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Optimized Visual Recognition Through a Deep Convolutional Neural Network With Hierarchical Modular Organization

IEEE Access 2024年 12卷 95517-95528页

作者： Clinton Sitepu, Ade Liu, Chuan-Ming National Taipei University of Technology College of Electrical Engineering and Computer Science Taipei10608 Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan

Artificial Intelligence, including machine learning and deep convolutional neural networks (DCNNs), relies on complex algorithms and neural networks to process and analyze data. DCNNs for visual recognition often require access to high-performance hardware, such as GPUs and cloud-based computing resources, to perform tasks efficiently. Visual recognition requires DCNNs with numerous layers. Fully connected layers in DCNNs are often the most computationally intensive. These layers connect every neuron in one layer to every neuron in the next layer, resulting in a large number of parameters to compute. To mitigate redundancy and make DCNNs more efficient, this article implements and demonstrates the concept to identifying and removing redundant or low-impact connections from fully connected layers using convolution neural network with hierarchical modular organization. The modularity of the DCNNs is built based on the cluster hierarchy of the similar image. These clusters are created based on a novel similarity metric, which measures how closely related images are to each other. The architecture uses multiple smaller DCNNs, referred to as modules, designed to progressively classify images into super clusters according to their similarity. Experimental results using popular image datasets show that the proposed DCNNs model to optimized number of operations by 49% to 99% and keeps its performance comparable. © 2024 The Authors.

关键词： Convolution

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Does Partner Asymmetry Facilitate Innovation Speed? Organizational Learning and Social Exchange Perspectives

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IEEE Transactions on Engineering Management 2024年 71卷 4144-4159页

作者： Hsiao, Yung-Chang Wu, Ming-Ho National University of Tainan Department of Business and Management College of Management Tainan700301 Taiwan National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering College of Electrical Engineering and Computer Science Kaohsiung824005 Taiwan

The right partner and high innovation speed are crucial for a successful research and development (R&D) alliance in the high-tech industry. Does homogeneity or heterogeneity between partners benefit innovation speed? This study examined the links between partner asymmetries (technological heterogeneity and network resource asymmetry) and innovative speed from both economic and social dual perspectives by focusing on biopharmaceutical industries. The results revealed an inverse U-shaped relationship between technology heterogeneity and innovation speed. In addition, a positive linear relationship exists between network resource asymmetry and innovation speed for 1991-2000 alliances and for biotech-biotech partner-type alliances;significantly negative moderating effects of time to market occurred on these linear relations. Finally, this study makes important theoretical and practical contributions to partner selection literature on R&D alliances in the biopharmaceutical industry. © 1988-2012 IEEE.

关键词： Behavioral research

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An enhanced AMBTC for color image compression using color palette

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Multimedia Tools and Applications 2024年第11期83卷 31783-31803页

作者： Xiong, Lizhi Zhang, Mengtao Yang, Ching-Nung Kim, Cheonshik School of Computer Science Nanjing University of Information Science and Technology Nanjing China Department of Computer Science and Information Engineering National Dong Hwa University Hualien Taiwan Department of Computer Engineering Sejong University Seoul Korea Republic of

Digital image has been used in various fields as an essential carrier. Many color images have been constantly produced since their more realistic description, which takes up much storage space and network bandwidth. Thus, color image compression has become an essential key technology. Absolute Moment Block Truncation Coding (AMBTC) has been widely studied as one of the classical image compression methods. However, in the existing methods, the visual quality of the reconstructed images and the compression rate are all relatively low. Therefore, this paper proposes an enhanced AMBTC for color image compression using a color palette. In the proposed method, the K-means clustering algorithm is utilized for training the image's palette pattern. The color palette obtained by K-mean will be more suitable for reconstructing this image than the standard color palette, and the visual quality will be higher. The six clustered central pixels are matched with the palette through a color difference formula, and the obtained index values are used as the quantization levels. Huffman coding is used to build a bitmap to achieve a higher compression rate, that is, a lower bit rate. At last, a block of a color image can be represented by six index values and a bitmap. Experimental results and theoretical analysis demonstrate that the proposed method has better visual quality and bit rate than similar schemes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Color

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A fusion of CNN And SIFT For multicultural facial expression recognition

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Multimedia Tools and Applications 2025年 1-19页

作者： Qadir, Inam Iqbal, Muhammad Amjad Ashraf, Samman Akram, Sheeraz Department of Computer Science University of Central Punjab Lahore Pakistan Faculty of Computer Science and Information Technology The Superior University Lahore Pakistan Lahore Pakistan

