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Predicting Test Smell Categories based on Issue Descriptions using information Retrieval and Machine Learning Techniques 10

Predicting Test Smell Categories based on Issue Descriptions...

引用

10th international conference on Digital Arts, media and technology and 8th ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2025

作者： Withunsapphasiri, Jiramed Phannachitta, Passakorn Doungsa-Ard, Chartchai Chiang Mai University College of Arts Media and Technology Thailand

ISBN: (纸本)9798331543273

Effective software testing is essential for achieving high-quality software products. Various strategies and methods are used to ensure the software meets requirements and functionality, but test smell is one of the challenges to achieving effective software testing. These smells contain various issues, such as execution behavior, semantic inconsistencies, dependency management, inefficiencies, and code-related problems. This study investigates applying information retrieval (IR) and machine learning techniques to predict test smell categories by analyzing natural language issue descriptions and shifting from code-based to issue-based approaches. The result reveals that preprocessing techniques impact model performance. Techniques such as Spacy demonstrated better performance due to their sophisticated language models, while TF-IDF proved effective in distinguishing semantic inconsistencies in textual issue descriptions. Oversampling appeared as a crucial method for handling data imbalance and enhancing model performance across all test smell categories. However, N-gram requires further investigation to capture the relationship in issue descriptions. These findings show the possibility of combining natural language processing with machine learning to predict and address test smell categories, contributing to more reliable software testing practices and reducing the mistakes that will happen. This study can potentially explore additional preprocessing strategies, oversampling techniques, and machine learning models to optimize predictive performance. © 2025 IEEE.

关键词： Model checking

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Image-Mixed Glitch Sounds 10

Image-Mixed Glitch Sounds

引用

作者： Amano, Noriki Notre Dame Seishin University Department of Information Science and Data Science Okayama Japan

ISBN: (纸本)9798331543273

Glitch art is the art that uses errors (glitches) in images, videos, and sounds that are intentionally created by destroying digital data or physically manipulating electronic devices. This type of glitch art is interesting in that it expresses unpredictable, accidental beauty. We are exploring the methods and effects of creating glitch art, and as part of this research, we attempted to create glitch sounds by mixing sound and images. Specifically, we simultaneously loaded image and sound data into the image processing software Photoshop to create multi-layered data. This generates glitches in the sound data. Our goal is not to generate glitch sounds using a random method, but rather to generate glitch sounds using a method based on legitimate manipulation of images, which are on a different level from sound, and to make the glitch sound visually enjoyable. We have confirmed that this goal can be achieved using the method used in this research. This research is unique in that it simultaneously realizes the audibility and visualization of glitches, and creates a new type of art that brings about a shift in aesthetic sensibilities. © 2025 IEEE.

关键词： Photointerpretation

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EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

引用

2025 IEEE international conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 IEEE.

关键词： Light field semantic segmentation Mamba State space model

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A Novel Framework for Deepfake Image Detection Using Deep Learning Approach 22

A Novel Framework for Deepfake Image Detection Using Deep Le...

引用

22nd international Learning and technology conference, L and T 2025

作者： Jaiswal, Ambika Chamatkar, Aruna Kharat, Akash Prakash Sharma, Bhisham Dhaou, Imed Ben Chaurasia, Suhashini Awadhesh Sant Gadge Baba Amravati University P.G. Department of Computer Science Maharashtra Amravati India Kamala Nehru College Maharashtra Nagpur India Saraswati College Shegaon Department of Computer Science Shegaon India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Hekma School of Engineering Computing Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

Deepfake, fast growing field in the age of multimedia and AI, have been attracted attention in the recent years. Deep learning algorithms are used to create real digital content which are difficult to distinguish from authentic content. Deepfakes can serve multiples purposes, including educational content, academic research, social entertainment so it spread wrong information, information manipulation, damage to reputation, many frauds. Day to day deepfake crime is increasing. Detection of deepfake is big challenging issue in the digital forensics. A strong approach needs to be created from protecting against the media deepfake. The main work of this paper is to build a efficient framework for deepfake detection. The paper discusses deepfakes, detection techniques, datasets, proposed model. Novel proposed approach has been created that are based on the integration of CNN and VGG16. The deepfake dataset has been used to utilized the network architecture. Proposed model has been achieved the 95% accuracy and 94% precision score. The developed model is superior than the other state of art developed methods. © 2025 IEEE.

关键词： Dataset Deep Learning Deepfake Image processing

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Effective Minimization of Fraud Using a Hybrid Novel Approach on Social media 4

Effective Minimization of Fraud Using a Hybrid Novel Approac...

