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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Priyanka, S. Keerthika, S. Arunesh, J. Priyadharshini, S. Sanjay, S. Anudeep, C. Kongu Engineering College Department of Artificial Intelligence Tamil Nadu India

ISBN: (纸本)9798350343274

Drowsy driving is a major contributor to traffic accidents and a major risk to public safety. The development of driver sleepiness detection systems has found success in recent years with the use of machine learning techniques. This work proposes a comprehensive approach to machine learning algorithms - such as Support Vector Machines (SVM), Random Forest, Decision Tree, and K-Nearest Neighbours (KNN) - for the detection of driver sleepiness. In this paper, investigate how these algorithms perform when trained on a picture dataset. Using a camera mounted in the car, the suggested system first takes live pictures of the driver's face. After that, facial landmarks are taken out of these pictures and used as input data for machine learning models. To identify the patterns linked to drowsiness, each algorithm is trained using a labelled dataset that includes both alert and sleepy facial expressions. Using cross-validation approaches, the performance of each algorithm is assessed in terms of accuracy, precision, recall, and F1-score. By informing sleepy drivers in real time so they can take the appropriate safety measures or take a break, the suggested system has the potential to improve road safety. An important way to assess the suitability of various machine learning algorithms for driver sleepiness detection applications is to compare them. To improve the system's accuracy and resilience in real-world situations, more research can concentrate on incorporating other sensor data, such as eye movement patterns or steering habits. © 2023 IEEE.

关键词： Support vector machines

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A Novel Approach To Auto Dipping system Of Vehicles Based On LiDAR 3

A Novel Approach To Auto Dipping System Of Vehicles Based On...

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3rd international conference on Advances in Computing, Communication, Embedded and Secure systems, ACCESS 2023

作者： Kumar, Shiv Garg, Vikash Kumar Bansal, Ankit Singh, Kiran Deep Sainik Institute of Mgt and Tech Punjab Bathinda India Sliet Department of Computer Science & Engineering Punjab Longowal India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350346169

Automation is part of life these days. Every gadget in our routine lives is part of artificial intelligence these days. Vehicles are also part of this update these days. Almost all the automations around us that concern vehicles are useful. From the auto-engine-check system to the automatic cleaning of the wind screen and the auto-door-lock system to the anti-lock-braking system and the auto-air-bag system, all are useful and are part of vehicle safety these days. Furthermore, the auto-dip system is important in vehicle automation. Nearly 50% of accidents these days are due to bad driving at night. Dipping headlights play a vital role in visibility at night. Several papers have been published in response to this concern about the course of scarcity. Some are widely used in the market, but they have limitations, such as not being able to provide legally required removal or working in every climate. The framework is required and won't influence the exhibition of vehicles. This paper proposes an innovative system of auto-dipping using LiDAR that is accurate and will work in every atmospheric condition. Moreover, the auto-dipping system that is proposed is handy and innumerable in terms of weight. © 2023 IEEE.

关键词： Artificial intelligence

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Ship Trajectory Prediction with Social History LSTM 3

Ship Trajectory Prediction with Social History LSTM

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3rd international conference on Computer, Control and Robotics, ICCCR 2023

作者： Zhao, Wenfeng Zhang, Xudong School of Computer and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9781665492126

Ship trajectory prediction is an important research topic in ship navigation automation. It can effectively help ship pilots to obtain comprehensive Marine traffic information and reduce the potential collision risk of ships. Compared with pedestrian and vehicle trajectory prediction, due to the existence of AIS system for ships, it is easier to obtain historical data of ships, so it is easier to explore the periodicity of trajectories in time. Therefore, based on the previous research on trajectory prediction, we propose a new history module, which adds the agent's long-term route intention to the model to guide the model to predict the ship trajectory. At the same time, we adopt a data-driven idea and pre-train a State Refinement module to obtain high-dimensional feature representations of velocity. Experiments show that our model is superior to the existing current algorithms in long-term trajectory prediction on self-made data sets. © 2023 IEEE.

