Computer-based Intelligent System Research Articles

Real-Time Sign Language Detection using TensorFlow, OpenCV and Python

Abstract: Deaf and hard-of-hearing persons, as well as others who are unable to communicate verbally, utilise sign language to communicate within their communities and with others. Sign languages are a set of preset languages that communicate information using a visual-manual modality. The dilemma of real-time finger-spelling recognition in Sign Language is discussed. We gathered a dataset for identifying 36 distinct gestures (alphabets and numerals) and a dataset for typical hand gestures in ISL created from scratch using webcam images. The system accepts a hand gesture as input and displays the identified character on the monitor screen in real time. This project falls under the category of human-computer interaction (HCI) and tries to recognise multiple alphabets (a-z), digits (0-9) and several typical ISL hand gestures. To apply Transfer learning to the problem, we used a Pre-Trained SSD Mobile net V2 architecture trained on our own dataset. In the vast majority of situations, we constructed a robust model that consistently classifies Sign language. Many studies have been done in the past in this area employing sensors (such as glove sensors) and other image processing techniques (such as edge detection technique, Hough Transform, and so on), but these technologies are quite costly, and many people cannot afford them. During the study, various human-computer interaction approaches for posture recognition were investigated and evaluated. The optimum solution was determined to comprise a set of image processing approaches with Human movement categorization. Without a controlled background and low light, the system can detect chosen Sign Language signs with an accuracy of 70-80%. As a result, we're creating this software to assist such folks because it's free and simple to use. However, aside from a small group of people, not everyone is familiar with sign language, and they may need an interpreter, which may be cumbersome and costly. This research intends to bridge the communication gap by building algorithms that can anticipate alphanumeric hand motions in sign language in real time. The main goal of this research is to create a computer-based intelligent system that will allow deaf persons to interact effectively with others by utilising hand gestures. Keywords: Pre-Trained SSD Mobile net V2, Sign Language , HCI

Case-Based Reasoning Using The Nearest Neighbor Method For Detection Of Equipment Damage To PLN Power Plant

Predictive Maintenance (PdM) at the PLN Power Plant is a periodic monitoring of equipment activities before the equipment is damaged in more severe conditions. According to an expert or PdM owner that maintenance analysis is not appropriate and efficiency has an impact on maintenance costs that are not small. In real conditions, the PdM owner analyzes equipment damage based on previous cases of damage equipment. Then we need a computer-based intelligent system that can help detect damage to equipment.Based on the Literature Review that has been done, Case-Based Reasoning can solve new problems using answers or experiences from old problems such as imitating human abilities. Case-Based Reasoning Process there is the most important step, which is to find the highest similarity value or the level of similarity between new cases and old cases by adapting solutions from old cases that have occurred (Sankar, 2004). In this study the process of similarity or approach using Nearest Neighbor.Testing on the system uses 20 test data and the measurement of system performance uses confusion matrix. Evaluation of testing using confusion matrix can be seen how accurately the system can classify data correctly that is equal to 97.98%. Then the precision value of 95% represents the number of positive categorized data that is correctly divided by the total data classified as positive. Furthermore, the test results of the equipment damage detection test data at the PLN plant with a threshold value of 0.75 using the nearest neighbor, the system has a performance with a 95% sensitivity level.

Identification of Diabetic Maculopathy Stages using Fundus Images

The impairment and blindness are caused due to Diabetes mellitus. Most of sick person are suffering with impairment diabetes and fifty to sixty percent of ill persons are suffering due to blindness diabetes. Present paper concentrated the identification process of diabetes mellitus using a computer-based intelligent system. The method consists of non-clinically and clinically significant maculopathy and usual eye images. With the help of morphological image processing techniques the fundus are extracted from the usual eye images. Later for comparison the extracted data is applied to classifiers named as (i) feed-forward ANN and (ii) (PNN) probabilistic neural network.

A Computer-Based Intelligent System for Automatic Tool Selection

Tool selection is one of the most significant process considerations when deciding how a machined product will be manufactured. The tool selection activity is rooted in feature geometry but it also has a direct impact on machinability and machine tool performance. This paper presents a computer-based intelligent system for the automatic selection of cutting processes and tools. The main objective of the research work was to develop a procedure for the selection of cutting tools and cutting parameters. This will help the designers and manufacturing planners to select an optimal set of cutting tools and cutting conditions for different material properties and to give users alternatives on how to reduce cost and time.

