Performance measure of dry bulk ports using shipping big data

The growing importance of big data has headed enterprises to advance their big data analytics capability to strengthen their firm performance. This study tests how big data capability impact on business intelligence infrastructure to achieve firm performance measures such as operational performance and marketing performance. This study is based on the recent literature on the knowledge-based view, big data capability, IT capability, and business intelligence. The primary survey of 272 responses from Chinese firms’ IT managers and big data analysts are used to uncover the relationship in the proposed model. The finding shows that the big data analytics capability significantly impacts on business intelligence infrastructure that in turn positively impact on operational performance and marketing performance. Further, the business intelligence infrastructure partially mediates between big data analytics capability and operational performance, and fully mediates between big data analytics capability and marketing performance. This research contributes to the information systems literature such as big data analytic capability, business intelligence, and firm performance measures, and thus offers grounds to extend more widespread studies in this field. This study adds to the literature on the theory and practical bases for big data capability and business intelligence infrastructure.

The dry bulk market BDI index fell from 11,793 points on May 20, 2008 to 290 points on February 10, 2016, which is a very certain "downward cycle" of the shipping market, showing an "L" shape. By citing random samples and big data, the paper analyzes the causes of the "L" type cycle. First, through random sample data, a binary measurement model with BDI index as the dependent variable and dry bulk market supply and demand as independent variables is established, and it is concluded that the "down cycle" of the dry bulk market is caused by the "supply factor"; then the paper continues to quote big data, that is, to use "whole sample" to further analyze the sub-variables of supply variables. Finally, the paper concludes that the "downward cycle" of the dry bulk shipping market is "L" shaped, which is caused by changes in the "market structure" of the shipbuilding market.

Performance measures and metrics (PMM) is identified to be an essential aspect of managing diverse supply chains. The PMM improves the firm’s performance by providing open and transparent communication between the various stakeholders of an organisation. The literature suggests that big data analytics has a positive impact on the supply chain and firm performance. Presently, the literature lack studies that recognise the PMM relevant to big data-driven supply chain (BDDSC). The present study is based on a comprehensive review of 66 papers published with the primary objective to identify the various PMMs used to evaluate the BDDSC. The findings suggest that the PMMs applicable to BDDSC can be classified into two non-mutually exclusive categories. The first category represents 24 performance measures used to evaluate the performance of the big data analytics capability and the second category represents 130 measures used for assessing the performance of BDDSC processes. The study also reports the emergence of new performance measures based on increasing use of predictive and social analytics in BDDSC. Based on the results of the study a framework on BDDSC performance measurement system is proposed which will guide the managers to have a robust performance measurement system in their organisation.

With the advent of 'heavy Artificial Intelligence' – big data, deep learning, and ubiquitous use of the internet, ethical considerations are widely dealt with in public discussions and governmental bodies. Within Computational Paralinguistics with its manifold topics and possible applications (modelling of long-term, medium-term, and short-term traits and states such as personality, emotion, or speech pathology), we have not yet seen that many contributions. In this article, we try to set the scene by (1) giving a short overview of ethics and privacy, (2) describing the field of Computational Paralinguistics, its history and exemplary use cases, as well as (de-)anonymisation and peculiarities of speech and text data, and (3) proposing rules for good practice in the field, such as choosing the right performance measure, and accounting for representativity and interpretability.

Bayesian Fill Volume Estimation Based on Point Level Sensor Signals

Performance measurement is the process of making an evidence-based improvement. It reveals the performance gains or gaps, depending on the entity to be measured, being an organization, people, equipment, processes, or systems. After development, big data analytics (BDA) systems massively fail in organizational settings. The reasons, however, are not fully understood. This paper investigates how organizations can quantify the performance of their BDA systems. To answer this question, we investigated performance measures and performance-contributing factors in the existing literature and surveyed users’ perceptions of our findings. The results show that metrics of efficiency and effectiveness can be used to measure the performance of the BDA System. The results also demonstrate that technology, competency, and working conditions are the key factors that contribute to the performance of the BDA System.

