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共 25762 条记录，以下是211-220 订阅

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

引用

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

作者： Pavan Kalyan S Arun Kumar B R Satish Kumar T Prem Raj S Pratik B Patil Pothireddy Harshini Dept. Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments.

关键词： Adaptation models Computational modeling Large language models Memory management Data structures Data models Computational efficiency Problem-solving Sustainable development Optimization

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Advances in Deep Learning for Tuberculosis Detection in Chest X-Rays: A Review of Diagnostic Accuracy

Advances in Deep Learning for Tuberculosis Detection in Ches...

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

作者： Jayakumar K G Muthupandi Dhayanidhi R S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Tuberculosis (TB) is one of the leading causes of deaths globally, mainly in low- and middle-income countries. Early and accurate detection is crucial for effective treatment and disease control. In this paper, models like CNNs-VGG16, ResNet, and DenseNet, to name a few, are taken into an exhaustive study. Optimization techniques, including transfer learning and ensemble methods, which can increase accuracy in the challenging diverse clinical settings, will be presented. Equitable healthcare outcome is achieved in terms of future research directions which include representative datasets and interpretability of the model.

关键词： Deep learning Accuracy Tuberculosis Transfer learning Medical services Feature extraction X-ray imaging Optimization Tuning Biomedical imaging

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Adaptive Privacy and Lightweight Security in Healthcare IoT: Challenges, Solutions and Future Directions

Adaptive Privacy and Lightweight Security in Healthcare IoT:...

引用

Innovation in Computing and Engineering (ICE), International Conference on

作者： Mukul Chauhan Tayyab Khan Dept. of Computer Science & Engineering Indian Institute of Information Technology Sonepat India

ISBN: (数字)9798350380460

ISBN: (纸本)9798350380477

IoT in healthcare has transformed patient care by enabling and implementing the real-time monitoring for the end patients or users. However, the development of connected devices introduces significant privacy and security challenges, especially while securing the healthcare data. In this paper, a comparative review of adaptive privacy and lightweight security mechanisms in the area of healthcare IoT is shown. A comparison of current methods based on criteria including data security, privacy preservation, scalability, resource efficiency, and real-time implementation is one of the major contributions. Furthermore, this review recognizes the research gaps in the recent researches and highlights promising techniques, including advanced encryption, federated learning, and the edge computing. By addressing these challenges, the study aims to guide the development of robust, efficient, and scalable solutions that ensure privacy and security in dynamic healthcare IoT environments.

关键词： Privacy Data privacy Technological innovation Reviews Scalability Medical services Real-time systems Encryption Security Edge computing

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An Intelligent CCTV based Surveillance System for Realtime crimes or life threat detection

An Intelligent CCTV based Surveillance System for Realtime c...

引用

International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Md. Niamatullah Naeem Ongsaiu Marma Mohammad Hasan Sujit Roy Dept. of Computer Science & Engineering Jamalpur Science & Technology University Jamalpur Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

It’s the time when people are more concerned about their safety than ever before. For this reason, sophisticated surveillance systems that can quickly identify any threats and take action to prevent it is essential to save life and property. An intelligent closed-circuit television (CCTV) surveillance system driven by the You Only Look Once (YOLO) object detection model is a robust approach to put the surveillance system to a new dimension. The proposed system is intended for monitoring real-time CCTV footage which generally focusing on identifying any weapons-carrying persons—a vital sign of possible threat and take appropriate action against it. By utilizing the effectiveness and precision of the YOLO model, the system quickly recognizes and detect these people as they enter the CCTV-covered region. Once detected, the system initiates a multi-tiered response mechanism that can involve loud alerts system, emergency police calls, targeted authority phone call and notifications, depending on the perceived level of threat for immediate action. The system is not limited to weapon detection only it is also capable of detecting fires or suspicious activity and triggering the response system to take action. This surveillance system adaptability guarantees monitoring and timely action in emergency situations, strengthening overall security protocols. With the help of this intelligent CCTV surveillance system public safety should be improved by avoiding possible threats, speeding up reaction and response times, and enabling prevent injuries and property from any incoming danger.

关键词： YOLO TV Surveillance Weapons Streaming media Cameras Real-time systems Threat assessment Public security Time factors

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Towards Low-Code Architecture and Development of Embedded Systems

Towards Low-Code Architecture and Development of Embedded Sy...

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IEEE International Conference on Software Architecture Workshops (ICSAW)

作者： Felipe Xavier Loek Cleophas Michel R. V. Chaudron Dept. of Math. and Computer Science Eindhoven University of Technology Eindhoven Netherlands

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

While the interest in low-code platforms grows, these technologies have mostly focused on administrative business applications, and other domains remain relatively untouched. We think much can be gained from introducing low-code concepts and tools to the embedded systems domain, which still suffers from a relatively complex development cycle. This paper presents a roadmap for creating an approach that allows the generalization of embedded software development through a low-code platform. We highlight five main tasks required to accomplish this endeavor, which will be researched in the near future: 1) studying existing low-code development characteristics; 2) defining a reference architecture for embedded systems; 3) identifying prevailing embedded system design patterns; 4) designing a multi-view approach with a consistent visual language; and 5) integrating formal verification into the platform.

关键词： Visualization Embedded systems Software architecture computer architecture Programming Reliability engineering Software reliability DSL Embedded software Guidelines

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Enhanced Realtime Sign Language Detection and Multilingual Translation System

Enhanced Realtime Sign Language Detection and Multilingual T...

