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FlashMe: Automatic Flashcard Generation 14

FlashMe: Automatic Flashcard Generation

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Nair, Nikhil Pikle, Shlok Save, Soham Varghese, Ryan Sonawane, Kavita St. Francis Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350335095

The task of studying and revising can be perceived as challenging due to the intricate nature of concepts presented in dense textbook material. Furthermore, it has been observed that students allocate as much as 50% of their study time towards study techniques that are deemed less effective [1]. Thus, encouraging students to employ efficient methods is important. Additionally, numerous studies have demonstrated that flashcards are an exceptionally efficient method for enhancing learning outcomes. [2]. To tackle this issue, our research indicates the utilization of various advanced Natural Language Processing models. These models would be combined to develop a multi-stage transformer architecture that enables the automated creation of flashcards from text containing technically rich literature. © 2023 IEEE.

关键词： Natural language processing systems

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Singh, Yashdeep Karmakar, Sushanta Indian Institute of Technology Guwahati Dept. of Computer Science & Engineering Guwahati India

ISBN: (纸本)9798350306941

In this work we investigate the problem of popular matching in a 3-uniform 3-partite hypergraph, where the first set contains a set of agents and the second and the third set contain different class of items respectively. Agents have a preference list for hyperedges, whereas the members of the other sets do not have preferences. Each agent votes in favor of a preferred matching. A matching M is called popular if there does not exist a matching M′ such that the number of agents that prefer M′ is more than the number of agents that prefer M. This paper provides a characterization of popular matching in a 3-uniform 3-partite hypergraph and shows that the problem of deciding whether a given 3-uniform 3-partite hypergraph has a popular matching is NP-hard. We also prove the NP-hardness of popular matching in a k-uniform k-partite hypergraph (k > 3). Assuming that a given 3-uniform 3-partite hypergraph admits at least one popular matching, we compare the cardinality of the maximum size popular matching (denoted as M∗) with a maximum matching (denoted as Mmax) in the 3-uniform 3-partite hypergraph and show that | Mast geq 23| Mmax|. © 2023 IEEE.

关键词： b- matching matching in hypergraph popular matching

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Text Classification using Machine Learning Algorithms on Encrypted Data 9

Text Classification using Machine Learning Algorithms on Enc...

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9th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2023

作者： Behera, Sagarika Kolipakula, Nikhita Swetha, P. Yogish, Yogish Prathuri, Jhansi Rani Cmr Institute of Technology Dept. of Computer Sc. and Engineering Bengaluru India

ISBN: (纸本)9798350334395

Text classification is a fundamental activity in natural language processing (NLP), that includes classifying text resources into predetermined groups. With the growing concerns over data privacy and confidentiality, there is a need to perform text classification on encrypted data to protect sensitive information. In recent years, homomorphic encryption has emerged as a powerful technique that makes computations possible on encrypted data without compromising its confidentiality. This paper provides an overview of text classification using machine learning algorithms on encrypted data. In this research work, we have taken a data set of movie reviews. The data set is cleaned by removing stop words and performing stemming, lemmatization, and tokenization. We utilized a semantic model with inverse document frequency weighting and term frequency weighting to represent the data (TF-IDF). For the classification of the data set, we have applied six machine learning algorithms Logistic Regression, Naïve Bayes, KNN, Support Vector Machine, Random Forest, and Decision Tree. The plain text is encrypted using a somewhat homomorphic encryption technique called the RSA method. The ciphertext is subjected to the machine learning algorithms mentioned above. We have compared the results for accuracy. The Random Forest algorithm was found to give 75.56% accuracy compared to other algorithms on encrypted data. In this work, we have calculated the time taken by each algorithm for execution on plain text and ciphertext. It was observed that the Random Forest method took more time and the KNN method took less time comparatively. © 2023 IEEE.

关键词： Logistic regression

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Smart Contract Cloning Identification Using BERT Model Based Representation Learning 20

Smart Contract Cloning Identification Using BERT Model Based...

