Cloud-based configurable data stream processing architecture in rural economic development.

Purpose: This study aims to address the limitations of traditional data processing methods in predicting agricultural product prices, which is essential for advancing rural informatization to enhance agricultural efficiency and support rural economic growth.

Methodology: The RL-CNN-GRU framework combines reinforcement learning (RL), convolutional neural network (CNN), and gated recurrent unit (GRU) to improve agricultural price predictions using multidimensional time series data, including historical prices, weather, soil conditions, and other influencing factors. Initially, the model employs a 1D-CNN for feature extraction, followed by GRUs to capture temporal patterns in the data.

A hybridized feature extraction for COVID-19 multi-class classification on computed tomography images.

COVID-19 has killed more than 5 million individuals worldwide within a short time. It is caused by SARS-CoV-2 which continuously mutates and produces more transmissible new different strains. It is therefore of great significance to diagnose COVID-19 early to curb its spread and reduce the death rate.

SegEIR-Net: A Robust Histopathology Image Analysis Framework for Accurate Breast Cancer Classification.

Background: Breast Cancer (BC) is a significant threat affecting women globally. An accurate and reliable disease classification method is required to get an early diagnosis. However, existing approaches lack accurate and robust classification.

Wearable Sensor Data Classification for Identifying Missing Transmission Sequence Using Tree Learning.

Wearable Sensor (WS) data accumulation and transmission are vital in analyzing the health status of patients and elderly people remotely. Through specific time intervals, the continuous observation sequences provide a precise diagnosis result. This sequence is however interrupted due to abnormal events or sensor or communicating device failures or even overlapping sensing intervals.

EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats.

Coronavirus disease 2019 (COVID-19) has caused severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) across the globe, impacting effective diagnosis and treatment for any chronic illnesses and long-term health implications. In this worldwide crisis, the pandemic shows its daily extension (i.e.

Smart Graphene-Based Electrochemical Nanobiosensor for Clinical Diagnosis: Review.

The technological improvement in the field of physics, chemistry, electronics, nanotechnology, biology, and molecular biology has contributed to the development of various electrochemical biosensors with a broad range of applications in healthcare settings, food control and monitoring, and environmental monitoring. In the past, conventional biosensors that have employed bioreceptors, such as enzymes, antibodies, Nucleic Acid (NA), etc., and used different transduction methods such as optical, thermal, electrochemical, electrical and magnetic detection, have been developed.

Effect of Gaussian filtered images on Mask RCNN in detection and segmentation of potholes in smart cities.

Accidents have contributed a lot to the loss of lives of motorists and serious damage to vehicles around the globe. Potholes are the major cause of these accidents. It is very important to build a model that will help in recognizing these potholes on vehicles.

Current Technologies for Detection of COVID-19: Biosensors, Artificial Intelligence and Internet of Medical Things (IoMT): Review.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease, technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology, etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which includes RT-PCR, antigen-antibody, and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include deep learning and transfer learning approach.

A Novel Approach for Multichannel Epileptic Seizure Classification Based on Internet of Things Framework Using Critical Spectral Verge Feature Derived from Flower Pollination Algorithm.

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients.

Computer aided detection of tuberculosis using two classifiers.

Tuberculosis caused by Mycobacterium tuberculosis have been a major challenge for medical and healthcare sectors in many underdeveloped countries with limited diagnosis tools. Tuberculosis can be detected from microscopic slides and chest X-ray but as a result of the high cases of tuberculosis, this method can be tedious for both microbiologist and Radiologist and can lead to miss-diagnosis. The main objective of this study is to addressed these challenges by employing Computer Aided Detection (CAD) using Artificial Intelligence-driven models which learn features based on convolution and result in an output with high accuracy.

Machine learning analysis on the impacts of COVID-19 on India's renewable energy transitions and air quality.

India is severely affected by the COVID-19 pandemic and is facing an unprecedented public health emergency. While the country's immediate measures focus on combating the coronavirus spread, it is important to investigate the impacts of the current crisis on India's renewable energy transition and air quality. India's economic slowdown is mainly compounded by the collapse of global oil prices and the erosion of global energy demand.

Towards an effective model for lung disease classification: Using Dense Capsule Nets for early classification of lung diseases.

Machine Learning and computer vision have been the frontiers of the war against the COVID-19 Pandemic. Radiology has vastly improved the diagnosis of diseases, especially lung diseases, through the early assessment of key disease factors. Chest X-rays have thus become among the commonly used radiological tests to detect and diagnose many lung diseases.

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application.

Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model.

Achieving data privacy for decision support systems in times of massive data sharing.

The world is suffering from a new pandemic of Covid-19 that is affecting human lives. The collection of records for Covid-19 patients is necessary to tackle that situation. The decision support systems (DSS) are used to gather that records.

Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images.

The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities.

ANFIS for prediction of epidemic peak and infected cases for COVID-19 in India.

Corona Virus Disease 2019 (COVID-19) is a continuing extensive incident globally affecting several million people's health and sometimes leading to death. The outbreak prediction and making cautious steps is the only way to prevent the spread of COVID-19. This paper presents an Adaptive Neuro-fuzzy Inference System (ANFIS)-based machine learning technique to predict the possible outbreak in India.

Advanced Deep Learning Algorithms for Infectious Disease Modeling Using Clinical Data: A Case Study on COVID-19.

Background: Dealing with the COVID-19 pandemic has been one of the most important objectives of many countries.Intently observing the growth dynamics of the cases is one way to accomplish the solution for the pandemic.

Introduction: Infectious diseases are caused by a micro-organism/virus from another person or an animal.

Deming least square regressed feature selection and Gaussian neuro-fuzzy multi-layered data classifier for early COVID prediction.

Coronavirus disease (COVID-19) is a harmful disease caused by the new SARS-CoV-2 virus. COVID-19 disease comprises symptoms such as cold, cough, fever, and difficulty in breathing. COVID-19 has affected many countries and their spread in the world has put humanity at risk.

Convolutional neural network for diagnosis of viral pneumonia and COVID-19 alike diseases.

Reverse-Transcription Polymerase Chain Reaction (RT-PCR) method is currently the gold standard method for detection of viral strains in human samples, but this technique is very expensive, take time and often leads to misdiagnosis. The recent outbreak of COVID-19 has led scientists to explore other options such as the use of artificial intelligence driven tools as an alternative or a confirmatory approach for detection of viral pneumonia. In this paper, we utilized a Convolutional Neural Network (CNN) approach to detect viral pneumonia in x-ray images using a pretrained AlexNet model thereby adopting a transfer learning approach.

Pandemic coronavirus disease (Covid-19): World effects analysis and prediction using machine-learning techniques.

Pandemic novel Coronavirus (Covid-19) is an infectious disease that primarily spreads by droplets of nose discharge when sneezing and saliva from the mouth when coughing, that had first been reported in Wuhan, China in December 2019. Covid-19 became a global pandemic, which led to a harmful impact on the world. Many predictive models of Covid-19 are being proposed by academic researchers around the world to take the foremost decisions and enforce the appropriate control measures.

Convolutional neural networks for the classification of chest X-rays in the IoT era.

Chest X-ray medical imaging technology allows the diagnosis of many lung diseases. It is known that this technology is frequently used in hospitals, and it is the most accurate way of detecting most thorax diseases. Radiologists examine these images to identify lung diseases; however, this process can require some time.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe.