Machine Learning Techniques For Classification Research Articles

Detection of attacks on software defined networks using machine learning techniques and imbalanced data handling methods

AbstractSoftware‐defined networks (SDNs) have gained popularity in recent years as a solution for the fundamental issues that affect traditional dispersed networks. The primary advantage of SDNs is the decoupling of the control plane from the data plane, which increases the flexibility of the network. The SDN represents a network architecture of the next generation, however, its configuration options are centralized, leaving it open for cyber‐attacks. This paper concentrates on the early identification of attacks in an SDN environment. When malicious traffic is affecting in an SDN topology, an artificial intelligence (AI) module in the topology is used to detect the attack and stop the attack source using machine learning (ML) techniques. The architecture presented in this research allows for the comparison of several ML classification techniques that are used to identify different sorts of network attacks. For attack detection, eight ML techniques are used, namely logistic regression (LR), linear discriminant analysis (LDA), Naïve Bayes (NB), k‐nearest neighbor (KNN), classification and regression tree (CART), AdaBoost (AB), random forest (RF), and support‐vector machine (SVM) classifiers. These techniques are tested on the InSDN dataset, which is a novel attack‐specific SDN dataset. The results show that the highest accuracy of 98.6% is achieved with the LDA classifier. Further improvement in the accuracy of classification models is observed when random over‐sampling, synthetic minority oversampling technique (SMOTE), random under‐sampling, and under‐sampling with Tomek links and near‐miss concept are applied to address the class imbalance problem. After applying these methods, the LDA classifier showed an accuracy of 98.79%.

Avaliação de recursos para o mapeamento de cobertura do solo em sub-bacia do rio Capibaribe-PE usando imagem Kompsat-2

Land use and land cover mapping is an important factor in geospatial analysis in watershed management. The integration of remote sensing images and Machine Learning classification techniques enable the identification and environmental monitoring of landscape elements. The MSC (MultiSpectral Camara) sensor on the Kompsat-2 satellite captures images of high spatial resolution, which allows the identification of terrestrial resources on a local scale. Six data models were developed for classifying land use and land cover by Random Forest in a Capibaribe River sub-basin. These models were created based on spectral indices and ranking of variable importance. The evaluation of the results was done through spatial quantification and accuracy analysis. Products based on bands and spectral indices showed global accuracy ranging between 94 and 98%, where the classes of Arboreal and Shrubby Vegetation stood out with estimates of accuracy of the producer and user above 80%. Products with the lowest data resources showed poor accuracy performance with overall accuracy values clustered below 60%. This study is the first to use adaptations of Kompsat-2 spectral data and computer learning methods to demonstrate the application of high-performance land cover mapping. Thus, this article contributed to the monitoring of the soil surface in urban sub-basins that need precise spatial information about the state of environmental conversation.

Improving forecasting model for fuzzy time series using the Self-updating clustering and Bi-directional Long Short Term Memory algorithm

This paper proposes a new model for creating fuzzy time series and conducting future forecasts using the Bi-directional Long Short-term Memory model (Bi-LSTM). This study contributes by using both classical machine learning and deep learning techniques. Firstly, a self-updating clustering algorithm is used to determine the optimal number of clusters for time series. Secondly, the study describes a method for determining the fuzzy relationship between elements of time series and the identified clusters. Thirdly, the developed model creates the fuzzy time series based on the established rules. Finally, forecasting for the future value using the Bi-LSTM model with some improvement in optimizing the parameters and increasing the accuracy of forecasting result. The steps of the proposed model are clearly illustrated by a numerical example. Moreover, this model also tested on various datasets, including M3, M4, and M5 datasets. By evaluating its performance using parameters such as sMAPE and RMSSE, the proposed model has shown significant enhancements when compared to existing models. Additionally, the model’s effectiveness is further demonstrated through its successful application in forecasting the VN index stock in Vietnam.

Applications of Different Classification Machine Learning Techniques to Predict Formation Tops and Lithology While Drilling.

