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共 39991 条记录，以下是221-230 订阅

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Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-Based Heterogeneous System with Algorithm-based Fault Tolerance 18

Parallelized 0/1 Knapsack Algorithm Optimization in CPU-GPU-...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Abid, Marzan Binte Shaha, Shuvo Kabir, Upama Arefin, Md. Fahim University of Dhaka Dept. of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350331011

A heterogeneous high-performance computing (HPC) system is an aggregation of CPUs and GPUs through high-speed interconnection. As graphics processing units (GPUs) can conduct rapid mathematical and vector operations due to their hardware properties, GPU-based heterogeneous systems can optimize algorithms that show data parallelism. Since the dynamic programming (DP) solution of the 0/1 knapsack algorithm is inherently data-parallel, this paper optimized the algorithm to maximize the advantages of a GPU-based parallel environment. Furthermore, this research identified that the algorithm was well suited to four distinct GPU-launch approaches. Among them, the "single-cell calculation by single thread with a single kernel launch"approach outperforms others by reducing the complexity from O(n∗w) to O(n). Besides, these HPC systems are susceptible to the slightest of errors. Checkpoint recovery (CP/R) is the most commonly used fault tolerance and recovery system, which suffers from huge checkpoint and recovery overhead. To reduce this overhead in CPU-GPU-based heterogeneous systems, an algorithm-based fault tolerance (ABFT) technique has been proposed for a particular class of algorithm: 0/1 knapsack. The experimental results verify the feasibility of the proposed ABFT technique and demonstrate that the system can recover from failures with minimal overhead. © 2024 IEEE.

关键词： Graphics processing unit

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Securing Microservice Architecture: Load Balancing and Role-Based Access Control 3

Securing Microservice Architecture: Load Balancing and Role-...

引用

3rd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2024

作者： Thakur, Payal Talwandi, Navjot Singh Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350387193

This study focuses on the security aspects within microservice architecture, particularly addressing load balancing and role-based access control (RBAC). Exploring the intersection of load balancing techniques and RBAC mechanisms, the research aims to enhance the security posture of microservices. By evaluating strategies for efficient load distribution and implementing RBAC protocols, the study seeks to fortify the architecture against potential vulnerabilities. The integration of load balancing and RBAC not only ensures optimized resource utilization but also strengthens access control measures, bolstering the overall security framework in microservice-based systems. © 2024 IEEE.

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Object Detection Model for Tacking System Implemtation using Deep Learning Technique 5

Object Detection Model for Tacking System Implemtation using...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh Thakur, Payal Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Thing discovery and tracking using deep learning has emerged as a cutting-edge technology with applications spanning autonomous vehicles, surveillance systems, robotics, and more. This paper provides an overview of the principles, methodologies, and recent advancements in the field of object detection and tracking using deep learning techniques. We delve into the fundamental concepts of convolutional neural networks (CNNs), region-based approaches, and tracking algorithms that have revolutionized object detection. Furthermore, we explore the challenges and potential applications of this technology, from real-time video analysis to enhancing safety and security. The research in this domain continues to push the boundaries of what is achievable, making thing discovery and following by means of bottomless erudition a key enabler of future smart systems. Object detection and tracking are important tasks in computer vision that have numerous applications in various domains, including surveillance, robotics, and autonomous driving. Deep learning algorithms have shown remarkable performance in these tasks, enabling accurate and efficient detection and tracking of objects in complex environments. This paper presents an overview of object detection and tracking using deep learning, highlighting the key techniques and algorithms used in these tasks. We discuss popular deep learning architectures, such as Faster R-CNN, YOLO, and SSD, and their applications in object detection. We also review deep learning-based tracking algorithms, such as Siamese networks and correlation filters, and their performance in object tracking. Finally, we discuss the challenges and future directions of object detection and tracking using deep learning. © 2024 IEEE.

关键词： computer vision Convolutional Neural Networks (CNNs) Deep learning Object detection Object tracking Real-time video analysis and Surveillance systems Region-based approaches

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Automation of Data Analysis and Web-Scraping to Value Used Items 3

Automation of Data Analysis and Web-Scraping to Value Used I...

