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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Vinod Jain Anupam Yadav GLA University Mathura U.P India

ISBN: (数字)9781665419604

ISBN: (纸本)9781665429986

The detection of various types of flowers, leaves, based on their characteristics, is very useful in many fields of agriculture and medical research. machine learning algorithms are applied in this article to the identification of flowers on the basis of their characteristics. machine learning algorithms K-nearest neighbor, Random Forest and Decision Tree are applied in a data set of flowers and their precision is calculated. algorithms are implemented on a data collection in the Python programming language. It is found that the performance of KNN machine learning algorithm is best in detection of flowers.

关键词： machine learning algorithms Vegetation Forestry Data collection Decision trees Random forests Python

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Pathological Prostate Gleason Score Prediction Using MRI Radiomics and machine learning algorithms

Pathological Prostate Gleason Score Prediction Using MRI Rad...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Soroush Bagheri Ghasem Hajianfar Abdollah Saberi Mehrdad Oveisi Isaac Shiri Habib Zaidi Department of Medical Physics Kashan University of Medical Sciences Kashan Iran Rajaie Cardiovascular Medical and Research Center Iran University of Medical Science Tehran Iran Department of Medical Imaging Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Switzerland Kings College London London United Kingdom Geneva University Neurocenter Geneva University Geneva Switzerland Department of Nuclear Medicine and Molecular Imaging University of Groningen University Medical Center Groningen RB Groningen Netherlands Department of Nuclear Medicine University of Southern Denmark Odense Denmark

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Prostate cancer (PCa) is one of the most critical diseases in all-male groups. Clinical diagnosis and follow-up is commonly performed using MRI, which proved to have good specificity and sensitivity for identifying prostate cancer. The pathologic grade or PCa aggressivity is calculated by the Gleason score as a scale. Gleason patterns are numbered in rising cellular disorder and misfortune of standard glandular design from 1 to 5. As of lately, in addition to conventional parameters extracted from MRI, there has been noticeable progress in the extraction of high-throughput quantitative features from medical images, called radiomics. In this work, we enrolled 140 patients with prostate cancer. The whole prostate was segmented manually to extract 51 radiomic features, including shape-based, histogram based, and texture-base features. Data were randomly split into 70% for train and 30% as test datasets, respectively. Z-score normalization was applied to features, and after normalization of features, these were fed to different feature selection and classifiers. We demonstrated the feasibility of differentiation of the Gleason score (3+4 from 4+3) using MRI radiomics and machine learning algorithms.

关键词： Pathology machine learning algorithms Sensitivity Magnetic resonance imaging Feature extraction Prostate cancer Standards

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Malware Detection Method using Tree-based machine learning algorithms

Malware Detection Method using Tree-based Machine Learning A...

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Computing (ICOCO), IEEE International Conference on

作者： Satoshi Okada Mariko Fujimoto Wataru Matsuda Takuho Mitsunaga The University of Tokyo / Toyo University Tokyo Japan Toyo University / Nagoya Institute of Technology Tokyo Japan NTT Social Informatics Laboratories Tokyo Japan Toyo University Tokyo Japan

ISBN: (纸本)9781665436908

Malware detection engines are required a high True Positive Rate (TPR) and low False Positive rate (FPR). For example, to obtain the VB100 certification conducted by Virus Bulletin in the U.K., the detector's TPR must be 99.5% or higher, and FPR less than 0.01%. However, it is difficult for signature-based malware detection engines to detect zero-day or new variants of malware. For such malware, an approach based on machine learning is considered effective because the features of specimens are analyzed in a complex manner. Therefore, we created a malware detection model from surface analysis logs, and PE header dumps by machine learning using the FFRI Dataset 2018. Furthermore, we verified the accuracy under the constraint that the FPR is less than a certain value. As a result, we succeeded in creating a new model with high accuracy: when FPR is set to be less than 1%, TPR is 99.7%, when FPR is set to be less than 0.1%, TPR is 98.7%, and when FPR is set to be less than 0.01% and TPR is 94.5%. In addition, we revealed features with high contribution to malware detection in this model.

关键词： machine learning algorithms Computer viruses Conferences Forestry Detectors Feature extraction Malware

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Impact of machine learning algorithms in Intrusion Detection Systems for Internet of Things

Impact of Machine Learning Algorithms in Intrusion Detection...

