An Efficient CNN for Hand X-Ray Classification of Rheumatoid Arthritis

Hand Radiography (RA) is one of the prime tests for checking the progress of rheumatoid joint inflammation in human bone joints. Recognizing the specific phase of RA is a difficult assignment, as human abilities regularly curb the techniques for it. Convolutional neural network (CNN) is the center for hand recognition for recognizing complex examples. The human cerebrum capacities work in a high-level way, so CNN has been planned depending on organic neural-related organizations in humans for imitating its unpredictable capacities. This article accordingly presents the convolutional neural network (CNN) which has the ability to naturally gain proficiency with the qualities and anticipate the class of hand radiographs from an expansive informational collection. The reproduction of the CNN halfway layers, which depict the elements of the organization, is likewise appeared. For arrangement of the model, a dataset of 290 radiography images is utilized. The result indicates that hand X-rays are rated with an accuracy of 94.46% by the proposed methodology. Our experiments show that the network sensitivity is observed to be 0.95 and the specificity is observed to be 0.82.

APPLYING NEURAL NETWORKS IN RECOGNIZING CONDITIONALLY GRAPHICAL ELECTRICAL SYMBOLS

The article discusses the issue of using artificial neural networks for recognizing the conditionally graphical designations of electrical engineering, in particular, the convolutional neural networks and the R-CNN object recognition model, which is most suitable for solving the task at hand. Recognition of images of a specific picture is a task set for the complex information processing systems, as well as control and decision-making systems. The classification of various technological or natural objects, analog and digital signals is developed by a set of specific characteristics and properties. Defining the type and features of an object finds its application in different branches of science: machine learning, diagnostics, meteorology, video surveillance and security systems, in virtual reality systems and image search. However, research has not yet been carried out for solving the applied problems and achieving the required parameters (e.g. in recognizing conditional graphical symbols of electrical engineering). The neural networks have been found to have the highest quality and most promising among all mathematical models and methods of pattern recognition. As for the interactivity, the output result of image recognition work is a necessary and sufficient answer, which does not have a stable work on the variability of objects within categories and their invariant transformations. The scheme of the model R-CNN has been studied in detail, the importance of the training sample and its influence on the quality of pattern recognition by the neural network have been grounded. The application of the RoI Pooling method for object recognition in the image is shown in general, due to which there have been selected several regions of interest indicated through the bounding boxes.

Industry 4.0: Hand Recognition on Assembly Supervision Process

In the assembly industry, the process of assembling components is very important in order to produce a quality product. Assembly of components should be carried out sequentially based on the standards set by the company. For companies that still operate the assembly process manually by employee, sometimes errors occur in the assembly process, which can affect the quality of production. In order to be carried out the assembly process according to the procedure, a system is needed that can detect employee hands when carrying out the assembly process automatically. This study proposes an artificial intelligence-based real-time employee hand detection system. This system will be the basis for the development of an automatic industrial product assembly process to welcome the Industry 4.0. To verify system performance, several experiments were carried out, such as; detecting the right and left hands of employees and detecting hands when using accessories or not. From the experimental results it can be concluded that the system is able to detect the right and left hands of employees well with the resulting FPS average of 15.4.

CURSOR MANIPULATION WITH HAND RECOGNITION USING COMPUTER VISION

This paper aims to make a software program which will Track/Monitor your hand movement in front of the screen through a webcam and will move the cursor of the computing system with respect to your hand movement and can do certain fixed tasks like Right Click, Left Click, Scroll, Drag, Switch Between Programs, Go back, Forward, etc. This program will work in background and use convolutional Neural Networks Model (SSD) to convolve each and every video frame coming from input and at the end will classify the image into classes after further processing of the predicted class it will do necessary operations on Mouse/ Trackpad driver to perform desired operations.

Visual Hand Recognition in Hand Laterality and Self-Other Discrimination Tasks: Relationships to Autistic Traits and Positive Body Image

In a study concerning visual body part recognition, a “self-advantage” effect, whereby self-related body stimuli are processed faster and more accurately than other-related body stimuli, was revealed, and the emergence of this effect is assumed to be tightly linked to implicit motor simulation, which is activated when performing a hand laterality judgment task in which hand ownership is not explicitly required. Here, we ran two visual hand recognition tasks, namely, a hand laterality judgment task and a self-other discrimination task, to investigate (i) whether the self-advantage emerged even if implicit motor imagery was assumed to be working less efficiently and (ii) how individual traits [such as autistic traits and the extent of positive self-body image, as assessed via the Autism Spectrum Quotient (AQ) and the Body Appreciation Scale-2 (BAS-2), respectively] modulate performance in these hand recognition tasks. Participants were presented with hand images in two orientations [i.e., upright (egocentric) and upside-down (allocentric)] and asked to judge whether it was a left or right hand (an implicit hand laterality judgment task). They were also asked to determine whether it was their own, or another person’s hand (an explicit self-other discrimination task). Data collected from men and women were analyzed separately. The self-advantage effect in the hand laterality judgment task was not revealed, suggesting that only two orientation conditions are not enough to trigger this motor simulation. Furthermore, the men’s group showed a significant positive correlation between AQ scores and reaction times (RTs) in the laterality judgment task, while the women’s group showed a significant negative correlation between AQ scores and differences in RTs and a significant positive correlation between BAS-2 scores and dprime in the self-other discrimination task. These results suggest that men and women differentially adopt specific strategies and/or execution processes for implicit and explicit hand recognition tasks.

Recognizing Your Hand and That of Your Romantic Partner

Although the hand is an important organ in interpersonal interactions, focusing on this body part explicitly is less common in daily life compared with the face. We investigated (i) whether a person’s recognition of their own hand is different from their recognition of another person’s hand (i.e., self hand vs. other’s hand) and (ii) whether a close social relationship affects hand recognition (i.e., a partner’s hand vs. an unknown person’s hand). For this aim, we ran an experiment in which participants took part in one of two discrimination tasks: (i) a self–others discrimination task or (ii) a partner/unknown opposite-sex person discrimination task. In these tasks, participants were presented with a hand image and asked to select one of two responses, self (partner) or other (unknown persons), as quickly and accurately as possible. We manipulated hand ownership (self (partner)/other(unknown person)), hand image laterality (right/left), and visual perspective of hand image (upright/upside-down). A main effect of hand ownership in both tasks (i.e., self vs. other and partner vs. unknown person) was found, indicating longer reaction times for self and partner images. The results suggest that close social relationships modulate hand recognition—namely, “self-expansion” to a romantic partner could occur at explicit visual hand recognition.