检索结果-内蒙古大学图书馆

Energy efficient deep reinforcement learning approach to control the traffic flow in iot networks for smart city

Journal of Ambient Intelligence and Humanized Computing 2024年第12期15卷 3945-3961页

作者： Dhanvijay, Mrinai M. Patil, Shailaja C. Department of Electronics and Telecommunication Modern Education Society’s Wadia College of Engineering Pune India Department of Electronics and Telecommunication Rajarshi Shahu College of Engineering Pune India

Traffic flow control plays a crucial role in the development of smart cities, as it directly impacts various key components of urban infrastructure. With the rapid developments in artificial intelligence and large amount of data, there has been growing interest in leveraging deep reinforcement learning (RL) techniques for traffic control. These methods have shown promise in improving intersection efficiency and reducing travel time. However, existing approaches often overlook the spatial and temporal correlations between intersections, leading to suboptimal performance and increased waiting times. A decentralized traffic management system that leverages a Graph-Structured Correlation time spatial attention (GSCTSA) network and Asynchronous Advantage Actor-Critic (GSCTSA -A3C)is proposed. The system utilizes traffic controllers installed at each traffic light to collect and process real-time traffic data from nearby sensors and cameras. By processing the data at the edge layer, closer to the intersections, instead of relying on cloud-based solutions, real-time decision-making becomes feasible. The proposed GSCTSA -A3C model combines LSTM, correlational attention, and Graph Attention Networks with the Asynchronous Advantage Actor-Critic (A3C) RL algorithm. It captures spatio-temporal correlations and optimizes traffic light timings to improve traffic flow and reduce congestion. The GSCTSA module extracts essential spatio-temporal correlations, while the A3C algorithm learns optimal traffic control policies. Experimental outcomes demonstrate the effectiveness of the GSCTSA -A3C approach in achieving efficient traffic management in IoT networks for smart cities. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

Statistically significant feature-based heart murmur detection and classification using spectrogram image comparison of phonocardiogram records with machine learning techniques

引用

Australian Journal of Electrical and electronics engineering 2024年第3期21卷 243-257页

作者： Careena, P. Preetha, M. Mary Synthuja Jain Arun, P. Department of electronics and communication engineering Noorul Islam Centre For Higher Education Tamilnadu India Department of Electronics and Communication Engineering Amal Jyothi College of Engineering Kerala India Department of Electronics and Communication Engineering St. Joseph’s College of Engineering and Technology Kerala India

Computerized evaluation of valve anomalies from cardiac sound is a well-tried endeavor in cardiology. Conversely, automated methods for the diagnosis of cardiovascular diseases mainly depend on the features collected from the cardiac signal. Analyzing phonocardiogram (PCG) signals can yield useful information into the mechanics of the heart. A machine learning technique for detecting and classifying murmurs is proposed, which takes into account the statistically significant features derived from comparing spectrogram images obtained by the Short-Time Fourier Transform (STFT) of the PCG signals. The spectrograms are compared by Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Matrix (SSIM). Finally, these similarity index features are fed into various decision trees, both with and without PCA to classify like normal heart sound and murmurs like systolic, diastolic, and continuous. The SSIM and PSNR alone offer accuracy of 88.23% and 87.94%, respectively for distinguishing normal and murmur and are differ with a P-value of 2.05 × 10−19. The PCA enabled coarse tree performs better in terms of classification accuracy of 85% and 92.50% during training and testing, respectively. The results show that this method can accurately detect and classify heart murmurs, outperforming conventional methods. ©, Engineers Australia.

关键词： Cardiology

来源：评论

学校读者我要写书评

暂无评论

Convolutional Neural Network Classification of Coleoptera through Keras and TensorFlow 24

Convolutional Neural Network Classification of Coleoptera th...

引用

10th International Conference on Computing and Artificial Intelligence, ICCAI 2024

作者： Angelia, Randy Echavez Torres, John Louie B. Cagadas, Cristelle Ann G. Manigo, Annalee Computer Engineering Program College of Engineering Education University of Mindanao Philippines

ISBN: (纸本)9798400717055

An application was developed to address the time-consuming and subjective nature of physical examination and human judgment to classify the diverse families of Coleoptera more efficiently. Specifically focusing on the Coleoptera families in the Philippines, the application utilizes a Convolutional Neural Network (CNN)-based model as its classification architecture. To enhance the accuracy of the CNN model, researchers gathered images of Coleoptera from various families. The program achieves an impressive 94.44% accuracy rate and has demonstrated successful results in classifying Coleoptera families. This application offers a more streamlined and reliable approach to Coleoptera classification, benefiting researchers and enthusiasts. © 2024 ACM.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Development of an RFID-based Children’s Disaster and Emergency PreparednessDevice 24

Development of an RFID-based Children’s Disaster and Emerge...

引用

8th International Conference on E-Society, E-education and E-Technology, ESET 2024

作者： Broas, Pauline Ingrid Derama, Lovely Mae Rosales, Rosban Bacus, John Computer Engineering Program College of Engineering Education University of Mindanao Davao City Philippines

ISBN: (纸本)9798400707094

RFID Technology has grown increasingly popular over recent years due to its efficiency. This study examines the development, implementation, and impact of an RFID-based cognitive learning system focusing on child preparedness in disasters and emergencies. This study aims to design a stand-alone interactive device, fabricate an RFID-based touchscreen interactive device for children, conduct functionality testing on the accuracy of the eLearnCards and responsiveness of the LCD Touchscreen, and evaluate system accuracy and performance on children using a paired T-test. A quantitative research approach is adopted, encompassing the collection and analysis of pre-test and post-test scores from a stratified random sample of 60 children from New Matina Elementary School with an age range of 7-12 years old. The results of this study signify that the RFID-based cognitive learning system substantially contributes to enhancing students’ disaster preparedness knowledge. This study concludes that it underscores the considerable potential of RFID-based educational technology in reinforcing disaster preparedness among children. © 2024 Copyright held by the owner/author(s).

关键词： Disasters

来源：评论

学校读者我要写书评

暂无评论

Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121

引用

Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3519-3531页

作者： Muthukumar, A. Raj, M. Thanga Ramalakshmi, R. Meena, A. Kaleeswari, P. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Electronics and Communication Engineering KLN College of Engineering Tamil Nadu Pottapalayam India

An additional tool for swaying public opinion on social media is to present recent developments in the creation of natural language. The term "Deep fake" originates from deep learning technology, which effortlessly and seamlessly steers someone toward digital media. Artificial Intelligence (AI) techniques are a crucial component of deep fakes. The generative powers of generative capabilities greatly reinforce the advancements in language modeling for content generation. Due to low-cost computing infrastructure, sophisticated tools, and readily available content on social media, deep fakes propagate misinformation and hoaxes. These technologies make it simple to produce misinformation that spreads fear and confusion to everyone. As such, distinguishing between authentic and fraudulent content can be challenging. This study presents a novel automated approach for the identification of deep fakes, based on Adaptive Gaining Sharing Knowledge (AGSK) and using DenseNet121 architecture. During pre-processing, the image’s sensitive data variance or noise is eliminated. Following that, CapsuleNet is used to extract the feature vectors. The deep fake is identified from the resulting feature vectors by an AGSK with DenseNet121 architecture, together with the hyper-parameter that has been optimized using the AGSK model. Propaganda and defamation pose less of a concern, and the results of the suggested deepfake image recognition model show how reliable and successful the model is. The accuracy of detection is 98% higher than other cutting-edge models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

A Modified 2:1 Multiplexer-Based Low Power Ternary ALU for IoT Applications

引用

Journal of engineering Science and Technology Review 2024年第5期17卷 104-109页

作者： Allwin Devaraj, S. Magdalin Mary, D. Kannan, P. Esakki Rajavel, S. Thangaraj, Cynthia Anbuselvi Gurumoorthy, K.B. William, Blanie Scrimshaw Department of Electronics and Communication Engineering Francis Xavier Engineering College Tamilnadu Tirunelveli627003 India Department of Electrical and Electronics Engineering Sri Krishna College of Technology Tamilnadu Coimbatore641042 India Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Tamilnadu Coimbatore641021 India Department of Electronics and Communication Engineering SEA College of Engineering and Technology Karnataka Bengaluru560049 India Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Tamilnadu Coimbatore641407 India Department of Computer Science and Engineering Rohini College of Engineering and Technology Tamilnadu Kanyakumari629401 India

The ternary logic has a benefit over the binary logic which provides a secured solution to achieve a trade-off between the area and power of the design. However, from the structure of the ternary Aritmetic Logic Unit (ALU), it is clear that its architecture increases the area, propagation delay, and power consumption. To overcome this drawback, a loopback algorithm is proposed to achieve low power and high throughput Internet of Things (IoT) processors. The loopback algorithm reduces the number of processing stages in multipliers and adders which can significantly reduce area and power dissipation. The proposed 2:1 multiplexer-based approach reduces the need for a decoder and results in low power consumption. The proposed design will be implemented in Xilinx ISE 13.0 and simulation will be done in Modelsim. The modified Ternary ALU (TALU) performs finer than the previous TALU method. The number of registers used in this architecture is reduced by up to 25% than the existing system therefore there is a reduction in power dissipation. © (2024), (International Hellenic University - School of Science). All rights reserved.

关键词： Logic design

来源：评论

学校读者我要写书评

暂无评论

A transformer based real-time photo captioning framework for visually impaired people with visual attention

引用

Multimedia Tools and Applications 2024年第41期83卷 88859-88878页

作者： Muhammed Kunju, Abubeker Kiliyanal Baskar, S. Zafar, Sherin A R, Bushara S, Rinesh A, Shafeena Karim Kerala Kanjirapally India Faculty of Engineering Department of Electronics and Communication Engineering Karpagam Academy of Higher Education Coimbatore India Department of Computer and Electronics Engineering SEST- Jamia Hamdard New Delhi India Department of Electronics and Communication Engineering KMEA College of Engineering Kerala Cochin India Department of Computer Science and Engineering V.S.B College of Engineering Technical Campus Coimbatore India

In recent years, transformer-based photo captioning frameworks plays a crucial role in improving individuals’ overall well-being, self-reliance, and inclusivity by giving them access to visual content via written and voiced explanations. This research investigates a two-layer transformer architecture to capture extensive relationships in visual data effectively. Using a two-layer transformer design, visual attention mechanism enables the model to autonomously identify relevant regions within a picture while generating a particular word within the anticipated caption. To get the features from the images, a pre-trained Inception V3 model is used, and the feature maps are extracted from the final convolutional block. The dataset used in this research was Flick8k, Flick30k, among bespoke datasets. The model was trained on a TPU platform and implemented on a Raspberry Pi 4B graphics processing unit. To assess the efficacy of the two-layer transformer model for photo captioning, a series of experiments were undertaken using benchmark datasets and with state-of-the-art models. The quality of the generated captions was evaluated using quantitative criteria such as BLEU, BLEUR, ROUGE and F1 score. Furthermore, a series of qualitative assessments were conducted, employing human evaluators, to measure the overall coherence, and fluency of the captions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computer vision

来源：评论

学校读者我要写书评

暂无评论

Android-Based Application for Ripeness Classification and Shelf-Life Determination of Philippine Bananas 2

Android-Based Application for Ripeness Classification and Sh...

引用

2nd International Conference on Computer Graphics and Image Processing, CGIP 2024

作者： Lumpay, Fernandrigo R. Canoy, Jon Waldson T. Caballero, Ryu Mhar A. Nepa, Kimberly M. College of Engineering Education University of Mindanao Computer Engineering Program Davao City Philippines

ISBN: (纸本)9798350374186

Lauded as the world's most popular fruit, bananas are one of the main exports of the Philippines. It contains essential nutrients that can have a protective impact on health. Commonly, farmers can distinguish the different types of bananas and potentially predict their shelf-life. Nevertheless, human judgment is not always reliable. It needs to have support and evidence to minimize the margin of error. Hence, this study focuses on developing an android application to determine the Philippine bananas' ripeness, variety, and shelf-life. This application identifies different bananas sold in the local market, specifically Cardava, Lakatan, and Latundan. Also, the application determines the shelf-life of bananas, such as the remaining days for unripe bananas to become ripe and ripe bananas to overripe. A Convolution Neural Network-based (CNN) model classifier is used as an architecture categorizing the Philippine local market bananas. The proponents used real-life image data to increase the accuracy of the CNN model. The application was proven effective and has reached 90% accuracy. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Channel estimation and MIMO combining architecture in millimeter wave cellular system with few ADC bits

引用

Journal of engineering and Applied Science 2024年第1期71卷 40页

作者： Raji, Akeem Abimbola Orimolade, Joseph Folorunso Amusa, Kamoli Akinwale Adejumobi, Isaiah Adediji Department of Electrical and Electronics Engineering College of Engineering and Environmental Studies Olabisi Onabanjo University Ibogun Campus Ogun State Nigeria Department of Electrical and Electronics Engineering Caristas Institute of Higher Education Hong Kong Department of Electrical and Electronics Engineering Federal University of Agriculture Abeokuta Nigeria

Hybrid combiner and precoder architectures, radio frequency (RF) chain, analog phase shifters, digital-to-analog converter (DAC), and analog-to-digital converter (ADC) are components of a millimeter wave cellular system. Prior works in the area of millimeter wave cellular system design employ receiver with infinite bit and large amount of RF chain that scales linearly with the quantity of transmitting and receiving antennas. This mode of design no doubt increases power demand or requirement of a typical millimeter wave system. In this work, hybrid architecture with few RF chains and small number of ADC bits are proposed and are used as candidate for millimeter wave channel estimation and cellular communication. In that connection, least square (LS), orthogonal matching pursuit (OMP), compressed sampling matching pursuit (CoSAMP), and deep learning (DL) techniques are utilized for analytical investigation. Indeed, computational results reveal that, when ADC consisting of uniform mid- rise quantizer is employed, the performance of 4 and 6 bits at signal-to-noise ratio (SNR) values of − 10 dB and 20 dB is at par with infinite bit (unquantized case). As a validation, DL compares favorably well with adaptive compressed sensing (ACS) technique previously used in the literature for channel estimation, while OMP and CoSAMP show better performance than ACS. © The Author(s) 2024.

关键词： Millimeter waves

来源：评论

学校读者我要写书评

暂无评论

Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms

引用

Environmental Monitoring and Assessment 2025年第3期197卷 1-36页

作者： RajasundrapandiyanLeebanon, T. Murugan, N. S. Sakthivel Kumaresan, K. Jeyabose, Andrew Department of Electrical and Electronics Engineering TamilNadu College of Engineering Tamil Nadu Coimbatore India Department of Mechanical Engineering Park College of Engineering & Technology TamilNadu Coimbatore India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Solar radiation plays a critical role in the carbon sequestration processes of terrestrial ecosystems, making it a key factor in environmental sustainability among various renewable energy sources. This study integrates two advanced signal processing techniques—empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD)—with machine learning (ML) algorithms, including multilayer perceptron (MLP), random forest regression (RFR), support vector regression (SVR), and ridge regression, to forecast long-term solar radiation. Meteorological data spanning 13 years (2000–2012) from seven locations across India (Bhubaneswar, Chennai, Delhi, Hyderabad, Nagpur, Patna, and Trivandrum) were used for training and testing. The optimal model was identified based on performance metrics, including the highest linear correlation coefficient (R), and the lowest mean absolute error (MAE) and root mean square error (RMSE). The results indicate that EEMD integrated with ML algorithms consistently outperformed EMD-based approaches. Among the ML models evaluated, EEMD integrated with MLP achieved the best performance across all locations, with RMSE = 0.332, MAE = 0.26, and R2 = 0.99. Furthermore, a comparative analysis with previous studies demonstrated that the proposed approach provides superior accuracy, underscoring its efficacy in solar radiation forecasting. © The Author(s) 2025.

关键词： Empirical mode decomposition

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：