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Transforming Education System through Artificial intelligence and machine Learning 3

Transforming Education System through Artificial Intelligenc...

引用

3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Tyagi, Mani Ranjan, Shiv Smiti Gupta, Anubhuti Amity University Amity Business School Greater Noida India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

We are now living in the fourth industrial revolution, in which artificial intelligence (AI) is changing every aspect of our social interactions. AI has begun to generate new teaching and learning solutions in the education sector, which are being tested in various contexts. This paper will explain the extent to which artificial intelligence (AI) can be used in the education sector and its benefits. AI has progressed from simple rule-based systems to data-driven systems with sophisticated capabilities. This paper discusses the use of artificial intelligence (AI) in education to improve teaching and learning outcomes. AI, as a new technology, will bring about various changes in the 'learning experience' by creating an adjustable learning environment that will result in a 'personalized learning experience.' We will see some examples of how AI is being used in education. © 2022 IEEE.

关键词： Embedded systems

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An AI-Centred Adaptive Cognitive Network Traffic Modeling & Measurement Technique for EIS 3

An AI-Centred Adaptive Cognitive Network Traffic Modeling & ...

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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 Ahmed Khan, Suyeb 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 Baba Kuma Singh Ji Engineering College Amritsar 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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EvauleBlock: Evaluating Answers in E-Learning Applications with Enhanced Security and intelligence through DRL and Blockchain Integration 3

EvauleBlock: Evaluating Answers in E-Learning Applications w...

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3rd international conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Parthasarathy, R. Kiruthiga Devi, M. Karthick, V. Kumar, P. Mahendran, Rakesh Kumar Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Dr. Mgr Educational and Research Institute Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350369083

Evaluating the candidate answers during online examination by a human examiner is wearisome task. Along with that, security concerns also associated in the E-Learning environment in terms of false data injection and modification attacks respectively. The state of the art work, either focus on designing intelligent and robust question answering model using machine Learning (ML)/Deep Learning (DL) algorithm, or designing a secure framework for E-Learning environment using blockchain. To neutralize that issue, we design a secure and intelligent model for E-Learning environment using Deep Reinforcement Learning (DRL) and blockchain methodology respectively named EvauleBlock. The EvauleBlock composed of four major processes such as data assortment, data pre-processing, intelligent answer evaluation, and blockchain storage. In data assortment stage, the training of the proposed DRL algorithm named Multi Agent Deep Deterministic Policy Gradient (MADDPG) is taken place by crawling the related question answering data from the various websites. Followed by, we have performed pre-processing for the candidate written answers using adequate pre-processing steps. Once the answers are pre-processed, it is then provided to the MADDPG algorithm which employs four agents for validating the candidate answers by checking the semantic, syntactic, keyword, and context similarity scoring respectively. At last, the overall scores are securely stored in the blockchain using Proof of Work (PoW) consensus algorithm. The implementation of this work is carried out using python 3.11.0 tool and performance of the proposed model are validated and compared with adequate performance metrics. The results are promising when compared to the conventional models. © 2024 IEEE.

关键词： Answer Evaluation Blockchain Deep Reinforcement Learning E-Learning Question Answering system

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Harnessing the Potential of AI and ML in Medical Tourism: Challenges, Opportunities, and Ethical Implications 3

Harnessing the Potential of AI and ML in Medical Tourism: Ch...

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3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Tripathi, Rajan Prasad Shalki Amity University in Tashkent Department of Information Technology and Engineering Tashkent Uzbekistan Amity University in Tashkent Department of Management and Social Sciences Tashkent Uzbekistan

ISBN: (纸本)9798350342338

This paper examines the emerging role of Artificial intelligence (AI) and machine Learning (ML) in enhancing medical tourism, specifically focusing on the journey of patients from Uzbekistan to India. As medical tourism flourishes, the integration of AI and ML offers unprecedented opportunities to improve patient experiences and outcomes, particularly for Uzbek patients seeking advanced medical care in India. We explore how AI and ML can revolutionize various aspects of medical tourism, including personalized patient care, accurate diagnostics, and efficient treatment planning. The study examines the use of AI-driven solutions for seamless coordination of healthcare services, allowing everything from remote consultations to post-treatment care, thereby eliminating geographical and language constraints. However, major difficulties and ethical concerns are raised by the introduction of such technology. Data privacy, algorithmic bias, and the administration of international healthcare data are among the topics examined in depth. We stress the need for strong legal and ethical frameworks to be put in place to protect patient data and provide access to quality healthcare for everyone. The purpose of this research is to provide light on the opportunities and challenges that AI and ML present for influencing the future of medical tourism between Uzbekistan and India. © 2023 IEEE.