Accurate prediction of formation tops and lithology plays a critical role in optimizing drilling processes, cost reduction, and risk mitigation in hydrocarbon operations. Although several techniques like well logging, core sampling, cuttings analysis, seismic surveys, and mud logging are available for identifying formation tops, they have limitations such as high costs, lower accuracy, manpower-intensive processes, and time or depth lags that impede real-time estimation. Consequently, this study aims to leverage machine learning models based on easily accessible drilling parameters to predict formation tops and lithologies, overcoming the limitations associated with traditional methods. Data from two wells (A and B) in the Middle East, encompassing drilling mechanical parameters such as rate of penetration (ROP), drill string rotation (DSR), pumping rate (Q), standpipe pressure (SPP), weight on bit (WOB), and torque, were collected for real-field analysis. Machine learning models including Gaussian naive Bayes (GNB), logistic regression (LR), and linear discriminant analysis (LDA) were trained and tested on the data set from well A, while the data set from well B was utilized for model validation as unseen data. The formations of wells A and B consist of four lithologies, namely, sandstone, anhydrite, carbonate/shale, and carbonates, necessitating the development of multiclass classification models. The drilling parameters, specifically the WOB and ROP, exhibited a strong influence on lithology identification. Among the models, GNB demonstrated exceptional performance in predicting formation lithology from the drilling parameters, achieving accuracy and nearly perfect precision, recall, and F1 score for the different classes. LDA and LR models accurately predicted sandstone and carbonate lithologies, although some misclassifications occurred in approximately 5% of points for anhydrite and around 20% in carbonate/shale formations. During validation, the models demonstrated accuracies of around 0.96, 0.95, and 0.92 for the GNB, LR, and LDA, respectively. The study highlights the efficacy of the developed machine learning models in accurately predicting the formation lithology and tops in real time. This is achieved by utilizing readily available drilling parameters, making the approach highly accurate and cost effective by leveraging existing real-time drilling data.

An effective autism spectrum disorder screening method using machine learning classification techniques

SummaryNowadays, autism spectrum disorder (ASD) is one of the fastest‐growing developmental disorders globally. Screening test consumes more time and expensive to detect the autism signs. Due to advances in artificial intelligence and machine learning (ML), Autism can be predicted at the initial stage. Numerous researches have been conducted using different methods, but none of these researches presented any anticipated results about the capability to predict autism traits under different age groups. Therefore, this paper proposes an effectual prediction method based upon ML strategy to develop a mobile application for predicting ASD of any age people. The autism prediction model is developed by five ML classifiers, such as Gaussian Naive Bayes, Decision Tree Classifier, K‐Nearest Neighbors (KNN), Multinomial Logistic Regression (MLR), Support Vector Machine (SVM) and also a mobile application is developed using proposed prediction method. The proposed method is analyzed with IAPQ records gathered from certain areas in Erode district, Tamil Nadu, India. From the analysis, the SVM classifier achieves maximum sensitivity of 23.14%, 6.04%, 5.89%, 11.03% than other classifiers, like Gaussian Naive Bayes, Decision Tree Classifier, KNN, and MLR.

Linear programming based computational technique for leukemia classification using gene expression profile.

Cancer is a serious public health concern worldwide and is the leading cause of death. Blood cancer is one of the most dangerous types of cancer. Leukemia is a type of cancer that affects the blood cell and bone marrow. Acute leukemia is a chronic condition that is fatal if left untreated. A timely, reliable, and accurate diagnosis of leukemia at an early stage is critical to treating and preserving patients' lives. There are four types of leukemia, namely acute lymphocytic leukemia, acute myelogenous leukemia, chronic lymphocytic in extracting, and chronic myelogenous leukemia. Recognizing these cancerous development cells is often done via manual analysis of microscopic images. This requires an extraordinarily skilled pathologist. Leukemia symptoms might include lethargy, a lack of energy, a pale complexion, recurrent infections, and easy bleeding or bruising. One of the challenges in this area is identifying subtypes of leukemia for specialized treatment. This Study is carried out to increase the precision of diagnosis to assist in the development of personalized plans for treatment, and improve general leukemia-related healthcare practises. In this research, we used leukemia gene expression data from Curated Microarray Database (CuMiDa). Microarrays are ideal for studying cancer, however, categorizing the expression pattern of microarray information can be challenging. This proposed study uses feature selection methods and machine learning techniques to predict and classify subtypes of leukemia in gene expression data CuMiDa (GSE9476). This research work utilized linear programming (LP) as a machine-learning technique for classification. Linear programming model classifies and predicts the subtypes of leukemia Bone_Marrow_CD34, Bone Marrow, AML, PB, and PBSC CD34. Before using the LP model, we selected 25 features from the given dataset of 22283 features. These 25 significant features were the most distinguishing for classification. The classification accuracy of this work is 98.44%.

