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MeQANet: Deep Learning-based Framework for Classifying Medical Query Answering in Bengali

MeQANet: Deep Learning-based Framework for Classifying Medic...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Tasnia, Fairooz Hoque, Mohammed Moshiul Chittagong University of Engineering and Technology Dept. of Computer Science and Engineering Chattagram Bangladesh

ISBN: (纸本)9798331529765

The automatic classification of medical QA pairs is crucial for improving healthcare efficiency, allocating specialized care, optimizing resource distribution, and facilitating robust data analysis. Bengali, the 7th most widely spoken language globally, has a growing volume of health-related queries. However, unlike English, there is no benchmark large-scale medical QA dataset, pre-trained language model, or human baseline score for medical QA in Bengali. This paper introduces a deep learning-based framework called MeQANet that leverages CNN and BiLSTM techniques for classifying medical QA texts in Bengali. It also presents a novel medical QA corpus in Bengali (BnMedQA), consisting of approximately 4500 medical QA pairs across eight domains: Orthopedic, ENT, Gastroenterology and Hepatology, Gynecology, Pediatrics, General Medicine, Dermatology, and Cardiology. Various techniques (including LR, SVM, RF, MNB, AdaBoost (AB), CNN, BiL-STM, and CNN+BiLSTM) are employed to perform the classification tasks. Experimental results indicate that the CNN+BiLSTM approach achieved the highest F1-score of 0.85 among all employed techniques. © 2024 IEEE.

关键词： Query languages

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Addressing the Long-Tailed Data Distribution in Bone Marrow Cell Identification through Class Balance Deep Classification Model 2

Addressing the Long-Tailed Data Distribution in Bone Marrow ...

引用

2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ghosh, Rakesh Shankar Shill, Pintu Chandra Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350317558

The accurate classification of bone marrow cells has long been a critical component of diagnosing blood diseases. However, traditional methods rely on subjective human interpretation and lack standardized quantitative criteria. To address this challenge, this research proposes a Class Balance Deep Classification Model (CBDCM) designed to classify bone marrow cells using EfficientNet to handle the long-tailed data distribution problem. In order to validate the propose model by applying it to proper dataset and analyzing the experimental outcomes of the propose method. Notably, CBDCM attains impressive precision, sensitivity, and specificity values of 86.14%, 87.53%, and 99.45%, respectively. Simulation results show that CBDCM outperforms, demonstrating superior performance in a detailed comparison with both deep neural networks and traditional machine learning techniques. The findings of this study, with significant implications for addressing class imbalance in datasets, hold promise for standardizing the classification of bone marrow cells, potentially revolutionizing the diagnosis of blood diseases. © 2024 IEEE.

关键词： bone marrow cell CBDCM data imbalance deep learning EfficientNet gaussian smoothing

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Binary Classification of Breast Tumours using CBAM-Enhanced Collaborative Network 3

Binary Classification of Breast Tumours using CBAM-Enhanced ...

引用

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

作者： Das, Soumit Nivel, Mst Fabia Akter Rajshahi University of Engineering & Technology Dept. of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350388282

For women breast cancer is a life-threatening problem, making the importance of early detection crucial. Expert pathologists assess cell shapes in breast histopathology images at various magnification levels for accurate diagnosis. Unfortunately, a shortage of pathologists in many countries heightens the risk of human error in disease identification and treatment. To address this challenge, this study proposed a CBAM-Enhanced Collaborative Network for classifying benign and malignant tumors in breast cancer histopathological images, offering a potential solution to enhance accuracy and efficiency in diagnosis. The model included a transfer learning branch using a pretrained DenseNet121 structure with integrated CBAM (Convolutional Block Attention Module). Simultaneously, a collaborative branch employed Inception-ResNet-v2 with CBAM for feature extraction from histopathological images. The feature fusion module then optimized information from both branches. The proposed method achieved impressive binary classification accuracies on the BreaKHis dataset: 98.31% overall, with 98.33%, 98.08%, 99.67%, and 97.80% for magnification factors of 40x, 100x, 200x, and 400x, respectively. CBAM-Enhanced Collaborative Network also exhibits strong generalization capabilities, as evidenced by its superior performance on the ICIAR2018 BACH Challenge dataset. © 2024 IEEE.

