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Today, the use of machine learning and artificial intelligence due to many advantages such as simplicity, high speed, high accuracy in predicting various processes, no need for complex equipment and tools and the availability of many applications in science and fields. Has found various including statistics, mathematics, physics, chemistry, biochem...
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... is those issues whose output is a continuous number or a set of continuous numbers, such as house price forecasts based on information such as area, number of rooms, etc. Classification refers to those issues whose output is part of a set, such as predicting whether an email is spam or predicting the type of illness a person has out of ten diseases. Figure 1 shows a schematic of supervised learning (Caruana & Niculescu-Mizil, 2006). ...
Citations
... Machine Learning (ML) models are now massively used to tackle a wide variety of real-world problems [19,53]. Such models have thus a direct impact into people lives, and there are increasing transparency and fairness concerns that practitioners should be aware of when deploying ML methods into real applications [2,9]. ...
... On the other hand, machine learning (ML), is a subset of artificial intelligence that focuses on algorithms and statistical models that allow computers to learn from data and make predictions or take actions without being explicitly programmed. ML algorithms analyze datasets to identify patterns, relationships, and insights, and use that knowledge to make accurate predictions or decisions [10], [11]. In the context of heat-related illness prediction, IoT and ML can significantly contribute to improving early detection and prevention. ...
With the increasing occurrence of heat-related illnesses due to rising temperatures worldwide, there is a need for effective detection and prediction systems to mitigate the risks. Heat stroke, a life-threatening condition occurs when the body’s temperature exceeds 104 degrees Fahrenheit (40 degrees Celsius). It can happen due to prolonged exposure to temperatures. When the body struggles to cool itself down adequately. The internet of things (IoT) and machine learning (ML) are two advancing technologies that have the potential to revolutionize industries and enhance our lives in numerous ways. Currently, monitoring devices are primarily used to diagnose when individuals suffering from heatstroke are at the location. This paper delves into the exploration of utilizing the IoT and ML algorithms to predict heat strokes. It reviews existing studies in this field, focusing on how IoT has been deployed and the application of machine learning techniques. The research aims to define the integration of IoT devices and ML algorithms that has a great potential to detect and predict heat-related illnesses such as heat stroke at an early stage.
... The algorithm has clustered the data into categories or patterns, as represented by the organized groups of dots. (Nozari & Sadeghi, 2021) The article is organized as follows: Section II explains the fundamental concepts and principles of clustering in data mining. Section III discusses the challenges associated with data clustering from a data mining perspective. ...
Clustering is a crucial technique in both research and practical applications of data mining. It has traditionally functioned as a pivotal analytical technique, facilitating the organization of unlabeled data to extract meaningful insights. The inherent complexity of clustering challenges has led to the development of a variety of clustering algorithms. Each of these algorithms is tailored to address specific data clustering scenarios. In this context, this paper provides a thorough analysis of clustering techniques in data mining, including their challenges and applications in various domains. It also undertakes an extensive exploration of the strengths and limitations characterizing distinct clustering methodologies, encompassing distance-based, hierarchical, grid-based, and density-based algorithms. Additionally, it explains numerous examples of clustering algorithms and their empirical results in various domains, including but not limited to healthcare, image processing, text and document clustering, and the field of big data analytics.
... Machine learning is a branch of AI that enables computers to learn from data in order to acquire decision-making capabilities and perform tasks without human intervention [34]. ML models are trained on a set of examples, known as training data, in order to be able to make predictions on newer or unseen data. ...
Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating artificial intelligence (AI) across various applications to enhance production processes. We cite two critical areas where AI can play a key role in industry: product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.
... Machine learning is a branch of AI that enables computers to learn from data in order to acquire decision making capabilities, and perform tasks without human intervention [33]. ML models are trained on a set of examples known as training data, in order to be able to make predictions on newer never before seen data. ...
