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Exploring the Construction Path of Characteristic Volunteer Service Teams in "Double First-Class" Universities under the Volunteer Service Framework

Exploring the Construction Path of Characteristic Volunteer ...

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作者： Xianying Li Yunkai Zhang Jiangyan Sun Kexin Li Yunhan Zhao Hao Ding School of Computer Science and Technology Harbin Institute of Technology Student affairs office Harbin Institute of Technology School of Language and Literature Harbin Institute of Technology

Volunteer service, as an important component of the Communist Youth League's work with youth in "Double First-Class" universities, plays an indispensable role in carrying out ideological and political education and nurturing socialist core values. Currently, volunteer service, primarily driven by social forces, focuses mainly on public welfare-oriented social service projects. The sustainability of volunteer service forces in universities is generally limited, highlighting the urgent need for universities to establish relevant volunteer service systems to meet educational demands. Starting with the definition of characteristic volunteer service teams in "Double First-Class" universities, this article discusses the importance and necessity of building such teams. Additionally, it analyzes and discusses the current status of the construction of characteristic volunteer service teams in "Double First-Class" universities, addressing the related issues. Finally, taking the "Fun Programming into Campus" volunteer service team at Harbin Institute of technology(Weihai) as an example, the article provides suggestions on how to build characteristic volunteer service teams in "Double First-Class" universities. These suggestions are intended to have a certain reference value for further promoting the comprehensive development of characteristic volunteer service teams in "Double First-Class" universities.

关键词： Double First-Class Exploring the Construction Path of Characteristic Volunteer Service Teams in Service Universities under the Volunteer Service Framework

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An Efficient Deep Learning Framework using CapsNet and SOM for Multidrug-Resistant Tuberculosis Detection and Analysis in CXR Images

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Procedia computer Science 2025年 258卷 3251-3263页

作者： Ceronmani Sharmila V Remya K Sam Praveen P Associate Professor Department of ECE SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu Assistant Professor Department of Information Technology Hindustan Institute of Technology and Science Padur Chennai Tamil Nadu PG Student Department of Computer Science and Information Systems Birla Institute of Technology and Science Pilani Rajasthan

Early detection of Multidrug-resistant Tuberculosis (MDR-TB) is crucial for illness prevention, lowering mortality risk, and preventing the spread of infection to people. The chest X-ray images(CXRI), which is affordable and widely available data can be used for MDR-TB disease screening in clinics. However, radiologists must manually review CXRI, which places a significant strain on them and increases inter-observer variability. Therefore, it is difficult for researchers to suggest a reliable and affordable CAD method for detecting MDR-TB. In this study, Capsule Neural Networks and Self Organizing Maps, simple and effective deep learning networks were introduced to perform detection and analysis tasks respectively. CapsNet is used to reliably identify MDR-TB from the patient’s CXR images. SOM clusters and visualizes the data at each layering stage. This work proposes an innovative approach of fusing CapsNet and SOM to produce higher accuracy. Comparative analysis between three classification methods under CapsNet (Dynamic routing, Transformation matrix, Convolutional capsules) is also performed. ConvCaps outperformed than the other two methods with the accuracy of 89.47%. The model is assessed by few parameters such as Precision, Recall, Accuracy, and F1-score for each method. The accuracy of the proposed fused CapsNet and SOM model is 93.6%.

关键词： CapsNet Multidrug-resistant Tuberculosis SOM CXR Images

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Classification of Skin Cancer Images Using Convolutional Neural Networks

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SSRN 2022年

作者： Agarwal, Kartikeya Singh, Tismeet Student of Computer Science and Engineering Netaji Subhas University of Technology India

Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and histopathological examination. Skin cancer occurs when errors (mutations) occur in the DNA of skin cells. The mutations cause the cells to grow out of control and form a mass of cancer cells. The aim of this study was to try to classify images of skin lesions with the help of convolutional neural networks. The deep neural networks show humongous potential for image classification while taking into account the large variability exhibited by the environment. Here we trained images based on the pixel values and classified them on the basis of disease labels. The dataset was acquired from an Open Source Kaggle Repository(Kaggle Dataset)which itself was acquired from ISIC(International Skin Imaging Collaboration) Archive. The training was performed on multiple models accompanied with Transfer Learning. The highest model accuracy achieved was over 86.65%. The dataset used is publicly available to ensure credibility and reproducibility of the aforementioned result. © 2022, The Authors. All rights reserved.