引用

4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Prasanna, N.Merrin Jadala, Vijaya Chandra Raju, S.Hrushikesava Choudhury, Nabanita Adinarayna, S. Krishnaiah, Vv Jaya Rama Annamacharya University Dept. of Ece Andhra Pradesh Rajampet516216 India Sr University Department of Computer Science and Artificial Intelligence Telangana Warangal506371 India Koneru Lakshmaiah Education Foundation Department of Cse Guntur522502 India Assam Down Town University Faculty of Computer Technology Assam Guwahati India Department of CSE Visakhapatnam India

ISBN: (纸本)9798331523923

In a social context, optimizing information retrieval and user engagement is paramount. The challenges identified are the large volume of data, ensuring accuracy, and performance requires evolutionary structures. A novel approach is needed that combines machine learning algorithms with evolutionary optimization techniques to automatically recommend and refine tags on known segments of social media content. This safeguards against user attention diversion and potential risks to personal information. Activities encompass automated form feedback solicitation, user-reported content negativity flagged with custom tags in red, precise identification and processing, and accuracy validation. The process initiates with an initial set of tags generated through natural language processing (NLP) techniques, subsequently honed using a genetic algorithm. This iterative process refines tag selection based on content relevance, markedly augmenting tagging precision. Concurrently, a machine learning model analyzes user behavior patterns and preferences, fine-tuning tag recommendations based on historical data. The hybrid approach surpasses conventional tagging systems, exhibiting adaptability and learning capabilities that dynamically adjust tag recommendations to mirror emerging trends and individual user interests. Additionally, integrated user feedback mechanisms fine-tune the hybrid optimization model, enabling continuous enhancement and customization. Presently, web platform owners prioritize user engagement by eliminating misleading content segments. As the volume of social media content burgeons, this approach offers a scalable and effective solution to elevate content discoverability and relevance in the digital landscape. © 2025 IEEE.

关键词： Genetic algorithms

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A Survey on Fake News Detection Using Multivariate Feature Selection and Hybrid Deep Learning Approach 1

A Survey on Fake News Detection Using Multivariate Feature S...

引用

1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Dongre, Aditya Kumar Kalaiarasi, G. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350355611

Fake news detection has emerged as a critical challenge in the digital age, where misinformation spreads rapidly across social media and news platforms. This survey explores the efficacy of using multivariate feature selection combined with hybrid deep learning approaches to enhance the accuracy and robustness of fake news detection systems. The paper reviews various feature selection techniques, including chi-square, information gain, and correlation-based methods, that help in identifying the most relevant features for detecting fake news. These techniques are crucial in reducing dimensionality, improving processing time, and enhancing the performance of deep learning models. Furthermore, the study examines hybrid deep learning frameworks, which integrate multiple architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers to capture both linguistic and contextual information from news data. This survey provides a comprehensive analysis of recent advancements, highlighting key research contributions, datasets, and evaluation metrics used in fake news detection. The integration of multivariate feature selection with hybrid deep learning models offers a promising approach to addressing the complexities of fake news identification. Finally, the paper outlines the challenges and future research directions in improving model generalization, handling adversarial content, and adapting to evolving patterns of misinformation. © 2025 IEEE.

关键词： Adversarial machine learning

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Comparative Analysis of Infant Cry Classification using Machine Learning and Deep Learning Techniques 1

Comparative Analysis of Infant Cry Classification using Mach...

引用

1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Ramulu, Kothuri Parashu Jogannagari, Pranathi Reddy Indur Institute of Engineeing & Technology Department of Computer Science and Engineering Siddipet India University of Alabama at Birmingham Department of Computer Science Birmingham United States

ISBN: (纸本)9798331529246

Infant Cry Classification is the process of analyzing acoustic signals from an infant's cry for extracting relevant features to determine the cause of crying, and assess the infant's health condition. The process is typically divided into four main stages: pre-processing, feature extraction, feature fusion, and classification. In the feature extraction stage, spectrograms along with frequency transforms such as the wavelet Transform, Cosine Transform, and Fourier Transforms are employed to capture important characteristics of the cry. The feature fusion stage is critical as it integrates information from various scales and contexts obtained during feature extraction, allowing for a more comprehensive representation of the cry's features. In current times, both Machine Learning (ML) and Deep Learning (DL) techniques are employed for classification. The ML techniques predominantly utilized in this process are: Multi-Class Support Vector Machines (SVM), Hyperparameter-Optimized SVM, Kernel-based SVM, Ensemble Boosting methods, Random Forest classifiers, K-Nearest Neighbors (K-NN), and Bayesian Optimization. In the case of DL architectures, models such as Style and Latent Guided Adversarial Networks (SLGAN), Multi-Layer Perceptron (MLP), Convolutional Neural Network - Sparse Convolution Net (CNN-SCNet), 1D CNN, and Dual-CNN are frequently employed to ensure high accuracy. The performance of these classification algorithms is evaluated in terms of the following metrics: sensitivity, specificity, accuracy, precision, and F-measure. © 2025 IEEE.

