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Information Technology (ACIT)

作者： Muhamad Irsan Rosilah Hassan Taj-Aldeen Naser Abdali Mohamad Khairi Ishak School of Computing Telkom University Bandung Indonesia Department of Information Technology College of Engineering and Information Technology Ajman University U.A.E Faculty of Basic Education University of Misan Misan Iraq Department of Electrical and Computer Engineering Ajman University Ajman United Arab Emirates

Instances of COVID-19 transmission occur daily due to individuals failing to maintain distance or engaging in physical contact with others who may be contaminated with the virus. To mitigate this issue, this study has developed a system to detect human subjects practicing social distancing. The system utilizes a Raspberry Pi 4 Model B 8GB device in combination with a Logitech HD Webcam C270 camera. To detect human subjects, the Convolutional Neural Network is employed, utilizing the You Only Look Once (YOLO) method. In the testing phase of the tool, the system successfully identifies human subjects and assesses their proximity to others. It also detects instances of social distancing violations. The system achieved an average mean Average Precision (mAP) of 0.9792, a Precision of 0.9482, a Recall of 0.9819, and an f1 score of 0.9648.

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MetaTrading: An Immersion-Aware Model Trading Framework for Vehicular Metaverse Services

arXiv

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arXiv 2024年

作者： Wu, Hongjia Zeng, Hui Xiong, Zehui Kang, Jiawen Cai, Zhiping Chan, Tse-Tin Niyato, Dusit Han, Zhu Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore School of Automation Guangdong University of Technology China College of Computer National University of Defense Technology China College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Updates of extensive Internet of Things (IoT) data are critical to the immersion of vehicular metaverse services. However, providing high-quality and sustainable data in unstable and resource-constrained vehicular networks remains a significant challenge. To address this problem, we put forth a novel immersion-aware model trading framework that incentivizes metaverse users (MUs) to contribute learning models trained by their latest local data for augmented reality (AR) services in the vehicular metaverse, while preserving their privacy through federated learning. To comprehensively evaluate the contribution of locally trained learning models provided by MUs to AR services, we design a new immersion metric that captures service immersion by considering the freshness and accuracy of learning models, as well as the amount and potential value of raw data used for training. We model the trading interactions between metaverse service providers (MSPs) and MUs as an equilibrium problem with equilibrium constraints (EPEC) to analyze and balance their costs and gains. Moreover, considering dynamic network conditions and privacy concerns, we formulate the reward decisions of MSPs as a multi-agent Markov decision process. Then, a fully distributed dynamic reward method based on deep reinforcement learning is presented, which operates without any private information about MUs and other MSPs. Experimental results demonstrate that the proposed framework can effectively provide higher-value models for object detection and classification in AR services on real AR-related vehicle datasets compared to benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Markov processes

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Deep Learning and Sentiment Analysis Improve E-commerce Sales Prediction

Deep Learning and Sentiment Analysis Improve E-commerce Sale...

引用

Data Science and Network Security (ICDSNS), International Conference on

作者： Geetha K Swarnalatha K. Ananda Babu. J Poranki V L R N Sai Sudha Ramachandra A C Dept. of CSE GST GITAM University Bengaluru Campus Bengaluru India Dept of AI&DS MIT Thandavapura Mysore India Department of Information Science and Engineering Malnad College of Engineering Hassan India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India

The dataset labelled “Amazon and Yelp Reviews“ is a useful resource for sentiment analysis and opinion mining. It has roughly 1.5 million reviews from Amazon and Yelp for a wide range of products and services, as well as the relevant information for each review, which includes product ratings, review dates, and review titles. The dataset's size and diversity make it a good choice for training large-scale deep learning models to handle sentiment analysis tasks. The recommended approach for undertaking sentiment analysis using the “Amazon and Yelp Reviews“ dataset includes three steps: collecting data on a daily basis, preparing user comments, and applying a BiLSTM model for sentiment analysis. Assessing consumer sentiment entails evaluating comments, goods, and businesses to develop predictions about how people feel about various products and services. Some of the measures used in the model evaluation process include accuracy, precision, recall, and F1-score. The proposed method achieves an average accuracy of 87.3%, which is comparable to existing deep learning methods such as character-level convolutional neural networks (CNNs)- LSTM and deep transfer learning. The model's errors may be evaluated to discover where it can be improved, and additional assessment metrics such as AUC- ROC, AUC-PR, G-mean, and MCC can be used to determine how well the model is doing. The dataset and methods presented have the potential to be useful for a wide range of applications, including customer feedback analysis, product and service development, and online reputation management.

