Adamu Abubakar

A Review of the Applications of Bio-inspired Flower Pollination Algorithm☆

Abstract The Flower Pollination Algorithm (FPA) is a novel bio-inspired optimization algorithm that mimics the real life processes of the flower pollination. In this paper, we review the applications of the Single Flower Pollination Algorithm (SFPA), Multi-objective Flower Pollination Algorithm an extension of the SFPA and the Hybrid of FPA with other bio-inspired algorithms. The review has shown that there is still a room for the extension of the FPA to Binary FPA. The review presented in this paper can inspire researchers in the bio-inspired algorithms research community to further improve the effectiveness of the PFA as well as to apply the algorithm in other domains for solving real life, complex and nonlinear optimization problems in engineering and industry. Further research and open questions were highlighted in the paper.

A new approach for forecasting OPEC petroleum consumption based on neural network train by using flower pollination algorithm

Display Omitted We proposed a new forecasting method based on mete-heuristic algorithm.The method was applied to forecast OPEC petroleum consumption.The new method outperforms previous methods in forecasting OPEC petroleum consumption.The new method is an alternative means of forecasting OPEC petroleum consumption. Petroleum is the live wire of modern technology and its operations, with economic development being positively linked to petroleum consumption. Many meta-heuristic algorithms have been proposed in literature for the optimization of Neural Network (NN) to build a forecasting model. In this paper, as an alternative to previous methods, we propose a new flower pollination algorithm with remarkable balance between consistency and exploration for NN training to build a model for the forecasting of petroleum consumption by the Organization of the Petroleum Exporting Countries (OPEC). The proposed approach is compared with established meta-heuristic algorithms. The results show that the new proposed method outperforms existing algorithms by advancing OPEC petroleum consumption forecast accuracy and convergence speed. Our proposed method has the potential to be used as an important tool in forecasting OPEC petroleum consumption to be used by OPEC authorities and other global oil-related organizations. This will facilitate proper monitoring and control of OPEC petroleum consumption.

Computational intelligence techniques with application to crude oil price projection: a literature survey from 2001–2012

This paper is an attempt to survey the applications of computational intelligence techniques for predicting crude oil prices over a period of ten years. The purpose of this research is to provide an exhaustive overview of the existing literature which may assist prospective researchers. The reviewed literature covers a spectrum of publications on the proposed model, source of experimental data, period of data collection, year of publication and contributors. The overall trend of the publications in this area of research issued within the last decade is also addressed. The existing body of research has been analyzed and new research directions have been outlined that have been previously ignored. It is expected that researchers across the globe may thus be encouraged to re-direct their attention and resources in order to keep on searching for an optimum solution.

Exploring end-user preferences of 3D mobile interactive navigation design

The main aim of 3D mobile interactive navigation support is to offer end-users easier means of accomplishing basic and complex navigation tasks in a user friendly way. Unfortunately, not until the end-users make use of the end-product or prototype, their needs, preferences, and their true understanding will not be acknowledged. As a result of these considerations, this paper presents a qualitative interview study to explore the end-users preferences for 3D mobile interactive navigation system, so as to determine design preferences of the application. The study uses a prototype of the design and conducted photo-diary method interviews, where the prototype visual representations is presented to the prospective end-user in support of an interview, which makes the interview more concrete. The result of the study provided clues about the features that were not foreseen in the earlier design process, the features that need to be upgrade and the features that end-users value most.

A Review of the Advances in Cyber Security Benchmark Datasets for Evaluating Data-Driven Based Intrusion Detection Systems☆

Abstract Cybercrime has led to the loss of billions of dollars, the malfunctioning of computer systems, the destruction of critical information, the compromising of network integrity and confidentiality, etc. In view of these crimes committed on a daily basis, the security of the computer systems has become imperative to minimize and possibly avoid the impact of cybercrimes. In this paper, we review recent advances in the use of cyber security benchmark datasets for the evaluation of machine learning and data mining-based intrusion detection systems. It was found that the state-of-the-art cyber security benchmark datasets KDD and UNM are no longer reliable, because their datasets cannot meet the expectations of current advances in computer technology. As a result, a new ADFA Linux (ADFA-LD)cyber security benchmark dataset for the evaluation of machine learning and data mining-based intrusion detection systems was proposed in 2013 to meet the current significant advances in computer technology. ADFA-LD requires improvement in terms of full descriptions of its attributes. This review can be used by the research community as a basis for abandoning the previous state-of-the-art cyber security benchmark datasets and starting to use the newly introduced benchmark dataset for effective and robust evaluation of machine learning and data mining-based intrusion detection system.

