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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Gupta, Anish Tyagi, Lalit Kumar Singh Sisodia, Vibhash Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (纸本)9798331508685

Long Short-Term Memory (LSTM) networks have garnered interest for their ability to remember temporal relations in sequential input. This research employs LSTM networks to estimate prices, which is crucial for stock market predictions. First, we explain the basic concepts of LSTM networks, then we demonstrate how they may be used for time series forecasting by analyzing and interpreting sequential data. Thus, the study begins with a brief overview of LSTM architecture and its potential advantages over traditional models. They show how important feature engineering and data preparation are for stock market prediction using LSTMs. We also consider optimizing LSTM modeling assessment methodologies and hyper parameters. LSTM networks are used in single-step and multi-step stock market forecasting, according to this publication. We employ fundamental research on news emotions and economic indexes to examine how environmental influences affect model efficiency. We also suggest ways to reduce overfitting and data scarcity, which are LSTM's drawbacks for stock market prediction. © 2025 IEEE.

关键词： Marketplaces

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Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices

Machine and Deep Learning Classifications for IoT-Enabled He...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Tyagi, Lalit Kumar Gupta, Anish Sisodia, Vibhash Singh Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science & Engineering Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (纸本)9798331508685

The Internet of Things (IoT) is transforming academic research and will impact various industries, including healthcare. IoT has transformed healthcare from blast-shaped outdated centralized methods to decentralized smart devices. This led to tailored health systems. Recent advances in wearables, sensor networks, and computing cloud have made large-scale health care IoT usage easier. Despite its serious limitations, IoT can change medicine. Maintenance of various appliances, pricing increases, and the need for greater storage space all issues. IoT is a diverse and durable technology, thus this study will analyze its health sector use. How machine learning and deep learning interact is being studied. The Internet of Things (IoT) health system architecture is also covered in this article, along with its benefits and use cases. This helps highlight IoHT difficulties and encourages academics to create innovative solutions. This paper examines the current work of numerous researchers in machine learning and deep learning, which are heavily linked with IoT-based healthcare approaches. © 2025 IEEE.

关键词： Deep learning

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Long Short-Term Memory (LSTM) Networks For Stock Market Prediction

Long Short-Term Memory (LSTM) Networks For Stock Market Pred...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Anish Gupta Lalit Kumar Tyagi Vibhash Singh Sisodia Department of Computer Science & Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

关键词： Time-frequency analysis Biological system modeling Computational modeling Time series analysis Predictive models Data models Stock markets Forecasting Long short term memory Tuning

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Machine and Deep Learning Classifications for IoT-Enabled Healthcare Devices

Machine and Deep Learning Classifications for IoT-Enabled He...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Lalit Kumar Tyagi Anish Gupta Vibhash Singh Sisodia Department of Computer Science & Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali India Sri Bhawani Niketan Institute of Technology and Management Jaipur India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

关键词： Deep learning Ethics Accuracy Data security Medical services Transforms Internet of Things Predictive analytics Wearable devices Biomedical imaging

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A comprehensive study on skin cancer detection using artificial neural network (ANN) and convolutional neural network (CNN)

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Clinical eHealth 2023年第1期6卷 76-84页

作者： Shah, Aarushi Shah, Manan Pandya, Aum Sushra, Rajat Sushra, Ratnam Mehta, Manya Patel, Keyur Patel, Kaushal Ahmedabad International School Gujarat Ahmedabad India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Raysan Gujarat Gandhinagar India Department of Computer Engineering School of Technology Pandit Deendayal Energy University Raysan Gujarat Gandhinagar India Atmiya Vidya Mandir Gujarat Surat India Anand Niketan School Gujarat Ahmedabad India Department of Computer Science and Technology Charusat University Gujarat India

