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An Automatic POS Tagger System for Code Mixed Indian Social Media Text 5th

An Automatic POS Tagger System for Code Mixed Indian Social...

引用

5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Basisth, Nihar Jyoti Sachan, Tushar Kumari, Neha Pandey, Shyambabu Pakray, Partha Department of Computer Science and Engineering National Institute of Technology Silchar Assam Silchar India

ISBN: (纸本)9783031488788

For a range of Natural Language Processing (NLP) applications, including Sentiment Analysis, Sarcasm Detection, Information Retrieval, Question Answering, and Named Entity Identification, text derived from multiple users’ posts and what they comment on social media constitute significant information (IR). All such applications require part-of-speech (POS) tagging to add tag information to the raw text. Code-mixing, a social media user’s natural desire to submit content in multiple languages, presents a difficulty to POS tagging. In addition, sophisticated and freestyle writing increases the intricacy of the issue. For POS tagging of Code-Mixed Indian social media text, a supervised algorithm using Hidden Markov Model (HMM) with the Viterbi algorithm has been developed to address the problem. The suggested system has been trained and tested using publicly accessible social media text in Indian languages (ILs), particularly Bengali, Telugu, English, and Hindi. On the basis of the F-measure, the accuracy of the system-annotated tags have been assessed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hidden Markov models

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Deep Learning and Dynamic BPSO for Road Accident Severity Prediction using Twitter Data 15

Deep Learning and Dynamic BPSO for Road Accident Severity Pr...

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Raul, Sanjib Kumar Rout, Rashmi Ranjan Somayajulu, Dvln National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India

ISBN: (纸本)9798350370249

The traffic and medical authorities utilize accident severity prediction through social media postings for better administration and robust emergency response. Deep learning-based models can be used for accident severity prediction using social media data. However, most of the deep learning methods can only perform well when trained with an optimal set of hyper-parameters. This necessitates the use of meta-heuristics in conjunction with deep learning. Moreover, the increase in depth of the networks warrants partial training of the network to make the training process efficient. In this work, we propose a novel partial training-based hybrid deep neural network on convolutional neural networks (CNN) and long short-term memory (LSTM), which is partially trained, and the hyperparameters are optimized using a dynamic version of binary particle swarm optimization (DBPSO) known as accident tweet hybrid deep dynamic BPSO (AT-HDBPSO). Experiments show that the proposed AT-HDBPSO turns out to be efficient in training compared to traditional deep learning-based training. Finally, the proposed method outperforms other baseline and state-of-the-art techniques in prediction accuracy. © 2024 IEEE.

关键词： Tweets

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A Novel Framework for Securing Information in Cyber Security System using Authentication, Intrusion Detection and Deep Learning-based Risk Prediction Tasks 5

A Novel Framework for Securing Information in Cyber Security...

引用

5th IEEE International Conference for Emerging technology, INCET 2024

作者： Naik, R. Lakshman Jain, Sourabh Department of Computer Science and Engineering Indian Institute of Information Technology Haryana Sonepat India

ISBN: (纸本)9798350361155

The cyber security has become a hot research topic nowadays. The term cyber security is frequently employed replaceable with the term data security. In recent days, each system faces the cyber security threat virtually and the model framework should have the capacity to combine the security features and work as the system's integral elements. Various works have been conducted to discover the answer to the evaluation challenge of cyber security and numerous architectures have been designed. However, the techniques face distinct issues though it is functioning well initially at the development time. Hence, an efficient and advanced cyber security framework is implemented in this paper to prevent cyber attacks using deep learning. Initially, to predict the risks in cyber security devices, the necessary data is fetched from the available resources. The gathered data is forwarded to the authentication operation and then the authenticated data is given to the intrusion detection stage. After that, the intrusion-detected data is subjected to the risk prediction stage, where the Weighted Recurrent Neural Network (WRNN) is supported to forecast the risk level of the network. Here, the Dung Beetle Optimizer (DBO) is utilized to tune the weights optimally in WRNN. This authentication, intrusion detection, and risk prediction operations have enriched the security of the cyber networks. Lastly, the efficacy and the security of the implemented model are analyzed with its related models by utilizing several performance metrics. © 2024 IEEE.

关键词： Forecasting

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Multi-classifier approach to detect Alzheimer's Disease based on Handwriting Analysis 3

Multi-classifier approach to detect Alzheimer's Disease base...

