Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

Evaluation of a software positioning tool to support SMEs in adoption of big data analytics

Big data analytics has been widely adopted by large companies to achieve measurable benefits including increased profitability,customer demand forecasting,cheaper development of products,and improved stock control.Small and medium sized enterprises(SMEs)are the backbone of the global economy,comprising of 90%of businesses worldwide.However,only 10%SMEs have adopted big data analytics despite the competitive advantage they could achieve.Previous research has analysed the barriers to adoption and a strategic framework has been developed to help SMEs adopt big data analytics.The framework was converted into a scoring tool which has been applied to multiple case studies of SMEs in the UK.This paper documents the process of evaluating the framework based on the structured feedback from a focus group composed of experienced practitioners.The results of the evaluation are presented with a discussion on the results,and the paper concludes with recommendations to improve the scoring tool based on the proposed framework.The research demonstrates that this positioning tool is beneficial for SMEs to achieve competitive advantages by increasing the application of business intelligence and big data analytics.

Game Theory Based Model for Predictive Analytics Using Distributed Position Function

This paper presents a game theory-based method for predicting the outcomes of negotiation and group decision-making problems. We propose an extension to the BDM model to address problems where actors’ positions are distributed over a position spectrum. We generalize the concept of position in the model to incorporate continuous positions for the actors, enabling them to have more flexibility in defining their targets. We explore different possible functions to study the role of the position function and discuss appropriate distance measures for computing the distance between the positions of actors. To validate the proposed extension, we demonstrate the trustworthiness of our model’s performance and interpretation by replicating the results based on data used in earlier studies.

Toward Data-Driven Digital Therapeutics Analytics:Literature Review and Research Directions

With the advent of digital therapeutics(DTx),the development of software as a medical device(SaMD)for mobile and wearable devices has gained significant attention in recent years.Existing DTx evaluations,such as randomized clinical trials,mostly focus on verifying the effectiveness of DTx products.To acquire a deeper understanding of DTx engagement and behavioral adherence,beyond efficacy,a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis.In this work,the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets,to investigate contextual patterns associated with DTx usage,and to establish the(causal)relationship between DTx engagement and behavioral adherence.This review of the key components of datadriven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets,which helps to iteratively improve the receptivity of existing DTx.

Edge-Coordinated Energy-Efficient Video Analytics for Digital Twin in 6G

Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital twin.In this paper,with the aim of developing energy-efficient digital twin in 6G,we investigate real-time video analytics based on cameras mounted on mobile devices with edge coordination.This problem is challenging because 1)mobile devices are with limited battery life and lightweight computation capability,and 2)the captured video frames of mobile devices are continuous changing,which makes the corresponding tasks arrival uncertain.To achieve energy-efficient video analytics in digital twin,by taking energy consumption,analytics accuracy,and latency into consideration,we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework,which can utilized digital twin models to achieve joint offloading decision and configuration selection.The edge nodes help to collect the information on network topology and task arrival.Extensive simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.

A Boosted Tree-Based Predictive Model for Business Analytics

Business Analytics is one of the vital processes that must be incorpo-rated into any business.It supports decision-makers in analyzing and predicting future trends based on facts(Data-driven decisions),especially when dealing with a massive amount of business data.Decision Trees are essential for business ana-lytics to predict business opportunities and future trends that can retain corpora-tions’competitive advantage and survival and improve their business value.This research proposes a tree-based predictive model for business analytics.The model is developed based on ranking business features and gradient-boosted trees.For validation purposes,the model is tested on a real-world dataset of Universal Bank to predict personal loan acceptance.It is validated based on Accuracy,Precision,Recall,and F-score.The experimentfindings show that the proposed model can predict personal loan acceptance efficiently and effectively with better accuracy than the traditional tree-based models.The model can also deal with a massive amount of business data and support corporations’decision-making process.

