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

作者： Rajasekar, P. Devendraiah, K.M. Tamizhselvi, A. Yadav, Droupti Priyadharshini, A. Suganthi, D. Srm Institute of Science and Technology Department of Data Science and Business System Kattankulathur India V. V. Sangha's Independent P U College Ballari India St. Joseph's College of Engineering Department of Information Technology Chennai India School of Basic Sciences Csjm University Department of Environmental Science and Technology Kanpur India Erode India Saveetha College of Liberal Arts and Sciences Simats Chennai India

ISBN: (纸本)9798350352931

Landslides are natural disasters that are devastating and unpredictable. Community notification and casualty reduction require early warning systems. Using data analytics and machine learning technology (MLT), IoT-enabled early warning models can monitor environmental variables and predict landslides. However, current models struggle to anticipate *** innovative strategy suggested by this work involves feature extraction, preprocessing, and model training. The first step to high-quality data is preparing the landslip grid points with Multicollinearity Analysis. Simulating salp chains improves feature selection during feature extraction with the Salp Swarm Algorithm (SSA). During training, a Graph-Based Convolutional Neural Network (G-CNN) is used for optimum predictions. The suggested strategy was more predictive than before, averaging 92.76% Due to its accuracy, the G-CNN outperforms graph-based methods and standard CNNs in handling complex environmental *** SSA-based feature extraction with G-CNN training improves landslip prediction accuracy, a promising early warning system development. This technique could improve natural catastrophe prediction and response by using IoT and ML advances. © 2024 IEEE.

关键词： Convolutional neural networks

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Interactive Blood Bank Repository: Django AWS Approach 5

Interactive Blood Bank Repository: Django AWS Approach

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Sujatha, E. Manickavasagan, V. Lakshminarayanan, S. Ani Minisha, R. Rekha, K.S. Loganathan, V. Department of Computer Science and Engineering Saveetha Engineering College Tamil Nadu India Department of Computer Science and Business Systems Sri Sairam Engineering College Tamil Nadu India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu India Department of Information Technology Saveetha Engineering College Tamil Nadu India Department of Artificial Intelligence and Data Science Vel Tech Multi Tech Dr Rr and Dr Sr Engineering College Tamil Nadu India

ISBN: (纸本)9798350385649

Everyday, there are a growing number of people who require blood transfusions and there is no substitute for human blood. The secure blood donors are voluntary non-remunerated blood donors from risk populations. Donors in India and across the globe are three types namely, Voluntary donors, Professional donors and replacement donors. The deadline-driven web framework that supports Django's clean, practical design and speedy development. The existing system has a manual work restriction. This has restrictions on securely controlling the task for timely results declaration, and has a significant resource consumption. Web-based blood donation is the coordination of the supply and demand of blood through an online portal. The main goal of this research study is to give blood donors and blood searchers an online platform and to develop an engaging method of connecting the two groups. The suggested system provides a great deal of information regarding blood types, blood supply, and blood donation specifics to raise awareness among the public. Additionally, other system features like password-based security protection, report generation, chat functionality, and stock shortages can even improve the management tasks by streamlining them. © 2024 IEEE.

关键词： Economics

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Singe Image Dehazing using Deep Neural Networks 2

Singe Image Dehazing using Deep Neural Networks

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2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023

作者： Doshi, Nisarg Bhavsar, Sagar Rajeswari, D. Srinivasan, R. College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India College of Engineering and Technology Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798350315905

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear *** first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r.t SSIM values. © 2023 IEEE.

关键词： Image fusion

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images 5

A DCNN with Jellyfish Questoptimization Algorithm in Order t...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Aravinda, K. Manjunatha, B. Kavin, Balasubramanian Prabhu Gangadevi, E. Balusamy, Balamurugan Gite, Shilpa New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Srm Institute of Science and Technology Department of Data Science and Business Systems Chengalpattu Kattankulathur India Loyola College Department of Computer Science Chennai India Delhi India Aiml Department Pune India

ISBN: (纸本)9798350383522

Incomplete closure around the spinal cord is the underlying cause of spina bifida, a birth condition. Multiple methods exist for identifying the presence of spina bifida. Ultrasound can be used to look for spinal axis deformities. However, due to the distortion inherent in prenatal ultrasound pictures, this method requires the expertise of a skilled physician in order to pinpoint the anomaly. Examining ultrasound pictures visually may be a subjective and error-prone process. The diagnosis procedure for spinal deformities may benefit from a computerised support system that can automatically locate the site of the abnormality. An algorithm for determining whether the entire spine in an ultrasound picture is healthy or diseased would be the first step in developing such software. In this study, we combine the power of the Jellyfish Search Optimisation Algorithm (JFSOA) with a deep convolution neural network to better identify SB. Improved classification accuracy is achieved by JFSOA's optimal selection of the SDL model's learning rate. Foetal ultrasound scans taken in the second trimester are widely regarded as a useful screening technique for identifying birth defects. The foetal spine picture from an ultrasound scan taken at the 18th week of pregnancy is used. Preprocessing pipelines, such as contrast improvement, intensity modification, and denoising, are used to improve the quality of ultrasound pictures and eliminate noise. Using this method, the suggested model can reliably recognise pictures of people with spina bifida. The suggested approach accurately identifies and labels ultrasound pictures as normal or defective based on the attributes retrieved © 2024 IEEE.

