Big Data and Cognitive Computing最新文献_第5页

Crafting a Museum Guide Using ChatGPT4 使用ChatGPT4制作博物馆指南

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-09-04 DOI: 10.3390/bdcc7030148

Georgios Trichopoulos, M. Konstantakis, G. Caridakis, A. Katifori, Myrto Koukouli

引用次数: 0

Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review 制药和医学领域的创新机器人技术和人工智能：为医疗保健的未来铺平道路——综述

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-30 DOI: 10.3390/bdcc7030147

M. Stasevych, V. Zvarych

{"title":"Innovative Robotic Technologies and Artificial Intelligence in Pharmacy and Medicine: Paving the Way for the Future of Health Care—A Review","authors":"M. Stasevych, V. Zvarych","doi":"10.3390/bdcc7030147","DOIUrl":"https://doi.org/10.3390/bdcc7030147","url":null,"abstract":"The future of innovative robotic technologies and artificial intelligence (AI) in pharmacy and medicine is promising, with the potential to revolutionize various aspects of health care. These advances aim to increase efficiency, improve patient outcomes, and reduce costs while addressing pressing challenges such as personalized medicine and the need for more effective therapies. This review examines the major advances in robotics and AI in the pharmaceutical and medical fields, analyzing the advantages, obstacles, and potential implications for future health care. In addition, prominent organizations and research institutions leading the way in these technological advancements are highlighted, showcasing their pioneering efforts in creating and utilizing state-of-the-art robotic solutions in pharmacy and medicine. By thoroughly analyzing the current state of robotic technologies in health care and exploring the possibilities for further progress, this work aims to provide readers with a comprehensive understanding of the transformative power of robotics and AI in the evolution of the healthcare sector. Striking a balance between embracing technology and preserving the human touch, investing in R&D, and establishing regulatory frameworks within ethical guidelines will shape a future for robotics and AI systems. The future of pharmacy and medicine is in the seamless integration of robotics and AI systems to benefit patients and healthcare providers.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46567026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Enhancing Speech Emotions Recognition Using Multivariate Functional Data Analysis 利用多元函数数据分析增强语音情感识别

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-25 DOI: 10.3390/bdcc7030146

Matthieu Saumard

{"title":"Enhancing Speech Emotions Recognition Using Multivariate Functional Data Analysis","authors":"Matthieu Saumard","doi":"10.3390/bdcc7030146","DOIUrl":"https://doi.org/10.3390/bdcc7030146","url":null,"abstract":"Speech Emotions Recognition (SER) has gained significant attention in the fields of human–computer interaction and speech processing. In this article, we present a novel approach to improve SER performance by interpreting the Mel Frequency Cepstral Coefficients (MFCC) as a multivariate functional data object, which accelerates learning while maintaining high accuracy. To treat MFCCs as functional data, we preprocess them as images and apply resizing techniques. By representing MFCCs as functional data, we leverage the temporal dynamics of speech, capturing essential emotional cues more effectively. Consequently, this enhancement significantly contributes to the learning process of SER methods without compromising performance. Subsequently, we employ a supervised learning model, specifically a functional Support Vector Machine (SVM), directly on the MFCC represented as functional data. This enables the utilization of the full functional information, allowing for more accurate emotion recognition. The proposed approach is rigorously evaluated on two distinct databases, EMO-DB and IEMOCAP, serving as benchmarks for SER evaluation. Our method demonstrates competitive results in terms of accuracy, showcasing its effectiveness in emotion recognition. Furthermore, our approach significantly reduces the learning time, making it computationally efficient and practical for real-world applications. In conclusion, our novel approach of treating MFCCs as multivariate functional data objects exhibits superior performance in SER tasks, delivering both improved accuracy and substantial time savings during the learning process. This advancement holds great potential for enhancing human–computer interaction and enabling more sophisticated emotion-aware applications.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48425737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Applied Digital Twin Concepts Contributing to Heat Transition in Building, Campus, Neighborhood, and Urban Scale 应用数字孪生概念促进建筑、校园、社区和城市规模的热传导

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-25 DOI: 10.3390/bdcc7030145

Ekaterina Lesnyak, Tabea Belkot, Johannes Hurka, Jan Philipp Hörding, L. Kuhlmann, Pavel Paulau, Marvin Schnabel, P. Schönfeldt, Jan Middelberg

