A Real-Time CNN-Based Lightweight Mobile Masked Face Recognition System

Due to the global spread of the Covid-19 virus and its variants, new needs and problems have emerged during the pandemic that deeply affects our lives. Wearing masks as the most effective measure to prevent the spread and transmission of the virus has brought various security vulnerabilities. Today we are going through times when wearing a mask is part of our lives, thus, it is very important to identify individuals who violate this rule. Besides, this pandemic makes the traditional biometric authentication systems less effective in many cases such as facial security checks, gated community access control, and facial attendance. So far, in the area of masked face recognition, a small number of contributions have been accomplished. It is definitely imperative to enhance the recognition performance of the traditional face recognition methods on masked faces. Existing masked face recognition approaches are mostly performed based on deep learning models that require plenty of samples. Nevertheless, there are not enough image datasets containing a masked face. As such, the main objective of this study is to identify individuals who do not use masks or use them incorrectly and to verify their identity by building a masked face dataset. On this basis, a novel real-time masked detection service and face recognition mobile application was developed based on an ensemble of fine-tuned lightweight deep Convolutional Neural Networks (CNN). The proposed model achieves 90.40% validation accuracy using 12 individuals- 1849 face samples. Experiments on the five datasets built in this research demonstrate that the proposed system notably enhances the performance of masked face recognition compared to the other state-of-the-art approaches. 2022 IEEE.Scopus2-s2.0-8513276413

Smart Wearable Patient Tracking Systems

Nowadays Smart Wearable Systems (SWS) subject, emerges as one of the most popular research topic. Becouse of being comprimised from multiple disciplines, this area is favored by researchers of different areas. Devices of SWS are designed to measure physiological parameters for illnesses, chronic diseases, daily activity monitoring and other purposes, now it is finding more and more place in our lives. We can see the effects of SWS in health, sports, entertainment, education, business and other fileds, especially for the projects of health sector many products are available to the end users. Different SWS that are classified according to its features and methods of use have common features such as ability to make high-capacity wireless communication and housing embedded sensors. When invetigating the studies of SWS that include various forms and numbers of sensors, it is seen that most preferred sensors are; ECG sensor, Glucose sensor, body temperature sensor, blood pressure sensor, SpO(2) sensor and Accelerometer. General details of SWS and leading research projects from past to present are described within the scope of this article

SUPPORT VECTOR MACHINES COMBINED WITH FEATURE SELECTION FOR DIABETES DIAGNOSIS

Clinical Decision Support Systems (CDSS) are used as a service software which provides huge support to clinical decision making process where the main properties of a patient are matched to a tangible clinical knowledge. Within this gathered important information about patients, the medical decisions can be made more accurately. In this paper we present a CDSS that uses four physiological parameters of patients such as Pre-prandial Blood Glucose, Postprandial Blood Glucose, Hemoglobin A1C (HbA(1c)) and Glucose in Urine to produce a prediction about the possibility of being diabetic. According to collected reference data provided from hospitals, the disease can be predicted by comparing the input data of patients. If the system cannot procure a prediction about patients' status with these parameters, then the second phase which uses soft computing techniques is put into process with requesting additional data about patients. Our conducted experiments show that the diagnosis can be established in a breeze by getting the patients information with % 80 accuracy. Support Vector Machines were applied to achieve maximum success rate with nine different physiological parameters such as; Pregnancy, glucose, blood pressure, skin fold, insulin, Hemoglobin A1C, body mass index, family tree and age. Four different Kernel Functions are implemented in case studies and classification process is optimized by reducing attributes with feature selection algorithms. This represents an improvement in classification of CDSS, while reducing computational complexity

Wallet-Based Transaction Fraud Prevention Through LightGBM With the Focus on Minimizing False Alarms

