Intelligent diagnosis system based on artificial intelligence models for predicting freezing of gait in Parkinson’s disease

IntroductionFreezing of gait (FoG) is a significant issue for those with Parkinson’s disease (PD) since it is a primary contributor to falls and is linked to a poor superiority of life. The underlying apparatus is still not understood; however, it is postulated that it is associated with cognitive disorders, namely impairments in executive and visuospatial functions. During episodes of FoG, patients may experience the risk of falling, which significantly effects their quality of life.MethodsThis research aims to systematically evaluate the effectiveness of machine learning approaches in accurately predicting a FoG event before it occurs. The system was tested using a dataset collected from the Kaggle repository and comprises 3D accelerometer data collected from the lower backs of people who suffer from episodes of FoG, a severe indication frequently realized in persons with Parkinson’s disease. Data were acquired by measuring acceleration from 65 patients and 20 healthy senior adults while they engaged in simulated daily life tasks. Of the total participants, 45 exhibited indications of FoG. This research utilizes seven machine learning methods, namely the decision tree, random forest, Knearest neighbors algorithm, LightGBM, and CatBoost models. The Gated Recurrent Unit (GRU)-Transformers and Longterm Recurrent Convolutional Networks (LRCN) models were applied to predict FoG. The construction and model parameters were planned to enhance performance by mitigating computational difficulty and evaluation duration.ResultsThe decision tree exhibited exceptional performance, achieving sensitivity rates of 91% in terms of accuracy, precision, recall, and F1- score metrics for the FoG, transition, and normal activity classes, respectively. It has been noted that the system has the capacity to anticipate FoG objectively and precisely. This system will be instrumental in advancing consideration in furthering the comprehension and handling of FoG

Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity

Rapid technological development has changed drastically the automotive industry. Network communication has improved, helping the vehicles transition from completely machine- to software-controlled technologies. The autonomous vehicle network is controlled by the controller area network (CAN) bus protocol. Nevertheless, the autonomous vehicle network still has issues and weaknesses concerning cybersecurity due to the complexity of data and traffic behaviors that benefit the unauthorized intrusion to a CAN bus and several types of attacks. Therefore, developing systems to rapidly detect message attacks in CAN is one of the biggest challenges. This study presents a high-performance system with an artificial intelligence approach that protects the vehicle network from cyber threats. The system secures the autonomous vehicle from intrusions by using deep learning approaches. The proposed security system was verified by using a real automatic vehicle network dataset, including spoofing, flood, replaying attacks, and benign packets. Preprocessing was applied to convert the categorical data into numerical. This dataset was processed by using the convolution neural network (CNN) and a hybrid network combining CNN and long short-term memory (CNN-LSTM) models to identify attack messages. The results revealed that the model achieved high performance, as evaluated by the metrics of precision, recall, F1 score, and accuracy. The proposed system achieved high accuracy (97.30%). Along with the empirical demonstration, the proposed system enhanced the detection and classification accuracy compared with the existing systems and was proven to have superior performance for real-time CAN bus security

Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices

With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment

Framework for Predicting and Modeling Stock Market Prices Based on Deep Learning Algorithms

The creation of trustworthy models of the equities market enables investors to make better-informed choices. A trading model may lessen the risks that are connected with investing and make it possible for traders to choose companies that offer the highest dividends. However, due to the high degree of correlation between stock prices, analysis of the stock market is made more difficult by batch processing approaches. The prediction of the stock market has entered a technologically advanced era with the advent of technological marvels such as global digitization. For this reason, artificial intelligence models have become very important due to the continuous increase in market capitalization. The novelty of the proposed study is the development of the robustness time series model based on deep leaning for forecasting future values of stock marketing. The primary purpose of this study was to develop an intelligent framework with the capability of predicting the direction in which stock market prices will move based on financial time series as inputs. Among the cutting-edge technologies, artificial intelligence has become the backbone of many different models that predict the direction of markets. In particular, deep learning strategies have been effective at forecasting market behavior. In this article, we propose a framework based on long short-term memory (LSTM) and a hybrid of a convolutional neural network (CNN-LSTM) with LSTM to predict the closing prices of Tesla, Inc. and Apple, Inc. These predictions were made using data collected over the past two years. The mean squared error (MSE), root mean squared error (RMSE), normalization root mean squared error (NRMSE), and Pearson’s correlation (R) measures were used in the computation of the findings of the deep learning stock prediction models. Between the two deep learning models, the CNN-LSTM model scored slightly better (Tesla: R-squared = 98.37%; Apple: R-squared = 99.48%). The CNN-LSTM model showed a superior performance compared with the single deep learning LSTM and existing systems in predicting stock market prices

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries

Artificial Intelligence Algorithm-Based Economic Denial of Sustainability Attack Detection Systems: Cloud Computing Environments

