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On Some Topological Structures of Transformation Groups

IAENG International Journal of Applied Mathematics 2024年第5期54卷 887-893页

作者： Dhanasekar, Keerthana Visalakshi, V. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

This paper explores fundamental aspects of irresolute maps and investigates separation axioms, specifically semi-T2 and semi normal properties. The study introduces Irr-topological transformation groups, Irr*-topological transformation groups, I*-topological transformation groups and I-topological transformation groups, providing an in-depth analysis of their interrelations. Through detailed examination, the topological properties of these groups are elucidated, complemented by relevant examples and counterexamples. This comprehensive exploration aims to enhance our understanding of irresolute maps, separation axioms, and the intricate relationships within the introduced transformation groups. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Topology

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DDoS and Botnet Attacks: A Survey of Detection and Prevention Techniques

DDoS and Botnet Attacks: A Survey of Detection and Preventio...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Pranav, S.A. Sathya Priya, S. HariHaran, B. Srm Institute Of Science And Technology Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The Internet of Things (IoT) heralds a innovative generation in communication via enabling regular gadgets to supply, receive, and percentage records easily. IoT applications, which prioritise venture automation, aim to present inanimate items autonomy;they promise increased consolation, productivity, and automation. However, strong safety, privateness, authentication, and recuperation methods are required to understand this goal. In order to assemble give up-to-quit secure IoT environments, this newsletter meticulously evaluations the security troubles and risks inherent to IoT applications. It emphasises the vital necessity for architectural *** paper starts by conducting an examination of security worries before exploring emerging and advanced technologies aimed at nurturing a sense of trust, in Internet of Things (IoT) applications. The primary focus of the discussion revolves around how these technologies aid in overcoming security challenges and fostering an ecosystem for IoT. © 2024 IEEE.

关键词： Botnet

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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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Sentiment Analysis using Speech Recognition for Improved Customer Operations Using Convolutional Neural Networks 2

Sentiment Analysis using Speech Recognition for Improved Cus...

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2nd International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2024

作者： Hema, D. Deva Sujith, N. Rithika, P.J. Bhavesh, S. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Dept. of Computer Science SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9798331541217

This paper presents the application of advanced speech recognition technologies to transcribe and analyze customer interactions, enhancing both business efficiency and customer experience. Motivated by the need for human-like interaction in automated systems, our work introduces "Humanization through Automation," a system designed to retain personal touches while optimizing operational efficiency. The approach involves capturing high-quality audio, followed by noise reduction and normalization to facilitate effective feature extraction using Spectrogram Conversion and Mel-Frequency Cepstral Coefficients (MFCC). The use of Convolutional, Pooling, and Dropout Layers refines detail detection and reduces overfitting, while Fully Connected Layers integrate and analyze features, using activation functions for accurate classification and sentiment analysis. By embedding sentiment detection within Interactive Voice Response (IVR) systems via Convolutional Neural Networks (CNNs), our system can interpret emotional tones, allowing dynamic responses and connecting customers with suitable representatives based on emotional state. This integration of speech recognition and sentiment analysis not only elevates interaction quality but also strengthens customer relationships, optimizes sales strategies, and fosters organizational growth, transitioning from traditional customer service models to solutions emphasizing efficiency and personalization. © 2024 IEEE.

关键词： Sales

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Detecting Mental Stress Using K-NN Classifier and IoT Devices 11th

Detecting Mental Stress Using K-NN Classifier and IoT Device...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Prabu Shankar, K.C. Hema, M. Computer Science and Engineering SRM Institute of Science & Technology Kattankulathur Chennai India

ISBN: (纸本)9789819717231

Mental tension has become a constant in the fast-paced world of today. It is a problem that keeps getting worse and, if left unchecked, could have serious health effects. Consequently, it is of highest importance to detect stress in individuals to prevent any detrimental results. With its sophisticated algorithms and skills, machine learning can be essential in solving this problem. In this study, a theoretical framework for stress detection using wearable technology with machine learning algorithms and other cutting-edge features is proposed. Heart rate variability (HRV) and speech emotion recognition (SER), two crucial modules that help identify stress in people, are implemented in the proposed system. The SER module detects the emotions in speech, whereas the HRV module examines fluctuations in heart rate. It is anticipated that the suggested system will offer a precise and trustworthy stress detection method, opening the door to better health results. HRV for short is a method to help identifying stress. Many machine learning classification techniques, including logistic regression, k-nearest neighbors, random forest classifier, decision tree classifier, and neural network classifier (NNC), are used to create this particular module. The k-NN model outperformed the other algorithms, scoring a 99.7% accuracy rate. The Python programming language was chosen because of its abundance of libraries and header files to make implementation easier. Streamlit, an open-source app framework used in the domains of machine learning and data science for developing apps connected to ML modules, was used to deploy the HRV module locally. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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On Orthogonal (phi, digamma)-Contraction Type Mappings and Relevant Fixed Point Theorems with Applications

