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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Bharathi, L. Vasoya, Nimish H. Kansal, Lavish Gehlot, Anita Swathi, Bajila Khan, Suyeb Ahmed Ramachandra College of Engineering Department of Electronics and Communication Engineering Andhra Pradesh Eluru India Children's University Department of Student Startup Gujarat Gandhinagar382021 India Lovely Professional University Phagwara India Uttaranchal University Uttaranchal Institute of Technology India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India Hoshiar Nagar Amritsar Baba Kuma Singh Ji Engineering College India

ISBN: (纸本)9798350399264

The IoT is the foundation of the Enterprise Information System (EIS), which collects a lot of business information. For EIS, timely, accurate data analysis transfer are crucial. For net management traffic optimization in EIS, Network infrastructure traffic is crucial. Nevertheless, the needs for fine-grain traffic monitoring are in conflict with the monitoring overheads precision. Long-standing promises of artificial intelligence (AI) include the ability to recognise the physical attributes of network activity take appropriate steps in response. In order to decrease network monitoring overheads increase measurement precision, we present an AI centred between light Adapt Measuring System (ALAMM) for SDN in this study. Initially, we simulate forecast the network congestion information using AI. We suggest an adaptive strategy to choose the rate of traffic sample centred on the outcomes of traffic forecasting. Next, we instruct switching devices to evaluate the coarse-grain traffic of flow pattern networks using the measuring primitives. In order to retrieve the Fine grained (FG) assessment optimise the measured values, the matrices refilling optimizing approach is finally suggested. Simulated outcomes demonstrate that our method can accurately and efficiently determine network activity. © 2023 IEEE.

关键词： Software defined networking

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Random Matrix Theory Based Radio Frequency Fingerprinting Identification of WiFi Signal

Random Matrix Theory Based Radio Frequency Fingerprinting Id...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： You Jiang Jian Xiong Junqi Wu Bo Liu Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Student Innovation Center Shanghai Jiao Tong University Shanghai China School of Computer Science University of Technology Sydney Ultimo NSW Australia

ISBN: (数字)9798350364262

ISBN: (纸本)9798350364279

This paper introduces a method of Radio Frequency Fingerprint (RFF) recognition based on Random Matrix Theory (RMT). RFF identification is a physical layer method to identify devices that exchanging messages in the network. The extracted subtle features of the transmitters working as the biometric fingerprint can uniquely represent the identity of the devices. However, the differences in RFF become extremely subtle when transmitters are the same type of industrial standard equipment and it is very difficult to identify the devices under this scenario. In the proposed method, a random matrix of the I/Q (In-phase/Quadrature) data of bit-similar Universal Software Radio Peripheral (USRP) devices is constructed firstly, and the extracted features are sent to the Convolutional Neural Networks (CNN) network for recognition. Experiments are carried out under Signal-to-Noise Ratio (SNR) $\in[-10,20] \mathrm{dB}$ between different datasets. It demonstrates a 0.98 accuracy in identification, outweighting the traditional classification method.

关键词： Radio frequency Wireless sensor networks Accuracy Radio transmitters Fingerprint recognition Feature extraction Object recognition

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Architecture Design of B5G and 6G Millimeter-Wave Radio Access Network: Using Wireless Communications to Increase Coverage, Capacity and Performance 16

Architecture Design of B5G and 6G Millimeter-Wave Radio Acce...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Al-Jumaily, Abdulmajeed Alrubaee, Saif H. Jiménez, Víctor P. Gil Al-Saegh, Ali M. Mahmood, Ahmed A. Al-Jumeily, Dhiya Universidad Carlos Iii de Madrid Department of Signal Theory and Communications Madrid Spain University of Technology Department of Registration and Student Affairs Baghdad Iraq Middle Technical University Department of Space Technology Engineering Baghdad Iraq Universidad Carlos Iii de Madrid Department of Electrical Engineering Electronics and Automation Madrid Spain Liverpool John Moores University School of Computer Science & Mathematics Liverpool United Kingdom

ISBN: (纸本)9798350381344

In the Beyond-5G (B5G) and sixth generation (6G), several advanced techniques are based on high-frequency millimeter-wave wireless communications. This paper proposes the design of a novel B5G and 6G wireless radio access network (RAN) architecture. This architecture aims to be a key enabling factor for significantly increasing network capacity, promoting the deployment of new services, and integrating with the evolution of the previous cellular wireless access network architecture. Furthermore, the proposed architecture is adaptable to the various characteristics of high-frequency millimeter-wave communication. It leverages on the allocation of each layer resources of the different networks for more efficient deployment. © 2023 IEEE.

