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Deep Learning Approaches for Feature Extraction in Big data Analytics 10

Deep Learning Approaches for Feature Extraction in Big Data ...

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Dhaygude, Amol Dattatray Varma, Raj A. Yerpude, Poonam Swarnkar, Suman Kumar Kumar Jindal, Rupesh Rabbi, Fazle University of Washington Department of Data Science Seattle United States Maharashtra Pune India Indian Institute of Technology Dept of Computer Science & Engineering Bhilai India Shri Shankaracharya Institute of Professional Management and Technology Dept of Computer Science and Engineering Raipur India National Institute of Design Dept of Computer Science & Engineering Ahmedabad India Australian Computer Society Australia

ISBN: (纸本)9798350382471

In the context of big data analytics, this study examines the use of algorithms based on deep learning for feature extraction. Traditional methods usually have trouble sifting through the complexity and volume of data to find the important elements. We investigate the application of auto encoders, transformer-based models, convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to address this problem. Our comprehensive review of existing literature compares these deep learning techniques with traditional methods and highlights their adaptability to large-scale datasets. The efficacy and precision of these methodologies are demonstrated by empirical investigations conducted on authentic datasets across a range of disciplines, including but not limited to time-series analysis, picture identification, and natural language processing. Despite challenges like computational requirements and model interpretability, our findings indicate that deep learning-based feature extraction holds significant promise for enhancing big data analytics, leading to valuable insights and discoveries in various fields. © 2023 IEEE.

关键词： Autoencoders Big data Analytics CNN) Deep Learning Efficiency Feature Extraction Image Recognition Natural Language Processing RNNs Scalability Time-Series Analysis Real-World datasets Transformer Models

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An Analytical Review of Large Language Models Leveraging KDGI Fine-Tuning,Quantum Embedding’s,and Multimodal Architectures

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computers, Materials & Continua 2025年第6期83卷 4031-4059页

作者： Uddagiri Sirisha Chanumolu Kiran Kumar Revathi Durgam Poluru Eswaraiah G Muni Nagamani Department of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of TechnologyVijayawada520007India Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522302India Department of Computer Science and Engineering(Data Science) AVN Institute of Engineering and TechnologyHyderabad501510India Department of Computer Science and Engineering(Data Science) Vignan’s Institute of Management and Technology forWomenHyderabad501301India Department of Computer Science and Engineering Andhra Loyola Institute of Engineering and TechnologyVijayawada520008India

A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across *** studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and *** gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment *** this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly *** propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal *** methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance *** for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are *** investigation’s scope,mad,and methods are described,but the primary results are *** work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy *** medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote ***-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple *** discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain *** framework helps academics and practitioners identify,adapt,and innovate LLMs for different *** work

关键词： Large languagemodels quantum embeddings fine-tuning techniques multimodal architectures ethical AI scenarios

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A Multimodal Content Moderation System using Adversarial Machine Learning 3

A Multimodal Content Moderation System using Adversarial Mac...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Barveen, A. Mohamed Faizal, M.K. Geetha, S. M.I.E.T. Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Tamilnadu Trichy India University College of Engineering Department of Computer Applications Tamilnadu Trichy India

ISBN: (纸本)9798350375190

Memes are a visual representation of images with text embedded in them that conveys the thoughts and feelings of a peculiar audience and is mainly intended to elicit humor. It is widely spread across social media platforms in the guise of sardonic images, humorous jokes, and several other viral sensations. After the profound success of the late fusion technique that combines multimodal features, researchers have used stacked LSTM to extract textual features, whereas VGG16 has been used to extract visual features. Apart from this, BiLSTM and CNN have been used to extract contextual features. This study suggests a unique deep learning-based method for classifying objectionable memes within a multimodal dataset to address this problem. The proposed system uses machine learning to train the classifier on labeled data, which allows it to categorize the information accurately. The proposed system uses both text and image data. To improve user experience and safety, offensive images are blurred, and offensive words are shuffled using adversarial training. © 2024 IEEE.

关键词： Image enhancement

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Image Segmentation Survey: Classical and Deep Learning Methods 4

Image Segmentation Survey: Classical and Deep Learning Metho...

