检索结果-内蒙古大学图书馆

Iraqi Journal for computer science and Mathematics 2024年第3期5卷 108-127页

作者： Hamdi, Mustafa Maad Rashid, Sami Abduljabbar Nafea, Ahmed Adil Department of Computer Science College of Computer Science and IT University of Anbar Ramadi Iraq Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Johor Batu Pahat86400 Malaysia Department of Artificial Intelligence College of Computer Science and IT University of Anbar Ramadi Iraq

VANETs are highly attractive and is used in maximum of the applications of cross-regional communication. To increase the coverage of the vehicular network, Unmanned Arial Vehicles (UAVs) are introduced, and they get connected with the satellite networks to perform heterogeneous communication. With the help of this connectivity, the communication quality of ground level to air medium is increased. Currently the vehicle usage is highly increased and as a results of communication link failure, improper resource allocation are arises whither abruptly assumes a stability about a network with that increases an energy consumption and communication delay in the heterogeneous networks. In these conditions, thus study is idea of Resource Allocation and Edge Computing for Dual Hop Communication (RAEDH) in introduced in satellite assisted UAVs enabled VANETs. The major sections of the approach are UAV assisted mobile computing, resource allocation among the vehicles and the UAVs, and dual communication among the vehicles and the *** these methods the input resources are properly allocated and that reduces the power utility and communication delay. Initially, the vehicular network is established, incorporating trusted components like TA, RSU, and CRS. Subsequently, mobile edge computing reduces energy consumption through computation offloading and optimized UAV trajectory selection. Resource allocation, facilitated by whale optimization, ensures effective utilization across vehicles. The implementation of this method is done in NS3, and the scenario is analyzed using two parameters like number of vehicles and its speed. The output parameters that remain thought-out over a performance examination stay throughput, end-to-end delay, energy efficiency, packet loss, packet delivery ratio, and routing overhead, and as well those results are compared with the earlier methods. Finally, dual-hop transmission between vehicles and UAVs enhances delivery ratio and throughput. from

关键词： Satellites

来源：评论

学校读者我要写书评

暂无评论

Crack Detection on Brick Walls by Convolutional Neural Networks Using the Methods of Sub-dataset Generation and Matching 1

引用

1st International Conference and 2nd International Conference on Deep Learning Theory and Applications, DeLTA 2020 and DeLTA 2021

作者： Talukder, Mehedi Hasan Ota, Shuhei Takanokura, Masato Ishii, Nobuaki Department of Computer Science and Engineering Faculty of Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Industrial Engineering and Management Faculty of Engineering Kanagawa University Yokohama Japan

ISBN: (数字)9783031373206

ISBN: (纸本)9783031373190

The appearance of cracks is considered an initial sign of the deterioration of structures such as concrete and brick walls. Crack detection plays an important role in ensuring the safety and durability of structures. Conventionally, a maintenance engineer performs crack detection manually, which is laborious and time-consuming. Therefore, a systematic crack detection method is required. Among the existing crack detection methods, convolutional neural networks (CNNs) are more effective;however, CNNs often fail in the case of brick walls. There are several types of bricks, and some may appear to have cracks owing to their structure. Additionally, the joining points of bricks may appear as cracks;therefore, CNN fails. It is theorized that CNN performance can be improved if sub-datasets are generated based on the image attributes, and a proper sub-dataset is selected by matching the test image with the sub-datasets. In this study, sub-dataset generation and matching methods are proposed to improve the performance of crack detection in brick walls using CNN. CNN training is conducted with each sub-dataset generated by the proposed sub-dataset generation method, while crack detection is performed using a proper trained CNN that is selected using the proposed matching method. For numerical experiments, training datasets are first prepared by manual image cropping and rotation, after which the performance of crack detection is evaluated by cross-validation. Numerical experiments show that the proposed method improves crack detection in brick walls. This study will help to ensure the safety of structures as well as the safety of human life. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Crack detection

