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In the agriculture and healthcare industries, AI has been deployed to achieve better crop production, disease prediction, continuous monitoring, efficient supply chain management, improved operational efficiency, and reduced water waste, with the main goal of designing standard, reliable product quality control methods and the search for new ways o...
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... general, machine learning algorithms are divided into two groups: generative models and discriminative models. Various supervised machine learning techniques are employed in this study to analyze activity recognition. Fig. 1 depicts the relationship between artificial intelligence and machine learning. As a result, Nave Bayes, kNN, and SVM exhibit a higher capacity to properly recognize the activities. The Nave Bayes method is a generative model, whereas kNN and SVM (Radial Bias and Polynomial) are discriminative ...
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... The first assumption made by SVMs is that the dataset is linear and hence constructs a hyperplane. The use of the kernel is thereafter applied to reduce the error in the dataset generated by the assumptions of SVMs [38]. The Alternating DT is an ML technique that aims to improve and generalize the decision tree classification algorithm. ...
... The implementation of Generative AI has increased the efficiency of investment portfolio management (Kalayci et al., 2020;Pallathadka, Mustafa, et al., 2023;Pallathadka, Ramirez-Asis, et al., 2023). ...
The study investigates the convergence of digital transformation, artificial intelligence (AI), and Islamic finance. In particular, it examines the ethical consequences that may arise from the integration of Generative AI in the sustainable development of Islamic financial services and products. This research fills a void in the current body of knowledge by examining the ethical consequences of generative AI in the context of Islamic finance. Using an interdisciplinary framework that integrates Islamic finance and technological ethics, the study seeks to make scientific and practical contributions. At the intersection of AI technology and Islamic finance, it is anticipated that new theories will emerge, as well as ethical principles that will serve as a guide for technology developers, policymakers, and Islamic financial institutions. The study has the potential to lead in creating a sustainable and inclusive Islamic finance ecosystem by ethically integrating Generative AI.
... According to [2], ML-enabled precision agriculture can offer benefits such as increased yield, reduced input cost, and improved environmental sustainability. Also, [8] discuss in his article that integrating ML can transform agriculture by fostering data-driven decisions and optimizing farming practices. Guided by these insights, our research aims to investigate diverse ML algorithms to assess their compatibility and efficacy within the BambooShoots.AI application. ...
This research initiated a comprehensive investigation to determine the most suitable Machine Learning (ML) algorithms for bamboshoots.AI application. Convolutional neural networks (CNNs) and support vector machines (SVMs) were cited as two essential ML techniques in a comprehensive review of ten papers. To further assess these two algorithms in the context of the Bambooshoots.ai application, an experimental analysis was also carried out. The CNN model had been trained over 25 epochs with 8 batches of data per epoch, demonstrating a consistent increase in accuracy, and reached 97.94% by the end of training. In contrast, the SVM model provided an accuracy of approximately 57.14%. The experimental results indicated that the CNN model had been better at classifying bamboo shoot images, thus making it the preferred choice for the BambooShoots.AI application. The significant difference in accuracy between the two models, as well as the consistent performance of CNNs in image-based tasks, informed this decision. The results of this research were thrilling because they showed that machine learning could be used to help understand and manage bamboo shoots. This could have been a big help to farmers and could have led to new ways of doing things in agriculture.
... One of the main goals of this study is to find the most important factors that lead to trust, credibility, and community involvement in Saudi Arabia's healthcare system. By looking closely at the part that ethical practices, good governance structures, and communitycentered initiatives play, the study aims to find the most important things that affect how people in Saudi Arabia think about hospitals and how well they do in the long run (Pallathadka et al., 2023). ...
In the Kingdom of Saudi Arabia, the healthcare sector has been evolving rapidly, presenting a dynamic landscape where the pursuit of financial viability intersects with the imperative of social responsibility. This study investigates the intricate relationship between profitability and social responsibility and their collective impact on achieving institutional excellence in government and private hospitals. Employing an exploratory approach, a comprehensive analysis of qualitative and quantitative data was conducted to discern the patterns and trends shaping the healthcare landscape in the context of the Saudi Arabian society. The study reveals that while financial viability remains a crucial aspect for the sustenance and growth of hospitals, social responsibility has emerged as a key determinant in building trust and establishing long-term credibility within the community.
... The use of ML techniques in this study allowed us to gain new insights into the acute effects of low-dose insecticide exposure on medfly, providing valuable information for understanding the risks of pesticide abuse on non-target organisms and the environment. Our work, therefore, substantially contributes to biomedical research as it integrates the principles of One Health and EcoHealth, 49 which highlight the interconnectedness of human health and the environment. Indeed, by investigating the sublethal effects of biocides on non-human organisms, we can better understand and prevent their potential impact on human health and ecosystems. ...
Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.
... Artificial intelligence models and digital soil mapping have been used in the past to predict soil fertility, providing a decision-making tool capable of predicting the most suited crops to plant based on soil pH, soil nutrients, soil moisture, environmental variables, and other factors [13]. For precision farming, machine learning and deep learning algorithms are the most frequently used types of artificial intelligence [14]. The lack of widespread adoption of digital soil mapping and other digital innovative solutions is a barrier to high productivity in agricultural systems in developing countries, despite the fact that its use has been on the rise internationally. ...