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20th IEEE India Council International Conference, INDICON 2023

作者： Kumar, Vishnu Pranav, Pisipati Srinivas, M. National Institute of Technology Warangal Dept. of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350305593

The use of Smart Contracts and Blockchains has experienced a significant rise, leading to the automation of various financial and corporate operations. However, the occurrence of flaws and vulnerabilities in smart contracts has resulted in significant financial losses, raising concerns about their security. It is crucial to enhance the maintenance and reliability of smart contract codes. To detect the clones and bugs in a smart contract, in this paper, we propose a deep learning based model that learns the embedding as a continuous numeric-valued vector for each smart contract. Our approach involves five steps. First, we divide the smart contract Solidity program into streams along with their corresponding structural information. Second, we tokenize the textual data of the smart contract and create a corpus. Third, we employ the BERT word embedding model on the smart contract's tokens corpus to learn feature representation for each token, generating a fixed-length vector of size 768. Fourth, we compute the embedding of each smart contract by considering the weighted sum of tokens within the contract. Finally, to measure clone similarity, we use hierarchical clustering on the feature vectors of smart contracts. The experimental results obtained using our approach on 22,000 Ethereum blockchain Solidity contracts show a clone ratio of 92.14%, and a precision of 93% in detecting clone-related bugs, representing significantly higher accuracy compared to conventional algorithms. © 2023 IEEE.

关键词： BERT Model Blockchain Bug Detection Clone Detection Ethereum Smart Contract Solidity programming

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Detect Bangladeshi Mango Leaf Diseases Using Lightweight Convolutional Neural Network 3

Detect Bangladeshi Mango Leaf Diseases Using Lightweight Con...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahbub, Nosin Ibna Naznin, Feroza Hasan, Md Imran Shifat, Syed Mahfuzur Rahman Hossain, Md. Alamgir Islam, Md Zahidul Islamic University Dept. of Information and Communication Technology Kushtia7003 Bangladesh Green University Dept. of Computer Science and Engineering Dhaka Bangladesh Islamic University Dept. of Computer Science and Engineering Kushtia7003 Bangladesh University of Global Village Dept. of Computer Science and Engineering Barishal Bangladesh

ISBN: (纸本)9798350345360

This research concentrates on the diagnosis of common mango leaf diseases in Bangladesh using image processing via deep learning. Mango production could be raised by at least 28% globally if the crop could be safeguarded from a variety of diseases. However, without the assistance of an expert, it is challenging for the farmer to detect the disease at the appropriate time. Few studies have been conducted to identify the mango leaf disease present in Bangladesh. So far, no study has been done to identify the seven distinct mango leaf diseases reported in Bangladesh. We proposed a lightweight convolutional neural network (LCNN) in this paper to accurately classify seven distinct mango leaf diseases as well as normal mango leaf. To assess the proposed LCNN model, performance is compared to several pre-trained models such as VGG16, Resnet50, Resnet101, and Xception, and it is found that LCNN achieves the highest testing accuracy (98%). © 2023 IEEE.

关键词： Convolutional neural networks

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Boosting-Based Machine Learning Approaches for Diabetes Prediction using Indian Demographic and Health Survey Data 3

Boosting-Based Machine Learning Approaches for Diabetes Pred...

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

作者： Kallur, Shanta Yaligar, Suman Thotad, Puneeth N. Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated to Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India

ISBN: (纸本)9798350364569

Traditional diagnostic techniques are time-consuming and prone to error. Performance is added however time and cost are reduced by using computer-aided diagnostic methods. This study aims to calculate how healthy boosting-based machine learning classifiers perform in automatically identifying diabetes in the Indian context. For this experiment, Indian demographic and health survey 2021 data on women aged 19 to 79 are considered. This data includes clinical, anthropometric, and biochemical components. 12,103 of the 706,504 responders who tested positive for diabetic testing were considered. The predictive exploration-based boosting machine learning models are adopted, such as adaptive boosting, categorical boosting, extreme gradient boosting, gradient boosting, and light gradient boosting models. Kernel Principal Component Analysis is employed for feature extraction. The Extreme Gradient Boosting model demonstrated strong performance on the diabetes dataset both before and after applying Kernel PCA. The metrics including accuracy, F1-score, precision, and recall for the XGBoost model showed improvements: accuracy increased from 81% to 83%, F1-score from 81% to 82%, precision from 81% to 88%, and recall from 81% to 76%.In contrast, Adaptive Boosting performed poorly, while Categorical Boosting, Gradient Boosting, and Light Gradient Boosting models achieved moderate performance levels. Overall, the model attained optimal performance across metrics such as accuracy, precision, Area Under the Curve (AUC), and recall. © 2024 IEEE.

