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2nd International Conference on Artificial Intelligence and Machine Learning Applications, AIMLA 2024

作者： Maniraj, P. Murugan, A. Durai Gokul, R. Shalini, A.S. Kesavan, K. Kavin, P. M. Kumarasamy College of Engineering Department of Electrical and Electronics Engineering Karur India M. Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Computer Science and Business Systems Karur India M. Kumarasamy College of Engineering Electrical and Electronics Engineering Karur India

ISBN: (纸本)9798350349221

With the developing prevalence of electric bicycles (e-bicycles) as a feasible and proficient method of transportation, guaranteeing their security has turned into a foremost concern. Electric bicycles are important resources helpless against robbery and harm, to conquer this issue the venture proposes the improvement of an Electric Vehicle Burglary Location and Warning Framework utilizing IOT innovation. The framework integrates sensors to screen electric vehicles for unapproved access and utilizes Radio Recurrence ID innovation to improve security. After recognizing an unfair RFID section or any dubious movement, the framework sets off a caution to tell the proprietor and naturally locks the back tire, forestalling robbery. This framework could be an instrument for deflecting the robbery of electric vehicles and keeping proprietors from monetary misfortunes. In a period of developing IOT-based security arrangements, this framework offers an important expansion to safeguard electric vehicle and their venture. © 2024 IEEE.

关键词： Electric vehicles

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Prognostic Stacking Machine Learning Model for Intensive Care Unit Admission Prediction of COVID Patients 4th

Prognostic Stacking Machine Learning Model for Intensive Car...

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4th Doctoral Symposium on Computational Intelligence, DoSCI 2023

作者： Sharma, Richa Pandey, Himanshu Agarwal, Ambuj Kumar Srivastava, Dolley Department of Computer Science and Engineering Maharishi University of Information Technology Lucknow India Department of Computer Science and Engineering Faculty of Engineering and Technology University of Lucknow Lucknow India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

ISBN: (纸本)9789819937158

Statistical methods and models are the backbone of machine learning, which allows computers to autonomously discover and exploit data patterns. The ultimate objective is for computers to accurately forecast outcomes based on historical data. In this work, we present two related research projects: one on stacking and the other on boosting, two ensemble learning approaches. This research investigates its behavior from several angles and sheds light on its relationship to related ensemble learning schemes by showing that it is typically the best option and that stacking can be used to replicate the vast majority of ensemble learning schemes. The COVID-19 clinical dataset has been used to test these strategies for use in anticipating and preparing healthcare systems to avert collapse, where collapse is defined as an over-capacity demand of ICU beds. F1-measures stacking is superior to other methods in terms of accuracy, precision, and recall, as shown by the findings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： COVID-19

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Optimizing Bearing Fault Detection:CNN-LSTM with Attentive TabNet for Electric Motor systems

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computer Modeling in engineering & sciences 2024年第12期141卷 2399-2420页

作者： Alaa U.Khawaja Ahmad Shaf Faisal Al Thobiani Tariq Ali Muhammad Irfan Aqib Rehman Pirzada Unza Shahkeel Nautical Science Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Marine Engineering Department Faculty of MaritimeKing Abdulaziz UniversityJeddah22230Saudi Arabia Artificial Intelligence and Sensing Technologies(AIST)Research Center University of TabukTabuk71491Saudi Arabia Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia

Electric motor-driven systems are core components across industries,yet they’re susceptible to bearing *** fault diagnosis poses safety risks and economic instability,necessitating an automated *** study proposes FTCNNLSTM(Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory),an algorithm combining Convolutional Neural Networks,Long Short-Term Memory Networks,and Attentive Interpretable Tabular *** model preprocesses the CWRU(Case Western Reserve University)bearing dataset using segmentation,normalization,feature scaling,and label *** architecture comprises multiple 1D Convolutional layers,batch normalization,max-pooling,and LSTM blocks with dropout,followed by batch normalization,dense layers,and appropriate activation and loss ***-tuning techniques prevent *** were conducted on 10 fault classes from the CWRU *** was benchmarked against four approaches:CNN,LSTM,CNN-LSTM with random forest,and CNN-LSTM with gradient boosting,all using 460 *** FTCNNLSTM model,augmented with TabNet,achieved 96%accuracy,outperforming other *** establishes it as a reliable and effective approach for automating bearing fault detection in electric motor-driven systems.

