检索结果-内蒙古大学图书馆

5th International Conference on Electrical Information and Communication Technology, EICT 2021

作者： Podder, Nitun Kumar Shill, Pintu Chandra Rana, Humayan Kabir Omit, Shudeb Babu Sen Mehedee Hasan Al Shahriar, M. Shafiul Azam, M. Khulna University of Engineering Technology Dept. of Computer Science Engineering Khulna Bangladesh Green University of Bangladesh Dept. of Computer Science Engineering Dhaka Bangladesh Z. H. Sikder University of Science and Technology Dept. of Computer Science Engineering Shariatpur Bangladesh Pabna University of Science and Technology Dept. of Computer Science Engineering Pabna Bangladesh

ISBN: (纸本)9781665409063

The coronavirus disease (COVID-19) caused by SARS-COV-2, a highly infectious pathogen, genetically similar to SARS-COV is an unprecedented worldwide health crisis. Rapidly accumulating clinical research revealed that COVID-19 is manifested by various neurological symptoms and also affects the brain in many ways including direct infection to systemic inflammation. Which indicates it may substantially increase the incidence of developing neurodegenerative diseases (NDGDs). To discourse this issue we studied the computable frameworks to address the gene expression association of COVID-19 and NDGDs to identify the link among them. We analyzed GEO microarray datasets from COVID-19 and NDGDs including Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease. We constructed disease-gene relationship networks and identified dysregulated pathways, ontological pathways, protein-protein interaction (PPI) network, and protein-drug interaction (PDI) network. We observed that COVID-19 associated genes share 19, 26, 20, 19, 22 differentially expressed genes with Epilepsy, Stroke, Multiple Sclerosis, Alzheimer's disease, and Parkinson's disease respectively. Gene expression dysregulation, PPI and PDI relationship networks, different pathways suggest that COVID-19 may have a significant link to the development of these NDGDs. This analysis may help to develop therapeutic strategies and raise awareness about the influence of COVID-19 on the progression of NDGDs. © 2021 IEEE.

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

On Vertex-Euclidean Deficiency of Complete Fan Graphs and Complete Wheel Graphs

引用

Journal of Combinatorial Mathematics and Combinatorial Computing 2024年 121卷 41-52页

作者： Gao, Zhen-Bin Guo, Ricky Kwong, Harris Lee, Sin-Min Qiu, Wei College of General Education Guangdong University of Science and Technology Dongguan523000 China Dept. of Computer Science Univ. of Calif. at Los Angeles Los AngelesCA90095 United States Dept. of Math. Sci. SUNY Fredonia FredoniaNY14063 United States 1786 Plan Tree Drive UplandCA91784 United States College of Math. Sci.Harbin Engineering Univ. Harbin150001 China

A simple graph G with p vertices is said to be vertex-Euclidean if there exists a bijection f : V(G) → {1, 2, . . ., p} such that f(v1) + f(v2) > f(v3) for each C3-subgraph with vertex set {v1, v2, v3}, where f(v1) 2) 3). More generally, the vertex-Euclidean deficiency of a graph G is the smallest integer k such that G ∪ Nk is vertex-Euclidean. To illustrate the idea behind this new graph labeling problem, we study the vertex-Euclidean deficiency of two new families of graphs called the complete fan graphs and the complete wheel graphs. We also explore some related problems, and pose several research topics for further study. © 2024 the Author(s)

关键词： Wheels

来源：评论

学校读者我要写书评

暂无评论

Opportunistic Migration for Hybrid Memories While Mitigating Aging Effects

Opportunistic Migration for Hybrid Memories While Mitigating...

引用

IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Aswathy N S Hemangee K. Kapoor Dept. of Computer Science and Engineering Indian Institute of Technology Guwahati Guwahati Assam

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Hybrid memory systems composed of Non-volatile memory (NVM) and DRAM to exploit the high density of NVM and low access latency of DRAM. Phase Change Memory (PCM), a type of non-volatile memory, is a viable choice for main memory. High write latency and high voltage requirements for PCM lead to Biased Temperature Instability (BTI) aging and performance degradation. De-stressing the memory circuit at regular intervals controls BTI aging. Memory performance can be enhanced by migrating the highest write count memory pages across memory units. Migration and de-stress halt the service of regular requests and affect the performance of the system. Therefore, it is crucial to control migration and de-stress to enhance hybrid memory performance while mitigating BTI aging. We propose DOPMig, a de-stress-aware page migration technique. The policy migrates write-intensive pages to DRAM at regular intervals but opportunistically parallel to the de-stress operation. This method of background migration helps to reduce the migration overheads and improves performance. DOPMig achieves a performance gain of 22%, and improves memory service rate by 15%, and increases DRAM access by 24%.

