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检索条件"机构=Dept. of Computer and Electrical Engineering and Computer Science"

共 55306 条记录，以下是131-140 订阅

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

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2024 International Conference on electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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Elastic Blockchain Security Solution for Edge Computing Supporting Mobile IoT Devices

Elastic Blockchain Security Solution for Edge Computing Supp...

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2024 International Conference on electrical and computer engineering Researches, ICECER 2024

作者： Almubark, Hameed Jedidi, Ahmed Al-Raweshidy, Hamed S. Brunel University London Dept. of Computer Science United Kingdom Ahlia University Dept. of Computer Engineering Manama Bahrain Brunel University of London Dept. of Electronic and Electrical Engineering United Kingdom

ISBN: (纸本)9798331539733

When integrated, blockchain technology and cloud computing (EC) may improve security and protect data. However, when data is sent to EC, the Internet of Things (IoT) device layer cannot guarantee data security. As a result, in the IoT ecosystem, end users and IoT devices are susceptible to assaults. Additionally, as the number of IoT devices rises and generates a large quantity of data, especially in mobile IoT devices, security concerns rise dramatically. High security and privacy standards are necessary for industrial Internet of Things (IIoT) applications in order to safeguard data and guarantee that IoT devices may seamlessly switch to another network. By utilising its decentralised structure, consensus process, and encryption features, blockchain technology can offer a solution. However, because IIoT devices have limited resources, the encryption procedure might be complicated. Actually, in order to secure communication between IIoT devices and EC, this research suggests a new security mechanism built on blockchain technology. Additionally, two security tiers will be used by the Elastic Blockchain Security Solution (EBC) to create a dynamic system for mobile IIoT devices. To evaluate the EBC solution's effectiveness, scalability, and performance, as well as its consensus method and encryption capabilities, it was put into practice and tested in a number of scenarios © 2024 IEEE.

关键词： Industrial internet of things (IIoT)

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A Case for Speculative Address Translation with Rapid Validation for GPUs 57

A Case for Speculative Address Translation with Rapid Valida...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Park, Junhyeok Kwon, Osang Lee, Yongho Kim, Seongwook Byeon, Gwangeun Yoon, Jihun Nair, Prashant J. Hong, Seokin Sungkyunkwan University Dept. of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University Dept. of Semiconductor Convergence Engineering Korea Republic of University of British Columbia Dept. of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350350579

A unified address space is vital for heterogeneous systems as it enables efficient data sharing between CPUs and GPUs. However, GPU address translation faces challenges due to high TLB pressure, particularly with irregular and memory-intensive applications. Compared to an ideal scenario, we observe that address translation overheads cause a slowdown of up to 34.5% in modern heterogeneous systems. This paper introduces Avatar, a novel framework to accelerate address translation in GPUs. Avatar comprises two key components: Contiguity-Aware Speculative Translation (CAST) and In-Cache Validation (CAVA) mechanisms. Avatar identifies the potential for predicting virtual-to-physical address mapping by monitoring contiguous pages that lie in both virtual and physical address spaces. Leveraging this insight, CAST speculatively translates virtual addresses into physical addresses. This speculative address translation enables immediate data fetching into GPUs while addressing translation occurs in the background, reducing TLB-miss overhead. Unfortunately, modern GPUs lack support for speculative execution, which limits CAST's performance gain. Data fetched from speculated physical addresses is unusable until validation. CAVA addresses this limitation by quickly validating speculated physical addresses. To this end, CAVA embeds page mapping information into each 32B sector of 128B cache lines. Thus, CAVA enables fetching a sector block from memory for a speculated address and rapidly validating the speculative translation using the embedded mapping information. Our experiments show that Avatar achieves a 90.3% (high) speculation accuracy and improves GPU performance by 37.2% (on average). © 2024 IEEE.

关键词： Virtual addresses

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Multi-label Bengali Abusive Comments Classification using Problem Transformation Method 20

Multi-label Bengali Abusive Comments Classification using Pr...

