A genetic Algorithm-Based feature selection

This article details the exploration and application of Genetic Algorithm (GA) for feature selection. Particularly a binary GA was used for dimensionality reduction to enhance the performance of the concerned classifiers. In this work, hundred (100) features were extracted from set of images found in the Flavia dataset (a publicly available dataset). The extracted features are Zernike Moments (ZM), Fourier Descriptors (FD), Lengendre Moments (LM), Hu 7 Moments (Hu7M), Texture Properties (TP) and Geometrical Properties (GP). The main contributions of this article are (1) detailed documentation of the GA Toolbox in MATLAB and (2) the development of a GA-based feature selector using a novel fitness function (kNN-based classification error) which enabled the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. The results obtained were compared with various feature selectors from WEKA software and obtained better results in many ways than WEKA feature selectors in terms of classification accuracy

Antioxidant activity and chemical composition of Clarias gariepinus from nature and captivity

Gradual shift from eating healthy to unhealthy food is a major contributor to development of noncommunicable diseases . This study compared the antioxidant activity and chemical composition of wild and cultured Clarias gariepinus. A total of 10 fish each were collected from both environments. Nutrient composition, antioxidant activity, fatty acid and heavy metals load of the samples were determined using standard methods. Significant difference was observed in the proximate composition (crude protein (CP), Ether Extract (EE) and Nitrogen free extract in both samples, with higher values of CP (54.98 ± 0.66%) and EE (34.17 ± 0.33%) observed in the wild sample. No significant difference was observed in the values for sodium, potassium, phosphorus, calcium, magnesium, zinc and iron. However, manganese (350.93 ppm) was significantly (P<0.05) higher in the cultured species. Scavenging activity against 2, 2-diphenyl-1-picrylhydrazyl (DPPH) was not statistically different (P > 0.05) at 50 mg/ml concentration. Hydrogen peroxide radical scavenging activity was however low in cultured and wild samples (1.27 and 8.12 mg/100 g) respectively. Heavy metals level in both samples were not statistically different (P>0.05). It was concluded that the cultured C. gariepinus compared favourably with the wild species in their mineral composition, antioxidant activity, and heavy metal content as opposed to local belief

Genetic Algorithms as a Feature Selection Tool in Heart Failure Disease

A great wealth of information is hidden in clinical datasets, which could be analyzed to support decision-making processes or to better diagnose patients. Feature selection is one of the data pre-processing that selects a set of input features by removing unneeded or irrelevant features. Various algorithms have been used in healthcare to solve such problems involving complex medical data. This paper demonstrates how Genetic Algorithms offer a natural way to solve feature selection amongst data sets, where the fittest individual choice of variables is preserved over different generations. In this paper, a Genetic Algorithm is introduced as a feature selection method and shown to be effective in aiding understanding of such data

Objectively measured physical activity and cardiac biomarkers: A cross sectional population based study in older men.

BACKGROUND: N-terminal pro-brain natriuretic peptide (NT-proBNP) and high sensitivity Troponin T (hsTnT) are markers of cardiac injury used in diagnosis of heart failure and myocardial infarction respectively, and associated with increased risk of cardiovascular disease. Since physical activity is protective against cardiovascular disease and heart failure, we investigated whether higher levels of physical activity, and less sedentary behaviour were associated with lower NT-proBNP and hsTnT. METHODS AND RESULTS: Cross sectional study of 1130 men, age 70-91years, from the British Regional Heart Study, measured in 2010-2012. Fasting blood samples were analysed for NT-proBNP and hsTnT. Physical activity and sedentary behaviour were measured using ActiGraph GT3X accelerometers. Relationships between activity and NT-proBNP or hsTnT were non-linear; biomarker levels were lower with higher total activity, steps, moderate/vigorous activity and light activity only at low to moderate levels of activity. For example, for each additional 10min of moderate/vigorous activity, NT-proBNP was lower by 35.7% (95% CI -47.9, -23.6) and hsTnT by 8.4% (95% CI -11.1, -5.6), in men who undertook <25 or 50min of moderate/vigorous activity per day respectively. Biomarker levels increased linearly with increasing sedentary behaviour, but not independently of moderate/vigorous activity. CONCLUSION: Associations between biomarkers and moderate/vigorous activity (and between hsTnT and light activity) were independent of sedentary behaviour, suggesting activity is driving the relationships. In these older men with concomitantly low levels of physical activity, activity may be more important in protecting against cardiac health deterioration in less active individuals, although reverse causality might be operating

