ECG-based cardiac arrhythmias detection through ensemble learning and fusion of deep spatial-temporal and long-range dependency features.

Cardiac arrhythmia is one of the prime reasons for death globally. Early diagnosis of heart arrhythmia is crucial to provide timely medical treatment. Heart arrhythmias are diagnosed by analyzing the electrocardiogram (ECG) of patients.

LSCS-Net: A lightweight skin cancer segmentation network with densely connected multi-rate atrous convolution.

The rising occurrence and notable public health consequences of skin cancer, especially of the most challenging form known as melanoma, have created an urgent demand for more advanced approaches to disease management. The integration of modern computer vision methods into clinical procedures offers the potential for enhancing the detection of skin cancer . The UNet model has gained prominence as a valuable tool for this objective, continuously evolving to tackle the difficulties associated with the inherent diversity of dermatological images.

Design and development of patient health tracking, monitoring and big data storage using Internet of Things and real time cloud computing.

With the outbreak of the COVID-19 pandemic, social isolation and quarantine have become commonplace across the world. IoT health monitoring solutions eliminate the need for regular doctor visits and interactions among patients and medical personnel. Many patients in wards or intensive care units require continuous monitoring of their health.

Cancer detection and classification using a simplified binary state vector machine.

Cancer is an invasive and malignant growth of cells and is known to be one of the most fatal diseases. Its early detection is essential for decreasing the mortality rate and increasing the probability of survival. This study presents an efficient machine learning approach based on the state vector machine (SVM) to diagnose and classify tumors into malignant or benign cancer using the online lymphographic data.

Smartphone Sensors for Indoor Positioning.

The explosive growth and wide proliferation of mobile devices, the majority of which are smartphones, led to the inception of several novel and intuitive services, including on-the-go services, online customer services, and location-based services (LBS) [...

An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.

The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM).

Optimization of Imbalanced and Multidimensional Learning Under Bayes Minimum Risk and Savings Measure.

The full potential of data analysis is crippled by imbalanced and high-dimensional data, which makes these topics significantly important. Consequently, substantial research efforts have been directed to obtain dimension reduction and resolve data imbalance, especially in the context of fraud detection analysis. This work aims to investigate the effectiveness of hybrid learning methods for alleviating the class imbalance and integrating dimensionality reduction techniques.

Reduced features set neural network approach based on high-resolution time-frequency images for cardiac abnormality detection.

A suitable temporal and spectral processing of the electrocardiogram (ECG) signals can facilitate the visual interpretation and discrimination between known patterns for classification. This paper proposes a non-invasive hybrid neural network and time-frequency (TF) based method to detect and classify commonly found cardiac abnormalities in ECG signals including congestive heart failure, ventricular tachyarrhythmia, intracardiac atrial fibrillation, arrhythmia, malignant ventricular ectopy, normal sinus rhythm, and postictal heart rate oscillations in partial epilepsy. Non-stationary raw ECG signals are collected from an online healthcare dataset source 'PhysioBank' that contains physiologic signals.

Data Transmission Enhancement Using Optimal Coding Technique Over Channel for Interbody Communication.

Wireless actuators and sensors are examples of sophisticated technologies. Another breakthrough is the use of wireless medical devices, which provide scalable and cost-effective solutions for wearable device integration. wireless body area networks devices reduce surgery invasiveness and provide continuous health monitoring.

Recurrent Neural Network and Reinforcement Learning Model for COVID-19 Prediction.

Detection and prediction of the novel Coronavirus present new challenges for the medical research community due to its widespread across the globe. Methods driven by Artificial Intelligence can help predict specific parameters, hazards, and outcomes of such a pandemic. Recently, deep learning-based approaches have proven a novel opportunity to determine various difficulties in prediction.

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning.

Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones.

A deep learning-based social distance monitoring framework for COVID-19.

The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. In the current situation, as there are no vaccines available; therefore, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus.

Genetic Causes of Severe Childhood Obesity: A Remarkably High Prevalence in an Inbred Population of Pakistan.

Monogenic forms of obesity have been identified in ≤10% of severely obese European patients. However, the overall spectrum of deleterious variants (point mutations and structural variants) responsible for childhood severe obesity remains elusive. In this study, we genetically screened 225 severely obese children from consanguineous Pakistani families through a combination of techniques, including an in-house-developed augmented whole-exome sequencing method (CoDE-seq) that enables simultaneous detection of whole-exome copy number variations (CNVs) and point mutations in coding regions.

Lymphocytic Pleural Effusion and an Enzyme Involved in Purine Metabolism: A Tertiary Care Experience in Karachi, Pakistan.

Background: The levels of adenosine deaminase (ADA) are increased in tubercular pleural effusion and its determination has acquired popularity as a diagnostic test which is inexpensive and is readily accessible. Pleural fluid ADA showed sensitivity (86.36%), specificity (61.

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance.

Loss-of-function mutations in ADCY3 cause monogenic severe obesity.

Study of monogenic forms of obesity has demonstrated the pivotal role of the central leptin-melanocortin pathway in controlling energy balance, appetite and body weight . The majority of loss-of-function mutations (mostly recessive or co-dominant) have been identified in genes that are directly involved in leptin-melanocortin signaling. These genes, however, only explain obesity in <5% of cases, predominantly from outbred populations .