引用

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

作者： Madhur Kakkar John Daniel D. Sudha Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Sign language is the communication medium that is utilized by the hearing-impaired community. The medium is visual and it uses hand signs and symbols for communication. Over 300 different types of sign languages present in the world today. Sign language uses gestures and hand movements for the majority of the words and finger spellings for the rest. Nonverbal speakers face a lot of challenges while interacting with verbal speakers. Even non-verbal speakers who speak different sign languages face difficulties communicating with each other. This project proposes an enhanced real-time system which detects gestures and finger spellings of multiple sign languages, and translates them into multiple spoken languages. The proposed system aims to utilize CNN network for finger spelling recognition and Long Short-Term Memory (LSTM) network for gesture interpretation. The system will use computer vision for accurate gesture detection and Google Translator API for recognizing signs which will be translated into multiple spoken languages. Initial tests with isolated datasets indicate promising accuracy for both finger spelling and gesture recognition. The goal of the completed system is to help aid communication between verbal and non-verbal speakers and to provide an efficient mode of communication between the two communities.

关键词： Hands Deep learning Sign language computer vision Translation Face recognition Medical services Real-time systems Multilingual Long short term memory

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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IEEE Conference on Wireless Communications and Networking

作者： Wahab Khawaja Rune H. Jacobsen Sajid Hussain Ismail Guvenc Dept. Electrical and Computer Engineering Aarhus University Aarhus Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology Mirpur AJK Pakistan Dept. Electrical and Computer Engineering North Carolina State University Raleigh NC USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

关键词： Analytical models Simulation Time series analysis Scattering Artificial neural networks Machine learning Receivers Euclidean distance Autonomous aerial vehicles Millimeter wave communication

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Interdisciplinary Approaches to Identify Genetic Biomarkers Impacting Lung Cancer Progration

Interdisciplinary Approaches to Identify Genetic Biomarkers ...

引用

International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Nitun Kumar Podder M D Anamul Haque Salma Khatun Dept. of Computer Science & Engineering Pabna University of Science and Technology Pabna Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Lung cancer is the leading cause of cancer deaths worldwide. It is the most common form of cancer globally, accounting for 12.3% of all cancer cases, with approximately 1.2 million new diagnoses recorded in the year 2000. While smoking is a primary cause of most lung cancers, it is important to note that nonsmokers can also be susceptible to this deadly cancer. Cancer occurs when cells mutate and proliferate uncontrollably. It is essential to monitor lung cancer, as patients face a significant risk of developing comorbidities, including stroke, colorectal cancer, encephalopathy, endometrial cancer, glioblastoma, and Parkinson's disease. To accurately diagnose and develop cancer treatments, it is essential to identify a specific set of influential genes. In this study, we utilized the Cancer Genome Atlas (TCGA) data set to detect significant genes tied to lung cancer and their relationship to comorbidities. Consequently, we conducted preprocessing of the data set and carried out the Kruskal-Wallis test along with Bonferroni correction methods to identify significant genes. A total of 14 significant abnormal genes have been identified from a pool of 18,784 genes. To guess what these important genes do, we looked at functional pathways, gene ontology, protein-protein interactions, protein-chemical interactions, and protein-drug interactions. We also employed the Cox proportional hazard model and the product limit (P.L.) estimator to investigate the significant impact of clinical and genetic factors on survival in lung cancer patients. This investigation lays the groundwork for pinpointing genes that influence cancer and comprehending the effects of lung cancer on the progression of comorbid conditions.

关键词： Proteins Parkinson's disease Lung cancer Genomics Stroke (medical condition) Ontologies Hazards Monitoring Faces Cancer

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A Comprehensive Review on Lunar Crater Detection using Image Processing

A Comprehensive Review on Lunar Crater Detection using Image...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Omkar Mane Prathamesh Nagawade Mann Pathak Sahil Phadtare Deepak Uplaonkar Dept. of Computer Engineering International Institute of Information Technology (IIT) Pune India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

This review investigates the application of machine learning techniques, particularly the YOLO algorithm, and proposes a better alternative to the existing models for automated lunar crater detection. It addresses the challenges associated with manual identification methods, including the presence of overlapping features and the management of extensive data volumes. Additionally, the paper examines the development of a web-based application designed for lunar surface analysis. The review synthesizes current advancements in image processing techniques for crater detection, highlighting potential enhancements in efficiency and accuracy for lunar geological studies.

关键词： YOLO Adaptation models Machine learning algorithms Accuracy Reviews Geology Computational modeling Moon Real-time systems Surface treatment

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Comparative Analysis of Machine Learning Methods in Climate Data Extraction and Forecasting

Comparative Analysis of Machine Learning Methods in Climate ...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Parth Ajit Khajgiwale Atul Naveen Nishtha Jethani Ajitkumar Shitole Dept. of Computer Engineering International Institute of Information Technology Hinjewadi Pune India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

Climate forecasting plays a critical role in understanding and mitigating the impacts of climate change. Advances in machine learning (ML) have significantly enhanced the accuracy of climate projections, particularly when dealing with large and complex datasets derived from regional climate models (RCMs) and global climate models (GCMs). This study investigates the application of various ML techniques for predicting precipitation based on temperature data extracted from climate datasets. We evaluate a range of regression models, including linear models and advanced ensemble methods such as Gradient Boosting and XGBoost, along with deep learning approaches like Convolutional Neural Networks (CNNs). Through a rigorous evaluation of these models, we focus on their performance as measured by Mean Squared Error (MSE) to identify the most effective techniques for capturing the intricate relationships within climate data. By analyzing existing studies and performance metrics, we aim to provide insights into the capabilities and limitations of these ML models in improving climate predictions. Furthermore, this research highlights the need for incorporating additional climate variables and optimizing model configurations to enhance predictive accuracy in climate forecasting.

关键词： Temperature measurement Deep learning Adaptation models Temperature distribution Accuracy Precipitation Predictive models Ensemble learning Forecasting Meteorology

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