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Denoising Medical Images Using Deep Learning in IoT Environment

Computers, Materials & Continua 2021年第12期69卷 3127-3143页

作者： Sujeet More Jimmy Singla Oh-Young Song Usman Tariq Sharaf Malebary School of Computer Science and Engineering Lovely Professional UniversityJalandharIndia Software Department Sejong UniversitySeoulKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology in RabighKing Abdulaziz UniversityJeddah21911Saudi Arabia

Medical Resonance Imaging(MRI)is a noninvasive,nonradioactive,and meticulous diagnostic modality capability in the field of medical ***,the efficiency of MR image reconstruction is affected by its bulky image sets and slow process ***,to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network(SANR_CNN)for eliminating noise and improving the MR image reconstruction *** proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality,and SARN algorithm is used for building a dictionary learning technique for denoising large image *** proposed SANR_CNN model also preserves the details and edges in the image during *** experiment was conducted to analyze the performance of SANR_CNN in a few existing models in regard with peak signal-to-noise ratio(PSNR),structural similarity index(SSIM),and mean squared error(MSE).The proposed SANR_CNN model achieved higher PSNR,SSIM,and MSE efficiency than the other noise removal *** proposed architecture also provides transmission of these denoised medical images through secured IoT architecture.

关键词： Medical resonance imaging convolutional neural network denoising contrast enhancement internet of things rheumatoid arthritis

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Hadiyyissa Automatic Speech Recognition Using Deep Learning Approach

Hadiyyissa Automatic Speech Recognition Using Deep Learning ...

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Information and Communication Technology for Development for Africa (ICT4DA), International Conference on

作者： Selamu Shamore Amin Tuni Gure Mohammed Abebe Yimer Department of Computer Science Wachemo University Hossana Ethiopia Department of Information System Arsi University Arsi Ethiopia Faculty of Computing and Software Engineering Arba Minch University Arba Minch Ethiopia

Speech Recognition focuses on developing advanced technologies that can recognize and translate spoken language into text. Automatic Speech Recognition (ASR) is an Artificial Intelligence (AI) technology that captures human voice from a mi-crophone and analyzes it based on spoken words to comprehend natural language. In Ethiopia, ASR models have been developed for specific local languages such as Amharic, Tigrigna, Wolaytta, and Afaan Oromo, but there has been a lack of research on ASR models for the Hadiyyisa language, which is also spoken in Ethiopia. The research study designed and developed an ASR model for the Hadiyyisa language. The researchers collected data from native speakers of the Hadiyyisa language and used Python programming to design and develop the deep learning model for the Hadiyissa ASR model. Different methods and techniques were used in the study to achieve the research objectives and complete the entire research process. The proposed model utilized deep learning algorithms including RNN, LSTM, BiLSTM, and CNN, along with suitable feature extraction techniques such as Mel Frequency Cepstral Coefficients (MFCC). The accuracy of the proposed ASR model was evaluated through experimentation, which demonstrated that the BiLSTM model outperformed the other deep learning models with an overall accuracy of 90.95 % Based on the experimental evidence, the proposed model was found to be better suited for ASR for the Hadiyyisa language.

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Efficient Regression with Feature Selection Based on L12-Norm

Efficient Regression with Feature Selection Based on L12-Nor...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Yu Lei Qin Li Jane You School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China School of Telecommunications Engineering Xidian University Xi’an China Department of Computing The Hong Kong Polytechnic University Hong Kong China

Feature selection is one of the most important components of many machine learning applications. Most of the existing sparse coding-based methods only concerned with the regularization of the transformation matrix, but ignoring the sparsity of projected features in the intrinsic feature space. In this paper, we introduce a novel regularization with ℓ 12 -norm, called class specificity distribution term. It aims to ensure that samples with the same label could obey a specific distribution, while data from the different categories should have different distributions in the intrinsic feature space. Then we propose a novel embedded feature selection framework based on regression and class specificity distribution term measured by ℓ 12 -norm, termed RL12. An efficient augmented Lagrange multipliers based optimization algorithm is proposed to solve the ℓ 12 -norm regularization iteratively. Experimental results demonstrate the effectiveness of our method.

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Cloned Code Clustering for the software Product Line engineering Approach to Developing a Family of Products

Cloned Code Clustering for the Software Product Line Enginee...

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IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Taeyoung Kim Jihyun Lee Sungwon Kang SW Beacon Lab SW Campus HL Mando Corporation Seongnam Republic of Korea Department of Software Engineering Jeonbuk National University Jeonju Republic of Korea School of Computing KAIST Daejeon Republic of Korea

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

Cloned code clustering identifies identical or similar code fragments, i.e., cloned code, from products of a product family, and then constructs cloned code clusters, a set of files, each of which contains the same cloned code. It is an essential step for migrating from the Clone-And-Own (CAO) approach to the software Product Line engineering approach for developing a family of products. This paper proposes a method for cloned code clustering based on files that share cloned code. The method proposed in this paper identifies clusters to use for constructing a product line platform at the source code level in such a way that it works regardless of cloning-in-the-small or cloning-in-the-large; it does not need to know what code is original or what code is cloned; and its results remain consistent regardless of the comparison order or the relative similarity of products of a product family. For evaluation we applied our method to Apo-Games and ArgoUML developed with the CAO approach and confirmed that our method correctly constructs code clusters.

