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2024 IEEE International Conference on communication, Computing and Signal Processing, IICCCS 2024

作者： Adnan, Myasar Mundher Farkhodjonovich, Muydinov Firuzjon Shrivastava, Rohit Bhandari, Anika Aravind, A.R. Sandeep Kumar, S. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Fergana Medical Institute Of Public Health Head Of Educational and Methodological Department Fergana Uzbekistan Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Department of Computer and Communication Engineering Karnataka Udupi India

ISBN: (纸本)9798350390759

In this research, RL is proposed as a solution towards achieving the dynamic optimization of 5G network throughput hindered by the complexity of Intersystems environments. To this extent, the optimization problem is formulated and modeled within the framework of Markov Decision Process and the study resorts to techniques from Deep Reinforcement Learning leading to the design of adaptive algorithms that optimise various network parameters in real-time. It uses simulated raw 5G network data and other data open for public usage for training it and the application itself is coded in Python programming language with TensorFlow for implementing different algorithms. As for the performance, it could be seen that the RL-optimized approach is superior to traditional methods and leads to better system performance in terms of throughput, latency, energy efficiency and user satisfaction in a number of scenarios. The study identifies the possibility to apply RL to enable the learning capability of 5G networks in supporting new services and optimizing the network performance. This research finding can be beneficial for the advancement of 5G wireless communication technology by demonstrating the versatility and advantages of utilizing RL in the network. © 2024 IEEE.

关键词： Deep reinforcement learning

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Digital-Twin Enabled Time Ahead Resource Allocation for Integrated Fiber-Wireless Connected Vehicular Network

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Journal of communications and Information Networks 2024年第3期9卷 296-308页

作者： Akshita Gupta Saurabh Jaiswal Martin Maier Vivek Ashok Bohara Anand Srivastava Wirocomm Research Group Department of Electronics&Communication EngineeringIndraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Department of Computer Science&Applied Mathematics Indraprastha Institute of Information Technology Delhi(IIITD)New Delhi 110020India Optical Zeitgeist Laboratory Institute National de la Recherche ScientifiqueMontreal H5A 1K6Canada

The digital twin(DT)is envisaged as a catalyst for pioneering ecosystems of service provision within an immersive environment born from the convergence of virtual and physical ***,DT could enhance the performance of edge-intelligent connected vehicular networks by allocating network resources efficiently based on the key performance indicators(KPIs)of vehicular data ***,this work addresses the key challenge of computation and spectrum resource allocation for vehicular *** allocate the optimal resource allocation,we subdivided the problem into:traffic classification,collective learning,and resource allocation *** order to do so,this paper concentrates on two crucial vehicular applications:brake application and lane-change *** utilize a random forest model to collectively learn vehicular data traffic in the upcoming time ***,a time-ahead resource allocation algorithm is proposed to improve the quality of service(QoS)by intelligently offloading vehicular data traffic to a DT-based integrated fiber-wireless(Fi-Wi)connected vehicular *** evaluate the performance of the resource allocation strategy in terms of resources required by the network alongside the packet loss *** was observed that there was a 44.74%increase in cost as the total computation resources increased from F=50 to 100 GHz,whereas the PLR of the network decreased by 71.43%.

关键词： connected vehicles digital twin edgeintelligence fiber-wireless machine learning resource allocation

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A RECENT REVIEW ON IoT BASED TECHNIQUES AND APPLICATIONS

A RECENT REVIEW ON IoT BASED TECHNIQUES AND APPLICATIONS

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2024 International Conference on Sustainable Computing and Integrated communication in Changing Landscape of AI, ICSCAI 2024

作者： Singh, Arjun Kumar, Sandeep Kumar, Sandeep Bahuguna, Renu Singh, Mahesh Kumar Raj, Nikhil Department Of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida India Department Of Information Technology Raj Kumar Goel Institute Technology Ghaziabad India Department Of Electronics and Communication Engineering G.L. BAJAJ Institute of Technology & Management Greater Noida India Department Of Cyber Security COER University Roorkee India Department Of Computer Science and Engineering Dronacharya Group of Institutions Greater Noida India