引用

3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Dadhania, Jatan K. Chaudhuri, Abhilasha Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat India

ISBN: (纸本)9798350384598

Alzheimer's disease (AD) is a degenerative brain condition that predominantly impacts older individuals, resulting in a gradual decline in cognitive function and memory. Effective management and treatment of AD rely heavily on early detection. In this study, we propose a machine learning (ML) model for predicting AD using a dataset of handwriting tasks performed by test subjects with varying degrees of cognitive impairment. Handwriting analysis has shown potential as a low-cost, non-invasive method for early AD identification. These data were used to train various ML classifiers to classify individuals as either AD or non-AD. Baseline evaluations were conducted using multiple classifiers on the entire feature set and on individual tasks. The insights gained from these evaluations were then utilized to create a more optimized model combining the strength of multiple classifiers with the help of the TOPSIS method. The optimized model achieved a promising accuracy of 97% on the test set, demonstrating its potential for early AD detection. Our findings suggest that handwriting analysis, coupled with ML techniques, holds significant promise as a tool for early AD prediction and warrants further investigation. © 2024 IEEE.

关键词： Adversarial machine learning

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Segmentation of Brain Tumor using Modified Linknet Architecture from MRI Images

Segmentation of Brain Tumor using Modified Linknet Architect...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Suraj Venkatesh Babu, G.S. Skanda Kumar, C.S. Kowshik, R.G. Ashok, K.N. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Segmentation of Cancerous cell is the most significant tasks in medical image processing. It is believed that early diagnosis of cancerous cell is essential for enhancing treatment options and raising patient survival rates. The manual segmentation is dependent on radiotherapist involvement and expertise. MRI scans are often speedy and an excellent diagnostics step for medical professionals than CT scans. As a result, in an emergency, doctors advise getting an MRI scan. However, there is a chance for inaccuracy because there is a lot of MRI data. This has made automatic cancerous cell segmentation a feasible process. Currently, ML methods are in use for segmentation. This research proposes segmentation of cancerous cell using modified LinkNet architecture from MRI images. The proposed modified LinkNet architecture incorporates advanced features such as attention mechanisms and residual connections to improve its ability to capture intricate details and subtle variations within brain images. The model is trained with a diverse and extensive dataset comprising various types and sizes of brain tumors, providing robust performance across different clinical scenarios. © 2024 IEEE.

关键词： Diseases

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An Ensemble-Based Approach for Cyber Attack Detection in Financial Systems 5th

An Ensemble-Based Approach for Cyber Attack Detection in Fin...

引用

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

作者： Kumar, Dhanisht Srivastava, Harshit Bhamare, Kartik S. Kumar, Malay Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad India

ISBN: (纸本)9783031609343

Digital commerce is the process of exchanging money or assets using electronic or digital channels such as the internet or mobile devices. Instead of physical cash or checks, individuals or businesses can use digital platforms to transfer money, make payments or conduct financial transactions securely and efficiently. As more and more people conduct financial transactions online, the risk of cyberattacks targeting financial institutions will increase. This article presents an integrated approach to detecting cyber-attacks in the financial sector. The proposed method combines learning models including decision trees, random forests, and gradient boosting to increase the accuracy and robustness of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

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Snowfall Forecasting with Enhanced Accuracy: Leveraging Multi-Class SVM for Meteorological Predictions 7th

Snowfall Forecasting with Enhanced Accuracy: Leveraging Mult...

引用

7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Ranjith Kumar, M. Sudeesh Kumar, V. Tharun Kaarthik, G.K. Chatiyode, Veda Srinivasan Anusha, Janani Revathi, P. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Department of Computer Science and Engineering C Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India

ISBN: (纸本)9783031699856

The use of multi-class Support Vector Machines (SVM) for snowfall prediction is an advanced meteorological application. The study emphasizes how valuable snowfall forecasts are in various contexts, including agriculture, aviation, transportation management, and climate reporting. This approach presents a novel methodology for classifying snowfall types by analyzing various meteorological factors such as temperature range, wind dynamics, and precipitation. A 400-entries of datasets were pulled from GitHub to demonstrate the methodology. The sample is selected to reduce the complexity and to increase the performance. The dataset is split into a testing set (30%) and a training set (70%), which aids in assessing the model’s performance in a wide range of parameters. When the algorithm was first tested, it could distinguish different types of snowfall with a remarkable 83% accuracy rate based on indicators such as Temperature (min & max), Precipitation, Wind Speed, and Wind Gust. By optimizing the SVM configuration through hyper-parameter tuning approaches such as grid search, we were able to further refine our model’s accuracy to an astounding 91%. This improvement in forecast accuracy signifies a significant move away from older, more expensive instrument-based methods and towards data-driven snowfall analysis. Our results show how machine learning can revolutionize weather prediction systems, providing a more effective and economical method of comprehending and predicting snowfall patterns. © IFIP International Federation for Information Processing 2024

