Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

Automatic leaf disease detection techniques are effective for reducing the time-consuming effort of monitoring large crop farms and early identification of disease symptoms of plant leaves. Although crop tomatoes are seen to be susceptible to a variety of diseases that can reduce the production of the crop. In recent years, advanced deep learning methods show successful applications for plant disease detection based on observed symptoms on leaves. However, these methods have some limitations. This study proposed a high-performance tomato leaf disease detection approach, namely attention-based dilated CNN logistic regression (ADCLR). Firstly, we develop a new feature extraction method using attention-based dilated CNN to extract most relevant features in a faster time. In our preprocessing, we use Bilateral filtering to handle larger features to make the image smoother and the Ostu image segmentation process to remove noise in a fast and simple way. In this proposed method, we preprocess the image with bilateral filtering and Otsu segmentation. Then, we use the Conditional Generative Adversarial Network (CGAN) model to generate a synthetic image from the image which is preprocessed in the previous stage. The synthetic image is generated to handle imbalance and noisy or wrongly labeled data to obtain good prediction results. Then, the extracted features are normalized to lower the dimensionality. Finally, extracted features from preprocessed data are combined and then classified using fast and simple logistic regression (LR) classifier. The experimental outcomes show the state-of-the-art performance on the Plant Village database of tomato leaf disease by achieving 100%, 100%, 96.6% training, testing, and validation accuracy, respectively, for multiclass. From the experimental analysis, it is clearly demonstrated that the proposed multimodal approach can be utilized to detect tomato leaf disease precisely, simply and quickly. We have a potential plan to improve the model to make it cloud-based automated leaf disease classification for different plants.

Deep learning methods have shown early progress in analyzing complicated ECG signals, especially in heartbeat classification and arrhythmia detection. However, there is still a long way to go in terms of health-related data analysis. This research provides a duel structured and bidirectional Recurrent Neural Network(RNN) method for arrhythmia classification that addresses the issues with multilayered dilated convolution neural network (CNN) models. Initially, the data is preprocessed by Chebyshev Type II filtering that is faster and do not use statistical characteristics. Noise from the preprocesed filter is aslo removed by using Daubechies wavelet that can able to solve fractal problems and signal discontinuities. An then Z-normalization is done using Pan-Tompkins normalization technique for handling of different normally distributed samples. Finally, a generative adversarial network (GAN)-based synthetic signal is generated for recreation of signal to handle imbalanced signal class. The proposed Bidirectional RNN with Dilated CNN (BRDC) appears to take advantage of the potentiality of multilayered dilated CNN and bidirectional RNN unit (bidirectional gated recurrent Units, BiGRU - bidirectional long short-term memory, BiLSTM) architecture to generate fusion features. Finally, the signals are classified by fully connected layer and Rectified Linear Unit (ReLU) activation function. The PhysioNet 2017 challenge dataset is used to train and validate the proposed model. By combining fusion features with dilated CNN, the learned model significantly improves the classification performance and interpretability. The experimental findings show that, for MIT-BIH provided ECG (electrocardiogram) data to identify arrhythmia, the proposed BRDC model outperforms existing models with 99.90 % accuracy, 98.41 % F1, 97.96 % precision, and 99.90 % recall during training. One of the significant findings of this study is that the proposed approach can significantly reduce time length when employing RNN networks with multilayered dilated CNN. Overall, our hybrid model using BiGRU-BiLSTM and multi-layered dilated CNN provides a cost-effective ECG signal reduction and high-performance automated recognition technique to identify arrhythmia. Our future improvement will focus on the classification of numerous arrhythmia signal-based data, automatic and cloud based ECG classification.

Gathering a large number of people in a shared physical area is very common in urban culture. Although there are limitless examples of mega crowds, the Islamic religious ritual, the Hajj, is considered as one of the greatest crowd scenarios in the world. The Hajj is carried out once in a year with a congregation of millions of people when the Muslims visit the holy city of Makkah at a given time and date. Such a big crowd is always prone to public safety issues, and therefore requires proper measures to ensure safe and comfortable arrangement. Through the advances in computer vision based scene understanding, automatic analysis of crowd scenes is gaining popularity. However, existing crowd analysis algorithms might not be able to correctly interpret the video content in the context of the Hajj. This is because the Hajj is a unique congregation of millions of people crowded in a small area, which can overwhelm the use of existing video and computer vision based sophisticated algorithms. Through our studies on crowd analysis, crowd counting, density estimation, and the Hajj crowd behavior, we faced the need of a review work to get a research direction for abnormal behavior analysis of Hajj pilgrims. Therefore, this review aims to summarize the research works relevant to the broader field of video analytics using deep learning with a special focus on the visual surveillance in the Hajj. The review identifies the challenges and leading-edge techniques of visual surveillance in general, which may gracefully be adaptable to the applications of Hajj and Umrah. The paper presents detailed reviews on existing techniques and approaches employed for crowd analysis from crowd videos, specifically the techniques that use deep learning in detecting abnormal behavior. These observations give us the impetus to undertake a painstaking yet exhilarating journey on crowd analysis, classification and detection of any abnormal movement of the Hajj pilgrims. Furthermore, because the Hajj pilgrimage is the most crowded domain for video-related extensive research activities, this study motivates us to critically analyze the crowd on a large scale.

With an increasing number of people on the planet today, innovative human-computer interaction technologies and approaches may be employed to assist individuals in leading more fulfilling lives. Gesture-based technology has the potential to improve the safety and well-being of impaired people, as well as the general population. Recognizing gestures from video streams is a difficult problem because of the large degree of variation in the characteristics of each motion across individuals. In this article, we propose applying deep learning methods to recognize automated hand gestures using RGB and depth data. To train neural networks to detect hand gestures, any of these forms of data may be utilized. Gesturebased interfaces are more natural, intuitive, and straightforward. Earlier study attempted to characterize hand motions in a number of contexts. Our technique is evaluated using a vision-based gesture recognition system. In our suggested technique, image collection starts with RGB video and depth information captured with the Kinect sensor and is followed by tracking the hand using a single shot detector Convolutional Neural Network (SSD-CNN). When the kernel is applied, it creates an output value at each of the m × n locations. Using a collection of convolutional filters, each new feature layer generates a defined set of gesture detection predictions. After that, we perform deep dilation to make the gesture in the image masks more visible. Finally, hand gestures have been detected using the well-known classification technique SVM. Using deep learning we recognize hand gestures with higher accuracy of 93.68% in RGB passage, 83.45% in the depth passage, and 90.61% in RGB-D conjunction on the SKIG dataset compared to the state-of-the-art. In the context of our own created Different Camera Orientation Gesture (DCOG) dataset we got higher accuracy of 92.78% in RGB passage, 79.55% in the depth passage, and 88.56% in RGB-D conjunction for the gestures collected in 0-degree angle. Moreover, the framework intends to use unique methodologies to construct a superior vision-based hand gesture recognition system.