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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Uribe-Quevedo, Alvaro Arevalo, Camilo Villegas, Julián Sun, Winnie Ontario Tech University Faculty of Business and It OshawaON Canada University of Aizu Department of Computer Science and Engineering Fukushima Aizuwakamatsu Japan Ontario Tech University Faculty of Health Sciences OshawaON Canada

ISBN: (纸本)9798350340181

Reminiscence Therapy (RT) is a non-pharmacological treatment focused on helping individuals with dementia recollect memories through their senses. Traditionally, RT therapy has relied on multimedia, where caregivers choose videos, pictures, and audio files. Since cross-sensory stimuli play an important role during RT, the adoption of Virtual Reality (VR) has spiked as the technology becomes commonplace. Our previous work has focused on prototyping a VR RT tool through co-design and usability testing, utilizing hand tracking for ease of use. In this research, we aim to further understand usability, presence, and cognitive load effects by comparing a 2D Graphical User Interface (GUI), a radio-style 3D User Interface (3DUI), a curved-style 3DUI, and a spatial audio User Interface UI that allows playing, stopping, and adjusting the volume of an audio file. Our preliminary study indicates that a traditional 2D GUI has higher usability, and lower cognitive load, while a curved-style 3DUI with 3D push buttons and a slider for volume around the user has a higher presence than the radio-style 3DUI and the spatial audio UI. © 2023 IEEE.

关键词： Virtual reality

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An Authentication Based Scheme for Mobile Applications Using THJWT

An Authentication Based Scheme for Mobile Applications Using...

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2022 Workshop on computer Networks and Communications, WCNC 2022

作者： Chopra, Sarvesh Singh, Amritpal Singh, Aman Department of Computer Science Lovely Professional University Punjab India Faculty of Engineering Universidade Bie Kuito Angola

Information and Communication Technology (ICT) integrates multiple devices and applications that are connected directly to the Internet communications. The development of customer server systems in technology, such as IoT based, cloud based, and smart homes systems is growing rapidly in the current era of technology. User authentication is an important concern for these applications. THJWT i.e., Tuned Hybrid JSON Web Token for JWT-based properties has been particularly evident in the recent growth of client server requests. JWTs (JSON Web Token) are used for authentication of subsequent customer requests without making regular calls to a resource server or database. In this paper, we have introduced the process of authenticating and verifying JWT on each client request based on the random stamp values to verify client authentication on the server data. The effectiveness of the proposed approach is enhanced by case studies that demonstrate time and space complexity. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Authentication

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Wireless control of a D.C. Motor Used for Driving a Conveyor Belt

Wireless control of a D.C. Motor Used for Driving a Conveyor...

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2024 International Conference on Circuit, systems and Communication, ICCSC 2024

作者： Livint, Petru Banu, Ioan-Viorel Culea, George Manale, Bouderbala Majout, Btissam Dusa, Alexandru University 'Vasile Alecsandri' of Bacau Department of Energetics and Science Computer Romania Sidi Mohammed Ben Abdellah University Fez Limas Laboratory Faculty of Sciences Dhar El Mahraz Morocco Sidi Mohammed Ben Abdellah University Fez Laboratory of Engineering Modellingand Systems Analysis Morocco 'Dunarea de Jos' University of Galati Department of Automatic and Electrical Engineering Romania

ISBN: (纸本)9798350365306

This article presents an experimental stand for the remote control of a D.C. motor used to drive a conveyor belt. This is based on two development boards Arduino Uno and two shields NRF24L01. The D. C. motor is supplied from a D.C. voltage source by means of the H type bridge. The emitter circuit sends the control signal generated through a potentiometer to the receptor circuit by means of radio waves at a frequency of 2.4 GHz. The receptor circuit receives the analogue signal emitted by the emitting circuit, transforms it into a PWM (pulse width modulation) signal and uses it for controlling the transistors of the H-bridge. For implementing and validating the remote control system of the D.C. motor two programs have been issued in the programming environment Arduino IDE for the two circuits. emitter and receptor. The remote control system of the D.C. motor has been running in the same way as the direct control of this. The remote control is used in the case of the electric actuation systems made with D.C. motors continuous used in spaces inaccessible to the human operator. © 2024 IEEE.

