road accidents using machine learning algorithms apart from the regular factors. The results from the experiments were appreciable for the dataset collected from nearby police stations and hospitals of Bangalore and Tumakuru road and kaggle dataset. The testing results were found to be near to the manual analysis. © 2022 IEEE.

关键词： data mining

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EFFICIENT DDoS ATTACK DETECTION USING machine learning TECHNIQUES 3

EFFICIENT DDoS ATTACK DETECTION USING MACHINE LEARNING TECHN...

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3rd IEEE international Power and Renewable Energy conference (IPRECON)

作者： Nazarudeen, Fathima Sundar, Sumod TKM Coll Engn Ctr Artificial Intelligence Kollam Kerala India

ISBN: (纸本)9781665491754

Distributed Denial-of-Service (DDoS) attacks are deliberate attempts to interrupt the regular traffic of a specific server, network, organization, by flooding the victim or its neighbouring servers with network traffic. Identification of such attacks using various models is challenging due to the substantial modifications in their regular pattern and traffic rates. An automated detection approach is used to mitigate this issue, by limiting the feature space, which minimizes the model's overfitting and computational time. The CICDDoS2019 data set containing extensive DDoS attacks are used to train and access the proposed methodology in a cloud-based context. The relevant features are extracted using the Extra Tree classifier and they are fed to the Decision Tree, XGBoost, and Random Forest. Consequently, the proposed model can be used to detect DDoS attacks effectively.

关键词： CICDDoS2019 dataset DDoS attacks data preprocessing Feature selection Classification

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Detailed review on Integrated Healthcare Prediction System Using Artificial Intelligence and machine learning 3

Detailed review on Integrated Healthcare Prediction System U...

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3rd international conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Parasar, Deepa Ali, Arif Pillai, N. Muthuvairavan Shahi, Archana Alfurhood, Badria Sulaiman Pant, Kumud Amity University Maharashtra Cse India Dev Bhoomi Uttarakhand University Department of Cse Uttarakhand Dehradun India R.M.D Engineering College Computer Science and Business Systems Kavarapettai India Chitkara University Chitkara Business School India Princess Nourahbint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Saudi Arabia Graphic Era Deemed to Be University Department of Biotechnology Uttarakhand Dehradun India

ISBN: (纸本)9798350399264

The intricacy and expansion of statistics in increased care sector will lead to an increase in the application of artificial intelligence (AI). There are several different AI techniques in use today by consumers, health professionals, and sciences companies. Guidelines for care and prognosis, patient and commitment, and organizational chores are the key application areas. Challenges will prevent the work of health professionals from being substantially computerized for a considerable length of time, despite the fact that AI can do healthcare tasks in many instances equally as well or better than people. Ethics issues and the implementation of artificial intelligence in healthcare are also discussed. AI technology is essential for producing effective healthcare applications. The definition of several effective software programmers may help in the growth of the health predictive model. Computer vision and cloud services are critical elements for building smart healthcare *** collection and analysis of a wide range of health care data may provide useful insights quickly. Artificial intelligence is also crucial in the process of illness detection and prognosis. This smart and efficient approach may help with early illness diagnosis. Sensors connected through wireless networks make it possible to track the actions of a whole population. Sensors embedded in the human body may aid in the recognition of emotions and facial expressions by using neural networks. There is a plethora of technological implementations that contribute to a more advanced data processing *** primary focus of this research is to determine how effectively various efficient technology's function when used in the development of healthcare prediction models. AI uses a wide variety of sensor-based methods to boost healthcare's efficacy. Another area of study has substantial implications for the healthcare system. To help identify problems in biomedical goods, modelling tools may be pro

关键词： Forecasting

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ECG data mining Approach for Detection of Arrhythmia Using machine learning

ECG Data Mining Approach for Detection of Arrhythmia Using M...

