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.

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.

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.

“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.

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.

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.

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.

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.

Data Processing and Artificial Neural Network in the Background of Big Data

The existing significance of big data technology lies not only in collecting massive information,but also in professional processing and analysis.It transforms information into data and extracts valuable knowledge from data.The advent of the era of big data has brought us a new development model,but also produced many emerging industries,such as cloud computing,artificial intelligence and so on.Based on this,this paper studies the artificial neural network and back propagation algorithm in this context,so that computer technology can better serve human beings,which is of great significance to promote the further development of artificial intelligence technology.

Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management

Information technologies involving artificial Intelligence, big data, Internet of Things devices and blockchain have been developed and implemented in many engineering fields worldwide. Existing review articles focus on developments and characteristics of individual topics and the associated deployment in the energy sector. These technologies, all based on communication, information, and data analysis, are naturally coherent and integrable. This article reviews the literature and patents in four closely related fields and aims to provide a holistic view of how they are related and their integrability in relation to smart energy management strategies. Artificial intelligence models forecast energy use and load profiles as well as schedule resources to ensure reliable performance and effective utilization of energy resources. Training artificial intelligence models requires immense volumes of data. Utilizing big data systems and data mining enables the discovery of new functions and relationships, which determines the performance of artificial intelligence. Data mining also refines the information;thus, artificial intelligence is trained iteratively with more accurate data. Smart energy management can be further enhanced through advanced digital technologies like Internet of Things and blockchain. An Internet of Things platform containing edge, fog and cloud layers helps connect artificial intelligence to other hardware and software devices and systems. Furthermore, an Internet of Things platform efficiently transmits and stores data, improving access and availability to stakeholders for data mining. Emerging technologies such as blockchain and cryptocurrency facilitate energy trading and can be designed in the cloud layer of an Internet of Things platform to supplement data storage. Providing an efficient and seamless integration of artificial intelligence, big data, and advanced digital technologies will be an important factor in the emerging transition of the energy sector to a lower-carbon system.

Application status,problems and suggestions of artificial intelligence in medical field

介绍了新一代人工智能应用发展的背景,梳理了人工智能在医学影像、辅助诊断、健康管理、疾病预 测、药物研发领域中的应用现状,从数据、算法、主体责任、法律法规、评估标准、人才保障方面分析了人工 智能在医疗领域应用中的问题与挑战,并从行业整体发展的角度出发,对医学人工智能的应用发展提出相关建议.

Artificial intelligence-assisted psychosis risk screening in adolescents:Practices and challenges

Artificial intelligence-based technologies are gradually being applied to psychiatric research and practice.This paper reviews the primary literature concerning artificial intelligence-assisted psychosis risk screening in adolescents.In terms of the practice of psychosis risk screening,the application of two artificial intelligence-assisted screening methods,chatbot and large-scale social media data analysis,is summarized in detail.Regarding the challenges of psychiatric risk screening,ethical issues constitute the first challenge of psychiatric risk screening through artificial intelligence,which must comply with the four biomedical ethical principles of respect for autonomy,nonmaleficence,beneficence and impartiality such that the development of artificial intelligence can meet the moral and ethical requirements of human beings.By reviewing the pertinent literature concerning current artificial intelligence-assisted adolescent psychosis risk screens,we propose that assuming they meet ethical requirements,there are three directions worth considering in the future development of artificial intelligenceassisted psychosis risk screening in adolescents as follows:nonperceptual realtime artificial intelligence-assisted screening,further reducing the cost of artificial intelligence-assisted screening,and improving the ease of use of artificial intelligence-assisted screening techniques and tools.

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.

Dirty Data between Errors and Their Handling—A Firsthand Experience in Solving Dirty Data from Within

Managing large amounts of data is becoming part of everyday life in most organizations. Handling, analyzing, searching, and making predictions from big data is becoming the norm for many organizations of many interests. Big data provides the foundations for more benefits and higher values to be extracted from big data. As big data comes with countless benefits, it also comes with many challenges to fulfilling its expectations. Some of those problems haunting big data banks are being termed dirty data. This paper focuses on dirty data while working on an organization’s natural live information system. The author was responsible for studying and analyzing a faltering information system and planning and carrying out the required solutions and fixes. The importance of the work carried out lies in the high level of dirty data observed in the system. Therefore, this paper is based on the part of dirty data—the paper focuses on how the team suffered from dirty data and how it was dealt with.

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.

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.

Exploring deep learning for landslide mapping:A comprehensive review

A detailed and accurate inventory map of landslides is crucial for quantitative hazard assessment and land planning.Traditional methods relying on change detection and object-oriented approaches have been criticized for their dependence on expert knowledge and subjective factors.Recent advancements in highresolution satellite imagery,coupled with the rapid development of artificial intelligence,particularly datadriven deep learning algorithms(DL)such as convolutional neural networks(CNN),have provided rich feature indicators for landslide mapping,overcoming previous limitations.In this review paper,77representative DL-based landslide detection methods applied in various environments over the past seven years were examined.This study analyzed the structures of different DL networks,discussed five main application scenarios,and assessed both the advancements and limitations of DL in geological hazard analysis.The results indicated that the increasing number of articles per year reflects growing interest in landslide mapping by artificial intelligence,with U-Net-based structures gaining prominence due to their flexibility in feature extraction and generalization.Finally,we explored the hindrances of DL in landslide hazard research based on the above research content.Challenges such as black-box operations and sample dependence persist,warranting further theoretical research and future application of DL in landslide detection.

The Role of Big Data and Machine Learning in the Integration and Implementation of Historical,Current,and Continuously Gathered Earth Data

We are faced with more challenges than ever with respect to the amount,variety,and sheer volume of earth data.Not only do we have data of many different ages and origins,we also have a wide variety of data,much of which has just been recently digitized,or is in a format that can cannot easily be integrated into algorithms,georeferenced data.

Social Media and Stock Market Prediction: A Big Data Approach

Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market.