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Steganography is the technique for secretly hiding messages in media such as text, audio, image, and video without being discovered. Image is one of the most essential media for concealing data, making it hard to identify hidden data not visible to the human eye. In general, the cover image and the encrypted image are symmetrical in terms of dimension size, resolution, and qualities. This makes the difference difficult to perceive with the human eye. As a result, distinguishing between the two symmetric images required the development of methods. Steganalysis is a technique for identifying hidden messages embedded in digital material without having to know the embedding algorithm or the “non-stego” image. Due to their enormous feature vector dimension, which requires more time to calculate, the performance of most existing image steganalysis classification (ISC) techniques is still restricted. Therefore, in this research, we present a steganalysis classification method based on one of the texture features chosen, such as segmentation-based fractal texture analysis (SFTA), local binary pattern (LBP), and gray-level co-occurrence matrix (GLCM). The classifiers employed include Gaussian discriminant analysis (GDA) and naïve Bayes (NB). We used a public database in our proposed method and applied it to IStego100K datasets to be able to assess its performance. The experimental results reveal that in all classifiers, the SFTA feature surpassed all of the texture features, making it a great texture feature for image steganalysis classification. In terms of feature dimension and classification accuracy (CA), a comparison was made between the suggested SFTA-based GDA approach and various current ISC methods. The outcomes of the comparison are obvious show that the proposed method surpasses current methods.

Because textured contact lenses obscure the iris’s natural texture, they pose a serious threat to the accuracy of iris recognition systems and may make identity theft possible. Therefore, this work proposes a reliable method for textured contact lens detection that uses efficient global texture descriptors and effective feature selection with classification techniques. Run-Length and Zernike Moments are effective global texture descriptors that have been extracted from preprocessed iris images that were acquired from the IIIT-D CLI dataset. To improve classification performance, Ant Colony Optimization (ACO) was used to decrease the dimensionality of the feature vectors. Support Vector Machine (SVM) and Logistic Regression (LOG), two classifiers, have been evaluated with different descriptor pairings. According to findings from experiments, Zernike features optimized by ACO and paired with LOG produced the greatest accuracy of 98.04%, greatly surpassing previous methods. The efficacy of the presented approach for safe and dependable iris-based biometric systems is demonstrated by its exceptional results with regard to accuracy, recall, precision, and F1-score.

Human gesture image recognition is the process of identifying, deciphering, and classifying human gestures in images or video frames using computer vision algorithms. These gestures can vary from the simplest hand motions, body positions, and facial emotions to complicated gestures. Two significant problems affecting the performance of human gesture picture recognition methods are ambiguity and invariance. Ambiguity occurs when gestures have the same shape but different orientations, while invariance guarantees that gestures are correctly classified even when scale, lighting, or orientation varies. To overcome this issue, hand-crafted features can be combined with deep learning to greatly improve the performance of hand gesture image recognition models. This combination improves the model’s overall accuracy and dependability in identifying a variety of hand movements by enhancing its capacity to record both shape and texture properties. Thus, in this study, we propose a hand gesture recognition method that combines Reset50 model feature extraction with the Tamura texture descriptor and uses the adaptability of GAM to represent intricate interactions between the features. Experiments were carried out on publicly available datasets containing images of American Sign Language (ASL) gestures. As Tamura-ResNet50-OptimizedGAM achieved the highest accuracy rate in the ASL datasets, it is believed to be the best option for human gesture image recognition. According to the experimental results, the accuracy rate was 96%, which is higher than the total accuracy of the state-of-the-art techniques currently in use.

New computer-aided design and computer-aided manufacturing (CAD-CAM) materials have been reported to have suitable mechanical and physical properties for endocrown restorations. However, there is a lack of literature on evaluating the retention of endocrown using these materials. This in vitro study aimed to compare the retention of endocrowns fabricated from 3 different CAD-CAM materials. Sixty human mandibular third molars were divided into 3 groups ( n  = 20) according to the material used: (e.max CAD, Ivoclar AG, Schaan, Liechtenstein), (HC, Shofu, Koyoto, Japan), and (Brilliant Crios, Coltene, Altstätten, Switzerland) ( n  = 20). Specimens were stored in distilled water at 37 °C for 1 week and subjected to 5000 thermal cycles. A universal testing machine (5500R/1123, Instron, Norwood, USA) was used to measure the tensile force. All results were statistically analyzed using one-way ANOVA (α = 0.05), and different failure modes were demonstrated. The results revealed that e. max CAD showed the highest mean tensile force value (402.35 ± 15.812) N, and the lowest mean tensile force value was for Brilliant Coris (118.90 ± 12.430) N ( P  < .001). It is concluded that e.max CAD endocrowns showed the highest tensile force values after thermocycling in comparison to other materials with a significant difference, which may have a promising impact on the survival of endocrown restorations.

