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Revolutionizing Diabetes Prediction Models: Leveraging Advanced Feature Reduction and Classification Methods 22

Revolutionizing Diabetes Prediction Models: Leveraging Advan...

引用

22nd International Learning and Technology Conference, L and T 2025

作者： Attiah, Ameera Alagha, Mawaddah Bukassim, Lujain El-Kafrawy, Passent College of Engineering Effat University Computer Science Dept. Jeddah Saudi Arabia

ISBN: (纸本)9798331532215

The global challenge of diabetes demands innovative approaches for early diagnosis and effective management. This paper investigates the integration of advanced dimensionality reduction and feature selection techniques to improve diabetes prediction models. Utilizing Principal Component Analysis (PCA) and the Artificial Bee Colony (ABC) algorithm, the study focuses on optimizing feature selection to enhance model accuracy, computational efficiency, and interpretability. Employing two diverse datasets - the Behavioral Risk Factor Surveillance System (BRFSS) and the Pima Indians Diabetes Dataset - the research evaluates the performance of Random Forest and Logistic Regression models. Results reveal significant improvements in predictive accuracy and computational efficiency, particularly with ABC-selected features, reducing feature dimensionality while maintaining high accuracy. This study underscores the potential of combining PCA and ABC to revolutionize predictive healthcare analytics, offering robust tools for diabetes management and prevention. © 2025 IEEE.

关键词： Dimensionality reduction

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Real-Time Gaze Tracking for Online Examination

Real-Time Gaze Tracking for Online Examination

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Bisht, Preyanshu Kumar, Suresh Netaji Subhas University of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798331508685

The exponential growth in online education has increased the demand for automated systems to ensure academic integrity during online examinations. A real-time proctoring system addresses this need by monitoring a student's eye gaze and head movements during an exam. This system leverages Dlib's pre-trained frontal face detector and 68-point facial landmark predictor to detect facial features and track the direction of eye movement and head position. By analyzing these metrics, the system can flag behaviors associated with potential cheating, such as looking away from the screen for extended periods. This paper discusses the design and implementation of the proctoring system, details the detection algorithms, and evaluates its effectiveness for real-time monitoring. © 2025 IEEE.

关键词： Online systems

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Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning

引用

2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Pradeep Kumar, B.P. Bhagya, D.J. Gagana, G. Mani, N. Dept. of Computer Science and Engineering Atria Institute of Technology Karnataka Bangalore India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538538

By examining patterns in the text data and metadata linked to job adverts, The use of learning algorithms has grown in popularity in the detection of fraudulent job postings. For this, Research Machine emphasises the application of a number of algorithms, such as Naive Bayes and Random Forest, Together with models for deep learning, like CNN and Bidirectional LSTM, and AdaBoost. To extract useful features These techniques depend on text preparation techniques such as lemmatisation, tokenisation, and TF-IDF vectorisation. To differentiate between authentic and fraudulent posts, models are instructed in datasets and evaluated using measures such as accuracy, accuracy, memory, and F1-score. Advanced classifiers and group techniques have proven to be quite successful in enhancing detection performance while lowering false positives and negatives. © 2025 IEEE.

关键词： Random forests

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Smart Vehicle Procurement System using Blockchain Technology 3

Smart Vehicle Procurement System using Blockchain Technology

引用

3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kumar, Boddu Manoj Rajeswari, G. Duraipandian, Kavitha David, Shiela Dept. of Computer Science and Engineering with specialization in Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331527549

Blockchain technology has become more widespread in our lives revolutionizing various industries through improved security, transparency, and operational efficiency. Its decentralized ledger system is particularly effective in areas like finance and supply chain management ensuring data integrity and reducing the need for middlemen. This study explores how blockchain technology is used in the process of buying vehicles highlighting its benefits compared to methods. Traditional vehicle purchases involve a network of dealers, banks and regulators leading to inefficiencies, higher costs and potential fraud. The usual process involves steps like financing checking vehicle history and changing ownership. Each requiring extensive paperwork and third-party intervention. In contrast using blockchain for vehicle purchases can simplify things through contracts that automate tasks like verifying history securely transferring ownership instantly settling payments. The unchangeable nature of records ensures that all transaction data remains secure against tampering fostering trust between buyers and sellers. Furthermore, blockchain can cut costs significantly by removing the need for middlemen such, as escrow services and title companies while reducing burdens. The advanced security elements built into technology help reduce the chances of fraudulent actions like altering odometer readings or selling stolen cars. This study showcases a comparison illustrating how blockchain can simplify the process of buying vehicles offering an economical and secure experience. © 2025 IEEE.

