This paper introduces a novel concept of understanding cerebral dynamics by exploring the acoustic signals generated by blood flow in the brain (BFB) and mechanical resonant frequencies produced by the brain. The concept of this paper will be a groundbreaking approach to brain signal analysis through acoustic signals of BFB. Traditional methods for brain wave capture and analysis mostly depend on fMRI and EEG signals, which are now very popular and have some limitations in accessibility, cost, and real-time analysis capabilities. In this study, we seek the existing gaps in the current brain signal-capturing methods, and our theoretical underpinnings hypothesize that sound produced by blood flow in the brain (BFB) can be an innovative approach to capture the brain signal in a more user-friendly and accessible way. The feasibility of capturing and analyzing these BFB-sound is also discussed in this paper. We proposed a theoretical framework to capture the BFB sound through the human ear. The successful completion of this concept architecture will serve in different applications, from diagnosing neurological disorders to monitoring brain health, underscoring its potential to revolutionize non-invasive brain diagnostics. By synthesizing current knowledge and proposing innovative techniques, this paper aims to pave the way for new frontiers in understanding brain function through the brain sound generated by blood flow and captured from the human ears.

In this paper, we introduce a machine learning (ML)-based approach for Chronic Kidney Disease (CKD) and diabetes prediction and perform feature effect analysis through SHAP (SHapley Additive exPlanations). We utilize two publicly available clinical datasets and build five ML classifier models for the analysis. Among all models, CatBoost provides the best performance for both CKD and diabetes prediction. Our CatBoost model has an accuracy of 0.95, mean 10-fold cross-validation of 0.96, ROC-AUC score of 0.99, precision of 0.96, recall of 0.96, and F1-score of 0.96 for CKD. The CatBoost model shows an accuracy of 0.99, mean 10-fold cross-validation of 0.97, ROC-AUC score of 1.0, precision of 1.0, recall of 0.98, and F1-score of 0.99 for diabetes. We perform comprehensive feature effect analysis by computing SHAP values. This gives insights into the contribution of every feature to the model's predictions. The achieved results highlight that CatBoost is a robust choice for accurate and reliable predictions of CKD and diabetes. The findings of this study contribute to the corresponding field of ML approaches for medical diagnosis and illustrate the significance of feature effect analysis in understanding model predictions. With excellent results, our research has the potential to enhance the clinical decision-making process and improve patient outcomes.

Natural Language Processing (NLP) is one of the fundamental domains of Artificial Intelligence (AI). In this paper, we present a systematic review of NLP based research for recognizing Bangla speech with regular and regional dialects. We describe 23 research papers based on Bangla speech recognition in regular accents and regional dialects. Due to the cultural diversity, Bangla has many dialects with distinctive regional pronunciations, complex vocabulary, and syntax. These characteristics of Bangla create several challenges for implementing NLP successfully. In this paper, we focus on NLP's vital role in speech recognition, which is essential to virtual assistants, transcription services, and language-learning applications. We discuss several methods such as advanced language models, comprehensive datasets, and continuous adaptation to overcome the challenges of recognizing Bangla with NLP. Several algorithms, such as Deep Neural Networks, Gaussian Mixture Models (GMM), Linear Predictive Coding (LPC), Mel frequency cepstral coefficients (MFCC), etc. have been developed to detect the spoken words in Bangla. These existing research works have faced various challenges like scarcity of data, dialectal variability, computational resource requirements, etc. For mitigation of the challenges and further advancement in this research area, we discuss some research scopes including dialect identification through large datasets, low-resource dialect modeling, development of deep learning model for end-to-end ASR systems, continuous learning, etc. This research will help us understand the present status and challenges associated with NLP-based Bangla speech recognition.

