Research ArticleOpen accessEvery word in the titleA NOVEL APPROACH BASED ON GENETIC FUZZY CLUSTERING AND ADAPTIVE NEURAL NETWORKS FOR SALES FORECASTINGAttariuas Hichama, Bouhorma Mohameda, El Fallahi AbdellahbThis article proposes a new hybrid sales forecasting system based on genetic fuzzy clustering and Back-Propagation (BP) Neural Networks with adaptive learning rate (GFCBPN).The proposed architecture consists of three stages: (1) utilizing Winter’s Exponential Smoothing method and Fuzzy C-Means clustering, all normalized data records will be categorized into k clusters; (2) using an adapted Genetic Fuzzy System (MCGFS), the fuzzy … …Journal of Computer Science, Vol. 9, Issue 8 · 2 Jul 2013 · 6,669 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleSpeed Estimation of Adaptive Fuzzy-Controlled Piezo-Electric Motor using MLP-Neural NetworkShebel ALSABBAH, Tariq MUGHRABIThe speed of ultrasonic motor of piezo-electric type is usually measured using mechanical sensors such as pulse encoders. However, these sensors are costly and bulky. In this paper, a numerical speed estimation approach of a piezo-electric motor (PEM) is implemented using multilayer perception neural network (MLP-NN). The proposed model evaluates rotational speed and load torque based on the amplitude and … …American Journal of Applied Sciences, Vol. 5, Issue 11 · 30 Nov 2008 · 6,785 accessesView PDFCite
Research ArticleOpen accessEvery word in the titleVehicle Identification using Fuzzy Adaline Neural NetworkGokul Murugesan, Shriram K. Vasudevan, Sivaraman Ramachandran et al.Video surveillance is an important aspect in today’s world, where a particular scene of area requires monitoring to avoid terrorist attacks and unauthorized entries. Vehicle recognition is an important area in object tracking and recognition. Objects may be of rigid or non-rigid in nature with varying velocity and have different features. Important features like shape, logo, color and texture are … …Journal of Computer Science, Vol. 9, Issue 6 · 6 Jun 2013 · 7,176 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Integrated Machine Learning Model for Heart Disease Classification and CategorizationSrikanth Meda, Raveendrababu BhogapathiProgressions in the coordination among the machine learning algorithms, helped to achieve high accuracy and reliability in decision-making systems. Due to the impact and importance of heart diseases in real life, designing the efficient heart disease prediction model become a pivotal aspect today. Former research scholars applied the popular supervised machine learning models like Decision Trees, Naïve Bayes, ANN's, and … …Journal of Computer Science, Vol. 18, Issue 4 · 19 Apr 2022 · 6,535 accessesView PDFCite
Research ArticleOpen accessIn the keywordsEfficient Handling of Medical Data Classification in Cloud-Edge Network using Optimization AlgorithmS. S. Saranya, N. Sabiyath FatimaNowadays, a cloud-edge computing framework with IoT offers different medicinal facilities by classifying a massive amount of patients’ health data through a Deep Neural Network. But, how to optimize task scheduling while carrying multiple tasks from multiple edge devices in real-time was still challenging. This article introduces a cooperative cloud-edge computing structure to effectively perform the fuzzy DNN classification into … …Journal of Computer Science, Vol. 17, Issue 11 · 30 Nov 2021 · 6,480 accessesView PDFCite
Research ArticleOpen accessIn the keywordsAn Overview of Research Challenges for Classification of Cardiotocogram DataC. Sundar, M. Chitradevi, G. GeetharamaniCardiotocography (CTG) is a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC). The most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography … …Journal of Computer Science, Vol. 9, Issue 2 · 2 Apr 2013 · 7,827 accessesView PDFCite
Research ArticleOpen accessIn the keywordsOptimal Control Algorithms for Second Order SystemsDanilo Pelusi, Raffaele MascellaProportional Integral Derivative (PID) controllers are widely used in industrial processes for their simplicity and robustness. The main application problems are the tuning of PID parameters to obtain good settling time, rise time and overshoot. The challenge is to improve the timing parameters to achieve optimal control performances. Remarkable findings are obtained through the use of Artificial Intelligence techniques as … …Journal of Computer Science, Vol. 9, Issue 2 · 2 Apr 2013 · 7,029 accessesView PDFCite
Research ArticleOpen accessIn the keywordsGradient Descent Learning for Utility Current Compensation using Active Regenerative PWM FilterR. Balamurugan, G. GurusamyProblem statement: Harmonic analysis is a primary matter of power quality assessment. Its main intention is to check the utility whether it is delivering the loads without any deviations in voltages and currents. The problem is due to proliferation of Electronic converters and power electronics which gave birth to numerous new applications, offering unmatched comfort to the customers. Approach: Harmonics … …Journal of Computer Science, Vol. 7, Issue 12 · 14 Oct 2011 · 6,545 accessesView PDFCite
Research ArticleOpen accessIn the keywordsDermoscopic Image Segmentation using Machine Learning AlgorithmL. Padma Suresh, K.L. Shunmuganathan, S.H. Krishna VeniProblem statement: Malignant melanoma is the most frequent type of skin cancer. Its incidence has been rapidly increasing over the last few decades. Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the structure of interest in medical images. Approach: This study explains the task of segmenting skin lesions in … …American Journal of Applied Sciences, Vol. 8, Issue 11 · 7 Oct 2011 · 7,342 accessesView PDFCite
Research ArticleOpen accessIn the keywordsApplication of Neuro-Fuzzy Techniques for Solar RadiationW. A. Rahoma, U. Ali Rahoma, A. H. HassanProblem statement: The prediction is very useful in solar energy applications because it permits to estimate solar data for locations where measurements are not available. The developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. Approach: The forecasting ability of some models could be further enhanced with … …Journal of Computer Science, Vol. 7, Issue 10 · 11 Aug 2011 · 7,077 accessesView PDFCite