E-wallets’ rising popularity can be attributed to the fact that they facilitate a wide variety of financial activities such as payments, transfers, investments, etc., and eliminate the need for actual cash or cards. The confidentiality, availability, and integrity of a user’s financial information stored in an electronic wallet can be compromised by threats such as phishing, malware, and social engineering; therefore, fintech platforms employ intelligent fraud detection mechanisms to mitigate the problem. The purpose of this study is to detect fraudulent activity using cutting-edge machine learning techniques on data obtained from the leading e-wallet platform in Turkey. After a comprehensive analysis of the dataset’s features via feature engineering procedures, we found that the LightGBM approach had the highest detection accuracy of fraudulent activity with 97% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249. This approach resulted in the establishment of a machine-learning model suitable for use by relatively small fraud detection teams

Wearable sensor-based evaluation of psychosocial stress in patients with metabolic syndrome

The prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic disturbances, infections, and nutritional deficiencies have now revealed as contributing factors to develop metabolic diseases. This study presents a multivariate methodology for the modeling of stress on metabolic syndrome (MES) patients. We have developed a supporting system to cope with MES patients' anxiety and stress by means of several biosignals such as ECG, GSR, body temperature, SpO(2), glucose level, and blood pressure that are measured by a wearable device. We employed a neural network model to classify emotions with HRV analysis in the detection of stressor moments. We have accurately recognized the stressful situations using physiological responses to stimuli by utilizing our proposed affective state detection algorithm. We evaluated our system with a dataset of 312 biosignal records from 30 participants and the results showed that our proposed method achieved an average accuracy of 92% and 89% in distinguishing stress level in MES and other groups respectively. Both being the focus of an MES group and others proved to be highly arousing experiences which were significantly reflected in the physiological signal. Exposure to the stress in MES and cardiovascular heart disease patients increases the chronic symptoms. An early stage of comprehensive intervention may reduce the risk of general cardiovascular events in these particular groups. In this context, the use of e-health applications such as our proposed system facilitates these processes

Neurophysiological and biosignal data for investigating occupational mental fatigue: MEFAR dataset

The prevalence of mental fatigue is a noteworthy phenomenon that can affect individuals across diverse professions and working routines. This paper provides a comprehensive dataset of physiological signals obtained from 23 participants during their professional work and questionnaires to analyze mental fatigue. The questionnaires included demographic information and Chalder Fatigue Scale scores indicating mental and physical fatigue. Both physiological signal measurements and the Chalder Fatigue Scale were performed in two sessions, morning and evening. The present dataset encompasses diverse physiological signals, including electroencephalogram (EEG), blood volume pulse (BVP), electrodermal activity (EDA), heart rate (HR), skin temperature (TEMP), and 3-axis accelerometer (ACC) data. The NeuroSky MindWave EEG device was used for brain signals, and the Empatica E4 smart wristband was used for other signals. Measurements were carried out on individuals from four different occupational groups, such as academicians, technicians, computer engineers, and kitchen workers. The provision of comprehensive metadata supplements the dataset, thereby promoting inquiries about the neurophysiological concomitants of mental fatigue, autonomic activity patterns, and the repercussions of a cognitive burden on human proficiency in actual workplace settings. The accessibility of the aforementioned dataset serves to facilitate progress in the field of mental fatigue research while also laying the groundwork for the creation of customized fatigue evaluation techniques and interventions in diverse professional domains

A decision support system to determine optimal ventilator settings

Background: Choosing the correct ventilator settings for the treatment of patients with respiratory tract disease is quite an important issue. Since the task of specifying the parameters of ventilation equipment is entirely carried out by a physician, physician ' s knowledge and experience in the selection of these settings has a direct effect on the accuracy of his/her decisions. Nowadays, decision support systems have been used for these kinds of operations to eliminate errors. Our goal is to minimize errors in ventilation therapy and prevent deaths caused by incorrect configuration of ventilation devices. The proposed system is designed to assist less experienced physicians working in the facilities without having lung mechanics like cottage hospitals