关键词： Millimeter waves

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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Activity Recognition in Video Frames for Enhancing Human-Robot Collaboration: A Machine Learning Perspective

Activity Recognition in Video Frames for Enhancing Human-Rob...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： Ved Prakash Chaubey Shamneesh Sharma Aman Kumar Arun Malik Praveen Malik Isha Batra Delivery and Student Success UpGrad Campus upGrad Education Private Limited Bangalore Karnataka India School of Computer Science & Engineering Lovely Professional University Punjab India School of Electronics and Communication Engineering Lovely Professional University Punjab India

Human activity recognition is a prominent area of investigation within computer vision research. Its potential applications span diverse fields such as security monitoring, healthcare, and human-computer interaction. The identification and classification of human activity through the utilization of machine learning techniques has yielded a plethora of proposed models within this dynamic field of scientific inquiry. By employing diverse kinetic models that are linked to the learning of spatial or temporal features, the characteristics of a collection of image or video data are extracted. The primary aim of this model is to identify and classify ongoing activities. This objective has been successfully achieved by various deep-layer trained models that have been implemented in this field. To fulfill the specified criteria, the proposed approach utilizes the Long Recurrent Convolutional Network (LRCN) and Long Short-Term Memory (LSTM) convolutional neural network architectures. The implementation of Vision-Based Human Activity Recognition utilizes the 3D Convolutional Neural Network (CNN) model. The model undergoes training utilizing the UCF-50 dataset. The classification of the photos by the model is contingent upon the action being performed. The model will receive videos as input, which will undergo appropriate pre-processing before being classified based on the activity. The vision-based human activity identification system offers several advantages, including but not limited to accuracy, cost-effectiveness, and user-friendliness.

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Early Detection of Forest Fire using Deep Image Neural Networks

Early Detection of Forest Fire using Deep Image Neural Netwo...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： M.K. Nallakaruppan Sofia Pillai Garima Bharadwaj Balamurugan Balusamy School of Information Technology and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Noida Institute of Engineering and Technology Delhi-NCR India Department of Electronics and Communication Engineering Noida Institute of Engineering and Technology Delhi-NCR India Student Engagement ShivNadar University Delhi-NCR India

Forest wealth is the biggest proportionality in the environmental sustainability. The forest wealth is mainly endangered by various natural calamities such as flood, hurricane, fire,volcano, land striding and so on. The forest is also threatened with man made damages such as afforestation, wild-trafficking, animal trade, illegal expansions. The human oriented calamities can be controlled with the existence of severe laws and regulations. The natural calamities are uncontrollable in nature. The only phenomena which can be controlled by human is forest fire. This proposed work provides insights on the detection of the forest fire in early stages through satellite imaging and the same is supplied to the nearest monitoring stations. In these stations we apply deep image neural network models such as InceptionV3, VGG-16, Resnet 50 and DenseNet 201 to determine the presence of forest fire or not through binary classification. The results show that the Densenet 201 has the deepest convolutional layer among all these methods, provides the accuracy of 98.46 % by outperforming all other models.

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Management of Covid-19 Detection Using Artificial Intelligence in 2020 Pandemic 21

Management of Covid-19 Detection Using Artificial Intelligen...

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5th International Conference on Medical and Health Informatics, ICMHI 2021

作者： Ghaderzadeh, Mustafa Aria, Mehrad Student Research Committee Department and Faculty of Health Information Technology and Management School of Allied Medical Science Shahid Beheshti University of Medical Sciences Tehran Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (纸本)9781450389846

Successful early detection of Covid-19 disease plays an important role in improving the effectiveness of treatment and managing the pandemic. Various diagnostic methods for the rapid detection of COVID-19 are presented. The first and most important test to detect Covid19 is the PCR test. Studies have shown that PCR testing is time-consuming, expensive, and has a large number of false negatives. As a trend in the scientific community, artificial intelligence has succeeded in Covid19 detection and diagnosis. This article identifies the key achievements reflected in the performance measurement indicators of the application of artificial intelligence algorithms in Covid-19 detection. Besides, this study discusses the finding and future lessons as a roadmap for Corona Pandemic Age. Mean diagnosis of all AI algorithms in the studies performed through Radiology modality had sensitivity with an average higher than 95% and a specificity of higher than 92%, which have a higher diagnostic rate than of traditional radiological methods. Based on relevant research in the field of diagnosis of Covid-19, the Health care managers and Specialists could be able to manage the Pandemic much better and scientifically. Equipping devices and Radiological Center by AI Algorithms and software can cause increases covid19 identification tests and hence can cover more people exposed to Diagnosing Test. This study presents a comprehensive review of Artificial Intelligence techniques and evolving deep learning techniques for Covid-19 Detection. © 2021 ACM.

