Intelligent Systems with Applications最新文献_第9页

Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis 医疗和保健分析研究中机器学习应用的演变：文献计量分析

Intelligent Systems with Applications Pub Date : 2024-09-19 DOI: 10.1016/j.iswa.2024.200441

Samuel-Soma M. Ajibade , Gloria Nnadwa Alhassan , Abdelhamid Zaidi , Olukayode Ayodele Oki , Joseph Bamidele Awotunde , Emeka Ogbuju , Kayode A. Akintoye

{"title":"Evolution of machine learning applications in medical and healthcare analytics research: A bibliometric analysis","authors":"Samuel-Soma M. Ajibade , Gloria Nnadwa Alhassan , Abdelhamid Zaidi , Olukayode Ayodele Oki , Joseph Bamidele Awotunde , Emeka Ogbuju , Kayode A. Akintoye","doi":"10.1016/j.iswa.2024.200441","DOIUrl":"10.1016/j.iswa.2024.200441","url":null,"abstract":"<div><div>This bibliometric research explores the global evolution of machine learning applications in medical and healthcare research for 3 decades (1994 to 2023). The study applies data mining techniques to a comprehensive dataset of published articles related to machine learning applications in the medical and healthcare sectors. The data extraction process includes the retrieval of relevant information from the source sources such as journals, books, and conference proceedings. An analysis of the extracted data is then conducted to identify the trends in the machine learning applications in medical and healthcare research. The Results revealed the publications published and indexed in the Scopus and PubMed database over the last 30 years. Bibliometric Analysis revealed that funding played a more significant role in publication productivity compared to collaboration (co-authorships), particularly at the country level. Hotspots analysis revealed three core research themes on MLHC research hence demonstrating the importance of machine learning applications to medical and healthcare research. Further, the study showed that the MLHC research landscape has largely focused on ML applications to tackle various issues ranging from chronic medical challenges (e.g., cardiological diseases) to patient data security. The findings of this research may be useful to policy makers and practitioners in the medical and healthcare sectors and to global research endeavours in the field. Future studies could include addressing issues such as growing ethical considerations, integration, and practical applications in wearable technology, IoT, and smart healthcare systems.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200441"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of inventory management through computer vision and machine learning technologies 通过计算机视觉和机器学习技术优化库存管理

Intelligent Systems with Applications Pub Date : 2024-09-19 DOI: 10.1016/j.iswa.2024.200438

William Villegas-Ch , Alexandra Maldonado Navarro , Santiago Sanchez-Viteri

{"title":"Optimization of inventory management through computer vision and machine learning technologies","authors":"William Villegas-Ch , Alexandra Maldonado Navarro , Santiago Sanchez-Viteri","doi":"10.1016/j.iswa.2024.200438","DOIUrl":"10.1016/j.iswa.2024.200438","url":null,"abstract":"<div><p>This study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitations of traditional methods and pre-existing automated systems. The platform uses convolutional neural networks and open-source libraries such as TensorFlow and PyTorch to recognize and accurately classify products from images captured in real time. Practical implementation in a natural warehouse environment allowed the proposed platform to be compared with traditional systems, highlighting significant improvements, such as a 45% reduction in the time required for inventory counting and a 9% increase in inventory accuracy. Despite facing challenges such as staff resistance to change and technical limitations on image quality, these difficulties were overcome through effective change management strategies and algorithm improvements. The findings of this study identify the potential for computer vision technology to transform warehouse operations, offering a practical and adaptable solution for inventory management.</p></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200438"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001121/pdfft?md5=fddb74ba205cf2dbd45a73920fc45d01&pid=1-s2.0-S2667305324001121-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142272161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DeepInvesting: Stock market predictions with a sequence-oriented BiLSTM stacked model – A dataset case study of AMZN 深度投资：利用面向序列的 BiLSTM 叠加模型预测股市--AMZN 数据集案例研究

Intelligent Systems with Applications Pub Date : 2024-09-19 DOI: 10.1016/j.iswa.2024.200439

