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Real-time video objects detection, tracking, and recognition are challenging issues due to the real-time processing requirements of the machine learning algorithms. In recent years, video processing is performed by deep learning (DL) based techniques that achieve higher accuracy but require higher computations cost. This paper presents a recent survey of the state-of-the-art DL platforms and architectures used for deep vision systems. It highlights the contributions and challenges from over numerous research studies. In particular, this paper first describes the architecture of various DL models such as AutoEncoders, deep Boltzmann machines, convolution neural networks, recurrent neural networks and deep residual learning. Next, deep real-time video objects detection, tracking and recognition studies are highlighted to illustrate the key trends in terms of cost of computation, number of layers and the accuracy of results. Finally, the paper discusses the challenges of applying DL for real-time video processing and draw some directions for the future of DL algorithms.

Neurodegenerative diseases such as Alzheimer's, Huntington's and Parkinson's diseases have multifaceted nature because of the different factors contributing to their progression. The complex nature of neurodegenerative diseases has developed a pressing need to design multitarget-directed ligands to address the complementary pathways involved in these diseases. The major enzyme targets for development of therapeutics for Alzheimer's disease are cholinesterase and β-secretase enzymes. In this review, we discuss recent advances in profiling single target inhibitors based on these enzymes to multitarget-directed ligands as potential therapeutics for this devastating disease. In addition, therapeutics based on iron chelation strategy are discussed as well.

Face masks are widely used in various industries and jobs, such as healthcare, food service, construction, manufacturing, retail, hospitality, transportation, education, and public safety. Masked face recognition is essential to accurately identify and authenticate individuals wearing masks. Masked face recognition has emerged as a vital technology to address this problem and enable accurate identification and authentication in masked scenarios. In this paper, we propose a novel method that utilizes a combination of deep-learning-based mask detection, landmark and oval face detection, and robust principal component analysis (RPCA) for masked face recognition. Specifically, we use pretrained ssd-MobileNetV2 for detecting the presence and location of masks on a face and employ landmark and oval face detection to identify key facial features. The proposed method also utilizes RPCA to separate occluded and non-occluded components of an image, making it more reliable in identifying faces with masks. To optimize the performance of our proposed method, we use particle swarm optimization (PSO) to optimize both the KNN features and the number of k for KNN. Experimental results demonstrate that our proposed method outperforms existing methods in terms of accuracy and robustness to occlusion. Our proposed method achieves a recognition rate of 97%, which is significantly higher than the state-of-the-art methods. Our proposed method represents a significant improvement over existing methods for masked face recognition, providing high accuracy and robustness to occlusion.

The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.

Biometric authentication is a widely used method for verifying individuals’ identities using photoplethysmography (PPG) cardiac signals. The PPG signal is a non-invasive optical technique that measures the heart rate, which can vary from person to person. However, these signals can also be changed due to factors like stress, physical activity, illness, or medication. Ensuring the system can accurately identify and authenticate the user despite these variations is a significant challenge. To address these issues, the PPG signals were preprocessed and transformed into a 2-D image that visually represents the time-varying frequency content of multiple PPG signals from the same human using the scalogram technique. Afterward, the features fusion approach is developed by combining features from the hybrid convolution vision transformer (CVT) and convolutional mixer (ConvMixer), known as the CVT-ConvMixer classifier, and employing attention mechanisms for the classification of human identity. This hybrid model has the potential to provide more accurate and reliable authentication results in real-world scenarios. The sensitivity (SE), specificity (SP), F1-score, and area under the receiver operating curve (AUC) metrics are utilized to assess the model’s performance in accurately distinguishing genuine individuals. The results of extensive experiments on the three PPG datasets were calculated, and the proposed method achieved ACCs of 95%, SEs of 97%, SPs of 95%, and an AUC of 0.96, which indicate the effectiveness of the CVT-ConvMixer system. These results suggest that the proposed method performs well in accurately classifying or identifying patterns within the PPG signals to perform continuous human authentication.

