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Deep Analysis of Machine Learning Techniques for the Effective Retrieval of Images Based on Content

Deep Analysis of Machine Learning Techniques for the Effecti...

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Disruptive Technologies (ICDT), International Conference on

作者： Ashish Jain Sudeep Varshney Dept. of Computer Science and Engineering SET Sharda University Greater Noida India Dept. of Computer Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida India

Our research aims to investigate how machine learning techniques can be applied for efficient image retrieval based on content. It is a very difficult process to obtain accurate images from vast digital image collections. CBIR has been particularly interested in this area. It has been shown that content- or feature-based approaches are more effective than text-based query techniques. Research has shown that the performance of these methods varies depending on the type and size of the data set analyzed, with some algorithms performing well for specific small data sets and others for larger data sets. This article provides a comprehensive and systematic review of effective methods for obtaining the image. The paper's objective is to examine different ML classifiers that are applied via the researchers and compare their results based on accuracy and performance. These algorithms include SVM, KNN, random forest, BoVW, Naive Bayes, and LDA. This review paper can assist researchers in selecting the best classifier for their research, as well as in improving existing classifiers by combining them with other algorithms.

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Rameshwari Kapoor Rashmi Kuliyal Nilesh Bhanot Piyush Bagla Ruchira Rawat Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Dept. of Computer Science and Engineering Graphic Era (Deemed to be University) University Dehradun Uttarakhand India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70 % on the driver distraction dataset.

关键词： Deep learning Training Analytical models Accuracy Road accidents Transfer learning Convolutional neural networks Driver behavior Vehicles Residual neural networks

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Reddy, Arra Keerthan Vardhan Rapeti, Harsha Reddy, Doduguru Amarnath Srinivas, M. Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems © 2025 IEEE.

关键词： Deep neural networks

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Design and Development of an IoT Based Smart Vaccine Carrier and Real-Time Monitoring System

Design and Development of an IoT Based Smart Vaccine Carrier...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Raisa T. Hasanat Nafees Mansoor Nabeel Mohammed Mohammad S. Rahman Sifat Momen Mirza Rasheduzzaman EA Technology Capenhurst United Kingdom Dept. of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering United International University Dhaka Bangladesh Dept. of Electrical and Electronic Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Vaccination stands as the most reliable defense against a spectrum of life-threatening diseases. However, the eq-uitable distribution of vaccines in developing and least-developed nations face formidable challenges due to the absence of efficient and transparent cold chain management systems. This article introduces a real-time data-centric monitoring system designed to track the distribution and transportation of vaccines. Additionally, this research presents an energy-efficient vaccine carrier based on thermoelectric cooling, boasting minimal power consumption requirements. This innovative carrier ensures the safe transportation of vaccines even to the most remote regions. Moreover, the proposed system regulates the temperature and humidity of individual carriers by continuously monitoring sensor data. Furthermore, the system includes real-time tracking and reporting features. Anticipated outcomes include a significant increase in vaccination coverage in remote areas and enhanced transparency in the distribution process.

关键词： Temperature sensors Temperature distribution Power demand Cold chain Transportation Humidity Real-time systems Vaccines Reliability Monitoring

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ADNet: An Advanced Artificial Neural Network for Early Diagnosis of Alzheimer's Disease

ADNet: An Advanced Artificial Neural Network for Early Diagn...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md. Arif Hasan Rifat Nusrat Jahan Tanha Taranum Muhammad Nazrul Islam Faiz Al Faisal Department of Computer Science and Engineering Green University of Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Alzheimer's disease is a progressive neurological disorder, often referred to as dementia, that requires early and accurate diagnosis for effective treatment and management. It's a complex disease type that originates in the brain and only gets worse with timestamps, which is why it should be diagnosed early and accurately. Traditional diagnostic methods often fall short, posing challenges to healthcare professionals. This paper introduces a novel deep learning technique, ADNet, a specialized convolutional neural network (CNN) developed to classify Alzheimer's disease into four stages. For MRI image processing, we applied preprocessing techniques, including Green Fire Blue conversion and contrast enhancement using CLAHE, to improve image clarity. To address data imbalance, various data augmentation methods were implemented. During experimentation, pre-trained models like VGG16, MobileNet-V2, InceptionV3, and ResNet-50 achieved accuracies of 85%, 85%, 79 %, and 67 %, respectively, but fell short of our goals. Our ADNet model, however, attained an impressive 97.26% accuracy. This work offers significant contributions to Alzheimer's research and advances deep learning applications in medical imaging and neurological disease diagnosis.

