AUC-ROC Score Research Articles

Ai in Fraud Detection: Evaluating the Efficacy of Artificial Intelligence in Preventing Financial Misconduct

AI is anticipated to enhance competitive advantages for financial organisations by increasing efficiency through cost reduction and productivity improvement, as well as by enhancing the quality of services and goods provided to consumers. AI applications in finance have the potential to create or exacerbate financial and non-financial risks, which could result in consumer and investor protection concerns like biassed, unfair, or discriminatory results, along with challenges related to data management and usage. The AI model's lack of transparency may lead to pro-cyclicality and systemic risk in markets, posing issues for financial supervision and internal governance frameworks that may not be in line with a technology-neutral regulatory approach. The primary objective of this research is to explore the effectiveness of Artificial Intelligence in preventing financial misconduct. This study extensively examines sophisticated methods for combating financial fraud, specifically evaluating the efficacy of Machine Learning and Artificial Intelligence. When examining the assessment metrics, this study utilized various metrics like accuracy, precision, recall, F1 score, and the ROC-AUC. The study found that Deep Learning techniques such as “Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks /Long Short-Term Memory, and Auto encoders” achieved high precision and AUC-ROC scores in detecting financial fraud. Voting classifiers, stacking, random forests, and gradient boosting machines demonstrated durability and precision in the face of adversarial attacks, showcasing the strength of unity.

A Comparative analysis of Advanced Machine Learning Techniques for Enhancing Brain Tumor Detection

Detection and classification of brain tumors are prominent tasks in neuroimaging, as it ensures accurate and timely diagnosis for effective treatment planning. This study explores the utility of machine learning models, such as convolutional neural networks, VGG 16 and 19 architectures, and recurrent neural networks, in improving the detection of brain tumor anomaly in both CT and MRI. A total of 3400 images were integrated and collected from multiple sources and prepared for usage by conducting meticulous preprocessing and feature extraction. Following the preparation process, the models were trained using stratified dataset split with a 70/30 ratio. The testing results indicated that the VGG 16 and VGG 19 architectures yielded the highest performance results, as they produced the optimal precision, recall, and F1-score values that reached up to 96.7%, 97.2%, and 96.5%, respectively, along with the highest AUC-ROC scores. In comparison, the CNN and RNN models presented lower performance results in each measured metric. The superiority of the VGG architectures strengthes the idea that the complexity and the capacity of the model to memorize and retain the imaging features is crucial for the accurate detection of the tumors. As such, the improved results can be helpful for healthcare professionals, as they provide powerful tools to ensure precise detection and characteristics of brain tumors. Nevertheless, further research is required to validate the results with a larger dataset, and the adherence of the study to the black-box nature of the model poses limitations on the interpretation. In any case, the study supplements the growing body of research in medical imaging and contributes to the prevention and management of brain tumors.

MLCapsNet +: A multi-capsule network for the identification of the HIV ISs along important sequence positions

The most studied sub-category of the retrovirus is the human immunodeficiency virus (HIV), which is a type of virus of the Retroviridae family. The HIV integration site (HIV IS)/ integration sites (HIV ISs) denote a crucial entity in the entire process of infection and its rebound if there is an interruption in therapy. It determines the steps involved in the formation of latent viral reserve. This work proposes a very deep neural network framework, where each of the layers of the multi-layered network is itself a neural network (NN). The attention mechanism is used for the extraction of the importance of positions in terms of an attention map. This framework will use the Capsule Network along with the attention mechanism to increase the explainability about the presence of local features. Convolutional neural networks (CNNs), which are specialized for image-based recognition and classification, have many drawbacks with one of these being its invariance to translation. This drawback has been overcome by the Capsule Networks. The proposed model also identifies the HIV ISs achieving a performance better than the State-of-the-art methods. Support Vector Machine (SVM), Random Forest (RF) and Logistic Regression (LR) classifiers have been used. Only two state-of-the-art methods are present in the literature that achieve these two goals and a comparison of this work with those two works has been provided here. This work performs way better than the state-of-the-art work in detecting the HIV IS. The comparison is presented here in terms of AUC-ROC, AUC-PR, accuracy, confusion matrix, and Fβ score.