Cloud computing is currently the most cost-effective means of providing commercial and consumer IT services online. However, it is prone to new flaws. An economic denial of sustainability attack (EDoS) specifically leverages the pay-per-use paradigm in building up resource demands over time, culminating in unanticipated usage charges to the cloud customer. We present an effective approach to mitigating EDoS attacks in cloud computing. To mitigate such distributed attacks, methods for detecting them on different cloud computing smart grids have been suggested. These include hard-threshold, machine, and deep learning, support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF) tree algorithms, namely convolutional neural network (CNN), and long short-term memory (LSTM). These algorithms have greater accuracies and lower false alarm rates and are essential for improving the cloud computing service provider security system. The dataset of nine injection attacks for testing machine and deep learning algorithms was obtained from the Cyber Range Lab at the University of New South Wales (UNSW), Canberra. The experiments were conducted in two categories: binary classification, which included normal and attack datasets, and multi-classification, which included nine classes of attack data. The results of the proposed algorithms showed that the RF approach achieved accuracy of 98% with binary classification, whereas the SVM model achieved accuracy of 97.54% with multi-classification. Moreover, statistical analyses, such as mean square error (MSE), Pearson correlation coefficient (R), and the root mean square error (RMSE), were applied in evaluating the prediction errors between the input data and the prediction values from different machine and deep learning algorithms. The RF tree algorithm achieved a very low prediction level (MSE = 0.01465) and a correlation R2 (R squared) level of 92.02% with the binary classification dataset, whereas the algorithm attained an R2 level of 89.35% with a multi-classification dataset. The findings of the proposed system were compared with different existing EDoS attack detection systems. The proposed attack mitigation algorithms, which were developed based on artificial intelligence, outperformed the few existing systems. The goal of this research is to enable the detection and effective mitigation of EDoS attacks

Artificial Intelligence Algorithms for Detecting and Classifying MQTT Protocol Internet of Things Attacks

The Internet of Things (IoT) grew in popularity in recent years, becoming a crucial component of industrial, residential, and telecommunication applications, among others. This innovative idea promotes communication between physical components, such as sensors and actuators, to improve process flexibility and efficiency. Smart gadgets in IoT contexts interact using various message protocols. Message queuing telemetry transfer (MQTT) is a protocol that is used extensively in the IoT context to deliver sensor or event data. The aim of the proposed system is to create an intrusion detection system based on an artificial intelligence algorithm, which is becoming essential in the defense of the IoT networks against cybersecurity threats. This study proposes using a k-nearest neighbors (KNN) algorithm, linear discriminant analysis (LDA), a convolutional neural network (CNN), and a convolutional long short-term memory neural network (CNN-LSTM) to identify MQTT protocol IoT intrusions. A cybersecurity system based on artificial intelligence algorithms was examined and evaluated using a standard dataset retrieved from the Kaggle repository. The dataset was injected by five attacks, namely brute-force, flooding, malformed packet, SlowITe, and normal packets. The deep learning algorithm achieved high performance compared with the developing security system using machine learning algorithms. The performance accuracy of the KNN method was 80.82%, while the accuracy of the LDA algorithm was 76.60%. The CNN-LSTM model attained a high level of precision (98.94%) and is thus very effective at detecting intrusions in IoT settings

Developing Cybersecurity Systems Based on Machine Learning and Deep Learning Algorithms for Protecting Food Security Systems: Industrial Control Systems

Industrial control systems (ICSs) for critical infrastructure are extensively utilized to provide the fundamental functions of society and are frequently employed in critical infrastructure. Therefore, security of these systems from cyberattacks is essential. Over the years, several proposals have been made for various types of cyberattack detection systems, with each concept using a distinct set of processes and methodologies. However, there is a substantial void in the literature regarding approaches for detecting cyberattacks in ICSs. Identifying cyberattacks in ICSs is the primary aim of this proposed research. Anomaly detection in ICSs based on an artificial intelligence algorithm is presented. The methodology is intended to serve as a guideline for future research in this area. On the one hand, machine learning includes logistic regression, k-nearest neighbors (KNN), linear discriminant analysis (LDA), and decision tree (DT) algorithms, deep learning long short-term memory (LSTM), and the convolution neural network and long short-term memory (CNN-LSTM) network to detect ICS malicious attacks. The proposed algorithms were examined using real ICS datasets from the industrial partners Necon Automation and International Islamic University Malaysia (IIUM). There were three types of attacks: man-in-the-middle (mitm) attack, web-server access attack, and telnet attack, as well as normal. The proposed system was developed in two stages: binary classification and multiclass classification. The binary classification detected the malware as normal or attacks and the multiclass classification was used for detecting all individual attacks. The KNN and DT algorithms achieved superior accuracy (100%) in binary classification and multiclass classification. Moreover, a sensitivity analysis method was presented to predict the error between the target and prediction values. The sensitivity analysis results showed that the KNN and DT algorithms achieved R2 = 100% in both stages. The obtained results were compared with existing systems; the proposed algorithms outperformed existing systems

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries

An Investigation into the Determinants of Investment Awareness: Evidence from the Young Saudi Generation

Investment awareness enables people to make sound and effective decisions in their investment activities. It is evident that many individuals were unsuccessful in managing their investment efficiently. Thus, this study aims to be pioneering among its kind to explore and examine the determinants of investment awareness. To achieve this purpose, we develop a comprehensive quantitative model that investigates the impact of the most essential and suggested drivers of investment awareness (i.e., financial literacy, spending patterns, self-control, saving behavior, attitude toward risk, and family financial socialization). Using convenience sampling, an online questionnaire (using five-point Likert scales) was distributed to a sample of students representing the young generation aged between 18–35 years old from the School of Business and Applied College at King Faisal University. To ratify the validity and reliability of the questionnaire, a pilot test was carried out. For analyzing and interpreting that data, we utilized partial least squares structural equation modeling (PLS-SEM). The reported results of 409 responses show that financial literacy, self-control, saving behavior, and family financial socialization have positive and significant influences on investment awareness. Conversely, spending patterns and attitude toward risk are found to be positively but insignificantly related to investment awareness. These results ratify the need to formulate and implement operative actions to decrease the issue of investment awareness. This study adds to the body of knowledge on the determinants of investment awareness, specifically among the young generation (university students). Furthermore, examining the level of investment awareness could offer vital implications to policymakers, educational institutions, and families on how to enhance the awareness of young investors and support them in making good investment decisions