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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer science 2024年第5期5卷 1-22页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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Heterogeneous Face Recognition Algorithm: Convolution Neural Network Approach 5

Heterogeneous Face Recognition Algorithm: Convolution Neural...

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

作者： Sujatha, E. Manickam, M. Ani Minisha, R. Rekha, K.S. Indumathy, T. Department of Computer Science and Engineering Tamil Nadu Chennai India School of Computing Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rr and Dr Sr Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India Department of Medical Electronics Tamil Nadu Chennai India

ISBN: (纸本)9798350385649

Heterogeneous face recognition, also known as HFR, is an approach to facial recognition that attempts to match facial pictures that have been gathered through a variety of sensory modalities. HFR is primarily used for forensics, network security, and all commercial applications. The heterogeneous face recognition (HFR) algorithm is robust in handling challenges that exist in conventional face recognition. This is since heterogeneous face images display an enormous amount of intra- class variation, and it does not support hybrid modality of face images. This study provides a unique technique to transfer learning as a means of mastering invariant facial feature characteristics in visual (VIS) face pictures. A wrapping procedure combines end to end connections of two components for deep network. A discriminator has the details of fine-grained data for improving the quality of image which depends on wavelets. The system is made up of two different modules: one deals with face recognition, while the other determines a person's gender. It analysis about the characteristics of gender from the image using Convolutional Neural Networks that have been pre-trained. To train CNN in the face recognition module, in particular, the use of the open-source datasets Labelled Faces within the Wild, YouTube Face, and VGGFace2 results in an improvement in accuracy. Deep learning techniques and a substantial number of photographic examples of people's faces are what make it feasible for the proposed recognition to function so admirably. The problem of studying recognition remains challenging due to the limited size of the sample and the information gap between the senses. © 2024 IEEE.

关键词： Face recognition

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Machine Learning based Sales Prediction and Characterization using Consumer Perceptions 6

Machine Learning based Sales Prediction and Characterization...

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6th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2022

作者： Sreemathy, J. Prasath, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Tamilnadu Coimbatore India Srm Institute of Science and Technology School of Computing Department of Networking and Communications Tamilnadu Chennai India

ISBN: (数字)9781665469418

ISBN: (纸本)9781665469418

In today's age of automated scenarios and digital lifestyle, online shopping has really made its way to everyone's household, with one touch anyone can order the required products. The use of digital marketing over conventional marketing is often favored. It is beneficial to both social media marketing professionals and technicians. When conducting research, one may gain preliminary insights into consumers' perceptions of social media advertisements and online buying habits. Online knowledge exchange allows researchers, academics, and business people to swiftly and easily connect with individuals while conducting searchable mobile brand website research. This research provides a methodical description of a study that only aids consumers in making the optimal smartphone decision for their own parametric needs. A given dataset will be examined utilising machine learning methods, such as brand name predictions with regression and precise results. Groups of people are frequently paid by brands to create internet evaluations, which may be favourable to them or unfavourable to their competitors. © 2022 IEEE.

关键词： Knowledge management

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Improving Image Captioning by Mimicking Human Reformulation Feedback at Inference-time

arXiv

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arXiv 2025年

作者： Berger, Uri Abend, Omri Frermann, Lea Stanovsky, Gabriel School of Computer Science and Engineering The Hebrew University of Jerusalem School of Computing and Information Systems University of Melbourne Australia

Incorporating automatically predicted human feedback into the process of training generative models has attracted substantial recent interest, while feedback at inference time has received less attention. The typical feedback at training time, i.e., preferences of choice given two samples, does not naturally transfer to the inference phase. We introduce a novel type of feedback - caption reformulations - and train models to mimic reformulation feedback based on human annotations. Our method does not require training the image captioning model itself, thereby demanding substantially less computational effort. We experiment with two types of reformulation feedback: first, we collect a dataset of human reformulations that correct errors in the generated captions. We find that incorporating reformulation models trained on this data into the inference phase of existing image captioning models results in improved captions, especially when the original captions are of low quality. We apply our method to non-English image captioning, a domain where robust models are less prevalent, and gain substantial improvement. Second, we apply reformulations to style transfer. Quantitative evaluations reveal state-of-the-art performance on German image captioning and English style transfer, while human validation with a detailed comparative framework exposes the specific axes of improvement. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Facial Emotion Recognition Using a Local Binary Pattern based Deep Learning 2

