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This paper proposes a novel approach to automatic recognition of handprinted Bangla (an Indian script) numerals. A modified Topology Adaptive Self-Organizing Neural Network is proposed to extract a vector skeleton from a binary numeral image. Simple heuristics are considered to prune artifacts, if any, in such a skeletal shape. Certain topological and structural features like loops, junctions, positions of terminal nodes, etc. are used along with a hierarchical tree classifier to classify handwritten numerals into smaller subgroups. Multilayer perceptron (MLP) networks are then employed to uniquely classify the numerals belonging to each subgroup. The system is trained using a sample data set of 1800 numerals and we have obtained 93.26% correct recognition rate and 1.71% rejection on a separate test set of another 7760 samples. In addition, a validation set consisting of 1440 samples has been used to determine the termination of the training algorithm of the MLP networks. The proposed scheme is sufficiently robust with respect to considerable object noise.

A preprocessing stage in every speech/music applications including audio/speech separation, speech/speaker recognition and audio/genre transcription task is inevitable. The importance of such pre-processing stage is originated from the requisite of determining each frame of the given signal is belonged to which classes, namely: speech only, music only or speech/music mixture. Such classification can significantly decrease the computational burden due to exhaustive search commonly introduced as a problem in model-based speech recognition or separation as well as music transcription scenarios. In this paper, we present a new method to separate mixed type audio frames based on support vector machine (SVM) and neural network. We present a feature type selection algorithm which seeks for the most appropriate features to discriminate possible classes (hypotheses) on the mixed signal. We also propose features based on eigen-decomposition on the mixed frame. Experimental results demonstrate that the proposed features together with the selected audio classifiers achieve acceptable classification results. From the experimental results, it is observed that the proposed system outperforms other classification systems including k-nearest neighbor (k-NN) and multi-layer perceptron (MLP).

In this study, the effectiveness of an SPAD-502 portable chlorophyll (Chl) meter was evaluated for estimating the Chl contents in leaves of some medicinal and aromatic plants. To predict the individual chlorophyll concentration indexes of St. John’s wort (Hypericum perforatum L.), mint (Mentha angustifolia L.), melissa (Melissa officinalis L.), thyme (Thymus sp.), and echinacea (Echinacea purpurea L.), models were developed using SPAD value. Multi-layer perceptron (MLP), adaptive neuro fuzzy inference system (ANFIS), and general regression neural network (GRNN) were used for determining the chlorophyll concentration indexes.

Inertial navigation system (INS) is often integrated with satellite navigation systems to achieve the required precision at high-speed applications. In global navigation system (GPS)/INS integration systems, GPS outages are unavoidable and a severe challenge. Moreover, because of the usage of low-cost microelectromechanical sensors (MEMS) with noisy outputs, the INS will get diverged during GPS outages, and that is why navigation precision severely decreases in commercial applications. In this paper, we improve GPS/INS integration system during GPS outages using extended Kalman filter (EKF) and artificial intelligence (AI) together. In this integration algorithm, the AI receives the angular rates and specific forces from the inertial measurement unit (IMU) and velocity from the INS at $t$ and $t-1$. Therefore, the AI has positioning and timing data of the INS. While the GPS signals are available, the output of the AI is compared with the GPS increment; so that the AI is trained. During GPS outages, the AI will practically play the GPS role. Thus, it can prevent the divergence of the GPS/INS integration system in GPS-denied environments. Furthermore, we utilize neural networks (NNs) as an AI module in five different types: multi-layer perceptron (MLP) NN, radial basis function (RBF) NN, wavelet NN, support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS). To evaluate the proposed approach, we utilize a real dataset that has been gathered by a mini-airplane. The results demonstrate that the proposed approach outperforms the INS and GPS/INS integration systems with the EKF during GPS outages. Meanwhile, the ANFIS also reached more than 47.77% precision compared to the traditional method.

