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A Heart Disease Prediction Model using Merged XGBoost-SVM Classifier and Particle Swarm Optimization 5

A Heart Disease Prediction Model using Merged XGBoost-SVM Cl...

引用

5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Majumder, Annwesha Banerjee Gupta, Somsubhra Majumder, Sourav Singh, Dharmpal JIS College of Engineering Dept. of Information Technology Kalyani India Swami Vivekananda University Dept. of Comouter Science Kolkata India Capgemini Kolkata India JIS College of Engineering Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350395235

This study aims to develop an advanced heart disease prediction model using machine learning techniques to address the global health challenge of early identification and accurate diagnosis, especially considering the projected deficits in healthcare by 2030. The proposed model integrates two powerful algorithms, XGBoost and Support Vector Machine Classifier, leveraging their complementary strengths to achieve effective classification. To enhance the model's performance, feature selection has been conducted using the LASSO method, which identifies the most relevant features, while outlier removal based on the Z score ensures data quality. StandardScaler has been utilized to standardize the data, mitigating any biases caused by differing scales. Furthermore, Particle Swarm Optimization has been employed to optimize the model's performance by fine-tuning its parameters. The reported accuracy of 91.8% underscores the model's potential to accurately predict heart disease, offering valuable support in prevention efforts. These findings highlight the significance of technological advancements in healthcare, particularly in addressing the growing demand for accurate and timely diagnoses. © 2024 IEEE.

关键词： Support vector machines

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Land Use Land Cover Classification Using Machine Learning and Remote Sensing data: A Case Study of Karnaprayag, Uttarakhand, India 1

Land Use Land Cover Classification Using Machine Learning an...

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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Saini, Rashmi Singh, Suraj Govind Ballabh Pant Institute of Engineering and Technology Dept. of Computer Science and Engineering Pauri Garhwal India

ISBN: (纸本)9798350364590

Land use and land cover (LULC) classification is crucial for planning and sustainable development, especially in mountainous regions. This study focuses on the Tehsil Karnaprayag, situated in the vicinity of the Greater Himalaya. With a total area of 1858.903 km2 and elevations ranging from 647m to 7641m. Using multispectral Sentinel-2 imagery and Machine Learning techniques i.e. Extreme gradient boosting (XGBoost) and K-nearest neighbor (KNN). This study aims to classify seven distinct land cover classes: snow, forest, water bodies, grassland, agricultural land, fallow land, and built-up areas. The classification results demonstrate the effectiveness of XGBoost classifier achieving an accuracy of 90.22% and a kappa value of 0.882, while KNN achieved an accuracy of 88.59% with a kappa value of 0.862. The analysis reveals that forest and fallow land are the predominant land cover types in the study area, constituting nearly 60% and 15% of the total area respectively. Notably, XGBoost exhibits the highest class-specific accuracy of 97.60% for the snow class, demonstrating its utility in discriminating between challenging land cover types. This research highlights the importance of LULC classification in informing land management strategies and facilitating sustainable development initiatives in mountainous regions. The findings provide valuable insights into the spatial distribution and composition of land cover in Tehsil Karnaprayag, essential for effective planning and conservation efforts in the Himalayan ecosystem. © 2024 IEEE.

关键词： Sustainable development

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Unique Approach of Contextual Uniqueness Assessment of Research Papers Using Multi-Label Classification and Similarity Detection 26

Unique Approach of Contextual Uniqueness Assessment of Resea...

引用

26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Islam, Md. Zihadul Moon Taher, Sidrat Muztahid, Shaikh Adib Biswas, Arpon Shopnil, Md. Shahariar Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

Research papers should possess a significant amount of novelty. However, proper paraphrasing of entire paper may not be identified as plagiarism. Therefore, in this work a unique approach of similarity detection technique has been proposed. At first the abstracts are used to classify which area the paper belongs to, then papers of the same area are extracted and then contextual similarity is detected using cosine similarity. For training and testing purposes, two datasets have been created using data from IEEE Xplore. Since, almost every paper has some elements of paraphrased contents, therefore, a maximum acceptable threshold has also been proposed in this paper. Maintaining the maximum threshold, how well cosine similarity was able to distinguish the novel, paraphrased, and fully plagiarised works has also been analyzed. © 2023 IEEE.

关键词： Contextual Similarity Detection Cosine Similarity Deep Learning Multi-Label Classification

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Using IOT and WSNs Technology for Monitoring an Aquaponic Greenhouse System 4

Using IOT and WSNs Technology for Monitoring an Aquaponic Gr...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Touhami, Achouak Bourouis, Amina Benahmed, Tariq Touhami, Sana Touhami, Nawal Benahmed, Khelifa Ali Kafi university center Dept. Mathematics and Computer Science Tindouf Algeria Tahri Mohammed university Dept. Mathematics and Computer Science Bechar Algeria Ahmed Draria university Dept. Electrical Engineering Adrar Algeria

ISBN: (纸本)9798331519872

The increasing population, climate fluctuations, soil erosion, limited water resources, and concerns about food security highlight the need for effective and sustainable approaches to food production. Aquaponics represents a sustainable method that conserves land and water while minimizing its environmental impact. This technique operates as a closed-loop, soil-less farming system, requiring meticulous monitoring, control, and management. Monitoring an aquaponic greenhouse system involves tracking various parameters to maintain the health and productivity of both plants and fish. Our research introduces a design and algorithms for monitoring an aquaponic greenhouse, utilizing wireless sensor networks (WSN), aquaponic systems, and Internet of Things (IoT) technology. © 2024 IEEE.

关键词： Aquaponic greenhouse Closed-loop Fish Internet of things Plant Wireless sensor networks

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Relaxing Super Localization Frame Rate Requirements Utilizing a Novel 2D Interpolation Technique

Relaxing Super Localization Frame Rate Requirements Utilizin...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Tuccio, Giulia Afrakhteh, Sajjad Iacca, Giovanni Demi, Libertario University of Trento Dept. of Information Engineering and Computer Science Italy

ISBN: (纸本)9798350346459

Ultrasound Localization Microscopy (ULM) by-passes the conventional diffraction limit, allowing the imaging of the vasculature at a microscopic resolution through precise localization of intravenously injected microbubbles. Thanks to their resolution, ULM generated images hold the promise to be the next diagnostic frontier for diseases that modify the vascular structure, such as cancer. However, deploying ULM in clinical settings is currently limited by the prolonged acquisition times and high frame rates needed. To relax the high frame rate requirement, we propose a new time-efficient ULM (TEULM) pipeline. The proposed TEULM introduces the radial basis functions (RBFs) as interpolators upstream of the ULM framework, to recover the missing information consequence of a reduced frame rate. We test TEULM using in vivo Rat's Brain and Kidney datasets acquired at a high frame rate (1 KHz). The results confirm the ability of TEULM to reconstruct super-resolution (SR) images with a similarity score (Dice score) of 80% for both datasets when simulating a frame rate acquisition of 100 Hz;i.e., we can relax the frame rate requirement for ULM by a factor 10 while keeping the SR image quality acceptable. © 2023 IEEE.

关键词： Interpolation

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Finding Type 2 Diabetes Using Sparse Balanced Support Vector Machine in Electronic Health Records

Finding Type 2 Diabetes Using Sparse Balanced Support Vector...

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2023 IEEE IAS Global Conference on Emerging Technologies, GlobConET 2023

作者： Kaur, Ranjanpal Sekhon, Er. Navjot Kaur Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350331790

Early Type 2 Diabetes (T2D) diagnosis is crucial for optimal integrated therapy and patient follow-up. There has been a huge growth in the quantity of accessible Electronic Health Record (EHR) data, and developments in Machine Learning (ML) methodologies have been as amazing. Maintaining and modelling a huge data set can cause over fitting, model interpretability, and computational expenditure. We used Sparse Balanced Support Vector Machine (SB-SVM) to detect type 2 diabetes in an original EHR dataset (named FIMMG dataset). In particular, we have chosen just those aspects of the EHR collected from a homogeneous group of participants before the diagnosis of T2D, including exemptions, examinations, and medicine prescriptions. The proposed solution is trustworthy compared to other ML and Deep Learning methods utilised by the state-of-the-art to handle this problem. The SB-SVM offers the best prediction performance-computation time tradeoff, according to the data. In addition, the induced sparsity makes the model more interpretable while automatically handling high-dimensional data and the normally uneven distribution of classes. © 2023 IEEE.

关键词： Support vector machines

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Accurate Detection of Aquatic Debris in Ocean Surfaces using AlexNet Algorithm over ResNet-50 Algorithm 3

Accurate Detection of Aquatic Debris in Ocean Surfaces using...

