Diabetes is a faction of metabolic ailments distinguished by hyperglycemia which is the consequence of a defect, in the action of insulin, insulin secretion, or both and producing various abnormalities in the human body. In recent years, the utilization of intelligent systems has been expanded in disease classification and numerous researches have been proposed. In this research article, a variant of Convolutional Neural Network (CNN) that is, Functional Link Convolutional Neural Network (FLCNN) is proposed for the diabetes classification. The main goal of this article is to find the potential of a computationally less complex deep learning network like FLCNN and applied the proposed technique on a real dataset of diabetes for classification. This article also presents the comparative studies where various other machine learning techniques are implemented and outcomes are compared with the proposed FLCNN network. The performance of each classification techniques have been evaluated based on standard measures and also validated with a non‐parametric statistical test such as Friedman. Data for modeling diabetes classification is collected from Bombay Medical Hall, Upper Bazar, Ranchi, India. Accuracy achieve by the proposed classifier is more than 90% which is closer to the other state‐of‐the‐art implemented classifiers.
Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.
A number of renewable or non-renewable sources of energy present these days are not able to meet our need of power. The need of power is increasing day by day as the population is increasing so we are finding different ways to generate power. In this paper we are generating power by walking on the stairs and the power which is generated can be stored for the further use. We can use this system in malls, homes, schools, companies, college, metro etc. When the group of people walk on these stairs, power is generated by the continuous movement of the persons. Here mechanical power is converted into electrical power.The power which is generated can be used to charge mobile.
The recent development in artificial intelligence is quite astounding in this decade. Especially, machine learning is one of the core subareas of AI. Also, ML field is an incessantly growing along with evolution and becomes a rise in its demand and importance. It transmogrified the way data is extracted, analyzed, and interpreted. Computers are trained to get in a self-training mode so that when new data is fed they can learn, grow, change, and develop themselves without explicit programming. It helps to make useful predictions that can guide better decisions in a real-life situation without human interference. Selection of ML tool is always a challenging task, since choosing an appropriate tool can end up saving time as well as making it faster and easier to provide any solution. This chapter provides a classification of various machine learning tools on the following aspects: for non-programmers, for model deployment, for Computer vision, natural language processing, and audio for reinforcement learning and data mining.
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