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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

Journal of Advanced Research 2023年 48卷 191-211页

作者： Ukwuoma, Chiagoziem C. Qin, Zhiguang Belal Bin Heyat, Md Akhtar, Faijan Bamisile, Olusola Muaad, Abdullah Y. Addo, Daniel Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China IoT Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen 518060 China Centre for VLSI and Embedded System Technologies International Institute of Information Technology Telangana Hyderabad 500032 India Department of Science and Engineering Novel Global Community Educational Foundation Hebersham 2770 NSW Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center Chengdu University of Technology Chengdu China Department of Studies in Computer Science University of Mysore Manasagangothri Mysore India Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul 05006 South Korea

Introduction: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. Objectives: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. Methods: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. Results: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. Conclusion: The proposed hybrid deep learning framewo

关键词： Chest X-ray imaging Explainable artificial intelligence (XAI) Pneumonia identification Self-attention network Transfer ensemble learning Transformer encoder (TE)

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Mahfuzur Rahman Shuvo Ashifur Rahman Vishwanath Akuthota Tanay Paul Mahfujul Islam Md Sadi Ashraf Prosenjit Roy Md Tanzim Reza Department of Management Information System International American University United States Department of Computer Science and Engineering Chittagong University of Engineering and Technology Bangladesh TechOptima Hyderabad India Department of Computer Science and Engineering BRAC University Bangladesh Department of Computer Science Prairie View A & M University Texas United States

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

The automobile service industry’s explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of $\mathbf{8 5 \%}$, recall of $\mathbf{7 0 . 8 \%}$, and an F1-score of $\mathbf{7 7 . 2 \%}$, demonstrating the efficacy of integrating classical ML with generative AI. The system’s average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system’s Pro

关键词： Radio frequency Measurement Generative AI Navigation Scalability Hybrid power systems User experience Reliability Random forests Multi-agent systems

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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining 23

PSMiner: A Pattern-Aware Accelerator for High-Performance St...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Hao Qi Yu Zhang Ligang He Kang Luo Jun Huang Haoyu Lu Jin Zhao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China and Zhejiang-HUST Joint Research Center for Graph Processing Zhejiang Lab China Department of Computer Science University of Warwick United Kingdom

ISBN: (纸本)9798350323481

Streaming Graph Pattern Mining (GPM) has been widely used in many application fields. However, the existing streaming GPM solution suffers from many unnecessary explorations and isomorphism tests, while the existing static GPM ones require many repetitive operations to compute the full graph. In this paper, we propose a pattern-aware incremental execution approach and design the first streaming GPM accelerator called PSMiner, which integrates multiple optimizations to reduce redundant computation and improve computing efficiency. We have conducted extensive experiments. The results show that compared with the state-of-the-art software and hardware solutions, PSMiner achieves the average speedups of 770.9× and 60.4×, respectively.

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Parallel Acceleration of Real-time Feature Extraction Based on SURF Algorithm 15

Parallel Acceleration of Real-time Feature Extraction Based ...

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15th International Conference on computer Research and Development, ICCRD 2023

作者： Yu, Jiyang Huang, Dan Li, Jinyang Li, Wenjie Wang, Xianjie Shi, Xiaolong China Academy of Space Technology Beijing Institute of Spacecraft System Engineering Beijing China China Research and Development Academy of Machinery Equipment Chongqing University of Technology Chongqing Beijing China Chongqing Feima Innovation Research Institute Intelligent Equipment Research Department Chongqing China College of Computer Science and Engineering Chongqing University of Technology Chongqing China

ISBN: (纸本)9781665487504

Visual feature extraction is the key to target registration calculation. Compared with SIFT, SURF feature extraction algorithm has less computational complexity and better applicability when image signal-to-noise ratio is low. In this paper, an FPGA-based SURF algorithm for real-time feature extraction and parallel acceleration is designed for large-field scene registration applications of space targets. Firstly, the application scenario of space target is analyzed, and the grayscale features of the data containing the target used for registration are counted. Then, the five main parts of SURF algorithm were analyzed, and the algorithm was simplified based on the gray feature of the image. Finally, according to the simplified results, the parallel pipeline architecture of each module is designed based on FPGA. SURF computing modules are also given in this paper the design of the architecture and data flow of information, in XC7K325 completed the whole design of experiment, the results show that the design for 1024 × 1024 pixel image, single frame image processing time need only 51 us, resource usage is not more than 18.6%, power consumption is not more than 7.5 W, The computational efficiency is 87% higher than the previous design. Low signal-to-noise ratio (SNR) was used to simulate space target scene data for simulation test calculation, and the SURF descriptor error was less than 3%, ensuring the system's calculation error while effectively utilizing resources. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Deep Learning Techniques for Advanced Large-Scale Data Processing

Deep Learning Techniques for Advanced Large-Scale Data Proce...

