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Interpretable MOOC Dropout Prediction using Different Ensemble Methods and XAI 2

Interpretable MOOC Dropout Prediction using Different Ensemb...

引用

2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Khan, Labib Hasan Haque, Mohammed Ashfaqul Ibrahim, Esaba Ahnaf Mostakim, Moin Hossain, Muhammad Iqbal Brac University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9781665456302

Massive open online course (MOOC) has been around for several years, which started to gain traction in 2012 when Coursera was established. MOOCs use pre-recorded lectures and scheduled weekly tests to provide content and access to students over the internet. The dataset used in this study is taken from the KDDCUP 2015 dataset, a publicly available dataset. Here, 12 features are considered, they are browser access, navigate, average chapter delays, etc to comprehend the possibility of dropout. This research study intends to predict the dropout of a learner so that it can be prevented through manual interaction. Additionally, XAI is implemented to inter-pret the models to suggest MOOC platforms, which feature impact dropout the most. Further, different ensemble learning techniques, namely voting classifier and stacking are used. The proposed voting classifier uses five of the best-performing machine learning models. Then, the performance of the model is evaluated by using multiple metrics such as precision, recall, and F1-score. Finally, a recall of 97.636 % has been obtained with stacking and f1-score of 91.603 % has been obtained with the hard voting classifier. © 2023 IEEE.

关键词： Forecasting

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Optimizing Wind Speed Estimation with Least Squares Generative Adversarial Networks: A Case Study of Pakistan 6

Optimizing Wind Speed Estimation with Least Squares Generati...

引用

6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ashfaq, Iqra Zahid, Hasan Naveed, Fajr Abid, Aaisha Uddin, Zaki NUST CEME Dept. of Electrical Engineering Islamabad Pakistan GIKI Dept. of Electrical Engineering Swabi Pakistan Fast NUCES Dept. of Computer Science Islamabad Pakistan NUST CEME Dept. of Mechatronics Engineering Islamabad Pakistan

ISBN: (纸本)9798350355703

Wind energy has emerged as a key player in sustainable power generation. Accurate prediction of wind speed is critical for optimizing wind farm operations, energy output forecasting, and reducing dependency on traditional power sources. In this study, we propose a Least-Squares Generative Adversarial Network (LSGAN) for wind speed prediction in a specific region of Pakistan, utilizing data from Tandoghulamali, which includes features like wind speeds at various altitudes and wind direction. The model is designed to predict wind speed at 80 meters, a crucial target variable for wind turbine operations. The LSGAN model effectively leverages adversarial training, achieving high accuracy with a Mean Absolute Error (MAE) of 1.54 and a Root Mean Squared Error (RMSE) of 1.77. Through a detailed comparison of actual versus predicted values, the model demonstrates its ability to generalize well across unseen data, with stable performance during the adversarial learning process. This approach opens up new possibilities for applying advanced machine learning techniques in renewable energy forecasting, particularly in regions with sparse historical data. © 2024 IEEE.

关键词： Wind forecasting

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Automatic Music Generation of Indian Classical Music based on Raga 8

Automatic Music Generation of Indian Classical Music based o...

引用

8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Adhikary, Sneha Manasa, S.M. Shreesha, S.K. Bhat, Shreya Kamatchi Priya, L. PES University Dept. of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350334012

The use of machine learning algorithms for the generation of new music has garnered increasing interest in recent years. With the aim of creating music that does not currently exist in the market, various machine learning techniques are being employed, including LSTM, RBM, GCA, FSM, and GAN. This research paper focuses specifically on the application of Wasserstein Generative Adversarial Networks (WGANs) for the generation of Indian classical music based on raga. The study uses MIDI piano music as input and trains the WGAN on a dataset of Indian classical music recordings. The results of the study indicate that the WGAN is capable of producing new music that is stylistically and acoustically similar to the Indian classical music present in the training dataset. Additionally, the generated music was found to be comparable to human-generated music. This study highlights the potential success of utilizing WGANs for creating music in the Indian classical genre, based on raga. © 2023 IEEE.

关键词： Music

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A Novel Approach for Recognizing Real-Time American Sign Language (ASL) Using the Hand Landmark Distance and Machine Learning Algorithms 9

A Novel Approach for Recognizing Real-Time American Sign Lan...

