Detecting human presence indoors with millimeter-wave frequency-modulated continuous-wave (FMCW) radar faces challenges from both moving and stationary clutters. This work proposes a robust and real-time capable human...
详细信息
Parkinson's disease (PD) diagnosis involves the assessment of a variety of motor and non-motor symptoms. To accurately diagnose PD, it is necessary to differentiate its symptoms from those of other conditions. Dur...
详细信息
The enhancement of Intrusion Detection Systems (IDS) is required to ensure protection of network resources and services. This is a hot research topic, especially in the presence of advanced intrusions and at...
详细信息
The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and ena...
详细信息
The factory has adopted an extensive ecosystem of connected devices and IoT sensors, utilizing cloud computing for real-time decision-making. Secure cloud storage serves as the backbone, managing vast datasets and enabling centralized control. By leveraging advanced analytics and machine learning on the cloud, the factory has implemented predictive maintenance, minimizing downtime and optimizing production. The integration of Hybrid PSO-GA for machines and supply chain processes streamlines operations, allowing for remote monitoring and control to enhance operational agility. Cutting-edge advancements in New Generation information Technologies (New IT) are crucial in driving the evolution of smart manufacturing. The proliferation of Internet-connected devices in these environments generates substantial data throughout the product lifecycle. Adopting a cloud-based smart manufacturing strategy provides numerous services and applications for analysing massive datasets and fostering significant cooperation in manufacturing operations. However, challenges such as latency, bandwidth congestion, and network unavailability hinder its effectiveness for real-time applications requiring fast, low-latency performance. These issues are efficiently addressed by integrating cloud computing with edge computing, extending the cloud’s capabilities to the edge. This paper presents a hierarchical reference architecture for smart manufacturing, leveraging cloud computing. The proposed approach employs a hybrid PSO-GA scheduling function that combines Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to optimize task start times and reduce latency. The optimal solution from this hybrid approach updates task start times, with subsequent scheduling performed using a selected algorithm. The proposed novel hybrid PSO-GA model integrates AI-driven optimization, IoT, and digital twins to enhance real-time decision-making and adapt to dynamic data streams in smart manufacturing. Its
In recent years, incidents involving unmanned aerial vehicles (UAVs) have increased significantly, raising concerns over security and privacy, especially concerning civilian and military facilities. Vision-based appro...
详细信息
Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area eve...
详细信息
Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the Internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
详细信息
Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Animal emotion detection, including elephant emotions, is highly possible, but what the traditional emotion detection approaches highlight is their blatant ignorance of adopting edge-enabled intelligence and serverles...
详细信息
The popularity of the Internet and digital consumer gadgets has fundamentally changed our society and daily lives by making digital data collection, transmission, and storage exceedingly easy and convenient. However, ...
详细信息
The agriculture industry's production and food quality have been impacted by plant leaf diseases in recent years. Hence, it is vital to have a system that can automatically identify and diagnose diseases at an ini...
详细信息
暂无评论