Big Data and Cognitive Computing

This research aims to study and assess state-of-the-art physics-informed neural networks (PINNs) from different researchers’ perspectives. The PRISMA framework was used for a systematic literature review, and 120 research articles from the computational sciences and engineering domain were specifically classified through a well-defined keyword search in Scopus and Web of Science databases. Through bibliometric analyses, we have identified journal sources with the most publications, authors with high citations, and countries with many publications on PINNs. Some newly improved techniques developed to enhance PINN performance and reduce high training costs and slowness, among other limitations, have been highlighted. Different approaches have been introduced to overcome the limitations of PINNs. In this review, we categorized the newly proposed PINN methods into Extended PINNs, Hybrid PINNs, and Minimized Loss techniques. Various potential future research directions are outlined based on the limitations of the proposed solutions.

Zaharaddeen Karami Lawal

Hayati Yassin

Daphne Teck Ching Lai

Azam Che Idris

BDCC

Physics-Informed Neural Network (PINN) Evolution and Beyond: A Systematic Literature Review and Bibliometric Analysis

物理信息神经网络（PINN）的演进与超越：系统文献综述与文献计量分析

Outlier detection is a statistical procedure that aims to find suspicious events or items that are different from the normal form of a dataset. It has drawn considerable interest in the field of data mining and machine learning. Outlier detection is important in many applications, including fraud detection in credit card transactions and network intrusion detection. There are two general types of outlier detection: global and local. Global outliers fall outside the normal range for an entire dataset, whereas local outliers may fall within the normal range for the entire dataset, but outside the normal range for the surrounding data points. This paper addresses local outlier detection. The best-known technique for local outlier detection is the Local Outlier Factor (LOF), a density-based technique. There are many LOF algorithms for a static data environment; however, these algorithms cannot be applied directly to data streams, which are an important type of big data. In general, local outlier detection algorithms for data streams are still deficient and better algorithms need to be developed that can effectively analyze the high velocity of data streams to detect local outliers. This paper presents a literature review of local outlier detection algorithms in static and stream environments, with an emphasis on LOF algorithms. It collects and categorizes existing local outlier detection algorithms and analyzes their characteristics. Furthermore, the paper discusses the advantages and limitations of those algorithms and proposes several promising directions for developing improved local outlier detection methods for data streams.

Omar Alghushairy

Raed Alsini

Terence Soule

Xiaogang Ma

A Review of Local Outlier Factor Algorithms for Outlier Detection in Big Data Streams

大数据流中局部离群因子算法用于异常检测的综述

Mining and analysis of the big data of Twitter conversations have been of significant interest to the scientific community in the fields of healthcare, epidemiology, big data, data science, computer science, and their related areas, as can be seen from several works in the last few years that focused on sentiment analysis and other forms of text analysis of tweets related to Ebola, E-Coli, Dengue, Human Papillomavirus (HPV), Middle East Respiratory Syndrome (MERS), Measles, Zika virus, H1N1, influenza-like illness, swine flu, flu, Cholera, Listeriosis, cancer, Liver Disease, Inflammatory Bowel Disease, kidney disease, lupus, Parkinson’s, Diphtheria, and West Nile virus. The recent outbreaks of COVID-19 and MPox have served as “catalysts” for Twitter usage related to seeking and sharing information, views, opinions, and sentiments involving both of these viruses. None of the prior works in this field analyzed tweets focusing on both COVID-19 and MPox simultaneously. To address this research gap, a total of 61,862 tweets that focused on MPox and COVID-19 simultaneously, posted between 7 May 2022 and 3 March 2023, were studied. The findings and contributions of this study are manifold. First, the results of sentiment analysis using the VADER (Valence Aware Dictionary for sEntiment Reasoning) approach shows that nearly half the tweets (46.88%) had a negative sentiment. It was followed by tweets that had a positive sentiment (31.97%) and tweets that had a neutral sentiment (21.14%), respectively. Second, this paper presents the top 50 hashtags used in these tweets. Third, it presents the top 100 most frequently used words in these tweets after performing tokenization, removal of stopwords, and word frequency analysis. The findings indicate that tweets in this context included a high level of interest regarding COVID-19, MPox and other viruses, President Biden, and Ukraine. Finally, a comprehensive comparative study that compares the contributions of this paper with 49 prior works in this field is presented to further uphold the relevance and novelty of this work.