Agriculture is essential to a flourishing economy. Although soil is essential for sustainable food production, its quality can decline as cultivation becomes more intensive and demand increases. The importance of healthy soil cannot be overstated, as a lack of nutrients can significantly lower crop yield. Smart soil prediction and digital soil mapping offer accurate data on soil nutrient distribution needed for precision agriculture. Machine learning techniques are now driving intelligent soil prediction systems. This article provides a comprehensive analysis of the use of machine learning in predicting soil qualities. The components and qualities of soil, the prediction of soil parameters, the existing soil dataset, the soil map, the effect of soil nutrients on crop growth, as well as the soil information system, are the key subjects under inquiry. Smart agriculture, as exemplified by this study, can improve food quality and productivity.
... E-commerce and product recommendations [8,9] Image, speech, and pattern recognition [8,9] User behavior analytics and context-aware smartphone applications [8,9] Healthcare services [10][11][12] Traffic prediction and transportation [8,13] Internet of things (IoT) and smart cities [13] Cybersecurity and threat intelligence [14] Natural language processing and sentiment analysis [15] Sustainable agriculture [16] Industrial applications [17] 1. 1 ...
... Consequently, the type of data obtained determines the machine learning algorithm. From this point of view, the four categories listed below can be defined [8][9][10]: ...
... The goal, also referred to as the function, is the output of the model and determines the type of model to be used. The different types of ML can be defined as follows [8][9][10]: ...
The success of machine learning (ML) techniques in the formerly difficult areas of data analysis and pattern extraction has led to their widespread incorporation into various aspects of human life. This success is due in part to the increasing computational power of computers and in part to the improved ability of ML algorithms to process large amounts of data in various forms. Despite these improvements, certain issues, such as privacy, continue to hinder the development of this field. In this context, a privacy-preserving, distributed, and collaborative machine learning technique called federated learning (FL) has emerged. The core idea of this technique is that, unlike traditional machine learning, user data is not collected on a central server. Nevertheless, models are sent to clients to be trained locally, and then only the models themselves, without associated data, are sent back to the server to combine the different locally trained models into a single global model. In this respect, the aggregation algorithms play a crucial role in the federated learning process, as they are responsible for integrating the knowledge of the participating clients, by integrating the locally trained models to train a global one. To this end, this paper explores and investigates several federated learning aggregation strategies and algorithms. At the beginning, a brief summary of federated learning is given so that the context of an aggregation algorithm within a FL system can be understood. This is followed by an explanation of aggregation strategies and a discussion of current aggregation algorithms implementations, highlighting the unique value that each brings to the knowledge. Finally, limitations and possible future directions are described to help future researchers determine the best place to begin their own investigations.
... Machine learning techniques are widely used in applications that support agricultural activities. By providing useful applications and crop insights, machine learning techniques benefited farmers by minimizing agricultural losses [9][10]. ...
AI impacts surrounding human life, such as the economy, health, education, and agricultural production; however, the crop prices in the harvest season are still on manual calculation, which causes doubts about accuracy. In this study, an image-based approach is proposed to help farmers calculate rice prices more accurately. YOLOv5 is used to detect and extract the scales in the images taken from the harvesting of rice crops. Then, various image processing techniques, such as brightness balance, background removal, etc., are compiled to determine the needle position and number on the extracted scale. Lastly, geometric transformations are proposed to calculate the weight. A real dataset of 709 images is used for the experiment. The proposed method achieves good results in terms of mAP@0.5 at 0.995, mAP@[0.5:0.95] at 0.830 for scale detection, and MAE at 3.7 for weight calculation.
... Healthcare services [11][12][13]; • Image, speech and pattern recognition [14,15]; • Internet of Things (IoT) and smart cities [14,16]; • Cybersecurity and threat intelligence [17]; • Natural language processing and sentiment analysis [18]; • User behavior analytics and context-aware smartphone applications [14,15]; • E-commerce and product recommendations [14,15]; • Sustainable agriculture [19]; ...
Machine Learning (ML) and Deep Learning (DL) are derivatives of Artificial Intelligence (AI) that have already demonstrated their effectiveness in a variety of domains, including healthcare, where they are now routinely integrated into patients’ daily activities. On the other hand, data heterogeneity has long been a key obstacle in AI, ML and DL. Here, Multimodal Machine Learning (Multimodal ML) has emerged as a method that enables the training of complex ML and DL models that use heterogeneous data in their learning process. In addition, Multimodal ML enables the integration of multiple models in the search for a single, comprehensive solution to a complex problem. In this review, the technical aspects of Multimodal ML are discussed, including a definition of the technology and its technical underpinnings, especially data fusion. It also outlines the differences between this technology and others, such as Ensemble Learning, as well as the various workflows that can be followed in Multimodal ML. In addition, this article examines in depth the use of Multimodal ML in the detection and prediction of Cardiovascular Diseases, highlighting the results obtained so far and the possible starting points for improving its use in the aforementioned field. Finally, a number of the most common problems hindering the development of this technology and potential solutions that could be pursued in future studies are outlined.
... In recent years, Machine Learning (ML) algorithms have successfully solved various tasks, reaching state-of-theart in multiple areas [1][2][3][4][5][6][7]. This is not only due to the development of new algorithms (more powerful and prominent), but also the selection of good hyperparameters contributed to this advance. ...
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.
