Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by challenges in social interaction, communication, and restricted repetitive behaviours. Early diagnosis and intervention signifi...
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This note surveys developments in particle physics due to advances made in the fields of statistics, machinelearning, and artificial intelligence. With the aid of examples and recent work, this article attempts to gi...
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ISBN:
(纸本)9783031585012;9783031585029
This note surveys developments in particle physics due to advances made in the fields of statistics, machinelearning, and artificial intelligence. With the aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machinelearning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.
machinelearning (ML) systems increasingly perform complex decision-making and prediction tasks - e.g., in autonomous driving - based on patterns inferred from large quantities of data. The inclusion of ML increases t...
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The classification of soil types for agricultural management systems and environmental engineering is extremely important for land use planning and environmental conservation. Generally speaking, soil classification i...
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The classification of soil types for agricultural management systems and environmental engineering is extremely important for land use planning and environmental conservation. Generally speaking, soil classification involves time-consuming resource-intensive methodologies where uneconomical laboratory tests are necessarily needed. Artificial intelligence-based machinelearning techniques are being leveraged to compensate for this. Before progressing into machinelearning classification methods, statistical analysis are required to understand the type of data and compatibility of applied algorithms. This paper gives an examination of soil type dataset collected from southern Syria by conducting clustering behavior, correlation analysis, and artificial neural network-based classification. Five soil features were involved in the current approach, namely E/N coordinates, elevation values, slope percentage, and rainfall depth. The conducted analysis has leveraged a total of 25 types of soils regardless of the total quantity in each target. It is commonly rare to reach high classification accuracies in such adopted cases of work, however, the resulted 94.9% accuracy significantly advances soil recognition in all considered aspects. It is demonstrably concluded that high potentiality is noted in artificial neural networks where future proposals are written for algorithm optimization perspectives. This proposed methodology will minimize the resources requirements, consumed time of mapping, and manpower necessity which will collectively boost land management practices.
Personalised learning recommendation systems are an essential component in the process of enhancing the effectiveness of websites that provide online education. Educational information that is tailored to the specific...
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Personalised learning recommendation systems are an essential component in the process of enhancing the effectiveness of websites that provide online education. Educational information that is tailored to the specific requirements of each student is the responsibility of these systems, which are accountable for delivering it. This research study aims to present a novel approach to personalized learning recommendation systems that makes use of collaborative filtering and machinelearning techniques. The purpose of this research study is to present this method. In our method, we make use of collaborative filtering techniques to explore the interactions that take place between users and products, as well as to identify patterns of similarity among pupils. The use of machinelearning models, such as decision trees and neural networks, enables us to present learners with tailored recommendations based on their preferences, historical behaviors, and performance indicators. This is accomplished through the utilization of these models. The solution that is being provided offers recommendations that are not only dynamic but also adaptable;these recommendations alter over time regardless of how learners interact with the platform.
Embedded systems such as Raspberry Pi have gained popularity due to their small size, low cost, and ability to run various applications, including machinelearning algorithms. Various studies have investigated the per...
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ISBN:
(纸本)9798350348194;9798350348187
Embedded systems such as Raspberry Pi have gained popularity due to their small size, low cost, and ability to run various applications, including machinelearning algorithms. Various studies have investigated the performance of Raspberry Pi for machinelearning tasks, including image classification and object detection. These studies indicate that Raspberry Pi can serve as a viable platform for running machinelearning algorithms. In this study, we review the current state of research on the CPU performance of running face recognition algorithms on Raspberry Pi. This research presents the testing done on a facial recognition algorithm using machinelearning to reach the determination value needed for a real-time facial recognition-based software to decide the pass-fail criteria for a familiar face in a database of choice. This paper presents the results and analysis of the timing constraints of running a machinelearning algorithm on a Raspberry Pi 3 Model B.
This study presents a novel approach to quantifying physical exertion using various machinelearning algorithms. Such techniques include, among others, random forests, decision trees, linear regression, and gradient b...
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Daily lives have been made much easier with the invasion of IoT devices in many fields such as health care, education, smart homes, agriculture, etc. Nowadays these devices are prone to many cyber attacks that exist c...
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There are increasing concerns about power quality disturbances (PQDs) at many phases of energy generation, transformation, distribution, and consumption due to the increasing interconnection of various energy systems....
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Intrusion detection system is used against harmful attacks also attacker's tools and strategies are always evolving. But implementing an authorized IDS system is equally a significant job. Several experiments were...
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