关键词： Cosine transforms

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Harnessing AI for Agriculture: Plant Pest Detection on Web Application Using Deep Learning 10

Harnessing AI for Agriculture: Plant Pest Detection on Web A...

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作者： Nuankaew, Wongpanya S. Yangwattana, Yotsapron Kamwaree, Sasikan Sararat, Thapanapong Nuankaew, Pratya University of Phayao School of Information and Communicatin Technology Phayao56000 Thailand

ISBN: (纸本)9798331543273

This research develops a mobile web application for pest identification using deep learning to help farmers manage pest outbreaks. The system enables farmers to capture pest images with their mobile cameras and receive alerts and recommendations. The objectives include developing pest classification models, creating web applications, and assessing user satisfaction. YOLOv5 architecture was tested for accuracy, speed, and efficiency. Developed using Flask and Python with SQLite for data management, results showed YOLOv5m6 achieved the highest Recall value of 0.8281 in 4.007 hours, followed by YOLOv5m at 0.8234 in 3.622 hours, and YOLOv5l at 0.8261 in 19.424 hours. Despite similar Recall values, YOLOv5m6 was more efficient. The system demonstrates AI's potential to improve pest management, though challenges in real-time processing and image complexity remain. © 2025 IEEE.

关键词： Deep learning

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An Approach for Aspect Based Sentiment Analysis Using Multivariate Feature Extraction and Machine Learning 1

An Approach for Aspect Based Sentiment Analysis Using Multiv...

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1st international conference on AIML-Applications for Engineering and technology, ICAET 2025

作者： Patil, Priyanka Pravin Gupta, Sunil School of Computer & System Sciences JNU Jaipur India

ISBN: (纸本)9798350355611

Aspect-based sentiment analysis (ABSA) is a nuanced field within natural language processing that focuses on extracting sentiment information at a granular level, targeting specific aspects of entities within a text. This paper presents an innovative approach to ABSA by leveraging multivariate feature extraction techniques combined with advanced machine learning algorithms. Our method begins with a comprehensive pre-processing phase that includes tokenization, part-of-speech tagging, and dependency parsing to structure the textual data. We then employ multivariate feature extraction techniques, such as term frequency-inverse document frequency (TF-IDF), word embeddings, and syntactic dependency features, to capture the contextual and semantic richness of the text. These features are fed into machine learning models, including support vector machines (SVM), random forests, and deep learning architectures like recurrent neural networks (RNN) etc. We evaluate the performance of these models using a well-defined benchmark dataset, highlighting the effectiveness of combining multivariate features with machine learning in improving the accuracy of aspect-based sentiment classification. Our experimental results demonstrate that the proposed approach significantly outperforms traditional sentiment analysis methods, providing more precise and insightful sentiment evaluations at the aspect level. This advancement holds promising applications in fields such as customer feedback analysis, social media monitoring, and market research, where understanding specific sentiment nuances is crucial. © 2025 IEEE.

关键词： Semantics

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Research on the Transfer Effect of Traditional Painting Styles Empowered by Artificial Intelligence 24

Research on the Transfer Effect of Traditional Painting Styl...

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3rd international conference on Artificial Intelligence and Intelligent information processing, AIIIP 2024

作者： Chen, Cui Wang, Hongjuan School of New Media Beijing Institute of Graphic Communication Beijing China

ISBN: (纸本)9798400707308

With the vigorous development and technological advancement of artificial intelligence, style transfer technology driven by deep learning has become a hot topic in the research of artificial intelligence computer vision. This technology not only promotes the innovation pace of artificial intelligence, but also opens up a new cross disciplinary chapter by combining with traditional painting. Style transfer technology not only opens up a new path for the dissemination of traditional painting, but also promotes its deep practice of protection and innovation. This paper reviews the development of style transfer technology, and discusses the transfer effects of different style transfer models in different traditional painting styles in China through experiments, which provides a reference direction for the development of traditional painting in the field of style transfer, and also provides guidance and enlightenment for future practitioners in model selection. © 2024 Copyright held by the owner/author(s).

关键词： Traditional painting

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