ISBN: (纸本)9798350355338

IoT known as Internet of Things is an advanced mechanics which has changed the way humans’ smart devices, smart things, data, and information are connected throughout the world. IoT is a single concept of embedding everything. IoT evolution is yet in its premature steps, and there is a slew of interconnected hurdles to overcome. The IoT has the potential to boost the globe's accessibility, probity, availability, scalability, secrecy, and interoperability. Although, numerous challenges and concerns must be overcome before the IoT's full promise can be realized. Various components of IoT data are recognized and defined, including data sources, data collection, data processing, and transmitting devices. In terms of analytical and substantial data management, as well as transmission networks, the challenges that arise from the need to manage large amounts of heterogeneous data over their systems are mentioned. Sensors can be placed in isolated or far, huge, low-population localities in some applications, while sensors are used to monitor tight locations in others. Clustering the detectors lattice’s nodes in multiple clusters is fundamentally beneficial clearly for scalability grounds, & it can be used to create conservation or utilization schedules that can significantly extend the lattice's life. Current study compares and contrasts common and advanced clustering schemes, as well as size, type of data collected, and performance as a function of non-uniformity and noise level. A combined met heuristic approach, a combined metaheuristic approach, WOA combined with SA, is also used in the proposed work. © 2024 IEEE.

关键词： Clustering IoT (Internet of Things) Metaheuristic Algorithms Simulated Annealing WOA Algorithm (Whale Optimization Algorithm)

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Enhancing Cloud Virtualization Security with a Z-Score Based Conjugate Self-Organizing Migration ML Model 15

Enhancing Cloud Virtualization Security with a Z-Score Based...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Sidana, Sagar Thakur, Sujata Negi Sinha, Anurag Indhumathi, N. Raj, Petheru Al-Khayyat, Ahmed Modi, Vyom Mishra, Aditya Kumari, Anita Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India Graphic Era Hill University Department of computer science and engineering India Ignou School of Computing and information Science New Delhi India Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Marwari Education Foundations Group Of Institutions Rajkot India Gla University Department of computer science and engineering Applications Mathura India Rkdf University Department f English Literature India

ISBN: (纸本)9798350370249

This study presents a new machine learning model for detecting malware in cloud computing settings called Conjugate Self-Organizing Migration (CSOM), which is based on Z-score. Feature vectors are standardized by the model through the use of Z-score normalization, which improves its flexibility with changing data distributions. By merging self-organizing migratory processes with statistical analysis, the CSOM model effectively reduces the threat of growing malware. Using Z-score normalization to guarantee correct representation of changing data, the imaging approach depicts the cloud environment as a dynamic, self-organizing landscape. Through conjugate migration techniques, the CSOM model dynamically modifies synaptic weights to minimize false positives and enable the identification of tiny fluctuations in harmful actions. The Z-score-based CSOM model is effective in detecting malware at high accuracy rates in a variety of dynamic cloud environments, as demonstrated by the experimental results. The model demonstrates its flexibility in responding to changing cyberthreats with its strong performance in real-world settings. Finally, with its better accuracy and adaptability, the suggested Z-score-based CSOM model offers a potential approach for cloud computing virus detection. In order to handle the constantly shifting environment of cloud-based cyber threats, further research may examine scalability and more refinement. © 2024 IEEE.

关键词： Cloud Computing Conjugate Self-Organizing Migration Machine Learning Model Malware Detection Z-score

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Optimizing Resource Allocation in Healthcare Facilities Through IoT and Machine Learning Predictive Analysis 3rd

Optimizing Resource Allocation in Healthcare Facilities Thro...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Dankan Gowda, V. Sharma, Avinash Mukherjee, Saptarshi Rahman, Mirzanur Barshe, Shweta B. Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Makaut Maulana Abul Kalam Azad University of Technology West Bengal Nadia Kolkata India Department of Information Technology Gauhati University Assam Guwahati India Department of Computer Engineering Bharati Vidyapeeth College of Engineering Mumbai Navi India

ISBN: (纸本)9789819751457

In the evolving landscape of health care, optimal resource allocation emerges as a critical determinant of patient outcomes and facility efficiency. This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) predictive analysis in healthcare facilities to optimize resource allocation. With the omnipresence of IoT devices and the analytic prowess of ML, our research aims to develop a model that can predict real-time resource demands, ranging from patient care equipment to manpower distribution. Drawing data from wearable sensors, embedded devices, and facility infrastructure, the proposed ML model demonstrates significant improvement in predicting short-term resource needs. Preliminary results indicate a reduction in resource wastage, enhanced patient care, and increased operational efficiency in healthcare facilities that incorporated the proposed system. However, the research also acknowledges potential limitations, including data security concerns and system scalability. The findings presented highlight the transformative potential of IoT and ML in revolutionizing resource management in health care. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data security Embedded devices Internet of Things (IoT) Machine learning (ML) Operational efficiency Wearable sensors