关键词： Artificial intelligence machine Learning Medical Tourism

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Ingredients for Hybrid intelligence: Towards an Integrated Theory and Application 3

Ingredients for Hybrid Intelligence: Towards an Integrated T...

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3rd IEEE international conference on Human-machine Systems, ICHMS 2022

作者： Rockbach, Jonas D. Fuchs, Sven Witte, Thomas E.F. Bluhm, Luka-Franziska Fraunhofer Fkie Human-Machine Systems Wachtberg Germany

ISBN: (数字)9781665452380

ISBN: (纸本)9781665452380

Human-autonomy teaming aims at integrating humans with capable machine agents. In this work, we discuss a framework that is aimed at providing a theoretical basis for the integration of heterogeneous agents into a hybrid agent, i.e., a sociotechnical organism that is comprised of interacting human and artificial agents. The approach is tuned to identifying the challenges that any intelligent agent would face and translating these into designs for hybrid agent mechanisms. Specifically, an agent architecture that integrates different classes of intelligence is discussed in the context of hybrid intelligence. © 2022 IEEE.

关键词： Ergonomics

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Biases, Epistemic Filters, and Explainable Artificial intelligence 3

Biases, Epistemic Filters, and Explainable Artificial Intell...

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3rd international conference on Hybrid Human-Artificial intelligence, HHAI-WS 2024

作者： Moruzzi, Sebastiano Ferrari, Filippo Riscica, Filippo Dipartimento delle Arti Università di Bologna via Azzo Gardino 23 Bologna40126 Italy

This paper examines the role of biases and epistemic filters in Explainable AI (XAI) and Generative AI (GenAI). The increasing integration of AI into social frameworks raises questions about human-machine interaction and governance within democratic systems. The study emphasizes the importance of incorporating social epistemology to address the complexities of AI-related epistemic questions, traditionally analyzed from an individualist perspective but now requiring a social approach. It highlights the need for transparent AI explanations to assist AI-based decision-making, considering stakeholders’ biases and the effectiveness of XAI methods. The concept of epistemic filters—omission and discredit filters—is introduced to analyze how these biases impact AI outputs and user interactions. The paper also discusses the challenges of evaluating GenAI outputs and the necessity of prompt engineering skills, proposing a research agenda for employing epistemic filters to enhance XAI techniques. Ultimately, the goal is to ensure AI systems are not only technically accurate but also contextually appropriate, transparent, and fair, addressing both technical and cognitive biases. © 2024 Copyright for this paper by its authors.

关键词： Bandpass filters

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Automated Database Management System using Artificial intelligence 3

Automated Database Management System using Artificial Intell...

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3rd international conference on Electronics and Sustainable Communication Systems, ICESC 2022

作者： Sreehari, K. Anas, T.P. Krishna, A.S. Anish Mohamed Suhail, B. Mohanraj, T. Karpagam Academy of Higher Education Faculty of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (数字)9781665479714

ISBN: (纸本)9781665479714

Face recognition from an image or video is a promising biometrics research topic. Video surveillance cameras are popular in public places, and they provide important safety benefits. Since the entity does not require any assistance, the face recognition in a surveillance system has become a critical research topic. It is well-known all over the world. Face recognition outperforms other biometric recognition techniques in terms of uniqueness and validity. The human face recognition is a challenging topic in the computer vision due to its diverse and dynamic nature. Precision and recognition speed are considered as the critical challenges in this industry. The primary objective of the proposed work is to examine various face detection and identification methodologies in order to deliver a comprehensive solution for facial recognition and detection, which is more effective and has a great response in an image-based way. This gives a solution based on facial rich dataset research by incorporating imagery, attitudes, ethnicity, and colour. © 2022 IEEE.

