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Analysis of Relationship Between learning Outcomes and Student’s Exam Results Using Association Rule Mining and Fuzzy Inference Rules 14th

Analysis of Relationship Between Learning Outcomes and Stude...

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14th international conference on Applications of Fuzzy systems, Soft Computing, and Artificial Intelligence Tools, ICAFS 2020

作者： Salahli, M.A. Gasimzadeh, T. Alasgarova, F. Guliyev, A. Department of Computer and Instructional Technologies Education Çanakkale Onsekiz Mart University Çanakkale Turkey Department of Instrument Making Engineering Azerbaijan State Oil and Industry University 20 Azadlig Avenue BakuAZ 1010 Azerbaijan Department of Information Technology and Natural Sciences Azerbaijan Tourism and Management University Koroglu Rehimov Street 822/23 BakuAZ 1072 Azerbaijan

ISBN: (纸本)9783030640576

The academic success of students largely depends on the correct selection and evaluation of learning outcomes. The most effective tool for measuring student knowledge is exams. In this context, the scores of the students in the exams are the important indicators of their learning achievements. In this study the relationship between learning outcomes and exam results of students is analyzed. The research was carried out based on mathematics exam scores of students studying in the 6th grade of secondary school and the data related to the learning outcomes of this course. To reveal this relationship, an approach based on fuzzy logic and data mining methods is considered. The weights of the learning outcomes in the questions were analyzed, then the relationships between the outcomes and scores were determined using the Apriori algorithm. Then fuzzy inference rules were created based on the most frequent items determined as the results of the Apriori algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Students

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Multi-scale Salient Features for Person Re-identification

Multi-scale Salient Features for Person Re-identification

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2021 international conference on Computer engineering and Innovative Application of VR, ICCEIA VR 2021

作者： Dong, Yachao Liu, Hongzhe Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing100101 China

Person re-identification is a technology that uses computer vision to match and retrieve the same target person across cameras, which has a wide range of applications in the fields of intelligent video surveillance, intelligent security, and unmanned driving. Aiming at the problem of low accuracy caused by complex background information, occlusion and other factors in person re-identification, the extracted features are not discriminative and robust, resulting in low accuracy, a person re-identification method based on multi-scale saliency features is proposed. Firstly, the saliency detection is used to extract the distinctive feature areas with discriminative power in person, so that the features of distinguishing person inages are more prominent. Secondly, these saliency features are merged with global features and the fused features are divided into different scales, and the global features are divided into three different scales to enhance the expression of features. Finally, according to the difference of global and local features, three kinds of loss functions are combined for learning. In the inference stage, the global feature and the local feature are fused into a new feature vector for metric learning. The proposed method has been used in the person re-identification public datasets Market1501 and DukeMTMC-reID to do a lot of effective verification experiments. The experimental results show that the features extracted by the proposed method have strong distinguishability and robustness. © Published under licence by IOP Publishing Ltd.

关键词： Security systems

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machine learning-Based Fault Prediction of Electromechanical System with Current and Vibration Signals 16th

Machine Learning-Based Fault Prediction of Electromechanical...

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16th international conference on Vibration engineering and Technology of machinery, VETOMAC 2021

作者： Gangsar, Purushottam Singh, Vikas Chouksey, Manoj Parey, Anand Mechanical Engineering Department Shri G S Institute of Technology and Science Indore India School of Mechanical Engineering IIT Indore Indore India

ISBN: (纸本)9789819947201

This paper describes the development of intelligent fault diagnosis for electromechanical systems (EMS) and examines various combinations of mechanical and electrical faults in such systems. Specifically, the study focuses on a three-phase asynchronous motor (IM) with an outer rotor bearing system. The faults investigated in this study include a healthy system (F1), healthy motor with outer bearing faults (F2), healthy motor with unbalance in outer rotor (F3), bearing fault in the motor with healthy outer rotor (F4), inner motor bearing and outer rotor bearing fault (F5), motor bearing fault with outer unbalanced rotor (F6), motor stator fault with outer healthy rotor (F7), motor stator fault with outer unbalanced rotor (F8), motor stator fault with outer bearing fault (F9), and motor bearing fault with outer bearing fault and unbalanced rotor (F10). In order to detect combined faults in the motor-rotor-bearing assembly, the paper proposes using wavelet characteristics extracted from the current and vibration signals, which are used to develop a support vector machine (SVM)-based defect detection system. Finally, the paper concludes with a discussion of the research results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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3rd international conference on Big-Data and Cloud Computing, ICBDCC 2019

3rd International Conference on Big-Data and Cloud Computing...