引用

9th IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Ahmed, Shahan Kar, Sumit Kumar Basak, Sarnali North South University Dept. of Electrical and Computer Engineering Dhaka Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Savar Bangladesh

ISBN: (纸本)9798350319651

Several sign language recognition techniques and models have been prevalent to ease the communication gap between people with hearing disabilities and people who don't understand sign language. The proposed model is a vision-based approach aims to find a better system to recognize sign gestures using the distance between hand landmarks. This process doesn't rely on complex image processing techniques, making it robust towards challenging lighting conditions, noisy backgrounds, and image resolution. To accurately detect 26 American Sign Language (ASL) letters, the model only extracts 12 features. As a result, it can recognize the letters more rapidly than the traditional image-based method and can be trained with lesser computational resources. Therefore, low-configured devices can likewise utilize the method described in this paper. Despite a number of sign language recognition methods are currently in widespread use, the inability of those models to handle the orientation of some specific letter combinations (D, G, U, H, I, and J) has a severe effect on their overall effectiveness. This proposed algorithm, added with features 11 and 12, can solve the anomalies in the orientation of similar pair alphabets. Therefore, the four most popular algorithms, Naive Bayes, Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN), have been used to predict the letters of the American Sign Language alphabet. Regardless of the algorithm employed, the model was highly efficient because of the carefully curated dataset, which included attributes directly related to the ASL alphabet signs. SVM demonstrated the best accuracy among these algorithms, outperformina the others by 97 %. © 2023 IEEE.

关键词： Support vector machines

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Understanding Facial Expression of Children with Autism Using Learning Theory

Understanding Facial Expression of Children with Autism Usin...

引用

2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Zaman, Anushka Khan, Evea Zerin Rabiul Alam, Md. Golam Chakrabarty, Amitabha Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350341072

It is challenging for autistic individuals to gather socially and empathically, as we understand, so this subject matter differs from the identification of a common human facial expression. In our research, we have taken pictures of children and teenagers from school of special children based in Bangladesh, aged between 5 to 20 with Autism Spectrum Disorder (ASD) and identified their emotions from their images. Our objective is to find a technique that will allow their emotions to be accurately perceived and thus, make it easy for them including other individuals around them to interact socially without any barriers. In our paper, we implemented VGG16 and six Machine Learning Algorithms along with one Feature Extraction technique to detect Facial Expression of children with autism. The proposed system has showcased an accuracy of 75% for VGG16, 68% for Random Forest Classifier, 61% for Random Forest Classifier along with PCA, 61% for Support Vector Machine (SVM) and 67% for SVM with PCA, 56% for Logistic Regression and 53% for Logistic Regression along with PCA, 52% for Linear Discriminant Analysis (LDA) and 54% for LDA with PCA, 46% for Decision Tree and 50% for Decision Tree along with PCA and 46% for Gaussian Naïve Bayes and 38% for Gaussian Naïve Bayes along with PCA. © 2023 IEEE.

关键词： Support vector machines

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Real-Time Machine Learning for Precision Agriculture: Targeted Weed Detection and Adaptive Agrochemical Application

Real-Time Machine Learning for Precision Agriculture: Target...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Bhatt, Pooja Mishra, Amit Kumar Tripathi, Neha School of Agriculture Graphic Era Hill University Uttarakhand Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

Precision farming is increasingly being seen as the way toward sustainable improvement in crop yields, with a minimum degradation of the environment because of agricultural practices. In general, these traditional methods of agrochemical application, be they herbicides or pesticides, have a tendency to be overused, and this form of application, apart from economic problems, often creates a series of ecological issues. The highlighted target of this paper is an innovative real-time machine learning-based system designed for the precise detection and classification of crops and weeds that would enable targeted and adaptive agrochemical applications. The system is based on the core of a classifier from a Random Forest, trained on a custom dataset of images taken under several environmental conditions. In real-time, this classifier can differentiate between crops and weeds as the main input into an adaptive spraying mechanism that proportionally changes the chemical application rate based on the plant density and canopy size. Field tests can confirm that this system cuts the use of agrochemicals by up to 45% while maintaining a very high level of classification accuracy under challenging conditions. This system will offer an interesting solution for modern agriculture to drastically cut costs, environmental impacts, and health risks about conventional spraying methods, all while ensuring optimum crop health protection and yield enhancement. © 2024 IEEE.

