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Predicting Student Dropout: A Machine Learning Approach

Predicting Student Dropout: A Machine Learning Approach

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International Conference on computing and Networking Technology (ICCNT)

作者： Sreedham Deb Mst. Sakira Rezowana Sammy Abdur Nur Tusher Md. Rabbi Salehin Sakib Md. Firoz Hasan Ajharul Islam Aunik Computer Science and Engineering Daffodil International University Dhaka Bangladesh Management Information Systems Begum Rokeya University Rangpur Bangladesh Computer Science Wayne State College Wayne Nebraska United States

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Student dropout is a pervasive problem in the education system, with a significant number of students failing to complete their studies due to various reasons such as academic difficulties, financial issues, personal problems, and more. Dropout not only affects the student’s academic and career prospects but also has significant financial and social costs for educational institutions and society. Identifying at-risk students and providing targeted interventions to prevent dropout has been a longstanding goal of educational researchers and practitioners. In recent years, machine Learning algorithms have emerged as a promising method for predicting student attrition and pinpointing the primary elements that contribute to it. Predicting student attrition and pinpointing the primary elements that contribute to it. this research paper offers an extensive investigation of the application of machine-learning algorithms-in-predicting student attrition. the study is based on dataset of student’s academic and demographic information collected from a major university in the Bangladesh. the dataset comprises information on over 400 students who enrolled in the university between 2015 and 2020, including their academic records, demographic characteristics, and enrollment history. Analyze datasets utilizing a variety of machine learning approaches such as support vector machines, random forests, logistic regression etc. Evaluate the performance of these algorithms using metrics such as accuracy, precision, recall, and F1 score. Our study finds that machine learning algorithms can effectively predict student dropout with high accuracy, precision, and recall. the best-performing algorithm is Random Forest with a precision of 0.78, recall of 0.78, and $F 1$-score of 0.78. Logistic regression and KNN algorithms also perform reasonably well, with a precision of 0.75 and 0.76, respectively.

关键词： Support vector machines Measurement Logistic regression Machine learning algorithms Accuracy Predictive models Nearest neighbor methods Prediction algorithms History Random forests

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Advances in Yoga Training Tools: A CNN-RF Based Pose Identification System

Advances in Yoga Training Tools: A CNN-RF Based Pose Identif...

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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Shiva Mehta Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

A hybrid model that is presented in this article is called CNN-RF which uses convolutional Neural Networks in conjunction with Random Forest algorithms. there is a novel model that supports the task of yin yoga from the video motion data in real-time, with precision and speed. the model deals with the problem of accurate identification of body images in yoga due to the ease of its solution. It uses a deep learning approach in order to extract distinctive features, which are combined and further categorized by the ensemble learning algorithm. With these techniques, a situational quotient of 94 is reachable. 115%. the sum of the performance of the five two poses was excellent (the maximum accuracy of 94). Surprisingly, the MNEs have shifted the marketing of their goods and services according to China’s new domestic market (with a $75 \%$ market share), focusing on recall (with a $98 \%$ effectiveness rate). Support Vector Machine (SVM, 99% precision and 95% recall, scores around 98%), and F1-scores (above 94%), signifies its importance. the outcomes obtained at the confusion matrix prove we have a good classification accuracy and, mostly, observed confusion between postures that are similar is little. they call attention to the issues to improve the future services. Error rate showed a considerable decrease during the training stages, from the error rate of $18 \%$ dropping to $4 \%$, demonstrating very high quality of the training in 10 epochs. the model’s reliability in statistical and the speed of gaining training points makes it feasible to be applied in yoga training systems, health monitoring, and rehabilitation machines. this brings the essential e-bikes features, user motivation and accident reduction. the above work provides a solid basis for AI and physical wellness to merge by proposing an approximation method that can enhance safety and effectiveness in yoga training with the help of technology.

关键词： Training Support vector machines Accuracy Error analysis Streaming media Feature extraction Real-time systems Convolutional neural networks Random forests Monitoring

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Distinctive Zucchini Classification in India: Leveraging CNN and RNN Models

Distinctive Zucchini Classification in India: Leveraging CNN...