An optimisation study on integrating and incentivising Thermal Energy Storage (TES) in a dwelling energy system

In spite of the benefits from thermal energy storage (TES) integration in dwellings, the penetration rate in Europe is 5%. Effective fiscal policies are necessary to accelerate deployment. However, there is currently no direct support for TES in buildings compared to support for electricity storage. This could be due to lack of evidence to support incentivisation. In this study, a novel systematic framework is developed to provide a case in support of TES incentivisation. The model determines the costs, CO2 emissions, dispatch strategy and sizes of technologies, and TES for a domestic user under policy neutral and policy intensive scenarios. The model is applied to different building types in the UK. The model is applied to a case study for a detached dwelling in the UK (floor area of 122 m2), where heat demand is satisfied by a boiler and electricity imported from the grid. Results show that under a policy neutral scenario, integrating a micro-Combined Heat and Power (CHP) reduces the primary energy demand by 11%, CO2 emissions by 21%, but with a 16 year payback. Additional benefits from TES integration can pay for the investment within the first 9 years, reducing to 3.5–6 years when the CO2 levy is accounted for. Under a policy intensive scenario (for example considering the Feed in Tariff (FIT)), primary energy demand and CO2 emissions reduce by 17 and 33% respectively with a 5 year payback. In this case, the additional benefits for TES integration can pay for the investment in TES within the first 2 years. The framework developed is a useful tool is determining the role TES in decarbonising domestic energy systems

Scheduling of projects under the condition of inflation

This paper (see also [4]) develops a linear programming (LP) model for an efficient scheduling and management of big projects. The model has an inflation factor as one of its major features and incorporates an extensive modification of existing project scheduling models. The paper includes numerical examples to demonstrate the usefulness of the model and the consequences of excluding inflation factor during project cost-estimation, planning and scheduling in an environment of high inflation. By transforming the variables of the LP model, a new model with fewer constraints is obtained, thereby reducing the amount of effort required to find an optimal solution.cost model project management LP inflation

A computational approach to plant leaves identification

The manual classification of plants’ species based on images of their leaves is timeconsuming and prone to human error. It also requires expert in specific knowledge agricultural domain. The essence of computer-based plants classification system is to augment the manual type so as to increase speed, efficiency, and accuracy of recognition. This study shows the basic principles behind the application of image processing techniques in agriculture. It shows how descriptors are extracted from the images of plant species and further processed for automated image classification of plant species

A two-step optimization model for quantifying the flexibility potential of power-to-heat systems in dwellings

Coupling the electricity and heat sectors is receiving interest as a potential source of flexibility to help absorb surplus renewable electricity. The flexibility afforded by power-to-heat systems in dwellings has yet to be quantified in terms of time, energy and costs, and especially in cases where homeowners are heterogeneous prosumers. Flexibility quantification whilst accounting for prosumer heterogeneity is non-trivial. Therefore in this work a novel two-step optimization framework is proposed to quantify the potential of prosumers to absorb surplus renewable electricity through the integration of air source heat pumps and thermal energy storage. The first step is formulated as a multi-period mixed integer linear programming problem to determine the optimal energy system, and the quantity of surplus electricity absorbed. The second step is formulated as a linear programming problem to determine the price a prosumer will accept for absorbing surplus electricity, and thus the number of active prosumers in the market.A case study of 445 prosumers is presented to illustrate the approach. Results show that the number of active prosumers is affected by the quantity of absorbed electricity, frequency of requests, the price offered by aggregators and how prosumers determine the acceptable value of flexibility provided. This study is a step towards reducing the need for renewable curtailment and increasing pricing transparency in relation to demand-side response