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Road Accident Prediction in Highways using Machine Learning algorithms

Road Accident Prediction in Highways using Machine Learning ...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Jambula Snehith Reddy M. R. Rashmi Lee Hoong Pin Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India Faculty of Engineering and QS INTI International University Nilai Malaysia

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Road accidents are increasing day by day. In India, every year nearly 33% of road accidents take place in national highways and 23% in state highways. this paper focuses on predicting the road accidents in national and state highways in India using machine learning algorithms. the data is curated using annual reports published on road accidents in India by ministry of road transport & highways also, the reports on the compensation to the victims by motor vehicle accident fund trust. Road accident details are captured from the year 1990-2022. this covers the information about monthwise, state-wise accidents due to all types of vehicles, road environment, type of traffic control, vehicle condition, weather condition, rural/urban areas and so on. the data contains information about total number vehicles registered, road length, number of accidents in a year, number of people killed and the number of people injured. the road accidents and the number of vehicles causing accidents are predicted using machine learning algorithms viz., Linear Regression, Ridge Regression, Decision Tree, K-Nearest Neighbour (KNN) and Multilayer Perceptron algorithms. Multilayer perceptron model outperformed all other prediction models with highest R2-Score of 0.93278 and Mean Squared Error (MSE) of 0.277503 and Root Mean Squared Error (RMSE) of 0.387880 for five-fold cross validation. Prediction helps in identifying the critical regions/roads where the number of accidents are more. this helps in implementing accident preventive measures like restricting speed limit in such highways.

关键词： Road accidents Machine learning algorithms Roads Multilayer perceptrons Predictive models Nearest neighbor methods Traffic control Prediction algorithms Real-time systems Regression tree analysis

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5th international Workshop on Advanced algorithms and Control Engineering, IWAACE 2021

5th International Workshop on Advanced Algorithms and Contro...

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2021 5th international Workshop on Advanced algorithms and Control Engineering, IWAACE 2021

the proceedings contain 121 papers. the topics discussed include: research on oxygen saturation based on statistical analyses;application of machine learning in recommendation system;research on location of cold chain logistics distribution center with low carbon in Beijing-Tianjin-Hebei area on the basis of RNA-artificial fish swarm algorithm;health evaluation method of supercapacitor based on data mining;research on network traffic anomaly detection method based on deep learning;microservice monitoring and handoff algorithm based on dual-node hot standby;application of particle swarm optimization on reliability sampling inspection program for success or failure product;and quantitative research on the science and technology investment and economic growth of Guangdong province from the perspective of intelligent data analysis.

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Adaptive Optimal Control of Discrete-Time Linear Systems with Discounted Value: Off-Policy Reinforcement Learning

Adaptive Optimal Control of Discrete-Time Linear Systems wit...

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Data-driven Optimization of Complex Systems (DOCS), international conference on

作者： Liao Zhu Chunxiuzi Liu Jingsheng Xu Ping Guo International Academic Center of Complex Systems Beijing Normal University Zhuhai China School of Systems Science Beijing Normal University Beijing China School of Law Henan University of Urban Construction Pingdingshan China

this manuscript is engaged in the intricacies of optimizing the control conundrums inherent to enigmatic, unknown discrete-time linear systems, underscored by an intractable discounted infinite-horizon value function. To forge a data-driven conductor, we unveil the discounted algebraic Riccati equation (D-ARE) and cast an analytical gaze upon the on-policy reinforcement learning (RL) algorithm. From this springboard, we erect two computational scaffoldsnamely, the model-based and model-free off-policy RL algorithmsto surmount the D-ARE impasse. Notably, the congruence between off-policy RL algorithms and their on-policy kin emerges resplendent. the model-free off-policy RL algorithm, akin to a discerning connoisseur, imbibes the essence of D-ARE solution from the subtle ballet of system state trajectory data, eschewing the need for the ostentation of system dynamics. Subsequently, the hallowed crucible of simulation, enlivened by the direct current servo motor, buttresses the efficacy of our proposed algorithm while painting a vivid tableau of the discount factor's symphonic influence upon the resultant conductor.