来源：评论

学校读者我要写书评

暂无评论

Skin Disease Detection Using Neural Networks 2

Skin Disease Detection Using Neural Networks

引用

2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Gupta, Meenu Kumar, Rakesh Nandan Pradhan, Anil Kumar Obaid, Ahmed J. Chandigarh University Department of Computer Science & Engineering Punjab Mohali India University of Kufa Faculty of Computer Science and Mathematics Dept of Computer Science Najaf Iraq College of Engineering Uruk University Baghdad Iraq

ISBN: (纸本)9798350370188

Skin diseases like acne, psoriasis, eczema, and dermatitis affect millions worldwide. Skin cancer and melanoma are diseases that happen due to exposure to UV radiation. The early detection of skin diseases is crucial for effective treatment and prevention of further complications. This work comprehensively reviews various image-based techniques for skin disease detection, including traditional computer vision methods such as Convolutional Neural Network (CNN), Random Forest (RF), Naïve Bayes (NB), k-nearest Neighbour (k-NN), and Support Vector Machine (SVM). ISIC and dermofit datasets comprised 26,150 images divided into three categories: training (20,150), testing (3000), and validation (2000), respectively. Receiver Operating Characteristics (ROC) and Mean Squared Error (MSE) are used to calculate the accuracy and precision of models. As a result, it concluded that CNN outperformed with 94.91% accuracy. The proposed system can aid dermatologists in making accurate diagnoses and improve patient care. © 2024 IEEE.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Mutual character dialogue generation with semi-supervised multitask learners and awareness

引用

International Journal of Information Technology (Singapore) 2024年第3期16卷 1357-1363页

作者： Ahrari Khalaf, Ayesheh Hassan Abdalla Hashim, Aisha Olowolayemo, Akeem Department of Electrical and Computer Engineering Faculty of Engineering International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia Department of Computer Science Faculty of Information and Communication Technology International Islamic University Malaysia (IIUM) Kuala Lumpur Malaysia

Consistent efforts have been ongoing to improve the friendliness and reliability of informal dialogue systems. However, most research focuses solely on mimicking human-like answers. Therefore, the interlocutors’ awareness features of the dialogue system are left unexplored. Meanwhile, cognitive science research reveals that awareness is a crucial indicator of an effective, high-quality informal conversation. This research aims to boost the quality of the conversational generation system by factoring in awareness of the interlocutors in the design and training of the dialogue system model. The Generative Pre-Trained Transformer-2 (GPT-2) model was implemented into the Persona Perception (P²) Bot to achieve the objectives of this study. This was to precisely develop model's understanding, P² Bot was implemented using a transmitter–receiver-based structure. The P² Bot leverages mutual persona awareness to improve the quality of customized dialogue generation. GPT-2 is a 1.5B parameter transformer model that produces state-of-the-art accuracy in a zero-shot setting on seven of the eight evaluated language modeling datasets. The observations of the proposed model on a sizable open-source dataset, PERSONA-CHAT, proved successful, with improvement above the state-of-the-art baselines in both automatic measures and human assessments. The model has achieved 82.2% accuracy on Hits@1 performance metrics in the original data and 68.8% on the revised data. On the human evaluation, the model scored an average of 2.66, pointing out that the responses provided were coherent and informative. A dialogue generation model with character and awareness which can communicate like an informative human expert was introduced. This study presents the submerging of GPT-2 model on a mutual persona perception dialogue generating model. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cognitive science Conversational agent Dialogue generation Generative Pre-trained Transformer 2 (GPT-2) Natural language understanding (NLU)

来源：评论

学校读者我要写书评

暂无评论

A salient feature establishment tactic for cassava disease recognition

引用

Artificial Intelligence in Agriculture 2024年第4期14卷 115-132页

作者： Jjayu Zhang Baohua Zhang Zixuan Chen Innocent Nyalala Kunjie Chen Junfeng Gao College of Engineering Nanjing Agricultural UniversityNanjing210000 Jjiangsu ProvinceChina Department of Computer Science University of AberdeenUK Faculty of Science Department of Computer ScienceEgerton UniversityNjoroKenya Nanjing Institute of Agricultural Mechanization Ministry of Agriculture and Rural AffairsNanjingJiangsu 210014China

