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检索条件"主题词=Machine Learning Algorithms"

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Comparative Analysis of machine learning Models in Online Payment Fraud Prediction

Comparative Analysis of Machine Learning Models in Online Pa...

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Intelligent Methods, Systems, and Applications (IMSA)

作者： Ahmed Younes Shdefat Mennatallah Mohamed Shahd Khaled Farrah Hany Hanaa Fathi Diaa Salama AbdElminaam College of Engineering and Technology American University of the Middle East Egaila Kuwait Faculty of Computer Science Misr International University Cairo Egypt Applied Science Research Center Applied Science Private University Amman Jordan Jadara Research Center Jadara University Irbid Jordan Information Systems department Faculty of Computers and Artificial Intelligence Benha University Benha Egypt MEU Research Unit Middle East University Amman Jordan

ISBN: (数字)9798350362633

ISBN: (纸本)9798350362640

With the substantial increase in Cybercrime, the pressing need for effective solutions in the light of the rising amount of corporate financial losses, is a concern f or many researchers. The widespread adoption of ecommerce and diverse online payment methods has contributed to the surge in a major form of cybercrime—online payment fraud. This further highlights the critical need to control and reduce the widespread danger of fraudulent behaviour on the internet. However, predicting online payment fraud is difficult as fraudsters are constantly changing their tactics, transactions happen quickly and in large volumes, transactions involve different regions and countries, stolen data is often used and other factors. Our goal is to improve the accuracy and usability of online payment fraud prediction through the use of machine learning. A well-established and widely recognized dataset specifically designed for studying online payment fraud was used. The six proposed algorithms trained and tested were (SVM), Decision Tree, Naïve Bayes, Random Forest, (KNN) and Logistic Regression. After rigorous testing, Decision Tree presented the highest accuracy in performance of 98.58%. However, as ten-fold cross validation was implemented on the dataset, Decision Tree suffered a loss while Random Forest had an increase in accuracy making it the best model with accuracy of 98.47%. As shown in this paper, machine learning has proven to be a reliable and ever-evolving approach for the prediction of Online Payment Fraud and results can be further improved in collaboration with financial and cybercrime specialists.

关键词： Support vector machines Accuracy machine learning algorithms Predictive models Prediction algorithms Fraud Bayes methods

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Early Identification of as Disorder using machine learning Based Classifier System Implementation

Early Identification of as Disorder using Machine Learning B...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： G Karthika Priya Dharshini M Meenakshi T J Nandhini Eppili Jaya Riyad E. Abed Fay Al-Taee Abdul Hussein Mohsen Saveetha Engineering college Saveetha nagarThandalam Chennai Saveetha school of Engineering Saveetha University Thandalam Chennai Department of Computer Science and Engineering Saveetha School of Engineering - SIMATS Saveetha University Chennai India Department of ECE Aditya Institute of Technology and Management Tekkali Srikakulam Andhra Pradesh India College of Health and Medical Technology National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq Computer Engineer Department Al-kunooze University College Basra Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this work, we utilized resting-state functional connectivity (FC) data to construct diagnostic classifiers using machine learning classifiers like random forest algorithms on four different autism spectral disorders (ASD) samples with a total of participants, ranging in age from 6 to 18 years. We purposefully altered the makeup of each sample to vary the degrees of gender diversity and symptom intensity, allowing for some overlap between groups and accounting for the wide range of ASD symptoms. Each sample had specific inclusion requirements, such as: all genders with an open-ended severity range; male participants with an open-ended severity; all genders with an emphasis on higher severity levels; and male participants only with higher severity levels. There were subjects in each subset (ASD, TD), carefully matched for head motion and age, of whom were assigned to training and to validation. Within every sample, random forest classifiers were trained. Sample through sample exhibited classification accuracies of corresponding to after testing in validation samples. Important factors affecting classifier performance were shown to vary among sample sets, including the connections within the cingulo-opercular task control (COTC) network and the interactions between COTC ROIs and the default mode and dorsal attention networks. Our findings demonstrate how difficult it can be to develop diagnostic classifiers based on the characteristics of ASD samples. The efficacy of classifiers is enhanced with greater uniformity about gender and symptom intensity. Nonetheless, it is crucial to recognize the inherent variability of ASDs, as relying exclusively on performance metrics may not be sufficient to accurately assess the usefulness of classifiers in practical applications.

