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Intelligent Computational Model for Energy Efficiency and AI Automation of Network Devices in 5G communication Environment

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Tsinghua Science and Technology 2024年第6期29卷 1728-1751页

作者： Ashish Bagwari Jaganathan Logeshwaran M.Raja P.Devisivasankari Jyotshana Bagwari Vikas Rathi Asma Mohammed Elbashir Saad Department of Electronics and Communication Engineering Uttarakhand Technical UniversityDehradun 248007India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore 641202India Department of Computer Science and Engineering CMR Institute of TechnologyKarnataka 560037India Department of Information Science and Engineering CMR Institute of TechnologyKarnataka 560037India Advanced and Innovative Research Laboratory Dehradun 248001India Department of Electronics and Communication Engineering Graphic Era(Deemed to be University)Dehradun 248007India Department of Physics College of Science and HumanitiesPrince Sattam Bin Abdulaziz UniversityAl-Kharj 16273Saudi Arabia

Currently, the 4G network service has caused massive digital growth in high use. Video calling has become the go-to Internet application for many people, downloading even huge files in minutes. Everyone is looking for and buying only 4G Subscriber Identity Module (SIM)-capable mobiles. In this case, the expectation of 5G has increased in line with 2G, 3G, and 4G, where the G stands for generation, and it does not indicate Internet or Internet speed. 5G includes next-generation features that are more advanced than those available in 4G network services. The main objective of 5G is uninterrupted telecommunication service in hybrid energy storage system. This paper proposes an intelligent networking model to obtain the maximum energy efficiency and Artificial Intelligence (AI) automation of 5G networks. There is currently an issue where the signal cuts out when crossing an area with one tower and traveling to an area with another tower. The problem of “call drop”, where the call is disconnected while talking, is not present in 5G. The proposed Intelligent Computational Model (ICM) model obtained 96.31% network speed management, 90.63% battery capacity management, 92.27% network device management, 93.57% energy efficiency, and 88.41% AI automation.

关键词： hybrid energy storage system 4G digital growth 5G downloading Subscriber Identity Module(SIM) generation network energy efficiency Artificial Intelligence(Al)automation

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Experimental Fire Detection and Alarm System Based on the Integration of IoT and Image Processing

Experimental Fire Detection and Alarm System Based on the In...

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2024 IEEE International Conference on Consumer electronics-Asia, ICCE-Asia 2024

作者： Vuong, Dat Tran, Van Vu Tran, Van Thanh Nhat Tran, Huynh Anh Hao Le, Pham Hoang Son, Tran The Vietnam-Korea University of Information and Communication Technology Faculty of Computer Engineering and Electronics Da Nang Viet Nam

ISBN: (纸本)9798331530839

Fires are considered a risk that causes human death and property loss. To reduce fire damage, fires must be detected and warned promptly. This paper aims to design and experimentally implement a fully-function fire detection and warning system. The developed system is based on a Wireless Sensor Network (WSN) that includes various nodes distributed across the city. Each node has two main blocks which are the image processing and communication blocks. The image processing block applies the EfficientN et B0 model to detect the fire and smoke, whereas the communication block uses the Module 4G to contact the house owner over a mobile phone network and LoRa gateway to send the signal warning to the fire center over the a LoRaWAN. The Module 4G is responsible for sending captured images of suspected fire if any to the house owner's device for getting his/her acknowledgement. Then an urgent alarm message consisting of fire position and risk level are sent to the fire center if the fire is acknowledged by the house owner, otherwise, the alarm is ceased. Besides, the proposed system is installed the gas sensor, smoke sensor, water spray system and light-sound alarm to augment the reliability of fire detection. The proposed system is experimentally demonstratedand achieved positive results: Delay time sending from the proposed fire detection system to the user is recorded as 5 seconds and from the user to the fire center is recorded as 10 seconds, within a LoRaWAN coverage of 1.5km. © 2024 IEEE.

