An improved biometric stress monitoring solution for working employees using heart rate variability data and Capsule Network model.

Biometric stress monitoring has become a critical area of research in understanding and managing health problems resulting from stress. One of the fields that emerged in this area is biometric stress monitoring, which provides continuous or real-time information about different anxiety levels among people by analyzing physiological signals and behavioral data. In this paper, we propose a new approach based on the CapsNets model for continuously monitoring psychophysiological stress.

TRI-POSE-Net: Adaptive 3D human pose estimation through selective kernel networks and self-supervision with trifocal tensors.

Accurate and flexible 3D pose estimation for virtual entities is a strenuous task in computer vision applications. Conventional methods struggle to capture realistic movements; thus, creative solutions that can handle the complexities of genuine avatar interactions in dynamic virtual environments are imperative. In order to tackle the problem of precise 3D pose estimation, this work introduces TRI-POSE-Net, a model intended for scenarios with limited supervision.

An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques.

Diabetes is thought to be the most common illness in underdeveloped nations. Early detection and competent medical care are crucial steps in reducing the effects of diabetes. Examining the signs associated with diabetes is one of the most effective ways to identify the condition.

Evaluation of a multiphase cascaded H-bridge inverter for induction motor operation.

Multi-phase systems are becoming more popular for applications requiring high power and precise motor control, even if single-phase AC power is still frequently utilized in households and some enterprises. While both systems have benefits over single-phase, there are trade-offs associated with each. Because of its balanced operation and effective power transfer, the three-phase (3-Φ) system is the most widely used multi-phase system.

Preventive effect of LMT18-32 on induced periodontitis.

Periodontitis is a severe gum infection leading to chronic inflammation in the gums, damage of tissues around teeth, and destruction of alveolar bones. is the major causative pathogen that induces periodontitis. Numerous probiotic bacteria are reported to produce antibacterial substances against pathogens especially oral pathogens, and these are proposed as preventive measures for periodontitis.

BRON: A blockchained framework for privacy information retrieval in human resource management.

The correctness and the true validated data in Human Resource Management (HRM) are important for organizations as the data plays an impactful role in recruiting, developing, and retaining a skilled workforce. On one hand, the validated data in an organization helps in recruiting legitimate skillful employees; on the other hand, keeping the employee's data safe and maintaining privacy laws such as compliance with the General Data Protection Regulation (GDPR) is also an organization's responsibility. Besides, transparency in human resource management operations is crucial because it promotes trust and fairness within an organization.

Enhancing handwritten text recognition accuracy with gated mechanisms.

Handwritten Text Recognition (HTR) is a challenging task due to the complex structures and variations present in handwritten text. In recent years, the application of gated mechanisms, such as Long Short-Term Memory (LSTM) networks, has brought significant advancements to HTR systems. This paper presents an overview of HTR using a gated mechanism and highlights its novelty and advantages.

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network.

Blood cancer has emerged as a growing concern over the past decade, necessitating early diagnosis for timely and effective treatment. The present diagnostic method, which involves a battery of tests and medical experts, is costly and time-consuming. For this reason, it is crucial to establish an automated diagnostic system for accurate predictions.

Electrochemical Sensors for Heavy Metal Ion Detection in Aqueous Medium: A Systematic Review.

Heavy metal ions (HMIs) are very harmful to the ecosystem when they are present in excess of the recommended limits. They are carcinogenic in nature and can cause serious health issues. So, it is important to detect the metal ions quickly and accurately.

Sensitivity analysis of bi-metal stacked-gate-oxide hetero-juncture tunnel fet with Si0.6Ge0.4 source biosensor considering non-ideal factors.

This article provides insights in designing a dielectrically modulated biosensor by adopting high-k stacked gate oxide proposition in a bi-metal hetero-juncture Tunnel Field Effect Transistor (BM-SO-HTFET) with Si0.6Ge0.4 source.

PODE: privacy-enhanced distributed federated learning approach for origin-destination estimation.

The statewide consumer transportation demand model analyzes consumers' transportation needs and preferences within a particular state. It involves collecting and analyzing data on travel behavior, such as trip purpose, mode choice, and travel patterns, and using this information to create models that predict future travel demand. Naturalistic research, crash databases, and driving simulations have all contributed to our knowledge of how modifications to vehicle design affect road safety.

Designing optimal prototype filters for maximally decimated Cosine Modulated filter banks with rapid convergence.

An analytic design of a prototype filter for M-channel maximally decimated cosine-modulated Near Perfect Reconstruction (NPR) filter banks is proposed in this work. The prototype filter is created using the least-square (CLS) method with weighted constraints, which is one-dimensional and requires single-parameter optimization. Compared to existing approaches, this suggested method achieves rapid convergence by analytically determining the optimal step size, ensuring the 3 dB cutoff frequency at π/2 M.

Improving prediction of maternal health risks using PCA features and TreeNet model.

Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother's health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women's safety.

Enhancing underwater target localization through proximity-driven recurrent neural networks.

Environmental monitoring, ocean research, and underwater exploration are just a few of the marine applications that require precise underwater target localization. This study goes into the field of underwater target localization using Recurrent Neural Networks (RNNs) enhanced with proximity-based approaches, with a focus on mean estimation error as a performance metric. In complex and dynamic underwater environments, conventional localization systems frequently face challenges such as signal degradation, noise interference, and unstable hydrodynamic conditions.

Meta-atom loaded circularly polarized triple band patch antenna for Wi-Fi, ISM and X-band communications.

Microstrip antennas usually suffer from high losses, gain, and efficiency degradation. It is a challenging task to miniaturize the patch antenna without degrading the performance parameters. To mitigate the above problems, a microstrip patch antenna loaded with stubs and printed on the ground plane loaded with dumbbell meta-atom is presented in this paper.

Privacy preserved and decentralized thermal comfort prediction model for smart buildings using federated learning.

Thermal comfort is a crucial element of smart buildings that assists in improving, analyzing, and realizing intelligent structures. Energy consumption forecasts for such smart buildings are crucial owing to the intricate decision-making processes surrounding resource efficiency. Machine learning (ML) techniques are employed to estimate energy consumption.

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance.

Background: Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy.

White blood cells classification using multi-fold pre-processing and optimized CNN model.

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features.

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.

The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival.

Internet of Things Framework for Oxygen Saturation Monitoring in COVID-19 Environment.

The pandemic/epidemic of COVID-19 has affected people worldwide. A huge number of lives succumbed to death due to the sudden outbreak of this corona virus infection. The specified symptoms of COVID-19 detection are very common like normal flu; asymptomatic version of COVID-19 has become a critical issue.

Effect of Lactobacillus plantarum LMT1-48 on Body Fat in Overweight Subjects: A Randomized, Double-Blind, Placebo-Controlled Trial.

Background: We investigated whether Lactobacillus plantarum strain LMT1-48, isolated from Korean fermented foods and newborn feces, is a suitable probiotic supplement to treat overweight subjects.

Methods: In this randomized, double-blind, placebo-controlled clinical trial, 100 volunteers with a body mass index of 25 to 30 kg/m2 were assigned randomly (1:1) to receive 2×1010 colony forming units of LMT1-48 or to a placebo treatment group. Body composition was measured by dual-energy X-ray absorptiometry, and abdominal visceral fat area (VFA) and subcutaneous fat area were measured by computed tomography scanning.

Improved ECG Watermarking Technique Using Curvelet Transform.

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient's data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals.

Lactobacillus plantarum LMT1-48 exerts anti-obesity effect in high-fat diet-induced obese mice by regulating expression of lipogenic genes.

Obesity is a major health problem and is known to be closely associated with metabolic diseases. Abnormal hepatic accumulation of fat causes fatty liver or hepatic steatosis, and long-term consumption of a high-fat diet is known to be a key obesity-causing factor. Recent studies have demonstrated that probiotics such as Lactobacillus strains, exert an anti-obesity effect by regulating adipogenesis.

Energy Efficient Range-Free Localization Algorithm for Wireless Sensor Networks.

In this paper, an energy-efficient localization algorithm is proposed for precise localization in wireless sensor networks (WSNs) and the process is accomplished in three steps. Firstly, the beacon nodes discover their one-hop neighbor nodes with additional tone requests and reply packets over the media access control (MAC) layer to avoid collision of packets. Secondly, the discovered one-hop unknown nodes are divided into two sets, i.

Antiobesity Effects of LMT1-48 Accompanied by Inhibition of in the Intestine of Diet-Induced Obese Mice.

The gut microbiota is the most important environmental factor that plays a role in inducing obesity. The gram-negative bacteria, strains, recently identified in obese mice are considered to be pathogenic bacteria in the gut. Probiotics are important members of the gut microbiota and exert beneficial effects, including inhibiting the growth of potential pathogenic bacteria.