Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast.Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year.

TLBO (Teaching -learning -based optimization) is a nature inspired optimization algorithm. There are many evolutionary algorithms like genetic algorithm, ant colony optimization, particle swarm optimization etc. All these algorithms depend on algorithmic parameters. A small change in these algorithmic parameters may cause a large change in the effectiveness of the algorithm. In this scenario TLBO is coming to picture. TLBO is independent of algorithmic parameters. TLBO follows the Teacher -Student and Student -Student interaction in the class room. TLBO have two phases, Teacher Phase and Learner Phase. The key feature of TLBO is, in the first stage algorithm attains average learning, in the second stage algorithm pick the best solution. In teacher phase, teacher is one of the learners among the population who has best knowledge level. Teacher tries to improve the mean knowledge level of class up to his level. When learners reached teacher's knowledge level, algorithm needs a new teacher with more knowledge. In the learner phase, learners interact with each other to improve their knowledge. This technique will be used in the learning of the parameters of the RBF network

Heart diseases are the most important reasons behind the high rate of morbidity and mortality among the world's population. In clinical data analysis, heart disease prediction is represented as an important problem. Progressively, the number of data is increasing, to analyze and processing it is very difficult and specially, it turns out to be to maintain the e-healthcare data. In addition, the prediction model in machine learning is considered as a necessary feature in this paper. Thus, this work concentrates to present a novel heart disease prediction technique by means of considering particular processes such as Feature Extraction, Record, minimization of Attribute, and Classification. At first, in feature extraction, both the higher-order and statistical features are extracted. Then, minimization of attribute and record is performed; to solve the curse of dimensionality the Component analysis Principle Component Analysis (PCA) acts an important role. At last, using the Neural Network (NN) model the prediction process is performed which consumes the dimensionally minimized features. Additionally, one of the main contributions of this article is to work on accurate prediction. Therefore, for the weight optimization of NN, the meta-heuristic techniques are exploited in this work. A novel optimization algorithm named Archimedes Optimization Algorithm (AOA) is proposed which resolves the aforesaid optimization issues. At last, the outcomes of the proposed method states that its efficiency over the other conventional methods.