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检索条件"机构=Dept. of Civil Engineering and Computer Science Engineering"

共 35989 条记录，以下是91-100 订阅

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Recognition of Fingerprint Images using CNN for Cybercrime Detection System 2

Recognition of Fingerprint Images using CNN for Cybercrime D...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Kavitha, T. Patil, Bhimaraya Saraswathi, S. Rajarajeswari, S. Patil, Anita New Horizon College of engineering Dept. of Computer Engineering Bangalore India Sri Sivasubramaniya Nadar College of Engineering Dept. of Computer Science Engineering Chennai India Vellore Institute of Technology School of computer science and engineering Vellore India City Engineering College Dept. of Information Science Bengaluru India

ISBN: (纸本)9798350372892

Fingerprint identification plays a crucial role in crime detection, but manual analysis is time-consuming and error-prone. This research aims to develop an automated system for accurate and efficient fingerprint matching using machine learning. The proposed approach involves image acquisition, preprocessing, feature extraction, and classification. Fingerprint images are enhanced, binarized, and segmented. Minutiae (ridge endings and bifurcations) are extracted as features. A convolutional neural network (CNN) is trained on a dataset of fingerprint images for classification. By comparing algorithms, Random Forest achieved over 8 1 % accuracy, outperforming Decision Trees (nearly 75% accuracy). The trained CNN model can accurately match fingerprints from crime scenes with potential suspects, providing reliable evidence. The developed system offers law enforcement agencies an automated and accurate fingerprint identification tool, improving crime-solving efficiency and reducing manual effort. The proposed approach showcases the potential of machine learning in forensic applications. © 2024 IEEE.

关键词： Random forests

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Content Based Classification of Short Messages using Recurrent Neural Networks in NLP

Content Based Classification of Short Messages using Recurre...

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2024 International Conference on Artificial Intelligence, computer, Data sciences, and Applications, ACDSA 2024

作者： Bennet, Deepthi Tabitha Bennet, Preethi Samantha Thiagarajan, Priya Sundarakantham, K. Thiagarajar College of Engineering Dept. of Applied Mathematics and Computational Science Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India Thiagarajar College of Engineering Dept. of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350394528

Short message spam poses a significant threat for all mobile phone users, as it can act as an efficient tool for cyberattacks including spreading malware and phishing. Traditional anti-spam measures are only minimally effective against modern spammers. Intelligent analysis of the content to categorise the messages as Spam (unwanted and unsolicited) or Ham (useful messages) is therefore essential to safeguard the user from such attacks. Various artificial intelligence (AI) techniques are proving to be useful in the analysis of the content of such short messages to classify and filter spam. We have trained, validated and tested seven such AI techniques on the SMS spam collection dataset to identify the best model for designing and developing a content based classification system. Recurrent Neural Networks (RNN) have shown the highest performance metrics (Test Accuracy: 99.28%) and hence our proposed system includes a RNN model for classification. A web app of this system has also been deployed where a single SMS can be input and the designed system can classify it as Spam or Ham. The designed system is compared against existing systems and is found to be significantly better. © 2024 IEEE.

关键词： Recurrent neural networks

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Personalized Movie Recommendation Based on User Preferences Using Optimized Sequential Transformer Model

Personalized Movie Recommendation Based on User Preferences ...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Ravikumar, R.N. Jain, Sanjay Sarkar, Manash Amity University Rajasthan Dept. of Computer Science and Engineering Jaipur India Atria Institute of Technology Dept. of Computer Science and Engineering Banglore India

ISBN: (纸本)9798350379716

This study explores the application of a Transformer based model to the 'MovieLens 1M' and 'MovieLens 10M' datasets provided by GroupLens, which includes one million and ten movie ratings. The Transformer model, known for its proficiency in handling sequence-to-sequence tasks, employs multi-head self-attention mechanisms and position-wise feed-forward networks. Our implementation, developed in PyTorch, benefits from its capacity for automatic differentiation and GPU acceleration, which significantly enhances the training efficiency. The model underwent rigorous hyperparameter tuning and optimization to adapt optimally to the characteristics of the dataset. Performance evaluation using the Normalized Discounted Cumulative Gain (NDCG) metric demonstrates that the Transformer model substantially outperforms a conventional popular recommendation system across various top-K rankings. Specifically, for the MovieLens 1M dataset, the Transformer model achieved NDCG scores of 0.0455, 0.0805, 0.0995, and 0.1273 at top 1, 3, 5, and 10, respectively. Comparatively, the popular recommendation system scored 0.0023, 0.0044, 0.0061, and 0.0091 at these same rankings. This trend of superior performance by the Transformer model is also consistent across the MovieLens 10M dataset, further confirming its effectiveness in ranking highly relevant items at the top of the list. Also, to address quality and utility of recommendations, MAP, Precision, Recall, F1 Score, Coverage and Serendipity metrics evaluated. These results underscore the effectiveness of Transformer models in extracting and leveraging complex patterns in sequence data, demonstrating a clear superiority over traditional methods in recommendation systems. This study highlights the potential of deep learning architectures to revolutionize fields reliant on understanding user preferences and sequential data interactions. © 2024 IEEE.

关键词： Recommender systems

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Predictive Modeling and Dynamic Analysis of Price Trends in eCommerce using ML Technique 3

Predictive Modeling and Dynamic Analysis of Price Trends in ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Banerjee, Pallab Prasad, Vishal Thakur, Kanika Mitra, Dipra Gaurav, Kumar Kanrar, Soumen Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotias University Dept. of Computer Science Engineering Greater Noida India

ISBN: (纸本)9798350364569

In the ever-evolving landscape of eCommerce, staying ahead of pricing trends is paramount for both consumers and sellers. This project presents an advanced Ecommerce Product Price Tracker with Price Prediction capabilities, aimed at empowering users with foresight into future pricing dynamics. Supervised Machine Learning algorithms, encompassing regression and time series analysis, form the backbone of predictive modeling, enabling accurate forecasts of forthcoming price movements. The resultant tool not only provides real-time tracking of current product prices but also furnishes predictive analytics, empowering users with actionable insights. By bridging the gap between data-driven insights and eCommerce dynamics, this project contributes to fostering efficiency and efficacy in the vibrant eCommerce ecosystem. © 2024 IEEE.

关键词： Predictive analytics

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

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

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

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

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

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

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

Assessing Landslide Susceptibility in Active Tectonic Zones ...

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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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Deep Learning-Driven Cloud Intrusion Detection: Internet of Things and Cloud Networks 1

Deep Learning-Driven Cloud Intrusion Detection: Internet of ...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Moon, Iffath Tanjim Islam Mondal, Md. Nazrul Rajshahi University of Engineering & Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798331518578

In this paper, we have utilized deep learning approaches to detect cloud intrusion for the Internet of Things (IoT). The emerging growth of IoT and cloud environments has revolutionized many industries by allowing real-time data sharing and storage. Cloud platforms offer the essential infrastructure for managing the massive volumes of data produced by IoT devices. However, due to several flaws, including insufficient encryption, insecure APIs, and weak authentication, intruders can exploit the vulnerabilities to interrupt services and obtain unauthorized access. Therefore, deep learning can be an effective solution to detect cloud intrusion in IoT. In this research, we have developed individual convolutional neural network (CNN) and long short-term memory (LSTM) models, along with a hybrid CNN-LSTM model for multiclass classification and binary classification to detect cloud intrusion. In addition, we have examined which models perform the best for cloud intrusion detection for IoT among the individual and hybrid models. The proposed models are effective in detecting cloud intrusion, as CNN can extract spatial features, and LSTM can capture temporal dependencies from network traffic data. Furthermore, we have utilized one of the most recent and comprehensive datasets for IoT, the ToN-IoT dataset, containing real-world data. In multiclass classification, the hybrid CNN-LSTM has given the best performance, achieving an accuracy of 97.65%, precision of 97.72%, recall of 97.65%, f1 score of 97.67%, and AUC of 99.93%. In binary classification, all CNN, LSTM, and hybrid CNN-LSTM have demonstrated high performance, where LSTM has achieved the highest accuracy of 99.97%. Moreover, we have evaluated the performance of multiclass classification for detecting nine distinct kinds of attacks. In this instance, CNN has shown better performance in detecting backdoor attacks. Thus, this research can make the IoT platform more secure by efficiently detecting cloud intrusion. © 2025

关键词： Network intrusion

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