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Distributed Computing and Electrical Circuits and electronics (ICDCECE), IEEE International Conference on

作者： Sudha Arvind S. Arvind Vamshi Kumar Silveri Govardhan Potey Parameshwar Nunavath Ranadheep Podishetty Department of Electronics and Communication Engineering CMR Hyderabad India Department of Computer Science and Engineering Hyderabad Institute of Technology and Management Hyderabad India

Security analysts have the knowledge necessary to identify the types of traffic that are present in a network, identify instances of abnormally high traffic, and identify the network nodes responsible. Assisting users in locating servers and the destinations of data flows, network traffic visualization can also help researchers identify online threats. With Wireshark’s capabilities as a packet tracer and capture tool, network analysis starts and ends with the packet capture parameters selected in this study. Wireshark is used in this study to create a network tracker. In order to identify the source Internet Protocol (IP) and destination IP on a Google map, will capture the data packets using packet tracer, analyze them, and then translate the IP addresses into latitude and longitude values. This will let users visualize network traffic on a Google map and track it.

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Research on Forecast Algorithm of Financial Time Series Analysis Based on Markov Model 2

Research on Forecast Algorithm of Financial Time Series Anal...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Devi, M. Esanmurodova, N. Sahaya Sheela, M. Jasmine Hephzipah, J. Alkkhayat, A.H. Sarala, R. New Prince Shri Bhavani College Of Engineering And Technology India Tashkent Institute of Irrigation and Agricultural Mechanization Engineers National Research University Tashkent Uzbekistan Tamil Nadu Chennai India Department of Electronics & Communication Engineering R.M.K. Engineering College RSM Nagar Tamil Nadu Chennai India College of technical engineering The Islamic university Najaf Iraq Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798350305418

The financial system is both transparent and convoluted, with deep connections between the myriad of economic factors that are at play. The contemporary view of finance can be broken down into three distinct components: the time value of money, asset pricing, and risk management. The crux of the issue is finding the appropriate distribution of resources across time periods while operating in an uncertain environment. In the subject of forecasting financial time series, numerous studies have proven that the link between input and output variables is not stable, and that both variables always vary with time. This has been demonstrated by the findings of the studies. Because data comprises a significantly greater amount of information, it is only natural to give more weight to more current information. The study of financial markets now would be incomplete without the inclusion of time series analysis, both as a theoretical framework and a practical methodology. One of the methods that are considered to be part of the mainstream in quantitative financial analysis is the time series analysis method. This study makes use of the Markov model, which is a technique for forecasting financial time series that has been shown to reduce the amount of error associated with forecasting. © 2023 IEEE.

关键词： analytical forecasting algorithms financial time series Markov

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Deep Learning with Backtracking Search Optimization Based Skin Lesion Diagnosis Model

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computers, Materials & Continua 2022年第1期70卷 1297-1313页

作者： C.S.S.Anupama L.Natrayan E.Laxmi Lydia Abdul Rahaman Wahab Sait Jose Escorcia-Gutierrez Margarita Gamarra Romany F.Mansour Department of Electronics&Instrumentation Engineering V.R.Siddhartha Engineering CollegeVijayawada520007India Department of Mechanical Engineering Saveetha School of EngineeringSIMATSChennai602105India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Archives and Communication King Faisal University31982Kingdom of Saudi Arabia Electronic and Telecommunicacions Program Universidad Autonoma del CaribeBarranquilla08001Colombia Department of Computational Science and Electronic Universidad de la CostaCUCBarranquilla08001Colombia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Nowadays,quality improvement and increased accessibility to patient data,at a reasonable cost,are highly challenging tasks in healthcare *** of Things(IoT)and Cloud Computing(CC)architectures are utilized in the development of smart healthcare *** entities can support real-time applications by exploiting massive volumes of data,produced by wearable sensor *** advent of evolutionary computation algorithms andDeep Learning(DL)models has gained significant attention in healthcare diagnosis,especially in decision making *** cancer is the deadliest disease which affects people across the *** skin lesion classification model has a highly important application due to its fine-grained variability in the presence of skin *** current research article presents a new skin lesion diagnosis model i.e.,Deep Learning with Evolutionary Algorithm based Image Segmentation(DL-EAIS)for IoT and cloud-based smart healthcare ***,the dermoscopic images are captured using IoT devices,which are then transmitted to cloud servers for further ***,Backtracking Search optimization Algorithm(BSA)with Entropy-Based Thresholding(EBT)i.e.,BSA-EBT technique is applied in image *** by,Shallow Convolutional Neural Network(SCNN)model is utilized as a feature *** addition,Deep-Kernel Extreme LearningMachine(D-KELM)model is employed as a classification model to determine the class labels of dermoscopic *** extensive set of simulations was conducted to validate the performance of the presented method using benchmark *** experimental outcome infers that the proposed model demonstrated optimal performance over the compared techniques under diverse measures.

