Iraqi Journal for Computers and Informatics
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DYNAMIC THRESHOLDING GA-BASED ECG FEATURE SELECTION IN CARDIOVASCULAR DISEASE DIAGNOSIS
Get PDFElectrocardiogram (ECG) data are usually used to diagnose cardiovascular disease (CVD) with the help of a revolutionary algorithm. Feature selection is a crucial step in the development of accurate and reliable diagnostic models for CVDs. This research introduces the dynamic threshold genetic algorithm (DTGA) algorithm, a type of genetic algorithm that is used for optimization problems and discusses its use in the context of feature selection. This research reveals the success of DTGA in selecting relevant ECG features that ultimately enhance accuracy and efficiency in the diagnosis of CVD. This work also proves the benefits of employing DTGA in clinical practice, including a reduction in the amount of time spent diagnosing patients and an increase in the precision with which individuals who are at risk of CVD can be identified
AN OVERVIEW SMART ASSISTANT SYSTEM FOR OLD PEOPLE USING INTERNET OF THINGS
Get PDFThe Internet of Things is a technology that applied in the field of healthcare, especially elderly patients, and allows patients to be tracked without the need for direct physical interaction with patients. Diseases and other consequences can be recognized early, especially those who are more likely to have a disorder in their physiological data. It is critically necessary to create new approaches and technology in order to improve health care for the aged population at a price that is more cheap and in a form that is simpler to use. In addition, patients and members of their families get a sense of peace when they are aware that they are being observed and will be assisted in the event that any complications emerge. This study uses a literature review to explore the ideas behind healthcare system components, in addition this study examines the characteristics, requirements, and definitions of internet of things. The primary purpose of this study is to introduce the reader to the various sensors and other healthcare system components utilised for the purpose of monitoring the elderly. However, this work will help future researchers who desire to do study in this field of healthcare systems and assist efficient knowledge acquisition by providing a solid foundation
An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application
Get PDFWith technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper
ENSEMBLE MACHINE LEARNING APPROACH FOR IOT INTRUSION DETECTION SYSTEMS
Get PDFThe rapid growth and development of the Internet of Things (IoT) have had an important impact on various industries, including smart cities, the medical profession, autos, and logistics tracking. However, with the benefits of the IoT come security concerns that are becoming increasingly prevalent. This issue is being addressed by developing intelligent network intrusion detection systems (NIDS) using machine learning (ML) techniques to detect constantly changing network threats and patterns. Ensemble ML represents the recent direction in the ML field. This research proposes a new anomaly-based solution for IoT networks utilizing ensemble ML algorithms, including logistic regression, naive Bayes, decision trees, extra trees, random forests, and gradient boosting. The algorithms were tested on three different intrusion detection datasets. The ensemble ML method achieved an accuracy of 98.52% when applied to the UNSW-NB15 dataset, 88.41% on the IoTID20 dataset, and 91.03% on the BoTNeTIoT-L01-v2 dataset
COMPARATIVE STUDY OF FONT RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS AND TWO FEATURE EXTRACTION METHODS WITH SUPPORT VECTOR MACHINE
Get PDFFont recognition is one of the essential issues in document recognition and analysis, and is frequently a complex and time-consuming process. Many techniques of optical character recognition (OCR) have been suggested and some of them have been marketed, however, a few of these techniques considered font recognition. The issue of OCR is that it saves copies of documents to make them searchable, but the documents stop having the original appearance. To solve this problem, this paper presents a system for recognizing three and six English fonts from character images using Convolution Neural Network (CNN), and then compare the results of proposed system with the two studies. The first study used NCM features and SVM as a classification method, and the second study used DP features and SVM as classification method. The data of this study were taken from Al-Khaffaf dataset [21]. The two types of datasets have been used: the first type is about 27,620 sample for the three fonts classification and the second type is about 72,983 sample for the six fonts classification and both datasets are English character images in gray scale format with 8 bits. The results showed that CNN achieved the highest recognition rate in the proposed system compared with the two studies reached 99.75% and 98.329 % for the three and six fonts recognition, respectively. In addition, CNN got the least time required for creating model about 6 minutes and 23- 24 minutes for three and six fonts recognition, respectively. Based on the results, we can conclude that CNN technique is the best and most accurate model for recognizing fonts
DIAGNOSE EYES DISEASES USING VARIOUS FEATURES EXTRACTION APPROACHES AND MACHINE LEARNING ALGORITHMS
Get PDFOphthalmic diseases like glaucoma, diabetic retinopathy, and cataracts are the main cause of visual impairment worldwide. With the use of the fundus images, it could be difficult for a clinician to detect eye diseases early enough. By other hand, the diagnoses of eye disease are prone to errors, challenging and labor-intensive. Thus, for the purpose of identifying various eye problems with the use of the fundus images, a system of automated ocular disease detection with computer-assisted tools is needed. Due to machine learning (ML) algorithms\u27 advanced skills for image classification, this kind of system is feasible. An essential area of artificial intelligence)AI (is machine learning. Ophthalmologists will soon be able to deliver accurate diagnoses and support individualized healthcare thanks to the general capacity of machine learning to automatically identify, find, and grade pathological aspects in ocular disorders. This work presents a ML-based method for targeted ocular detection. The Ocular Disease Intelligent Recognition (ODIR) dataset, which includes 5,000 images of 8 different fundus types, was classified using machine learning methods. Various ocular diseases are represented by these classes. In this study, the dataset was divided into 70% training data and 30% test data, and preprocessing operations were performed on all images starting from color image conversion to grayscale, histogram equalization, BLUR, and resizing operation. The feature extraction represents the next phase in this study ,two algorithms are applied to perform the extraction of features which includes: SIFT(Scale-invariant feature transform) and GLCM(Gray Level Co-occurrence Matrix), ODIR dataset is then subjected to the classification techniques Naïve Bayes, Decision Tree, Random Forest, and K-nearest Neighbor. This study achieved the highest accuracy for binary classification (abnormal and normal) which is 75% (NB algorithm), 62% (RF algorithm), 53% (KNN algorithm), 51% (DT algorithm) and achieved the highest accuracy for multiclass classification (types of eye diseases) which is 88% (RF algorithm), 61% (KNN algorithm) 42% (NB algorithm), and 39% (DT algorithm)
