This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data signals were obtained from a temperature-to-voltage converter by using an Arduino Uno microcontroller board and MATLAB. Then, faults were simulated in normal data to get 100 samples of each fault, in which one sample is composed of 1000 data elements. A support vector machine (SVM) was used for data classification in a one-versus-rest manner. The statistical time-domain features, extracted from a sample, were used as a single observation for training and testing SVM. The number of features varied from 5 to 10 to examine the effect on accuracy of SVM. Three different kernel functions used to train SVM include linear, polynomial, and radial-basis function kernels. The fault occurrence event in fault samples was chosen randomly in some cases to replicate a practical scenario in industrial systems. The results show that an increase in the number of features from 5 to 10 hardly increases the total accuracy of the classifier. However, using ten features gives the highest accuracy for fault classification in an SVM. An increase in the number of training samples from 40 to 60 caused an overfitting problem. The k-fold cross-validation technique was adopted to overcome this issue. The increase in number of data elements per sample to 2500 increases the efficiency of the classifier. However, an increase in the number of training samples to 400 reduces the capability of SVM to classify stuck fault. The receiver operating characteristics curve comparison shows the efficiency of SVM over a neural network.

Skin cancer is a prevalent form of malignancy globally, and its early and accurate diagnosis is critical for patient survival. Clinical evaluation of skin lesions is essential, but it faces challenges such as long waiting times and subjective interpretations. Deep learning techniques have been developed to tackle these challenges and assist dermatologists in making more accurate diagnoses. Prompt treatment of skin cancer is vital to prevent its progression and potentially life-threatening consequences. The use of deep learning algorithms can improve the speed and accuracy of diagnosis, leading to earlier detection and treatment. Additionally, it can reduce the workload for healthcare professionals, allowing them to concentrate on more complex cases. The goal of this study was to develop reliable deep learning (DL) prediction models for skin cancer classification; (i) deal with a typical severe class imbalance problem, which arises because the skin-affected patients’ class is significantly smaller than the healthy class; and (ii) interpret the model output to better understand the decision-making mechanism (iii) Propose an End-to-End smart healthcare system through an android application. In a comparison examination with six well-known classifiers, the effectiveness of the proposed DL technique was explored in terms of metrics relating to both generalization capability and classification accuracy. A study used the HAM10000 dataset and an optimized CNN to identify the seven forms of skin cancer. The model was trained using two optimization functions (Adam and RMSprop) and three activation functions (Relu, Swish, and Tanh). Furthermore, an XAI-based skin lesion classification system was developed, incorporating Grad-CAM and Grad-CAM++ to explain the model’s decisions. This system can help doctors make informed skin cancer diagnoses in their early stages, with an 82% classification accuracy and 0.47% loss accuracy.

Generative Adversarial Networks are a class of artificial intelligence algorithms that consist of a generator and a discriminator trained simultaneously through adversarial training. GANs have found crucial applications in various fields, including medical imaging. In healthcare, GANs contribute by generating synthetic medical images, enhancing data quality, and aiding in image segmentation, disease detection, and medical image synthesis. Their importance lies in their ability to generate realistic images, facilitating improved diagnostics, research, and training for medical professionals. Understanding its applications, algorithms, current advancements, and challenges is imperative for further advancement in the medical imaging domain. However, no study explores the recent state-of-the-art development of GANs in medical imaging. To overcome this research gap, in this extensive study, we began by exploring the vast array of applications of GANs in medical imaging, scrutinizing them within recent research. We then dive into the prevalent datasets and pre-processing techniques to enhance comprehension. Subsequently, an in-depth discussion of the GAN algorithms, elucidating their respective strengths and limitations, is provided. After that, we meticulously analyzed the results and experimental details of some recent cutting-edge research to obtain a more comprehensive understanding of the current development of GANs in medical imaging. Lastly, we discussed the diverse challenges encountered and future research directions to mitigate these concerns. This systematic review offers a complete overview of GANs in medical imaging, encompassing their application domains, models, state-of-the-art results analysis, challenges, and research directions, serving as a valuable resource for multidisciplinary studies.