关键词： Belts

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Design and Implementation of Telemedicine System Using Light Fidelity and PIC16F877A Microcontroller 14

Design and Implementation of Telemedicine System Using Light...

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14th International Conference on Information science and Technology, ICIST 2024

作者： Ezilarasan, M.R. Leung, Man-Fai Dai, Xiangguang Vel Tech Rangarajan Dr. Sagunthala R and D Instituteof Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computing and Information Science Anglia Ruskin University Faculty of Science and Engineering Cambridge United Kingdom Information Processing and Control of Chongqing Municipal Institutions of Higher Education Chongqing Three Gorges University Key Laboratory of Intelligent Chongqing Wanzhou404100 China

ISBN: (纸本)9798350353334

Medical Body Area Networks (MBANs) increasingly utilize wireless communication technologies to enhance the freedom and convenience for patients and caregivers. Currently, radio frequency (RF) is the prevalent medium in healthcare applications;however, the potential for electromagnetic interference with precision medical equipment remains a concern. This study introduces a novel design and implementation of a medical healthcare information system using the newly developed Wireless Visible Light Communication (VLC) technology, also known as Light Fidelity (Li-Fi). Visible Light Emitting Diodes (LEDs), expected to soon surpass traditional incandescent and fluorescent bulbs as the primary light source due to their energy efficiency, facilitate this technology. In this research, patient health is monitored using sensors, with results displayed on an LCD. An LED lightbulb transmits the data as light signals, which are captured by a photo-detector at the receiver's end. This setup not only displays the output on an LCD but also alerts caregivers through an auditory signal, beeping every 15 seconds for respiration and every 30 seconds for heart rate monitoring. Moreover, these lighting fixtures serve dual functions: illumination and wireless data transmission, using the rapid switching capability of LEDs. This prototype VLC-based system offers a promising alternative for data services and monitoring in hospital areas where RF use is restricted. © 2024 IEEE.

关键词： Liquid crystal displays

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Optimized Siamese Humboldt Squid Attention Network Architecture for Cyber-Attack Detection in IoT 4.0 4

Optimized Siamese Humboldt Squid Attention Network Architect...

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4th International Conference on Sustainable Expert systems, ICSES 2024

作者： Mohamed, M. Varusai Nainwal, Ankita Ayshwarya, B. Kumar, H.S. Rabadiya, Dhaval Taqui, Syed Noeman Department of Computer Application Tamil Nadu Chennai600048 India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun248 002 India Kristu Jayanti College Department of Computer science Karnataka Bengaluru560043 India Department of Mechanical Engineering Karnataka Nitte India Parul University Faculty of Management studies P.O. Limda Ta. Waghodia Gujarat Vadodara391760 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

The rapid expansion of IoT within Industry 4.0 has heightened the risk of cyber-attacks, underscoring the need for advanced detection mechanisms. Existing cyber-attack detection systems in this context often struggle with reduced accuracy, high computational time, complexity, and elevated error rates. This study addresses these challenges by proposing an Optimized Siamese Humboldt Squid Attention Network (OSH-SqAtNet) architecture for cyber-attack detection in IoT Industry 4.0 environments. The input data, sourced from datasets like CSE-CIC-IDS2018, undergoes preprocessing via a Mixture of Conventional Fast Bilateral Filter (MC2FB) to enhance data quality. A Gold Rush Optimizer (GRO) is then employed for feature selection, isolating the most significant features to optimize classification performance. The preprocessed and optimized data are classified using the OSH-SqAtN et architecture for accurate cyber-attack detection. For secure data transmission, Rabin Fuzzy Cryptosystem assisted Elliptic Curve Cryptography (RFC-ECC) is utilized. Experimental simulations conducted on the Python platform reveal that the proposed method outperforms existing techniques, achieving higher accuracy (98%) and recall (0.993%). These results demonstrate the approach's superior efficacy and potential for advancing cybersecurity in IoT Industry 4.0. © 2024 IEEE.

