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An Ensemble-based ML Model to Predict Cardiac Disease 7

An Ensemble-based ML Model to Predict Cardiac Disease

引用

7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Jannat Hema, Nur E. Samayeen Zeest, Nubras Mahjabin, Mohammad Ratul Nazrul Islam, Muhammad Military Institute of Science And Technology Department of Biomedical Engineering Dhaka Bangladesh Military Institute of Science And Technology Department of Computer Science And Engineering Dhaka Bangladesh

ISBN: (纸本)9798350359299

In contemporary times cardiovascular disease has emerged as a predominant health concern. It underscores a critical necessity for early and precise predictions to facilitate effective prevention and intervention strategies. This investigation is centered on a meticulous comparative analysis of heart disease prediction which employs advanced machine learning techniques. Three distinct machine learning models such as Random Forest, Light GBM and Gradient Boosting are deployed to analyze and predict heart disease based on an available dataset. The study commences with comprehensive data preprocessing, incorporating sophisticated sampling techniques. Subsequently, the dataset is stratified into training and testing sets, and the three machine learning models are systematically implemented. Evaluation metrics, including accuracy, precision, recall, and F1- score, are employed to assess the predictive capabilities of the models. Three combinations are selected for ensemble modeling based on their better performance for the used dataset. Notably, the ensemble model of Random Forest and XGBoost attained a peak accuracy of 99%. The study highlights the use of machine learning, particularly ensemble model in enhancing diagnostic accuracy for heart disease prediction, holding the potential to significantly contribute to early detection and timely intervention, thereby alleviating healthcare burdens related to cardiovascular health. © 2024 IEEE.

关键词： Forecasting

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An IoT and Blockchain-Based Framework for Sustainable Vertical Farming 2

An IoT and Blockchain-Based Framework for Sustainable Vertic...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Munim, Kazi Md. Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350360523

With ongoing global challenges like population growth, resource deficiency, climate change, and global warming, the demand for efficient procedures to produce sustainable, efficient, and safe food is increasing. Thus vertical farming, a pioneer emerging technology may play a vital role in addressing such challenges in agriculture. This article presents an innovative framework integrating IoT and blockchain to offer an efficient, reliable, and secure ecosystem for vertical farming. The IoT devices allow real-time monitoring and controlling of essential parameters like temperature, moisture, light, nutrient levels, and alike, while the Blockchain technology provides a transparent and immutable ledger of the records regarding crop growth and resource consumption of the farm, enhancing the framework's traceability. A prototypical system of this framework was developed and simulated in the lab environment to evaluate its performance for a specified use case. By generating required control instructions to maintain the ideal environment, the prototypical system proved to be effective, efficient, and reliable. © 2024 IEEE.

关键词： Blockchain

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Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation

Automated Literature Review Using NLP Techniques and LLM-Bas...

引用

2024 International Conference on Innovations in Science, engineering and Technology, ICISET 2024

作者： Ali, Nurshat Fateh Mohtasim, Md. Mahdi Mosharrof, Shakil Krishna, T. Gopi Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350355499

This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted in an increased demand for automation. Developing a system capable of automatically generating the literature reviews from only the PDF files as input is the primary objective of this research work. The effectiveness of several Natural Language Processing (NLP) strategies, such as the frequency-based method (spaCy), the transformer model (Simple T5), and retrieval-augmented generation (RAG) with Large Language Model (GPT-3.5-turbo), is evaluated to meet the primary objective. The SciTLDR dataset is chosen for this research experiment and three distinct techniques are utilized to implement three different systems for auto-generating the literature reviews. The ROUGE scores are used for the evaluation of all three systems. Based on the evaluation, the Large Language Model GPT-3.5-turbo achieved the highest ROUGE-1 score, 0.364. The transformer model comes in second place and spaCy is at the last position. Finally, a graphical user interface is created for the best system based on the large language model. © 2024 IEEE.

关键词： Graphical user interfaces

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ForTT-Gen: Network Traffic Generator for Malware Forensics Analysis Training 12

ForTT-Gen: Network Traffic Generator for Malware Forensics A...

