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2024 IEEE International Conference on Computer Vision and Machine Intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

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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...

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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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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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Comprehensive Methodologies for Breast Cancer Classification: Leveraging XAI LIME, SHAP Bagging Boosting, and Diverse Single Classifiers 4

Comprehensive Methodologies for Breast Cancer Classification...

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

作者： Avlani, Dhruvi T. Abhijna, M.B. Sai Disha, G.S. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Information Science and Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence and & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350354218

Breast cancer is one of the most common causes for deaths owed to cancer. It is very crucial and necessary to detect cancer at early phases. There are various Machine Learning techniques and computational techniques available for the aim of diagnosis of breast cancer data. This paper offers a Machine Learning model to perform automated diagnosis for the classification of breast cancer. This method employs single classifier model and bagging boosting for feature selection. Also, four algorithms SVM, Decision Tree, KNN and Logistic Regression which gives the highest accuracy of 88%. The system was trailed on BCGENES dataset. The performance of the system is measured on the basis of accuracy and precision. The incorporation of XAI methods, such as SHAP values and LIME, provides a nuanced understanding of feature importance and individual predictions, elevating model interpretability. The amalgamation of machine learning classifiers, hyperparameter tuning, and XAI techniques contributes to advanced screening tools, fostering trust among clinicians and researchers. © 2024 IEEE.

关键词： Logistic regression

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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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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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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions 3

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Vivek, Kolla Swetha, Gurrala Kumar M, V. T. Ram Pavan Veni, Anusuri Krishna Praveen, Neti Kale, Masthan Rao Dept. of Cse Andhra Pradesh Ongole India Dept. of Cse Telangana India Kakaraparti Bhavanarayana College Dept. of Computer Science Andhra Pradesh Vijayawada India Madanapalle Institute of Technology & Science Dept. of Computer Science & Engineering - Data Science Andhra Pradesh Madanapalle517325 India Srkr Engineering College Dept. of Computer Science and Design Andhra Pradesh Bhimavaram India Dept. of Cse Andhra Pradesh Ongole India

ISBN: (纸本)9798331509675

Smart cities have emerged as a response to the fast expansion of metropolitan areas;these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions. © 2025 IEEE.

关键词： Digital Technology Drainage Monitoring Fire detection Smart City Waste Management

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Deep Learning-based Electric Kickboard Helmet Detection under Night Driving Conditions

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Transactions of the Korean Institute of Electrical Engineers 2022年第10期71卷 1411-1419页

作者： Kim, Daun Jeong, Jin-Woo Dept. of Data Science Seoul National University of Science and Technology Korea Republic of Dept. of Industrial Engineering Seoul National University of Science and Technology Korea Republic of

As the use of electric kickboards has increased recently, the frequency of safety accidents is also increasing. Even though the government amended the road traffic law to make it compulsory to wear a helmet, the helmet-wearing rate is still remarkably low. In this work, we propose a novel method to monitor and detect whether a user of an electric kickboard is wearing a helmet through a deep learning-based object detection network, thereby increasing the helmet-wearing rate. For continuous driver monitoring, the proposed method captures the user's face in a front view using the user's mobile phone attached to the kickboard. In addition, to handle both day and night driving conditions, various image conversion, enhancement, and augmentation techniques were considered and evaluated. The experimental results showed the feasibility of the proposed method. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Object detection

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Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GRU 4

Text Sentiment Analysis of Film Reviews Using Bi-LSTM and GR...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Mouthami, K. Yuvaraj, N. Thilaheswaran, K.K. Lokeshvar, K.J. Kpr Institute of Engineering and Technology Dept. of Computer Science Engineering Coimbatore India Kpr Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350300093

Sentiment analysis is a vast subject to explore in natural language processing (NLP) techniques. The film reviews were analyzed and segregated into positive, neutral, and negative reviews. The proposed model examines two distinct datasets, one with multi-class labels and the other with binary-type class labels. The preprocessing is done by using the bag of words and skip-gram word2vec, variety of classifiers, including Two state Gated recurrent units (TS-GRU), was used for binary classification, and the Bidirectional Long Short-Term Memory (Bi-LSTM for the multi-class situation was implemented. Bi-LSTM was utilized in the model, in which symmetric Bi-LSTM replaces the LSTM approaches to get around the high computational cost of training the normal LSTM. The Bi-LSTM has equivalent accuracy to the LSTM while significantly reducing computational costs. Sentiment analysis offers a glimpse of how consumers feel about certain movies. This endeavor is undertaken via the study of natural language processing, in which sentiment analysis is a branch which examines how words are arranged and utilized to extract meaning from user writing. This helps to investigate further the methods for deciphering such data thanks to significant advancements in machine learning techniques;with Keras API and the Internet Movie database (IMDb) dataset, this study compares single and multi-branch CNN with Bidirectional LSTMs with different kernel sizes. GRU achieved 98.24% of accuracy, and bi-LSTM achieved 98.65% of accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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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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