A dermatologist-like automatic classification system is developed in this paper to recognize nine different classes of pigmented skin lesions (PSLs), using a separable vision transformer (SVT) technique to assist clinical experts in early skin cancer detection. In the past, researchers have developed a few systems to recognize nine classes of PSLs. However, they often require enormous computations to achieve high performance, which is burdensome to deploy on resource-constrained devices. In this paper, a new approach to designing SVT architecture is developed based on SqueezeNet and depthwise separable CNN models. The primary goal is to find a deep learning architecture with few parameters that has comparable accuracy to state-of-the-art (SOTA) architectures. This paper modifies the SqueezeNet design for improved runtime performance by utilizing depthwise separable convolutions rather than simple conventional units. To develop this Assist-Dermo system, a data augmentation technique is applied to control the PSL imbalance problem. Next, a pre-processing step is integrated to select the most dominant region and then enhance the lesion patterns in a perceptual-oriented color space. Afterwards, the Assist-Dermo system is designed to improve efficacy and performance with several layers and multiple filter sizes but fewer filters and parameters. For the training and evaluation of Assist-Dermo models, a set of PSL images is collected from different online data sources such as Ph2, ISBI-2017, HAM10000, and ISIC to recognize nine classes of PSLs. On the chosen dataset, it achieves an accuracy (ACC) of 95.6%, a sensitivity (SE) of 96.7%, a specificity (SP) of 95%, and an area under the curve (AUC) of 0.95. The experimental results show that the suggested Assist-Dermo technique outperformed SOTA algorithms when recognizing nine classes of PSLs. The Assist-Dermo system performed better than other competitive systems and can support dermatologists in the diagnosis of a wide variety of PSLs through dermoscopy. The Assist-Dermo model code is freely available on GitHub for the scientific community.

Glutamine (Gln) is converted to excitatory (glutamate, aspartate) and inhibitory (γ-amino butyric acid) amino acid neurotransmitters in brain, and is a source of energy during glucose deprivation. Current research utilized an Analytical Quality by Design approach to optimize levels and combinations of critical gas pressure (sheath, auxiliary, sweep) and temperature (ion transfer tube, vaporizer) parameters for high-sensitivity mass spectrometric quantification of brain tissue glutamine. A Design of Experiments (DOE) matrix for evaluation of relationships between these multiple independent variables and a singular response variable, e.g. glutamine chromatogram area, was developed by statistical response surface methodology using central composite design. A second-order polynomial equation was generated to identify and predict singular versus combinatory effects of synergistic and antagonistic factors on chromatograph area. Predicted versus found outcomes overlapped, with enhanced area associated with the latter. DOE methodology was subsequently used to evaluate liquid chromatographic variable effects, e.g. flow rate, column temperature, and mobile phase composition on the response variable. Results demonstrate that combinatory AQbD-guided mass spectrometric/liquid chromatographic optimization significantly enhanced analytical sensitivity for Gln, thus enabling down-sized brain tissue sample volume procurement for quantification of this critical amino acid.

Cardiovascular disorders are often diagnosed using an electrocardiogram (ECG). It is a painless method that mimics the cyclical contraction and relaxation of the heart’s muscles. By monitoring the heart’s electrical activity, an ECG can be used to identify irregular heartbeats, heart attacks, cardiac illnesses, or enlarged hearts. Numerous studies and analyses of ECG signals to identify cardiac problems have been conducted during the past few years. Although ECG heartbeat classification methods have been presented in the literature, especially for unbalanced datasets, they have not proven to be successful in recognizing some heartbeat categories with high performance. This study uses a convolutional neural network (CNN) model to combine the benefits of dense and residual blocks. The objective is to leverage the benefits of residual and dense connections to enhance information flow, gradient propagation, and feature reuse, ultimately improving the model’s performance. This proposed model consists of a series of residual-dense blocks interleaved with optional pooling layers for downsampling. A linear support vector machine (LSVM) classified heartbeats into five classes. This makes it easier to learn and represent features from ECG signals. We first denoised the gathered ECG data to correct issues such as baseline drift, power line interference, and motion noise. The impacts of the class imbalance are then offset by resampling techniques that denoise ECG signals. An RD-CNN algorithm is then used to categorize the ECG data for the various cardiac illnesses using the retrieved characteristics. On two benchmarked datasets, we conducted extensive simulations and assessed several performance measures. On average, we have achieved an accuracy of 98.5%, a sensitivity of 97.6%, a specificity of 96.8%, and an area under the receiver operating curve (AUC) of 0.99. The effectiveness of our suggested method for detecting heart disease from ECG data was compared with several recently presented algorithms. The results demonstrate that our method is lightweight and practical, qualifying it for continuous monitoring applications in clinical settings for automated ECG interpretation to support cardiologists.

