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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66490 条记录，以下是161-170 订阅

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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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Project or Factorize? A Case Study of Multiview CCA and PARAFAC2 Tensor Factorization 58

Project or Factorize? A Case Study of Multiview CCA and PARA...

引用

58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Chen, Jia Papalexakis, Evangelos E. University of California Dept. of Electrical and Computer Engineering Riverside United States Uuniversity of Californiav Dept. of Computer Science and Engineering Riverside United States

ISBN: (纸本)9798350354058

Consider learning the shared representations from multiple unlabeled views. Previous work either projects different views to the same space while enforcing the agreement among the projected views such as multiview canonical correlation analysis (MCCA), or factorizes different views while ensuring the common latent components across the views such as PARAFAC2, a tensor decomposition method. In this paper we first investigate a fundamental question: 'Do these two approaches learn different representations?' Preliminary numerical results suggest that in practice they do, which, in turn, begs the question of how we can leverage this observation in order to compute a superior representation. In this paper, we present a simple proof-of-concept scheme which augments MCCA with PARAFAC2 representations and vice-versa, and we demonstrate on multiple real datasets that such scheme can improve upon the baseline representation, paving the way for future research on optimally combining the strengths of projection and factorization methods for multiview representation learning. © 2024 IEEE.

关键词： Tensors

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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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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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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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Utilizing TFLite and Machine Learning for the Early Detection of Mango Leaf Disease: An Automated Flutter Application 5

Utilizing TFLite and Machine Learning for the Early Detectio...

引用

5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Patel, Aastha Ravikumar, R.N. Betgeri, Santushti Singhal, Shilpa Singh, Sushil Kumar Takodara, Mitul Dept. of Computer Engineering Marwadi Education Foundation's Group of Institutions Rajkot India Dept. of Computer Engineering Marwadi University Rajkot India

ISBN: (纸本)9798350361155

The mango, one of the most important tropical fruits in the world economically, is constantly in danger of going extinct due to a number of diseases. In order to achieve optimal production and quality, prompt intervention and efficient management depend on the early and accurate detection of these diseases. Expert visual inspection is a common component of traditional illness diagnosis techniques, although it can be laborious, subjective, and prone to human mistake. In this research work, we applied Machine Learning techniques to create an automated system for detecting mango disease. The application uses a TFLite model, which was trained on a huge set of images of mango leaves with the following classifications: Powdery Mildew, Bacterial Canker, Black Sooty Mold, Cutting Weevil, Die Back, Anthracnose, and Healthy Leaves. The mango leaf disease dataset is obtained from Kaggle with 300 images. We have evaluated the model with 10 epochs and achieved 100% of accuracy. © 2024 IEEE.

关键词： Fruits

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A comprehensive step of XAI for Bengali and English text detection and recognition from natural scene images 2

A comprehensive step of XAI for Bengali and English text det...

引用

2nd International Conference on Networking, Embedded and Wireless systems, ICNEWS 2024

作者： Dutta, Mithun Das, Utpol Kanti Das, Jugal Krishna Rangamati Science and Technology University Dept. of Computer Science and Engineering Rangamati Bangladesh Jahangirnagar University Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350367461

A critical first step in many applications, including augmented reality, document analysis, and scene comprehension, is text detection from images. Even though text identification for the English language has advanced significantly, there are particular difficulties when applying these methods to languages such as Bengali because of variations in script, morphology, and typographic characteristics. This article offers a wonderful method for leveraging the Faster R-CNN framework to identify and categorize text from images that contain Bengali and English sentences. The study utilizes a dataset named "Bengali and English text image dataset (BETID)"that consists of images with text in both Bengali and English scripts, taken from a variety of sources such as books, documents, natural scenes, internet images, and landscapes. Pre-processing methods are used to improve the resilience and generalization of the model, including image scaling, normalization, and augmentation. The Faster R-CNN architecture is tuned and trained to identify text regions in images and categorize Bengali or English text. The performance of the text Classification model is evaluated using evaluation measures such as accuracy on the BETID dataset. The proposed Faster R-CNN model achieved an average of 97.90% accuracy, which is better than other deep learning models. Empirical findings showcase the capacity of transfer learning to alleviate data scarcity concerns and enhance text detection precision for low-resource languages such as Bengali.) © 2024 IEEE.

关键词： Deep learning

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A Hybrid Flood Forecasting Approach Using Hidden Markov Model and Machine Learning Techniques 7

A Hybrid Flood Forecasting Approach Using Hidden Markov Mode...

