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Early-stage stroke prediction based on Parkinson and wrinkles using deep learning

Neural Computing and Applications 2024年第30期36卷 18781-18805页

作者： Haritha, T. Babu, A. V. Santhosh Department of Information Technology Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tamilnadu Tiruchengode India

The application of noninvasive methods to enhance healthcare systems has been facilitated by the development of new technology. Among the four major cardiovascular diseases, stroke is one of the deadliest and potentially fatal, but, if detected early enough, a patient's life may be spared. Most stroke research has centered on MRI and CT scans for uncomplicated categorization. This medical approach (imaging) is costly, time-consuming and needs the utilization of complex technology. To make up for these shortcomings, however, there has been a lot of interest in adopting noninvasive, measurable EEGs. Nevertheless, the raw data should be classified before the proper characteristics can be formed, both the forecasting algorithms and the analytical techniques demand time. As a result, this work proposes a deep learning-based model that aims to predict the chance of stroke at an early stage utilizing Parkinson's disease and wrinkles as markers. A patient may have a stroke disease if they are diagnosed with both Parkinson's disease and wrinkles. To the best of our knowledge, this research is the first to use these biomarkers to predict the risk of having a stroke. The proposed model achieves a higher accuracy of 94.7% on the considered dataset. Additionally, the recommended model was evaluated and tested in terms of loss, training time, accuracy, recall, and F1-score versus the other existing models. With less price and pain than present testing approaches, these discoveries are predicted to result in major enhancements in the early detection of strokes. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Forecasting

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Enhancing User Trust and Interpretability in AI-Driven Feature Request Detection for Mobile App Reviews: An Explainable Approach

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IEEE Access 2024年 12卷 114023-114045页

作者： Gambo, Ishaya Massenon, Rhodes Lin, Chia-Chen Ogundokun, Roseline Oluwaseun Agarwal, Saurabh Pak, Wooguil Obafemi Awolowo University Department of Computer Science and Engineering Ile-Ife220282 Nigeria National Chin-Yi University of Technology Department of Information Computer Science and Information Engineering Taichung411 Taiwan Kaunas University of Technology Department of Centre of Real Time Computer Sciences Kaunas44249 Lithuania Landmark University Department of Computer Science Omu-Aran251103 Nigeria Yeungnam University Department of Information and Communication Engineering Gyeongsan38541 Korea Republic of

Mobile app developers struggle to prioritize updates by identifying feature requests within user reviews. While machine learning models can assist, their complexity often hinders transparency and trust. This paper presents an explainable Artificial Intelligence (AI) approach that combines advanced explanation techniques with engaging visualizations to address this issue. Our system integrates a bidirectional Long Short-Term Memory (BiLSTM) model with attention mechanisms, enhanced by Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). We evaluate this approach on a diverse dataset of 150,000 app reviews, achieving an F1 score of 0.82 and 89% accuracy, significantly outperforming baseline Support Vector Machine (F1: 0.66) and Convolutional Neural Network (CNN) (F1: 0.72) models. Our empirical user studies with developers demonstrate that our explainable approach improves trust (27%) when explanations are provided and correct interpretation (73%). The system's interactive visualizations allowed developers to validate predictions, with over 80% overlap between model-highlighted phrases and human annotations for feature requests. These findings highlight the importance of integrating explainable AI into real-world software engineering workflows. The paper's results and future directions provide a promising approach for feature request detection in app reviews to create more transparent, trustworthy, and effective AI systems. © 2013 IEEE.

