Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of the video is a critical process due to the negative pattern of the image. In the detection of forged images or videos, various image processing techniques were implemented. Existing methods are ineffective in detecting new threats or false images. This article has proposed You Only Look Once-Local Binary Pattern Histogram (YOLO-LBPH) to detect fake videos. YOLO is used to detect the face in an image or a frame of a video. The spatial features are extracted from the face image using a EfficientNet-B5 method. Spatial feature extractions are fed as input in the Local Binary Pattern Histogram to extract temporal features. The proposed YOLO-LBPH is implemented using the large scale deepfake forensics (DF) dataset known as CelebDF-FaceForensics++(c23), which is a combination of FaceForensics++(c23) and Celeb-DF. As a result, the precision score is 86.88% in the CelebDF-FaceForensics++(c23) dataset, 88.9% in the DFFD dataset, 91.35% in the CASIA-WebFace data. Similarly, the recall is 92.45% in the Celeb-DF-Face Forensics ++(c23) dataset, 93.76% in the DFFD dataset, and 94.35% in the CASIA-Web Face dataset.

• Keywords
• Celeb DF-Face Forensics++, Celeb-DF, Deepfake, FaceForencies++, LBPH, YOLO,
• Publication type
• Journal Article MeSH

BACKGROUND: One of the major challenges in the analysis of gene expression data is to identify local patterns composed of genes showing coherent expression across subsets of experimental conditions. Such patterns may provide an understanding of underlying biological processes related to these conditions. This understanding can further be improved by providing concise characterizations of the genes and situations delimiting the pattern. RESULTS: We propose a method called semantic biclustering with the aim to detect interpretable rectangular patterns in binary data matrices. As usual in biclustering, we seek homogeneous submatrices, however, we also require that the included elements can be jointly described in terms of semantic annotations pertaining to both rows (genes) and columns (samples). To find such interpretable biclusters, we explore two strategies. The first endows an existing biclustering algorithm with the semantic ingredients. The other is based on rule and tree learning known from machine learning. CONCLUSIONS: The two alternatives are tested in experiments with two Drosophila melanogaster gene expression datasets. Both strategies are shown to detect sets of compact biclusters with semantic descriptions that also remain largely valid for unseen (testing) data. This desirable generalization aspect is more emphasized in the strategy stemming from conventional biclustering although this is traded off by the complexity of the descriptions (number of ontology terms employed), which, on the other hand, is lower for the alternative strategy.

• Keywords
• Biclustering, Enrichment analysis, Gene expression, Ontology, Symbolic machine learning,
• MeSH
• Molecular Sequence Annotation MeSH
• Data Mining methods   MeSH
• Drosophila melanogaster genetics   MeSH
• Semantics * MeSH
• Cluster Analysis MeSH
• Gene Expression Profiling * MeSH
• Machine Learning MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Silva invasion pattern can help predict lymph node metastasis risk in endocervical adenocarcinoma. We analysed Silva pattern of invasion and lymphovascular invasion to determine associations with clinical outcomes in stage IA and IB1 endocervical adenocarcinomas. International Federation of Gynecology and Obstetrics (FIGO; 2019 classification) stage IA-IB1 endocervical adenocarcinomas from 15 international institutions were examined for Silva pattern, presence of lymphovascular invasion, and other prognostic parameters. Lymph node metastasis status, local/distant recurrences, and survival data were compared using appropriate statistical tests. Of 399 tumours, 152 (38.1%) were stage IA [IA1, 77 (19.3%); IA2, 75 (18.8%)] and 247 (61.9%) were stage IB1. On multivariate analysis, lymphovascular invasion (p=0.008) and Silva pattern (p<0.001) were significant factors when comparing stage IA versus IB1 endocervical adenocarcinomas. Overall survival was significantly associated with lymph node metastasis (p=0.028); recurrence-free survival was significantly associated with lymphovascular invasion (p=0.002) and stage (1B1 versus 1A) (p=0.002). Five and 10 year overall survival and recurrence-free survival rates were similar among Silva pattern A cases and Silva pattern B cases without lymphovascular invasion (p=0.165 and p=0.171, respectively). Silva pattern and lymphovascular invasion are important prognostic factors in stage IA1-IB1 endocervical adenocarcinomas and can supplement 2019 International Federation of Gynecology and Obstetrics staging. Our binary Silva classification system groups patients into low risk (patterns A and B without lymphovascular invasion) and high risk (pattern B with lymphovascular invasion and pattern C) categories.

