Artificial Intelligence and Cognitive Computing

Artificial intelligence (AI) is a subject garnering increasing attention in both academia and the industry today. The understanding is that AI-enhanced methods and techniques create a variety of opportunities related to improving basic and advanced business functions, including production processes, logistics, financial management and others. As this collection demonstrates, AI-enhanced tools and methods tend to offer more precise results in the fields of engineering, financial accounting, tourism, air-pollution management and many more. The objective of this collection is to bring these topics together to offer the reader a useful primer on how AI-enhanced tools and applications can be of use in today’s world. In the context of the frequently fearful, skeptical and emotion-laden debates on AI and its value added, this volume promotes a positive perspective on AI and its impact on society. AI is a part of a broader ecosystem of sophisticated tools, techniques and technologies, and therefore, it is not immune to developments in that ecosystem. It is thus imperative that inter- and multidisciplinary research on AI and its ecosystem is encouraged. This collection contributes to that

A model of enterprise systems capabilities

This study has developed a model of ES capabilities to analyze the extent and quality of the use of ES in organizational contexts. The model consists of six general ES capabilities that can be used and deployed by organizations: 1) transaction automation, 2) decision-making process support, 3) monitoring performance, 4) customer service, 5) coordination, and 6) process management automation. The model itself was initially formulated from concepts in IS and ES literature. Then, the model was applied, validated and tuned through an in-depth case study.Enterprise systems, ES capabilities, ES use

Classifying Indian Classical Dances By Motion Posture Patterns

Dance is a classic form of human motion which is usually performed as a reaction of expression to music. The Indian classical dances, for instance, require multiple complicated movements that relates to body motion postures and hand gestures with high similarities. Past studies showed interests using various methods to classify dances. The most common method used is the Hidden Markov Models (HMM), apart from using the correlation matrix method and hierarchical cluster analysis. Nevertheless, less effort has been placed in analysing the Indian dance by using the data mining approach. Therefore, the objectives in this work are to (i) distinguish different types of Indian classical dances, (ii) classify the type of dance based on motion posture patterns and (iii) determine the effects of attributes on the classification accuracy. This study involves five types of Indian classical dances (Kathak, Bharatanatyam, Kuchipudi, Manipuri and Odissi) motion postures. The data mining approaches were used to classify the motion posture patterns by type of dances. A total of 15 dance videos were collected from the public available domain for body joints tracking processes using the Kinovea software. Data mining analysis was performed in three stages: data pre�processing, data classification and knowledge discovery using the WEKA software. RandomForest algorithm returned the highest classification accuracy (99.2616%). On attribute configuration, y-coordinates of left wrist (LW(y)) was identified as the most significant attribute to differentiate the Indian classical dance classes

Adopting Business Intelligence (BI) For Performance Monitoring Through USMIR

Motivation for USM to continuously measure, monitor and improve the performance comes from various factors, mainly competition to acquire best student that contribute to the quality of researchers, research and publications, secure funding, accreditation, rating and status. USM tum to ICT with the formation of USMiR project, to have cohesive, useful and sustainable Information Repository regardless of platforms, routine operating procedures as well as bureaucracies. This case study looks into the factors that contributed to the failure of USMiR project to deliver its objectives. The study begins with understanding the current state pertaining to the data flow in USM, by using MyRA audit process as an example, for easier understanding. Also, it looks into KPI-MS online platform and USMiR architecture as well as standard operating procedure in placed. Current issues such as data/information mostly confined at every schools/PTJs or at particular database which is very time consuming to be gathered, lack of data and process integration poor solution architecture design and stakeholder momentum towards USMiR project were discussed. To further understand the potential root causes of those issues, a comprehensive analysis is performed using interview, reference of documents, Fishbone and Pareto analysis tools. The goal for this study is to understand and find a solution to the problems faced by USMiR in meeting its objective

An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls

This study presents an intelligent Decision Support System (DSS) aimed at bridging the theoretical-practical gap in groundwater management. The ongoing demand for sophisticated systems capable of interpreting extensive data to inform sustainable groundwater decision- making underscores the critical nature of this research. To meet this challenge, telemetry data from six randomly selected wells were used to establish a comprehensive database of groundwater pumping parameters, including flow rate, pressure, and current intensity. Statistical analysis of these parameters led to the determination of threshold values for critical factors such as water pressure and electrical current. Additionally, a soft sensor was developed using a Random Forest (RF) machine learning algorithm, enabling real-time forecasting of key variables. This was achieved by continuously comparing live telemetry data to pump design specifications and results from regular field testing. The proposed machine learning model ensures robust empirical monitoring of well and pump health. Furthermore, expert operational knowledge from water management professionals, gathered through a Classical Delphi (CD) technique, was seamlessly integrated. This collective expertise culminated in a data-driven framework for sustainable groundwater facilities monitoring. In conclusion, this innovative DSS not only addresses the theory-application gap but also leverages the power of data analytics and expert knowledge to provide high-precision online insights, thereby optimizing groundwater management practices

A study of multicomponent gas mixtures using various analytical methods for stack emission measurements

