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The Role of Machine Learning in Equipping Students with Emerging Opportunities

The Role of Machine Learning in Equipping Students with Emer...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Sengar, Alok Singh Bahadure, Nilesh Bhaskarrao Mishra, Amit Kumar Chaudhari, Vaibhav Pandey, Neeraj Kumar Dumka, Ankur Noida International University School of Sciences Department of Computer Science Greater Noida India Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Nutanix Technologies India Pvt Ltd Technical Staff-2 Karnataka Bengaluru India Graphic Era Deemed to be University Dept. of Computer Science & Engineering Dehradun India Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

The growth of the machine learning (ML) process is crucial to evaluate the basic influence on the growth of students' life. Main advantage of using ML in education is that it can be used to generate individualized learning programs that meet the desires of each learner. Large-scale information on student performance, learning preferences, and styles may be examined by ML algorithms to build personalized learning paths. This level of personalization accounts for each learner's unique learning style and pace to ensure that they receive just the appropriate amount of challenge and support. For instance, adaptive learning platforms with machine learning abilities can maximize student engagement and learning efficiency by dynamically modifying the material trouble in response to real-time student performance. Another way that machine learning is being incorporated into education is through the presence of relevant, up-to-date knowledge to the syllabus that tracks business trends. The need for expertise in fields like data analytics, cybersecurity, and artificial intelligence is rising as the labor market changes. Educational institutions may identify growing skill requirements and modify their courses accordingly by regularly analyzing labor market data with the use of ML. By ensuring that students possess the abilities that employers value, they increase their competitiveness and adaptability in the labor market. There are some different prospectuses that allows determining the basic impact of the motivated student's aspects to the improvement of their studies. In addition, maintaining appropriate ideas in the machine learning process allows for the organization of some better approaches where the student's activity is analyzed appropriately. All of the opportunities defined in the machine learning process are determined basic approach which is described the student's improvement in their growth process. There is the basic approach where the impact of the machine learni

关键词： Federated learning

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Automated Brain Tumor Detection Using Soft Computing-Based Segmentation Technique 3

Automated Brain Tumor Detection Using Soft Computing-Based S...

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3rd International Conference on Computing and Information Technology, ICCIT 2023

作者： Zubair, Muhammad Umair, Muhammad Owais, Muhammad University of Central Punjab Faculty of Information Technology & Computer Science Lahore Pakistan Khalifa University Kucars C2PS Dept of Electrical Engineering & Computer Science United Arab Emirates

ISBN: (纸本)9798350321487

Development and growth of abnormal cells within the brain results in brain tumor. In this study, a novel segmentation methodology is proposed for the segmentation of tumor. The proposed model consists of two phases. In the first phase, the brain CT image from the medical database is pre-processed to remove artifacts and noise. For Image segmentation, a Hierarchical Self Organizing Map (HSOM) is used that provides promising segmentation results. The conformist Self Organizing Map (SOM), which was used to categorize the picture row by row, is extended by the HSOM. Thus, the HSOM with vector quantization speeds up calculation at this lowest level of the weight vector, where there are more tumor pixels. The proposed automated system is tested on Kaggle (online available) database and achieves an accuracy of 98.94%. © 2023 IEEE.

关键词： Self organizing maps

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LSTM-Based Prediction of Active Earth Pressure Coefficient Against Rigid Retaining Walls 8

LSTM-Based Prediction of Active Earth Pressure Coefficient A...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Salima, Attache Mohamed, Sayah Labib, Terrissa Sadek Ikram, Maouche Noureddine, Zerhouni University of Biskra Dept. Hydraulic and Civil Engineering Biskra Algeria University of Oran1 Dept. Computer Science Oran Algeria University of Biskra Dept. Computer Science Biskra Algeria UMR6174 Dept. AS2M CNRS Femto-st Besançon France

ISBN: (纸本)9798350309249

The application of a Long Short-Term Memory (LSTM) neural networks have been explored in this study to predict the active earth pressure coefficient (AEPC). The AEPC is a critical parameter in geotechnical engineering for designing retaining structures. Traditional methods rely on empirical formulas and manual calculations, which may lack accuracy and efficiency. By leveraging the capabilities of LSTM neural networks, which are well-suited for sequential data analysis and long-term dependencies, this research aims to develop a more accurate and reliable prediction model. The performance of the LSTM model is evaluated based on metrics such as Mean Squared Error (MSE) and determination coefficient (R2), and score function (S) to ensure its predictive accuracy. The obtained experimental results (MSE = 0.0003, R2 = 0.97, and S = 0.0014), even for a limited number for training epochs, demonstrate that the LSTM neural networks can effectively outperforms and predict the active earth pressure coefficient with high precision compared to conventional methods widely used in this field. © 2024 IEEE.

