<abstract>
<title style='display:none'>Abstract</title>
The paper presents an approach for controlling a linefollowing robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following robot on a running track with different configurations is then evaluated. The results show that the line-following robot responded more efficiently with an artificial neural network control algorithm than with a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following robot to adapt to environmental conditions and overcome obstacles on the track more effectively.
</abstract>

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

<abstract>
<title style='display:none'>Abstract</title>
This paper presents a method for automatically measuring plants’ heights in indoor hydroponic plantations using the OpenCV library and the Python programming language. Using the elaborated algorithm and Raspberry Pi-driven system with an external camera, the growth process of multiple pak choi cabbages (Brassica rapa L. subsp. Chinensis) was observed. The main aim and novelty of the presented research is the elaborated algorithm, which allows for observing the plants’ height in hydroponic stations, where reflective foil is used. Based on the pictures of the hydroponic plantation, the bases of the plants, their reflections, and plants themselves were separated. Finally, the algorithm was used for estimating the plants’ heights. The achieved results were then compared to the results obtained manually. With the help of a ML (Machine Learning) approach, the algorithm will be used in future research to optimize the plants’ growth in indoor hydroponic plantations.
</abstract>

Application of the OpenCV library in indoor hydroponic plantations for automatic height assessment of plants

<abstract>
<title style='display:none'>Abstract</title>
In this paper, the processing of the data of a 3D light detection and distance measurement (LiDAR) sensor mounted on a mobile robot is demonstrated, introducing an innovative methodology to manage the data and extract useful information. The LiDAR sensor is placed on a mobile robot which has a modular design that permits the easy change of the number of wheels, was designed to travel through several environments, and saves energy by changing the number and arrangement of the wheels in each environment. In addition, the robot can recognize landmarks in a structured environment by using a classification technique on each frame acquired by the LiDAR. Furthermore, considering the experimental tests, a new simple algorithm based on the LiDAR data processing together with the inertial data (IMU sensor) through a Kalman filter is proposed to characterize the robot’s pose by the surrounding environment with fixed landmarks. Finally, the limits of the proposed algorithm have been analyzed, highlighting new improvements in the future prospective development for permitting autonomous navigation and environment perception with a simple, modular, and low-cost device.
</abstract>

Processing of LiDAR and IMU Data for Target Detection and Odometry of a Mobile Robot

<abstract>
<title style='display:none'>Abstract</title>
A low-cost small-scale autonomous vehicle refers to a self-driving vehicle that is designed to be affordable and suitable for smaller applications or specific purposes. In this study, the firefly algorithm was utilized to address obstacle avoidance challenges in the presence of dynamic or statically positioned uncertain obstacles. The autonomous vehicle successfully reached the intended destination, demonstrating a satisfactory level of accuracy. Regardless of the starting point, the vehicle arrived at the predetermined position within an area measuring 5 meters in diameter. The achievement of such results can be attributed to the cost-effective selection of sensors, utilization of a simple algorithm, and the implementation of a moderately powered processor and circuit components.
</abstract>

Low-Cost Small-Scale Autonomous Vehicle

<abstract>
<title style='display:none'>Abstract</title>
With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.
</abstract>

AI-Based Yolo V4 Intelligent Traffic Light Control System

<abstract>
<title style='display:none'>Abstract</title>
The number of automobiles on the road grows in lockstep with the advancement of vehicle manufacturing. Road accidents appear to be on the rise, owing to this growing proliferation of vehicles. Accidents frequently occur in our daily lives, and are the top ten causes of mortality from injuries globally. It is now an important component of the worldwide public health burden. Every year, an estimated 1.2 million people are killed in car accidents. Driver drowsiness and weariness are major contributors to traffic accidents this study relies on computer software and photographs, as well as a Convolutional Neural Network (CNN), to assess whether a motorist is tired. The Driver Drowsiness System is built on the Multi-Layer Feed-Forward Network concept CNN was created using around 7,000 photos of eyes in both sleepiness and non-drowsiness phases with various face layouts. These photos were divided into two datasets: training (80% of the images) and testing (20% of the images). For training purposes, the pictures in the training dataset are fed into the network. To decrease information loss as much as feasible, backpropagation techniques and optimizers are applied. We developed an algorithm to calculate ROI as well as track and evaluate motor and visual impacts.
</abstract>

Machine Learning and Artificial Intelligence Techniques for Detecting Driver Drowsiness

<abstract>
<title style='display:none'>Abstract</title>
We propose a Computer Vision and Machine Learning equipped model that secures the ATM from fraudulent activities by leveraging the use of Haar cascade (HRC) and Local Binary Pattern Histogram (LBPH) classifier for face detection and recognition correspondingly, which in turn detect fraud by utilizing features, like PIN and face recognition, help to identify and authenticate the user by checking with the trained dataset and trigger real-time alert mail if the user turns out to be unauthorized also. It does not allow them to log in into the machine, which resolves the ATM security issue. this system is evaluated on the dataset of real-world ATM camera feeds, which shows an accuracy of 90%. It can effectively detect many frauds, including identity theft and unauthorized access which makes it even more reliable.
</abstract>

Unlocking the Future of Secure Automatic Machines: Leveraging Facereg with HRC &amp; LBPH

<abstract>
<title style='display:none'>Abstract</title>
The paper discusses the threat of decommissioning to the thermal power plant (TPP) heat exchanger tubes because of erosion and develops a computer-integrated control system (CICS) for the process of distribution of steam coal flows with different indicators of abrasive materials content, which is based on fuzzy logic.
The problem of rapid decommissioning of TPP heat exchangers, particularly abrasive damage to furnace screen tubes, economizer, superheater, etc. This may indicate a discrepancy between the expected fuel ash content and the actual one, as well as a high content of abrasive impurities in steam coal.
The work aims to develop a CICS of the wear resistance of the heat exchange surface of a steam boiler of a coal-fired power plant by measuring and fuzzy control of the content of abrasive impurities in steam coal. The problems of damage to the equipment of the TPP boiler are investigated, and a system for controlling the wear resistance of the surface by automatic fuzzy control of the quality of coal is developed. The results were investigated during coal preparation and combustion in the furnace of a thermal power plant to investigate the effectiveness of the proposed fuzzy controller. The model results confirm the feasibility of the fuzzy control method for the system with different coal quality parameters.
</abstract>

The Coal Quality CICS That Increases the Wear Resistance of Heat Exchanger Tubes

<abstract>
<title style='display:none'>Abstract</title>
Socio-cognitive computing is a paradigm developed for the last several years in our research group. It consists of introducing mechanisms inspired by inter-individual learning and cognition into metaheuristics. Different versions of the paradigm have been successfully applied in hybridizing Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithms, Differential Evolution, and Evolutionary Multi-agent System (EMAS) metaheuristics. In this paper, we have followed our previous experiences in order to propose a novel mutation based on socio-cognitive mechanism and test it based on Evolution Strategy (ES). The newly constructed versions were applied to popular benchmarks and compared with their reference versions.
</abstract>

(<italic>μ</italic> +<italic>λ</italic>) Evolution Strategy with Socio-Cognitive Mutation

<abstract>
<title style='display:none'>Abstract</title>
Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with high dimensionality problems to select the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system.
</abstract>

Using Reinforcement Learning to Select an Optimal Feature Set

Publication Ethics and Malpractice Statement

Editorial Policy

Peer Review Process

Instruction for Authors

Open Access License