Artificial intelligence (AI) is rapidly reshaping business and society. Fueled and guided by big data, AI is making a profound impact on lives, livelihoods, and future opportunities. AI and big data are changing customer experiences, business operations, strategies, and entire industry sectors. State-of-the-art AI algorithms and big data outperform human experts on a range of challenging prediction problems. However, these same algorithms can be bias, fragile, and unable to explain their output. These flaws can lead to regulatory and legal issues. In this chapter, we explore the nature of AI systems and the performance measures used to evaluate their decisions. We discuss the legal imperative to broaden these metrics beyond narrow mathematical measures of optimality such that they include explanation capabilities. Making algorithms and data transparent is not enough to determine if AI decisions are discriminatory, unfair, or wrong. Explanations can ensure AI behavior is law abiding and trustworthy.

In this issue, Mohareb and colleagues sought to validate the 2012 appropriate use criteria for diagnostic catheterization. The editorialists discuss the opportunities and challenges of applying the...

The rapid progress of modern technologies generates a massive amount of high-throughput data, called Big Data, which provides opportunities to find new insights using machine learning (ML) algorithms. Big Data consist of many features (also called attributes); however, not all these are necessary or relevant, and they may degrade the performance of ML algorithms. Feature selection (FS) is an essential preprocessing step to reduce the dimensionality of a dataset. Evolutionary algorithms (EAs) are widely used search algorithms for FS. Using classification accuracy as the objective function for FS, EAs, such as the cooperative co-evolutionary algorithm (CCEA), achieve higher accuracy, even with a higher number of features. Feature selection has two purposes: reducing the number of features to decrease computations and improving classification accuracy, which are contradictory but can be achieved using a single objective function. For this very purpose, this paper proposes a penalty-based wrapper objective function. This function can be used to evaluate the FS process using CCEA, hence called Cooperative Co-Evolutionary Algorithm-Based Feature Selection (CCEAFS). An experiment was performed using six widely used classifiers on six different datasets from the UCI ML repository with FS and without FS. The experimental results indicate that the proposed objective function is efficient at reducing the number of features in the final feature subset without significantly reducing classification accuracy. Based on different performance measures, in most cases, naïve Bayes outperforms other classifiers when using CCEAFS.

The internet of things (IoT) is an internet among things through advanced communication without human’s operation. The effective use of data classification in IoT to find new and hidden truth can enhance the medical field. In this paper, the big data analytics on IoT based healthcare system is developed using the Random Forest Classifier (RFC) and MapReduce process. The e-health data are collected from the patients who suffered from different diseases is considered for analysis. The optimal attributes are chosen by using Improved Dragonfly Algorithm (IDA) from the database for the better classification. Finally, RFC classifier is used to classify the e-health data with the help of optimal features. It is observed from the implementation results is that the maximum precision of the proposed technique is 94.2%. In order to verify the effectiveness of the proposed method, the different performance measures are analyzed and compared with existing methods.

This study addresses the challenge of extracting valuable information and selecting key variables from large datasets, essential across statistics, computational science, and data science. In the age of big data, where safeguarding personal privacy is paramount, this study presents an online learning algorithm that leverages differential privacy to handle large‐scale data effectively. The focus is on enhancing the online group lasso approach within the differential privacy realm. The study begins by comparing online and offline learning approaches and classifying common online learning techniques. It proceeds to elucidate the concept of differential privacy and its importance. By enhancing the group‐follow‐the‐proximally‐regularized‐leader (GFTPRL) algorithm, we have created a new method for the online group lasso model that integrates differential privacy for binary classification in logistic regression. The research offers a solid validation of the algorithm’s effectiveness based on differential privacy and online learning principles. The algorithm’s performance was thoroughly evaluated through simulations with both synthetic and actual data. The comparison is made between the proposed privacy‐preserving algorithm and traditional non‐privacy‐preserving counterparts, with a focus on regret bounds, a measure of performance. The findings underscore the practical benefits of the differential privacy‐preserving algorithm in tackling large‐scale data analysis while upholding privacy standards. This research marks a significant step forward in the fusion of big data analytics and the safeguarding of individual privacy.