关键词： Training Autism machine learning algorithms Forestry Magnetic heads Task analysis Random forests

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Android Malware Detection Using machine learning

Android Malware Detection Using Machine Learning

引用

IEEE International Conference on Internet of Things and Intelligence System (IOTAIS)

作者： Shaikha Al Ali Ali Suleiman Ghina Hallal Sultan Alseiari Yiguang Ma Salam Dhou Fadi Aloul Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798331521219

ISBN: (纸本)9798331521226

Malware, or malicious software, poses a significant threat to systems and networks. Malware attacks are becoming extremely sophisticated, and the ability to detect and prevent them is becoming more challenging. Detecting and preventing malware is crucial for several reasons, including the security of personal information, data loss and tampering, system disruptions, financial losses, and reputation damage. This paper presents a machine learning approach for Android malware detection. In this work, several machine learning algorithms were utilized, namely k-Nearest neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector machine (SVM) and other ensemble classifiers including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting machine (LGBM) and CatBoost. It was found that SVM using radial basis function (RBF) kernel achieved the highest performance with an accuracy of 99.5%. This work proved the feasibility of using machine learning in detecting malware and improving the security of mobile devices. The results of this work can be used to build more robust systems to protect devices and networks from malicious attacks.

关键词： Support vector machines Performance evaluation machine learning algorithms machine learning Nearest neighbor methods Malware Mobile handsets Security Kernel Protection

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Training and Modeling with Privacy in Network Data Using machine learning

Training and Modeling with Privacy in Network Data Using Mac...

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Innovations in Intelligent Systems and Applications Conference

作者： Onat Can Baba Engin Masazade Marmara University Electrical and Electronics Engineering Istanbul Turkey

ISBN: (数字)9798350379433

ISBN: (纸本)9798350379440

Privacy concerns have become paramount in today's data-driven landscape, particularly with the widespread adoption of Internet of Things (IoT) devices. This paper explores the integration of machine learning algorithms, specifically Gaussian Naive Bayes, with privacy-preserving techniques like Differential Privacy to address these concerns. We delve into this innovative approach's principles, methods, and implications. Using a dataset representative of IoT network traffic, we demonstrate how Gaussian Naive Bayes with Differential Privacy can effectively classify data while safeguarding individual privacy, highlighting its significance in pervasive connectivity and data-driven decisionmaking.

关键词： Training Differential privacy Technological innovation machine learning algorithms Gaussian processes Telecommunication traffic machine learning Libraries Bayes methods Internet of Things

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Mitigating Racial Algorithmic Bias in Healthcare Artificial Intelligent Systems: A Fairness-Aware machine learning Approach

Mitigating Racial Algorithmic Bias in Healthcare Artificial ...

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International Conference on Smart Sensors and Application (ICSSA)

作者： Tiffany Meg Nyawambi Henry Muchiri Strathmore Institute of Mathematical Sciences Strathmore University Nairobi Kenya School of Computing and Engineering Sciences Strathmore University Nairobi Kenya