Nirmalya Thakur

Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox

关于COVID-19和猴痘的Twitter公众话语的情感分析与文本分析

The way humans and artificially intelligent machines interact is undergoing a dramatic change. This change becomes particularly apparent in domains where humans and machines collaboratively work on joint tasks or objects in teams, such as in industrial assembly or disassembly processes. While there is intensive research work on human–machine collaboration in different research disciplines, systematic and interdisciplinary approaches towards engineering systems that consist of or comprise human–machine teams are still rare. In this paper, we review and analyze the state of the art, and derive and discuss core requirements and concepts by means of an illustrating scenario. In terms of methods, we focus on how reciprocal trust between humans and intelligent machines is defined, built, measured, and maintained from a systems engineering and planning perspective in literature. Based on our analysis, we propose and outline three important areas of future research on engineering and operating human–machine teams for trusted collaboration. For each area, we describe exemplary research opportunities.

Basel Alhaji

Janine Beecken

Rüdiger Ehlers

Jan Gertheiss

Felix Merz

Jörg P. Müller

Michael Prilla

Andreas Rausch

Andreas Reinhardt

Delphine Reinhardt

Christian Rembe

Niels-Ole Rohweder

Christoph Schwindt

Stephan Westphal

Jürgen Zimmermann

Engineering Human–Machine Teams for Trusted Collaboration

工程化人机团队以实现可信协作

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

Divya Garikapati

Sneha Sudhir Shetiya

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

自动驾驶汽车：人工智能的演变与当前行业格局

Recommender systems aim to predict the preferences of users and suggest items of interest to them in various domains. While traditional recommendation techniques consider users as individuals, some approaches aim to satisfy the needs of a group of people. Multi-agent systems can be used to develop such recommendations, where multiple intelligent agents interact with each other to achieve a common goal, i.e., deciding which item to recommend. Particularly, negotiation techniques can be used to find a decision that aims at maximizing the satisfaction of all group members. The proposed approach introduces a multi-agent recommender system for a group of users by considering their personality traits, relationships and social interactions during the negotiation process that leads to the generation of recommendations. While traditional recommendation techniques do not take into account the effects of personality traits and relationships between individuals, our approach demonstrates that personality traits, especially personality types in the context of conflict management, and social relationships can significantly impact on the group recommendation. The results indicate that the opinion of an individual can be influenced when she is part of a group that cooperates towards a shared goal. Overall, the proposed approach shows that recommender systems can benefit from considering that factors. This work contributes to understanding the impact of personality traits and social relationships on group recommendations and suggests potential directions for future research.

Ariel Monteserin

Daiana Elin Madsen

Daniela Godoy

Silvia Schiaffino

Integrating Social Relationships and Personality into MAS-Based Group Recommendations

将社会关系和个性整合到基于MAS的群体推荐中

Sentiment analysis via artificial intelligence, i.e., machine learning and large language models (LLMs), is a pivotal tool that classifies sentiments within texts as positive, negative, or neutral. It enables computers to automatically detect and interpret emotions from textual data, covering a spectrum of feelings without direct human intervention. Sentiment analysis is integral to marketing research, helping to gauge consumer emotions and opinions across various sectors. Its applications span analyzing movie reviews, monitoring social media, evaluating product feedback, assessing employee sentiments, and identifying hate speech. This study explores the application of both traditional machine learning and pre-trained LLMs for automated sentiment analysis of customer product reviews. The motivation behind this work lies in the demand for more nuanced understanding of consumer sentiments that can drive data-informed business decisions. In this research, we applied machine learning-based classifiers, i.e., Random Forest, Naive Bayes, and Support Vector Machine, alongside the GPT-4 model to benchmark their effectiveness for sentiment analysis. Traditional models show better results and efficiency in processing short, concise text, with SVM in classifying sentiment of short length comments. However, GPT-4 showed better results with more detailed texts, capturing subtle sentiments with higher precision, recall, and F1 scores to uniquely identify mixed sentiments not found in the simpler models. Conclusively, this study shows that LLMs outperform traditional models in context-rich sentiment analysis by not only providing accurate sentiment classification but also insightful explanations. These results enable LLMs to provide a superior tool for customer-centric businesses, which helps actionable insights to be derived from any textual data.

