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Comparative Study of Word2Vec, FastText, and Glove Embeddings for Synonym Identification in Bugis Language

Comparative Study of Word2Vec, FastText, and Glove Embedding...

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2024 Beyond Technology Summit on Informatics International Conference, BTS-I2C 2024

作者： Arianto, Wahyu Ramadhan Yuyun Abrar, Ahmad Umar, Najirah Nasrullah Nurfaedah Gusnawaty Jarin, Asril Handayani University of Makassar Dept. of informatics engineering Makassar Indonesia National Research and Innovation Agency Research center for data and information science Bandung Indonesia Handayani University of Makassar Dept. of informatics management Makassar Indonesia Hasanuddin University Dept. of Regional Literature Makassar Indonesia

ISBN: (纸本)9798331508579

This research aims to analyze the performance of word vectors generated by three pre-trained word embedding models, namely Word2Vec, Fast'Text, and Glove, to detect synonyms at the word level in the Bugis language. The word vectors from each model were integrated with a Bugis language dataset containing 12,060 monolingual texts, and then trained using the Long Short-Term Memory (LSTM) model. The results show that Word2Vec has the highest accuracy of 0.9412, followed by Glove with 0.9353 and FastText with 0.9262. Additional experiments with cosine similarity revealed that Glove performed best for predicting synonyms across all word occurrence frequency categories, while FastText and Word2Vec had inferior results. These findings indicate that although high training accuracy is achieved, it does not always imply that the model will excel in predicting synonyms. © 2024 IEEE.

关键词： Embeddings

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Comparative Study of Inductive Graph Neural Network Models for Text Classification

Comparative Study of Inductive Graph Neural Network Models f...

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Pandian, Saran Chaurasia, Uttkarsh Ranjan, Shudhanshu Saxena, Shefali Daiict Data Science Gandhinagar India Nit Dept. of Computer Science and Engineering Hamirpur India Presidency University Dept. of Computer Science and Engineering Banglore India Nit Dept. of Electronics and Communication Hamirpur India

ISBN: (纸本)9781665461092

Graph neural networks(GNN) are a special variant of neural networks which help in dealing with unstructured data such as graph data. The advent of the GNN has helped in dealing with problems in different domains, especially in the domain of Natural Language Processing(NLP). In NLP, GNNs are used to implement tasks such as text classification which has a wide variety of applications. There are two ways to represent the text data using GNN namely, Inductive and transductive. In this paper, we apply the approach of the inductive model using different variants of GNN. We observed that the GAT variant gave better performance compared to other variants. Moreover, we observed that the complexity of the model and the dataset size influences the entropy of the output. © 2022 IEEE.

关键词： Entropy

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An Identification of Guava Fruit Disease Using ML 3

An Identification of Guava Fruit Disease Using ML

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

作者： Ahmed, Khandakar Rabbi Salam, Towfika Nandi, Subarna Hasan, Nazmul Rashid, Omor Faruk Al Miraz, Shafiun Reza, Md. Shafi Rahman, Farjana International University of Business Agriculture and Technology Uttara Model Town Miyan Research Institute Dhaka Bangladesh Kazla Dept. of Mechatronics Engineering Rajshahi6204 Bangladesh Dept. of Computer Science and Engineering Rajshahi Bangladesh London South Bank University Dept. of Data Science 103 Borough Rd LondonSE1 0AA United Kingdom Varendra University Dept. of Computer Science and Engineering Rajshahi Bangladesh International University of Business Agriculture and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh Jeonbuk National University Graduate School of Integrated Energy of AI North Jeolla Jeonju Korea Republic of

ISBN: (纸本)9798350388282

Fruits contain a lot of fiber. Fruits contain biologically active substances that improve our health. This study focuses on identifying and classifying guava fruit diseases. Guava disease has become a significant issue for guava production, threatening farmers' socioeconomic growth. As a result of this phenomenon, an automated computer vision-based guava disease diagnosis system was developed that can detect malicious guava and direct it to early cure approaches, reducing relative economic loss. In light of this, we present in this study a guava illness detection and categorization recommendation approach based on convolutional neural networks (CNN). This paper employs transfer learning to classify images using our customized convolutional neural network (CNN) model architecture. The photographs in the data set were obtained from the Mendeley data website. The data set includes 306 images of healthy and unhealthy guava fruits and leaves. The four disease classes targeted by the data sets are dot, Canker, mummification, and Rust. We obtained an average accuracy of 74.72% for training and 34.74% for testing from the data set used. © 2024 IEEE.