A comprehensive analysis for classification and regression of surface points based on geodesics and machine learning algorithms

PurposeFeature extraction from 3D datasets is a current problem. Machine learning is an important tool for classification of complex 3D datasets. Machine learning classification techniques are widely used in various fields, such as text classification, pattern recognition, medical disease analysis, etc. The aim of this study is to apply the most popular classification and regression methods to determine the best classification and regression method based on the geodesics.Design/methodology/approachThe feature vector is determined by the unit normal vector and the unit principal vector at each point of the 3D surface along with the point coordinates themselves. Moreover, different examples are compared according to the classification methods in terms of accuracy and the regression algorithms in terms of R-squared value.FindingsSeveral surface examples are analyzed for the feature vector using classification (31 methods) and regression (23 methods) machine learning algorithms. In addition, two ensemble methods XGBoost and LightGBM are used for classification and regression. Also, the scores for each surface example are compared.Originality/valueTo the best of the author’s knowledge, this is the first study to analyze datasets based on geodesics using machine learning algorithms for classification and regression.

Characterization and discrimination of spike protein in SARS-CoV-2 virus-like particles via surface-enhanced Raman spectroscopy.

Non-infectious virus-like particles (VLPs) are excellent structures for development of many biomedical applications such as drug delivery systems, vaccine production platforms, and detection techniques for infectious diseases including SARS-CoV-2 VLPs. The characterization of biochemical and biophysical properties of purified VLPs is crucial for development of detection methods and therapeutics. The presence of spike (S) protein in their structure is especially important since S protein induces immunological response. In this study, development of a rapid, low-cost, and easy-to-use technique for both characterization and detection of S protein in the two VLPs, which are SARS-CoV-2 VLPs and HIV-based VLPs was achieved using surface-enhanced Raman spectroscopy (SERS). To analyze and classify datasets of SERS spectra obtained from the VLP groups, machine learning classification techniques including support vector machine (SVM), k-nearest neighbors (kNN), and random forest (RF) were utilized. Among them, the SVM classification algorithm demonstrated the best classification performance for SARS-CoV-2 VLPs and HIV-based VLPs groups with 87.5% and 92.5% accuracy, respectively. This study could be valuable for the rapid characterization of VLPs for the development of novel therapeutics or detection of structural proteins of viruses leading to a variety of infectious diseases.

An Efficient Filter and Wrapper based Selection Methods along With Random Forest and Support Vector Machines Classification Technique in Health Care System

Health care Management System (HMS) is a key to successful management of any health care industry. Health care management systems have so many research dimensions such as identifying disease and diagnostic, drug discovery manufacturing, Bioinformatics’ problem, personalized treatments, Patient image analysis and so on. Heart Disease Prediction (HDP) is a process of identifying heart disease in advance and recognizes patient health condition by applying techniques on patient heart related symptoms. Now a day’s the problem of identifying heart diseases is solved by machine learning techniques. In this paper we construct a heart disease prediction method using combined feature selection and classification machine learning techniques. According to the existing study the one of the main difficult in heart disease prediction system is that the available data in open sources are not properly recorded the necessary characteristics and there is some lagging in finding the useful features from the available features. The process of removing inappropriate features from an available feature set while preserving sufficient classification accuracy is known as feature selection. A methodology is proposed in this paper that consists of two phases: Phase one employs two broad categories of feature selection techniques to identify the efficient feature sets and it is given to the input of our second phase such as classification. In this work we will concentrate on filter-based method for feature selection such as Chi-square, Fast Correlation Based Filter (FCBF), Gini Index (GI), RelifeF, and wrapper-based method for feature selection such as Backward Feature Elimination (BFE), Exhaustive Feature Selection (EFS), Forward Feature Selection (FFS), and Recursive Feature Elimination (RFE). The UCI heart disease data set is used to evaluate the output in this study. Finally, the proposed system's performance is validated by various experiments setups.

Combining Two Classification Methods for Predicting Jakarta Bay Seabed Type Using Multibeam Echosounder Data

Classification of seabed types from multibeam echosounder data using machine learning techniques has been widely used in recent decades, such as Random Forest (RF), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Nearest Neighbor (NN). This study combines the two most frequently used machine learning techniques to classify and map the seabed sediment types from multibeam echosounder data. The classification model developed in this study is a combination of two machine learning classification techniques, namely Support Vector Machine (SVM) and K-Nearest Neighbor (K-NN). This classification technique is called SV-KNN. Simply, SV-KNN adopts these two techniques to carry out the classification process. The SV-KNN technique begins with determining test data by specifying support vectors and hyperplanes, as was done on the SVM method, and executes the classification process using the K-NN. Clay, fine silt, medium silt, coarse silt, and fine sand are the five main classes produced by SVKNN. The SV-KNN method has an overall accuracy value of 87.38% and a Kappa coefficient of 0.3093.

