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Online Harassment Detection using machine learning 5

Online Harassment Detection using Machine Learning

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Ahirwar, Raj Ajay, M. Sathyabalan, N. Lakshmi, K. Periyar Maniammai Inst Sci & Technol PMIST Thanjavur Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Online communication has become more accessible with the recent digitalization techniques. this advancement also comes with its own advantages and disadvantages. the major disadvantage is the occurrence of bullying and online harassment in cyberspace, which is referred to as cyberbullying. A lot of effort has been taken to deal with this problem. the sufferers will only know the extent of their psychological and mental consequences. Several research studies have proposed different techniques for identifying cyberbullying, however the majority of them focus on a specific job, such as text recognition systems by using machine learning (ML) techniques or voice recognition systems using Natural Language Processing (NLP) techniques. To overcome this drawback, the proposed research study offers a multitasking approach, which can work with both text and audio data.

关键词： SVM Logistic regression Random forest Naive Bayes Naturallanguage processing machine learning

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Research on Safety Hazard Identification Method for Construction Sites Based on YOLOv9 Algorithm

Research on Safety Hazard Identification Method for Construc...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Hong Zeng Xinjie Wu Yiran Lyu Chunyi Yu Intelligent Construction Department Chongqing Jianzhu College Chongqing China Construction Management Department Chongqing Jianzhu College Chongqing China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

this study establishes a framework system for identifying construction safety hazards, addressing issues such as insufficient perception of construction safety scene information, insufficient utilization of unsafe behavior data, and insufficient analytical capabilities in the current construction industry. through computer vision understanding, various potential safety hazards are automatically identified, and the YOLOv9 algorithm is used for model validation. the identification and positioning of people and hazardous areas are analyzed, with an overall average recognition accuracy of 92%. this method can provide safety warnings and reminders for construction sites, and provide important reference and guidance for active control and management of construction site safety.

关键词： Hazardous areas machine learning algorithms Safety management Object detection machine learning Inspection Safety pattern recognition Object recognition Security

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Gene Expression data Analysis using Supervised machine learning Algorithm in data mining for Breast Cancer Prediction 5

Gene Expression Data Analysis using Supervised Machine Learn...

引用

5th international conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Nathiya, S. Sumitha, J. Dr.SNS Rajalakshmi College of Arts and Science Department of Computer Science Tamil Nadu Coimbatore India

ISBN: (纸本)9798350367171

the human body is composed of cells, and each cell has a variety of components. the human body's cells are essential for both the construction and operation of living things. Cells in the human body are capable of dividing and self-destructing as necessary. this natural functioning can occasionally alter;for example, certain cells may grow uncontrollably or improperly, giving rise to tumors. When these tumors develop into malignant ones and spread to nearby body parts, the condition is known as cancer. Specifically, if the cells in the breast can grow uncontrollably then this condition is known as breast cancer. A gene is a piece of genetic material that is present in every human cell. the genes that code for the proteins that determine how each cell functions. Gene expression is the name given to this function that produce protein. Occasionally, variations in the genetic material, or mutated genes, produce alterations in the values of gene expression. Breast cancer is caused by these altered gene expression levels. Some diagnostic techniques are necessary to detect this altered gene expression for breast cancer, and they are highly effective in helping medical professionals to pinpoint the disease's biomarker. thus, the goal of this research is to use gene expression data to create a model that can accurately identify breast cancer biomarkers. the suggested model in this study, a pruned neural network method, is designed to predict breast cancer while requiring less memory and execution time and offering the best accuracy. this suggested model is evaluated against current techniques, including support vector machines and decision trees, and it outperforms them with a 98 % accuracy rate. Having a larger number of gene characteristics and fewer samples is a drawback of using gene expression data. therefore, the wrapper approaches are employed as a preprocessing strategy in this research work to remove the undesirable genes. the gene expression data is preprocessed be

关键词： Support vector machines

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Method and data analysis based on ECG signal for emotion recognition 5

Method and data analysis based on ECG signal for emotion rec...

引用

5th international conference on Artificial Intelligence and Computer Science, AICS 2023

作者： Chen, Jie Lin, Meirong Zheng, Zhiguo Lv, Hongyan College of Information Engineering Hainan Vocational University of Science and Technology Hainan Haikou571126 China

ISBN: (纸本)9781510668621

Emotion recognition is an important link in utilizing the relationship between computers and emotion measurement. the development of medical technology requires people to no longer be limited to physical health, and psychological health is also increasingly valued. Collecting ECG signals and using machine learning for emotion recognition is an important development direction in the field of artificial intelligence. After the original signal is processed and noise filtered, features can be extracted. In this method, the TFIDF algorithm is used. High-dimensional data after Fourier transform could have a higher recognition rate in the SVM classifier. ECG signals as an intrinsic physiological characteristic signal of the human body, could more intuitively express human emotions and serve as a daily monitoring method for emotions, which helps in self-diagnosis and self-analysis of emotions. the average recognition rate of this paper for the five emotions is about 77% with the fact that all data comes from real life scenarios. © 2023 SPIE.

关键词： Emotion recognition

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Credit Card Fraud Detection Using Advanced machine learning Techniques 5

Credit Card Fraud Detection Using Advanced Machine Learning ...

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5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Aditi, A. Dubey, Aman Mathur, Ankit Garg, Preeti Kiet Group of Institutions Delhi-NCR India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

the world is witnessing rapid growth in Ecommerce and hence in the use of plastic money and with this dramatic increase in card uses there is an explosion in credit card fraud. With the number of credit card users, fraud cases are also rising. In real case scenarios, the fraudulent transaction is intermixed with the genuine transaction so simpler ways like pattern matching are not good to detect these frauds. Execution of an accurate and effective fraud detecting system is of major importance to all financial card issuing bodies. Several ways are based on approximate reasoning, AI, data mining, sequence alignment that is identifying regions of similarities, inheritable programming, etc, which are highly used in the detection of these credit card frauds. An innovative approach and a clear understanding of these technologies will help design better systems for credit card detection. © 2022 IEEE.

关键词： pattern matching

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Multiple Class Breast Cancer Detection Method Based on Deep learning and MIRRCNN Model 5

Multiple Class Breast Cancer Detection Method Based on Deep ...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Sajiv, G. Ramkumar, G. SIMATS Saveetha Sch Engn Chennai Tamil Nadu India

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

the DCNN is a powerful deep learning system. Breast cancer classification, segmentation, and detection have previously been shown to be improved by DCNNs in a range of medical imaging modalities. As a global disease, breast cancer is one of the most frequent and most destructive. In this paper, the MIRRCNN model was employed to classify breast cancer based on recurrent residual convolutional neural networks (RNNs). Inception (Inception-v4), ResNet, and the Recurrent Convolutional Neural Network are only a few of the robust models in the DCNN family. Compared to Inception, Residual, and RCNN networks, the MIRRCNN is superior in object recognition. A publicly accessible database is utilised to classify breast cancer using the MIRRCNN method. Researchers compared their findings in terms of picture, patches, and patient-level categorization to previous machine learning and data mining methodologies. this model surpasses previous methods in terms of sensitivity, AUC, the ROC chart, and overall accuracy for both datasets.

关键词： Deep Convolutional Neural Network Segmentation ResNet RCNN Inception CNN

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DeepAdversaryDefense: A Deep Model to Identify and Prevent Adversarial Attacks against Medical Speech recognition 5

DeepAdversaryDefense: A Deep Model to Identify and Prevent A...

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5th international conference on Informatics and data-Driven Medicine (IDDM)

作者： Panwar, Kirtee Singh, Akansha Singh, Krishna Kant Bennett Univ SCSET Greater Noida India Amity Univ ASET Noida India

Deep learning models have made significant progress in safety-critical environments such as health-care systems, machine-learning based robots, Autonomous Intelligent Vehicles (AIV)), aviation software, etc. Deep learning models can learn from input data, the property of learning has its own drawbacks as these models can be easily affected by minor disturbances in input examples. these input examples are generally created purposely by attackers and are known as adversarial examples. A small malicious change in input can cause the model to generate incorrect output. Majority of works in literature are towards understanding and generation of adversarial attack. Most of these attacks do not effectively resist detection networks. On the other hand, adversarial example detectors have inadequate evaluation. In this paper, a secure medical speech recognition (MSR) system is proposed that can prevent malicious attacks. Adversarial examples that pose security concerns can be detected and filtered out. With the proposed model such system-malicious inputs designed to perform an attack on safety-critical applications, even if the adversary has no access to the underlying model are prevented.

