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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Patil, Swapnil Chandre, Pankaj R. Vanarote, Viresh Pathan, Shafi Nimbalkar, Madhukar Shinde, Gitanjali Department of Computer Science and Engineering MIT School of Computing MIT Art Design and Technology University Loni Kalbhor India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhwa India

ISBN: (纸本)9789819998104

Profanity detection has become increasingly important in various industries, including media and online content moderation. In this paper, we propose a novel approach to identifying profane words from audio using natural language processing (NLP) techniques. We first convert the audio files into textual transcripts using automatic speech recognition (ASR) tools. We then apply pre-processing techniques such as tokenization, stemming, and stop-word removal to the transcripts. Finally, we train various machine learning models, such as Naive Bayes, Support Vector Machines (SVMs), and Random Forests, to classify the transcripts as either containing profane language or not. The proposed approach can be integrated into various applications, such as content moderation tools, online chat systems, and automatic transcription services. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Audio recordings

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An Efficient Approach of Brain Tumor Detection & Extraction using BWT with Auto Enhance Technique 3

An Efficient Approach of Brain Tumor Detection & Extraction ...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Bahadure, Nilesh Bhaskarrao Gonge, Sudhanshu Patni, Jagdish Chandra Shah, Bhoomi Raju, Nagrajan Patil, Prasenjeet D. Department of Computer Science and Engineering School of Technology GSFC University Gujarat Vadodara India Maharashtra Pune India Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Electrical and Electronics Engineering SASTRA University Tamil Nadu Thanjavur India Department of Computer Science and Engineering MIT ADT University Pune India

Brain tumor detection and extraction of infected areas with utmost accuracy is essential in medical image analysis. Despite ex- cessive research, segmentation operation for brain tumor detection remains an exigent issue because medical images have diverse image contents, non-uniformity object texture, occlusion, image noise, and other factors that consistently influence and affect per- formance analysis. This paper introduces the most efficient brain tumor detection and extraction technique, using Berkely Wavelet Transformation-based segmentation with auto-enhancement techniques. If the input images are too dark or their intensity is not good enough, the auto-enhance technique will automatically adjust the image quality. The extensive experimental analysis and sim- ulation, with a comparison of the results with other state-of-the-art techniques available, prove the significance and effectiveness of the proposed algorithm. The primary intent of this paper is to develop a simple but performance-oriented algorithm for brain tumor detection and extraction, which is suitable for most imaging modalities and gives the best performance even if images are of poor quality. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Image segmentation

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Predictive Modelling of Rice Blast Disease Utilizing Ensemble Voting Classifiers in Machine Learning 8

Predictive Modelling of Rice Blast Disease Utilizing Ensembl...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Revathi, A. Poonguzhali, S. School of Computing Sciences Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350386578

Rice is the second-most significant cereal plant, next to wheat, and the most commonly cultivated staple food in India. Rice production has declined in recent years due to the spread of several rice diseases, changes in weather conditions, and soil quality. Rice blast, also known as RB, is the most devastating of all rice plant diseases and causes a vast amount of annual loss. The key challenge is to minimize crop loss due to rice blast disease while also increasing agricultural output. It is critical to predict the disease ahead of time so that the required safeguards can be taken. As manual prediction of rice blast disease requires a huge amount of labor and time, it is a must to implement an automated system using machine learning (ML) techniques, which are widely used artificial intelligence (AI) methods for prediction models. In the proposed system, the sample data set of 15,400 has been fed as an input to the ML algorithms via Nïve Bayes, random forest, KNN, and voting classifiers for prediction. The results show that the ensemble method of combining the machine learning algorithm-based prediction technique offers improved accuracy than the random forest algorithm, with an accuracy of 99.5%. The proposed methodology will greatly benefit agriculturalists with increased profits and safeguard food for the country. © 2024 IEEE.

关键词： Agriculture Artificial Intelligence Disease Prediction Machine Learning Supervised Learning

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The Impact of Transformer Models and Regression Algorithms on Automated Short Answer Grading 5

The Impact of Transformer Models and Regression Algorithms o...

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5th IEEE International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nampoothiri, Abhijith. N. Thushara, M.G. Pradeesh, N. Anand Krishna, K.R. Santhosh, Abhai Abhijith Kumar, S. Kumar, Suryan Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Omnex Software Solutions Pvt. Ltd Thoraipakkam Chennai India

ISBN: (纸本)9798350356236

Grading short answer questions in higher education often poses significant challenges due to its subjective nature and time-consuming process. As educational institutions expand, the need for efficient and objective grading solutions becomes critical. This paper investigates the use of machine learning and natural language processing to automate the evaluation of short answer responses. Our study employs a two-phase methodology: first, several transformer models - including BERT, Sentence-BERT, T5, Genism, and XLNet - are used to transform text into numerical representations. In the second phase, various regression techniques, such as Linear Regression, Support Vector Machine (SVM), Random Forest, and AdaBoost, are applied to these representations to estimate scores. Our findings indicate that the combination of the BERT model with Random Forest performs the best, demonstrating superior accuracy with the lowest error metrics. This optimal model pairing is proposed for integration into the Ample Learning Management System (LMS) to enhance grading efficiency and provide quicker, more reliable feedback. The significant implications of this research provides teachers with a robust tool for automated grading, which can enhance the educational experience by delivering timely and accurate evaluations. © 2025 IEEE.