Accurate classification of cassava disease,particularly in field scenarios,relies on object semantic localization to identify and precisely locate specific objects within an image based on their semantic meaning,thereby enabling targeted classification while suppressing rrelevant noise and focusing on key semantic *** advancement of deep convolutional neural networks(CNNs)paved the way for identifying cassava diseases by leveraging salient semantic features and promising high *** study proposes an approach that incorporates three innovative elements to refine feature representation for cassava disease ***,a mutualattention method is introduced to highlight semantic features and suppress irrelevant background features in the feature ***,instance batch normalization(IBN)was employed after the residual unit to construct salient semantic features using the mutualattention method,representing high-quality semantic features in the ***,the RSigELUD activation method replaced the conventional ReLU activation,enhancing the nonlinear mapping capacity of the proposed neural network and further improving fine-grained leaf disease classification performance This approach significantly aided in distinguishing subtle disease manifestations in cassava *** proposed neural network,MAIRNet-101(Mutualattention IBN RSigELUD Neural Network),achieved an accuracy of 95.30%and an F1-score of 0.9531,outperforming EfficientNet-B5 and *** evaluate the generalization capability of MAIRNet,the FGVC-Aircraft dataset was used to train MAIRNet-50,which achieved an accuracy of 83.64%.These results suggest that the proposed algorithm is well suited for cassava leaf disease classification applications and offers a robust solution for advancing agricultural technology.

关键词： Cassava disease classification Irrelevant feature depression Semantic foreground representation Salient semantic features Negative eigenvalue maintain

来源：评论

学校读者我要写书评

暂无评论

Assessment of Spring Water Security for Small Community Using Internet of Things (IoT) 7th

Assessment of Spring Water Security for Small Community Usin...

引用

7th International Conference on Architecture and Civil engineering, ICACE 2023

作者： Nadzri, Mohamad Nazrul Hafiz Mohd Othman, Maidiana Razali, Noor Afiza Mat Nordin, Norzaima Department of Civil Engineering Faculty of Engineering Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia Department of Aeronautic and Aviation Engineering of Computer Science Faculty of Defence Science and Technology Universiti Pertahanan Nasional Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9789819707508

The securing of clean and sustainable water sources is a fundamental entitlement of individuals and an essential factor in the sustained advancement of societies. Alternative water resources, springs can serve as a primary water source for small, remote communities. The preservation of the security and quality of spring water is of the highest priority. This study provides an assessment of the security of spring water resources within the community, facilitated through the utilization of an Internet of Things (IoT) monitoring system. The study aimed to assess the variations in temperature, turbidity, pH, TDS, and electrical conductivity of spring water on the NDUM campus over the course of a day, specifically during the morning, afternoon, and evening periods. The monitoring was conducted over a span of one year to capture potential patterns and changes. The obtained data is sent to the cloud by using an IoT-based ThingSpeak application to monitor the quality of water. As a result of the collected data, the system provides an efficient water quality parameter monitoring system, and early warning can be taken to reduce any harmful effects to the environment. The outcome of this study may contribute to the proposal of a new way of monitoring water quality on campus by using a real-time system. In addition, the outcome also provides baseline information about water quality for the welfare of society and supports the green campus campaign, which may also help in future water security and sustainability planning for the NDUM campus area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Water quality

来源：评论

学校读者我要写书评

暂无评论

Physician Scheduling using Constraint Satisfaction Programming 11

Physician Scheduling using Constraint Satisfaction Programmi...