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International Conference on Computing and Networking technology (ICCNT)

作者： Mp Sunil Jyoti Shekhawat T. Vanaja Vazeer Ali Mohammed Shailesh J. Thaware Vaishali Singh Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer & Information System Lewis University Illinois USA Department of Computer Engineering Vishwakarma Institute of Technology Pune INDIA Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Deep getting to know techniques have become increasingly more popular due to their potential to improve huge-scale records processing performance. Leveraging specialized hardware which includes GPUs, deep getting to know algorithms can attain mind-blowing pace will increase compared to traditional algorithms. further, deep gaining knowledge of fashions can discover complicated non-linear relationships in big datasets, permitting greater accurate overall performance and less fake predictions. Latest advances in deep mastering have enabled it to be implemented to remedy a plethora of information processing duties, from photo recognition to natural language processing. CNNs, RNNs, and GANs are a number of the maximum famous deep gaining knowledge of fashions used in tackling complex data processing obligations. agencies consisting of Google, fb, and Amazon use these sophisticated frameworks to build predictive models that help improve numerous programs. Deep getting to know strategies offer exquisite ability to take massive-scale information processing to the following stage. corporations are increasingly more becoming privy to the emerging opportunities, and actively investing in deep mastering to liberate the untapped strength of their information.

关键词： Deep learning Accuracy Scalability Information processing Predictive models Data processing Prediction algorithms Hardware Natural language processing Optimization

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Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第5期12卷 20-42页

作者： Rajendra Mahla Baseem Khan Om Prakash Mahela Anup Singh Department of Electrical Engineering National Institute of Technology Kurukshetra India Department of Electrical and Computer Engineering Hawassa University Ethiopia Power System Planning Division Rajasthan Rajya Vidyut Prasaran Nigam Limited JaipurIndia Department of Electrical Engineering Apex Institute of Engineering and Technology JaipurIndia

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)*** are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared *** algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and *** work has been performed using the MATLAB *** of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).

关键词： Fast kurtogram power quality disturbance ruled-based decision tree wavelet packet transform

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Adaptive Intrusion Detection in Cyber-Physical systems Using Reinforcement Learning-Based Autoencoders

Adaptive Intrusion Detection in Cyber-Physical Systems Using...

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Integrated Intelligence and Communication systems (ICIICS), International Conference on

作者： N. Rajathi Divya. S s. Anitha Elavarasi G. Saritha V. Jeya Ramya Department of Information Technology Kumaraguru College of Technology Coimbatore India Department of Computer Science and Business System Bannari Amman Institute of Technology Erode India Department of Computer Science and Engineering Sona College of Technology Salem India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The increasing integration of Cyber-Physical systems (CPS) in critical infrastructure presents unique challenges for ensuring robust cybersecurity. Traditional Intrusion Detection systems (IDS) often struggle with the dynamic, complex nature of CPS environments, making adaptive solutions essential. This paper presents an Adaptive Intrusion Detection system (AIDS) that leverages Reinforcement Learning-Based Autoencoders (RL-AE) to detect and respond to cyber intrusions in CPS environments. The proposed model combines the power of autoencoders for unsupervised anomaly detection with reinforcement learning to continuously adapt and improve detection accuracy. Experimental results on benchmark CPS datasets demonstrate that the RL-AE-based IDS achieves a detection accuracy of 98.3%, a precision rate of 96.8%, and a recall rate of 97.5%, effectively minimizing false positives to 2.4%. The system also demonstrated rapid adaptability, with a convergence time of less than 50 iterations in response to new attack patterns. These results highlight the model's ability to enhance system resilience and promote secure and efficient CPS operations in industrial and critical infrastructure applications. This approach provides a scalable and flexible IDS solution, promoting secure and resilient CPS operations in real-time, high-stakes environments.

关键词： Adaptation models Adaptive systems Accuracy Autoencoders Intrusion detection Reinforcement learning Cyber-physical systems Real-time systems Critical infrastructure Anomaly detection

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Evaluating the Privacy and Security Implications of Mobile Apps

Evaluating the Privacy and Security Implications of Mobile A...