关键词： Ships

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Analysis and prediction of industrial energy consumption behavior based on big data and artificial intelligence

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ENERGY REPORTS 2023年 9卷 395-402页

作者： Wu, Qiong Ren, Hongbo Shi, Shanshan Fang, Chen Wan, Sha Li, Qifen Shanghai Univ Elect Power Coll Energy & Mech Engn Shanghai 200090 Peoples R China State Grid Shanghai Elect Power Res Inst Shanghai 200433 Peoples R China

To promote the energy-saving transformation of industrial enterprises effectively, it is important to accurately grasp the energy consumption behavior of enterprises. In this study, the multi-type industrial enterprises in an industrial park located in Shanghai are selected for analysis. Based on the collection and processing of the measured heating (steam) data, by employing the big data analysis method, the cluster analysis is carried out from different dimensions including user difference, load fluctuation and typical daily characteristics. Following which, the multi-type load characteristic curve is obtained, and the energy consumption characteristics of the whole industrial park and different types of industrial enterprises are discussed. On this basis, the energy load forecasting model of industrial enterprises is developed based on the LSTM neural network, and the effect of introducing meteorological data on the accuracy of load forecasting is analyzed. (c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Benefit distribution Integrated energy system Benefit-cost analysis Indirect benefits

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Multi-Frequency Damping Adaptive Control Strategy for Active Dampers Based on MultiHarmonic detection 3

Multi-Frequency Damping Adaptive Control Strategy for Active...

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3rd IEEE international conference on Industrial Electronics for Sustainable Energy systems, IESES 2023

作者： Gao, Hailong Xiao, Xuequan Zhou, Yushan Zhang, Xiao Zhao, Yihang State Grid Jiangsu Electric Power Company Dept. of Xuzhou Power Supply Branch Xuzhou China China University of Mining and Technology Dept of Electrical and Power Engineering Xuzhou China

ISBN: (纸本)9798350324754

As the interface between new energy and the power grid, grid-connected converters are easy to cause multifrequency oscillation of the system. So, this paper proposes an active damper to dampen multiple harmonics by simulating virtual resistance. The basic idea of the active damper is that use the cascaded multiple Second-Order Generalized Integrator with the Frequency-Locked Loops (SOGI-FLL) to detect and extract the unknown multiple harmonics automatically. Then, for different content of resonance, the active damper adjusts the virtual resistance value adaptively to enhance the stability of the system. Finally, the simulations of MATLAB/Simulink are carried out and the results verify the accuracy and effectiveness of the proposed scheme. © 2023 IEEE.

关键词： MATLAB

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Utilizing Social Media Platforms for Artificial intelligence-Based detection and Developing Effective Communications in Disaster-Affected Areas 3

Utilizing Social Media Platforms for Artificial Intelligence...

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

作者： Waghmare, Ganesh Kumar, Saurabh Udhayakumar, R. Raja Mannar, B. Singh, Rajesh Rawat, Navneet Lexicon Management Institute of Leadership and Excellence Maharashtra Pune India Invertis University Invertis Institute of Computer Applications Bareilly India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of English Tamil Nadu Chennai India St.Theresa Intl College Lecturer - Business Administration Thailand Uttaranchal University Uttaranchal Institute of Technology India Graphic Era Deemed to Be University Department of Management Studies Uttarakhand Dehradun248002 India

ISBN: (纸本)9798350399264

Disaster response organizations must have timely access to current and pertinent disaster information in order to formulate wise management decisions and decrease the impact of disasters. Social media has developed recently as a proactive avenue for disaster communications. It has made it easier for groups that deal with disasters to get a tonne of information in real time about them from the people that are affected. Researchers and professionals are now able to fully realise the significance of such a user-generated content for aiding sensible decision-making in catastrophe management thanks to advancements in artificial intelligence. This paper provides an overview of how artificial intelligence techniques are now being used to evaluate social media content connected to disasters in order to support disaster management at various stages. It gives a brief overview of a few increasingly digitally systems that use information from social media to manage disasters. This study also highlights a number of research opportunities and obstacles for handling disaster-related information on social media. © 2023 IEEE.

关键词： Disasters

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Evaluation of Machine Learning Algorithms Performance in Comparison to Cloud Privacy Legislation 3

Evaluation of Machine Learning Algorithms Performance in Com...

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3rd international conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Patil, Sourabh Bhatnagar, Vishal Singh, Shyla Netaji Subhas University of Technology Department of Computer Science and Engineering New Delhi India

ISBN: (纸本)9798350300710

An Intrusion detection system (IDS) is a security tool that checks network data for any indication of malicious activity and notifies the network administrator. The proposed system puts into practice an anomaly-based IDS using a variety of supervised machine approaches. Anomaly and normal, the binary classification employed by the IDS model, are used to categorize, or identify anomalous network packets (0). The model evaluation is compiled using metrics for machine learning evaluation. The anomaly-based IDS model outperforms signature-based IDS in terms of its high computational cost, confined coverage, lack of adaptability, and high false positive rate. The dataset is utilized to train our IDS Model and it is trained and evaluated using various classifiers. Notably, the Random Forest Classifier achieves an exceptional accuracy rate of 99.92%, surpassing all other classifiers, thereby aligning with the cloud privacy legislation's objective of ensuring data confidentiality and integrity. © 2023 IEEE.

