DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.

This research explores the use of gated recurrent units (GRUs) for automated brain tumor detection using MRI data. The GRU model captures sequential patterns and considers spatial information within individual MRI images and the temporal evolution of lesion characteristics. The proposed approach improves the accuracy of tumor detection using MRI images.

An intelligent neural network model to detect red blood cells for various blood structure classification in microscopic medical images.

Biomedical image analysis plays a crucial role in enabling high-performing imaging and various clinical applications. For the proper diagnosis of blood diseases related to red blood cells, red blood cells must be accurately identified and categorized. Manual analysis is time-consuming and prone to mistakes.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large datasets, however, challenges arise when dealing with sensitive medical data. Restrictions on sharing these medical data result in limited publicly available datasets thereby impacting the performance of the deep learning models.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data.

Therapeutic Efficacy of a Formulation Prepared with L., Forssk., and Honey on Uncomplicated Pelvic Inflammatory Disease Analyzed with Machine Learning Techniques.

A single-blind double-dummy randomized study was conducted in diagnosed patients (n = 66) to compare the efficacy of Linseeds ( L.), Psyllium ( Forssk.), and honey in uncomplicated pelvic inflammatory disease (uPID) with standard drugs using experimental and computational analysis.

A Multimodal Network Security Framework for Healthcare Based on Deep Learning.

As the network is closely related to people's daily life, network security has become an important factor affecting the physical and mental health of human beings. Network flow classification is the foundation of network security. It is the basis for providing various network services such as network security maintenance, network monitoring, and network quality of service (QoS).

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions.

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs.

Probabilistic Approach to COVID-19 Data Analysis and Forecasting Future Outbreaks Using a Multi-Layer Perceptron Neural Network.

The present outbreak of COVID-19 is a worldwide calamity for healthcare infrastructures. On a daily basis, a fresh batch of perplexing datasets on the numbers of positive and negative cases, individuals admitted to hospitals, mortality, hospital beds occupied, ventilation shortages, and so on is published. Infections have risen sharply in recent weeks, corresponding with the discovery of a new variant from South Africa (B.

MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.

The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors.

DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the accurate detection of brain cancer. However, due to the inaccuracy of artificial diagnostic systems, medical professionals are not effectively incorporating them into the diagnosis process of Brain Cancer.

IIMFCBM: Intelligent Integrated Model for Feature Extraction and Classification of Brain Tumors Using MRI Clinical Imaging Data in IoT-Healthcare.

Accurate classification of brain tumors is vital for detecting brain cancer in the Medical Internet of Things. Detecting brain cancer at its early stages is a tremendous medical problem, and many researchers have proposed various diagnostic systems; however, these systems still do not effectively detect brain cancer. To address this issue, we proposed an automatic diagnosing framework that will assist medical experts in diagnosing brain cancer and ensuring proper treatment.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread.

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data.

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases.

Assessment of Lagrangian particle dispersion model "LAPMOD" through short range field tracer test in complex terrain.

In this paper the mesoscale application of the 3D Lagrangian particle dispersion model LAPMOD has been assessed for a field tracer test performed in a short-range complex terrain. The meteorological input was provided through the diagnostic model CALMET, the meteorological pre-processor of the CALPUFF model. The CALMET/LAPMOD coupled system was used to simulate the hourly averaged ground level concentration at 47 discrete receptors.

Predicting eco-conscious consumer behavior using theory of planned behavior in Pakistan.

Sustainability of the environment has become pivotal in the modern world, and there have been enormous efforts by the world leaders and organizations to reduce the effects of hazardous production on the environment. This has led companies to implement pro-environment programs and work on sustainability to shift consumption from conventional products to green products. This study incorporates green trust, environmental concerns, and intrinsic religious orientation as a moderator into the theory of planned behavior.

Effect of race, gender and age on thyroid and thyroid stimulating hormone levels in North West Frontier Province, Pakistan.

Background: Thyroid is one of the ductless endocrine gland, which is located immediately below the larynx on either side of and anterior to the trachea. The principal hormones of thyroid gland are thyroxine (T4) and triiodothyronine (T3). The current study was carried out to investigate the impact of race, gender and area on the levels of Thyroxine (T4), Triiodothyronine (T3) and Thyroid Stimulating Hormone (TSH) in normal healthy individuals.

Effect of 90 decibel noise of 4000 hertz on blood pressure in young adults.

Background: Almost every individual dislikes excessive and unnecessary noise. Noise exerts various adverse psychological and physiological effects, on human body including a rise in blood pressure.

Methods: 117 volunteer medical students, aged 18-23 years were exposed to 90 decibel noise of 4000 hertz for 10 minutes, produced by audiometer in a sound-proof room.