Improved Instance Selection Methods for Support Vector Machine Speed Optimization

Support vector machine (SVM) is one of the top picks in pattern recognition and classification related tasks. It has been used successfully to classify linearly separable and nonlinearly separable data with high accuracy. However, in terms of classification speed, SVMs are outperformed by many machine learning algorithms, especially, when massive datasets are involved. SVM classification speed scales linearly with number of support vectors, and support vectors increase with increase in dataset size. Hence, SVM classification speed can be enormously reduced if it is trained on a reduced dataset. Instance selection techniques are one of the most effective techniques suitable for minimizing SVM training time. In this study, two instance selection techniques suitable for identifying relevant training instances are proposed. The techniques are evaluated on a dataset containing 4000 emails and results obtained compared to other existing techniques. Result reveals excellent improvement in SVM classification speed

Oil well characterization and artificial gas lift optimization using neural networks combined with genetic algorithm

This paper examines the characterization of six oil wells and the allocation of gas considering limited and unlimited case scenario. Artificial gas lift involves injecting high-pressured gas from the surface into the producing fluid column through one or more subsurface valves set at predetermined depths. This improves recovery by reducing the bottom-hole pressure at which wells become uneconomical and are thus abandoned. This paper presents a successive application of modified artificial neural network (MANN) combined with a mild intrusive genetic algorithm (MIGA) to the oil well characteristics with promising results. This method helps to prevent the overallocation of gas to wells for recovery purposes while also maximizing oil production by ensuring that computed allocation configuration ensures maximum economic accrual. Results obtained show marked improvements in the allocation especially in terms of economic returns. © 2014 Chukwuka G. Monyei et al

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

The Just-In-Time (JIT) scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA), is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS) and Simulated Annealing (SA). SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm

Nigeria's energy poverty: Insights and implications for smart policies and framework towards a smart Nigeria electricity network

© 2017 Elsevier Ltd A thorough and exhaustive review of relevant literature and associated works is carried out to critically examine energy poverty in Nigeria with respect to ownership and income. Using the desktop approach and empirical formulas, the persistent failure of public infrastructure like healthcare, education and security to the poor electricity generation, transmission and distribution capacity in the country is examined; alongside current government's contribution to buoying our generation capacity and electricity access through policies and investment. The findings of the review reveal the urgent need for the smart roll out of distributed generation units in order to stimulate and encourage the ongoing diversification of the economy and also the need for a sustainable road map that incorporates the successes of countries faced with similar challenges. This review paper also proposes the need for palliatives in form of subsidized solar home systems (SHSs) through a sustainable and economically viable means for off grid homes to assuage the effects of non-availability of grid electricity

Benchmarking RCGAu on the Noiseless BBOB Testbed

RCGAu is a hybrid real-coded genetic algorithm with “uniform random direction” search mechanism. The uniform random direction search mechanism enhances the local search capability of RCGA. In this paper, RCGAu was tested on the BBOB-2013 noiseless testbed using restarts till a maximum number of function evaluations (#FEs) of 105 × D are reached, where D is the dimension of the function search space. RCGAu was able to solve several test functions in the low search dimensions of 2 and 3 to the desired accuracy of 108. Although RCGAu found it difficult in getting a solution with the desired accuracy 108 for high conditioning and multimodal functions within the specified maximum #FEs, it was able to solve most of the test functions with dimensions up to 40 with lower precisions

A biased load manager home energy management system for low-cost residential building low-income occupants

© 2018 Elsevier Ltd This research paper presents the development of a biased load manager home energy management system for low-cost residential building occupants. As a smart grid framework, the proposed load manager coordinates the operation of the inverter system of a low cost residential apartment consisting of rooftop solar photovoltaic panels, converter and battery, and provides a platform for discriminating residential loads into on-grid and off-grid supply classes while maximizing solar irradiance for optimum battery charging and improving consumer comfort from base levels. Modelled in a Matlab simulation environment, the system incorporates a converter system for maximum power point tracking using a hopping algorithm, with a dedicated mechanism for smart dispatch of specified loads to meet the users' comfort based on the priority ranking of the loads. Results obtained indicate a 34% reduction in electricity cost, 26% reduction in carbon emissions and a 4% increase in comfort level for the photovoltaic/battery/utility option compared to the utility only option. The results further show that cost is a major factor affecting the users' comfort and not necessarily dispatch of appliances to meet energy needs. The research can be useful for encouraging the adoption of the photovoltaic/battery/utility option by low/middle income energy users in developing countries

Classification of Phishing Email Using Random Forest Machine Learning Technique

Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature) were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN) and false positive (FP) rates

Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network

Particle Swarm Optimization Algorithm for Optimizing Assignment of Blood in Blood Banking System

This paper reports the performance of particle swarm optimization (PSO) for the assignment of blood to meet patients’ blood transfusion requests for blood transfusion. While the drive for blood donation lingers, there is need for effective and efficient management of available blood in blood banking systems. Moreover, inherent danger of transfusing wrong blood types to patients, unnecessary importation of blood units from external sources, and wastage of blood products due to nonusage necessitate the development of mathematical models and techniques for effective handling of blood distribution among available blood types in order to minimize wastages and importation from external sources. This gives rise to the blood assignment problem (BAP) introduced recently in literature. We propose a queue and multiple knapsack models with PSO-based solution to address this challenge. Simulation is based on sets of randomly generated data that mimic real-world population distribution of blood types. Results obtained show the efficiency of the proposed algorithm for BAP with no blood units wasted and very low importation, where necessary, from outside the blood bank. The result therefore can serve as a benchmark and basis for decision support tools for real-life deployment

Age estimation via face images: a survey

Abstract Facial aging adversely impacts performance of face recognition and face verification and authentication using facial features. This stochastic personalized inevitable process poses dynamic theoretical and practical challenge to the computer vision and pattern recognition community. Age estimation is labeling a face image with exact real age or age group. How do humans recognize faces across ages? Do they learn the pattern or use age-invariant features? What are these age-invariant features that uniquely identify one across ages? These questions and others have attracted significant interest in the computer vision and pattern recognition research community. In this paper, we present a thorough analysis of recent research in aging and age estimation. We discuss popular algorithms used in age estimation, existing models, and how they compare with each other; we compare performance of various systems and how they are evaluated, age estimation challenges, and insights for future research