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International Conference on Advanced Computer Control, ICACC

作者： Jinsi Jose Deepa V Jose Karna Srinivasa Rao Justin Janz CHRIST University Bangalore India

ISBN: (纸本)9781665439206

The importance of security aspects is increased recently due to the enormous usage of IoT devices. Securing the system from all sorts of vulnerabilities is inevitable to use IoT applications. Intrusion detection systems are power mechanism which provides this service. The introduction of artificial intelligence into intrusion detection systems can further enhance its power. This paper is an attempt to understand the impact of machine learning algorithms in attack detection. Using the UNSW-NB 15 dataset, the impact of different machine learning algorithms is assessed.

关键词： machine learning algorithms Intrusion detection Internet of Things Security Artificial intelligence

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A Critical Analysis of Diabetes Detection using machine learning algorithms

A Critical Analysis of Diabetes Detection using Machine Lear...

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Advances in Computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Lalit Kumar Prashant Johri School of Computing Science & Engineering Galgotias University Greater Noida India

ISBN: (纸本)9781665438124

Diabetes is a long-term incurable disease that transpires when the pancreas is unable to manufacture insulin, or when insulin produced in the body cannot be used properly. The pancreas manufactures a hormone called Insulin which acts like an essential constituent that allows glucose from the food a person consumes to go from the stream of blood into the body cells and manufacture energy needed by the body. All food made up of carbohydrates is sampled down to glucose in the blood. Glucose gets into the cells with the help of insulin. In the case where the body finds it difficult to produce insulin or make it come into use properly, it gives rise to higher levels of glucose in the blood and that condition is known as hyperglycemia. Over a long period of time, a high level of glucose can cause damage to the body and lead to the improper functioning of different organs. A lot of statistics by the World Health Organization claim an increase in the figures of patients suffering from diabetes. A rapid growth in the figure of diabetic patients in India is really alarming. A large amount of medical data is produced. It is significant to collect, save, assimilate and foresee the health issues of such patients using constant observations and technical innovations. With the help of innovation, it is essential to build a plan that saves and observes the data related to diabetes and detects the upcoming dangers. Its timely detection and examination is still a task for researchers. Medical experts want a trustworthy prediction plan to detect Diabetes. Various machine learning algorithms are necessary for observing the data from various perspectives and outlining it into valuable data. The main motive is to figure out new formats and then to analyze these patterns to furnish important and significant data for the users.

关键词： Technological innovation machine learning algorithms Organizations Diabetes Glucose Pancreas Insulin

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Credit Card Fraud Detection System using machine learning algorithms and Fuzzy Membership

Credit Card Fraud Detection System using Machine Learning Al...

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Modern Trends in Information and Communication Technology Industry (MTICTI), International Conference of

作者： Ahmed Qasim Abdulghani Osman Nuri Ucan Khattab M. Ali Alheeti Institute of Graduate Studies University of Altınbaş Turkey School of Science and Engineering University of Altınbaş Turkey College of Computer Science and Information Technology University Of Anbar Iraq

ISBN: (纸本)9781665414456

Fraudulent transactions have skyrocketed in tandem with the rise in Credit Card users. Since legitimate and fraudulent transactions look similar, it is nearly impossible to tell one from the other. This paper proposes a fraud detection system that uses machine learning (ML) and a fuzzy membership function to identify fraudulent transactions. The ML techniques used were Logistic regression (LR), Linear Discriminant Analysis (LDA), and the boosting algorithm XGBoost to create models for the proposed system. The dataset from Kaggle was used for training and testing these models. Many performance metrics were used to evaluate the proposed system models’ efficiency: confusion matrix, accuracy, precision, f1, recall, and AUC. The results showed the superiority of the XGBoost model over the other models.

关键词： Training Measurement Industries machine learning algorithms Credit cards Market research Linear discriminant analysis

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NIRS and machine learning algorithms as a non-invasive technique to discriminate and classify cooked broiler and duck meat

Applied Food Research

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Applied Food Research 2025年第1期5卷

作者： Kamrunnahar Khan Bristy Dip Ghosh Md. Abul Hashem Department of Animal Science Faculty of Animal Husbandry Bangladesh Agricultural University Mymensigh 2202 Bangladesh

