A synthetic player for Ayὸ board game using alpha-beta search and learning vector quantization

Game playing especially, Ayὸ game has been an important topic of research in artificial intelligence and several machine learning approaches have been used, but the need to optimize computing resources is important to encourage the significant interest of users. This study presents a synthetic player (Ayὸ) implemented using Alpha-beta search and Learning Vector Quantization network. The program for the board game was written in Java and MATLAB. Evaluation of the synthetic player was carried out in terms of the win percentage and game length. The synthetic player had a better efficiency compared to the traditional Alpha-beta search algorithm

Template Matching Based Sign Language Recognition System for Android Devices

An android based sign language recognition system for selected English vocabularies was developed with the explicit objective to examine the specific characteristics that are responsible for gestures recognition. Also, a recognition model for the process was designed, implemented, and evaluated on 230 samples of hand gestures.  The collected samples were pre-processed and rescaled from 3024 ×4032 pixels to 245 ×350 pixels. The samples were examined for the specific characteristics using Oriented FAST and Rotated BRIEF, and the Principal Component Analysis used for feature extraction. The model was implemented in Android Studio using the template matching algorithm as its classifier. The performance of the system was evaluated using precision, recall, and accuracy as metrics. It was observed that the system obtained an average classification rate of 87%, an average precision value of 88% and 91% for the average recall rate on the test data of hand gestures.  The study, therefore, has successfully classified hand gestures for selected English vocabularies. The developed system will enhance the communication skills between hearing and hearing-impaired people, and also aid their teaching and learning processes. Future work include exploring state-of-the-art machining learning techniques such Generative Adversarial Networks (GANs) for large dataset to improve the accuracy of results. Keywords— Feature extraction; Gestures Recognition; Sign Language; Vocabulary, Android device

Lightweight Agents, Intelligent Mobile Agent and RPC Schemes: A Comparative Analysis

This paper presents the performance comparison of Lightweight Agents, Single Mobile Intelligent Agents and Remote Procedure Call which are tools for implementing communication in a distributed computing environment. Routing algorithms for each scheme is modeled based on TSP. The performance comparison among the three schemes is based on bandwidth overhead with retransmission, system throughput and system latency. The mathematical model for each performance metric is presented, from which mathematical model is derived for each scheme for comparison. The simulation results show that the LWAs has better performance than the other two schemes in terms of small bandwidth retransmission overhead, high system throughput and low system latency. The Bernoulli random variable is used to model the failure rate of the simulated network which is assumed to have probability of success p = 85% and the probability of failure q = 15%. The network availability is realized by multiplicative pseudorandom number generator during the simulation. The results of simulation are presented

A low cost course information syndication system

This study presents a cost-effective, reliable, and convenient mobile web-based system to facilitate the dissemination of course information to students, to support interaction that goes beyond the classroom. The system employed the Really Simple Syndication (RSS) technology and was developed using Rapid Application Development (RAD) methodology. The design of the system was modelled using Unified Modeling Language (UML) diagrams, while its implementation was done using Java Micro Edition (JME) and “PHP: Hypertext Preprocessor” (PHP).A simulation technique was used to evaluate the proposed system performance by comparing the approach used in its design to one adopted in a similar study, using response time and bandwidthconsumption as metrics. The results obtained revealed that the performance of the proposed syndication system was better. Similarly, an experiment to investigate the students’ perception of the system was conducted, with students’ responses revealing a tremendous success of this project

A fuzzy semantic information retrieval system for transactional applications

In this paper, we present an information retrieval system based on the concept of fuzzy logic to relate vague and uncertain objects with un-sharp boundaries. The simple but comprehensive user interface of the system permits the entering of uncertain specifications in query forms. The system was modelled and simulated in a Matlab environment; its implementation was carried out using Borland C++ Builder. The result of the performance measure of the system using precision and recall rates is encouraging. Similarly, the smaller amount of more precise information retrieved by the system will positively impact the response time perceived by the users

Two-stage capacity optimization approach of multi-energy system considering its optimal operation

