检索结果-内蒙古大学图书馆

2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ghosh, Manas Sing, Jamuna Kanta RCC Institute of Information Technology Department of Computer Application Kolkata India Jadavpur University Department of Computer Science & Engineering Kolkata India

ISBN: (纸本)9798350371314

The advent of deep neural networks has provided a competitive edge in facial analysis efforts, especially the detection and recognition of faces from video images. This paper outlines a methodology wherein a face database is created from an outdoor video clip, and a rank-level fusion mechanism has been proposed for face recognition using a database that makes use of the probabilities produced by the softmax functions of multiple deep convolutional neural network (CNN) models. These probabilities are referred to as certainty indices for the potential classes to which a test sample may belong. For each of the CNN models, fuzzy ranks are obtained for a subset of classes with the top certainty indices by applying a Gaussian distribution function followed by a complementation operation. For each class, appearing in one or more subsets corresponding to deep network models, average of the certainty indices is complemented and fused with the sum of complemented fuzzy ranks. While calculating the cumulative sum of the fuzzy ranks, a penalty, is foisted to the class for not being present in any subset. This can avoid the chance of becoming an unexpected winner. The class with the lowest final ranking was identified as the class pertaining to the test sample. In addition to testing with the custom-made outdoor video-based face dataset, the projected method was evaluated with a standard surveillance indoor dataset, ChokePoint. Considering all test sets on ChokePoint dataset, the average recognition accuracy of the proposed method is 99.54%. The same for custom-made dataset is 93.96%. For both datasets, precision and macro-average scores of the proposed method in all cases are higher than the individual deep CNN models. © 2024 IEEE.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Student’s Health Exercise Recognition Tool for E-Learning Education

引用

Intelligent Automation & Soft Computing 2023年第1期35卷 149-161页

作者： Tamara al Shloul Madiha Javeed Munkhjargal Gochoo Suliman AAlsuhibany Yazeed Yasin Ghadi Ahmad Jalal Jeongmin Park Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering United Arab Emirates UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Korea

Due to the recently increased requirements of e-learning systems,multiple educational institutes such as kindergarten have transformed their learning towards virtual *** student health exercise is a difficult task but an important one due to the physical education needs especially in young *** proposed system focuses on the necessary implementation of student health exercise recognition(SHER)using a modified Quaternion-basedfilter for inertial data refining and data fusion as the pre-processing ***,cleansed data has been segmented using an overlapping windowing approach followed by patterns identification in the form of static and kinematic signal ***,these patterns have been utilized to extract cues for both patterned signals,which are further optimized using Fisher’s linear discriminant analysis(FLDA)***,the physical exercise activities have been categorized using extended Kalmanfilter(EKF)-based neural *** system can be implemented in multiple educational establishments including intelligent training systems,virtual mentors,smart simulations,and interactive learning management methods.

关键词： E-learning exercise recognition online physical education student’s healthcare

来源：评论

学校读者我要写书评

暂无评论

Detection of Cyber Attack in IoT Based Model Using ANN Model with Genetic Algorithm 5

Detection of Cyber Attack in IoT Based Model Using ANN Model...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Srivastava, Animesh Sharma, Hemendra Shanker Rawat, Rishikesh Garg, Navin Graphic Era Hill University Department of Computer Science & Engineering Dehradun India Coer University Department of Computer Science & Application Roorkee India Bansal College of Engineering Bhopal India Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350383522

Thanks to the IoT, our everyday lives are now more accessible, connected, and convenient than ever before. Because it facilitates the effortless transfer of massive amounts of data between interconnected devices, it leaves the network vulnerable to various forms of intrusion and attack. As the number of interconnected devices grows, the need for reliable attack detection systems to forestall malicious actions becomes paramount. This paper introduces a strategy for detecting attacks on the Internet of Things (IoT) that integrates Genetic Algorithms (GA) and Artificial Neural Networks (ANN) models. This paper introduces a model for an artificial neural network that uses genetic algorithms to detect attacks on the Internet of Things. While the GA adjusts the hyperparameters of the ANN model, the ANN model is responsible for determining whether network traffic is malicious or not. To evaluate the proposed system, an Internet of Things network traffic dataset was utilized. As demonstrated by the outcomes, the system has a commendable rate of detection. The system can spot DoS attacks. The results show that our method is great at finding attacks on the Internet of Things. we achieved a 99.3 percent accuracy rate with our suggested ANN with GA. © 2024 IEEE.

关键词： artificial neural networks attack detection deep learning Internet of Things

来源：评论

学校读者我要写书评

暂无评论

Applying Bayesian belief in LoRa: smart parking case study

引用

Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7857-7870页

作者： Gupta, Sakshi Snigdh, Itu School of Computer Application Lovely Professional University Punjab Phagwara India Department of Computer Science and Engineering Birla Institute of Technology Mesra Jharkhand Ranchi India

With the popularity of LoRa, many IoT applications are now being realized with this Low Power Wide Area Network (LPWAN) technology. Apart from ensuring low power and long-range communications, LoRa is also interference-free and hence an obvious choice for applications in urban scenarios. We dedicate our study to analyze and infer the impact of LoRa parameters like SF (Spreading Factor), BW (Bandwidth), CR (Coding Rate) on network performance metrics like delay, throughput for a smart parking scenario. Suited to the smart parking case study, we also present network performance observations for varied LOS settings (Line-of-Sight), NON-LOS, and obstruction. We try to identify the most significant parameters under different network conditions and determine the relationship among them through canonical correlation through our article. We also propose a Bayesian belief network to infer the best possible combination of LoRa parameters for achieving high-quality network efficiency under the environment scenarios. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Bayesian networks

来源：评论

学校读者我要写书评

暂无评论

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

引用

computers, Materials & Continua 2023年第1期74卷 363-378页

作者： Khalid Hamid Muhammad Waseem Iqbal Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Due to a tremendous increase in mobile traffic,mobile operators have started to restructure their networks to offload their *** directions will lead to fundamental changes in the design of future Fifthgeneration(5G)cellular *** the formal reason,the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph *** number that can be uniquely calculated by a graph is known as a graph *** the last two decades,innumerable numerical graph invariants have been portrayed and used for correlation *** any case,no efficient assessment has been embraced to choose,how much these invariants are connected with a network *** paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical *** K-banhatti sombor invariants(KBSO)and Contrharmonic-quadratic invariants(CQIs)are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or *** the hexagonal networks are used in mobile base stations in layered,forms called *** review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced *** deduced outcomes can be utilized for the modeling of mobile cellular networks,multiprocessors interconnections,microchips,chemical compound synthesis and memory interconnection *** results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network(MBSN)for the high load of traffic and minimal traffic also.

关键词： Hexagonal networks k-banhatti sombor invariants maple network graph artificial intelligence contraharmonic-quadratic invariants honeycomb

来源：评论

学校读者我要写书评

暂无评论

Energy-Efficient Shop Scheduling Using Space-Cooperation Multi-Objective Optimization

IEEE Transactions on Sustainable Computing

引用

IEEE Transactions on Sustainable Computing 2024年第3期10卷 601-615页

作者： Ding, Jiepin Xia, Jun Yang, Yaning Zhou, Junlong Chen, Mingsong Li, Keqin East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai200062 China Ningxia Normal University School of Physics and Electronic Information Engineering Guyuan756500 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China State University of New York Department of Computer Science New PaltzNY12561 United States

Since Industry 5.0 emphasizes that manufacturing enterprises should raise awareness of social contribution to achieve sustainable development, more and more meta-heuristic algorithms are investigated to save energy in manufacturing systems. Although non-dominated sorting-based meta-heuristics have been recognized as promising multi-objective optimization methods for solving the energy-efficient flexible job shop scheduling problem (EFJSP), it is hard to guarantee the quality of the Pareto front (e.g., total energy consumption, makespan) due to the lack of population diversity. This is mainly because an improper individual comparison inevitably reduces population diversity, thus limiting exploration and exploitation abilities during population updates. To achieve efficient population evolution, this paper introduces a novel space-cooperation multi-objective optimization (SCMO) method that can effectively solve EFJSP to obtain scheduling schemes with better trade-offs. By cooperatively evaluating the similarity among individuals in both the decision space and objective space, we propose a space-cooperation population update method based on a three-vector representation that can accurately eliminate repetitive individuals to derive higher-quality Pareto solutions. To further improve search efficiency, we propose a difference-driven local search, which selectively changes the positions of operations with higher differences to search for neighbors effectively. Based on the Taguchi method, we conduct experiments to obtain a suitable parameter combination of SCMO. Comprehensive experimental results show that, compared to state-of-the-art methods, our SCMO method achieves the highest HV and NR and the lowest IGD, with an average of 0.990, 0.952, and 0.001, respectively. Meanwhile, compared to traditional local search approaches, our difference-driven local search obtains twice the HV on instance Mk12 and reduces the solving time from 1521s to 475s. © 2016 IEEE.

