检索结果-内蒙古大学图书馆

5th International Conference on Recent Developments in Control, Automation and Power engineering, RDCAPE 2023

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity School of Engineering and Technology Amity University Noida India Amity Innovation and Design Centre Amity University Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9789819600465

This paper investigates the effectiveness of two restart methods, local and global restarts, in enhancing the performance of the quantum-inspired evolutionary algorithm (QiEA) to solve optimization problems. The study compares the result of these modifications by testing their performance against the widely recognized and challenging IEEE CEC 2022 benchmark functions. The objective is to analyze the impact of the restart methods on the convergence and optimization outcomes of the QiEA. Through experimental evaluation, it was sought to determine whether the inclusion of restart methods in the QiEA framework would lead to improved convergence toward the optimal solution. Also, a novel Greedy Local Restart algorithm is proposed. The results demonstrate that the QiEA variants consistently outperform the basic QiEA, achieving superior optimization results in terms of convergence and accuracy. Restart approaches are thus observed as an indispensable asset in handling complex optimization problems, greatly enhancing the effectiveness and applicability of evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

Innovative Video Classification Method based on Deep Learning Approach

引用

Recent Patents on engineering 2025年第2期19卷 95-107页

作者： Hemamalini, V. Jayasutha, D. Vinothini, V.R. Devi, R. Manjula Kumar, A. Anitha, E. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Arjun College of Technology Coimbatore Thamaraikulam India Department of Mathematics Bannari Amman Institute of Technology Sathyamangalam Erode638401 India Department of Computer Science and Design Kongu Engineering College Perundurai Erode638060 India Department of Civil Engineering University Institute of Engineering & Technology Maharshi Dayanand University Rohtak124001 India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore641202 India

Background: The automated classification of videos through artificial neural networks is addressed in this work. To explore the concepts and measure the results, the data set UCF101 is used, consisting of video clips taken from YouTube to recognize actions. The study is carried out with the authors' resources to determine the feasibility of independent research in the area. Methods: This work was developed in the Python programming language using the Keras library with Tensorflow as the back-end. The objective is to develop a network that presents performance compatible with the state of the art in terms of classifying videos according to the actions taken. Results: Given the hardware limitations, there is considerable distance between the implementation possibilities in this work and what is known as the state-of-the-art. Conclusion: Throughout the work, some aspects in which this limitation influenced the development are presented, but it is shown that this realization is feasible and that obtaining expressive results is possible 98.6% accuracy is obtained in the UCF101 data set, compared to the 98 percentage points of the best result ever reported, using, however, considerably fewer resources. In addition, the importance of transfer learning in achieving expressive results as well as the different performances of each architecture are reviewed. Thus, this work may open doors to carry patent-based outcomes. © 2025 Bentham Science Publishers.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advances in Facial Emotion Recognition: Deep Learning Approaches and Future Prospects

Advances in Facial Emotion Recognition: Deep Learning Approa...

引用

2024 MIT Art, design and Technology school of computing International Conference, MITADTSoCiCon 2024

作者： Deshmukh, Shrikala Chaudhary, Snehal Gayakwad, Milind Kadam, Kalyani More, Nitin S. Bhosale, Amol Amity University Mumbai India College of Engineering Department of Information Technology Pune India College of Engineering Pune India Symbiosis Institute of Technology Pune India Pune India MIT Art Design and Technology University School of Computing Department of CSE Pune India

ISBN: (纸本)9798350362879

Facial Emotion Recognition (FER) has evolved significantly with the advent of deep learning techniques, offering novel approaches to interpret and process facial expressions. This paper reviews recent advancements in FER, emphasizing the role of deep neural network architectures and their application across diverse fields including security, health, and human-machine interfaces. We analyze various methodologies, technological advancements, challenges, and future trends in FER, providing insights into the current state-of-The-Art. We conclude by discussing the limitations of current FER systems and propose future directions for research, particularly in extending emotion recognition beyond basic emotions and incorporating multimodal approaches. © 2024 IEEE.