Pawanjit Singh Ghatora

Seyed Ebrahim Hosseini

Shahbaz Pervez

Muhammad Javed Iqbal

Nabil Shaukat

Sentiment Analysis of Product Reviews Using Machine Learning and Pre-Trained LLM

基于机器学习和预训练大语言模型的产品评论情感分析

Customer satisfaction is not just a significant factor but a cornerstone for smart cities and their organizations that offer services to people. It enhances the organization’s reputation and profitability and drastically raises the chances of returning customers. Unfortunately, customer support service through online chat is often not rated by customers to help improve the service. This study employs artificial intelligence and data augmentation to predict customer satisfaction ratings from conversations by analyzing the responses of customers and service providers. For the study, the authors obtained actual conversations between customers and real agents from the call center database of Jeddah Municipality that were rated by customers on a scale of 1–5. They trained and tested five prediction models with approaches based on logistic regression, random forest, and ensemble-based deep learning, and fine-tuned two pre-trained recent models: ArabicT5 and SaudiBERT. Then, they repeated training and testing models after applying a data augmentation technique using the generative artificial intelligence, GPT-4, to improve the unbalance in customer conversation data. The study found that the ensemble-based deep learning approach best predicts the five-, three-, and two-class classifications. Moreover, data augmentation improved accuracy using the ensemble-based deep learning model with a 1.69% increase and the logistic regression model with a 3.84% increase. This study contributes to the advancement of Arabic opinion mining, as it is the first to report the performance of determining customer satisfaction levels using Arabic conversation data. The implications of this study are significant, as the findings can be applied to improve customer service in various organizations.

Rihab Fahd Al-Mutawa

Arwa Yousuf Al-Aama

Arabic Opinion Classification of Customer Service Conversations Using Data Augmentation and Artificial Intelligence

使用数据增强和人工智能对客户服务对话进行阿拉伯语意见分类

Human core and skin temperature (Tcr and Tsk) are crucial indicators of human health and are commonly utilized in diagnosing various types of diseases. This study presents a deep learning model that combines a long-term series forecasting method with transfer learning techniques, capable of making precise, personalized predictions of Tcr and Tsk in high-temperature environments with only a small corpus of actual training data. To practically validate the model, field experiments were conducted in complex environments, and a thorough analysis of the effects of three diverse training strategies on the overall performance of the model was performed. The comparative analysis revealed that the optimized training method significantly improved prediction accuracy for forecasts extending up to 10 min into the future. Specifically, the approach of pretraining the model on in-distribution samples followed by fine-tuning markedly outperformed other methods in terms of prediction accuracy, with a prediction error for Tcr within ±0.14 °C and Tsk, mean within ±0.46 °C. This study provides a viable approach for the precise, real-time prediction of Tcr and Tsk, offering substantial support for advancing early warning research of human thermal health.

Xinge Han

Jiansong Wu

Zhuqiang Hu

Chuan Li

Boyang Sun

Forecasting Human Core and Skin Temperatures: A Long-Term Series Approach

预测人体核心和皮肤温度：一种长期序列方法

Eye-movement assessment is a key component of neurological evaluation, offering valuable insights into neural deficits and underlying mechanisms. This narrative review explores the emerging subject of digital eye-movement outcomes (DEMOs) and their potential as sensitive biomarkers for neurological impairment. Eye tracking has become a useful method for investigating visual system functioning, attentional processes, and cognitive mechanisms. Abnormalities in eye movements, such as altered saccadic patterns or impaired smooth pursuit, can act as important diagnostic indicators for various neurological conditions. The non-invasive nature, cost-effectiveness, and ease of implementation of modern eye-tracking systems makes it particularly attractive in both clinical and research settings. Advanced digital eye-tracking technologies and analytical methods enable precise quantification of eye-movement parameters, complementing subjective clinical evaluations with objective data. This review examines how DEMOs could contribute to the localisation and diagnosis of neural impairments, potentially serving as useful biomarkers. By comprehensively exploring the role of eye-movement assessment, this review aims to highlight the common eye-movement deficits seen in neurological injury and disease by using the examples of mild traumatic brain injury and Parkinson’s Disease. This review also aims to enhance the understanding of the potential use of DEMOs in diagnosis, monitoring, and management of neurological disorders, ultimately improving patient care and deepening our understanding of complex neurological processes. Furthermore, we consider the broader implications of this technology in unravelling the complexities of visual processing, attention mechanisms, and cognitive functions. This review summarises how DEMOs could reshape our understanding of brain health and allow for more targeted and effective neurological interventions.

Lisa Graham

Rodrigo Vitorio

Richard Walker

Gill Barry

Alan Godfrey

Rosie Morris

Samuel Stuart

Digital Eye-Movement Outcomes (DEMOs) as Biomarkers for Neurological Conditions: A Narrative Review

数字眼动结果（DEMOs）作为神经系统疾病的生物标志物：叙述性综述

Abbreviation	BDCC
Journal Impact	3.78
Quartiles(Global)	COMPUTER SCIENCE, THEORY & METHODS(Q1)

ISSN	2504-2289
h-index	33

Publication Information	Publisher: Multidisciplinary Digital Publishing Institute (MDPI)，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2017，Proportion of original research papers: 93.08%，Self Citation Rate:8.10%， Gold OA Rate: 100.00%
Journal Fees	$Details
Average review cycle	网友分享经验：11 Weeks

Big Data and Cognitive Computing

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience