Negative Rate Research Articles

AC-YOLO: Multi-category and high-precision detection model for stored grain pests based on integrated multiple attention mechanisms

The existing detection models for stored grain pests have low accuracy and poor generalization ability in fine-grained detection tasks involving numerous species, minor inter-class differences, and significant intra-class variations. This study addresses these issues by constructing a dataset comprising 3227 images of 12 species of grain pests and proposing an enhanced YOLOv7 algorithm for grain pest detection. This algorithm integrates the Convolutional Block Attention Module (CBAM) into the backbone network to enhance the perception of crucial features, thereby improving detection accuracy. Furthermore, the algorithm’s neck network incorporates the convolutional self-attention mechanism, ACmix, which improves algorithm efficiency and target sensitivity, reducing the rate of false negatives. The Efficient Complete Intersection over Union (ECIoU) is employed as the detection box loss function to augment the model’s generalization capabilities. Experimental results demonstrate that employing the proposed algorithm for detecting the 12 grain pests yields an mAP@0.5 and F1-score of 91.9% and 89.6%, respectively. Compared to the original algorithm, this represents an improvement of 6.2 and 3.4 percentage points, and the detection speed remains nearly constant. Moreover, the comprehensive performance of this algorithm surpasses that of Faster RCNN, SSD, other YOLO series, and the Deformable-detr algorithm. This method facilitates high-precision, real-time detection under conditions involving small targets and complex backgrounds, presenting a viable approach for developing intelligent grain pest detection systems.

Usability of the Japanese Late-Stage Elderly Questionnaire for screening major depression.

Older adults with major depression are at risk of frailty and long-term care needs. Consequently, screening for major depression is imperative to prevent such risks. In Japan, the Late-Stage Elderly Questionnaire was developed to evaluate older adults' holistic health, including mental well-being. It comprises one specific question to gauge life satisfaction, but the effectiveness of this question to screen for major depression remains unclear. Therefore, we aimed to assess the usability of this question to screen for major depression. This retrospective cohort study used a large, commercially available claims database in Japan. Participants were older adults aged ≥75 years who completed the Late-Stage Elderly Questionnaire and were classified with and without new major depression within 1 year. We evaluated the questionnaire's ability to screen for major depression using C-statistics, developing three models to assess the cut-off value based on responses to the life satisfaction question ('Satisfied', 'Somewhat satisfied', 'Somewhat unsatisfied', or 'Unsatisfied'), estimating the sensitivity and specificity of each model. Among 11 117 older adults, 77 newly experienced major depression within 1 year. The C-statistic for screening major depression was 0.587. The model setting the cut-off between 'Somewhat unsatisfied' and 'Unsatisfied' the demonstrated lowest sensitivity and highest specificity, while the model setting the cut-off between 'Satisfied' and 'Somewhat satisfied' demonstrated highest sensitivity and lowest specificity. Our results suggest that due to its poor screening ability and high rate of false negatives, the question assessing life satisfaction in the Late-Stage Elderly Questionnaire may not be useful for screening major depression in older adults and may require modification.

A Model of Zombie Firms and the Perils of Negative Real Interest Rates

A Model of Zombie Firms and the Perils of Negative Real Interest Rates

DNA-AuNP encoders array Integrating a multi-modal coupled analysis approach for noninvasive precision screening of myocardial function injury disease in children

Limited-species serum biomarkers could only partially reflect the information on myocardial function injury disease in children, leading to some important information being inevitably lost, which affects the judgment of the whole information, resulting in a high rate of false positives and negatives. There is a critical unmet need for noninvasive precision screening for myocardial function injury disease in children. Here, we introduced a multi-modal coupled analysis of the DNA-AuNP encoders to achieve this goal. The DNA-AuNP encoders react with the serum specimen from myocardial function injury disease in children to generate the multi-modal response signals, such as the fluorescence spectrum, size distribution, and surface ζ-potential distribution. Interestingly, we used dark-field microscopy to observe the interface changes between the DNA-AuNP encoder and serum at the microscopic level. Different machine learning algorithms analyze the correlation of a clinical outcome with multi-modal response signals from a target disease. As the number of modal response signals increases, our approach monotonically increases screening performance. The three-modal coupled analysis provided an average accuracy of up to 91.7% using 37 serum specimens in children from myocarditis, myocardial injury, and health. This study provides a highly generic tool for noninvasive screening using a drop of serum.

