Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especi...
详细信息
ISBN:
(纸本)9789811965814;9789811965807
Meteorological satellites are widely used for collecting information about the atmosphere. Due to the indirect relation between satellite data and measurements of meteorological parameters, mathematical models, especially those based on artificial intelligence, have been developed for meteorological modeling. Indeed, in recent years, machinelearning has enabled fundamental advances in the modeling of random systems. In this context, we will show the contribution of techniques based on artificial intelligence in the estimation of precipitation. Based on the expertise of our research laboratories in this field, the objective of this paper is to present our recent results and developments using machinelearning, such as ANN, SVM, and RF. For the classification and estimation of rainfall intensities, satellite images were used for the implementation of these techniques. The training and validation was carried out by comparing the satellite images to the corresponding radar images. The results of these artificial intelligence-based techniques indicate very interesting performance.
In this paper, we modeled a system to recognize human activities using integrated sensors like gyroscopes and accelerometers in smartphones. To perform human activity recognition (HAR) accurately, appropriate machine ...
详细信息
Bridges are the essential part of our transportation in daily life. Infrastructure safety and integrity are crucial to prevent accidents and ensure smooth traffic flow. Regular inspections require careful observation ...
详细信息
Cyber threats are becoming more frequent and complex, making decision systems crucial to cybersecurity. The review paper uses machinelearning to examine decision systems and cybersecurity, highlighting its fundamenta...
详细信息
ISBN:
(纸本)9789819601424
Cyber threats are becoming more frequent and complex, making decision systems crucial to cybersecurity. The review paper uses machinelearning to examine decision systems and cybersecurity, highlighting its fundamentals, applications, and challenges. The paper begins with a detailed cybersecurity overview, emphasizing the need for strong decision-making systems to address changing threats. We define and discuss decision systems in the fundamentals section, emphasizing their importance in cybersecurity. An analysis of cybersecurity decision system development highlights significant achievements and technological progress. This paper focuses on machinelearning in cybersecurity decision systems. Analyzing cybersecurity machinelearning methods allows for a thorough examination of their use in decision-making. We discuss the pros and cons of integrating machinelearning into decision-making systems and its impact on cybersecurity. Intrusion Detection systems (IDS), Threat Intelligence Platforms (TIP), Incident Response and Management, and Adaptive Security Architectures are examined in the methods and processes section. Each system will be examined for its function, machinelearning algorithms, and case studies to demonstrate their practicality. Assessing decision system effectiveness requires metrics and benchmarks. This paper analyzes decision system performance metrics and cybersecurity machinelearning model benchmarks and datasets. Using these metrics to compare decision systems reveals their strengths and weaknesses. The review concludes with future machinelearning-based decision system challenges and new trends and technologies. To maintain cybersecurity resilience, recommendations are made to address future challenges and improve decision-making systems. In-depth machinelearning analysis in this review paper expands cybersecurity decision system knowledge. This resource helps researchers, practitioners, and policymakers understand, implement, and improve cybe
The interpretation and detection of human emotions are crucial functions of the center-nervous system. Physiological signals are used widely to develop emotion recognition systems in recent years. Detection of emotion...
详细信息
As the carrier of integrated circuit, semiconductor lead frame is an important part of chip electrical connection. In the manufacturing process, the lead frame must go through multiple processes, which will lead to de...
详细信息
ISBN:
(纸本)9798350375084;9798350375077
As the carrier of integrated circuit, semiconductor lead frame is an important part of chip electrical connection. In the manufacturing process, the lead frame must go through multiple processes, which will lead to defects on the surface. However, it is difficult to obtain defect data in actual manufacturing. In this paper, an algorithm for surface defect detection of lead frame based on knowledge distillation is proposed. We propose an unsupervised model learning strategy. The algorithm uses normal images for training and uses global context information and local texture information for defect detection. The experimental results show that the proposed method can effectively detect the surface defects of the lead frame. This method can not only detect structural defects such as dirt, scratches, and pin deformation, but also detect logical defects such as plating area deviation.
Aerodynamic optics has important research value in many fields such as aerospace optical sensors, remote sensing detection and so on. The traditional gas radiation calculation models are insufficient in terms of accur...
详细信息
The proceedings contain 112 papers. The topics discussed include: protecting vehicle location privacy with contextually-driven synthetic location generation;enhancing spatio-temporal quantile forecasting with curricul...
ISBN:
(纸本)9798400711077
The proceedings contain 112 papers. The topics discussed include: protecting vehicle location privacy with contextually-driven synthetic location generation;enhancing spatio-temporal quantile forecasting with curriculum learning: lessons learned;critical features tracking on triangulated irregular networks by a scale-space method;revisiting the bus stop problem in road networks;augmentation techniques for balancing spatial datasets in machine and deep learning applications;discretized random walk models for efficient movement interpolation;prompt mining for language models-based mobility flow forecasting;privacy preserved taxi demand prediction system for distributed data;and deep reinforcement learning for multi-period facility location: pk-median dynamic location problem.
Currently, several studies are being developed using machinelearning with the aim of replacing real sensors with virtual sensors. One of the biggest challenges is obtaining quality data to enable the system modeling....
详细信息
ISBN:
(纸本)9798331531768;9798331531751
Currently, several studies are being developed using machinelearning with the aim of replacing real sensors with virtual sensors. One of the biggest challenges is obtaining quality data to enable the system modeling. This article proposes the replacement of a light dependent resistor sensor, applied to a refrigerator, which detects whether the door is open or closed and an anomaly test when the door is forgotten open for too long. The use of tiny machinelearning is applied to ESP32 to enable the use of regression and classification models to detect the door opening and anomaly. The tested metrics for the regression were the root mean squared error. The comparative results between the virtual and real sensors are satisfactory with an accuracy of 99.59% for classification and a root mean squared error value of 0.01 for regression. Furthermore, a prototype was developed, and the model was embedded in the ESP32 microcontroller.
The increasing impact of climate change and global warming highlights the urgent need for sustainable agriculture practices to ensure food security. This research focuses on utilizing machinelearning techniques to ac...
详细信息
暂无评论