关键词： Decentralized finance

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Using Partial Least Squares Regression to Estimate the State Quantities of Kunigel-V1 Bentonite by Near-Infrared Reflection Spectra

Using Partial Least Squares Regression to Estimate the State...

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Geo-EnvironMeet 2025: Innovative Developments toward Sustainability

作者： Murase, Sota Ruan, Kunlin Ito, Daichi Wang, Hailong Komine, Hideo Dept. of Civil and Environmental Engineering Faculty of Science and Engineering Waseda Univ. Okubo Shinjuku-ku Tokyo Japan Faculty of Science and Engineering Waseda Univ. Okubo Shinjuku-ku Tokyo Japan Dept. of Civil Engineering Univ. of Tokyo Japan

ISBN: (数字)9780784485682

ISBN: (纸本)9780784486016

Bentonite was selected as candidate buffer material in deep geological disposal project for handling high-level radioactive waste. In the context of the buffer construction, efficient and accurate assessment of parameters such as water content and dry density is essential. This study explores the possibility of using near-infrared spectroscopy to non-destructively measure these properties of Kunigel-V1 bentonite. Infrared reflectance spectra of 60 specimens with different water contents and compaction pressures were obtained using a near-infrared spectral sensor. The data were processed, and calibration curves were generated through partial least squares (PLS) regression. The results demonstrated high coefficients of determination between measured and estimated water contents, as well as for wet density, degree of saturation, and volumetric water content. For parameters indirectly related to water content, such as dry density and void ratio, exhibited promising coefficients of determination. These findings indicate the potential application of near-infrared spectroscopy and PLS regression for estimating the state quantities of compacted Kunigel-V1 bentonite. © ASCE.

关键词： Bentonite

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Predicting the Likelihood of a Recurrent Febrile Seizure Using Thermographic Images and Deep Learning with Rules Generation 1

Predicting the Likelihood of a Recurrent Febrile Seizure Usi...

引用

1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Patil, Pratima Deshpande, Deepa S. MGM University Jawaharlal Nehru Engineering College Dept. of Computer Science and Engineering Chhatrapati Sambhajinagar431003 India

ISBN: (纸本)9798350355611

Recurrent febrile seizures are a common concern in children, often triggered by rapid changes in body temperature during fever episodes. For rapid action and care, early and accurate seizure recurrence prediction is crucial. Limited techniques were developed for predicting the likelihood of a Recurrent Febrile Seizure using a Thermographic image. Several drawbacks were considered for recurrent febrile seizure prediction such as lower performance, high time consumption, and error rate. Hence, the proposed technique is introduced to provide an efficient Deep Learning (DL)-based technique. Initially, the data is gathered from datasets, and the Intra-Attentional Convolutional ResNet (InAtt-CRNet) with the Yolov5-Face model detects the face and reduces the processing time by tacking the face with a bounding box. The patient's forehead or nose temperature is monitored using the Region of Interest (ROI) finding approach following face detection and tracking in each frame. Then, the temperature is predicted using the Enhanced Convolutional Capsule Network (EC2Net). The body temperature, age, duration of fever, and family history are used to construct the clinical decision rule. The Twin-directional Gated Long Short-term memory (TdG-LSTM) is used to predict the likelihood of a subsequent febrile seizure. The proposed technique achieves 98.16% accuracy, 98.45% precision, 0.12 RMSE, and 29 seconds as processing time. © 2025 IEEE.

关键词： Long short-term memory

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Determination of In Situ Rock Density and Elastic Moduli with SH-Love Wave Tomography

Determination of In Situ Rock Density and Elastic Moduli wit...