Ashkan Safari, Mohammad Ali Badamchizadeh

{"title":"DeepInvesting: Stock market predictions with a sequence-oriented BiLSTM stacked model – A dataset case study of AMZN","authors":"Ashkan Safari, Mohammad Ali Badamchizadeh","doi":"10.1016/j.iswa.2024.200439","DOIUrl":"10.1016/j.iswa.2024.200439","url":null,"abstract":"<div><div>Intelligent forecasters are now being considered in the stock market, providing essential insights and strategic guidance to investors and traders by presenting analytical tools and predictive models, thus enabling informed decision-making and mitigating financial risks in this dynamic market. The importance of intelligent analyzers in stock trading routines is considered in this work, where DeepInvesting, a multimodal deep learning model tailored for stock price prediction, is introduced. Employing a Sequence-Oriented, Long-Term Dependent (SoLTD) architecture featuring Bidirectional Long Short-Term Memory (BiLSTM) networks, DeepInvesting is applied to essential features of the Amazon Corp. (AMZN) market dataset, gathered from Yahoo Finance, including Closing, Opening, High, Low, Volume, and Adj Close prices. Exceptional performance in forecasting Closing, Opening, High, Low, and Adj Close prices is demonstrated, with minimal Mean Absolute Percentage Error (MAPE) and Root Mean Squared Percentage Error (RMSPE) scores, coupled with high R-squared (R<sup>2</sup>) values, manifesting a robust fit to the data, as well as computational complexity, and Rates Per Second (RPS) metrics in comparison to other models of KNN, LSTM, RNN, CNN, and ANN. Finally, challenges in the accurate prediction of trading volumes are identified, highlighting an area for future enhancement.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200439"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001133/pdfft?md5=7fc986df27640d36742f23b85b7b526b&pid=1-s2.0-S2667305324001133-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology 基于人工智能的智能电网用电预测模型--利用区块链技术迈向智慧城市的明智之举

Intelligent Systems with Applications Pub Date : 2024-09-18 DOI: 10.1016/j.iswa.2024.200440

Emran Aljarrah

{"title":"AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology","authors":"Emran Aljarrah","doi":"10.1016/j.iswa.2024.200440","DOIUrl":"10.1016/j.iswa.2024.200440","url":null,"abstract":"<div><div>A smart grid (SG) is the financial benefit of a complicated and smart power system that can keep up with rising demand. It has to do with saving energy and being environmentally friendly. Growing populations and new technologies have caused a big rise in energy use, causing big problems for the environment and energy security. It is essential and significant to use blockchain technology and artificial intelligence (AI) to solve problems with power control. Data can be collected using a smart city in a power-consumed smart grid data and pre-process using a Z-Score normalization technique. It can extract features using a Spatial-Temporal Correlation (STC) to assess smart grid power usage within the context of a smart city using large-scale, high-dimensional data. Ensuring data integrity, privacy, and trust among grid applicants, transmit the data securely and reliably to a centralized or distributed cloud platform utilizing blockchain technology—a secure transmission and storage using Distributed Authentication and Authorization (DAA) protocol. To achieve precise load forecasting, a short-term recurrent neural network with an improved sparrow search algorithm (LSTM-RNN-ISSA) is incorporated. The smart grid may then record the projected results. Communication can be done on a smart grid with the users; the Blockchain-Based Smart Energy Trading with Adaptive Volt-VAR Optimization (BSET-AVVO) algorithm can be used for effective communication—a quick balancing electrical load and supply via a task-oriented communication mechanism in real-time demand response. Finally, our proposed method performs successfully better than the existing approaches.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200440"},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001145/pdfft?md5=d544863ef72dcd52a4a7ed799b0b670e&pid=1-s2.0-S2667305324001145-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal fusion: A study on speech-text emotion recognition with the integration of deep learning 多模态融合：融合深度学习的语音文本情感识别研究

Intelligent Systems with Applications Pub Date : 2024-09-08 DOI: 10.1016/j.iswa.2024.200436