关键词： Forecasting

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Accurate Recurrent Neural Network for Active Earth Pressure Coefficient Estimation

Accurate Recurrent Neural Network for Active Earth Pressure ...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Salima, Attache Mohamed, Sayah Sadek Labib, Terrissa Ikram, Maouche Noureddine, Zerhouni University of Biskra Dept. Hydraulic and Civil Engineering Biskra Algeria University of Oran1 Dept. Computer Science Oran Algeria University of Biskra Dept. Computer Science Biskra Algeria UMR6174 Cnrs Femto-st Dept. AS2M Beançon France

ISBN: (纸本)9798350372229

Compared to traditional civil engineering approaches, artificial intelligence (AI) applied to data-centric systems has shown extraordinary predictive ca-pabilities. As a result, Artificial Intelligence (AI) is frequently used to model the complexity of behavior of a large number of geotechnical engineering materials. This study proposes a new recurrent neural networks (RNN) model to predict accurately the lateral earth pressure coefficient Kaγ of a retaining wall for different geometric configurations on frictional soil. The performance of the RNN model has been assessed using the determination coefficient and mean square error. The results obtained are contrasted with those obtained by the analytical and numerical methods. It has been demonstrated that our model gives more accurate outcomes than analytical and numerical techniques with a precision of up to 10-4 © 2024 IEEE.

关键词： active earth pressure coefficient deep learning predictive modeling recurrent neural network retaining wall

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A LLC Step-up PV Micro-converter With An Integrated High-Frequency AC Inter-Connected Energy Storage Unidirectional Power Interface

A LLC Step-up PV Micro-converter With An Integrated High-Fre...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Besharra, Maria Moury, Sanjida Kanathipan, Kajanan Goel, Nishith Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada Lakehead University Dept. of Electrical Engineering and Computer Science Barrie Canada Research and Development Cistech Limited OttawaONK2E 7V7 Canada

ISBN: (纸本)9798350316445

This paper presents an LLC photovoltaic (PV) step-up converter with an integrated high-frequency (HF) AC-link power interface for distributed energy storage (such as a battery) structure. The proposed converter consists of a unidirectional battery charging and discharging circuits that are either coupled or HF inter-connected with the main PV step-up LLC converter. This configuration directly feeds surplus PV energy to the battery via a single-stage DC/DC conversion process. The descriptions of the proposed power interface and the circuit analysis are provided in this paper. Results on a 600W, 500kHz converter for a 400V DC grid and 96V battery module as well as preliminary hardware results on a 100W, 500kHz 400V DC grid and 48V battery prototype, are provided to highlight the features of the proposed work. © 2023 IEEE.

关键词： energy storage high frequency AC-link Photovoltaic (PV) soft switching

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Online Hierarchical Conformance Refinement Planning for Autonomous Robots 28

Online Hierarchical Conformance Refinement Planning for Auto...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Kamperis, Oliver Michael Wang, Yongjing Castellani, Marco University of Birmingham Dept. of Computer Science BirminghamB15 2TT United Kingdom University of Birmingham Dept. of Mechanical Engineering BirminghamB15 2TT United Kingdom

ISBN: (纸本)9798350335859

Planning is a fundamental reasoning ability required by any general-purpose autonomous robot that will be deployed into real-world domains. This paper presents our ongoing work towards a novel domain-independent planning paradigm, called Hierarchical Conformance Refinement (HCR), capable of rapid online partial-planning (that is planning partly interleaved with execution), which aims to maximize productivity and minimize down-times of robots. HCR planning achieves this by employing two core concepts involved in human decision making;abstract reasoning and problem decomposition. Our approach combines the expressive power of Answer Set Programming (ASP) with the speed of hierarchical refinement planning. Together, these allow large and complex problems with long plan lengths, to be broken down and solved as many smaller and more manageable partial-problems, in a general way. This exponentially reduces execution latency and total planning times, for the trade-off of small losses in plan quality. Simulated experiments ran on an extension of the blocks world domain show that HCR planning reduced overall planning time by between 43.8 to 97.6 %, and execution latency time by 87.1 to 98.7%, over classical ASP based planning, but could increase plans lengths by 0.0 to 9.0% over the minimum possible length, from our easiest to hardest problems tested. © 2023 IEEE.

关键词： Decision making

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Direct Electrostriction Measurement using SPM for Fiber Type Identification

Direct Electrostriction Measurement using SPM for Fiber Type...

