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Networking and Communications (ICNWC), International Conference on

作者： m.K. mohamed Faizal S. Geetha A. Barveen Department of Computer Science and Engineering M.I.E.T. Engineering College Trichy Tamilnadu Department of Computer Applications University College of Engineering Trichy Tamilnadu

The face-morphing attack, which occurs in private, public, and governmental institutions, is one of the most well-known in today’s world. Face recognition systems tend to be vulnerable if the face images are manipulated with duplicate images. manipulated images are combined with the original image so that the images look like legitimate ones. Several face recognition studies are being conducted to determine whether the face images are manipulated. Using the DL algorithm, the face image is trained to attain the original and morphed face images by recognizing the face images. DL algorithms determine the images by classifying whether they are morphs or not recognizable to humans. In this paper, the foremost emphasis is on diagnosing the face recognition from those face-morphed images using the different DL techniques. Different DL techniques are effectively compared, where the ViT transformer attains improved accuracy when compared to Resnet, RNN, and CNN, respectively. This paper provides an overview of the various deep learning algorithms for detecting those face recognition images that focus on challenges and issues in the facial datasets from Face Recognition Kaggle dataset with training and testing image dataset. It determines the higher contrast in image efficiency and the evaluation of the face recognized images with an improved image.

关键词： Training Deep learning Image recognition Face recognition Transformers Classification algorithms Testing

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A modified UTAUT model for the acceptance and use of digital technology for tackling COVID-19

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Sustainable Operations and computers 2022年 3卷 118-135页

作者： Akinnuwesi, Boluwaji A. Uzoka, Faith-michael e. Fashoto, Stephen G. mbunge, elliot Odumabo, Adedoyin Amusa, Oluwaseun O. Okpeku, moses Owolabi, Olumide Department of Computer Science Faculty of Science and Engineering University of Eswatini KwaluseniM201 Swaziland Department of Mathematics and Computing Mount Royal University CalgaryABT3E6K6 Canada Department of Computer Science Faculty of Science Lagos State University Lagos Ojo Nigeria Department of English Studies Faculty of Arts Adekunle Ajasin University Akungba-Akoko Ondo State Nigeria Department of Genetics University of KwaZulu-Natal South Africa Department of Computer Science University of Abuja Abuja Nigeria

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilization of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (Pe), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational influence and benefit (OIB) and government expectancy and benefits (GeB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as Pe, FC, SI, OIB and GeB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation. © 2021

关键词： COVID-19

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Leveraging ensemble Learning model for Human Gait Recognition in Human computer Interface 2

Leveraging Ensemble Learning Model for Human Gait Recognitio...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Pant, Suman Bhat, Niyati Shobana, G. Sindhuja, R. Lekha, D. Srimathi, S. School of CA & IT Shri Guru Ram Rai University India B.E Computer engineering University of Mumbai Mumbai India Department of Computer Applications College of Science and Humanities SRM Institute of Science and Technology Kattankulathur India Department of Medical Electronics Saveetha Engineering college India CSE Department R.M.K. College of Engineering and Technology India Department of Bio Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9798350340235

Human Gait Recognition (HGR) is a biometric system that is exploited for security purpose over the past few years. The efficiency of gait detection is affected by different features namely walking surfaces, wearing clothes, and carrying a bag. moreover, detection from divergent views is a complicated problem in HGR. Numerous approaches were proposed for HGR using classical and DL methods but the conventional technique is not appropriate for massive data. This study leverages ensemble Learning model for Human Gait Recognition in Human computer Interface (eLmHGA-HCI) technique. The eLmHGA-HCI technique intends to recognize various kinds of human gaits to assist HCI applications. In the eLmHGA-HCI technique, Time Domain (TD) and Frequency Domain (FD) features from the input signals. For gait recognition, an ensemble of three DL models takes place namely Bidirectional Long Short Term memory (BiLSTm), Gated Recurrent Unit (GRU) and Recurrent Neural Network (RNN). To exhibit the enhancedgait recognition results of the eLmHGA-HCI technique, a widespread simulation analysis is done. The simulation outcomes pointed out the enhanced performance of the eLmHGA-HCI approach over other existing techniques. © 2023 Ieee.

