Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Spatial-temporal data modelling and processing for personalised decision support

The purpose of this research is to undertake the modelling of dynamic data without losing any of the temporal relationships, and to be able to predict likelihood of outcome as far in advance of actual occurrence as possible. To this end a novel computational architecture for personalised ( individualised) modelling of spatio-temporal data based on spiking neural network methods (PMeSNNr), with a three dimensional visualisation of relationships between variables is proposed. In brief, the architecture is able to transfer spatio-temporal data patterns from a multidimensional input stream into internal patterns in the spiking neural network reservoir. These patterns are then analysed to produce a personalised model for either classification or prediction dependent on the specific needs of the situation. The architecture described above was constructed using MatLab© in several individual modules linked together to form NeuCube (M1). This methodology has been applied to two real world case studies. Firstly, it has been applied to data for the prediction of stroke occurrences on an individual basis. Secondly, it has been applied to ecological data on aphid pest abundance prediction. Two main objectives for this research when judging outcomes of the modelling are accurate prediction and to have this at the earliest possible time point. The implications of these findings are not insignificant in terms of health care management and environmental control. As the case studies utilised here represent vastly different application fields, it reveals more of the potential and usefulness of NeuCube (M1) for modelling data in an integrated manner. This in turn can identify previously unknown (or less understood) interactions thus both increasing the level of reliance that can be placed on the model created, and enhancing our human understanding of the complexities of the world around us without the need for over simplification. Read less Keywords Personalised modelling; Spiking neural network; Spatial-temporal data modelling; Computational intelligence; Predictive modelling; Stroke risk predictio

Assessing the factors of green computing adoption among manufacturing employees: an analysis of the electrical and electronic sector

Recent trends and heavy uses of IT products and electronic gadgets have led to a proliferation of green computing studies because these wastes are not biodegradable. A significant amount of previous studies has been performed on green computing at the organizational level with most studies tend to focus on developed countries. The present study examined numerically the most influential factor towards the employees’ intention to adopt green computing and measure the intention level of employees in green computing adoption. The current study explored five adoption factors with five hypotheses have been established. These hypotheses were theorized from Theory of Planned Behaviour with emphasis on environmental concern. Previous studies from Malaysia context have primarily concentrated on green computing in education sector compared to manufacturing sector. Hence the study was conducted at electrical and electronic industries located at southern Malaysia. Questionnaires were purposely distributed to 250 respondents, however only 110 responses were valid that yielded response rate of 56%. Respondents are among the employees in IT and administration department that equipped with ICT application. The finding verifies the most influential factor affecting green computing adoption is environmental concern with 43.8%. All factors were proved to have positive correlation to the green computing intention. This correlation is related to the high intention level of employees in practicing green computing due to headquarters initiatives and the conditions set by the importing countries. Social norms have less impact towards behavioral intention to practice green computing that manifested by the low correlation percentage. The current study contributes to our knowledge on the green computing intention among manufacturing employees in Malaysia besides the implications of the results and future research directions. This study will help the policy makers in promoting environmental awareness among users of computing devices

Flow Shop Scheduling Problem: a Computational Study

A computational study has been developed to obtain optimal / near optimal solution for the flow shop scheduling problem with make-span minimization as the primary criterion and the minimization of either the mean completion time, total waiting time or total idle time as the secondary criterion. The objective is to determine a sequence of operations in which to process ‘n’ jobs on ‘m’ machines in same order (flow shop environment) where skipping is allowed. The Simulation approach for deterministic and stochastic flow shop scheduling has been developed. It reads and manipulates data for 500 jobs on 500 machines. Different factorial experiments present a comparative study on the performance of different dispatching rules, such as FCFS, SPT, LPT, SRPT and LRPT with respect to the objectives of minimizing makespan, mean flow time, waiting time of jobs, and idle time of machines. The proposed model is evaluated and found to be relatively more effective in finding optimal/ near optimal solutions in many cases. The influence of the problem size in computational time for this model is discussed and recommendations for further research are presented