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International Conference on computing and Networking Technology (ICCNT)

作者： Satvik Vats Rishabh Sharma Computer Science and Engineering Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the proposed study works at three types of zucchini-Arka Sumeet, Arka Anamika, and Ahkra Manik-in India. It uses a mix of Convolutional Neural Networks (CNN) and recurrent Neural Networks (RNN) models to classify them. A big set of $\mathbf{1 1, 0 0 0}$ high-quality pictures showing different stages of zucchini growth was gathered and cleaned up to make all the data match. the suggested new design, a mix of CNN and RNN, was made to find complex plant pictures. It can also understand how the zucchini grows over time better by looking at its patterns. the model is very good, with a success rate of $\mathbf{9 6. 9 \%}$. It does an excellent job telling zucchini types apart. Measurement tools like precision, recall, and F1-score along with specificity show if the model is good at correctly classifying things. the Confusion Matrix helps us understand where mistakes happen in classification, so can improve models better. this study shows how the mix of CNN-RNN is important for the proper grouping of zucchini types. It gives a strong base to help precision farming uses. the usefulness of the model in farming choices, getting rid of diseases, and planning specific crop strategies shows its real importance. More progress is needed in making neural networks better, adding more data, and fixing models. this will make them flexible for all kinds of farming situations and improve their performance. this study shows a good, solid way to classify types of zucchini in India. It could have important effects on broader farming tech uses.

关键词： Recurrent neural networks Computational modeling Crops Solids Data models Planning Convolutional neural networks Farming Diseases Strain

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Developing a Deep Residual Network For the Early Identification of Adult-Onset Diabetes

Developing a Deep Residual Network For The Early Identificat...

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International Conference on computing and Networking Technology (ICCNT)

作者： Mary Hanna Priyadharshini R Kanishkka S.N Affreen Unisa C Poornima Dept of Computer Science and Engineering (Artificial Inteligence and Machine Learning) Vel Tech high Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Diabetes is a chronic metabolic disorder characterized by elevated blood glucose levels, resulting in progressive damage to essential organs such as the heart, blood vessels, eyes, kidneys, and nerves. Primarily affecting adults, type 2 diabetes, the most common form, emerges when the body exhibits resistance to insulin or fails to generate a sufficient quantity. the past three decades have witnessed a substantial rise in the prevalence of type 2 diabetes globally, spanning nations with diverse income levels. In the quest to improve patient care through the early detection of diabetes-related complications, this study employs machine learning (ML) models to identify and classify type 2 diabetes. the research evaluates the effectiveness of five ML models-K-nearest neighbor (K-NN), Bernoulli Naive Bayes (BNB), decision trees (DT), logistic regression (LR), and support vector machines (SVM)-as well as three deep learning models-Res-Net, MultiLayer Perceptron (MLP), and Long Short-Term Memory (LSTM)-in identifying individuals with *** a Kaggle-hosted dataset that includes information from 2768 patients, both with and without diabetes, the study considers various factors such as previous pregnancies, blood glucose levels, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index, genetic background, family history of diabetes, age, and ultimate outcome. the results indicate that the deep learning models demonstrated notable performance. the Res-Net model, with a success rate of $98 \%$, emerged as the most effective in diabetes identification, closely followed by the MLP and LSTM models with success rates of $\mathbf{9 4. 2 \%}$ and $87.2 \%$, respectively. In summary, the use of deep learning models for early diabetes detection shows significant promise, as evidenced by the favorable performance of the ResNet model in this investigation.

关键词： Deep learning Support vector machines Training Accuracy Predictive models Diabetes Glucose Insulin Long short term memory Blood

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ParkinSense: A Fuzzy Inference System designed to aid in diagnosing Parkinson’s Disease

ParkinSense: A Fuzzy Inference System designed to aid in dia...

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International Conference on computing and Networking Technology (ICCNT)

作者： Aaryan Mahadik Veena Trivedi Kunal Khubchandani Raj Tandon Bhavika Valecha TE Computer Engg VESIT Chembur CMPN Dept Faculty VESIT Chembur

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder that poses significant challenges in early detection and precise classification of its severity. this research paper introduces a machine learning and fuzzy logic based approach to address these challenges by developing a predictive model for Parkinson’s disease detection and severity classification. the study utilizes a diverse dataset comprising clinical information and speech features from individuals with and without Parkinson’s disease. through comprehensive data preprocessing and feature engineering, the paper aims to extract relevant patterns and biomarkers associated with the disease and to predict a probability of the disease using a fuzzy approach. A variety of machine learning algorithms, including but not limited to support vector machines, random forests, and neural networks, are employed to train and evaluate the model. the focus is on achieving high accuracy, sensitivity, and specificity in detecting the presence of Parkinson’s disease. Additionally, the model is designed to classify the severity levels of Parkinson’s disease, providing valuable insights into disease progression. the findings aim to enhance our understanding of the disease’s underlying mechanisms and facilitate more targeted and personalized treatment strategies. the proposed model’s performance is assessed through rigorous validation techniques, including cross-validation and independent testing. the paper aspires to bridge the gap between machine learning advancements and the pressing clinical needs in Parkinson’s disease diagnosis and severity assessment, fostering a more informed and proactive approach to patient care. this research investigates Parkinson’s disease detection through machine learning, incorporating features such as shimmer and frequency analysis. By employing advanced algorithms, the study aims to enhance diagnostic precision by examining subtle vocal and movement characteristics associated with P

