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In this paper, an Advanced Quantum-based Neural Network Classifier (AQNN) is proposed. The proposed AQNN is used to form an objectionable Web content filtering system (OWF). The aim is to design a neural network with a few numbers of hidden layer neurons with the optimal connection weights and the threshold of neurons. The proposed algorithm uses the concept of quantum computing and genetic concept to evolve connection weights and the threshold of neurons. Quantum computing uses qubit as a probabilistic representation which is the smallest unit of information in the quantum computing concept. In this algorithm, a threshold boundary parameter is also introduced to find the optimal value of the threshold of neurons. The proposed algorithm forms neural network architecture which is used to form an objectionable Web content filtering system which detects objectionable Web request by the user. To judge the performance of the proposed AQNN, a total of 2000 (1000 objectionable + 1000 non-objectionable) Website's contents have been used. The results of AQNN are also compared with QNN-F and well-known classifiers as backpropagation, support vector machine (SVM), multilayer perceptron, decision tree algorithm, and artificial neural network. The results show that the AQNN as classifier performs better than existing classifiers. The performance of the proposed objectionable Web content filtering system (OWF) is also compared with well-known objectionable Web filtering software and existing models. It is found that the proposed OWF performs better than existing solutions in terms of filtering objectionable content.

The authors continued their research on the development of an intelligent automatic ships pilot containing a controller based on fuzzy logic. Its features are determined by the optimizer based on a genetic algorithm. It also contains a modular unit of neural network models of ship navigation paths, as well as a neural network classifier. This paper is devoted to the description of a neural network classifier designed to classify the movement patterns of marine vessels to identify the peculiarities of the ship depending on its type and sailing conditions. The introduction of such classifier to an autopilot allows for more precise consideration of multivariate and difficult to formalize factors affecting the vessel while operating, such as varying weather conditions, irregular waves, hydrodynamic characteristics of the vessel, draft, water under the keel, rate of the vessel sailing, etc. The article outlines the technique concerning the development of a neural network classifier and the results of its computer modelling on the example of a refrigerated transport vessel type. The authors used such methods for obtaining and processing findings as spectral estimation, machine learning methods, in particular, neural network technology and computer or simulation modelling. [ABSTRACT FROM AUTHOR]

This article presents a fully integrated gesture and gait classification system-on-chip (SoC) for rehabilitation application. In order to reduce the power consumption and area cost on the analog front end, special analog-to-digital converter (ADC)-less mixed-signal feature extraction (MSFE) circuits were designed to directly generate eight commonly used time-domain features to eliminate the area cost of ADC. A fully connected neural network classifier was implemented supporting: 1) on-chip learning to deliver user-specific training for better classification accuracy; 2) dedicated neural network layer to support gait classification; and 3) multi-chip data communication, which transfers only low-dimensional features from the neural network to minimize the communication bottleneck in a sensor fusion environment. A 12-channel test chip was fabricated in a 65-nm low-power process to demonstrate the proposed techniques. The measurements show an average power of $1~\mu \text{W}$ per channel and a 3-ms computational latency as required by the stringent rehabilitation requirement. In addition, the MSFE circuits achieve $3\times $ saving of area compared with the conventional approach, while the communication bandwidth was reduced by $100\times $ due to the transferring of only low-dimensional feature data from the neural network among multiple chips. [ABSTRACT FROM AUTHOR]

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One of the most prevalent forms of cancer worldwide is oral cancer which has a high rate of mortality. Diagnosis and treatment of oral premalignant lesions at an early stage reduces the death rate. The objective of this work is to detect malignancies by analyzing color features of digital true color oral images. A dataset of around 433 oral lesion images has been created that includes benign, premalignant and malignant lesions. The proposed method was experimented on this dataset. Different classifiers have been trained using various color features. The neural network classifier detects abnormalities with an accuracy of 94.82%. Results indicate that the color features have better potential in identifying benign and malignant oral lesions. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) uses non-invasive and non-radiative imaging to detect inhomogeneous electrical properties in tissues. The inverse problem of EIT is a highly nonlinear, ill-posed problem, which causes inaccuracy in target size calculation. We propose a novel approach to discretize the target size and use a neural network (NN) classifier to classify the unknown size in discrete steps. The target size is discretized into distinct steps, and each step can be a unique class. The data is pre-processed with the cumulative distribution transform (CDT) to enhance distinguishability. First, the NN is trained with simulated datasets, divided into time difference (t-d) group and CDT group. After training, the NN classifier is tested by experimental data recorded in a phantom experiment. Linear discriminant analysis (LDA) is performed to assess the distinguishability of classes. There is a significant increase in distance between classes after the CDT pre-processing. The density of the classes has an upward trend with a higher degree of clustering after CDT pre-processing. The CDT data clustering into distinguishable classes is essential to excellent NN classification results. Such an approach is a significant paradigm shift by turning the cumbersome inverse calculation with uncertain accuracy into a classification problem with predetermined step errors. The accuracy and resolution can be further extended by increasing the discretization steps. [ABSTRACT FROM AUTHOR]

Sarcasm is a type of sentiment where people express negative sentiment using positive connotation words in text and vice-versa. In this work, we propose a cross-domain sarcasm detection framework that allows acquisition, storage and processing of tweets for detecting sarcastic content in online reviews. We conduct our experiments on Amazon product review dataset namely the Sarcasm Corpus Version1 having about 2000 reviews. We use Support Vector Machines (SVM) and Neural Networks (NN) for detecting sarcasm using lexical, pragmatic, linguistic incongruity and context incongruity features. We report the results and present a comparative evaluation of SVM and NN classifiers for single domain sarcasm detection indicating their suitability for the task. Then, we use these models for cross-domain sarcasm detection. The experimental results indicate the reliability of our approach.

Imaging modalities play a major role in early detection and diagnosis of various medical conditions related to the patient. Retinal image segmentation has been taken up for investigation in this research paper to efficiently detect the presence of eye disorder which could be indicators of major onset of conditions like hypertension, cataracts, diabetic retinopathy, age-related macular disorders, etc. A machine learning method for classification of given pixels in the search space into regions containing blood vessels and those that do not contain blood vessels is implemented using a three-stage neural classifier in this paper. Prior to classification, an optimization algorithm namely ant colony optimization derived from nature-inspired phenomena is used to provide an optimal feature vector set to set high standards for the neural network based classification approach. The novelty and merits of the paper lie in back tracing of the segmentation process in which optimization is done first on the preprocessed features followed by classification for segmented output on the optimized features. This results in elimination of redundant feature vectors which tend to occupy much memory as well increase the computational overhead on the process. The entire implemented system is automated by the machine learning process and tested on 30 samples, 15 each on DRIVE and STARE databases. Classification rates of nearly 98% on an average scenario have been achieved for segmentation and 96.5% for abnormality detection. The performances have been compared against Bayesian set models and standalone ANN models. [ABSTRACT FROM AUTHOR]

Exploration (factor, cluster, and decision tree), regression (multiple linear regression), and neural network (regression, classification) models of clustering, approximation and prediction of the stability constants of cation complexes with ionophore antibiotics (nonactin, monactin, dinactin, trinactin, ennatin B, monensin A, and valinomycin) according to the properties of organic solvents (methanol, ethanol, acetonitrile, and nitrobenzene) and cations (Li+, Na+, K+, Rb+, Cs+, Tl+, Ag+, NH4+, Mg2+, Ca2+, Sr2+, Ba2+, and Mn2+) have been developed. It has been shown that neural network performance is better than that of multiple linear regression (the correlation coefficient on the training sample 0.756 compared to 0.697). The neural network classifier and the classification tree has confirmed the clustering of stability of ionophore–cation complexes carried out by the exploratory k-means method by 97.2%. The prognostic capabilities of the constructed multilayer perceptron have been demonstrated. [ABSTRACT FROM AUTHOR]

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human-computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg-Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F1 score, and Mathews correlation coefficient. [ABSTRACT FROM AUTHOR]

Stress relaxation plays an important role in the design of underground stopes. The aim of this paper is to assess the stope stability in connection with the stress relaxation using a classification approach. Three types of stress relaxation were clearly defined, namely partial relaxation, tangential relaxation and full relaxation. A neural network classifier was implemented to assess the stability of the stopes on the basis of case histories of stope performances. The results of the classification were compared to existing empirical methods of quantifying the stress relaxation. Overall, the present study shows higher classification accuracies, especially when the stress relaxation was considered. The results suggested that the relaxation type can be a good predictor of stability. Relaxed stope (full and tangential stress relaxation) cases are the most critical in the sense that lower accuracies were obtained and the probability of correct classification is rather erratic. [ABSTRACT FROM AUTHOR]

An ability to categorise and recognise a spoken language is an essential task in a multi-lingual society like India. Language identification (LID) is the process of identifying the language spoken by some unknown speaker using a given speech sample. In this article, textural descriptors extracted from spectrogram image and evolutionary feature selection is presented for Indian language identification. Language-specific long-term cues and prosodic information present in various frequency zones of the spectrogram image can efficiently modelled using textural descriptors. Firstly, an input audio sample is converted into a spectrogram visual representation which characterises the band of frequencies of a signal with respect to time. Then, completed local binary pattern (CLBP), local binary pattern histogram Fourier (LBPHF) and discrete Wavelet transform based texture descriptors are used to extract the features from the spectrogram image. Later, using grey wolf optimiser (GWO) feature selection, irrelevant and redundant features are removed, and only optimal features are selected from the dataset. GWO-based feature selection supports to construct the classification model with optimal features and the performance of the classifier is optimised. Finally, using the artificial neural network classifier and Indic-TTS database 96.9659% accuracy was obtained. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG), a non-invasive diagnostic technique, used for detecting cardiac arrhythmia. From last decade industry dealing with biomedical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Atrial Fibrillation (AF) is an irregular rhythm of the human heart. During AF, the atrial moments are quicker than the normal rate. As blood is not completely ejected out of atria, chances for the formation of blood clots in atrium. These abnormalities in the heart can be identified by the changes in the morphology of the ECG. The first step in the detection of AF is preprocessing of ECG, which removes noise using filters. Feature extraction is the next key process in this research. Recent feature extraction methods, such as Auto Regressive (AR) modeling, Magnitude Squared Coherence (MSC) and Wavelet Coherence (WTC) using standard database (MIT-BIH), yielded a lot of features. Many of these features might be insignificant containing some redundant and non-discriminatory features that introduce computational burden and loss of performance. This paper presents fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT) for extracting relevant features from the ECG signal. The sparse matrix factorization method is used for developing fast and efficient CS-SCHT algorithm and its computational performance is examined and compared to that of the HT and NCHT. The applications of the CS-SCHT in the ECG-based AF detection is also discussed. These fast CS-SCHT features are optimized using Hybrid Firefly and Particle Swarm Optimization (FFPSO) to increase the performance of the classifier.

Introduction: The COVID-19 pandemic had a global impact and created an unprecedented emergency in healthcare and other related frontline sectors. Various Artificial-Intelligence-based models were developed to effectively manage medical resources and identify patients at high risk. However, many of these AI models were limited in their practical high-risk applicability due to their "black-box" nature, i.e., lack of interpretability of the model. To tackle this problem, Explainable Artificial Intelligence (XAI) was introduced, aiming to explore the "black box" behavior of machine learning models and offer definitive and interpretable evidence. XAI provides interpretable analysis in a human-compliant way, thus boosting our confidence in the successful implementation of AI systems in the wild., Methods: In this regard, this study explores the use of model-agnostic XAI models, such as SHapley Additive exPlanations values (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), for COVID-19 symptom analysis in Indian patients toward a COVID severity prediction task. Various machine learning models such as Decision Tree Classifier, XGBoost Classifier, and Neural Network Classifier are leveraged to develop Machine Learning models., Results and Discussion: The proposed XAI tools are found to augment the high performance of AI systems with human interpretable evidence and reasoning, as shown through the interpretation of various explainability plots. Our comparative analysis illustrates the significance of XAI tools and their impact within a healthcare context. The study suggests that SHAP and LIME analysis are promising methods for incorporating explainability in model development and can lead to better and more trustworthy ML models in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Nambiar, S and S.)

Models of multiple linear regression and multilayer artificial neural network have been developed for modeling and predicting the stability constants of sodium and potassium coronates basing on the properties of aqueous-organic solvents (water-methanol, water-propan-2-ol, water-acetonitrile, and water-acetone). The values of the coronates stability constants in water-ethanol solvents have been predicted, and the predictions of the models of multiple linear regression and an artificial neural network models have been compared. The contributions of electrostatic, cohesive, and electron-donating interactions to the increase in the stability of the coronates have been quantitatively assessed basing on the models of multiple linear regression and the principle of free energies linearity. Neural network models based on unsupervised (multilayer perceptrons) and supervised (Kohonen networks) learning algorithms have been developed to classify the stability of sodium and potassium coronates. The neural network classifiers have fully confirmed the classification of the coronated stability via the k-means exploration method. [ABSTRACT FROM AUTHOR]

