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共 26723 条记录，以下是51-60 订阅

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Enhancing Hospital Mortality Prediction for Heart Failure Patients through Hyperparameter Optimization and Interpretability Analysis with LIME and SHAP 1

Enhancing Hospital Mortality Prediction for Heart Failure Pa...

引用

1st International Conference for Women in Computing, InCoWoCo 2024

作者： Meena, S. Murthy, Monisha Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Heart failure is one of the primary causes for deaths caused in the hospital. Predicting mortality rate of such patients is extremely important for the efficient use of health care resources. This research aims to estimate the hospital mortality rate of Heart Failure(HF) patients using machine learning(ML). Using computational technique, the obtained model has given the most efficient accuracy. This paper uses the classification model to detect two outcomes (0 and 1, i.e., if the patient will survive or not). The classification models used for the hospital mortality prediction are, single classifiers such as Logistic regression, SVM, KNN, Decision tree (Ginni and Entropy);with Logistic regression obtaining highest accuracy of 88.9%, Bagging and boosting models such as Randomforest, Adaptive boosting, Gradient boosting, XG boosting, Categorical boosting;with CatBoost obtaining highest accuracy of 90.2%. To achieve the best accuracy, ensemble models were used out of which ensemble classifier ensemble gave 91.1% accuracy. To understand the features playing important role in decision making, explainable AI(XAI) is used. LIME and SHAP are considered. LIME generated local explanations, it explained the decision of a complex model for a specific instance or observation. In contrast, SHAP provided global explanations, it explained the overall behavior of the model across all instances. © 2024 IEEE.

关键词： Adaptive boosting

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Detection of PCOS Leveraging Machine Learning and Interpretation Using Explainable AI 1

Detection of PCOS Leveraging Machine Learning and Interpreta...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Madhavi, Bindu V. Firdouse, Asfiya Kodipalli, Ashwini Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798331518943

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder impacting women globally. This research aims to early predict and detect PCOS which is needed to reduce long-term complications. Since it is considered to be a hard-to-diagnose disorder, machine learning is utilized for this purpose. This study leveraged the power of computational algorithms trained on patient data for model prediction where Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score. In addition to this, Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score. By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model's accuracy was observed. Besides, three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy. Additionally, Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were used for model interpretability. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Detection of Breast Cancer Through a Novel Framework: A Machine Learning Approach with Explainable AI Interpretation 1

Enhancing Detection of Breast Cancer Through a Novel Framewo...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Vidyashree, R. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798331518943

Breast cancer, the most prevalent cancer among women, significantly contributes to increased mortality rates and ranks as the second leading cause of cancer-related deaths in women. Early detection is crucial for effective treatment, yet traditional diagnostic approaches often involve multiple laboratory tests supervised by medical experts. To mitigate human error and expedite breast cancer detection, there is a pressing need for automated systems capable of accurate and timely diagnosis. This study aims to investigate the application of machine learning (ML) techniques in classifying breast cancer using features from digitized images of fine-needle aspiration (FNA) breast mass samples. Machine learning methods are instrumental in addressing this imperative. The study conducts a comparative analysis of various ML classification methods, including Logistic Regression, Support Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Trees, Bagging-Boosting, and ensemble models, using a breast cancer dataset to establish a novel framework. The performance of each algorithm is assessed based on metrics such as accuracy, precision, recall, and F1 score. The findings underscore the effectiveness of ensemble classifier models, achieving a notable accuracy rate of 98.87%. Additionally, the integration of explainable Artificial Intelligence enhances the interpretability of the models by providing valuable insights into their decision-making processes. © 2024 IEEE.

关键词： Contrastive Learning

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Multiple Approaches Towards Authentication Using Keystroke Dynamics 2

Multiple Approaches Towards Authentication Using Keystroke D...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Arsh, Aditya Kar, Nirmalya Das, Smita Deb, Subhrajyoti Dept. of Computer Science & Engineering NIT Agartala Tripura India Dept. of Computer Science & Engineering ICFAI University Tripura India