Global Warming: Predicting OPEC Carbon Dioxide Emissions from Petroleum Consumption Using Neural Network and Hybrid Cuckoo Search Algorithm

Background Global warming is attracting attention from policy makers due to its impacts such as floods, extreme weather, increases in temperature by 0.7°C, heat waves, storms, etc. These disasters result in loss of human life and billions of dollars in property. Global warming is believed to be caused by the emissions of greenhouse gases due to human activities including the emissions of carbon dioxide (CO2) from petroleum consumption. Limitations of the previous methods of predicting CO2 emissions and lack of work on the prediction of the Organization of the Petroleum Exporting Countries (OPEC) CO2 emissions from petroleum consumption have motivated this research. Methods/Findings The OPEC CO2 emissions data were collected from the Energy Information Administration. Artificial Neural Network (ANN) adaptability and performance motivated its choice for this study. To improve effectiveness of the ANN, the cuckoo search algorithm was hybridised with accelerated particle swarm optimisation for training the ANN to build a model for the prediction of OPEC CO2 emissions. The proposed model predicts OPEC CO2 emissions for 3, 6, 9, 12 and 16 years with an improved accuracy and speed over the state-of-the-art methods. Conclusion An accurate prediction of OPEC CO2 emissions can serve as a reference point for propagating the reorganisation of economic development in OPEC member countries with the view of reducing CO2 emissions to Kyoto benchmarks—hence, reducing global warming. The policy implications are discussed in the paper.

Bio-inspired computation: Recent development on the modifications of the cuckoo search algorithm

Abstract Presently, the Cuckoo Search algorithm is attracting unprecedented attention from the research community and applications of the algorithm are expected to increase in number rapidly in the future. The purpose of this study is to assist potential developers in selecting the most suitable cuckoo search variant, provide proper guidance in future modifications and ease the selection of the optimal cuckoo search parameters. Several researchers have attempted to apply several modifications to the original cuckoo search algorithm in order to advance its effectiveness. This paper reviews the recent advances of these modifications made to the original cuckoo search by analyzing recent published papers tackling this subject. Additionally, the influences of various parameter settings regarding cuckoo search are taken into account in order to provide their optimal settings for specific problem classes. In order to estimate the qualities of the modifications, the percentage improvements made by the modified cuckoo search over the original cuckoo search for some selected reviews studies are computed. It is found that the population reduction and usage of biased random walk are the most frequently used modifications. This study can be used by both expert and novice researchers for outlining directions for future development, and to find the best modifications, together with the corresponding optimal setting of parameters for specific problems. The review can also serve as a benchmark for further modifications of the original cuckoo search.

Usability evaluation of hospital websites in Nigeria: what affects end users’ preferences?

Hospital providers need to deliver satisfactory services in a specialized field which involves a great number of stakeholders with different concerns, needs and requirements. Some hospitals’ policies have been focused on providing health and medical services to the public. Less attention has been given to the responsibility to provide useful, accurate health information of high quality to their key publics mainly by facilitating interactive communication with patients, citizens and physicians and community services. To date, hospitals are turning increasingly towards the Internet and have developed their own web presence in order to enhance interactive communication practices. The research evaluated the usability of hospital websites in Nigeria, focusing on two websites in south- west of the country. Evaluation criteria for assessment were developed. The results provided empirical evidence that websites should be easy to use as well as aesthetically pleasing but must be rich in information content.

Co — Active neuro-fuzzy inference systems model for predicting crude oil price based on OECD inventories

This paper present a novel approach to crude oil price prediction based on co-active neuro-fuzzy inference systems (CANFIS) instead of the commonly use fuzzy neural network and adaptive network-based fuzzy inference systems due to superiority and robustness of the CANFIS model. Monthly data of West Texas Intermediate crude oil price and organization for economic co-operation and development (OECD) inventories, obtained from US Department of Energy were used to built the propose model. The CANFIS prediction model was trained, validated and tested. The performance of our approach is measured using mean square error, root mean square error, mean absolute error and regression. Suggestion from the results shows that the CANFIS demonstrated a high level of generalization capability with relatively very low error and high correlation which exhibited successful prediction performance of the proposal. The model has the potential of being developed into real life systems for use by both government and private businesses for making strategic planning that can boost economic activities.

A Review on Soft Set-Based Parameter Reduction and Decision Making

Many real world decision making problems often involve uncertainty data, which mainly originating from incomplete data and imprecise decision. The soft set theory as a mathematical tool that deals with uncertainty, imprecise, and vagueness is often employed in solving decision making problem. It has been widely used to identify irrelevant parameters and make reduction set of parameters for decision making in order to bring out the optimal choices. In this paper, we present a review on different parameter reduction and decision making techniques for soft set and hybrid soft sets under unpleasant set of hypothesis environment as well as performance analysis of the their derived algorithms. The review has summarized this paper in those areas of research, pointed out the limitations of previous works and areas that require further research works. Researchers can use our review to quickly identify areas that received diminutive or no attention from researchers so as to propose novel methods and applications.