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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Asad Majeed Mahnoor Iftikhar Raja Hashim Ali Usama Arshad Muhammad Zeeshan Shabbir Ali Zeeshan Ijaz Nisar Ali Ali Aftab Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan Faculty of Electronic Systems Engineering University of Regina Regina Canada

Sorting and searching are critical processes for effecttive data analysis. In this paper, we evaluate the performance of various sorting and searching algorithms and compare their time and space complexities on both sorted and unsorted data. The algorithms we analyzed include six common sorting algorithms (insertion, radix, bucket, merge, bubble, and quick sort) and three search algorithms (linear, binary, and jump search). The results of our study provide insights into the best algorithms to use for different input sizes and types of data. It was found that for small input sizes, all algorithms perform similarly, but for larger input sizes, insertion and radix sorts are better for time complexity while bubble sort is better for space complexity. Additionally, jump search outperformed linear and binary search algorithms in both time and space complexity. Besides, difference between time and space complexity of sorted and unsorted data was significant.

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FPPL: An Efficient and Non-IID Robust Federated Continual Learning Framework

arXiv

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arXiv 2024年

作者： He, Yuchen Shen, Chuyun Wang, Xiangfeng Jin, Bo School of Computer Science and Technology East China Normal University Shanghai200062 China Shanghai Formal-Tech Information Technology Co. Lt Shanghai200062 China School of Software Engineering Tongji University Shanghai200092 China

Federated continual learning (FCL) aims to learn from sequential data stream in the decentralized federated learning setting, while simultaneously mitigating the catastrophic forgetting issue in classical continual learning. Existing FCL methods usually employ typical rehearsal mechanisms, which could result in privacy violations or additional onerous storage and computational burdens. In this work, an efficient and non-IID robust federated continual learning framework, called Federated Prototype-Augmented Prompt Learning (FPPL), is proposed. The FPPL can collaboratively learn lightweight prompts augmented by prototypes without rehearsal. On the client side, a fusion function is employed to fully leverage the knowledge contained in task-specific prompts for alleviating catastrophic forgetting. Additionally, global prototypes aggregated from the server are used to obtain unified representation through contrastive learning, mitigating the impact of non-IID-derived data heterogeneity. On the server side, locally uploaded prototypes are utilized to perform debiasing on the classifier, further alleviating the performance degradation caused by both non-IID and catastrophic forgetting. Empirical evaluations demonstrate the effectiveness of FPPL, achieving notable performance with an efficient design while remaining robust to diverse non-IID degrees. Code is available at: https://***/ycheoo/FPPL. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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2M3DF: Advancing 3D Industrial Defect Detection with Multi Perspective Multimodal Fusion Network

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Asad, Mujtaba Azeem, Waqar Jiang, He Mustafa, Hafiz Tayyab Yang, Jie Liu, Wei Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Department of Automation Shanghai200240 China Lahore Garrison University Department of Software Engineering Lahore54000 Pakistan China University of Mining and Technology School of Information and Control Engineering Jiangsu Xuzhou221116 China Zhejiang Normal University School of Computer Science and Technology Jinhua321004 China

In the context of Industrial Anomaly Detection (IAD), ensuring the quality of manufactured products is critical. Traditional 2D based methods often fail to capture anomalies present in complex 3D shapes. For effective anomaly detection in 3D shapes, it is essential to incorporate global semantic context, local geometric structure, and color information of the object. To fully leverage these features, we propose a network named 2M3DF, that leverages knowledge from multi-view RGB images and corresponding point cloud information for enhanced anomaly detection performance. Our model initially employs pre-trained feature extractors that generate local features from multi-view RGB images and corresponding point clouds. The novel inter-modality feature representation and fusion module first adapts these inter-modality features and then effectively aligns and aggregates these multimodality features on a pixel-to-point basis. To learn the normality from point-wise fused multimodal features, we fit a multivariate Gaussian distribution to model the normal feature distribution. Comprehensive experimental evaluations using the MVTec3D-AD and Eyecandies dataset validate the effectiveness of our propose model and demonstrate significant improvements in comparison to existing state-of-the-art methods. Our model achieves a 96.6% mean I-AUROC while delivering real-time results. © 1991-2012 IEEE.

关键词： Normal distribution

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PowerGen: Resources Utilization and Power Consumption Data Generation Framework for Energy Prediction in Edge and Cloud Computing

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Procedia computer Science 2024年 238卷 385-395页

作者： Leila Ismail Huned Materwala Intelligent Distributed Computing and Systems (INDUCE) Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The explosive adoption of IoT applications in different domains, such as healthcare, transportation, and smart home and industry, has led to the pervasive adoption of edge and cloud computing. Large-scale edge and cloud data centers, consisting of thousands of computing servers, are hungry-energy infrastructure exacerbating issues such as environmental carbon footprint and high electricity costs. Developing energy-efficient solutions for cloud infrastructure requires knowledge of the correlation between computing server resource utilization and power consumption. Power consumption modeling exhibits this relationship and is crucial for energy savings. In this paper, we propose PowerGen, a framework to generate server resources utilization and corresponding power consumption dataset. The proposed framework will aid academic researchers to formulate correlations between resources utilization and power consumption by using power prediction models, and evaluate energy-aware resource management approaches in an edge-cloud computing system. It will help edge and cloud administrators to evaluate the energy-efficiency of heterogenous severs architectures in a datacenter. We exemplify the applicability of the dataset, generated by our proposed framework, in power prediction modeling and energy-aware scheduling for green computing scenarios.