引用

12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Bistene, Jonas Vasconcelos Das Chagas, Clayton Escouper Dos Santos, Anderson Fernandes Pereira De Souza Dias, Gabriela Moutinho Salles, Ronaldo Moreira Defense Engineering Graduate Program Military Institute of Engineering Rio de Janeiro Brazil Military Institute of Engineering Department of Computer Engineering Rio de Janeiro Brazil Ciicesi Estg Polytechnic of Porto Porto Portugal

ISBN: (纸本)9798350330366

The generation and replication of network traffic are essential tasks for testing, analyzing, simulating, and evaluating the behavior and efficiency of systems, protocols, applications, and network services. However, it faces various challenges and limitations when generating or replicating network traffic in a realistic, responsive, and scalable manner, using appropriate models. This work proposes a modeling of a program for generating and replicating network traffic, called Forensic Training Traffic Generator (ForTT-Gen), aims to address the challenge associated with generating and replicating network traffic generated by malware by creating files that can be used in the analysis of network packets inherent to forensic analysts' training. ForTT-Gen is a tool that can generate and replicate network traffic using a hybrid model that combines the replication of real data and the generation of synthetic data through statistical techniques. Experimental results demonstrate the model's ability to reliably. Accurately reproducing the statistical patterns of the original traffic, is evidenced by a determination coefficient of 1 and a Pearson coefficient of 0.9, all within a 0,58 confidence interval with 95% certainty. © 2024 IEEE.

关键词： Malware

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Detection and Grading of Diabetic Retinopathy from Fundus Images by Applying Transfer Learning 5th

Detection and Grading of Diabetic Retinopathy from Fundus Im...

引用

5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Raza, Md Aasif Berwal, Krishan Department of Computer Engineering National Institute of Technology Kurukshetra India Military College of Telecommunication Engineering Mhow India

ISBN: (纸本)9783031609343

Diabetic Retinopathy (DR) is a common complication of diabetes that can cause damage to the veins in the retina, resulting in significant vision loss. Timely treatment can help prevent this vision loss, but due to delays in diagnosis and a shortage of ophthalmologists, patients often experience vision problems before receiving proper care. Therefore, it is crucial to detect DR at an early stage. Automating the analysis of diabetic retinopathy is essential for addressing these challenges. While deep learning has achieved high accuracy in binary classification, its performance in multi-stage classification, particularly in the early stages of the disease, is less impressive. To address the abovementioned problem, Densely Connected Convolutional Networks are proposed for detecting Diabetic Retinopathy in retinal images. The proposed method has two cases (case 1- Binary classification, i.e., DR and No DR, and case 2 Multilevel classification, i.e., classification with five stages of DR). Also, the proposed method is tested with a publicly available retinal image dataset, i.e., APTOS. The accuracy of binary and multiclass classifications is 99% and 95% for the APTOS. The accuracy of the DR screening system is improved due to preprocessing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Ophthalmology

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A Hybrid Deep Learning Framework for Estimating Human 3D Pose from 2D Joint Positions

A Hybrid Deep Learning Framework for Estimating Human 3D Pos...

引用

2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Sultana, Zinia Islam, Muhammad Nazrul Kabir, Md. Hasanul Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh Computer Science and Engineering Islamic University of Technology Gazipur Bangladesh

ISBN: (纸本)9798350350289

For computers to understand human activity or behavior in a variety of scenarios, reliable 3D human posture estimation is a prerequisite. Several difficulties have made such work more complex as it is influenced by various factors, including image quality, background, garment texture and diversity, body shape, and the presence of other objects alongside persons in the image which has depicted the necessity of adopting the technique of computer vision. While much work has been done on 2D human pose estimation, showing state-of-the-art performance, the objective of this research is to expand the capacity for 3D human pose estimation. We have investigated deep neural networks comprising of linear layers with residual blocks. And proposed a hybrid deep learning framework and raised the number of residual blocks to achieve this objective. The hybrid network comprises of two parallel models estimation and combines them to estimate 3D pose. This research also showed comparative training results. Finally, the proposed architecture was evaluated on H3WB dataset and presented the evaluation results considering the evaluation metrics of the mean per joint position error (MPJPE) and the percentage of correct keypoints (PCK). The proposed architecture performed about 50% better in terms of MPJPE and PCK@150mm for three residual blocks. © 2024 IEEE.

关键词： Textures

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An Ensemble Based Approach for Green Area Segmentation from Satellite Images 3

An Ensemble Based Approach for Green Area Segmentation from ...

引用

3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Kabir, Alisha Zerin, Maliha Siddiqui, M N Alam Anannya, Tasmiah Tamzid Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350388282

Recent statistics show an alarming depreciation in green space in all kinds of regions. Image segmentation using deep learning models is a promising and quick method to determine the green space of an area and aid environment specialists in taking necessary actions. Hence, the objective of this research is to compare state-of-the-art deep learning models and then propose the best approach for segmenting green space from aerial images. Traditional Deep learning and Ensemble Learning techniques were implemented with a publicly available and a self-made data set containing 5108 and 1737 images respectively. All the models were evaluated using suitable performance metrics and the Ensemble Learning approach was found to perform better. The best-performed segmentation model is obtained by incorporating Segnet, UNet and DeepLabV3+ with accuracy, precision, and dice-coefficient of 0.94, 0.96 and 0.91. © 2024 IEEE.

