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Cardiomegaly Detection Using Deep Learning 4

Cardiomegaly Detection Using Deep Learning

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Suganthi, S.U. Sivabalan, A. Akash, M.V. Barath, B. Goushik, G. Anto Akash, P. Sri Sairam Institute of Technology Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India Sri Sairam Engineering College Dept. of Artificial Intelligence And Data Science Tamil Nadu Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

This research introduces an innovative method for forecasting cardiomegaly, a common heart condition marked by an enlarged heart, by combining deep learning and machine learning methods. Using the ResNet50 architecture, we examine chest X-ray images to identify cardiomegaly indications. At the same time, a Random Forest algorithm analyses clinical data in a table format, such as age, gender, and different cardiac indicators, to improve prediction accuracy. Our approach combines these models to use unstructured images and structured health data, resulting in a comprehensive diagnostic tool. The data, collected from public health databases, was carefully labelled to guarantee accurate training and validation. Preliminary results showed the promising accuracy of the hybrid model, surpassing individual approaches, and demonstrating the potential of integrated models to improve diagnostic capabilities in cardiology. This study helps improve medical imaging and predictive analytics in healthcare, providing a productive solution for early detection and management of cardiomegaly. The results highlight the promise of combining AI models to change diagnostic methods and enhance patient results. © 2024 IEEE.

关键词： Deep learning

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A Survey on Fish Size Measurement in Aquatic Environments

A Survey on Fish Size Measurement in Aquatic Environments

引用

2024 IEEE Bangalore Humanitarian Technology Conference, B-HTC 2024

作者： Ankitha, K. Venugopala, P.S. Kumaki, Takeshi NMAMIT Dept. of Artificial Intelligence and Data Science Nitte India Ritsumeikan University Dept. of Electronic and Computer Engineering Kusatsu Shiga Japan

ISBN: (纸本)9798350344509

Fish size measurement in aquatic environments plays a pivotal role in understanding fish biology, ecology, and fisheries management. This survey explores various methodologies employed for fish size measurement, emphasizing their significance in aquaculture, fisheries, Ocean and ecological research. Techniques encompass both contact and non-contact methods, with advancements in imaging technologies allowing for non-invasive measurements. These approaches involve the use of cameras, image processing software, and advanced machinery to estimate fish size without causing stress or harm to the fish. While these methods offer promising avenues for precise measurements, challenges exist, including accuracy concerns and environmental limitations. Consequently, there's ongoing research to refine these techniques and ensure their applicability across different fish species and aquatic conditions. Overall, this survey highlights the evolving background of fish size measurement techniques and their importance in various domains within aquatic sciences. © 2024 IEEE.

关键词： Fish

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Optimizing Multimodal Transformers for Medical Image Captioning: Enhancing Automated Descriptions via AI Systems 6

Optimizing Multimodal Transformers for Medical Image Caption...

引用

6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Arman, Mithila Jahan, Md. Khurshid Dhrubo, Ahmed Faizul Haque Rhaman, Md. Mahfuzur Choya, Sumaya Binte Zilani Dohan, Din Mohammad Ul Islam Sajid, Md. Ashiq Rabiul Alam, Md. Golam BRAC University Dept. of CSE Dhaka Bangladesh North South University Dept. of ECE Dhaka Bangladesh George Mason University Dept. of Computational Data Science FairfaxVA United States American International University-Bangladesh Dept. of CSE Dhaka Bangladesh

ISBN: (纸本)9798331506520

In contemporary diagnostic workflows, medical image captioning has emerged as a pivotal advancement, combining deep learning methodologies and transformer architectures to enhance accuracy and efficiency in medical interpretations. This paper proposes the optimization of multimodal transformers for automated medical image captioning, focusing on integrating Vision Transformers (ViT) and Bidirectional Auto-Regressive Transformers (BART) with novel variations such as Swin Transformers and GPT-2. We use a robust multimodal AI framework to explore how these architectures synergize to generate coherent and diagnostically relevant captions for radiological images. We assess the performance of multiple transformer models by employing the ROCO dataset, containing paired X-ray images and expert-generated reports. Our findings demonstrate that the ViT + BART combination yields the most stable and accurate captions, minimizing training and validation loss. In contrast, DEiT + MBART displayed instability, highlighting the need for further hyperparameter tuning. Through this comparative analysis, we underscore the critical role of transformer-based models in reducing the cognitive load on medical professionals, enhancing diagnostic accuracy, and promoting real-time, automated medical image interpretation. © 2025 IEEE.

关键词： Medical imaging

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An Al Powered Exam Proctoring: Comprehensive Monitoring for Integrity

An Al Powered Exam Proctoring: Comprehensive Monitoring for ...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Mewada, Dhruv Gaikwad, Shlok Gharat, Bhavika Kamble, Priyanshu Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence&Data Science Mumbai India

ISBN: (纸本)9798350359688

In recent times, remote learning has experienced significant growth, yet the challenge of conducting academic examinations in a secure manner persists. Various approaches have been adopted by universities, such as collecting assignments susceptible to plagiarism or implementing remote proctoring with manual supervision. As our current lifestyle appears to be transitioning towards a new normal, it becomes imperative to explore effective solutions. This paper introduces a proposed remedy: the development of an AI-based integrated system designed to prevent cheating during examinations. The system not only detects fraudulent activities but also records evidence, ensuring both security and cost-effectiveness. © 2024 IEEE.

