Smart Home minimizes user's intervention in monitoring home settings and controlling appliances. This paper presents an approach to the development of applications by integrating Internet Things (IoT) with Web services Cloud computing. The focuses on: (1) embedding intelligence into sensors actuators using Arduino platform, (2) networking smart things Zigbee technology, (3) facilitating interactions services, (4) improving data exchange efficiency JSON format. Moreover, we implement three...

Recent advances in wireless networking and big data technologies, such as 5G networks, medical analytics, the Internet of Things, along with recent developments wearable computing artificial intelligence, are enabling development implementation innovative diabetes monitoring systems applications. Due to life-long systematic harm suffered by patients, it is critical design effective methods for diagnosis treatment diabetes. Based on our comprehensive investigation, this article classifies...

The Internet of Things presents the user with a novel means communicating Web world through ubiquitous object-enabled networks. Cloud Computing enables convenient, on demand and scalable network access to shared pool configurable computing resources. This paper mainly focuses common approach integrate (IoT) under name CloudThings architecture. We review state art for integrating Things. examine an IoT-enabled smart home scenario analyze IoT application requirements. also propose...

Accurate emotion recognition from speech is important for applications like smart health care, entertainment, and other services. High accuracy Chinese challenging due to the complexities of language. In this paper, we explore how improve recognition, including signal feature extraction classification methods. Five types features are extracted a sample: mel frequency cepstrum coefficient (MFCC), pitch, formant, short-term zero-crossing rate energy. By comparing statistical with deep by Deep...

Industrial Internet of Things (IIoT) is producing massive data which are valuable for knowing running status the underlying equipment. However, these involve various operation events that span some time, raise questions on how to model long memory states, and predict based historical accurately. This paper aims develop a method of: (1) analyzing equipment working condition sensed data; (2) building prediction forecasting designing deep neural network (3) improving accuracy by systematic...

This paper proposes a new deep learning network - RCNN4SPTL (RCNN -based Foreign Object Detection for Securing Power Transmission lines), which is suitable detecting foreign objects on power transmission lines. The uses RPN (Region Proposal Network) to generate aspect ratio of the region proposals align with size objects. an end training improve its performance. Experimental results show that significantly improves detection speed and recognition accuracy, compared original Faster RCNN.

Wind farms are typically located at high latitudes, resulting in a risk of blade icing. Data-driven approaches offer promising solutions for icing detection, but they rely on considerable amount data. Data exchange between multiple wind would improve the performance detection models, due to spatio-temporal dependencies capable reflecting different meteorological conditions. The traditional centralized approach faces many challenges, including requirement storage and computational capacity...

Millions of patients suffer from rare diseases around the world. However, samples are much smaller than those common diseases. Hospitals usually reluctant to share patient information for data fusion due sensitivity medical data. These challenges make it difficult traditional AI models extract disease features prediction. In this paper, we propose a Dynamic Federated Meta-Learning (DFML) approach improve We design an Inaccuracy-Focused (IFML) that dynamically adjusts attention different...

Abstract Recently, the development of new technologies such as Internet Things, Big Data and Cloud Computing have promoted intelligentization many industries deeply changed way human production living styles. Last year, AlphaGo defeated top level experts Go chess times, marking start a IT era, here is not Information Technology but Intelligence Technology. The challenge K-12 science education to cultivate young students with advanced scientific literacy help them keep up fast under...

Gear fault diagnosis (GFD) based on vibration signals is a popular research topic in industry and academia. This paper provides comprehensive summary systematic review of signal-based GFD methods recent years, thereby providing insights for relevant researchers. The authors first introduce the common gear faults their signal characteristics. overview compare feature extraction methods, such as adaptive mode decomposition, deconvolution, mathematical morphological filtering, entropy. For each...

Bearing fault diagnosis is essential for improving the efficiency of industrial operations. The inherently multi-modal nature bearing vibration signals presents significant challenges in diagnosis. To address this issue, we propose a novel Multi-Channel Broad Learning System (MCBLS). MCBLS comprises multiple Systems (BLSs) and incorporates aPositive-Negative Weighted Voting mechanism (PNWV). first employs empirical mode decomposition to break down signal into intrinsic functions (IMFs)...

Predicting interactions between drugs and their targets is vital for drug discovery repositioning. Conventional techniques are slow labor-intensive, while deep learning algorithms offer efficient solutions. However, often focus on single representations or simplistic combinations, leading to suboptimal feature representation. Moreover, the prevalent use of convolutional neural networks (CNNs) in image representation neglects necessity both local global information Drug-Target Interaction...

Service composition provides value-adding services through composing basic Web services, which may be provided by various organizations. Cloud computing presents an efficient managerial, on-demand, and scalable way to integrate computational resources (hardware, platform, software). However, existing architecture lacks the layer of middleware enable dynamic service composition. To accelerate on-demand composition, authors explore paradigm in for Pervasive Computing environments propose a...

Recently education blockchain driven smart has become focus of attention, and related system frameworks key technologies are presented. However, problems difficult to model, experiment, optimize in need be further solved, driving mechanisms, application scenarios other issues analysis. This paper first introduces blockchain, challenges issues, then based on introduction parallel intelligence theory it proposes its mechanism, function distribution, data transfer, elaborated; At last, several...

Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different dimensions collected from embedded sensors. This paper proposes a graph network-based modeling approach for IoT equipment (called GNNM-IoT), which considers both data, vertices denote sensor edges relationships vertices. The GNNM-IoT model's sensors with produce nonlinear complex relationships. We have evaluated the using...

Federated learning (FL) brings collaborative intelligence into industries without centralized training data to accelerate the process of Industry 4.0 on edge computing level. FL solves dilemma in which enterprises wish make use with security concerns. To industrial Internet things further leverage FL, existing achievements are developed from three aspects: 1) define terminologies and elaborate a general framework for accommodating various scenarios; 2) discuss state-of-the-art fundamental...

Ship classification based on synthetic aperture radar (SAR) images is a crucial component in maritime surveillance. In this article, the feature selection and classifier design, as two key essential factors for traditional ship classification, are jointed together, novel model combining kernel extreme learning machine (KELM) dragonfly algorithm binary space (BDA), named BDA-KELM, proposed which conducts automatic searches optimal parameter sets (including penalty factor) at same time....