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20th International Conference on electrical engineering, Computing science and Automatic Control, CCE 2023

作者： Khan, Tahsin Tasnia Hassan, Abid Ahamed, Md Faysal Islam, Samiul Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh North South University Dept. of Electrical & Computer Engineering Dhaka Bangladesh International Standard University Dept. of Computer Science & Engineering Dhaka Bangladesh

ISBN: (纸本)9798350306767

Sentiment analysis on the Bengali language is limited to binary and multi-class classifications. This paper focuses on the multi-label classification of the Bengali abusive social media comments dataset having 10220 rows of different social media negative comments using the problem transformation method. The texts are categorized into five labels: toxic, threat, obscene, insult, and racism. Three different multi-label approaches: Binary Relevance, Label Powerset, and Classifier Chain combined with three popular machine learning algorithms: Multinomial Naive Bayes, Random Forest, and Logistic Regression are implemented in this research. This study evaluates the performance of these models by applying them to various classification scenarios, considering five-label, four-label, three-label, and two-label classifications independently. Our research outcomes are evaluated with some performance indicators: Accuracy, Precision, Recall, F1-Scores, Confusion matrix, Macro-average, and Hamming score. The combination of Label Powerset and Logistic Regression outperformed the other two classifier-model combinations because of their quick adaptation to the dataset with an accuracy of 88.07% for five label classifications, 90.12% for four label classifications, 90.56% considering three label classifications and 92.67% for two label classification individually. With 88.07% accuracy, the Label Powerset with Logistic Regression approach can be applied to classify five types of sentiment in a single text. © 2023 IEEE.

关键词： Classification (of information)

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Automated Identification of Pneumonia Employing Convolution Neural Networks and the VGG-16 Model 1

Automated Identification of Pneumonia Employing Convolution ...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Srivastava, Anamika Gupta, Anuj Kaushik, Deepti Gupta, Neha Chadha, Shikha Chauhan, Rosey Noida Institute Of Engineering Technology Dept. of Computer Science & Engineering Gerater Noida India Sharda Unviersity Dept. of Computer Science & Engineering Gerater Noida India School of Engineering and Technology IIMT University Dept. of Computer Science and Engineering U.P Meerut India ABES Engineering College Dept. of Computer Science UP Ghaziabad India

ISBN: (纸本)9798350353648

Pneumonia, an inflammatory lung disease, impacts the small air sacs known as alveoli and is typically caused by the bacteria Streptococcus pneumonia. Symptoms commonly include a combination of productive or dry cough, chest pains, difficulty breathing, and fever. In India, where Covid-19 is prevalent, community-acquired pneumonia (CAP) poses a significant risk factor for mortality, accounting for one-third of fatalities, as reported by the World Health Organization (WHO).Diagnosing and predicting the outcome of pneumonia typically involves examining chest imaging radiographs of patients exhibiting clinically similar symptoms (e.g., fever, dyspnea, cough, and sputum production). Therefore, the development of an automated method for pneumonia detection could offer valuable advantages, particularly in remote regions, facilitating prompt treatment. Deep learning (DL) models enable the analysis of various chest radiographs, with Convolutional Neural Networks (CNNs) receiving considerable attention in disease classification. Leveraging features learned. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of Digitized Visual Search Task for Assessment of Aging Effects 2

Design of Digitized Visual Search Task for Assessment of Agi...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Navalyal, Geeta Navalyal, Apoorva Naik, Sayali Dept. of Computer Science & Engineering Uttarakhand Dehradun India University of Florida Dept. of Electrical & Computer Engineering GainesvilleFL United States Grant Thornton Karnataka Bengaluru India

ISBN: (纸本)9798350372847

Mild cognitive impairments (MCI) develop in adults due to the normal aging process and may sometimes progress to a more serious cognitive decline stage resulting in Dementia. If the cognitive decline is identified at the MCI stage, it can be delayed or prevented from progressing by different interventions. A typical indicator of cognitive impairments that might be caused due to an injury, aging, or other factors is found to be a decreased search efficiency in visual search tasks. Here we propose a model to assess this efficiency by designing a digitized, easy-to-use visual search task capable of incorporating multiple variables in the visual stimulus, affecting the search capabilities. The variables under consideration include the number of distracting elements, target size, target distance from the center of the stimulus area, and habituation effects. This designed stimulus is tested on participants from different age groups. © 2024 IEEE.

关键词： aging cognitive impairments Visual search task

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Inexpert Sensor Fusion Using an Adaptive Kalman Filter Approach 5

Inexpert Sensor Fusion Using an Adaptive Kalman Filter Appro...

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5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Saab, Samer Lebanese American University Dept. of Electrical and Computer Engineering Byblos Lebanon

ISBN: (数字)9798331519957

ISBN: (纸本)9798331519957

Sensor fusion in complex systems often relies on well-calibrated, expert sensors to achieve accurate and reliable outcomes. Integrating untrained or inexpert sensors into such networks poses significant challenges such as the cost associated with manually training them for the overall network objective. This paper presents an adaptive Kalman filter (AKF) approach to seamlessly incorporate an inexpert sensor into an established sensor network. By leveraging the existing trained sensors, the proposed method dynamically adjusts the inexpert sensor's parameters, enabling it to contribute effectively to the overall system. We evaluate the performance of this approach using absolute errors and Kullback-Leibler (KL) divergence. Preliminary results show that the proposed AKF achieves a KL divergence on the order of 10-4 in a binary classification task, significantly outperforming a recursive density estimation method. It is also shown that in a diverging multivariate target tracking problem, after being trained, two inexpert sensors successfully contribute to a sensor network, even after temporarily removing the expert sensor. © 2025 IEEE.

