Methodologies and Applications of Artificial Intelligence in Systems Engineering

Urinary tract infection (UTI) is a type of health disorder, an infection in the urinary glands mainly caused by bacteria. Currently, conventional early detection methods that have been established involve rapid dipstick strip test and urine culture analysis, which have suboptimal accuracy and effectiveness. Several retrospective studies regarding UTI bacteria classification have shown promising results, but still have limitations regarding prediction accuracy and technical simplicity. This study aims to implement a method based on artificial intelligence (AI) in classifying images of bacteria that causes UTIs. Eight artificial intelligence methods based on deep neural networks were used in the study; the models were evaluated and compared based on the prediction's effectiveness and accuracy. This study also seeks to create the easiest method of classifying bacteria causing UTIs using a computer-based application with the best obtained AI-based model. The best training results using an intelligent approach placed DenseNet201 as the method with the highest accuracy (83.99%). Then, the output model was used as a knowledge reference for the designed computer-based application. Real-time prediction results will appear in the application window.

Section: Introductionmentioning

confidence: 99%

Urinary Tract Infection Bacteria Classification: Artificial Intelligence-based Medical Application

Fadlil,

Fathurrahman,

Lin

et al. 2023

“…Two crucial parameters efficient performance and optimal energy management require thorough and targeted research. The controller should deliver the fastest possible speed while consuming the least amount of energy [26]- [31]. Due to the fluctuating road conditions, the EV with a wind turbine is very nonlinear, time-independent, and unreliable.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Controller with PID, FOPID, and NPID Compensators for Tracking Control of Electric – Wind Vehicle

Shamseldin

2022

This paper presents a new combination between the Model Reference Adaptive Control (MRAC) with several types of PID’s controllers (PID, Fractional order PID (FOPID), and Nonlinear PID (NPID)) optimized using a new Covid-19 algorithm. The proposed control techniques had been applied on a new model for an electric-wind vehicle, which can catch the wind that blows in the opposite direction of a moving vehicle to receive wind; a wind turbine is installed on the vehicle’s front. The generator converts wind energy into electricity and stores it into a backup battery to switch it when the primary battery is empty. The simulation results prove that the new model of electric–wind vehicles will save power and allow the vehicle to continue moving while the other battery charges. In addition, a comparative study between different types of control algorithms had been developed and investigated to improve the vehicle dynamic response. The comparison shows that the MRAC with the NPID compensator can absorb the nonlinearity (air resistance and wheel friction) where it has a minimum overshoot, rise time, and settling time (35 seconds) among other control techniques compensators (PID and FOPID).

“…Artificial Intelligence (AI) is a suitable algorithm for robot vision if we aim for an intelligent system. Since AI focuses on developing computer software to make computing tasks smarter, AI research applications' ultimate focus is to develop computational approaches for intelligent behaviour [5]. The demand for an intelligent system with real-time control in manufacturing processes and productions is increasing rapidly [6].…”

Section: Introductionmentioning

confidence: 99%

Disease Detection of Solanaceous Crops Using Deep Learning for Robot Vision

Hidayah

Radzi

Razak

et al. 2022

Traditionally, the farmers manage the crops from the early growth stage until the mature harvest stage by manually identifying and monitoring plant diseases, nutrient deficiencies, controlled irrigation, and controlled fertilizers and pesticides. Even the farmers have difficulty detecting crop diseases using their naked eyes due to several similar crop diseases. Identifying the correct diseases is crucial since it can improve the quality and quantity of crop production. With the advent of Artificial Intelligence (AI) technology, all crop-managing tasks can be automated using a robot that mimics a farmer's ability. However, designing a robot with human capability, especially in detecting the crop's diseases in real-time, is another challenge to consider. Other research works are focusing on improving the mean average precision and the best result reported so far is 93% of mean Average Precision (mAP) by YOLOv5. This paper focuses on object detection of the Convolutional Neural Network (CNN) architecture-based to detect the disease of solanaceous crops for robot vision. This study's contribution involved reporting the developmental specifics and a suggested solution for issues that appear along with the conducted study. In addition, the output of this study is expected to become the algorithm of the robot's vision. This study uses images of four crops (tomato, potato, eggplant, and pepper), including 23 classes of healthy and diseased crops infected on the leaf and fruits. The dataset utilized combines the public dataset (PlantVillage) and self-collected samples. The total dataset of all 23 classes is 16580 images divided into three parts: training set, validation set, and testing set. The dataset used for training is 88% of the total dataset (15000 images), 8% of the dataset performed a validation process (1400 images), and the rest of the 4% dataset is for the test process (699 images). The performances of YOLOv5 were more robust in terms of 94.2% mAP, and the speed was slightly faster than Scaled-YOLOv4. This object detection-based approach has proven to be a promising solution in efficiently detecting crop disease in real-time.