关键词： Electric motor-driven systems bearing faults automation fine tunned convolutional neural network long short-term memory fault detection

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Effect of Pore Water on the Thermal Diffusivity of Water-Saturated Rocks at Extremely Low Temperatures

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Zairyo/Journal of the Society of Materials science, Japan 2022年第3期71卷 214-220页

作者： Kamoshida, Naoto Okawara, Masafumi Department of Systems Innovation Engineering Faculty of Science and Engineering Iwate University Ueda Morioka020-8551 Japan

Currently, the effect of pore water on the thermal diffusivity of rocks at low temperatures is unclear. In this study, the thermal diffusivities of Berea sandstone (12.8%-porosity) and Himekami granite (1.09%) were measured using an optional heating method. Consequently, the change in the thermal diffusivity of saturated Himekami granite (approximately 100%-water saturation) increased continuously and gradually, as observed in dry granite (approximately 0%). However, when cooling from room temperature to −10°C, the thermal diffusivity of saturated Berea sandstone increased discontinuously and rapidly, in contrast to the dry sandstone in which it increased continuously and gradually. Subsequently, the effect of porosity and pore structure on the thermal diffusivity of saturated rocks was examined during the freezing process by comparing the results of Kimachi sandstone (20.2%-porosity) and Ogino tuff (28.7%) measured using the same method by the Kamoshida et al. (2013). Results show that the change in the thermal diffusivity during the cooling process of saturated rocks is depended by the pore structure (pore size distribution) instead of the rock's water content (porosity). Therefore, rocks containing macropores, such as Berea and Kimachi sandstones, can be assumed particle-dispersed complexes (the rock-forming minerals are the matrix, and the pores are the particles). Thus, the changed behavior of thermal diffusivity during the cooling process can be estimated by measuring the pore size distribution and thermophysical properties of rock-forming minerals. © 2022 The Society of Materials science, Japan

关键词： Pore size

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Relevance Classification of Flood-related Twitter Posts via Multiple Transformers

Relevance Classification of Flood-related Twitter Posts via ...

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2022 MediaEval Workshop, MediaEval 2022

作者： Mukhtiar, Wisal Rizwan, Waliiya Habib, Aneela Afridi, Yasir Saleem Hasan, Laiq Ahmad, Kashif Department of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Department of Computer Science Munsters Technological University Cork Ireland

In recent years, social media has been widely explored as a potential source of communication and information in disasters and emergency situations. Several interesting works and case studies of disaster analytics exploring different aspects of natural disasters have been already conducted. Along with the great potential, disaster analytics comes with several challenges mainly due to the nature of social media content. In this paper, we explore one such challenge and propose a text classification framework to deal with Twitter noisy data. More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts, achieving the highest F1-score of 0.87. © 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： Disasters

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Remarks on a Nonlinear Model Predictive Control Using an Echo State Network 6

Remarks on a Nonlinear Model Predictive Control Using an Ech...

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6th International Symposium on computer, Consumer and Control, IS3C 2023

作者： Takahashi, Kazuhiko Sasaki, Miku Kimura, Reika Hashimoto, Masafumi Doshisha University Faculty of Science and Engineering Department of Information Systems Design Kyoto Japan Doshisha University Faculty of Science and Engineering Department of Intelligent Information Engineering Kyoto Japan

ISBN: (纸本)9798350301953

In this study, we explored the possibility of using reservoir computing;especially echo state networks (ESNs) to control systems. We used the ESNs to obtain a prediction model to achieve optimal model predictive control (MPC) of nonlinear systems. The model's effectiveness was investigated by comparing the control performance with that of the MPC using the radial basis function network (RBFN) as the prediction model. We conducted computational training and testing experiments of the ESN-based nonlinear MPC (NMPC) for testing the tracking task of the discrete-time nonlinear plant. The experimental results show that although the ESN-based NMPC accurately performs the control task, it does not outperform the RBFN-based NMPC. This study demonstrates the potential application of ESNs for controlling nonlinear systems. © 2023 IEEE.

关键词： Model predictive control

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RUL Estimation for Power Electronic Devices Using RNNs

RUL Estimation for Power Electronic Devices Using RNNs

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2024 Prognostics and System Health Management Conference, PHM 2024

作者： Cai, Congrui Lu, Zhonghai School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Embedded Systems Master Program Sweden School of Electrical Engineering and Computer Science Department of Electrical Engineering KTH Royal Institute of Technology Sweden

ISBN: (纸本)9798350360585

Recurrent Neural Networks (RNNs) are commonly used in data-driven approaches to estimate the Remaining Useful Lifetime (RUL) of power electronic devices. RNNs are preferred because their intrinsic feedback mechanisms are better suited to model time-series data. However, the impact of RNN complexity on estimation accuracy is rarely discussed in the literature. This issue is important because choosing a lower-complexity model that delivers the same or similar performance as a higher-complexity model can increase implementation efficiency. In the paper, we use three RNN models, namely, the vanilla version, LSTM (Long Short Term Memory) and GRU (Gated Recurrent Unit) to conduct RUL estimation for power electronic devices. We use two accelerated aging datasets, one dataset targeting the package failure of MOSFETs, and the other dataset targeting package failure of power diodes. Our study shows that a lower-complexity RNN does not necessarily deliver a lower performance. Similarly, a higher-complexity model does not assure a higher performance. As such, our work highlights the importance of selecting a proper neural network for RUL estimation not biased towards complex models. This is especially useful and important for implementing such RUL estimation techniques in embedded resource-constrained and speed-limited computins platforms. © 2024 IEEE.

关键词： Long short-term memory

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Facial Image-Based Autism Detection:A Comparative Study of Deep Neural Network Classifiers

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computers, Materials & Continua 2024年第1期78卷 105-126页

作者： Tayyaba Farhat Sheeraz Akram Hatoon SAlSagri Zulfiqar Ali Awais Ahmad Arfan Jaffar Faculty of Computer Science and Information Technology The Superior UniversityLahore54600Pakistan Intelligent Data Visual Computing Research(IDVCR) Faculty of Computer Science and Information TechnologyThe Superior UniversityLahore54600Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh12571Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive *** and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like *** study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local *** research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate *** addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the *** findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation ***,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test *** validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD *** research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.

关键词： Autism Autism Spectrum Disorder(ASD) disease segmentation features optimization deep learning models facial images classification

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Experiment Design and Verification for Assessing the Acquisition of Strategic Planning Ability 15

Experiment Design and Verification for Assessing the Acquisi...

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15th International Congress on Advanced Applied Informatics Winter, IIAI-AAI-Winter 2023

作者： Manatphaiboon, Natchanon Hamachi, Shogo Yucel, Zeynep Leelaprute, Pattara Monden, Akito Kasetsart University Faculty of Engineering Department of Computer Engineering Bangkok Thailand Okayama University Department of Computer Science Okayama Japan

ISBN: (纸本)9798350383829

Exposing healthy subjects to the Tower of London (ToL) task several times enables the observation of the evolution of skill level over time as well as its reflections on eye gaze patterns. In order to identify such effects, one needs a carefully designed set of ToL tasks (i.e. problem sets). In particular, problem complexity should be gradually increasing within each problem set and comparable across different problem sets, as assessed in terms of objective measures, to enable smooth progress and the accumulation of experience. In that respect, this study uses goal hierarchy, search depth, and the number of optimal solutions for assessing complexity and establishing the gradual relation. Namely, two problem sets, Base-1 and Base-2, are designed. To satisfy the comparability condition, six color permutations are derived from Base-1 (experiments-1 to -6) and delivered to the subjects on two consecutive days. Finally, another problem set derived from Base-2 with the exact same structural properties is delivered on the last day (experiment-7). We conducted experiments with 15 participants and examined the evolution of their planning, trial, and execution times, as well as rule violations and the number of moves beyond optimal. The results suggest that there are indeed improvements in speed and accuracy in experiment-7 compared to experiment-1, even though both are novel to the participants. Additionally, these improvements are shown to be similar and consistent for different (initial) problem sets. Moreover, the proposed problem sets' difficulty and planning time for all experiments have a strong positive correlation, averaging 0.86 (σ=0.06). In that respect, we believe that the eye-gaze patterns also have the potential to withhold important information and are worth further investigation in future studies. © 2023 IEEE.

关键词： execution experience goal hierarchy Learning planning search depth Tower of London task

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IoT Based systems and Services: Recent Security Concerns and Feasible Solutions 1

IoT Based Systems and Services: Recent Security Concerns and...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Attaullah, Hafiz Muhammad Memon, Shahzad Erkan, Ozlem Feyza Khawar, Ruba Mohammad Ali Jinnah University Department of Computer Science Karachi Pakistan Beykoz University Faculty of Engineering and Architecture Department of Computer Engineering Istanbul Turkey NED University of Engineering and Technology Faculty of Information Sciences and Humanities Karachi Pakistan

ISBN: (纸本)9798350373042

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated. © 2024 IEEE.

关键词： Network security

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