关键词： Shellfish

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Streamlining Information Capture and Retrieval with AI-Enabled systems 3

Streamlining Information Capture and Retrieval with AI-Enabl...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sudhamsu, Gadug Agarwal, Ankita Dhivrani, Rekha Bhanu, K.S. Jeyalaxmi, M. Nawadkar, Ashwini R. Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Atlas SkillTech University Department of UGDX Maharastra Mumbai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

artificial intelligence (AI) is an emerging era that has proven to have a fantastic capacity to revolutionize how facts are captured and retrieved. AI-enabled systems offer an expansion of abilities to streamline the system of accumulating, managing, storing, and retrieving data. AI-enabled structures provide several benefits, such as using predictive analytics and herbal Language Processing (NLP) to extract records more effectively and correctly and automated pass-referencing competencies to simplify locating records. AI-enabled systems make indexing and saving statistics easier, allowing users to look for the relevant data they need quickly. AI-enabled structures offer higher security measures and encryption techniques for defensive touchy facts. Functions like facial recognition, gadget mastering algorithms, and get admission to hold data safe and far from malicious actors. Such systems may control and store statistics securely, correctly, and managed. AI-enabled systems provide an excellent suite of features for streamlining the manner of seizure and retrieval of statistics. AI answers, including predictive analytics, natural language processing, and automated cross-referencing, allow groups to quickly get the right of entry to the relevant information they need, even by providing higher safety features for included facts. In destiny, this technology might also grow to be even greater advanced, assisting businesses in gaining facts to electricity predictive analytics, helping in advertising efforts, and enhancing the organizational operation. © 2023 IEEE.

关键词： information organizational predictive processing security

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Profile face recognition based on elements by normalizing global and local features 8

Profile face recognition based on elements by normalizing gl...

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8th International Iranian Conference on Signal Processing and Intelligent systems, ICSPIS 2022

作者： Khoshnevisan, Erfan Hassanpour, Hamid Alyannezhadi, Mohammad. M. Shahrood University of Technology Faculty of Computer Engineering Iran Unicerversity of Science and Technology of Mazandaran Department of Mathematics Iran

ISBN: (纸本)9781665476232

Despite recent advances in face recognition using deep learning, pose changes are still one of the challenging problems. In this paper, we presented a method to normalize the image in the feature space by capturing local consistency during training. we used facial elements such as eyes, lips, nose, and hairstyle in addition to its general state for recognition. For this purpose, all the elements were detected and localized by semantic segmentation and then normalized. Specifically, an Attention-based convolutional neural network is introduced to integrate local features with their long-range dependencies yielding better feature representations and hence generate representation that preserves identities better, especially for larger pose angles. We used the face/head segmentation dataset for training and the FERET dataset for testing, and the results were good for images with the most difficult angular conditions. The performance of this model on the +90° images of the FERET dataset for 980 samples was 49.6%compared to VGGFace which was only 2.6% accurate. © 2022 IEEE.

关键词： Face recognition

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IT'S ALL IN THE EMBEDDING! FAKE NEWS DETECTION USING DOCUMENT EMBEDDINGS

arXiv

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arXiv 2023年

作者： Truicǎ, Ciprian-Octavian Apostol, Elena-Simona Computer Science and Engineering Department Faculty of Automatic Control and Computers University Politehnica of Bucharest Bucharest Romania

With the current shift in the mass media landscape from journalistic rigor to social media, personalized social media is becoming the new norm. Although the digitalization progress of the media brings many advantages, it also increases the risk of spreading disinformation, misinformation, and malformation through the use of fake news. The emergence of this harmful phenomenon has managed to polarize society and manipulate public opinion on particular topics, e.g., elections, vaccinations, etc. Such information propagated on social media can distort public perceptions and generate social unrest while lacking the rigor of traditional journalism. Natural Language Processing and Machine Learning techniques are essential for developing efficient tools that can detect fake news. Models that use the context of textual data are essential for resolving the fake news detection problem, as they manage to encode linguistic features within the vector representation of words. In this paper, we propose a new approach that uses document embeddings to build multiple models that accurately label news articles as reliable or fake. We also present a benchmark on different architectures that detect fake news using binary or multi-labeled classification. We evaluated the models on five large news corpora using accuracy, precision, and recall. We obtained better results than more complex state-of-the-art Deep Neural Network models. We observe that the most important factor for obtaining high accuracy is the document encoding, not the classification model's complexity. © 2023, CC BY-NC-ND.