IMPROVING THE PRIORITIZATION PROCEDURE OF PATIENTS WITH COVID-19 IN HOSPITALS BASED ON DECISION-MAKING TECHNIQUES: A SYSTEMATIC REVIEW
Get PDFCoronavirus-specific antibodies can be detected in the blood of people who have recently recovered from coronavirus disease-2019 (COVID-19). Convalescent-Plasma (CP) transfusion process proved that it\u27s among the most efficient protocols, and it\u27s used in hospitals to treat various infections and diseases. Several medical issues have been addressed due to the growing interest in creating Artificial Intelligence (AI) applications. However, considering the virus\u27s enormous potential harm to global public health, such uses are insufficient. This proposed systematic review and meta-analysis aims to obtain an overview of COVID-19, highlight the limits of decision-making approaches, and give healthcare professionals information about the technique\u27s advantages. Between 2016 and 2021, five databases, namely IEEE Xplore, Web of Science, PubMed, Science Direct, and Scopus, were utilized to run four sequences of search queries. As a result, 477 studies are found to be relevant. Only six studies were thoroughly examined and included in this review after screening articles and using proper inclusion criteria, highlighting the lack of research on this crucial topic. Studies\u27 findings were reviewed to identify the gaps in all the evaluated papers. Motivations, problems, constraints, suggestions, and case examples were thoroughly examined. This study seeks to answer how we support the researchers with collected information for managing transfusion of the highest quality CP to the most critical COVID-19 patients across telemedicine hospitals
Review of Detection Denial of Service Attacks using Machine Learning through Ensemble Learning
Get PDFToday\u27s network hacking is more resource-intensive because the goal is to prohibit the user from using the network\u27s resources when the target is either offensive or for financial gain, especially in businesses and organizations. That relies on the Internet like Amazon Due to this, several techniques, such as artificial intelligence algorithms like machine learning (ML) and deep learning (DL), have been developed to identify intrusion and network infiltration and discriminate between legitimate and unauthorized users. Application of machine learning and ensemble learning algorithms to various datasets, consideration of homogeneous ensembles using a single algorithm type or heterogeneous ensembles using several algorithm types, and evaluation of the discovery outcomes in terms of accuracy or discovery error for detecting attacks. The survey literature provides an overview of the many approaches and approaches of one or more machine-learning algorithms used in various datasets to identify denial of service attacks. It has also been shown that employing the hybrid approach is the most common and produces better attack detection outcomes than using the sole approaches. Numerous machine learning techniques, including support vector machines (SVM), K-Nearest Neighbors (KNN), and ensemble learning like random forest (RF), bagging, and boosting, are illustrated in this work (DT). That is employed in several articles to identify different denial of service (DoS) assaults, including the trojan horse, teardrop, land, smurf, flooding, and worm. That attacks network traffic and resources to deny users access to the resources or to steal confidential information from the company without damaging the system and employs several algorithms to obtain high attack detection accuracy and low false alarm rates
PARALLEL PROCESSING OUTCOMES OF E-ABDULRAZZAQ ALGORITHM USING MULTI-CORE TECHNIQUE
Get PDFThe string matching problem is considered one of the substantial problems in the fields of computer science like speech and pattern recognition, signal and image processing, and artificial intelligence (AI). The increase in the speedup of performance is considered an important factor in meeting the growth rate of databases, Subsequently, one of the determinations to address this issue is the parallelization for exact string matching algorithms. In this study, the E-Abdulrazzaq string matching algorithm is chosen to be executed with the multi-core environment utilizing the OpenMP paradigm which can be utilized to decrease the execution time and increase the speedup of the algorithm. The parallelization algorithm got positive results within the parallel execution time, and excellent speeding-up capabilities, in comparison to the successive result. The Protein database showed optimal results in parallel execution time, and when utilizing short and long pattern lengths. The DNA database showed optimal speedup execution when utilizing short and long pattern lengths, while no specific database obtained the worst results
Artificial Intelligence in Identifying or Mitigating Threats in Internet of Things
Get PDFThe Internet of Things (IoT) is generally acknowledged as a dramatic change spearheaded by scientists and business executives. The IoT has the potential to improve our daily lives by connecting smart devices to the internet. Due to the limited resources and distant deployment of these IoT devices, securing them is a significant challenge today. This paper focuses primarily on mitigating threats and attacks on realistic artificial intelligence, such as network architecture for smart devices. The text on mitigating attacks in networks, especially those involving mobile nodes, is discussed. We develop and test a new countermeasure against all mitigated attack variants. The proposed approach combines node location and trust-based parent selection. The result demonstrates the viability of the suggested countermeasure. In addition, demonstrating the superiority of the suggested countermeasure involves considering the precision of detecting the attack and the delay in isolating the attacker