关键词： Emotion Recognition

来源：评论

学校读者我要写书评

暂无评论

Hardware Implementation of Block Ciphers - A Case Study on Encrypted Image Transfer Over Universal Asynchronous Receiver Transmitter

Hardware Implementation of Block Ciphers - A Case Study on E...

引用

2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Sharmila, S. Bhuvaneswaran, R.S. Natarajan, Kailash Prathiba, A. Vaithiyanathan, D. Ramanujan Computing Centre College of Engineering Guindy Anna University Chennai India School of Electronics Engineering Center for Nanoelectronics and Vlsi Design Vit Chennai India National Institute of Technology Delhi Department of Electronics and Communication Engineering Delhi India

ISBN: (纸本)9798350300857

Communication protocols such as Universal Asynchronous Receiver/Transmitter (UART), Serial Peripheral Interface (SPI), Inter-Integrated Circuits (I2C), and Computer Area Networks (CAN) exist to facilitate communication among devices. This work studies a comparison of the additional overhead of offering Advanced Encryption Standard (AES)-128 and PRESENT-64 algorithms with reconfigurable UART on the transmitter and receiver side to offer data encryption. Advanced Encryption Standard (AES) in the basic loop architecture and PRESENT in the fully unrolled architecture for their implementation in the proposed study of secured data transfer over the UART channel. AES-128 consumes almost three times more clock cycles to encrypt and decrypt than PRESENT-64. Considering that AES-128 takes only double the input size of PRESENT-64 and requires thrice the number of clock cycles, AES-128 is more time-consuming. Xilinx Field Programmable Gate Array (FPGA) implementation results suggest that PRESENT-64 consumes slightly more onboard resources than AES-128 since the proposed work employs an unrolled PRESENT architecture. The complete architecture is specified using Verilog HDL and implemented in Xilinx families, namely Artix7, Spartan6, Virtex6, and Virtex7 series of FPGAs. © 2023 IEEE.

关键词： Data transfer

来源：评论

学校读者我要写书评

暂无评论

Coverage Hole Aware Data Routing for Mobile Sink Wireless Sensor Networks 14

Coverage Hole Aware Data Routing for Mobile Sink Wireless Se...

引用

14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Wedaj, Fisseha Teju Hawbani, Ammar Wang, Xingfu Al-Gunid, Haithm M. Yilma, Getinet Farooq Qaisar, Muhammad Umar University of Science and Technology of China School of Computer Science and Technology Hefei China Shenyang Aerospace University School of Computer Science Shenyang China Adama Science and Technology University School of Electrical Engineering and Computing Adama Ethiopia Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen China

ISBN: (纸本)9798350361889

In wireless sensor networks (WSNs), the coverage hole problem is one of the challenging problems that needs an effective solution. Data routing protocols in WSNs aim to disseminate the sensors' data to the central device with the minimum possible network resource usage. However, suppose there is a coverage hole in the middle of the source and destination nodes, and the source node doesn't have information about the hole. In that case, the data may choose the longer and more repetitive path to reach the destination, increasing resource usage, mainly energy and time. The condition is even more challenging when the network employs a mobile data collector. This paper proposes a Coverage Hole Aware Data Routing (CHADR) protocol for Mobile Sink WSNs that determines the shorter hole bypassing path in advance at the source node using the hole and data collector location information. CHADR evaluates the path efficiency using directional angle and expected hops count. After determining the path, data will be transmitted to the mobile sink using an Opportunistic routing technique. We proved CHADR performance with experimental simulations. © 2024 IEEE.

