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Real-Time 3D Texture and Motion Analysis for Face Anti-spoofing Using Deep Learning and computer Vision 11th

Real-Time 3D Texture and Motion Analysis for Face Anti-spo...

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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Dommati, Manikanta Kiliroor, Cinu C Department of Computer Science Indian Institute of Information Technology Kottayam Kerala Kottayam India

ISBN: (纸本)9789819717231

Along with the rise of face recognition systems, there is a growing concern over the vulnerability of these systems to spoofing attacks, where an attacker can present a fake or a manipulated face image to bypass the authentication process. Regular updates in the anti-spoofing systems are required to stop attacks on the recognition systems. The existing systems use convolution neural networks as binary classifiers to differentiate between a real face and a spoofed face. We propose a face anti-spoofing system that aims to differentiate between a live and spoofed face image by employing a two levels of verification and. The proposed system is designed to work with RGB cameras and does not require any 3D camera hardware or stereo vision cameras, making it accessible for more practical applications. The proposed system uses a transfer learning technique to perform first level of verification and validate in the second level using computer vision. computer vision aims to build a high-security model for cutting out all the attacks on the facial recognition system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Unveiling Fake News with Machine Learning and Natural Language Processing Techniques 2

Unveiling Fake News with Machine Learning and Natural Langua...

引用

2nd International Conference on Emerging Trends in Information technology and Engineering, ic-ETITE 2024

作者： Kavitha, S. Anchitaalagammai, J.V. Murali, S. Deepalakshmi, R. Anuradha, G. Thaya Rani, V. Velammal College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India

ISBN: (纸本)9798350328202

The detection of fake news using machine learning and natural language processing techniques. With the proliferation of misinformation on social media and digital platforms, identifying fake news has become crucial. The project preprocesses textual data, applies various machine learning models, including logistic regression, Naïve Bayes, decision trees, random forest, neural networks, support vector machines, and AdaBoost Classifier, and employs dimensionality reduction techniques like PCA and t-SNE. The dataset is categorized into cases with and without speaker and political party information, and label encoding is used for numeric conversion. The results highlight the challenges and complexities of fake news detection. © 2024 IEEE.

关键词： Logistic regression

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A Deep Learning Framework for Classifying and Mitigating Bias in News Reporting 9th

A Deep Learning Framework for Classifying and Mitigating B...

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9th Future Technologies Conference, FTC 2024

作者： Mitra, Abhijit Das, Rivujit Paul, Jayanta Majumder, Rajdeep Saha, Arghyadeep Sengupta, Nandita Sil, Jaya Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Shibpur711103 India P.O. Box 55040 Manama Bahrain

ISBN: (纸本)9783031731242

The media plays a significant role in shaping public perception, but media bias can distort this influence, often seen in one-sided or polarizing content, particularly in word choice. Such bias not only distorts public discourse but also challenges information integrity, especially with news spreading rapidly on social media. Our research aims to address the limitations of current bias detection models and manual classification inefficiencies. We focus on identifying and counteracting bias to promote unbiased reporting. Our study primarily aims to classify news biases into left, right, and center perspectives while mitigating biases for impartial reporting. We introduce a novel approach using a Transformer-based deep learning framework enhanced by multi-task learning techniques. Our methodology includes advanced deep learning models, notably GPT-2, fine-tuned for superior performance in metrics like BLEU and ROUGE scores. To support our model’s efficacy, we developed a unique dataset tailored to our research objectives. Our findings represent a significant stride towards achieving accuracy and fairness in news reporting, signaling a new era in digital media integrity. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Contrastive Learning

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LeukoDeepNet: A Novel Approach in Automated Identification of Leukemia Diseases in Microscopic Human Blood Cells

LeukoDeepNet: A Novel Approach in Automated Identification o...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Hossain, Md. Monwar Sakib, Najmus Milu, Insan Ara Jennifer, Sanjeda Sara Morshed, Md. Adnan Wasif Reza, Ahmed Arefin, Mohammad Shamsul East West University Department of Computer Science and Engineering Dhaka Bangladesh Chittagong University of Engineering and Technology Department of Computer Science and Engineering Chittagong Bangladesh

ISBN: (纸本)9798331529765

Leukemia, a critical hematologic malignancy, demands early and accurate diagnosis for effective treatment. In this research, we present a comprehensive approach to the detection of leukemia based on microscopic human blood cell images. Our research is dedicated to the early detection of leukemia by leveraging deep learning techniques on microscopic blood cell images. The study introduces a new modified VGG16 model, LeukoDeepNet, and compares it with conventional Convolutional Neural Network (CNN) and Inception (GoogLeNet) models. LeukoDeepNet stands out with remarkable training and validation accuracies of 99.96% and 92.63%, along with low training and validation losses of 0.0013 and 0.6295, respectively. These findings underscore the success of the approach and its potential to enhance both leukemia diagnosis and treatment. © 2024 IEEE.

关键词： Convolutional neural networks

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Securing the Digital Realm: Unmasking Fraud in Online Transactions Using Supervised Machine Learning Techniques 6th

Securing the Digital Realm: Unmasking Fraud in Online Transa...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Reddy, G. Yuktha Kokatnoor, Sujatha Arun Kumar, Sandeep Department of Computer Science and Engineering School of Engineering and Technology Christ University Bangalore India

ISBN: (纸本)9789819774227

A key component of contemporary banking systems and e-commerce platforms is identifying fraud in online transactions. Traditional rule-based techniques are insufficient for preventing sophisticated fraud schemes because of the increasing complexity and number of expanding online transactions. This research study examines the development of fraud detection methods, emphasizing data analytics and machine learning (ML) models. The study also focuses on the fact that developing efficient fraud detection systems requires continuous observation, data preprocessing, feature selection, and testing of models. Seven ML models, Logistic Regression (LR), Decision Trees (DT), k-Nearest Neighbors (kNN), Naïve Bayes (NB), Support Vector Machine (SVM), Random Forests (RF), and Extreme Gradient Boosting (XGBoost) are considered for classifying the dataset into fraudulent or not. During the experimentation study, it was observed that XGBoost yielded the highest accuracy of 99% when compared to other models. Users can determine which features significantly influence the model's predictions by using XGBoost's feature significance insights. Additionally, XGBoost provides integrated support for managing missing values in data, negating the requirement for imputation and other preprocessing procedures. Due to these, it performed better. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Nearest neighbor search

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Abstractive Summarization Evaluation for Prompt Engineering 8th

Abstractive Summarization Evaluation for Prompt Engineering

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8th International Visual Informatics Conference, IVIC 2023

作者： Chakraborty, Shayak Pakray, Partha Department of Computer Science and Engineering National Institute of Technology Silchar - 788010 Assam India

ISBN: (纸本)9789819973385

The task of summarizing large documents for easier and faster readability is widely acknowledged and a standard task in the field of Natural Language Processing. The metrics that are used to measure the working of this task are based on statistical measures such as n-grams and Longest Common Subsequences. Abstractive summarization is a type of automatic text summarization which refers to creating the summary from the main document without entirely copying words from the original document. With the advent of Deep learning architectures, abstract summarization has increased in popularity. The Large language models which are used for abstractive text summarization need proper prompts to generate summary. Engineering proper prompts is important as the quality of the summary generated depends on the prompt. In this paper, an abstractive measure of similarity is proposed where the textual similarity is measured by using Euclidean distance to compare a Principal Component based transformed BERT Embedding vector of the document and the summary. The metric is used to create prompts for Generative Pretrained Transformer and Text to Text Transfer Transformer models which are standard state-of-the-art language models. The summary generated shows significant improvement and the prompts generated by using the abstractive comparison metric are seen to have the perplexity almost the same as the document thus promising better summarization results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Principal component analysis

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A Hybrid Text-Image Response System for Enhanced Customer Interaction Using Advanced Deep Learning Models 4

A Hybrid Text-Image Response System for Enhanced Customer In...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Ashok, P. Pathmanaban, J. Rajadurai, S. Yuvaraj, B. Bharathraj, S.M. Nawaz, S. M. Mustafa Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331508845

In recent times, the integration of chatbots into customer interaction systems has transformed the way businesses engage with their customers. As businesses worldwide seek to implement chatbot systems to improve customer relationships, the evolution of these chatbots extends beyond text-based capabilities. With advancements such as Google's Gemini pro and OpenAI's GPT-4, chatbots now possess the ability to comprehend image-related queries. However, the challenge lies in developing interactive chatbots tailored to specific needs, capable of providing responses not only in text but also with relevant images - a feature that can significantly enhance problem-solving for small businesses. This paper proposes an innovative system designed to address user queries using both textual responses and pertinent images. Drawing inspiration from recent research, we present a novel approach that leverages the power of Lang chain, Meta's Llama-2-7b chat model, coupled with sentence transformers all-MiniLM-L6-v2 model for mapping the right image for the user query. This amalgamation of advanced models aims to create a sophisticated chatbot system adept at delivering informative responses enriched with both text and visual content. Our system represents a significant step forward in customer interaction, offering a versatile solution applicable across diverse domains, including customer service, education, and research. © 2024 IEEE.

关键词： all-MiniLM-L6-v2 model Chatbots Hugging-Face Sentence transformers Image-Based Data Innovative Approach Interactive Chatbot Large-Language Models Llama-2-7b chat model Multimodal Interaction Natural Language Processing Textual Conversations User Queries

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Detection of Schizophrenia Using Attention Mechanism with Convolutional Neural Network on EEG Signal 2

Detection of Schizophrenia Using Attention Mechanism with Co...

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2nd International Conference on Power, Electrical, Electronics and Industrial Applications, PEEIACON 2024

作者： Bhuiyan, Mohammad Khairul Alam Biswas, Dipannita Khulna University of Engineering & Technology Department of Computer Science and Engineering Khulna9203 Bangladesh

ISBN: (纸本)9798331517984

Schizophrenia (SZ) is a challenging mental disorder, requiring accurate diagnosis for effective treatment. Early diagnosis of schizophrenia can facilitate a normal life for patients. Electroencephalogram (EEG) data contains valuable insights into brain network connectivity, enabling the detection of abnormalities associated with schizophrenia. Hence, this study proposes a methodology employing a deep learning-based model utilizing deep convolutional neural networks (CNNs) with an Attention mechanism to classify schizophrenia patients. In this research, each EEG signal undergoes decomposition with Continuous Wavelet Transform (CWT) to extract relevant features that capture the underlying neurophysiological characteristics of brain activity. These features are then utilized to distinguish schizophrenia patients from healthy individuals using the proposed method, which is evaluated on an EEG dataset comprising 45 schizophrenic patients and 39 healthy subjects. To provide a comparison, various deep learning models like ResNet-50, CNN and DenseNet Model are applied. The results indicate that the proposed model outperforms all other methods tested. © 2024 IEEE.

关键词： Convolutional neural networks

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Wi-Fi CSI-based Vibration Sensing for Industrial Fault Detection 2

Wi-Fi CSI-based Vibration Sensing for Industrial Fault Detec...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Tias, Tahsin Azad Asaduzzaman Department of Computer Science and Engineering Chittagong University of Engineering & Technology Chattogram4349 Bangladesh

ISBN: (纸本)9798331508227

In the modern landscape of ubiquitous wireless technologies, WiFi sensing has emerged as a promising method to gather environmental data through WiFi Channel State Information (CSI). This paper presents a comprehensive framework designed to identify frequency levels of vibrating objects and detect faults in industrial settings. Passive sensing using CSI data as in the Industrial Internet of Things (IIoT), wireless sensing has become increasingly popular for fault detection. The proposed methodology comprises four key stages: setting up the experimental environment, collecting CSI data, preprocessing the data, and predicting frequency levels and faults. CSI data were meticulously gathered across a frequency range of 0 to 20 Hz. Subsequent data processing involved feature selection using Recursive Feature Elimination with Cross-Validation (RFECV) and comparison with other feature selection techniques after preprocessing the raw CSI data. The processed data were then analyzed using both Machine Learning (ML) and Deep Learning (DL) models. Among the ML models, CatBoost demonstrated superior performance for frequency identification, achieving an accuracy of 95.52%. For fault detection, the Random Forest (RF) model excelled, attaining an accuracy of 98.77%. These models were evaluated based on various metrics, including accuracy, precision, recall, and f1 score, confirming their efficacy and robustness in industrial applications. The findings highlight the potential of Wi-Fi CSI-based sensing as a reliable and efficient solution for industrial fault detection and frequency identification. ©2024 IEEE.

关键词： Wi-Fi

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ICDP: An Improved Convolutional Neural Network Model to Detect Pneumonia from Chest X-Ray Images 2nd

ICDP: An Improved Convolutional Neural Network Model to Det...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Md. Hasib, Khan Oli Ullah, Md. Imran Nazir, Md. Akter, Afsana Saifur Rahman, Md. Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka1216 Bangladesh

ISBN: (纸本)9789819989362

Pneumonia is a serious respiratory infection that can be fatal if not diagnosed promptly. However, even experienced radiologists can find it challenging to arrive at an accurate assessment of pneumonia based on chest X-ray pictures due to their blurriness. In order to solve this issue, we have come up with a novel solution of automated system (ICDP) that uses deep learning techniques to improve diagnostic accuracy. Using deep convolutional neural networks (CNNs), we have built our system. Our model incorporates several layers designed for identifying features in X-ray images and determining whether there is pneumonia or not. In our experiments, we found that our proposed model achieved 95% accuracy in diagnosing pneumonia, and its performance improved with training. Our approach has the potential to improve radiologists’ ability to identify pneumonia, which might ultimately save many lives. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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