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Global Conference for Advancement in technology (GCAT)

作者： V. Saravanan Talluri Upender E. d. Kanmani ruby P. deepalakshmi desidi Narsimha reddy Ananthi S N Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of CSE CMR College of Engineering and Technology. Medchal Hyderabad Telangana Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science Technology School of Computing Director - Accreditation and Ranking Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Data Consultant (Data Governance Data Analytics: Enterprise Performance Management AI&ML) Soniks Consulting LLC Texas USA Department of Computer Science and Engineering S.A. Engineering College Chennai

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The dynamic nature of cyber threats demands that threat detection mechanisms be continuously improved in order to protect digital assets and infrastructures. In cybersecurity, this study investigates the use of machine learning (ML) algorithms for enhanced threat detection. This work intends to improve threat detection systems' accuracy, speed, and adaptability by utilizing ML algorithms' capabilities. The abstract begins by outlining the significance of machine learning in transforming traditional cybersecurity measures. It highlights the limitations of conventional signature-based methods, which often fail to detect novel and sophisticated threats. In contrast, ML-based approaches offer dynamic and proactive defenses capable of identifying previously unseen attack vectors. Furthermore, the paper delves into the practical implementation of ML models in real-world cybersecurity environments. It covers data collection and preprocessing techniques critical for training accurate and reliable models, as well as the challenges associated with maintaining and updating these models to adapt to evolving threat landscapes. The importance of feature selection and engineering in improving model performance is also emphasized. To validate the effectiveness of ML approaches, the study presents case studies and experimental results from various cybersecurity applications. The abstract concludes by addressing the future prospects and potential advancements in machine learning for cybersecurity. It discusses the role of emerging technologies, such as deep learning and federated learning, in enhancing threat detection capabilities. Additionally, it considers the ethical and privacy concerns associated with deploying ML models in cybersecurity and the need for robust governance frameworks. In summary, this study presents a thorough analysis of machine learning techniques for enhanced threat detection in cybersecurity, including useful applications, advantages, and difficulties.

关键词： Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Machine learning data collection Threat assessment data models Vectors Computer security

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Federated Learning-Based Intelligent Indoor Smoke and Fire detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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Telecommunications and Intelligent Systems (ICTIS), International Conference on

作者： Mohamed rafik Aymene Berkani Ammar Chouchane Yassine Himeur Anes Abdennebi Seref Sagiroglu Abbes Amira Research Laboratory in Advanced Electronics Systems (LSEA) University Yahia Fares of Medea Medea Algeria Laboratory of LI3C University Biskra University Center of Barika Barika Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Software and Information Technology Engineering École de Technologie Supérieure Montréal Canada Artificial Intelligence and Big Data Analytics Security R&D Center Gazi University Ankara Turkey Department of Computer Science University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331527396

ISBN: (纸本)9798331527402

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1d for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable dL-CNN1d model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments.

关键词： Privacy Smart buildings Costs Federated learning Atmospheric modeling data models Convolutional neural networks Wearable sensors Monitoring Intelligent sensors

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Optical parameters extraction of soil and its microplastics contamination using terahertz spectroscopy

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Heliyon 2024年第19期10卷 e38326页

作者： Meenaakumari, M. Shoba, S. Manikandan, E. Karthigeyan, K.A. Nithyapriyan, S.A. Meikeerthivasan, V. Hemeshwar, S. Centre for Advanced Materials and Innovative Technologies Vellore Institute of Technology Campus Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Campus India Centre for Advanced Data Sciences Vellore Institute of Technology Chennai Campus India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

The primary focus of this study investigates the potential of continuous-wave terahertz frequency-domain spectroscopy(CW-THz-FdS) as a rapid, non-destructive technique for detecting microplastic contamination in soil. Focusing on low-density polyethylene (LdPE) as a model microplastic, we prepared soil mixtures with varying LdPE concentrations (1 %,5 %,10 %,15 % by weight). We used a THz-FdS system operating between 0.1 and 0.7 THz to analyze the optical properties (transmission coefficient, attenuation and refractive index) of these mixtures and LdPE samples separately. Notably both soil and LdPE particles were of micrometer scale. Our results revealed distinct, concentration-dependent variations in these properties. The refractive index for the higher concentration (15 %) ranged from 2.2 to 2.3, while the attenuation coefficient varies between 10 and 14 cm⁻¹. Crucially, we identified polylactic acid [PLA] as a suitable sample holder material due to its high transmission in the THz range. Consequently, this study suggests that terahertz spectroscopy can be a promising technique for detecting microplastic contamination in soil, offering a non-destructive and sensitive method for environmental monitoring. © 2024 The Authors

