The Internet of Things (IoT) is a major contributor to the vast amount of data generated worldwide, significantly impacting the big data market. However, this data holds value only when utilized for insights and appli...
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In the rapidly evolving e-commerce industry, the ability to select high-quality data for model training is essential. This study introduces the High-Utility Sequential Pattern Mining using SHAP values (HUSPM-SHAP) mod...
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This paper introduces a 5G multi-frequency antenna design method based on multi-objective sequential domain patching. By etching helical metamaterials on radiation patches and loading asymmetric electric-inductive-cap...
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Despite the rapid advances in electrocatalysts based on two-dimensional(2D)transition metal dichalco-genides(TMDs)materials,they are subject to serious aggregation,poor conductivity and the presence of inactive basal ...
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Despite the rapid advances in electrocatalysts based on two-dimensional(2D)transition metal dichalco-genides(TMDs)materials,they are subject to serious aggregation,poor conductivity and the presence of inactive basal ***,we have successfully demonstrated the in-situ construction of NiSe_(2)-MoSe_(2) heterostructure arrays on carbon cloth(NiSe_(2)-MoSe_(2)/CC)by a facile two-step hydrothermal *** presence of the synergistic effect in the heterostructures effectively optimizes the poor conductivity and hydrophilicity,and thus enables fast electron transfer,leading to enhanced electrochemical ***-thermore,density functional theory calculations reveal that the electrons redistribution at the heterojunc-tion interface and the reduced Gibbs free energy of hydrogen adsorption for hydrogen evolution reaction(HER)/the Gibbs free energy change value of rate-determining step for oxygen evolution reaction(OER),thus enhancing the HER/OER catalytic ***,the device displays a good performance with a low overpotential of 98 and 310 mV for HER and OER,respectively,and a low cell voltage of 1.59 V for its corresponding electrolyzer(10 mA cm^(-2)).This work presents the high-performance water splitting of bifunctional electrocatalysts based on 2D TMDs materials and offers a novel design concept of interface engineering.
Avalanche photodiode detectors (APDs) at X-ray synchrotrons are typically limited to recording at most one photon per synchrotron pulse. Digitizing the APD amplifier outputs enables signal processing to accurately mea...
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This paper leverages insights from my previous works to analyze and predict customer behavior in different areas using data mining and machine learning techniques. The research focuses on identifying and interpreting ...
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Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, ...
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Worldwide, cardiovascular and chronic respiratory diseases account for approximately 19 million deaths annually. Evidence indicates that the ongoing COVID-19 pandemic directly contributes to increased blood pressure, cholesterol, as well as blood glucose levels. Timely screening of critical physiological vital signs benefits both healthcare providers and individuals by detecting potential health issues. This study aims to implement a machine learning-based prediction and classification system to forecast vital signs associated with cardiovascular and chronic respiratory diseases. The system predicts patients' health status and notifies caregivers and medical professionals when necessary. Utilizing real-world data, a linear regression model inspired by the Facebook Prophet model was developed to predict vital signs for the upcoming 180 seconds. With 180 seconds of lead time, caregivers can potentially save patients' lives through early diagnosis of their health conditions. For this purpose, a Naïve Bayes classification model, a Support Vector Machine model, a Random Forest model, and genetic programming-based hyper tunning were employed. The proposed model outdoes previous attempts at vital sign prediction. Compared with alternative methods, the Facebook Prophet model has the best mean square in predicting vital signs. A hyperparameter-tuning is utilized to refine the model, yielding improved short- and long-term outcomes for each and every vital sign. Furthermore, the F-measure for the proposed classification model is 0.98 with an increase of 0.21. The incorporation of additional elements, such as momentum indicators, could increase the model's flexibility with calibration. The findings of this study demonstrate that the proposed model is more accurate in predicting vital signs and trends. IEEE
Self Supervised Representation Learning (SSRepL) can capture meaningful and robust representations of the Attention Deficit Hyperactivity Disorder (ADHD) data and have the potential to improve the model's performa...
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Radars centered in the S-band face challenges in through-wall target detection applications due to high attenuation through lossy dielectrics. This paper discusses applications in target detection, angle-of-arrival (A...
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Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known ...
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Memtransistors in which the source-drain channel conductance can be nonvolatilely manipulated through the gate signals have emerged as promising components for implementing neuromorphic *** the other side,it is known that the complementary metal-oxide-semiconductor(CMOS)field effect transistors have played the fundamental role in the modern integrated circuit ***,will complementary memtransistors(CMT)also play such a role in the future neuromorphic circuits and chips?In this review,various types of materials and physical mechanisms for constructing CMT(how)are inspected with their merits and need-to-address challenges *** the unique properties(what)and poten-tial applications of CMT in different learning algorithms/scenarios of spiking neural networks(why)are reviewed,including super-vised rule,reinforcement one,dynamic vision with in-sensor computing,*** exploiting the complementary structure-related novel functions,significant reduction of hardware consuming,enhancement of energy/efficiency ratio and other advan-tages have been gained,illustrating the alluring prospect of design technology co-optimization(DTCO)of CMT towards neuro-morphic computing.
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