With the depletion of fossil fuel and climate change, multi-energy systems have attracted widespread attention in buildings. Multi-energy systems, fuelled by renewable energy, including solar and biomass energy, are gaining increasing adoption in commercial buildings. Most of previous capacity design approaches are formulated based upon conventional operating schedules, which result in inappropriate design capacities and ineffective operating schedules of the multi-energy system. Therefore, a two-stage capacity optimization approach is proposed for the multi-energy system with its optimal operating schedule taken into consideration. To demonstrate the effectiveness of the proposed capacity optimization approach, it is tested on a renewable energy fuelled multi-energy system in a commercial building. The primary energy devices of the multi-energy system consist of biomass gasification-based power generation unit, heat recovery unit, heat exchanger, absorption chiller, electric chiller, biomass boiler, building integrated photovoltaic and photovoltaic thermal hybrid solar collector. The variable efficiency owing to weather condition and part-load operation is also considered. Genetic algorithm is adopted to determine the optimal design capacity and operating capacity of energy devices for the first-stage and second-stage optimization, respectively. The two optimization stages are interrelated; thus, the optimal design and operation of the multi-energy system can be obtained simultaneously and effectively. With the adoption of the proposed novel capacity optimization approach, there is a 14% reduction of year-round biomass consumption compared to one with the conventional capacity design approach

Development and testing of a graphical FORTRAN learning tool for novice programmers

To address the difficulties associated with computer programming, this article first looks at some reasons why students, especially engineering students, find programming such a daunting prospect, and it proposes a programming learning tool managed by a Deterministic Finite Automaton (DFA). The DFA machine used a graphical environment provided by Simulink to teach the FOR-mula TRANslator (FORTRAN) programming language to science students. The proposed programming learning tool and the traditional method of teaching were compared and evaluated. The results of evaluation indicated that there was an improvement in learning effectiveness of the proposed learning tool

Big data analytics system for costing power transmission projects

© 2019 American Society of Civil Engineers. Inaccurate cost estimates have significant impacts on the final cost of power transmission projects and erode profits. Methods for cost estimation have been investigated thoroughly, but they are not used widely in practice. The purpose of this study is to leverage a big data architecture, to manage the large and diverse data required for predictive analytics. This paper presents a predictive analytics and modeling system (PAMS) that facilitates the use of different data-driven cost prediction methods. A 2.75-million-point dataset of power transmission projects has been used as a case study. The proposed big data architecture fits this purpose. It can handle the diverse datasets used in the construction sector. The three most prevalent cost estimation models were implemented (linear regression, support vector regression, and artificial neural networks). All models performed better than the estimated human-level performance. The primary contribution of this study to the body of knowledge is an empirical indication that data-driven methods analysed in this study are on average 13.5% better than manual methods for cost estimation of power transmission projects. Additionally, the paper presents a big data architecture that can manage and process large varied datasets and seamless scalability

Performance comparison of deep learning and boosted trees for cryptocurrency closing price prediction

The emergence of cryptocurrencies has drawn significant investment capital in recent years with an exponential increase in market capitalization and trade volume. However, the cryptocurrency market is highly volatile and burdened with substantial heterogeneous datasets characterized by complex interactions between predictors, which may be difficult for conventional techniques to achieve optimal results. In addition, volatility significantly impacts investment decisions; thus, investors are confronted with how to determine the price and assess their financial investment risks reasonably. This study investigates the performance evaluation of a genetic algorithm tuned Deep Learning (DL) and boosted tree-based techniques to predict several cryptocurrencies' closing prices. The DL models include Convolutional Neural Networks (CNN), Deep Forward Neural Networks, and Gated Recurrent Units. The study assesses the performance of the DL models with boosted tree-based models on six cryptocurrency datasets from multiple data sources using relevant performance metrics. The results reveal that the CNN model has the least mean average percentage error of 0.08 and produces a consistent and highest explained variance score of 0.96 (on average) compared to other models. Hence, CNN is more reliable with limited training data and easily generalizable for predicting several cryptocurrencies' daily closing prices. Also, the results will help practitioners obtain a better understanding of crypto market challenges and offer practical strategies to lower risks

Benchmarks for energy access: Policy vagueness and incoherence as barriers to sustainable electrification of the global south

© 2019 The unavailability of tangible policy benchmarks continues to mitigate against sustainable electrification in the global south. Furthermore, incoherent policy benchmarks as to what should constitute clean energy allow for varying interpretations and divergent options in electrifying households across the global south. The multiplicity of policies to deepen access to improved energy services in the global south notwithstanding, ‘success’ is not in sight until definite and uniform benchmarks guide the roll-out of electrification schemes