SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted ECG CLASSIFICATION USING NEURAL NETWORK(University of Bridgeport, 2018) Alhassani, Ahmad; Faezipour, MiadAn electrocardiogram (ECG) is one of the biomedical signals that is considered a very useful approach to providing information about heart problems. This thesis has been done to contribute to making machines of observation of hearts have more ability for making accurate and fast diagnosis so that life of more patients might be saved. Physios Bank was the source of our dataset. It has many real examples of heart diseases that we can choose for our studies. In this research, there are five heart cases that were used for this research, normal N, atrial premature beat PAC, premature ventricular contraction PVC, left bundle branch block beat LBBB, and right bundle branch block beat RBBB. Classifying these five cases with a high efficiency and accuracy using neural network is our final goal. To achieve this goal, ECG signals must go through specific procedures or steps. The first procedure was ECG signal preprocessing. This step has three sup steps, signal filtering, signal detrending, and signal smoothing. The second procedure is extracting features of ECG signals. The forth one is classifying ECG signals using neural network. Finally, the results of NN will be saved for future purposes. Our system was implemented by using MATLAB because it is a very powerful software for signal processing and signal analysis. Our research was ended with some good achievements and optimizations. For example, discovering good techniques for filtering, finding new way for features extraction, building one neural network to classify multiple heart diseases, and making a high accuracy with 96.88% percent.65 0Item Restricted WIRELESS SIGNAL IDENTIFICATION/CLASSIFICATION USING DEEP LEARNING(Stevens Institute of Technology, 2023-12) Samarkandi, Abdullah; Yao, Yu-DongWe exploit deep learning convolutional neural networks (CNN) on a constellation diagram to identify QAM modulation of different orders in static, slow, and frequency selective fading channels. Although constellation diagrams have been studied and classified in literature, most of the work focused on noise. Little has been done to study the effect of multipath fading channels. We develop a highly accurate modulation classification method by exploiting deep learning with the constellation diagram. Based on the experimental results, our CNN model achieves a classification accuracy of 100% at -10 dB signal-to-noise ratio (SNR) under a multipath Rayleigh fading channel. Then, we use CNN on joint image representation and propose an automatic modulation classification algorithm to classify the communication signals. The combined representations include a constellation diagram, an ambiguity function (AF), and an eye diagram. Experimentation results show that combining constellation and eye diagrams achieves superior classification performance compared to having these representations separately. Combining AF and an eye diagram results in improvement at low SNR. Finally, we extract features from each of the three datasets (Constellation, Eye diagram, AF) using transfer learning with pre-trained model and then train the new classifier on top of these features. We compare the results of the feature extraction to the results of the joint image representation.20 0Item Restricted Symbolic Computation of Squared Amplitudes in High Energy Physics with Machine Learning(Saudi Digital Library, 2023-12-21) Alnuqaydan, Abdulhakim; Eides, Michael; Gleyzer, SergeiThe calculation of particle interaction squared amplitudes is a key step in the calculation of cross sections in high-energy physics. These complex calculations are currently performed using domain-specific symbolic algebra tools, where the computational time escalates rapidly with an increase in the number of loops and final state particles. This dissertation introduces an innovative approach: employing a transformer-based sequence-to-sequence model capable of accurately predicting squared amplitudes of Standard Model processes up to one-loop order when trained on symbolic sequence pairs. The primary objective of this work is to significantly reduce the computational time and, more importantly, develop a model that efficiently scales with the complexity of the processes. To the best of our knowledge, this model is the first that encapsulates a wide range of symbolic squared amplitude calculations and, therefore, represents a potentially significant advance in using symbolic machine learning techniques for practical scientific computations.33 0Item Restricted Seeing in the Dark: Towards Robust Pedestrian Detection at Nighttime(Saudi Digital Library, 2023-12-24) Althoupety, Afnan; Feng, Wu-chi“At some point in the day, everyone is a pedestrian” a message from the National Highway Traffic Safety Administration (NHTSA) about pedestrian safety. In 2020, NHTSA reported that 