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2023 7th International Conference On Computing, Communication, Control And Automation, ICCUBEA 2023

作者： Sri Sakthi, L. Chitra, P. Harini Sree, M.S. Subbulakshmi, P. Umayal, C. Thiagarajar College of Engineering Dept. of Computer Science and Business Systems Madurai India

ISBN: (纸本)9798350304268

Floods are one of the most common and adverse disasters that the world faces. In India, floods are seemed as the disaster that happens often and pose extreme risk to lives and belongings. Forecasting floods can greatly reduce the risk and effect of the forthcoming flood. A large quantity of parameters is answerable for flood occurrence. The amount of rainfall and temperature are the most vital parameters for inflicting floods. Hence rainfall and temperature are used as determinants inside the look at. Machine learning model such as logistic regression, Naive bayes, k-Nearest neighbours, support vector machines (SVM) and random forests generally generate a model or computation method to label the results. Further, in latest days, hybridization techniques are expected to yield better results, wherein multiple models are combined to provide a new model. The proposed work is to develop a hybrid model called HMM-ML which includes Hidden Markov model with the above cited ML models. as a result, HMM acts as a first-class-tuner to stimulate the model. The developed model is validated against the region Kozhikode located in the State of Kerala, India which gets the maximum amount of rainfall during the period between June - August. © 2023 IEEE.

关键词： Flood prediction Hidden Markov Model(HMM) Hybridization Machine Learning (ML) Rainfall Temperature

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Weather-Dependent Fast Power Flow in Distribution systems: A Meteorological-Data-Aided Method

Weather-Dependent Fast Power Flow in Distribution Systems: A...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Zhang, Yuanshuo Zhang, Ying Wang, Lei Zhou, Anping Oklahoma State University Dept. Electrical and Computer Engineering Stillwater United States Southern Methodist University Dept. Electrical and Computer Engineering Dallas United States

ISBN: (纸本)9798331541125

The weather-dependent variability of line impedance can substantially impair the accuracy of traditional power flow calculations in electric grids, especially under varying and extreme weather conditions. Recently, significant advancements have been made in weather-dependent power flow (WDPF) algorithms to address this issue in transmission systems. However, determining line parameters in three-phase unbalanced distribution grids remains time-consuming due to the nonlinear and analytically intractable nature of heat-balance functions. To achieve fast and high-fidelity calculations, this paper proposes a novel data-aided WDPF algorithm leveraging readily available weather information to calibrate line parameters in real time. The method enhances the classic backward-forward sweep approach for unbalanced distribution grids by incorporating real-time updates of line parameters based on the thermal model of line conductors during the iterative power flow process. Additionally, well-trained neural networks are employed to expedite power flow calculations by solving line temperature variables. Case studies on two unbalanced IEEE feeders demonstrate that the proposed algorithm surpasses state-of-the-art WDPF algorithms in computational efficiency and accuracy under diverse weather conditions. © 2025 IEEE.

关键词： DC distribution systems

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Selfish Users in Listen-Before-Talk Co-existence of Cellular-Wi-Fi Networks: Counteraction Methods 21

Selfish Users in Listen-Before-Talk Co-existence of Cellular...

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21st International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, WiOpt 2023

作者： Saibharath, S. Mishra, Sudeepta Arora, Gazal Sharma, Anamika Hota, Chittaranjan Dept. of Computer Science and Information systems BITS Pilani Hyderabad Campus India Dept. of Computer Science and Engineering IIT Ropar Punjab India

ISBN: (纸本)9783903176553

5G operates in the unlicensed spectrum to expand its capacity. This spectrum provides more uplink and downlink frequency bands in 5G to satisfy ultra-dense and scalability requirements. Wi-Fi is a prominent wireless technology that operates in unlicensed bands. Due to the proximity of cellular and Wi-Fi channels, utilization of both Wi-Fi and 5G New Radio (NR) can cause interference. The co-existence of 5G NR and Wi-Fi devices lacks the means of communication for negotiation and coordination among them. Listen-Before-Talk and Duty-cycling are standard Medium Access Control (MAC) mechanisms to enable co-existence. Due to a lack of coordination, network utilization becomes unfair with the existence of selfish devices. The selfish users could maximize throughput and affect other Wi-Fi and NR users. Here, we investigate the effects of selfish nodes on this Wi-Fi Co-existence with 5G. These nodes disobey exponential backoff to boost their channel acquisition. We study the effect of misbehavior through the Order Gain metric. Through standard CSMA/CA settings with overall channel occupancy of 99.75%, we demonstrate that selfish users profoundly impact fair co-existence. We study the side effects through throughput, channel sensing, and acquisition for various backoff window access patterns. Finally, we go through counteraction methods to overcome the selfish user impact. © 2023 IFIP.

关键词： Wi-Fi

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