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共 565 条记录，以下是21-30 订阅

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Predicting student Performance Through Classroom Concentration: A Review on Machine Learning and Computer Vision Approaches 21

Predicting Student Performance Through Classroom Concentrati...

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21st IEEE student conference on research and development, scored 2023

作者： Umar, Umar Sunusi Rana, Muhammad Ehsan Hameed, Vazeerudeen Abdul Asia Pacific University of Technology and Innovation School of Computing Kuala Lumpur Malaysia

ISBN: (纸本)9798350318821

student performance prediction is a complex problem in which a computer predicts student's future performance while they engage in their studies. To enable effective educational interventions during a course, accurate early forecasts of a student's performance is essential. The review proposed a silent method and the latest data for capturing student behaviour is classroom video monitoring. For student performance prediction dynamics to be understood and subsequently improved, it is essential to understand students' attention spans and what kinds of behaviours may advise the absence of attention. The ability to detect student attention is one of the features of computer vision systems used to monitor classrooms. This review included the pertinent Educational Data Mining literature on identifying dropouts and students at risk from classroom data. For example, during class hours students are to concentrate, focus and give all their attention in class. A machine learning approach can be used to rate each student's concentration with the help of computer vision algorithms since most studies use student data from colleges such as quiz marks and exam scores. The assessment outcomes revealed that a variety of Machine Learning (ML) and computer vision techniques are developed to understand and address the main problems, including guessing about to-fail learners and student dropouts. It has been validated that machine learning systems are important for detecting at-risk students and dropout frequencies which help for improving student success. To find the basics that can have an impact on how well students perform academically machine learning algorithms can be used to study a student's daily routine such as their study habits, social interactions, and extracurricular activities. Since it applies the most relevant advanced methods for each specific task, this student concentration score using computer vision offers the ideal answer for monitoring classrooms. The integration

关键词： students

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Based on a size of Microplastics, Multi-Channel Microwave Resonant MEMS Sensor 21

Based on a size of Microplastics, Multi-Channel Microwave Re...

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21st IEEE student conference on research and development, scored 2023

作者： Lee, Kwon-Hong Kim, Jin-Hyoung Shin, Kyu-Sik Cha, Cheol-Ung Lee, Hyung-Min Korea University School of Electrical Engineering Seoul Korea Republic of Smart Sensor Research Center Korea Electronics Technology Institude Seongnam-si Korea Republic of

ISBN: (纸本)9798350318821

In this paper, the multi-channel high-frequency resonant MEMS sensor, which can measure the concentration of microplastics while classifying their sizes, is proposed. The MEMS filter with multiple reservoir channels fabricated through the in-house MEMS process sorts microplastics injected through the microfluidic channel by size and accumulates them in the reservoir. The microwave resonant sensor, used to measure the concentration of size-classified and accumulated microplastics, is applied the CMSRR structure to achieve notch frequency shift sensitivity based on their concentration. The classification characteristic of the multi-channel MEMS filter was verified through experiments using the microplastics dispersion. The performance of microwave resonant sensor was simulated through the commercial simulation tool. © 2023 IEEE.

关键词： Microwave resonators

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Circuit Analysis and Simulation Modelling of a LCC-LCC Compensation Network Resonant Converter for Wireless Power Transfer Applications 21

Circuit Analysis and Simulation Modelling of a LCC-LCC Compe...

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21st IEEE student conference on research and development, scored 2023

作者： Hong, Lim Wei Begam, K.M. Aparow, Vimal Rau Nath, Shibajee Ming, Teo Jin Mehranzamir, Kamyar Research Group Department of Electrical and Electronic Engineering Semenyih Malaysia University of Nottingham Malaysia Department of Electrical and Electronic Engineering Semenyih Malaysia

ISBN: (纸本)9798350318821

Wireless power transfer (WPT) eliminates the need for wires to transfer power, making it more convenient, reduces risk of hazard caused by cables and allows self-driving battery powered vehicles to charge without human operation. However, the wireless power transfer efficiency decreases dramatically as misalignment between the transmitter (Tx) coil and receiver (Rx) coil increases. In this paper, LCC-LCC compensation network is integrated into the design of the WPT system to improve both misalignment and output load tolerance of the WPT system. Kirchhoff's Voltage Law (KVL) was done, and the equations produced were used to calculate the values of the components and a MATLAB Simulink simulation model was constructed. The simulation results will be compared with theoretical calculations to verify the mathematical equations. The design of a 30V, 2.5A, 75W LCC-LCC compensation network resonant converter operating at 85kHz achieved an efficiency of 81%. © 2023 IEEE.

