IAES International Journal of Artificial Intelligence (IJ-AI)
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Improved unmanned aerial vehicle control for efficient obstacle detection and data protection
Get PDFThe article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields
An ensemble-based approach for effective distributed denial of service attack detection in software defined networking
Get PDFSoftware defined networking (SDN) is a network framework that aims to redefine network characteristics through the programmability of network components, faster and larger network monitoring, centralized network operation, and effective detection of fraudulent traffic and special malfunctions. However, SDN networks are vulnerable to security threats that can cause complete network failure. To address this issue, in this paper, machine learning techniques are suggested for the swift detection of attacks. Various methods for detecting distributed denial of service (DDoS) attacks are evaluated, and the study identifies the most precise method for categorizing such attacks within a SDN network. The results indicate that the proposed system achieves high accuracy in detecting DDoS attacks, with ensemble learning achieving 99% accuracy. This indicates a remarkable improvement percentage in comparison to the approaches of decision tree (DT), k-nearest neighbors (KNN), and support vector machine (SVM)
Under-sampling technique for imbalanced data using minimum sum of euclidean distance in principal component subset
Get PDFImbalanced datasets are characterized by a substantially smaller number of data points in the minority class compared to the majority class. This imbalance often leads to poor predictive performance of classification models when applied in real-world scenarios. There are three main approaches to handle imbalanced data: over-sampling, under-sampling, and hybrid approach. The over-sampling methods duplicate or synthesize data in the minority class. On the other hand, the under-sampling methods remove majority class data. Hybrid methods combine the noise-removing benefits of under-sampling the majority class with the synthetic minority class creation process of over-sampling. In this research, we applied principal component (PC) analysis, which is normally used for dimensionality reduction, to reduce the amount of majority class data. The proposed method was compared with eight state-of-the-art under-sampling methods across three different classification models: support vector machine, random forest, and AdaBoost. In the experiment, conducted on 35 datasets, the proposed method had higher average values for sensitivity, G-mean, the Matthews correlation coefficient (MCC), and receiver operating characteristic curve (ROC curve) compared to the other under-sampling methods.
Optimizer algorithms and convolutional neural networks for text classification
Get PDFLately, deep learning has improved the algorithms and the architectures of several natural language processing (NLP) tasks. In spite of that, the performance of any deep learning model is widely impacted by the used optimizer algorithm; which allows updating the model parameters, finding the optimal weights, and minimizing the value of the loss function. Thus, this paper proposes a new convolutional neural network (CNN) architecture for text classification (TC) and sentiment analysis and uses it with various optimizer algorithms in the literature. Actually, in NLP, and particularly for sentiment classification concerns, the need for more empirical experiments increases the probability of selecting the pertinent optimizer. Hence, we have evaluated various optimizers on three types of text review datasets: small, medium, and large. Thereby, we examined the optimizers regarding the data amount and we have implemented our CNN model on three different sentiment analysis datasets so as to binary label text reviews. The experimental results illustrate that the adaptive optimization algorithms Adam and root mean square propagation (RMSprop) have surpassed the other optimizers. Moreover, our best CNN model which employed the RMSprop optimizer has achieved 90.48% accuracy and surpassed the state-of-the-art CNN models for binary sentiment classification problems.
Driver inattention detection system using multi-task cascaded convolutional networks
Get PDFDriver inattention has emerged as a critical concern impacting road safety, resulting in an alarming surge in accidents and fatalities. This research introduces a novel system for detecting inattention, structured across six levels: perception, facial feature extraction, tracking driver face, and driver secondary task using pre-trained deep learning models, inattention detection, risk estimation, and alert. The system is based on image processing captured from two strategically positioned cameras that simultaneously capture the driver’s activities while driving and their facial expressions. The second contribution concerns the driver facial features extraction using multi-task cascaded convolutional networks (MTCNN), and it is comparison with the histogram of gradient (HOG)-based frontal face detector, and haar feature based cascade classifier. The algorithms were compared based on their runtime efficiency, robustness in handling varying lighting conditions, and various head movements. The MTCNN achieves high performance, reaching accuracy levels ranging from 96.4% to 99.5% on two datasets including realistic driving scenarios: the DrivFace dataset and, the driver drowsiness dataset. The comparative analysis sheds light on the strengths and weaknesses of each algorithm, providing valuable insights for selecting the most suitable face detection algorithm to use in our system
Enhancing the smart parking assignment system through constraints optimization