关键词： cloud computing edge computing dataset generation energy-efficiency green computing machine learning internet of things power consumption modeling resource management scheduling

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BOAformer: Object Detection Network Based on Background-Object Attention Transformer

BOAformer: Object Detection Network Based on Background-Obje...

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International Conference on Communications, Information System and computer engineering (CISCE)

作者： Yong Yang Chenbin Liang Shuying Huang Xiaozheng Wang School of Computer Science and Technology Tiangong University Tianjin China School of Electronics and Information Engineering Tiangong University Tianjin China School of Software Tiangong University Tianjin China School of Control Science and Engineering Tiangong University Tianjin China

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

At present, deep learning has achieved great success in the field of object detection. To ensure that positive samples in the image are not missed, most deep-learning object detection methods set many prediction boxes and use the same prediction operation on them. Although these methods can obtain higher predictive values for high certainty objects, they may identify objects with lower certainty, such as small or fuzzy objects, as negative samples or miss them. To address this issue, this paper presents an object detection network based on background-object attention transformer (BOAformer), which improves the accuracy of object detection by establishing the relationship between background and object features. Firstly, the existing classification model is employed to obtain scores of object prediction boxes. On this basis, a scoring mechanism is designed to filter out high scoring object prediction boxes to reduce the computational complexity of the later network. Then, to improve the accuracy of object detection, BOAformer is constructed, which obtains auxiliary classification values of objects by learning the relationship between background and object features, and adds them to the initial detection results. Finally, many experiments are conducted to verify the effectiveness of BOAformer. The experimental results show that the designed network improves the accuracy of object detection and can detect small or lost objects in the background.

关键词： Training Visualization Accuracy Computational modeling Semantic segmentation Object detection Predictive models

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Med-3D: 3D Reconstruction of Medical Images based on Structure-from-Motion via Transfer Learning

Med-3D: 3D Reconstruction of Medical Images based on Structu...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Quan, Hongyan Dong, Jiashun Qian, Xiaoxiao East China Normal University School of Computer Science and Technology Shanghai China East China Normal University School of Software Engineering Shanghai China

ISBN: (纸本)9781665401265

The problem of 3D reconstruction of medical images is very challenging due to the disadvantages of less texture and serious speckle noise. In this paper, we propose a medical image 3D reconstruction framework based on transfer learning and Structure-from-Motion. Unlike some existing methods, which need special hardware devices, or be effective for a particular modal of images, our unified framework does not depend on any special equipment and can be applied to diverse modalities of medical images, such as ultrasonic image, computed tomography (CT) etc. Its contribution covers three aspects: First, it uses Structure-From-Motion(SFM) to estimate the camera structure and pose of consecutive frames end-to-end, which is suitable for clinical auxiliary diagnosis. Second, strong and robust photogrammetry knowledge, including depth, the camera structure and pose, can be pre-learned from the natural images with the richer texture, and then, are transferred to the medical images learning. Besides, Med-3D is proposed, a GAN model based on self-supervised learning framework, in the generator, the spatial coplanar characteristics to constrain the cloud points reconstructed from a single captured frame or adjacent frames are considered for learning more precise features. Experimental results on the medical data of ultrasound images, and CT in the open clinical library, demonstrate that our method can reconstruct geometric structures from 2D slices and has excellent accuracy in terms of reconstruction effects. © 2021 IEEE.

关键词： Textures

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XVAE-mViT: Explinable Hybrid Artificial Intelligence Framework for Predicting COVID-19 from Chest X-Ray and CT Scans

XVAE-mViT: Explinable Hybrid Artificial Intelligence Framewo...

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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Daniel Addo Mugahed A. Al-antari Shijie Zhou Kwabena Sarpong Ertan Bütün Muhammed Talo Humam AbuAlkebash Chiagoziem C. Ukwuoma School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Artificial Intelligence DaeyangAI Center Sejong University Seoul Republic of Korea Department of Computer Engineering Firat University Elazığ Turkey Department of Software Engineering Firat University Elazig Turkey Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey The College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan China

The COVID-19 virus has rapidly spread as a global pandemic, causing significant impacts on public health, economies, and daily life worldwide. Accurately and quickly predicting COVID-19 is crucial to maintaining stronger healthcare systems. This paper introduces a novel hybrid model of artificial intelligence that combines the benefits of the Variational Auto-Encoder (VAE) with the attention mechanism based on the Vision Transformer (Vi $T$ ). The novel encoder network is structured with four sequential blocks, each involving residual connections of two multiscale kernel depth-wise separable convolution (MKnDSC) modules. The mobile Vi $T$ is coupled with the V AE to serve as the classification head for predicting COVID- 19 using chest X-ray (CXR) and computed tomography (CT) scan modalities. We achieved promising classification results with overall accuracies of 96.16% and 95.42% using CXR and CT images, respectively. The proposed hybrid AI framework appears to be a practical solution, especially considering its lightweight structure of 2.15 million parameters and 0.68 FLOPs.