关键词： Energy transfer

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Design Performance of Fiber Optic-Surface Plasmon Resonance (FO-SPR) Sensors for Detecting Palm Oil Concentration 22

Design Performance of Fiber Optic-Surface Plasmon Resonance ...

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22nd IEEE student conference on research and development, scored 2024

作者： Lestiyanti, Yuni Salim, Mohd Rashidi Abdullah, Ahmad Sharmi Bin Manap, Hadi Bin Nameh, Sevia Mahdaliza Idrus Sutan Yulianti, Ian Tresna, Wildan Panji Universiti Teknologi Malaysia Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Malaysia Pahang Al Sultan Abdullah Faculty of Electrical & Electronic Engineering Technology Pekan Malaysia Universitas Negeri Semarang Department of Physics Semarang Indonesia Research Center for Photonics Tangerang Indonesia

ISBN: (纸本)9798331510077

Fiber Optic-Surface Plasmon Resonance (FOSPR) has gained significant attention across various applications due to its compact size, high portability, and flexibility compared to traditional prism-based SPR sensors. FO-SPR also enables in situ applications in challenging environments, such as liquids, gases, and confined spaces, and has lower production costs due to simpler, more accessible components. This makes FO-SPR highly suitable for developing sensors to monitor the quality of palm cooking oil concentration. This study aims to assess the FO- SPR design's effectiveness in detecting palm oil concentration through simulation. The sensor design was simulated in both transmission and reflection modes using the Finite Difference Time Domain (FDTD) method. The effect of heating duration on refractive index changes was observed, with heating from 30 to 210 minutes resulting in a refractive index change from 1.4645 to 1.4749. The simulation results show a good linear response to changes in palm oil concentration in both modes. In transmission mode, the sensor demonstrated a sensitivity of 1682.69 nm/RIU, detection accuracy (DA) of 0.54, quality factor (QF) of 52.31 / RIU, and figure of merit (FOM) of 20.44. In reflection mode, similar performance was observed, with sensitivity, DA, QF, and FOM values of 1781.25 nm/RIU, 1.23,119.13 / RIU, and 165.48, respectively. These findings indicate that FO-SPR, in both transmission and reflection modes, shows strong potential as a sensor for palm cooking oil concentration. © 2024 IEEE.

关键词： Surface plasmon resonance

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Comparative Study of Recurrent Neural Networks for Electric Vehicle Battery State of Charge Estimation 21

Comparative Study of Recurrent Neural Networks for Electric ...

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21st IEEE student conference on research and development, scored 2023

作者： Hui, Choong Ting Manoharan, Aaruththiran Begam, K.M. Sooriamoorthy, Denesh University of Nottingham Malaysia Department of Electrical and Electronic Engineering Selangor Malaysia Research Group Department of Electrical and Electronic Engineering Selangor Malaysia Asia Pacific University Taman Teknologi Malaysia School of Engineering Jalan Teknologi 5 Kuala Lumpur57000 Malaysia

ISBN: (纸本)9798350318821

To slow down global climate change, the automotive industry is shifting their attention gradually from internal combustion engine vehicles to electric vehicles. Lithium ion batteries (LiBs) are used to store energy for the propulsion system of electric vehicles. An accurate state-of-charge (SOC) estimator can provide safe and reliable usage of electric vehicles. From the findings in recent literature, it was found that using recurrent artificial neural networks for SOC estimation produce high estimation accuracy. This work investigates two variants of the recurrent neural network studied extensively in the literature, namely, gated recurrent unit (GRU-RNN) and long short-term memory (LSTM-RNN). LiB discharge data when exposed to FUDS, US06 and BJDST driving profiles at 0°C, 25°C and 45°C from CALCE dataset were used for training and evaluating the performance of the ANNs. LSTM-RNN was found to achieve 0.0117% maximum mean squared error (MSE) and GRU-RNN with 0.00975% maximum MSE. However, when tested with real time battery discharge data, it was found that LSTM-RNN is robust to noise than GRU-RNN, thus ensuring more stability in the SOC estimations. © 2023 IEEE.