Using Logistic Regression and Support Vector Classification to Predict Cancer

This study investigates the application of machine learning (ML) algorithms in the early diagnosis of breast cancer, focusing on logistic regression and Support Vector Classification (SVC). Utilizing a dataset from Kaggle, which includes diverse clinical features from breast mass samples, the research conducts a comparative analysis of these models in terms of accuracy and interpretability. Our findings reveal that both logistic regression and SVC demonstrate high precision in distinguishing between benign and malignant tumors, with SVC showing a marginally superior performance due to its higher sensitivity and lower rate of false negatives. The study emphasizes the potential of ML in enhancing cancer diagnostic processes, highlighting the importance of non-invasive, cost-effective, and accurate diagnostic alternatives. It also addresses the challenges of model interpretability and the need for more transparent ML applications in clinical settings. This research paves the way for future advancements in medical diagnostics, offering promising directions for integrating ML algorithms into clinical decision-making and patient care.

Venetoclax plus a hypomethylating agent versus cytarabine, aclarubicin, and granulocyte colony-stimulating factor chemotherapy as a first-line therapy for newly diagnosed acute myeloid leukemia: A propensity score-matched analysis.

Both venetoclax plus a hypomethylating agent (VEN/HMA) and cytarabine, aclarubicin, and granulocyte colony-stimulating factor (CAG) are low-intensity regimens for older patients with acute myeloid leukemia (AML) that show good efficacy and safety. It is unknown how VEN/HMA compares with the CAG regimen for the treatment of newly diagnosed AML. The outcomes of patients with newly diagnosed AML treated with VEN/HMA were compared with those of patients treated with a CAG-based regimen. Propensity score matching between these two cohorts at a 1:1 ratio was performed according to age at diagnosis, sex, Eastern Cooperative Oncology Group performance status, state of fitness, and European LeukemiaNet (ELN) 2022 risk stratification to minimize bias. A total of 84 of 96 patients in the VEN/HMA cohort were matched with 84 of 147 patients in the CAG cohort. VEN/HMA resulted in a better response than the CAG-based regimens, as indicated by a higher composite complete remission (CRc) rate (82.1% vs. 60.7%; p=.002) and minimal residual disease negativity rate (88.2% vs. 68.2%; p=.009). In patients with an ELN adverse risk, VEN/HMA was associated with a higher CRc rate compared to CAG (80.5% vs. 58.3%; p=.006). VEN/HMA was associated with longer event-free survival (EFS) (median EFS, not reached vs. 4.5 months; p=.0004), whereas overall survival (OS) was comparable between the two cohorts (median OS, not reached vs. 18 months; p=.078). The VEN/HMA regimen may result in a better response than CAG-based treatment in older patients with newly diagnosed AML.

Optimizing Screening for Intrastriatal Interventions in Huntington's Disease Using Predictive Models.

Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD. The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions. We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to identify the 10 most important variables. A random forest algorithm was applied to build a predictive model for putamen volume >2500 mm3 and caudate volume >2000 mm3 bilaterally. Using the same 10 predictors, we constructed a logistic regression model with predictors significant at P < 0.05. The random forest model with 1000 trees and minimal terminal node size of 5 resulted in 83% area under the curve (AUC). The logistic regression model retaining age, CAG repeat size, and symbol digit modalities test-correct had 85.1% AUC. A probability cutoff of 0.8 resulted in 5.4% false positive and 66.7% false negative rates. Using easily obtainable clinical data and machine learning-identified initial predictor variables, random forest, and logistic regression models can successfully identify people with sufficient striatal volumes for inclusion cutoffs. Adopting these models in prescreening could accelerate clinical trial enrollment in HD and other neurodegenerative disorders when volume cutoffs are necessary enrollment criteria. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Hand gesture recognition with deep residual network using Semg signal.

To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition. The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset. The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924. The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.

