检索结果-内蒙古大学图书馆

Internet of things technology, research, and challenges: a survey

Multimedia Tools and Applications 2025年第11期84卷 8455-8490页

作者： Vishwakarma, Amit Kumar Chaurasia, Soni Kumar, Kamal Singh, Yatindra Nath Chaurasia, Renu Management science and technology Khalifa University Abu Dhabi United Arab Emirates Computer science & Engineering SGT University Gurugram India Department of Information Technology IGDTUW New Delhi India Electrical Engineering IIT Kanpur Kanpur India Computer science & Engineering AIT Rooma Kanpur India

The world of digitization is growing exponentially;data optimization, security of a network, and energy efficiency are becoming more prominent. The Internet of Things (IoT) is the core technology of modern society. This paper is based on a survey of recent and past technologies used for IoT optimization models, such as IoT with Blockchain, IoT with WSN, IoT with ML, and IoT with big data analysis. Suppose anyone wants to start core research on IoT technologies, research opportunities, challenges, and solutions. In that case, this paper will help me understand all the basics, such as security, interoperability, standards, scalability, complexity, data management, and quality of service (QoS). This paper also discusses some recent technologies and the challenges in implementation. Finally, this paper discusses research possibilities in basic and applied IoT Domains. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Impact of machine learning-based imputation techniques on medical datasets- a comparative analysis

引用

Multimedia Tools and Applications 2025年第9期84卷 5905-5925页

作者： Tiwaskar, Shweta Rashid, Mamoon Gokhale, Prasad Department of Computer Engineering Faculty of Science and Technology Vishwakarma University Pune India

In the realm of medical datasets, particularly when considering diabetes, the occurrence of data incompleteness is a prevalent issue. Unveiling valuable patterns through medical data analysis is crucial for early and precise medical predictions. However, the quality of data and the proper handling of missing data hold significant significance. To address this challenge, imputation stands as a robust approach. The main goal of this paper aims to provide a comprehensive investigation into the effects brought about by Machine Learning (ML) based imputation techniques, specifically K Nearest Neighbor Imputation (KNNI), Multiple Imputation by Chained Equations (MICE), and MissForest. Results of all three techniques are compared with the complete dataset for five missing rates (10% to 50%), and evaluated using four categories of evaluation criteria i.e. (1) model performance, (2) imputation error rate (Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Coefficient of Determination (R^2) values), (3) Pearson correlation analysis and, (4) model selection basis (Bayesian information criterion (BIC), Akaike information criterion (AIC), values). Model performance includes accuracy, precision, recall, F1 score, and Matthews Correlation Coefficient (Mcoff) score of four ML classifiers viz. (a) Random Forest (RF), (b) Support vector machine (SVM), (c) AdaBoost, (d) XGBoost (XGB). For all missing rate cases, the MissForest technique is better than the KNNI and MICE in accuracy and Mcoff in 80% of cases, precision in 40% of cases, recall in 60% of cases, F1 score, MAE, RMSE, R^2 in 100% of cases, AIC in 80% of cases, and BIC values in 100% of cases. Also, the correlation analysis confirms that the MissForest imputation preserves association between the variables, like the complete dataset. Overall, our findings suggest that MissForest is a better machine learning-based imputation technique for handling missing data in diabetes research. © The Author(s), under exclusive lice

关键词： Adaptive boosting

来源：评论

学校读者我要写书评

暂无评论

ASSESSING THE ROLE OF PROJECT MANAGER IN AGILE ENVIRONMENTS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

引用

UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2024年第4期2024卷 95-108页

作者： Tudose, Cătălin Boiangiu, Costin Anton Bădoi, Iulia Bianca Iordache, Elena Alexandra Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

Agile methodology does not formally define the role of a project manager, and the Scrum methodology distributes this role among team members. However, the success of software development projects often depends heavily on this role, so typically, one person assumes the responsibility of a project manager in Agile environments. Project managers must adapt their strategies to address the various challenges of this constantly evolving market as more companies embrace Agile approaches to improve their flexibility and responsiveness. The objective of this article is to evaluate project managers’ roles in Agile environments by examining various aspects of their responsibilities and contributions. Project managers play crucial roles in planning, organizing, and guiding teams, with a primary focus on enhancing communication, managing resources, mitigating risks, and fostering collaboration. Key insights into the role of project managers in Agile software teams will be explored through a systematic review of the literature. The paper will also investigate how project managers balance internal and external dynamics, embrace change, and promote innovation to lead successful relationship management in Agile environments. © 2024, Politechnica University of Bucharest. All rights reserved.

