A multi-patch-based deep learning model with VGG19 for breast cancer classifications in the pathology images.

Purpose: Breast cancer encompasses various subtypes with distinct prognoses, necessitating accurate stratification methods. Current techniques rely on quantifying gene expression in limited subsets. Given the complexity of breast tissues, effective detection and classification of breast cancer is crucial in medical imaging.

XAI-driven CatBoost multi-layer perceptron neural network for analyzing breast cancer.

Early diagnosis of breast cancer is exceptionally important in signifying the treatment results, of women's health. The present study outlines a novel approach for analyzing breast cancer data by using the CatBoost classification model with a multi-layer perceptron neural network (CatBoost+MLP). Explainable artificial intelligence techniques are used to cohere with the proposed CatBoost with the MLP model.

An explainable Artificial Intelligence software system for predicting diabetes.

Implementing diabetes surveillance systems is paramount to mitigate the risk of incurring substantial medical expenses. Currently, blood glucose is measured by minimally invasive methods, which involve extracting a small blood sample and transmitting it to a blood glucose meter. This method is deemed discomforting for individuals who are undergoing it.

An Interpretable Approach with Explainable AI for Heart Stroke Prediction.

Heart strokes are a significant global health concern, profoundly affecting the wellbeing of the population. Many research endeavors have focused on developing predictive models for heart strokes using ML and DL techniques. Nevertheless, prior studies have often failed to bridge the gap between complex ML models and their interpretability in clinical contexts, leaving healthcare professionals hesitant to embrace them for critical decision-making.

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.

Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning.

AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients.

Due to an aging population, assisted-care options are required so that senior citizens may maintain their independence at home for a longer time and rely less on caretakers. Ambient Assisted Living (AAL) encourages the creation of solutions that can help to optimize the environment for senior citizens with assistance while greatly reducing their challenges. A framework based on the Internet of Medical Things (IoMT) is used in the current study for the implementation of AAL technology to help patients with Type-2 diabetes.

Fine-Tuned DenseNet-169 for Breast Cancer Metastasis Prediction Using FastAI and 1-Cycle Policy.

Lymph node metastasis in breast cancer may be accurately predicted using a DenseNet-169 model. However, the current system for identifying metastases in a lymph node is manual and tedious. A pathologist well-versed with the process of detection and characterization of lymph nodes goes through hours investigating histological slides.

Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.

Machine Learning methods can play a key role in predicting the spread of respiratory infection with the help of predictive analytics. Machine Learning techniques help mine data to better estimate and predict the COVID-19 infection status. A Fine-tuned Ensemble Classification approach for predicting the death and cure rates of patients from infection using Machine Learning techniques has been proposed for different states of India.

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices.

Biomass derived efficient conversion of levulinic acid for sustainable production of γ-valerolactone over cobalt based catalyst.

Biomass feedstocks offer very promising sustainable production of fuels and chemicals as fossil fuels generate greenhouse gases and are going to become scarce. Nevertheless, establishing value addition to biomass waste to produce commodity chemicals by combining economic and environmental performances is complex. In this context, hydrogenation of biomass based levulinic acid at normal atmospheric reaction conditions using robust cobalt supported on porous heterogeneous catalyst has been studied at 200 °C in a continuous process.

Additional food supplements as a tool for biological conservation of predator-prey systems involving type III functional response: A qualitative and quantitative investigation.

Provision of additional food supplements for the purpose of biological conservation in ecosystems has of late been intensely researched by agriculturalists, biologists and mathematicians. The study of these ecosystems is usually done using the predator-prey systems. In these ecological studies it has been observed that the quality and quantity of additional food supplements provided play a crucial role in the growth of the predators and thereby influence the eventual state of the ecosystem.

Ordered mesoporous ferrosilicate materials with highly dispersed iron oxide nanoparticles and investigation of their unique magnetic properties.

Ordered mesoporous ferrosilicate materials with highly dispersed iron oxide nanoparticles are directly synthesized through a hydrothermal approach under acidic conditions. The obtained samples possess a high surface area (up to 1236 m(2) g(-1)) and a large pore volume (up to 1.1 cm(3) g(-1)).

Polymeric micelle assembly for the direct synthesis of functionalized mesoporous silica with fully accessible Pt nanoparticles toward an improved CO oxidation reaction.