引用

3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Shriya, Y.S. Babu, Aarav Chandrashekhar, Naren Verma, Siddhant Jagdish Shanthala, P.T. PES University Computer Science Engineering Dept Bangalore India

ISBN: (纸本)9798350372120

Second-hand products such as phones, laptops, car's, etc. make up a substantial part of the market. They aid a sustainable lifestyle by reducing the use of new resources and diminishing waste disposal. With the evolution of second-hand shopping, pricing has become a very tedious and cumbersome process. Traditionally, models are pre-trained on static datasets. With the trends in the market being sporadic, the outcome of such models is greatly affected. One approach to counter this would be to update the dataset periodically, which requires significant resource allocation. This approach is better suited when dealing with a dataset that contains a large range of products together but may prove inefficient when encountering a specific item. Through this study, a pipeline is established that integrates real-time web scraping with machine learning, which is achieved by using the Selenium tool, automating data preparation of the web scraped data, and implementing a machine learning model. The model considers essential item features to provide accurate predictions for specific items queried by a user. This paper also helps us perform a comparative study of various machine learning models that are considered ideal for regression analysis. When dealing with a dataset, limited in size, the need to pick a model that can handle the sparsity in data becomes critical. Tuning the Random Forest model to 30 trees showed the best results when compared to the other regression models. It predicted accurate prices on multiple iterations with varying inputs, whereas the other models did not account for the change in the features. © 2024 IEEE.

关键词： Costs

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Optimizing Job Search: A Multi-Model Approach with DRNN-BES, Collaborative Filtering, and LLM-NER Resume Analysis 3

Optimizing Job Search: A Multi-Model Approach with DRNN-BES,...

引用

3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Jayakumar, Naveenkumar Patil, Netra Pokal, Roshankumar S. Dev, Rudra Pratap Nair, Sidharth Vellore Institute of Technology School of Computer Science and Engineering Vellore India College of Engineering Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350354348

This research introduces an innovative job recommendation platform designed to optimize the job search process through advanced technology integration. The platform features a sophisticated Resume Analyzer leveraging NLP and ML to meticulously extract candidate information. Coupled with a Recommendation System that employs mixture models such as DRNN-BES and collaborative filtering, it delivers personalized job suggestions tailored to individual profiles. Additionally, the system utilizes state-of-the-art web scraping technologies to continuously update its database with diverse and current job listings from various online sources, ensuring a wide range of opportunities are accessible. This integration of technologies not only refines job-matching precision but also significantly improves the efficiency of connecting job seekers with appropriate opportunities. Initial experimental results underscore the enhanced effectiveness and accuracy of our recommendation system, particularly when incorporating user behavioral data into the collaborative filtering algorithm. This paper demonstrates the transformative potential of integrating AI into job seeking, offering a scalable, unbiased, and time-efficient approach to modern employment challenges. © 2024 IEEE.

关键词： Collaborative filtering

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SARCASAM Analysis in Social Networks Using Deep Learning Algorithm 4

SARCASAM Analysis in Social Networks Using Deep Learning Alg...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Priya, M. Kumar, Vijaya K Vennila, P. Prasanna, M.A. Dept. of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam611002 India Dept. of Computer Science and Engineering Women's Engineering College Puducherry605008 India Dept. of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy621112 India

Twitter is the one of the biggest social media sites, where users may share their thoughts, ideas, and opinions as well as discuss current events and live tweets. In the subject of opinion mining, creating reliable and effective algorithms for sarcasm detection on Twitter is an intriguing task. Sarcasm is the use of positive language to convey depressing emotions while speaking in opposition to one's own intentions. Sarcasm is frequently employed on social networking and micro blogging platforms, where users can offend others and find it difficult to express their true feelings. The deep learning technique utilised in the current algorithms to identify these sarcastic tweets has the limitation of not being able to predict continuous variables. A novel deep learning algorithm is proposed to identify both positive and negative terms as well as sarcasm in comments. Deep neural networks are used to classify the comments into positive and negative word categories. Customers' opinions are mined using sentiment analysis to find and extract information from the text. Sarcastically stated statements from social networking sites can be quickly categorised and recognised by using VADER (Valence Aware Dictionary and Sentiment Reasoner). © 2025 The Authors.