{"title":"Applied Digital Twin Concepts Contributing to Heat Transition in Building, Campus, Neighborhood, and Urban Scale","authors":"Ekaterina Lesnyak, Tabea Belkot, Johannes Hurka, Jan Philipp Hörding, L. Kuhlmann, Pavel Paulau, Marvin Schnabel, P. Schönfeldt, Jan Middelberg","doi":"10.3390/bdcc7030145","DOIUrl":"https://doi.org/10.3390/bdcc7030145","url":null,"abstract":"The heat transition is a central pillar of the energy transition, aiming to decarbonize and improve the energy efficiency of the heat supply in both the private and industrial sectors. On the one hand, this is achieved by substituting fossil fuels with renewable energy. On the other hand, it involves reducing overall heat consumption and associated transmission and ventilation losses. In addition to refurbishment, digitalization contributes significantly. Despite substantial research on Digital Twins (DTs) for heat transition at different scales, a cross-scale perspective on heat optimization still needs to be developed. In response to this research gap, the present study examines four instances of applied DTs across various scales: building, campus, neighborhood, and urban. The study compares their objectives and conceptual frameworks while also identifying common challenges and potential synergies. The study’s findings indicate that all DT scales face similar data-related challenges, such as gathering, ownership, connectivity, and reliability. Also, hierarchical synergy is identified among the DTs, implying the need for collaboration and exchange. In response to this, the “Wärmewende” data platform, whose objectives and concepts are presented in the paper, promotes research data and knowledge exchange with internal and external stakeholders.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45613028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model 增强慢性肾脏疾病的早期检测:一个鲁棒的机器学习模型

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-16 DOI: 10.3390/bdcc7030144

M. Arif, A. Mukheimer, Daniyal Asif

{"title":"Enhancing the Early Detection of Chronic Kidney Disease: A Robust Machine Learning Model","authors":"M. Arif, A. Mukheimer, Daniyal Asif","doi":"10.3390/bdcc7030144","DOIUrl":"https://doi.org/10.3390/bdcc7030144","url":null,"abstract":"Clinical decision-making in chronic disorder prognosis is often hampered by high variance, leading to uncertainty and negative outcomes, especially in cases such as chronic kidney disease (CKD). Machine learning (ML) techniques have emerged as valuable tools for reducing randomness and enhancing clinical decision-making. However, conventional methods for CKD detection often lack accuracy due to their reliance on limited sets of biological attributes. This research proposes a novel ML model for predicting CKD, incorporating various preprocessing steps, feature selection, a hyperparameter optimization technique, and ML algorithms. To address challenges in medical datasets, we employ iterative imputation for missing values and a novel sequential approach for data scaling, combining robust scaling, z-standardization, and min-max scaling. Feature selection is performed using the Boruta algorithm, and the model is developed using ML algorithms. The proposed model was validated on the UCI CKD dataset, achieving outstanding performance with 100% accuracy. Our approach, combining innovative preprocessing steps, the Boruta feature selection, and the k-nearest neighbors algorithm, along with a hyperparameter optimization using grid-search cross-validation (CV), demonstrates its effectiveness in enhancing the early detection of CKD. This research highlights the potential of ML techniques in improving clinical support systems and reducing the impact of uncertainty in chronic disorder prognosis.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48683149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Ransomware Detection Using Machine Learning: A Survey 利用机器学习进行勒索软件检测：综述

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-16 DOI: 10.3390/bdcc7030143

Amjad Alraizza, Abdulmohsen Algarni

{"title":"Ransomware Detection Using Machine Learning: A Survey","authors":"Amjad Alraizza, Abdulmohsen Algarni","doi":"10.3390/bdcc7030143","DOIUrl":"https://doi.org/10.3390/bdcc7030143","url":null,"abstract":"Ransomware attacks pose significant security threats to personal and corporate data and information. The owners of computer-based resources suffer from verification and privacy violations, monetary losses, and reputational damage due to successful ransomware assaults. As a result, it is critical to accurately and swiftly identify ransomware. Numerous methods have been proposed for identifying ransomware, each with its own advantages and disadvantages. The main objective of this research is to discuss current trends in and potential future debates on automated ransomware detection. This document includes an overview of ransomware, a timeline of assaults, and details on their background. It also provides comprehensive research on existing methods for identifying, avoiding, minimizing, and recovering from ransomware attacks. An analysis of studies between 2017 and 2022 is another advantage of this research. This provides readers with up-to-date knowledge of the most recent developments in ransomware detection and highlights advancements in methods for combating ransomware attacks. In conclusion, this research highlights unanswered concerns and potential research challenges in ransomware detection.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43798277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Breast Cancer Classification Using Concatenated Triple Convolutional Neural Networks Model 基于级联三重卷积神经网络模型的癌症乳腺分类