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Geotechnical Frontiers 2025: Soil Characterization and Improvement

作者： Tran, Khiem T. Chen, Ruoyu McVay, Michael Dept. of Civil and Coastal Engineering Univ. of Florida GainesvilleFL United States Dept. of Civil Engineering and Architecture Jiangsu Univ. of Science and Technology Jiangsu Zhenjiang China

ISBN: (数字)9780784486016

Detailed information on in situ rock density and variability is important for the design and construction of shallow foundations. This paper presents an application of SH- and Love-waves full-waveform inversion method (2D SH-Love FWI) for the determination of rock density over a large volume without the requirement of borings. There are two main advantages of the presented SH-Love wave approach. First, SH- and Love-waves (horizontal source) are more sensitive to material density than vertical S-wave, P-wave, and Rayleigh waves (vertical source), allowing for more accurate extraction of the density. Second, the SH-Love FWI provides both density and S-wave velocity (Vs), and the shear modulus can be directly computed for analyses of foundation bearing capacity and settlement. To assess the method’s feasibility, SH-wave seismic testing and rock coring were conducted at a Florida site. The SH-Love FWI method was able to characterize both density and Vs of a complex substructure profile to 18-m dept. at a high resolution (sub-meter pixels). The profile consists of two soil layers at 0-2 m and 4-6 m dept.s with a density of about 1,400 kg/m3, and two rock layers at 2-4 m dept. and below 6 m dept. with a density of about 1,600 kg/m3. The density values from the seismic testing well agree with those from rock coring samples. The results show the proof of concept that the mass density can be obtained from the seismic testing with good accuracy. © ASCE.

关键词： Soil testing

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Hybrid Fake News Detection Model: Bagging and Logistic Regression Approach 14

Hybrid Fake News Detection Model: Bagging and Logistic Regre...

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14th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2025

作者： Kumar, Shivam Tiwari, Naveen Kumar Bajpai, Abhishek Yadav, Shashank Dept. of Computer Science and Engineering Rajkiya Engineering College Kannauj UP Kannauj India

ISBN: (纸本)9798331531935

In Today’s world or from Past Scenario’s Fake News has a Huge Impact on Everyone’s Lives including Politics, Sports, Education, External Foreign Affairs, Defense, etc. This project aims to develop a model for identifying and detecting fake news. In this model, we implement an ensemble method that leverages the TF IDF vectorization technique to extract meaningful textual features and employ a Bagging Classifier with Logistic Regression as the base estimator to enhance classification accuracy. To build this classifier, we utilized the scikit learn library. Our work was implemented using Jupyter Notebook, an online platform. For feature extraction, we employed a well-known vectorizer called Term Frequency Inverse Document Frequency (TF-IDF). We split our data into training sets (75%) and test sets (25%). The proposed model achieves an accuracy of 99.15% on the test dataset, with a precision of 99.45%, a recall of 99%, and an F1-score of 99.17%. It outperforms standalone Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting classifiers. © 2025 IEEE.

关键词： Logistic regression

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Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

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5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-dept. and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

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Experimental Study on Effects of Biopolymer on CO2 Gas Hydrates in Sandy Soil

Experimental Study on Effects of Biopolymer on CO2 Gas Hydra...

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Geotechnical Frontiers 2025: Foundations, Retaining Structures, and Geosynthetics

作者： Chea, Sokpheanika Kang, Chul-Hwan Kim, Jin Hwang, Hyun-Joong Cho, Gye-Chun Dept. of Civil and Environmental Engineering Korea Advanced Institute of Science and Technology Korea Republic of

ISBN: (数字)9780784485972

Gas hydrates have been increasingly recognized as a potential renewable energy source for the last two decades. Additionally, various studies have shown that the formation of gas hydrates within sediments can enhance soil stiffness by reducing the permeability of sediments, suggesting usage as a ground improvement material, especially in marine sedimentary. Enhancing the formation rate of gas hydrates is crucial for practical applications. Much research has been done exploring substances known as promoters that could increase the forming rate of gas hydrates. This paper aims to review types of conventional and emerging promoters and introduces biopolymers, specifically xanthan gum, as a novel promoter. We have conducted a series of high-pressure, low-temperature condition experiments to see the effects of biopolymer on gas hydrate formation. Results showed remarkable CO2 gas consumption differences due to biopolymers, indicating the potential for biopolymers as an environment-friendly soil improvement material. © ASCE.

关键词： Renewable energy

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