关键词： Teaching

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APFT: Adaptive Phoneme Filter Template to Generate Anti-Compression Speech Adversarial Example in Real-Time

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5152-5165页

作者： Huang, Yihuan Ren, Yanzhen Sun, Zongkun Zhai, Liming Wang, Jingmin Liu, Wuyang Wuhan University School of Cyber Science and Engineering Wuhan430072 China Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Central China Normal University School of Computer Science Wuhan430079 China

Automatic Speech Recognition (ASR) systems are widely used for speech censoring. Speech Adversarial Example (AE) offers a novel approach to protect speech privacy by forcing ASR to mistranscribe. However, existing speech AE faces two challenges in real-time voice communication scenarios, such as IP telephone, voice chat, or video conference, it cannot be generated in real-time, and its defensive capability is significantly reduced after the essential audio compression for network transmission. In this paper, we propose Adaptive Phoneme Filter Template (APFT) to address these issues. The key features of APFT include: 1) Phoneme-level Templates for universal AE generation in real-time, 2) Filter, which eliminates redundant signals to improve compression robustness. 3) Adaptive Band Filtering, which limits the attack area from the frequency band without affecting the attack effectiveness and improves speech quality. The comprehensive experimental results show that APFT has four advantages: 1) Real-time Generation, with AE generation time below 1.1ms for 1s speech;2) Compression Robustness, achieving a WER of 0.64 under AAC and Opus codecs;3) Transferability, with an average WER of 0.72 across datasets and ASR systems;4) Stealthiness, achieving a MOS of 4.07 for high-quality speech. In addition, the experiment on Telegram voice calls further proves the practical applicability of APFT. © 2005-2012 IEEE.

关键词： Speech enhancement

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Machine Learning-Based Detection of Blood Cancer from Microscopic Images: A Comparative Study 9

Machine Learning-Based Detection of Blood Cancer from Micros...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Manoj, Milan Sreekumar, Vishnu Reghuram, Surya Anjali, T. Ramlal, Nandakishor Prabhu Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Altair Engineering Ab Stockholm Sweden

ISBN: (纸本)9798350377972

Early and accurate detection of blood cancer is critical for improving patient outcomes. This article is proposed to develop and evaluate the effectiveness of using machine learning techniques for automation in blood cancer detection based on microscopic images of blood cells. A comparison will be conducted using three of the most used models: Support Vector Machine, Decision Tree, and Random Forest on their capability in the classification process of distinguishing blood cells from benign and malig-nant groups. The methodology is divided into the following steps: image preprocessing, extraction of color histogram and texture features through the Gray-Level Co-occurrence Matrix (GLCM), and optimization of hyperparameters for model training. Evaluation metrics used are accuracy, precision, recall, and F1-score. Random Forest has been seen to perform with a high accuracy of 99.08% with all the categories showing robust classification. It was followed by SVM with an accuracy of 96.15 % and Decision Tree with 94.92%. The results present Random Forest as a valuable resource for the automated diagnosis of blood cancer. This paper reiterates the promise that machine learning holds in boosting diagnostic efficiency and encourages future studies into more advanced approaches such as deep learning for further improvement in clinical applications. © 2024 IEEE.

关键词： Diseases

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Rumor detection using dual embeddings and text-based graph convolutional network

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Discover Artificial Intelligence 2024年第1期4卷 1-15页

作者： Pattanaik, Barsha Mandal, Sourav Tripathy, Rudra M. Sekh, Arif Ahmed School of Computer Science and Engineering XIM University Odisha Bhubaneswar752050 India Department of Computer Science UiT The Arctic University Tromsø9017 Norway

Social media platforms like Twitter and Facebook have gradually become vital for communication and information exchange. However, this often leads to the spread of unreliable or false information, such as harmful rumors. Currently, graph convolutional networks (GCNs), particularly TextGCN, have shown promise in text classification tasks, including rumor detection. Their success is due to their ability to identify structural patterns in rumors and effectively use neighborhood information. We present a novel rumor detection model using TextGCN, which utilizes a word-document graph to represent rumor texts. This model uses dual embedding from two pre-trained transformer models: generative pre-trained transformers (GPT) and bidirectional encoder representations from transformers (BERT). These embeddings serve as node representations within the graph, enhancing rumor detection. Combining these deep neural networks effectively extracts significant contextual features from rumors. This graph undergoes convolution, and through graph-based learning, the model detects a rumor. We evaluated our model using publicly available rumor datasets, such as PHEME, Twitter15, and Twitter16. It achieved 88.64% accuracy on the PHEME dataset, surpassing similar models, and performed well on Twitter15 and Twitter16 with accuracies of 81.98% and 83.41%, respectively. © The Author(s) 2024.