6,516 pedestrians were killed in traffic crashes and a pedestrian was killed every 81 minutes on average in the United States. In relation to light condition, 77% of pedestrian fatalities occurred in the dark, 20% in daylight, 2% in dusk, and 2% in dawn. To tackle the issue from a technological perspective, this dissertation addresses the problem of pedestrian detection robustness in dark conditions, benefiting from image processing and learning-based approaches by: (i) proposing a pedestrian- luminance-aware brightening framework that moderately corrects image luminance so that pedestrians can be more robustly detected, (ii) proposing an image-to-image translation framework that learns the mapping between night and day domains through the game training of generators and discriminators and thus alleviates detecting dark pedestrian using the synthetic night images, and (iii) proposing a multi-modal framework that pairs RGB and infrared images to reduce the light factor and make pedestrian detection a fair game regardless the illumination variance.11 0Item Restricted DETECTING MANIPULATED AND ADVERSARIAL IMAGES: A COMPREHENSIVE STUDY OF REAL-WORLD APPLICATIONS(UCF STARS, 2023-11-06) Alkhowaiter, Mohammed; Zou, CliffThe great advance of communication technology comes with a rapid increase of disinformation in many kinds and shapes; manipulated images are one of the primary examples of disinformation that can affect many users. Such activity can severely impact public behavior, attitude, and be- lief or sway the viewers’ perception in any malicious or benign direction. Additionally, adversarial attacks targeting deep learning models pose a severe risk to computer vision applications. This dissertation explores ways of detecting and resisting manipulated or adversarial attack images. The first contribution evaluates perceptual hashing (pHash) algorithms for detecting image manipulation on social media platforms like Facebook and Twitter. The study demonstrates the differences in image processing between the two platforms and proposes a new approach to find the optimal detection threshold for each algorithm. The next contribution develops a new pHash authentication to detect fake imagery on social media networks, using a self-supervised learning framework and contrastive loss. In addition, a fake image sample generator is developed to cover three major image manipulating operations (copy-move, splicing, removal). The proposed authentication technique outperforms the state-of-the-art pHash methods. The third contribution addresses the challenges of adversarial attacks to deep learning models. A new adversarial-aware deep learning system is proposed using a classical machine learning model as the secondary verification system to complement the primary deep learning model in image classification. The proposed approach outperforms current state-of-the-art adversarial defense systems. Finally, the fourth contribution fuses big data from Extra-Military resources to support military decision-making. The study pro- poses a workflow, reviews data availability, security, privacy, and integrity challenges, and suggests solutions. A demonstration of the proposed image authentication is introduced to prevent wrong decisions and increase integrity. Overall, the dissertation provides practical solutions for detect- ing manipulated and adversarial attack images and integrates our proposed solutions in supporting military decision-making workflow.31 0Item Restricted FRUITPAL: AN IOT-ENABLED FRAMEWORK FOR AUTOMATIC MONITORING OF FRUIT CONSUMPTION IN SMART HEALTHCARE(Saudi Digital Library, 2023-11-06) Alkinani, Abdulrahman; Mohanty, Saraju; Kougiagnos, EliasThis research proposes FruitPAL and FruitPAL 2.0. They are full automatic devices that can detect fruit consumption to reduce the risk of disease. Allergies to fruits can seriously impair the immune system. A novel device (FruitPAL) detecting fruit that can cause allergies is proposed in this thesis. The device can detect fifteen types of fruit and alert the caregiver when an allergic reaction may have happened. The YOLOv8 model is employed to enhance accuracy and response time in detecting dangers. The notification will be transmitted to the mobile device through the cloud, as it is a commonly utilized medium. The proposed device can detect the fruit with an overall precision of 86%. FruitPAL 2.0 is envisioned as a device that encourages people to consume fruit. Fruits contain a variety of essential nutrients that contribute to the general health of the human body. FruitPAL 2.0 is capable of analyzing the consumed fruit and then determining its nutritional value. FruitPAL 2.0 has been trained on YOLOv5 V6.0. FruitPAL 2.0 has an overall precision of 90% in detecting the fruit. The purpose of this study is to encourage fruit consumption unless it causes illness. Even though fruit plays an important role in people’s health, it might cause dangers. The proposed work can not