关键词： Dermatology

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Banana Leaf Disease Detection using Advanced Convolutional Neural Network

Banana Leaf Disease Detection using Advanced Convolutional N...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： E Mohanraj R Hariharan S Keerthana K B Cavin Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Any plant’s ability to grow disease-free is crucial for both the environment and human existence. Nevertheless, various diseases, viruses, and fungi affect the plant and highly influence the yield quality and production quantity. If the disease and its origin are not appropriately diagnosed, illness control procedures might be a waste of effort and resources, causing additional plant losses. Plant disease tracking by manual process is quite challenging. It requires a considerable amount of work, expertise in plant diseases, and protracted processing durations. Traditional neural networks require a large number of parameters to train effectively, which can make them slow and computationally expensive. The previous study includes the leaf disease detection performed by CNN, R-CNN, and SVM models. Using the Faster R-CNN, which outperforms other models in terms of computation time and accuracy, this study intends to develop a banana leaf disease detection system.

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Machine Learning Techniques for Diabetes Prediction: A Comparative Analysis

Machine Learning Techniques for Diabetes Prediction: A Compa...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： J Mythili T Surendhar P Suryaprakash K Suresh Kumar Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

As the prevalence of diabetes mellitus rises, more families are being impacted. Most diabetics don’t know much about their health situation or the risks they face before getting a diagnosis. A unique data mining-based method was suggested for the prediction of type 2 diabetes mellitus (T2DM) in this study. The accuracy and robustness of the prediction model are the primary priorities, ensuring flexibility so that it may be used to other datasets. The model is made up of two parts: the enhanced Logistic Regression with Multilayer Perception (LRMLP) and Naïve Bayes (NB) method with a set of preprocessing techniques. To compare the findings with those of other studies, the Diabetes Dataset for Pima Indians was used with the Environment for Knowledge Analysis toolkit. According to the findings, the model’s prediction accuracy is higher than that of earlier research. Additionally, this approach guarantees that the dataset quality is enough. The system was applied to two more diabetes datasets in order to assess its effectiveness further. Both research findings show strong performance. The model is shown to be helpful for managing diabetic health in a realistic manner as a result. For experimental analysis, data from the University of California, Irvine’s (UCI) machine learning repository’s Pima Indian Diabetes Database (PIDD) is used. These classification algorithms’ performance is evaluated using a variety of statistical metrics, including sensitivity (recall), precision, F-score, and accuracy. The measurement of correctly and erroneously classified cases is called accuracy. According to the testing findings, LR-MLP outscored other classification algorithms and provides the maximum accuracy of 96.3%.

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Crime Type Prediction based on Various Occurrence using Parallel LSTM

Crime Type Prediction based on Various Occurrence using Para...

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Sustainable Computing and Smart Systems (ICSCSS), International Conference on

作者： M Saradha T Nithesh Priyan D U Shreeram S Viknesh Department of Computer Science and Engineering Faculty of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India Department of Computer Science and Engineering Student of Computer Science and Engineering K S Rangasamy College of Technology Tiruchengode India

Crime is a widespread societal issue that has a negative impact on people's standard of living and the nation's prosperity. It's a major consideration for potential residents and tourists alike when deciding whether or not to settle in a given area. As crime rates rise, police departments have a growing need for cutting-edge GIS and data mining tools to enhance crime analytics and strengthen public safety. The suggested method includes preprocessing, feature selection, and evaluating the model's performance. We begin by cleaning up the raw crime statistics. For more predictable signals, this comprises both spatial and temporal regularization. Feature selection is performed using a rough spanning tree. To measure the effectiveness of the model, we employ parallel LSTM. When compared to two established approaches, the new strategy fares quite well.

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Feature Selection for Binary Dataset using Dragonfly Algorithm

Feature Selection for Binary Dataset using Dragonfly Algorit...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Zaid Tariq Raouf Dhafar Hamed Abd Informatics Institute for Postgraduate Student Baghdad Iraq College of Computer Science and Information Technology University of Anbar Ramadi Iraq

In contemporary times, the proliferation of data dimensionality has introduced many challenges within the realm of machine learning. Consequently, identifying and selecting pertinent features have assumed paramount importance. Consequently, a diverse array of techniques for feature selection has been proffered. Among them, Metaheuristic techniques are particularly significant within this milieu, with a focus on the dragonfly algorithm garnering notable attention. Metaheuristic algorithms are rooted in the emulation of swarm intelligence, drawing inspiration from the collective behaviors exhibited by insects. This scholarly inquiry aims to enhance the efficacy of classification outcomes by leveraging the dragonfly algorithm to select the most salient features. In this paper, the proposed approach is rigorously evaluated across three distinct datasets, namely, “Ansur,” “Predict 5-Year Career Longevity for NBA Rookies,” and “Chronic Kidney Disease,” all of which were procured from the Kaggle repository. The study used four machine learning classifiers, namely, XGBoosting, K-Nearest Neighbors (KNN), Decision Tree, and Gaussian Naive Bayes (Gaussian-NB), for classification and evaluation. The empirical findings unveiled a notable enhancement in classification performance. Notably, the accuracy metrics exhibit substantial improvements across all classifiers. For instance, within chronic kidney disease classification, both XGBoost and Decision Tree classifiers yielded a remarkable accuracy rate of 100%, thereby underscoring the efficacy of the proposed dragonfly algorithm-based feature selection technique in augmenting the predictive capabilities of machine learning models.