关键词： Fruits

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Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framework Using Mask R-CNN and ResNet

Pneumonia Diagnosis in Chest X-rays: A Deep Learning Framewo...

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Jay Vithlani Kanchan Dabre Pratham Gala Satishkumar L. Varma Dept. of Computer Science and Engineering(Data Science) Dwarkadas J. Sanghvi College of Engineering Mumbai India Dept. of Computer Science and Engineering(Data Science) Mumbai India Pillai College of Engineering Mumbai India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Pneumonia remains a significant factor in global mortality rates, highlighting the need for prompt and accurate diagnostic methods to improve patient outcomes. While radiographic examination is a standard diagnostic tool, accurate chest X-ray interpretation might be difficult because of the visual similarities between pneumonia and other respiratory issues. This research explores deep learning techniques for detecting and localizing pneumonia in chest X-ray images. This work has investigated Mask R-CNN’s performance with various backbone architectures. This approach includes comprehensive data preparation, including text removal, image resizing, and employing data augmentation techniques. The study examines different backbone architectures, loss functions, and optimization strategies to find the optimal configuration. The bounding boxes generated by the models identify potential pneumonia infections, and their performance is evaluated using various metrics at IoU thresholds between 0.5 and 0.95. The combined results of the Mask R-CNN model as base model and backbone model as ResNet-101 demonstrates superior performance, with an average IoU of 0.75, precision (88.21%), recall (86.03%), and an F1 score (87.11%). This proposed method not only performs competitively with current models but also provides precise localization through bounding boxes and segmentation masks, potentially enhancing the diagnostic process for radiologists. This research represents a significant advancement in utilizing deep learning for detecting and localizing pneumonia in CXR-images.

关键词： Deep learning Analytical models Visualization Pneumonia Accuracy Reliability X-ray imaging Standards Optimization Biomedical imaging

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A Cloud-based Auto-Scaling System for Virtual Resources to Back Ubiquitous, Mobile, Real-Time Healthcare Applications 3

A Cloud-based Auto-Scaling System for Virtual Resources to B...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Khader Basha, Sk Purimetla, Nagamalleswara Rao Roja, D. Vullam, Nagagopiraju Dalavai, Lavanya Vellela, Sai Srinivas Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of CSE AP Guntur India Chalapathi Institute of Engineering and Technology Dept. of CSE AP Guntur India

ISBN: (纸本)9798350343632

The utilisation of virtualization technologies in conjunction with cloud computing has emerged as a significant and influential trend within the Information Technology (IT) sector. Cloud computing has significantly transformed the development landscape of mobile ubiquitous services by introducing notable attributes such as reliability and cost-effectiveness. Consequently, it has successfully penetrated the market, bringing about substantial changes in the industry. While it is possible to easily convert numerous types of best-effort mobile ubiquitous applications to cloud infrastructures, it is crucial to thoroughly analyse essential elements in order to enhance the effectiveness of cloud computing infrastructures for real-time healthcare applications. When programme clients observe an anomaly, there is a substantial increase in the data transfer rate. Nevertheless, there are inherent issues in the current allocation of computational resources by server-side processes to effectively manage the fluctuating and time-varying data quantities. This article introduces a novel auto-scaling technique for servers, which aims to dynamically allocate virtual resources. The suggested method's efficacy in scaling virtual resources up and down in response to the number of real-time tasks is evaluated through testing conducted on an Amazon EC2 platform. The obtained findings demonstrate the efficient performance of the mechanism. © 2023 IEEE.