关键词： Routing protocols

来源：评论

学校读者我要写书评

暂无评论

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

引用

Big Data Mining and Analytics 2024年第2期7卷 247-270页

作者： Mona Ahamd Alghamdi Abdullah S.A.L-Malaise AL-Ghamdi Mahmoud Ragab Information Systems Department Faculty of Computing and Information Technology(FCIT)King Abdulaziz University(KAU)Jeddah 21589Kingdom of Saudi Arabia Information Systems Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah 22246Kingdom of Saudi Arabia Information Technology Department FCITKAUJeddah 21589Kingdom of Saudi Arabiaand also with Mathematics DepartmentFaculty of ScienceAl-Azhar UniversityNaser City 11884Egypt

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving *** energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy *** objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily *** proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and *** and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy *** model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,*** stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized *** results of this study show that prediction accuracy is high,and the model’s stability is high as *** model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in ***,this study shows that it can be inferred.

关键词： energy consumption prediction Artificial Intelligence(AI) Deep Learning(DL) snapshot ensemble

来源：评论

学校读者我要写书评

暂无评论

Scientometric review and visualisation of City Information Modeling (CIM) trends and future directions

引用

Discover Cities 2025年第1期2卷 1-31页

作者： Okonta, Ebere Donatus Israel, Godspower Chukwudi Okeke, Francis Ogochukwu School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UK Department of Architecture University of Jos Plateau State Nigeria School of Engineering Technology and Design Canterbury Christ Church University Kent UK

City Information Modelling (CIM) has emerged as a promising approach for developing smart and sustainable cities. The study conducted a scientometric review and visualisation of global CIM research from 1996 to 2023 through a co-author, co-word, and co-citation analysis on 303 bibliographic records from the Scopus core collection database using VOSviewer and Gephi software. Prominent research domains encompass areas such as Smart City, Digital Twin, CIM, 3D City Models, Geographic Information Systems (GIS), Building Information Modelling (BIM), Big Data, and the Internet of Things (IoT). Leading institutions contributing significantly to CIM research include NEC Laboratories Europe in Germany, the University of Florence in Italy, Loughborough University in the United Kingdom, South China University of Technology in China, the Georgia Institute of Technology in the United States, and the National University of Singapore in Singapore. These institutions have emerged as influential contributors to the field of CIM research. The United States, China, Germany, the Netherlands, South Korea, and the United Kingdom have emerged as leaders in citations for CIM research. Additionally, the study identified key publication outlets such as Sustainable Cities and Society, ISPRS journals, and Lecture Notes in Computer Science, emphasizing their pivotal role in disseminating CIM research. The study further identified some of the practical applications of CIM in Urban developments, such as Energy-Efficient Urban Planning, Urban Digital Twins for Participatory Planning, Geographical Digital Twins for Renewable Energy, Crisis Management Information Systems, Innovative Land Acquisition, Building Energy Consumption Optimization, and so on. Areas such as Artificial Intelligence, Blockchain, Human Factors, design, Linked Data Benchmark, Photogrammetry, Photorealistic Texture, Virtual Reality, Ontology, and Semantic Web require considerable attention and warrant further studies. The study

关键词：

来源：评论

学校读者我要写书评

暂无评论

Exploring the Role of quality management practices (QMS) in mitigating Construction Failures and building collapse

引用

Discover Civil engineering 2024年第1期1卷 1-25页

作者： Okonta, Ebere Donatus Okeke, Francis Ogochukwu Ojelabi, Elizabeth Tofunmi Akinola, Ayodeji Victor School of Computing Engineering and Digital Technologies Teesside University Middlesbrough UK School of Engineering Technology and Design Canterbury Christ Church University Kent UK School of Environmental Studies Federal Polytechnic Ilaro Ilaro Nigeria