关键词： Activity recognition Deep learning ensemble learning Gait recognition Human computer interface

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A Novel Nash-Based matching Approach for multirobot Task Allocation in Distributed Robotic Networks 7th

A Novel Nash-Based Matching Approach for Multirobot Task All...

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7th eAI International Conference on Robotics and Networks, ROSeNeT 2023

作者： Hamidoğlu, Ali Gül, Ömer melih Gültekin, Gökhan Koray Kadry, Seifedine Nimer Department of Mathematics Bahcesehir University İstanbul Turkey Department of Computer Engineering Bahçeşehir University İstanbul Turkey Department of Electrical-Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey Department of Applied Data Science Noroff College Kristiansand Norway Ajman University Ajman United Arab Emirates

ISBN: (纸本)9783031644948

efficiency is paramount in distributed robotic networks, where multiple autonomous robots collaborate to perform complex tasks. In this context, the identification of the most efficient path for robots, considering both distance and cost, plays a crucial role in the development of an effective matching algorithm for addressing multirobot task allocation (mRTA) challenges. This study presents a novel cooperative Nash game framework that serves as a distributed matching method for addressing the task allocation problem in a distributed robotic network consisting of robots and tasks. A particular mRTA problem is investigated where each robot moves at a constant speed determined by maximizing energy harvesting while minimizing energy consumption during the motion. In this framework, Nash equilibrium is established as a near-optimal approach for matching based on distance. In the numerical experiments, the performances are assessed for various scenarios involving 10 robots and 20 robots with the same num.e. of tasks. Here, the Hungarian algorithm is used as an optimal benchmark algorithm to demonstrate the reliability of the theoretical findings and the robustness of the proposed model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Distributed systems energy harvesting Game theory multirobot systems Task allocation

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A Comprehensive Review of the Occlusion Identification Based Gait Recognition mechanism

A Comprehensive Review of the Occlusion Identification Based...

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Ieee Students' Conference on electrical, electronics and computer science (SCeeCS)

作者： Beauty Kumari Prof. Jyoti Bharti Department of Computer Science Engineering (C.S.E.) MANIT Bhopal M.P. INDIA

This pattern module of gait has been developed by the capturing process of a scene of obj ects during the walking cycle of subjects, but in the case of a populated or multi-person environment, the region of interest or subject is often obstructed by the numerous static and dynamic obj ects. Due to the obstruction (process of occlusion), the information developed by the gait pattern model has been discarded or lost. This process of obstruction of the subject due to several environmental objects during the development of the gait pattern is known as occlusion. Numerous techniques, including Lagrange's inter-polation, piece-wise cubic hermite interpolation, spline inter-polation, periodic curve fitting, linear curve fitting, and quadratic curve fitting, were applied in the reconstruction of these hidden sequences of the pattern gait module. This work provides an introduction to the mechanisms involved in occluded-based gait reconstruction and explores the difficulties encountered in trying to describe this system. This paper also describes the literature reported by numerous researchers over the past few years. The primary goal of this paper is to provide a comprehensive review of the current methods for recovering occluded gait patterns based on research.

关键词： Legged locomotion Face recognition Biological system modeling Fingerprint recognition Security Curve fitting Splines (mathematics)

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Design of Arrow Shaped microstrip Patch Antenna for X and Ku- Band Applications 1

Design of Arrow Shaped Microstrip Patch Antenna for X and Ku...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Yuvaraj, D. Nageswara Rao, m.V. Shanmuga priya, P. Naveneetha krishanan, S. Department of Computer Science Cihan University Kurdistan region Duhok Iraq Dept. of ECE GMR Institute of Technology RajamSrikakulam Andhra Pradesh India Department of Computer Science and Engineering M.I.E.T Engineering College Trichy India Department of Electronics and Communication Engineering SACS MAVMM Engineering College Madurai India