only alert people to fruit that can cause allergies, but also it encourages people to consume fruit that is beneficial for their health.20 0Item Restricted ENHANCING IOT DEVICES SECURITY: ENSEMBLE LEARNING WITH CLASSICAL APPROACHES FOR INTRUSION DETECTION SYSTEM(Saudi Digital Library, 2023-11-15) Alotaibi, Yazeed; Ilyas, MohammadThe Internet of Things (IoT) refers to a network of interconnected nodes constantly engaged in communication, data exchange, and the utilization of various network protocols. Previous research has demonstrated that IoT devices are highly susceptible to cyber-attacks, posing a significant threat to data security. This vulnerability is primarily attributed to their susceptibility to exploitation and their resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are employed. This study aims to contribute to the field by enhancing IDS detection efficiency through the integration of Ensemble Learning (EL) methods with traditional Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance, we initially utilize a binary ML classification approach to classify IoT network traffic as either normal or abnormal, employing EL methods such as Stacking and Voting. Once this binary ML model exhibits high detection rates, we extend our approach by incorporating a ML multi-class framework to classify attack types. This further enhances IDS performance by implementing the same Ensemble Learning methods. Additionally, for further enhancement and evaluation of the intrusion detection system, we employ DL methods, leveraging deep learning techniques, ensemble feature v selections, and ensemble methods. Our DL approach is designed to classify IoT network traffic. This comprehensive approach encompasses various supervised ML, and DL algorithms with ensemble methods. The proposed models are trained on TON-IoT network traffic datasets. The ensemble approaches are evaluated using a comprehensive metrics and compared for their effectiveness in addressing this classification tasks. The ensemble classifiers achieved higher accuracy rates compared to individual models, a result attributed to the diversity of learning mechanisms and strengths harnessed through ensemble learning. By combining these strategies, we successfully improved prediction accuracy while minimizing classification errors. The outcomes of these methodologies underscore their potential to significantly enhance the effectiveness of the Intrusion Detection System.23 0Item Restricted Physics and AI-Driven Anomaly Detection in Cyber-Physical Systems(Saudi Digital Library, 2023) Alotibi, Faris; Tipper, DavidOrganizations across various sectors are moving rapidly to digitization. Multiple applications in cyber-physical systems (CPSs) emerged from interconnectivity such as smart cities, autonomous vehicles, and smart grids, utilizing advanced capabilities of the Internet of Things (IoTs), cloud computing, and machine learning. Interconnectivity also becomes a critical component in industrial systems such as smart manufacturing, smart oil, and gas distribution grid, smart electric power grid, etc. These critical infrastructures and systems rely on industrial IoT and learning-enabled components to handle the uncertainty and variability of the environment and increase autonomy in making effective operational decisions. The prosperity and benefits of systems interconnectivity demand the fulfillment of functional requirements such as interoperability of communication and technology, efficiency and reliability, and real-time communication. Systems need to integrate with various communication technologies and standards, process and analyze shared data efficiently, ensure the integrity and accuracy of exchanged data, and execute their processes with tolerable delay. This creates new attack vectors targeting both physical and cyber components. Protection of systems interconnection and validation of communicated data against cyber and physical attacks become critical due to the consequences of disruption attacks pose to critical systems. In this dissertation, we tackle one of the prominent attacks in the CPS space, namely the false data injection attack (FDIA). FDIA is an attack executed to maliciously influence decisions, that is CPSs operational decisions such as opening a valve, changing wind turbine configurations, charging/discharging energy storage system batteries, or coordinating autonomous vehicles driving. We focus on the development of anomaly detection techniques to protect CPSs from this emerging threat. The anomaly detection mechanisms leverage both physics of CPSs and AI to improve their detection capability as well as the CPSs' ability to mitigate the impact of FDIA on their operations.51 0Item Restricted Computational intelligence approaches applied to various domains(Saudi Digital Library, 2023-03-04) Alibrahim, Hussain; Ludwig, SimoneOver the past decade, machine learning has revolutionized a wide range of fields, from self-driving cars