The incidence and mortality of cancer are increasing, making it a leading cause of death worldwide. Conventional treatments such as surgery, radiotherapy, and chemotherapy face significant limitations due to therapeutic resistance. Autophagy, a cellular self-degradation mechanism, plays a crucial role in cancer development, drug resistance, and treatment. This review investigates the potential of autophagy inhibition as a therapeutic strategy for cancer. A systematic search was conducted on Embase, PubMed, and Google Scholar databases from 1967 to 2024 to identify studies on autophagy inhibitors and their mechanisms in cancer therapy. The review includes original articles utilizing in vitro and in vivo experimental methods, literature reviews, and clinical trials. Key terms used were “Autophagy”, “Inhibitors”, “Molecular mechanism”, “Cancer therapy”, and “Clinical trials”. Autophagy inhibitors such as chloroquine (CQ) and hydroxychloroquine (HCQ) have shown promise in preclinical studies by inhibiting lysosomal acidification and preventing autophagosome degradation. Other inhibitors like wortmannin and SAR405 target specific components of the autophagy pathway. Combining these inhibitors with chemotherapy has demonstrated enhanced efficacy, making cancer cells more susceptible to cytotoxic agents. Clinical trials involving CQ and HCQ have shown encouraging results, although further investigation is needed to optimize their use in cancer therapy. Autophagy exhibits a dual role in cancer, functioning as both a survival mechanism and a cell death pathway. Targeting autophagy presents a viable strategy for cancer therapy, particularly when integrated with existing treatments. However, the complexity of autophagy regulation and the potential side effects necessitate further research to develop precise and context-specific therapeutic approaches.

Hypertensive retinopathy (HR) and diabetic retinopathy (DR) are retinal diseases closely associated with high blood pressure. The severity and duration of hypertension directly impact the prevalence of HR. The early identification and assessment of HR are crucial to preventing blindness. Currently, limited computer-aided methods are available for detecting HR and DR. These existing systems rely on traditional machine learning approaches, which require complex image processing techniques and are often limited in their application. To address this challenge, this work introduces a deep learning (DL) method called HDR-EfficientNet, which aims to provide an efficient and accurate approach to identifying various eye-related disorders, including diabetes and hypertensive retinopathy. The proposed method utilizes an EfficientNet-V2 network for end-to-end training focused on disease classification. Additionally, a spatial-channel attention method is incorporated into the approach to enhance its ability to identify specific areas of damage and differentiate between different illnesses. The HDR-EfficientNet model is developed using transfer learning, which helps overcome the challenge of imbalanced sample classes and improves the network’s generalization. Dense layers are added to the model structure to enhance the feature selection capacity. The performance of the implemented system is evaluated using a large dataset of over 36,000 augmented retinal fundus images. The results demonstrate promising accuracy, with an average area under the curve (AUC) of 0.98, a specificity (SP) of 96%, an accuracy (ACC) of 98%, and a sensitivity (SE) of 95%. These findings indicate the effectiveness of the suggested HDR-EfficientNet classifier in diagnosing HR and DR. In summary, the HDR-EfficientNet method presents a DL-based approach that offers improved accuracy and efficiency for the detection and classification of HR and DR, providing valuable support in diagnosing and managing these eye-related conditions.