关键词： Image classification

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Enhancing Traffic Control Strategies through Dynamic Simulation and Reinforcement Learning 3

Enhancing Traffic Control Strategies through Dynamic Simulat...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Sebastian, Alona Mary Athulram, K.R. Michael, Colin Sunil, Diya Anna Preetha, K.G. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798350375190

In urban areas, traffic congestion is a major problem causing increased travel times, higher consumption of fuel, and environmental pollution. The traditional traffic control systems cannot adapt to dynamic traffic conditions, causing inefficient traffic flow and inefficient use of road infrastructure. In this paper, we propose a novel method to enhance traffic management strategies through dynamic simulation and reinforcement learning (RL). A simulation framework is presented that models the complex interactions among vehicles, traffic signals, and infrastructure in a realistic urban environment. Within this framework, RL agents are deployed to learn adaptive traffic control policies that minimize congestion and optimize traffic flow. This approach uses the ability of RL to continuously learn and adjust to shifting traffic circumstances, which results in improved efficiency and responsiveness compared to static or rule-based control systems. Finally, evaluate the effectiveness of the approach through extensive simulations and demonstrate its capability to significantly reduce congestion, decrease travel times, decrease average waiting time and enhance overall traffic management. By integrating dynamic simulation and RL-based control strategies, the proposed approach offers a guaranteed solution for addressing the challenges of urban traffic congestion and advancing the field of intelligent transportation systems. © 2024 IEEE.

关键词： Reinforcement learning

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Mental State Report From Social Media Activity

Mental State Report From Social Media Activity

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Ajit, Akshay Mathew, Crispin Denny, David Johns Devananth, H. Dhanya, P.M. Rajagiri School of Engineering and Technology Dept. of Computer Science and Engineering Ernakulam India

ISBN: (纸本)9798331540685

This paper is about making user experience with social media better, by developing the complete analysis tool that would parse textual data from different platforms like Instagram, Facebook or Reddit. By developing a custom browser extension, the user-generated content is captured in real-time and can also be processed subsequently. The project comprises two key components: Emotion Detection and Hate Speech Detection. The Emotion Detection system uses sophisticated Natural Language Processing NLP techniques, such as BERT models to identify users' emotional state through their social media posts. This information is then used to develop a daily mental state report by which users are able to gain insights into their emotional health over time. Further, the Hate Speech Detection system leverages machine learning algorithms to detect and mark words that may be harmful or offensive in user- generated content. This feature enhances positive and inclusive Internet environment. The project goes beyond the analysis;it includes a pragmatic component suggesting specific tasks for users depending on their mental state reports. These recommendations are aimed at promoting positive interaction and enhancing the user's overall well-being. This project offers a comprehensive insight into users' emotional experiences on social media using state-of-the-art NLP and machine learning approaches. The combination of task recommendations and text summarization increases interaction among users, creating a pleasant atmosphere where people can enjoy using social media. The browser extension is a multipurpose tool that does not merely analyze but provides some improvements to the user's mental health and online activity. © 2024 IEEE.

关键词： Emotion Recognition

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BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Semantic Segmentation of X-Ray Images of Human Hand Bone 3

BoneSegNet: Enhanced 2D-TransUnet Model for Multiclass Seman...

引用

3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Venkatesh Nagaraju, Y. Darshan, D. Sahanashree, K.J. Nagamani, P.N. Basapur, Satish B University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

Multiclass segmentation of hand bones in X-ray images is crucial for various medical applications, including diagnostic assistance and surgical planning. This study presents BoneSegNet, a deep learning-based segmentation model designed to classify hand bones into seven distinct anatomical classes: Distal phalanges, Intermediate phalanges, Proximal phalanges, Metacarpals, Carpals, Ulna, and Radius. Our model, BoneSegNet leverages the strengths of both convolutional neural networks (CNNs) and transformer encoders incorporating the UNet architecture. Our model architecture begins with patch and positional embeddings of the input image, followed by a series of transformer encoder layers to capture long-range dependencies and contextual information. The transformer encoder is equipped with multi-head attention mechanisms and multi-layer perceptron's (MLPs) with GELU activation and dropout for regularization. Skip connections at specific layers are stored and later utilized in the decoder. The decoding path consists of a series of deconvolution and convolution blocks designed to progressively upscale the feature maps while incorporating skip connections from the encoder. This hierarchical structure ensures detailed spatial information is preserved and enhanced. The final output layer employs a 1x1 convolution with a sigmoid activation function to produce the multiclass segmentation map. The model is trained and evaluated on a dataset of hand X-ray images. It achieves an average accuracy of 99.25%, an average Dice score of 0.81, an average mIoU score of 0.70, sensitivity of 0.79, and specificity of 0.997. These results demonstrate the model's effectiveness in accurately segmenting and classifying hand bones, making it a valuable tool for medical professionals. © 2024 IEEE.

关键词： Semantic Segmentation

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Machine Learning Approaches for Survival Prediction in Covid-19 Patients: A Comparative Analysis

Machine Learning Approaches for Survival Prediction in Covid...