关键词： Neurological diseases Deep learning Training Technological innovation Accuracy Magnetic resonance imaging Computational modeling Brain modeling Convolutional neural networks Alzheimer's disease

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Contribution of Algorithm Visualizations to Students' Learning Skills: A Pedagogical Approach

Contribution of Algorithm Visualizations to Students' Learni...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Harem Ali Kheder Asaf Varol Dept. of Computer Engineering College of Engineering and Computer Sciences Lebanese French University Erbil Iraq Dept. of Engineering Management and Technology College of Engineering and Computer Science The University of Tennessee at Chattanooga Chattanooga TN US

ISBN: (数字)9798350330366

ISBN: (纸本)9798350330373

The Domain Algorithm visualizations are multimedia-based representations of algorithms that help students learn how they operate and behave. Algorithm visualizations provide numerous benefits to instructors and students, including an improved understanding of difficult algorithms in programming. By stripping down the complexities of algorithms and presenting them in simplified visual ways, students can obtain a more thorough knowledge of how they perform. Instructional approaches, such as guided instruction or discovery learning, are critical to the pedagogical effectiveness of algorithm representations. Guided instructions can considerably boost learners' overall performance in problem-solving activities that incorporate visualizations. At the same time, discovery education promotes exploration and experimentation, yet it may result in longer learning times and increased mental demands. A combination of these approaches is required for optimal effectiveness of instruction. In this paper, we investigate how the pedagogical effectiveness of algorithm visualizations should be determined by a variety of elements, including student characteristics, visual and cognitive processing ability, algorithm representation, and instructional context based on a literature review and interviews with field experts, and a bunch of recommendations is provided that has witnessed by authors during their long teaching periods.

关键词： Visualization Education Linguistics computer science education Complexity theory Security Problem-solving

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Predicting Age from Brain Connectivity Matrix: A Stacked Regression Approach

Predicting Age from Brain Connectivity Matrix: A Stacked Reg...

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International Symposium on Applied Computational Intelligence and informatics ( SACI)

作者： Gabriel Pojoga Victor Teslaru Otilia ZvoriȘteanu Ștefan-Daniel Achirei Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iasi Iasi Romania

ISBN: (数字)9798331515478

ISBN: (纸本)9798331515485

Accurately predicting age from functional connectome data can provide valuable insights into brain development and neurodegenerative processes. In this study, we apply various machine learning algorithms to age prediction using functional connectivity features. The preprocessing pipeline included missing value imputation and categorical variable encoding. We experimented with traditional regression models and neural networks, ultimately achieving the best performance with a stacking ensemble. The ensemble leveraged a Multi-Layer Perceptron meta-learner to reduce base model biases, improving predictive accuracy. To enhance diversity, base models were selected based on their individual performance and prediction correlation. Our results demonstrate that ensemble learning can effectively handle the complexity of neuroimaging data, offering a robust approach for age prediction and potential applications in neurological research.

关键词： Neuroimaging Computational modeling Precision medicine Stacking Diversity reception Proteomics Predictive models Brain modeling Ensemble learning Biological neural networks

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Quantum transport simulation of a mid-wave infrared Ga-based type-II superlattice for curved focal-plane arrays 13

Quantum transport simulation of a mid-wave infrared Ga-based...

