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Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Distributed Denial of Service (DDoS) stands as a critical cybersecurity concern, representing a malicious tactic employed by hackers to disrupt online services, network resources, or host systems, rendering them inaccessible to legitimate users. DDoS attack detection is essential as it has a wide-ranging impact on the field of computer science. This is quantitative research to evaluate Multilayer Perceptron (MLP) classification algorithm with different optimization methods and different activation functions on UDP-based DDoS attack detection. The CIC-DDoS2019 DDoS evaluation dataset, known for its inclusion of modern DDoS attack types, was instrumental in this study by the Canadian Institute for Cyber Security. The CIC-DDoS2019 dataset encompasses eleven DDoS attack datasets, which are UDP, UDP-Lag, NTP, and TFTP datasets were utilized in this investigation. This study proposes a novel feature selection approach. It specifically targets datasets related to UDP-based DDoS attacks. The approach aims to identify groups of features that share the uncorrelated characteristic. It means None of the features within a subset have a significant relationship with each other as measured by three correlation methods: Pearson, Spearman, and Kendall. To further validate the proposed approach, the researchers conducted experiments on a specially crafted DDoS attack dataset. MLP classification algorithm along with ADAM optimization method and Tanh activation function produce the better results for UDP-based DDoS attack detection. This combination produces the better accuracy values of 99.97 for UDP Flood attack, 99.77 for UDP-Lag attack, 99.70 for NTP attack, 99.93 for TFTP attack and 99.76 for UDP customized DDoS attack. [ABSTRACT FROM AUTHOR]

Cloud detection in satellite imagery is one the most important problems of satellite meteorology. The accuracy of cloud detection significantly determines the quality of other hydrometeorological products. The paper presents an algorithm for detecting clouds in satellite images, which is based on a convolutional neural network with a modified U-Net architecture. Multispectral satellite imagery from the MSU-GS instrument operating onboard Arktika-M No 1 satellite are used as input data. The algorithm accuracy was estimated through machine learning metrics and comparison with reference masks compiled via manual decryption of the satellite images by an experienced image interpreter. In addition, the results are compared with similar products based on data of SEVIRI and VIIRS instruments. The accuracy of a cloud mask obtained following the suggested algorithm is 92% compared to a reference mask for sun-illuminated areas and 89% for dark areas. [ABSTRACT FROM AUTHOR]

Memes have become a fundamental part of online communication and humour, reflecting and shaping the culture of today’s digital age. The amplified Meme culture is inadvertently endorsing and propagating casual Misogyny. This study proposes V-LTCS (Vision- Language Transformer Combination Search), a framework that encompasses all possible combinations of the most fitting Text (i.e. BERT, ALBERT, and XLM-R) and Vision (i.e. Swin, ConvNeXt, and ViT) Transformer Models to determine the backbone architecture for identifying Memes that contains misogynistic contents. All feasible Vision-Language Transformer Model combinations obtained from the recognized optimal Text and Vision Transformer Models are evaluated on two (smaller and larger) datasets using varied standard metrics (viz. Accuracy, Precision, Recall, and F1-Score). The BERT-ViT combinational Transformer Model demonstrated its efficiency on both datasets, validating its ability to serve as a backbone architecture for all subsequent efforts to recognize Multimodal Misogynous Memes.

Gene Expression Programming (GEP) is a powerful evolutionary algorithm with simple, linear and compact chromosomes, which has been applied in many fields to solve a large variety of complex problems such as logistic regression, function finding and time series prediction. Since online signature data are composed of discrete points, it is difficult to represent by functional forms, resulting in a limited amount of information used in calculating feature values. Hausdorff distance is utilized as a similarity measure to compute the maximum distance between two point sets, which reduces computational complexity compared with other distance measures. The main contributions of this work are: (1) In preprocessing stage, GEP is used to make signature curve continuous and control each parameter to obtain a fitting curve. Curve fitting is to find a suitable function that is the best fitting for a given set of data; (2) In feature extraction stage, curvature and torsion are utilized to construct eight feature sets for characterizing each user's signatures, and then Hausdorff distance is proposed to calculate the distances between feature sets of two signatures to form an eight-dimensional feature vector; (3) In verification stage, combined with Feed-Forward BP Neural Network classifier, distance matrices consisting of feature vectors are trained and tested many times. The best performances can be provided with false rejection rate, false acceptance rate, average error rate and standard deviation. The experimental results implemented on three available online signature databases MCYT-100, SVC2004 and SUSIG indicate the effectiveness and robustness of our proposed method. [ABSTRACT FROM AUTHOR]