The most generic and understandable way of communication is by observing facial expressions;Facial Expression Recognition(FER) performance was affected by the differences in ethnicity, culture, and geography. This research proposes a feature grafting-based novel technique to recognize multicultural facial expressions. The Viola-Jones algorithm detects the facial region of an image that is most concentrated. Feature Grafting is used to combine high-level and low-level features. A multicultural facial expression database was created using still images of male and female subjects obtained from three datasets: TFEID, Radboud, and JAFFE. The model is designed and tested using this dataset and the proposed technique. The obtained accuracy using the proposed technique was 94.34% which is better than the state-of-the-art method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Grafting (chemical)

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Comparison of MobileNetV2 and VGG19 for the Categorization of Thermal Images

Comparison of MobileNetV2 and VGG19 for the Categorization o...

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International Conference on Applied Soft Computing and Communication Networks, ACN 2023

作者： Muften, Haider Ali Khayeat, Ali Retha Hasoon College of Computer Science and Information Technology Computer Science Department University of Kerbala Karbala Iraq

ISBN: (纸本)9789819720033

Object detection in thermal images is vital for diverse applications, utilizing machine learning algorithms to analyze infrared radiation. This study focuses on classifying contrast-enhanced thermal images (CLAHE) using MobileNetV2 and VGG19 architectures. Our research compares their performance metrics, emphasizing accuracy, F1 score, and recall. Results indicate MobileNetV2 outperforms VGG19, achieving 95% accuracy and 96.21% F1 score, whereas VGG19 scored 90.33% and 90.57%, respectively. This study demonstrates the effectiveness of MobileNetV2 in thermal image classification, showcasing its potential for real-world applications. Our findings underscore the applicability of transfer learning techniques in thermal image analysis, providing valuable insights for future research and development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Object detection

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Enhanced sentiment analysis framework: Ensemble attention enhanced bidirectional long-short-term encoder for accurate classification of consumer reviews

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Alexandria Engineering Journal 2025年 127卷 265-283页

作者： Almalki, Ali Jaber Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia

The fast growth of today's technology on social media, the web, etc caused the ongoing and fast creation of opinionated textual data. In this context, internet reviews by consumers determine upcoming consumers whether can buy a particular product or not. Because the thoughts regarding the products are expressed by commenting on reviews. The sentiment analysis is more supportive for comprehending feedback and the attitude of consumers regarding the product's characteristics. More sentiment classification tasks are carried out by the researchers but attained low level output due to difficulty in learning contextual words. To rectify this type of issue, current work focuses on developing a sentiment analysis framework by progressive techniques. The novel namely Ensemble Attention Enhanced Bidirectional Long-short-term Encoder (EAE-BiLE) is developed. The developed method analyzes online reviews and gains reviews from Amazon and the second task is to form high-quality input. The BERT model generates contextual word embeddings and Bi-directional Long-Short-Term Memory (Bi-LSTM) processes these embeddings in order to capture both backward and forward dependencies from the text. The novel method makes the accurate classification by focusing on important and relevant parts in the text using an attention mechanism that assigns different weights. Detailed experiments that expose the performance of the developed method are performed in this work. This requires a few evaluation metrics that can measure the effectiveness of the developed method, which is accomplished by F1-score, recall, accuracy, and precision. The developed EAE-BiLE framework yields higher rates of 97.2 %, 97.2 %, 99.1 %, and 98.4 % for precision, F1-score, accuracy, and recall. Its error performance is also low, which represents the developed method makes the precise classification. The developed EAE-BiLE excels with higher effectiveness than prior techniques for efficacious sentiment classification. © 2025 T

关键词： Emotion Recognition

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DeepGAN: Utilizing generative adversarial networks for improved deep learning

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International Journal of Knowledge-Based and Intelligent Engineering Systems 2024年第4期28卷 732-748页

作者： V, Edward Naveen A, Jenefa T.M, Thiyagu A, Lincy Taurshia, Antony Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology India Department of Computer Science and Engineering Karunya Institute of Technology and Sciences India Division of Computer Science and Engineering Karunya Institute of Technology and Sciences India Department of Computer Science and Engineering National Engineering College India