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Infrared Sensors, Devices, and Applications XIII 2023

作者： Glennon, John Bellotti, Enrico Div. of Material Science and Engineering Boston University BostonMA02215 United States Dept. of Electrical and Computer Engineering Boston University BostonMA02215 United States

ISBN: (纸本)9781510665880

Type-II strained-layer superlattices have theoretically predicted advantages that make them an attractive material system for use in infrared technology. However, the influence of structural disorder has been demonstrated to negatively impact the performance of these materials. A recent development in infrared technology is the use of curved focal-plane arrays which promise improved uniformity of sensitivity and a reduction in the optical complexity of the device. Curving of the wafer introduces mechanical strain into the absorber layer which can also impact the vertical carrier mobility. This work reports on an analysis of the influence of external strain and disorder on the vertical hole mobility in curved focal-plane arrays utilizing a mid-wave InAs/GaSb absorber layers. A series of quantum transport calculations is performed over a range of strain configurations for two different structures: an ideal superlattice and a superlattice with graded interfaces and disorder. Finally, the impact these effects have on the quantum efficiency of a hypothetical curved-FPA is approximated, and a potential avenue for improved device design is suggested. © 2023 SPIE. All rights reserved.

关键词： Quantum efficiency

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Evaluating Machine Learning Methods for Bangla Text Emotion Analysis

Evaluating Machine Learning Methods for Bangla Text Emotion ...

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Innovation in Technology (APCIT), Asia Pacific Conference on

作者： Tahmina Akter Mst. Sharmin Akter Tanjim Mahmud Dilshad Islam Mohammad Shahadat Hossain Karl Andersson Dept. of Computer Science and Engineering Port City International University Chittagong Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Dept. of Physical and Mathematical Sciences Chattogram Veterinary and Animal Sciences University Bangladesh Dept. of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Cybersecurity Laboratory Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361537

ISBN: (纸本)9798350361544

Text-based emotion identification goes beyond simple sentiment analysis by capturing emotions in a more nuanced way, akin to shades of gray rather than just positive or negative sentiments. This paper details our experiments with emotion analysis on Bangla text. We collected a corpus of user comments from various social media groups discussing socioeconomic and political topics to identify six emotions: sadness, disgust, surprise, fear, anger, and joy. We evaluated the performance of four widely used machine learning algorithms—RF, DT, k-NN, and SVM—alongside three popular deep learning algorithms—CNNs, LSTM, and Transformer Learning—using TF-IDF feature extraction and word embedding techniques. The results showed that among the machine learning algorithms, DT, RF, k-NN, and SVM achieved accuracy scores of 82%, 84%, 73%, and 83%, respectively. In contrast, the deep learning models CNN and LSTM both achieved higher performance with an accuracy of 85% and 86% respectively. These findings highlight the effectiveness of traditional ML and DL approaches in detecting emotions from Bangla social media texts, indicating significant potential for further advancements in this area.

关键词： Deep learning Support vector machines Technological innovation Sentiment analysis Machine learning algorithms Accuracy Social networking (online)

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Printable Fused Silica Based Microchamber Integrated With Graphene Chemi-Resistive Sensors For Direct On-Chip Soil Testing

Printable Fused Silica Based Microchamber Integrated With Gr...

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2023 IEEE Applied Sensing Conference, APSCON 2023

作者： Jose, Sabitha Ann Atwa, Yahya Mitchell, Neil Shakeel, Hamza Queen's University Belfast School of Electronics Electrical Engineering and Computer Science Belfast United Kingdom Umm Al-Qura University Dept. Electrical Engineering Makkah Saudi Arabia

ISBN: (纸本)9781665461634

In this study, we demonstrate a graphene-based portable gas sensor with an integrated fused silica micro chamber for direct on-chip sample injection to detect a low concentration (-10 ppm) of acetone from a soil sample at room temperature and pressure. A chemiresistive gas sensor was first fabricated by a conformal transfer process using a commercial monolayer graphene film. Printable fused silica micro gas chamber (internal volume of 50 microliter), fabricated separately, was permanently attached with the graphene chemiresistor using an ultra-violet light source. A relative resistance change of 1% (-20 ppm of acetone) was achieved by injection of a headspace sample for acetone calibration. This direct on-chip injection method shows an excellent potential for detecting volatile organic compounds from a soil sample without requiring complex sample extraction steps © 2023 IEEE.

关键词： Fused silica

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