Energy Efficiency Prediction using Artificial Neural Network

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin

An Overview of the Use of Neural Networks for Data Mining Tasks

In the recent years the area of data mining has experienced a considerable demand for technologies that extract knowledge from large and complex data sources. There is a substantial commercial interest as well as research investigations in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from datasets. Artificial Neural Networks (NN) are popular biologically inspired intelligent methodologies, whose classification, prediction and pattern recognition capabilities have been utilised successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks

Chemical and biological reactions of solidification of peat using ordinary portland cement (OPC) and coal ashes

Construction over peat area have often posed a challenge to geotechnical engineers. After decades of study on peat stabilisation techniques, there are still no absolute formulation or guideline that have been established to handle this issue. Some researchers have proposed solidification of peat but a few researchers have also discovered that solidified peat seemed to decrease its strength after a certain period of time. Therefore, understanding the chemical and biological reaction behind the peat solidification is vital to understand the limitation of this treatment technique. In this study, all three types of peat; fabric, hemic and sapric were mixed using Mixing 1 and Mixing 2 formulation which consisted of ordinary Portland cement, fly ash and bottom ash at various ratio. The mixtures of peat-binder-filler were subjected to the unconfined compressive strength (UCS) test, bacterial count test and chemical elemental analysis by using XRF, XRD, FTIR and EDS. Two pattern of strength over curing period were observed. Mixing 1 samples showed a steadily increase in strength over curing period until Day 56 while Mixing 2 showed a decrease in strength pattern at Day 28 and Day 56. Samples which increase in strength steadily have less bacterial count and enzymatic activity with increase quantity of crystallites. Samples with lower strength recorded increase in bacterial count and enzymatic activity with less crystallites. Analysis using XRD showed that pargasite (NaCa2[Mg4Al](Si6Al2)O22(OH)2) was formed in the higher strength samples while in the lower strength samples, pargasite was predicted to be converted into monosodium phosphate and Mg(OH)2 as bacterial consortium was re-activated. The Michaelis�Menten coefficient, Km of the bio-chemical reaction in solidified peat was calculated as 303.60. This showed that reaction which happened during solidification work was inefficient. The kinetics for crystallite formation with enzymatic effect is modelled as 135.42 (1/[S] + 0.44605) which means, when pargasite formed is lower, the amount of enzyme secretes is higher

Elephant Search with Deep Learning for Microarray Data Analysis

Even though there is a plethora of research in Microarray gene expression data analysis, still, it poses challenges for researchers to effectively and efficiently analyze the large yet complex expression of genes. The feature (gene) selection method is of paramount importance for understanding the differences in biological and non-biological variation between samples. In order to address this problem, a novel elephant search (ES) based optimization is proposed to select best gene expressions from the large volume of microarray data. Further, a promising machine learning method is envisioned to leverage such high dimensional and complex microarray dataset for extracting hidden patterns inside to make a meaningful prediction and most accurate classification. In particular, stochastic gradient descent based Deep learning (DL) with softmax activation function is then used on the reduced features (genes) for better classification of different samples according to their gene expression levels. The experiments are carried out on nine most popular Cancer microarray gene selection datasets, obtained from UCI machine learning repository. The empirical results obtained by the proposed elephant search based deep learning (ESDL) approach are compared with most recent published article for its suitability in future Bioinformatics research.Comment: 12 pages, 5 Tabl