A significant quantity of malware is created on purpose every day. Users of smartphones and computer networks now mostly worry about malware. These days, malware detection is a major concern in the cybersecurity area. Several factors can impact malware detection performance, such as inappropriate features and classifiers, extensive domain knowledge, imbalanced data environments, computational complexity, and resource usage. A significant number of existing malware detection methods have been impacted by these factors. Therefore, in this paper, we will first identify and determine the best features and classifiers and then use them in order to propose the malware detection method. The comparative strategy and proposed malware detection procedure consist of four basic steps: malware transformation (converting images of malware from RGB to grayscale), feature extraction (using the ResNet-50, DenseNet-201, GoogLeNet, AlexNet, and SqueezeNet models), feature selection (using PCA method), classification (including GDA, KNN, logistic, SVM, RF, and ensemble learning), and evaluation (using accuracy and error evaluation metrics). Unbalanced Malimg datasets are used in experiments to validate the efficacy of the results that were obtained. According to the comparison findings, KNN is the best machine learning classifier. It outperformed the other classifiers in the Malimg datasets in terms of both accuracy and error. In addition, DenseNet201 is the best pretrained model in the Malimg dataset. Therefore, the proposed DenseNet201-KNN methods had an accuracy rate of 96% and a minimal error rate of 3.07%. The proposed methods surpass existing state-of-the-art approaches. The proposed feature extraction is computationally quicker than most other methods since it uses a lightweight design and fewer feature vector dimensions.

ET is used in camel reproduction to increase the reproductive potential of elite females selected for production and show. This retrospective study analyzed the association between factors related to embryo flushing (flushing fluid turbidity and debris), embryo quality (grades 1–4), recipient uterine status (tone and endometrial microcalcifications (EM)), farm and its locations (Qassim and Hail), as well high and low temperature on the likelihood of establishment of the 2-month pregnancy rate (PR) in dromedary camels. A total of 4360 embryos were transferred to 2947 recipients in this study. Logistic regression analysis (binary) was applied to evaluate the association between the selected factors and PR in month two. The likelihood of PR was affected by embryo quality (p < 0.01), EMs (p < 0.01), and farm (p < 0.05) and its location (p < 0.01). Transferring embryos to low (p < 0.01) or medium (p < 0.05) EM recipients decreased the likelihood of PR by ~1.3 times compared to uteri without EM. Grade 3 or 4 embryo transfer decreased the likelihood of PR by ~1.9 and 2.6 times, respectively, compared to grade 1 embryos (p< 0.01). In Saudi Arabia, applying an ET program in dromedaries in the Hail region raised the prediction of PR over the Qassim region by 1.2 times. Temperature changes had no effect on PR; however, higher temperature only affected the PR when grade 4 embryos were transferred. In conclusion, the likelihood of PR was higher with the transfer of grade 1 or 2 embryos in an EM-free uterus in the Hail region (Saudi Arabia). PR was only affected by higher temperature in the case of transferring grade 4 embryos.

Image watermarking is one of many methods for preventing unauthorized alterations to digital images. The major goal of the research is to find and identify photos that include a watermark, regardless of the method used to add the watermark or the shape of the watermark. As a result, this study advocated using the best Gabor features and classifiers to improve the accuracy of image watermarking identification. As classifiers, discriminant analysis (DA) and random forests are used. The DA and random forest use mean squared energy feature, mean amplitude feature, and combined feature vector as inputs for classification. The performance of the classifiers is evaluated using a variety of feature sets, and the best results are achieved. In order to assess the performance of the proposed method, we use a public database. VOC2008 is a public database that we use. The findings reveal that our proposed method’s DA classifier with integrated features had the greatest TPR of 93.71 and the lowest FNR of 6.29. This shows that the performance outcomes of the proposed approach are consistent. The proposed method has the advantages of being able to find images with the watermark in any database and not requiring a specific type or algorithm for embedding the watermark.