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Insights into software Development Approaches: Mining Q&A Repositories

arXiv

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arXiv 2023年

作者： Khan, Arif Ali Khan, Javed Ali Akbar, Muhammad Azeem Zhou, Peng Fahmideh, Mahdi M3S Empirical Software Engineering Research Unit University of Oulu OuluFI-90014 Finland Department of Software Engineering University of Science and Technology Bannu28100 Pakistan Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta15210 Finland College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Business University of Southern Queensland ToowoombaQLD4350 Australia

Context: software practitioners adopt approaches like DevOps, Scrum, and Waterfall for high-quality software development. However, limited research has been conducted on exploring software development approaches concerning practitioners’ discussions on Q&A forums. Objective: We conducted an empirical study to analyze developers’ discussions on Q&A forums to gain insights into software development approaches in practice. Method: We analyzed 13,903 developers’ posts across Stack Overflow (SO), software engineering Stack Exchange (SESE), and Project Management Stack Exchange (PMSE) forums. A mixed method approach, consisting of the topic modeling technique (i.e., Latent Dirichlet Allocation (LDA)) and qualitative analysis, is used to identify frequently discussed topics of software development approaches, trends (popular, difficult topics), and the challenges faced by practitioners in adopting different software development approaches. Findings: We identified 15 frequently mentioned software development approaches topics on Q&A sites and observed an increase in trends for the top-3 most difficult topics requiring more attention. Finally, our study identified 49 challenges faced by practitioners while deploying various software development approaches, and we subsequently created a thematic map to represent these findings. Conclusions: The study findings serve as a useful resource for practitioners to overcome challenges, stay informed about current trends, and ultimately improve the quality of software products they develop. © 2023, CC BY.

关键词： Project management

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An Intelligent Approach to Segment the Liver Cancer using Machine Learning Method 4

An Intelligent Approach to Segment the Liver Cancer using Ma...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Anand, L. Maurya, Mahesh Seetha, J. Nagaraju, D. Ravuri, Ananda Vidhya, R.G. School of Computing Srm Institute of Science and Technology Department of Networking and Communications India University of Mumbai K. C. College of Engineering Management and Research Mith Bunder Road Near Hume Pipe Maharashtra Thane400603 India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sri Venkatesa Perumal College of Engineering and Technology Department of CSE Andhra Pradesh Puttur India Senior Software Engineer Intel Corporation HillsboroOR97124 United States Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350300093

Cancer Liver cancer is difficult to detect and diagnose early. Machine learning has improved liver cancer diagnosis in recent years. This abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme Learning Machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. The autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. The CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. This simplifies feature selection and improves the model's capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. The proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. This unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach's therapeutic promise and application in real-world healthcare settings, additional datasets are needed. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing low quality in radiograph datasets using wavelet transform convolutional neural network and generative adversarial network for COVID-19 identification 4

Enhancing low quality in radiograph datasets using wavelet t...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Nneji, Grace Ugochi Cai, Jingye Jianhua, Deng Monday, Happy Nkanta Chikwendu, Ijeoma Amuche Oluwasanmi, Ariyo James, Edidiong Christopher Mgbejime, Goodness Temofe School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan China School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9781665413220

The coronavirus disease of 2019 (COVID-19) pandemic has caused a global public health epidemic since there is no 100% vaccine to cure or prevent the further spread of the virus. With the ever-increasing number of new infections, creating automated methods for COVID-19 identification of Chest X-ray images is critical to aiding clinical diagnosis and reducing the time-consumption for image interpretation. This paper proposes a novel joint framework for accurate COVID-19 identification by integrating an enhanced super-resolution generative adversarial network with a noise reduction filter bank of wavelet transform convolutional neural network on both Chest X-ray and Chest Tomography images for COVID-19 identification. The super-resolution utilized in this study is to enhance the image quality while the wavelet transform Convolutional Neural Network architecture is used to accurately identify COVID-19. Our proposed architecture is very robust to noise and vanishing gradient problem. We used public domain datasets of Chest x-ray images and Chest Tomography to train and check the performance of our COVID-19 identification task. This experiment shows that our system is consistently efficient by accuracy of 0.988, sensitivity of 0.994, and specificity of 0.987, AUC of 0.99, F1-score of 0.982 and 0.989 for precision using the Chest X-ray dataset while for Chest Tomography dataset, an accuracy of 0.978, sensitivity of 0.981, and specificity of 0.979, AUC of 0.985, F1-score of 0.961 and precision of 0.980. These performances have also outweighed other established state-of-the-art learning methods. © 2021 IEEE.

关键词： Coronavirus

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