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A Comparative Study of Deep Image Retrieval Models Leveraging Deep Features

A Comparative Study of Deep Image Retrieval Models Leveragin...

引用

Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Ashish Jain Sudeep Varshney Dept. of Computer Science and Engineering SET Sharda University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This review investigates how deep learning methods can be utilized for efficient image retrieval based on content. Obtaining accurate images from vast digital collections poses significant challenges, motivating research in CBIR. The effectiveness of these methods varies depending on the dataset’s type and size, with certain algorithms excelling with specific dataset characteristics. An extensive and well-structured review of successful image retrieval techniques is given in this paper. Our primary objective is to evaluate various deep learning models applied by researchers and compare their performance based on the outcome of evaluation matrices. These models encompass CNNs, DBNs, and other deep architectures tailored for image retrieval tasks. By synthesizing insights from this review, researchers can make informed decisions regarding model selection and potentially enhance retrieval performance by leveraging advanced deep learning features. The importance of deep features in image retrieval is the ability to capture complex visual patterns and semantic information that cannot be easily extracted by traditional handcrafted features. With the increasing volume of online images, Image retrieval using deep learning has become crucial for applications like object recognition, image retrieval, and image search engines.

关键词： Deep learning computers Visualization Reviews Image retrieval Semantics Search engines Feature extraction Object recognition data mining

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Enhancing Synthetic Reduced Nearest-Neighbor with Two-Layer Neural Networks: A Step Forward in Image Classification

Enhancing Synthetic Reduced Nearest-Neighbor with Two-Layer ...

引用

IEEE Workshop on Signal Processing Systems (SIPS)

作者： Azar Alizadeh Mukesh Singhal dept. Electrical Engineering and Computer Science nUniversity of California Merced Merced USA

ISBN: (数字)9798350373752

ISBN: (纸本)9798350373769

Synthetic Reduced Nearest Neighbor (SRNN) models, operating exclusively on synthetic samples or prototypes, represent a significant stride in the field of nearest-neighbor algorithms. Central to this innovation is enhancing the inter-pretability and optimization of the model, achieved through specialized techniques. This study introduces a novel Two-Layer Neural-SRNN model for classification tasks, diverging from traditional Expectation Maximization (EM) methodologies. The TLN-SRNN model significantly advances efficiency and scalability, outperforming traditional methods in speed while maintaining accuracy. Our empirical findings highlight the model's rapid convergence and robust performance across diverse datasets, establishing it as a notable innovation in the field of machine learning.

关键词： Technological innovation Scalability Neural networks Signal processing algorithms Prototypes Machine learning Signal processing Optimization Image classification Convergence

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Fake Account Detection Using ANN Based Model in Machine Learning

Fake Account Detection Using ANN Based Model in Machine Lear...

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science, Technology, engineering and Management (ICSTEM), International Conference on

作者： Y. Alekya Rani Ganja Srividya Allam Balaram Kotha Harish Kumar Ajmeera Kiran Manda Silparaj Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad Dept of CSE Stanley College of Engineering & Technology for Women Hyderabad Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Computer science Engineering (Data science) CVR College of Engineering Hyderabad Department of Computer Science and Engineering (CS) CVR College of Engineering Hyderabad

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

In the digital age, the proliferation of fake accounts on various platforms poses significant challenges in terms of cybersecurity, misinformation, and online harassment. To combat this issue, this project aims to develop a robust fake account detection system using machine learning techniques. The project involves the collection of user data, feature engineering,data preprocessing, and the selection of suitable machine learning algorithms. The model is trained and evaluated using relevant metrics to distinguish between real and fake accounts effectively. Once deployed, this system continuously monitors user activities and alerts administrators when fake accounts are detected, enabling timely and legal compliance. The success of this project depends on the quality and quantity of data as well as the ability to adapt and improve the detection methods over time, in a constant battle against evolving fake account creation techniques.

关键词： Measurement Machine learning algorithms Social networking (online) Law Artificial neural networks Forestry Information age

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Forecasting EV Market Trends in India: A Deep Learning Approach for Two/Three-Wheelers 12

Forecasting EV Market Trends in India: A Deep Learning Appro...

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12th International Electrical engineering Congress, iEECON 2024

作者： Mishra, Rishav Dev Kumar Dash, Santanu Chudjuarjeen, Saichol Korkua, Suratsavadee School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Vellore Institute Of Technology TIFAC-CORE Vellore India Rajamangala University Of Technology Krungthep Faculty Of Engineering Dept. Electrical Engineering Bangkok Thailand School Of Engineering And Technology Walailak University Thailand

ISBN: (纸本)9798350383591

This research paper presents a comprehensive analysis of machine learning models for predicting future sales of electric vehicles (EVs) in the Indian Market. With a specific focus on 2 and 3 wheeler sales. The study compares different machine learning approaches, including Linear Regression, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) Neural Network, Through an evaluation of their respective errors, LSTM Neural Network method demonstrates superior prediction accuracy, making it the preferred model for forecasting EV sales, specifically 2 wheeler and 3 wheeler sales over the next five years. The paper aims to present the forecasted sales figures, in hopes of providing valuable insights into the future trajectory of EV sales and serving as a vital resource for stakeholders in the electric vehicle industry. © 2024 IEEE.

