A fusion approach using GIS, green area detection, weather API and GPT for satellite image based fertile land discovery and crop suitability.

Proper utilization of agricultural land is a big challenge as they often laid over as waste lands. Farming is a significant occupation in any country and improving it further by promoting more farming opportunities will take the country towards making a huge leap forward. The issue in achieving this would be the lack of knowledge of cultivable land for food crops.

Neural harmony: revolutionizing thyroid nodule diagnosis with hybrid networks and genetic algorithms.

In the contemporary world, thyroid disease poses a prevalent health issue, particularly affecting women's well-being. Recognizing the significance of maternal thyroid (MT) hormones in fetal neurodevelopment during the first half of pregnancy, this study introduces the HNN-GSO model. This groundbreaking hybrid approach, utilizing the MT dataset, integrates ResNet-50 and Artificial Neural Network (ANN) within a Glow-worm Swarm Optimization (GSO) framework for optimal parameter tuning.

Factors Associated with Prehospital Delay in Patients with Acute Stroke in South India.

Background: Early hospital presentation is critical in the management of acute ischemic stroke. The effectiveness of stroke treatment is highly dependent on the amount of time lapsed between onset of symptoms and treatment. This study was aimed to identify the factors associated with prehospital delay in patients with acute stroke.

A Foreground Prototype-Based One-Shot Segmentation of Brain Tumors.

The potential for enhancing brain tumor segmentation with few-shot learning is enormous. While several deep learning networks (DNNs) show promising segmentation results, they all take a substantial amount of training data in order to yield appropriate results. Moreover, a prominent problem for most of these models is to perform well in unseen classes.

Automated Detection and Classification of Oral Squamous Cell Carcinoma Using Deep Neural Networks.

This work aims to classify normal and carcinogenic cells in the oral cavity using two different approaches with an eye towards achieving high accuracy. The first approach extracts local binary patterns and metrics derived from a histogram from the dataset and is fed to several machine-learning models. The second approach uses a combination of neural networks as a backbone feature extractor and a random forest for classification.

Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network.

The critical structure and nature of different bone marrow cells which form a base in the diagnosis of haematological ailments requires a high-grade classification which is a very prolonged approach and accounts for human error if performed manually, even by field experts. Therefore, the aim of this research is to automate the process to study and accurately classify the structure of bone marrow cells which will help in the diagnosis of haematological ailments at a much faster and better rate. Various machine learning algorithms and models, such as CNN + SVM, CNN + XGB Boost and Siamese network, were trained and tested across a dataset of 170,000 expert-annotated cell images from 945 patients' bone marrow smears with haematological disorders.

Deep Neural Network Models for Colon Cancer Screening.

Early detection of colorectal cancer can significantly facilitate clinicians' decision-making and reduce their workload. This can be achieved using automatic systems with endoscopic and histological images. Recently, the success of deep learning has motivated the development of image- and video-based polyp identification and segmentation.

A robust approach for industrial small-object detection using an improved faster regional convolutional neural network.

With the increasing pace in the industrial sector, the need for a smart environment is also increasing and the production of industrial products in terms of quality always matters. There is a strong burden on the industrial environment to continue to reduce impulsive downtime, concert deprivation, and safety risks, which needs an efficient solution to detect and improve potential obligations as soon as possible. The systems working in industrial environments for generating industrial products are very fast and generate products rapidly, sometimes leading to faulty products.

A mini-review on the impact of COVID 19 on vital organs.

COVID-19 (Corona Virus Disease-2019) is an infectious disease caused by a novel coronavirus, known as the acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This is a highly contagious disease that has already affected more than 220 countries globally, infecting more than 212 million people and resulting in the death of over 4.4 million people.

A mini-review on the effects of COVID-19 on younger individuals.

Coronavirus disease 2019 (COVID-19) pandemic has uprooted our lives like never before since its onset in the late December 2019. The world has seen mounting infections and deaths over the past few months despite the unprecedented measures countries are implementing, such as lockdowns, social distancing, mask-wearing, and banning gatherings in large groups. Interestingly, young individuals seem less likely to be impacted by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for COVID-19.

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques.

As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes the diagnosis process more challenging, in which the most of the cells are superimposed.

Critical texture pattern feature assessment for characterizing colonies of induced pluripotent stem cells through machine learning techniques.

The objectives of this study are to assess various automated texture features obtained from the segmented colony regions of induced pluripotent stem cells (iPSCs) and confirm their potential for characterizing the colonies using different machine learning techniques. One hundred and fifty-one features quantified using shape-based, moment-based, statistical and spectral texture feature groups are extracted from phase-contrast microscopic colony images of iPSCs. The forward stepwise regression model is implemented to select the most appropriate features required for categorizing the colonies.

Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies.

Distributional Variations in the Quantitative Cortical and Trabecular Bone Radiographic Measurements of Mandible, between Male and Female Populations of Korea, and its Utilization.

It is important to investigate the irregularities in aging-associated changes in bone, between men and women for bone strength and osteoporosis. The purpose of this study was to characterize the changes and associations of mandibular cortical and trabecular bone measures of men and women based on age and to the evaluation of cortical shape categories, in a large Korean population. Panoramic radiographs of 1047 subjects (603 women and 444 men) aged between 15 to 90 years were used.

Automated Detection of Cancer Associated Genes Using a Combined Fuzzy-Rough-Set-Based F-Information and Water Swirl Algorithm of Human Gene Expression Data.

This study describes a novel approach to reducing the challenges of highly nonlinear multiclass gene expression values for cancer diagnosis. To build a fruitful system for cancer diagnosis, in this study, we introduced two levels of gene selection such as filtering and embedding for selection of potential genes and the most relevant genes associated with cancer, respectively. The filter procedure was implemented by developing a fuzzy rough set (FR)-based method for redefining the criterion function of f-information (FI) to identify the potential genes without discretizing the continuous gene expression values.

Automatic detection of osteoporosis based on hybrid genetic swarm fuzzy classifier approaches.

Objectives: This study proposed a new automated screening system based on a hybrid genetic swarm fuzzy (GSF) classifier using digital dental panoramic radiographs to diagnose females with a low bone mineral density (BMD) or osteoporosis.

Methods: The geometrical attributes of both the mandibular cortical bone and trabecular bone were acquired using previously developed software. Designing an automated system for osteoporosis screening involved partitioning of the input attributes to generate an initial membership function (MF) and a rule set (RS), classification using a fuzzy inference system and optimization of the generated MF and RS using the genetic swarm algorithm.

Texture analysis of mandibular cortical bone on digital dental panoramic radiographs for the diagnosis of osteoporosis in Korean women.

Objective: To determine whether individual measurements or a combination of textural features and mandibular cortical width (MCW) derived from digital dental panoramic radiographs (DPRs) are more useful in assessment of osteoporosis.

Study Design: Textural features were obtained by using fractal dimension (FD) and gray-level co-occurrence matrix (GLCM). Digital DPRs and bone mineral densities (BMDs) of the lumbar spine and the femoral neck were obtained from 141 female patients.

The combination of a histogram-based clustering algorithm and support vector machine for the diagnosis of osteoporosis.

Purpose: To prevent low bone mineral density (BMD), that is, osteoporosis, in postmenopausal women, it is essential to diagnose osteoporosis more precisely. This study presented an automatic approach utilizing a histogram-based automatic clustering (HAC) algorithm with a support vector machine (SVM) to analyse dental panoramic radiographs (DPRs) and thus improve diagnostic accuracy by identifying postmenopausal women with low BMD or osteoporosis.

Materials And Methods: We integrated our newly-proposed histogram-based automatic clustering (HAC) algorithm with our previously-designed computer-aided diagnosis system.

Computer-aided measurement of mandibular cortical width on dental panoramic radiographs for identifying osteoporosis.

Aim: To develop a computer-aided diagnosis system to continuously measure mandibular inferior cortical width on dental panoramic radiographs and evaluate the system's efficacy in identifying postmenopausal women with low-skeletal bone mineral density.

Methods: Mandibular inferior cortical width was continuously measured by enhancing the original X-ray image, determining cortical boundaries, and evaluating all distances between the upper and lower boundaries in the region of interest. The system's efficacy in identifying osteoporosis at the lumbar spine and the femoral neck was evaluated for 100 women (≥50 years): 50 in the development of the tool and 50 in its validation.

Diagnosis of osteoporosis from dental panoramic radiographs using the support vector machine method in a computer-aided system.

Background: Early diagnosis of osteoporosis can potentially decrease the risk of fractures and improve the quality of life. Detection of thin inferior cortices of the mandible on dental panoramic radiographs could be useful for identifying postmenopausal women with low bone mineral density (BMD) or osteoporosis. The aim of our study was to assess the diagnostic efficacy of using kernel-based support vector machine (SVM) learning regarding the cortical width of the mandible on dental panoramic radiographs to identify postmenopausal women with low BMD.

Antibacterial activity of leaves and inter-nodal callus extracts of Mentha arvensis L.

Objective: To determine the anti-bacterial efficacy of chloroform, ethanol, ethyl acetate and water extracts of inter-nodal and leaves derived calli extracts from Mentha arvensis (M. arvensis) against Salmonella typhi(S. typhi), Streptococcus pyogenes(S.