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, in fulfillment of the requirements for the degree Master of Science. Johannesburg, 2017.Multicomponent gas mixtures are inherently challenging to produce in the laboratory because of matrix effects, boiling points and reactivity amongst other factors. Therefore, methods must be continuously developed to control these challenges. The purpose of this work was to study these complex gas mixtures to improve their measurements with emphasis on the reduction of uncertainty. There are three critical steps to be followed in gas metrology for primary reference gas mixtures of the highest metrological level; purity analysis of source gases, gravimetric preparation and verification/validation which includes stability testing. Purity analysis of select source gases was quantified using various techniques. This methodology incorporated the use of molar masses and their uncertainties in order to obtain purity values for the chemical composition of gas mixtures. While many preparation methods such as permeation and dynamic methods are available, a static gravimetric method was used to prepare the complex stack and automotive gas mixtures following International Standard Organisation: 6142-1. For the mole fraction range of interest, four components (carbon dioxide, carbon monoxide, sulphur dioxide and nitric oxide) excluding propane, were obtained from analysis by non-dispersive spectroscopy techniques calibrated by several standard gas mixtures of different mole fractions. Propane was analysed by a gas chromatograph coupled with flame ionisation detection. Multipoint calibration was used to evaluate the linearity or nonlinearity of the detector. The final results for the stack gas mixture components showed an achievement of 0.4% to 0.8% percentage relative expanded uncertainty and 0.4% to 1.3% for carbon dioxide depending on the matrix of the standard gas mixtures used, 0.5% to 1% for propane, 0.8% to 1.8% for nitric oxide, 2% to 6% for carbon monoxide and 0.3% to 2.3% for sulphur dioxide. One of the most important suppositions drawn was the incidence of synergistic effects associated with calibration by nonrepresentative standard gas mixtures when these were used for analysis for some of the components of stack mixtures. To evaluate improvements in measurement capability, the results of the current work were compared to the data of the laboratory in 2008-2011 and there was an improvement in the measurement of carbon dioxide, carbon monoxide, propane and nitric oxide. These improvements are attributed to rigorous purity analysis of starting materials, reduction of uncertainty and developments in measurement expertise. In this work, different measurement and calibration methods were used to analyse the components of the new stack gas mixtures. The stability of these components was evaluated by analysing them at different times and the statistical D-test was used to check for significant instability. An unknown stack sample was compared with the standard gas mixtures prepared for this work. In combination with same matrix and same concentrations, single point calibration was found suitable for stack gas measurement. To reiterate the concept of matrix effect, the results of carbon dioxide in a mixture containing carbon monoxide and oxygen as well in nitrogen, were used to show how differences in matrix often give erroneous results and same conclusions cannot be made for different mixtures. While the data of this measurement was unsatisfactory, an improved method developed for this type of emission multicomponent was very successful. Emission industries also require automotive primary reference gas mixtures. These are equally important and complex multicomponent mixtures measured and improved in this work. A very precise and repeatable single point method was developed for the analysis of the components of automotive mixtures. The repeatability of the gas chromatography method was 0.2% for oxygen, 0.1% for carbon monoxide, 0.5% for carbon dioxide and 0.3% for propane. The percentage relative expanded uncertainty was 0.4% for oxygen, 0.8% for carbon monoxide, 0.8% for carbon dioxide and 0.5% for propane. However, its limitation was the use of different calibration gases for each analysis. This led to inconsistencies in the calculated mole fractions, non-predictability and instability. A proficiency testing scheme was coordinated by the laboratory for automotive emission as part of this study. Given the complexity of the samples, the work aimed to check any improvements that could be made to the capability of measurement over the years. This new method using gas chromatography coupled with different detectors (residual gas analyser) was successful in verifying the gravimetric values very V accurately. Finally, the results of the stack gas mixtures were ≤1% relative except carbon monoxide and ≤1% for automotive mixtures. This work aimed to support the emission industry by providing it with representative and accurate reference gas mixtures, extend the accreditation scope of the laboratory and improve its calibration and measurement capability for multicomponent gas mixtures.LG201

Modeling Clinicians’ Cognitive and Collaborative Work in Post-Operative Hospital Care

abstract: Clinicians confront formidable challenges with information management and coordination activities. When not properly integrated into clinical workflow, technologies can further burden clinicians’ cognitive resources, which is associated with medical errors and risks to patient safety. An understanding of workflow is necessary to redesign information technologies (IT) that better support clinical processes. This is particularly important in surgical care, which is among the most clinical and resource intensive settings in healthcare, and is associated with a high rate of adverse events. There are a growing number of tools to study workflow; however, few produce the kinds of in-depth analyses needed to understand health IT-mediated workflow. The goals of this research are to: (1) investigate and model workflow and communication processes across technologies and care team members in post-operative hospital care; (2) introduce a mixed-method framework, and (3) demonstrate the framework by examining two health IT-mediated tasks. This research draws on distributed cognition and cognitive engineering theories to develop a micro-analytic strategy in which workflow is broken down into constituent people, artifacts, information, and the interactions between them. It models the interactions that enable information flow across people and artifacts, and identifies dependencies between them. This research found that clinicians manage information in particular ways to facilitate planned and emergent decision-making and coordination processes. Barriers to information flow include frequent information transfers, clinical reasoning absent in documents, conflicting and redundant data across documents and applications, and that clinicians are burdened as information managers. This research also shows there is enormous variation in how clinicians interact with electronic health records (EHRs) to complete routine tasks. Variation is best evidenced by patterns that occur for only one patient case and patterns that contain repeated events. Variation is associated with the users’ experience (EHR and clinical), patient case complexity, and a lack of cognitive support provided by the system to help the user find and synthesize information. The methodology is used to assess how health IT can be improved to better support clinicians’ information management and coordination processes (e.g., context-sensitive design), and to inform how resources can best be allocated for clinician observation and training.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201