ISBN: (纸本)9781607685395

A small size printed patch antenna to cover applications of X and Ku band is suggested in this paper. The suggested antenna consists of an arrow shaped patch on front view of FR 4 substrate and a partial ground on the other side. Initial design started with a rectangular patch of dimensions 10.7 mm x 7 mm. Then it is modified into arrow shaped patch in order to improve the reflection coefficient at lower and higher *** measurement of the antenna is 12 x 15 x 1.6 mm3. The antenna parameters are optimized using Fem based HFSS software. The operating range of frequencies include from 6.75 GHz to 18.96 GHz. The proposed antenna displays a good omnidirectional radiation pattern and maximum gain of 7.41 dBi at 17 GHz. Simulated S11 parameters, VSWR, Gain and current distributions are offered in this paper and S11 simulated parameters are in good agreement with measured parameters. © The electrochemical Society

关键词： Arrow shaped Ku band Printedmicrostrip antenna X band

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Digital Twin-Assisted Load-Balanced User Association and AoI-Aware Scheduling in IoT Networks

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Ieee Access 2025年 13卷 95888-95910页

作者： Shaharyar Khwaja, Ahmed Tri Vo, Nhat Nwadiugwu, Williams-Paul Woungang, Isaac Anpalagan, Alagan Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering TorontoONM5B 2K3 Canada Toronto Metropolitan University Department of Computer Science TorontoONM5B 2K3 Canada York University Lassonde School of Engineering Department of Electrical and Computer Engineering TorontoONM3J 1P3 Canada Laurentian University School of Engineering and Computation Science SudburyONP3E 2C6 Canada

In this paper, we propose a digital twin (DT) assisted-user device (UD)-base station (BS) association for cellular-supported internet of things (IoT) networks, and scheduling of uplink transmission of these devices to their associated BSs considering the age of information (AoI). Unlike existing work, we consider the scenario of a main base station (mBS) and several smaller base stations (SBSs), which is suitable for 5G and beyond 5G technology. We propose a novel Q-learning-inspired algorithm for simultaneous uplink throughput maximization and load balancing among the different BSs to associate the UDs away from the mBS and distribute them.e.enly between the SBSs as much as possible. We further take into account the maximum UD-association limit and limited coverage radius of each BS, which makes it closer to a realistic scenario. Another contribution is the proposal and analysis of heuristics-based scheduling of the UDs transmission to their respective BSs with the aim to reduce the overall AoI. Scheduling is important as many applications in the IoT networks require near-time or near-real-time updates for taking timely decisions and maintaining acceptable performance and safety levels. The AoI is an effective metric for the timeliness of information at a receiver, and the computational simplicity of the heuristics-based approach ensures that the scheduling decisions are taken promptly. Simulations results and comparison with several benchmark methods show that the proposed solution can achieve load balancing with a slight degradation in the cumulative maximum achievable throughput achieved in the network. The results also provide valuable insights into the joint performance of the proposed model and the benchmark UD-BS association and scheduling solutions with different packet generation rates for transmission by the UDs. © 2013 Ieee.

关键词： mobile telecommunication systems

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Transformer encoder and Decoder method for Forest estimation and Change Detection 2

Transformer Encoder and Decoder Method for Forest Estimation...

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2nd Ieee International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kotin, Kiran Kashinath Kumar, Sharan Alabdeli, Haider Kumar, Gotte Ranjith Ramachandra, A.C. Smt. Kamala and Sri Venkappa M. Agadi College of Engineering and Technology Department of Ece Karnataka Lakshmeshwar India H.K.E. Society's S.M.V. College of Engineering Department of Ece Karnataka Raichur India The Islamic University Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

Forest Change Detection (FCD) is difficult component of natural assess monitoring and conservation strategy, enabled informing decision-making. Different techniques using strength of Artificial Intelligence (AI) has implemented to detect and classify modifications in forest cover by Remote Sensing (RS) data. In this research, proposed a Transformer encoder and Decoder (TeD) method for forest estimation and change detection. The multi Feature Fusion (mFF) is used for effective combine features of various scales. Next, multilevel decoding named as multi Scale and multi Decoder (mSmD) is used at fine the scales much excessively. At last, loss is function is used for minimizing loss of mSmD. The performance of proposed method is analysed with performance measures of Intersection over Union (IoU) and Accuracy. The proposed method attained IoU of 86.23% and 87.45% in forest and background Attained mIoU of 85.21% and 88.92% in forest and background respectively. The proposed method attained accuracy of 92.89% and 92.33% in forest and background. Attained mAcc of 90.54% and 91.48% in forest and background respectively. The proposed method performed effective than previous methods like multiscale Channelwise Cross Attention Network (mCCANet), UNet and Generative Adversarial Networks (GAN). © 2024 Ieee.