关键词： Snow

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Cloud-Based Cancer Prediction System for Enhancing Security and Authentication Using Deep Learning

Cloud-Based Cancer Prediction System for Enhancing Security ...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kiran Kumar, P.N. Neha, B.S. Sree, K. Theja Harshitha, K.M. Prathusha, K. S J C Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

Cloud-based applications are ubiquitously employed across diverse fields within the ever-evolving landscape of technology, serving as integral roots for innovation and efficiency. A meticulous evaluation of the skin cancer prediction has been done in this research, with a focus on deep neural network-based approaches. By scrutinizing a subset of 187 studies leveraging image processing techniques alongside Convolutional Neural Networks (CNNs), a profound understanding of their collective impact within various sectors emerges, allowing for a detailed exploration of their effectiveness and applications. Cancer prediction being the pivotal event of this research draw on the applications of convolutional neural network. By delving into a curated selection of studies showcasing deep neural network applications, a nuanced exploration of their efficacy within this sector is attainable, presenting an opportunity to glean insights into their practical implications and advancements. This insightful exploration acts as a beacon for future research endeavors, bringing attention to the progression of deep architectures in addressing pressing healthcare challenges and beyond. These findings underscore the paramount importance of these advancements in refining the accuracy of cancer prediction systems. In summation, this research illuminates the profound influence of deep learning methodologies, particularly convolutional neural networks, on the forefront of skin cancer prediction. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-time Face mask Detection Using Deep Learning and MobileNet V2

Real-time Face mask Detection Using Deep Learning and Mobile...

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International Conference on VLSI and Microwave and Wireless Technologies, ICVMWT 2021

作者： Verma, Akshay Srivastava, Manish Kumar Master of Technology Department of Computer Science and Engineering MMM University of Technology Gorakhpur India Assistant Professor Department of Computer Science and Engineering MMM University of Technology Gorakhpur India

ISBN: (纸本)9789811903113

COVID-19 has affected the whole world severely. Lockdowns and quarantines are imposed all over the world to prevent its spread. Hand sanitizers and face masks were made compulsory for individuals to apply for safety of their own and their society. This project will check the presence or the absence of masks on the face of a person. There could be more than a single person in the input provided, and the input could vary from images to GIFs to Livestreams. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision

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APFormer: Anti-Phishing Transformer for Website-Phishing Detection Via Joint Feature Learning

APFormer: Anti-Phishing Transformer for Website-Phishing Det...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Memon, Anam Manjotho, Ali Asghar National University of Modern Languages Department of Computer Science Hyderabad Pakistan Mehran University of Engineering and Technology Department of Computer Systems Engineering Jamshoro Pakistan

ISBN: (纸本)9798350349719

Phishing is a cybercrime activity where the perpe-Trator tricks users into believing that a fraudulent website is authentic, revealing sensitive information. Recently, the frequency of phishing attacks has increased, targeting both individuals and corporate sectors, thereby rendering traditional methods of detecting phishing websites less effective. These methods often produce high false positive rates due to the complexities involved in creating a strong correlation between brand identity and website features. Furthermore, sophisticated attacks, such as DOM element manipulation tricks, evade the existing methods and convince the user with visual and behavioural similarity. We propose a transformer-based approach for website phishing detection using joint feature learning. The approach jointly learns textual, visual, and heuristic multi-modal features to establish a stronger connection between brand identity and the webpage. The proposed model comprises a feature extraction module and a transformer network. The feature extraction module extracts textual, visual, and heuristic features from the DOM tree, while the transformer module classifies the input page as legitimate or phishing by learning the feature mapping to brand identity. Our method outperforms current state-of-The-Art methods on benchmark datasets by achieving a 3.5% increase in recall and a 5.9% reduction in false positive rate. © 2024 IEEE.

关键词： Websites

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