A key problem domain inside Robotic Process Automation is the automatic discovery of workflow process schemes. Considering current process mining technologies, graph-based approaches dominate the industry. On the other hand, the conventional methods suffer from low time efficiency and varying accuracy. Machine learning-based methods can provide better efficiency, but they have significant limitations considering schema flexibility. The paper presents a novel neural network-based schema induction model for the discovery of event patterns containing parallel and optional sequences of different actors. This model can process more complex event graphs and situations than the conventional methods. The performed analysis and test results show the unique power of this approach in process schema mining. [ABSTRACT FROM AUTHOR]

This work is devoted to the sentiment classification of Russian sentences into four classes: positive, negative, mixed, and neutral. Unlike most modern works in this area, a mixed-sentiment class of sentences is introduced. Mixed-sentiment sentences contain both positively and negatively inflected speech at the same time. To solve this problem, the following tools were applied: an attention-based LSTM neural network, a dual attention-based GRU neural network, a and BERT neural network with several output layer modifications providing classification into four classes. Experiments comparing the effectiveness of various neural networks have been carried out on three corpora of Russian-language sentences. Two corpora are made up of users reviews: one with clothing reviews and the other with hotel reviews. The third corpus is made up of news articles from Russian publications. The best average weighted F-measure in the experiments of 0.90 is achieved by a BERT model on the clothing review corpus. The best F-measures for positive and negative sentences are noted on the same corpus, amounting to 0.92 and 0.93, respectively. The best classification results for neutral and mixed-sentiment sentences are achieved on the news corpus. For them, the F-measure is 0.72 and 0.58, respectively. As a result of the experiments, a significant superiority of BERT transfer neural networks over the previous generation LSTM and GRU neural networks is shown, which is most pronounced when classifying texts with weakly expressed sentiments. An analysis of the errors shows that "adjacent" sentiment classes (positive/negative and mixed) account for a larger proportion of errors in classification using BERT than "opposite" classes (positive and negative, neutral and mixed). [ABSTRACT FROM AUTHOR]

The present study evaluates the application of the hybrid machine learning methods to detect changes of land use with a focus on agricultural lands through remote sensing data processing. Two spectral images by Landsat 8 were applied to train and test the machine learning model. Feed forward neural network classifier was utilized as the machine learning model in which two evolutionary algorithms including particle swarm optimization and invasive weed optimization were applied for the training process. Moreover, three conventional training methods including Levenberg–Marquardt back propagation (LM), Scaled conjugate gradient backpropagation (SCG) and BFGS quasi-Newton backpropagation (BFG) were used for comparing the robustness and reliability of the evolutionary algorithms. Based on the results in the case study, evolutionary algorithms are not a reliable method for detecting changes through the remote sensing analysis in terms of accuracy and computational complexities. Either BFG or LM is the best method to detect the agricultural lands in the present study. BFG is slightly more robust than the LM method. However, LM might be preferred for applying in the projects due to low computational complexities. [ABSTRACT FROM AUTHOR]

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods. [ABSTRACT FROM AUTHOR]

Abstract Background Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer’s Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. Results We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. Conclusions The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone.

