检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Hosseini, Hossein Kazemi, Arefeh Zare, Mohammad Sobhan Zojaji, Zahra Mohammadi, Amir Hossein Nematbakhsh, Mohammad Ali Department of software engineering University of Isfahan Isfahan Iran ADAPT Research Centre Dublin City University Dublin Ireland Department of software engineering Faculty of Computer Engineering University of Isfahan Isfahan Iran

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve the preprocessing, embedding, retrieval, prompt construction, language modeling, and response evaluation of the system. In this paper, we address the challenges towards implementing a real-world RAG system for Persian language called PersianRAG. We propose novel solutions to overcome these obstacles and evaluate our approach using several Persian benchmark datasets. Our experimental results demonstrate the capability of the PersianRAG framework to enhance question answering task in Persian. © 2024, CC BY-NC-ND.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Fused Machine Learning Approach for Intrusion Detection System

引用

computers, Materials & Continua 2023年第2期74卷 2607-2623页

作者： Muhammad Sajid Farooq Sagheer Abbas Atta-ur-Rahman Kiran Sultan Muhammad Adnan Khan Amir Mosavi School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam 31441Saudi Arabia Department of CIT The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Gachon UniversitySeongnam13120Korea John von Neumann Faculty of Informatics Obuda UniversityBudapest1034Hungary Institute of Information Engineering Automation and MathematicsSlovak University of Technology in BratislavaBratislava81107Slovakia Faculty of Civil Engineering TU-DresdenDresden01062Germany

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of *** interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and *** learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit *** intrusion detection system controls the flow of network traffic with the help of computer *** deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network *** this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network *** Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious *** proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

关键词： Fused machine learning heterogeneous network intrusion detection

来源：评论

学校读者我要写书评

暂无评论

Self-Optimizing Memory Controllers: Proposing Request-level Scheduling 2

Self-Optimizing Memory Controllers: Proposing Request-level ...

引用

2nd International Conference on computer Science, engineering and Applications, ICCSEA 2022

作者： Shawky, Alaa Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Faculty of Engineering Electronics and Communications Dept. Cairo Egypt Siemens Digital Industries Software Cairo Egypt Ain Shams University Faculty of Engineering Computer and Systems Engineering Dept. Cairo Egypt

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

Self-Optimizing Memory Controllers present a great potential in the future of memory controllers. As they alleviate the burden of designing an optimal memory scheduling policy, while providing adaptability to different workloads and systems. Previous state-of-art controllers scheduled memory accesses on the command level. This causes the agent to lose sight of the relation between commands and can lead to the issuing of unuseful sequences of precharge/activate of rows or closing banks that should have been left open, and vice versa. In this work we propose a bank-aware self-optimizing memory controller that schedules memory references on the request-level, presenting to the agent the actual problem it needs to tackle. This simplifies the evaluation of system attributes and eliminates the need for correction mechanisms that was needed in previous controllers. With the proposed algorithm a maximum of 18% increase in the data bus utilization is achieved compared to the commonly used FR-FCFS algorithm. © 2022 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Task Offloading and Resource Allocation in IoT Based Mobile Edge Computing Using Deep Learning

引用

computers, Materials & Continua 2023年第8期76卷 1463-1477页

作者： Ily s Abdullaev Natalia Prodanova KAruna Bhaskar ELaxmi Lydia Seifedine Kadry Jungeun Kim Dean of the Faculty of Economics Department of Management and MarketingFaculty of EconomicsUrgench State UniversityUrganch220100Uzbekistan Basic Department Financial Control Analysis and Audit of Moscow Main Control DepartmentPlekhanov Russian University of EconomicsMoscow117997Russia Department of Computer Science and Engineering KL Deemed to UniversityVaddeswaramGunturAndhra PradeshIndia Department of Computer Science and Engineering GMR Institute of TechnologyAndhra PradeshRajamIndia Department of Applied Data Science Noroff University CollegeKristiansandNorway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblosLebanon Department of Software Kongju National UniversityCheonan31080Korea

Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data *** city benefitted from offloading to edge *** a mobile edge computing(MEC)network in multiple *** comprise N MDs and many access points,in which everyMDhasM independent real-time *** study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)*** proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system *** addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted *** TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading ***,the SGO algorithm is used for the parameter tuning of the DBN *** simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.

关键词： Mobile edge computing seagull optimization deep belief network resource management parameter tuning

来源：评论

学校读者我要写书评

暂无评论

Comparison of YOLO Algorithms for Vehicle Accident Detection and Classification

Comparison of YOLO Algorithms for Vehicle Accident Detection...

