Landslides pose a significant threat to life, infrastructure and the environment in the Western Ghats region of Karnataka. This study develops a comprehensive Landslide Susceptibility Mapping (LSM) model by integratin...
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IoT edge computing facilitates data to be processed at a location closer to the place where it is generated. Placing computing closer allows for faster and more reliable service to the users. It also benefits latency-...
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Aquatic lives are very sensitive in nature and is difficult to manage them in an aquarium. A slight change in the water temperature, dissolved oxygen content or feeding scheme may result in the death of those fishes. ...
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This project aims to solve the problem of securely storing and retrieving luggage in popular public places. There have been various methods that solve the above problem but there is a need for a more simple and effici...
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Breast cancer is a common cause of death among women *** imaging is a valuable diagnostic tool in breast cancer ***,the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to t...
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Breast cancer is a common cause of death among women *** imaging is a valuable diagnostic tool in breast cancer ***,the accuracy of computer-aided diagnosis systems for breast cancer classification is limited due to the lack of well-annotated *** study proposes a deep learning(DL)-based framework for breast mass classification using ultrasound images,which incorporates a novel data augmentation technique,generative adversarial network(GAN),and transfer learning(TL).Automating early tumor identification and classification in breast cancer diagnosis can save lives by improving the accuracy of diagnoses and reducing the need for invasive ***,the limited availability of wellannotated datasets for ultrasound images of breast cancer has hampered the development of accurate computer-aided diagnosis *** accuracy of breast mass classification using ultrasound images is limited due to the lack of well-annotated *** data augmentation techniques have limitations in applications with strict guidelines,such as medical ***,there is a need to develop a novel data augmentation technique to improve the accuracy of breast mass classification using ultrasound *** proposed framework can be extended to other medical imaging applications,where the availability of well-annotated datasets is *** GAN-based data augmentation technique and TL-based feature extraction can be used to improve the accuracy of classification models in other medical imaging ***,the proposed framework can be used to develop accurate computer-aided diagnosis systems for breast cancer detection in clinical *** proposed framework incorporates a DL-based approach for breast mass classification using ultrasound *** framework includes a GAN-based data augmentation technique and TL for feature *** dataset used for training and testing the model is the breast ultraso
A complete examination of Large Language Models’ strengths, problems, and applications is needed due to their rising use across disciplines. Current studies frequently focus on single-use situations and lack a compre...
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A complete examination of Large Language Models’ strengths, problems, and applications is needed due to their rising use across disciplines. Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance, strengths, and weaknesses. This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies. In 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 reviewed. We propose LLM application taxonomy by grouping techniques by task focus—healthcare, chemistry, sentiment analysis, agent-based simulations, and multimodal integration. Advanced methods like parameter-efficient tuning (LoRA), quantum-enhanced embeddings (DeepKet), retrieval-augmented generation (RAG), and safety-focused models (GalaxyGPT) are evaluated for dataset requirements, computational efficiency, and performance measures. Frameworks for ethical issues, data limited hallucinations, and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted. The investigation’s scope, mad, and methods are described, but the primary results are not. The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings perform better for context-heavy applications. In medical text normalization, ChatGPT-4 outperforms previous models, while two multimodal frameworks, GeoRSCLIP, increase remote sensing. Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance, demonstrating the necessity for adaptive models in multiple domains. To discover the optimum domain-specific models, explain domain-specific fine-tuning, and present quantum and multimodal LLMs to address scalability and cross-domain issues. The framework helps academics and practitioners identify, a
This research study provides a comprehensive evaluation of the performance of various hardware configurations in assessing Satoshi Nakamoto's consensus mechanisms, specifically Proof of Work (PoW). The analysis fo...
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Health Cure is a groundbreaking medical initiative aimed at revolutionizing early disease detection through the integration of advanced medical technology and sophisticated algorithms. This paper focuses on identifyin...
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ISBN:
(数字)9798350363289
ISBN:
(纸本)9798350363289
Health Cure is a groundbreaking medical initiative aimed at revolutionizing early disease detection through the integration of advanced medical technology and sophisticated algorithms. This paper focuses on identifying and evaluating seven critical diseases, including COVID-19, brain tumors, breast cancer, diabetes, Alzheimer's, pneumonia, and heart disease. The complexity of disease mechanisms and diverse symptoms poses a significant challenge in creating effective early diagnosis tools and efficient treatment plans on a global scale. This work leverages a variety of techniques, such as Random Forest, Convolutional Neural Networks (CNN), and XG Boost, to address the unmet need for accurate and timely disease diagnosis. Traditional diagnosis based on symptoms proves challenging for medical professionals to make the reliable detection of diseases a formidable task. To overcome these challenges, machine learning (ML) and deep learning algorithms plays a pivotal role in predicting and detecting high-risk diseases. The integration of supervised ML algorithms and deep learning techniques demonstrates substantial potential in outperforming existing disease diagnosis systems. After the prediction we design a user interface for users to get the test results immediately in our home just a few clicks. In this work, detect and predict seven diseases in one platform or multiple diseases under one platform using ML and DL techniques. And provide a user interface for users to get a test results immediately in our home just a few clicks after that provide a food, medicine and doctor recommendations. And our novel idea is detect heart beat rate by using heart beat sensor(MAX30100) for monitoring real time heart beat sensing in heart disease UI page, By supplying pertinent symptoms, machine learning is utilized in disease inference systems to forecast human illnesses. This paper presents a comprehensive platform capable of detecting and predicting multiple diseases using ML and DL m
Online content moderation faces significant challenges in identifying offensive language across diverse linguistic environments. This study compares the performance of five advanced BERT models mBERT, BERT Base, BERT ...
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Reversible data hiding in encrypted images (RDHEI) is an evolving field that aims to embed secret data within encrypted images in such a way that the hidden data can be extracted completely while the original image ca...
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