Vision transformer-equipped Convolutional Neural Networks for automated Alzheimer's disease diagnosis using 3D MRI scans.

Alzheimer's disease (AD) is a neurodegenerative ailment that is becoming increasingly common, making it a major worldwide health concern. Effective care depends on an early and correct diagnosis, but traditional diagnostic techniques are frequently constrained by subjectivity and expensive costs. This study proposes a novel Vision Transformer-equipped Convolutional Neural Networks (VECNN) that uses three-dimensional magnetic resonance imaging to improve diagnosis accuracy.

Dual therapy of cancer using optimal control supported by swarm intelligence.

Background: The scientific revolution in the treatment of many illnesses has been significantly aided by stem cells. This paper presents an optimal control on a mathematical model of chemotherapy and stem cell therapy for cancer treatment.

Objective: To develop effective hybrid techniques that combine the optimal control theory (OCT) with the evolutionary algorithm and multi-objective swarm algorithm.

Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods.

Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as .

Sentiment Analysis and Sarcasm Detection using Deep Multi-Task Learning.

Social media platforms such as Twitter and Facebook have become popular channels for people to record and express their feelings, opinions, and feedback in the last decades. With proper extraction techniques such as sentiment analysis, this information is useful in many aspects, including product marketing, behavior analysis, and pandemic management. Sentiment analysis is a technique to analyze people's thoughts, feelings and emotions, and to categorize them into positive, negative, or neutral.

Conventional machine learning and deep learning in Alzheimer's disease diagnosis using neuroimaging: A review.

Alzheimer's disease (AD) is a neurodegenerative disorder that causes memory degradation and cognitive function impairment in elderly people. The irreversible and devastating cognitive decline brings large burdens on patients and society. So far, there is no effective treatment that can cure AD, but the process of early-stage AD can slow down.

An Overview of Deep Learning Methods for Left Ventricle Segmentation.

Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images.

Fully Automatic Left Ventricle Segmentation Using Bilateral Lightweight Deep Neural Network.

The segmentation of the left ventricle (LV) is one of the fundamental procedures that must be performed to obtain quantitative measures of the heart, such as its volume, area, and ejection fraction. In clinical practice, the delineation of LV is still often conducted semi-automatically, leaving it open to operator subjectivity. The automatic LV segmentation from echocardiography images is a challenging task due to poorly defined boundaries and operator dependency.

Comparative studies of deep learning segmentation models for left ventricle segmentation.

One of the primary factors contributing to death across all age groups is cardiovascular disease. In the analysis of heart function, analyzing the left ventricle (LV) from 2D echocardiographic images is a common medical procedure for heart patients. Consistent and accurate segmentation of the LV exerts significant impact on the understanding of the normal anatomy of the heart, as well as the ability to distinguish the aberrant or diseased structure of the heart.

Machine Learning Application of Transcranial Motor-Evoked Potential to Predict Positive Functional Outcomes of Patients.

Intraoperative neuromonitoring (IONM) has been used to help monitor the integrity of the nervous system during spine surgery. Transcranial motor-evoked potential (TcMEP) has been used lately for lower lumbar surgery to prevent nerve root injuries and also to predict positive functional outcomes of patients. There were a number of studies that proved that the TcMEP signal's improvement is significant towards positive functional outcomes of patients.

Multiclass Convolution Neural Network for Classification of COVID-19 CT Images.

In the late December of 2019, a novel coronavirus was discovered in Wuhan, China. In March 2020, WHO announced this epidemic had become a global pandemic and that the novel coronavirus may be mild to most people. However, some people may experience a severe illness that results in hospitalization or maybe death.

Inter-Multilevel Super-Orthogonal Space-Time Coding Scheme for Reliable ZigBee-Based IoMT Communications.

The Internet of Things (IoT) technology has revolutionized the healthcare industry by enabling a new paradigm for healthcare delivery. This paradigm is known as the Internet of Medical Things (IoMT). IoMT devices are typically connected via a wide range of wireless communication technologies, such as Bluetooth, radio-frequency identification (RFID), ZigBee, Wi-Fi, and cellular networks.

A Study on Distortion Estimation Based on Image Gradients.

Image noise is a variation of uneven pixel values that occurs randomly. A good estimation of image noise parameters is crucial in image noise modeling, image denoising, and image quality assessment. To the best of our knowledge, there is no single estimator that can predict all noise parameters for multiple noise types.

