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Predictive analytics is the science of decision-making that eliminates guesswork out of the decision-making process and applies proven scientific procedures to find right solutions. Predictive analytics provides ideas on the occurrences of future downtimes and rejections thereby aids in taking preventive actions before abnormalities occur. Considering these advantages, predictive analytics is adopted in various diverse fields such as health care, finance, education, marketing, automotive, etc. Predictive analytics tools can be used to predict various behaviors and patterns, thereby saving the time and money of its users. Many open-source predictive analysis tools namely R, scikit-learn, Konstanz Information Miner (KNIME), Orange, RapidMiner, Waikato Environment for Knowledge Analysis (WEKA), etc. are freely available for the users. This chapter aims to reveal the best accurate tools and techniques for the classification task that aid in decision-making. Our experimental results show that no specific tool provides the best results in all scenarios; rather it depends upon the datasets and the classifier.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

A growing body of research outlines how to best facilitate and ensure methodological rigor when using dictionary-based computerized text analyses (DBCTA) in organizational research. However, these best practices are currently scattered across several methodological and empirical manuscripts, making it difficult for scholars new to the technique to implement DBCTA in their own research. To better equip researchers looking to leverage this technique, this methodological report consolidates current best practices for applying DBCTA into a single, practical guide. In doing so, we provide direction regarding how to make key design decisions and identify valuable resources to help researchers from the beginning of the research process through final publication. Consequently, we advance DBCTA methods research by providing a one-stop reference for novices and experts alike concerning current best practices and available resources.

Purpose – This chapter observes the dynamics between various aspects of current pop music production, particularly in respect to digital culture, and the preservation and access challenges faced by a wealth of analogue sound artefacts. I argue for the need to consider the activity of ‘fringe piracy’ – that is online music distribution that specialises in out-of-print analogue editions and bootleg trading – as worthy of civic merit: as participatory heritage recovery, preservation and dissemination.

Methodology – I narrate and interpret a series of contexts pertaining to deep changes in popular music production and consumption in the last decade. I will do so primarily by focusing on online activity, while unravelling its relationships with traditional modes of music production, dissemination and consumption (i.e. the music industry as defined by vinyl records, cassettes and CDs throughout the second half of the Twentieth Century). I further contrast the mechanics of ‘grey areas’ of online music access against mainstream web platforms such as iTunes. The author has performed extensive participant observation throughout various online platforms in the last decade, particularly the ones mentioned along the chapter. Additional content has been developed as a consequence of both online and offline discussions, as well as conference panels and symposia (Codebits, 2010; South By South West, 2011; Syracuse University London, 2011).

Findings – I argue that the current, wide field of possibilities for music production and dissemination stands in radical contrast with an ongoing and strengthened orthodoxy on the part of media labels and distributors. I further argue that, in contrast with this orthodoxy that stems from consumer culture, an exponential availability of recording and editing tools is encouraging a discreet civic mission of digital transcription, and subsequent historical preservation, of analogue artefacts that would otherwise face the prospect of fading into obscurity and possible definitive loss. This, however, seems to be occurring in gradual oblivion of contextual placement, but rather in line with a culture of interchangeable sampling of a purely sensorial and/or affective nature.

Originality – Most debates on the subject of music piracy tend to focus on a polarisation of the underlying issues, while mainly addressing its legal and political aspects. There is a need to unravel the cultural, aesthetic and civic parameters that emerge from a phenomenon that is, ultimately, anything but polarised: instead, one finds it is paved with complexity and ambivalence.

This chapter discusses the steps taken to access and use the ACS five-year data. The format of the data is discussed pointing out the fact that there is no requirement that an ACS five-year variable holds data for the same field year to year. The development of a cross-reference table is discussed allowing the data to be accessed by a common label.

This literature review is on advanced computer analytics, which is a major trend in the field of Human Resource Management (HRM). The authors focus specifically on computer-assisted text analysis (CATA) because text data are a prevalent yet vastly underutilized data source in organizations. The authors gathered 341 articles that use, review, or promote CATA in the management literature. This review complements existing reviews in several ways including an emphasis on CATA in the management literature, a description of the types of software and their advantages, and a unique emphasis on findings in employment. This examination of CATA relative to employment is based on 66 studies (of the 341) that bear on measuring constructs potentially relevant to hiring decisions. The authors also briefly consider the broader machine learning literature using CATA outside management (e.g., data science) to derive relevant insights for management scholars. Finally, the authors discuss the main challenges when using CATA for employment, and provide recommendations on how to manage such challenges. In all, the authors hope to demystify and encourage the use of CATA in HRM scholarship.

Food supply chain transparency and traceability is very important to address the issue regarding quality and safety. In traditional tracing system, with increasing the complexity of supply chain making product recalls difficult to manage and putting human lives at risk. To eliminate such types of risks, blockchain technology gives more efficient and reliable system for food tracing. Recently, there is an exponential rise in adoption of blockchain technology and most disparate IOT (Internet of things) devices in agriculture and food supply chain. It is an evolving technology that comforts the food supply chains by providing transparent data records and manage the food movement in the chain using distributed (P2P) network. That is more secured and there's no need for third party verifications. Our focus in this research will be on the Indian wheat supply chain and issues related to food losses caused by a lack of transparency and traceability. In order to improve the transparency of the wheat supply chain, we created an end-to-end smart wheat supply chain solution that combines blockchain technology, NFC tags, IoTs, and smart contracts. The solution is supported by entity relationship diagrams, information and money flow sequence diagrams, and a blockchain network diagram. We also used a security algorithm and the “NFC-Tag writer by NXP” program to validate and assess our system. This work could serve as a springboard for more in-depth research in this area. Depending on the existing situation in the industry, this research can also advise corporate procedures to deploy blockchain-based applications in the supply chain and logistics industry.

The purpose of this study is to identify the specific skills and abilities within the broad category of data analytics that current business professionals believe are most important for accounting graduates. Data analytics knowledge is clearly important, but this category is broad. Therefore, this study identifies the specific skills and abilities that are most important for accounting graduates so that faculty can create classroom materials most beneficial for the future accounting graduates. In 2013, the Association to Advance Collegiate Schools of Business developed new standards for accounting programs, including standard A7, related to information technology and analytics. The intent of the standard clearly focuses on increasing the level of technology and analytics studied within the accounting curriculum. However, the specific details and methods for achieving the intent of A7 remain an open question. This chapter uses prior research focused on business analytics education to identify potential analytic skills, tools, techniques, and management issues of concern within the accounting profession. A survey of 342 accounting professionals identifies suggested areas of analytic competencies for accounting graduates. Specifically, the authors find preferences for skills related to data interpretation and communication over any individual technical skills or statistical knowledge. These skills suggest a role for accountants as intermediaries who may need to translate analytic activities into business language. Post hoc, the authors examine the survey results for differences based on respondent characteristics. Interestingly, female respondents report lower beliefs about the importance of analytic skills. The authors also find some differences when examining different demographics within the respondents.