A method for cloud detection using the machine-learning algorithm based on a convolutional neural network is presented. Input data are satellite images received from the MSU-MR multispectral low-resolution scanning unit onboard the Meteor-M No. 2 satellite. The developed method can be an alternative to the traditional algorithms of cloud detection based on the calculation of differential indices and thresholds. The algorithm is verified using the machine-learning metrics, comparing the resulting cloud mask with the reference one obtained by interpreting the satellite image by an experienced meteorologist. It was also compared (for verification) with a similar product based on VIIRS spectroradiometer data. The cloud mask computed using the algorithm allows the automatic thematic processing of satellite images. [ABSTRACT FROM AUTHOR]

In recent years, face recognition has rapidly developed in the field of smartphones and control systems. It has been used to unlock the telephone and face-payment applications. With such rapid development, more and more demands are placed on the security of face recognition. However, face biometric-based recognition technologies are still vulnerable to spoofing attacks. Thus, developing robust and reliable antispoofing attack detection is critical to guarantee the security of facial analysis-based authentication. As deep learning techniques have achieved satisfactory performances in computer vision, they can also be employed to face spoofing detection. We present a multichannel linear local binary pattern optimization algorithm, which combines with Lucas–Kanade optical flow algorithm. The extracted facial features are fused and sent to a deep belief network classifier for classification and learning, and finally tested on the MSU-mobile facial spoofing database. Compared with existing deep leaning-based detection methods, our face spoofing detection algorithm has better scalability and robustness. To evaluate the performance of the proposed algorithm, the experiments are conducted on three crossed standard spoofing databases and excellent performance is also achieved. [ABSTRACT FROM AUTHOR]

Machine learning-guided optical proximity correction, called ML-OPC in this paper, has recently been proposed to alleviate long runtime of model-based OPC. ML-OPC using regression methods has been presented but with limited prediction accuracy. We propose neural network classifier-based OPC (NNC-OPC), in which a neural network classifier serves as a mask bias model. A few techniques are applied to enhance basic NNC-OPC: parameterization of layout segments using polar Fourier transform signals, dimensionality reduction through weighted principal component analysis, and sampling of training layout segments. Training segments are typically imbalanced over the range of mask biases, which may cause large prediction error for segments that appear less frequently. This is resolved by three techniques: 1) synthetic data generation; 2) class reorganization; and 3) an adaptive learning rate. Experiments with NNC-OPC with all techniques applied indicate that prediction error of mask bias and training time are reduced by 29% and 80%, respectively, compared to state-of-the-art ML-OPC with regression methods. [ABSTRACT FROM AUTHOR]

This paper presents a two-stage algorithm for automatic text detection and recognition. In the first stage, using a stroke width transform and an improved connected component, an edge analysis method detects a candidate character region. Subsequently, a text region is located by filtering and linking characters with similar font sizes and colors. For the second stage, a histogram of oriented gradient is employed as a feature descriptor, and a neural network classifier is built with dynamic-group-based hybrid particle swarm optimization (DGHPSO) for character recognition. In DGHPSO, each group's threshold value of similarity depends on the threshold values of fitness and distance. In addition, a local search algorithm is used to improve the search for a global optimum. The proposed algorithm was experimentally validated; it outperformed a number of recently published studies in terms of the text recognition rate when tested on the ICDAR 2003 database and the Street View Text database. [ABSTRACT FROM AUTHOR]

Highlights • Robust multi-parametric NCO-tracking controllers do not entail a discretization of the continuous-time dynamics. • The robust-counterpart multi-parametric dynamic optimization (mp-DO) problem retains the same complexity as nominal mp-DO. • Data classifiers based on deep learning can accurately describe the critical regions in (nominal or robust) mp-DO. • The methodology is demonstrated for a fluid catalytic cracking (FCC) unit and a chemical reactor cascade. Abstract This paper presents an extension of the recent multi-parametric (mp-)NCO-tracking methodology by Sun et al. [Comput. Chem. Eng. 92 (2016) 64–77] for the design of robust multi-parametric controllers for constrained continuous-time linear systems in the presence of uncertainty. We propose a robust-counterpart formulation and solution of multi-parametric dynamic optimization (mp-DO), whereby the constraints are backed-off based on a worst-case propagation of the uncertainty using either interval analysis or ellipsoidal calculus and an ancillary linear state feedback. We address the case of additive uncertainty, and we discuss approaches to dealing with multiplicative uncertainty that retain tractability of the mp-NCO-tracking design problem, subject to extra conservativeness. In order to assist with the implementation of these controllers, we also investigate the use of data classifiers based on deep learning for approximating the critical regions in continuous-time mp-DO problems, and subsequently searching for a critical region during on-line execution. We illustrate these developments with the case studies of a fluid catalytic cracking (FCC) unit and a chemical reactor cascade. [ABSTRACT FROM AUTHOR]