关键词： Phase change materials Degradation Nonvolatile memory Memory management Random access memory High-voltage techniques Aging Performance gain Phase change memory Proposals

来源：评论

学校读者我要写书评

暂无评论

Fair and Efficient Congestion Management for Low Voltage Distribution Networks

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Aswin Ramesh Vadavathi Gerwin Hoogsteen Johann Hurink dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble and Hybrid CNN-LSTM Models

Enhanced Detection of DoS/DDoS Attacks in SDN Using Ensemble...

引用

Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Maharin Afroj Khan Md Sadbin Rifat Md. Saifur Rahman Dept. of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Software-Defined Networking (SDN) faces significant challenges in mitigating Attacks known as denial of service (DoS) and distributed DDoS due to its centralized control plane architecture. This centralization makes the control plane an attractive target for attackers, highlighting the need for effective detection and mitigation strategies. In this study, we propose three innovative methodologies to enhance the accuracy of detecting DoS/DDoS attacks in SDN environments. Our first methodology employs an ensemble model with a voting classifier, which achieves a detection accuracy of 97.4%. This approach leverages multiple weak learners to enhance the robustness of detection. The second methodology utilizes an ensemble with a stacking technique, attaining a superior accuracy of 99%, demonstrating the efficacy of integrating multiple base models in a hierarchical fashion. The third approach introduces a novel hybrid model that blends LSTM networks with CNN, achieving a competitive accuracy of 98.51%. A comparative analysis of these methods reveals that, while all three significantly enhance detection rates, the stacking ensemble technique outperforms the others. This finding underscores the potential of advanced ensemble techniques in effectively addressing the dynamic and evolving cyber threats within SDN environments. Our study offers valuable insights into strengthening SDN infrastructures against DoS/DDoS attacks, contributing to the ongoing efforts to secure modern network architectures.

关键词： Logistic regression Accuracy Prevention and mitigation Stacking Network architecture Robustness Real-time systems Ensemble learning Software defined networking Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Based Segmentation of Weed Images in crop fields using U-Net

Deep Learning Based Segmentation of Weed Images in crop fiel...

引用

Advancement in Technology (ICONAT), International Conference for

作者： K. Jairam Naik Dept. of Computer Science & Engineering National Institute of Technology Raipur Raipur India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Weeds are problems for crop yields because they exert harmful effects on the yields by competing with cultivated crops for necessary resources like nutrients, sunlight, and soil. As a result, continual attempts are being made to create safer and more effective herbicide formulations. For precision weeding and to reduce pesticide pollution, segmenting weeds in photos is extremely important. However, weeds and crops are comparable in the field setting, making it challenging to precisely segregate weed from intricate field photos. A deep learning-based system for segmenting weeds from photos was proposed in this paper. This algorithm can distinguish between weeds and crops and soil in photos. Utilizing a condensed U-net, this semantic segmentation method was created. An image augmentation strategy was suggested because image tagging for the semantic segmentation of weeds is challenging. The classification algorithms used in the modified U-Net are Support Vector Machine (SVM), Random Forrest (RF) and Decision Tree (DT). These were also used for predicting the weed part in a sample image before their segmentation. The threshold accuracy for segmenting the image using to be around 51.85% with a segmentation time of approximately 01.02 ms whereas our modified U-Netis obtained 91.03% accuracy and the segmentation time of 50.35ms. The results showed the modified U-Net successfully differentiated weeds from images with numerous other plants. This study seeks to demonstrate that the weed-associated image segmentation technique is effectively identifying and separating weeds in intricate field conditions.

关键词： Training Support vector machines Radio frequency Image segmentation Accuracy Pollution Semantic segmentation Training data Soil Classification algorithms

来源：评论

学校读者我要写书评

暂无评论

Vision Transformer Inference on a CNN Accelerator

Vision Transformer Inference on a CNN Accelerator

引用

IEEE International Conference on computer Design: VLSI in computers and Processors, (ICCD)

作者： Changjae Yi Hyunsu Moh Soonhoi Ha Dept. of Computer Science and Engineering Seoul National University Seoul South Korea

ISBN: (数字)9798350380408

ISBN: (纸本)9798350380415

Following the remarkable performance demonstrated by the Transformer architecture in the field of computer vision as well as natural language processing (NLP), there is a growing demand for embedded systems capable of executing Vision Transformer (ViT) applications as well as Convolutional Neural Network (CNN) applications efficiently. Since CNN accelerators are already widely used commercially, this paper explores the possibility of using existing CNN accelerators to support ViT rather than developing separate accelerators for each. CNN accelerators inherently have some limitations in efficiently handling operations in transformers: matrix multiplication (MM) operations with two non-constant matrices and nonlinear operations. To overcome these limitations, we first propose a novel technique to efficiently handle MM operations without special reshaping hardware in an adder-tree type CNN accelerator. And we propose an optimal scheduling method to minimize the idle time caused by offloading computation of nonlinear operations of the Transformer. Additionally, we investigate the possibility of executing layer normalization and GELU operations on the accelerator with minor extensions. The experimental results validate the effectiveness of the proposed methods.

