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Optical Mark Recognition with Object Detection and Clustering 6

Optical Mark Recognition with Object Detection and Clusterin...

引用

6th International Conference on Electrical engineering and Information and Communication technology, ICEEICT 2024

作者： Tabassum, Kaifa Rahman, Zhalok Shahjalal University of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

OMR, or optical mark recognition, is the automated analysis of human-marked documents. Presently, many competitive examinations rely on multiple-choice questions, and the answers to these questions are recorded in OMR sheets. In this study, we present an automated answer detection system from an OMR sheet image using computer vision techniques. Typically, evaluating an OMR sheet from an image involves scanning, extracting data, and interpreting the results through various image processing techniques. However, traditional approaches have limitations. They cannot detect empty responses or recognize multiple choices properly without a specified layout or total number of questions stated. We took a unique approach by employing an object detection model, YOLOv8n, to detect and classify OMR marks. To address overlapping bounding boxes, we implemented the DBSCAN clustering algorithm to effectively group the columns and determine question order. Our system is designed to work with multiple layouts, as it can detect columns through clustering. Furthermore, it can identify unanswered questions as well as multiple marked options. To train and evaluate our model, we created a custom dataset with five different OMR sheet layouts. We achieved a remarkable precision of 96.5%, a recall of 99.8%, and an mAP (mean average precision) of 97.3%. These results demonstrate the efficacy of our approach to OMR sheet analysis. © 2024 IEEE.

关键词： Object detection

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Optical Character Recognition of Persian Epigraphs by Using Artificial Neural Networks in MATLAB

Optical Character Recognition of Persian Epigraphs by Using ...

引用

2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khan, Sumaiyya Bardekar, Aashish Sipna College of Engineering & Technology Dept. of Computer Science and Engineering Amravati India

ISBN: (纸本)9798331517953

Persian, also known as Farsi, was once a dominant language and cultural influence worldwide. Today, it is nearly extinct in the Indian subcontinent. However, for centuries, Farsi played a major role in high culture and administration across the subcontinent. Persian characters are complex, with various features such as ascenders, descenders, vertical lines, and curves. Automatic translation of Persian characters from inscriptions into English is currently unavailable. The process of deciphering and translating inscribed text in epigraphy can be time-consuming. This paper specifically focuses on identifying Persian epigraphs. First, the inscription image is cleaned up, and images containing Persian characters are obtained. Using preprocessing techniques, the images are then prepared for input to artificial neural networks. The artificial neural networks are trained to effectively recognize various patterns of Persian characters, as they can learn complex patterns and adapt to changes in images, resulting in high accuracy in Persian character recognition. Finally, the proposed method's performance is evaluated using accuracy, precision, and speed metrics. The results demonstrate that the method accurately and rapidly detects Persian characters in various images. This research has the potential to advance Persian character recognition and image processing, enabling broader applications such as converting image documents into text, character recognition in documents, and text translation with Persian image content. © 2024 IEEE.

关键词： MATLAB

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Detecting Ransomware Threats Using Machine Learning Approach 8

Detecting Ransomware Threats Using Machine Learning Approach

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Guruprasad, Sunitha Rao, Arnav G. Shetty, Sanmitha Chitharanjan Bastian, Rose Khader, Sarah Abdul St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India Visvesvaraya Technological University Karnataka Belagavi India Ramaiah Institute of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350350593

The rise of ransomware-related cyber-attacks poses a severe threat to organizations across various sectors. This research addresses the urgent need for robust detection mechanisms using advanced machine learning (ML) models. Ransomware, characterized by irreversible encryption of user data, demands proactive strategies for identification and prevention. This study leverages publicly available ransomware and benign executable files from sources such as VirusTotal which are run in a sandboxed environment to obtain a dataset to train an ML model capable of differentiating between diverse ransomware samples and classifying them based on specific characteristics. The process begins with meticulous data preprocessing ensuring the dataset's quality. Feature extraction algorithms are employed to identify relevant characteristics, streamlining the dataset and optimizing model performance. The model is trained on the dataset to effectively learn patterns. Evaluation metrics, including accuracy, precision, recall, F1-score, and the ROC curve, provide comprehensive insights into the model's effectiveness. © 2024 IEEE.

关键词： Adversarial machine learning

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Accelerating Cloud Deployment Through Containerization Technologies

Accelerating Cloud Deployment Through Containerization Techn...

