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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Prabhu, R. Murugan, V. Senthil School of Computing SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350365269

This article presents a novel approach for extracting shorelines from high-resolution satellite images. Traditionally, manual techniques have been employed for shoreline measurement, which is time-consuming. However, advancements in remote sensing technology, particularly in optical imagery, allow for the automatic extraction of accurate and detailed shorelines from high-resolution data. The conventional remote sensing methods have typically relied on pixel and object-based approaches for shoreline extraction, focusing solely on object size. In contrast, mathematical morphology techniques consider both object size and shape. This paper presents the Morphological Shoreline Index (MSI) algorithm, which is based on structural feature extraction, for extracting the shorelines. To validate the effectiveness of the proposed algorithm, three distinct high-resolution satellite images of Chennai, India, captured by the LISS-III sensor with a spatial resolution of 23.5 meters, have been used. © 2024 IEEE.

关键词： Mathematical morphology

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Real-Time Violence Detection and Alert System using MobileNetV2 and Cloud Firestore 2

Real-Time Violence Detection and Alert System using MobileNe...

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2nd IEEE International Conference on networking and Communications, ICNWC 2024

作者： Thomas, Mannu Balamurugan, P. SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350365269

The aggressive action greatly jeopardizes individual safety and general well-being. Various alternative tactics have been used to reduce violent behavior, including the installation and maintenance of surveillance systems. It is crucial for monitoring systems to automatically detect aggressive behavior and promptly provide warning signals to the authorities. computer vision and image processing have emerged as crucial study areas for identifying deviant and violent behavior, drawing in new researchers. The paper introduces a real- time violence detection and alert system that identifies violence in video streams by utilizing MobileNetV2. The system provides a scalable, precise, and adaptable public safety solution by using Cloud Firestore for data storage and administration, and a Telegram Bot for instant notifications. © 2024 IEEE.

关键词： Image processing

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Streamlining Multi-Cloud Infrastructure Orchestration: Leveraging Terraform as a Battle-Tested Solution 1

Streamlining Multi-Cloud Infrastructure Orchestration: Lever...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Ghosh, Aniruddha Srivastava, Sudhanshu Supraja, P. Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India

ISBN: (纸本)9798350372748

Cloud computing provides the agility required to dynamically adjust applications in response to changing demands. Despite the proliferation of cloud service providers, the issue of vendor lock-in continues to pose significant challenges. Recent service disruptions have underscored the perils of exclusive reliance on a single provider. Existing cloud orchestration tools, while designed to mitigate these issues by facilitating deployments across multiple cloud providers, are often bound to provider-specific models. This necessitates users' familiarity with the unique offerings of each provider and may limit adaptability in the face of errors. To address these challenges, a custom wrapper is proposed that operates on top of Terraform, a leading Infrastructure-as-Code (IaC) tool. This wrapper enables diverse cloud deployments through a customized configuration file, simplifying the process of auditing, configuring, and securing various deployments. Practical experiments confirm the seamless deployment of infrastructure across various cloud providers, notably AWS and Azure. Specifically, the successful deployment of a Linux VM on both platforms showcases the adaptability of the approach to diverse cloud environments. The solution also maintains an easy learning curve, ensuring accessibility and industry-friendliness for users managing infrastructure across different cloud providers. © 2024 IEEE.

关键词： Codes (symbols)

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IoT-Enabled Arrhythmia Detection using RNNs for ECG Analysis 5

IoT-Enabled Arrhythmia Detection using RNNs for ECG Analysis

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Binny, Bessel Praneel, N. Niranjan, A.L. Mathew, Albin Binu Vijay Vasanth, A. SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur India

ISBN: (纸本)9798331518097

In today's era, where major changes are taking place in Healthcare Systems, further details on heart diseases have to be regularly provided with emphasis on the management and preventative strategies for arrhythmias in particular. The current study addresses a research problem on whether it is possible to use the data obtained from ECG to make predictions about the presence of heart diseases, especially arrhythmia using machine learning techniques. Arrhythmia is an abnormal heart rhythm where the heart beats too fast or too slow or becomes random without self-identifying and this can be life- threatening if not diagnosed within the right period. This system aims at predicting electrical activity that leads to arrhythmia through the analysis of the ECG signal so that the medical professional is notified in good time for appropriate action. In this study, the authors' of the article will design, implement and evaluate the machine learning-based methods of disease prediction with the ultimate goal to enhance the sensitivity and specificity of cardiac disease predictive diagnostic services. © 2024 IEEE.

关键词： Electrocardiography

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WSO-KELM: War Strategy Optimization-Based Kernel Extreme Learning Machine for Automatic Software Fault Prediction Model

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Journal of The Institution of Engineers (India): Series B 2025年第1期106卷 145-163页

作者： Elci, J. Brundha Nandagopalan, S. Department of Computer Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India Department of Information Science and Engineering Vemana Institute of Technology Visvesvaraya Technological University Karnataka Belagavi India

The software development projects’ testing part is usually expensive and complex, but it is essential to gauge the effectiveness of the developed software. Software Fault Prediction (SFP) primarily serves to detect faults within software components. The scale of software projects is getting larger, making it impossible and time-consuming to analyze the flaws manually. Artificial Intelligence (AI) techniques are mainly incorporated to solve this problem. The massive data retrieved from the software repositories helps in identifying the features that are highly correlated with fault prediction and the features that are not. This paper introduces a Kernel Extreme Learning Machine (KELM) for SFP. To optimize the hyperparameters of the classifier and also enhance the classification accuracy, the War Strategy Optimization (WSO) algorithm is employed here. The experimental evaluations are conducted using different baseline actual-world datasets of software programs such as Java-coded open-source and PROMISE datasets. The experimental outcomes are conducted using different performance metrics that include sensitivity, Accuracy, and F1-score with values of 0.96%, 0.97%, and 0.96%, respectively. The results show that KELM is beneficial, and the optimization provided by the WSO algorithm also helps us obtain higher classification accuracy for the SFP problem and improve the fault detection process. © The Institution of Engineers (India) 2024.

关键词： Software reliability

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Performance Analysis of Multiple Leaf Disease Detection in Plants Using CNN Model 1

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3rd International Conference on Mechanical and Energy Technologies, ICMET 2023

作者： Kumar, Avinash Kalaiselvi, K. Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Faculty of Engineering and Technology Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Agriculture provides food for everyone, even in situations of rapid population growth. It is advisable to detect plant illnesses in their early phases of development in order to produce enough food for the entire population. Modern deep learning methods, machine learning, and Convolutional neural network have the capability to transform crop management and disease detection in the field of precision agriculture. This project offers a holistic response that makes use of these cutting-edge technologies to solve a variety of issues that farmers must deal with, including the timely and accurate identification of plant diseases and the provision of efficient treatment options. The development and optimization of deep learning models, particularly CNNs, to accurately identify plant diseases forms the basis of this study. These models were developed using large image datasets that included both healthy plants and various illness symptoms. The use of CNNs makes it possible to extract complex information from images, facilitating accurate disease classification. We present a novel multimodal fusion strategy because we understand that there are many other aspects besides visual cues that have an impact on plant health. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification (of information)

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A no-code swarm simulation framework for agent-based modeling using nature-inspired algorithms

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International Journal of Information technology (Singapore) 2024年第7期16卷 4693-4699页

作者： Hasan, Ishraq Islam, Rubyeat Sharmin, Nusrat Md. Akhtaruzzaman Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Swarm robotics describes the coordination among multiple robots assigned to perform a single task collectively and work as a system. The system is usually used in search and-rescue missions in adverse natural environments. Research in this field involves testing theories and algorithms by using physical robots which requires expensive environment setup. This study presents a novel no-code simulation framework for swarm robotics. The software framework provides a set of building block of various components to customize the research and development process. Besides the various platforms that exist nowadays, this proposed framework overcomes the common problem of low flexibility in modeling, resulting significant improvements in the development of any simulation through customization of algorithms and multi-agent modeling. For rapid development of simulation instances, this study proposes a swarm simulation framework that allows multi-agent modeling in a no-code development environment by defining the number of agents and the behaviors they are expected to exhibit. A random environment is generated where the agents and specific target is defined to test swarm robots for search and rescue operations. The framework mainly proposes a no-code environment and provides the opportunity to test the swarm behavior and relevant theories with flexibilities to make necessary adjustments. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Multi-agent modeling No-code environment Simulation framework Swarm robotics

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Predictive Analytics in Hotel Recommendations Using Machine Learning 5

Predictive Analytics in Hotel Recommendations Using Machine ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Siddharth Dey, Shayan Sharma, Shreyanshi Prathaban, Banu Priya School of Computing College of Engineering and Technology SRM Institute of Science and Technology Department of Networking and Communications Kattankulathur603203 India School of Computing SRM Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications Kattankulathur603203 India

ISBN: (纸本)9798331518097

In a world where travellers want the perfect stay, a new system emerges as the one that goes beyond simple hotel recommendations. By analysing details like interaction words and prices, it predicts hotel ratings using a powerful Random Forest Regressor which performs better than methods like Linear Regressor. With careful data processing and encoding, it achieves high accuracy, measured through precise metrics like RMSE, MSE and Max Error. It achieves a great RMSE score of 0.424. This system offers a scalable solution, turning every hotel suggestion into a personalized experience, redefining the way people find their ideal stay. © 2024 IEEE.

关键词： Hotels

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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Identification of Human Activity from Video Streaming Smartphone Data Using Intensified VGG16

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International Journal of Engineering, Transactions B: Applications 2025年第6期38卷 1340-1352页

作者： Yadav, R.K. Daniel, A. Semwal, V.B. Department of Computer Science and Engineering Amity University Gwalior India Data Science and Engineering Manipal University Jaipur India Department of Computer Science and Engineering Maulana Azad National Institute Technology Bhopal India

Human activity recognition (HAR) techniques pick out and interpret human behaviors and actions by analyzing data gathered from various sensor devices. HAR aims to recognize and automatically categorize human activities using patterns and attributes taken from sensor data. HAR is complex in implementing the algorithm for a self-recorded dataset, including challenges such as age variation, wearing different clothes, environment and surface, the direction of the smartphone camera, and many more. The paper aims to propose a VGG16 deep learning framework including an activation function and different optimizers for classifying human activity from the real-time captured dataset;further, we compare the evaluated results with existing results. The proposed methods achieved 99.88% accuracy with excellent precision, recall, and F_measure values. Comparing the evaluated result with existing outcomes over the WISDM and UCI-HAR datasets. The new things in the article are a self-captured dataset of various aged male, female, and healthy volunteers to perform seven activities. Furthermore, this research uses Tensor Processing Units (TPU) available on Kaggle to improve classification accuracy while reducing error rates and speeding up execution. ©2025 The author(s).

关键词： Smartphones

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