Ensuring product quality and integrity is paramount in the rapidly evolving landscape of industrial manufacturing. Although effective to a certain degree, traditional quality control methods often fail to meet the demands for efficiency, accuracy, and adaptability in today’s fast-paced production environments. The advent of Deep Learning (DL) and Computer Vision (CV) technologies has opened new vistas for automated defect detection, promising to revolutionize the way industries approach quality control and inspection. This systematic review focuses on recent advancements in DL and CV applications for automated defect detection in manufacturing processes. It provides a comprehensive overview of state-of-the-art techniques for detecting, classifying, and predicting defects, highlighting the significant strides made in addressing challenges such as varying lighting conditions, complex defect patterns, and the seamless integration of these technologies into existing manufacturing workflows. Through a critical analysis of current methodologies, this study identifies key areas of opportunity, outlines the challenges that persist and suggests directions for future research. This review synthesizes findings from a broad spectrum of industrial applications, offering insights into the potential of DL and CV to enhance quality control mechanisms. By charting the progress and pinpointing the gaps in current practices, this paper aims to serve as a valuable resource for researchers, practitioners, and policymakers seeking to leverage the benefits of DL and CV for improved product management and manufacturing excellence.

The dynamic hand skeleton data have become increasingly attractive to widely studied for the recognition of hand gestures that contain 3D coordinates of hand joints. Many researchers have been working to develop skeleton-based hand gesture recognition systems using various discriminative spatial-temporal attention features by calculating the dependencies between the joints. However, these methods may face difficulties in achieving high performance and owing generalizability due to their inefficient features. To overcome these challenges, we proposed a Multi-branch attention-based graph and a general deep-learning model to recognize hand gestures by extracting all possible types of skeleton-based features. We used two graph-based neural network channels in our multi-branch architectures and one general neural network channel. In graph-based neural network channels, one channel first uses the spatial attention module and then the temporal attention module to produce the spatial-temporal features. In contrast, we produced temporal-spatial features in the second channel using the reverse sequence of the first branch. In the last branch, general deep learning-based features are extracted using a general deep neural network module. The final feature vector was constructed by concatenating the spatial-temporal, temporal-spatial, and general features and feeding them into the fully connected layer. We included position embedding and mask operation for both spatial and temporal attention modules to track the sequence of the node and reduce the computational complexity of the system. Our model achieved 94.12%, 92.00%, and 97.01% accuracy after evaluation with MSRA, DHG, and SHREC’17 benchmark datasets, respectively. The high-performance accuracy and low computational cost proved that the proposed method outperformed the existing state-of-the-art methods.

Sign Language Recognition (SLR) represents a revolutionary technology aiming to establish communication between deaf and non-deaf communities, surpassing traditional interpreter-based approaches. Existing efforts in automatic sign recognition predominantly rely on hand skeleton joint information, steering clear of image pixels to address challenges like partial occlusion and redundant backgrounds. Many researchers have been working to develop automatic sign recognition using hand skeleton joint information instead of image pixels to overcome partial occlusion and redundant background problems. However, body motion and facial expression play an essential role in increasing the inner gesture variance in expressing sign language emotion besides hand information for large-scale sign word datasets. Recently, some researchers have been working to develop muti-gesture-based SLR recognition systems, but their performance accuracy and efficiency are unsatisfactory for real-time deployment. Addressing these limitations, we propose a novel approach – a two-stream multistage graph convolution with attention and residual connection (GCAR) – designed to extract spatial-temporal contextual information. The multistage GCAR system, incorporating a channel attention module, dynamically enhances attention levels, particularly for non-connected skeleton points during specific events within spatial-temporal features. The methodology involves capturing joint skeleton points and motion, offering a comprehensive understanding of a person’s entire body movement during sign language gestures and feeding this information into two streams. In the first stream, joint key features undergo processing through sep-TCN, graph convolution, deep learning layer, and a channel attention module across multiple stages, generating intricate spatial-temporal features in sign language gestures. Simultaneously, the joint motion is processed in the second stream, mirroring the steps of the first branch. The fusion of these two features yields the final feature vector, which is then fed into the classification module. The model excels in capturing discriminative structural displacements and short-range dependencies by leveraging unified joint features projected onto a high-dimensional space. Owing to the effectiveness of these features, the proposed method achieved significant accuracies: 90.31%, 94.10%, 99.75%, and 34.41%, for the WLASL, PSL, MSL, and ASLLVD large-scale datasets, respectively, with 0.69 million parameters. The high-performance accuracy, coupled with stable computational complexity, demonstrates the superiority of the proposed model. This innovative approach is anticipated to redefine the landscape of sign language recognition, setting a new standard in the field.