Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Sports Prediction Model through Cloud Computing and Big Data Based on Artificial Intelligence Method

This article delves into the intricate relationship between big data, cloud computing, and artificial intelligence, shedding light on their fundamental attributes and interdependence. It explores the seamless amalgamation of AI methodologies within cloud computing and big data analytics, encompassing the development of a cloud computing framework built on the robust foundation of the Hadoop platform, enriched by AI learning algorithms. Additionally, it examines the creation of a predictive model empowered by tailored artificial intelligence techniques. Rigorous simulations are conducted to extract valuable insights, facilitating method evaluation and performance assessment, all within the dynamic Hadoop environment, thereby reaffirming the precision of the proposed approach. The results and analysis section reveals compelling findings derived from comprehensive simulations within the Hadoop environment. These outcomes demonstrate the efficacy of the Sport AI Model (SAIM) framework in enhancing the accuracy of sports-related outcome predictions. Through meticulous mathematical analyses and performance assessments, integrating AI with big data emerges as a powerful tool for optimizing decision-making in sports. The discussion section extends the implications of these results, highlighting the potential for SAIM to revolutionize sports forecasting, strategic planning, and performance optimization for players and coaches. The combination of big data, cloud computing, and AI offers a promising avenue for future advancements in sports analytics. This research underscores the synergy between these technologies and paves the way for innovative approaches to sports-related decision-making and performance enhancement.

Big Data 4.0: The Era of Big Intelligence

Big data has had significant impacts on our lives,economies,academia and industries over the past decade.The current equations are:What is the future of big data?What era do we live in?This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta(big data)to big intelligence.More specifically,this article will analyze big data from an evolutionary perspective.The article overviews data,information,knowledge,and intelligence(DIKI)and reveals their relationships.After analyzing meta as an operation,this article explores Meta(DIKE)and its relationship.It reveals 5 Bigs consisting of big data,big information,big knowledge,big intelligence and big analytics.Applying meta on 5 Bigs,this article infers that 4 Big Data 4.0=meta(big data)=big intelligence.This article analyzes how intelligent big analytics support big intelligence.The proposed approach in this research might facilitate the research and development of big data,big data analytics,business intelligence,artificial intelligence,and data science.

Systematic Survey on Big Data Analytics and Artificial Intelligence for COVID-19 Containment

Artificial Intelligence(AI)has gained popularity for the containment of COVID-19 pandemic applications.Several AI techniques provide efficient mechanisms for handling pandemic situations.AI methods,protocols,data sets,and various validation mechanisms empower the users towards proper decision-making and procedures to handle the situation.Despite so many tools,there still exist conditions in which AI must go a long way.To increase the adaptability and potential of these techniques,a combination of AI and Bigdata is currently gaining popularity.This paper surveys and analyzes the methods within the various computational paradigms used by different researchers and national governments,such as China and South Korea,to fight against this pandemic.The process of vaccine development requires multiple medical experiments.This process requires analyzing datasets from different parts of the world.Deep learning and the Internet of Things(IoT)revolutionized the field of disease diagnosis and disease prediction.The accurate observations from different datasets across the world empowered the process of drug development and drug repurposing.To overcome the issues generated by the pandemic,using such sophisticated computing paradigms such as AI,Machine Learning(ML),deep learning,Robotics and Bigdata is essential.

Key issues and progress of industrial big data-based intelligent blast furnace ironmaking technology

Blast furnace (BF) ironmaking is the most typical “black box” process, and its complexity and uncertainty bring forth great challenges for furnace condition judgment and BF operation. Rich data resources for BF ironmaking are available, and the rapid development of data science and intelligent technology will provide an effective means to solve the uncertainty problem in the BF ironmaking process. This work focused on the application of artificial intelligence technology in BF ironmaking. The current intelligent BF ironmaking technology was summarized and analyzed from five aspects. These aspects include BF data management, the analyses of time delay and correlation, the prediction of BF key variables, the evaluation of BF status, and the multi-objective intelligent optimization of BF operations. Solutions and suggestions were offered for the problems in the current progress, and some outlooks for future prospects and technological breakthroughs were added. To effectively improve the BF data quality, we comprehensively considered the data problems and the characteristics of algorithms and selected the data processing method scientifically. For analyzing important BF characteristics, the effect of the delay was eliminated to ensure an accurate logical relationship between the BF parameters and economic indicators. As for BF parameter prediction and BF status evaluation,a BF intelligence model that integrates data information and process mechanism was built to effectively achieve the accurate prediction of BF key indexes and the scientific evaluation of BF status. During the optimization of BF parameters, low risk, low cost, and high return were used as the optimization criteria, and while pursuing the optimization effect, the feasibility and site operation cost were considered comprehensively.This work will help increase the process operator’s overall awareness and understanding of intelligent BF technology. Additionally, combining big data technology with the process will improve the practicality of data models in actual production and promote the application of intelligent technology in BF ironmaking.

“Deep-time Digital Basin” Based on Big Data and Artificial Intelligence

1 Introduction Information technology has been playing an ever-increasing role in geoscience.Sphisicated database platforms are essential for geological data storage,analysis and exchange of Big Data(Feblowitz,2013;Zhang et al.,2016;Teng et al.,2016;Tian and Li,2018).The United States has built an information-sharing platform for state-owned scientific data as a national strategy.

Urban Big Data and the Development of City Intelligence

This study provides a definition for urban big data while exploring its features and applications of Chi- na＇s city intelligence. The differences between city intelligence in China and the ＂smart city＂ concept in other countries are compared to highlight and contrast the unique definition and model for China＇s city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intel- ligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China＇s urban big data. Considering the supporting and core role that urban big data plays in city intel- ligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China＇s urban de- velopment. In conclusion, it is imperative that China make full use of its unique advantages-including using the nation＇s current state of development and resources, geographical advantages, and good hu- man relations-in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.

Artificial Intelligence Self-Organising (AI-SON) Frameworks for 5G-Enabled Networks: A Review

The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Smart Society and Artificial Intelligence:Big Data Scheduling and the Global Standard Method Applied to Smart Maintenance

The implementation of artificial intelligence(AI)in a smart society,in which the analysis of human habits is mandatory,requires automated data scheduling and analysis using smart applications,a smart infrastructure,smart systems,and a smart network.In this context,which is characterized by a large gap between training and operative processes,a dedicated method is required to manage and extract the massive amount of data and the related information mining.The method presented in this work aims to reduce this gap with near-zero-failure advanced diagnostics(AD)for smart management,which is exploitable in any context of Society 5.0,thus reducing the risk factors at all management levels and ensuring quality and sustainability.We have also developed innovative applications for a humancentered management system to support scheduling in the maintenance of operative processes,for reducing training costs,for improving production yield,and for creating a human–machine cyberspace for smart infrastructure design.The results obtained in 12 international companies demonstrate a possible global standardization of operative processes,leading to the design of a near-zero-failure intelligent system that is able to learn and upgrade itself.Our new method provides guidance for selecting the new generation of intelligent manufacturing and smart systems in order to optimize human–machine interactions,with the related smart maintenance and education.

Data,Analytics,and Intelligence

We are living in an age of big data,analytics,and artificial intelligence(AI).After reviewing a dozen different books on big data,data analytics,data science,AI,and business intelligence(BI),there are the current questions:(1)What are the relationships between data,analytics,and intelligence?(2)What are the relationships between big data and big data analytics?(3)What is the relationship between BI and data analytics?This article first discusses the heuristics of the Greek philosopher Plato and French mathematician Descartes and how to reshape the world.Then it addresses the above questions based on a Boolean structure,which destructs big data,data analytics,data science,and AI into data,analytics,and intelligence as the Boolean atoms.Data,analytics,and intelligence are reorganized and reassembled,based on the Boolean structure,to data analytics,analytics intelligence,data intelligence,and data analytics intelligence.The research will analyse each of them after examining the system intelligence.The proposed approach in this research might facilitate the research and development of big data,data analytics,AI,and data science.

An intelligent prediction model of epidemic characters based on multi-feature

The epidemic characters of Omicron(e.g.large-scale transmission)are significantly different from the initial variants of COVID-19.The data generated by large-scale transmission is important to predict the trend of epidemic characters.However,the re-sults of current prediction models are inaccurate since they are not closely combined with the actual situation of Omicron transmission.In consequence,these inaccurate results have negative impacts on the process of the manufacturing and the service industry,for example,the production of masks and the recovery of the tourism industry.The authors have studied the epidemic characters in two ways,that is,investigation and prediction.First,a large amount of data is collected by utilising the Baidu index and conduct questionnaire survey concerning epidemic characters.Second,theβ-SEIDR model is established,where the population is classified as Susceptible,Exposed,Infected,Dead andβ-Recovered persons,to intelligently predict the epidemic characters of COVID-19.Note thatβ-Recovered persons denote that the Recovered persons may become Sus-ceptible persons with probabilityβ.The simulation results show that the model can accurately predict the epidemic characters.

Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

From Big Data to Intelligent Tutoring: Exploring the Application of Big Data Technology in the Ideological and Political Education for College Counselors

This paper explores the application of big data technology in ideological and political education for college counselors.Big data-driven personalized counseling for students,social network analysis,and public opinion monitoring can optimize the efficiency of counselor work.However,there are still challenges concerning data privacy protection,technical support,and training.By scientifically evaluating and addressing these challenges,big data technology is expected to enhance the effectiveness of college counselors and improve the quality of ideological and political education.In the future,with technological advancements,big data technology will become an essential tool for higher education management.