In the realm of global computer security, safeguarding sensitive data and computer systems stands as an ongoing challenge. Striking the delicate balance between convenient access for legitimate users and thwarting imposter attacks is imperative. Traditional authentication methods, primarily reliant on username and password schemes, have been the longstanding cornerstone of cyber system authentication. Yet, this conventional approach is marred by inherent vulnerabilities, including password exchange, shoulder surfng, brute force attacks, dictionary attacks, speculation, phishing, and emerging threats. In response to these shortcomings, biometrics technology has emerged as a reliable alternative for authentication and verification, with Keystroke Dynamics being a well-established method. This research advances Keystroke Dynamics-based authentication through cutting-edge technologies, including machine learning, deep learning, and neural networks. A quantitative assessment demonstrates remarkable results - Machine Learning achieves 99.9% accuracy with the Random Forest model, Deep Learning combines CNN with GRU to reach 99.31% accuracy, and the DBN model registers at 98.01%. In the realm of neural networks, Bi-CNN and FFM-NN excel, with accuracy rates of 96.8% and 94.7%, respectively. These findings lay the foundation for robust, secure authentication systems, ensuring the protection of sensitive data and computer assets in our interconnected world. © 2024 Elsevier B.V.. All rights reserved.

关键词： Authentication

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Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning 10

Automatic Identification of Teff (Eragrostis Tef) Flour Adul...

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10th International Conference on Systems and Informatics, ICSAI 2024

作者： Bochera, Zekiyos Abayneh Gizaw, Solomon Clement, Onime Addis Ababa University Dept. of Computational Data Science Addis Ababa Ethiopia Addis Ababa University Dept. of Computer Science Addis Ababa Ethiopia Trieste Italy

ISBN: (纸本)9798331510138

Food adulteration, the act of mixing foodstuffs with lower-quality foodstuffs or other food and non-food items, has become a global issue, particularly in developing countries like Ethiopia. This is a significant problem as it can lead to nutritional deficiencies and in some cases, illnesses, diseases or even death. The identification of adulterated food typically requires physio-chemical laboratory tests that vary depending on the type and level of adulterant. Teff flour, a daily staple food in Ethiopia, is often adulterated with wood flour and gypsum powder. In this research work, we utilized computer vision techniques to extract textural and color features from images of both adulterated and pure Teff flour. We then trained a Support Vector Machine (SVM) based machine learning model to identify adulterated Teff flour and predict the level of adulteration. The presented results show varying levels of prediction accuracy depending on the type of Teff flour, the level of adulteration. The developed model was also found to provide good results with images sizes ranging from 128 × 128 to 2048 × 2048 pixels. The results show overall model accuracy was above 90% when the adulteration level of red Teff flour is more than 25% and above 84% when the adulteration level of white wood flour was above 25%. © 2024 IEEE.

关键词： Food chemistry

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Symptoms-based Classification of Disease Using Various ML Algorithms and Interpretation using LIME and SHAP KERNELS 4

Symptoms-based Classification of Disease Using Various ML Al...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Soujanya, K.J. Kodipalli, Ashwini Gosai, Sanjeev Sneha, B.J. Rao, Trupthi Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India

ISBN: (纸本)9798350354218

Using a variety of machine learning techniques, this research study suggests a unique method for classifying diseases using symptom-based analysis. To improve model transparency and comprehension, the study makes use of the interpretability tools, SHAP kernels and LIME. The research attempts to close the gap between interpretability and prediction accuracy in medical diagnosis by combining two approaches. A thorough assessment of symptom-based categorization is ensured by the use of many machine learning methods. The application of LIME and SHAP kernels aids in the clarification of model predictions and provides information on the primary symptoms that impact illness categorization. This study advances the creation of accurate and comprehensible illness categorization models, promoting confidence in the use of machine learning methods in healthcare. © 2024 IEEE.

关键词： Adversarial machine learning

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5G User Equipment (UE) Positioning and Localization Estimation using Machine Learning

5G User Equipment (UE) Positioning and Localization Estimati...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Survase, Sanket Diwan, Raman Lal, Kumari Nidhi Kumar, Shishupal VNIT Nagpur Dept. of Computer Science and Engineering Nagpur India IIIT Nagpur Dept. of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350378092

The emergence of 5G technology has ushered in a new era of connectivity, making communications faster and cheaper. One of the main challenges of 5G networks is accuracy and efficiency, which are important for many applications such as driver less cars, smart cities and virtual reality. This research paper explores the integration of machine learning techniques to improve the accuracy and reliability of 5G infrastructure. The plan uses rich data generated by 5G networks, combining various parameters such as signal strength, flight time and angle of arrival. Machine learning algorithms are used to process this data and create good models for the correct location. The system is designed to adapt to dynamic and complex urban environments where traditional methods often encounter limitations. The main contribution of this research includes the development of a new machine learning-based distribution algorithm suitable for 5G networks, a comprehensive evaluation of performance comparison with the existing system, and evaluation of recommendations for feasibility and implementation. The results showed significant improvements in accuracy and reliability, demonstrating the potential of machine learning to revolutionize the 5G workplace. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-Enabled Covert Surveillance Camera Identification through IR Sensor 2