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A Machine Learning Approach for Predicting Efficient CPU Scheduling Algorithm

A Machine Learning Approach for Predicting Efficient CPU Sch...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Swapnil Biswas Md. Shakil Ahmed Md. Jobayer Rahman Anika Khaer Md. Motaharul Islam Department of Computer Science and Engineering United International University Bangladesh

CPU scheduling is a critical aspect of an operating system design as it dictates how processes are prioritized for execution. In a uniprocessor, only a single process at a time can be executed simultaneously. the CPU scheduler considers various factors to determine the most effective process order. thus, it's vital to decide quickly on the CPU scheduling strategy. In this research, a number of different machine learning algorithms are compared in order to determine which one offers the most suitable CPU scheduling method that is very effective and has the quickest turnaround time to carry out the new process requests. Implementing machine learning techniques in operating systems research can improve critical aspects such as response time, throughput, turnaround time, and waiting time. By enhancing the overall performance of the system, users are delivered a faster and more efficient computing experience. this analysis showcases the significance of utilizing machine learning techniques as a solution to the issues arising due to the ever-increasing complexity and demands of modern computing. By optimizing resource utilization and improving overall performance, we can reduce energy consumption and environmental impact while meeting the demands of modern computing. this aligns with the objectives of sustainable technology and Industry 5.0 to integrate physical and digital systems for sustainable and efficient production.

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Research on Improved ICP for 3D Reconstruction of Lightning Arrester

Research on Improved ICP for 3D Reconstruction of Lightning ...

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

作者： Senjian Lu Beiping Hou Qinlong Zhang Yangbin Zheng Fangkun Shao Shihao Ge Wen Zhu School of Automation and Electrical Engineering Zhejiang University of Science and Technology Hangzhou China Zhejiang International Science and Technology Cooperation Base of Intelligent Robot Sensing and Control China Technology

the lightning arrester is an electrical device used to prevent damage to the insulation of electrical equipment caused by over voltages, including lightning and switching impulses. Creepage distance, umbrella distance, and diameter are crucial parameters for lightning arrester protection. However, traditional manual detection methods are laborious and cumbersome. Moreover, due to the highly symmetrical structure of the lighting arrester itself, the traditional methods have difficulties such as easy reverse registration, etc. this paper proposes an approach for obtaining multi-angle point cloud maps by using robotic arm and depth camera, and a quadratic point cloud registration algorithm is presented, it incorporates both the FGR (fast global registration) registration and improved ICP (Iterative Closest Point) registration algorithms. Specifically, the FGR registration algorithm is initially employed for the approximate registration of two point-cloud, and then the KICP (ICP uses KDTREE to accelerate the ICP algorithm) is used for fine registration. the experimental results demonstrate that the average registration time is 60% faster than traditional method with high registration accuracy. therefore, this method has good Real time performance and robustness for reconstructing lightning arrester.

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Exploring A Novel Data Augmentation Strategy for Enhanced In-Vehicle Security Analysis

Exploring A Novel Data Augmentation Strategy for Enhanced In...

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Sustainable Technologies for Industry 4.0 (STI), international conference on

作者： Kabid Hassan Shibly Rownak Borhan Lubana Akter Md. Abdul Based Division of Information Science Nara Institute of Science and Technology Japan Dept of Computer Science and Engineering Dhaka International University Bangladesh

In the sphere of automotive cybersecurity, navigating the complexities of behavior management within structured security databases poses significant challenges due to the heterogeneous distribution of data. this uneven data spread obscures a minority of identifiable attack instances amidst a larger pool of unlabeled data, thereby diminishing the performance of machine learning algorithms, particularly in aspects of data deterioration and equilibrium. Notwithstanding, the domain of semi-supervised learning, which leverages both labeled and unlabeled data for model development, remains relatively unexplored within the cybersecurity context. this work presents the Prototype Mixing Generative Network (PMGN), integrated with Multivariate Imputation by Chained Equations (MICE) imputation method and class conditioning, to tackle these problems. Specifically designed for semi-supervised learning, this method includes a new data augmentation technique to mitigate the inherent data imbalances found in security databases. In application across diverse security datasets, the PMGN methodology demonstrates superior performance over conventional methods, underscoring its enhanced efficacy. A major step forward in the field of semi-supervised learning for in-vehicle cybersecurity has been made with the integration of PMGN. Crucially, this research aligns with Industry 5.0 and Sustainable Technology objectives by fortifying vehicular digital frameworks, a critical element in the evolution towards sustainable and intelligent transportation systems.

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Advanced Feature Engineering and Sampling Technique for Effective Heart Disease Prediction System using Machine Learning Technique

Advanced Feature Engineering and Sampling Technique for Effe...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： John Oloruntoba Awoyemi Bosede Olanike Awoyemi Tolulope Anthonia Ugwu Atolase Mercy Oluwasola Temitope Oladokun Chukwuemeka Christian Ugwu Dept. of Computer Science Federal University Oye-Ekiti Oye-Ekiti Nigeria Dept. of Economics Afe Babalola University Ado-Ekiti Nigeria Dept. of Information Systems Federal University of Technology Akure Nigeria International Brewery PLC- AB-Inbev Lagos Nigeria Dept. of Data Science Federal University of Technology Akure Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Predicting heart disease is crucial in healthcare, where accurate detection can significantly impact patient outcomes. A major challenge in prediction is imbalanced data, with underrepresented samples in the minority class leading to biased models. this study addresses this by combining advanced feature engineering with SMOTE (Synthetic Minority Oversampling Technique) to manage class imbalance. the objectives include refining data through feature engineering, building predictive models using machine learning algorithms, and assessing performance metrics. algorithms such as Random Forest, SVM, Decision Tree, Logistic Regression, and K Nearest Neighbor are applied. Results show that robust feature engineering notably enhances performance, with Random Forest achieving the highest accuracy (0.9604), followed by SVM (0.954) and Decision Tree (0.947). the findings highlight the role of feature engineering and sampling in improving heart disease prediction models, with meaningful implications for healthcare advancements.