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Gene Disease Classification from Biomedical Text via Ensemble Machine Learning

Gene Disease Classification from Biomedical Text via Ensembl...

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International Conference on Developments in eSystems Engineering (DESE)

作者： Rabea F. Ghazi Dhafar Hamed Abd Informatics Institute for Postgraduate Student Baghdad Iraq College of Computer Science and Information Technology University of Anbar Ramadi Iraq

Detecting connections between genes and diseases is a vital endeavor in bioinformatics and genomics, carrying significant implications for the entire comprehension of the molecular underpinnings of various diseases. The rapid increase in the number of documents in the field of biomedicine has resulted in a significant burden and time requirement for manually curating relationships within this literature. In order to tackle this particular difficulty, the present study introduced a resilient ensemble machine-learning methodology that aimed at automating the classification of gene-disease relationships through the analysis of biomedical text. The proposed model was meant to leverage ensemble learning capabilities by integrating different base classifiers which are Decision Trees, Random Forest, AdaBoost, Bagging, CatBoost, Extra Trees and XGBoost with two feature extraction TF and TF-IDF. This ensemble architecture aimed to enhance the accuracy and dependability of gene-disease association predictions by utilizing a wide range of variables obtained from biomedical literature, including abstracts and various ensemble configurations and evaluating performance using standard metrics which are precision, recall, and F1-score, AUC, and accuracy. The study findings provided evidence supporting the efficacy of the ensemble methodology in enhancing both accuracy and resilience when compared to the performance of individual classifiers. The highest accuracy was achieved with XGBoost and TF 0.979%.

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Using Machine Learning Techniques to Moderate CO2 Emission in KSA Future Smart Cities

Using Machine Learning Techniques to Moderate CO2 Emission i...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Arwa H. Alshanbari Shahd H. Altalhi Samaher S. Alsharif Ghalia M. Alharthi Razan W. Althubiti Hossam Meshref Computer Science Department Master's in Data Science graduate student College of Computers and Information Technology Taif University Taif Kingdom of Saudi Arabia Computer Science Department College of Computers and Information Technology Taif University Taif Kingdom of Saudi Arabia

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

As the world grapples with the escalating challenge of climate change, the role of innovative technologies in mitigating environmental impacts has never been more important. This study is inspired by the pioneering smart city of NEOM, Saudi Arabia; one of the forefronts of sustainable urban development, leveraging machine learning (ML) techniques to predict and mitigate carbon dioxide (CO 2 ) emissions in the cities of Saudi Arabia. These cities are striving towards NEOM's approach to achieving zero emissions to the greatest extent possible, because NEOM needs the surrounding cities to have low CO 2 emission as well to achieve its goal. This research aims to create models based on machine learning that can precisely predict CO 2 emissions, utilizing a detailed dataset of vehicle emissions from Kaggle. We apply the following ML models: Logistic Regression, Gradient Boosting, AdaBoost, Bagging, Backpropagation Neural Networks, Naive Bayes, and Support Vector Machines (SVM). The models are evaluated based on their Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) as well as Confusion Matrix in predicting CO 2 emissions. Our analysis achieved the highest performance with an R 2 value of 0.99. Gradient Boosting and Bagging achieved the highest accuracies of 99%. Through a comprehensive review of recent studies and the application of diverse machine learning approaches, this proposed research identifies key challenges in reducing CO 2 emissions in smart cities, emphasizing the critical role of ML in enhancing the accuracy of forecasts across various sectors. Ultimately, that will support policy makers to make informed decisions to sustain a better urban development.

关键词： Support vector machines Technological innovation Accuracy Smart cities Reviews Neural networks Predictive models Boosting Carbon emissions Bagging

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Improving the Performance of the K-Nearest Neighbors Algorithm with Parallelization in Dask

Improving the Performance of the K-Nearest Neighbors Algorit...

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International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Amin Rezanejad Amir Seyed Danesh Master’s student in Computer Engineering University of Guilan Rasht Iran Faculty of Technology and Engineering East of Guilan University of Guilan Rudsar-Vajargah Iran

This study implements the K-Nearest Neighbors (K-NN) algorithm for classification tasks using the Python programming language in both serial and parallel modes. The execution times are measured and compared between the serial and parallel implementations, demonstrating a performance improvement in the parallel execution. The research aims to emphasize the significance of parallelization and computational efficiency enhancement in machine learning algorithms, specifically focusing on K-NN, using Python and tools such as DASK. Such studies are valuable for enhancing the performance of tasks related to data scientists and data analysts, benefiting from the accessibility and ease of parallelization offered by Python and related frameworks.

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