A near real-time automated oil spill detection and early warning system using Sentinel-1 SAR imagery for the Southeastern Mediterranean Sea

ABSTRACT The ecological and environmental impact of marine oil pollution underlines the importance and necessity of an oil spill surveillance system. This study proposes an operational automated oil spill detection and early warning system to help take quick action for oil combating operations. Oil slicks in the spaceborne Sentinel-1 synthetic aperture radar (SAR) data are detected by a trained deep learning-based oil object detector. These detected oil objects are segmented into binary masks based on the similarity and discontinuity of the backscattering coefficients, and their trajectory is simulated. The detection process was tested on one-year SAR acquisitions in 2019, covering the Southeastern Mediterranean Sea; the false discovery rate (FDR) and false negative rate (FNR) are 23.3% and 24.0%, respectively. The system takes around 1.5 h from downloading SAR images to providing slick trajectory simulation. This study highlights the capabilities of using deep learning-based techniques in an operational oil spill surveillance service.

A novel method for detecting credit card fraud problems.

Credit card fraud is a significant problem that costs billions of dollars annually. Detecting fraudulent transactions is challenging due to the imbalance in class distribution, where the majority of transactions are legitimate. While pre-processing techniques such as oversampling of minority classes are commonly used to address this issue, they often generate unrealistic or overgeneralized samples. This paper proposes a method called autoencoder with probabilistic xgboost based on SMOTE and CGAN(AE-XGB-SMOTE-CGAN) for detecting credit card frauds.AE-XGB-SMOTE-CGAN is a novel method proposed for credit card fraud detection problems. The credit card fraud dataset comes from a real dataset anonymized by a bank and is highly imbalanced, with normal data far greater than fraud data. Autoencoder (AE) is used to extract relevant features from the dataset, enhancing the ability of feature representation learning, and are then fed into xgboost for classification according to the threshold. Additionally, in this study, we propose a novel approach that hybridizes Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to tackle class imbalance problems. Our two-phase oversampling approach involves knowledge transfer and leverages the synergies of SMOTE and GAN. Specifically, GAN transforms the unrealistic or overgeneralized samples generated by SMOTE into realistic data distributions where there is not enough minority class data available for GAN to process effectively on its own. SMOTE is used to address class imbalance issues and CGAN is used to generate new, realistic data to supplement the original dataset. The AE-XGB-SMOTE-CGAN algorithm is also compared to other commonly used machine learning algorithms, such as KNN and Light GBM, and shows an overall improvement of 2% in terms of the ACC index compared to these algorithms. The AE-XGB-SMOTE-CGAN algorithm also outperforms KNN in terms of the MCC index by 30% when the threshold is set to 0.35. This indicates that the AE-XGB-SMOTE-CGAN algorithm has higher accuracy, true positive rate, true negative rate, and Matthew's correlation coefficient, making it a promising method for detecting credit card fraud.

Diagnosis of skin lesion using shift-invariant network and an improved grey wolf optimizer

The global incidence of skin cancer has been rising, resulting in increased mortality and morbidity if left untreated. Accurate diagnosis of skin malignancies is crucial for early intervention through excision. While various innovative medical imaging techniques, such as dermoscopy, have improved the way we examine skin cancers, the progress in medical imaging for identifying skin lesions has not kept pace. Skin lesions exhibit diverse visual features, including variations in size, shape, boundaries, and artifacts, necessitating an efficient image-processing approach to assist dermatologists in decision-making. In this research, we propose an automated skin lesion classifier called GreyNet, which utilizes optimized convolutional neural networks (CNNs) or shift-invariant networks (SIN). GreyNet comprises three components: (i) a trained fully deep CNN for semantic segmentation, relating input images to manually labeled standard scans; (ii) an enhanced dense CNN with global information exchange and adaptive feature salvaging module to accurately classify each pixel in histopathological scans as benign or malignant; and (iii) a binary grey wolf optimizer (BGWO) to improve the classification process by optimizing the network’s hyperparameters. We evaluate the performance of GreyNet in terms of lesion segmentation and classification on the HAM10000 database. Extensive empirical results demonstrate that GreyNet outperforms existing lesion segmentation methods, achieving improved dice similarity score, volume error, and average processing time of 1.008±0.009, 0.903±0.009%, and 0.079±0.010 s, respectively. Moreover, GreyNet surpasses other skin melanoma classification models, exhibiting improved accuracy, precision, specificity, sensitivity, false negative rate, false positive rate, and Jaccard similarity score (JSS) of 96.5%, 97%, 96.2%, 92.1%, 3.8%, 3%, and 89.5%, respectively. Based on our experimental analysis, we conclude that GreyNet is an efficient tool to aid dermatologists in identifying skin melanoma.