Facial Emotion Recognition Using a Local Binary Pattern base...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Tayubi, Iftikhar Aslam Pawar, Dilip N. Kiran, Ajmeera Reddy, Pundru Chandra Shaker Sharma, Nipun Chitra, D. King Abdulaziz University Faculty of Computing and Information Technology Rabigh Jeddah Saudi Arabia Bharati Vidyapeeth College of Engineering Instrumentation Engineering Maharashrra Navi Mumbai India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Presidency University Electronics and Communication Engineering Karnataka Bangalore India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Facial-emotion-recognition(FER) is being conducted with the goals of analyzing the psychological characteristics of juvenile offenders and promoting the use of deep learning to the extraction of psychological features. Initially, we talk about how certain personality traits are linked to being good at reading facial expressions. To build a facial emotion identification model from this data, we add more convolutional layers to a CNN and combine it with other neural networks like VGGNet, AlexNet, and LeNet-5. Next, a Central-Local-Binary-Pattern(CLBP) method, which is optimized, is incorporated into the CNN to build a CNN-CLBP framework for FER. After face photos have been preprocessed and key parameters have been optimized, the algorithm is subjected to a validity study. The average detection rate for the CNN-CLBP is 88.16%, which is greater than the recognition rate for other approaches. In addition, image preparation and parameter tuning boost the recognition accuracy of this approach without resorting to poor-tting. In addition, the CNN-CLBP system accurately identifies 97% of pleased and startled expressions, but 22.54% of sad ones. The study's findings can be used as a starting point for further investigations into the mental health of teen offenders. © 2024 IEEE.

关键词： Local binary pattern

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Deep learning-based cognitive digital twin system for wrist pulse diagnostic and classification 2nd

Deep learning-based cognitive digital twin system for wrist ...

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2nd International Conference on Next-Generation computing and Information Systems, ICNGCIS 2023

作者： Chaudhary, Jitendra Kumar Parimalam, T. Alghayadh, Faisal Yousef Keshta, Ismail Soni, Mukesh Degadwala, Sheshang School of Computing Graphic Era Hill University Bhimtal Campus Uttrakhand India Department of Computer Science Nandha Arts and Science College Tamilnadu Erode India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Department of Computer Engineering Sigma University Gujarat Vadodara India

ISBN: (纸本)9781032738659

In analyzing and recognizing wrist pulse signals, it isn’t easy to mine the nonlinear information of wrist pulse signals using analysis methods such as time and frequency. Traditional machine learning methods require the manual definition of features and cannot perform self-learning of features. A cognitive digital twin technique for pulse analysis and recognition based on threshold-less recursive graph and CNN is proposed. The wrist pulse signal is converted into a threshold-free recursive graph based on the nonlinear dynamics’ theory. The VGG-16 CNN automatically extracts the nonlinear features of the recursive graph, and a pulse condition classification model is established. Experimental finding several that the classification of the proposed method accuracy can reach 98.14%, as compared with the existing pulse classification methods. This study offers a novel concept and strategy for classifying pulse signals, and it has application to the objectification of pulse diagnosis. © 2025 The Author(s).

关键词： Graphic methods

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Improved coverage measurements through machine learning algorithms in a situational aware channel condition for indoor distributed massive MIMO mm-wave system

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International Journal of Vehicle Information and Communication Systems 2022年第1期7卷 1-31页

作者： Prakash, Vankayala Chethan Nagarajan, G. Subramaniyaswamy, V. Ravi, Logesh Pondicherry Engineering College Pondicherry University Pondicherry Kalapet India School of Computing SASTRA Deemed University Tamil Nadu Thanjavur India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Avadi India