关键词： Intrusion detection

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Forest Fire detection via the Internet of Things-Based systems 3

Forest Fire Detection via the Internet of Things-Based Syste...

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3rd international conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Negi, Prafful Kathuria, Samta Singh, Rajesh Pachouri, Vikrant Kathuria, Namrata Uttaranchal Institue of Technology Uttaranchal University Dehradun India Uttaranchal University Division of Research & Innovation Dehradun India Uit Uttaranchal University Division of Research & Innovation Dehradun India Law College Dehradun Uttaranchal University Dehradun India

ISBN: (纸本)9798350322842

One of the disasters caused by natural or human causes directly affect the economy, society, and environment on a large scale and makes it more difficult to meet some of the Sustainable Development Goals. The promotion of sustainable forest management help to achieve environmental sustainability, a goal that has gained international attention. Internet of Things (IoT), a revolutionary technology of the fourth industrial revolution, brings us toward achieving sustainable forest management by preventing hazards from occurring in the forest by employing a large number of smart devices to gather data, monitor the forest, and timely detection of fire. The current study extensively assesses how IoT is utilized to design various tools to promptly detect fire and manage it. Finally, based on the analysis, the challenges and suggestions for future improvement are made with regard to attaining forest sustainability by preventing forest fires and making the IoT based system more reliable. © 2023 IEEE.

关键词： Internet of things

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The Development and Implementation of Deep Learning-Based Pneumonia detection 3

The Development and Implementation of Deep Learning-Based Pn...

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

作者： Kumar, Pavan Nijhawan, Ginni Babu, M.Kishore Joshi, Kapil Anusha, Pasupunooti Shivakumar, K. Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering &technology Department of Information Technology Hyderabad India Lovely Professional University Phagwara India Koneru Lakshmaih Education Foundation Kl Business School Andhra Pradesh Guntur India Uttaranchal University Uttaranchal Institute of Technology India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Sr University School of Cs and Ai Department of Cs and Ai Warangal India

ISBN: (纸本)9798350399264

The most common cause of death worldwide is pneumonia. It affects 7% of the planet's population &, on average, results in the deaths of 700, 000 children annually. Using X-rays as the primary diagnostic tool, this disease is identified. Using principles of Transfer Learning & Image Augmentation, we have developed a wholly original image processing-based Deep Learning technique to identify respiratory problems. With a memory score of 97.44% and also a precision score of 96.00%, we were generally prepared to succeed. In this program, the patient must attach a chest X-ray that's been acquired by the laboratory. The computer will then examine the report for errors and, if any are found, will alert the patient that something is wrong with their health. Additionally, the system will display the message "Healthy"if indeed the report has no issues. By adopting this technique, the patient can avoid spending more money to see a doctor if they are not experiencing any pneumonia-related issues. Additionally, patients may save money in this way. © 2023 IEEE.

关键词： Deep learning

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Advanced Manufacturing Process Development for Automation in the Industry using Artificial intelligence-Based system 3

Advanced Manufacturing Process Development for Automation in...

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

作者： Kumaravel, D. Tomar, Prashant Tulanovna, Kodirova Dilshodkhan Sharma, Sushant Pawar, Krupal Prabhakar Patil, Pravin P Amet University Tamil Nadu Chennai India Parul University Parul Institute of Technology Department of Mechanical Engineering Gujarat Vadodara India Candidate of Technical Sciences Docent Uzbekistan Fergana Polytechnic Institute Department of Chemistry and Chemical Technology Fergana Uzbekistan Faculty of Engineering and Technology Fet Mriirs Manav Rachna International Institute of Research and Studies Department of Mechanical Engineering India Mechanical Engineering Rajiv Gandhi College of Engineering India Graphic Era Deemed to Be University Department of Mechanical Engineering India

ISBN: (纸本)9798350399264

The record of answer books and attendance (RABA) in any examination system is an unexplained but necessary component of educational institutions to register the presence and trace the unique number of answer sheets of a certain applicant. Manually writing in the RABA of pupils traditionally is time-consuming, difficult to manage, and prone to human mistakes. To address these issues, an AI-based system with facial recognition and a fingerprint-based biometric system of digital RABA is presented. In this work, an automated system for face recognition and fingerprint identification using biometrics is proposed, and the answer sheet barcode is subsequently scanned for RABA input using a camera module. Finally, all of these records are stored in a database and may be retrieved at any moment by the institution of the university. This approach provides a faster and more precise means of avoiding proxy attendance. © 2023 IEEE.

关键词： Face recognition

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