Pt-decorated mesoporous silica is directly prepared using a polymeric micelles assembly approach using an asymmetric triblock copolymer, poly(styrene-b-2-vinylpyridine-b-ethylene oxide) as the structure directing agent. Strongly immobilized, fully accessible, and uniformly dispersed Pt nanoparticles on mesoporous silica wall exhibit superior catalytic activity toward CO oxidation.

Dynamics of additional food provided predator-prey system with mutually interfering predators.

Use of additional/alternative food source to predators is one of the widely recognised practices in the field of biological control. Both theoretical and experimental works point out that quality and quantity of additional food play a vital role in the controllability of the pest. Theoretical studies carried out previously in this direction indicate that incorporating mutual interference between predators can stabilise the system.

Direct carbonization of Al-based porous coordination polymer for synthesis of nanoporous carbon.

Nanoporous carbon (NPC) is prepared by direct carbonization of Al-based porous coordination polymers (Al-PCP). By applying the appropriate carbonization temperature, both high surface area and large pore volume are realized for the first time. Our NPC shows much higher porosity than other carbon materials (such as activated carbons and mesoporous carbons).

Direct synthesis of nanoporous carbon nitride fibers using Al-based porous coordination polymers (Al-PCPs).

We report a new synthetic route for preparation of nanoporous carbon nitride fibers with graphitic carbon nitride polymers, by calcination of Al-based porous coordination polymers (Al-PCPs) with dicyandiamide (DCDA) under a nitrogen atmosphere.

Role of quantity of additional food to predators as a control in predator-prey systems with relevance to pest management and biological conservation.

Necessity to understand the role of additional food as a tool in biological control programs is being increasingly felt, particularly due to its eco-friendly nature. A thorough mathematical analysis in this direction revealed the vital role of quality and quantity of the additional food in the controllability of the predator-prey systems. In this article controllability of the additional food--provided predator-prey system is studied from perspectives of pest eradication and biological conservation.

Fabrication of mesoporous carbons with rod and winding road like morphology using NbSBA-15 templates.

Here we demonstrate for the first time the fabrication hexagonally ordered mesoporous carbon materials with different morphology and pore diameters using NbSBA-15 mesoporous silica template with different niobium content. The materials were characterized by several characterization techniques such as XRD, HRSEM, HRTEM, elemental mapping, ICP-AES, and EDS analysis. We also demonstrate that the morphology of the materials can be controlled by simply tuning the morphology of the parent NbSBA-15 template, whose morphology can be tuned by adjusting the loading of niobium in the framework wall structure of SBA-15.

A validated stability-indicating LC method for acetazolamide in the presence of degradation products and its process-related impurities.

The objective of the current study was to develop a validated, specific and stability-indicating reverse phase liquid chromatographic method for the quantitative determination of acetazolamide and its related substances. The determination was done for an active pharmaceutical ingredient, its pharmaceutical dosage form in the presence of degradation products, and its process-related impurities. The drug was subjected to stress conditions of hydrolysis (acid and base), oxidation, photolysis and thermal degradation as per International Conference on Harmonization (ICH) prescribed stress conditions to show the stability-indicating power of the method.

A rapid method for the synthesis of nitrogen doped TiO2 nanoparticles for photocatalytic hydrogen generation.

A facile, fast, and economic method of doping TiO2, synthesized by conventional precipitation route with N has been developed. By this method, stable N doped TiO2 can be prepared within a short duration of time. The method adopted was to treat the TiO2 powder synthesized by simple precipitation with trioctyl amine (TOA) at 320 degrees C for 2 hours followed by calcination at 400 degrees C for 2 hours to obtain the N-doped TiO2.

Time optimal control of an additional food provided predator-prey system with applications to pest management and biological conservation.

Use of additional food has been widely recognized by experimental scientists as one of the important tools for biological control such as species conservation and pest management. The quality and quantity of additional food supplied to the predators is known to play a vital role in the controllability of the system. The present study is continuation of a previous work that highlights the importance of quality and quantity of the additional food in the dynamics of a predator-prey system in the context of biological control.

Eu3+ and Dy3+ doped YPO4 nanoparticles: low temperature synthesis and luminescence studies.

Eu3+ and Dy3+ doped YPO4 nanoparticles dispersible in methanol/water were prepared by the reaction of Y3+ and Eu3+/Dy3+ ions with ammonium dihydrogen phosphate in ethylene glycol medium at 160 degrees C. Nature and extent of strain associated with lattice has been found to change with incorporation of Eu3+/Dy3+ ion in the nanoparticles as well as the heat treatment temperature. Based on the TEM studies, it has been established that particles are highly crystalline with an average particle size of around 5 nm.