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Lakkadwala, Aayushya Lakkadwala, Prashant Acropolis Institute of Technology and Research Dept. of Computer Science and Engineering Indore India Acropolis Institute of Technology and Research Dept. of Information Technology Indore India

ISBN: (纸本)9798350388800

This research investigates the effectiveness of containerization technologies, with a focus on Docker, in accelerating cloud application deployments. The study examines diverse methodologies aimed at optimizing container image size, refining container orchestration mechanisms for expedited deployments, and evaluating the integration of serverless functions within containers to reduce deployment durations. By synthesizing existing scholarly literature with empirical analyses, this paper aims to elucidate the efficacy of containerization in streamlining cloud deployment processes. It offers insights into optimal practices and identifies prospective avenues for future research and development in the field. © 2024 IEEE.

关键词： Cloud platforms

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Effective Polyp Segmentation in Medical Imaging: A Semi-Supervised Approach with Progressive Supervision 3

Effective Polyp Segmentation in Medical Imaging: A Semi-Supe...

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3rd International Conference on Big Data, Information and computer Network, BDICN 2024

作者： Ye, Shunlong Ji, Zexuan Nanjing University of Science and Technology Dept. of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350363821

Medical imaging plays a pivotal role in diagnosing and monitoring various diseases, and accurate polyp segmentation is essential for early disease detection and treatment planning. However, the scarcity of well-Annotated medical imaging data hampers the development of effective segmentation models. Semi-supervised learning stands out as a critical approach to address this issue. Traditional semi-supervised medical image segmentation models leverage strong-weak perturbation consistency losses to provide supervision signals. While these losses are effective in promoting consistency, they often exhibit a drawback, that is the dominance of strong perturbations during early training stages. This dominance hinders the model's ability to extract meaningful features, ultimately impacting its overall performance. In response to these challenges, we introduce a novel training method applied to polyp segmentation. We employ a progressive approach, gradually introducing strong-weak perturbation supervision signals. This gradual guidance empowers the model to focus on learning relevant features from the beginning of training, mitigating the issue of early dominance and enhancing its performance. Our method is evaluated on two widely-used polyp segmentation datasets and surpasses state-of-The-Art methods, demonstrating substantial improvements in segmentation accuracy. © 2024 IEEE.

关键词： Self-supervised learning

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Deep Feature Fusion Based Brain Tumor Classification From Imbalance MRI Dataset Using Weighted Loss Functions 2

Deep Feature Fusion Based Brain Tumor Classification From Im...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Tasnim, Taieba Parvin, Farjana Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi Bangladesh

ISBN: (纸本)9798331517984

Deep learning using MRI images to classify brain tumours presents a significant challenge due to imbalanced training datasets, where one tumor type may have far more samples than others. The overrepresented class may perform worse in classifiers as a result of this imbalance. In order to tackle this issue, the paper examines how feature learning in CNNs is affected by weighted loss functions to manage data imbalance. The article propose deep feature fusion, where particular CNN model features that were trained using three distinct loss functions are fused and classified using softmax classifier. This approach lead to notable improvements in brain tumor classification compared to a CNN trained with standard cross-entropy loss. Notably, the proposed methods substantially reduce classification errors between uneven class samples. The proposed model achieved an impressive 94.5% classification accuracy, accompanied by an average Fl-score of 94.6%, average precision of 94.6 %, and average recall of 95%. These findings underscore the effectiveness of our methodology in improving brain tumor classification accuracy, offering a promising avenue for more reliable diagnosis and treatment planning in clinical settings, © 2024 IEEE.

关键词： Deep learning

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Device Identification using Network Traces in a Smart Home IoT Network 17

Device Identification using Network Traces in a Smart Home I...

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17th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2023

作者： Brahma, Jwngfu Matam, Rakesh Choudhury, Nikumani Indian Institute of Information Technology Dept. of Computer Science & Engineering Guwahati India Birla Institute of Technology & Science Dept. of Computer Science & Information Systems Hyderabad India

ISBN: (纸本)9798350307672

Device identification is a crucial aspect of securing networks, particularly in the context of the Internet of Things (IoT), where a vast variety of devices are interconnected. Recently, there has been significant research on developing techniques for identifying IoT devices based on their unique characteristics, called as device fingerprints. These techniques use machine learning algorithms, which can effectively learn and classify devices based on their features. However, device identification remains a challenging task due to the diversity of IoT devices and the constant evolution of their characteristics. This paper presents our attempt at device identification by analyzing distinct device characteristics observed during network communication and using different machine learning techniques. © 2023 IEEE.

关键词： Internet of things

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

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

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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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