关键词： computer vision Embedded systems Computational modeling Machine vision Graphics processing units Optimal scheduling Transformers Natural language processing Hardware Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

AI-Based Indian Currency Detection for Visually Challenged Users

AI-Based Indian Currency Detection for Visually Challenged U...

引用

IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： S. Shopika A S Senthil Velan Dharun Prakash SR Pavithra S R Poornima Lakshmi R. Ponnusamy Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331510022

ISBN: (纸本)9798331510039

For denomination recognition, this Indian Currency Detection System will use a CNN trained on an extremely large and diverse set of Indian banknotes from $$ 10 to $$ 2000. It possesses an amicable interface through which images can be captured using the camera of the user's smartphone, providing real-time audio feedback to denominate the banknote without requiring any visual assistance. Made with the features of capturing an image, real-time recognition, and user-friendly design, these are made to add a great experience for smooth navigation by a blind person. The novel and innovative features make this Indian Currency Detection System unique. Using CNN, which has been trained on the comprehensive set of Indian banknotes from $$10 to $$2000, the software lets a user capture banknote image directly by the smartphone camera. It allows knowing the denomination through real-time audio feedback without any visual assistance. Such characteristics include image capture, real-time identification, and designing user-friendly user interfaces that make easy navigation of the surroundings possible for the visually impaired. It does more than detect currency. It significantly contributes to the discussion regarding the development and availability of more assistive technologies. It is usable and aims to bring about greater accessibility and inclusivity in promoting financial independence among visually impaired people. Contributing to the ability of cash transactions among the visually impaired to gain confidence in freedom and security, this project aligns with a broader discussion related to assistive technologies.

关键词： Visualization Image recognition Navigation Blindness Assistive technologies User interfaces Cameras Real-time systems Security Currencies

来源：评论

学校读者我要写书评

暂无评论

Prediction of Diabetes Using Machine Learning Approach

Prediction of Diabetes Using Machine Learning Approach

引用

Computing, sciences and Communications (ICCSC), International Conference on

作者： Niraj Kumar Sinha Meena Pundir Tanu Dhiman Dept. of Computer Science & Engineering Chandigarh University Gharuan Mohali Punjab India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Diabetes is a chronic disease posing a significant global health challenge, with the number of affected individuals projected to nearly double by 2045. This disease, which is characterized by high blood sugar, can cause severe complications such as kidney failure, blindness, and heart problems. To address this pressing concern this study target to enhanced machine learning technology-based system for the early understanding of diabetes with high accuracy. The suggested method makes use of the output of different technique, such as Decision Tree, Random Forest, K-Nearest Neighbor and Naive Bayes, to maximize their benefits and enhance overall prediction performance. The study evaluates the accuracy of each algorithm individually and selects the techniques with the best result as the final predictive system. The results of this study support ongoing initiatives to apply innovative data science methods for diabetes early diagnosis and treatment, ultimately assisting in the avoidance of the disease's severe consequences and enhancing patient outcomes. Based on the Random Forest classifier model, this model generated an accuracy of 96.83%.

关键词： Accuracy Computational modeling Pressing Nearest neighbor methods Prediction algorithms Diabetes Bayes methods Random forests Kidney Diseases

来源：评论

学校读者我要写书评

暂无评论

Reducing Quantum Measurement Repetitions in Image Classification through Probability Loss

Reducing Quantum Measurement Repetitions in Image Classifica...

引用

Quantum Computing and engineering (QCE), IEEE International Conference on

作者： Kwangyeul Choi Kyoung Keun Park Taehyun Kim Dept. of Computer Science and Engineering Seoul National University Seoul Republic of Korea

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Quantum computing holds significant promise for solving complex problems more efficiently than classical computers. However, a major challenge lies in the repetitive measurement steps required to obtain reliable results from quantum states. This paper introduces the concept of probability loss to reduce the need for repeated measurements in a hybrid quantum-classical image classification model. For experiment, a hybrid model is introduced which is inspired by the Vision Transformer architecture, our model divides input images into patches and processes them as sequences.

关键词： computers computer vision Quantum computing Computational modeling computer architecture Quantum state Transformers Loss measurement Reliability Image classification

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：