关键词： Intelligent models skin lesion dermoscopic images smart healthcare internet of things

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Smart Electric Vehicle (EVs) Charging Network Management Using Bidirectional GRU - AM Approaches 2

Smart Electric Vehicle (EVs) Charging Network Management Usi...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Gandhi, Mohd. Asif Priya, K. Charan, Piyush Sharma, Ritu Rao, G. Nageswara Suganthi, D. Department of Mechanical Engineering Anjuman-I-Islam's Kalsekar Technical Campus Maharashtra Panvel India Marudhar Kesari Jain College for Women Department of Computer Science Tamil Nadu Vaniyambadi India Manav Rachna University Department of Electronics and Communication Engineering Haryana Faridabad India Ggits Madhya Pradesh Jabalpur India Lakireddy Bali Reddy College of Engineering Jntuk Kakinada Electrical and Electronics Engineering Department Andhra Pradesh Mylavaram India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science Thandalam Tamilnadu Chennai India

ISBN: (纸本)9798350347579

Electric Vehicles (EVs) are now essential since electrifying transportation has shown to be a game-changer in raising the sustainable and eco-friendly platform in global industry. Integrating Electric Vehicle Charging System (EVCS) as a new entity into the power distribution system is one of the most important and challenging concerns. The development of an EVCS network infrastructure is a key step toward the broad adoption of EVs. In order to make informed judgments about transmission, distribution, energy allocation, and charging station placement, the control center or central aggregator must have an accurate forecast of occupancy, consumption, and energy or charging demand. Data analytics and other methods have made it possible to regularly get information from the EVCS for the purposes of archiving and processing all of the data collected. This proposed approach to presents a solution to the aforementioned problems with energy demand forecasting for EVCS networks. Three steps make up the proposed method: preprocessing, feature selection, and model performance evaluation. Preprocessing data via normalization, feature selection by K-Means, and ultimately model evaluation via K-means. The proposed model has superior results to the LSTM, GRU, and BIGRU - AM models. © 2023 IEEE.

关键词： Data Analytics

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Proactive Network Optimization Using Deep Learning in Predicting IoT Traffic Dynamics

Proactive Network Optimization Using Deep Learning in Predic...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： S. Anu Priya Niyati Bhat B. Rajesh Kanna S. Rajalakshmi R. Beaulah Jeyavathana Srimathi S Department of Computer Applications Vels Institute of Science Technology & Advanced Studies ((VISTAS) Chennai B.E Computer Engineering University of Mumbai Mumbai Department of Electronics and Communication Engineering Alagappa Chettiyar College of Engineering and Technology Karaikudi Department of Electronics and Communication Engineering RMK College of Engineering and Technology Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai Department of Bio Technology Saveetha School of Engineering SIMATS Chennai Tamilnadu India

