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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Ganesan, R. Manjula, G. Chandan, Madhavarapu Thamarai, I. Rajavelu, S. Bhatt, Upendra Mohan Sarkar, Swagata Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600062 India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Thiruvallur601206 India Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering Andhra Pradesh Vaddeswaram522302 India Panimalar Engineering College Department of Computer Science and Engineering Chennai city campus Tamil Nadu 600029 India Department of Physics Tamil Nadu Dindigul624622 India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun248002 India Sri Sairam Engineering College Department OfArtificial Intelligence and Data Science Tamil Nadu Chennai600044 India

ISBN: (纸本)9781665456302

Weeds compete for natural resources both in forest areas, harming the development of native vegetation, and in agricultural areas, affecting crop quality. The need then arises to classify these species, so that mechanical or chemical methods can be applied appropriately to contain the pests. This research presents the application and comparison of machine learning techniques, with the aim of automating the classification of images for agricultural challenges, such as the detection of defective seeds, and weeds and the category between these and native vegetation, while finally, the architecture of a convolutional neural network is presented. As a differential, the network's self-learning ability stands out, as images are captured in less than ideal conditions at varying heights and lighting levels in most cases. This research is expected to provide important information on artificial intelligence techniques that can be used in the classification of weed images, a factor that will contribute to the forestry and agricultural sector. © 2023 IEEE.

关键词： Classification (of information)

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Robust Heart Rate Monitoring System: Contactless Approach using Fast Fourier Transform 15

Robust Heart Rate Monitoring System: Contactless Approach us...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Sujatha, E. divya, M. Nandhini, I. Raju, d. Naveen Chennai India Chennai India Department of Electronics and Communication Engineering School of Electrical and Communication Vel Tech Rangarajan R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Today, to be alive in this world we need to have all our body parts working in good condition and since technology is improving faster day by day everything has become automatic and AI enabled. Measuring the heart-rate (HR) of people has multiple applications in telemedicine, Internet-of-Things (IoT), sports, security, etc. However, sometimes it becomes difficult to use a classic method for measuring HR and it does not support scalability. This project is about giving a solution that works live and gives the HR of a person using web camera. Face detection is combined with object tracking is used to produce a set of frames, which are sampled in the later stages of the pipeline for color variations. The average of the color, in a region of interest (ROI) is chosen on the face, representing a signal which corresponds to the heart rate. Signal processing techniques are used to extract the frequency from the signals obtained. The algorithm used is FFT (Fast Fourier Transform) to make image transformation between frequency and special domain. This method is precise and efficient. And using this system another method is also enabled in which the video of a person is uploaded and the heart rate is found through video. By utilizing object tracking combined with face detection, this method reduces the processing power needed and allows better scaling. The accuracy is 98% compared to the real time existing systems. © Grenze Scientific Society, 2024.

关键词： Fast Fourier transforms

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Enhancing Predictive Maintenance in Industrial IoT through Machine learning Models 4

Enhancing Predictive Maintenance in Industrial IoT through M...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Rani, A. Jaya Mabel Prithivirajan, V. Shiney, S Arumai Babaiah, Ch. Swamy, Hemanth Sivagamidevi, G. Saveetha University Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India S A Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Mlr Institute of Technology Department of Electronics and Communication Engineering Dundigal Hyderabad India Motorola Solutions India Dhanalakshmi College of Engineering Department of Ai & Ds Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

Significant production losses and increased maintenance costs can arise from unplanned downtime in industrial settings caused by machine faults. In order to identify issues early and prevent significant breakdowns, predictive maintenance approaches make use of data collected from Internet of Things (IoT)-enabled devices installed in operating equipment. Here, we present a predictive maintenance system that employs machine learning methods, namely AdaBoost, to categorize various types of machine pauses in real-time, with a focus on knitting machines. Six different types of machine stops were classified using machine learning: idle stop, lycra stop, full roll stop, gate stop, feeder stop, and needle stop. We did this by preprocessing and inputting machine learning model data, which included machine speeds and steps. After a rigorous training and optimization process that combines cross-validation and hyperparameter tuning, the model achieves an impressive accuracy rate of 92% on the test dataset. These results highlight how our suggested solution might help the textile sector become more productive and efficient overall by precisely detecting machine stoppage and enabling timely maintenance interventions. © 2024 IEEE.