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years,huge volumes of healthcare data are getting generated in various forms.The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker.Due to such massive generation of big data,the utilization of new methods based on Big Data Analytics(BDA),Machine Learning(ML),and Artificial Intelligence(AI)have become essential.In this aspect,the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning(BDA-CSODL)technique for medical image classification on Apache Spark environment.The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately.BDA-CSODL technique involves different stages of operations such as preprocessing,segmentation,fea-ture extraction,and classification.In addition,BDA-CSODL technique also fol-lows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image.Moreover,a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor.Stochastic Gradient Descent(SGD)model is used for parameter tuning process.Furthermore,CSO with Long Short-Term Memory(CSO-LSTM)model is employed as a classification model to determine the appropriate class labels to it.Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique.A wide range of simulations was conducted on benchmark medical image datasets and the com-prehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

Cyberattack Detection Framework Using Machine Learning and User Behavior Analytics

This paper proposes a novel framework to detect cyber-attacks using Machine Learning coupled with User Behavior Analytics.The framework models the user behavior as sequences of events representing the user activities at such a network.The represented sequences are thenfitted into a recurrent neural network model to extract features that draw distinctive behavior for individual users.Thus,the model can recognize frequencies of regular behavior to profile the user manner in the network.The subsequent procedure is that the recurrent neural network would detect abnormal behavior by classifying unknown behavior to either regu-lar or irregular behavior.The importance of the proposed framework is due to the increase of cyber-attacks especially when the attack is triggered from such sources inside the network.Typically detecting inside attacks are much more challenging in that the security protocols can barely recognize attacks from trustful resources at the network,including users.Therefore,the user behavior can be extracted and ultimately learned to recognize insightful patterns in which the regular patterns reflect a normal network workflow.In contrast,the irregular patterns can trigger an alert for a potential cyber-attack.The framework has been fully described where the evaluation metrics have also been introduced.The experimental results show that the approach performed better compared to other approaches and AUC 0.97 was achieved using RNN-LSTM 1.The paper has been concluded with pro-viding the potential directions for future improvements.

Heterogeneous Ensemble Feature Selection Model(HEFSM)for Big Data Analytics

Big Data applications face different types of complexities in classifications.Cleaning and purifying data by eliminating irrelevant or redundant data for big data applications becomes a complex operation while attempting to maintain discriminative features in processed data.The existing scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.Recently ensemble methods have made a mark in classification tasks as combine multiple results into a single representation.When comparing to a single model,this technique offers for improved prediction.Ensemble based feature selections parallel multiple expert’s judgments on a single topic.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.The major goal of this research is to suggest HEFSM(Heterogeneous Ensemble Feature Selection Model),a hybrid approach that combines multiple algorithms.Further,individual outputs produced by methods producing subsets of features or rankings or voting are also combined in this work.KNN(K-Nearest Neighbor)classifier is used to classify the big dataset obtained from the ensemble learning approach.The results found of the study have been good,proving the proposed model’s efficiency in classifications in terms of the performance metrics like precision,recall,F-measure and accuracy used.

Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

Developing Blue Spots Model for Tennessee Using GIS, and Advanced Data Analytics: Literature Review

Climate change and global warming results in natural hazards, including flash floods. Flash floods can create blue spots;areas where transport networks (roads, tunnels, bridges, passageways) and other engineering structures within them are at flood risk. The economic and social impact of flooding revealed that the damage caused by flash floods leading to blue spots is very high in terms of dollar amount and direct impacts on people’s lives. The impact of flooding within blue spots is either infrastructural or social, affecting lives and properties. Currently, more than 16.1 million properties in the U.S are vulnerable to flooding, and this is projected to increase by 3.2% within the next 30 years. Some models have been developed for flood risks analysis and management including some hydrological models, algorithms and machine learning and geospatial models. The models and methods reviewed are based on location data collection, statistical analysis and computation, and visualization (mapping). This research aims to create blue spots model for the State of Tennessee using ArcGIS visual programming language (model) and data analytics pipeline.

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.

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.

AI and data analytics are in full swing in the textile sector

The implementation of artificial intelligence and data analytics varies from management automation to product inspection.These technologies detect visual defects and measure wrinkles in the fabric.Also,machine learning algorithms identify previously hidden operating patterns to optimize business processes.Moreover,AI tracks consumer behavior to provide better recommendations and get insights into market fluctuations.This way,data-driven solutions improve workflows,control the labor pool,and enhance end-product quality.