The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations. [ABSTRACT FROM AUTHOR]

Remote sensing data are widely used in numerous applications. A conventional task solved using remote sensing images is their classification. The classification maps are commonly produced by some pre-trained classifiers applied either to uncompressed or compressed images where lossy compression is often needed and employed in practice due to the necessity to reduce data volume at stages of image transfer and storage. Then, the classification accuracy depends on the characteristics of an image, a classifier, and a coder used. The main subject of this paper is the factors that determine classification accuracy. One of them is compressed image quality. We fix the quality of compressed image quality characterized by the peak signal-to-noise ratio for several coders and rely on the same training approach. Our goal is twofold. First, we would like to consider classification accuracy for two approaches to classifier training: based on undistorted data and images with simulated distortions. Second, our desire is to compare the performance of different techniques of image compression. The task of this paper is to obtain an idea is it worth training the neural network classifier for uncompressed images or images of similar quality to the quality of compressed data to be classified. The coder’s influence on classification results is of special interest as well. The main results are the following. First, the classification accuracy is almost the same for classifiers trained for uncompressed and simulated compressed data for the general distortion model. Second, there is a certain difference in the classification results for different compression techniques studied. Lightly better classification results are observed for data produced by more sophisticated (modern) coders. Experiments have been carried out for two real-life three-channel Sentinel-2 images of Kharkiv and the Kharkiv region having different complexity. Four typical classes have been considered. As a conclusion, it is possible to state that either the general model of distortions must be modified or the classifier training should be performed for data produced by the corresponding compression technique.

The article investigates the possibility of identifying the presence of SKOS (Simple Knowledge Organization System) relations between concepts represented by terms on the base of their vector representation in general natural language models. Several language models of the Word2Vec and GloVe families are considered, on the basis of which an artificial neural network (ANN) classifier of SKOS relations is formed. To train and test the efficiency of the classifier, datasets formed on the basis of the DBPedia and EuroVoc thesauri are used. The experiments performed have shown the high efficiency of the classifier trained using GloVe family models, while training it with use of Word2Vec models looks impossible in the bounds of considered ANN-based classifier architecture. Based on the results, a conclusion is made about the key role of taking into account the global context of the use of terms in the text for the possibility of identifying SKOS relations.

Background: Dealing with the high dimension of both neuroimaging data and genetic data is a difficult problem in the association of genetic data to neuroimaging. In this article, we tackle the latter problem with an eye toward developing solutions that are relevant for disease prediction. Supported by a vast literature on the predictive power of neural networks, our proposed solution uses neural networks to extract from neuroimaging data features that are relevant for predicting Alzheimer's Disease (AD) for subsequent relation to genetics. The neuroimaging-genetic pipeline we propose is comprised of image processing, neuroimaging feature extraction and genetic association steps. We present a neural network classifier for extracting neuroimaging features that are related with the disease. The proposed method is data-driven and requires no expert advice or a priori selection of regions of interest. We further propose a multivariate regression with priors specified in the Bayesian framework that allows for group sparsity at multiple levels including SNPs and genes. Results: We find the features extracted with our proposed method are better predictors of AD than features used previously in the literature suggesting that single nucleotide polymorphisms (SNPs) related to the features extracted by our proposed method are also more relevant for AD. Our neuroimaging-genetic pipeline lead to the identification of some overlapping and more importantly some different SNPs when compared to those identified with previously used features. Conclusions: The pipeline we propose combines machine learning and statistical methods to benefit from the strong predictive performance of blackbox models to extract relevant features while preserving the interpretation provided by Bayesian models for genetic association. Finally, we argue in favour of using automatic feature extraction, such as the method we propose, in addition to ROI or voxelwise analysis to find potentially novel disease-relevant SNPs that may not be detected when using ROIs or voxels alone. [ABSTRACT FROM AUTHOR]

Remote sensing images have found numerous applications nowadays. A traditional outcome or intermediate result of their processing is a classification map. Such maps are usually obtained from a pre-trained classifier and it is desired to have the produced classification maps as accurately as possible. The basic subject of this article is the factors determining this accuracy. The main among them are the quality of remote sensing data and classifier type, parameters and training approach. Image quality can be degraded due to several factors. One of them is distortions introduced by lossy compression that is widely used due to a huge volume of acquired data and the necessity to sufficiently decrease their size at transmission, storage and/or dissemination stages. Because of this, the main goal of this paper is to consider classification and lossy compression jointly. In particular, this means that the classifier learning can be performed for original (uncompressed, compressed in a lossless manner) images (if they are available) as well as for compressed data at hand (offered to a user for classification and further analysis). The task of this paper is to consider and compare these two options. The first one is the classifier learning for original images and further application to compressed data, where images can be compressed with different compression ratios while producing compressed data of different quality. The second option is the use of the classifier learning for compressed images, where compression parameters for training data can be approximately the same as for the images to which the classifier is applied. The main result is that the latter methodology can provide certain benefits compared to the classifier learning for original data if one has to classify compressed remote sensing data. Simulation data are obtained for a classifier based on a convolutional neural network. As images for training and verification, four real-life three-channel (visible range) Sentinel-2 remote sensing images of Kharkiv and Kharkiv region are employed that possess different complexity of the content and have four main classes. The practical recommendations are given. In conclusion, we can state that it is worth having classifiers trained for several degrees of compression and it is reasonable to compress complex structure images with special care.

When radar receives target echoes to form plots, it is inevitably affected by clutter, which brings a lot of imprecise and uncertain information to target recognition. Traditional radar plot recognition algorithms often have poor performance in dealing with imprecise and uncertain information. To solve this problem, a radar plot recognition algorithm based on adaptive evidence classification (RPREC) is proposed in this paper. The RPREC can be considered as the evidence classification version under the belief functions. First, the recognition framework based on the belief functions for target, clutter, and uncertainty is created, and a deep neural network model classifier that can give the class of radar plots is also designed. Secondly, according to the classification results of each iteration round, the decision pieces of evidence are constructed and fused. Before being fused, evidence will be corrected based on the distribution of radar plots. Finally, based on the global fusion results, the class labels of all radar plots are updated, and the classifier is retrained and updated so as to iterate until all the class labels of radar plots are no longer changed. The performance of the RPREC is verified and analyzed based on the real radar plot datasets by comparison with other related methods.

The subject of study in this article is the features of structural organization and functioning of the improved Hamming network as a model of neural network heteroassociative memory for classification by discriminant functions. The goal is to improve the neural network classifier based on the Hamming network, which implements the criterion of maximum similarity using discriminant functions and does not have restrictions on the representation of input data (not only binary data). The tasks: analyze the capabilities of associative memory models using neural networks as an example; analyze the features of classification on the principles of discriminant analysis; develop the structure of a neural network classifier as a model of neural network heteroassociative memory; perform simulation modeling of the classification process on the example of medical diagnosis. The methods used are a mathematical model of the functioning of a neural network as a classifier, and simulation in C#. The following results have been obtained: the structure of the neural network classifier has been improved through the formation connection matrix of a hidden layer from pre-calculated coefficients of linear discriminant functions, and the connection matrix of the output layer in the form symmetrical matrix with zeros on the main diagonal. This allows not only to simplify m connections, where m is the number of classes, in the structure of the output layer of the neural network classifier, but also to speed up the classification process, as well as to implement classification by the maximum of discriminant functions. Conclusions. The scientific novelty of the results obtained is as follows: the neural network classification method has been improved using pre-calculated elements of the connection matrices in the hidden and output layers of the classifier, which does not imply a long process of direct neural network learning with using discriminant functions; the structural organization of a neural network classifier is proposed, which is an improvement of the Hamming network as a model of heteroassociative memory, that allows using this classifier in a decision support system for medical diagnosis; the removal of positive feedback in neurons of the competitive (output) layer is implemented, which allows not only simplifies the structure of the neural network classifier but also speeds up the classification process almost 2 times, which is confirmed by the simulation results.