引用

Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Ahmed N. Nusari Abdulfattah E. Ba Alawi Ferhat Bozkurt İbrahim Yücel Özbek Electrical and Electronics Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Computer and Control Engineering Department Faculty of Engineering Sana’a University Sana’a Yemen Computer Engineering Department Faculty of Engineering Ataturk University Erzurum Turkey Software Engineering Department Faculty of Engineering & IT Taiz University Taiz Yemen Electrical and Electronics Department Faculty of Engineering Ataturk University Erzurum Turkey

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

Early detection of vehicle accidents is essential for immediate response and to decrease the severity of injuries while vehicle accidents occur. Among the advanced algorithms YOLOv6, YOLOv7, YOLOv8, and YOLOv9 are compared in this article to determine their performance in detecting vehicle accidents that are categorized as "severity" or "moderate." The study emphasizes how important deep learning is to enhancing accident detection systems. Out of all the models evaluated, YOLOv8m has the highest precision (0.979), and the highest recall (0.927) for YOLOv8n. Furthermore, YOLOv9c performs admirably in mAP50, receiving a score of 0.977, indicating its resilience in identifying accidents under a variety of circumstances. YOLOv9e is noteworthy for its better mAP50-95, which reaches a high of 0.941, demonstrating its ability to localize accident incidents accurately. These results provide valuable insight into how well particular algorithms perform in real-world situations. Through a close examination of their performance measures, this research aids in the advancement of more effective accident detection systems, which in turn improves emergency response operations and lessens the effects of accidents on people and communities. This comparison research emphasizes how crucial it is to use state-of-the-art technology to increase accident detection accuracy and enable timely and efficient response.

关键词： YOLO Deep learning Technological innovation Accuracy Computational modeling Scalability Emergency services

来源：评论

学校读者我要写书评

暂无评论

Streamlining Attendance with Voice Recognition via Gaussian Mixture Model

Streamlining Attendance with Voice Recognition via Gaussian ...

引用

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Ali, Noor Ali, Khadija Ali, Fatimah Ali, Aaminah Ali, Nisar Ali, Raja Hashim A-levels Lahore Grammer School 55 Main Gulberg Lahore54660 Pakistan Department of Software Engineering NUST College of Electrical and Mechanical Engineering Islamabad43701 Pakistan Department of Artificial Intelligence Faculty of Computer Science & Engineering GIK Institute of Engineering Sciences & Technology Topi23460 Pakistan Department of Business University of Europe for Applied Sciences Potsdam14469 Germany Faculty of Engineering and Applied Science University of Regina ReginaS4S0A2 Canada

ISBN: (纸本)9798331504465

Voice recognition systems are crucial because they allow seamless human-computer interaction and improve accessibility for users of all abilities. The use of these technologies in hands-free control, language translation, virtual assistants, transcription services, and hands-free control is revolutionising how we engage with technology and enhancing convenience and productivity in general. Several attendance systems based on voice recognition exist, but we wanted to deploy an attendance system with a good graphical user interface specifically for students of GIK Institute. For this purpose, we wanted to make a user-friendly and accurate voice recognition system based and trained on self-provided data of ten students. This study introduces an AI-driven attendance system, which demonstrates high efficiency and accuracy in identifying students’ daily class attendance. To achieve this, the Gaussian Mixture Model approach was employed. The paper also delves into the utilization of libraries and methods, encompassing the training and validation of well-known machine learning models. Additionally, the system’s performance, its strengths, weaknesses and potential areas for improvement are also discussed in the study. © 2024 IEEE.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Data and Ensemble Machine Learning Fusion Based Intelligent software Defect Prediction System

引用

computers, Materials & Continua 2023年第6期75卷 6083-6100页

作者： Sagheer Abbas Shabib Aftab Muhammad Adnan Khan Taher MGhazal Hussam Al Hamadi Chan Yeob Yeun School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Software Faculty of Artificial Intelligence and SoftwareGachon UniversitySeongnam13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUKMBangiSelangor43600Malaysia College of Engineering and IT University of Dubai14143UAE EECS Department Center for Cyber Physical SystemsKhalifa UniversityAbu Dhabi127788UAE

The software engineering field has long focused on creating high-quality software despite limited *** defects before the testing stage of software development can enable quality assurance engineers to con-centrate on problematic modules rather than all the *** approach can enhance the quality of the final product while lowering development *** defective modules early on can allow for early corrections and ensure the timely delivery of a high-quality product that satisfies customers and instills greater confidence in the development *** process is known as software defect prediction,and it can improve end-product quality while reducing the cost of testing and *** study proposes a software defect prediction system that utilizes data fusion,feature selection,and ensemble machine learning fusion techniques.A novel filter-based metric selection technique is proposed in the framework to select the optimum features.A three-step nested approach is presented for predicting defective modules to achieve high *** the first step,three supervised machine learning techniques,including Decision Tree,Support Vector Machines,and Naïve Bayes,are used to detect faulty *** second step involves integrating the predictive accuracy of these classification techniques through three ensemble machine-learning methods:Bagging,Voting,and ***,in the third step,a fuzzy logic technique is employed to integrate the predictive accuracy of the ensemble machine learning *** experiments are performed on a fused software defect dataset to ensure that the developed fused ensemble model can perform effectively on diverse *** NASA datasets are integrated to create the fused dataset:MW1,PC1,PC3,PC4,and *** to the results,the proposed system exhibited superior performance to other advanced techniques for predicting software defects,achieving a remarkable accuracy rate of 92.08%.