Brain-Computer Interface is an emerging field that focuses on transforming brain data into machine commands. EEG-based BCI is widely used due to the non-invasive nature of Electroencephalogram. Classification of EEG signals is one of the primary components in BCI applications. Steady-State Visually Evoked Potential (SSVEP) paradigms have gained importance because of lesser training time, higher precision, and improved information transfer rate compared to P300 and motor imagery paradigms. In this paper, a novel hybrid Anchoring-based Particle Swarm Optimized Scaled Conjugate Gradient Multi-Layer Perceptron classifier (APS-MLP) is proposed to improve the classification accuracy of SSVEP five classes viz. 6.66, 7.5, 8.57, 10 and 12 Hz, signals. Scaled Conjugate Gradient descent anchors the initial position of Particle Swarm Optimization. The best position, Pbest, of each particle initializes an SCG-MLP, the accuracy of APS-MLP is obtained by averaging the accuracies of each SCG-MLP. The proposed method is compared with standard classifiers namely, k-NN, SVM, LDA and MLP. In which, the proposed algorithm achieves improved training and testing accuracies of 88.69% and 95.4% respectively, which is 12–15% higher than the standard EEG-based BCI classifiers. The proposed algorithm is robust, with a Cohen’s kappa coefficient of 0.96, and will be used in applications such as motion control and improving the quality of life for people with disabilities.

This paper proposes an intelligent classification technique to identify two categories of MRI volume data as normal and abnormal. The manual interpretation of MRI slices based on visual examination by radiologist/physician may lead to incorrect diagnosis when a large number of MRIs are analyzed. In this work, the textural features are extracted from the MR data of patients and these features are used to classify a patient as belonging to normal (healthy brain) or abnormal (tumor brain). The categorization is obtained using various classifiers such as support vector machine (SVM), radial basis function, multilayer perceptron and k-nearest neighbor. The performance of these classifiers are analyzed and a quantitative indication of how better the SVM performance is when compared with other classifiers is presented. In intelligent computer aided health care system, the proposed classification system using SVM classifier can be used to assist the physician for accurate diagnosis.

In IP networks, packets forwarding performance can be improved by adding more nodes and dividing the network into smaller segments. Being able to measure and predict traffic flows to direct to a given segment can be crucial in respecting traffic shaping, scheduling and QoS. This paper proposes to model network packets forwarding performance for optimization and prediction purposes by using multi-layer feed-forward neural network model that uses sigmoid functions to activate the hidden nodes. Gradient descent technique has been considered to optimize and enhance the MLP accuracy. Simulations of MPL neurons training stages pointed out a relative improvement of the forwarding process when network posses a larger density of neurons. Numerical results validated our theoretical analysis and confirmed that to enhance the forwarding process, it is necessary to divide the network into small segments by optimizing resources allocation.

Colonoscopy allows doctors to check the abnormalities in the intestinal tract without any surgical operations. The major problem in the Computer-Aided Diagnosis (CAD) of colonoscopy images is the low illumination condition of the images. This study aims to provide an image enhancement method and feature extraction and classification techniques for detecting polyps in colonoscopy images. We propose a novel image enhancement method with a Pyramid Histogram of Oriented Gradients (PHOG) feature extractor to detect polyps in the colonoscopy images. The approach is evaluated across different classifiers, such as Multi-Layer Perceptron (MLP), Adaboost, Support Vector Machine (SVM), and Random Forest. The proposed method has been trained using the publicly available databases CVC ClinicDB and tested in ETIS Larib and CVC ColonDB. The proposed approach outperformed the existing state-of-the-art methods on both databases. The reliability of the classifiers’ performance was examined by comparing their F1 score, precision, F2 score, recall, and accuracy. PHOG with Random Forest classifier outperformed the existing methods in terms of recall of 97.95%, precision 98.46%, F1 score 98.20%, F2 score of 98.00%, and accuracy of 98.21% in the CVC-ColonDB. In the ETIS-LARIB dataset it attained a recall value of 96.83%, precision 98.65%, F1 score 97.73%, F2 score 98.59%, and accuracy of 97.75%. We observed that the proposed image enhancement method with PHOG feature extraction and the Random Forest classifier will help doctors to evaluate and analyze anomalies from colonoscopy data and make decisions quickly.

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.