Purpose: To describe the relationships between foveal structure and visual function in a cohort of individuals with foveal hypoplasia (FH) and to estimate FH grade and visual acuity using a deep learning classifier. Design: Retrospective cohort study and experimental study. Participants: A total of 201 patients with FH were evaluated at the National Eye Institute from 2004 to 2018. Methods: Structural components of foveal OCT scans and corresponding clinical data were analyzed to assess their contributions to visual acuity. To automate FH scoring and visual acuity correlations, we evaluated the following 3 inputs for training a neural network predictor: (1) OCT scans, (2) OCT scans and metadata, and (3) real OCT scans and fake OCT scans created from a generative adversarial network. Main Outcome Measures: The relationships between visual acuity outcomes and determinants, such as foveal morphology, nystagmus, and refractive error. Results: The mean subject age was 24.4 years (range, 1–73 years; standard deviation = 18.25 years) at the time of OCT imaging. The mean best-corrected visual acuity (n = 398 eyes) was equivalent to a logarithm of the minimal angle of resolution (LogMAR) value of 0.75 (Snellen 20/115). Spherical equivalent refractive error (SER) ranged from −20.25 diopters (D) to +13.63 D with a median of +0.50 D. The presence of nystagmus and a high-LogMAR value showed a statistically significant relationship (P < 0.0001). The participants whose SER values were farther from plano demonstrated higher LogMAR values (n = 382 eyes). The proportion of patients with nystagmus increased with a higher FH grade. Variability in SER with grade 4 (range, −20.25 D to +13.00 D) compared with grade 1 (range, −8.88 D to +8.50 D) was statistically significant (P < 0.0001). Our neural network predictors reliably estimated the FH grading and visual acuity (correlation to true value > 0.85 and > 0.70, respectively) for a test cohort of 37 individuals (98 OCT scans). Training the predictor on real OCT scans with metadata and fake OCT scans improved the accuracy over the model trained on real OCT scans alone. Conclusions: Nystagmus and foveal anatomy impact visual outcomes in patients with FH, and computational algorithms reliably estimate FH grading and visual acuity.

In this report, we are presenting our work on performance analyses of five different neural network classifiers viz. MLP, DL4JMLP, logistic regression, SGD and simple logistic classifier in lung nodule detection using WEKA interface. To the best of our knowledge, this report demonstrates first use of WEKA for comparative performance analyses of neural network classifiers in identifying lung nodules from lung CT-images. A total of 624 handcrafted features from 52 numbers of lung CT-images collected randomly from Lung Image Database Consortium (LIDC) were fed into WEKA to evaluate the performances of the classifiers under four different categories of computation. Performances of the classifiers were observed in terms of 11 important parameters viz. accuracy, kappa statistic, root mean squared error, TPR, FPR, precision, sensitivity, F-measurement, MCC, ROC area and PRC area. Results show 86.53%, 77.77%, 55.55%, 94.44% & 88.88% accuracy as well as 0.91, 0.86, 0.68, 0.91 & 0.93 ROC area for MLP, DL4JMLP, logistic, SGD and simple logistic classifier respectively at tenfold cross-validation by taking 66% of the data set for training and 34% for testing and validation purpose. SGDClassifier has been found the best performing followed by simple logistic classifier for the purpose. [ABSTRACT FROM AUTHOR]

The paper presents results of using a neural network classifier to analyze images of malignant skin lesions obtained using a hyper-spectral camera. Using a three-block neural network of VGG architecture, we conducted the classification of a set of two-dimensional images of melanoma, papilloma and basal cell carcinoma, obtained in the range of 530 – 570 and 600 – 606 nm, characterized by the highest absorption of melanin and hemoglobin. The sufficiency of the inclusion in the training set of two-dimensional images of a limited spectral range is analyzed. The results obtained show significant prospects of using neural network algorithms for processing hyperspectral data for the classification of skin pathologies. With a relatively small set of training data used in the study, the classification accuracy for the three types of neoplasms was as high as 96 %.

