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检索条件"机构=Artificial Intelligence and Data Science Faculty of Engineering and Technology"

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Realizing the Potential of Metaverse to Revolutionize the Future of Healthcare 2

Realizing the Potential of Metaverse to Revolutionize the Fu...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Ishtaiwi, Abdelraouf Hadi, Wael Al-Qerem, Ahmad University Of Petra Department Of Data Science And Artificial Intelligence Amman Jordan University Of Petra Department Of Information Security Amman Jordan Zarqa University Faculty Of Information Technology Computer Science Department Zarqa Jordan

ISBN: (纸本)9798350394962

The emergence of metaverse technology, underpinned by virtual reality, augmented reality, and artificial intelligence, holds profound implications for transforming healthcare. This paper reviews the current applications of metaverse in medical education and training, mental healthcare, remote patient monitoring, surgical procedures, and biomedical research. It analyzes the challenges around adopting this technology, spanning technical limitations, ethical dilemmas, threats to privacy, and regulatory gaps. Finally, it proposes strategies and future directions to harness the metaverse's potential to increase healthcare access, efficiency, personalization and democratization. This encompasses research on innovative use cases, developing ethical governance frameworks, and policy reforms to incentivize equitable metaverse adoption across the healthcare ecosystem. A nuanced, evidence-based approach can enable the metaverse to usher a new paradigm of patient-centric healthcare delivery. © 2024 IEEE.

关键词： Augmented reality

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Twitter Based Complaint Management System for Rail Transit 2

Twitter Based Complaint Management System for Rail Transit

引用

2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Saranya, K. Sudharson, D. Aathmika, M. Shakthi, Vel S Shree, Anunitha S Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore India Department of Artificial Intelligence and Data Science Kumaraguru College of Technology Coimbatore India Information Science and Engineering Department Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

People can now more easily express their opinions and ideas to a wider audience through social media platforms such as Twitter, Facebook, and Instagram. Among these social media applications, Twitter has the most trusted users and is one of the most used, with many users tweeting millions of tweets per day. Trusted users, including small and large organizations or companies, respond to customer's tweets that convey their thoughts, like complaints or suggestions, by retweeting them using the microblogging service provided by Twitter. In today's competitive world, Indian Railways manages the second largest railway system by size, which encourages users to communicate to them through tweets on Twitter. This results in a sheer volume of data and makes it impossible for the Railway Administration to answer each user's query or complaint. Sometimes these complaints may include some urgent matters or questions requiring prompt attention. The proposed method focuses on implementing a website where the tweets are classified into different categories based on their temporal nature and are prioritized in cases of emergency. This proposed solution helps the railway administration to easily reply to the tweets and know their quality of service based on the users' feedback. © 2023 IEEE.

关键词： Quality of service

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Brain-Powered Vehicles for Individuals with Disabilities Leveraging AI 4

Brain-Powered Vehicles for Individuals with Disabilities Lev...

引用

4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Jaison, T. Jisna Thambi, Priyanka E. Pinheiro, Nikita Scms School of Engineering and Technology Dept. of Artificial Intelligence & Data Science Karukutty India Kalamasery India St. Paul's College Dept. of Computer Science Kalamasery India

ISBN: (纸本)9798350387490

This paper provides a novel method for enhancing mobility and independence for people with disabilities via integrating Brain-pc interface (BCI) generation with artificial intelligence (AI) for vehicle control. The proposed device permits customers to control important vehicle features which includes steerage, braking, and acceleration the usage of their Brain indicators, captured through non-invasive EEG sensors. Preliminary checking out with diverse person agencies established an outstanding accuracy price of over ninety% in deciphering person intentions and efficaciously controlling the automobile. The device adapts to person neural styles, ensuring personalized and dynamic control, and extensively improves person autonomy and self-assurance. However, demanding situations which include sign noise, user variability, and long-time period device stability stay. This studies demonstrates the capacity for BCIs and AI to revolutionize transportation for individuals with disabilities, supplying an exceptional level of independence. Future trends will focus on improving signal clarity, improving machine stability, and addressing regulatory issues to make sure safe, real-world application. © 2024 IEEE.