关键词： Face recognition

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ReM-YOLO: A New Lightweight Vehicle Parts Target Detection Algorithm

ReM-YOLO: A New Lightweight Vehicle Parts Target Detection A...

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2023 international conference on pattern recognition, machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Yu, Tao Li, Lei Luo, Xu Li, Qiang National University of Defense Technology College of Intelligence Science and Technology Changsha China

ISBN: (纸本)9798350346596

In the scene of equipment maintenance, the equipment parts target detection technology can provide technical support for maintenance personnel, and lightweight algorithms based on deep learning have been much concerned, which have the advantages of strong feature extraction and short delay time. YOLOv7 is considered as a new algorithm in the YOLO series, which offers many optimized modules to improve target detection abilities. However, YOLOv7 has problems such as huge amount of computation and parameters, serious memory consumption, and the over-optimized structure. In this paper, a lightweight algorithm ReM-YOLO based on YOLOv7 is proposed to improve the network structure. YOLOv7 is improved by adding C3 blocks, MobileOne blocks and Rep-DSC blocks to reduce the model size while maintaining high precision, and a non-parameter SimAM attention module is employed to further improve the detection accuracy. Compared to YOLOv7, the ReM-YOLO has better improvements in precision and recall, and the model size is reduced by 1/3 size of YOLOv7. It has been observed that experimental tests are carried out on our dataset of vehicle engine components with the high accuracy rate of 96.2%. The improved algorithm helps further experiments about model compression effectively. © 2023 IEEE.

关键词： Feature extraction

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Ultra-Gain DC-DC Converter fed 3-Φ Inverter for Variable-Speed Drive Applications 3

Ultra-Gain DC-DC Converter fed 3-Φ Inverter for Variable-Sp...

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3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Swaroop, K.P. Duvvuri, Sssr Sarathbabu Varma, Sagiraju Dileep Kumar Garapati, Durga Prasad Koduri, Omkar Narasimharaju, V.S.N. Bhimavaram Department of Electrical & Electronics Engineering Bhimavaram534202 India Bhimavaram Department of Artificial Intelligence Bhimavaram534202 India Bhimavaram Department of Electrical & Electronics Engineering Bhimavaram534202 India

ISBN: (纸本)9798350304732

In this manuscript, a newly developed high voltage gain DC-DC converter is considered for application oriented research on variable speed AC drives. The induction machine is modeled in generalized synchronous rotating reference frame. Conventional sine and space vector pulse width modulation strategies available in the literature are chosen in the present work for generating pulses for three-phase inverter. The performance analysis of high-gain DC-DC Converter fed to inverter driven induction motor using MATLAB/Simulink 2023a environment with tabulated results. Extensive simulations are performed on two critical case studies to compare & validate that proposed strategy. © 2023 IEEE.

关键词： Induction motor Sinusoidal Pulse Width Modulation (SPWM) Space vector ultra gain

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machine Learning Model for Traffic Prediction and pattern Extraction in High-Speed Optical Networks 3rd

Machine Learning Model for Traffic Prediction and Pattern Ex...

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3rd international conference on Computing and Communication Networks, ICCCN 2023

作者： Rai, Saloni Garg, Amit Kumar Department of Electronics and Communications Deenbandhu Chhotu Ram University of Science and Technology Murthal India

ISBN: (纸本)9789819708918

The tremendous development of network traffic causes the need to develop new network applications. machine learning provides a pertinent platform to enhance currently used network optimization methods. The information about future traffic volumes is vital for the network operators. This paper presents an issue in traffic prediction and pattern extraction in high-speed optical networks and fills the literature gap by using a multiprocessor module from Python to enhance the efficiency of the work. An ML approach based on regression is designed. In the investigation, a dataset has been simulated and generated using the SNDLIB library and Python module GNPy estimation tool, which provides dynamic traffic matrices stated for various real network topologies and mimics real-world data. The performance of the proposed system is evaluated using different evaluation indexes like Mean Square Error (MSE), Mean Absolute Error (MAE), Max error (ME), and processing time based on the tuning of hyper-parameters. Outcomes of findings indicate better results compared to the existing technique. The findings confirm that proposed approach’s efficiency is better and seems to be a promising solution to the current network problem. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Python

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