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The IoT-Based Smart Shopping Trolley System

The IoT-Based Smart Shopping Trolley System

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IoT, communication and Automation Technology (ICICAT), International Conference on

作者： Sakshi Maurya Gayatri Sahu Anupriya Yadav Bhawna Shukla Gaurav Agrawal Narendra Kumar Department of Electronics and Communication KIET Group of Institutions Ghaziabad India Department of Computer Science and Engineering

This research paper presents an IoT -based smart trolley system for supermarkets that aims to provide customers with an improved shopping experience. The system consists of a smart trolley equipped with sensors and microcontrollers that interact with a cloud-based server and a mobile application. This involves designing the hardware and software components of the smart trolley, including the IoT sensors, RFID reader, microcontroller, display screen, and user interface. The system will communicate with a cloud-based server that stores and processes data from the sensors and provides real-time analytics for store managers. The trolley can detect the items placed in it and display the total cost of the purchase on an LCD screen. The mobile application allows customers to view the list of items in their trolley, their prices, and the total cost, search for items, and navigate to their locations in the supermarket. The cloud-based server collects and analyzes data from the trolleys and generates insights for supermarket management, such as popular products, inventory levels, and customer footfall. The proposed system has the potential to enhance the shopping experience for customers and provide valuable insights into supermarket management.

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IoT-Enabled Health Monitoring System for Safeguarding Vital Organs with Cloud-Based Diagnosis and Advanced Algorithms 4

IoT-Enabled Health Monitoring System for Safeguarding Vital ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Ahmad, Salman Khursheed Ikra, K.M. Sharma, Preeti Kaushik, Yogita Choudhary, Amar Tripathi, Pradeep Kumar Gl Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India Mangalmay Institute of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science -DS Ghaziabad India Sunder Deep Group of Institutions Department Mca Ghaziabad India Alliance University Department of Electronics and Communication Engineering Bengaluru India Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India

ISBN: (纸本)9798350360165

This study presents a novel IoT-enabled fitness tracking device intended to improve patient care through the capture of real-time physiological records and predictive analytics. The system uses a variety of sensors, including an Arduino UNO, an LM35, a MAX30100 pulse charge and SPo2 sensor, and a stress sensor, to continuously gather health data from ten different patients. Proactive health assessments and early intervention are made possible by the combination of cloud-based analysis and advanced system learning models, such as Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbours (KNN), and Recurrent Neural Networks (RNN). Tested on real-world datasets, the experimental results show how effective the device is at making accurate and timely predictions. Notably, SVM is the most accurate variant, closely followed by ANN, KNN, and RNN. The confusion matrices ensure accurate and thorough examination of each version's performance, aiding in selecting the most relevant set of rules for real-time health monitoring to be used by clinicians. The present study, as groundbreaking work, demonstrates a major leap in merging IoT with device expertise in the healthcare sector that usages new possibilities in proactive and personalized treatment strategies. The recommended tool's innovative and reliable predictive analytics can potentially revolutionize patient transportation in the healthcare system. © 2024 IEEE.

关键词： Electronic health record

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Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 387-399页

作者： Kolikipogu, Ramakrishna Shivaputra Muniyandy, Elangovan Maroor, Jnaneshwar Pai Lakshmi, Gundala Venkata Rama Konduri, Bhagavan Naveenkumar, R. Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Dr. Ambedkar Institute of Technology Bengaluru India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Business Administration Nitte Institute of Professional Education Nitte (Deemed to Be University) Karnataka Mangaluru India Department of Computer Science and Engineering CVR College of Engineering Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Computer Science and Engineering Chandigarh Colleges of Engineering Chandigarh Group of Colleges Jhanjeri Punjab Mohali 140307 India