关键词： Fuzzy logic Support vector machines Machine learning algorithms Pressing Sensitivity and specificity Predictive models Feature extraction Random forests Diseases Testing

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the program decision logic approach to predicated execution 99

The program decision logic approach to predicated execution

引用

Proceedings of the 26th annual international symposium on computer architecture

作者： David I. August John W. Sias Jean-Michel Puiatti Scott A. Mahlke Daniel A. Connors Kevin M. Crozier Wen-mei W. Hwu Center for Reliable and High-Performance Computing University of Illinois Urbana-Champaign IL Logic Systems Laboratory (DI-LSL) Swiss Federal Institute of Technology of Lausanne (EPFL) CH-1015 Lausanne Switzerland Hewlett-Packard Laboratories Hewlett-Packard Palo Alto CA

ISBN: (纸本)9780769501703

Modern compilers must expose sufficient amounts of Instruction-Level Parallelism (ILP) to achieve the promised performance increases of superscalar and VLIW processors. One of the major impediments to achieving this goal has been inefficient programmatic control flow. Historically, the compiler has translated the programmer's original control structure directly into assembly code with conditional branch instructions. Eliminating inefficiencies in handling branch instructions and exploiting ILP has been the subject of much research. However, traditional branch handling techniques cannot significantly alter the program's inherent control structure. the advent of predication as a program control representation has enabled compilers to manipulate control in a form more closely related to the underlying program logic. this work takes full advantage of the predication paradigm by abstracting the program control flow into a logical form referred to as a program decision logic network. this network is modeled as a Boolean equation and minimized using modified versions of logic synthesis techniques. After minimization, the more efficient version of the program's original control flow is re-expressed in predicated code. Furthermore, this paper proposes extensions to the HPL PlayDoh predication model in support of more effective predicate decision logic network minimization. Finally, this paper shows the ability of the mechanisms presented to overcome limits on ILP previously imposed by rigid program control structure.

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Machine Learning Vulnerabilities at the Edge: A Study of Multi-stage Gray-box Attacks*

Machine Learning Vulnerabilities at the Edge: A Study of Mul...

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International Conference on computing and Networking Technology (ICCNT)

作者： Ayush Sinha Ishan Shrivastava Nachiket Tapas O. P. Vyas C3iHub Indian Institute of Technology Kanpur U.P India Department of Information Technology Indian Institute of Information Technology Allahabad Devghat Jhalwa Prayagraj India Department of Computer Science and Engineering Chhattisgarh Swami Vivekanand Technical University Bhilai Chhattisgarh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the growing use of machine learning (ML) models at the network edge has created new opportunities for adversaries to exploit vulnerabilities. Adversarial machine learning (AML) attacks manipulate input data, causing misclassification in sectors such as healthcare, autonomous vehicles, and finance. this research provides a pre-emptive analysis of a multi-stage gray-box attack, which can achieve results similar to a white-box attack. Unlike white-box attacks, this gray-box attack uses real-time edge data to train a surrogate model from the targeted ML model. the adversary then adjusts input data for the surrogate model to identify classifier weaknesses and design a more potent attack. Detection of this attack is challenging because it does not require full access to the targeted classifier’s parameters or training data. We examine a real life scenario, each reflecting distinct adversarial real-time data instances. the surrogate model is chosen from six ML models trained on the complete dataset. Simulations show that the multi-stage gray-box attack misleads the ML classifier, achieving a high evasion rate with less data compared to a white-box attack. Our findings reveal the susceptibility of ML classifiers at the network edge to multi-stage gray-box attacks, highlighting potential impacts on performance and reliability. We also evaluate strategies to address these vulnerabilities, including robust encryption algorithms and advanced anomaly detection techniques.

关键词： Training Image edge detection Prevention and mitigation Training data Medical services Data models Real-time systems Adversarial machine learning Reliability Glass box

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Toward a Dynamic Network-Centric Distributed Cloud Platform for Scientific Workflows: A Case Study for Adaptive Weather Sensing

Toward a Dynamic Network-Centric Distributed Cloud Platform ...