关键词： Long short-term memory

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A Comparative Analysis of Machine Learning Techniques for Heart Disease Prediction 21

A Comparative Analysis of Machine Learning Techniques for He...

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21st IEEE student conference on research and development, scored 2023

作者： Alqushaibi, Alawi Ragab, Mohammed Gamal Sumiea, Ebrahim Hamid Al-Selwi, Safwan Mahmood Hasan, Mohd Hilmi Abdulkadir, Said Jadid Alhussian, Hitham Al-Asbahi, Mahmoud Mustafa Universiti Teknologi PETRONAS Computer and Information Sciences Perak Malaysia Universiti Teknologi PETRONAS Centre for Research in Data Science Perak Malaysia Universiti Malaysia Terengganu Faculty of Ocean Engineering Technology and Informatics Terengganu Malaysia

ISBN: (纸本)9798350318821

Detection of heart disease is critical, as it is a common and serious health condition. Predicting its occurrence is vital in the healthcare domain, and machine learning techniques are increasingly being utilized to accurately identify patients at risk. To evaluate how well machine learning models can effectively predict heart disease, we used a pre-existing dataset from the UCI repository's Cleveland database. Though the dataset originally contained 303 records and 76 attributes, we followed the literature by narrowing the focus to 14 relevant attributes in order to improve the precision of various algorithmic approaches. Our study employed five machine learning classifiers, namely KNearest Neighbors (KNN), CatBoost Classifier (CBC), Decision Tree Classifier (DT), XGBClassifier (XGBC), and Support Vector Classifier (SVM). We evaluated their performance using different metrics like accuracy, precision, AUC, and F1 score on the heart disease dataset. We found that the CatBoost Classifier had the best performance, achieving high accuracy and AUC with 0.88% and 0.94% values, respectively. This suggests that machine learning algorithms could aid in identifying heart disease at an early stage and have significant implications for improving patient outcomes in healthcare. © 2023 IEEE.

关键词： Forecasting

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Explaining Deep Learning Decisions Via Fuzzy Inference System on Medical Images 21

Explaining Deep Learning Decisions Via Fuzzy Inference Syste...

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21st IEEE student conference on research and development, scored 2023

作者： Murad, Nafeesa Yousuf Hasan, Mohd Hilmi Azam, Muhammad Hamza Yousuf, Nadia Khalique, Sonia Abdul Universiti Teknologi PETRONAS Computer and Information Sciences Perak Seri Iskandar32610 Malaysia Universiti Teknologi PETRONAS Centre for Research in Data Science Perak Seri Iskandar32610 Malaysia Benazir Bhutto Shaheed University Computer and Information Sciences Karachi Pakistan

ISBN: (纸本)9798350318821

Explaining deep learning models has become crucial in various domains, including medical image processing. Understanding these models' decision making processes is complicated as deep learning act like a black box when it comes on decision making. This can also help in advancements of medical imaging analysis, by allowing for more accurate diagnostic and treatment decisions and, ultimately, better patient care. In this study, we addressed the requirement for interpretability, by fusing the strength of CNNs with a Takagi-Sugeno-Kang (TSK) fuzzy inference system. We have utilized ResNet50, a transfer learning form of CNN, to classify a batch of 1000 images from dataset of chest cancer into four classes: 'Adenocarcinoma,' 'Squamous Cell,' 'Normal,' and 'Large Cell'. The resolution of each image was varied, which was afterwards rectified in the preprocessing step, and this model attained 94.5 percent accuracy. We tried to improve the transparency and dependability of the model's decision-making process. In this regard, we applied PCA to minimize the dimensions of predicted probabilities obtained from CNN for looking insight into the CNN model's decision making process. Fuzzy c-means clustering is used to group the decision depending on the number of fuzzy rules. We extracted important insights from CNN's predictions and offer a thorough justification of its decision-making process by incorporating the TSK fuzzy inference system. © 2023 IEEE.

关键词： Fuzzy inference

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The Application of Clustering Algorithm in College student Information Management

The Application of Clustering Algorithm in College Student I...