IoT-Enabled Covert Surveillance Camera Identification throug...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Ruksana, Shaik Imran, Shaik Farheen, Shaik Fatima Kireeti, Krovi Mohan Sailaja, K.L. VR Siddhartha Engineering College Dept. of Artificial Intelligence and Data Science Vijayawada India VR Siddhartha Engineering College Dept. of Computer Science and Engineering Vijayawada India

ISBN: (纸本)9798350371314

A rising number of spy cameras installed in sensitive locations globally poses an escalating threat to personal privacy. Detecting these small and well-concealed cameras with the naked eye is challenging. Despite the abundance of Android apps, accurate detection remains elusive, with many requiring subscriptions and some charging users without prior notice. Furthermore, most IoT devices are designed to detect wired cameras, overlooking the prevalence of wireless spy cameras. Existing studies often rely on impractical methods, such as wireless transmission detection. The primary objective of this paper is to develop a cost-effective, scalable, robust, and user-friendly spy camera detector. This detector identifies all night vision cameras emitting infrared light, promptly alerting the user through a beep sound from the attached buzzer. In addressing the imperative need for heightened privacy protection, the IoT spy camera detector adept.y spots concealed cameras via IR signals. By offering user-friendly location alerts through a reassuring buzzer, it actively contributes to enhancing security measures against evolving privacy threats, creating a safer environment for all. © 2024 IEEE.

关键词： Cost effectiveness

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An Analysis of Human Factors in Malware Attacks

An Analysis of Human Factors in Malware Attacks

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2024 International Conference on computer and Applications, ICCA 2024

作者： Alotaibi, Jawza Al Ja'am, Jihad M. Hoda, Mohammed Oryx Universal College in Partnership with Liverpool John Moores University Dept. of Computing and Data Science Doha Qatar Kuwait College of Science and Technology Dept. of computer Science and Engineering Doha Kuwait

ISBN: (纸本)9798350367560

This research study investigates human responses to malware attacks, focusing on attacker motivations, individual reactions, and key factors influencing user vulnerability and resilience. Using a structured survey questionnaire, the study gathered data from 104 participants with diverse demographics across various occupations. The data was analyzed using SPSS statistical software and Python-based machine learning algorithms. The survey included demographic information and responses to cybersecurity-related questions about human behavior and experience. Machine learning techniques identified ten critical factors as significant predictors of vulnerability to malware attacks. The prediction model indicated that 55 out of 104 participants were vulnerable, with higher vulnerability observed among female participants and those in non-IT professions. This study contributes valuable insights into the human factors influencing cybersecurity resilience, providing a foundation for developing effective strategies to mitigate malware attack vulnerability. © 2024 IEEE.

关键词： Cyber attacks

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The Role of data Visualization in Improving the Sustainability of Smallholdings 2

The Role of Data Visualization in Improving the Sustainabili...

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2nd International Conference on Technology Innovation and Its Applications, ICTIIA 2024

作者： Maulana, Ahmad Candra, Ade Amalia, Amalia Universitas Sumatera Utara Dept. of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Dept. of Information Technology Medan Indonesia Universitas Sumatera Utara Dept. of Computer Science Medan Indonesia

ISBN: (纸本)9798350351613

Indonesia is the largest palm oil producer in the world, with smallholder oil palm plantations being a significant contributor. However, information regarding smallholder oil palm plantations is very limited. This research aims to provide an alternative solution by designing a dataset that can be used as a basis for decision-making. This research analyzed data from 2818 smallholders in 64 sub-districts in Sumatra. The level of sustainability was visualized and analyzed using QGIS based on three factors, namely plant age, group status, and certification status. The results show that the districts of Nassau, Sungai Apit, Kerumutan, Batang Asam, and Batang Toru have the highest level, while Batang Lubu Sutam, Salapian, Torgamba, Pematang Bandar, and Bilah Hulu subdistricts have the lowest level of sustainability. The sustainability levels per sub-district were visualized to cluster the level of sustainability per region for large-scale smallholding programs. © 2024 IEEE.

关键词： Information management

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