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-16 DOI: 10.3390/bdcc7030142

M. Alshayeji, Jassim Al-Buloushi

{"title":"Breast Cancer Classification Using Concatenated Triple Convolutional Neural Networks Model","authors":"M. Alshayeji, Jassim Al-Buloushi","doi":"10.3390/bdcc7030142","DOIUrl":"https://doi.org/10.3390/bdcc7030142","url":null,"abstract":"Improved disease prediction accuracy and reliability are the main concerns in the development of models for the medical field. This study examined methods for increasing classification accuracy and proposed a precise and reliable framework for categorizing breast cancers using mammography scans. Concatenated Convolutional Neural Networks (CNN) were developed based on three models: Two by transfer learning and one entirely from scratch. Misclassification of lesions from mammography images can also be reduced using this approach. Bayesian optimization performs hyperparameter tuning of the layers, and data augmentation will refine the model by using more training samples. Analysis of the model’s accuracy revealed that it can accurately predict disease with 97.26% accuracy in binary cases and 99.13% accuracy in multi-classification cases. These findings are in contrast with recent studies on the same issue using the same dataset and demonstrated a 16% increase in multi-classification accuracy. In addition, an accuracy improvement of 6.4% was achieved after hyperparameter modification and augmentation. Thus, the model tested in this study was deemed superior to those presented in the extant literature. Hence, the concatenation of three different CNNs from scratch and transfer learning allows the extraction of distinct and significant features without leaving them out, enabling the model to make exact diagnoses.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42639297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD) 基于作者的圣训分类数据集(ABCD)

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-14 DOI: 10.3390/bdcc7030141

A. Ramzy, Marwan Torki, Mohamed Abdeen, O. Saif, Mustafa ElNainay, AbdAllah Alshanqiti, E. Nabil

{"title":"Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD)","authors":"A. Ramzy, Marwan Torki, Mohamed Abdeen, O. Saif, Mustafa ElNainay, AbdAllah Alshanqiti, E. Nabil","doi":"10.3390/bdcc7030141","DOIUrl":"https://doi.org/10.3390/bdcc7030141","url":null,"abstract":"Religious studies are a rich land for Natural Language Processing (NLP). The reason is that all religions have their instructions as written texts. In this paper, we apply NLP to Islamic Hadiths, which are the written traditions, sayings, actions, approvals, and discussions of the Prophet Muhammad, his companions, or his followers. A Hadith is composed of two parts: the chain of narrators (Sanad) and the content of the Hadith (Matn). A Hadith is transmitted from its author to a Hadith book author using a chain of narrators. The problem we solve focuses on the classification of Hadiths based on their origin of narration. This is important for several reasons. First, it helps determine the authenticity and reliability of the Hadiths. Second, it helps trace the chain of narration and identify the narrators involved in transmitting Hadiths. Finally, it helps understand the historical and cultural contexts in which Hadiths were transmitted, and the different levels of authority attributed to the narrators. To the best of our knowledge, and based on our literature review, this problem is not solved before using machine/deep learning approaches. To solve this classification problem, we created a novel Author-Based Hadith Classification Dataset (ABCD) collected from classical Hadiths’ books. The ABCD size is 29 K Hadiths and it contains unique 18 K narrators, with all their information. We applied machine learning (ML), and deep learning (DL) approaches. ML was applied on Sanad and Matn separately; then, we did the same with DL. The results revealed that ML performs better than DL using the Matn input data, with a 77% F1-score. DL performed better than ML using the Sanad input data, with a 92% F1-score. We used precision and recall alongside the F1-score; details of the results are explained at the end of the paper. We claim that the ABCD and the reported results will motivate the community to work in this new area. Our dataset and results will represent a baseline for further research on the same problem.","PeriodicalId":36397,"journal":{"name":"Big Data and Cognitive Computing","volume":" ","pages":""},"PeriodicalIF":3.7,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47848493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Bat Algorithm for Web Service Selection with QoS Uncertainty 具有QoS不确定性的Web服务选择智能Bat算法

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-10 DOI: 10.3390/bdcc7030140

Abdelhak Etchiali, Fethallah Hadjila, Amina Bekkouche

引用次数: 1

Executable Digital Process Twins: Towards the Enhancement of Process-Driven Systems 可执行数字过程双胞胎:迈向过程驱动系统的增强

IF 3.7

Big Data and Cognitive Computing Pub Date : 2023-08-08 DOI: 10.3390/bdcc7030139

F. Corradini, Sara Pettinari, B. Re, Lorenzo Rossi, F. Tiezzi

引用次数: 0