ISBN: (数字)9798350307757

ISBN: (纸本)9798350307764

Problems have arisen in allocating and managing network resources effectively because of the proliferation of IoT gadgets. This study aims to provide a deep learning-based strategy for forecasting and analyzing IoT traffic patterns to solve this problem. The model uses recurrent neural networks (RNNs) with Long Short-Term Memory (LSTM) cells to capture the complex temporal dependencies in IoT data streams. Following careful data gathering from various IoT sources, a thorough preprocessing phase is performed to account for missing values and outliers. Effective modeling is enabled by applying feature engineering approaches to distill meaningful information and by temporally aggregating the dataset. The heart of the study is the creation of a sequence-to-sequence architecture that allows for reliable forecasting of future IoT traffic trends. Mean Squared Error (MSE) and other performance metrics measure the quality of the proposed model's predictions. In addition to its predictive abilities, the model's analytical section reveals interesting facts about the fundamental habits underpinning IoT traffic. This dual purpose helps network managers better comprehend the ever-changing IoT ecosystem and allocate scarce resources. The results of this study have important implications for network optimization, providing a preventative strategy for dealing with the difficulties posed by the exponential growth of IoT data traffic. Regarding accuracy and interpretability, the suggested deep learning model significantly contributes to the development of intelligent network resource management in the IoT era.

关键词： Deep learning Analytical models Recurrent neural networks Ecosystems Predictive models Traffic control Internet of Things

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Machine Learning-driven Quality Control Strategies for Industrial IoT Applications 4

Machine Learning-driven Quality Control Strategies for Indus...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Dankan Gowda, V. Irshad, Muhammad Aashir Mary, P. Arockia Prasad, Y. Durga Prasad, K.D.V. Tarigonda, Hariprasad Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Northumbria University 110-114 Middlesex St LondonE17HT United Kingdom V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Mohan Babu University Department of Mechanical Engineering School of Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350354218

This paper introduces a new solution for improving Quality of Control in Industrial Internet of Things (IIoT) systems by using a machine learning approach. The planned system utilizes monitoring data originating from different sensors installed in the production line to detect faults as well as estimate maintenance requirements. Besides, the proposed system uses the state-of-art machine learning techniques like Convolutional Neural Networks for defect detection and Recurrent Neural Networks for Predictive maintenance with high accuracy and efficiency. computer simulations indicate a high increase in the coverage of defects, predictive capability, and overall product quality. The combination of IoT and machine learning presents a convenient and evolutionary solution that becomes progressively enhanced as more data becomes available, and give a solid framework for the sustained optimization of standards in industrial practices. Future work seeks to enhance the developed models and extend the implemented system's functionalities to other application domains and bigger data sets. © 2024 IEEE.

关键词： Smart manufacturing

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Antennas and Other Peripherals for IoT

Antennas and Other Peripherals for IoT

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International Conference on communication and electronics Systems (ICCES)

作者： Sudhir K. Routray Mahesh K. Jha Abhishek Javali M. Pappa Department of Computer Science and Engineering CMR University Bangalore India Department of Electronics and Communication Engineering CMR Institute of Technology Bangalore India

Internet of things (IoT) is predominantly wireless in nature and thus it utilizes the main principles of wireless communication and networking. In order to design and deploy IoT networks, several wireless network components are needed. The common among them are the antennas, antenna support systems, edge computing facilities, gateways, and data centers. However, IoT nodes and sensors are small in size and they are exposed to limited resource environments. Keeping that in mind, the wireless components needed for IoT are designed according to their matching applications and demanded characteristics. This study analyzes the antennas and other peripheral devices required in IoT deployment. Also, this study discusses about the issues related to the architecture used for large scale and small scale deployments.

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Protecting Data from DDOS Attack in a Cloud Based Intrusion Detection System Security through Enhanced RSA Algorithm 2

Protecting Data from DDOS Attack in a Cloud Based Intrusion ...