关键词： Electro-optic sampling Micro plastic Non-invasive Terahertz spectroscopy

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Twitter Spam detection Using Word Vector and Binary Classifier

Twitter Spam Detection Using Word Vector and Binary Classifi...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： ramaprabha Marimuthu Gurusigaamani Ayyanar Muthulingam Vinoth N.A. S Sivasathiya Ganesan Kardeepa Ponnuchamy Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer science and Engineering Kalasalingam Academy of Research and Education Virudhunagar India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai India Department of Artificial Intelligence and Data Science Easwari Engineering College Ramapuram Chennai India

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics.

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Efficient diabetes detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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distributed Systems, Computer Networks and Cybersecurity (ICdSCNC), International Conference on

作者： A S Gowri P Jose Karthik S M S Berlin Shaheema K M Karuppasway r. Balamurugan Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department for Applied Research The Gandhigram Rural Institute (Deemed to be University) Gandhigram India Department of Artificial Intelligence and Data Science Stella Mary’s College of Engineering Azhikal India Department of Mathematics and Statistics Bharath Institute of Higher Education and Research Chennai India Department of Information Technology DMI College of Engineering Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with rBF function and achieve the accuracy as 98,04%.

关键词： Support vector machines Accuracy Convolution Machine learning Feature extraction data models diabetes data mining Convolutional neural networks Principal component analysis

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Initial Study on Applicability of Thermal Imaging for Microplastics Identification in Aquatic Medium

SSRN

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SSrN 2023年

作者： Manikandan, E. Pravin, Sheena Christabel Kiruthika, V. Gopikashree, S. Chandan, Sai Gokul H.T. Karthigeyan, K.A. Shoba, S. Centre for Innovation and Product Development Vellore Institute of Technology Chennai Campus Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai Campus Chennai India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai Campus Chennai India

Owing to plastic's durability, plastic waste management is challenging. The current issue is that millions of tonnes of plastic end up in the ocean, possibly resulting in microplastic pollution and becoming the most prevalent underwater contaminant. Efficient identification of microplastic pollution is a scientific challenge because as the size of the particles decreases, it becomes more difficult to recognize and detect them. In this work, we demonstrate a deep learning-based approach for microplastics identification. The dataset is acquired using the thermal imaging technique. data collection is followed by a data pre-processing step and finally, a classification step. The reported best accuracy, 97.42%, is achieved using the CNN model. The proposed approach allows for the deployment of increasingly diversified models as deep-learning models are still progressing. © 2023, The Authors. All rights reserved.

关键词： data handling

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Multiple Target research of Construction Progress on the Basis of on BIM and decide Tree Algorithms

Multiple Target Research of Construction Progress on the Bas...

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International Conference on Smart Technologies For Smart Nation (SmartTechCon)

作者： S. Selvakanmani E. Manigandan C. Hazarathaiah Yadav Satheesh Kumar S Brijesh Singh B. Karthik Department of Information Technology RMK Engineering College RSM Nagar Chennai Department of Computer Applications Saveetha College of Liberal Arts and Sciences Thandalam Chennai Tamilnadu India Department of Chemistry Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Sathyamangalam Tamilnadu India Department of MBA CMR Institute Of Technology Bangalore India Department of Electrical and Electronics Engineering Sona College of Technology Salem India

Because of India's growing urbanization, coupled with dwindling urban land supply and rising land costs, the construction of high-rise structures has emerged as the dominant method to urban planning and development. The rapid growth of India's economy and the rapid pace of urbanization have resulted in an increasing urban population, raising the requirement for effective land usage. High-rise structures have gradually been the main center of urban construction projects in response to these *** primary goal of this study is to look into the use of Building Information Modeling (BIM) in building projects with the goal of optimizing the use of information received from it. Numerous graphs and algorithmic formulas are developed during the study to examine and illustrate these features. According to the findings of the study, a significant amount, accounting for 28.2% of the total, is related to various focused research procedures. As a result, the interest and significance of this field of investigation are expected to endure and develop in the coming years..