Transcranial Electrical Motor Evoked Potential in Predicting Positive Functional Outcome of Patients after Decompressive Spine Surgery: Review on Challenges and Recommendations towards Objective Interpretation.

Spine surgeries impose risk to the spine's surrounding anatomical and physiological structures especially the spinal cord and the nerve roots. Intraoperative neuromonitoring (IONM) is a technology developed to monitor the integrity of the spinal cord and the nerve roots via the surgery. Transcranial motor evoked potential (TcMEP) (one of the IONM modalities) is adopted to monitor the integrity of the motor pathway of the spinal cord and the motor nerve roots.

Current Trends in Readmission Prediction: An Overview of Approaches.

Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission.

A comparative study of multiple neural network for detection of COVID-19 on chest X-ray.

Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure.

Optimally-Weighted Image-Pose Approach (OWIPA) for Distracted Driver Detection and Classification.

Distracted driving is the prime factor of motor vehicle accidents. Current studies on distraction detection focus on improving distraction detection performance through various techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). However, the research on detection of distracted drivers through pose estimation is scarce.

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19.

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images.

Effects of land use configuration, seasonality and point source on water quality in a tropical watershed: A case study of the Johor River Basin.

Land use plays a significant role in determining the spatial patterns of water quality in the Johor River Basin (JRB), Malaysia. In the recent years, there have been several occurrences of pollution in these rivers, which has generated concerns over the long-term sustainability of the water resources in the JRB. Specifically, this water resource is a shared commodity between two states, namely, Johor state of Malaysia and Singapore, a neighbouring country adjacent to Malaysia.

A framework for assessing the adequacy of Water Quality Index - Quantifying parameter sensitivity and uncertainties in missing values distribution.

Water quality monitoring is a pillar in water resource management, but it can be resource intensive, especially for developing countries with limited resources. As such, Water Quality Indices (WQI) are developed to summarise general water quality, but efforts to assess the utility, flexibility, and practicality of WQI have been limited. In this study, we introduced an additional step to the traditional WQI development framework by introducing an adjusted form of WQI (WQI) to handle missing values, and capitalise on the remaining available information for the development of a WQI.

Speckle Noise Diffusion in Knee Articular Cartilage Ultrasound Images.

Background: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e.

Water Use by Inland Aquaculture in Thailand: Stakeholder Perceptions, Scientific Evidence, and Public Policy.

This paper assesses the significance of stakeholder discourses on uses of water by aquaculture for public policy. Our discourse analysis focuses on the experiences with inland aquaculture in Thailand, drawing from interviews with stakeholders, and evidence in public documents such as newspapers and television news reports. A key finding is that fish farms suffer significant losses from polluted run-off entering water bodies where fish are grown.

Microwave steam activation, an innovative pyrolysis approach to convert waste palm shell into highly microporous activated carbon.

Microwave-steam activation (MSA), an innovative pyrolysis approach combining the use of microwave heating and steam activation, was investigated for its potential production of high grade activated carbon (AC) from waste palm shell (WPS) for methylene blue removal. MSA was performed via pyrolytic carbonization of WPS to produce biochar as the first step followed by steam activation of the biochar using microwave heating to form AC. Optimum yield and adsorption efficiency of methylene blue were obtained using response surface methodology involving several key process parameters.

Temporal Variability of Faecal Contamination from On-Site Sanitation Systems in the Groundwater of Northern Thailand.

We investigated the impacts of on-site sanitation systems to local groundwater. In this year-long study, we monitored the response of faecal contamination levels to hydroclimatological factors including rainfall and groundwater table. Concentration of faecal indicators-E.

Hydrological connectivity and Burkholderia pseudomallei prevalence in wetland environments: investigating rice-farming community's risk of exposure to melioidosis in North-East Thailand.

In our analysis of 136 water samples from wetland environments (rice paddies, natural wetland sites, man-made water bodies) in rural areas of North-East Thailand, Burkholderia pseudomallei was most prevalent in rice paddies (15 of the 30 positive sites). The high prevalence in the water of rice fields is indicative of the inherent vulnerability of farmers in rural agricultural areas in this area of Thailand and likely other locations in the tropics. Nearly all B.

Correction: Pathogenic waterborne free-living amoebae: An update from selected Southeast Asian countries.

[This corrects the article DOI: 10.1371/journal.pone.