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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sheeba, T.M. Albert Antony Raj, S. Anand, M. Srm Institute of Science and Technology College of Science and Humanities Department of Computer Applications Chennai India Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9781665462167

Image segmentation is one of the most important processes involved in image processing. Segmenting an image is breaking it up into smaller pieces, or segments. These are the areas where it is most helpful because processing the complete image would be inefficient for tasks like object recognition or image compression. Image segmentation is the process of dividing an image's elements for further processing. This article looked at a number of image segmentation techniques, including the threshold approach, edge-based method, and clustering-based method. The segmentation method that is most effective for separating images in OCT images is clustering-based segmentation. To reduce the speckle noise during preprocessing, the wiener filter approach is used. With regards to the threshold, edge-based (Sobel, Canny, Robert's), and clustering-based segmentation techniques, calculate MSE (mean square error) and PSNR (peak signal to noise ratio) values for the segmented image quality. © 2023 IEEE.

关键词： Image segmentation

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A Robust MAES Encryption And Decryption Algorithm using Ankaru Model Through Fog Computing

A Robust MAES Encryption And Decryption Algorithm using Anka...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Karunya, S. Sara, Belina V.J. Angayarkkani, K. Sowmiya, J. Sonia, A. Jeganraj, P. Dwaraka Doss Goverdhan Doss Vaishnav College Department of Information Technology & Bca Chennai India Srm Institute of Science & Technology Department of Computer Applications Chennai India Aalim Muhammed Salegh College of Engineering Department of Cse Chennai India Srm Institute of Science & Technology Master of Computer Applications Chennai India Srm Institute of Science & Technology Department of BCA-DS Chennai India

ISBN: (纸本)9798350360660

The world as a whole now depends on the data that people have stored online and how these developments are tracked in the future. To satisfy the requirements of safeguarding the information of an individual, a company, or a rapidly growing sector, there is only one standard. The first phase, encryption, and the second, decryption, are absolutely necessary to complete. Data is encrypted for protection, then it is decrypted for retrieval. The Advanced Encryption Standard provides the highest level of security for data protection, ensuring that its processing remains hidden. Data on edge devices is encrypted using the AES method by Fog. Since AES has never experienced a data breach, safety and security are the most fundamental truths that may be discovered through its use. Data from Edge devices is encrypted and then modified using fog computing to secure it using the AES technique. The data is then recovered by decryption. The secret key is primarily concerned with data entering and returning to the source, which transforms the proper propagation outcome. © 2024 IEEE.

关键词： advanced encryption standard advanced encryption standard and transformation cryptography decryption modified

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IoT-Based Crop Yield Prediction System in Indian Sub-continent Using Machine Learning Techniques

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Remote Sensing in Earth Systems sciences 2023年第3-4期6卷 156-166页

作者： Nithya, V. Josephine, M.S. Jeyabalaraja, V. Department of Computer Applications Dr. M.G.R. Educational and Research Institute Tamil Nadu Chennai India Department of Computer Science Engineering Velammal Engineering College Tamil Nadu Chennai India

The agricultural sector holds significant importance in the socio-economic structure of India. The inability of farmers to make informed decisions regarding the optimal crop selection for their land, based on non-scientific and traditional methods, poses a significant challenge for a nation where farming constitutes the livelihood of nearly 58% of the population. At times, farmers have experienced challenges in selecting appropriate crops that align with the soil conditions, sowing season, and geographical location. By collecting data from sensors in the fields, ranchers can easily ascertain the extent of the crops, which helps preserve scarce resources and lessens the negative effects of weeds on the environment. The majority of Indian farmers choose conventional farming methods when deciding how their crops should be grown. Regardless, ranchers do not jeopardize agricultural yields, which are sensitive to abiotic factors such as soil type. In this proposed method, two types of datasets have been collected from the southern and northern parts of the country through IoT-based sensors. The crop recommendation system has been processed with various machine learning algorithms like logistic regression, KNN, decision tree, and support vector machine through the collected dataset. The accuracy of 97% and 96% has been achieved for processed datasets in the southern and northern parts of India. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Accuracy Crop recommendation Decision tree IoT KNN Logistic regression Machine learning Precision farming SVM Training

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Text-Conditioned Image Synthesis - A Review

Text-Conditioned Image Synthesis - A Review

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2023 IEEE Silchar Subsection Conference, SILCON 2023

作者： Saha, Piyush Ghosh, Sumon Bhandari, Dinabandhu Calcutta Institute of Engineering and Management Dept. of Electrical Engineering Kolkata India Heritage Institute of Technology Dept. of Computer Applications Kolkata India Heritage Institute of Technology Dept. of Computer Science Kolkata India

ISBN: (纸本)9798350314144

With the advent of Generative Adversarial Networks and diffusion models, image synthesis conditioned on text description has been an active area of research. Generative Adversarial Networks are a flexible and intuitive way of conditional image synthesis and significant progress has been made in the last few years regarding visual realism, diversity, and semantic alignment. In recent years, Diffusion probabilistic models have been shown to perform better in the task of image synthesis and have been used extensively for the task of text-to-image synthesis, significantly improving visual realism, semantic alignment, and generation of high-resolution images. However, the field still faces many challenges such as the generation of high-resolution images with multiple objects and the development of suitable and reliable evaluation metrics. In this review, we contextualize the state-of-the-art text-conditioned image generation models, and critically examine current evaluation strategies, architectures, model training, and datasets. This review complements previous surveys on text-conditioned image synthesis which we believe will help researchers to further advance this field. © 2023 IEEE.

关键词： Generative adversarial networks

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Face Recognition using Adaboost Classifier For The Visually Impaired 8

Face Recognition using Adaboost Classifier For The Visually ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Avinash, N.J. Krishnaraj Rao, N.S. Rama Moorthy, H. Mangalore Institute of Technology and Engineering Department of Electronics and Communication Engineering Mangalore Karnataka Moodabidre India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India Nitte Institute of Professional Education Department of Computer Applications Karnataka Mangaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The paper suggests an approach to facial recognition of physical challenges. The most difficult challenge for blind persons is identifying impediments outside and behind them. They mostly rely on other people to help them make up for their blindness. To overcome this, they need to be supported by someone else people. In their daily lives, they face many problems when it comes to moving from one place to another. There is no guidance to help them when they need it. This study suggests a facial recognition system for those with vision impairments utilizing the Raspberry Pi to help prevent these situations. There is a camera designed for face recognition and a microphone designed for voice recognition in this case, so these components can be combined and people with visual disabilities can use it easily and behaves reasonably. In this work, a method is presented for detecting, classifying, and locating objects within complex environments. The paper applies the techniques of image processing and Adaboot classifiers to achieve an accuracy of more than 90 © 2024 IEEE.

关键词： Face recognition

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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Anomaly Detection in Industrial Quality Control with computer Vision and Deep Learning

Anomaly Detection in Industrial Quality Control with Compute...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Mary, A. Jenita Umesh, V. Khairnar, Prerana Nilesh Sahana, R. Sangeethaa, S.N. Shanthi, H.J. Srm Institute of Science and Technology Faculty of Science and Humanities Vadapalani City Campus Department of Computer Science and Applications Chennai India Ksou Mysore Department of It Mysuru India Visvesvaraya Institute of Technology Department of Electronics and Telecommunication Engineering Chincholi Nashik India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Department of Artificial Intelligence and Machine Learning Chennai India Bannari Amman Institute of Technology Department of Computer Science & Engineering Sathyamangalam India Hindustan Institute of Technology and Science Deemed to Be University Department of Computer Applications Padur India

ISBN: (纸本)9798350375442

This study examines the impact of the dynamic manufacturing environment on companies' efforts to improve customer satisfaction through product quality enhancement, production cost reduction, and sustainability realisation. The research focuses on the current trends and challenges companies face in adapting in response to the dynamic nature of the industrial industry. This study seeks to offer practical recommendations by analyzing these factors for companies to meet the evolving demands of their customers while ensuring long-term success and competitiveness. This study examines the impact of anomaly detection on product quality, specifically focusing on the commonly employed method of visual quality inspection. This study examines the methods of visual quality inspection for products, comparing manual and automatic approaches. This study examines the limitations of manual inspection as a monotonous task, which can result in the oversight of errors and subjective assessments. In order to keep up with the needs of smart production and the advent of Industry 4.0, manufacturing as a whole is actively seeking computerized quality inspection solutions. Innovative inspection systems can be created through the effective use of big data. This research abstract discusses the widespread use of machine learning in automated quality inspection systems to enhance performance and support data analytics methods. This study uses a convolutional neural network architecture to extract complex features and patterns from images. This enables the model to differentiate between normal and abnormal conditions effectively. This research presents a proposed model trained on a diverse dataset, including a comprehensive collection of potential defects and irregularities. This research abstract presents a proposed framework for validating effectiveness through extensive experiments on datasets from diverse industrial domains. This study presents the findings of a research project that evaluated

关键词： Smart manufacturing

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Intrusion Detection Mechanism Using Deep Learning 5

Intrusion Detection Mechanism Using Deep Learning

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

作者： Ananthi, P. Nirmaladevi, K. Naveen Kumar, S. Kongu Engineering College Department of Computer Technology-PG Tamilnadu Erode638060 India Madanapalle Institute of Technology & Science Department of Computer Applications India M.Sc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331518097

In today's connected and data-driven world, networks and digital systems need to be protected from malicious attacks. The effectiveness of conventional Intrusion Detection Systems (IDS) in recognizing and impeding novel threats and intricate methodologies has its limits. This study introduces a new method for improving network security by using deep learning techniques in the design and development of an intrusion detection system (IDS). The suggested Intrusion Detection System uses deep learning techniques, specifically Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs), to identify and react to known and new security threats. Data can be taught to be encoded into a lower-dimensional representation and then decoded back into its original form using autoencoders (AEs). Autoencoders are a cutting-edge intrusion detection system that can lower false positives and raise overall detection accuracy because of their capacity to automatically learn from and adjust to the ever-changing threat landscape. They enhance computer networks capacity to spot irregularities and possible security threats. With the deep learning model obtaining a high accuracy of 99% in real-world threat detection, the results show that deep learning is effective in developing intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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Comprehensive Earth System Analysis for Advancing Community Resilience: Integrating Remote Sensing with Hybrid GAN-RNN Techniques

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 788-803页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Lakkshmanan, Ajanthaa Ramya, Saminathan Pallavi, Lanke Thiagarajan, Ranganathan Department of Computer Applications Sindhi College Chennai India Department of Computing Technologies SRM Institute of Science and Technology Kattankaluthur Chengalpattu District India Department of Computer Science and Engineering Kongu Engineering College Perundurai Tamil Nadu Erode India Department of CSE B V Raju Institute Of Technology Narsapur India Department of Information Technology Prathyusha Engineering College Thiruvallur India

Effective disaster prediction is essential for climate resilience, especially in areas prone to extreme weather events. Conventional models often face challenges in predicting complex or uncommon events, largely due to data scarcity and the difficulty of modeling temporal dependencies in sequential data. This paper introduces an innovative hybrid GAN-RNN model that leverages remote sensing and IoT sensor data to improve disaster prediction accuracy. By combining generative adversarial networks (GANs) for generating synthetic data and recurrent neural networks (RNNs) for recognizing temporal patterns, this model forms a potent predictive tool. Achieving a remarkable testing accuracy of 98.16%, the hybrid GAN-RNN model significantly surpasses traditional predictive methods. Comprehensive evaluations demonstrate its enhanced capability to predict complex and rare events effectively, establishing it as a crucial resource for disaster risk management and environmental monitoring. This research advances AI-driven methodologies in climate resilience, offering a solid framework for future innovations in the field. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： AI-driven solutions Climate resilience Data augmentation Disaster prediction Environmental monitoring Hybrid GAN-RNN IoT sensors Remote sensing Synthetic data Temporal pattern recognition

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Transmission of ECG in Android Application by Cloud Server 1

Transmission of ECG in Android Application by Cloud Server

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Elavarasan, N. Bindu, Garikapati Buvaneswari, B. Kanaga Priya, P. Srinivasan College of Art's and Science Department of Computer Applications Perambalur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Guntur India Panimalar Engineering College Department of Information Technology Chennai India Computer Science and Engineering KPR Institute of Engineering and Technology Arasur India

ISBN: (纸本)9798350323795

This paper proposes an approach for transmitting electrocardiographic (ECG) signals through a cloud server from an application named App1 to a remote application called App2. The objective is to enable real-time monitoring and analysis of ECG signals, facilitating remote healthcare services and enhancing the accessibility of medical data. The proposed approach leverages the capabilities of cloud computing and network connectivity to establish a seamless and secure communication channel between the two applications. The proposed approach has the potential to revolutionize remote healthcare services by enabling real-time transmission of ECG signals through a cloud server. It facilitates continuous monitoring of patients' cardiac activity, enhances collaboration between healthcare professionals, and expands the reach of healthcare services to remote areas. However, further research and development are required to address potential challenges such as network latency, data privacy concerns, and system scalability to accommodate a large number of users and data streams. © 2023 IEEE.

关键词： Cloud computing

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