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2nd IEEE International Conference on communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Naik, Pramod Kumar Divya, S. Mohan, M. Velusamy, Jayaraj Radhakrishnan, P. Rajasekaran, Arun Sekar Dayananda Sagar University Bengaluru Department of Cse India Biomedical Engineering Dr. N.G.P. Institute of Technology India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India School of Cs and Ai Sr University Telangana Warangal India Sr University Department of Ece Telangana Warangal India

ISBN: (纸本)9798350369960

Since cloud computing platforms have become an essential component in a range of different industries, it is still challenging to ensure that user data and system resources will remain secure inside these settings. This paper proposes a methodology to identify and mitigate unwanted traffic and packets, specifically duplicate packets, in cloud computing envi- ronments. The procedure requires the creation of an IntrusionFinder and Detector (IF-AD) system that allots secondary storage and keeps user information in a secure manner. This technique does a comparison between the sizes of downloaded files and the sizes of the original files, searching for any variations between the two that would suggest a probable distributed denial of service assault (DDoS). The RSA cryptography procedure is employed in order to further strengthen the degree of security that is provided by any future file transfers that may take place. The problem that has been identified and a potential solution for has been proposed with the intention of enhancing the safety of cloud-based systems by identifying and preventing unauthorised access to files as well as modifications to them. © 2023 IEEE.

关键词： Cloud computing decryption duplicate packets encryption Intrusion-Finder and Detector RSA

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Predicting the Frequency Bands and the Path Loss in Wireless communication Systems using Random Forests 3

Predicting the Frequency Bands and the Path Loss in Wireless...

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3rd International Conference on Smart electronics and communication, ICOSEC 2022

作者： Senthilkumar, C. Nirmala, P. Ahila, S. Sobitha Geetha, M. Ramesh, S. Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai602105 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Proactive and predictive design in the next wireless generation is critical to avoiding the flaws of prior generations and achieving the 5G goal services pillars. Base stations are needed to perform and make judgments to maintain communication dependability as wireless devices become more commonplace. Machine Learning (ML) is used in this research to help base stations anticipate the frequency bands and the route loss. There is a comparison between the ML algorithms Multilayer Perceptron and Random Forests. In order to keep up with the demands of the new radios, systems that use various bands need an immediate reaction from devices to change bands quickly. For this reason, ML approaches are required to learn and help a radio base station in shifting between multiple bands in response to data-driven decisions. Afterwards, the findings are compared to those of different deep learning approaches. To guarantee that the projected works would succeed, these strategies are used to several case studies. Unsupervised algorithms were added to the random forests in order to improve the accuracy of the learning process. © 2022 IEEE.

关键词： Deep learning

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Leveraging Machine Learning for Constructing Robust Automated Real-Time Data Analysis Systems

Leveraging Machine Learning for Constructing Robust Automate...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Mishra, Awakash Mukuntharaj, C. Sathi, G. Dhabliya, Dharmesh Kharbas, Vikash Kumar Muthukumar, B. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Department of Animation and Virtual Reality Karnataka Bangalore India

ISBN: (纸本)9798350329773

This paper provides a technique to leverage system-study strategies to develop robust automatic real-time information evaluation structures. In particular, it covers the usage of supervised and reinforcement learning to pressure the strong automated characteristic engineering of a massive variety of data analytics tasks, from time collection forecasting to anomaly detection. The paper additionally proposes an architecture that applies deep knowledge of algorithms to reap statistics from uncooked and aggregated statistics streams. The proposed architecture improves upon current fashions by introducing a hierarchical statistics shape to constitute the enter information, supervised gaining knowledge of strategies to initialize the version, and reinforcement learning to refine the styles based totally on the statistics move's dynamics. Checks conducted on a public dataset exhibit the effectiveness of the proposed framework. This method has potential applications across numerous domains, such as financial services, telecoms, and analytics inside the net of things. © 2023 IEEE.

关键词： Reinforcement learning

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