Rehabilitation and disability researchers are increasingly considering utlizing machine learning (ML) algorithms to enhance accessibility for people with disabilities (PwD) as they interact with their environments. PwD often experience environmental barriers in the community and private buildings due to a lack of accessible infrastructure design and prior accessibility information. Such environmental barriers may inhibit PwD from full participation and impede in one’s overall independence and quality of life. The availability of healthcare services and information are essential for increased participation in occupations and optimal independence. In the current era of connected health, information has the ability to be accessed anywhere and providing accessibility content for PwD to use can enhance occupational performance factors. The purpose of this study was to 1) identify existing accessibility measurement challenges and barriers and 2) propose an intelligent accessibility evaluation and assessment for buildings, and 3) leverage mobile applications to address major challenges of accessibility measurement. This research aims to improve the accuracy and reliability of the accessibility measurement using a smarter system.

Heart Rate (HR) and Heart Rate Variability (HRV) is an essential measurement to know the heart’s cardiovascular condition. Many works have been done for measuring HR-HRV based on the facial video non-invasively. In this paper, based on our previous work experience of measuring HR-HRV by Remote photoplethysmography signals (rPPG) analysis, we have built a prediction model from the 10-second time series data extracted from a facial video. In this work, we have used the instantaneous public dataset with several data models to predict the HR-HRV, and stress levels exclusively from the dataset. We have used here some of the popular algorithms appropriate for this task. We have also analyzed the stress level classification on the gender of a subject using the same facial videos with 16 different classifiers resulting in almost perfect accuracy for several classifiers.

Accurate and valid health information is crucial for effective medical management. Failure to collect adequate information from physical and mental health examinations can be a barrier to Virtual medical platforms and telemedicine. In this paper, we propose the non-invasive “Dream” project prototype to monitor and record heart rate (HR), heart rate-variation (HRV) (for physical health), and stress (for mental health) using only a smart-phone. This non-invasive mobile application, “Dream” uses the front camera to capture video to calculate HR-HRV and stress. The full “Dream” project encompasses our previous facial video HR-HRV and stress work. We have also compared our proposed “Dream” project with 39 works in this area. We found a significant positive difference between our proposed “Dream” project compared to other projects in respect to user accessibility, application, cost-effectiveness, hospitalization monitoring, and human health status. Furthermore, we can apply “Dream” in remote human health monitoring, driver monitoring, and creating vital sign records non-invasively without a health care assistant. Especially during a pandemic, this virtual health monitoring system can be useful for scaled-up telemedicine to serve the remote population.

Next Step Clinic (NSC) is a neighborhood-based mental clinic in Milwaukee in the USA for early identification and intervention of Autism spectrum disorder (ASD) children. NSC's primary goal is to serve the underserved families in that area with children aged 15 months to 10 years who have ASD symptoms free of cost. Our proposed and implemented Electronic Medical Records (NSC: EMR) has been developed for NSC. This paper describes the NSC: EMR's design specification and whole development process with the workflow control of this system in NSC. This NSC: EMR has been used to record the patient’s medical data and make appointments both physically or virtually. The integration of standardized psychological evaluation form has reduced the paperwork and physical storage burden for the family navigator. By deploying the system, the family navigator can increase their productivity from the screening to all intervention processes to deal with ASD children. Even in the lockdown time, due to the pandemic of COVID-19, about 84 ASD patients from the deprived family at that area got registered and took intervention through this NSC: EMR. The usability and cost-effective feature has already shown the potential of NSC: EMR, and it will be scaled to serve a large population in the USA and beyond.

This paper describes a cloud-based heart rate (HR) and heart rate variability (HRV) monitor, which can monitor an individual's heart rate and heart rate variability using minimal resources at the user's end. This HR and HRV monitor does not involve any contact sensors or costly medical equipment, and it does not require a high-definition camera to video record the user's face. This HR and HRV monitoring system simply requires the webcam of a personal computer or the camera of a mobile device such as a cell phone or tablet in order to video record the face of the user. The system utilizes the resources of the user's device while recording and encoding the video segments before transferring them to a server. All the processes to calculate the HR and HRV occur at the server.