关键词： Diagnosis

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Automated Emotion Recognition from Facial Expressions using Convolutional Neural Network

Automated Emotion Recognition from Facial Expressions using ...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Syed Aman Hussain Nareddy Yashwanth Reddy Junnuthula Srivardhan Ajay Sharma Shamneesh Sharma Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Education Private Limited Bangalore Karnataka India Symbiosis Institute of Digital and Telecom Management Constituent of Symbiosis International Deemed University Pune India

ISBN: (数字)9798350368710

ISBN: (纸本)9798350368727

In the era of digital communication, it is more about being able to detect emotions than anything else if we are to maintain this complicated and demanding human society. Emotion recognition research is pulling out all the stops, resulting in all manner of methods to recognize human feelings. Emotions are often transmitted through words and body language, and facial expressions play an important part in communicating them. In this paper, the use of a Convolutional Neural Network (CNN) is proposed as a real-time emotion recognition system based on machine learning algorithm, and the system is able to process video feeds. The classifier breaks emotions down into seven categories, with an accuracy of about 81 percent. In addition, the research extends to the use of various image datasets to improve emotion forecasting accuracy.

关键词： Industries Emotion recognition Accuracy Machine learning algorithms Streaming media Digital communication Real-time systems Convolutional neural networks Feeds Forecasting

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Next-Gen Assistive Technology: AI Applications for Deaf-Mute and Blind Communities

Next-Gen Assistive Technology: AI Applications for Deaf-Mute...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Tushar Bharti Shashwat Singh Ved Prakash Chaubey Shivangini Gupta Shamneesh Sharma Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Delivery and Student Success upGrad Education Private Limited Bangalore Karnataka India SIDTM Symbiosis International Deemed University Pune Maharashtra India NRI Samara State Medical University Russia

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

In today's interconnected world, communication gaps still exist between individuals with sensory impairments, specifically the blind and deaf-mute communities. This paper introduces BridgeAI, a novel assistive technology platform designed to overcome these communication barriers by leveraging cutting-edge AI technologies. BridgeAI employs Convolutional Neural Networks (CNN) for sign language recognition, speech-to-text, and text-to-speech models tailored for communication between the blind and deaf-mute individuals. The key innovation lies in the use of a custom-built dataset, enhanced through MediaPipe for real-time hand tracking, alongside an integrated Region of Interest (ROI)-based approach to improve gesture recognition accuracy. This study presents the architecture, implementation, and results of BridgeAI, which achieved a sign language recognition accuracy of 96.8%, demonstrating significant potential in improving accessibility for the blind and deaf-mute communities.

关键词： Technological innovation Sign language Accuracy Assistive technologies Real-time systems Text to speech Convolutional neural networks Artificial intelligence Informatics Speech to text

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Smart Traffic Management Solution integrating Machine Learning and Internet of Things

Smart Traffic Management Solution integrating Machine Learni...

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Innovative engineering sciences and Technological Research (ICIESTR), International Conference on

作者： Isha Batra Arun Malik Shamneesh Sharma Saikat Gochhait School of Computer Science and Engineering Lovely Professional University Punjab India Delivery and Student Success-upGrad Campus upGrad Education Private Limited Bangalore India SIDTM Symbiosis International Deemed University Pune Maharashtra India NRI Samara State Medical University Russia

ISBN: (数字)9798350348637

ISBN: (纸本)9798350348644

Increase in population and trend for luxurious lifestyle has led to huge increase in the number of vehicles. This change has further fascinated the researchers to focus on smart transportation systems. Numerous applications are offered in smart transportation systems like smart parking, detecting event of accidents, smart lights, and many more. Detecting event of accidents and informing vehicles on the move is the most prominent area of research now days. Technologies such as Internet of Things (IoT), Machine Learning (ML) can help achieve goals of smart transportation system. In this research paper, a smart architecture for accident detection and information dissemination is proposed. Initially, the data is collected using IoT sensor. After data collection, decision making is made to find out whether accident has occurred or not and the associated congestion in network using ML techniques. This information is finally disbursed to the neighboring vehicles using a secure method for data communication. The vehicles after receiving this information will decide whether to continue moving or choose some alternate path for communication. This research work emphasizes results after applying ML method and evaluates the performance of method in terms of accuracy, precision, and recall.

关键词： Support vector machines Accuracy Decision making Machine learning Vectors Smart transportation Internet of Things Vehicle dynamics Accidents Principal component analysis

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