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Racial bias in artificial intelligent (AI) systems is a pervasive problem that undermines fairness and perpetuates inequality. This paper presents an analysis of bias-mitigating algorithms applied to standard machine learning classifiers with the goal of reducing racial bias in healthcare access. The study integrates Reweighing, Adversarial Debiasing, and Exponentiated Gradient (with demographic parity) methods with two base classifiers: Logistic Regression and Random Forest Classifier. The evaluation framework employs both traditional predictive performance metrics—accuracy, precision, recall, and F1 score—and fairness metrics, including average odds difference, disparate impact score, equal opportunity difference, and balanced accuracy. The study finds that after the application of bias-mitigating algorithms, the Random Forest Classifier augmented with the Reweighing algorithm is the superior model, achieving an accuracy of 88.01%, precision of 89%, recall of 97%, and F1 score of 93%, while also demonstrating a balance with respect to fairness. This indicates the model's capability to maintain equity across diverse racial groups and the Random Forest Classifier model's compatibility with the Reweighing technique. The model was then interpreted using Local Interpretable Model-Agnostic Explanations to offer clear insights into the decision-making process. By highlighting effective strategies for mitigating racial algorithmic bias and fostering interpretability, this paper contributes foundational knowledge crucial for ethical AI development in the healthcare sector.

关键词： Measurement Logistic regression Ethics machine learning algorithms Medical services Predictive models Prediction algorithms Classification algorithms Artificial intelligence Random forests

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CIL4EMTD: A Novel Class Incremental learning Method for Encrypted Malware Traffic Detection

CIL4EMTD: A Novel Class Incremental Learning Method for Encr...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Jiarui Li Lei Du Yilu Chen Liyi Zeng Hao Li Zhaoquan Gu School of Computer Science and Technology Harbin Institute of Technology (Shenzhen) Shenzhen China Department of New Networks Pengcheng Laboratory Shenzhen China National Key Laboratory of Advanced Communication Networks Shijiazhuang China

ISBN: (数字)9798350391367

ISBN: (纸本)9798350391374

With the widespread adoption of traffic encryption technologies, detecting attack behaviors in encrypted malware traffic has become a critical and challenging problem. The encrypted malware traffic contains a lot of redundant information and noise, which necessitates additional computational resources. In addition, new attack categories continue to emerge over time, resulting in traditional detection methods losing effectiveness in identifying these new attacks. To handle the above challenge, in this paper, we propose CIL4EMTD, a novel Class Incremental learning method targeted for Encrypted Malware Traffic Detection. Our method leverages clustering techniques to enrich the extraction of dataset features, while specifically customizing LwF for detecting encrypted malware to optimize the stability of model training via efficient knowledge distillation. To verify the effectiveness of CIL4EMTD, we conduct experiments comparing it with other class incremental learning methods. The experiment results show that CIL4EMTD demonstrates state-of-the-art performance on a real-world dataset, achieving a recognition accuracy of 94.34% for new categories while maintaining the average accuracy for known categories within a margin of less than a 10% decline.

关键词： Training Incremental learning Accuracy machine learning algorithms Noise Network security Feature extraction Malware Stability analysis Cryptography

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Comparison of machine learning Model for Lung Cancer Detection Using Classification Techniques

Comparison of Machine Learning Model for Lung Cancer Detecti...

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Aerospace Electrical Engineering and Informatics (ICAAEEI), International Conference of Adisutjipto on

作者： Athiyatur Rohmah Yoga Pristyanto Ika Nur Fajri Afrig Aminuddin Agung Nugroho Atik Nurmasani Department of Information System Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9798331507930

ISBN: (纸本)9798331507947

Lung cancer is one of the most common types of cancer that causes death in the world. In 2020, lung cancer had caused 1,796,144 deaths. This research provides a solution for lung cancer detection using machine-learning classification methods. This research aims to determine the performance of classification algorithms used for building classification models for lung cancer detection. Those algorithms include Support Vector machine, Decision Tree, Random Forest, and Naïve Bayes. The Support Vector machine algorithm provides performance with an accuracy score of 96.5%, a precision score of 95.83%, and a recall score of 96.66%. The Decision Tree algorithm came up with a performance with an accuracy score of 92%, a precision score of 92.18%, and a recall score of 94.9%. The Random Forest algorithm performs with an accuracy score of 95.5%, a precision score of 95.62%, and a recall score of 94.9%. Moreover, the Naïve Bayes algorithm provides performance with an accuracy score of 80%, a precision score of 79.69%, and a recall score of 80.17%. The comparison of the performance of those four algorithms shows that the Support Vector machine algorithm provides the best performance in building classification models for lung cancer detection.