关键词： Long short-term memory

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From Pixels to Diagnosis: Convolutional Neural Networks in Tuberculosis Screening

From Pixels to Diagnosis: Convolutional Neural Networks in T...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Tanvir Mahtab Zihan Abduz Zami Mohiuddin Ahmed Rakibul Islam Fahim Ahmed Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh Dept. of CSE UIU Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Tuberculosis (TB) is a chronic lung disease that constitutes one of the top 10 global causes of death. Timely and precise identification of TB is critical, as untreated cases can pose life-threatening risks. Traditional radiographic interpretation by trained professionals is subjective, time-consuming, and prone to human error. This study proposes an automated deep learning-based approach for TB detection from chest X-ray images, based on EfficientNet-B0 architecture. The Convolutional Neural Network (CNN) extracts discriminative features, allowing it to differentiate between normal and abnormal lung patterns indicative of TB. Our choice of the EfficientNet-B0 architecture played a pivotal role in the success of our TB detection model. Training and evaluation were conducted on three distinct datasets: Tuberculosis (TB) Chest X-ray database, Shenzhen Hospital CXR Set, and Montgomery County CXR Set. The key breakthrough is attaining an accuracy of 99.29% on the Tuberculosis (TB) Chest X-ray database and 98.50% on the combined dataset. This study presents a promising approach for automated TB screening using readily available chest X-ray images, potentially improving diagnostic accuracy and efficiency, especially in resource-limited settings. We compared our scores with those of other works and found that ours significantly outperforms them.

关键词： Training Tuberculosis databases Computational modeling computer architecture X-rays Feature extraction

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Detection and Treatment of Epilepsy Disease using EEG Signals

Detection and Treatment of Epilepsy Disease using EEG Signal...

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Cybernation and Computation (CYBERCOM), International Conference on

作者： Shiv Ashish Dhondiyal Sushil Chandra Dimri Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331542344

ISBN: (纸本)9798331542351

Epilepsy Seizures is a neurological illness that causes repeated seizures and affects millions of people throughout the world. From the studies it has been detected that 70% peoples in the world suffer from epilepsy disease. The prognosis and medical care of epileptic seizure are critical for improving the quality of life of affected individual. Electroencephalography (EEG) is a valuable technique for diagnosing and managing epilepsy because it allows for real-time monitoring of brain electrical activity. Recent advancements in signal processing techniques, such as the Discrete Wavelet Transform (DWT), have shown significant promise in enhancing the diagnosis and treatment of epilepsy. This paper purposed an optimize Random Forest (RF) algorithm by using the DWT application in the analysis of EEG signals for the prognosis of epilepsy disease along with its treatment like medicines, therapies and other treatments methods to cure epilepsy disease. The suggested approach provides clinicians with a promising tool for early and precise identification of epileptic seizures, which is critical for making improvements in patient outcomes and quality of life. The simulation results show that the purposed optimize support vector machine algorithm gives better results with an accuracy of 98% along with the sensitivity, specificity and f1_score of 97%, 96% and 98% respectively.

关键词： Support vector machines Sensitivity Simulation Epilepsy Signal processing algorithms Transforms Signal processing Electroencephalography Discrete wavelet transforms Prognostics and health management

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Design and Implementation of Low Power Circuits Based on 5nm Double Gate MOSFET Technology Using Different Materials

Design and Implementation of Low Power Circuits Based on 5nm...

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IEEE Electron Devices Kolkata Conference (EDKCON)

作者： Shrabanti Kundu Jyotsna Kumar. Mandal Dept. of Computer Science and Engineering University of Kalyani Kalyani Nadia WB India

ISBN: (数字)9798350374643

ISBN: (纸本)9798350374650

This paper presents a comprehensive investigation of 5nm double gate MOSFETs (DG MOSFETs) circuits using different substrate semiconductor materials for low-power devices. A comparative analysis of characteristics of SiGe, InGaAs and GaN as substrate materials is performed. The study reveals that InGaAs offers superior performance due to its higher electron mobility, lower threshold voltage and thermal stability than other materials studied here for low-power applications. The electrical properties of DG MOSFETs on these substrates have been investigated by calculating key parameters such as threshold voltages (Vth), Ion and Ioff currents. The results show that InGaAs-based DG MOSFETs exhibit superior performance with threshold voltage, improved leakage current and Ion/Ioff ratio compared to other substrate materials. Additionally, a lower threshold voltage has been observed for InGaAs-based devices, indicating reduced power consumption. These findings demonstrate the potential of InGaAs as a preferred substrate material for low-power, high-performance DG MOSFET devices. Double gate MOSFET circuits are designed using InGaAs and power dissipation is calculated. The results show a significant reduction in power consumption and improved circuit performance compared to traditional Si-based devices. This research demonstrates the potential of InGaAs-based DG MOSFETs for low-power electronic devices, enabling the development of energy-efficient circuits for various applications.