There are many data mining algorithms and methodologies for various fields and various problematic. Each data mining researcher/practitioner should describe the intrinsic quality of the discovered models and patterns. In addition he/she is faced with assessing the performance of his/her own solution(s) in order to make –fair– comparisons with state of the art approaches. Which methodology, which benchmarks, which measures of performance, which tools, which measures of interest, which scenarios, etc., should be used, and why? Every one should answer these questions, and assessing the quality and the performance of a data mining process is a critical issue. Assessing the quality and the performance is a critical issue for classical situations and even more in the Big Data era where we can not manage this issue with classical and current approaches. Clearly, large scale data, complex data and streaming data bring new challenges (e.g. large scale inference, fake correlations, necessity of perpetual validation). In this setting, the Quality Issues, Measures of Interestingness and Evaluation of data mining models Workshop series provide an open forum for intensive discussions and the exchange of new ideas and best practices on quality issues among data mining researchers. QIMIE focuses on the theory, the techniques, the links between the discovery stage and the quality assessment stage, and the practices that can ensure that the discovered knowledge of a data mining process is of quality. It is organized with the IEEE Task Force on Evaluation and Quality issues (Data Mining and Big Data Analytics Technical Committee, IEEE Computational Intelligence Society).

The growth of technologies like Artificial Intelligence (AI), Big Data & Internet of Things (IoT), etc. has marked many advancements in the technological world since the last decade. These technologies have a wide range of applications. One such application is “Chatterbot or “Chatbot”. Chatbots are conversational AIs, which mimics the human while conversing & eliminates the need of human by automating mundane tasks. In the study undertaken, we have created a chatbot in education domain & it is named as “College Enquiry Chatbot”, This chatbot is a web-based application that analyses and understands user's queries and provides an instant and accurate response. Rasa technology is used to construct this chatbot. It's an open-source technology, which uses its two main packages i.e., Rasa Core & Rasa Natural Language Understanding (NLU) in order to build a Contextual AI Chatbot. NLU is used to infer the intent and to extract the necessary entities from user input & the Rasa Core provides the output by building a probabilistic model with the help of Recurrent Neural Network (RNN). Evaluation of the model is done by getting a confusion matrix and performance measures like Precision, Accuracy & F1 Score which come out to be 0.628, 0.725 and 0.669 respectively on average basis. This chatbot's accuracy, lack of dependability on human resources, 24 x 7 accessibility and low maintenance creates various opportunities for its implementation. This conversational agent can not only be used in educational institutions but also in places where enquiry becomes a tedious task.

With the evolution of Internet standards and advancements in various Internet and mobile technologies, especially since web 4.0, more and more web and mobile applications emerge such as e-commerce, social networks, online gaming applications and Internet of Things based applications. Due to the deployment and concurrent access of these applications on the Internet and mobile devices, the amount of data and the kind of data generated increases exponentially and the new era of Big Data has come into existence. Presently available data structures and data analyzing algorithms are not capable to handle such Big Data. Hence, there is a need for scalable, flexible, parallel and intelligent data analyzing algorithms to handle and analyze the complex massive data. In this article, we have proposed a novel distributed supervised machine learning algorithm based on the MapReduce programming model and Distance Weighted k-Nearest Neighbor algorithm called MR-DWkNN to process and analyze the Big Data in the Hadoop cluster environment. The proposed distributed algorithm is based on supervised learning performs both regression tasks as well as classification tasks on large-volume of Big Data applications. Three performance metrics, such as Root Mean Squared Error (RMSE), Determination coefficient (R2) for regression task, and Accuracy for classification tasks are utilized for the performance measure of the proposed MR-DWkNN algorithm. The extensive experimental results shows that there is an average increase of 3% to 4.5% prediction and classification performances as compared to standard distributed k-NN algorithm and a considerable decrease of Root Mean Squared Error (RMSE) with good parallelism characteristics of scalability and speedup thus, proves its effectiveness in Big Data predictive and classification applications.

Anesthesia Quality and Safety