The construction industry in Nigeria faces critical challenges due to frequent building collapses, highlighting the necessity for robust Quality Management Systems (QMS). This study investigates the current state of QMS adoption and its effectiveness in mitigating building failures. Despite the recognised importance of QMS, the industry shows a low adoption rate, with only 34.8% of firms holding ISO certification and 60.9% lacking formal QMS frameworks. A significant barrier identified is insufficient top management support, impacting 71.4% of the companies surveyed. The study employs a mixed-methods approach, including a comprehensive literature review and a survey of 22 industry stakeholders across major Nigerian cities. This methodology allows for a detailed examination of QMS practices, highlighting the critical role of management commitment, communication, and training in enhancing quality outcomes. The research fills a gap by providing a holistic analysis of QMS issues across different regions, which have been previously understudied. The findings underscore the necessity for enhanced regulatory frameworks and standardised practices to ensure consistent quality management. The study contributes to the international discourse on construction quality by emphasising systemic improvements rather than merely technical fixes. It calls for further research into region-specific challenges and broader adoption of formal QMS practices to improve safety and reliability in Nigeria's construction sector.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Comparative Study of Classification Algorithms Using Big Data Analytics for Flight Delays Prediction 5th

Comparative Study of Classification Algorithms Using Big Dat...

引用

5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Asiri, Mona Hassan AL-Malaise AL-Ghamdi, Abdullah S. Fayoumi, Ayman G. Ragab, Mahmoud Department of Information Systems Collage of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Information Systems Department School of Engineering Computing and Design Dar Al-Hekma University Jeddah34801 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Information Technology Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia

ISBN: (纸本)9789819771776

Flight delays are the most pressing cause of concern in the airline sector because they can weaken and harm airlines, passengers, and airports. Flight delay prediction has become an essential step in decision-making processes within commercial aviation. Due to the developments of recent technologies, there are many prediction models for flight delays;however, more development of accurate prediction models is still needed. Machine learning, statistical models, and other techniques can be used to create quick and accurate flight delay prediction models. Furthermore, methods for feature selection can be developed to get rid of dimensionality issues. This study aimed to compare four classification algorithms using "big data analytics" to predict flight delays, a function that can improve the whole airline sector. The selected classification algorithms were the "Gaussian Naïve Bayes," "Gradient Boosted Tree" classifier, "Logistic Regression," and "Random Forest" classifier. Many assessment techniques were applied to each one of the suggested models to show the better performance of these models. The comparison used the resulting confusion matrix to summarize prediction results for the following classification problems: "F1 Score", "Accuracy," "Recall," "Precision," "Area Under ROC," and "Area Under PR." The assessment results showed the enhanced outcomes of the "Gradient Boosted Tree" Classifier with existing techniques, and all results of "Logistic Regression" and "Naïve Bayes" are equal. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

AEGAN: Generating imperceptible face synthesis via autoencoder-based generative adversarial network

AEGAN: Generating imperceptible face synthesis via autoencod...

引用

作者： Che, Aolin Yang, Jing-Hua Guo, Cai Dai, Hong-Ning Xie, Haoran Li, Ping Faculty of Innovation Engineering Macau University of Science and Technology China School of Computing and Information Engineering Hanshan Normal University Chaozhou China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing and Decision Sciences Lingnan University Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong School of Design The Hong Kong Polytechnic University Hong Kong

Face recognition (FR) systems based on convolutional neural networks have shown excellent performance in human face inference. However, some malicious users may exploit such powerful systems to identify others' face images disclosed by victims' social network accounts, consequently obtaining private information. To address this emerging issue, synthesizing face protection images with visual and protective effects is essential. However, existing face protection methods encounter three critical problems: poor visual effect, limited protective effect, and trade-off between visual and protective effects. To address these challenges, we propose a novel face protection approach in this article. Specifically, we design a generative adversarial network (GAN) framework with an autoencoder (AEGAN) as the generator to synthesize the protection images. It is worth noting that we introduce an interpolation upsampling module in the decoder in order to let the synthesized protection images evade recognition by powerful convolution-based FR systems. Furthermore, we introduce an attention module with a perceptual loss in AEGAN to enhance the visual effects of synthesized images by AEGAN. Extensive experiments have shown that AEGAN not only can maintain the comfortable visual quality of synthesized images but also prevent the recognition of commercial FR systems, including Baidu and iKLYTEK. © 2023 John Wiley & Sons Ltd.

关键词： Face recognition

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：