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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Bachani, Viren Roychowdhury, Anish Dasgupta, Abhijit Ei-Alfy, Hazem BSc. Data Science SP Jain School of Global Management Sydney Australia SP Jain School of Global Management Data Science Dept. Sydney Australia SP Jain School of Global Management Data Science Dept. Mumbai India School of Computer Science University of Sydney Sydney Australia Alexandria University Faculty of Engineering Alexandria Egypt

ISBN: (纸本)9798350395914

Image segmentation is a prerequisite to almost all computer vision applications. It enables the extraction of meaningful information from visual inputs by partitioning images into segments with shared features. This review paper begins by outlining different image segmentation tasks. It then surveys a broad spectrum of segmentation techniques, ranging from classical frameworks to deep-learning (DL) architectures. We perform this through a systematic literature analysis across a handful of applications as part of our methodology. A key contribution of our survey is that it elaborates on a few situations where classical approaches outperform DL-based methods in accuracy, along with the added advantage of computational efficiency. In some cases, these findings also provoke interest in revisiting classical methods, prompting strategies that leverage the strengths of both paradigms. © 2024 IEEE.

关键词： Markov processes

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Machine Learning Challenges of E-government Models of Cloud Computing in Developing Countries 18

Machine Learning Challenges of E-government Models of Cloud ...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Tamilarasi, R. Kumar, P. N. Santhosh Ghantasala, G. S. Pradeep Rao, D. Nageswara Bathla, Priyanka Gupta, Gaurav Sonia Alliance University Department of Computer Science and Engineering Bengaluru India Hyderabad India Alliance University Department of Computer Science and Engineering Bangalore India Chandigarh University Punjab India Shoolini University Solan India Shoolini University Yogananda School of AI Computer and Data Sciences Solan India

ISBN: (纸本)9789380544519

Cloud computing usage in electronic government (e-government) is developing across nations and presents a range of difficulties. Infrastructure problems, such as unreliable internet connections and insufficient devices, make it difficult to integrate cloud solutions efficiently. Concerns about data security and privacy arise when private information about citizens is stored in the cloud and could be hacked or accessed without permission. Economic limits and resource constraints could prevent cloud infrastructure installation, thereby reducing e-government service scalability and efficiency. Compliance with various regulations adds complexity to the legal and governmental aspects of managing cloud data. A scientific investigation reveals that while cloud computing-driven e-government services offer numerous advantages, factors such as IT infrastructure, internet accessibility, and confidence in emerging technologies could pose barriers to their adoption. Also, recommend trust-focused cloud computing solutions for e-government services (CCSE-GS). Finally, this study focuses on the number of obstacles that developing countries must overcome to fully realize the potential of cloud computing for e-government services. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Challenges about e-government Cloud Computing E-Government models IT infrastructure

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An Optimized Hybrid Deep Learning and IoT-Enabled Health System in Retina Image Analysis for Blindness Detection 2

An Optimized Hybrid Deep Learning and IoT-Enabled Health Sys...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Yamini, J. Kanimozhi, T. Jenefa, A. Bagyalakshmi, K. Aarthi, K. Dhivya, P. Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Machine learning Coimbatore India Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India Ppg Institute of Technology Department of Computer Science and Engineering Coimbatore India Sns College of Technology Department of Mathematics Coimbatore India Kalaignar Karunanidhi Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350368413

Early signs of blind identification and retina disorder in retina image has become prominent. Deep learning techniques are used to improve the accuracy in detecting pathological conditions from the retina images. However, the models have some challenges such as suboptimal feature extraction and inefficient filtering methods. Most of the traditional methods often result in overfitting and under fitting, thus decreasing the generalizability and robustness of the model. The proposed hybrid approach combines bilateral filtering with Convolutional Neural Networks (CNN). In this approach, bilateral filtering acts as a pre-processing technique to enhance image quality by reducing noise and preserving edges for the correct extraction of features. The CNN is applied for feature extraction and it is used to self-learn complex patterns from filtered images. The proposed approach gives an accuracy of 95.65% with a loss of 0.18 without overfitting and underfitting. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Event Coreference Resolution Using Prompt Learning and U-shaped Module 4

Improving Event Coreference Resolution Using Prompt Learning...

