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From Image to Emotion: Exploring CNN Architectures for Facial Emotion Recognition

From Image to Emotion: Exploring CNN Architectures for Facia...

引用

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

作者： Mahmud Rafi, M Shahriar Alam, Tazrian Chowdhury, Linkon Roy Chowdhury, Bristy East Delta University Dept. of Computer Science and Engineering Chattogram Bangladesh Bgc Trust University Dept. of Computer Science and Engineering Chattogram Bangladesh

ISBN: (纸本)9798331529765

Facial Emotion Recognition (FER) has made significant accomplishments over the past few years while the implications of it are quite extensive and are found in different domains. In this paper, a deep learning-based architecture is proposed for FER where we mainly focus on the main components, including dataset preparation;preprocessing methods;feature extraction techniques;classification algorithm;and XAI approaches. The proposed model based on convolutional neural networks (CNNs) operates with feature extraction from facial images. Preprocessing techniques such as resizing, normalization, and augmentation are used to elevate the quality and variety of the dataset. Due to the architecture of VGG16 and ResNet50, we implement three CNN models to extract discriminant features from facial expressions. Meanwhile, each model works as a classifier so that it can make accurate classifications. On the other hand, we use saliency maps to give the model the computation process in a more interpretable manner. Through multiple experimentation and evaluation, the established model shown the high accuracy of the regional analysis of facial emotions, and, as a result, FE R systems can be updated by the improved performance and explainability. © 2024 IEEE.

关键词： Convolutional neural networks

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The Impact of Compiler Warnings on Code Quality in C++ Projects 32

The Impact of Compiler Warnings on Code Quality in C++ Proje...

引用

32nd IEEE/ACM International Conference on program Comprehension, ICPC 2024

作者： Johansson, Albin Holmberg, Carl Neto, Francisco G. De Oliveira Leitner, Philipp Chalmers University of Technology Dept. of Computer Science and Engineering Gothenburg Sweden Chalmers - University of Gothenburg Dept. of Computer Science and Engineering Gothenburg Sweden

ISBN: (纸本)9798400705861

Modern compilers often offer a variety of warning flags, which developers can enable to get feedback on code that, while syntactically correct, may be problematic. In the case of C++, one example of such 'correct but problematic' code is code that leads to undefined behavior (UB). The usage of compiler warnings has long been suspected as a way to decrease bugs and increase code quality. However, empirical evidence that supports this hypothesis is rare. In this study, we present evidence from a study of 127 open source C++ projects. We categorize their usage of compiler warnings into five groups based on which warning flags are being used, and analyse the relationship between compiler warnings and five quality metrics (bugs, critical issues, vulnerabilities, code smells, and technical debt) using Bayesian analysis. We conclude that, in general, compiler warnings indeed correlate with, and potentially cause, higher code quality, with the clearest impact being on the number of critical issues in a project. Using stricter warning flags expectantly correlates with higher code quality in our study objects. However, there are substantial differences between projects, which we attribute to the project's individual development culture. That is, while warnings matter, other factors such as quality culture, are likely to be even more important to source code quality. © 2024 ACM.

关键词： Quality control

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Accidental Prone Area Detection in Dhaka Metropolitan City Using Machine Learning Models 5

Accidental Prone Area Detection in Dhaka Metropolitan City U...

引用

5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Jerin, Foyjunnesa Akhi, Amatul Bushra Tasin, Mobin Mohtadi Sultana, Naznin Akbar, Md. Atef Sharmin, Shayla Daffodil International University Dept. of Computer Science and Engineering Dhaka Bangladesh North South University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350351378

Dhaka, known for its status as one of the world's most densely populated areas, grapples with numerous challenges associated with urbanization and safety. This study identifies accident-prone zones within the Dhaka Metropolitan City by analyzing data collected from 2019 to 2023. Ten specific locations were investigated using various machine learning models: Decision Tree Classifier, Gradient Boosting Classifier, Random Forest, Gaussian Naive Bayes, and Logistic Regression. Evaluation metrics, including precision, recall, accuracy, F1-score, and standard deviation, were utilized, with the Random Forest classifier exhibiting the highest accuracy at 94.39%. The analysis encompassed both monthly and annual accident data, revealing trends and probabilities associated with different incident types. The study's implications suggest potential advancements in accident investigation by incorporating advanced machine learning techniques, integrating diverse datasets, and expanding the geographical coverage to encompass the entire metropolitan area. This comprehensive approach aims to bolster accident prevention measures and enhance urban safety. Overall, this research contributes to a deeper understanding of accident-prone regions and proposes strategies for targeted interventions to improve safety within metropolitan areas. © 2024 IEEE.

关键词： Accidents

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Assessing Landslide Susceptibility in Active Tectonic Zones using Convolutional Neural Networks (CNN)

Assessing Landslide Susceptibility in Active Tectonic Zones ...

引用

2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Dhinakaran, K. Iyyappan, V. Divya, E. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamil Nadu India Chennai Institute of Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331510022

Assessing landslide susceptibility is vital in mitigating natural disasters, particularly in zones with active tectonics. This paper will discuss a novel approach based on a CNN architecture in a U -Net designed to assess landslide susceptibility in these zones of active tectonism. The model thus takes advantage of spatial data sources from remote sensing and GIS and multi-coupled geospatial factors such as slope, elevation, rainfall, and tectonic activity in a multivariate framework for predicting landslide-prone areas. The U-Net CNN architecture is used as it captures the local and global spatial patterns and results in very precise susceptibility maps. The selected historical data about landslides was then fed into the trained and validated model to predict potential landslide occurrence zones, indicating high accuracy in susceptibility mapping. The CNN-based U-Net approach suggests a higher order of precision and spatial resolution than the conventional machine learning approach. This work portrays the capabilities of deep learning approaches in developing landslide risk assessment with much-improved precision. It thus offers an essential tool for disaster risk management along with urban planning for tectonically active regions. © 2024 IEEE.

关键词： Convolutional neural networks

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Neural Style Transfer: A Comparative Study 15

Neural Style Transfer: A Comparative Study

引用

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

作者： Suryamritha, M. Balaji, Varshini Kannan, Srinidhi Singh, Tripty Sharma, Mansi Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India University Dept. of Computer Science and Engineering India

ISBN: (纸本)9798350370249

Neural Style Transfer (NST) has emerged as a powerful technique for artistic image synthesis by fusing the base image with style source. In this study, we present a comparative analysis of NST using popular convolutional neural network (CNN) architectures, using pre-trained models such as VGG19, DenseNet121, ResNet50, and MobileNetV2. The methodology involves leveraging the deep features extracted by the models to balance content and style in synthesized images. We delve into the specifics of the model's layer utilization, loss function definition, and TensorFlow implementation details. Experimental results demonstrate the model's ability to produce visually compelling images that seamlessly merge content and style characteristics. Densenet121 has demonstrated superior performance compared to the other models in the evaluation. This work not only contributes to the field of NST but also offers a user-friendly TensorFlow framework, fostering further exploration and advancements in the realm of computer vision and image processing. © 2024 IEEE.

关键词： Artistic image synthesis CNN DenseNet121 image processing MobileNetV2 Neural Style Transfer ResNet50 TensorFlow VGG-19

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Triple-Node-Upset Recoverable Radiation-Hardened by Design Latch

Triple-Node-Upset Recoverable Radiation-Hardened by Design L...

引用

2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Paparsenos, Evangelos Tsiatouhas, Yiorgos Lab Dept. of Computer Science and Engineering Ioannina Greece

ISBN: (纸本)9798350318845

CMOS technology evolution enhances integrated circuits (ICs) performance characteristics at the cost of their increased susceptibility to radiation and thus to the occurrence of single-event upsets (SEUs) that may lead to soft errors generation. SEUs may affect multiple nodes in a circuit (multiple node upsets - MNUs). Fortunately, these disturbances do not cause permanent damage to the circuits. Various techniques have been proposed to deal with SEUs that concurrently affect one, two or three nodes. In this paper, we propose the design of a latch that offers tolerance up to the level of triple-node upsets (TNUs). This is achieved by using redundancy in the hardware which provides the ability to store a logic value in multiple nodes within the latch as well as by using multiple feedback paths which allow it to recover its correct state in the event of an SEU. Compared to other techniques dealing with the same problem, our proposed method provides faster recovery time (higher than 16.87%) after an SEU, and at the same time reduced power consumption and higher speed performance. © 2024 IEEE.

