The article proposes the construction of a prefabricated monolithic reinforced concrete overlap consisting of beams hollow triangular section. It is shown that in such overlap the effect of spatial work is much higher than the analogous effect in traditional overlap that consists of U-shaped or T-beams and slabs. The technique of determining the forces of interaction of individual beams in the composition of the overlap is given. The technique is based on a discrete-continual method developed by the author, which is adapted to the calculation of overlaps that consist of considered beams. The technique of determining the effort between the shelf and the ribs of a beam during its bending is presented. It is based on the theory of compound rods. The algorithm of calculation taking into account the spatial work is presented as well as the principles of constructing overlaps consisting of beams hollow triangular section, taking into account the change in their rigidity as a result of cracks formation. An approach to the determination of the rigidity of beams with normal torsion fractures is given, based on the approximation of numerical experimental data.

The extended utilization of picture-enhancing or manipulating tools has led to ease of manipulating multimedia data which includes digital images. These manipulations will disturb the truthfulness and lawfulness of images, resulting in misapprehension, and might disturb social security. The image forensic approach has been employed for detecting whether or not an image has been manipulated with the usage of positive attacks which includes splicing, and copy-move. This paper provides a competent tampering detection technique using resampling features and convolution neural network (CNN). In this model range spatial filtering (RSF)-CNN, throughout preprocessing the image is divided into consistent patches. Then, within every patch, the resampling features are extracted by utilizing affine transformation and the Laplacian operator. Then, the extracted features are accumulated for creating descriptors by using CNN. A wide-ranging analysis is performed for assessing tampering detection and tampered region segmentation accuracies of proposed RSF-CNN based tampering detection procedures considering various falsifications and post-processing attacks which include joint photographic expert group (JPEG) compression, scaling, rotations, noise additions, and more than one manipulation. From the achieved results, it can be visible the RSF-CNN primarily based tampering detection with adequately higher accurateness than existing tampering detection methodologies.

<span>Detecting hybrid tampering attacks in an image is extremely difficult; especially when copy-clone tampered segments exhibit identical illumination and contrast level about genuine objects. The existing method fails to detect tampering when the image undergoes hybrid transformation such as scaling, rotation, compression, and also fails to detect under small-smooth tampering. The existing resampling feature extraction using the Deep learning techniques fails to obtain a good correlation among neighboring pixels in both horizontal and vertical directions. This work presents correlation aware convolution neural network (CA-CNN) for extracting resampling features for detecting hybrid tampering attacks. Here the image is resized for detecting tampering under a small-smooth region. The CA-CNN is composed of a three-layer horizontal, vertical, and correlated layer. The correlated layer is used for obtaining correlated resampling feature among horizontal sequence and vertical sequence. Then feature is aggregated and the descriptor is built. An experiment is conducted to evaluate the performance of the CA-CNN model over existing tampering detection methodologies considering the various datasets. From the result achieved it can be seen the CA-CNN is efficient considering various distortions and post-processing attacks such joint photographic expert group (JPEG) compression, and scaling. This model achieves much better accuracies, recall, precision, false positive rate (FPR), and F-measure compared existing methodologies.</span>

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The tomato crop is a significant staple with a high commercial value on the Indian market and is produced in enormous quantities. Diseases are harmful to the health of the plant, which has an impact on its growth. It is essential to monitor the progress of the farmed crop to ensure minimal losses. Many different tomato diseases attack the crop's leaves at frightening rates. The primary goal of the proposed effort is to identify a simple method for detecting tomato leaf disease while employing limited computational resources to produce outcomes that are comparable to state-of- the-art. Automatic feature extraction is used by neural network models to help categorize the input image into the appropriate illness classifications. The average accuracy of this suggested system is between 94 to 95 percent, demonstrating the viability of the neural network approach even in challenging circumstances. Keywords: Agriculture, Convolution Neural Network, machine learning, Support Vector Machine.