The economics, reliability, and carbon efficiency of hybrid microgrid systems (HMSs) are often in conflict; hence, a reasonable design for the sizing initial is important. In this article, we propose an improved two-archive many-objective evolutionary algorithm (TA-MaEA) based on fuzzy decision to solve optimization problem HMSs. For HMS simulated costs, loss power supply probability, pollutant emissions, balance considered as objective functions. proposed algorithm, employ two archives with...

In the traditional cloud-based Internet of Vehicles (IoV) architecture, it is difficult to guarantee low latency requirements current intelligent transportation system (ITS). As a supplement cloud computing, fog computing can effectively alleviate bottlenecks bandwidth and resources improve quality service (QoS) IoV. However, as distributed that operates near users, has complicated network structure. complex dynamic IoV environment, manage these with different attributes provide high-quality...

Security is crucial for industrial wireless sensor networks (IWSNs); therefore, in this article, we simultaneously consider the security, lifetime, and coverage issues by deploying nodes relay an environment to analyze multipath routing enhancing security. For security issue, computation of disjoint paths converted a maximum flow problem. Then, deployment problem transformed into multiobjective optimization problem, which address employing six state-of-the-art serial algorithms two...

As one of the next-generation network technologies for data centers, wireless center networks have important research significance. Smart architecture optimization and management are vital networks. With ever-increasing demand resources, deployment servers on rise. However, traditional wired links among expensive inflexible. Benefitting from development intelligent other techniques, this article studies a high-speed topology A radio propagation model based heat map is constructed. The...

Fuzzy rough theory can describe real-world situations in a mathematically effective and interpretable way, while evolutionary neural networks be utilized to solve complex problems. Combining them with these complementary capabilities may lead fuzzy network the interpretability prediction capability. In this article, we propose modifications existing models of then develop powerful framework for by inheriting merits both aforementioned systems. We first introduce neurons enhance consequence...

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play role warning people about and controlling pollution. We used support vector regression (SVR) random forest (RFR) to build models for predicting the Quality Index (AQI) Beijing nitrogen oxides (NOX) concentration Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), determination (R2) were evaluate performance models....

The deployment of a very large number readers in limited space may increase the probability collision among radio-frequency identification (RFID) and reduce dependability controllability Internet-of-Things (IoT) systems. Intelligent computing technologies can be used to realize intelligent management by scheduling resources circumvent issues. In this article, an improved RFID reader anticollision model is constructed modifying measure index, introducing constraint function, simultaneously...

In this study, a portable electronic nose (E-nose) prototype is developed using metal oxide semiconductor (MOS) sensors to detect odors of different wines. Odor detection facilitates the distinction wines with properties, including areas production, vintage years, fermentation processes, and varietals. Four popular machine learning algorithms-extreme gradient boosting (XGBoost), random forest (RF), support vector (SVM), backpropagation neural network (BPNN)-were used build identification...

Chinese liquors from different plants have unique flavors attributable to the use of various bacteria and fungi, raw materials, production processes. Accurately identifying flavor is not always possible through subjective consciousness a taster. A quartz crystal microbalance (QCM)-based electronic nose (e-nose) can perform this task because its keen ability imitate human senses. It does so by using sensor array pattern-recognition system. In paper, behavior system based on random forest (RF)...

Abstract Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 limited due to irregularities in specimen handling. We propose deep learning framework that identifies from medical images as an auxiliary testing method improve sensitivity. use pseudo-coloring methods and platform for annotating X-ray computed tomography train the convolutional neural network, which achieves performance similar experts provides high...

Meat adulteration is a global problem which undermines market fairness and harms people with allergies or certain religious beliefs. In this study, novel framework in one-dimensional convolutional neural network (1DCNN) serves as backbone random forest regressor (RFR) regressor, named 1DCNN-RFR, proposed for the quantitative detection of beef adulterated pork using electronic nose (E-nose) data. The 1DCNN extracted sufficient number features from multichannel input matrix converted raw...

Abstract Effective remaining useful life (RUL) prediction of bearings is essential for the predictive maintenance rotating machinery. However, effectiveness many existing RUL methods depends on expert experience and signal processing algorithms, which limiting application these in real-life scenarios. This study proposes a novel end-to-end deep learning framework consisting multi-scale attention-based dilated causal convolutional (MADCC) module multi-layer temporal network (MTCN) to predict...

Camellia oil, recognized as a high-quality edible oil endorsed by the Food and Agriculture Organization, is confronted with authenticity issues arising from fraudulent adulteration practices. These practices not only pose health risks but also lead to economic losses. This study proposes novel machine learning framework, referred transformer encoder backbone support vector regressor (TES), coupled an electronic nose (E-nose), for detecting varying levels in camellia oil. Experimental results...

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and estimate the content fat protein which are indicators quality. The E-nose a low cost non-destructive device. For source identification, features based on odor from E-nose, composition (Dairy Herd Improvement, DHI analytical data) analysis fusion analyzed by principal component (PCA) linear discriminant (LDA) for dimension reduction then...

Alzheimer’s disease (AD) is the most prevalent neurodegenerative causing dementia and poses significant health risks to middle-aged elderly people. Brain magnetic resonance imaging (MRI) widely used diagnostic method for AD. However, it challenging collect sufficient brain data with high-quality annotations. Weakly supervised learning (WSL) a machine technique aimed at effective feature representation from limited or low-quality In this paper, we propose WSL-based deep (DL) framework...

Chinese green tea is known for its health-functional properties. There are many categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas labeled as famous (FGTSGI) and expensive. However, the subtle differences between categories complicate fine-grained classification of GTSGI. This study proposes a novel framework consisting convolutional neural network backbone (CNN backbone) support vector machine classifier (SVM...

Accurate and fast rolling bearing fault diagnosis is required for the normal operation of rotating machinery equipment. Although deep learning methods have achieved excellent results diagnosis, performance most declines sharply when working conditions change. To address this issue, we propose a one-dimensional lightweight subdomain adaptation network (1D-LDSAN) faster more accurate diagnosis. The framework uses convolutional neural backbone rapid extraction advanced features from raw...

Accurate tooth segmentation and numbering are the cornerstones of efficient automatic dental diagnosis treatment. In this paper, a multitask learning architecture has been proposed for accurate in panoramic X-ray images. A graph convolution network was applied annotation target region, modified convolutional neural network-based detection subnetwork (DSN) used recognition boundary regression, an effective region (RSSN) segmentation. The features extracted using RSSN DSN were fused to...

The interrelation and complementary nature of multi-omics data can provide valuable insights into the intricate molecular mechanisms underlying diseases. However, challenges such as limited sample size, high dimensionality differences in omics modalities pose significant obstacles to fully harnessing potential these data. prior knowledge gene regulatory network pathway information harbors useful gene-gene interaction functional module information. To effectively integrate make full use...

Chinese liquors are internationally well-known fermentative alcoholic beverages. They have unique flavors attributable to the use of various bacteria and fungi, raw materials, production processes. Developing a novel, rapid, reliable method identify multiple is positive significance. This paper presents pattern recognition system for classifying ten brands based on multidimensional scaling (MDS) support vector machine (SVM) algorithms in quartz crystal microbalance (QCM)-based electronic...