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An ensemble learning-based machine learning with voting mechanism for chronic disease prediction

An ensemble learning-based machine learning with voting mech...

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2024 International Conference on Artificial Intelligence, computer, data sciences, and Applications, ACDSA 2024

作者： Soualihou, Soualihou Ngnamsie Kim, Jeong-Dong Zonyfar, Candra Lee, Hyun Lee, Taek Lee, Jung-Been Sun Moon University Dept. of Computer Science and Electronic Convergence Asan Korea Republic of Sun Moon University Dept. of Computer Science and Engineering Asan Korea Republic of Sun Moon University Division of Computer Science and Engineering Asan Korea Republic of

ISBN: (纸本)9798350394528

Chronic diseases present a significant challenge in healthcare, often requiring ongoing medical attention and posing limitations on patients' daily activities. Diagnosis of such diseases is hindered by the absence of specific symptoms making them hard to detect and or prevent. Addressing this issue, researchers have turned to computational approaches, analyzing patients' medical records to predict the presence or absence of chronic diseases with promising results. However, there is potential for further improvement. This paper introduces an ensemble classifier, ELVot-CroDiP, designed to enhance chronic disease prediction. ELVot-CroDiP harnesses the collective strengths of various machine learning algorithms through a majority voting mechanism, thereby boosting predictive accuracy. The model's performance was evaluated using precision, recall, f1-score, and accuracy across five medical datasets, including heart disease, chronic kidney disease, diabetes, and heart stroke. Comparative analysis shows that ELVot-CroDiP offers superior performance in predicting chronic diseases compared to existing models, as evidenced by its higher scores in the mentioned evaluation metrics for each dataset tested. © 2024 IEEE.

关键词： Forecasting

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A Computational Analysis of Optimization Techniques to Solve Assignment Problem 2

A Computational Analysis of Optimization Techniques to Solve...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Kuma, Yash Sahoo, Ramesh Ch. Dixit, Prashant Dept. of Computer Science & Engineering Manav Rachna International Institute of Research and Studies Haryana Faridabad India Dept. of Computer Science & Engineering Galgotias University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798331541217

In the field of combinatorial optimization, numerous methods have been developed to address complex problems, including assignment, scheduling, and resource allocation. This paper presents a comparative analysis of seven prominent optimization techniques: Hungarian Algorithm, Greedy Algorithm, Auction Algorithm, Linear Programming, Genetic Algorithm, Simulated Annealing, and Constraint Programming. We evaluate these methods on computational efficiency, scalability, and practical applicability, focusing on assignment problems. The analysis reveals that Hungarian Algorithm, Linear Programming, and Constraint Programming consistently deliver high accuracy across all matrix sizes, making them ideal for scenarios where solution precision is paramount. Conversely, the Greedy Algorithm excels in speed on smaller matrices but suffers from significant time and accuracy issues as matrix size increases. Additionally, while both Constraint Programming and Genetic Algorithm achieve high accuracy, their execution times rise considerably with matrix size, especially for Genetic Algorithm, which becomes impractical for large-scale problems. Thus, Hungarian Algorithm, Linear Programming, and Constraint Programming are optimal for accuracy-focused tasks, while Greedy Algorithm is preferable for smaller, speed-critical cases. © 2024 IEEE.

关键词： Genetic algorithms

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

引用

1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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Nova: Revolutionizing Agile Project Management with Scrum and Kanban Integration for Enhanced Team Collaboration and Productivity 5

Nova: Revolutionizing Agile Project Management with Scrum an...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Manaswini, Poluri Shaik, Sajjad Ashwani, Siddhant Balakrishnan, Roshni M. Rajagopal, Shinu M. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350376685

In the realm of software project management, Agile methodologies such as Scrum and Kanban have become quintessential for fostering adaptability and efficiency. "Nova,"a novel project management tool, embodies these methodologies to offer a user-friendly interface that supports the dynamic needs of modern software development teams. This paper explores Nova's unique approach that integrates the foundational principles of Agile which are Scrum, and Kanban, thereby facilitating a seamless and effective management platform. Through a comparative analysis with traditional project management tools, this study evaluates how Nova enhances team collaboration, project tracking, and outcome predictability. Emphasizing its ease of use and adaptability, the research highlights Nova's potential to improve project delivery times and overall team productivity. © 2024 IEEE.

关键词： Lean production

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Speech Emotion Recognition Using ML 7

Speech Emotion Recognition Using ML

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7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Niharika, S.M. Soumya, A. Rv College of Engineering Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350343144

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions. © 2023 IEEE.