A mathematical method for constraint-based cluster analysis towards optimized constrictive diameter smoothing of saphenous vein grafts

This study was concerned with the cluster analysis of saphenous vein graft data to determine a minimum number of diameters, and their values, for the constrictive smoothing of diameter irregularities of a cohort of veins. Mathematical algorithms were developed for data selection, transformation and clustering. Constrictive diameter values were identified with interactive pattern evaluation and subsequently facilitated in decision-tree algorithms for the data clustering. The novel method proved feasible for the analysis of data of 118 veins grafts, identifying the minimum of two diameter classes. The results were compared to outcome of a statistical recursive partitioning analysis of the data set. The method can easily be implemented in computer-based intelligent systems for the analysis of larger data sets using the diameter classes identified as initial cluster structure.

Automatic identification of diabetic maculopathy stages using fundus images

Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets.

Computer-Based Identification of Breast Cancer Using Digitized Mammograms

High-quality mammography is the most effective technology presently available for breast cancer screening. Efforts to improve mammography focus on refining the technology and improving how it is administered and X-ray films are interpreted. Computer-based intelligent system for identification of the breast cancer can be very useful in diagnosis and its management. This paper presents a comparative approach for classification of three kinds of mammogram namely normal, benign and cancer. The features are extracted from the raw images using the image processing techniques and fed to the two classifiers namely: the feedforward architecture neural network classifier, and Gaussian mixture model (GMM) for comparison.. Our protocol uses, 360 subjects consisting of normal, benign and cancer breast conditions. We demonstrate a sensitivity and specificity of more than 90% for these classifiers.

AUTOMATED IDENTIFICATION OF EYE DISEASES USING HIGHER-ORDER SPECTRA

A computer-based intelligent system for the classification of eye diseases can be very useful for their diagnosis and management. With age, the incidence of ocular pathology rises, thereby decreasing normal eye function. The most common causes of age-related eye disorders and visual impairment in the elderly are cataracts and iridocyclitis (inflammation of the iris, i.e. the colored part of the eye, and of the ciliary body). For proper care and management of eyes, we need a system which can automatically classify these eye diseases. The method proposed in this study is based on higher-order spectral (HOS) features that capture contour and shape information, while providing robustness to shift, rotation, changes in size, and noise. The parameters are extracted from the raw images using the HOS techniques, and fed to the classifiers for classification. This paper presents the classification of three kinds of eye classes using four-layer feedforward and Gaussian mixture model (GMM) classifiers. Our protocol used 122 subjects who had three different kinds of eye disease conditions. We demonstrated a sensitivity of 100% for the classifier, with a specificity of 90%. Our systems are clinically ready to test on large data sets.

Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry

Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities.

Cardiac state diagnosis using higher order spectra of heart rate variability

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart, by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability analysis is an important tool to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Like many bio-signals, HRV signals are nonlinear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of nonlinear systems and provides good noise immunity. In this work, we studied the HOS of the HRV signals of normal heartbeat and seven classes of arrhythmia. We present some general characteristics for each of these classes of HRV signals in the bispectrum and bicoherence plots. We also extracted features from the HOS and performed an analysis of variance (ANOVA) test. The results are very promising for cardiac arrhythmia classification with a number of features yielding a p-value <0.02 in the ANOVA test.

A computer-based intelligent system for automatic tool selection

This paper presents a computer-based intelligent system for the automatic selection of cutting processes and tools within a concurrent engineering environment. The main objective of the research work was to develop a procedure for the selection of cutting tools, develop a dynamic programming-based system that utilises mathematical modules and heuristic data to determine and calculate cutting parameters and total component cost. This will help the designers and manufacturing planners to select an optimal set of cutting tools and cutting conditions for different material properties and to give users alternatives on how to reduce cost and time. The system comprised of several modules; the knowledge acquisition module, the knowledge base module, the inference engine, the user interface, and the database. The developed system, which was designed to cover different component shapes including cylindrical, prismatic, different types of machining techniques, conventional and less conventional, has the potential to deal with complex products that are made up of multiple components. The system is capable of selecting cutting tools. It calculates cutting conditions and estimates component cost, based on the properties of the work piece material and features attributes, which include surface finish and tolerances, as well as using a number of production criteria such as material removal rate, tool life, machining time, and cost.

A computer-based intelligent system for Design for Assembly

A computer-based intelligent system for Design for Assembly (DFA) within a concurrent engineering environment is presented in this article. The main objective of the current research work is to develop an expert system that supports new techniques for design for assembly and to give users the possibility to assess and reduce the total production cost at an early stage during the design process. The system enables designers to minimise the number of components of a product and select the most economic assembly technique for that specific product.