特金罕山国家自然保护区野生球根花卉资源综合评价

目的:为探究特金罕山国家自然保护区野生球根花卉资源,筛选出适合当地城市绿化的野生球根花卉种类。方法:采用野外实地调查和文献检索的方法,调查特金罕山国家自然保护区野生球根花卉资源,并运用层次分析法从观赏价值、适应性、开发价值3个方面对其进行综合评价。结果:特金罕山国家自然保护区共有野生球根花卉37种,隶属12科,28属,其中,百合科种类最多,14种。根据综合评价结果,37种野生球根花卉分为4个等级:综合价值高的Ⅰ级(R>4.0)5种;综合价值较高的Ⅱ级(3.7<R≤4.0)10种;综合价值一般的Ⅲ级(3.5<R≤3.7)12种;综合价值较低的Ⅳ级(R≤3.5)10种。结论:特金罕山国家自然保护区野生球根花卉资源丰富,建议加强资源保护并优化开发利用方式,丰富城市园林绿化球根花卉种类。

根际化学与生物多样性的表征方法:组学技术的机遇与挑战

根际是联结植物、土壤和微生物的重要界面,是化学与生物过程耦合最活跃的区域.根际环境影响土壤中有机和无机污染物的行为,而研究方法的完善与提升有助于阐释根际中复杂的过程与作用机制.本文从传统的化学和生物学方法到新兴的组学技术对根际科学的方法学研究进展进行了综述,重点讨论了当今组学技术在根际研究中应用机遇与挑战,同时展望了今后需要关注的科学问题.根际化学组分的传统分析方法涵盖了光谱、色谱、质谱和色质联用等技术,主要聚焦于低分子量有机酸等小分子的定性定量测定,导致对根际化学多样性的认知偏差;传统的根际微生物研究依赖于培养技术,对微生物多样性的描述存在很大的局限.揭示根际异质性和复杂性,亟需采用高端的技术,组学方法显示出极大的优势,显著提高了研究者对根际科学的认识.基于靶向和非靶向代谢组学有利于深入研究根际复杂的化学多样性过程;基于宏基因组学、转录组学和蛋白组学等组学工具能够提供微生物组基因和蛋白的表达、功能特性等更详细的信息,可以全面地揭示根际微生物的多样性.应该强调的是,未来多组学整合分析更是表征根际化学与生物多样性的一个强有力工具,但需要更多的模型、框架和计算基础来实现根际基因、蛋白、转录和代谢水平的多层次关联,以助于挖掘根-微生物-土壤界面大量尚未揭示的关键过程、机理及生态环境效应.

基于多阶解析信号的磁源变深度成像方法

解析信号是磁法数据处理与解释的常用工具。本文从不同阶次解析信号及其垂向导数关系出发,引入深度缩放因子,构建了磁源变深度成像函数。该方法利用深度成像的极大值反映场源空间位置,利用反演深度及成像极大值估计场源构造指数。另外,结合不同深度缩放因子、不同阶次的成像结果提高方法的可靠性与适用性。模型试验及实例应用表明,相对于解析信号比值和局部波数的DEXP(depth from extreme points)方法,本文方法在使用更低阶次导数的情况下,能够获得更强的计算稳定性、更高的空间成像分辨率和更准确的场源参数反演结果。

出行即服务系统理论与实践:回顾与展望

交通是城市绿色低碳转型中最受关注的领域之一,也是数字化渗透及数字平台最为活跃的领域。出行即服务(Mobility as a Service, MaaS)系统是绿色交通的典型代表,是一种新型交通组织和供给方式,反映了当前出行需求的深刻变化和城市交通组织范式转变的耦合。全球范围内已出现了上百个大小规模不等和模式各异的MaaS实践创新,北京MaaS是中国持续至今、影响最大的MaaS实践。目前MaaS实践提出的理论和方法主要基于欧美发达国家,无法充分描述和分析中国实践。在文献研究的基础上,延伸纳入了中国经验,提出了具有全球普适性的一个MaaS系统分析框架,强调辨析全球范围内的MaaS异同均可以从三个维度展开,即嵌入的社会背景、发展目标和产生的社会经济环境影响;并应用此框架对国内外五个典型MaaS进行了比较研究,重点解码了北京MaaS的激励机制、商业模式和商业生态。本文旨在推动MaaS理论和研究方法的全球发展,重点提出了四个方面的关注:(1)MaaS系统的发展再次考验着城市交通如何回归其公共属性;(2)MaaS实践嵌入在城市社会背景中,具有明显的差异性。模式选择是对城市既有社会背景和交通格局的继承,但也可能就此发生转向。MaaS打开了一次城市交通转型的机会窗口;(3)MaaS系统的可持续运营依然面临挑战;(4)数字技术带来数据产权、数据隐私和安全等亟待解决的新问题。所有研究案例表明,数字技术的快速发展需要匹配治理模式创新,MaaS生态的协同进化至关重要。