关键词： Radiation hardening

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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

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Principal Component Analysis and Deep Learning-Based Traffic Anomaly Detection in WSN

Principal Component Analysis and Deep Learning-Based Traffic...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Shakya, Vandana Choudhary, Jaytrilok Singh, Dhirendra Pratap Manit Dept. of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350348460

Wireless Sensor Networks (WSNs) have advanced quickly due to the fast expansion of wireless networks. Yet, because of their ease of use and versatility, security concerns have grown. This means that conducting research on intrusion protection in WSNs is now essential. Denial of Service (DoS) assaults are among the most common types of network attacks. They are dangerous because they take down the target network in order to accomplish their goal. Within WSNs, where devices function with limited resources, a denial-of-service attack has the potential to be disastrous. This research suggests a novel solution for WSNs, which are susceptible to assaults because to their devices' little storage capacity. To find abnormalities in DoS traffic within WSNs, the technique combines a Deep Convolutional Neural Network (DCNN) with Principal Component Analysis (PCA). By detecting and reducing the effects of DoS assaults, and by utilising the complementary capabilities of PCA and DCNN in this particular situation, the goal is to improve the security of WSNs. Compared with other traditional DL architectures, the proposed model has a more simplified structure and better feature extraction capabilities. This special combination gives it the power to quickly identify anomalous network activity in WSNs devices, especially those with limited storage. Because of its lightweight design, the suggested model addresses the inherent resource limits and guarantees optimal performance in the context of WSNs. A variety of assessment measures, such as confusion matrices, different classification metrics, and Receiver Operating Characteristic (ROC) curves, are used to verify the effectiveness of the suggested model. These metrics are used to evaluate the model's categorization performance in a rigorous manner. Extensive experimental comparisons reveal that the small size of the proposed model outperforms other popular models for anomalous traffic detection with regards to classification performance

关键词： Deep Convolutional Neural Network (DCNN) Denial of Service (DoS) KNN Principal Component Analysis (PCA) WSNs

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PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier 3

PhishVQC: Optimizing Phishing URL Detection with Correlation...

引用

3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Shahriyar, Farhan Tanbhir, Gazi Raihan Chy, Abdullah Md Tanzin, Mohammed Abdul Al Arafat Mashrafi, Jisan World University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Southern University Bangladesh Dept. of Computer Science and Engineering Chittagong Bangladesh Brac University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798331523893

Phishing URL detection is crucial in cybersecurity as malicious websites disguise themselves to steal sensitive information. Traditional machine learning techniques struggle to perform well in complex real-world scenarios due to large datasets and intricate patterns. Motivated by quantum computing, this paper proposes using Variational Quantum Classifiers (VQC) to enhance phishing URL detection. We present PhishVQC, a quantum model that combines quantum feature maps and variational ansatzes such as RealAmplitude and EfficientSU2. The model is evaluated across two experimental setups with varying dataset sizes and feature map repetitions. PhishVQC achieves a maximum macro average F1-score of 0.89, showing a 22% improvement over prior studies. This highlights the potential of quantum machine learning to improve phishing detection accuracy. The study also notes computational challenges, with execution wall times increasing as dataset size grows. © 2025 IEEE.

关键词： Qubits

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Enhancing Artificial Intelligence and Machine Learning Understanding Through EnVision: A Virtual Reality Approach 1

Enhancing Artificial Intelligence and Machine Learning Under...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Patra, Louise Kumari, Manisha Gopalapillai, Radhakrishnan Dept. of Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350365016

The quality of teaching and learning process can be improved through innovative methods like use of virtual reality. We introduce EnVision, a groundbreaking Virtual Reality based approach to revolutionize Artificial Intelligence and Machine Learning (AI/ML) education. EnVision offers a unique approach where users build models through immersive learning experiences. Using interactive tutorials mixed with real-time data integration and visualization, users learn AI/ML concepts with ease. Through qualitative and quantitative methods, we evaluate EnVision's effectiveness and user satisfaction, contributing to the advancement of innovative educational gaming technologies. The evaluation results show that EnVision provides the AI/ML practitioners engaging learning experiences. © 2024 IEEE.

关键词： Virtual environments

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