Predictive Analysis of Endoscope Demand in Otolaryngology Outpatient Settings

Background: There has been a trend to transit reprocessing of flexible endoscopes from a high-level disinfectant (HLD) centralized manner to sterilization performed by nursing staff in some Ear, Nose, and Throat (ENT) clinics. In doing so, the clinic nursing staff are responsible for predicting and managing clinical demand for flexible endoscopes. The HLD disinfection process is time-consuming and requires specialized training and competency to be performed safely. Solely depending on human expertise for predicting the flexible endoscope demands is unreliable and produced a concern of an inadequate supply of devices available for diagnostic purposes. Method: The demand for flexible endoscopes for future patient visits has not been well studied but can be modeled based on patients’ historical information, provider, and other visit-related factors. Such factors are available to the clinic before the visit. Binary classifiers can be used to help inform the sterile processing department of reprocessing needs days or weeks earlier for each patient. Results: Among all our trained models, Logistic Regression reports an average AUC ROC score of 89% and accuracy of 80%. Conclusion: The proposed framework not only significantly reduces the reprocessing efforts in terms of time spent on communication, cleaning, scheduling, and transferring scopes, but also helps to improve patient safety by reducing the exposure risk to potential infections.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis

BackgroundLong-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting.MethodsWe constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People’s Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets.ResultsThe results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process.ConclusionsOur diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

BERT-Based Medical Chatbot: Enhancing Healthcare Communication through Natural Language Understanding

The advent of modern technologies like Artificial Intelligence(AI), Internet of Things(IoT) and Deep Learning(DL) has ushered in a transformative era in healthcare, offering innovative solutions towards personalized healthcare by enhancing the quality of various medical services. Our proposed methodology involves the development of a BERT-based medical chatbot, leveraging cutting-edge deep learning technology to significantly enhance healthcare communication and accessibility. The traditional challenges faced by medical chatbots, such as imprecise understanding of medical conversations, inaccurate responses to jargon, and the inability to offer personalized feedback, are addressed through the utilization of Bidirectional Encoder Representations from Transformers (BERT). The performance metrics of our chatbot underscore its effectiveness. With an accuracy of 98%, the chatbot ensures a high level of precision in handling medical queries. The precision score of 97% attests to the accuracy and reliability of its responses. The AUC-ROC score of 97% indicates the chatbot's exceptional ability to predict specific diseases based on user queries and symptoms, showcasing its robust predictive power. Furthermore, a recall of 96% demonstrates the chatbot's capability to avoid missing cases in medical diagnoses, ensuring comprehensive coverage of potential conditions. The F1 score of 98% showcases the chatbot's proficiency in delivering accurate and personalized healthcare information, striking a harmonious balance between precision and recall. Our BERT-based medical chatbot not only addresses the limitations of traditional approaches but also achieves a remarkable performance with high accuracy, precision, predictive power, and comprehensive coverage, making it a valuable tool for advancing the quality of healthcare services.

A reliable diabetic retinopathy grading via transfer learning and ensemble learning with quadratic weighted kappa metric

The most common eye infection in people with diabetes is diabetic retinopathy (DR). It might cause blurred vision or even total blindness. Therefore, it is essential to promote early detection to prevent or alleviate the impact of DR. However, due to the possibility that symptoms may not be noticeable in the early stages of DR, it is difficult for doctors to identify them. Therefore, numerous predictive models based on machine learning (ML) and deep learning (DL) have been developed to determine all stages of DR. However, existing DR classification models cannot classify every DR stage or use a computationally heavy approach. Common metrics such as accuracy, F1 score, precision, recall, and AUC-ROC score are not reliable for assessing DR grading. This is because they do not account for two key factors: the severity of the discrepancy between the assigned and predicted grades and the ordered nature of the DR grading scale. This research proposes computationally efficient ensemble methods for the classification of DR. These methods leverage pre-trained model weights, reducing training time and resource requirements. In addition, data augmentation techniques are used to address data limitations, improve features, and improve generalization. This combination offers a promising approach for accurate and robust DR grading. In particular, we take advantage of transfer learning using models trained on DR data and employ CLAHE for image enhancement and Gaussian blur for noise reduction. We propose a three-layer classifier that incorporates dropout and ReLU activation. This design aims to minimize overfitting while effectively extracting features and assigning DR grades. We prioritize the Quadratic Weighted Kappa (QWK) metric due to its sensitivity to label discrepancies, which is crucial for an accurate diagnosis of DR. This combined approach achieves state-of-the-art QWK scores (0.901, 0.967 and 0.944) in the Eyepacs, Aptos, and Messidor datasets.