关键词： Tweets

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Graph Contrastive Learning for Truth Inference 40

Graph Contrastive Learning for Truth Inference

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Liu, Hao Liu, Jiacheng Tang, Feilong Li, Peng Chen, Long Yu, Jiadi Zhu, Yanmin Gao, Min Yang, Yanqin Hou, Xiaofeng Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong East China Normal University School of Data Science and Engineering Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China University of Aizu School of Computer Science and Engineering Japan Simon Fraser University School of Computing Science Canada

ISBN: (纸本)9798350317152

Crowdsourcing has become a popular paradigm for collecting large-scale labeled datasets by leveraging numerous annotators. However, these annotators often provide noisy labels due to varying expertise. Truth inference aims to infer accurate consensus labels from noisy crowdsourced annotations. Existing approaches rely heavily on hand-engineered assumptions or ground truth data, limiting their applicability. To address this, we propose GOVERN, a graph contrastive learning framework for truth inference without such external supervision. GOVERN employs a novel graph data augmentation strategy to generate views capturing worker coordination patterns. A contrastive objective then encourages invariant representations across views, enabling the discovery of features related to the hidden consensus. Further, a label correction method based on k-nearest neighbors refines noisy pseudo-labels to supervise model training. Comprehensive experiments on 9 real-world datasets demonstrate that GOVERN outperforms state-of-the-art truth inference techniques. © 2024 IEEE.

关键词： Crowdsourcing

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A deep learning approach for image and text classification using neutrosophy

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International Journal of Information Technology (Singapore) 2024年第2期16卷 853-859页

作者： Wajid, Mohd Anas Zafar, Aasim Wajid, Mohammad Saif School of Computing Science and Engineering Galgotias University Greater Noida India Department of Computer Science Aligarh Muslim University Aligarh India School of Engineering and Sciences Tecnológico de Monterrey Monterrey Mexico

The proliferation of data on the web and on personal computers is a direct result of the proliferation of new technologies and gadgets. Most of these pieces of information are collected through a number of different methods (text, image, video, etc.). This type of information is also vital for e-commerce websites. The products on these websites feature both images and descriptions in text form, making them multimodal in nature. Earlier classification and information retrieval algorithms focused largely on a single modality. This study leverages multimodal data for categorization utilising neutrosophic fuzzy sets for uncertainty management for information retrieval tasks. This work employs image and text data and, inspired by prior ways of embedding text over an image, seeks to classify the images using neutrosophic classification algorithms. Neurosophic convolutional neural networks (NCNNs) are used to learn feature representations of the generated images for classification tasks. We demonstrate how a pipeline based on NCNN may be applied to learn representations of the unique fusion method. Traditional convolutional neural networks are subject to unexpected noisy conditions in the test phase, and as a result, their performance for the categorization of noisy data degrades. Comparing our technique against individual sources on multi-modal classification dataset provides good results. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Convolutional neutral network Early fusion Fuzzy logic Information retrieval Late fusion Multimodal data Neutrosophic logic

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Accurate and Optimized Labelling of Fashion Products Through Attention Based Spiking Neural Networks 10

Accurate and Optimized Labelling of Fashion Products Through...

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10th International Conference on Advanced computing and Communication Systems, ICACCS 2024

作者： Vithyatharshana, N. Thrinayani, Yedhoti Kulkarni, Yogeshwar Nitin Singh, Rimjhim Padam Kanchan, Sneha Dept. of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer science Malaysia

ISBN: (纸本)9798350384369

Automation of garment classification has become the backbone of e-commerce, influencing product search, personalization, and customer experience. This paper uses the Fashion MNIST dataset, a rich collection of clothing images to investigate the potential of Spiking Neural Networks (SNNs) for accurate and efficient classification. SNNs are an excellent choice for widespread usage and real-world integration due to their low power consumption and biomimicry. With the intention of revolutionizing product search, personalization, and customer experience, SNNs are made to address style differences, perspectives, and illumination. In order to fully utilize Fashion MNIST, this paper suggests strategic improvements to the SNN architecture. By addressing key challenges and introducing novel optimizations, our research contributes to a more robust and intelligent future for automated fashion product labelling. An SNN model with 93.6% accuracy has been developed as the base model for all the applications for the same. This data-driven approach leads the way to flawless product searches, highly customized recommendations, and a more intelligent fashion technology in future. © 2024 IEEE.

关键词： Deep learning

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