Sine tangent search algorithm enabled LeNet for cotton crop classification using satellite image

Economic growth of country largely depends on crop production quantity and quality. Among various crops, cotton is one of the major crops in India, where 23 percent of cotton gets exported to various other countries. To classify these cotton crops, farmers consume much time, and this remains inaccurate most probably. Hence, to eradicate this issue, cotton crops are classified using deep learning model, named LeNet in this research paper. Novelty of this paper lies in utilization of hybrid optimization algorithm, named proposed sine tangent search algorithm for training LeNet. Initially, hyperspectral image is pre-processed by anisotropic diffusion, and then allowed for further processing. Also, SegNet is deep learning model that is used for segmenting pre-processed image. For perfect and clear details of pre-processed image, feature extraction is carried out, wherein vegetation index and spectral spatial features of image are found accurately. Finally, cotton crop is classified from segmented image and features extracted, using LeNet that is trained by sine tangent search algorithm. Here, sine tangent search algorithm is formed by hybridization of sine cosine algorithm and tangent search algorithm. Then, performance of sine tangent search algorithm enabled LeNet is assessed with evaluation metrics along with Receiver Operating Characteristic (ROC) curve. These metrics showed that sine tangent search algorithm enabled LeNet is highly effective for cotton crop classification with superior values of accuracy of 91.7%, true negative rate of 92%, and true positive rate of 92%.

Evaluation of traditional targeted axillary dissection eligibility criteria for node-positive breast cancer after neoadjuvant chemotherapy in a prospective multicenter registry

IntroductionTargeted axillary dissection (TAD) is performed after neoadjuvant systemic therapy (NST) to decrease the rate of non-therapeutic axillary dissection (ALND) for patients with node-positive breast cancer. In order to ensure the oncologic safety of TAD, eligibility criteria resulting in a low false negative rate (FNR) have been proposed. The purpose of this study was to evaluate the utility of the traditional criteria. MethodsData was collected from a prospective multicenter registry. In order to ascertain FNRs, pathologic findings in the sentinel lymph nodes (LN)s, malignant clipped LN, and axillary contents were determined. The FNRs within TAD eligibility criterion groups were compared. ResultsA total of 110 patients underwent TAD and ALND, and were therefore eligible for analysis. TAD retained a low FNR in advanced clinical T-N stage compared with earlier disease (T stage: 95% CI 0.00–11.93, p = 0.42; N stage: 95% CI 0.00–8.76, p = 0.31). Presentation with ≥4 abnormal LNs on axillary ultrasound did not predict a high TAD FNR (95% CI 0.00–5.37, p = 0.16). No significant differences were noted in TAD FNR when single was compared with dual tracer (blue dye vs dual tracer 95% CI 0.72–52.49, p = 0.13; radiotracer vs dual tracer 0.04–20.11, p = 0.51). Excision of the clipped LN and only one SLN was as accurate as excision of the clipped LN and ≥2 SLNs (95% CI 0.00–10.61, p = 0.38). ConclusionsTAD retained a low FNR among patients traditionally considered ineligible for this technique. However, excision of the clipped LN and at least one SLN remained essential to a low FNR.

LSCO: Light spectrum chimp optimization based spinalnet for live face detection and recognition

Face detection (FD) is considered to be of utmost significance in monitoring and safety systems. Finding concealed faces is a crucial research issue, particularly in populated or high-security settings like events and retail stores, where they may pose a security danger. To bridge this gap, a Light Spectrum Chimp Optimization (LSCO) enabled SpinalNet is introduced for live face detection and recognition. At first, an input image is fed up into the pre-processing phase to enhance the contrast image and remove the unwanted noises utilizing the Type 2 Fuzzy and Cuckoo Search (T2FCS) filter. Thereafter, the face is detected utilizing Viola-Jones which classifies the images based on the simple feature values to accurately identify and analyze human faces. Moreover, the detected image is passed to the feature extraction to acquire the face component features like eyes, nose, mouth, etc from the human face image. Here, the features employed are Local Directional Pattern Variance (LDPv), Local Directional Number Pattern (LDNP), Local Gabor Binary Pattern (LGBP) and statistical features like, mean, variance, kurtosis, skewness, and Convolutional Neural Networks (CNN) features. At last, face recognition is accomplished by the proposed LSCO which is tuned by SpinalNet. Here, LSCO is the integration of Light Spectrum Optimizer (LSO) and Chimp optimization (ChOA). The suggested technique has produced accuracy, True positive rate (TPR), True Negative rate (TNR) and execution time obtained 93.5%, 90.7%, 87.5% and 0.236 sec respectively.