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Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning

arXiv

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arXiv 2023年

作者： Wang, Shengbo Li, Ke Yang, Yin Cao, Yuting Huang, Tingwen Wen, Shiping College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom College of Science and Engineering Hamad Bin Khalifa University Doha5855 Qatar Science Program Texas A & M University at Qatar Doha23874 Qatar Australian AI Institute Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

Breaking safety constraints in control systems can lead to potential risks, resulting in unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even among similar tasks. In this paper, we develop a novel adaptive safe control framework that integrates meta learning, Bayesian models, and control barrier function (CBF) method. Specifically, with the help of CBF method, we learn the inherent and external uncertainties by a unified adaptive Bayesian linear regression (ABLR) models, which consists of a forward neural network (NN) and a Bayesian output layer. Meta learning techniques are leveraged to pre-train the NN weights and priors of the ABLR model using data collected from historical similar tasks. For a new control task, we refine the meta-learned models using a few samples, and introduce pessimistic confidence bounds into CBF constraints to ensure safe control. Moreover, we provide theoretical criteria to guarantee probabilistic safety during the control processes. To validate our approach, we conduct comparative experiments in various obstacle avoidance scenarios. The results demonstrate that our algorithm significantly improves the Bayesian model-based CBF method, and is capable for efficient safe exploration even with multiple uncertain constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Fast and Secure Coding for Wireless Networks

Fast and Secure Coding for Wireless Networks

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International Conference on Computing and Networking Technology (ICCNT)

作者： Seema Sambargi Y.V. Dongre N. V. Balaji Saniya Khurana Sowmiya S Sidhant Das School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka comp Vishwakarma Institute of Technology Pune Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Speedy and comfy coding for wireless networks is an essential problem in the trendy computing landscape. Wi-fi networks have become increasingly commonplace in recent years, and they present specific demanding situations for code builders. To ensure certain cozy, reliable, and efficient overall performance of network applications, developers must be able to quickly and appropriately generate code for wireless structures. The way to meet these requirements, builders have to be capable of selecting the most appropriate coding approaches to apply if they want to limit errors and maximize security. The main venture to developing easy and reliable code for wireless structures is the complexity of the underlying structures. The ever-increasing complexity of underlying community technologies renders conventional coding techniques inadequate. The elevated demands for real-time records and connection transmission exacerbate this. To address this venture, rapid and at-ease coding strategies need to be carried out. Those strategies can encompass, however are not constrained to, principles including comfy coding, exceptional coding practices, and diverse safety protocols. Principals of comfy coding confer with a method that emphasizes comfortable practices at some point in the improvement and protection of the codebase. This consists of proper protection practices along with authentication, encryption, enter validation, error dealing, and malicious code prevention. This technique allows make certain that the code is written in a strong, comfortable, and efficient manner.

关键词： Codes Protocols Wireless networks Encoding Complexity theory Security Reliability Communication system security Protection Wireless fidelity

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Structural Regularization Based Discriminative Multi-View Unsupervised Feature Selection

SSRN

引用

SSRN 2023年

作者： Zhou, Shixuan Song, Peng Yu, Yanwei Zheng, Wenming School of Computer and Control Engineering Yantai University Yantai264005 China College of Computer Science and Technology Ocean University of China Qingdao266400 China Key Laboratory of Child Development and Learning Science Ministry of Education Southeast University Nanjing210096 China

Multi-view unsupervised feature selection (MUFS) has recently aroused considerable attention, which can select the compact representative feature subset from original multi-view data. Despite the promising preliminary performance, most previous MUFS methods fail to explore the discriminative ability of multi-view data. In addition, they usually utilize spectral analysis to maintain the geometrical structure, which will inevitably increase the difficulty of parameter selection. To address these issues, we present a novel MUFS method, named structural regularization based discriminative multi-view unsupervised feature selection (SDFS). Specifically, we calculate the similarity matrix of sample space from different views and automatically weight each view-specific graph to learn a consensus similarity graph, in which these two types of graphs can promote each other. Further, we treat the learned latent representation as the cluster indicator, and employ a graph regularization without introducing additional parameters to maintain the geometrical structure of data. Besides, a simple yet efficient iterative updating algorithm with theoretical convergence property is developed. Extensive experiments on several benchmark datasets verify that the designed model is superior to several state-of-the-art MUFS models. © 2023, The Authors. All rights reserved.

关键词： Spectrum analysis

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Monolithic Germanium–Tin on Si Avalanche Photodiodes

arXiv

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arXiv 2024年

作者： Rudie, Justin Amoah, Sylvester Wang, Xiaoxin Kumar, Rajesh Abernathy, Grey Akwabli, Steven Grant, Perry C. Liu, Jifeng Li, Baohua Du, Wei Yu, Shui-Qing Material Science and Engineering University of Arkansas FayettevilleAR72701 United States Department of Electrical Engineering and Computer Science University of Arkansas FayettevilleAR72701 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Arktonics LLC 1339 S. Pinnacle Dr. FayettevilleAR72701 United States Materials Science & Engineering Program University of Arkansas FayettevilleAR72701 United States Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR72701 United States

We demonstrate monolithically grown germanium-tin (GeSn) on silicon avalanche photodiodes (APDs) for infrared light detection. A relatively thinner Ge buffer design was adopted to allow effective photo carriers to transport from the GeSn absorber to the Si multiplication layer such that clear punch-through behavior and a saturated primary responsivity of 0.3 A/W at 1550 nm were observed before avalanche breakdown in GeSn/Si APDs for the first time. The spectral response covers 1500 to 1700 nm. The measured punch-through and breakdown voltages are 15 and 17 V, respectively. Undisputed multiplication gain was obtained with the maximum value of 4.5 at 77 K, and 1.4 at 250 K, directly in reference to the saturated primary responsivity from the same device rather than a different GeSn p-i-n photodiode in previous reports. A peak responsivity was measured as 1.12 A/W at 1550 nm and 77 K. © 2024, CC0.