关键词： Forest Change Detection multi Feature Fusion multi Scale and multi Decoder Remote Sensing and Transformer encoder and Decoder

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Role of Dialog and explicit A.I. for Building Trust in Human-Robot Interaction

Role of Dialog and Explicit A.I. for Building Trust in Human...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Pandya, Diptanshu Pathak, Rakesh Kumar, Vipin Jain, Abhishek Jain, Arpit mursleen, mohd Medicaps University Department of C.S.E. M.P. Indore India Medi-Cap University Department of C.S.E. Indore India Uttaranchal University Department of C.S.E. Dehradun India Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350323887

Robots and people can communicate similarly. Human-robot contact therefore has increasingly assimilated into society. meanwhile, technology is continually being improved for greater communication with people. A.I. for dialogue and content detection is thus necessary. A.I. has become a tool for enhancing interactions between humans and robots. People's increased online presence has resulted in a variety of content, and explicit A.I. assists in analysing and filtering that content by age group. Nonetheless, dialoguer AI aids in the process of responding in accordance with the input given, making it important for human-robot interaction. The following study has covered the working approach for analysing the function of Vocal Artificial Intelligence and explicit Artificial Intelligence in Human Interaction. For a thorough understanding, difficulties are also listed in a table format. A thorough examination also covers the function of explicit and conversational Ai in interpersonal interactions. © 2023 Ieee.

关键词： Human robot interaction

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Apple Fruit leaf Disease Detection and Classification using Quantum Behaved Particle Swarm Optimization 2

Apple Fruit leaf Disease Detection and Classification using ...

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2nd Ieee International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： math, Gayatri Vishwanath, P. Adnan, myasar mundher Reddy, R Archana Sundaram, S. m.e.akshi Raichur Visvesvaraya Technological University H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Belgaum India H.K.E. Society's S.M.V. College of Engineering Department of ECE Karnataka Raichur India The Islamic University Najaf Iraq School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India

ISBN: (纸本)9798350317558

Detecting and classifying apple fruit leaf diseases manually is a time-consuming and expensive task, requiring expertise. This process not only affects the quality of the leaf disease image but also demands expert intervention. In the dataset, there are 1615 healthy images distributed among four classes: apple scab, apple rust, apple black rot, and healthy apple. Data augmentation techniques were involved image inversion, noise injection, colour enhancement, rotation and scaling. Despite these efforts, the existing model faces challenges in an inefficient search space with high dimensions and local optima. To enhance the Quantum-Behaved Particle Swarm Optimization (QBPSO) based Long Term-Short memory (LSTm) dimension the search space is expanded globally, independently detecting the leaf disease. Feature extraction is performed using Squeeze Net layers to analyse the leaf disease, reducing parameters to improve the detection process. The fire modulus in the analysis is involved in the process. The hyperparameter tuning process utilizes QBPSO to independently detect and classify the leaf disease, with the Schrödinger equation playing a crucial role in the process. The obtained results demonstrate that the proposed method achieves better accuracy of 98.76%, Precision of 98.72%, Sensitivity of 98.82% and F1-score of 98.70%. on the plant dataset. These results ensure detection and classification performance compared to other existing methods, such as Discrete Wavelet Transformation (DWT), Butterfly Optimization Algorithm (BOA) and Chaotic Salp Swarm Algorithm (CSSA). © 2024 Ieee.

关键词： Apple Leaf Disease Butterfly Optimization Algorithm Chaotic Salp Swarm Algorithm Long Term-Shor memory and Quantum-Behaved Particle Swarm Optimization

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