关键词： Heart Support vector machines Accuracy Machine learning algorithms Refining Medical services Predictive models Decision trees Random forests Diseases

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Optimizing threshold Parameters in NLP-based Classification of Koshur Devnagari Scripted Poems

Optimizing Threshold Parameters in NLP-based Classification ...

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Smart Electronics and Communication (ICOSEC), international conference on

作者： Preety Sagar Jatinderkumar R. Saini Jaikranti College of Computer Science & Management Studies Savitribai Phule Pune University (SPPU) Pune India Symbiosis Institute of Computer Studies and Research Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331504403

ISBN: (纸本)9798331504410

Classification of poetry using Natural Language Processing (NLP) involves categorizing poems into different genres, styles, or themes based on analyzing their linguistic features. the authors, in their previous research paper, had designed a baseline SaSa algorithm for the categorization of Koshur poems into five different categories using the value of threshold as two and received an accuracy of $\mathbf{8 9 \%}$. the threshold value of two means that the tokens with frequencies less than or equal to two will not be considered. It was advantageous because it reduces the algorithm’s time and enhances accuracy. In this study, the authors investigate the effect of threshold variation on the accuracy of a poetry classification model. the experiments were conducted on a diverse dataset of poems to demonstrate how minor adjustments to the threshold value can lead to notable changes in classification accuracy and metrics values. the authors here experimented with threshold values starting from 1 to 5. the findings highlight the importance of finetuning threshold parameters in NLP-based classification tasks for optimal performance.

关键词： Measurement Accuracy Linguistics Natural language processing Classification algorithms

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Compared Various algorithms of Artificial Intelligence to Predict Slope Hazards

Compared Various Algorithms of Artificial Intelligence to Pr...

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IEEE Eurasia conference on IOT, Communication and Engineering (ECICE)

作者： Lu Zheng Yu-Tong Lin Jin-Hang Huang Shu-Lian Zhang Rui Zhou Yu-Shan Chen Zhong-Yuan Chen Zen-Jung Lee School of Intelligent Costruction Fuzhou University of International Studies and Trade Shouzhan Changle Fuzhou China School of Advanced Manufacturing Fuzhou University Quanzhou China

With the development of the economy and infrastructure construction, a large number of slopes have been formed, and the threat of landslides to the safety of people’s lives and property is increasing. therefore, it is particularly important to monitor slope status effectively. the continuous progress of artificial intelligence (AI) has solved the problem of slope safety and its prediction. However, there are too many factors affecting the slope so the predictions of the models vary. To obtain the best prediction model, continuous experiments are needed. therefore, we applied AI in slope monitoring. Data was collected and processed through a system, and five algorithms were compared in terms of performance: linear regression (LR), deep learning (DL), decision tree (DT), support vector regression (SVR), and multi-layer perceptron (MP). Slope settlement was predicted based on pore water pressure, rainfall, cracks, and humidity. the root mean square error and experimental results were analyzed based on each model, and the deep learning algorithm model was incorporated into the prediction system. the research results indicated that slope hazards could be predicted with the lowest root mean square error to prevent hazards.

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A Data Analytics Strategy to Identify mHealth Apps Quality Improvements. A Case Study 21

A Data Analytics Strategy to Identify mHealth Apps Quality I...

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5th international conference on Medical and Health Informatics, ICMHI 2021

作者： Montenegro, Carlos Armas, Rolando Larco, Andres Yanez, Cesar Escuela Politecnica Nacional Quito Ecuador International Associated Laboratory LIA-MODO Ecuador

ISBN: (纸本)9781450389846

this study uses a dataset that comes from the employment of the Mobile App Rating Scale (MARS) model. the dataset comprehends 565 Spanish Language Apps for People with special skills, published during the last twenty years. this work aims to identify influential features to improve mHealth apps quality using a data analytics strategy that integrates the MARS model, Principal Components Analysis (PCA), and Genetic algorithms (GA). the results show that it is feasible to find specific characteristics that could improve the apps. the process used involves a practical contribution conducted to the stakeholders promoting the enhancement of the quality of life using mHealth approach. © 2021 ACM.

关键词： Genetic algorithms

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