to speech recognition, web search, and even the human genome. However, the success of machine learning algorithms depends on a thorough understanding of the problem, mechanisms, properties, and constraints. This dissertation explores various aspects of machine learning, including hyperparameter optimization, nature-inspired algorithms for semi-supervised learning, image encryption using Particle Swarm Optimization with a logistic map and image originality. In the first chapter, three models - Genetic Algorithm, Grid Search, and Bayesian Optimization - are compared to improve classification accuracy for neural network models. The objective is to build a neural network model with a set of hyperparameters that can improve classification accuracy for data mining tasks, which aim to discover hidden relationships between input and output data to predict accurate outcomes for new data. The second chapter focuses on using nature-inspired algorithms, such as Particle Swarm Optimization (PSO) and Anti Bee Colony (ABC), to correctly cluster unlabelled data in semi-supervised learning problems. Two hybrid versions of K-means clustering, one with PSO and the other with ABC, are developed. The third chapter uses PSO to develop an image encryption algorithm using the logistic map to aid in the encryption process. The optimization problem is formulated by converting the image encryption problem into an optimization problem. In the final chapter, a new algorithm is developed using different techniques such as classification, optimization, and image analysis to detect whether an image is original or has been edited and modified. Overall, this dissertation investigates a variety of machine learning techniques and their practical applications across numerous fields. The techniques have the potential to be applied in diverse areas, such as biology, meteorology, healthcare, and finance.12 0Item Restricted Retrieval and Labeling of Documents Using Ontologies: Aided by a Collaborative Filtering(2023) Alshammari, Asma Abdulkarim; Bhatnagar, RajInformation retrieval is one of the common tasks in today’s world and retrieval systems are aided by various text mining and analysis methods. The objective of retrieval is to obtain information resources from a collection that are relevant to a specified query. The retrieval process begins with a query provided by a user. A search engine is then started to find the relevant resources. Typically, the queries are formed using the same terms (words) that also occur within the resources. The situations of a document matching the non-occurring terms are illustrated by the following examples: we want to retrieve documents relevant to some query terms that do not explicitly occur in the documents but are relevant to their contents. We want to retrieve documents using queries that contain labels from the ontology tree, and these labels may not explicitly occur in documents. We may have a large collection of documents in an organization, and various user communities that may want to refer to the documents using their community-specific ontologies. Several information retrieval methods use clustering of documents followed by determining signatures for each cluster describing the terms predominantly present in each of the clusters. We have designed and implemented a clustering algorithm that partitions the data space in a step-wise manner and seeks to optimize clusters that have good-quality signatures representing the documents in the clusters. The clustering algorithm is modeled on a bi-clustering strategy using the spectral co-clustering method at each step and then optimizing towards clusters that have strong representative signatures. We have shown that this clustering algorithm performs better than other known clustering algorithms such as K-Means and Latent Dirichlet Allocation (LDA). We have accomplished our goal of improving information retrieval systems’ capabilities and performance by presenting a new method to generate predicted terms for the documents by using Singular Value Decomposition (SVD) based collaborative filtering methods. We have shown that retrievals made using such recommended terms for documents retrieve correct documents with reasonably high accuracy. In addition, including predicted terms in the clustering process improves the purity of clusters and the quality of retrieval. We have achieved our goal of integrating ontological labels with information retrieval by adding terms to a document from ontologies and using a collaborative filtering approach to associate ontology labels with other relevant documents. We have tested the performance of our method with many cases of integrating ontologies: single ontology label, single large ontology with all complexities of an ontology tree, and multiple ontology trees. We have tested this method on our document collections and have obtained promising results. Our method has higher performance than other existing methods.51 0