Skin cancer is a significant health risk that requires early detection for effective treatment. This paper discusses two automated techniques, Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), which make use of deep learning techniques for skin cancer detection. Through evaluation of research on skin cancer detection using ANN and CNN, the effectiveness and performance of these techniques in early and efficient diagnosis of skin cancer were established. The study found that ANN and CNN were successful in early detection of skin cancer using different data sets and hybrid models, demonstrating the potential for these technologies to improve accuracy in skin cancer detection. The paper highlights the novelty of using deep learning techniques for skin cancer detection and emphasises the critical need for an automated system for skin lesion recognition to reduce effort and time in the diagnosis process. The possible applications of this study include the development of more efficient and accurate skin cancer detection systems that can lead to earlier diagnosis and improved treatment outcomes. Overall, this research underscores the importance of using advanced technologies, such as ANN and CNN, in the fight against skin cancer and highlights the potential impact of these techniques in improving patient outcomes. © 2023

关键词： Artificial Neural Network Convolutional Neural Network Skin cancer

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An Efficient Lion Optimization Based Cluster Formation and Energy Management in WSN Based IoT 2nd

An Efficient Lion Optimization Based Cluster Formation and E...

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2nd International Conference on Intelligent Computing and Optimization, ICO 2019

作者： Yuvaraj, D. Sivaram, M. Mohamed Uvaze Ahamed, A. Nageswari, S. Department of Computer Science Cihan University - Erbil Kurdistan Region Erbil Iraq Department of Computer Science Cihan University - Duhok Kurdistan Region Duhok Iraq Department of Computer Networking Lebanese French University - Erbil Kurdistan Region Erbil Iraq Department of Computer Science and Engineering Bhararh Niketan Engineering College Theni India

ISBN: (纸本)9783030335847

For the past few decades, Wireless sensor networks (WSNs) disseminate less expensive sensor nodes (SN’s) in its area, where nodes plays significant role in Internet of Things (IoT). WSN based IoT, nodes are generally resource constraints in numerous ways, like energy, storage, computing resources and so on. Effectual routing protocols (RP) are essential for longer lifetime and to attain superior energy utilization. Here, a novel energy efficient k- centroid Lion optimization based routing protocol (kEE-LOP) to enhance network performance. Anticipated kEE-LOP comprises three essential factors: distributed behaviour of lion based cluster formation technique to facilitate nodes organization, novel algorithm for constructing adaptive clusters based on lion migration and k-nodes centroid based cluster head rotation to distribute load to all sensor nodes eventually, thereby a novel mechanism for reducing energy utilization for distance communications. In specific, residual energy of nodes is measured with kEE-LOP for long distance communication. Specifically, nodes’ residual energy is measured as kEE-LOP for computing CH centroid’s position. Simulation results specifies kEE-LOP outperforms superior than LEACH, LEACH-C. As well as, kEE-LOP is more appropriate for networks that is essential for longer lifetime. © 2020, Springer Nature Switzerland AG.

关键词： Internet of things

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Forest fire management using machine learning techniques

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Measurement: Sensors 2023年 25卷

作者： Anandaram, Harishchander M, Nagalakshmi Cosio Borda, Ricardo Fernando K, Kiruthika S, Yogadinesh Centre for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Department of CSE Marri Laxman Reddy Institute of Technology and Management India Management Professional School Director of the Universidad Autónoma del Perú Universidad Autónoma del Perú Lima Peru Department of Computer Science and Business Systems Panimalar Engineering College Tamilnadu India Bharath Niketan Engineering College Tamilnadu India

As per the latest survey produced by the Forest Survey, the forest cover is 19.27% of the geographic area. According to this report every country can meet the human needs of 16% of the world's population from the 1% of the world's forest resource. The Forest Survey said that 90% of the forest fires created by humans. They pose a threat not only to the forest wealth but also this leads to the main threat to biodiversity, a change in the ecosystem. The environment gets dry and twinges, which leads to produce flames in the forest. There are two types of forest fire i) Surface Fire and ii) Crown Fire iii) Ground Fire. Surface Fire: The forest fire starts its flame primarily as a surface fire, spreading along the ground with the help of dry grasses and so on. Crown Fire: It starts flame on the crown of the shrubs, bushes and trees and sustained on the surface. This type of fire is very dangerous because resinous material given off burning logs burn furiously. If there is a slope with fire then the fire spread from the top of the slope to the down. Ground fire occurs in the humus and peaty layers beneath the litter of under composed portion of forest floor with intense heat but practically no flame. Such fires are relatively rare and have been recorded occasionally at high altitudes in Himalayan fir and spruce forests. In Remote sensing field, the knowledge of surface temperature plays a vital role. By using brightness and emissivity feature, temperature mapping and analysis can be done. The surface temperature values are measured to detect the forest fire from the ASTER image. ASTER stands for Advanced Space borne Thermal Emission and Reflection Radiometer. ASTER image contains 5 thermal bands (wave length ranges from 8.125 μm to 11.65 μm) and these are used in comparison. To convert digital numbers to exoatmospheric radiance, ASTER thermal bands are used. The converted exoatmospheric radiance is then mapped into surface radiance using the Emissivity Normalization method

