Systematic Look at Machine Learning Algorithms � Advantages, Disadvantages and Practical Applications

Dineva, Kristina; Atanasova, Tatiana

doi:10.5593/sgem2020/2.1/s07.041

Cited by 18 publications

(9 citation statements)

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“…The branch of artificial intelligence (AI) called ML is a prime technology to study. During the last years, the availability of vast amounts of data and information has allowed for more correct predictions to be made using ML models, while traditional machine learning algorithms are supported by methods that lead to the development of faster and more dynamic algorithms with better accuracy [ 3 ]. Machine learning models have the capability for learning and adapting according to the problem that needs to be solved, while conventional programming options are limited by the fact that the individuals who implement them are expected to already possess knowledge of the idiosyncrasies of the system the solution is being tailored for [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

Loukatos¹,

Kondoyanni²,

Alexopoulos³

et al. 2023

Sensors

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The digital transformation of agriculture is a promising necessity for tackling the increasing nutritional needs on Earth and the degradation of natural resources. Toward this direction, the availability of innovative electronic components and of the accompanying software programs can be exploited to detect malfunctions in typical agricultural equipment, such as water pumps, thereby preventing potential failures and water and economic losses. In this context, this article highlights the steps for adding intelligence to sensors installed on pumps in order to intercept and deliver malfunction alerts, based on cheap in situ microcontrollers, sensors, and radios and easy-to-use software tools. This involves efficient data gathering, neural network model training, generation, optimization, and execution procedures, which are further facilitated by the deployment of an experimental platform for generating diverse disturbances of the water pump operation. The best-performing variant of the malfunction detection model can achieve an accuracy rate of about 93% based on the vibration data. The system being implemented follows the on-device intelligence approach that decentralizes processing and networking tasks, thereby aiming to simplify the installation process and reduce the overall costs. In addition to highlighting the necessary implementation variants and details, a characteristic set of evaluation results is also presented, as well as directions for future exploitation.

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Section: Introductionmentioning

confidence: 99%

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

Loukatos¹,

Kondoyanni²,

Alexopoulos³

et al. 2023

Sensors

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“…machine learning models possess the ability to learn and adjust according to the problem, whereas traditional programming alternatives are constrained since those implementing them are expected to already understand the intricacies of the system for which the solution is being customized [48]. In recent years, the accessibility of extensive volumes of data and information has enabled more fast and accurate ML models [49]. The swift growth in available data, facilitated by improved sensor inventions, has significantly elevated the significance of machine learning, transforming it into a potent instrument for numerous applications between various disciplines.…”

Section: Related Work and Rationalementioning

confidence: 99%

Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education

Kondoyanni,

Loukatos,

Arvanitis

et al. 2024

Sustainability

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Considering that the fusion of education and technology has delivered encouraging outcomes, things are becoming more challenging for higher education as students seek experiences that bridge the gap between theory and their future professional roles. Giving priority to the above issue, this study presents methods and results from activities assisting engineering students to utilize recent machine-learning techniques for tackling the challenge of water resource preservation. Cost-effective, innovative hardware and software components were incorporated for monitoring the proper operation of the corresponding agricultural equipment (such as electric pumps or water taps), and suitable educational activities were developed involving students of agricultural engineering. According to the evaluation part of the study being presented, the implementation of a machine-learning system with sufficient performance is feasible, while the outcomes derived from its educational application are significant, as they acquaint engineering students with emerging technologies entering the scene and improve their capacity for innovation and cooperation. The study demonstrates how emerging technologies, such as IoT, ML, and the newest edge-AI techniques can be utilized in the agricultural industry for the development of sustainable agricultural practices. This aims to preserve natural resources such as water, increase productivity, and create new jobs for technologically efficient personnel.

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“…Although relationships between housing choices and the determinants are atypical and nonlinear (Jun et al, 2020;Liao et al, 2015), most empirical studies have used conventional methods such as latent class analysis and logistic regression, which are unable to tackle the joint effects and nonlinear relationships among variables (Liao et al, 2015). Machine learning (ML) algorithms can address these limitations by tackling interactions among categorical and continuous variables with nonlinear relationships and generating better accuracies even with large data sets comprising noisy data (Dineva & Atanasova, 2020;Medeiros et al, 2021;Wibrin et al, 2006). Thanks to the power of ML methods in solving complex problems (Chi-Hsien & Nagasawa, 2019;Choi & Lim, 2020;Orogun & Onyekwelu, 2019;Pantano & Dennis, 2019), we propose an innovative ML approach to analyse the complicated consumer behaviour of high involvement products such as housing choices by using the Australian housing market as the natural laboratory for this research.…”

Section: Introductionmentioning

confidence: 99%

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

Arachchige

Quach

Roca

et al. 2022

J of Consumer Behaviour

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Adopting the conservation of resources theory, this research explores the joint influence of multiple factors affecting the choice of housing, an instance of a high involvement product. We develop an innovative Machine Learning approach to identify the most significant set of factors affecting consumers' housing choices. It was found that housing choices were primarily bound by energy resources and secondarily by other personal, conditional, and object resources. The study identified 25 key factors, many of which previous studies have not explored. The new factors include government payments, vehicle possession, time living with children, marriage duration, current location duration, and health conditions. The results suggest that joint effects of factors are more prominent than individual effects in influencing the choice of high‐involvement products. Further, the study suggests that ML methods are more robust than traditional methods and can be applied to analyse other types of high involvement products. The findings can assist real estate investors, policymakers and other stakeholders to understand sophisticated market behaviours to develop better and tailored housing strategies.

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Systematic Look at Machine Learning Algorithms � Advantages, Disadvantages and Practical Applications

Cited by 18 publications

References 0 publications

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

On-Device Intelligence for Malfunction Detection of Water Pump Equipment in Agricultural Premises: Feasibility and Experimentation

Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education

Understanding high‐involvement product purchase through an innovative machine learning approach: A case of housing type choice

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