引用

Device Intelligence, Computing and Communication Technologies, (DICCT), international conference on

作者： Rahema Naaz Shafeeq Ahmad Department of Computer Science & Engineering Azad Institute of Engineering and Technology Lucknow India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

The Electrocardiogram (ECG) is very important signal generated from human body that provides information for assessment of variation in cardiac activities. These ECG data are generally distorted due to high power line or magnetic field associated noise that results as a problem in error during diagnosis of heart related disorders on analysis through biomedical analysis software. This work contributes for very significant stage that is introduced to reduce the anomaly in the ECG signals to perform accurate automated diagnosis. This work follows a methodology of an automated machine learning algorithm is developed for accurate estimation of ECG data segment cycle using clustering approach integrated with Discrete Wavelet Transform (DWT). The key findings of this works cover the development and analysis of an approach to overcome the errors in detection of T-wave using Implantable Cardioverter Defibrillator (ICD). The work reports the solution of issues using fuzzy C-means clustering behind prediction of fatal Ventricular Arrhythmias (VA) for performing standard classifications.

关键词： machine learning algorithms Accuracy databases Arrhythmia Clustering algorithms Transforms Electrocardiography Discrete wavelet transforms Classification algorithms Standards

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Bridging the Gap: From Food recognition to Accurate Weight Estimation

Bridging the Gap: From Food Recognition to Accurate Weight E...

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3rd international conference on Disruptive Technologies, ICDT 2025

作者： Maharana, Bhagyashree Goyal, Mayank Kumar Abidi, Ali Imam Sharda School of Engineering & Technology Sharda University Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331519582

Accurate estimation of food weight is of utmost importance in the assessment of diet in nutrition, healthcare, and wellness. Although great progress has been achieved with machine learning and computer vision in image-based food recognition, the task of accurate weight estimation is still a challenging one. This review summarizes the current methodologies that address the gap, focusing on the reduction of errors and novel innovations. The review compares 2D and 3D computational methods, emphasizes the advantages of hybrid multimodal models, and assesses their strengths and weaknesses. The role of datasets and annotation techniques is analyzed in detail, highlighting the importance of having diverse and accurate datasets for model improvement. Standard evaluation metrics and benchmarks are addressed to evaluate accuracy and comparability across methods. Realism emerging trends of self-supervised learning, multimodal data integration, and real-time edge computing applications are searched for as answers to these practical constraints found in reality: variability in food, occlusion, environmental factors all challenge;solution proposed here. This review is aimed at outlining future research guidance by summarizing recent innovations, addressing gaps, and providing a roadmap for practical, scalable, and accurate dietary assessment systems. © 2025 IEEE.

关键词： Self-supervised learning

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SRv6-INT Enabled Network Monitoring and Measurement: Toward High-Yield Network Observability for Digital Twin 3rd

SRv6-INT Enabled Network Monitoring and Measurement: Toward...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Zhang, Xu Feng, Chuan Han, Pengchao Liu, Wei Gong, Xiaoxue Zhang, Qiupei Guo, Lei School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Institute of Intelligent Communication and Network Security Chongqing University of Posts and Telecommunications Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China School of Information Engineering Guangdong University of Technology Guangzhou510006 China

ISBN: (纸本)9789819616978

Network monitoring and measurement is an important part of realizing the network digital twin. However, it introduces the problem of high cost when obtaining the status data of physical networks. Therefore, to efficiently achieve network observability, we first introduce potential techniques and network scenarios for implementing network measurements. Then, a High-Yield Segment Routing over IPv6 based In-band Network Telemetry (HY-SRv6-INT) system is proposed in this paper. In particular, we design the HY-SRv6-INT system architecture. It consists of IPv6 switches implemented by the P4 language and a Software-Defined Networking (SDN) controller containing the HY-SRv6-INT application. Next, we experimentally demonstrate a packet-level path-associated network measurement process. In addition, the performance of the HY-SRv6-INT system is also quantitatively evaluated to verify its advantages. Finally, we discuss some research challenges and open issues for future network telemetry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Internet protocols

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Using Laplacian Support Vector machine for Gas-bearing Reservoir Identification

Using Laplacian Support Vector Machine for Gas-bearing Reser...