• Keywords
• Silva pattern, Stage, endocervical adenocarcinoma, lymphovascular invasion, pattern of invasion,
• MeSH
• Adenocarcinoma * pathology   MeSH
• Carcinoma * pathology   MeSH
• Humans MeSH
• Lymphatic Metastasis MeSH
• Uterine Cervical Neoplasms * MeSH
• Prognosis MeSH
• Retrospective Studies MeSH
• Neoplasm Staging MeSH
• Check Tag
• Humans MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Images of ocular fundus are routinely utilized in ophthalmology. Since an examination using fundus camera is relatively fast and cheap procedure, it can be used as a proper diagnostic tool for screening of retinal diseases such as the glaucoma. One of the glaucoma symptoms is progressive atrophy of the retinal nerve fiber layer (RNFL) resulting in variations of the RNFL thickness. Here, we introduce a novel approach to capture these variations using computer-aided analysis of the RNFL textural appearance in standard and easily available color fundus images. The proposed method uses the features based on Gaussian Markov random fields and local binary patterns, together with various regression models for prediction of the RNFL thickness. The approach allows description of the changes in RNFL texture, directly reflecting variations in the RNFL thickness. Evaluation of the method is carried out on 16 normal ("healthy") and 8 glaucomatous eyes. We achieved significant correlation (normals: ρ=0.72±0.14; p≪0.05, glaucomatous: ρ=0.58±0.10; p≪0.05) between values of the model predicted output and the RNFL thickness measured by optical coherence tomography, which is currently regarded as a standard glaucoma assessment device. The evaluation thus revealed good applicability of the proposed approach to measure possible RNFL thinning.

• Keywords
• Fundus images, Glaucoma, Local binary patterns, Markov random fields, Retinal nerve fiber layer, Texture analysis,
• MeSH
• Color * MeSH
• Optic Disk pathology   MeSH
• Fundus Oculi MeSH
• Glaucoma pathology   MeSH
• Humans MeSH
• Markov Chains * MeSH
• Nerve Fibers pathology   MeSH
• Normal Distribution MeSH
• Tomography, Optical Coherence MeSH
• Retinal Ganglion Cells pathology   MeSH
• Image Enhancement methods   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

The term COVID-19 is an abbreviation of Coronavirus 2019, which is considered a global pandemic that threatens the lives of millions of people. Early detection of the disease offers ample opportunity of recovery and prevention of spreading. This paper proposes a method for classification and early detection of COVID-19 through image processing using X-ray images. A set of procedures are applied, including preprocessing (image noise removal, image thresholding, and morphological operation), Region of Interest (ROI) detection and segmentation, feature extraction, (Local binary pattern (LBP), Histogram of Gradient (HOG), and Haralick texture features) and classification (K-Nearest Neighbor (KNN) and Support Vector Machine (SVM)). The combinations of the feature extraction operators and classifiers results in six models, namely LBP-KNN, HOG-KNN, Haralick-KNN, LBP-SVM, HOG-SVM, and Haralick-SVM. The six models are tested based on test samples of 5,000 images with the percentage of training of 5-folds cross-validation. The evaluation results show high diagnosis accuracy from 89.2% up to 98.66%. The LBP-KNN model outperforms the other models in which it achieves an average accuracy of 98.66%, a sensitivity of 97.76%, specificity of 100%, and precision of 100%. The proposed method for early detection and classification of COVID-19 through image processing using X-ray images is proven to be usable in which it provides an end-to-end structure without the need for manual feature extraction and manual selection methods.

• Keywords
• COVID-19 diagnosis, Haralick, K-nearest neighbor, Local binary pattern, Machine learning, Support vector machine, X-ray image,
• Publication type
• Journal Article MeSH

In recent years, computed tomography (CT) has become a standard technique in cardiac imaging because it provides detailed information that may facilitate the diagnosis of the conditions that interfere with correct heart function. However, CT-based cardiac diagnosis requires manual segmentation of heart cavities, which is a difficult and time-consuming task. Thus, in this paper, we propose a novel technique to segment endocardium and epicardium boundaries based on a 2D approach. The proposal computes relevant information of the left ventricle and its adjacent structures using the Hermite transform. The novelty of the work is that the information is combined with active shape models and level sets to improve the segmentation. Our database consists of mid-third slices selected from 28 volumes manually segmented by expert physicians. The segmentation is assessed using Dice coefficient and Hausdorff distance. In addition, we introduce a novel metric called Ray Feature error to evaluate our method. The results show that the proposal accurately discriminates cardiac tissue. Thus, it may be a useful tool for supporting heart disease diagnosis and tailoring treatments.

• Keywords
• Active shape models, Left ventricle segmentation, Level sets, Local binary patterns, Ray Feature error, Steered Hermite transform,
• MeSH
• Models, Biological MeSH
• Humans MeSH
• Tomography, X-Ray Computed methods   MeSH
• Heart Ventricles pathology   MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

An automatic method of segmenting the retinal vessel tree and estimating status of retinal neural fibre layer (NFL) from high resolution fundus camera images is presented. First, reliable blood vessel segmentation, using 2D directional matched filtering, enables to remove areas occluded by blood vessels thus leaving remaining retinal area available to the following NFL detection. The local existence of rather faint and hardly visible NFL is detected by combining several newly designed local textural features, sensitive to subtle NFL characteristics, into feature vectors submitted to a trained neural-network classifier. Obtained binary retinal maps of NFL distribution show a good agreement with both medical expert evaluations and quantitative results obtained by optical coherence tomography.