Improved Elman Deep Learning Model for Intrusion Detection System in Internet of Things

Many researchers have developed intrusion detection systems in the past using conventional techniques such as artificial neural networks, fuzzy clustering, evolutionary algorithms, association rule mining, and support vector machines. However, in terms of false negative rates and detection rates, these methods did not yield the best outcomes. To address these problems, we proposed a hybrid deep learning model (HDLM) based on intrusion detection and prevention in IoT devices. Initially, the data are collected from KDDCup-99 and NSL-KDD datasets. Then, the important features are extracted from the dataset using the Forward Feature Selection Algorithm (FFSA). Finally, the extracted features are given to the HDLM classifier. The proposed HDLM is a combination of Elman Recurrent Neural Network (ERNN) and Subtraction-Average-Based Optimizer (SABO). The performance of the suggested method is assessed using performance metrics including precision, recall, accuracy, sensitivity, specificity, and F_Measure. The experimental results show that the proposed method attained the maximum intrusion detection accuracy of 98.52%.

An Efficient Aspect-based Sentiment Classification with Hybrid Word Embeddings and CNN Framework

Background: As the e-commerce product reviews and social media posts are increasing enormously, the size of the database for polarity/ sentiment detection is a challenging task, and again, predicting polarities associated with respect to aspect terms end to end in a sentence is a havoc in real-time applications. Human behavior is influenced by the various opinions generated in society. Public opinion influences our decisions most often. Businesses and establishments always need to collect the opinion of the society, which they try to obtain using customer feedback forms and questionnaires or surveys, which help them to be aware of the shortcomings if any, and to use suggestions to improve quality. It works in the same way for customers as well and the opinions of other customers about a particular product can come in handy when deciding to buy a product. Objectives: In this work, an efficient Aspect-based Sentiment Classification technique has been introduced with a hybrid, multiple-word embedding methods and implemented using the CNN framework on large databases. Methods: Most of the traditional models have a limitation on the dependency for one or more similar types of aspect words for sentiment classification problem. However, these conventional models such as TF-ID, Word 2Vec and Glove method consumes much more time for word embedding process and Aspect terms generation and further process of aspect level sentiment classification. Further, these models are facing problems of high true negative rate and misclassification rate on large aspect databases in sentiment classification. In this article, we have introduced an efficient Proposed ensemble word embedding model in the CNN network and defined Hybrid Word2 Vec method, Hybrid Glove word embedding method and Hybrid Random Forest model for sentiment classification. Results: Experiments on a widely used benchmark prove that the proposed word embedding method- based classification technique results in to higher true positive rate with minimal misclassifications and also supports better runtime and accuracy than the traditional word embedding-based aspect level classification approaches. Conclusion: In this article, a hybrid ensemble feature ranking-based classification model is proposed on the large aspect databases. In this work, advanced multiple-word embedding methods are implemented to improve the essential feature extraction problem in the aspect level sentiment process. These multiple-word embedding methods are applied to the sentiment databases in the CNN framework.

Erken Evre Karaciğer Fibrozisini Öngörmede Shear Wave Elastografinin Rolü

Aim: We examined the relationship between shear wave elastography (SWE) values and histopathological results in our study. Thus, we found the sensitivity of SWE in demonstrating early fibrosis. Materials and Methods: A total of consecutive 70 patients with chronic hepatitis B were prospectively evaluated. The patients included in fibrosis stages (F) 0, 1, 2, 3 and 4 according to Ishak scoring were examined with SWE. SWE measurements of F2, F3 and F4 patients who were found to have early stage fibrosis were compared with those of F0 and F1 patients. Results: The velocity+SD, and kPa+SD values in the group requiring treatment (F2, F3 and F4) were significantly higher than the group not requiring treatment (F0 and F1 )(p &amp;lt; 0.05). The sensitivity rate of the 1.85 cut-off value for velocity+SD was 53.8%, the positive prediction rate was 80.8%, the specificity rate was 83.3%, and the negative prediction rate was 58.1%. For kPa+SD, the cut-off value of 10.8 had a sensitivity rate of 51.3%, a positive prediction rate of 95.2%, a specificity of 96.7%, and a negative predictive rate of 60.4%. A significant correlation was observed between the fibrosis score and the kPa+SD distribution. Conclusion: SWE can differentiate the patients requiring treatment (F2, F3 and F4) from the patients not requiring treatment.