关键词： Performance evaluation MOSFET Electric potential Power demand Logic gates MOSFET circuits Threshold voltage Indium gallium arsenide Substrates Thermal stability

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Enhancing Hyperledger Fabric Security with Lightweight Post-Quantum Cryptography and National Cryptographic Algorithms

Enhancing Hyperledger Fabric Security with Lightweight Post-...

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Open Innovations Association (FRUCT), Conference of

作者： Vinay Kumar Kasula Sarath Babu Rakki Rajkumar Banoth Dept. Information Technology University of the Cumberlands Williamsburg KY USA Dept. of Computer Science and Engineering JNTUH University College of Engineering Hyderabad TG India Dept. of Computer Science University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9789526524634

ISBN: (纸本)9798331595425

Hyperledger Fabric is a scalable and modular consortium blockchain platform designed for enterprise applications, where cryptographic algorithms play a fundamental role in ensuring data security and integrity. However, the native Fabric framework lacks support for national cryptographic standards, necessitating the integration of secure cryptographic mechanisms. This study proposes an enhanced approach to embedding national cryptographic algorithms into the Fabric platform. First, Graph-based Dependency Analysis is employed to investigate the interaction logic among Fabric components and the invocation of cryptographic functions, facilitating an efficient integration strategy. Next, the Lightweight Post-Quantum Cryptography (L-PQC) framework, which enhances resistance to quantum threats while maintaining computational efficiency, is utilized to integrate SM2, SM3, and SM4 cryptographic algorithms into Fabric’s Blockchain Cryptographic Service Provider (BCCSP) module. Subsequently, a Microservices-Based Cryptography Integration mechanism is designed to establish seamless mapping between Fabric’s cryptographic function calls and the national cryptographic algorithm interfaces, ensuring compatibility and interoperability. Finally, the implementation is evaluated using Blockchain Simulation Environments, where a fabric-gm consortium blockchain instance is deployed to validate the correctness and efficiency of the embedded cryptographic modules. Comparative analysis with the original Fabric platform reveals that the enhanced system introduces a 2.5% increase in network startup time, a 1.8× rise in transaction latency, and a 7.5% increase in dynamic certificate generation time, while maintaining operational performance within acceptable limits. The proposed integration strategy ensures secure and efficient cryptographic support within Hyperledger Fabric, making it resilient to emerging cryptographic threats.

关键词： Resistance Technological innovation Quantum computing Distributed ledger Heuristic algorithms System performance Fabrics Blockchains Cryptography Standards

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Big data Application in Cancer Classification by Analysis of RNA-seq Gene Expression

Big Data Application in Cancer Classification by Analysis of...

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Intelligent Technologies (CONIT), International Conference on

作者： Amruth A Ramanan R Rhea Paul Vishal S S Saravanan Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India Dept. of Computer Science and Engineering Amrita School of Computing Chennai India

computer science domain has got massive developments in the past decade, especially in the field of Big data and Artificial Intelligence. Many industries are adopting these technologies to improve their products and services. Healthcare is one such industry which comes across voluminous data. Big data platform is used to process the data and subsequently AI models can be used for prediction or classification. This paper focuses on proposing a Spark based pipeline to reduce the time required to process the voluminous data. With the aid of this new pipeline, we classify the type of cancer through analysis of RNA-seq gene expression levels. The Spark-based pipeline is run on default standalone mode and on a cluster. The study recorded that the Spark-based pipeline run on cluster mode improved the performance approximately by 11 times (regarding processing costs) when compared to the performance of Python-based pipeline.

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Integrating Artificial Intelligence and Educational Technology for Inclusive and Ethical Education Practices

Integrating Artificial Intelligence and Educational Technolo...

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Intelligent data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Lathika Ilayaraja Yukthika S S Subramani Prabakaran S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

AI and EdTech are transforming education by making it more accessible, personalized, and assessable, even in low and middle income countries (LMICs) facing resource constraints. However, challenges come with unequal access, issues in data privacy, and bias in AI model creations. As AI continues to become more popular, the issue of whether the work made by the students will be original. This paper focuses on the impact of artificial intelligence on learning in both its ethical and practical dimensions while highlighting the themes of inclusivity, fairness, and authenticity. Guidelines and policies for responsible AI in integration will be proposed further, alongside teacher training. Results should depict that this moral framework, policies, and teacher education should aid in ensuring that the implications of AI are positive and fruitful for learning. Ideal AI guidelines would foster an educational system that is inclusive, transparent, and considers uniqueness and innovation.

关键词： Training Ethics Technological innovation Large language models Education Educational technology Learning (artificial intelligence) Internet of Things Artificial intelligence Guidelines

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