关键词： Embeddings

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Urban traffic flow management on large scale using an improved ACO for a road transportation system

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International Journal of Intelligent Computing and Cybernetics 2023年第4期16卷 766-799页

作者： Somia Boubedra Cherif Tolba Pietro Manzoni Djamila Beddiar Youcef Zennir Department of Computer Science Faculty of Engineering SciencesUniversity of Badji Mokhtar AnnabaAnnabaAlgeria Universitat Politecnica de Valencia ValenciaSpain University of Oulu OuluFinland 20 August 1955 University of Skikda SkikdaAlgeria

Purpose:With the demographic increase,especially in big cities,heavy traffic,traffic congestion,road accidents and augmented pollution levels hamper transportation *** the optimal routes in urban scenarios is very challenging since it should consider reducing traffic jams,optimizing travel time,decreasing fuel consumption and reducing pollution levels *** this regard,the authors propose an enhanced approach based on the Ant Colony algorithm that allows vehicle drivers to search for optimal routes in urban areas from different perspectives,such as shortness and ***/methodology/approach:An improved ant colony algorithm(ACO)is used to calculate the optimal routes in an urban road network by adopting an elitism strategy,a random search approach and a flexible pheromone deposit-evaporate *** addition,the authors make a trade-off between route length,travel time and congestion ***:Experimental tests show that the routes found using the proposed algorithm improved the quality of the results by 30%in comparison with the ACO *** addition,the authors maintain a level of accuracy between 0.9 and ***,the overall cost of the found solutions decreased from 67 to *** addition,the experimental results demonstrate that the authors’improved algorithm outperforms not only the original ACO algorithm but also popular meta-heuristic algorithms such as the genetic algorithm(GA)and particle swarm optimization(PSO)in terms of reducing travel costs and improving overall fitness ***/value:The proposed improvements to the ACO to search for optimal paths for urban roads include incorporating multiple factors,such as travel length,time and congestion level,into the route selection ***,random search,elitism strategy and flexible pheromone updating rules are proposed to consider the dynamic changes in road network conditions and make the proposed approach more relevant and *** e

关键词： Urban traffic Routing problem Transportation networks Optimal path IT for transportation ACO meta-heuristic Nature-inspired computing Paper type Research

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A non-Restraining Sheep Activity Detection and Surveillance using Deep Machine Learning 16

A non-Restraining Sheep Activity Detection and Surveillance ...

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

作者： Ayub, Muhammad Yaseen Hussain, Abir Hassan, Muhammad Furqan Ul Khan, Bilal Khan, Farman Ali Al-Jumeily, Dhiya Khan, Wasiq Comsats University Islamabad Department of Computer Science Attock Campus Pakistan Sharjah University College of Engineering United Arab Emirates California State University School of Computer Science & Eng San BernardinoCA United States Liverpool John Moores University Faculty of Engineering & Technology Liverpool United Kingdom

ISBN: (纸本)9798350381344

The number of livestock farms and their sizes (particularly the sheep farms) are on the rise, in response to the growing demands of food supply chain for increasing population. The detection and monitoring of sheep activities particularly in huge farms is tedious and challenging task. Therefore, a reliable and cost-effective sheep activity detection system which can be utilized for the virtual fencing, is of high demand. Existing data-driven approaches use accelerometer data for sheep monitoring and activity detection however, there are several limitations with these methods such as generating high volume of data with noise, relatively expensive, and not very reliable. This study presents a non-invasive computer vision-based approach along with deep transfer learning for sheep detection and determining whether the corresponding state is 'active' or 'inactive'. We complied a primary dataset comprising sheep in diverse poses and activities in a realistic outdoor environment. A custom YOLOV5s model is trained over new dataset and validated on purely unseen sheep instances for the model evaluation. The statistical outcomes demonstrate the robustness of proposed approach for various sheep activity detection. Our method has diverse implications and uses in the development of reliable and economical systems for monitoring sheep, particularly in extensive farms and virtual fencing applications. © 2023 IEEE.

关键词： Deep learning

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