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2023 International conference on Data Science and Network Security, ICDSNS 2023

作者： Ma, Hui Jilin Agricultural Science and Technology University Jilin China

ISBN: (纸本)9798350301595

To solve the problem that the traditional students' information processing ability is limited with the massive increase of students' information, the application of clustering algorithm in the student information management system of colleges and universities is proposed. In this research, taking the application of clustering algorithm in three aspects of student comprehensive evaluation, student performance evaluation, and classroom teaching evaluation as examples, the clustering algorithm that is competent for student information data processing is selected. The experimental results show that there are 9 students in the second category of good, among which the student number 084210123 scored 70 points for data structure, which is lower than the lowest score of 73 in the third category of medium. This result is equivalent to the passing category of the fourth category, and it may be more appropriate to classify it as a passing category. The 10 teachers are divided into 3 categories, namely {2, 4, 6, 7, 10}, {5, 8} and {1, 3, 9}, and their grades are B, C, A. The experimental results are consistent with the division of the given data. It is concluded that the clustering algorithm has a good development space in the field of student information management. © 2023 IEEE.

关键词： Information management

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UX Law Implementation at Sevima edLink from Design Science research Perspective 2

UX Law Implementation at Sevima edLink from Design Science R...

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2nd International conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Rozas, Indri Tjahyanto, Aris Uin Sunan Ampel Surabaya Departement of Information System Surabaya Indonesia Institut Teknologi Sepuluh Nopember Departement of Information System Surabaya Indonesia

ISBN: (纸本)9798350351613

The ability to provide good usability in a Learning Management System (LMS) is crucial for its success. Usability measures the quality of the user experience when interacting with a product or system. In an LMS, usability is a key requirement because it significantly impacts the effectiveness of student learning. Moodle, one of the most widely used LMS platforms in academic institutions and universities, has been criticized for its low usability. According to an analysis, feature complexity is the primary issue for most users in Indonesia. Despite various challenges facing Educational Technology (EdTech) applications in Indonesia, Sevima edLink has flourished over the past five years, boasting a total of 1.6 million registered users and 35,000 active users per day. This research, therefore, conducted a case study on Sevima edLink to examine its development process and UX evaluation using the design science research method. The results of the usability evaluation, measured through the User Experience Questionnaire (UEQ) framework, showed that the scales for Attractiveness, Efficiency, Perspicuity, Dependability, Stimulation, and Novelty all scored above 60%. © 2024 IEEE.

关键词： research and development management

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Exploration Decay Policy (EDP) to Enhanced Exploration-Exploitation Trade-Off in DDPG for Continuous Action Control Optimization 21

Exploration Decay Policy (EDP) to Enhanced Exploration-Explo...

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21st IEEE student conference on research and development, scored 2023

作者： Sumiea, Ebrahim Hamid Abdulkadir, Said Jadid Alhussian, Hitham Al-Selwi, Safwan Mahmood Ragab, Mohammed Gamal Alqushaibi, Alawi Centre for Research in Data Science Universiti Teknologi PETRONAS Perak Seri Iskandar32610 Malaysia

ISBN: (纸本)9798350318821

The optimization of continuous action control tasks is a crucial step in deep reinforcement learning (DRL) applications. The goal is to identify optimal actions through the accumulation of experience in continuous action control tasks. This process can be achieved through DRL, which trains agents to develop a policy that maximizes the cumulative rewards gained from decision-making in dynamic environments. Balancing exploration and exploitation is a crucial challenge in acquiring this policy. To address the exploration-exploitation trade-off, the Exploration Decay Policy (EDP) implements a dynamic exploration noise strategy that adapts to the current training progress, enabling efficient exploration in the initial phases while gradually reducing exploration to focus on exploitation as training progresses. However, the fluctuating training stability across episodes in dynamic environments poses a challenge for exploitation policies to adapt accordingly. In this paper, we propose EDP to address exploration-exploitation trade-off dilemma. The objective is to dynamically modulate the noise scale, gradually decreasing it during periods of high training stability to promote exploration, while reducing it to maintain exploitation during periods of low training stability. The study introduces the EDP-DDPG method, enhancing continuous control tasks in Box2D environments. EDP-DDPG outperforms the standard DDPG by achieving higher rewards and quicker convergence. Its success stems from dynamically adjusting exploration noise every 25 episodes, balancing exploration and exploitation. This adaptive approach, reducing noise by 10% every 25 episodes, evolves from random to strategic limb movements, optimizing policy exploitation and adaptability in dynamic settings. © 2023 IEEE.

关键词： Reinforcement learning

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