Web different IoT apps offer lot more options for every player in the meat supply chain to monitor, control, and optimize their manufacturing method. Beginning within late 2017, industrial IoF2020 program will concentrate toward massive deployments of IoT via three use cases in the meat supply chain: pig farm management, poultry chain tracking, and for livestock visibility with openness. This initiative will include eleven collaborators spanning 5 nations. Installing and integrating IoT sensors for environmental and livestock management would alleviate producers’ difficulty getting knowledge that data needed regarding continuously tracking how well they are doing. Alert detection is additionally being created, integrating multiple sources of data in order to give farmers helpful input along with communicating news with other partners. By carrying out this, preventative or remedial measures can be done for infections, swine taint, bird mortality, feed waste, the natural world, et cetera. Additionally, EPCIS-based traceability from farm to fork will be allowed to ensure customers may get precise data about the source and caliber of their livestock. Each of these three use scenarios’ existing stage in work along with future plans are expected to be discussed. IoF2020 seeks to support the digital revolution in smart farming by tackling various educational and commercial problems while also promoting its use throughout Europe. It maintains mechanical necessary degree and security against physical along with toxic contaminants during industrial butchering of chicken, a lot measures have been taken. Buyers’ worries about food safety and health are the result of eating chicken increasingly. Our authorities have begun many kinds of initiatives aimed at improving safeguards for food. Our goal is to create an apparatus that alerts the factory farms affecting potential threats for product hygiene of food. With such setup, the detector can be mounted on top of the poultry slaugh

关键词： Food safety Internet of Things Meat processing Monitoring Supply chain

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Deep Learning Approaches for Real-Time Data Analytics in IoT Sensor Networks 3rd

Deep Learning Approaches for Real-Time Data Analytics in IoT...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Mary, P. Arockia Sharma, Avinash Dekka, Satish Gowda, V. Dankan Singh, Mandeep Department of Information Technology V.S.B. Engineering College Tamilnadu Karur India Director of Computer Application Chandigarh Group of Colleges Sahibzada Ajit Singh Nagar India Department of Computer Science and Engineering Lendi Institute of Engineering and Technology Andhra Pradesh Vizianagaram India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Department of University Institute of Computing Chandigarh University Mohalli Punjab Gharuan India

ISBN: (纸本)9789819751457

The rise of the Internet of Things (IoT) has made sensor networks ubiquitous, generating vast amounts of real-time data. However, the challenge lies not only in collecting this data but also in swiftly extracting meaningful insights. Traditional data processing methods often fall short in handling real-time IoT data streams. This research delves into deep learning algorithms as a solution for real-time analytics in IoT sensor networks. We examine suitable architectures for time-series data, such as 1D CNNs, GRUs, and LSTMs. The study also emphasizes the importance of efficient model training, rapid inference techniques, and data preprocessing. Our tests demonstrate that deep learning outshines traditional methods in accuracy and speed. Hence, when applied effectively, deep learning can offer dependable real-time analytics for IoT sensor networks, paving the way for smarter decisions in various IoT applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Data Analytics

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Automatic Detection of Tuberculosis with the Help of Image Processing and Machine Learning Classifier 7

Automatic Detection of Tuberculosis with the Help of Image P...

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7th International Conference on Computing in engineering and Technology, ICCET 2022

作者： Sharma, Anshu Malhotra, Rahul Sharma, Anurag Department of Computer Science & Engineering CT University Ludhiana India Department of Electronics & Communication Engineering CT Group of Institutions Jalandhar India Faculty of Engineering Design and Automation GNA University Phagwara India

ISBN: (纸本)9781839537042

Tuberculosis is the substantial irresistible disease present in human that principally influences lungs, this tuberculosis is one the main ten noticeable reasons for death for human. It has very typical symptoms so without testing it isn't sure that patient has infected with TB. In this paper, an automated framework dependent on image handling is proposed to help specialists and radiologists in deciphering Chest X-beams (CXR) for early identification of lung Tuberculosis (TB). CXR can show the abnormalities in lungs as well as TB which helps to precisely and rapidly distinguish TB on the grounds that early treatment will provide to patients and disease will stop spreading in body. The proposed system comprised of preprocessing, segmentation, features extraction, and classification. In this paper proposed a strategy of image segmentation and AI to conclusion and characterize the tuberculosis (TB). These tool and procedures are utilized to earlier uncover of tuberculosis (TB) illness. Additionally arrange the sort of TB. The framework recognizes the tuberculosis with more prominent precision. © 2022 The Institution of engineering and Technology.

关键词： Diseases

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