关键词： Support vector machines machine learning algorithms Accuracy Buildings Lung cancer Classification algorithms Bayes methods Decision trees Informatics Random forests

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Comparative Analysis of Classical and Quantum-Inspired machine learning Techniques for Indoor Localization: Unveiling the Potential of QKNN

Comparative Analysis of Classical and Quantum-Inspired Machi...

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IEEE India Conference (INDICON)

作者： Markkandan S Sharon Deena Reji Sairam B Shivani Narayanan Mridhula S School of Electronics Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This paper presents a comprehensive comparative analysis of classical and quantum-inspired machine learning techniques for WiFi-based indoor localization. We evaluate the performance of Support Vector machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), Quantum-inspired SVM (QSVM), and Quantum-inspired KNN (QKNN) algorithms using the UJI Indoor Localization dataset. Our results demonstrate the unexpected potential of QKNN, which performs comparably to classical KNN and outperforms other methods including QSVM. We provide insights into the relationship between Received Signal Strength (RSS) characteristics and localization accuracy, and investigate the importance of different Access Points in the localization process.

关键词： Location awareness Support vector machines Radio frequency machine learning algorithms Accuracy Nearest neighbor methods Indoor environment Random forests

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Transformative Potential of Consumer Insights into Sustainable Business Growth: machine learning Integrated with Marketing Analytics

Transformative Potential of Consumer Insights into Sustainab...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Ayush Bist Gurpreet Singh Shilpee Srivastava Department of Mathematics Chandigarh University Chandigarh India Department of CSE Chandigarh University Chandigarh India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

machine learning and marketing analytics have emerged as a well-known tool for transforming consumer insights into substantial business growth. Through the consumption of advanced procedures and data analytics methodologies, enterprises gain the ability to extract invaluable insights from extensive consumer data sets, leading to refined marketing strategies and enriched customer experiences. This research study analyzes the synergy between machine learning and marketing analytics, emphasizing their profound impact on consumer insights and subsequent business expansion. It explains a range of machine learning algorithms and data analytics tools pertinent to marketing endeavors, including predictive modeling, customer segmentation, and recommendation systems. providing businesses with robust tools for decision-making and strategy formulation. Additionally, this research study explores the real-world examples showcasing the transformative potential of machine learning and marketing analytics in optimizing marketing campaigns, personalizing customer interactions, and enabling sustainable growth. By integrating machine learning and marketing analytics, organizations can capitalize on unparalleled opportunities to gain a competitive advantage in today’s data-driven scenario, translating consumer insights into actionable strategies with unprecedented accuracy and efficacy.

关键词： Analytical models Data analysis machine learning algorithms Decision making machine learning Organizations Predictive models Market research Security Recommender systems

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Detection of Autism Spectrum Disorder using Deep learning Models

Detection of Autism Spectrum Disorder using Deep Learning Mo...

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International Conference on Inventive Computing and Informatics (ICICI)

作者： Kotha Chakradhar Kotte Thulasi Tharun Periyavaram Sandesh Kumar Reddy Thangam S Department of Computer Science & Engineering Amrita School Of Computing Bengaluru Amrita Vishwa Vidyapeetham India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

Autism Spectrum Disorder (ASD) is a serious developmental disorder that involves problems with social interaction and communication, as well as repetitive behavior. Early diagnosis and treatment are key to improving the lives of people with autism. One recent breakthrough in the field involves the use of cutting-edge machine learning algorithms to automatically identify ASD using different kinds of data like behavioral observations. The proposed approach combines various types of deep learning models such as YOLOv8n, Detectron2, VGG16, ResNet50, Xception, Inceptionv3, and MobileNetV2. Using these models’ capabilities, the goal of the research study is to increase the distinction between ASD-related behavior signals and normal development patterns. The effectiveness of our method was demonstrated by test results which showed it was highly accurate.

关键词： Deep learning Autism machine learning algorithms Accuracy Computational modeling Informatics Residual neural networks

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