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Computer vision based automatic evaluation method of Y2O3 steel coating performance with SEM image

Leveraging explainable AI and large-scale datasets for comprehensive classification of renal histologic types

Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence

AbstractSkin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.

A comparative template-switching cDNA approach for HTS-based multiplex detection of three viruses and one viroid commonly found in apple trees

Unlocking soybean meal pectin recalcitrance using a multi-enzyme cocktail approach

Integrated RNA sequencing analysis and machine learning identifies a metabolism-related prognostic signature in clear cell renal cell carcinoma

YOLO-STOD: an industrial conveyor belt tear detection model based on Yolov5 algorithm

PiERF1 regulates cold tolerance in Plumbago indica L. through ethylene signalling

Comparison of the safety and efficacy of dual antiplatelet therapy versus tenecteplase in patients with minor nondisabling acute ischemic stroke

Improving locomotor performance with motor imagery and tDCS in young adults

Triply periodic minimal surfaces for thermo-mechanical protection

Obtaining personalized predictions from a randomized controlled trial on Alzheimer’s disease

Study on the preparation and design of chenille/polyester integrated yarns and its acoustic properties

Sustainable material as a column filler in soft clay bed reinforced with encased column: numerical analysis

Application of the workload indicators of staffing need (WISN) to assess dietetic workforce needs in South African central and tertiary public hospitals

The association between urinary lead concentration and the likelihood of kidney stones in US adults: a population-based study

The antibacterial capabilities of alginate encapsulated lemon essential oil nanocapsules against multi-drug-resistant Acinetobacter baumannii

Patterns of variations in lipid molecular profile during larval development of red king crab, Paralithodes camtschaticus, and Japanese mitten crab, Eriocheir Japonica

Influence of heat-assisted vat photopolymerization on the physical and mechanical characteristics of dental 3D printing resins

AbstractThe effects of heat-assisted vat photopolymerization (HVPP) on the physical and mechanical properties of 3D-printed dental resins, including the morphometric stability of 3D-printed crowns, were investigated. A resin tank was designed to maintain the resin at 30, 40, and 50 ℃ during the 3D printing process. Test specimens were fabricated using a commercial dental resin, with untreated resin serving as the control group. Key properties such as viscosity, curing kinetics, surface microhardness, flexural properties, and dimensional accuracy were evaluated. The viscosity of the resin decreased significantly (P < 0.05) with increasing temperature, thereby enhancing its flow properties. Photo-DSC analysis revealed a 17.58% increase in peak heat flow at 50 ℃, indicating accelerated polymerization. Surface microhardness improved significantly (P < 0.05) with HVPP, though a slight reduction was observed at 50 ℃ compared to that at 30 and 40 ℃. The flexural strength, modulus, and resilience were significantly enhanced (P < 0.05) at higher temperatures, with 50 ℃ yielding the best mechanical properties. However, 3D morphometric analysis showed increased root mean square deviation from the CAD design at elevated temperatures. Our results suggest that HVPP enhances the durability of dental prostheses, although careful optimization of the printing temperature is essential to balance their strength and accuracy.

Passion fruit seed extract protects hydrogen peroxide-induced cell damage in human retinal pigment epithelium ARPE-19 cells