关键词： Predictive maintenance

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SVM-based Predictive Modeling of drowsiness in Hospital Staff for Occupational Safety Solution via IoT Infrastructure 2

SVM-based Predictive Modeling of Drowsiness in Hospital Staf...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Ganesh, B. Jai Vijayan, P. Vaidehi, V. Murugan, S. Meenakshi, R. Rajmohan, M. R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Vinayaka Mission's Kirupananda Variyar Arts and Science College Department of Computer Science Tamil Nadu Salem India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Hindustan Institute of Technology and Science Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

This research proposes an efficient approach to improve hospital worker safety on the job by detecting and preventing drowsiness. The system incorporates data from hospital-deployed Internet of Things (IoT) infrastructure and using Support Vector Machines (SVM) as its primary predictive modeling tool. data pertaining to ambient factors, work habits, and physiological indications of employees are collected in real-time by the IoT infrastructure. To identify patterns linked to sleepiness, the SVM model is trained on an extensive dataset. The next step is to implement the model in a healthcare setting so that it can detect signs of sleepiness and act accordingly in a timely manner. With the help of the IoT the predictive model can be dynamically adjusted and monitored in real-time, making it useful in a wide range of situations. The recommended approach is to enhance workplace safety by reducing the likelihood of accidents caused by sleepiness among healthcare workers. The SVM-based prediction model can correctly detect and forecast cases of sleepiness, the research subjected it to extensive testing and validation. This system's adoption helps with the creation of data-driven, proactive methods to improve hospital occupational safety. © 2024 IEEE.

关键词： Internet of things

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A Innovative Network Security Regulations dependent on Improved Support Vector Machine from the Outlook of Modern Cities

A Innovative Network Security Regulations Dependent on Impro...

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International Conference on computer and Communications Technologies (ICCCT)

作者： Vasanthan P S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The concerns about the security created by the PC have gotten more advanced and complicated. Interruption detection is a pragmatic subject in the area of PC security whose essential target is to identify uncommon assault or attacks and to guarantee the safety of inside frameworks. This paper likewise suggests a semi-class interruption recognition strategy that joins various classifiers to mastermind exemptions and regular activities in a PC framework. In the consideration preference tree learning-iterative dichotomy 3, the maltreatment recognition method is developed and is gathered by using the cumulative knowledge based on the peculiarity detection system performed by one class- uphold vector machine. As of late, individuals have paid more thoughtfulness regarding Id/interruption avoidance framework, which is firmly identified with the insurance and use of framework the executives. A couple of AI principles including neural framework, genetic programming, and progressed uphold vector machines, Bayesian framework, multivariate adaptable backslide splines, feathery deduction systems and other analogical frameworks has been scrutinized for the layout of interruption identification framework. In this article, we suggest a combination strategy dependent on dTl-Id3 and OC-SVM assess the presentation of the extended procedure by utilizing a particular dataset and a hybrid technique to upgrade the precision of IdS/IPS when stood out from a solitary help vector machine.

关键词： Surveys Support vector machines Urban areas Vectors data models Regulation Safety Registers Splines (mathematics) Pragmatics

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leveraging Consumer Technology for Healthcare Systems Using Blockchain Based Bio-Sensor devices

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Shabaz, Mohammad Rahman, Mohammad Zia Ur Alsaadi, Mahmood Raparthi, Mohan Maaliw, Renato R. Keshta, Ismail Soni, Mukesh Patni, Jagdish Chandra Byeon, Haewon Model Institute of Engineering and Technology Jammu India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P. India Department of computer science Al-Maarif University College Al Anbar Iraq Dallas Texas College of Engineering Southern Luzon State University Lucban Quezon Philippines Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune India School of advance Computing Alliance University Bengaluru India Department of Digital Anti-Aging Healthcare Inje University Gimhae Republic of Korea

Wireless biosensor devices have significantly enhanced level of convenience associated with patient treatment. Presently, most uncertified aggregate signature methods struggle to withstand key attacks that employ only specific keys. This paper presents an uncertified parallel key-isolated aggregate signature framework that utilizes blockchain technology in wireless medical sensor networks and biosensor devices for consumer electronics to tackle the challenges. The proximity of the signature verification and aggregation processes to end users is increased by integrating a bio-sensor device for consumer electronics into the cutting-edge framework. This action simultaneously enhances the safeguarding of patient confidentiality and mitigates computational burden on primary cloud server. By integrating the advantages of key-isolated and uncertified technologies, the proposed solution circumvents exposure concerns, difficult certificate administration, and key storage. This study demonstrates that the proposed method is resistant to Type I, Type II, and wholly selected key attacks when implemented in a random oracle model. This scheme shows a reduction in calculation overhead of 82.97%, 74.03%, 84.58%, and 86.79% correspondingly when compared to other schemes. Analysis of performance indicates that in comparison to pertinent uncertified signature systems, this solution can reduce communication overhead by a minimum of 25% and computational cost by a minimum of 74.03%. IEEE

关键词： Blockchain

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Advanced Fake Review detection via Aspect Extraction and TPA-BilSTM-CSSA 2

Advanced Fake Review Detection via Aspect Extraction and TPA...