关键词： Adversarial samples Automatic Speech recognition (ASR) Medical Speech recognition (MSR) vulnerabilities deep learning security Adversarial Loss

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On Divergence-Free Neural ODE for Classification 27th

On Divergence-Free Neural ODE for Classification

引用

27th international conference on pattern recognition, ICPR 2024

作者： Jarraya, Zakaria Drumetz, Lucas Benaïchouche, Simon Salem, Douraied Ben Rousseau, François IMT Atlantique LaTIM U1101 INSERM Brest France IMT Atlantique Lab-STICC UMR CNRS 6285 Brest France Univ Brest CHU Brest UMR 1101 Brest29200 France

ISBN: (纸本)9783031781858

Inspired by the principles of fluid dynamics, divergence-free neural ODEs represent a promising class of neural networks that can improve classification performance and robustness. In this work, we propose methods for constructing divergence-free vector fields in high-dimensional latent spaces and thus extending their application to real-world scenarios. Our experimental results show that divergence-free neural ODEs can outperform their conventional counterparts in three classification tasks. In particular, divergence-free models show remarkable resilience to adversarial perturbations. Furthermore, divergence-free models are more effective at preserving the underlying structure of the data. this research provides a physics-based perspective on machine learning applications, in particular classification tasks, by interpreting neural networks as dynamical systems. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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2022 IEEE 5th international conference on Artificial Intelligence and Big data, ICAIBD 2022

2022 IEEE 5th International Conference on Artificial Intelli...

引用

5th IEEE international conference on Artificial Intelligence and Big data, ICAIBD 2022

ISBN: (纸本)9781665499132

the proceedings contain 119 papers. the topics discussed include: thoughts on whether unmanned driving can be safe on the road;a review of research on feature extraction for emotion recognition based on EEG signals;boost speech recognition without training;research on the shared supply mode of urban public transportation under the Internet of things - a case study based on hello travel platform;deep reinforcement learning scheduling of container cloud workflow considering invalid time-consuming and reliability;review of panel data clustering methods and applications;fungi recognition based on coordinate attention and EfficientNetV2;prediction of protein post-translational modifications in rice based on multi-head self-attention;and research on the application of data mining in teaching system.

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Parkinson's Disease Classification with Self-supervised learning and Attention Mechanism 26

Parkinson's Disease Classification with Self-supervised Lear...

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Zhang, Yuchen Lei, Haijun Huang, Zhongwei Zhao, Menglu Li, Zhen Liu, Chuan-Ming Lei, Baiying Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol Nanjing Peoples R China Natl Taipei Univ Technol Comp Sci & Informat Engn Taipei Taiwan Shenzhen Univ Sch Biomed Engn Shenzhen Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Parkinson's disease (PD) is a neurodegenerative geriatric disease commonly occurring in middle-aged and elderly adults. Since PD is irreversible and its treatment only slows down its rate of development, the early diagnosis by accurate prediction is of great significance to retard its deterioration. However, the existing computer-assisted diagnosis methods for PD have limitations in exploring the implicit and spatial information in the brain. In view of this limitation, a 3D network based on self-supervised learning strategy and attention mechanism is proposed for PD classification in this paper. the proposed method put the input of successive frames from the preprocessed magnetic resonance imaging (MRI) data into 3D ResNet18 with the classifier module for PD classification. Specifically, the attention mechanism is used to explore the discriminative features. Meanwhile, a self-supervised learning pretext task and a regression task are designed to assist in training and improve the robustness of the proposed model. We use a 5-fold cross-validation strategy to corroborate our method's effectiveness on the Parkinson's Progression Markers Initiative (PPMI) dataset. the experimental results indicate that our proposed method has achieved an accuracy of 87.50% for PD classification, which outperforms the most state-of-the-art deep learning methods.

关键词： Parkinson's disease self-supervised learning attention mechanism magnetic resonance imaging

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