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Robotics, Artificial Intelligence and Intelligent Control (RAIIC), international conference on

作者： Hao Liang Shuying Ma Minglu Xu Ping Wang Chengdu Ingawa Electronics Technology Co. Ltd Chengdu China Chengdu Technological University Chengdu China Chengdu ForThink Technology Co. Ltd Chengdu China

ISBN: (数字)9798350352627

ISBN: (纸本)9798350352634

machine learning algorithms with automatic feature extraction capacity are a potent tool for breaking through the restrictions of old technologies in the new era of intelligent oil and gas exploration and have thus become widely used in geophysical exploration. When using seismic data for gas-bearing reservoir identification, a large amount of seismic data can be acquired, but it is more difficult to label the seismic data. To solve this difficulty, this research suggests using a semi-supervised machine learning method, the Laplace Support Vector machine (LapSVM), to learn the frequency variation properties of seismic waves in gas-bearing reservoirs and subsequently identify them. The frequency variation characteristics are derived from the high and low-frequency amplitudes obtained through S-transform. The application experiments of the field data show that, in comparison to the Support Vector machine (SVM), which only uses a small number of labeled samples for learning, the recognition based on LapSVM can use a large number of unlabeled samples for assisted learning, significantly improving recognition accuracy and enabling the effective detection of gas-bearing areas.

关键词： Support vector machines Training Laplace equations Accuracy Oils machine learning Reservoirs

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2025 international conference on Automation and Computation, AUTOCOM 2025

2025 International Conference on Automation and Computation,...

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3rd IEEE international conference on Automation and Computation, AUTOCOM 2025

ISBN: (纸本)9798331542375

The proceedings contain 296 papers. The topics discussed include: integrating automation and sustainability: Arduino-based solar panel cleaning system for renewable energy optimization;utilizing transforming portfolio management through automation using advanced deep reinforcement learning algorithms for optimized investment strategies;leveraging quantum computing for multi-criteria decision analysis in strategic business planning;secure multi-party computation for privacy preservation in collaborative networks;integration of continuous improvement approach in sustainable e-government services towards citizen satisfaction in health sector development of conceptual model;unlocking scholarly insights using deep learning-based evaluation method for research impact assessment in academic institutions;balancing technology with pedagogy in smart classrooms for English language;empirical study to select a place to stay in NCR area using data mining and machine learning;and the impact of ultra-wideband sensors in industrial automation and robotics applications.

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An Anomaly Detection Method for Multiple Time Series Based on Similarity Measurement and Louvain Algorithm 3

An Anomaly Detection Method for Multiple Time Series Based o...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Li, Shuya Song, Wenbin Zhao, Chao Zhang, Yifeng Shen, Weiming Hai, Jing Lu, Jiawei Xie, Yingshi Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Peoples R China Minist Environm Protect South China Inst Environm Sci Guangzhou 510655 Peoples R China Guangdong Prov Engn Lab Solid Waste Technol & Equ Guangzhou 510330 Peoples R China

Anomaly detection is an important task for data mining Detecting anomalies in the data collected from Municipal Solid Waste (MSW) incineration process is critical for their post processing, which helps to decrease emissions of typical flue gas pollutants and reduce costs. In this paper, we propose an unsupervised multiple time series anomaly detection model based on similarity measurement and Louvain algorithm, which consists of two stages. The first stage aims to find a subset of correlated time series data. The second stage is to detect anomalies in the subset, which can identify anomalous time windows and anomalous time subseries in the anomaly windows. The proposed method is applied on the data collected from an MSW incinerator in South China The results demonstrate the effectiveness of the proposed method. In addition, four similarity measure approaches are conducted and the results show that the model performs the best when using Pearson correlation coefficient, with an F2-score of 0.92. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： municipal solid waste incineration anomaly detection machine-learning Louvain algorithm similarity measurement

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Enhancing Management of Diverse Taiwanese Hotels Through machine learning and data mining

Enhancing Management of Diverse Taiwanese Hotels Through Mac...

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Social Sciences and Intelligent Management (SSIM), IEEE international conference on

作者： Yu-Hsiang John Huang Jenq-Foung Yao Cheng-Ying Yang Information Systems and Computer Science Georgia College & State University Milledgeville GA U.S.A Computer Science University of Taipei Taipei Taiwan

Using the capabilities of machine learning and data mining techniques, we formulated a Decision Tree model to aid hotel management in optimizing resource allocation for profit maximization. Additionally, we constructed an Artificial Neural Network (ANN) model to predict hotel occupancy rates based on various known predictors. To establish a baseline for correct prediction, we employed the ZeroR model that achieved an accuracy rate of 42.22%. In contrast, the developed Decision Tree model outperformed with a correct prediction rate of 73.33%, while the ANN model showed a rate of 70.56%. These results showed the effectiveness of both models in predicting outcomes for Taiwanese international tourist hotels.

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