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The role of Edge Computing in Enhancing Cloud Performance and reducing Latency

The Role of Edge Computing in Enhancing Cloud Performance an...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Puneet Sharma J Shyamala devi K Manikandan r Ani Minisha r Harini S. Karthik School of CS & AI SR University Warangal Telangana India Rajalakshmi Institute of Technology Chennai School of Computer Science & Engineering (SCOPE) Vellore Institute of Technology (VIT) Vellore Tamilnadu India Department of Artificial Intelligence and Data Science R.M. K. Engineering College Department of Computer Science and Engineering Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamilnadu India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing.

关键词： Cloud computing Machine learning algorithms Job shop scheduling Smart cities Processor scheduling Security reliability Low latency communication random forests Edge computing

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detection of Malfunctioning Modules in Photovoltaic Power Plants using Unsupervised Feature Clustering Segmentation Algorithm

arXiv

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arXiv 2022年

作者： dwivedi, divyanshi Yemula, Pradeep Kumar Pal, Mayukha Data Science Research Intern ABB Ability Innovation Center Hyderabad500084 India Department of Electrical Engineering Indian Institute of Technology Hyderabad502205 India Global R&D Leader Data Science at ABB Ability Innovation Center Hyderabad500084 India

The energy transition towards photovoltaic solar energy has evolved to be a viable and sustainable source for the generation of electricity. It has effectively emerged as an alternative to the conventional mode of electricity generation for developing countries to meet their energy requirement. Thus, many solar power plants have been set up across the globe. However, in these large-scale or remote solar power plants, monitoring and maintenance persist as challenging tasks, mainly identifying faulty or malfunctioning cells in photovoltaic (PV) panels. In this paper, we use an unsupervised deep-learning image segmentation model for the detection of internal faults such as hot spots and snail trails in PV panels. Generally, training or ground truth labels are not available for large solar power plants, thus the proposed model is highly recommended as it does not require any prior learning or training. It extracts the features from the input image and segments out the faults in the image. Here we use infrared thermal images of the PV panel as input, passed to a convolutional neural network which assigns cluster labels to the pixels. Further, optimize the pixel labels, features and model parameters using backpropagation based on iterative stochastic gradient descent. Then, we compute similarity loss and spatial continuity loss to assign the same label to the pixel with similar features and spatial continuity to reduce noises in the image segmentation process. The effectiveness of the proposed approach was examined on an online available dataset for the recognition of snail trails and hot spot failures in monocrystalline solar panels. © 2022, CC BY-NC-Nd.

关键词： Solar energy

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Advanced dual Module Weapon detection for Public Safety and Surveillance System

Advanced Dual Module Weapon Detection for Public Safety and ...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Manikanta Sirigineedi M. Sowmiya HemavathI. U V.C. Bharathi Sayali S Karmode r. Sathya Department of Information Technology Vishnu Institute of Technology Vishnupur Bhimavaram Andhra Pradesh India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology AVADI School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh India Information Technology Mahatma Gandhi Mission's College of Engineering & Technology Kamothe Navi Mumbai Maharashtra India Department of IT Kongunadu College of Engineering and Technology Trichy Tamil Nadu India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

This research presents a novel weapon detection system that combines state-of-the-art technologies for enhanced security. The system integrates a Convolutional Neural Network (CNN) for precise image-based weapon classification and the YOLO algorithm for real-time detection in live camera feeds. The proposed system generates email alerts to promptly inform users of detected weapons, enabling timely action to mitigate risks. By combining these advanced modules, the proposed system offers an effective solution for addressing weapon-related violence and improving security measures in various environments.

关键词： YOLO Weapons Surveillance real-time systems Public security Classification algorithms Security Convolutional neural networks Feeds Expert systems

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