关键词： Sensor nodes

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Enhancing the efficiency of lung cancer screening: predictive models utilizing deep learning from CT scans

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Neural Computing and Applications 2025年第17期37卷 11459-11477页

作者： Tawfeek, Medhat A. Alrashdi, Ibrahim Alruwaili, Madallah Shaban, Warda M. Talaat, Fatma M. Department of Computer Science Faculty of Computers and Information Menoufia University Shebin Elkom32511 Egypt Department of Computer Science College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University Sakakah Saudi Arabia Department of Communication and Electronics Engineering Nile Higher Institute for Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt

Lung cancer is the most lethal form of cancer. This paper introduces a novel framework to discern and classify pulmonary disorders such as pneumonia, tuberculosis, and lung cancer by analyzing conventional X-ray and CT scan images called lung cancer risk prediction (LCRP) model. LCRP has four modules, namely data collection and preprocessing, data augmentation module, image segmentation module, and prediction module. Actually, LCRP employs three deep learning models;sequential model, functional model, and transfer model on publicly available training datasets. Convolutional neural networks (CNNs) have emerged as a highly effective field in machine learning, particularly for image datasets in the field of biomedical applications. The primary goal is to validate these models by comparing their performance with other models in order to determine their effectiveness in addressing challenging datasets. Our research has revealed a noteworthy enhancement in the efficiency of binary and multi-class classification using mask R-CNN image segmentation. During the model training process, a combination of Adam and stochastic gradient descent dual optimizers has been used to improve performance. LCRP have outperformed current pre-trained models by minimizing training parameters, computational costs, and overhead. It introduces 98.5% accuracy, 88.7% specificity, 89% sensitivity, 89.2% precision, and 89.09% F-measure. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Lung cancer

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FoodExpert: Portable Intelligent Device for Rapid Screening of Pulse Quality and Adulteration

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood electronics 2024年第1期2卷 2-11页

作者： Pandey, Harsh Arya, Subhanshu Das, Debanjan Udutalapally, Venkanna International Institute of Information Technology Naya Department of Electronics & Communication Engineering Naya Raipur493661 India National Institute of Technology Department of Computer Science & Engineering Warangal506004 India

Pulses are one of the most important food crops in the world due to their higher protein content, approximately 21%-25%. Therefore, it is crucial to analyze the crop's quality and impurity levels. Stones, pebbles, marble chips, and synthetic dyes, such as lead chromate, metanil yellow, and artificial colors, are some of the impurities added to pulse products, accidentally or on purpose. The existing analysis techniques are mostly laboratory-based, time-consuming, costly, and require human examination. To address this issue, this article presents an intelligent system, FoodExpert, based on image processing that automatically uses an image of a pulse sample to identify the region of interest and essential attributes. Then, machine learning frameworks are used to predict pulse quality and adulteration levels based on the obtained parameters. On the test dataset, the suggested model had a 96% accuracy rate for pulse quality prediction and 94% accuracy for adulteration level prediction. The model was successfully deployed on a Raspberry Pi-based hardware prototype and mobile application. © 2023 IEEE.

关键词： Feature extraction

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Optimized Power Allocation in Downlink NOMA with Imperfect Successive Interference Cancellation

Optimized Power Allocation in Downlink NOMA with Imperfect S...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Biswas, Soumik Bera, Debasish Indian Institute of Information Technology Department of Electronics and Communication Engineering Kalyani India Indian Institute of Information Technology Department of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350350821

It is expected that physical-layer security will finally thrive to provide an extra layer of defence for a variety of devices in next-generation 6G wireless communication. In this work, physical-layer security using non-orthogonal multiple access (NOMA) with single-input-single-output (SISO) assumptions are considered where the users and eavesdroppers are spatially uniformly deployed. The channels between the transmitter (base station (BS)) and the NOMA users and eavesdroppers are considered to be independent Nakagami-m. We analyse the secrecy sum rate (SSR) of the NOMA system in the case of imperfect successive interfere cancellation (SIC) and propose an optimised power allocation scheme with minimised transmit power requirement to satisfy the Quality of Service (QoS) for all legitimate users. Closed-form solutions for the SSR are also derived. The performance of the proposed schemes has been evaluated both by analysis and by simulations. Results are evaluated for a wide range of the signal-to-noise ratio (SNR). © 2024 IEEE.