关键词： Adaptive boosting

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Footwear 3D Model Generation from Smartphone 2D Images Utilizing Photogrammetry 3

Footwear 3D Model Generation from Smartphone 2D Images Utili...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Abrar, Raiyan Islam, Md. Tariquel Naim, Mahapara Tabassum, Nuzhat Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350345360

3D modeling in the shoe industry is growing as the technology becomes more widely available. 3D modeling lets companies customize shoes for individual customers, making manufacturing more flexible and efficient. In the shoe industry of Bangladesh, using smartphone images for 3D modeling is a fairly new idea. This allows designers and manufacturers to collaborate and quickly share designs with customers for feedback. A photogrammetry pipeline is a cost-effective and efficient way to create 3D models. This study proposes a potential and affordable method for using photogrammetry to create 3D representations of footwear. The major goal was to determine whether photogrammetry, as opposed to RGB dept. camera photos, can more conveniently construct 3D models of footwear from a small number of smartphone images. In order to assure correct reconstruction, the pipeline required taking a collection of photos from 6-9 distinct perspectives and augmenting the dataset. Then, from these images, point clouds are produced, and from these aligned point clouds, the 3D mesh is produced. The mesh is then textured and smoothed to give color. In Bangladesh's shoe industry, this 3D modeling pipeline can be used for AR shoe try-on. © 2023 IEEE.

关键词： 3D modeling

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Nutrition Detection during Gestation Period using ML Algorithms 14

Nutrition Detection during Gestation Period using ML Algorit...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Dixit, Sunanda Sriram, Navayug Ashritha, S.B. Datta, Paidi Venkata Revanth Krishna, Bolem Jagadesh Dept. of Computer Science and Engineering BMS Institute of Technology and Management Bangalore India

The issue of low birth weight in infants is a significant concern in prenatal care, and it can have adverse effects on the newborn's health, sometimes leading to mortality. This problem contributes to high rates of child mortality worldwide. Artificial intelligence, particularly ML, offers potential solutions for predicting whether a fetus will be born small for its gestational age. Early detection of fetal developmental issues is critical, as timely intervention can increase gestation days and improve fetal weight at birth, reducing the risk of neonatal morbidity and mortality. This proposal aims to explore various machine learning methods for predicting small-for-gestational-age infants, emphasizing the importance of early detection and intervention to improve outcomes. © Grenze Scientific Society, 2023.

关键词： Machine learning

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An Innovative Machine Learning Pipeline for Stroke Prediction on Imbalanced Data 2

An Innovative Machine Learning Pipeline for Stroke Predictio...

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2nd International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2023

作者： Ullah, Md. Oli Raju, Shakil Ahmed Nazir, Md. Imran Akter, Afsana Rahman, Md. Saifur Bangladesh University of Business and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350358667

Stroke requires immediate medical care. The WHO states stroke is the leading cause of death and disability world-wide. Thus, early stroke detection is essential. Few studies projected with CNNs or other stacking approaches. CNN-ensemble classifier. This study used unequal Kaggle data. Random over-sampling balances data. After identifying the most important features, several machine learning algorithms are applied, including Logistic Regression, Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, K-Nearest Neighbor's Classifier, Gaussian Naive Bayes, Bernoulli Naive Bayes, and Stacking for Six Algorithms (Support Vector Machine, Decision Tree Classifier, Random Forest Classifier, KNN, BernoulliNB, GaussianNB). Training and testing evaluate each algorithm's stroke prediction accuracy. Stacking six algorithms gives the maximum accuracy, testing is \mathbf{99.9}\pm\% and training is 100%. Machine learning improves this classifier's stroke prediction. © 2023 IEEE.

关键词： Decision trees

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An Indicative Demanding Teaching Scenario for Primary Education with the Support of a Multi-layer Fog Computing Architecture 26

An Indicative Demanding Teaching Scenario for Primary Educat...

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Giannou, Vasiliki Mamalis, Basilis University of West Attica Dept. of Informatics and Computer Engineering Greece

ISBN: (纸本)9781450398541

Fog computing is a new, modern type of computing that distributes some of the storage, estimation, and processing constraints of cloud computing closer to the edge of the network. The impact of Cloud and Fog Computing in an educational context is now more important than ever. This paper presents innovative designed services of fog computing techniques in the educational sector. An original educational scenario is described, based on innovative fog computing techniques and the appropriate use of online collaborative learning tools. The aim of the paper is to propose an appropriate fog-based architecture to support education in Primary and Secondary Education as well as to provide appropriate recommendations and present some indicative education scenarios implemented based on the use of cloud and fog computing. The proposed scenario is properly documented, while specific implementation issues are presented and analyzed. © 2022 ACM.

关键词： Fog computing

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