引用

11th International Japan-Africa Conference on Electronics, Communications, and Computations, JAC-ECC 2023

作者： Bakr, Hend A. Salama, Ahmed M. Fares, Ahmed Zaky, Ahmed B. Cairo University Biomedical Engineering Program Faculty of Engineering Giza Egypt Benha University Computer Systems Engineering Program Faculty of Engineering at Shoubra Banha Egypt Computer science and information technology Programs Alexandria Egypt Benha University On leave from Computer Systems Engineering Program Faculty of Engineering at Shoubra Egypt

ISBN: (纸本)9798350369328

Physician scheduling is a critical task that impacts the quality of patient care, staff satisfaction, and operational efficiency in healthcare institutions. The traditional approach to physician scheduling is manual and time-consuming, which can result in errors, staff burnout, and suboptimal schedules. To address these challenges, researchers have turned to optimization techniques like CSP, which has shown promise in solving physician scheduling problems. This paper reviews the existing literature on CSP for physician scheduling and highlights the benefits and limitations of this approach. CSP's benefits include generating schedules quickly and efficiently, incorporating complex constraints and preferences, and handling changes and disruptions in real time. However, CSP also has some limitations, such as the need for a formalized model and the fact that it may not always generate the most intuitive schedules. Overall, the findings suggest that CSP is a promising approach to physician scheduling that can produce high-quality schedules while minimizing staff burnout and improving operational efficiency. © 2023 IEEE.

关键词： Constraint satisfaction problems

来源：评论

学校读者我要写书评

暂无评论

Analysis and Classification of Fake News Using Sequential Pattern Mining

引用

Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection Based on Bidirectional Long Short-Term Memory with Attention Mechanism

引用

computers, Materials & Continua 2023年第1期74卷 801-815页

作者： Yongjie Yang Shanshan Tu Raja Hashim Ali Hisham Alasmary Muhammad Waqas Muhammad Nouman Amjad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of TechnologyBeijing100124China Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and TechnologyTopi23460Pakistan Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Information Security and Cybersecurity Unit King Khalid UniversityAbhaSaudi Arabia Computer Engineering Department College of Information TechnologyUniversity of Bahrain32038Bahrain School of Engineering Edith Cowan UniversityJoondalup PerthWA6027Australia School of Engineering University of Management and TechnologyLahorePakistan

With the recent developments in the Internet of Things(IoT),the amount of data collected has expanded tremendously,resulting in a higher demand for data storage,computational capacity,and real-time processing *** computing has traditionally played an important role in establishing ***,fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility,location awareness,heterogeneity,scalability,low latency,and geographic ***,IoT networks are vulnerable to unwanted assaults because of their open and shared *** a result,various fog computing-based security models that protect IoT networks have been developed.A distributed architecture based on an intrusion detection system(IDS)ensures that a dynamic,scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is *** this study,we examined the time-related aspects of network traffic *** presented an intrusion detection model based on a twolayered bidirectional long short-term memory(Bi-LSTM)with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark *** showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy,precision,recall and F1 score.

关键词： Fog computing intrusion detection bi-LSTM attention mechanism

来源：评论

学校读者我要写书评

暂无评论

GRIPPY: A VR Grip Controller for Combating Sarcopenia in Elderly

GRIPPY: A VR Grip Controller for Combating Sarcopenia in Eld...

引用

2025 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2025

作者： Leong, Hoilam Wu, Jiang Lai, Yoke Chin Faulon, Loïc Sun, Xu Lee, Boon Giin University of Nottingham Faculty of Science and Engineering Department of Mechanical Materials and Manufacturing Engineering Ningbo China School of Computer Science China

ISBN: (纸本)9798331514846

As the global population ages, sarcopenia - age-related muscle decline - demands innovative solutions. This paper introduces GRIPPY, a VR grip controller that transforms basic handgrip exercises into immersive, gamified tasks. By integrating sensor-based interaction and VR gameplay, older adults can strengthen grip while maintaining higher motivation. A preliminary test with three participants (ages 68-80) revealed key usability insights, underscoring GRIPPY's potential for enhanced rehabilitation. Future work will refine its design and validate long-term efficacy. © 2025 IEEE.

关键词： Elderly Rehabilitation Grip Strength Immersive Exercise Interactive Training Sarcopenia Virtual Reality

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：