Artificial Intelligence and the Future of Education： Big Promises -Bigger Challenges

The history of educational technology in the last 50 years contains few instances of dramatic improvements in learning based on the adoption of a particular technology.An example involving artificial intelligence occurred in the 1990s with the development of intelligent tutoring systems( ITSs). What happened with ITSs was that their success was limited to well-defined and relatively simple declarative and procedural learning tasks(e. g.,learning how to write a recursive function in LISP; doing multi-column addition),and improvements that were observed tended to be more limited than promised(e. g.,one standard deviation improvement at best rather than the promised standard deviation improvement).Still,there was some progress in terms of how to conceptualize learning. A seldom documented limitation was the notion of only viewing learning from only content and cognitive perspectives( i. e.,in terms of memory limitations,prior knowledge,bug libraries,learning hierarchies and sequences etc.). Little attention was paid to education conceived more broadly than developing specific cognitive skills with highly constrained problems. New technologies offer the potential to create dynamic and multi-dimensional models of a particular learner,and to track large data sets of learning activities,resources,interventions,and outcomes over a great many learners. Using those data to personalize learning for a particular learner developing knowledge,competence and understanding in a specific domain of inquiry is finally a real possibility. While the potential to make significant progress is clearly possible,the reality is less not so promising. There are many as yet unmet challenging some of which will be mentioned in this paper. A persistent worry is that educational technologists and computer scientists will again promise too much,too soon at too little cost and with too little effort and attention to the realities in schools and universities.

Particle Swarm Optimization-Based Hyperparameters Tuning of Machine Learning Models for Big COVID-19 Data Analysis

Analyzing big data, especially medical data, helps to provide good health care to patients and face the risks of death. The COVID-19 pandemic has had a significant impact on public health worldwide, emphasizing the need for effective risk prediction models. Machine learning (ML) techniques have shown promise in analyzing complex data patterns and predicting disease outcomes. The accuracy of these techniques is greatly affected by changing their parameters. Hyperparameter optimization plays a crucial role in improving model performance. In this work, the Particle Swarm Optimization (PSO) algorithm was used to effectively search the hyperparameter space and improve the predictive power of the machine learning models by identifying the optimal hyperparameters that can provide the highest accuracy. A dataset with a variety of clinical and epidemiological characteristics linked to COVID-19 cases was used in this study. Various machine learning models, including Random Forests, Decision Trees, Support Vector Machines, and Neural Networks, were utilized to capture the complex relationships present in the data. To evaluate the predictive performance of the models, the accuracy metric was employed. The experimental findings showed that the suggested method of estimating COVID-19 risk is effective. When compared to baseline models, the optimized machine learning models performed better and produced better results.

Research on Machine Tool Fault Diagnosis and Maintenance Optimization in Intelligent Manufacturing Environments

In the context of intelligent manufacturing,machine tools,as core equipment,directly influence production efficiency and product quality through their operational reliability.Traditional maintenance methods for machine tools,often characterized by low efficiency and high costs,fail to meet the demands of modern manufacturing industries.Therefore,leveraging intelligent manufacturing technologies,this paper proposes a solution optimized for the diagnosis and maintenance of machine tool faults.Initially,the paper introduces sensor-based data acquisition technologies combined with big data analytics and machine learning algorithms to achieve intelligent fault diagnosis of machine tools.Subsequently,it discusses predictive maintenance strategies by establishing an optimized model for maintenance strategy and resource allocation,thereby enhancing maintenance efficiency and reducing costs.Lastly,the paper explores the architectural design,integration,and testing evaluation methods of intelligent manufacturing systems.The study indicates that optimization of machine tool fault diagnosis and maintenance in an intelligent manufacturing environment not only enhances equipment reliability but also significantly reduces maintenance costs,offering broad application prospects.

Research on the Application of Big Data and Artificial Intelligence Technology in Computer Network

With the continuous development of social economy,science and technology are also in continuous progress,relying on the Internet technology of big data era has come in an all-round way.On the basis of the development of cloud computing and Internet technology,artificial intelligence technology has emerged as the times require.It also has more advantages.Applying it to computer network technology can effectively improve the data processing efficiency and quality of computer network technology,and improve the convenience for people’s life and production.This paper studies and analyzes the practical application requirements of computer network,and discusses the application characteristics and timeliness of artificial intelligence technology.

International Papers Contribution on Artificial Intelligence Promotes the Application and Development of Big Data in the Petroleum Industry

Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.

迈向Intelligence导向的现代情报学

现代情报学应是与国家新时代发展相关联、具有国际视野,并能够接受历史检验的情报学。文章提出迈向现代情报学的发展策略:构建以Intelligence为导向的情报学知识体系,界定基于Intelligence属性的情报学核心问题域,重视情报学教育与情报工作的匹配,强化情报学研究的大数据思维,加强军民情报组织融合,重视情报历史研究等;并勾勒了情报学未来发展蓝图:成为统领信息科学和数据科学价值建构的智慧型学科,成为"指路人"和"智囊"角色的横断型学科,成为惠及上至政府下至普通民众的大众性学科。