In the realm of deep learning, Generative Adversarial Networks (GANs) have emerged as a topic of significant interest for their potential to enhance model performance and enable effective data augmentation. This paper addresses the existing challenges in synthesizing high-quality data and harnessing the capabilities of GANs for improved deep learning outcomes. Unlike traditional approaches that heavily rely on manually engineered data augmentation techniques, our work introduces a novel framework that leverages DeepGANs to autonomously generate diverse and high-fidelity data. Our experiments encompass a diverse spectrum of datasets, including images, text, and time series data. In the context of image classification tasks, we conduct experiments on the widely recognized CIFAR-10 dataset, which consists of 50,000 image samples. Our results demonstrate the remarkable efficacy of DeepGANs in enhancing model performance across various data domains. Notably, in image classification using the CIFAR-10 dataset, our innovative approach achieves an impressive accuracy of 97.2%. This represents a substantial advancement beyond conventional CNN models, underscoring the profound impact of DeepGANs in the realm of deep learning. In summary, this research sheds light on DeepGANs as a fundamental component in the pursuit of enhanced deep learning performance. Our framework not only overcomes existing limitations but also heralds a new era of data augmentation, with generative adversarial networks leading the way. The attainment of an accuracy rate of 97.2% on CIFAR-10 serves as a compelling testament to the transformative potential of DeepGANs, solidifying their pivotal role in the future of deep learning. This promises the development of more robust, adaptive, and accurate models across a myriad of applications, marking a significant contribution to the field. © 2024 – IOS Press. All rights reserved.

关键词： Generative adversarial networks

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Predicting Violence-Induced Stress in an Arabic Social Media Forum

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Intelligent Automation & Soft Computing 2023年第2期35卷 1423-1439页

作者： Abeer Abdulaziz AlArfaj Nada Ali Hakami Hanan Ahmed Hosni Mahmoud Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia

Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over *** interaction analysis aids in deﬁning people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and *** this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social *** searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on *** applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social *** extracted a set of tex-tual,spatial,and interaction attributes from various ***,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress *** depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in ***,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.

关键词： Arabic language analysis violence-induced stress detection hybrid model deep learning

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CRAQL: a novel clustering-based resource allocation using the Q-learning in fog environment

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International Journal of Cloud Computing 2024年第3期13卷 243-266页

作者： Ahlawat, Chanchal Krishnamurthi, Rajalakshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Fog computing is an emerging paradigm that provides services near the end-user. The tremendous increase in IoT devices and big data leads to complexity in fog resource allocation. Inefficient resource allocation can lead to resource starvation and unable to complete the task assignment within a specific time. Hence, to enhance the efficiency of the fog resources, it is critical to perform proper resource allocation. This work targets to provide the solution to the resource allocation problem with a novel clustering-based resource allocation using the Q-learning (CRAQL) model. For this purpose, the problem is defined as a decision-making problem and formulated as Markov decision process (MDP). Next, to find the optimal resource, an enhanced optimal resource allocation (EORA) algorithm is proposed and detailed study is performed to analyse the impact of various performance parameters. Simulation results show the comparison of the EORA versus conventional Brute force method by varying the performance parameters such as learning rate and number of trials. The experimental results exhibit optimal solutions with significant improvement in learning rate at an average probability of 0.5 within limited epochs. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Reinforcement learning

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A Secure Transmission of Encrypted Medical Data Based on Virtual Instruments 6th

A Secure Transmission of Encrypted Medical Data Based on Vir...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Abdulbaqi, Azmi Shawkat Department of Computer Science College of Computer Science and Information Technology University of Anbar Ramadi Iraq

ISBN: (纸本)9789819940707

Cardiovascular health is becoming more common over the world. Electrocardiography (ECG) is a test that utilizes an electrical signal in the heart to diagnose and monitor cardiac problems. Utilizing the myDAQ data acquisition device and LabVIEW, identifying cardiac illness in real-time is now not only possible but also simple. As a result, this article presents a system that can capture ECG signals in real-time, detect heart problems, and reveal whether a waveform is in range or not utilizing light-emitting diodes (LEDs). LabVIEW software was utilized to create and evaluate the ECG signal of a patient and to calculate associated parameters for the prototype home ECG system described in this article. These parameters include–the Rate of Heart, intervals-of-PR, intervals-of-QT, intervals-of-ST, waves-of-P, and waves-of-T durations. The distant station receives the recorded ECG signal through the Internet, as well as its related factors. Tests were done on the system utilizing ECG’s simulator, web-based-ECG, and actual ECG acquisition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Electrocardiograms

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