关键词： adaptive agrochemical application crop classification machine learning Precision agriculture Random Forest classifier weed detection

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Fuzzy Clustering based Cluster Head Selection for IoT enabled WSNs 10

Fuzzy Clustering based Cluster Head Selection for IoT enable...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gangwar, Saumitra Prasad, Ikkurthi Bhanu Pal, Vipin Yogita Yadav, Satyendra Singh Nit Meghalaya Dept. of Computer Science and Engineering Shillong India

ISBN: (纸本)9789380544472

This work presents a fuzzy mechanism for selecting the optimum cluster head (CH) inside a cluster. During the preliminary simulations, different parameters with different weights are taken into account to make sure that the best CH is chosen, which ameliorates the wireless sensor network (WSN) lifetime. Fuzzy c-means (FCM) clustering is used in the proposed work to select a CH based on the remaining energy ratio, the distance to the sink, the node history, and the node degree. By varying the weights for the above parameters using a brute-force approach, the best-performing weights have been acquired that extend the longevity of a WSN. The simulation results confirm that by incorporating the above-mentioned parameters, network stability is improved by 140.10% and 45.09% as compared to LEACH and FCM, respectively. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Energy efficiency

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User Profiling and Interest-based Machine Learning to Solve the Cold-Start Problem for a Recommendation Engine 10

User Profiling and Interest-based Machine Learning to Solve ...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Hasan, Sayed Nasir Khatwal, Ravi Sangam University Dept. of Computer Science and Engineering Bhilwara India

ISBN: (纸本)9789380544472

The cold-start problem is a major issue in recommender systems that becomes a challenge for e-commerce companies when processing large amounts of data. Several strategies have been proposed to overcome the cold-start problem using different approaches, but the user interest approach is a unique and standalone approach. In this study, we present an interest-based approach that establishes a relationship between user profile data and corresponding user interests. Our work will recommend the items to the cold-start users based on their pro-files and interest matching, and also promote the cold-start users. Our approach is unique in that we consider user interests rather than similarities between items and recommend items which are based on user interests. The experiments and related tests show that proposed method is efficient and successful as far as of classification time, training time, and precision. Although other classifiers were efficient in terms of accuracy, the Bayesian classifier and Chain Classifier had the shortest classification time as well as training time. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Recommender systems

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BTI Aging Influence and Mitigation in Neural Networks Oriented In-Memory Computing SRAMs 12

BTI Aging Influence and Mitigation in Neural Networks Orient...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Dilopoulou, Christina Tsiatouhas, Yiorgos University of Ioannina Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350321074

Static random access memories (SRAMs) are commonly used in In-Memory Computing (IMC) architectures that perform neural network related computations directly in the memory unit, overcoming the data transfer constrains of traditional Von-Neumann architectures. However, Bias Temperature Instability (BTI) aging is a growing reliability concern for nanometer technology SRAMs. In this paper, we examine the BTI aging influence on SRAM based IMC architectures and propose an aging mitigation method to enhance their reliability and lifetime. According to our simulation results, BTI aging can affect the computation reliability of IMC cells. Aging mitigation is achieved by periodically alternating active with inactive transistors and vice-versa in these cells. © 2023 IEEE.

关键词： Reliability

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Optimal IPL Playing 11 Team Selection 8

Optimal IPL Playing 11 Team Selection

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Ravichandran, Nilesh Nihal Chengappa, P.A. Rao Rebala, Pranav PES University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350334012

Since the inception of the Indian Premier League (IPL), the demand for quality data analysts has increased. The task to pick an ideal playing 11 from a huge squad is a real challenge and picking the best team can greatly improve a team's chances of winning. This project aims to predict the best possible playing 11 from a given set of players taking into consideration a number of factors like runs scored, runs conceded, strike rate, dot balls bowled etc. and this is essential as it can help pick an optimal eleven. Not only are the best batsmen and bowlers predicted but also the all-rounders which give a real balance to the team. Three models have been tested- XGBoost, Linear Regression and Random Forest. Linear Regression yields better results since it gives the least RMSE value. © 2023 IEEE.

关键词： Linear regression

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