关键词： Project management

来源：评论

学校读者我要写书评

暂无评论

Rice spikelet’s disease detection using hybrid optimization model and optimized CNN

引用

Soft Computing 2024年第21期28卷 12787-12806页

作者： Patel, Bharati Department of Computer Science and Engineering Faculty of Science and Technology ICFAI University Raipur India

Rice fields all across the world are affected by spikelet sterility, often known as rice spikelet's disease. It is characterized by the improper development of spikelet’s, which lowers grain output and quality. For optimal management and the avoidance of yield losses, this disease must be discovered early. In this study, a deep learning-based approach utilizing optimization techniques was proposed for accurate segmentation and disease detection in rice crops. The research aimed to address the vulnerability of rice crops to disease attacks from seed germination to mature spikelets. The proposed model consisted of three major phases: pre-processing, segmentation, feature extraction and disease detection. Gaussian filtering was employed to preprocess the raw rice crop images, while a new hybrid Whale Customized Gravitational Optimization Algorithm was utilized for segmentation. Disease detection was performed using a hybrid deep learning model called HybridNet, which combines convolutional neural networks (CNN) with an optimized recurrent neural network (RNN) model. The dataset intended for this proposed project is sourced from the CABI PlantwisePlus Knowledge Bank. The proposed model for rice spikelet disease detection achieved high accuracy with Sensitivity (0.9269), Specificity (0.9756), and Accuracy (0.9634). This indicates that the model effectively identifies and detects diseases in rice spikelets, demonstrating its reliable performance in disease management and crop protection. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Maximizing aged photovoltaic array power:a computer modelling study

引用

Clean Energy 2024年第1期8卷 171-187页

作者： Fadi N.Sibai Electrical&Computer Engineering Department Gulf University for Science and TechnologyMishrefKuwait

Photovoltaic(PV)modules age with time for various reasons such as corroded joints and terminals and glass coating defects,and their ageing degrades the PV array *** the help of the PV array numerical model,this paper explores the effects of PV module ageing on the PV array power,and the power gains and costs of rearranging and recabling aged PV modules in a PV *** numerical PV array model is first revised to account for module ageing,rearrangement and recabling,with the relevant equations presented *** updated numerical model is then used to obtain the array powers for seven different PV *** power results are then analysed in view of the attributes of the seven PV array examples.A guiding method to recommend recabling after rearranging aged modules is then proposed,leading to further significant power gains,while eliminating intra-row *** certain conditions are met,it was shown that recabling PV modules after rearranging them may lead to further significant power gains,reaching 57%and 98%in two considered PV array *** gains are possible in other arrays.A cost-benefit analysis weighing annual power gains versus estimated recabling costs is also given for the seven considered PV array examples to guide recabling decisions based on technical and economic *** the considered examples,recabling costs can be recovered in<4 *** with the powers of the aged arrays,power gains due to our proposed rearranging and recabling the PV arrays ranged between 73%and 131%in the considered examples—well over the gains reported in the ***,the cost of our static module rearrangement and recabling method outshines the costs of dynamic reconfiguration methods recently published in the literature.

关键词： solar energy optimizing array power photovoltaic array modelling photovoltaic modules ageing photovoltaic module rearrangement module recabling and reconfiguration PV array mismatch

来源：评论

学校读者我要写书评

暂无评论

Antenna Optimization using Machine Learning Algorithms and their Applications: A Review

引用

Journal of engineering science and technology Review 2024年第2期17卷 128-144页

作者： Pandey, Anil Kumar Singh, M.P. Department Of Computer Science & Engineering National Institute Of Technology Patna India

Antenna optimization using machine learning is a rapidly evolving field that leverages the power of artificial intelligence to design and improve antenna systems. Antenna optimization is a process of modifying antenna parameters to achieve desired performance metrics, such as gain, bandwidth, radiation pattern, and impedance matching. This paper presents a review of the most advanced development in antenna design and optimization by using machine learning techniques. The aim of this survey is to focus on different machine learning optimization techniques and their optimization capability with efficiency challenges. A deep outline from literature survey on optimization of antennas using machine learning are presented and listing various optimization algorithms and procedures that are applied to produce desired antenna characteristics and specifications. Firstly, a brief introduction of machine learning and its algorithms, later a quick explanation of antenna optimization process followed by an arranged introduction of different types of printed antenna designs using machine learning algorithm are reported. The methods emphasized in this survey have probably an effect on the imminent advancement of antennas for a variety of wireless applications. © (2024), (International Hellenic University – School of science). All Rights Reserved.

关键词： Microstrip antennas

来源：评论

学校读者我要写书评

暂无评论

Relay Type Link Fabrication Attack in SDN: A Review

IEEE Networking Letters

引用

IEEE Networking Letters 2025年第1期7卷 51-55页

作者： Metaferia, Getahun Lemma, Frezewd Adama Science and Technology University Department of Computer Science and Engineering Adama1881 Ethiopia

Software-defined Networking (SDN) is an innovative network architecture tailored to address the modern demands of network virtualization and cloud computing, which require features such as programmability, flexibility, agility, and openness to foster innovation. However, this architecture also brings forth new security challenges, particularly due to the separation of the data plane from the control plane. Our investigation centers on a specific vulnerability termed link fabrication, which can lead to topology poisoning. A compromised network topology can cause substantial disruptions across the entire network infrastructure. Through a systematic survey, we identified that significant research efforts have been directed towards mitigating link fabrication attacks. We classified the existing studies into six categories of vulnerabilities: Host-based, port amnesia, invisible assailant attack, topology freezing, switch-based link fabrication, and link latency. Furthermore, our survey highlights several open challenges in areas such as Programmable dataplane, dedicated attack trees and threat models, active defense and mitigation strategies, as well as controller awareness and machine learning. To address the vulnerabilities identified, we propose the implementation of a distance-bounding protocol concept at the control plane as a potential solution. © 2019 IEEE.

关键词： Fabrication Topology Network topology Control systems Relays Threat modeling Switches Routing Software defined networking Security

来源：评论

学校读者我要写书评

暂无评论

Leveraging machine learning for intelligent agriculture

引用

Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

来源：评论

学校读者我要写书评

暂无评论

A threefold chaotic image encryption technique for efficacious image data protection

引用

International Journal of computers and Applications 2024年第11期46卷 974-982页

作者： Anwar, Shamama Srivastava, Anshuman Department of Computer Science and Engineering Birla Institute of Technology Ranchi India

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Defect classification of glass substrate using deep neuro-fuzzy network with optimal parameter combination

引用

Granular Computing 2023年第4期8卷 839-849页

作者： Zhuang, Shun-Jie Lin, Cheng-Jian Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41107 Taiwan

Currently, numerous smart products are based on glass substrates. However, defects that occur during the production of glass substrates affect the quality and safety of the final products. Accordingly, we developed an optimal-parameter-combination-based deep neuro-fuzzy network (O-DNFN) for classifying defects in glass substrate images. The proposed O-DNFN comprises a deep neuro-fuzzy network (DNFN) and uses the Taguchi method. The fusion layer of the DNFN uses four feature fusion methods. The neuro-fuzzy network in the DNFN serves as replacement to a fully connected network for the classification of defects in glass substrate images. Because O-DNFN model parameter selection is challenging, we used the Taguchi method to determine the optimal parameter combination through fewer experiments. The experimental results revealed that the accuracy rates of the proposed O-DNFN with global max pooling fusion and an LeNet model in classifying defects in glass substrate images were 91.8% and 88%, respectively. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Defects

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：