Yanan Shang, Tianqi Fu

{"title":"Multimodal fusion: A study on speech-text emotion recognition with the integration of deep learning","authors":"Yanan Shang, Tianqi Fu","doi":"10.1016/j.iswa.2024.200436","DOIUrl":"10.1016/j.iswa.2024.200436","url":null,"abstract":"<div><p>Recognition of various human emotions holds significant value in numerous real-world scenarios. This paper focuses on the multimodal fusion of speech and text for emotion recognition. A 39-dimensional Mel-frequency cepstral coefficient (MFCC) was used as a feature for speech emotion. A 300-dimensional word vector obtained through the Glove algorithm was used as the feature for text emotion. The bidirectional gate recurrent unit (BiGRU) method in deep learning was added for extracting deep features. Subsequently, it was combined with the multi-head self-attention (MHA) mechanism and the improved sparrow search algorithm (ISSA) to obtain the ISSA-BiGRU-MHA method for emotion recognition. It was validated on the IEMOCAP and MELD datasets. It was found that MFCC and Glove word vectors exhibited superior recognition effects as features. Comparisons with the support vector machine and convolutional neural network methods revealed that the ISSA-BiGRU-MHA method demonstrated the highest weighted accuracy and unweighted accuracy. Multimodal fusion achieved weighted accuracies of 76.52 %, 71.84 %, 66.72 %, and 62.12 % on the IEMOCAP, MELD, MOSI, and MOSEI datasets, suggesting better performance than unimodal fusion. These results affirm the reliability of the multimodal fusion recognition method, showing its practical applicability.</p></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200436"},"PeriodicalIF":0.0,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001108/pdfft?md5=f20cf6e918be5af339bd33d538eaa064&pid=1-s2.0-S2667305324001108-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial intelligence and recommender systems in e-commerce. Trends and research agenda 电子商务中的人工智能和推荐系统。趋势和研究议程

Intelligent Systems with Applications Pub Date : 2024-09-06 DOI: 10.1016/j.iswa.2024.200435

Alejandro Valencia-Arias , Hernán Uribe-Bedoya , Juan David González-Ruiz , Gustavo Sánchez Santos , Edgard Chapoñan Ramírez , Ezequiel Martínez Rojas

{"title":"Artificial intelligence and recommender systems in e-commerce. Trends and research agenda","authors":"Alejandro Valencia-Arias , Hernán Uribe-Bedoya , Juan David González-Ruiz , Gustavo Sánchez Santos , Edgard Chapoñan Ramírez , Ezequiel Martínez Rojas","doi":"10.1016/j.iswa.2024.200435","DOIUrl":"10.1016/j.iswa.2024.200435","url":null,"abstract":"<div><p>Combining recommendation systems and AI in e-commerce can improve the user experience and decision-making. This study uses a method called bibliometrics to look at how these systems and artificial intelligence are changing. Of the 120 documents, 91 were analyzed. This shows a growth of 97.16% in the topic. The most influential authors were Paraschakis and Nilsson, with three publications and 43 citations. The magazine Electronic Commerce Research has four publications and 60 citations. China is the top country for citations, with 120, followed by India with 25 publications. The results show that research increased in 2021 and 2022. This shows a shift towards sentiment analysis and convolutional neural networks. The identification of new keywords, such as content-based image retrieval and knowledge graph, shows promising areas for future research. This study provides a solid foundation for future research in e-commerce recommender systems.</p></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200435"},"PeriodicalIF":0.0,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001091/pdfft?md5=7afc73524589c1244a248a6969677221&pid=1-s2.0-S2667305324001091-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Natural disasters detection using explainable deep learning 利用可解释深度学习检测自然灾害

Intelligent Systems with Applications Pub Date : 2024-09-01 DOI: 10.1016/j.iswa.2024.200430