关键词： Tweets

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Hybrid Quantum-Classical Transfer Learning for Real-Time Data Processing 17

Hybrid Quantum-Classical Transfer Learning for Real-Time Dat...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Chittem, Mahesh Babu Kumar Pradhan, Ashok SRM University AP Dept of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798331531195

Transfer learning is a set of techniques to apply skills or knowledge from a source task to a target task that is different but related, while Hybrid Quantum-Classical Transfer Learning (HQCTL) model extends the skills learned with quantum feature extraction specifically for edge computing which lacks resources. HQCTL combined with quantum-derived characteristics enhances accuracy, time, and real-time computation when it comes to classical aspects such as object detection or image analysis. In experimenting with images datasets such as COCO and PASCAL VOC the distribution of the framework generally presented higher accuracy and lower costs in terms of computation compared to either a purely classical or quantum approach. Of course, quantum enhanced feature extraction is still far from known and has greater potential for HQCTL as it helps to advance data representation which is optimal for the strict real-time processing in the IoT periphery. Possible research avenues include the development of different quantum representations of the problem, enhancements of the approach interconnectivity with various edge substrates, and the application of the framework to new machine learning tasks such as video analysis and time series prediction. Through presenting the HQCTL framework the potential of hybrid quantum-classical models to enhance edge AI applications while offering reliability, scalabilty and efficiency is demonstrated. © 2025 IEEE.

关键词： Transfer learning

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Deep Learning Based Detection of Anti-Reflective Obstacles in VLC Systems 6

Deep Learning Based Detection of Anti-Reflective Obstacles i...

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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Plissiti, Marina E. Papaioannou, Christoforos Sfikas, Yiorgos Papatheodorou, Georgios Poulis, Simon-Ilias Efthymiou, Aristides Tsiatouhas, Yiorgos University of Ioannina Computer Science and Engineering Dept Ioannina Greece

ISBN: (纸本)9798350344349

In Visible Light Communication (VLC) systems, the existence of non-reflective obstacles in the field of view of the receiver may cause deterioration or complete blockage of the optical signal which leads to an error-prone decoding process. We observe that image processing techniques, in combination with deep learning, provide effective ways to deal with this problem. In this work, we propose an efficient technique for the decoding of the signal in frames with partially occluded areas. First, a deep learning detector is used for the extraction of the obstacle, resulting in the determination of the regions of interest in each frame. Then, in a second step the decoding of the signal is employed, based on image processing techniques. The proposed method is evaluated in terms of the size of the obstacle and it is compared with other decoding schemes, providing very promising results. © 2024 IEEE.

关键词： Visible light communication

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Lora Performance Evaluation in Coherent Rayleigh Fading Channel 9

Lora Performance Evaluation in Coherent Rayleigh Fading Chan...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Ahmad, Yasser Asrul Hamid, Muhammad Nasrin Aqil Abdul Abdullah, Khaizuran Ismail, Ahmad Fadzil International Islamic University Malaysia Dept.of Electrical and Computer Engineering Kuala Lumpur Malaysia

ISBN: (纸本)9798350325218

This research presents an analysis of the performance of a LoraWAN communication system in Coherent channel conditions. The study uses Signal to Noise Ratio (SNR) and Packet Error Rate (PER) performance metrics. The data presented in this research is based on simulations using different values of spreading factors (SF7, SF8, and SF9) and bandwidths (125kHz, and 500kHz). The results show that the trade-off between SF and bandwidth should be considered when choosing the appropriate parameters for the system. The future works that can be done in this research include analyzing the system's performance under different modulation schemes and implementing the system in a real-world scenario. © 2023 IEEE.

关键词： Signal to noise ratio

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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