A new simulation and processing methodology based on open source tools to produce high fidelity synthetic aperture radar (SAR) simulations of ground vehicles of varying types, as well as analysis of an applied automatic target recognition (ATR) technique is presented in this Letter. This work is based around the RaySAR open‐source model and the outputs have been configured for both monostatic and bistatic geometries. Input CAD models of various military and civilian vehicles are used to produce the SAR imagery. This output imagery was then used to train a tiny you only look once convolutional neural network (CNN) classifier. The classification success of the CNN applied was showed to produce significantly accurate results and the whole pipeline of processing enabled rapid evaluation of potential ATR methods against targets of choice. [ABSTRACT FROM AUTHOR]

Diabetic retinopathy, a symptomless complication of diabetes, is one of the significant causes of vision impairment in the world. The early detection and diagnosis can reduce the occurrence of severe vision loss due to diabetic retinopathy. The diagnosis of diabetic retinopathy depends on the reliable detection and classification of bright and dark lesions present in retinal fundus images. Therefore, in this work, reliable segmentation of lesions has been performed using iterative clustering irrespective of associated heterogeneity, bright and faint edges. Afterwards, a computer-aided severity level detection method is proposed to aid ophthalmologists for appropriate treatment and effective planning in the diagnosis of non-proliferative diabetic retinopathy. This work has been performed on a composite database of 5048 retinal fundus images having varying attributes such as position, dimensions, shapes and color to make a reasonable comparison with state-of-the-art methods and to establish generalization capability of the proposed method. Experimental results on per-lesion basis show that the proposed method outperforms state-of-the-methods with an average sensitivity/specificity/accuracy of 96.41/96.57/94.96 and 95.19/96.24/96.50 for bright and dark lesions respectively on composite database. Individual per-image based class accuracies delivered by the proposed method: No DR-95.9%, MA-98.3%, HEM-98.4%, EXU-97.4% and CWS-97.9% demonstrate the clinical competence of the method. Major contribution of the proposed method is that it efficiently grades the severity level of diabetic retinopathy in spite of huge variations in retinal images of different databases. Additionally, the substantial combined performance of these experiments on clinical and open source benchmark databases support a strong candidature of the proposed method in the diagnosis of non-proliferative diabetic retinopathy. [ABSTRACT FROM AUTHOR]

The nearest neighbor partitioning (NNP) method is a high performance approach which is used for improving traditional neural network classifiers. However, the construction process of NNP model is very time-consuming, particularly for large data sets, thus limiting its range of application. In this study, a parallel NNP method is proposed to accelerate NNP based on Compute Unified Device Architecture(CUDA). In this method, blocks and threads are used to evaluate potential neural networks and to perform parallel subtasks, respectively. Experimental results manifest that the proposed parallel method improves performance of NNP neural network classifier. Furthermore, the application of parallel NNP in performance evaluation of cement microstructure indicates that the proposed approach has favorable performance. [ABSTRACT FROM AUTHOR]

Background and objective Myocardial Infarction (MI) is one of the most frequent diseases, and can also cause demise, disability and monetary loss in patients who suffer from cardiovascular disorder. Diagnostic methods of this ailment by physicians are typically invasive, even though they do not fulfill the required detection accuracy. Methods Recent feature extraction methods, for example, Auto Regressive (AR) modelling; Magnitude Squared Coherence (MSC); Wavelet Coherence (WTC) using Physionet database, yielded a collection of huge feature set. A large number of these features may be inconsequential containing some excess and non-discriminative components that present excess burden in computation and loss of execution performance. So Hybrid Firefly and Particle Swarm Optimization (FFPSO) is directly used to optimise the raw ECG signal instead of extracting features using the above feature extraction techniques. Results Provided results in this paper show that, for the detection of MI class, the FFPSO algorithm with ANN gives 99.3% accuracy, sensitivity of 99.97%, and specificity of 98.7% on MIT-BIH database by including NSR database also. Conclusions The proposed approach has shown that methods that are based on the feature optimization of the ECG signals are the perfect to diagnosis the condition of the heart patients. [ABSTRACT FROM AUTHOR]