Search results for "artificial intelligence"

Extraction of objects from structured backgrounds in the cat superior colliculus. Part II

1980

Specific changes occur in the cells of the uppers layers of the cat's superior colliculus when a two dimensional noise (background) is superimposed onto a deterministic signal (spot of light). Some of the measurements can be interpreted as meaning that some cells only react to certain relative movements of object (spot) and background (noise). The movement of the visual background is interpreted as environmental movement occurring due to the animal's own movement. The results of the measurements provide all the necessary presuppositions for a distinction between the animal's own velocity and that of the object (Part I). The experimental results can be interpreted with a model. The essential…

Rotational panoramic radiographs-unusual triple images

2015

Currently clinicians advice rotational panoramic radiography (RPR) for preliminary investigation. Despite few inherent limitations, rotational panoramic radiography still remains the diagnostic tool of choice. Abnormal structures such as a supernumerary tooth or a device falling within the certain central regions in conventional RPR images may mislead the clinicians towards an inaccurate diagnosis by producing multiple ghost images. Such cases must be treated with circumspect, and apart from RPR, additional imaging modalities need be employed to provide a judicious interpretation of the clinical situation. Thus this manuscript, we present a case where a paramedian supernumerary tooth which …

A Student's t‐based density peaks clustering with superpixel segmentation (tDPCSS) method for image color clustering

2020

Editorial – Preface to the Special Issue on Numerical Techniques Meet with OR

2021

Abstract This special issue of the Foundations of Computing and Decision Sciences, titled ”Numerical Techniques Meet with OR”, is devoted to the numerical techniques and their applications in real-world phenomena. The special issue and its editorial present numerical algorithms as they meet with different research topics such as, e.g., from operational research, supply chain management, geometrical structures and Covid-19 effects on financial applications. Besides, the special issue covers instructional information about numerical techniques which are useful for OR research problems and real-world applications on such issues.

Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (…

2021

Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Plat…

Machine learning for a combined electroencephalographic anesthesia index to detect awareness under anesthesia

2020

Spontaneous electroencephalogram (EEG) and auditory evoked potentials (AEP) have been suggested to monitor the level of consciousness during anesthesia. As both signals reflect different neuronal pathways, a combination of parameters from both signals may provide broader information about the brain status during anesthesia. Appropriate parameter selection and combination to a single index is crucial to take advantage of this potential. The field of machine learning offers algorithms for both parameter selection and combination. In this study, several established machine learning approaches including a method for the selection of suitable signal parameters and classification algorithms are a…

Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections.

2016

This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.

Prediction of banana quality indices from color features using support vector regression

2015

Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acid…

Deep learning approach for prediction of impact peak appearance at ground reaction force signal of running activity

2020

Protruding impact peak is one of the features of vertical ground reaction force (GRF) that is related to injury risk while running. The present research is dedicated to predicting GRF impact peak appearance by setting a binary classification problem. Kinematic data, namely a number of raw signals in the sagittal plane, collected by the Vicon motion capture system (Oxford Metrics Group, UK) were employed as predictors. Therefore, the input data for the predictive model are presented as a multi-channel time series. Deep learning techniques, namely five convolutional neural network (CNN) models were applied to the binary classification analysis, based on a Multi-Layer Perceptron (MLP) classifi…

Machine Learning Methods for One-Session Ahead Prediction of Accesses to Page Categories

2004

This paper presents a comparison among several well-known machine learning techniques when they are used to carry out a one-session ahead prediction of page categories. We use records belonging to 18 different categories accessed by users on the citizen web portal Infoville XXI. Our first approach is focused on predicting the frequency of accesses (normalized to the unity) corresponding to the user’s next session. We have utilized Associative Memories (AMs), Classification and Regression Trees (CARTs), Multilayer Perceptrons (MLPs), and Support Vector Machines (SVMs). The Success Ratio (SR) averaged over all services is higher than 80% using any of these techniques. Nevertheless, given the …