Driving cycles have a great influence on vehicles’ fuel economy, control performance and drivability. In this paper, vehicle speed is considered and twelve statistical features for driving pattern recognition are selected to obtain the characteristics of driving cycles. To extract the statistical features online from the speed distribution information, the sampling and updating windows are set and the number of features is reduced. Moreover, since the structure and parameters of neural network are crucial to the classifier, the structure and parameters of neural network, the sampling and updating window size, the feature subset selection are simultaneously optimized by genetic algorithm (GA) to improve the classifying accuracy and simplify neural network structure. The hybrid encoding/decoding and the structure operator are designed to optimize the whole neural network classifier. Four typical driving patterns, i.e., congested urban road, flowing urban road, suburban and highway, are selected based on multiple driving cycles. Simulation results show that the classifiers with GA optimized features have more powerful classification capability than principle component analysis (PCA) and kernel PCA (KPCA) based on k-nearest neighbor, support vector machine neural network classifiers and KPCA Convolutional neural network classifier. The proposed classifier obtains the satisfying classification accuracy with faster real-time classifying speed. [ABSTRACT FROM AUTHOR]

Quality control of mechanical components is crucial to ensure their expected performance and prevent their failure. For components manufactured additively, quality control performed in-process is particularly interesting, as the sequential deposition and remelting of layers represent a possibility to mitigate existing flaws. The first step towards closed-loop control is to ensure that the monitoring setup and the data analytics approach can flag and discriminate flaws. This study aims to assess the potential of a layerwise monitoring system associated with a supervised machine learning approach to identify and classify internal flaws in laser powder bed fusion of Hastelloy X. For that, systematically generated internal flaws were mapped ex-situ in 72 distinct process conditions. The outputs of the near-infrared long-exposure acquisition system were labeled according to the ex-situ characterization and used to train a fully convolutional neural network. The network was then used to classify previously unseen monitoring images into three classes, according to the predominant flaw type expected, lack of fusion, keyhole porosity, or residual porosity. Accuracy, precision and recall over 96% are obtained, indicating that the monitoring system combined with this supervised machine learning approach successfully identifies and classifies internal flaws. [Display omitted] [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) requires better descriptors to represent the face patterns as the facial region changes due to the movement of the face muscles during an expression. In this paper, a method of concatenating spatial pyramid Zernike moments based shape features and Law’s texture features is proposed to uniquely capture the macro and micro details of each facial expression. The proposed method employs multilayer perceptron and radial basis function feed forward artificial neural networks for recognizing the facial expressions. The suitability of the features in recognizing the expressions is explored across the datasets independent of the subjects or persons. The experiments conducted on JAFFE and KDEF datasets demonstrate that the concatenated feature vectors are capable of representing the facial expressions with better accuracy and least errors. The radial basis function based classifier delivers a performance with an average recognition accuracy of 95.86% and 88.87% on the JAFFE and KDEF datasets respectively for subject dependent FER.

This research builds a human action recognition system based on a single image or video capture snapshot. The TensorFlow Deep Learning models are developed using human keypoints generated by OpenPose. Four classifiers are considered: Neural Network, Random Forest, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) Classifiers. The models’ input layer are 50 points from x and y coordinate of 25 keypoints from OpenPose, and the output layer is the numerical representation of 11 human action labels which are 'hand-wave', 'jump', 'leg-cross', 'plank', 'ride', 'run', 'sit', 'lay-down', 'squat', 'stand', 'walk’. A total of 2132 images dataset was used for model training and testing. The results show the two best classifier models: Neural Network Classifier with 512 hidden nodes with an accuracy of 0.7733, and Random Forest Classifier with 60 estimators with an accuracy of 0.7752. Both models are then used as inference engines to recognize human action from images and real-time video. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of continuous hidden user authentication based on the analysis of keyboard handwriting is considered. The main purpose of the analysis is to continuously verify the identity of the subject during his work on the keyboard. The aim of the work is to increase the efficiency of hidden user authentication algorithms based on a neural network analysis of a dynamic profile, formed by keyboard handwriting. The idea of user authentication using keyboard handwriting is based on measuring the time of keystrokes and the intervals between keystrokes, followed by comparing the resulting data set with the stored dynamic user profile. Studies have shown that analyzing the average value of the time each key is pressed is inefficient. It is proposed to analyze the holding time of a combination of several keys and the time between their presses. An approach in which not the times of pressing individual keys, but the parameters of pressing the most common letter combinations are analyzed, will increase the accuracy of recognition of dynamic images. An algorithm and software implementation for Russian keyboard layout have been developed, experiments conducted on field data allow us to conclude that the proposed method is effectively used to authenticate the user using keyboard handwriting.

