检索结果-内蒙古大学图书馆

International Conference on Artificial Intelligence and Smart Environment, ICAISE 2022

作者： Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Salako, Dotun Patrick Farhaoui, Yousef Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Kwara State Ilorin240003 Nigeria Department of Electrical and Electronics Engineering Faculty of Engineering University of Lagos Akoka Lagos100213 Nigeria STI Laboratory T-IDMS Faculty of Sciences and Techniques Moulay Ismail University of Meknès Errachidia Morocco

ISBN: (纸本)9783031262531

The major challenge of inadequate healthcare services in developing countries has been linked to the unavailability of qualified medical personnel and inefficient diagnostic techniques adopted. The advent of technology in medicine has aided the improvement of medical processes including diagnosis. Many computerized systems have been implemented over the years for medical diagnosis some of which included machine learning techniques. But the existing methods lack the power of selecting the most relevant signs and symptoms of malaria and typhoid fever. Therefore, this study develops a system capable of Malaria and Typhoid Fever diagnosis using a Genetic Algorithm, a Neuro-Fuzzy Inference System called GENFIS. In order to train the NFIS, the GA module determines the optimal set of network parameters, saves them, and then distributes them to the appropriate hidden layer nodes. The study made use of the MATLAB environment to test and evaluate the system. The accuracy performance of the proposed model revealed an accuracy of 97.2%, thus performing better than some of the existing systems. If completely adopted, the proposed approach has the potential to lessen the major issues with NFIS systems. Furthermore, it might be used to address difficult issues in different fields. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Real Time Monitoring System of Drinking Water Quality Using Internet of Things 24

Real Time Monitoring System of Drinking Water Quality Using ...

引用

24th International Electronics Symposium, IES 2022

作者： Abidin, Zainul Maulana, Eka Nurrohman, Muhammad Yogi Wardana, Fitri Candra Warsito Universitas Brawijaya Faculty of Computer Science Department of Computer Engineering Malang Indonesia Universitas Brawijaya Faculty of Engineering Department of Electrical Engineering Malang Indonesia Universitas Brawijaya Master Program in Environmental Resources and Development Management Postgraduate School Malang Indonesia Universitas Brawijaya Faculty of Mathematics and Natural Sciences Department of Chemistry Malang Indonesia

ISBN: (数字)9781665489713

ISBN: (纸本)9781665489713

In order to reduce Polyethylene Terephthalate (PET) bottle plastic waste, Universitas Brawijaya (UB) provided reverse osmosis drinking water in some buildings. Water monitoring is needed to keep the water quality. Therefore, a water monitoring system based on Total Dissolved Solids (TDS), pH, and water flow was proposed. Since the UB buildings have good internet connection (WiFi), Internet of Things (IoT) was employed to the system to support real time monitoring. So, data of TDS, pH, and water flow can be periodically sent to cloud and displayed to a specific website. The system worked very well and the data can be summarized and used by operator as indicators of performance of the reverse osmosis machine. When water quality and performance decrease, operator must check filters membrane or water pump for further maintenance to support long life use. Although the sensors have been calibrated properly, some measurement errors occur. The measurement error percentages of TDS and pH are 0.62% and 0.94%, respectively. © 2022 IEEE.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Road traffic condition identification method based on triangular fuzzy number α - cut set coupling

Advances in Transportation Studies

引用

Advances in Transportation Studies 2021年第Special issue 1期2021卷 93-102页

作者： Chen, H.X. Patnaik, S. Faculty of Science Bengbu University Bengbu233000 China Department of Computer Science and Engineering SOA University Bhubaneswar751030 India

In order to overcome the problems of low discrimination rate, high false alarm rate and long average detection time in the process of road traffic condition discrimination, a new road traffic condition discrimination method based on triangular fuzzy number α - cut set coupling is proposed. Combined with the traffic flow characteristics of urban roads and the influencing factors of traffic congestion, this method analyzes the evaluation index of urban roads. Combined with the coupling of triangular fuzzy number α - cut set, the connection degree is determined in the process of set pair analysis and evaluation. Based on this, the behavior trajectory of vehicles is identified tojudge the traffic safety situation. Each index in the traffic safety sample matrix is dimensionless. According to the Lagrange indefinite multiplier method, the results of road traffic operation are obtained. The experimental results show that the misjudgment rate of the proposed method is always controlled within 10%, which is in line with the three-level alarm system, and greatly reduces the misjudgment rate of traffic accidents. The indicators of the proposed method are the lowest, which fully shows that the proposed method can achieve very satisfactory results. © 2021, Aracne Editrice. All rights reserved.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Dual Radial-Resonant Wide Beamwidth Circular Sector Microstrip Patch Antennas