DETECTING DECEPTIVE REVIEWS: AN INTEGRATED MACHINE LEARNING APPROACH

In recent years, online reviews have become a crucial factor in promoting products and services. However, the rise of fake reviews has posed a significant challenge. Businesses, marketers, and advertisers often resort to embedding fake reviews to attract customers or undermine their competitors. Deceptive reviews have become a common practice, as they serve as a means of promoting one''''s own business or tarnishing the reputation of rivals. Consequently, the identification of deceptive reviews has emerged as a critical and ongoing research area. This research paper presents a machine learning model approach to detect deceptive reviews. The study focuses on experiments conducted using a deceptive opinion spam corpus dataset, specifically targeting restaurant reviews. An n-gram model combined with max features is developed to identify deceptive content, with a particular emphasis on fake reviews. Additionally, a benchmark study is conducted to explore the performance of two different feature extraction techniques and their application in five machine learning classification techniques. The experimental findings demonstrate that the passive aggressive classifier outperforms other algorithms, achieving the highest accuracy not only in text classification but also in identifying fake reviews. Moreover, the research delves into the identification of deceptive reviews and explores diverse feature extraction and machine learning techniques to improve the model''''s accuracy.

CLASSIFICATION OF PARKINSON’S DISEASE AND OTHER NEUROLOGICAL DISORDERS USING VOICE FEATURES EXTRACTION AND REDUCTION TECHNIQUES

This study aimed to differentiate individuals with Parkinson's disease (PD) from those with other neurological disorders (ND) by analyzing voice samples, considering the association between voice disorders and PD. Voice samples were collected from 76 participants using different recording devices and conditions, with participants instructed to sustain the vowel /a/ comfortably. PRAAT software was employed to extract features including autocorrelation (AC), cross-correlation (CC), and Mel frequency cepstral coefficients (MFCC) from the voice samples. Principal component analysis (PCA) was utilized to reduce the dimensionality of the features. Classification Tree (CT), Logistic Regression, Naive Bayes (NB), Support Vector Machines (SVM), and Ensemble methods were employed as supervised machine learning techniques for classification. Each method provided distinct strengths and characteristics, facilitating a comprehensive evaluation of their effectiveness in distinguishing PD patients from individuals with other neurological disorders. The Naive Bayes kernel, using seven PCA-derived components, achieved the highest accuracy rate of 86.84% among the tested classification methods. It is worth noting that classifier performance may vary based on the dataset and specific characteristics of the voice samples. In conclusion, this study demonstrated the potential of voice analysis as a diagnostic tool for distinguishing PD patients from individuals with other neurological disorders. By employing a variety of voice analysis techniques and utilizing different machine learning algorithms, including Classification Tree, Logistic Regression, Naive Bayes, Support Vector Machines, and Ensemble methods, a notable accuracy rate was attained. However, further research and validation using larger datasets are required to consolidate and generalize these findings for future clinical applications.

Hybrid Quantum-Enhanced Machine Learning for Supply Chain Optimization

Abstract: Modern supply chains face intricate challenges demanding sophisticated optimization strategies.This research presents a pioneering approach, combining quantum computing and machine learning, to revolutionize supply chain optimization. A hybrid framework harmonizes the power of quantum computing, including quantum annealers and gate-based quantum devices, with classical machine learning techniques. This synthesis aims to surmount the limitations of conventional optimization methods. Through rigorous experimentation on real-world supply chain scenarios, our approach demonstrates substantial efficiency gains and enhanced solution quality in comparison to classical methods. The implications are far- reaching, promising substantial cost reductions, streamlined resource allocation, and sustainability improvements for industries relying on supply chain management. Our study contributes to the burgeoning field of quantum machine learning and offers immediate value to supply chain professionals grappling with complexity. As supply chains evolve, our research sets the stage for innovative solutions in the quest for optimization.

Random Forest Classification and Ionospheric Response to Solar Flares: Analysis and Validation

The process of manually checking, validating, and excluding data in an ionospheric very-low-frequency (VLF) analysis during extreme events is a labor-intensive and time-consuming task. However, this task can be automated through the utilization of machine learning (ML) classification techniques. This research paper employed the Random Forest (RF) classification algorithm to automatically classify the impact of solar flares on ionospheric VLF data and erroneous data points, such as instrumentation errors and noisy data. The data used for analysis were collected during September and October 2011, encompassing solar flare classes ranging from C2.5 to X2.1. The F1-score values obtained from the test dataset displayed values of 0.848; meanwhile, a more detailed analysis revealed that, due to the imbalanced distribution of the target class, the per-class F1-score indicated higher values for the normal data point class (0.69–0.97) compared to those of the anomalous data point class (0.31 to 0.71). Instances of successful and inadequate categorization were analyzed and presented visually. This research investigated the potential application of ML techniques in the automated identification and classification of erroneous VLF amplitude data points; however, the findings of this research hold promise for the detection of short-term ionospheric responses to, e.g., gamma ray bursts (GRBs), or in the analysis of pre-earthquake ionospheric anomalies.