A Learning Approach for The Identification of Network Intrusions Based on Ensemble XGBoost Classifier

The limitation of signature-based Intrusion Detection systems has given rise for the popularity of Machine learning (ML) approaches r for building such intrusion detection systems (IDSs). ML is a sub-filed of Artificial Intelligence that enables algorithms to learn from data and its applications have been widely accepted and used in many domains. To achieve a promising ML-based model that can identify attacks and intrusions in networks and the cyber space, different stages of machine learning approach like pre-processing, attribute selection, model building, hyper parameter tuning can be very important. CICIDS2017 intrusion dataset was used for all the experimentations. This study focuses on building cyber threat detection model based on the ensemble feature selection and classification method. Innovative approaches were used for the analysis and pre-processing of the dataset. Thereafter, XGboost algorithm was used for selecting relevant features from the default input attributes in each of the captures. Thereafter, the reduced features were employed in the identification of cyber intrusions. The average accuracy achieved in the 8 captures of the dataset is 98% while precision is 0.98. Also, recall is 0.98, f1-score is 0.98 while AUC ROC score is 0.99. The study concluded that XGBoost-based model was able to achieve promising results based on the proper dataset encoding, feature importance-based feature selection and tuning of the algorithm for intrusion identification.

Acoustic COVID-19 Detection Using Multiple Instance Learning.

In the COVID-19 pandemic, a rigorous testing scheme was crucial. However, tests can be time-consuming and expensive. A machine learning-based diagnostic tool for audio recordings could enable widespread testing at low costs. In order to achieve comparability between such algorithms, the DiCOVA challenge was created. It is based on the Coswara dataset offering the recording categories cough, speech, breath and vowel phonation. Recording durations vary greatly, ranging from one second to over a minute. A base model is pre-trained on random, short time intervals. Subsequently, a Multiple Instance Learning (MIL) model based on self-attention is incorporated to make collective predictions for multiple time segments within each audio recording, taking advantage of longer durations. In order to compete in the fusion category of the DiCOVA challenge, we utilize a linear regression approach among other fusion methods to combine predictions from the most successful models associated with each sound modality. The application of the MIL approach significantly improves generalizability, leading to an AUC ROC score of 86.6% in the fusion category. By incorporating previously unused data, including the sound modality 'sustained vowel phonation' and patient metadata, we were able to significantly improve our previous results reaching a score of 92.2%.

A Deep Learning Based Hybrid Model Development for Enhanced Credit Score Prediction

This paper presents a hybrid deep learning model that combines Deep Neural Networks (DNNs) and Recurrent Neural Networks (RNNs) to enhance credit score predictions, especially for people who have a short credit history. In these situations, traditional credit scoring techniques frequently fall short, misclassifying creditworthy applicants and costing lenders money. Neural network models and ensemble methods are used in the model’s data preparation to find intricate patterns. Metrics demonstrating the hybrid RNN+DNN model’s superior performance over standalone models include an AUC-ROC score of 0.7971 and enhanced outcomes via stratified K-fold cross-validation. The hybrid model also achieves high sensitivity, specificity, and accuracy. LSTM units, dense layers, batch size, epochs, L2 regularization, and dropout rates are all part of the model architecture. Although the study was successful, it had limitations that pertain to interpretability, computing requirements and dataset quality. To guarantee accuracy and equity in credit assessment, future research should concentrate on refining hyperparameters, increasing computational effectiveness, and verifying the model using actual credit scoring systems.

MSFlow: Multiscale Flow-Based Framework for Unsupervised Anomaly Detection.

Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives widespread applications, where only anomaly-free samples are available for training. Some UAD applications intend to locate the anomalous regions further even without any anomaly information. Although the absence of anomalous samples and annotations deteriorates the UAD performance, an inconspicuous, yet powerful statistics model, the normalizing flows, is appropriate for anomaly detection (AD) and localization in an unsupervised fashion. The flow-based probabilistic models, only trained on anomaly-free data, can efficiently distinguish unpredictable anomalies by assigning them much lower likelihoods than normal data. Nevertheless, the size variation of unpredictable anomalies introduces another inconvenience to the flow-based methods for high-precision AD and localization. To generalize the anomaly size variation, we propose a novel multiscale flow-based framework (MSFlow) composed of asymmetrical parallel flows followed by a fusion flow to exchange multiscale perceptions. Moreover, different multiscale aggregation strategies are adopted for image-wise AD and pixel-wise anomaly localization according to the discrepancy between them. The proposed MSFlow is evaluated on three AD datasets, significantly outperforming existing methods. Notably, on the challenging MVTec AD benchmark, our MSFlow achieves a new state-of-the-art (SOTA) with a detection AUORC score of up to 99.7%, localization AUCROC score of 98.8% and PRO score of 97.1%.

CNN Fusion: A Promising Technique for Ophthalmic Disorder Diagnosis

Ophthalmic disorders represent a major global health problem leading to visual impairment and even blindness if not detected and treated early. Deep learning is a popular approach where pre-trained models are now often used to diagnose a variety of diseases from different medical images. In order to provide an accurate and reliable categorization of ophthalmic illnesses, this study suggests a fusion or ensemble technique that harnesses the power of pre-trained models like ResNet50, DenseNet and EfficientNet. The proposed ensemble model enhances the performance by incorporating the benefits of three architectural paradigms, guiding the convergence of each one's distinct strengths to achieve improved overall classification result. The ensemble architecture has undertaken inclusive validation on multiple fundus image datasets, showing improved performance over lone models. An accuracy of 92% demonstrates the potential of ensemble model in the categorization of eye diseases like cataract, diabetic retinopathy, glaucoma and normal eyes. In addition to achieving cutting-edge accuracy, the ensemble also offers an AUC-ROC score of 1.00. This technique presents a viable alternative for boosting the accuracy and reliability in identifying various eye illnesses by combining diverse model capabilities. The empirical data and conclusions show that the ensemble model has the potential to make a substantial contribution to the field of ocular medical imaging and patient care by enabling precise and timely treatments that may significantly improve the lives of millions of people worldwide who are impacted by ophthalmic diseases.

Prediction of Cardiovascular Disease Based on Voting Ensemble Model and SHAP Analysis

Cardiovascular Diseases (CVD) or heart diseases cardiovascular diseases lead the list of fatal diseases. However, the treatment of this disease involves a time-consuming process. Therefore, new approaches are being developed for the detection of such diseases. Machine learning methods are one of these new approaches. In particular, these algorithms contribute significantly to solving problems such as predictions in various fields. Given the amount of clinical data currently available in the medical field, it is useful to use these algorithms in areas such as CVD prediction. This study proposes a prediction model based on voting ensemble learning for the prediction of CVD. Furthermore, the SHAP technique is utilized to interpret the suggested prediction model including the risk factors contributing to the detection of this disease. As a result, the suggested model depicted an accuracy of 0.9534 and 0.954 AUC-ROC score for CVD prediction. Compared to similar studies in the literature, the proposed prediction model provides a good classification rate.

Sparse Representations Optimization with Coupled Bayesian Dictionary and Dictionary Classifier for Efficient Classification