关键词： Job shop scheduling

来源：评论

学校读者我要写书评

暂无评论

Fake News Encoder Classifier (FNEC) for Online Published News Related to COVID-19 Vaccines

引用

Intelligent Automation & Soft Computing 2023年第7期37卷 73-90页

作者： Asma Qaiser Saman Hina Abdul Karim Kazi Saad Ahmed Raheela Asif Department of Computer Science and Information Technology NED University of Engineering and TechnologyKarachi75270Pakistan Department of Computer Science Iqra UniversityKarachi76400Pakistan Department of Software Engineering NED University of Engineering and TechnologyKarachi75270Pakistan

In the past few years,social media and online news platforms have played an essential role in distributing news content *** of the authenticity of news has become a major *** the COVID-19 outbreak,misinformation and fake news were major sources of confusion and insecurity among the general *** the first quarter of the year 2020,around 800 people died due to fake news relevant to *** major goal of this research was to discover the best learning model for achieving high accuracy and performance.A novel case study of the Fake News Classification using ELECTRA model,which achieved 85.11%accuracy score,is thus reported in this *** addition to that,a new novel dataset called COVAX-Reality containing COVID-19 vaccine-related news has been *** the COVAX-Reality dataset,the performance of FNEC is compared to several traditional learning models i.e.,Support Vector Machine(SVM),Naive Bayes(NB),Passive Aggressive Classifier(PAC),Long Short-Term Memory(LSTM),Bi-directional LSTM(Bi-LSTM)and Bi-directional Encoder Representations from Transformers(BERT).For the evaluation of FNEC,standard metrics(Precision,Recall,Accuracy,and F1-Score)were utilized.

关键词： Deep learning fake news detection machine learning transformer model classification

来源：评论

学校读者我要写书评

暂无评论

An Improved Designing of Neuroprosthetics Arm Using LDA

An Improved Designing of Neuroprosthetics Arm Using LDA

引用

2023 IEEE World Conference on Applied Intelligence and Computing, AIC 2023

作者： Singh, Anuj Purohit, Kamlesh Chandra Kumar, M. Anand Sharma, Sachin Department of Computer Science and Engineering Dehradun248002 India Department of Computer Application Dehradun248002 India

ISBN: (纸本)9798350310061

Neuroprosthetics have experienced a remarkable evolution in recent times, particularly with the advent of intelligent neuroprosthetics that leverages artificial intelligence (AI) technology. These devices have the potential to improve both the input and output of neurological systems, allowing individuals to control prosthetic limbs with their thoughts and perform everyday tasks such as picking and placing objects. the key to this innovative technology lies in the ability to collect brain signals through electroencephalography (EEG) devices, which can be then deciphered and utilized by the mechatronic components of the prosthetic limb. However, effectively separating relevant signals from irrelevant ones has been a significant challenge for the design and development of neuroprosthetics, as these signals fall into three distinct categories of brain communication. In this research paper, we explore the utilization of split signals in neuroprosthetics using LDA and how this varies from person to person depending on their mental state. Incorporating LDA methods into these devices seems to be the most viable solution for society to build a system that can efficiently differentiate and utilize brain signals, bringing us one step closer to unlocking the full potential of neuroprosthetics. It is found that with the adaptation of the LDA approach, 91% of the results are properly categorized and are best among the existing literature available to date. © 2023 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

AI-Driven Resource and Communication-Aware Virtual Machine Placement Using Multi-Objective Swarm Optimization for Enhanced Efficiency in Cloud-Based Smart Manufacturing

引用

computers, Materials & Continua 2024年第12期81卷 4743-4756页

作者： Praveena Nuthakki Pavan Kumar T. Musaed Alhussein Muhammad Shahid Anwar Khursheed Aurangzeb Leenendra Chowdary Gunnam Department of Computer Science&Engineering Koneru Lakshmaiah Education FoundationGuntur522302India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of AI and Software Gachon UniversitySeongnam-Si13120Republic of Korea Department of Electronics and Communication Engineering SRM UniversityAmaravati522502India

Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the *** these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource ***,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing *** manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place *** approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource *** proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.

关键词： Resource utilization smart manufacturing efficiency inter VM communication virtual machine placement cloud computing multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Intelligent Farming: IoT-Powered System for Efficient Soil Condition Analysis 13

Intelligent Farming: IoT-Powered System for Efficient Soil C...

引用

13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Sharma, Nandini Singh, Shiv Shanker Khenwar, Medha Gupta, Priyanka Gla University Department of Computer Engineering and Application Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Smart farming technology allows farmers to control and analyse variables in real-time, helping them optimize crop growth and manage their operations more efficiently. The use of specialized sensors, data collection tools, and advanced analytics in Internet of Things (IoT) systems is helping to bridge the gap between physical farm conditions and digital monitoring. In this study, we aim to develop a system that monitors soil conditions to support smart farming. The system provides farmers with live data on key environmental factors, such as temperature, humidity, soil moisture, and soil temperature, to improve crop yield and product quality. The IoT setup for this intelligent farming solution includes components like the DHT11 and humidity sensors, a DS18B20 temperature probe, LDR, water pump, and a 12V LED strip, all controlled by a NodeMCU. When the system is activated, it continuously monitors soil and environmental conditions and sends this information to the cloud for real-time access. If the soil moisture falls below a set threshold, the system automatically triggers irrigation. Our experimental results show that this model improves decision-making for managing farming conditions. © 2024 IEEE.

关键词： Smart agriculture

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：