关键词： Neurons

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Demystifying the Role of Big data Analytics in IIoT-Based Connected Railways 3

Demystifying the Role of Big Data Analytics in IIoT-Based Co...

引用

3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Gupta, Navya Narwal, Bhawna Batra, Shivangi Gera, Pooja Bhati, Pooja Department of Artificial Intelligence and Data Science Delhi India Department of Information Technology Delhi India Department of Mechanical and Automation Engineering Delhi India

ISBN: (纸本)9798350330724

Recent advances in sensors, networks, and hardware/software platforms have facilitated the development of Industrial Internet of Things (IIoT) applications in railway transportation. Nevertheless, the limited coverage and battery life of IIoT devices remain significant hurdles in realizing realistic service implementations. Against this backdrop, this study aims to scrutinize the impact of Industry 4.0 and big data analytics on railway engineering, considering factors such as post-COVID requisites and the security and privacy of rail users. Additionally, the research investigates the application of these ideas in India, which presents a challenging environment, and examines potential avenues for future research. © 2023 IEEE.

关键词： Big data

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Automated Image Captioning and Speech Synthesis 2

Automated Image Captioning and Speech Synthesis

引用

2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Mankawade, Amruta Pavitha, N. Vpatil, Amruta Vishwakarma Institute of Technology Department of Artificial Intelligence and Data Science India Vishwakarma University Department of Computer Engineering India Sjjtu Rajasthan India

ISBN: (纸本)9798350308846

An innovative system that creates natural language captions for supplied photos and transforms them to voice for increased accessibility is Automated Image Captioning and voice Synthesis. The suggested system extracts important information from the image and generates a relevant and coherent caption using cutting-edge technologies of deep learning like Convolutional Neural Networks & Recurrent Neural Networks. Text-to-speech synthesis techniques are then used to translate the generated captions to speech, allowing visually challenged users to access the material. The suggested approach was tested using typical benchmark datasets and yielded encouraging results. Because the system can create natural language captions and convert them to voice, it May be used in diverse applications, consisting of media accessibility & assistive technology. © 2023 IEEE.

关键词： Deep learning Image captioning LSTM RNN

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Design of Lightweight Strong Arbiter PUF Circuit Based on MOSFET Threshold Loss 15

Design of Lightweight Strong Arbiter PUF Circuit Based on MO...

引用

15th IEEE International Conference on ASIC, ASICON 2023

作者： Shao, Xilong Ma, Xuejiao Li, Gang Wenzhou University College of Electrical and Electronic Engineering Wenzhou China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou China

ISBN: (纸本)9798350312980

Arbiter Physical Unclonable Function (APUF), as a typical representative of strong PUF, suffers from limitations in hardware cost and randomness, severely constraining its application in the field of resource-constrained Internet of Things (IoT). This paper investigated the mechanism of MOSFET threshold loss and proposed a lightweight strong APUF structure of two kinds of MOSFETs. This structure utilizes the threshold loss that occurs when PMOS transmits low level and NMOS transmits high level, greatly extending the delay deviation of the rising and falling edges, thus enhancing the randomness of the PUF and passing 9 NIST randomness subtests. Each delay unit of the two proposed PUF circuits consists of only 16 MOS transistors, which is 8 transistors less than the delay unit of APUF. The full-custom layout area of the proposed delay unit is 4.68 μm2 in TSMC 65 nm process, reducing the area by 35.8% compared to the traditional APUF. © 2023 IEEE.

关键词： APUF Hardware Security Physical Unclonable Function Threshold Loss

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Supervised Satellite Image Retrieval using Defined Patterned with ASMC 2

Supervised Satellite Image Retrieval using Defined Patterned...