关键词： Kalman filters

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A Lightweight CNN-SVM Explainable AI Approach for Classification and Visualization of Grape Leaf Disease 3

A Lightweight CNN-SVM Explainable AI Approach for Classifica...

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3rd International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2024

作者： Peyal, Hasibul Islam Leion, Zannatul Mawa Abdal, Md. Nazmul Islam, Md. Injamul Miraz, Shafiun Remon, Mahmudur Rashid Kontho, Md. Mahin Rahaman Tasnim, Nishat Rajshahi University of Engineering & Technology Dept. of Electrical & Computer Engineering Rajshahi Bangladesh University of Liberal Arts Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Uttara University Dept. of Computer Science & Engineering Dhaka Bangladesh Rajshahi University of Engineering & Technology Rajshahi Bangladesh North South University Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Sher-e-Bangla Agricultural University Dept. of Agroforestry and Environmental Science Dhaka Bangladesh

ISBN: (纸本)9798350388282

Grape is highly esteemed as a significant agricultural crop in Bangladesh. Plant diseases primarily result from the presence of pathogens and pest insects, leading to a significant decline in productivity if not properly addressed. The fundamental aim of this research is to employ a streamlined CNN-SVM architecture, utilizing deep learning techniques, to accurately categorize grape leaves into three distinct disease classes and one healthy class. The proposed model surpasses the accuracy of the previously trained transfer learning models VGG-16 and VGG-19 while having approximately 257× to 267× times fewer parameters (0.537 M). On average, the proposed model achieves a classification accuracy of 99.18%, which is significantly higher than the 93.42% and 91.94% achieved by the transfer learning models, respectively. With a precision, recall, and F1 score close to 99%, the suggested model provides excellent results. The model's outstanding performance is further validated by its remarkable Area Under Curve (AUC) score of 99.98%. In addition to using less disc space (about 6 MB), the suggested model because of being lightweight shows a significant decrease in parameters. To visually display the disease identified by the proposed model, a transparent AI methodology has been utilized, specifically the Gradient Weighted Class Activation Mapping (Grad-CAM) technique. To better understand which area was responsible for the classification, a heatmap has been created. © 2024 IEEE.

关键词： Deep learning

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Design and Optimization of an IR-Based Automation System with Telegram Bot Integration for Remote Device Control 4

Design and Optimization of an IR-Based Automation System wit...

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4th International Conference on Robotics, electrical and Signal Processing Techniques, ICREST 2025

作者： Adnan Sharar Oyon, Md. Shihab Himel, Abid Hasan Mredul, Khaliduzzaman Ahmed, Abir Ali, M Tanseer Hossainl, Chowdhury Akram Rahman, Md. Abdur Dept. of Electrical and Electronic Engineering Dhaka Bangladesh Dhaka Bangladesh Dept. of Computer Engineering Dhaka Bangladesh

ISBN: (纸本)9798331530938

Automation is advancing rapidly, with infrared (IR) communication being a key method for controlling electronic devices. Despite this, challenges remain in optimizing IR controllers for consistent performance across varying angles and task loads. A 3D design and a robust algorithm have been developed to enhance the system's accuracy, while security is ensured by matching a unique TOKEN with the Telegram bot for each session. Error rates were analyzed across different angles, with specific zones determined based on performance. Minimal error rates defined the working zone, while higher rates indicated critical and invalid zones. Error rates increased as the transmitter moved further from the optimal angles, such as a 0-6 % error rate when operating within a 0° - 5° range. Reaction time was calculated across multiple tasks, showing an average of 5 seconds per command. Mismatch rates, observed during higher task loads, rose sharply when handling many tasks. For instance, the mismatch rate was 0 % for 12 tasks but 20 % when executing 20 tasks. Performance evaluations demonstrated optimal IR signal strength for reliable control within a distance of 10 to 15 inches, with diminishing effectiveness beyond 20 inches. These findings provide a foundation for developing more reliable and efficient IR control systems for practical applications. © 2025 IEEE.

关键词： Bot (Internet)

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