关键词： Avalanche photodiodes

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Prediction of groundwater table for Chennai Region using soft computing techniques

引用

Arabian Journal of Geosciences 2022年第9期15卷 1-19页

作者： Ramasamy, Viswanathan Alotaibi, Youseef Khalaf, Osamah Ibrahim Samui, Pijush Jayabalan, Jagan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science College of Computer and Information Systems Umm Al-Qura University Makkah Saudi Arabia Al-Nahrain University Al-Nahrain Nano-renewable Energy Research Center Baghdad Iraq Department of Civil Engineering National Institute of Technology Patna Patna India Department of Civil Engineering Galgotias University Greater Noida India

Groundwater is one of the fundamental sources for the people in Chennai Region; also, determination of groundwater depth will be utilized for prior planning on excavation and construction purposes. This research article determines the depth of groundwater table by adopting various soft computing techniques like support vector machine (SVM), adaptive neuro-fuzzy inference system (ANFIS), and backpropagation (BP). The dataset was collected from the Tamil Nadu Water Supply and Drainage Board (TWAD), Chennai. It consists of input latitude (Lx), longitude (Ly), and the depth of groundwater (dw). The data were processed, and it was incorporated in the above-stated predicting models. The color map was the yield from the developed models. The performance of the developed model was assessed by various statistical parameters, and it was found that all the adopted models yield better performance for predicting the groundwater table. The comparison between the models depicted that support vector machine gave the promising result in determining the groundwater table with an equation for the futuristic purposes. Multifarious statistical analysis was computed in order to justify the attainment of the adopted models. The Taylor diagram and William’s plot were also included in order to substantiate the capability of the developed soft computing models to foretell the groundwater depth for the Chennai City.

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Federated Machine Learning for Cardiac Disease Detection Using Internet of Medical Things

Federated Machine Learning for Cardiac Disease Detection Usi...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Kapil Aggarwal V.R. Vimal Upasana Patil Senthilkumar C Anup Ingle T Sampath Kumar Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation (K L Deemed to be University) Vaddeswaram Guntur Andhra Pradesh India Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Thandalam Chennai India Lingaraj Appa Engineering College Bidar Karnataka India Department of ECE Sri Krishna College of Technology Coimbatore Tamilnadu India Department of Electronics and Telecommunications Vishwakarma Institute of Information Technology Pune Maharashtra India School of ComputerScience and Artificial Intelligence SR University Warangal Telangana India

The abundant availability of medical data from healthcare organizations increases the application of Machine learning (ML) techniques in medical field. Reliable and trustworthy ML models needs to be developed in order to maintain the efficacy of the healthcare providers. ML models are used extensively to make reliable disease predictions while also safeguarding the data collected through Internet of Medical Things (IoMT) devices. Federated Learning (FL) techniques are suitable for preserving IoMT data since it retains only the trained models and progresses with information from dispersed consumers. FL techniques have the caliber to drastically transform medical industry by making prompt disease diagnosis to enhance efficacy of therapy. The accuracy of these FL techniques decreases due to the massive data being exchanged between local and remote sites. In order to mitigate this issue, a novel approach FedEDFA, that combines Federated Machine learning with meta-heuristic optimization algorithm is proposed. Enhanced Dragonfly Optimization algorithm is utilized to optimally choose the relevant characteristics and employ it further for disease prediction. This approach improves the resilience of the system in insecure networking environments. The performance of the proposed FedEDFA is assessed by applying it to UCI Cleveland dataset for predicting cardiovascular disease in a secure and privacy preserving way. The accuracy of the proposed approach is 98.3% which is superior to the other existing methods taken for comparison.

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Fovea Segmentation Using Semi-Supervised Learning

Fovea Segmentation Using Semi-Supervised Learning

引用

IEEE India Conference (INDICON)

作者： Ankita Ghosh Sahil Khose Yogish S. Kamath Neetha I. R. Kuzhuppilly J. R. Harish Kumar Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Dept. of Ophthalmology Kasturba Medical College Manipal Academy of Higher Education Manipal India Dept. of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Despite the accessibility of retinal fundus images in recent years, fovea segmentation is an exacting task due to the insufficiency of labelled data. In this paper, we propose a deep learning pipeline which utilizes unlabelled data alongside labelled data for the segmentation of fovea. We train 484 labelled images using the Deeplabv3+ architecture and deploy EfficientNet-B3 as the encoder in the framework. Additionally, we introduce semi-supervised learning in our pipeline and train 1200 unlabelled images by generating their pseudo labels. We evaluate our results on the Jaccard index, Dice score, sensitivity, specificity and accuracy. Our Dice score of 82.43% and Jaccard index of 70.52% surpasses the existing methods. We obtain 91.74% sensitivity, 99.75% specificity and 99.57% accuracy.

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