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3rd international conference on Big-Data and Cloud Computing, ICBDCC 2019

ISBN: (纸本)9789811552847

The proceedings contain 62 papers. The special focus in this conference is on Big-Data and Cloud Computing. The topics include: Classification of gender from face images and voice;an outlier detection approach on credit card fraud detection using machine learning: A comparative analysis on supervised and unsupervised learning;unmasking file-based cryptojacking;selection of a virtual machine within a scheduler (dispatcher) using enhanced join idle queue (ejiq) in cloud data center;an analysis of remotely triggered malware exploits in content management system-based web applications;gsm-based design and implementation of women safety device using internet of things;a novel approach on various routing protocols for wsn;fraud detection for credit card transactions using random forest algorithm;deep learning application in iot health care: A survey;L1 norm SVD-based ranking scheme: A novel method in big data mining;context aware text classification and recommendation model for toxic comments using logistic regression;self-supervised representation learning framework for remote crop monitoring using sparse autoencoder;determination of elements in human urine for transient biometrics;optimal placement of green power generation in the radial distribution system using harmony search algorithm;data security and privacy protection in cloud computing: A review;Re-ranking ODI batsman using JJ metric;intelligent cloud load balancing using elephant herd optimization;a quest for best: A detailed comparison between drakvuf-vmi-based and cuckoo sandbox-based technique for dynamic malware analysis;cloudstore: A framework for developing progressive streaming games;classification of dga botnet detection techniques based on dns traffic and parallel detection technique for dga botnet.

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Characterization of Flexible Patch Antennas on Planar and Curved Surfaces 3

Characterization of Flexible Patch Antennas on Planar and Cu...

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3rd international conference on Smart and intelligent learning for Information Optimization, CONSILIO 2021

作者： Khan, Habeeba Ali, Sayyed Arif Wajid, Mohd Alam, Muhammad Shah Department of Electronics Engineering ZHCET Aligarh Muslim University Aligarh India Electrical Engineering Department College of Engineering Imam Mohammad Ibn Saud Islamic University Riyadh11432 Saudi Arabia Centre for VLSI and Embedded Systems Technologies Lab IIIT Hyderabad Hyderabad India

This paper analyses patch antennas operating at 16 GHz on flexible Kapton polyimide substrate in various shapes, namely rectangular, circular, pentagon, hexagon, heptagon, and octagon. Their applications in medical areas, more particularly in wearable devices for E-health, are targeted. The bending effects of these antennas are studied, more specifically on gain, return loss, radiation characteristics, bandwidth, and beamwidth. A detailed comparison results showed that the rectangular patch had better performance even under the bending when the diameter of the surface is varied from 40mm to 120mm and maintains a gain of 4.8 dB at the given frequency. Under such extreme bending, the antennas operate satisfactorily with an efficiency of 43.424% to 47.41%. © Published under licence by IOP Publishing Ltd.

关键词： Microstrip antennas

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Disentangling Client Contributions: Improving Federated learning Accuracy in the Presence of Heterogeneous Data 21

Disentangling Client Contributions: Improving Federated Lear...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Liu, Chunming Alghazzawi, Daniyal M Cheng, Li Liu, Gaoyang Wang, Chen Zeng, Cheng Yang, Yang Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education School of Artificial Intelligence Wuhan China King Abdulaziz University Jeddah Saudi Arabia Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350329223

Federated learning (FL) is a promising paradigm that leverages distributed data sources to train machine learning models, thereby offering significant privacy advantages. However, the inherent statistical heterogeneity among clients, characterized by distinct data distributions and varying model performance, poses a substantial challenge. Such heterogeneity adversely affects both the convergence and overall performance of the global model within the FL framework. To address this issue, we introduce a novel FL algorithm, FedVa, which considers both the local data volume and model accuracy to determine client contributions, assigning respective weights. This consideration facilitates greater engagement from key clients during the aggregation process, thereby enhancing their influence on the global model refinement. Experimental results indicate that FedVa surpasses prevailing methods in managing statistical heterogeneity and enhancing global model accuracy, without incurring additional communication costs. Specifically, FedVa achieves a convergence speed 3.9 times faster than FedAvg on the MNIST dataset, and on the CIFAR-10 dataset, it results in a 2.3% increase in final model accuracy compared to FedAvg. Our code is publicly available at https://***/ChunmingLiu23/FedVa. © 2023 IEEE.

关键词： learning systems

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Computational Analysis of Human Navigation Trajectories in the VR Magic Carpet ™ Using K-Means 3rd

Computational Analysis of Human Navigation Trajectories in t...

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3rd international conference on Electronic engineering and Renewable Energy, ICEERE 2022

作者： Annaki, Ihababdelbasset Rahmoune, Mohammed Bourhaleb, Mohammed Embedded Systems Electronics and IT Team Research Laboratory in Applied Sciences National School of Applied Sciences P.B. 669 Oujda60000 Morocco

