检索结果-内蒙古大学图书馆

R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

来源：评论

学校读者我要写书评

暂无评论

Automated Counting System Based on Watershed Algorithm for Tilapia Fish Egg 1

Automated Counting System Based on Watershed Algorithm for T...

引用

1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Hassan, Mohamad Ghozali Harun, Nor Hazlyna Md Azhar, Amiera Syazlin Md Pauzi, Siti Naquiah Ja'afar, Noor Ashri Mansor, Nur Suhaili School of Technology Management and Logistics Universiti Utara Malaysia Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Malaysia Institute for Advanced and Smart Digital Opportunities School of Computing Sintok Malaysia

ISBN: (纸本)9798350306156

Aquaculture is a significant component of Malaysia's fishery sector and makes a substantial contribution to the country's economy. It is the source of income and employment for the nation. Tilapia fish is one of the main species cultured in the aquaculture industry, as eggs produced by this fish are considered very valuable both in terms of quantity and quality. A standard egg selection procedure to calculate the fish egg properly has not yet been implemented. The process of fish egg selection plays a vital role in determining the accurate count of fish eggs. This study focuses on developing an automated counting system for tilapia fish eggs using an image processing technique concentrating on the watershed algorithm. This algorithm enables the image of the fish eggs to be clustered in shaded colors, thus enabling the total number of fish eggs to be automatically counted. The results show that the automated counting system for tilapia fish eggs works successfully as the accuracy obtained was above 90%. It is believed that an automated counting system can help fish breeders to perform fish egg counting automatically and more effectively and efficiently. © 2023 IEEE.

关键词： Fish

来源：评论

学校读者我要写书评

暂无评论

The evolution,applications,and future prospects of large language models:An in-depth overview

The evolution,applications,and future prospects of large lan...

引用

作者： Jiayin Li School of Computing and Data Science Xiamen University Malaysia

The evolution of natural language processing has transpired through three primary phases,with large-scale language models significantly transforming the *** models have heightened the machine's capability to understand,produce,and interact with human language in unprecedented *** from RNNs to transformer models,transitioning from encoder-decoder frameworks to decoder-centric designs,and the journey from BERT to the Chat-GPT series have marked significant shifts in the academic ***,these sophisticated models have infiltrated a range of sectors,including finance,healthcare,biology,and education,revolutionizing both traditional and emerging ***,as these advancements are celebrated,the ethical and economic challenges they introduce must also be *** these pivotal issues and harnessing technology for societal betterment has become a priority for academia and industry alike,sparking intense research endeavors in recent *** review dives into the history of natural language processing,highlighting the pivotal developments and core principles of large language *** provides a comprehensive perspective on their adoption and influence within the financial sector,crafting a detailed narrative of their *** conclusion,the analysis reflects on the current challenges posed by these models and presents potential *** study stands as a definitive guide,offering readers an in-depth understanding of the development,application,and future trajectories of large-scale language models.

关键词： large language models natural language processing challenges and opportunities

来源：评论

学校读者我要写书评

暂无评论

MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

引用

31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

来源：评论

学校读者我要写书评

暂无评论

Deep Learning and Artificial Intelligence-Driven Advanced Methods for Acute Lymphoblastic Leukemia Identification and Classification: A Systematic Review

引用

Computer Modeling in Engineering & sciences 2025年第2期142卷 1199-1231页

作者： Syed Ijaz Ur Rahman Naveed Abbas Sikandar Ali Muhammad Salman Ahmed Alkhayat Jawad Khan Dildar Hussain Yeong Hyeon Gu Department of Computer Science Islamia CollegePeshawar25120Pakistan Department of Information Technology The University of HaripurHaripur22620Pakistan College of Technical Engineering The Islamic UniversityNajaf100986Iraq School of Computing Gachon UniversitySeongnam13120Republic of Korea Department of AI and Data Science Sejong UniversitySeoul05006Republic of Korea

Automatic detection of Leukemia or blood cancer is one of the most challenging tasks that need to be addressed in the healthcare *** of white blood cells(WBCs)in the blood or bone marrow microscopic slide images play a crucial part in early identification to facilitate medical *** Acute Lymphocytic Leukemia(ALL),the most preferred part of the blood or marrow is to be analyzed by the experts before it spreads in the whole body and the condition becomes *** researchers have done a lot of work in this field,to demonstrate a comprehensive analysis few literature reviews have been published focusing on various artificial intelligence-based techniques like machine and deep learning detection of *** systematic review has been done in this article under the PRISMA guidelines which presents the most recent advancements in this *** image segmentation techniques were broadly studied and categorized from various online databases like Google Scholar,science Direct,and PubMed as image processing-based,traditional machine and deep learning-based,and advanced deep learning-based models were *** Neural Networks(CNN)based on traditional models and then the recent advancements in CNN used for the classification of ALL into its subtypes.A critical analysis of the existing methods is provided to offer clarity on the current state of the ***,the paper concludes with insights and suggestions for future research,aiming to guide new researchers in the development of advanced automated systems for detecting life-threatening diseases.