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3rd International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Kamal, T.M. Bharatha Devi, N. Saveetha School of Engineering Dept. of Computer Science Chennai India

ISBN: (纸本)9798350306989

This research study intends to detect the aquatic debris accurately on Ocean surfaces using Novel AlexNet algorithm in comparison with ResNet-50 algorithm. Novel AlexNet and ResNet-50 are the two groups considered for this research with 20 iterations done for each group whose G-power is 80% and confidence level is 95%. The dataset consists of 724 JPG file format images depicting clear ocean and 324 JPG file format images depicting aquatic debris, including images with noise. This work demonstrates that the Novel AlexNet algorithm achieves an accuracy of 93.77% which identifies objects accurately over the ResNet50 algorithm with an accuracy of 91.47% and there is no statistical significance difference between the Novel Alexnet Algorithm and ResNet-50 Algorithm for the value of p=0.076 (Independent Sample T-test p © 2023 IEEE.

关键词： Debris

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Evaluation of Handwritten Descriptive Responses Using Machine Learning - A Survey 2

Evaluation of Handwritten Descriptive Responses Using Machin...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Tayal, Pankhuri Shetty, Parth Praveen Pratheeksha, P. Harshita, R. Sreenath, M.V. Pes University Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350308846

Evaluation of student performances is crucial to the process of learning in an education system. Currently, the most common approach adopted throughout the education industry is largely reliant on human resources as forms of evaluators. As the gap in student-teacher ratios increases, this process becomes complicated and cumbersome and such manual assessment techniques lead to unintentional biases and inconsistency. Technology has been playing a role in aiding evaluators in the recent trends. Instead of the traditional pen and paper-based methods, modes of assessments are being digitized and so are the correction processes. While there are several successful technologies available for grading multiple-choice questions, there is still growing research for methods of evaluating descriptive questions which need to account for a degree of variations in student answers. For the last few decades, there have been many researchers working on developing a system that would act as a bridge between traditional methods and digitized evaluation systems, by using techniques like Optical Character Recognition (OCR). Various Artificial Intelligence and Machine Learning techniques have been proposed to evaluate descriptive answers automatically. This paper provides a comprehensive survey of such systems and analyses the drawbacks of the current studies. It has been observed that these systems do not consider essay-type questions that do not have a defined answer. © 2023 IEEE.

关键词： Artificial Intelligence and Machine Learning Handwritten descriptive answer evaluation Natural Language Processing OCR

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Analysis of Fictional Character Backstories Using Natural Language Processing and Deep Learning 26

Analysis of Fictional Character Backstories Using Natural La...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Tanvir, Sifat Khan, Md. Rafid Jahan Oyshi, Ishmam Ishan Tonmoy, Md. Sahil Zaman Rafi, Mashrafi Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359015

With the overwhelming successes of industries like Marvel and DC over the years, fictional characters have become more and more popular among the general mass. However, generation of new fictional characters do not seem to be as appealing as before. This paper revolves around the idea of generating fictional character backstories using GPT-3.5. A dataset consisting of AI generated pre-existing and newly created fictional characters has been prepared. A sequential neural network model was designed by the authors and pre-trained using the popular Superheroes NLP dataset from Kaggle. The proposed model and some state-of-the-art pre-trained text classification BERT models have been compared side by side. The AI generated dataset has been used as the test data to see how the models perform in identifying the moral alignment of the characters. A comparative analysis is also done between the two datasets. © 2023 IEEE.

关键词： BERT Neural Network NLP RoBERTa Sequential Model

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Distributed and Synchronized Multilingual Audio Container for Video Streams 2

Distributed and Synchronized Multilingual Audio Container fo...

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Nidagundi, Pradnya S. Udupa, Prajna Dindore, Chinmayee Kothari, Palak Pujari, Nitin V. PES University Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347982

This paper presents a distributed and synchronised multilingual audio container for video streams. The system utilises a distributed architecture to separate audio and video streams, allowing for the independent manipulation and synchronization of multiple languages. The proposed solution addresses the challenges of providing efficient video streaming and has the potential to improve accessibility to foreign language educational content for non-native speakers. The proposed approach improves the scalability and flexibility of the system, as well as the overall user experience. The system is evaluated through simulations and real-world implementation, demonstrating its effectiveness in providing a seamless multilingual video streaming experience. The results show that the proposed system can synchronize multiple languages with high accuracy, and the distributed architecture ensures low latency and high scalability. © 2023 IEEE.

关键词： Synchronization

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