Ahmad M. Mustafa, Rand Agha, Lujain Ghazalat, Tariq Sha'ban

{"title":"Natural disasters detection using explainable deep learning","authors":"Ahmad M. Mustafa, Rand Agha, Lujain Ghazalat, Tariq Sha'ban","doi":"10.1016/j.iswa.2024.200430","DOIUrl":"10.1016/j.iswa.2024.200430","url":null,"abstract":"<div><p>Deep learning applications have far-reaching implications in people’s daily lives. Disaster management professionals are becoming increasingly interested in applying deep learning to prepare for and respond to natural disasters. In this paper, we aim to assist natural disaster management professionals in preparing for disasters by developing a framework that can accurately classify natural disasters and interpret the results using a combination of a deep learning model and an XAI method to ensure reliability and ease of interpretation without a technical background. Two main aspects categorize the novelty of our work. The first is utilizing pre-trained Models such as VGGNet19, ResNet50, and ViT for accurate classification of natural disaster images. The second is implementing three explainable AI techniques-Gradient-weighted Class Activation Mapping (Grad-CAM), Grad CAM++, and Local Interpretable Model-agnostic Explanations (LIME) to ensure the interpretability of the model’s predictions, making the decision-making process transparent and reliable. Experiments on the Natural disaster datasets (Niloy et al. 2021) and MEDIC with a ViT-B-32 model achieved a high accuracy of 95.23%. Additionally, explainable artificial intelligence techniques such as LIME, Grad-CAM, and Grad-CAM++ are used to evaluate model performance and visualize decision-making. Our code is available at.<span><span><sup>1</sup></span></span></p></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"23 ","pages":"Article 200430"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667305324001042/pdfft?md5=289fa5e7afac4b6fe86ff07bc28dfb3a&pid=1-s2.0-S2667305324001042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning model for estimating body weight of live pacific white shrimp in a clay pond shrimp aquaculture 用于估算泥塘对虾养殖中活太平洋南美白对虾体重的深度学习模型

Intelligent Systems with Applications Pub Date : 2024-09-01 DOI: 10.1016/j.iswa.2024.200434

Nitthita Chirdchoo , Suvimol Mukviboonchai , Weerasak Cheunta

{"title":"A deep learning model for estimating body weight of live pacific white shrimp in a clay pond shrimp aquaculture","authors":"Nitthita Chirdchoo , Suvimol Mukviboonchai , Weerasak Cheunta","doi":"10.1016/j.iswa.2024.200434","DOIUrl":"10.1016/j.iswa.2024.200434","url":null,"abstract":"<div><p>This paper presents a novel approach to address the essential challenge of accurately determining the total weight of shrimp within aquaculture ponds. Precise weight estimation is crucial in mitigating issues of overfeeding and underfeeding, thus enhancing efficiency and productivity in shrimp farming. The proposed system leverages image processing techniques to detect individual live shrimp and extract pertinent features for weight estimation within a clay pond environment. Specifically, an automated feed tray captures images of live shrimp, which are then processed using a combination of Detectron2, PyTorch, and CUDA (Compute Unified Device Architecture) for individual shrimp detection. Essential features such as area, perimeter, width, length, and posture are extracted through image analysis, enabling accurate estimation of shrimp weight. An Artificial Neural Network (ANN) model, utilizing these features, accurately predicts shrimp weight with a coefficient of determination (R<sup>2</sup>) of 94.50% when incorporating all extracted features. Furthermore, our system integrates a user-friendly web application that empowers farmers to monitor shrimp weight trends, facilitating precision feeding strategies and effective farm management. This study contributes a low-cost solution using a deep learning model to estimate the weight of live Pacific white shrimp in clay ponds, enabling daily weight calculations, helping farmers optimize feed quantities, providing shrimp size distribution insights, and reducing the Feed Conversion Ratio (FCR) for greater profitability. The procedure for shrimp feature extraction is also provided, including the calculation of shrimp length and width, as well as shrimp posture classification.</p></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"24 ","pages":"Article 200434"},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266730532400108X/pdfft?md5=11c3ca2d32627550f621452400e08cf3&pid=1-s2.0-S266730532400108X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable artificial intelligence for investigating the effect of lifestyle factors on obesity 用于研究生活方式因素对肥胖症影响的可解释人工智能

Intelligent Systems with Applications Pub Date : 2024-09-01 DOI: 10.1016/j.iswa.2024.200427

Tarek Khater , Hissam Tawfik , Balbir Singh

引用次数: 0

Editorial Note 编辑说明

Intelligent Systems with Applications Pub Date : 2024-09-01 DOI: 10.1016/j.iswa.2024.200418

引用次数: 0