Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.

BACKGROUND: Hair and scalp disorders present a significant challenge in dermatology due to their clinical diversity and overlapping symptoms, often leading to misdiagnoses. Traditional diagnostic methods rely heavily on clinical expertise and are limited by subjectivity and accessibility, necessitating more advanced and accessible diagnostic tools. Artificial intelligence (AI) and deep learning offer a promising solution for more accurate and efficient diagnosis. METHODS: The research employs a modified Xception model incorporating ReLU activation, dense layers, global average pooling, regularization and dropout layers. This deep learning approach is evaluated against existing models like VGG19, Inception, ResNet, and DenseNet for its efficacy in accurately diagnosing various hair and scalp disorders. RESULTS: The model achieved a 92% accuracy rate, significantly outperforming the comparative models, with accuracies ranging from 50% to 80%. Explainable AI techniques like Gradient-weighted Class Activation Mapping (Grad-CAM) and Saliency Map provided deeper insights into the model's decision-making process. CONCLUSION: This study emphasizes the potential of AI in dermatology, particularly in accurately diagnosing hair and scalp disorders. The superior accuracy and interpretability of the model represents a significant advancement in dermatological diagnostics, promising more reliable and accessible diagnostic methods.

Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time.

With an aging population and increased chronic diseases, remote health monitoring has become critical to improving patient care and reducing healthcare costs. The Internet of Things (IoT) has recently drawn much interest as a potential remote health monitoring remedy. IoT-based systems can gather and analyze a wide range of physiological data, including blood oxygen levels, heart rates, body temperatures, and ECG signals, and then provide real-time feedback to medical professionals so they may take appropriate action. This paper proposes an IoT-based system for remote monitoring and early detection of health problems in home clinical settings. The system comprises three sensor types: MAX30100 for measuring blood oxygen level and heart rate; AD8232 ECG sensor module for ECG signal data; and MLX90614 non-contact infrared sensor for body temperature. The collected data is transmitted to a server using the MQTT protocol. A pre-trained deep learning model based on a convolutional neural network with an attention layer is used on the server to classify potential diseases. The system can detect five different categories of heartbeats: Normal Beat, Supraventricular premature beat, Premature ventricular contraction, Fusion of ventricular, and Unclassifiable beat from ECG sensor data and fever or non-fever from body temperature. Furthermore, the system provides a report on the patient's heart rate and oxygen level, indicating whether they are within normal ranges or not. The system automatically connects the user to the nearest doctor for further diagnosis if any critical abnormalities are detected.

Moral Injury is a Risk Factor for Substance Use and Suicidality Among US Military Veterans with and without Traumatic Brain Injury.

A national survey of United States veterans was conducted, yielding 252 veterans with Traumatic Brain Injury (TBI) and 1235 veterans without TBI. Participants were asked questions about moral injury, suicidality, substance use, and other sociodemographic variables. Multivariable linear regression analysis was used to examine the previously described relationships. Increasing severity of moral injury was associated with higher scores on the substance use tool (b = 0.02, p = 0.04), although the magnitude of effect was not different from those without TBI (Z = - 0.57, p = 0.72). Increasing severity of moral injury was positively associated with suicidal behavior scores (b = 0.10, p < 0.01). The strength of this relationship was stronger in veterans with TBI than those without TBI (Z = 1.78, p = 0.04). Rehabilitation programs that treat veterans for TBI may need to consider the evaluation of moral injury given its association with adverse events in this population.

Ensemble of hybrid model based technique for early detecting of depression based on SVM and neural networks.

The prevalence of depression has increased dramatically over the last several decades: it is frequently overlooked and can have a significant impact on both physical and mental health. Therefore, it is crucial to develop an automated detection system that can instantly identify whether a person is depressed. Currently, machine learning (ML) and artificial neural networks (ANNs) are among the most promising approaches for developing automated computer-based systems to predict several mental health issues, such as depression. This study propose an ensemble of hybrid model-based techniques that aims to build a strong detection model that considers many psychological and sociodemographic characteristics of an individual to detect whether a person is depressed. Support vector machines (SVM) and multilayer perceptrons (MLP) are the two fundamental methods used to construct the suggested ensemble approach. The hybrid DeprMVM served as a meta-learner. In this study, the hybrid DeprMVM is a level-1 learner, whereas the SVM and MLP networks are level-0 learners. After the classifiers are trained and tested at level 0, their outputs are based on both the independent and dependent variables in the new data set that was used to train the meta-classifier. The training data class imbalance was reduced by applying the synthetic minority oversampling technique (SMOTE) and cluster sampling together, which improved the accuracy for detecting depression. Additionally, it can effectively reduce the risk of over-fitting from simply duplicating data points. To further confirm the effectiveness of the proposed method, various performance evaluation metrics were calculated and compared with previous studies conducted on this specific dataset. In conclusion, among all the techniques for identifying depression, the suggested ensemble approach had the best accuracy, at 99.39%, and an F1-score of 99.51%.

Cauli-Det: enhancing cauliflower disease detection with modified YOLOv8.

Cauliflower cultivation plays a pivotal role in the Indian Subcontinent's winter cropping landscape, contributing significantly to both agricultural output, economy and public health. However, the susceptibility of cauliflower crops to various diseases poses a threat to productivity and quality. This paper presents a novel machine vision approach employing a modified YOLOv8 model called Cauli-Det for automatic classification and localization of cauliflower diseases. The proposed system utilizes images captured through smartphones and hand-held devices, employing a finetuned pre-trained YOLOv8 architecture for disease-affected region detection and extracting spatial features for disease localization and classification. Three common cauliflower diseases, namely 'Bacterial Soft Rot', 'Downey Mildew' and 'Black Rot' are identified in a dataset of 656 images. Evaluation of different modification and training methods reveals the proposed custom YOLOv8 model achieves a precision, recall and mean average precision (mAP) of 93.2%, 82.6% and 91.1% on the test dataset respectively, showcasing the potential of this technology to empower cauliflower farmers with a timely and efficient tool for disease management, thereby enhancing overall agricultural productivity and sustainability.

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel.

Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient's heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.

Leaf diseases are a global threat to crop production and food preservation. Detecting these diseases is crucial for effective management. We introduce LeafDoc-Net, a robust, lightweight transfer-learning architecture for accurately detecting leaf diseases across multiple plant species, even with limited image data. Our approach concatenates two pre-trained image classification deep learning-based models, DenseNet121 and MobileNetV2. We enhance DenseNet121 with an attention-based transition mechanism and global average pooling layers, while MobileNetV2 benefits from adding an attention module and global average pooling layers. We deepen the architecture with extra-dense layers featuring swish activation and batch normalization layers, resulting in a more robust and accurate model for diagnosing leaf-related plant diseases. LeafDoc-Net is evaluated on two distinct datasets, focused on cassava and wheat leaf diseases, demonstrating superior performance compared to existing models in accuracy, precision, recall, and AUC metrics. To gain deeper insights into the model's performance, we utilize Grad-CAM++.