关键词： Amazon Elastic Compute Cloud Auto-scaling Cloud computing Healthcare Mobile ubiquitous

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A Machine Learning Based Approach for Frequency Response Prediction in Low Inertia Power System 11

A Machine Learning Based Approach for Frequency Response Pre...

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11th International Conference on Innovative Smart Grid Technologies - Asia, ISGT-Asia 2022

作者： Panwar, Akhilesh Rather, Zakir Hussain Doolla, Suryanarayana Liebman, Ariel Dargaville, Roger Indian Institute of Technology-Bombay Dept. of Energy Science and Engineering Mumbai India Monash University & Monash Energy Institute Dept. of Data Science & AI Civil Engineering Dept. Melbourne Australia

ISBN: (纸本)9798350399660

The uncertainty in securely operating the power system has increased with large-scale integration of inverter based resources (IBRs). IBR-driven displacement of conventional sources results in diminishing inherent frequency support capability of such systems as, unlike conventional synchronous generators, IBRs do not inherently support the grid frequency. Therefore, with increasing vulnerability, there is a need for an accurate frequency prediction model that could help a grid operator better plan system resources and securely operate the power system. Against this backdrop, this paper initially provides a critical insight into the frequency response of IBR-dominated systems to highlight the limitation of using a linear prediction model. Following the critical analysis, the paper proposes a data extraction framework and XGBoost algorithm-based regression model to predict RoCoF, frequency nadir, and quassi steady-state frequency of the IBR-dominated power system. The proposed approach has been implemented on a modified IEEE-39 bus system, and its comparative analysis with other state-of-art algorithms supports the superiority of the proposed method. © 2022 IEEE.

关键词： Forecasting

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AIML for Road Accident Analysis 4

AIML for Road Accident Analysis

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Rathore, Saurabh Pratap Singh Sanitha, P.C. Varshney, Manoj Verma, Anupama Vashisht, Manisha Saharan, Aaryan Icapsr Management & Research New Delhi India Dhanalakshmi Srinivasan University Dept. of Ai and Data Science Coimbatore India Mangalayatan University Dept. of Computer Engineering and Applications Aligarh India Usha Martin University Dept. of Business Management Ranchi India Lingayas Vidyapeeth University Dept. of Cse Faridabad India Chandigarh University Dept. of Cse Chandigarh India

ISBN: (纸本)9798350364729

Road traffic accidents pose a significant threat to both developed and developing nations, prompting global efforts to enhance road safety. With cars and drivers on the roads, pedestrians also face risks, often becoming innocent victims of accidents. Various factors contribute to road accidents, including driving under the influence, weather conditions, speed, road type, light conditions, road surface, and design. These accidents can cause severe, sometimes unseen injuries with lasting health implications. This study aims to explore road accidents in India using the random forest classifier algorithm and machine learning techniques. © 2024 IEEE.

关键词： Highway accidents

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WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery Benchmarking 38

WelQrate: Defining the Gold Standard in Small Molecule Drug ...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Yunchao Dong, Ha Wang, Xin Moretti, Rocco Wang, Yu Su, Zhaoqian Gu, Jiawei Bodenheimer, Bobby Weaver, Charles David Meiler, Jens Derr, Tyler United States Neural Science Dept. Amherst College United States Chemistry Dept. VU United States Computer Science Dept. University of Oregon United States Data Science Institute VU United States MD Anderson Cancer Center United States Electrical and Computer Engineering Dept. VU United States Psychology Dept. VU United States Institute of Chemical Biology VU United States Center for Structural Biology VU United States Pharmacology Dept. VU United States Germany Computer Science Dept. LU Germany Chemistry Dept. LU Germany