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International Conference on Electronics, Materials engineering and Nano-technology (IEMENTech)

作者： Shafiul Islam Mazumder Sazzadul Islam Shovon Md Tanvir Chowdhury Minhajul Islam Habiba Azad Raisa Habibur Rahman Monjurul Islam Sumon Md. Khalid Mahbub Khan Department of Computer Science and Engineering American International University Dhaka Bangladesh Department of Information System Central Michigan University Michigan United States Department of Computer Science and Engineering East West University Dhaka Bangladesh Department of Computer Science and Engineering University of Information Technology & Sciences (UITS) Dhaka Bangladesh

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

The rapid growth of mobile applications, combined with an increasing reliance on these apps for a variety of purposes, has prompted serious concerns about user privacy and data security. This study aims to assess the privacy practices, data handling methods, and security vulnerabilities of mobile apps in a variety of categories, including social media, e-commerce, health and fitness, and financial management. This study identifies major privacy and security issues by examining privacy policies and terms of service, as well as how these apps handle sensitive data. The study also examines free and premium apps' privacy procedures, taking into account the implications for user data protection. The findings illustrate differences in data handling and security standards across different app categories, highlighting the importance of transparent privacy policies, strong security mechanisms, and limited data sharing to protect user information. The research helps to define standards and best practices for promoting secure app activity, increasing user confidence, and guaranteeing a safer digital environment.

关键词： Privacy Social networking (online) Data security Ecosystems Data protection Mobile applications Electronic commerce Protection Standards Best practices

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Bioimaging of Dissolvable Microneedle Arrays: Challenges and Opportunities

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Research 2022年第4期2022卷 633-655页

作者： Yanni Wang Gehua Ma Guangzhi Gao Ji Tao Wenzhao Cao Haohao Sun Fengsen Ma Yilong Zhang Yen Wei Mei Tian Laboratory of Biologics and Biomaterials Collge of PharmacyZhejiang University of TechnologyHangzhou 310014China Cllge of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Human Phenome Institute Fudan UniversityShanghai 201203China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Life Science Research Center Frontier Crossing InstituteZhejiang University of TechnologyHangzhou 310023China Engineering Research Center of Intelligent Sensing and System Ministry of EducationHangzhou 310023China College of Computer Science and Technology Zhejiang University of TechnologyHangzhou 310023China Department of Chemistry and the Tsinghua Center for Frontier Polymer Research Tsinghua UniversityBeijing 100084China

The emergence of microneedle arrays(MNAs)as a novel,simple,and minimally invasive administration approach largely addresses the challenges of traditional drug *** particular,the dissolvable MNAs act as a promising,multifarious,and well-controlled platform for micro-nanotransport in medical research and cosmetic formulation *** effective delivery mostly depends on the behavior of the MNAs penetrated into the body,and accurate assessment is urgently *** imaging technologies offer high sensitivity and resolution visualization of cross-scale,multidimensional,and multiparameter information,which can be used as an important aid for the evaluation and development of new *** combination of MNA technology and imaging can generate considerable new knowledge in a cost-effective manner with regards to the pharmacokinetics and bioavailability of active substances for the treatment of various *** addition,noninvasive imaging techniques allow rapid,receptive assessment of transdermal penetration and drug deposition in various tissues,which could greatly facilitate the translation of experimental MNAs into clinical *** on the recent promising development of bioimaging,this review is aimed at summarizing the current status,challenges,and future perspective on in vivo assessment of MNA drug delivery by various imaging technologies.

关键词： needle mostly formulation

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Adaptive Varying Contention Window MAC Protocol Based on Underwater Acoustic Propagation Delay

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IEEE Sensors Journal 2023年第13期23卷 15019-15031页

作者： Guo, Yongan Obaidat, Mohammad S. Guo, Jing Wu, Tin-Yu Tey, Fu Jie Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education College of Telecommunications and Information Engineering Nanjing210003 China The King Abdullah II School of Information Technology University of Jordan Amman11942 Jordan The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China Amity University UP Noida201301 India Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China National Pingtung University of Science and Technology Department of Management Information Systems Pingtung912301 Taiwan National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106335 Taiwan

Underwater acoustic wave propagation time delay and fluctuations are much greater than radio waves. However, many of the protocols used for underwater acoustic networking and communication are designed according to the principles of terrestrial wireless communication networks, failing to give full consideration to utilizing the underwater acoustic propagation and its time delay variation. For this reason, this article proposes an adaptive varying contention window media access control (AVCW-MAC) protocol based on propagation delay. The protocol uses AVCW random backoff mechanism to differently set up the CW of each node based on the different propagation delays from each underwater sensor node to the cluster head node to improve the channel utilization. In addition, to avoid excessive difference in the CW of each node, the backoff factors K1 and K2 are introduced in the AVCW random backoff mechanism to improve the fairness of nodes' accessing the channel. Simulation results show that for the same amount of sensor nodes, the AVCW-MAC protocol has significantly improved network throughput and decreased average end-to-end latency when compared to legacy CW-MAC protocols. © 2001-2012 IEEE.

关键词： Underwater acoustics

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