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IEEE International Conference on e-Science and Grid computing

作者： Eric Lyons George Papadimitriou Cong Wang Komal thareja Paul Ruth J. J. Villalobos Ivan Rodero Ewa Deelman Michael Zink Anirban Mandal Electrical and Computer Engineering Department University of Massachusetts at Amherst MA USA Information Sciences Institute University of Southern California CA USA RENCI University of North Carolina at Chapel Hill NC USA Rutgers Discovery Informatics Institute NJ USA

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

Computational science today depends on complex, data-intensive applications operating on datasets from a variety of scientific instruments. A major challenge is the integration of data into the scientist's workflow. Recent advances in dynamic, networked cloud resources provide the building blocks to construct reconfigurable, end-to-end infrastructure that can increase scientific productivity. However, applications have not adequately taken advantage of these advanced capabilities. In this work, we have developed a novel network-centric platform that enables high-performance, adaptive data flows and coordinated access to distributed cloud resources and data repositories for atmospheric scientists. We demonstrate the effectiveness of our approach by evaluating time-critical, adaptive weather sensing workflows, which utilize advanced networked infrastructure to ingest live weather data from radars and compute data products used for timely response to weather events. the workflows are orchestrated by the Pegasus workflow management system and were chosen because of their diverse resource requirements. We show that our approach results in timely processing of Nowcast workflows under different infrastructure configurations and network conditions. We also show how workflow task clustering choices affect throughput of an ensemble of Nowcast workflows with improved turnaround times. Additionally, we find that using our network-centric platform powered by advanced layer2 networking techniques results in faster, more reliable data throughput, makes cloud resources easier to provision, and the workflows easier to configure for operational use and automation.

关键词： Radar Meteorology Generators Clouds Sensors Cloud computing Adaptive systems

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AI-Driven Dermatology: CNN-RF Hybrid Model for Skin Lesion’s Disease Classification

AI-Driven Dermatology: CNN-RF Hybrid Model for Skin Lesion’...

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International Conference on computing and Networking Technology (ICCNT)

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the spectrum of skin diseases, based on their nature, poses significant barriers to health that affect individuals internationally. thus, basic procedures such as accurate and early diagnosis are paramount to successful treatment. this paper presents a hybrid model called Convolutional Neural Network and Random Forest (CNNRF) for accurately diagnosing five different types of skin diseases: Melanoma, Benign nevus, Basal cell carcinomas 5CC, Dermatofibroma, vascular lesions. Resizing, normalization techniques, and data augmentation were applied to preprocess the dataset, including annotated photos of skin lesions. the CNN-RF method is based on Convolutional Neural Networks (CNNs) feature extraction and classification provided by Random Forests (RFs). Based on the results of the experiments, it was established that the proposed CNN-RF model achieved greater classification accuracy of 94% compared to the CNN model with $\mathbf{9 1 \%}$ and the RF model with $\mathbf{8 8 \%}$. Moreover, for the same experiment, the CNN-RF model established an accuracy of 93 per cent, recall of 94 per cent, and $\mathbf{F 1}$ score of 94 per cent. the model’s performance was demonstrated and analyzed using a confusion matrix and classification reports. the accuracy and recall rates were very high for this type of skin lesion, specifically up to 95% accuracy and 96% recall for Dermatofibroma and Vascular Lesions. Nonetheless, this research asserts the reliability of the proposed CNN-RF model as a feasible instrument for diagnosing skin diseases automatically, as well as with superior precision and stability. thus, the findings hold significant implications for clinical practice alerts, as the model can assist dermatologists in providing timely identification to improve the quality of treatment for their clients. Future work will focus on expanding the dataset and exploring other types of compound models to increase the accuracy of the diagnosis. this work contributes to the advancement of

关键词： Radio frequency Analytical models Accuracy Dermatology Feature extraction Skin Convolutional neural networks Lesions thermal stability Diseases

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Examining the Potential of Machine Learning in Reducing Prescription Drug Costs

Examining the Potential of Machine Learning in Reducing Pres...

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International Conference on computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Sairam Madasu Krishna J Rolla Ketan Gupta N. Yuvaraj Senior Software Engineer USA Senior Cloud Software Engineer Microsoft Charlotte North Carolina USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. the use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool.

关键词： Drugs Costs Accuracy Machine learning algorithms Software algorithms Pricing Medical services Organizations Machine learning Software

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