引用

Chinese Journal of Electronics 2023年第4期32卷 710-719页

作者： MAO Xiaohui LU Wenjun JI Feiyan XING Xiuqiong ZHU Lei Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

In this article, a design approach to a radial-resonant wide beamwidth circular sector patch antenna is advanced. As properly evolved from a U-shaped d ipole, a prototype magnetic dipole can be fit in the radial direction of a circular sector patch radiator, with its length set as the positive odd-integer multiples of onequarter wavelength. In this way, multiple TM0m（m = 1,2,...） modes resonant circular sector patch antenna with short-circuited circumference and widened E-plane beamwidth can be realized by proper excitation and perturbations. Prototype antennas are then designed and fabricated to validate the design approach. Experimental results reveal that the E-plane beamwidth of a dual-resonant antenna fabricated on air/Teflon substrate can be effectively broadened to 128°/120°, with an impedance bandwidth of 17.4%/7.1%, respectively. In both cases, the antenna heights are strictly limited to no more than 0.03-guided wavelength. It is evidently validated that the proposed approach can effectively enhance the operational bandwidth and beamwidth of a microstrip patch antenna while maintaining its inherent low profile merit.

关键词： Geometry Patch antennas Perturbation methods Prototypes Magnetic resonance Microstrip antennas Bandwidth

来源：评论

学校读者我要写书评

暂无评论

High-Resolution Optical Imaging Incorporated Non-Destructive Morphological Boundary Detection of Fruit Disorders: A Pilot Study 6

High-Resolution Optical Imaging Incorporated Non-Destructive...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Hakmana, Nethmi Tharushi Kahatapitiya, Nipun Shantha Weerasinghe, Sasmitha Kalupahana, Deshan Silva, Bhagya Nathali Ravichandran, Naresh Kumar Seong, Daewoon Han, Sangyeob Lee, Seung Yeol June, Hee-Young Jeon, Mansik Kim, Jeehyun Wijenayake, Udaya Wijesinghe, Ruchire Eranga Faculty of Engineering Sri Lanka Institute of Information Technology Department of Mechanical Engineering Malabe Sri Lanka Faculty of Engineering University of Sri Jayewardenepura Department of Computer Engineering Nugegoda Sri Lanka Faculty of Computing Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Malabe Sri Lanka Center for Scientific Instrumentation Korea Basic Science Institute Daejeon Korea Republic of School of Electronic and Electrical Engineering College of IT Engineering Kyungpook National University Daegu Korea Republic of ICT Convergence Research Center Kyungpook National University Daegu Korea Republic of Kyungpook National University Department of Plant Medicine Daegu Korea Republic of Institute of Plant Medicine Kyungpook National University Daegu Korea Republic of Faculty of Engineering Sri Lanka Institute of Information Technology Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

Plant diseases caused by various pathogens, such as fungi, bacteria, and viruses, lead to significant reductions in crop yield and quality. Early detection of these diseases is critical for preventing their spread and optimizing crop yield. Traditional methods of disease detection, while effective, often involve invasive or time-consuming procedures that may not provide real-time insights. This study investigates the use of Swept-Source Optical Coherence Tomography (SS-OCT) for non-destructive, high-resolution imaging to identify morphological abnormalities in fruit specimens affected by diseases. The research focuses on differentiating healthy regions, healthy boundaries, infected boundaries, and fully infected regions in apples. Using a 1310 nm center wavelength laser, the SS-OCT system captured detailed cross-sectional images of fruit tissue, revealing microstructural changes associated with disease progression. A qualitative assessment of the two-dimensional (2D) OCT images highlighted distinct layers in healthy tissues, noticeable structural deformities, and the merging of microstructural layers in the boundary and infected regions. Quantitative depth-profile analysis of axial-direction A-scan profiles further demonstrated the reduction and merging of intensity peaks correlating with tissue abnormalities in infected fruit specimens. These findings underscore the potential of SS-OCT as an effective tool for early disease detection in agricultural products, providing a means for timely intervention and optimized postharvest management. © 2024 IEEE.

关键词： Fruits

来源：评论

学校读者我要写书评

暂无评论

Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

引用

computer Systems science & engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin Saleh Bajaba Sarah B.Basahel E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

来源：评论

学校读者我要写书评

暂无评论

Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education

Revolutionizing Recruitment Management: Optimizing Advanced ...

引用

IEEE Conference on Systems, Process & Control (ICSPC)

作者： Ahmad Mukafi Andrian Nor Kamaliah Khamis Ahgalya Subbiah Halimatuz Zuhriyah Safira Faizah Faculty of Information Sciences and Engineering Management and Science University Section 13 Shah Alam Selangor Department Informatics Engineering Faculty Engineering and Computer Science Jakarta Global University Depok Indonesia

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme Programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems.