关键词： Fires

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Some investigation on DDOS attack models in mobile networks

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2019年第10期13卷 71-88页

作者： Yuvaraj, D. Sivaram, M. Ahamed, A. Mohamed Uvaze Nageswari, S. Department of computer science Cihan university Duhok Kurdistan Region Iraq Department of Computer Networking Labanese french University-Erbil Kurdistan Region Iraq Cihan University-Erbil Kurdistan region Iraq Department of Computer science and Engineering in Bhararh Niketan Engineering Theni India

With advancements in device and communication technologies, there has been a great revolution in development of net gadgets and communication technologies like 4G, 5G etc., in parallel, there has also been a widespread increase in ways to illegally hack data available over the internet and tamper the services offered to the customer. Common attacks include spoofing, Phishing, fraudulent extraction of transaction information and customer details. There have been attacks to cause traffic congestion over the network by introducing artificial infection packets over the internet. Hence, there is a great necessity in research for defence mechanism against these attacks to ensure smooth and safe provision of services to customers. This paper investigates and elaborates the different types of attacks that may be incident on a system or a network, their features and attack mechanisms which provide useful insights into developing an attack resistant system. Almost all types of attacks have been discussed systematically in this research paper with special emphasis on distributed denial of service attacks. © 2019 International Association of Online engineering.

关键词： Denial-of-service attack

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Preface

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International Journal of Product Development 2019年第2-3期23卷 85-88页

作者： Sharma, Tarun Kumar Pandey, Santosh K. Banerjee, Chitresh Banerjee, Arpita Department of CSE Amity School of Engineering and Technology Amity University Kant Kalwar303007 India Ministry of Communication and Information Technology Govt. of India Electronics Niketan 6 CGO Complex Lodhi Road New Delhi110003 India Amity Institute of Information Technology Amity University Rajasthan Amity Education Valley Kant Kalwar NH-11C Jaipur Delhi Highway Jaipur Rajasthan303007 India Department of Computer Science St. Xavier's College Hathroi Fort Road Church Road Jaipur Rajasthan302001 India

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Stateless multicasting using Multipath Diversity Routing in manet 3

Stateless multicasting using Multipath Diversity Routing in ...

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3rd International Conference on Communication and Signal Processing, ICCSP 2014

作者： Senthil Kumar, K. Sasi Kumar, S. Sugapriya, C. Gopalakrishnan, S. Department of Computer Science and Engineering Bharath Niketan Engineering College Theni Tamilnadu625536 India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamilnadu601206 India Department of Mathematics R.M.K. Engineering College Chennai Tamilnadu601206 India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindugal Tamilnadu601206 India

ISBN: (纸本)9781479933587

Mesh networks needs distinguished nodes to keep the information status which are depended by the conception of Multicast Routing Protocols. By considering traffic in wire- less sensor networks is so high which increases the packet delay during transmission. Receiver Based Multicast (RBM) is only the most beneficial method to receive packets in such heavy traffic conditions that is provided by the receiver based multicast protocols. In former cases, Path Diversity Routing (PDR) protocol is opted for transmission across the dynamic networks which choose the alternate path due to some disruption over the routing path and the efficiency of the entire network will also be impacted. To reduce the above drawbacks, Multipath Diversity Routing (MDR) protocol is presented that provides quick and authentic transmission under heavy traffic conditions among the nodes. MDR supports multipath transmission to reach the destination as it is in long distance. It also enhances the network performance that accomplishes eminent succession rate compare to other existing protocols. © 2014 IEEE.

关键词： Multicasting

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