关键词： online tests proctoring remote learning

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Boothless Toll Collection System 4

Boothless Toll Collection System

引用

4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Sarkar, Swagata Selvabharathi, M. Sarveswaran, D. Arun Raja, M. Raju, Aswin Sri Sairam Engineering College Dept. of Artificial Intelligence and Data Science Tamil Nadu Chennai India

ISBN: (纸本)9798331508845

As a developing country, it is critical that we prioritize the construction and improvement of our road infrastructure in order to stimulate economic growth and promote easier logistics operations. However, the current toll collection system is hampered by difficulties such as overcharging and traffic congestion, reducing its effectiveness. To solve these issues, we propose implementing an advanced toll collection system that includes GPS technology, an ANPR camera, and a variety of machine learning algorithms. This new method will transform toll collecting by properly calculating toll charges based on the actual distance traveled by vehicles on the highway, removing the need for drivers to stop at toll booths. The system uses GPS technology to precisely compute the distance traveled by each car, while ANPR cameras guarantee the journey's validity. As a result, vehicles will only be paid for what distance they have traveled, reducing overcharging concerns and congestion at toll gates. Our major goal is to create a smooth and efficient tolling mechanism that overcomes the limitations of traditional tollgate systems. We hope to improve the toll collection process, shorten wait times, and reduce the consumption of fuel associated with long lines at toll booths by utilizing cutting-edge technologies. By applying this innovative strategy, we hope to improve not only toll collecting efficiency but also commuters' overall driving experience. © 2024 IEEE.

关键词： Traffic congestion

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Optimization of Rainfall Prediction Using Satellite data Through Machine Learning and Deep Learning Algorithms

Optimization of Rainfall Prediction Using Satellite Data Thr...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Ramani, Kasarapu Reddy, Maddireddy Srilekha Bhavani, Kelavathi Feeza, Syed Bavesh, Vellampalli Sai Dept. of Data Science Tirupati India Sree Vidyanikethan Engineering College Department of Csse Tirupati India

ISBN: (纸本)9798350382693

In recent years, non-periodic rainfall has increased globally. The importance of accurate rainfall prediction model is increased in order to reduce the losses due to floods and drought. Rainfall prediction plays a crucial role in decision-making, flood and drought risk assessment and management. In agriculture, it guides many farmers in optimizing crop planting schedules and irrigation. It ensures better yields and reduces losses from droughts and floods. Accurate rainfall prediction benefits us in effective water resource management and energy sector planning. In India, the rainfall varies rapidly due to the vast geography. There are many factors which effects rainfall like distance from the sea, seasons, the presence of mountains, prevailing winds and precipitation. This research work mainly focuses on developing an efficient ensemble-based Machine Learning Techniques such as Extreme Gradient boosting algorithm (XG Boost) and Logistic Regression (LR) for predicting rainfall in different regions of India using satellite data and comparing results with the deep learning algorithms such as Convolutional Neural Network (CNN), Multiple Layer Perceptron (MLP), Long Short-Term Memory (LSTM), and Radial Basis Function Network (RBF). The aim of this paper is to overcome the drawbacks of existing systems and build improved prediction model with accuracy more than 90% in order to enhance water resource management, early warning systems, improved urban planning, agriculture, and climate change studies. © 2024 IEEE.

关键词： Radial basis function networks

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Exploring the Impact of Unsupervised Clustering Methods in Systematic Literature Reviews 56

Exploring the Impact of Unsupervised Clustering Methods in S...

引用

56th Annual SIGCSE Technical Symposium on Computer science Education, SIGCSE TS 2025

作者： Bolger, Emily Caballero, Marcos Dept. of Computational Mathematics Science and Engineering Michigan State University East Lansing MI United States Dept. of Computational Mathematics Science and Engineering Dept. of Physics and Astronomy Michigan State University East Lansing MI United States

ISBN: (纸本)9798400705328

About 15 years ago, education researchers conducted a systematic literature review (SLR) on change strategies for improving undergraduate STEM education instruction. Analyzing 191 articles from 1995 to 2008, the researchers identified four broad categories of change strategies through a comprehensive interdisciplinary literature review: (1) disseminating curriculum and pedagogy, (2) developing reflective teachers, (3) enacting policy, and (4) developing a shared vision. With recent developments in STEM education practices, it is imperative to conduct a follow-up SLR comparing the effects of change strategies and associated student success. Similarly, with the influx of scientific articles published in recent decades, it is time consuming to conduct comprehensive SLRs without the assistance of machine learning (ML) analysis techniques. Working with qualitative researchers, we investigate the impact and ability of using ML to assist in these analyses. While most ML approaches can be easily written with a few lines of code, using them to extract meaningful information for literature reviews is challenging. In this work, we describe our experience with integrating machine learning techniques into the analysis pipeline of SLRs. Specifically, this poster will: (1) share results from clustering analysis to identify themes of the chosen abstracts, (2) explore the effects of data bias on found clusters, (3) present the challenges of adding machine learning into SLRs, and (4) assess if the addition of ML in SLRs aligns with the expected goals of qualitative researchers. © 2025 Copyright held by the owner/author(s).

关键词： Clustering Instructional Change Strategies Machine Learning Systematic Literature Reviews

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

引用

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 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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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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