Developments in Image Processing Using Deep Learning and Reinforcement Learning.

The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

Deep-Learning-Based Classification of Cyclic-Alternating-Pattern Sleep Phases.

Determining the cyclic-alternating-pattern (CAP) phases in sleep using electroencephalography (EEG) signals is crucial for assessing sleep quality. However, most current methods for CAP classification primarily rely on classical machine learning techniques, with limited implementation of deep-learning-based tools. Furthermore, these methods often require manual feature extraction. Herein, we propose a fully automatic deep-learning-based algorithm that leverages convolutional neural network architectures to classify the EEG signals via their time-frequency representations. Through our investigation, we explored using time-frequency analysis techniques and found that Wigner-based representations outperform the commonly used short-time Fourier transform for CAP classification. Additionally, our algorithm incorporates contextual information of the EEG signals and employs data augmentation techniques specifically designed to preserve the time-frequency structure. The model is developed using EEG signals of healthy subjects from the publicly available CAP sleep database (CAPSLPDB) on Physionet. An experimental study demonstrates that our algorithm surpasses existing machine-learning-based methods, achieving an accuracy of 77.5% on a balanced test set and 81.8% when evaluated on an unbalanced test set. Notably, the proposed algorithm exhibits efficiency and scalability, making it suitable for on-device implementation to enhance CAP identification procedures.

A Systematic Review of UAV Applications for Mapping Neglected and Underutilised Crop Species’ Spatial Distribution and Health

Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.

AIoT-Driven Edge Computing for Rural Small-Scale Poultry Farming: Smart Environmental Monitoring and Anomaly Detection for Enhanced Productivity

The growing demand for chicken production has emphasized the importance of maintaining optimal conditions to improve quality and productivity.The integration of Artificial Intelligence (AI) and the Internet of Things(IoT) is recommended for the efficient management of the farm's environment. A potential solution is presented in this paper, utilizing IoT-based sensor nodes with ARM Cortex M3 - LPC 1769 and LORA technology to monitor chicken farms across diverse regions.The proposed solution incorporates a low-cost edge computing server-Jetson Nano device equipped with a machine learning model to categorize and monitor live environmental conditions in poultry farms. Real-time data from various branches is collected and analyzed using machine learning classification techniques including logistic regression, K nearest neighbors, and support vector machines.The performance of these algorithms is compared to identify the most effective approach. Upon evaluation, the K nearest neighbors emerges as the superior performer, achieving an impressive accuracy of 99.72% and an execution duration of 0.087 seconds on the Jetson Nano edge computing device. This cost-effective technology is tailored for small businesses in regions where farmers can gain valuable insights from data-driven decisions and closely monitor their operations. By incorporating AIoT into farm management, the challenges faced by small-scale poultry farming can be addressed, empowering farmers with enlightened techniques to improve overall productivity and quality.

Equivariant Graph Neural Networks for Toxicity Prediction.

Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.

MEDICAL IMAGING ENHANCEMENT WITH AI MODELS FOR AUTOMATIC DISEASE DETECTION AND CLASSIFICATION BASED ON MEDICAL IMAGES

The development of artificial intelligence (AI) in the healthcare industry has recently been of utmost importance. Early discovery, diagnosis, categorization, analysis, and treatment options are always positive medical breakthroughs. In making diagnoses and strategic decisions for healthcare, precise and consistent picture classification is essential. The semantic gap has emerged as the main problem with picture sorting. To bridge the gap, traditional machine learning techniques for classification mainly depend on low-level features rather than high-level ones, use some hand-crafted features, but compel intensive feature extraction and classification procedures. Deep convolution neural networks (CNNs), a potent technique that has significantly advanced in recent years, are successful in classifying images. A thorough assessment of pertinent studies is required to further assist readers in understanding the research and its main concepts captured. This study highlights the various AI models for automatic disease detection and classification based on medical images. This can include identifying tumors, lesions, fractures, or other abnormalities. The models discussed in this article include Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), One-Class Learning Models, Recurrent Neural Networks (RNNs), and 3D and Multimodal Models. Since these models work with various modalities, there are numerous instances where computed tomography (CT), positron emission computed tomography (PET), X-ray, magnetic resonance imaging (MRI), and ultrasound (US). Are mentioned to help complement the research.