This research develops a fuzzy-neural network model, using AI technologies and applies the model for effective control of profitability in paper recycling to improve production accuracy, reliability, robustness and to maximize profit generated by an industry, despite varying cost of production upon which ultimately profit, in an industry depend. Recycling reduces greenhouse gas emissions, conserves the natural resources on Earth, and saves space in the landfills for future generations of people. A sustainable future requires a high degree of recycling. However, Recycling industries face serious economic problems that increase the cost of recycling. Fuzzy logic has emerged as a tool to deal with uncertain, imprecise, partial truth or qualitative decisionmaking problems, to achieve robustness, tractability, and low cost, but it cannot automatically acquire the rules it uses to make those decisions. Neural networks have the ability to learn, generalize and process large amount of numerical data, but they are not good at explaining how they reach their decisions. The hybrid fuzzy-neural system has the ability to overcome the limitations of individual technique and enhances their strengths to handle financial trading. In order to achieve our objective, a study of a knowledge based system for effective control of profitability in paper recycling is carried out. The Mamdani's Max-Min technique is employed to infer data from the rules developed. This resulted in the establishment of some degrees of influence of input variables on the output. Fuzzy-Neural network model is developed using back propagation and supervised learning methods respectively. The outputs of Fuzzy logic serve as input to the neural network. To reinforce the proposed approach, we apply it to a case study performed on Paper recycling industry in Nigeria. A computer simulation is designed to assist the experimental decision for the best control action. The system is developed using MySQL, NetBeans, Java, MS Excel 2003, MatLab, etc. The obtained simulation and implementation fuzzy-neural results are investigated, compared and discussed.

Ubiquitous Computing is a trending innovation that allows a user to have access to many computers in a transparent manner anytime anywhere thereby enhancing computing confidence. However, the full potential of ubiquitous computing is not yet realised due to challenges including changing location of mobile users, poor network infrastructure, limited system resources, and poor transaction processing model. This work is concerned with the development of a proactive support for active transaction coordination in ubiquitous computing environment. The specific objectives are to identify relevant values of predefined key features of processing units that greatly impact on ubiquitous computing and to predict the processing capability of processing units using relevant values of the predefined features. An object-oriented analysis and system design methodology is employed and the proposed processing unit eligibility identification mechanism and neural network-based classifier is shown to effectively support ubiquitous computing.

This research work investigates the use of machine learning algorithms (Linear Regression and K-Nearest Neighbour) for NFL games result prediction. Data mining techniques were employed on carefully created features with datasets from NFL games statistics using RapidMiner and Java programming language in the backend. High attribute weights of features were obtained from the Linear Regression Model (LR) which provides a basis for the K-Nearest Neighbour Model (KNN). The result is a hybridized model which shows that using relevant features will provide good prediction accuracy. Unique features used are: Bookmakers betting spread and players' performance metrics. The prediction accuracy of 80.65% obtained shows that the experiment is substantially better than many existing systems with accuracies of 59.4%, 60.7%, 65.05% and 67.08%. This can therefore be a reference point for future research in this area especially on employing machine learning in predictions.

In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be reconsidered in the presence of data streams, offering a new research direction for the database community. Consequently, research has moved from the traditional database management system (DBMS) to the data stream management system (DSMS) which is powered by continuous queries. In this paper, continuous query (CQ) is introduced to motivate our discussion of its application areas.

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