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4th International Conference on computer science and Blockchain, CCSB 2024

作者： Zhang, Mengyao Long, Jun Huang, Wenti School of Software Xinjiang University Urumqi China Institute of Big Data Central South University Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China

ISBN: (纸本)9798350356502

Event Coreference Resolution (ECR) focuses on clustering event mentions that allude to identical actual events. Previous research primarily focuses on encoding event mentions without incorporating human interpretations, making it difficult to integrate human-summarized rules for guiding the ECR task. Furthermore, the semantic relations between event mentions have not been thoroughly explored, which impacts coreference judgment. To address these issues, we first utilize the powerful Longformer to acquire event representations at the document level. In addition, we introduce a prompt learning template to better emphasize event types and parameters, enhancing the interaction between event modeling and coreference judgment. Ultimately, we create matching vectors for event mentions using tensor matching and incorporate a U-shaped module to effectively identify the semantic relationships among these mentions, which enhances coreference discrimination. DPTU surpasses other ECR models on the KBP2017 dataset in cause of some experiments. © 2024 IEEE.

关键词： Semantics

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Predicting Flight Delays and Error Calculation Using Machine Learning Classifiers 5

Predicting Flight Delays and Error Calculation Using Machine...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Rajeshkumar, G. Vijaya, K. Sadesh, S. Arunkumar, P.M. Manoj Praveen, V. Kumar, M. Senthil Bannari Amman Institute of Technology Dept of Cse Sathyamangalam India Kongu Engineering College Department of Csd Perunduarai Erode India Velalar College of Engineering and Technology Department of Artificial Intelligence and Data Science Erode India Karpagam College of Engineering Department of Computer Science and Engineering Coimbatore641032 India Erode Sengunthar Engineering Collge Department of Artificial Intelligence and Data Science Perundurai India

ISBN: (纸本)9798350379945

Flight delays are a significant concern in the aviation industry, causing inconvenience for passengers and financial losses for airlines. Predicting flight delays accurately can help mitigate these issues by allowing both airlines and passengers to proactively manage schedules and resources. The delay might depend on weather, crew management, and resource planning. Existing prediction methods often lack accuracy or rely on historical data and statistical models, which may not capture the complex interactions and patterns present in flight operations to predict delays. This study proposes a novel approach for flight delay prediction using advanced machine learning techniques and tools. Specifically, machine-learning classifiers, such as Support Vector Machines (SVM) and Decision Tress, are employed to analyze a comprehensive live streaming dataset comprising various incorporated factors like flight operations, weather conditions, air traffic congestion, and aircraft performance. Proposed model is trained on a large dataset encompassing diverse routes and airlines and has achieved an accuracy of 95% in predicting delays by outperforming existing methods. © 2024 IEEE.

关键词： Traffic congestion

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Network Traffic Anomaly Detection Using Quantile Regression with Tolerance

Network Traffic Anomaly Detection Using Quantile Regression ...

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2023 IEEE International Black Sea Conference on Communications and Networking, BlackSeaCom 2023

作者： Alsan, Huseyin Fuat Guler, Ali Kerem Yildiz, Ekrem Kilinc, Sena Camlidere, Bora Arsan, Taner Kadir Has University Department of Computer Engineering Istanbul Turkey Turknet Department of Data Science Istanbul Turkey

ISBN: (纸本)9798350337822

Network traffic anomaly detection describes a time series anomaly detection problem where a sudden increase or decrease (called spikes) in network traffic is predicted. data is modeled with the trend and heteroscedastic noise component. Traditional autoregressive models struggle to capture data changes effectively, making anomaly detection difficult. Our approach is to generate upper and lower limits by using quantile regression. We use a deep learning based multilayer perceptron model to predict five data quantiles 1, 25, 50, 75, and 99. The upper and lower limits are calculated as differences between the quantile-1 and quantile-99. Any data that is outside these limits are considered as an anomaly. We also add tolerance to these limits to add flexibility to anomaly detection. Anomalies and non-anomalies are labeled to get a binary classification task. Anomaly detection is class imbalanced by nature;therefore, precision, recall, and F-1 score are computed to evaluate the proposed anomaly detection method. We conclude that choosing tolerance is a tradeoff between false alarms and missing anomaly detections. © 2023 IEEE.

关键词： Time series

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： data integrity

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