Get PDFTraffic in big cities has become a black spot for drivers. One of the major concerns is the parking problem that hinders urban mobility, particularly in big cities and other congested areas. This leads to an increase in accidents, a big consumption of fuel, and a spectacular augmentation of pollution. In this paper, we introduce a parking assignment system grounded in constraint programming to address the growing demand for efficient parking management in smart cities. Our system is designed to meet the requirements of groups of drivers seeking to reserve parking spaces simultaneously within the same period and geographical area. This entails imposing constraints on the desired parking type, including considerations such as walking and driving distances, parking costs, and availability. Within the scope of this study, we propose two formulations: constraint satisfaction programming (CSP) with an objective function and mixed-integer linear programming (MILP). Evaluation shows Choco, a CSP solver, is effective for smaller requests but slower for larger ones, while MILP excels for larger scenarios. Both solvers produce high-quality solutions meeting real-time response requirements. Our research offers innovative solutions for smart city management, considering parking type preferences, costs, and availability. We contribute significantly to parking space assignment methodologies, aiming to alleviate the time-consuming search for parking, reduce accidents, fuel consumption, and pollution
Real-time anomaly detection in electric motor operation noise
Get PDFAnomaly detection plays a very important role in many fields to identify abnormalities occurring in the system earlier. This study proposes a new abnormality detection solution for 3-phase electric motors based on their working noise. Normal and abnormal operating noise data sets for an electric motor were acquired in the laboratory. These datasets are converted into the corresponding two-dimensional gray spectrogram image sets. The normal set is used to train the autoencoder (AE) model to find the abnormality evaluation threshold. This threshold is validated again with anomalous data sets. The trained AE is then quantized to be installed on a system consisting of two duo-core microcontroller units (MCUs) for real-time testing. Free real-time operating system (FreeRTOS), a real-time operating system, is used to schedule tasks on the system. Experimental results show that the designed anomaly detector can accurately detect over 99% of abnormal events. The system can communicate with a supervisory control and data acquisition (SCADA) application running on the S7-1200 programmable logic controller (PLC) platform using the Modbus transmission control protocol (TCP) protocol. The SCADA application can continuously record evaluated results from the system and adjust abnormal thresholds for the system directly on the human-machine interface (HMI) screen
Evaluating sentiment analysis and word embedding techniques on Brexit
Get PDFIn this study, we investigate the effectiveness of pre-trained word embeddings for sentiment analysis on a real-world topic, namely Brexit. We compare the performance of several popular word embedding models such global vectors for word representation (GloVe), FastText, word to vec (word2vec), and embeddings from language models (ELMo) on a dataset of tweets related to Brexit and evaluate their ability to classify the sentiment of the tweets as positive, negative, or neutral. We find that pre-trained word embeddings provide useful features for sentiment analysis and can significantly improve the performance of machine learning models. We also discuss the challenges and limitations of applying these models to complex, real-world texts such as those related to Brexit.
Parallel multivariate deep learning models for time-series prediction: A comparative analysis in Asian stock markets
Get PDFThis study investigates deep learning models for financial data prediction and examines whether the architecture of a deep learning model and time-series data properties affect prediction accuracy. Comparing the performance of convolutional neural network (CNN), long short-term memory (LSTM), Stacked-LSTM, CNN-LSTM, and convolutional LSTM (ConvLSTM) when used as a prediction approach to a collection of financial time-series data is the main methodology of this study. In this instance, only those deep learning architectures that can predict multivariate time-series data sets in parallel are considered. This research uses the daily movements of 4 (four) Asian stock market indices from 1 January 2020 to 31 December 2020. Using data from the early phase of the spread of the Covid-19 pandemic that has created worldwide economic turmoil is intended to validate the performance of the analyzed deep learning models. Experiment results and analytical findings indicate that there is no superior deep learning model that consistently makes the most accurate predictions for all states' financial data. In addition, a single deep learning model tends to provide more accurate predictions for more stable time-series data, but the hybrid model is preferred for more chaotic time-series data
Intelligent fuzzy system to assess the risk of type 2 diabetes and diagnosis in marginalized regions
Get PDFDiabetes is one of the leading causes of death in the world and continues to rise. Type 2 diabetes mellitus is a life-threatening chronic degenerative disease if not appropriately controlled; risk factors and ineffective diagnosis continue to increase its prevalence. This study proposes an intelligent fuzzy system to make a diagnosis and predict the risk of developing type 2 diabetes mellitus. The system consists of two models; the R-T2DM model estimates if a person is at risk of developing type 2 diabetes mellitus. The DT2DM model is based on two systems: the symptomatology system estimates the level of symptoms the patient has, and the diagnosis system diagnoses type 2 diabetes mellitus. The results of this research were compared with those estimated by the team of doctors, and it was observed that the R-T2DM model obtained a success rate of 90.3%. The D-T2DM model got a success rate of 88.3% for the symptomatology system and 95.5% for the diagnosis system. The model developed in this study is focused on being applied in economically marginalized geographic areas of Mexico to improve the patient's quality of life