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2nd IEEE International Conference on data Science and Network Security, ICdSNS 2024

作者： Thakkar, Nilam Kumar, Arvind Sukumar, P. Sarode, deepika G. Patil, Harshal Vijayakumar, E. Ldrp - Institute of Technology & Research Ksv University Svkm Department of Information & Technology Gujarat India School of Computer Applications Mriirs Haryana Faridabad India Nandha Engineering College Department of Computer Science and Engineering Erode India Dr.D.Y.Patil Institute of Technology Department of Engineering Pune Pimpri India Iilm University Department of Computer Science and Engineering Greater Noida India KIT-Kalaignar Karunanidhi Institute of Technology Department of Mca Coimbatore India

ISBN: (纸本)9798350373110

In recent years, methods to detect bogus reviews have attracted the attention of numerous businesses and educational organizations. In order for reviews to accurately represent genuine user experiences and opinions, it is quite challenging to identify fake reviews. Supervised learning has been a popular approach to tackle the problem. The issue is that manually marking fake reviews with a degree of reliability that would be useful for training is very difficult, if not impossible. There are three phases to the suggested strategy preprocessing, feature extraction, and training the model. This proposed employs a series of preprocessing techniques to get the dataset's raw data ready for computational operations. dimensionality reduction and the quantification of content-related and text-segment-related features comprise feature extraction. To train the model, a TPA-BilSTM-CSSA was used. On average, the suggested method achieves a higher accuracy of 93.71% than BilSTM and CSSA. © 2024 IEEE.

关键词： Fake detection

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An Advanced decentralized Architecture enabled logical Identity based Encryption logic using Blockchain Assistance 4

An Advanced Decentralized Architecture enabled Logical Ident...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Geetha, R. Kalpana, S. Roopa Chandrika, R. Preetha, M. Santhoshini, P. Nithila, Ezhil E Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Eee Saveetha School of Engineering Tamil Nadu Chennai India Department of Computer Science Dubai United Arab Emirates Karpagam Academy of Higher Education Department of Computer Science and Engineering Tamil Nadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmd Engineering College Department of Electronics and Communications Engineering Tamil Nadu Chennai India Veltech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350349900

Using electronic signatures, blockchain technology, and identity-based encryption, essential documents may be certified and electronic contracts can be signed. All of the system's nodes, operations, and transactions may benefit from blockchain technology's distributed ledgers. This study introduces a novel encryption system, decentralized Identity based Encryption logic (dIEl), and assesses its efficacy by comparing it to the established Identity based Encryption (IBE) scheme. In order to guarantee the privacy and confidentiality of patients' data in cloud health systems, the suggested technique offers first-rate security using bilinear pairings. Additionally, this strategy authenticates users through challenge-response procedures. Furthermore, the suggested method guarantees the accuracy and integrity of shared data via the use of blockchain technology. Using the standard model, we can confirm that the suggested protocol is valid and that it is secure. Because all nodes are contributing data, cyberspace is more open and behavior can be more easily tracked. data certification and the contract signing procedure are both finished and in compliance. Users may protect themselves from fraud and identity theft with the help of the Identity based Management system's encryption features. When used together, these two technologies will provide a safer method of data storage while yet preserving user privacy. © 2024 IEEE.

关键词： decentralized finance

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Prediction of Rental demands using different Machine learning Algorithms

Prediction of Rental Demands using different Machine Learnin...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Ravikumar, S. Vijay, K. Pavithra, S. Prithi, S. Kannan, Sriram Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineeirng Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India VIT University School of Computer Science and Engineering Chennai India SRM IST School of Computing Department of Computational Intelligence Kattankulathur India Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350348217

A Bike rental expectation conjectures the interest for bikes rentals in reliance of climate conditions like the temperature and calendric data. In bike rental frameworks, it is vital that the managers have to realize the number of bikes to be placed in each and every station. This include empowers them in or-ganizing the necessary number of bikes at each station and choose whether a specific station needs to have additional number of bikes or it is not re-quired. In this venture we will foresee the no of bikes required for explicit climate and climatic conditions. © 2023 IEEE.

关键词： Machine learning

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Recognition of Identifying Malicious Cyber Attack Intention in Cyber Physical System

Recognition of Identifying Malicious Cyber Attack Intention ...

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International Conference on computer and Communications Technologies (ICCCT)

作者： P Vasanthan S. Srinivasan Dept. of Information Technology Sri Sairam Engineering College Dept. of Computer Science and Engineering R.M.D Engineering College

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

As the threat over the cyber-attack is predominantly increasing in every aspects such as based on network traffic, spoofing, phishing attacks the intrusion detection system is used. The network layer is maliciously attacked by the intruder. The intruder tries to capture network packets of the connection between client and server. Based upon the encryption standard, the intruder tries to comprise the user system. Intrusion cannot be easily detected since it is a manual process. In this novel approach, deep learning reinforcement technique is implemented. The bidirectional end to end communication along with the Bayesian inverse reinforcement learning is used to detect the malicious cyber intention in cyber physical system. Inverse Bayesian reinforcement learning technique is initiated to identify the behavior of the pattern recognition and to resample the dataset. This deals with the risk factor and determines whether the connection is comprised. In this work, the novel application is introduced with deep reinforcement learning to identify the intruder. The intrusion detection is labeled in dataset. These dataset implements reward functions to detect the intrusion over the cps.

关键词： Uncertainty Phishing Intrusion detection Telecommunication traffic Probability distribution Bayes methods Pattern recognition Servers Cyberattack Standards

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