Volume 12, Number 4

An Emotion monitoring system for a call-center is proposed. It aims to simplify the tracking and management of emotions extracted from call center Employee-Customer conversations. The system is composed of four modules: Emotion Detection, Emotion Analysis and Report Generation, Database Manager, and User Interface. The Emotion Detection module uses Tone Analyzer to extract them for reliable emotion; it also performs the Utterance Analysis for detecting emotion. The 14 emotions detected by the tone analyzer are happy, joy, anger, sad and neutral, etc. The Emotion Analysis module performs classification into the 3 categories: Neutral, Anger and Joy. By using this category, it applies the point-scoring technique for calculating the Employee Score. This module also polishes the output of the Emotion Detection module to provide a more presentable output of a sequence of emotions of the Employee and the Customer. The Database Manager is responsible for the management of the database wherein it handles the creation, and update of data. The Interface module serves as the view and user interface for the whole system. The system is comprised of an Android application for conversation and a web application to view reports. The Android application was developed using Android Studio to maintain the modularity and flexibility of the system. The local server monitors the conversation, it displays the detected emotions of both the Customer and the Employee. On the other hand, the web application was constructed using the Django Framework to maintain its modularity and abstraction by using a model. It provides reports and analysis of the emotions expressed by the customer during conversations. Using the Model View Template (MVT) approach, the Emotion monitoring system is scalable, reusable and modular.

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The security challenge on IoT (Internet of Things) is one of the hottest and most pertinent topics at the moment especially the several security challenges. The Botnet is one of the security challenges that most impact for several purposes. The network of private computers infected by malicious software and controlled as a group without the knowledge of owners and each of them running one or more bots is called Botnets. Normally, it is used for sending spam, stealing data, and performing DDoS attacks. One of the techniques that been used for detecting the Botnet is the Supervised Learning method. This study will examine several Supervised Learning methods such as; Linear Regression, Logistic Regression, Decision Tree, Naive Bayes, k- Nearest Neighbors, Random Forest, Gradient Boosting Machines, and Support Vector Machine for identifying the Botnet in IoT with the aim of finding which Supervised Learning technique can achieve the highest accuracy and fastest detection as well as with minimizing the dependent variable.

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ERP, or Enterprise Resource Planning systems help business management, which consists of a well-designed interface that incorporates different programs to integrate and manage all company functions at intervals of a company, these sets incorporate applications for human resources, monetary and accounting, sales and distribution, project management, materials management, SCM, or Supply Chain Management and quality management. Currently, organizations are running to improve their ability to survive in the global market competitions of the 21st century. While the organizations try to advance in their level of agility, changing and modifying the process of decision-making to make it more efficient and effective to satisfy the successive variations of the market. Different views are gathered regarding ERP implementation of ERP in manufacturing. Even we have taken certain essential components of ERP for a better understanding of ERP. Ease of use, usefulness, quality, and trust on ERP services have been taken an independent variable that affects user’s decision to adopt ERP. The role of ERP technology in manufacturing facilities are broken into more categories for detail concept. Quantitative data analysis methods were usually used for questionnaire data analysis which was utilized to analyze statistical data and after that collection of interview data was done. A researcher has applied different statistical tools like Chi-Square Tests, Anova, etc. to analyze the collected data. A researcher essential portion is to analyze and interpret data that relates to modifying data which explains the solution to the research question with some additional future recommendation for more quality research.

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The purpose of this research was to develop a structure for a network intrusion detection and prevention system based on the Bayesian Network for use in Cybersecurity. The phenomenal growth in the use of internet-based technologies has resulted in complexities in cybersecurity subjecting organizations to cyberattacks. What is required is a network intrusion detection and prevention system based on the Bayesian Network structure for use in Cybersecurity. Bayesian Networks (BNs) are defined as graphical probabilistic models for multivariate analysis and are directed acyclic graphs that have an associated probability distribution function. The research determined the cybersecurity framework appropriate for a developing nation; evaluated network detection and prevention systems that use Artificial Intelligence paradigms such as finite automata, neural networks, genetic algorithms, fuzzy logic, support-vector machines or diverse data-mining-based approaches; analysed Bayesian Networks that can be represented as graphical models and are directional to represent cause-effect relationships; and developed a Bayesian Network model that can handle complexity in cybersecurity. The theoretical framework on Bayesian Networks was largely informed by the NIST Cybersecurity Framework, General deterrence theory, Game theory, Complexity theory and data mining techniques. The Pragmatism paradigm used in this research, as a philosophy is intricately related to the Mixed Method Research (MMR). A mixed method approach was used in this research, which is largely quantitative with the research design being a survey and an experiment, but supported by qualitative approaches where Focus Group discussions were held. The performance of Support Vector Machines, Artificial Neural Network, K-Nearest Neighbour, Naive-Bayes and Decision Tree Algorithms was discussed. Alternative improved solutions discussed include the use of machine learning algorithms specifically Artificial Neural Networks (ANN), Decision Tree C4.5, Random Forests and Support Vector Machines (SVM).

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Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

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