关键词： Productivity Visualization Unified modeling language Prototypes Organizations Labor resources System analysis and design Programming profession Recruitment Testing

来源：评论

学校读者我要写书评

暂无评论

Automated Glaucoma Detection based on LBP Representation and GLRLM Feature Extraction Method

Automated Glaucoma Detection based on LBP Representation and...

引用

2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Ahmed, Zakia Lawgali, Ahmed Abdalla, Mohamed University of Omar Al-Mukhtar Faculty of Sciences Department of Computer Science Albayda Libya University of Benghazi Faculty of Information Technology Department of Computer Science Benghazi Libya University of Benghazi Faculty of Information Technology Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Glaucoma is a chronic and degenerative disease that causes irreversible damage to the nerve system of an eye, due to an increase in the Intra-ocular pressure in the retina. It reduces the vision area and leads to blindness. Early detection is critical to prevent permanent vision loss. The automated analysis of medical images contributes to the increase in the performance of classifying retinal images to pathological and nonpathological. This paper presents an automated glaucoma diagnosis approach by analyzing the texture of fundus images. The analysis was performed using Local Binary Pattern descriptor to represent grayscale and channels of retinal images and applied Gray Level Run-Length Matrix to describe patterns of texture. The classification was performed using two classifiers. The proposed approach provided the best classification results with 98%, 91%, and 97% of the three versions of RIM-ONE databases using Support Vector Machine classifier. Findings showed that local binary pattern representations and concatenated gray level run length matrix features achieved high results. Consequently, proving this approach is reliable and promising. © 2022 IEEE.

关键词： Local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Enhancing Performances of Deep Neural Networks with Ensemble Learning Methods for Complex Human Activity Recognition Using Wearable Sensors 10

Enhancing Performances of Deep Neural Networks with Ensemble...

引用

10th International Conference on Digital Arts, Media and Technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798331543273

Sensor-based human activity recognition (HAR) is a widely explored research area focused on identifying human behaviors by analyzing sensor-generated data, particularly one-dimensional time series signals. Such HAR systems have many real-world applications, including elderly health monitoring and athlete performance evaluation. This paper presents Ens-DeepNet, a novel ensemble learning approach designed for recognizing complex human activities using data from wearable sensors. While deep neural networks have proven effective for HAR tasks, their performance can be further improved through ensemble strategies. To this end, we propose an ensemble architecture that combines five distinct deep learning models - convolutional neural network (CNN), long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU). These models are integrated using a majority voting scheme to boost classification accuracy. The proposed method is evaluated using the UTwente dataset, which includes sensor data collected from ten individuals via tri-axial accelerometers and gyroscopes. Experimental findings demonstrate that the ensemble model achieves an impressive accuracy of 99.28%, surpassing the performance of each standalone model, whose accuracies range from 92.77%to 97.34%. This performance gain is particularly notable in recognizing complex activities such as eating, drinking coffee, smoking, and conversing. © 2025 IEEE.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

AI-Empowered Quality-of-Experience: Power Adaptation for Situation Aware Streaming of Live Surveillance Videos

AI-Empowered Quality-of-Experience: Power Adaptation for Sit...

引用

IEEE Conference on Wireless Communications and Networking

作者： Suvadip Batabyal Ozgur Ercetin Department of Computer Science & Engineering NIT Durgapur India Faculty of Engineering & Natural Sciences Sabanci University Istanbul Turkey

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

While artificial intelligence (AI) based algorithms have become an epitome of video quality analysis (VQA) for streaming services, the true power of AI is still to be harnessed for situation aware streaming in power constrained wireless networks. In this work, we propose an architecture for situation-aware streaming, that identifies important events from live-feeds of the surveillance cameras, and allocates optimal power that reduces the long-term power consumption. Real-time video surveillance is a crucial technology for smart cities, that requires deployment of large number of cameras; both road-side and aerial. The proposed architecture is designed to (i) reduce the total power consumption of all the surveillance cameras, thus reducing the greenhouse emissions, (ii) improve flight duration of aerial cameras (e.g. drones), (iii) reduce the manual searching of desired events/objects, and (iv) improve the overall QoE. It is imperative that only the important events (example, a car violating a red light) are of interest to a law enforcement officers. Hence, if the important sections of the video are received with high quality, the long-term QoE increases. The architecture has two modules, viz., a tiny neural network (having a small number of hidden layers) at the source, that incurs a small computational resource, albeit at the cost of low accuracy; and a deep neural network (DNN) (with many hidden layers) at the destination that is capable of determining events with high accuracy. We show that there exists an optimal number of frames that provides optimal QoE and is able to reduce the required power consumption of the transmitter compared to when situation awareness is not used.

关键词： Power demand Accuracy Wireless networks computer architecture Artificial neural networks Streaming media Cameras

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：