引用

2nd Edition of IEEE Delhi Section Owned Conference, DELCON 2023

作者： Gaikwad, Rohini Siddiqui, Rizwana Sies Graduate School of Technology Artificial Intelligence & Data Science Navi Mumbai India Sies Graduate School of Technology Computer Engineering Navi Mumbai India

ISBN: (纸本)9798350322064

It is suggested to use remotely sensed image retrieval with query-by-example approach. This often entails using query techniques that permit descriptive semantics, queries that incorporate user feedback, machine learning-capable systems, and systems that can gauge user satisfaction. This study uses the Advanced Split and Merge Clustering approach to extract features from the textures of similar grayscale images and the spectral distribution using two-dimensional Gabor functions. The resulting pattern will be the concatenation of texture features and Spectral Distribution (SD). Modified Earth Mover's Distance (EMD) is used to limit how much of each vector can be predefined in pieces. EMD technique for establishing center correspondence between the patterns uses interactive search. © 2023 IEEE.

关键词： Textures

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Soft Sensor Development Based on Large-Scale Pseudo Labeling Optimization 13

Soft Sensor Development Based on Large-Scale Pseudo Labeling...

引用

13th IEEE data Driven Control and Learning Systems Conference, DDCLS 2024

作者： Liu, Guangkun Jin, Huaiping Wang, Bin Yang, Biao Qian, Bin Kunming University of Science and Technology Faculty of Information Engineering and Automation Department of Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming650500 China

ISBN: (纸本)9798350361674

data-driven soft sensor techniques enable online prediction of key quality variables. However, in real industry, the expensive cost of manual labeling leads to the abundance of unlabeled data and the scarcity of labeled data, which becomes a major obstacle to establishing high-precision data-driven soft sensing. To address this problem, a method based on large-scale pseudo-labeling optimization, LSPLO, is proposed. This method, by introducing confidence assessment, fixes the pseudo labels assessed as high-confidence during optimization, while the low-confidence pseudo labels are used as new decision variables after dimensionality reduction to continue optimization. The original decision variables are reinitialized using superior pseudo labels, and stepwise dimensionality reduction and optimization are performed to obtain the optimal pseudo labels. The superiority of the proposed method LSPLO is demonstrated by a real industrial case of the Chlortetracycline (CTC) fermentation process. © 2024 IEEE.

关键词： Dimensionality reduction

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems 9

Graph Neural Networks for Optimizing Reliability in Vehicle-...

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Neelamegam, G. Revathy, J. Kannukkiniyal, M. Devendran, M. Akshya, J. Choudhry, Mani Deepak Vsb College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Christ the King Engineering College Department of Artificial Intelligence and Data Science Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India Hindustan institute of technology Department of Computer Science and Engineering Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798350377972

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models;it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data. © 2024 IEEE.

关键词： Graph neural networks

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CNN Based Plant Leaf Disease Detection Using Raw Leaf Images for Efficient Plant Health Monitoring in Agricultural IoT

CNN Based Plant Leaf Disease Detection Using Raw Leaf Images...

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2024 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Shibu, Pooja Reddy, Gangireddy Narendra Kumar St.Joseph College of Engineering and Technology Department of Artificial Intelligence and Data Science Kerala Palai686579 India National Institute of Technology Calicut Department of ECE Kerala Kozhikode673601 India

ISBN: (纸本)9798350376135

Agriculture is a major economic driver of both the high and low income countries across the globe. Hence it is important to modernize agricultural practices by incorporating modern technological developments as a continuous process. Among the various challenges, plant leaf diseases pose a significant threat, leading to reduced agricultural yields and economic losses. Early detection of these diseases is crucial for timely intervention, yet traditional methods relying on human visual inspection are often delayed, incomplete, and unreliable. To address this, our study employs a Convolutional Neural Network (CNN) to analyze plant leaf images taken from New plant dataset, resulting in a highly accurate model for detecting various leaf diseases of Apple, Potato, Strawberry and Tomato. The developed CNN model achieved an accuracy of 90.91%, offering a promising tool for improving agricultural productivity by minimizing disease-induced crop damage. © 2024 IEEE.

关键词： Agricultural economics

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