Recent technological advancements such as IoT and Big Data have granted industries extensive access to data, opening up new opportunities for integrating Artificial Intelligence (AI) across various applications to enhance production processes. We can cite two critical areas where AI can play a key role in industry such as product quality control and predictive maintenance. This paper presents a survey of AI applications in the domain of Industry 4.0, with a specific focus on product quality control and predictive maintenance. Experiments were conducted using two datasets, incorporating different machine learning and deep learning models from the literature. Furthermore, this paper provides an overview of the AI solution development approach for product quality control and predictive maintenance. This approach includes several key steps, such as data collection, data analysis, model development, model explanation, and model deployment.
... These are produced utilising predictive deep learning algorithms that have examined both what you are now looking for and what you have bought in the past in order to make suggestions for other goods you may need. Additional applications of artificial intelligence include adding colour to black-and-white photographs, identifying people, detecting illnesses, and functioning as virtual assistants (Nozari & Sadeghi, 2021). ...
... These are produced using predictive deep learning algorithms that have examined both what you are now looking for and what you have bought in the past in order to make suggestions for other goods you may need. Additional applications of artificial intelligence include adding colour to black-and-white photographs, identifying people, detecting illnesses, and functioning as virtual assistants (Nozari & Sadeghi, 2021). Other uses include fraud prevention, language translation, chat bots and service bots, facial recognition, and text translation. ...
AI's rapid development has already affected the economy and society, and it might revolutionize how goods and services are manufactured and categorized. This affects the country's production, employment rate, and competitiveness. This research aims to determine how emotions (good and negative) impact deep learning technologies and company startup intentions. The research also examined how emotion recognition influences online entrepreneurship education and how deep learning and entrepreneurship are connected. In conclusion, the research is one of the pioneering efforts that provides a comprehensive grasp of deep learning and facial recognition in entrepreneurship. 78 of the 650 replies were insufficient, according to the statistics. Some individuals constantly picked 3, while others submitted blank questionnaires. The answer is inadequate. 572 surveys were utilized for the study. Positive emotions affect the urge to establish a company, according to the research. People who are generally happy may be able to develop their entrepreneurial expertise and be more driven to behave entrepreneurially. Positive and negative emotions have a significant impact on the deep learning technology and entrepreneurial intention connection. The findings imply that individuals who often experience pleasant emotions may be better able to improve their entrepreneurial cognition and, as a consequence, their drive to engage in entrepreneurial activity. The facial recognition model in entrepreneurship significantly moderates the relationship between deep learning technology and entrepreneurial intention (=0.262, p 0.001); hence, hypotheses 6, 7, and 8 are validated. This provides support for the hypothesized results, claiming that facial recognition plays a significant role in determining the relationship between deep learning technology and entrepreneurial intention. The results would be significant for university students, academicians, and policymakers as they seek to link practices and outcomes. The study is among the pioneers on issues related to deep learning mechanisms in eastern China.
... The use of artificial intelligence in various industries has increased. Currently, the potential of artificial intelligence is being exploited in various business sectors and operations [1][2][3] . Artificial intelligence helps design thinking of business systems and learns from data to gain insights without involving any human input. ...
Purpose: Artificial intelligence (AI) has the potential to transform many aspects of business operations. This technology can be used in various fields such as data analysis and demand forecasting, improving logistics and transportation routes, and identifying inefficient points in the supply chain. This research aims to identify the dimensions and characteristics of sustainable and intelligent supply chains based on artificial intelligence are investigated. Methodology: This article uses systemic review to deeply examine the literature review and summarize the findings in the field. Findings: Using AI in supply chain will lead to improved response to changes in demand, reduced delivery times, and lower costs, and will lead to sustainable development. The integration of environmental, social, and economic aspects is continuously influencing general management decisions and especially supply chain management and operations management. Therefore, organizations are rethinking and redefining the concept of operations management using the supply chain approach based on smart technologies. Originality/value: In this paper, the application of artificial intelligence in sustainable supply chain management has been investigated and the effects of this technology on this field have been investigated. Also, the issue of artificial intelligence in the supply chain and the need to use it by businesses is also discussed. In addition, the potential applications of artificial intelligence in the sustainable supply chain are also investigated and a conceptual framework is also presented for it.