While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potential, thereby slowing the progress and transfer of innovation into real-world drug discovery. Thus, in this paper, we seek to establish a new gold standard for small molecule drug discovery benchmarking, WelQrate. Specifically, our contributions are threefold: WelQrate dataset collection - we introduce a meticulously curated collection of 9 datasets spanning 5 therapeutic target classes. Our hierarchical curation pipelines, designed by drug discovery experts, go beyond the primary high-throughput screen by leveraging additional confirmatory and counter screens along with rigorous domain-driven preprocessing, such as Pan-Assay Interference Compounds (PAINS) filtering, to ensure the high-quality data in the datasets;WelQrate Evaluation Framework - we propose a standardized model evaluation framework considering high-quality datasets, featurization, 3D conformation generation, evaluation metrics, and data splits, which provides a reliable benchmarking for drug discovery experts conducting real-world virtual screening;Benchmarking - we evaluate model performance through various research questions using the WelQrate dataset collection, exploring the effects of different models, dataset quality, featurization methods, and data splitting strategies on the results. In summary, we recommend adopting our proposed WelQrate as the gold standard in small molecule drug discovery benchmarking. The WelQrate dataset collection, along with the curation codes, and experimental scripts are all publicly available at ***. © 2024 Neural information processing systems foundation. All rights reserved.

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Leveraging data Mining for Cybersecurity Threat Detection 1

Leveraging Data Mining for Cybersecurity Threat Detection

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kaushik, Priyanka Chouhan, Ganga Singh Mishra, Atul Kumar Bandil, Deepika Kumari, Meenakshi Chandra Sekar, P. Chandigarh University Dept. of CSE Chandigarh India Madhav University Dept. of Commerce & Management Pindwara India Institute of Busi. Mang. & Commerce Dept. of Management Aligarh India Usha Martin University Dept. of Busi. Mang. & Commerce Jharkhand India Dhanalakshmi Srinivasan College of Engineering Dept. of AI & Data Science Coimbatore India

ISBN: (纸本)9798350356816

In the face of increasingly sophisticated cyber threats, the application of data mining techniques has become essential for effective cybersecurity threat detection. This paper examines how data mining methodologies, including clustering, classification, and anomaly detection, can be leveraged to enhance cybersecurity defenses. By systematically analyzing large datasets from various sources such as network traffic, system logs, and user activities, data mining tools can identify patterns and anomalies that signify potential security threats. Will discuss the integration of these techniques with machine learning algorithms to improve the accuracy and efficiency of threat detection systems. The study explores several data mining approaches, evaluating their strengths and limitations in detecting both known and novel cyber threats. Additionally, it addresses the challenges associated with data privacy, the volume and complexity of data, and the need for real-time analysis. The results illustrate that data mining not only aids in early threat detection but also provides a foundation for developing adaptive and proactive cybersecurity strategies. The paper concludes by outlining future directions for research and advancements in data mining for cybersecurity, emphasizing the need for ongoing innovation to counter emerging threats. © 2024 IEEE.

关键词： Phishing

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Zoom-DF: A dataset for Video Conferencing Deepfake 1

Zoom-DF: A Dataset for Video Conferencing Deepfake

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1st ACM Workshop on Security Implications of Deepfakes and Cheapfakes, WDC 2022, co-located with ACM AsiaCCS 2022

作者： Park, Geon-Woo Park, Eun-Ju Woo, Simon S. Computer Science & Engineering Dept. Sunkyunkwan University Suwon Korea Republic of Applied Data Science Dept. Sunkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9781450391788

With the rapid growth of deep learning methods, AI technologies for generating deepfake videos also have been significantly advanced. Nowadays, the manipulated videos such as deepfakes are so sophisticated that one cannot easily differentiate between real and fake, and one can create such videos with little effort. However, such technologies can be likely to be abused by people with malicious intents. To address this issue, approaches and efforts to detect deepfakes have been researched significantly. However, the performances of the detectors in general depends on the quantity and quality of the training data. In this paper, we introduce a new deepfake dataset, Zoom-DF, which can be injected during the remote meeting and video conferencing, to create a sequence of fake participant images. While most deepfake datasets focus on the face area, our dataset primarily targets for the remote meeting, and manipulates movements of the participants. We evaluate existing deepfake detectors on our new Zoom-DF dataset and present the performance results. © 2022 ACM.

关键词： Video conferencing

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