关键词： Mobile applications

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IaaSI: a device based interoperability as a service for IoMT devices

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14321-14332页

作者： Kotha, Aditya Manohar, Kota U, Venkanna Department of Computer Science and Engineering International Institute of Information Technology Naya Raipur India

Interoperability is a crucial aspect of the effective functioning of Internet of Things (IoT) devices, particularly in the healthcare industry. Although the use of IoT devices in healthcare has brought numerous benefits, such as remote sensing, monitoring, and data analysis, it has also introduced new challenges, notably in the area of interoperability. The lack of semantic and syntactic interoperability has hindered the ability of these devices to communicate and share data, leading to inefficiencies and limitations in their use. To address these challenges, this study proposes a solution that employs natural language processing (NLP) techniques to enhance the efficiency and effectiveness of healthcare IoT. Specifically, the solution utilizes Bidirectional Encoder Representations from Transformers (BERT) based string matching and Fuzzy Inference System (FIS) to facilitate data correlation with an existing vocabulary and a parser. The proposed approach was evaluated with real-world data from healthcare IoT devices, yielding an accuracy of 85.71% and an average processing delay of 0.46 s, thus demonstrating the potential of natural language processing techniques to enhance the interoperability of healthcare IoT devices. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Fuzzy logic

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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MultiSpectral diffusion: joint generation of wavelet coefficients for image synthesis and upsampling

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Multimedia Tools and Applications 2024年 1-22页

作者： Goudarzvand, Iman Eftekhari Moghadam, Amir Masoud Department of Computer and Information Technology Engineering Qazvin Branch Islamic Azad University Qazvin Iran

Diffusion models have become a prevalent framework in deep generative modeling across various modalities. However, despite producing high quality results, these models are computationally expensive and suffer from slow convergence. In this work, we address these challenges in image generation by leveraging the wavelet domain, which decomposes images into low and high-frequency components, each at half the resolution of the original image in both height and width. We observe that prioritizing the learning of low-frequency components over high-frequency details and masking out unnecessary high-frequency content in wavelet space can significantly enhance training convergence and reduce computational demands. This strategy simplifies the complexity associated with high-frequency details during training, allowing the model to capture the most representative features of the data distribution while maintaining a balance in detail preservation. To facilitate controlled learning across different wavelet coefficients, we employ a multitask loss function, with each task corresponding to the learning of a distinct wavelet subband. Additionally, to ensure consistency among wavelet coefficients, which is crucial for accurate reconstruction in pixel space, we introduce a multispectral cross-attention mechanism to aid the joint generation of different wavelet coefficients. The sampling process involves jointly generating wavelet coefficients, followed by an inverse wavelet transform to convert them back to pixel space. Our approach not only improves the training efficiency for unconditional image generation compared with the standard denoising diffusion probabilistic model (vanilla DDPM) but also uniquely supports the generation of high-frequency content conditioned on a low-resolution image, enabling both image generation and upsampling within a single model. To our knowledge, this capability is novel. Our model demonstrates superior performance in image generation compared with b

关键词： Inverse problems

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Trust and QoS-Driven Query Service Provisioning Using Optimization

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Intelligent Automation & Soft Computing 2023年第5期36卷 1827-1844页

作者： K.Narmatha K.Karthikeyan Department of Computer Science and Engineering SRM Institute of Engineering and TechnologyRamapuramChennaiIndia Department of Information Technology Coimbatore Institute of TechnologyCoimbatoreIndia

The growing advancements with the Internet of Things(IoT)devices handle an enormous amount of data collected from various applications like healthcare,vehicle-based communication,and smart *** research analyses cloud-based privacy preservation over the smart city based on query ***,there is a lack of resources to handle the incoming data and maintain them with higher privacy and ***,a solution based idea needs to be proposed to preserve the IoT data to set an innovative city environment.A querying service model is proposed to handle the incoming data collected from various environments as the data is not so trusted and highly sensitive towards *** handling privacy,other inter-connected metrics like efﬁciency are also essential,which must be considered to fulﬁl the privacy ***,this work provides a query-based service model and clusters the query to measure the relevance of frequently generated ***,a Bag of Query(BoQ)model is designed to collect the query from various *** is done with a descriptive service provisioning model to cluster the query and extract the query’s summary to get theﬁnal *** processed data is preserved over the cloud storage system and optimized using an improved Grey Wolf Opti-mizer(GWO).It is used to attain global and local solutions regarding privacy *** iterative data is evaluated without any over-ﬁtting issues and computational complexity due to the tremendous data handling *** on this analysis,metrics like privacy,efﬁciency,computational complexity,the error rate is *** simulation is done with a MATLAB 2020a *** proposed model gives a better trade-off in contrast to existing approaches.

