Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, a...
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Stress has a remarkable impact on various cognitive functions, demanding timely and effective detection using strategies deployed across interdisciplinary domains. It influences decision-making, attention, learning, and problem-solving abilities. As a result, stress detection and modeling have become important areas of study in both psychology and computerscience. This study links the fields of psychology and machine learning to deal with the urgent requirement of accurate stress detection methodologies and highlights sleep patterns as a key indicator for stress detection, discussing a novel approach to understand and determine stress levels. Psychologists use affective states to measure stress, which refers to a sense of feeling an underlying emotional state. However, most stress classification work has been limited to user-dependent models, which new users cannot use without additional training. This can be a significant time burden for new users trying to predict their affective states. Therefore, it is critical to address basic mental health issues in children and adults to prevent them from developing more complex problems on account of undergoing stress. The medical field processes vast amounts of medical data;the machine learning algorithms sift through patterns that might escape the human eye. The machine learning algorithms act as detectives, able to spot correlations and bring out a sense of complex information. The machine learning algorithms reveal fine correlations and patterns, aiding in more precise and prompt diagnoses particularly to focus fundamental mental health issues in individuals of all ages. This research work deploys an enhanced Multilayer Perceptron (MLP), exhibiting an extensive feature analysis for processing medical datasets, resulting in improved effectiveness in predicting stress levels. This helps us to diagnose issues more accurately and swiftly which improves the patient outcomes. The proposed and enhanced MLP model undergoes stri
Accidents caused by drivers who exhibit unusual behavior are putting road safety at ever-greater risk. When one or more vehicle nodes behave in this way, it can put other nodes in danger and result in potentially cata...
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The proposed underground tunnel for Mass Rapid Transit Line 1,Dhaka brings immense attention to the engineers and experts not only because of its construction challenges through a densely-built city,but also for the p...
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The proposed underground tunnel for Mass Rapid Transit Line 1,Dhaka brings immense attention to the engineers and experts not only because of its construction challenges through a densely-built city,but also for the potential tunneling-induced ground *** very preliminary study investigates the ground surface movement due to the progression of the tunnel boring machine(TBM)using numerical analysis.A series of finite element(FE)models have been developed using PLAXIS 3D,in which Mohr–Coulomb(MC),modified Cam-Clay(MCC),and hardening soil(HS)have been *** in-field data of Mashhad Metro Line-2 have been compared to verify PLAXIS 3D’s efficacy in tunnel ***,the outcomes of the FE analyses are compared with the existing empirical *** PLAXIS 3D analysis considering the MCC soil model exhibits strong agreement with the real monitored data,with a variance of only 3.85%.After simulating different stages of the tunnel construction,results are reported in terms of the distance of the inflexion point from the center,the settlement trough pattern,the maximum transverse settlements,and the vertical *** are also compared with the established empirical *** order to comprehend the surface settlement with various tunnel depths and diameters,a parameter dependency study has been carried *** analysis findings showed that increasing the TBM’s depth and radius causes the inflexion point’s distance from the center of the tunnel to increase by 4%and decrease by 5%,*** is also observed that as tunnel depth increases,the overall settlement of the tunnel lowers by 11%for every additional 5 meters of *** MCC model,out of the three,exhibits the most accurate value of the settlement compared to that obtained from the empirical solutions,and also the best-fit form to the Gaussian curve.
In an Internet of Things (IoT) assisted Wireless Sensor Network (WSN), the location of the Base Station (BS) remains important. BS serves as the central hub for data collection, aggregation and communication within th...
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Background: Human physical activity recognition is challenging in various research eras, such as healthcare, surveillance, senior monitoring, athletics, and rehabilitation. The use of various sensors has attracted out...
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Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required...
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Most optimization problems of practical significance are typically solved by highly configurable parameterized *** achieve the best performance on a problem instance,a trial-and-error configuration process is required,which is very costly and even prohibitive for problems that are already computationally intensive,*** problems associated with machine learning *** the past decades,many studies have been conducted to accelerate the tedious configuration process by learning from a set of training *** article refers to these studies as learn to optimize and reviews the progress achieved.
Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memris...
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Advancements in neuromorphic computing have given an impetus to the development of systems with adaptive behavior,dynamic responses,and energy efficiency *** charge-based or emerging memory technologies such as memristors have been developed to emulate synaptic plasticity,replicating the key functionality of neurons—integrating diverse presynaptic inputs to fire electrical impulses—has remained *** this study,we developed reconfigurable metal-oxide-semiconductor capacitors(MOSCaps)based on hafnium diselenide(HfSe2).The proposed devices exhibit(1)optoelectronic synaptic features and perform separate stimulus-associated learning,indicating considerable adaptive neuron emulation,(2)dual light-enabled charge-trapping and memcapacitive behavior within the same MOSCap device,whose threshold voltage and capacitance vary based on the light intensity across the visible spectrum,(3)memcapacitor volatility tuning based on the biasing conditions,enabling the transition from volatile light sensing to non-volatile optical data *** reconfigurability and multifunctionality of MOSCap were used to integrate the device into a leaky integrate-and-fire neuron model within a spiking neural network to dynamically adjust firing patterns based on light stimuli and detect exoplanets through variations in light intensity.
Disastrous situations pose a formidable challenge, testing our resilience against nature's fury and the race against time to prevent the loss of human life. It is noted that in such situations that Microblogging p...
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Purpose: The rapid spread of COVID-19 has resulted in significant harm and impacted tens of millions of people globally. In order to prevent the transmission of the virus, individuals often wear masks as a protective ...
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Surface wave inversion is a key step in the application of surface waves to soil velocity ***,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuris...
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Surface wave inversion is a key step in the application of surface waves to soil velocity ***,a common practice for the process of inversion is that the number of soil layers is assumed to be known before using heuristic search algorithms to compute the shear wave velocity profile or the number of soil layers is considered as an optimization ***,an improper selection of the number of layers may lead to an incorrect shear wave velocity *** this study,a deep learning and genetic algorithm hybrid learning procedure is proposed to perform the surface wave inversion without the need to assume the number of soil ***,a deep neural network is adapted to learn from a large number of synthetic dispersion curves for inferring the layer ***,the shear-wave velocity profile is determined by a genetic algorithm with the known layer *** applying this procedure to both simulated and real-world cases,the results indicate that the proposed method is reliable and efficient for surface wave inversion.
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