ISBN: (纸本)9789811962226

In this research, we use unsupervised machine learning clustering techniques, notably K-means (Jain in Pattern Recogn Lett 31:651–666, 2010 [1]), to explore human navigation using the VR Magic Carpet (Berthoz and Zaoui in Dev Med Child Neurol 57:15–20, 2015 [2]). This is a variant of the Corsi Block Tapping task (CBT) (Corsi in Human memory and the medial temporal region of the brain. McGill University, 1972 [3]) that was carried out within the experimental framework of virtual reality. The participant’s trajectory was captured as raw spatial data and afterward kinematically evaluated. Our previous research (Annaki et al. in Digital technologies and applications. ICDTA 2021. Lecture notes in networks and systems, vol 211. Springer, Cham, 2021 [4]) found three distinct groups. However, the classification remained unclear, suggesting that they include both types of people (ordinary and patients with cognitive spatial impairments). Based on this premise, we used K-means to distinguish patients’ navigation behavior from that of healthy people, highlighting the most significant differences and validating the feature on which our previous analysis was based. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Prediction of Heat Transfer Performance of Heat Pipe Using machine learning Approach 1

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1st international conference on Fluid, Thermal and Energy systems, ICFTES 2022

作者： Das, Sudev Jha, Ashutosh K. Johnsan, R. Bestha, Chandra S. Teja Reddy, V. Department of Chemical Engineering NIT Kerala Calicut673601 India

ISBN: (数字)9789819959907

ISBN: (纸本)9789819959891

The heat pipe is widely used for electronic cooling. ln the simplest configuration of heat pipe, it consists of a cylindrical vessel having three sections (Evaporator, Adiabatic, Condenser). It has a wide range of applications, from smartphones to satellites. Different working fluids are used in the heat pipe based on the operating conditions. This paper discusses the machine learning approach along with some analytical methods to predict the performance of Heat Pipes. Regression, Random Forest, Support Vector machine (SVM), and Gaussian Matrix Model (GMM) have been tested to find an optimal model based on 2500 experimental data points collected from different research papers and to show the relationship between different parameters. Our objective is to predict the heat transfer performance of a heat pipe by using different machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Regression analysis

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intelligent Moving machine (IMM) Based on Battery for an Efficient Regenerative Braking System 2nd

Intelligent Moving Machine (IMM) Based on Battery for an Eff...

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2nd international conference on Recent Advances in Mechanical engineering, RAME 2020

作者： Yadav, Amit Chaturvedi, D.K. Kant, Ajay Jain, Garima Kumar, Anil Dayalbagh Educational Institute Dayalbagh Agra India Department of Mechanical Production & Industrial and Automobile Engineering Delhi Technological University Delhi India

ISBN: (纸本)9789811596773

Modern-day electric vehicle technology is based on batteries and semiconductor devices;these systems have complementary features which can be successfully used in an electrical energy storage system for intelligent moving machine which is known as IMM. The utilization of the electrical energy storage system in electrical moving machine provides more advantages, like efficient regenerative braking energy, life span of battery, and improved acceleration of IMM. In this research paper, a new regenerative braking system (RBS) is developed for electric vehicle technology using electrical energy storage system and run by brushless DC (BLDC) motor. During the runtime of IMM when it takes brake, the BLDC acts as a generator. Hence, by using an appropriate artificial neural network technique and switching algorithm, the DC link voltage is boosted, and the energy is transferred to the superconductor devices and the battery through the inverter. To provide a reliable and smooth braking regeneration system, the braking force distribution is realized through a soft computing technique. Checking the performance of the regenerative braking system, different modeling and simulations performance test and experiments are carried out in indoor and outdoor environment. The IMM experiment result confirms high capacity of the future RBS technology. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial neural network (ANN) BLDC motor Control algorithm Electric vehicle (EV) Regenerative braking system (RBS)

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6G Vision: Toward Future Collaborative Cognitive Communication (3C) systems

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IEEE Communications Standards Magazine 2021年第2期5卷 60-67页

作者： Shahzadi, Sonia Iqbal, Muddesar Chaudhry, Nauman Riaz University of Gujrat London South Bank University

6G networks are expected to have a breakthrough by enabling the emergence of collaborative cognitive communication services over heterogeneous environments for industry 5.0 applications. These applications are required to adapt human-centric approach to make the most of human intuition and intelligence in Industry 4.0 *** calls for a transdisciplinarity research domain to investigate innovative systems with overlapping realms of Psychology, Sociology, Communication networks, Artificial Intelligence, Natural Language Processing and Collaborative Computing. The author at the Cognitive systems Research Centre, London South Bank University has coined the expression '3C systems' to refer to such artifacts which stands for 'Collabora-tive Cognitive Communication systems.' In this article, an innovative framework for 3C systems is proposed that is able to analyze and predict both the human as well as machine behaviors. It proactively diagnoses issues and recommends solutions without requiring any human intervention. The proposed concept of 3C systems would potentially contribute towards 6G standardization. The automation and orchestration aspects of this research have variety of applications stretched across city infrastructures, retail, business, tourism, health, law, education and travel. A thorough insight to a broad view of 6G vision has been presented towards envisioned 3C systems, while covering its enabling technologies. The experimental results for the proof of concept implementation has been presented. Results affirm the technical capabilities of the concept, to contribute to several industry 5.0 applications including, but not limited to holographic communication, self-driving vehicles, context-aware infrastructure and personalized interfaces. © 2017 IEEE.

关键词： Cognitive systems

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