• MeSH
• Fluorescein Angiography methods   MeSH
• Image Interpretation, Computer-Assisted methods   MeSH
• Humans MeSH
• Optic Nerve Diseases pathology   MeSH
• Nerve Net pathology   MeSH
• Reproducibility of Results MeSH
• Retinal Vessels pathology   MeSH
• Retinoscopy methods   MeSH
• Pattern Recognition, Automated methods   MeSH
• Sensitivity and Specificity MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Detached off-grids, subject to the generated renewable energy (RE), need to balance and compensate the unstable power supply dependent on local source potential. Power quality (PQ) is a set of EU standards that state acceptable deviations in the parameters of electrical power systems to guarantee their operability without dropout. Optimization of the estimated PQ parameters in a day-horizon is essential in the operational planning of autonomous smart grids, which accommodate the norms for the specific equipment and user demands to avoid malfunctions. PQ data for all system states are not available for dozens of connected / switched on household appliances, defined by their binary load series only, as the number of combinations grows exponentially. The load characteristics and eventual RE contingent supply can result in system instability and unacceptable PQ events. Models, evolved by Artificial Intelligence (AI) methods using self-optimization algorithms, can estimate unknown cases and states in autonomous systems contingent on self-supply of RE power related to chaotic and intermitted local weather sources. A new multilevel extension procedure designed to incrementally improve the applicability and adaptability to training data. The initial AI model starts with binary load series only, which are insufficient to represent complex data patterns. The input vector is progressively extended with correlated PQ parameters at the next estimation level to better represent the active demand of the power consumer. Historical data sets comprise training samples for all PQ parameters, but only the load sequences of the switch-on appliances are available in the next estimation states. The most valuable PQ parameters are selected and estimated in the previous algorithm stages to be used as supplementary series in the next more precise computing. More complex models, using the previous PQ-data approximates, are formed at the secondary processing levels to estimate the target PQ-output in better quality. The new added input parameters allow us to evolve a more convenient model form. The proposed multilevel refinement algorithm can be generally applied in modelling of unknown sequence states of dynamical systems, initially described by binary series or other insufficient limited-data variables, which are inadequate in a problem representation. Most AI computing techniques can adapt this strategy to improve their adaptive learning and model performance.

• MeSH
• Algorithms MeSH
• Renewable Energy MeSH
• Computer Systems MeSH
• Household Articles * MeSH
• Artificial Intelligence * MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

• Keywords
• DBN, Deep fake, Deep learning, Fisherface, LBPH, RBM,
• Publication type
• Journal Article MeSH

Zn-Cu alloys have attracted great attention as biodegradable alloys owing to their excellent mechanical properties and biocompatibility, with corrosion characteristics being crucial for their suitability for biomedical applications. However, the unresolved identification of intermetallic compounds in Zn-Cu alloys affecting corrosion and the complexity of the application environment hamper the understanding of their electrochemical behavior. Utilizing high-throughput first-principles calculations and machine-learning accelerated evolutionary algorithms for screening the most stable compounds in Zn-Cu systems, a dataset encompassing the formation energy of 2033 compounds is generated. It reveals that most of the experimentally reported Zn-Cu compounds can be replicated, especially the structure of R32 CuZn5 is first discovered which possesses the lowest formation energy of -0.050 eV per atom. Furthermore, the simulated X-ray diffraction pattern matches perfectly with the experimental ones. By formulating 342 potential electrochemical reactions based on the binary compounds, the Pourbaix diagrams for Zn-Cu alloys are constructed to clarify the fundamental competition between different phases and ions. The calculated equilibrium potential of CuZn5 is higher than that of Zn through the forward reaction Zn + CuZn5 ⇌ CuZn5 + Zn2+ + 2e-, resulting in microcell formation owing to the stronger charge density localization in Zn compared to CuZn5. The presence of chlorine accelerates the corrosion of Zn through the reaction Zn + CuZn5 + 6Cl- + 6H2O ⇌ Cu + 6ZnOHCl + 6H+ + 12e-, where the formation of ZnOHCl disrupts the ZnO passive film and expands the corrosion pH range from 9.2 to 8.8. Our findings reveal an accurate quantitative corrosion mechanism for Zn-Cu alloys, providing an effective pathway to investigate the corrosion resistance of biodegradable alloys.

• Publication type
• Journal Article MeSH