Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

doi:10.30534/ijatcse/2020/73952020

Cited by 13 publications

(5 citation statements)

References 52 publications

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“…It took a lot of new technology to incorporate this application with an internet connection, such as sensors and Arduino, like viewpoints, on the one hand, it would be very important to implement this application that allows this device the ability to be controlled remotely. To improve our results we intend to integrate hybrid methods such as genetic algorithms [28]- [30]. We can use drones in agriculture wich their multifunction in areas with high risk of infection can ensure easy and safe distant interventions [31].…”

Section: Resultsmentioning

confidence: 99%

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Amassmir

Tkatek

Abdoun

et al. 2022

IJEECS

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This paper proposes a comparison of three machine learning algorithms for a better intelligent irrigation system based on internet of things (IoT) for differents products. This work's major contribution is to specify the most accurate algorithm among the three machine learning algorithms (k-nearest neighbors (KNN), support vector machine (SVM), artificial neural network (ANN)). This is achieved by collecting irrigation data of a specific products and split it into training data and test data then compare the accuracy of the three algorithms. To evaluate the performance of our algorithm we built a system of IoT devices. The temperature and humidity sensors are installed in the field interact with the Arduino microcontroller. The Arduino is connected to Raspberry Pi3, which holds the machine learning algorithm. It turned out to be ANN algorithm is the most accurate for such system of irrigation. The ANN algorithm is the best choice for an intelligent system to minimize water loss for some products.

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Section: Resultsmentioning

confidence: 99%

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Amassmir

Tkatek

Abdoun

et al. 2022

IJEECS

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“…It required a lot of new technology to integrate this application with an internet connection, such as sensors and Arduino, such views, on the one hand, it would be extremely crucial to create an application that allows this item to be operated remotely. We aim to improve our results by utilizing a combination of methods, including genetic algorithms [32]- [34]. The versatility of drones makes them a valuable tool for agricultural purposes, particularly in areas where infection risks are high, as they allow for efficient and safe remote interventions [35].…”

Section: Figure 2 Schema Of Iot Implantationmentioning

confidence: 99%

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region

Tkatek,

Amassmir,

Belmzoukia

et al. 2023

IJECE

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Given the influence of several factors, including weather, soils, land management, genotypes, and the severity of pests and diseases, prescribing adequate nutrient levels is difficult. A potato’s performance can be predicted using machine learning techniques in cases when there is enough data. This study aimed to develop a highly precise model for determining the optimal levels of nitrogen, phosphorus, and potassium required to achieve both high-quality and high-yield potato crops, taking into account the impact of various environmental factors such as weather, soil type, and land management practices. We used 900 field experiments from Kaggle as part of a data set. We developed, evaluated, and compared prediction models of k-nearest neighbor (KNN), linear support vector machine (SVM), naive Bayes (NB) classifier, decision tree (DT) regressor, random forest (RF) regressor, and eXtreme gradient boosting (XGBoost). We used measures such as mean average error (MAE), mean squared error (MSE), R-Squared (RS), and R2Root mean squared error (RMSE) to describe the model’s mistakes and prediction capacity. It turned out that the XGBoost model has the greatest R2, MSE and MAE values. Overall, the XGBoost model outperforms the other machine learning models. In the end, we suggested a hardware implementation to help farmers in the field.

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“…Compared with the binocular system, the monocular vision system has lower costs and lower calibration difficulty. Still, it needs to project a target to determine the target's spatial position (Tkatek et al, 2020 ; Jammeli et al, 2021 ). To some extent, it increases the difficulty of the algorithm and increases the requirements for the environment.…”

Section: Research Model and Frameworkmentioning

confidence: 99%

Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

Liu

Ding

2022

Front. Neurorobot.

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The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.

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Artificial Intelligence for Improving the Optimization of NP-Hard Problems: A Review

Cited by 13 publications

References 52 publications

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

An intelligent irrigation system based on internet of things (IoT) to minimize water loss

Predictive fertilization models for potato crops using machine learning techniques in Moroccan Gharb region

Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence

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