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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Sejal Jain Sahil Hussain Shruti Gupta Computer Science and Engineering ABES Engineering College Ghaziabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Violent actions represent a serious danger to public safety and the overall well-being of society. Traditional surveillance systems operate primarily reactively, necessitating the development of proactive solutions to detect and mitigate such incidents. It is imperative for monitoring systems to identify violent behavior in real time and promptly notify the authorities. Deep learning and computer vision have emerged as pivotal areas of research for advancing automated violence detection *** paper proposes a Real-Time Violence Detection and Alert System that employs the MobileNetV2 architecture to ensure effective and accurate classification of frames. The system integrates Cloud Firestore for secure and scalable data management, providing a database that authorities can utilize for analyzing and investigating future violence cases. Additionally, it employs a Telegram Bot for immediate incident alerts. With 96% accuracy, the system demonstrates reliability and adaptability across various environments, offering a robust and scalable solution for public safety while contributing to proactive crime prevention.

关键词： Deep learning Accuracy Surveillance Scalability Chatbots Real-time systems Public security Security Reliability Testing

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Heart Disease Diagnosis using Deep Learning 2

Heart Disease Diagnosis using Deep Learning

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Kumar, Ashish Jain, Dhyanendra Singh, Prashant Das, Sanjoy Abes Engineering College Department of CSE-AIML Ghaziabad India Sunder Deep Engineering College Department of Computer Science and Engineering Ghaziabad India Indira Gandhi National Tribal University Department of Computer Science Engineering Manipur India

ISBN: (纸本)9798350369960

Developments for automation and advanced computing in the area of medical data processing has outcome with different new learning techniques. Deep learning has evolved as an advanced approach in machine learning applied to different old and new area of applications. Deep learning approaches have evolved as supervised, semi-supervised and un-supervised mode applied for different real time applications. The approach has shown a significant usage for image processing, computer vision, medical diagnosis, robotic and control operation application. Among various usage of machine learning approaches for automation, medical diagnosis has been observed as a new upcoming application. The criticality of data processing, response time, and accuracy in decision, tends the learning system more complex in usage for medical diagnosis. This paper outlines the developments made in the area of medical diagnosis and deep learning application for heart disease diagnosis. The application, database and the learning system used in the automation process is reviewed and outlined the evolution of deep learning approach for medical data analysis. © 2023 IEEE.

关键词： Deep learning approach Diagnosis and decision Heart disease diagnosis

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The Revolutionary Potential of Blockchain using Smart Contracts: Unlocking the Next Generation of Technology 5

The Revolutionary Potential of Blockchain using Smart Contra...

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5th IEEE International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2023

作者： Singh, Anirudh Pratap Jain, Dhyanendra Vats, Prashant Tiwari, Ayush Bhadana, Dev Abes Engineering College Department Of Cseaiml Ghaziabad India Scse Manipal University Jaipur Department Of Computer Science And Engineering Rajasthan India

ISBN: (纸本)9798350330861

Blockchain is a technology that uses decentralized network of nodes (miners), blockchain consists of blocks. Each block has three parts data, hash, hash of previous block. Once data is stored it is very difficult to change the data to temper the data. Miners verify the transaction by calculating its hash function for which they get to keep some commission. In this review paper will provide a deep understanding of Blockchain technology and its applications through a Smart contract used for industries like banking, healthcare, supply chain, security, and numerous others. Blockchain is a growing field of worlds with newer applications throughout the various types of fields in the industry from the use of cryptocurrency to hospital management and various others, these applications are only made possible because of How blockchain works and does things. © 2023 IEEE.

关键词： Smart contract

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Federated Approach for Privacy-Preserving Traffic Prediction Using Graph Convolutional Network

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Journal of Shanghai Jiaotong university(science) 2024年第3期29卷 509-517页

作者： LONARE Savita BHRAMARAMBA Ravi Pimpri Chinchwad College of Engineering Pune 411044India Department of Computer Science and Engineering GITAMViskhapatnam 530045India

Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning ***,data privacy and security are always a challenge in every field where data need to be uploaded to the *** learning(FL)is an emerging trend for distributed training of *** primary goal of FL is to train an efficient communication model without compromising data *** traffic data have a robust spatio-temporal correlation,but various approaches proposed earlier have not considered spatial correlation of the traffic *** paper presents FL-based traffic flow prediction with spatio-temporal *** work uses a differential privacy(DP)scheme for privacy preservation of participant's *** the best of our knowledge,this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP *** proposed framework trains the data locally at the client-side with *** then uses the model aggregation mechanism federated graph convolutional network(FedGCN)at the server-side to find the average of locally trained *** results of the proposed work show that the FedGCN model accurately predicts the *** scheme at client-side helps clients to set a budget for privacy loss.

关键词： federated learning(FL) traffic flow prediction data privacy graph convolutional network(GCN) differential privacy(DP)

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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Harnessing Blockchain to Address Plasma Donation Network Challenges

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computers, Materials & Continua 2023年第7期76卷 631-646页

作者： Shivani Batra Mohammad Zubair Khan Gatish Priyadarshi Ayman Noor Talal H.Noor Namrata Sukhija Prakash Srivastava Department of Computer Science and Engineering SRM UniversityDelhi-NCR1310029India Department of Computer Science and Information Taibah UniversityMedina42353Saudi Arabia Department of Computer Science and Engineering KIET Group of InstitutionsDelhi-NCRGhaziabad201206India College of Computer Science and Engineering Taibah UniversityMadinahSaudi Arabia College of Computer Science and Engineering Taibah UniversityYanbuMadinahSaudi Arabia Department of Computer Science and Engineering Banasthali VidyapithRajasthan304022India Department of Computer Science and Engineering Graphic Era(Deemed to Be University)Dehradun248002India