关键词： Acute lymphoblastic bone marrow segmentation classification machine learning deep learning convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts 41

MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Exp...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Jianan Cao, Zhiguang Wu, Yaoxin Song, Wen Ma, Yining Zhang, Jie Xu, Chi College of Computing and Data Science Nanyang Technological University Singapore School of Computing and Information Systems Singapore Management University Singapore Department of Information Systems Eindhoven University of Technology Netherlands Institute of Marine Science and Technology Shandong University China Singapore

Learning to solve vehicle routing problems (VRPs) has garnered much ***, most neural solvers are only structured and trained independently on a specific problem, making them less generic and *** this paper, we aim to develop a unified neural solver that can cope with a range of VRP variants ***, we propose a multi-task vehicle routing solver with mixture-of-experts (MVMoE), which greatly enhances the model capacity without a proportional increase in *** further develop a hierarchical gating mechanism for the MVMoE, delivering a good trade-off between empirical performance and computational ***, our method significantly promotes zero-shot generalization performance on 10 unseen VRP variants, and showcases decent results on the few-shot setting and real-world benchmark *** further conduct extensive studies on the effect of MoE configurations in solving VRPs, and observe the superiority of hierarchical gating when facing out-of-distribution *** source code is available at: https://***/RoyalSkye/Routing-MVMoE. Copyright 2024 by the author(s)

关键词： Vehicle routing

来源：评论

学校读者我要写书评

暂无评论

Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches 39

Finding Top-k Important Edges on Bipartite Graphs: Ego-betwe...

引用

39th IEEE International Conference on data Engineering, ICDE 2023

作者： Weng, Tongfeng Zhou, Xu Fang, Yixiang Tan, Kian-Lee Li, Kenli Hunan University College of Computer Science and Electronic Engineering China The Chinese University of HongKong School of Data Science Shenzhen China National University of Singapore School of Computing Singapore

ISBN: (纸本)9798350322279

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph;these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms. © 2023 IEEE.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

Harnessing the Power of Vocal Signals in COVID-19 Detection Utilizing Machine Learning

Harnessing the Power of Vocal Signals in COVID-19 Detection ...

引用

2024 IEEE International Conference on Big data, Bigdata 2024

作者： Mann, Aleesa Jadhav, Ajinkya P. Matovu, Richard Gupta, Vibhuti Meharry Medical College School of Applied Computational Sciences Department of Computer Science and Data Science NashvilleTN United States Gannon University Department of Computing and Information Science EriePA United States

ISBN: (纸本)9798350362480

The global COVID-19 pandemic has strained health-care systems and highlighted the need for accessible and efficient diagnostic methods. Traditional diagnostic tools, such as nasal swabs and biosensors, while accurate, pose significant logistical challenges and high costs, limiting their scalability. This paper explores an alternative, non-invasive approach to COVID-19 detection using machine learning algorithms to analyze vocal patterns, particularly cough and breathing sounds. Leveraging a publicly available dataset, we developed machine learning models capable of classifying audio samples as COVID-19 positive or negative. Our models achieve an AUC of up to 85% and an F1-score of 81%, demonstrating the potential of machine learning in enabling rapid, cost-effective COVID-19 diagnosis. These findings suggest that audio-based diagnostics could be a practical and scalable solution, particularly in resource-limited settings where traditional methods are less feasible. © 2024 IEEE.

关键词： COVID-19

来源：评论

学校读者我要写书评

暂无评论

Detecting the Traceability Issues in Supply chain Industries using Blockchain Technology

Detecting the Traceability Issues in Supply chain Industries...

引用

2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Subashini, B. Hemavathi, D. School of Computing SRM Institute of Science and Technology Department of Data Science and Business Systems India

ISBN: (纸本)9781665495295

Today, agriculture remains the most important economic activity in the world. Food care is a major concern for the whole society. For agriculture-based supply chain management, traceability is considered as the major requirement. Traceability helps in providing quality assurance and transparency to the end user. The antiquated traceability system has the complications of centralized systems, non-Transparency of information, unpredictable data, and the very easy formation of false data. The World Health Organization (WHO) reports that one out of ten people is affected by having tainted food. Globalization involving different food production processes and automation pushes the food supply chain into a complicated web. Numerous advancements have been investigated lately to determine food vulnerability and carry productivity to food related issues. One of the expected advances is block chain, which is as of now being effectively executed in monetary areas, for example, bit coin. Block chain can also be used in the food supply chain industries to improve traceability and help in the danger decrease of fakes and different types of unlawful occupation. This paper presents a systematic survey on agrifood supply chain traceability issues using various block chain platforms such as Ethereum and Hyperledger, as well as IOT techniques such as barcodes, RFID, NFC, and QR codes. The performance of several consensus algorithms is also investigated. © 2022 IEEE.

关键词： Transparency

来源：评论

学校读者我要写书评

暂无评论

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition 38

LF-ViT: Reducing Spatial Redundancy in Vision Transformer fo...

引用

38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Hu, Youbing Cheng, Yun Lu, Anqi Cao, Zhiqiang Wei, Dawei Liu, Jie Li, Zhijun AIoT Lab Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

ISBN: (纸本)1577358872

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed;if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT's prowess: it remarkably decreases Deit-S's FLOPs by 63% and concurrently amplifies throughput twofold. Code of this project is at https://***/edgeai1/***. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Image recognition

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：