This study investigated the utilization of near-infrared spectroscopy (NIRS) and machine learning methodologies to differentiate cooked broiler and duck meat. Nearinfrared spectral data were acquired using a portable spectrometer (700–1100 nm) from 40 samples (20 broilers and 20 duck breasts). The data were preprocessed and analyzed using principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). PCA revealed 87.97 % variance, demonstrating distinct groupings. Various machine learning algorithms, including linear discriminant analysis (LDA), support vector machine (SVM), Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, Decision Tree, Naive Bayes, Neural Network, and XGB, were evaluated. Hyperparameter tuning and 5-fold cross-validation were implemented to mitigate overfitting and enhance the generalization. Logistic Regression, SVM, and LDA achieved the highest accuracy (84.13 %, 84 %, and 83.50 %, respectively) and ROC AUC (92.99 %, 92.63 %, and 91.71 %, respectively), indicating robust discriminative power. Random Forest and Gradient Boosting also exhibited satisfactory performance, with consistent cross-validation scores (∼0.824). XGB demonstrated marginally superior recall compared to K-Nearest Neighbors, while Neural Network excelled in recall (90.74 %), a critical factor for detecting true positives. Decision Tree and Naive Bayes, although interpretable, underperformed relative to other algorithms. Logistic Regression and SVM have emerged as the most efficacious methods, attaining high accuracy and balanced precision and recall. This study underscores the potential of integrating spectroscopic data with advanced machinelearning techniques for meat classification and fraud detection.

关键词： Nir spectroscopy machine learning algorithms Principal component analysis Hyperparameter Optimization Ensemble Model

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Risks of Digital Transformation: Review of machine learning algorithms in Credit Card Fraud Detection

Risks of Digital Transformation: Review of Machine Learning ...

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International Informatics and Software Engineering Conference (UBMYK)

作者： Güneş Gürsoy Asaf Varol College of Engineering and Natural Sciences Maltepe University Istanbul Turkey

ISBN: (纸本)9781665407601

In addition to the advantages of the digital world, there are also disadvantages, which can harm people. With the spread of credit cards with the digital transformation, banks have become the targets of malicious hackers. In this study, firstly, information about artificial intelligence and digital transformation is given. In related studies, some machine learning methods such as Random Forest, Naive Bayes, K-Nearest Neighbor, Logistic Regression, Support Vector machines, Decision Tree, Artificial Neural Networks, Multilayer Perceptron and Ensemble learning have been used to detect credit card fraud and their algorithm performance has been demonstrated.

关键词： Support vector machines machine learning algorithms Digital transformation Software algorithms Predictive models Prediction algorithms Credit cards

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Investigation On Heart Disease Using machine learning algorithms

Investigation On Heart Disease Using Machine Learning Algori...

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International Conference on Computer Communication and Informatics (ICCCI)

作者： D. Sivabalaselvamani D. Selvakarthi L. Rahunathan S.Nandhini Eswari M. Pavithraa M. Sridhar Kongu Engineering College Perundurai Erode Tamilnadu India

Coronary delivery path coronary disease is caused in coronary corridors by atherosclerosis which results in heart failure and respiratory failure. Angiography, an exorbitantly tedious and deeply specialized intrusive technique, is used to conclude CAD (Coronary Artery Disease). Thus, experts are triggered by elective approaches, for example, AI calculations that will use non-intrusive scientific knowledge to assess and measure the severity of the cardiovascular condition. In this report, we present another way to deal with CAD examination combination, including recognizable peril factor realities utilizing the atom, swam upgrade mission strategy, and K-Means bundling computation decision of the relationship-based segment subset. The WEKA instrument is an open-source apparatus utilized for information pre-handling, grouping, and arranging. Overseen learning recreations, for example, multi-layer perceptron, multinomial strategic relapse, fluffy unordered principle acceptance calculation, and C4.5, are then used to exhibit CAD situations.

关键词： Heart Solid modeling machine learning algorithms machine learning Prediction algorithms Open source software Diseases

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Evaluation of Gender Bias in Facial Recognition with Traditional machine learning algorithms

Evaluation of Gender Bias in Facial Recognition with Traditi...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Mustafa Atay Hailey Gipson Tony Gwyn Kaushik Roy Winston-Salem State University Winston-Salem NC USA NC A&T State University Greensboro NC USA

ISBN: (纸本)9781728190495

The prevalent commercial deployment of automated facial analysis systems such as face recognition as a robust authentication method has increasingly fueled scientific attention. Current machine learning algorithms allow for a relatively reliable detection, recognition, and categorization of face images comprised of age, race, and gender. algorithms with such biased data are bound to produce skewed results. It leads to a significant decrease in the performance of state-of-the-art models when applied to images of gender or ethnicity groups. In this paper, we study the gender bias in facial recognition with gender balanced and imbalanced training sets using five traditional machine learning algorithms. We aim to report the machine learning classifiers which are inclined towards gender bias and the ones which mitigate it. Miss rates metric is effective in finding out potential bias in predictions. Our study utilizes miss rates metric along with a standard metric such as accuracy, precision or recall to evaluate possible gender bias effectively.

关键词： Training Measurement machine learning algorithms Image recognition Face recognition Static VAr compensators Prediction algorithms

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