关键词： Semantic Segmentation

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PPDHero: Requirements Elicitation and Development of a System to Empower New Mothers on Postpartum Depression 23th

PPDHero: Requirements Elicitation and Development of a Sy...

引用

23rd International Conference on Intelligent Systems Design and Applications, ISDA 2023

作者： Sara, Tasfia Akter Nazifa, Sadia Nur Tasneem, Shadmanee Shakib, Tasnim Ullah Islam, Muhammad Nazrul Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9783031648496

This research aims to develop a novel system for screening postpartum depression (PPD) and for providing education and support to new mothers to identify and manage symptoms and connect with health professionals. A requirement elicitation study was conducted following a semi-structured interview (n=6) and survey (n=102) and revealed a set of user requirements that need to be addressed in the proposed system. As such, extensive research and analysis of PPD and related information sources, including healthcare professionals, academic research papers, and online resources, served as a foundation for the development of the web-based system PPDHero. This system goes a step beyond by infusing gamification, setting us apart from the existing digital landscape. The usability of the proposed system was also evaluated through the System Usability Scale (SUS) approach, which was replicated with 18 participants including new mothers and doctors. The SUS scores received from the doctors (84.58) and new mothers (71.79) showed an acceptable quality of the proposed system from a usability perspective. The proposed PPDHero can improve the lives of new mothers by providing them with accessible and efficient tools to manage PPD symptoms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Usability engineering

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Early Detection of Diabetic Retinopathy Using Pretrained Models: A Focus on Initial Stages 1

Early Detection of Diabetic Retinopathy Using Pretrained Mod...

引用

1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Raza, Md Aasif Dua, Mohit Berwal, Krishan National Institute of Technology Kurukshetra Department of Computer Engineering Kurukshetra India Military College of Telecommunication Engineering Department of Communication Engineering Mhow India

ISBN: (纸本)9798350367720

Early detection of diabetic retinopathy is crucial because this disease currently has no cure. Identifying the condition at an early stage can significantly help in halting its progression and preventing severe vision loss. Numerous studies have focused on the detection and classification of diabetic retinopathy using various machine learning and deep learning techniques. In this work, we aim to detect the disease in its initial stages using a pre-trained VGG16 model, which is proved to be efficient in many image recognition tasks by recent researchers. Our initial approach achieved an accuracy of 79%, indicating a promising potential for early detection. To further improve the detection accuracy, we incorporated Ben Graham's preprocessing method, which enhances the finer details of the retinal images. This enhancement led to a 5% increase in accuracy, resulting in a final accuracy of 84.40%. These results underscore the effectiveness of advanced preprocessing techniques combined with deep learning models in the early detection of diabetic retinopathy. © 2024 IEEE.

关键词： Adversarial machine learning

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Usability Evaluation of Online Food Delivery Applications in Bangladesh: A Comparative Study

Usability Evaluation of Online Food Delivery Applications in...

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2024 IEEE International Women in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Islam Arif, Md. Ariful Kabir, Shahriar Islam, Zohirul Hussain Khan, Md Faruk Islam, Iyolita Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka Bangladesh Bangladesh University of Professionals Department of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331535476

The online food order and delivery service is becoming a very popular web-based application in Bangladesh. These days, a growing number of individuals are interested in ordering a variety of foods online. In addition, people are looking for quality food that they can purchase whenever and wherever they want. Online food delivery services are required to be intuitive, user-friendly, and accessible to increase their acceptability in Bangladesh. Therefore, the objective of this study is to explore and assess the existing online food delivery apps in Bangladesh and identify the best application from a usability perspective. To attain these objectives, four online food delivery apps (Food Panda, Hungrynaki, Pathao Food, and efood) were evaluated through Heuristic Evaluation, Cognitive Walkthrough, and System Usability Scale (SUS). The study's findings showed that each app has a number of usability issues requiring to be resolved in order to improve acceptance. The results indicated that from heuristic evaluation, Food Panda had the most number of problems and Hungrynaki had the least number of usability issues;from the cognitive walkthrough perspective, Food Panda and eFood was the mostly usable application;and from SUS evaluation score, Food Panda achieved best score from the users. Overall, Food Panda is the best application from the usability perspective. © 2024 IEEE.

关键词： Food ingredients

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