关键词： Channel state information

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Optimizing User Recommendations with Variational Autoencoders: Insights from MovieLens-1M and BookCrossing Datasets

Optimizing User Recommendations with Variational Autoencoder...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Samridhi, Kumari Sinha, Bam Bahadur Das, Trinanjan Indian Institute of Information Technology Electronics and Communication Engineering Ranchi India National Institute of Technology Computer Science and Engineering Sikkim India Indian Institute of Information Technology Computer Science and Engineering Ranchi India

ISBN: (纸本)9798350376876

Recommender systems utilize algorithms and data to predict user preferences based on their past choices. While these systems can be highly accurate, this increased accuracy can sometimes lead to predictability and monotony. In the realm of big data, recommender systems are both prevalent and valuable. Research on these systems has intensified in recent years, accompanied by a growing number of datasets. This paper examines the application of Variational Autoencoder (VAE)-based recommender systems using the MovieLens 1M and BookCrossing datasets. The study primarily evaluates the performance of the VAE model using Root Mean Square Error (RMSE) as the key metric. The VAE model was trained on these datasets to predict user preferences and generate personalized recommendations. Experimental results revealed an RMSE of less than 1 for both datasets, indicating the model's proficiency in accurately estimating user ratings for items. These findings suggest that VAE-based recommender systems are promising for enhancing recommendation accuracy and personalization. This research contributes to the understanding of VAE models within the context of recommender systems and provides insights for future research and potential improvements in this field. © 2024 IEEE.

关键词： Data accuracy

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AI-Driven Resource and communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

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computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

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A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Configuration

A Modified Hexagonal Shaped Sub-6 GHz 5G Antenna in MIMO Con...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Porwal, Rahul Garg, Rahul Kumar Raj, Nikhil Nair, M.V. Deepak Department of Electronics and Communication Engineering The LNM Institute of Information Technology Jaipur India Department of Computer Science and Engineering University of Engineering and Management Jaipur India

ISBN: (纸本)9798350379693

A line-fed Modified Hexagon shaped 2-port planar antenna is presented for 5G application. The MIMO antenna is designed through combining the techniques of a modifying the radiator and defected ground structure. In the MIMO configuration, two replicas of the MIMO element are arranged orthogonal to ensure the pattern diversity. A cross shaped structure is integrated with the ground to enhance interelement isolation. The size and operating bandwidth of the MIMO antenna are 28×56 mm2 and 2.54 GHz to 5.46 GHz, respectively. This design advantageously offers a wide -15 dB bandwidth from 3.3 GHz to 5.0 GHz with a percentage bandwidth of 40.96% and a peak realized gain of 3.1 dBi at 3.3 GHz. Maximum isolation value of 33.55 dB is achieved while it is never less than 16.85 dB for the frequency of interest. The envelope correlation coefficient (ECC) © 2024 IEEE.

关键词： 5G mobile communication systems

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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A Novel Routing Protocol over Time-Varying IoT Network Using RTS/CTS Framework 18

A Novel Routing Protocol over Time-Varying IoT Network Using...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Sharma, Neha Pandey, Prajjwal Aryan Rathore, Prateek Pandey, Om Jee Indian Institute of Technology BHU Department of Electronics Engineering Varanasi India National Institute of Technology Department of Electronics and Communication Engineering Silchar India The LNM Institute of Information Technology Department of Communication and Computer Engineering Jaipur India

ISBN: (纸本)9798350391725

In the context of 6G IoT networks, distributed computing has become a critical enabler for seamless communication across diverse sectors, such as smart manufacturing, intelligent transportation, healthcare, and defense. Many of these applications involve mobile IoT devices (IoDs) that change locations dynamically, requiring robust and efficient multi-hop data transmission frameworks. Selecting the optimal relay node in distributed 6G IoT networks is essential, as it directly impacts energy efficiency, latency, data interference, and throughput. Additionally, ensuring the availability of the relay node's queue is crucial to prevent data loss. To address these challenges, this work introduces a novel distributed computing framework based on the Request-To-Send (RTS) and Clear-To-Send (CTS) protocols, specifically designed for time-varying 6G IoT networks. The framework incorporates an RTS/CTS mechanism to manage distributed data transfer and employs the Optimal Relay IoD Selection with Q-Learning (OptRISQL) algorithm to determine the optimal relay path dynamically. By distributing computation across nodes, the proposed approach enhances network flexibility, reduces queue sizes, and minimizes memory costs, leading to improved overall network efficiency. Simulation results demonstrate that this distributed computing method effectively reduces interference and enhances throughput, outperforming other current state-of-the-art techniques. Index Terms-6G Internet of Things (IoT), Distributed Computing, Request-To-Send (RTS), Clear-To-Send (CTS), Quality-of-Service (QoS). © 2024 IEEE.

关键词： Smart manufacturing

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