关键词： Ensemble machine learning fusion software defect prediction fuzzy logic

来源：评论

学校读者我要写书评

暂无评论

Building a Full-text Search Index Using "Transformer" Neural Network

Building a Full-text Search Index Using "Transformer" Neural...

引用

International Conference on Application of Information and Communication Technologies (AICT)

作者： Vyacheslav Dobrynin Roman Abramovich Alexey Platonov Faculty of Software Engineering and Computer Systems ITMO University Saint Petersburg Russian Federation

The use of deep neural networks in information retrieval significantly improves its effectiveness, but negatively affects the performance of the process. To deal with this, we propose a new ranking model that uses the deep neural network of the "Transformer" architecture (in particular, BERT) for efficient information retrieval. In accordance with the proposed approach, contextualized vector representations are extracted from documents during indexing, after which these representations are clustered for each independent token. The resulting clusters reflect different meanings of the words and are indirectly used as inverted index keys. The values represent the documents in which these contextualized word meanings occur, along with the distances from each document to the contextualized embedding. Thus, after the indexing process, we obtain an index containing pre-calculated distances between the contextualized meanings of dictionary elements and documents. This approach helps us avoid the performance overhead of calculating distances online. At the search stage, the query is transformed into a set of contextualized vectors representing each query token, which allows us to use these vectors to retrieve most semantically close neighbor-tokens and use them to extract relevant documents from the index. This way of searching for contextualized embeddings consumes less memory and is more performant due to the use of an inverted index.

关键词：

来源：评论

学校读者我要写书评

暂无评论

American Sign Language Recognition Based on Machine Learning and Neural Network 2

American Sign Language Recognition Based on Machine Learning...

引用

2nd International Conference on Machine Learning and Intelligent Systems engineering, MLISE 2022

作者： Li, Lanxi Liu, Da Shen, Chenlin Sun, Jing The Ohio State University Computer Science And Engineering Columbus43201 United States Sichuan University Department Of Computer Science Chengdu610207 China Jilin University Faculty Of Software Engineering Changchun130012 China Institute Of Information And Software Engineering University Of Electronic Science And Technology Of China Chengdu610066 China

ISBN: (数字)9781665492461

ISBN: (纸本)9781665492461

Numerous disabilities such as deaf and mute are suffered from not being capable of communicating with normal people, it is necessary to find a way to solve this problem. A feasible method is Sign Language Recognition (SLR) which is a sort of pattern recognition technique. In this paper, machine learning and deep learning methods are applied to recognize and classify American Sign Language (ASL), and only 24 English letters are classified because letter J and Z require fingers to move. First, Principal Component Analysis (PCA) and manifold algorithms are used to do dimension reduction to accelerate the training of machine learning and visualize it. Second, various machine learning methods such as Random Forest Classification (RFC), K-Nearest Neighbor (KNN), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), and Stochastic Gradient Descent (SGD) are applied to classify the pattern. Since the SVM algorithm has several hyperparameters, this study uses the Grid Search method to find the best combination of hyperparameter which lead to predicting more accurately. It is found that different dimensionality reduction algorithms have unequal effects on the accuracy of each prediction model, and it can be concluded that the manifold algorithm is the best dimension reduction algorithm only for KNN but not for other prediction models, and PCA is much more feasible than KNN applied in such machine learning algorithms except KNN. Two deep learning algorithms such as Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) are also used in classification and their accuracy is highest among such algorithms mentioned above. © 2022 IEEE.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

A Review of Multi-omics and Machine Learning: Advancements and Applications in Cancer Research

A Review of Multi-omics and Machine Learning: Advancements a...

引用

Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Hazem Ayman Madonna Bassem Malak Madkour Mariam Fawzi Nada Amr Youmna Yousry Ziad Elgayar Ashraf Abdelraouf Department of Computer Engineering and Software Systems Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt

This comprehensive review starts with diving into the progress and real-world applications of combining multi-omics data analysis with machine learning techniques in cancer research. Multi-omics involves examining various biological data types like genomics, transcriptomics, proteomics, and metabolomics together to enhance our understanding of complex biological systems. By merging machine learning with multiomics data, we highlight the advantages for cancer studies, the deeper insights they yield and increased performance and results. Furthermore, we explore existing literature that showcases the integration of multi-omics and machine learning in cancer research. As part of our study, we conduct an experiment utilizing a multiomics dataset to predict the survival of breast cancer patients. We compare three distinct machine learning methods-ensemble, DeepProg, and DCAP-for survival prediction and conclude that despite the ensemble method that increased the prediction results of DeepProg over DCAP in multi-model setting, but the primitive capacity for DCAP is better in single model setting and achieves higher accuracy than DeepProg with noticeable margin 0.628 to 0.57 on C-Index metric, which strongly recommends using Denoising Autoencoder as the base for dimensionality reduction over the vanilla Autoencoder. Another empirical results conclude that using gaussian mixture model with diagonal covariance matrix for Clustering, which is used in DeepProg, might hinder the process for identifying reasonable clusters due to the assumption of no or zero correlation between different features which might not hold true in our problem.

关键词：

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：