Malware development has significantly increased recently, posing a serious security risk to both consumers and businesses. Malware developers continually find new ways to circumvent security research’s ongoing efforts to guard against malware attacks. Malware Classification (MC) entails labeling a class of malware to a specific sample, while malware detection merely entails finding malware without identifying which kind of malware it is. There are two main reasons why the most popular MC techniques have a low classification rate. First, Finding and developing accurate features requires highly specialized domain expertise. Second, a data imbalance that makes it challenging to classify and correctly identify malware. Furthermore, the proposed malware classification (MC) method consists of the following five steps: (i) Dataset preparation: 2D malware images are created from the malware binary files; (ii) Visualized Malware Pre-processing: the visual malware images need to be scaled to fit the CNN model’s input size; (iii) Feature extraction: both hand-engineering (Tamura) and deep learning (GoogLeNet) techniques are used to extract the features in this step; (iv) Classification: to perform malware classification, we employed k-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Extreme Learning Machine (ELM). The proposed method is tested on a standard Malimg unbalanced dataset. The accuracy rate of the proposed method was extremely high, making it the most efficient option available. The proposed method’s accuracy rate was outperformed both the Hand-crafted feature and Deep Feature techniques, at 95.42 and 96.84 percent.

BackgroundSpondylolysis and spondylolisthesis are a common problem in the population, especially in young males. There are many surgical methods used to treat isthmic lumbar spondylolisthesis and spondylolysis, either direct or indirect. Research has recorded the high efficiency of posterolateral fusion surgery, as one of the most common methods for treating isthmic lumbar spondylolisthesis and spondylolysis, improving clinical outcomes and achieving solid fusion, but some studies have recorded degenerative changes at the adjacent level. Furthermore, available research has recorded varying results in clinical outcomes and solid fusion for direct repair methods. However, they were able to preserve the adjacent level from degenerative changes. The smiley face rod technique is considered one of the modern methods of direct repair, and initial studies have shown satisfactory results in the treatment of isthmic lumbar spondylolisthesis and spondylolysis. However, it is still unknown how efficient this method is and whether it is like posterolateral fusion surgery. So our study aims to compare the smiley face shaped rod technique and posterolateral fusion surgery in terms of improved lower back and lower extremity pain, functional disability, fusion, occurrence of degenerative changes in the adjacent level, length of surgical incision, and amount of blood loss from the patient during surgery.ResultsThe age range of the patients was between 18 and 50 years, 17 males and 13 females, with a ratio of 1.3:1. The statistical results showed that the smiley face shaped rod technique method had an advantage over the instrumental posterolateral fusion method in the degree of improvement of back (p = 0.002) and lower limb pain (p = 0.028), also the amount of blood loss was less (p < 0.001) and the length of the surgical incision was smaller (p = 0.019). Although the two methods had the same efficiency in solid fusion and no degenerative changes occurred at the adjacent level during 1 year follow-up.ConclusionThe smiley face rod technique is a good option for the treatment of lumbar spondylolysis and Spondylolisthesis grade 1.

Background Sacrococcygeal pilonidal sinus disease (PSD) is an infection of the skin and subcutaneous tissue at the upper part of the natal cleft of the buttocks. Excision and healing by granulation “lay-open” method is still more preferable than other methods of midline closure or using flaps but the healing time is lengthy. The present study was performed to assess the healing promotion effect of platelet-rich plasma (PRP) on the pilonidal sinus wounds treated by the lay-open method. Methods One hundred patients suffering from PSD were randomly divided into two groups, they were treated by the lay-open method, at General surgery department, Kafr El-Sheik University hospital, Egypt, during the period from December 2018 to December 2019. Group (A) was adopted the regular dressing postoperatively, while group (B) was treated with PRP injection into the wound at 4 and 12 postoperative days. Results Accelerated rate of wound healing was detected in group (B) in day 10, with a significant difference detected in days 15, 20, 25 and 30 postoperative, with a mean time of complete healing 45 ± 2.6 days in group B, while it was 57 ± 2.4 days in group A with a p -value of 0.001 which indicates considerable effect in the treated group. Conclusions PRP injection is an effective new technique in accelerating the healing of pilonidal wound after surgery, with a significant decrease in post-operative pain, complications and an early return to work. Trial registration retrospectively registered. Trial registration number: 12/35/1016 issued on December 2018 from the Institution Review Board at Kafr El Sheikh University. ClinicalTrials.gov identifier: NCT04430413