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IAENG International Journal of Applied Mathematics 2024年第9期54卷 1788-1796页

作者： Dhanraj, Menaha Gnanaprakasam, Arul Joseph Department of Mathematics College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kanchipuram Tamil Nadu Kattankulathur603203 India Department of Mathematics College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kanchipuram Tamil Nadu Kattankulathur603203 India

In this article, we are motivated by various fixed point theorems for different types of orthogonal (, F) contractions in orthogonal Branciari (rectangular) b-metric spaces. We present examples that improve upon many existing results to highlight our key findings. Additionally, we provide an application to clarify the existence and uniqueness of solutions to integral equations. Finally, we present numerical results that contrast with the analytical findings. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Fixed point arithmetic

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Alzheimer's Disease Prediction using an Artificial Butterfly Optimizer with a Two-Layer CNN Approach 4

Alzheimer's Disease Prediction using an Artificial Butterfly...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Jevin, J.A. Umamageswari, A. Srm Institute of Science & Technology Department of Computer Science & Engineering Chennai India

ISBN: (纸本)9798350352931

Alzheimer's disease, the most common dementia, involves brain damage. Alzheimer's starts with mild symptoms and worsens over time, making it difficult to treat. Early detection can prevent disease progression or reduce symptoms before major impairment. This study uses multiple stages to detect Alzheimer's early. Model training, feature selection, and thorough data preprocessing are system essentials. Data scaling, missing value imputation, and outlier treatment prepared the clinical dataset. Information gain, correlation coefficient, and chi-square were used to choose Alzheimer's disease predictors. The model was trained on hundreds of clinical samples. The ABO-2layer CNN architecture achieved a maximum prediction accuracy of 94.56%, indicating that preprocessing and feature selection improve model performance. The high anticipated accuracy showed the significance of Alzheimer's disease early detection methods. This study improves feature selection and uses advanced CNN architectures to create tools that may help diagnose Alzheimer's early, one way to battle neurodegenerative diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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TRS Scheduling for Improved QoS Performance in Cloud System

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computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Morphological Classification of Galaxies using Enhanced Deep Learning Model 5

Morphological Classification of Galaxies using Enhanced Deep...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Nemade, Rohan Jha, Suraj Anand, Harsh Chandrasekaran, Saravanan Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350379884

Galaxies in astrophysics are one of its fundamental, their morphological classification remains an essential key to unlock countless doors about the structure, formation and history of our Universe. For nearly a century, astronomers have assigned galaxies to perceptual categories such as ellipticals, spirals and irregulars. However, the extraordinary expansion of the galaxy database catalysed and instigated by sky surveys of ever-increasing scales rendered this classification more manual than practical. Because of the learning capability and its scaling nature, to learn hierarchical abstract features from the visual data Convolutional Neural Networks (CNN) are powerful tools to recognize complex patterns and structures in images, such as spiral arms or central bulges that contribute significantly to accurate morphological classification. That is why CNN are particularly well suited to the problem of galaxy classification. While traditional classification work used to solve the problem, it is not feasible for today's huge amounts of generated data. This attempt to build and train a CNN model using galaxy images as our domain. The Enhanced CNN model can achieve the high accuracy to morphological type of galaxies. The Enhanced CNN increase both efficiency and classification accuracy, particularly when processing large dataset. The Galaxy Zoo dataset indicated that our study can provide a tool for other astronomical research in future. The Enhanced CNN gain the overall accuracy rate are 95.29% with the performance indicator were calculated including average precision, recall, F1-score and Area under curve (AUC) is 0.9513, 0.9531, 0.9525 and 0.9853 respectively. The Enhanced CNN outperforms the various existing state-of-the models. © 2024 IEEE.

关键词： Convolutional neural networks

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A Hybrid Deep Learning Solution for Real-Time DDoS Mitigation in IIoT Environments 5

A Hybrid Deep Learning Solution for Real-Time DDoS Mitigatio...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kamaleswari, S. Suganthi, N. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798331530013

The Industrial Internet of Things (IIoT) has revolutionized industrial operations by enhancing connectivity and automation. However, this interconnectivity also introduces significant vulnerabilities, particularly to Distributed Denial of Service (DDoS) attacks, which can disrupt critical industrial processes. This proposed model designed to fortify IIoT networks against DDoS threats. The proposed approach employs a hybrid model that integrates CNN and Bi-LSTMs networks to effectively capture spatial and temporal patterns in network traffic data. The framework encompasses including data collection, preprocessing, DDoS detection, and automated strategies. The framework using real-world IIoT traffic datasets, preliminary results prove that the work expressively improves DDoS detection capabilities while maintaining low false positive rates. This work ensuring the reliability and availability of industrial operations. Future research will explore advanced deep learning architectures and the integration of this framework with cutting-edge networking technologies for dynamic threat response. © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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