Plasma therapy is an extensively used treatment for critically unwell *** this procedure,a legitimate plasma donor who can continue to supply plasma after healing is ***,significant dangers are associated with supply management,such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal ***,from an ideological standpoint,less powerful people may be exploited throughout the contribution ***,there is a danger to the logistics system because there are now just some plasma *** research intends to investigate the blockchain-based solution for blood plasma to facilitate authentic plasma *** parameters,including electronic identification,chain code,and certified ledgers,have the potential to exert a substantial,profound influence on the distribution and implementation process of blood *** understand the practical ramifications of blockchain,the current study provides a proof of concept approach that aims to simulate the procedural code of modern plasma distribution ecosystems using a blockchain-based *** agent-based modeling used in the testing and evaluation mimics the supply chain to assess the blockchain’s feasibility,advantages,and constraints for the plasma.

关键词： Blockchain hyperledger fabric information visibility plasma donation network plasma quality

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Real Time Slumbering Detection Using DLIB

Real Time Slumbering Detection Using DLIB

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International Conference on Advances in Information Technology (ICAIT)

作者： Harsh Agrawal Pallavit Saxena Shiva Tiwari Sandhya Awasthi Kadambri Agarwal Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science and Engineering(Data Science) ABES Engineering College Ghaziabad India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Worldwide, driving when fatigued is still a major factor in traffic accidents. Our solution to this problem is a complete slumbering detection system that uses real-time monitoring, machine learning algorithms, and computer vision techniques to identify indicators of driver sleepiness. To recognize signs like eye closure, yawning, and head movement, our system makes use of many libraries, including dlib for facial landmark identification and OpenCV for face detection. Furthermore, the driver's past history of sleepiness is also stored in the database. To prevent tiredness the next time, the database viewer might take proactive steps to enhance his or her sleep. The likelihood of feeling drowsy increases significantly at night compared to the day. It should thus be possible to have a system that operates differently during the day and at night. We may tighten our system at night so that we are warned if someone yawns once or twice, and we can be alerted if someone yawns three or four times during the day. Head movement follows similar rules: during the day, we may ignore some head movement, while at night, we can pay close attention to even the tiniest movement.

关键词： Machine learning algorithms Sleep Databases Reviews Mouth Magnetic heads Real-time systems

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Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第03期10卷 439-450页

作者： Li, Xiaocan Wen, Jigang Xie, Kun Xie, Gaogang Liang, Wei Hunan University College of Computer Science and Electronics Engineering Changsha China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Chinese Academy of Sciences Computer Network Information Center China

Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GEANT, demonstrate the effectiveness of the proposed TRDN. © 2016 IEEE.

关键词： Data accuracy

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AI-Enhanced Precision Crop Rotation Management for Sustainable Agriculture

AI-Enhanced Precision Crop Rotation Management for Sustainab...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Hafiyya, R.M. Jemsheer Ahmed, P. Nabhan, P.A. Melethil, Safvan Mohammed, Nadhi Muhammed Murshid, A.P. Mea Engineering College Computer Science & Engineering India

ISBN: (纸本)9798350394399

This paper presents a novel approach to crop rotation management that integrates cutting-edge artificial intelligence (AI) techniques to improve and modernise agricultural processes. The study seeks to revolutionise traditional crop rotation tactics by utilising state-of-the-art machine learning techniques, namely supervised learning approaches. Through the use of real-time weather forecasts and historical crop performance data, AI-powered technology provides farmers with tailored advice for maximising crop rotations, ultimately leading to better agricultural results. The methodical methodology includes planning the project, gathering data, creating AI models, and deploying the system. Establishing precise project objectives, collecting and analysing soil data with care, creating AI-driven recommendations, and making sure that monitoring and improvement are ongoing are all crucial tasks. At every level, the system uses a range of instruments and software to guarantee a methodical, data-driven approach that produces improved crop rotation techniques that are advantageous to the environment and the agricultural industry. This study adds to the rapidly developing field of artificial intelligence in agriculture by offering a workable and creative answer to the problems farmers face when managing crop rotation. The results underline how AI may be used to improve farming techniques, increase crop yields, and promote sustainable farming, which makes it an important contribution to the field of precision agriculture research. © 2024 IEEE.

关键词： Crop rotation

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AI-Optimized Eco-Charge Regeneration Technology in EV 6

AI-Optimized Eco-Charge Regeneration Technology in EV

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sheeja, R. Akshaya, R. Akshaya, S. Jashwanthikaa, D.R. Easwari Engineering College Computer Science and Engineering India

ISBN: (纸本)9798350306613

In the modern era of transportation, improving the energy efficiency poses to be one of the major challenges faced by electric vehicles (EVs). When drivers apply brakes the braking systems convert kinetic energy into heat which leads to loss of power and energy. This loss of power hinders the performance and range optimization of EVs. In order to solve this problem, there is a need to find solutions that helps in increasing energy efficiency in EVs. This paper suggests a solution that can be used in optimizing the energy recovered during braking using Artificial Intelligence (AI). The application of AI algorithms helps in redirecting the kinetic energy to the battery resulting in an enhanced energy regeneration process. The proposed AI optimized aims to overcome the limitations of the traditional energy recovery systems. Through real time data-Analysis and adaptable control, the AI system maximizes the conversion of energy and ultimately improves both the energy efficiency and range of anxieties. This approach not only deals with the setbacks of existing braking systems, but also cope with the increasing demand for sustainable technologies EVs. The integration of AI into the process of energy recovery and optimization leads to a transformation in electric vehicle technology. © 2024 IEEE.

关键词： AI and ML algorithms braking force energy consumption energy optimization energy recovery EV

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