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

作者： Islam, Md. Muhiminul Shuvo, M.I.R. Khulna University of Engineering & Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350350289

The COVID-19 pandemic has underscored the significance of accurately predicting patient survival in order to promptly administer efficient medical care. The augmentation of biological and healthcare service data volume has been found to enhance the precision of disease prognosis, survival prediction, and other clinical assessments. Numerous biological characteristics serve as the underlying factors contributing to the etiology of numerous diseases. Therefore, it is imperative to have precise medical data that possesses appropriate characteristics in order to facilitate an analysis that exhibits exceptional clinical accuracy. In order to effectively analyze data, it is imperative to employ a machine learning model that is both exact and accurate in predicting sickness or survival outcomes. An expeditious and accurate assessment of the disease's magnitude is crucial during a particular phase of a pandemic, such as the Covid-19 outbreak. The primary aim of this research is to employ machine learning methodologies in order to forecast the survival outcomes of individuals diagnosed with COVID-19. This will be accomplished by using a publicly available dataset comprising various medical attributes pertaining to 383,499 COVID-19 patients, which was collected and made accessible by the Directorate General of Epidemiology, Secretariat of Health in Mexico. Various machine learning techniques, including Regression methods, Artificial Neural Networks, Random Forest Classifier, Support Vector Machine, AdaBoost, and XGBoost, are employed on the dataset that has undergone diverse preprocessing procedures. The experimental findings demonstrated that the system yielded numerous advantages in comparison to previous efforts in the same field. © 2024 IEEE.

关键词： COVID-19

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Nirnayaak: Revolutionizing Legal Research 4

Nirnayaak: Revolutionizing Legal Research

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4th International Conference on computer, Communication, Control and Information Technology, C3IT 2024

作者： Datta, Soumyajit Chakraborty, Arya Bhattacharya, Saptarshi Samui, Amit Chakraborty, Anurag Dasgupta, Kousik Kalyani Government Engineering College Dept. of Computer Science and Engineering Kalyani India

ISBN: (纸本)9798350389128

Nirnayaak is a novel and advanced legal software application, that is meticulously designed to expedite the retrieval of crucial legal information in an Indian Context, addressing the challenges posed by a growing caseload and inaccuracies in the legal system. Employing a groundbreaking architectural framework, it leverages OCR, Smart Text preprocessing, keyword extraction, a sophisticated LLM-based summarization and information retrieval and a feedback-based keyword ranking technique. This amalgamation of techniques enhances the accuracy and accessibility of legal data, enabling legal professionals, scholars, and the public to access pertinent information seamlessly just using natural language queries. We conducted benchmarks on popular keyword extraction techniques, demonstrating that our preprocessing techniques enhance their performance. Additionally, we evaluated LLMs to ascertain their proficiency in understanding contextual nuances. © 2024 IEEE.

关键词： Application programs

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SFNet: Synergistic Fusion Network for Text Localization in Natural Scene Images 3

SFNet: Synergistic Fusion Network for Text Localization in N...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Suman, Suman Champa, H.N. University of Visvesvaraya College of Engineering Dept. of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350381931

Prior methodologies for the detection of scene text have demonstrated promising outcomes across many evaluation metrics. Nevertheless, despite the utilization of deep neural network models, their effectiveness is sometimes limited in complicated situations due to the intricate interplay of several stages and components within the pipelines, which ultimately governs overall performance. This study presents a straightforward yet robust methodology for identifying textual content in practical environments. Phrases or text lines may be predicted in images of varying orientations and quadrilateral forms using the suggested pipeline's single neural network. This eliminates the need for superfluous intermediary steps such as candidate aggregation and word division. Due to the streamlined nature of our SFNet(Synergistic Fusion Network), our primary emphasis is directed at the development of loss functions and neural network design. The suggested approach has been evaluated using a well-established dataset including ICDAR 2015. The results of these experiments demonstrate that the new technique outperforms existing techniques in terms of both accuracy and efficiency. The proposed methodology demonstrates a performance metric, namely an F-score of 0.89 while operating at a frame rate of 13.2 frames per second and a resolution of 720p using the ICDAR 2015 dataset. © 2024 IEEE.

关键词： Pipelines

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WebTrek Learner : AI Integrated Cloud Based Learning Platform

WebTrek Learner : AI Integrated Cloud Based Learning Platfor...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Bhandari, Arjun Harisha, A. Rishav, Kumar Shifana, Mariyam Sameer, Jazil Sahyadri College of Engineering and Management Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331517892

In an era defined by the rapid proliferation of digital technology and connectivity, the need for virtual education has never been more critical. This article, referred to hereon as 'WebTrek Learner,' is a forward-thinking initiative aimed at catering quality virtual education through a dynamic and engaging approach of gaming. To further enhance the learning experience, WebTrek Learner incorporates cutting-edge LLM based AI technology to curate internal insights and content based on glossary documents. Cloud based a gaming platform enables lightweight experience to all potential learners. © 2024 IEEE.

关键词： Adversarial machine learning

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