关键词： Feature extraction

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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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Voice AI-Intelligence Based Voice Assistant 3

Voice AI-Intelligence Based Voice Assistant

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Garai, Pooja Nikam, Aditya Kerkar, Suyash Deshmukh, Samruddhi Mane, Atharva Bhise, Suvarna Vasantdada Patil College Of Engineering & Visual Arts Dept. of Artificial Intelligence & Data Science Mumbai India

ISBN: (纸本)9798331515911

Voice assistant applications have become integral parts of modern technology ecosystems, offering users convenient and efficient ways to interact with their devices. This paper introduces Voice Ai, a versatile voice assistant application developed using Android Studio and Google's Machine Learning Kit Machine learning(MLKit) for Optical Character Recognition (OCR) functionalities. The app includes features like setting alarms, making calls, opening applications, and conducting question-answer sessions powered by G4F. A comparative analysis between G4F and Google's backend is performed, assessing their performance and user satisfaction during question-answer interactions. Voice Ai simplifies daily tasks and boosts productivity, providing insights for developers, researchers, and end-users interested in AI-based voice assistant applications. © 2025 IEEE.

关键词： Adversarial machine learning

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Pattern Recognition in Lung Disease Classification: Leveraging Convolutional Neural Networks that Employs Local Phase Quantization Features 15

Pattern Recognition in Lung Disease Classification: Leveragi...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sree, M. Ramya Siddhartha, Mettu Reddy, Poli Vamsi Vardhan Singh, Tripty Nair, Rekha R. Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science and Engineering Bengaluru India Alliance University Alliance School of Advanced Computing Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350370249

Lung diseases are wide group of diseases related to the respiratory system, and is ranging from acute infections to chronic disorders with multifactorial causes. Recognition of the lung disease is very crucial in the proper diagnosis, forecasting through prognosis, and remedies. Hence proposed lung disease classification with feature extraction technique. The proposed work carried out feature extraction with Gray-Level Co-occurrence Matrix (GLCM) and Local Phase Quantization (LPQ) combined with Convolutional Neural Network(CNN). The goal is to create a model that reduces diagnostic errors, ensuring more precise early detection and classification of pulmonary disorders. This initiative facilitates the advancement of pulmonary medicine by promoting accurate radiologic imaging, while future directions emphasize integrating medical workflows to enable personalized and timely diagnosis and treatment. Additionally, the ResNet model demonstrates superior performance in hernia detection, achieving a test accuracy of 89.43% for GLCM based feature extraction and Pneumonia detection with 79.20% test accuracy for LPQ based feature extraction. © 2024 IEEE.

关键词： CNNs Deep learning Early detection Feature extraction GLCM Local Phase Quantization Lung disease diagnosis Medical imaging Texture analysis

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An Extended Reality Platform Powered by Large Language Models: A Case Study on Teaching Dance Costumes 7

An Extended Reality Platform Powered by Large Language Model...

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7th IEEE International Conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Garzarella, Silvia Vallasciani, Giacomo Cascarano, Pasquale Hajahmadi, Shirin Cervellati, Elena Marfia, Gustavo University of Bologna Dept. of the Arts Bologna Italy University of Bologna Dept. of Computer Science and Engineering Bologna Italy

ISBN: (纸本)9798331521578

In this paper, we present an Extended Reality (XR) platform powered by Large Language Models (LLMs), designed to support education in dance history and cultural heritage. The platform here applied to a case study on a dance costume, allows users to interact with 3D models, annotate them, and explore their historical and cultural contexts collaboratively. Using features like image-to-LLM queries, users can gain insights into costume details and performance history, and the annotation tools enable uploading multimedia resources, thereby simulating an archival research environment. By integrating LLMs, the platform provides tailored information and context on demand, enriching the user experience with detailed explanations about objects, such as costume construction, usage, and cultural significance. © 2025 IEEE.

关键词： Hosiery manufacture

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Slang Word Detection in the Context of User Profiling in the Social Media Platformes 4

Slang Word Detection in the Context of User Profiling in the...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Samanta, Bikram Shil, Ritika Pal, Alok Ranjan Pal, Antara College of Engg. and Management Dept. of Computer Science and Engg. Kolaghat India Atharva College of Engineering Dept. of Computer Science Engg. Mumbai India

ISBN: (纸本)9798350343670

On social media platforms, people express their views in various ways, sometimes using formal words and sometimes in informal form. The informal conversations may contain jargon words i.e. slang words which might be improper for all kinds of audience. In this work, an attempt has been made to identify slang words in a text, posted on the social media platform. The overall experiment has been carried out in 4 modules. In module-1, the suspicious words from the posted text have been identified using minimum edit distance. This experiment has been implemented by the help of a dataset of printable slang words collected from Kaggle. In module-2, the topic of discussion has been analyzed by using a knowledge based approach. This part of the experiment has been carried out by creating a newspaper archive from the online repository of the "Times of India"newspaper. But, this knowledge based strategy could not produce an appreciable accuracy at its baseline. Therefore, as a modification of this technique, in module-3, the context analysis task has been implemented combining knowledge based approach and machine learning based approach. In module-4, the module-1 and module-3 are merged to tag a suspicious word with its level of abusiveness. © 2024 IEEE.

关键词： Context Analysis Slang Word Social Media Transformer User Profiling

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