Among the numerous techniques followed to learn a linear classifier through the discriminative dictionary and sparse representations learning of signals, the techniques to learn a nonparametric Bayesian classifier jointly and discriminately with the dictionary and the corresponding sparse representations have drawn considerable attention from researchers. These techniques jointly learn two sets of sparse representations, one for the training samples over the dictionary and the other for the corresponding labels over the dictionary classifier. At the prediction stage, the representations of the test samples computed over the learned dictionary do not truly represent the corresponding labels, exposing weakness in the joint learning claim of these techniques. We mitigate this problem and strengthen the joint by learning a set of weights over the dictionary to represent the training data and further optimizing the same weights over the dictionary classifier to represent the labels of the corresponding classes of the training data. Now, at the prediction stage, the representation weights of the test samples computed over the learned dictionary also represent the labels of the corresponding classes of the test samples, resulting in the accurate reconstruction of the labels of the classes by the learned dictionary classifier. Overall, a reduction in the size of the Bayesian model’s parameters also improves training time. We analytically and nonparametrically derived the posterior conditional probabilities of the model from the overall joint probability of the model using Bayes’ theorem. We used the Gibbs sampler to solve the joint probability of the model using the derived conditional probabilities, which also supports our claim of efficient optimization of the coupled/joint dictionaries and the sparse representation parameters. We demonstrated the effectiveness of our approach through experiments on the standard datasets, i.e., the Extended YaleB and AR face databases for face recognition, Caltech-101 and Fifteen Scene Category databases for categorization, and UCF sports action database for action recognition. We compared the results with the state-of-the-art methods in the area. The classification accuracies, i.e., 93.25%, 89.27%, 94.81%, 98.10%, and 95.00%, of our approach on the datasets have increases of 0.5 to 2% on average. The overall average error margin of the confidence intervals in our approach is 0.24 compared with the second-best approach, JBDC, for which it is 0.34. The AUC–ROC scores of our approach are 0.98 and 0.992, which are better than those of others, i.e., 0.960 and 0.98, respectively. Our approach is also computationally efficient.

Security analysis of the data of social networks using AI techniques

This proposed research explores the potential of AI techniques, particularly user engagement prediction, for analyzing social network data and identifying potential security threats. Utilizing a Random Forest classifier, we developed a highly accurate model achieving 100% accuracy and a 1.0 AUC-ROC score. This exceptional performance demonstrates the ability of engagement prediction to accurately flag suspicious accounts with unusually low engagement, often associated with bots or fake profiles. Based on these findings, we implemented mitigation strategies such as flagging low-engagement accounts for further investigation and analyzing engagement trends to inform proactive security measures. Furthermore, our work opens doors for future research in combining engagement prediction with other AI techniques like sentiment analysis for even more sophisticated threat detection, ultimately contributing to the development of robust solutions for enhanced social network security and user privacy protection.

Фэйк-аккаунттарды анықтау мәселесінде машиналық оқытуды қолдану

Nowadays, due to the rapid growth of social networks, the possibility of various threats is increasing, one of which is fake accounts. The use of machine learning in the issue of identifying fake accounts on a social network is considered. The main goal of the work is to research and develop methods and models of machine learning for the effective detection of fake accounts in social networks. This avoids problems such as extortion, blackmail, harassment, fake requests, insulting a person, unauthorized access to personal information, illegal use of user information through research. The object of the study is fake accounts. Methods used in the research work: scaling methods, machine learning methods, feature selection methods, model evaluation methods. The subject area of the research work is Instagram. The uniqueness of the study is to determine the most reliable way to detect fake accounts using various, widely used methods. Machine learning classifiers were effectively used to detect a fake account: decision tree classifier; classifier KNN; logistic regression classifier; random forest classifier; support vector machine (SVM) classifier. The performance of the models was evaluated using various indicators, such as accuracy, ROC-curve, classification score. Some models, such as Decision Tree, Random Forest, and KNN, have shown stable and high AUC-ROC, Accuracy, Precision, Recall, and F1 scores both during initial testing and after retraining. This highlights the importance of choosing the right features when training a model and shows that not all features are equally useful for all models. As a result of the study, after verifying the effectiveness of using machine learning algorithms to identify fake

Accurate prediction of pressure losses using machine learning for the pipeline transportation of emulsions

One of the significant challenges to designing an emulsion transportation system is predicting frictional pressure losses with confidence. The state-of-the-art method for enhancing reliability in prediction is to employ artificial intelligence (AI) based on various machine learning (ML) tools. Six traditional and tree-based ML algorithms were analyzed for the prediction in the current study. A rigorous feature importance study using RFECV method and relevant statistical analysis was conducted to identify the parameters that significantly contributed to the prediction. Among 16 input variables, the fluid velocity, mass flow rate, and pipe diameter were evaluated as the top predictors to estimate the frictional pressure losses. The significance of the contributing parameters was further validated by estimation error trend analyses. A comprehensive assessment of the regression models demonstrated an ensemble of the top three regressors to excel over all other ML and theoretical models. The ensemble regressor showcased exceptional performance, as evidenced by its high R2 value of 99.7 % and an AUC-ROC score of 98 %. These results were statistically significant, as there was a noticeable difference (within a 95 % confidence interval) compared to the estimations of the three base models. In terms of estimation error, the ensemble model outperformed the top base regressor by demonstrating improvements of 6.6 %, 11.1 %, and 12.75 % for the RMSE, MAE, and CV_MSE evaluation metrics, respectively. The precise and robust estimations achieved by the best regression model in this study further highlight the effectiveness of AI in the field of pipeline engineering.