关键词： Cloud computing IoT privacy security grey wolf optimization

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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2220-2235页

作者： Jiufang Chen Kechen Liu Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou IEEE the College of Computer Science and Technology Chongqing University of Posts and Telecommunications the Department of Computer Science Columbia University the College of Computer and Information Science Southwest University the Department of Electrical and Computer Engineering Faculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Center of Research Excellence in Renewable Energy and Power Systems the Department of Electrical and Computer EngineeringFaculty of Engineeringand also with K.A.CARE Energy Research and Innovation CenterKing Abdulaziz University the Department of Electrical and Computer Engineering New Jersey Institute of Technology

High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable ***, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational ***, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices.

关键词： Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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Forecasting Directional Movement of Stock Prices using Deep Learning

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Annals of Data Science 2023年第5期10卷 1361-1378页

作者： Chandola, Deeksha Mehta, Akshit Singh, Shikha Tikkiwal, Vinay Anand Agrawal, Himanshu Department of Electrical Engineering and Computer Science York University Toronto Canada Hebert Werthim College of Engineering University of Florida Gainesville Gainesville United States Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Noida India

Stock market’s volatile and complex nature makes it difficult to predict the market situation. Deep Learning is capable of simulating and analyzing complex patterns in unstructured data. Deep learning models have applications in image recognition, speech recognition, natural language processing (NLP), and many more. Its application in stock market prediction is gaining attention because of its capacity to handle large datasets and data mapping with accurate prediction. However, most methods ignore the impact of mass media on the company’s stock and investors’ behaviours. This work proposes a hybrid deep learning model combining Word2Vec and long short-term memory (LSTM) algorithms. The main objective is to design an intelligent tool to forecast the directional movement of stock market prices based on financial time series and news headlines as inputs. The binary predicted output obtained using the proposed model would aid investors in making better decisions. The effectiveness of the proposed model is assessed in terms of accuracy of the prediction of directional movement of stock prices of five companies from different sectors of operation. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Large dataset

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Exploring the Latest Applications of OpenAI and ChatGPT: An In-Depth Survey

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computer Modeling in engineering & Sciences 2024年第3期138卷 2061-2102页

作者： Hong Zhang Haijian Shao School of Electrical Information Engineering Jiangsu University of TechnologyChangzhou213001China Department of Electrical and Computer Engineering University of NevadaLas Vegas89154USA

OpenAI and ChatGPT, as state-of-the-art languagemodels driven by cutting-edge artificial intelligence technology,have gained widespread adoption across diverse industries. In the realm of computer vision, these models havebeen employed for intricate tasks including object recognition, image generation, and image processing, leveragingtheir advanced capabilities to fuel transformative breakthroughs. Within the gaming industry, they have foundutility in crafting virtual characters and generating plots and dialogues, thereby enabling immersive and interactiveplayer experiences. Furthermore, these models have been harnessed in the realm of medical diagnosis, providinginvaluable insights and support to healthcare professionals in the realmof disease detection. The principal objectiveof this paper is to offer a comprehensive overview of OpenAI, OpenAI Gym, ChatGPT, DALL E, stable diffusion,the pre-trained clip model, and other pertinent models in various domains, encompassing CLIP Text-to-Image,education, medical imaging, computer vision, social influence, natural language processing, software development,coding assistance, and Chatbot, among others. Particular emphasis will be placed on comparative analysis andexamination of popular text-to-image and text-to-video models under diverse stimuli, shedding light on thecurrent research landscape, emerging trends, and existing challenges within the domains of OpenAI and *** a rigorous literature review, this paper aims to deliver a professional and insightful overview of theadvancements, potentials, and limitations of these pioneering language models.

关键词： OpenAI ChatGPT DALL E stable diffusion OpenAI Gym text-to-image text-to-video

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