Iris identification plays an important role in many applications like security, banking, access to buildings, and surveillance …. etc. Since the iris part of the eye image can be significantly affected by some factors, such as lighting conditions source, eyelids, eyelashes, pupil, sclera, and shadowing, therefore iris identification research is still wide and rich. The work proposed in this paper operates the iris identification system on the distorted colored images captured under visible light. The proposed idea minimizes the number of iris regions affected by distortion, by dividing the iris region into separable regions. Only the region without distortion part or region with distortion is less probable is used. The paper studies the effect of different color model such as HSV, YIQ, YCbCr, and RGB color models on iris identification. High quality feature extraction is introduced in this paper by using Contourlet Transform (CT). Euclidian Distance (ED) or Neural Network (NN) is used as classifiers. Simulation results show that the proposed method operating on non-distortion iris region outperforms the conventional method operating on the whole iris region for any selected color model and for standard databases (UPOL andUTIRIS) and a suggested one.

Background: Infrared (IR) spectroscopy allows intraoperative, optical brain tumor diagnosis. Here, we explored it as a translational technology for the identification of aggressive meningioma types according to both, the WHO CNS grading system and the methylation classes (MC)., Methods: Frozen sections of 47 meningioma were examined by IR spectroscopic imaging and different classification approaches were compared to discern samples according to WHO grade or MC., Results: IR spectroscopic differences were more pronounced between WHO grade 2 and 3 than between MC intermediate and MC malignant, although similar spectral ranges were affected. Aggressive types of meningioma exhibited reduced bands of carbohydrates (at 1024 cm -1 ) and nucleic acids (at 1080 cm -1 ), along with increased bands of phospholipids (at 1240 and 1450 cm -1 ). While linear discriminant analysis was able to discern spectra of WHO grade 2 and 3 meningiomas (AUC 0.89), it failed for MC (AUC 0.66). However, neural network classifiers were effective for classification according to both WHO grade (AUC 0.91) and MC (AUC 0.83), resulting in the correct classification of 20/23 meningiomas of the test set., Conclusions: IR spectroscopy proved capable of extracting information about the malignancy of meningiomas, not only according to the WHO grade, but also for a diagnostic system based on molecular tumor characteristics. In future clinical use, physicians could assess the goodness of the classification by considering classification probabilities and cross-measurement validation. This might enhance the overall accuracy and clinical utility, reinforcing the potential of IR spectroscopy in advancing precision medicine for meningioma characterization., Competing Interests: None., (© The Author(s) 2024. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)

Photovoltaic (PV) power forecasting is essential for the integration of renewable energy sources into the grid and for the optimisation of energy management systems. In this paper, we address the problem of probabilistic day-ahead forecasting of PV power generation for an operating plant with imperfect measurements and incomplete information. We compare four probabilistic forecasting methodologies: one physical irradiance-to-power method based on a model of the power plant and on weather forecasts, and four statistical methods based on quantile regression and classification techniques. We evaluate the performance of these methods in terms of deterministic and probabilistic accuracy, as well as the influence of the forecast horizon and the autoregressive component. The results show that statistical methods outperform the physical method, that conformalized quantile regression achieves the highest probabilistic accuracy, and that weather forecasts are more important than autoregressive predictors for the forecast procedure. To our knowledge, this is one of the first studies to compare different probabilistic forecasting approaches on the same case and provides information on the relative importance of the factors affecting the accuracy of the forecast. • Conformalized quantile regression attains superior probabilistic accuracy in tests. • Weather forecasts are pivotal for improved accuracy. • Statistical methods outperform a hybridised physical method in the test case. [ABSTRACT FROM AUTHOR]

In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k-mean, improved k-mean, fuzzy c-means and neural networks. The used dataset is labeled and creating a ýnew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network ýis the best clustering technique.