A Hybrid CNN Model for Deep Feature Extraction for Diabetic Retinopathy Detection and Gradation

Diabetic retinopathy (DR) is a complication of prolonged diabetic mellitus causing damage to the micro-vascular system in retina. Computer-aided detection of DR can significantly reduce time, effort, and cost in early diagnosis and thereby restrict further complications. In this paper, we present a deep learning based framework for automated DR stage classification from fundus retinal images.We design and develop a custom build Convolutional Neural Network (CNN) model named Model-IVS to extract meaningful and discriminative features from the retinal fundus images. The proposed hybrid-CNN combines the multi-scale pyramidal feature processing concept of inception model with the convolutional pipeline based feature extraction concept of VGG-Nets and adopts multi-scale feature concatenation based short-cut connections of DenseNet with learnable convolutions. For the binary classification tasks, we employ Model-IVS as a feature extractor and use Support Vector Machine (SVM) classifier on the reduced features obtained through principal component analysis. The proposed hybrid model is end-to-end trained incrementally on a class-balanced dataset curated from the train set of EyePACS dataset. The cross-dataset generalization of the proposed hybrid CNN based DR classification framework is evaluated on APTOS, MESSIDOR-1 and MESSIDOR-2 datasets. Experimental results demonstrate that our model has outperformed all the existing state-of-the-art methods for DR severity grading task on APTOS dataset. The proposed Model-IVS has achieved best accuracy, AUC-ROC, and kappa score of 84.33%, 0.978 and 0.899, respectively in DR severity grading task. In binary classification tasks like DR screening and referable DR predictions, our Model-IVS feature extraction network with SVM classifier on PCA features has also achieved comparable performance results w.r.t. the state-of-the-art methods. In DR screening task, we recorded an accuracy of 97.84%, 87.42%, and 89.67% on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively. In referable DR prediction, we achieved AUC-ROC score of 0.987, 0.928, and 0.873 on APTOS, MESSIDOR-1, and MESSIDOR-2 datasets, respectively.

Explainable artificial intelligence model for the detection of geographic atrophy using colour retinal photographs

ObjectiveTo develop and validate an explainable artificial intelligence (AI) model for detecting geographic atrophy (GA) via colour retinal photographs.Methods and analysisWe conducted a prospective study where colour fundus images were...

Deep Learning and Optimized Learning Machine for Brain Tumor Classification

Brain Tumorclassification in MRI images is a time-consuming and tedioustask for medical professionals. An accurate classification model can assist healthcare providers intreatingpatients with effective care. In this research work, an enhanced learning machine for classifying brain tumors has presented for medical specialist’s assistance. Deep learning architectures like Inception V3 and DenseNet201 are used to retrieve the categorization model's basic features. Along with the features collected using deep learning models, radiomic properties are integrated before classification in order to increase classification accuracy. Particle swarm optimized kernel Extreme Learning Machine (PSO-KELM) model has used to categorize the features into four groups like No Tumor, Gliomas, Meningiomas and Pituitary Tumors. Our system employs two benchmark datasets to evaluate the efficiency of the developed classification system using measures such as accuracy, recall, precision, false-positive rate, recall, precision, f1-score, and AUC ROC score, all of which our model performs better than the literaturevalues. In addition, the four existing optimized learning methods are individually compared with dataset 1 and five approaches are independently evaluated with dataset 2. Accuracy measure is used to authenticate the improved performance analysis of oursuggestedsystem. In training and testing phases, the suggested model accomplishesimproved accuracy than theState-of-Art deep learning approaches. Our system's classification accuracy is 96.17% and 97.92% for datasets 1 and 2, respectively. Similar to the training method, the proposed testing model's accuracy is improved as 97.97% and 98.21%, respectively.