In massive MIMO (Multiple Input Multiple Output) mm (millimetre) wave system, the channel conditions are measured and analysed for a better placement of reflectors or antennas. In order to increase the coverage area and to reduce interference among users factors such as pathloss and power delay profile are extracted from the Channel Impulse Response (CIR) i.e. from the received signal with respect to transmitter and receiver channel propagation conditions. In a distributed indoor massive MIMO mm-wave system, pathloss and power delay profile are evaluated for Line of Sight (LoS) and Non-Line of Sight (NLoS) environments at frequencies such as 28 and 39 GHz. Based on these factors, a dataset is constructed for 28 GHz. Algorithms such as Support Vector Machine, KNN and Fine Tree are considered. These algorithms are trained with a set of datasets and are tested for performance metrics such as Mean Absolute Error, Correlation Coefficient, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error are evaluated. Simulation results show that an accuracy of 94% and 95% using support vector machine, 93.8% and 94.5% accuracy using KNN and an accuracy of 93.2% and 93.8% using fine tree algorithm for pathloss and power delay profile respectively. © 2022 Inderscience Enterprises Ltd.

关键词： Mean square error

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Neural Networks and Deep Learning: Innovations and Applications in Soft computing 7

Neural Networks and Deep Learning: Innovations and Applicati...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Abdulkadar, Jahagirdar Imran Mohan, Chinnem Rama Bharati, P. Vijaya Palit, Shamik Rao, Gunda Srinivasa Mudholkar, Pankaj Savitribai Phule Pune University India Narayana Engineering College Department of Computer Science and Engineering Andhra Pradesh Nellore524004 India Vignans Institute of Engineering for Women Department of Cse Andhra Pradesh Visakhapatnam530049 India University of Stirling Department of Computing Science and Software Engineering RAK Campus United Arab Emirates Mallareddy University School of Management and Commerce Kompally500100 India Marwadi University Faculty of Computer Applications Gujarat Rajkot360003 India

ISBN: (纸本)9798350350067

The Paper explores different aspects of deep learning techniques and neural networks in the fields of healthcare, time-series forecasting, agriculture, and other relevant sectors through soft computing. The objective of the report is to examine the significant potential and applications of neural networks. While machine learning techniques called neural networks (NN) are utilized for audio, picture, and the processing of natural languages, it has huge implications in robotics where several machine learning algorithms are significantly required. Improvements in deep machine learning are also investigated in the article specifically in the fields of drug construction, genetics, recognizing faces, farming, biological medicine, biological informatics, medical treatment, natural language, multimedia analysis of data, and mobility prediction. However black box challenges and privacy concerns with data are often ignored in this research. The paper also outlines deep learning-driven data mining techniques for precisely predicting, displaying, evaluating, and classifying data. The report has also emphasized on addressing the potential privacy concerns and challenges associated to information representation. By reviewing recent literature sources about neural network techniques, the study proposes innovation strategies and potential improvement areas for the development of advanced technologies like deep learning and neural networks while addressing the key privacy concerns in data processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Automated Defect Detection in Electronic Components using Convolutional Neural Networks 9

Automated Defect Detection in Electronic Components using Co...

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Shobanadevi, A. Kottu, Sreekanth Kumar, K. R. Senthil Amudha, K. Praveena, K. Venkatesh, R. School of Computing Srm Institute of Science And Technology Department of Data Science And Business Systems Tamil Nadu Chennai600026 India Mallareddy University Department of Computer Science & Engineering Telangana Hyderabad500043 India R.M.K. Engineering College Department of Mechanical Engineering Tamil Nadu Kavaraipettai601206 India Department of Science And Humanities-Physics Tamil Nadu Kavaraipettai601206 India Mohan Babu University Erstwhile SreeVidyanikethan Engineering College Department of Electronics And Communication Engineering Andhra Pradesh 517102 India Department of Physics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350365092

This exploration paper explores the operation of convolutional neural networks(CNNs) in automating the discovery of blights in electronic factors. With the rapid-fire advancement of technology, the demand for high- quality electronic factors has surged, emphasizing the need for effective disfigurement discovery styles. using the capabilities of CNNs, this study aims to develop a robust and accurate automated system able to relate colorful types of blights to electronic factors. By training the CNN model on a different dataset of disfigurement images, the system learns to fete subtle variations and anomalies reflective of faults, thereby enhancing examination delicacy and effectiveness. The proposed approach offers promising results in streamlining disfigurement discovery processes, reducing homemade intervention, and ameliorating overall quality control in electronics manufacturing. Results indicate the effectiveness and trustability of CNNs in automating disfigurement discovery, paving the way for enhanced productivity and product trustability in electronic diligence. © 2024 IEEE.

关键词： Quality control

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