... Machine learning techniques can be categorized into three main divisions: reinforcement learning, unsupervised learning, and supervised learning [28], [29]. Among these, supervised learning is commonly used in practical applications and relies on labelled datasets. ...
span lang="EN-US">Vehicular Ad hoc Networks (VANETs) play a crucial role in Intelligent Transportation Systems (ITS), enabling seamless communication between vehicles and other entities. VANETs provide a wide range of services, allowing vehicles to communicate with each other and with roadside infrastructure. With the increasing amount of data generated by VANETs, machine learning approaches have emerged as valuable tools to address complex challenges in this domain. This paper presents a comprehensive literature review on the application of machine learning in VANETs. The paper discusses the potential challenges and future research directions in the field, emphasizing the need for more accessible machine learning solutions for VANETs. This review emphasizes the significant role of machine learning approach in advancing the capabilities of VANETs and shaping the future of intelligent transportation systems.</span
... Machine learning algorithms are a subset of artificial intelligence that relies on mathematical models and is utilized for optimal decision-making based on training data. These algorithms are also employed in simulating various processes (Nozari et al. 2021). ...
In Iran, similar to other developing countries, groundwater quality has been seriously threatened. Therefore, this study aimed to apply Machine Learning Algorithms (MLAs) in Groundwater Quality Modeling (GQM) and determine the optimal algorithm using the Best-Worst Method (BWM) in Ardabil province, Iran. Groundwater quality parameters included calcium (Ca2+), magnesium (Mg2+), sodium (Na+), potassium (K+), chlorine (Cl-), sulfate (SO-4), Total Dissolved Solids (TDS), bicarbonate (HCO-), Electrical Conductivity (EC) and acidity (pH). In the following, seven MLAs, including Support Vector Regression (SVR), Random Forest (RF), Decision Tree Regressor (DTR), K-Nearest Neighbor (KNN), Naïve Bayes, Simple Linear Regression (SLR) and Support Vector Machine (SVM), were used in the Python programming language, and groundwater quality was modeled. Finally, BWM was used to validate the results of MLAs. The results of examining the error statistics in determining the optimal algorithm in groundwater quality modeling showed that RF algorithm with values of MAE=0.28, MSE=0.12, RMSE=0.35 and AUC=0.93 was selected as the most optimal MLA. Schoeller diagram also showed that various ion ratios, including Na+K, Ca2+, Mg2+, Cl- and HCO3+CO3, in most of the sampled points had upward average values. Based on the results of BWM method, it can be concluded that a great similarity was observed between the results of RF algorithm and the classification of BWM method. These results showed that more than 50% of the studied area had low quality based on hydro-chemical parameters of groundwater quality. The findings of this research can assist managers and planners in developing suitable management models and implementing appropriate strategies for the optimal exploitation of groundwater resources.
... With the entry of cloud computing technology in the ranking of the best technologies in the digital field in 2015, the Internet of Things, cloud computing and artificial intelligence as complementary technologies have always been among the top three technologies in the ranking of the best digital technologies in terms of Have been patent registration (M. ) (Ghahremani nahr et al., 2021a (Sabet, 2021). ...
The Internet of Things is a new perspective on the information technology industry that encompasses all technical, social and economic concepts. Identifying priority application areas for this technology is one of the key points for its effective use. Governments also have a variety of tools for policy-making to support the development of this technology. Therefore, knowing which tool has a higher priority for support is a very important point that can not only prevent the loss of resources but also improve the speed of development. In this research, using the opinion of experts and using the TOPSIS method, an attempt has been made to identify the priority of IoT application areas as well as the priority of government support policy tools in these areas. The results of this research have shown that the important areas in this field respectively are Smart cities, Factories and industries, Shipping, Healthcare, Supply chain management, Buildings and houses and finally Agriculture and animal husbandry. Also Government policy tools respectively, in order of priority, are Financial and Investment Incentives, Flexible regulatory, Tax Exemption, Deploying IOT applications in E-government, Standards and Accreditation, Technology Infrastructure, Macro Policies, Application Infrastructure, Cybersecurity Regulation, Privacy Regulation.