关键词： Fetus Abnormalities Jellyfish Search Optimization Algorithm Spina bifida Detection Synergic deep convolution neural network Ultrasound

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Polar Region Climate Dynamics: Deep Learning and Remote Sensing Integration for Monitoring Arctic and Antarctic Changes

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Remote Sensing in Earth systems sciences 2024年第4期7卷 582-595页

作者： Maniraj, Subramanian Pitchiah Rose, Jesuharan Dafni Arunachalam, Rohini Rangasamy, Kotteeswaran Patil, Vishal Ratansing Kathirvelu, Selvakumarasamy Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Chennai India Department of Computer Science and Engineering Anil Neerukonda Institute of Technology and Sciences Andhra Pradesh Visakhapatnam India Joseph’s College of Engineering Chennai 600119 India Department of CSE(AIML) Vishwakarma Institute of Technology Pune India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India

The polar regions of the Earth, specifically the Arctic and Antarctic, play a pivotal role in regulating the planet’s climate systems. These regions are highly sensitive to climate change, with observable shifts such as diminishing sea ice, retreating glaciers, and rising temperatures. Understanding these dynamics is essential for predicting broader global climate impacts. This paper presents a novel approach for monitoring and predicting these changes by integrating an attention-based transformer model with a 3D convolutional neural network (3D-CNN). The proposed model is designed to capture both the spatial features of satellite imagery and the temporal dynamics of climate data, offering enhanced predictive capabilities for key climate variables such as sea ice extent, glacier movement, and temperature variations. The dataset was split into 80% training and 20% testing with tenfold cross-validation, and the model was trained using the Adam optimizer. The results show that the proposed model achieved a remarkable accuracy of 97.43%, outperforming traditional CNN and RNN models. The model also demonstrated a precision of 98.12% for sea ice extent prediction, a 96.45% precision for glacier movement detection, and a mean squared error (MSE) of 0.35 °C for temperature predictions. These findings suggest that deep learning models, specifically the integration of transformer and 3D-CNN architectures, can significantly contribute to polar region climate monitoring, offering improved tools for scientific research and policymaking. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. corrected publication 2024.

关键词： 3D convolutional neural network Attention-based transformer Climate dynamics Deep learning Glacier movement Polar region Remote sensing Sea ice extent Spatial–temporal analysis Temperature prediction

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Effective and Imperceptible Adversarial Textual Attack Via Multi-objectivization

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ACM Transactions on Evolutionary Learning and Optimization 2024年第3期4卷 1-23页

作者： Liu, Shengcai Lu, Ning Hong, Wenjing Qian, Chao Tang, Ke Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen College of Engineering South Tower 1088 Xueyuan Avenue Guangdong Shenzhen518055 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong999077 Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 10F Zhizhen Building Canghai Campus 3688 Nanhai Avenue Guangdong Shenzhen China National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing University Nanjing International College Area Xianlin Campus 163 Xianlin Avenue Jiangsu Nanjing China

The field of adversarial textual attack has significantly grown over the past few years, where the commonly considered objective is to craft adversarial examples (AEs) that can successfully fool the target model. However, the imperceptibility of attacks, which is also essential for practical attackers, is often left out by previous studies. In consequence, the crafted AEs tend to have obvious structural and semantic differences from the original human-written text, making them easily perceptible. In this work, we advocate leveraging multi-objectivization to address such an issue. Specifically, we reformulate the problem of crafting AEs as a multi-objective optimization problem, where the attack imperceptibility is considered as an auxiliary objective. Then, we propose a simple yet effective evolutionary algorithm, dubbed HydraText, to solve this problem. HydraText can be effectively applied to both score-based and decision-based attack settings. Exhaustive experiments involving 44,237 instances demonstrate that HydraText consistently achieves competitive attack success rates and better attack imperceptibility than the recently proposed attack approaches. A human evaluation study also shows that the AEs crafted by HydraText are more indistinguishable from human-written text. Finally, these AEs exhibit good transferability and can bring notable robustness improvement to the target model by adversarial training. © 2024 Copyright held by the owner/author(s).

关键词： Multiobjective optimization

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Text to Image Generation Using Attentional Generative Adversarial Network 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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Energy Efficient Routing Scheme for Performance Enhancement of MANET Through Dominating Set

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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Performance Evaluation of Optical Wireless Communication system in Fog Weather Conditions 2

Performance Evaluation of Optical Wireless Communication Sys...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Venusamy, Kanagaraj Kannadhasan, S. Prakash, E. Priyadarsini, K. Study World College of Engineering Department of Electronics and Communication Engineering Chennai Coimbatore India Rajalakshmi Engineering College Department of Mechatronics Engineering Chennai India School of Computing Srm Institute of Science & Technology Department of Data Science and Business Systems Chennai Kattankulathur India

ISBN: (纸本)9798350346640

Free space optical communication being an emerging technology, has Fog as a primary challenge on its way which makes fog modelling necessary to manage wireless networks. In this paper, an analytical study was made on the performance of five mathematical fog models employing the NRZ-OOK modulation scheme for transmission and a Si PIN photodiode as the photo receptor. This article focuses on numerous performance specifications including Signal to noise ratio [SNR], Bit error rate [BER] and channel capacity all against visibility under fog for propagations wavelengths of 850 nm and 1550 nm and simulations were carried out in MATLAB. The results indicate that the wavelength of 1550nm as more advantageous option over the others for FSO wireless links and an investigation on different models and their characteristics define their suitability for implementation. © 2023 IEEE.

关键词： Visibility

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