It Looks Like a Spinal Cord Tumor but It Is Not.

Differentiating neoplastic from non-neoplastic spinal cord pathologies may be challenging due to overlapping clinical and radiological features. Spinal cord tumors, which comprise only 2-4% of central nervous system tumors, are rarer than non-tumoral myelopathies of inflammatory, vascular, or infectious origins. The risk of neurological deterioration and the high rate of false negatives or misdiagnoses associated with spinal cord biopsies require a cautious approach. Facing a spinal cord lesion, prioritizing more common non-surgical myelopathies in differential diagnoses is essential. A comprehensive radiological diagnostic approach is mandatory to identify spinal cord tumor mimics. The diagnostic process involves a multi-step approach: detecting lesions primarily using MRI techniques, precise localization of lesions, assessing lesion signal intensity characteristics, and searching for potentially associated anomalies at spinal cord and cerebral MRI. This review aims to delineate the radiological diagnostic approach for spinal cord lesions that may mimic tumors and briefly highlight the primary pathologies behind these lesions.

Malicious Code Detection Using Machine Learning

Abstract: Malware, derived from "Malicious software," is a comprehensive term encompassing any software intentionally crafted to disrupt, damage, or illicitly access computer systems. It's critical to determine whether a file includes malware. The increase in malware is causing a lot of problems for businesses, including data loss and other problems. Malware can swiftly inflict significant damage to a system by slowing it down and encrypting a sizable amount of data on a personal computer. This suggests that lowering the number of false positives is important. A comprehensive description of the adaptable framework for machine learning algorithms may be found in this study. It is possible to detect malware with these methods. The justification for this is that these algorithms simplify the process of distinguishing between files that are infected with malware and those that are not. Modern antivirus and anti-malware tools offer effective protection against various malware attacks. Nevertheless, due to the constantly changing landscape of malicious activities, it is imperative to curate an up-to-date database of previous malware instances. This repository serves as a valuable resource for anticipating the characteristics of future attacks and facilitating swift responses. Different machine learning methods, including decision trees and random forests, are used in our malware detection process. The method with the highest accuracy is chosen, giving the system an excellent detection ratio. Additionally, the confusion matrix is used to calculate the false positive and false negative rates, which is how the system's performance is determined.

Computer-Aided Diagnosis System for Automated Detection of Mri Brain Tumors

Detection and classification of brain tumors in manual or traditional way is an area which could be improved by having such automated detection and clarification system for brain tumors. In this paper, enhanced Computer-Aided Diagnosis CAD software system is introduced for brain tumor detection and classification. Total of 229 brain MRI images was taken as dataset for the purpose of this research; those dataset images include 105 normal brain MRI images, and 124 abnormal brain MRI images. Proposed CAD system is specialized for Meningioma brain tumor detection and classification, and the technique could be generalized and implemented for Glioma, and Pituitary brain tumors as well, and the whole system was implemented using MATLAB software. We started by cropping the region of interest (ROI) of dataset images. Then, feature extraction was implemented using first order statistical features, as well as using of some wavelets filters in combination with the former. T-test is used to exclude features of no statistical significance (p-value &lt; 0.05). After that, different types of classifiers were used to separate the normal set from the abnormal one. Note that, we used an iterative approach to by changing features with many runs until we got best performance, where, best accuracy results were gotten with SVM-Kernel Function (Linear), KNN-1, KNN-3, and KNN-5 classifiers. Note also that, we used convolutional neural networks (CNN) from Deep Learning toolbox of MATLAB as a control method to compare, where the images were fed directly to the CNN. The results were evaluated using performance assessment techniques which are Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, Error Rate, and Area Under the Curve (AUC) of Reciever Operator Characteristic (ROC). With SVM classifier, the best gotten accuracy results were 91 % with CNN classifier, 82% with SVM classifier, and 77 % with KNN classifier. Furthermore, it was very beneficial to find such feature extraction techniques which gave acceptable accuracy results with three different classifiers; this was the case two times as mentioned the study. All proposed CAD system areas was developed and implemented using MATLAB software.