关键词： Codes Source coding Cloning software software product lines

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Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression

arXiv

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arXiv 2024年

作者： Su, Xiaoxin Zhou, Yipeng Cui, Laizhong Guo, Song College of Computer Science and Software Engineering Shenzhen University Shenzhen China School of Computing Faculty of Science and Engineering Macquarie University Sydney Australia Shenzhen University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology HKSAR Hong Kong

Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. To accelerate the communication speed, it has been explored to deploy a programmable switch (PS) in lieu of the parameter server to coordinate clients. The challenge to deploy the PS in FL lies in its scarce memory space, prohibiting running memory consuming aggregation algorithms on the PS. To overcome this challenge, we propose Federated Learning in-network Aggregation with Compression (FediAC) algorithm, consisting of two phases: client voting and model aggregating. In the former phase, clients report their significant model update indices to the PS to estimate global significant model updates. In the latter phase, clients upload global significant model updates to the PS for aggregation. FediAC consumes much less memory space and communication traffic than existing works because the first phase can guarantee consensus compression across clients. The PS easily aligns model update indices to swiftly complete aggregation in the second phase. Finally, we conduct extensive experiments by using public datasets to demonstrate that FediAC remarkably surpasses the state-of-the-art baselines in terms of model accuracy and communication traffic. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

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A deep learning-based approach for predicting ubiquitination sites using word embedding techniques

A deep learning-based approach for predicting ubiquitination...

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International Conference on Computer and Information Technology (ICCIT)

作者： Md. Faruk Hosen Md. Abul Basar Md. Samim Hossain Sakib Sarker Md. Suhag Ali Monir Morshed Muhammad Shahin Uddin Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Bangladesh Department of Computing and Information System Daffodil International University (DIU) Ashulia Dhaka Bangladesh Department of Computer Science and Engineering Uttara University Dhaka Department of Software Engineering Daffodil International University (DIU) Ashulia Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Ubiquitination is a prevalent and reversible post-translational modification (PTM) that controls apoptosis and plays a crucial role in protein breakdown and cell disorders. Ubiquitination also involved in the control of a number of physiological mechanisms such as cellular proliferation, gene transcription, intracellular signaling, DNA correction, and synthesis. However, experimental identification of ubiquitination sites prediction is time-consuming and costly, so it is an urgent need to construct effective predictors. In this paper, a novel predictor called UbiSite-DeepWET has been developed to accurately predict ubiquitination sites from sequences using a convolutional neural network (CNN) model. First, we perform nine feature extraction method, namely Amino Acid Composition (AAC), Dipeptide Compostion (DPC), Composition of k-spaced Amino Acid Pairs (CKSAAP), Pseudo-Amino Acid Composition (PAAC), Amino Acid index (AAindex) database, Word2Vec, GloVe and fastText, to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are used to remove unnecessary and irrelevant features for predicting ubiquitination sites. Finally, the best features are fed into the CNN classifier to build the UbiSite-DeepWET model for identifying the protein ubiquitination sites. Five-fold cross validation shows that the AUC values of Set1-Set6 datasets are 0.8846, 0.7764, 0.7881, 0.7740, 0.6870 and 0.7238, respectively. The Adaptive synthetic sampling approach (ADASYN) is applied in Set4 to Set6 unbalanced datasets, and the AUC values are 0.9929, 0.9975 and 0.9971, respectively. Additionally, we have created three independent test (IT) datasets which the AUC values are 0.8529, 0.6215 and 0.6001, respectively. The results demonstrate that the proposed technique UbiSite-DeepWET outperforms conventional ubiquitination prediction methods and offers new guidance for the identification of ubiquitination sites.

关键词： Protein engineering Additives Predictive models Amino acids Feature extraction Physiology Robustness Protein sequence Convolutional neural networks Bioinformatics

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Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yazhou Zou, Chunwang Wang, Bo Qin, Jing College of Intelligence and Computing Tianjin University Tianjin300350 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450002 China Department of Computational Neuroscience The Hong Kong Polytechnic University 999077 Hong Kong

Sarcasm detection, as a crucial research direction in the field of Natural Language Processing (NLP), has attracted widespread attention. Traditional sarcasm detection tasks have typically focused on single-modal approaches (e.g., text), but due to the implicit and subtle nature of sarcasm, such methods often fail to yield satisfactory results. In recent years, researchers have shifted the focus of sarcasm detection to multi-modal approaches. However, effectively leveraging multi-modal information to accurately identify sarcastic content remains a challenge that warrants further exploration. Leveraging the powerful integrated processing capabilities of Multi-Modal Large Language Models (MLLMs) for various information sources, we propose an innovative multi-modal Commander-GPT framework. Inspired by military strategy, we first decompose the sarcasm detection task into six distinct sub-tasks. A central commander (decision-maker) then assigns the best-suited large language model to address each specific sub-task. Ultimately, the detection results from each model are aggregated to identify sarcasm. We conducted extensive experiments on MMSD and MMSD 2.0, utilizing four multi-modal large language models and six prompting strategies. Our experiments demonstrate that our approach achieves state-of-the-art performance, with a 19.3% improvement in F1 score, without necessitating fine-tuning or ground-truth rationales. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers

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Science China(Information Sciences) 2024年第7期67卷 27-45页

作者： Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG School of Mathematics and Computer Science Zhejiang A&F University School of Software Dalian University of Technology Faculty of Applied Sciences Macao Polytechnic University Department of Electrical and Computer Engineering Lebanese American University School of Information Engineering Huzhou University Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU-BIT University

Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability, the interference factors in the process of WSN object localization cannot be effectively eliminated. In this paper, an explainable-AI-based two-stage solution is proposed for WSN object localization. In this solution, mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization. The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters. To address this,an explainable AI model is proposed to solve the localization problem. Since the relationship among the variables is difficult to fully include in the first-stage traditional model, we develop a two-stage explainable AI solution for this localization problem. The two-stage solution is actually a comprehensive consideration of the relationship between variables. The solution can continue to use the constraints unused in the firststage during the second-stage, thereby improving the performance of the solution. Therefore, the two-stage solution has stronger robustness compared to the closed-form solution. Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters. However, the two-stage solution performs better than the traditional solution, especially with a small number of mobile transceivers and sensors or in the presence of high noise. Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关键词： explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution

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Predicting just-in-time software defects to reduce post-release quality costs in the maritime industry

Predicting just-in-time software defects to reduce post-rele...

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作者： Kang, Jonggu Ryu, Duksan Baik, Jongmoon School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Software Engineering Jeonbuk National University Jeonju-si Korea Republic of

Background: The importance of software in maritime transportation is rapidly increasing as the industry seeks to develop and utilize innovative future ships, which can be realized using software technology. Due to the safety-critical nature of ships, software quality assurance (SQA) has become an essential prerequisite for such development. Objective: Based on the unique characteristics of the maritime domain, the purpose of this study was to achieve effective SQA resource allocation to reduce post-release quality costs. Method: software defect prediction (SDP) is employed to predict defects in newly developed software based on models trained with past software defects and to update information using machine learning. This study demonstrated that just-in-time SDP is applicable to maritime domain practice and can reduce post-release quality costs via combination with an estimation model, qCOPLIMO. Results: Using real-world datasets collected from the maritime industry, performance and cost-benefit analyses of SDP were performed. A successful model was obtained that meets the performance criterion of 0.75 in within-project defect prediction (WPDP) but not cross-project defect prediction (CPDP). In addition, the cost-benefit analysis results showed that 20% effort enables the detection of 56% of defects on average and that the post-release quality cost can be reduced by 37.3% in the maritime domain. Conclusion: SDP can be successfully applied to the maritime domain. Further, it is desirable to utilize WPDP instead of CPDP once minimum high-quality commits are available that can be identified as defective or not. Finally, SDP can help reduce review effort and post-release quality costs. © 2020 John Wiley & Sons Ltd

关键词： Defects

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Welcome to the Eighth International Workshop on Crowd-Based Requirements engineering (CrowdRE'24)

Welcome to the Eighth International Workshop on Crowd-Based ...

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IEEE International Requirements engineering Conference Workshops (REW)

作者： Julian Frattini Gouri Ginde Eduard C. Groen Oliver Karras Norbert Seyff Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden Dept. of Electrical and Software Engineering University of Calgary Canada Fraunhofer IESE Kaiserslautern Germany Department of Information and Computing Sciences Utrecht University Netherlands TIB - Leibniz Information Centre for Science and Technology Hannover Germany University of Applied Sciences and Arts Northwestern Switzerland Windisch Switzerland Department of Informatics University of Zurich Zurich Switzerland

ISBN: (数字)9798350395518

ISBN: (纸本)9798350395525

Welcome to the 8th International Workshop on Crowd-Based Requirements engineering (CrowdRE'24), where scientists and representatives engage in interactive discussions to analyze the state-of-the-art Crowd-Based Requirements engineering (CrowdRE) and to inspire each other in ways to move forward together. The discipline of CrowdRE seeks to address the challenges of traditional requirements engineering (RE) in scaling up to settings with thousands to millions of users of (software) products or (software-driven) services, who form a large and heterogeneous group that can be denoted as a ‘crowd’ [1], [2]. The online user feedback generated by the crowd, such as texts or usage data, can be a valuable source of requirements, problems, wishes, and needs. Responding quickly, effectively, and iteratively to this feedback can greatly increase a product's success. CrowdRE comprises any approach that provides RE with suitable means for this crowd paradigm, especially by involving the crowd and by collecting, harmonizing, analyzing, and interpreting their feedback.

关键词： Conferences Logic gates software Requirements engineering

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