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2023 Optical Fiber Communications Conference and Exhibition, OFC 2023

作者： Al-Shaikhli, Fatima O'Sullivan, Maurice Hui, Rongqing University of Kansas Dept. of Electrical Engineering and Computer Science LawrenceKS66045 United States Ciena Corporation OttawaK2K 0L1 Canada

ISBN: (纸本)9781957171180

关键词： Electrostriction

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Recurrent 3-D Multi-Level Visual Transformer For Joint Classification of Heterogeneous 2-d AND 3-D Radiographic Data

Recurrent 3-D Multi-Level Visual Transformer For Joint Class...

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IEEE International Conference on Image Processing

作者： Muhammad Owais Muhammad Zubair Taimur Hassan Divya Velayudhan Irfan Hussain Naoufel Werghi Dept. of Mechanical and Nuclear Engineering KUCARSDept Khalifa University UAE Faculty of IT & Computer Science University of Central Punjab Pakistan Dept.of Electrical Computer and Biomedical Engineering Abu Dhabi University UAE Dept.of Computer Science C2PS Khalifa University UAE

ISBN: (数字)9798350349399

ISBN: (纸本)9798350349405

Recent advancements in artificial intelligence algorithms for medical imaging show significant potential in automating the detection of lung infections from chest radiograph scans. However, current approaches often focus solely on either 2-D or 3-D scans, failing to leverage the combined advantages of both modalities. Moreover, conventional slice-based methods place a manual burden on radiologists for slice selection. To overcome these challenges, we propose the Recurrent 3-D Multi-level Vision Transformer (R3DM-ViT) model, capable of handling multimodal data to enhance diagnostic accuracy. Our quantitative evaluations demonstrate that R3DM-ViT surpasses existing methods, achieving an impressive accuracy of $96.67 \%$, F1-score of $96.88 \%$, mean average precision of $96.75 \%$, and mean average recall of $97.02 \%$. This research signifies a significant stride forward in the automated detection of lung infections through multimodal imaging.

关键词： Visualization Solid modeling Accuracy Image processing Image retrieval Lung Manuals

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Heterogeneous Wireless Access Networks for IoT and E-Health-A Survey 3

Heterogeneous Wireless Access Networks for IoT and E-Health-...

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

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

In a heterogeneous wireless environment, there are many different radio access technologies (RATs), such as Wi-Fi and 2G, 3G, 4G, and 5G, which each have a different degree of coverage and processing power to meet various service requirements. A mobile user may have access to various access networks in such a situation. On the PC of each user, numerous programs with varied Quality of Service (QoS) requirements can operate simultaneously. It would be advantageous for a multi-interface terminal to use two or more interfaces simultaneously in order to improve performance. However, using multiple networks at once could use up more energy than just using one interface. Therefore, energy use must to be taken into account when analyzing the Flow/Interface Relationship (FIA). This paper presents a novel method for choosing the ideal FIA that achieves the best trade-off between all the characteristics taken into account, dubbed Smart Tabu Search (STS). STS considers user preferences, network circumstances, network expenses, application QoS specifications, and battery life of mobile devices. We use simulations and testbed experiments to validate our concept. Professionals are now able to diagnose and monitor patients remotely because to the growing use of healthcare monitoring devices. One of the most frequent occurrences that affects the reliability of information transmission in any network is congestion, which is defined as the unchecked increase in traffic relative to network capacity. With the use of a mesh network, wireless sensors, and some of the most significant models for vital signs, the article aims to provide a realistic simulation environment for a healthcare system. The simulator environment is a helpful tool for assessing the dependability and effectiveness of the healthcare system in a realistic setting. However, the system is not appropriate for real-time applications due to the sluggish network adaption caused by the end-to-end based traffic regulation deci

关键词： Electrocardiograms

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Overview of Energy Harvesting and Sustainability in Smart Cities 3

Overview of Energy Harvesting and Sustainability in Smart Ci...

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

ISBN: (纸本)9798350330724

Sustainability is a key factor in the context of smart cities. In smart cities, promoting sustainable energy is one strategy for achieving sustainability and lowering greenhouse gas emissions. Different energy sources are needed to meet the need for affordable and dependable electrical energy, where the environmental impact typically outweighs the cost of generation. In order to increase sustainability in the electrical industry, smart cities are examined in this article. The methodological strategy for this involved conducting a systematic literature review utilizing the PRISMA procedure. 154 journal articles were examined in-depth for this review. Energy efficiency, renewable energy, and energy and urban planning were the themes that the results were organized into. The results of the study showed that: (a) there is a global academic publication landscape for research on smart cities and energy sustainability;(b) publications are unbalanced when comparing the results of studies on the energy use intensity of different continents to those on the energy sustain ability of smart cities;and (c) there is a strong focus on technology-related issues related to energy sustainability, efficiency, and renewables topics in the literature, but much less attention is given to these issues. Urban and energy authorities are informed by the insights produced, and scholars are given ideas for future research directions. © 2023 IEEE.

关键词： Smart city

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