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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Prabhu, R. Priya, M. Sangeetha Dayana, R. Suresh, M. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur Campus Chennai India Saveetha Engineering College Department of Computer Science and Engineering Thandalam Chennai India Jeppiaar Institute of Technology Department of Computer Science and Engineering Sriperumbudur Chennai India R.M.D. Engineering College Kavaraipettai Department of Mathematics Thiruvallur India

ISBN: (纸本)9798350361186

Slums (Informal Settlements) are the most dynamic areas whose geographical locations are inaccurate according to official statistics and maps because of the exponential expansion of urbanization strategies. As a result, a method has to be developed to distinguish between informal settlements and track their spatial patterns. This paper presents an integrated method for detecting slums from satellite images that employs homogeneous urban patches (HUP) and grey level co-occurrence matrix (GLCM)-based image segmentation. In order to extract meaningful spatial features, urban patches are segmented according to their homogeneity and GLCM analysis provides useful texture information about slum areas. The combination of both these GLCM and HUP features allows for the creation of a classification map that accurately identifies and maps slum areas. We utilized WorldView-2 (1.84m) satellite imagery of Madurai to test the effectiveness of proposed HUP approach. The validation process includes rigorous testing against ground truth data, with careful consideration given to the selection of training and testing datasets. The effectiveness of the proposed method is evaluated using metrics including overall accuracy, user accuracy, and producer accuracy Furthermore, the proposed algorithm outperformed existing GLCM-based approaches in terms of accuracy. © 2024 IEEE.

关键词： Image segmentation

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Enhancing Code Insights through Semantic Change Impact Evaluation

Enhancing Code Insights through Semantic Change Impact Evalu...

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International Conference on computing and Networking Technology (ICCNT)

作者： Chellu Vyshnavi Chukka Mishal Raj Gokul Gopakumar Guddeti Bindu Prasanna Meena Belwal Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In software development, system integrity is a measure of the impact code changes have on them. It is determined by the team’s comprehension. However, rapid evolution of change commits and interaction in complex code bases create problems. By evaluating the effect of reflection changes, this study contributes to the improvement of the comprehension of code, a critical success factor for quality and efficiency. This addresses issues by using semantic analysis, change detection, refactoring detection, and semantic change detection and diffing. Our approach documents the semantic differences between the original and changed source code and gives pragmatic conclusions on how to enhance code quality and maintainability. Specifically, we divide the code into AST files using simple python scripts for semantic analysis and compare them for differences to find implications. It executes the method through the focus on development workflows and major projects as it includes the past information while still simplifying argument parsing and file processing. Based on our study findings, a better understanding of code can help developers to improve their decision-making capacity and protect the performance during the enhancement activity of the program.

关键词： Codes Source coding Semantics Decision making Syntactics Reflection Software measurement Software development management Python Pragmatics

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Answer Prediction for Questions from Tamil and Hindi Passages

Answer Prediction for Questions from Tamil and Hindi Passage...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Namasivayam, Ram Vignesh Rajan, Manjusha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

Predicting answers for questions based on passages from Tamil and Hindi is a Question Answering (QA) task. It helps us to elicit information that is relevant to the queries from the passage. Answers are predicted by identifying the answer span from the context based on the questions. In this paper, four different approaches are used to solve the QA task for Tamil and Hindi Passages. Zero-shot transfer of QA models has been tested on the QA datasets available for Hindi and Tamil. Comparative analysis between Multilingual models that are fine-tuned using Tamil and Hindi QA datasets is carried out. In addition to that, the performance has also been evaluated after increasing the Hindi data points. As there are very few QA datasets available in Indic Languages, especially Tamil, a possible solution to overcome this challenge was tried and tested using the creation of a QA dataset for Tamil using Indic Language Translation models. Multilingual models were then used for fine-tuning with the translated Tamil QA dataset generated using Language translation and the results have been discussed. The predicted answers will be evaluated using Jaccard Similarity, Exact match (EM), and F1 Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Translation (languages)

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Deep Adaptive Algorithms for Local Urban Traffic Control: Deep Reinforcement Learning with DQN

Deep Adaptive Algorithms for Local Urban Traffic Control: De...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： P V R Subba Rao Venkata Ramana Murthy Polisetty K Krishna Jayanth N V Sai Manoj Vankadari Mohith R Prasanna Kumar Computer Science and Engineering Amrita Vishwa Vidyapeetham School of Computing Chennai

The paper uses Deep Reinforcement Learning (DRL) to traffic signal regulation, solving urban traffic congestion. Our research paper demonstrates the simulation of intricate traffic conditions with microscopic accuracy, utilizing the Simulation of Urban Mobility (SUMO) platform. As the foundation for DRL, a state representation and action space are established. The learning process is guided by the Deep Q-Network (DQN) architecture, precisely set hyperparameters, and epsilon-greedy exploration. To improve stability, training data is gathered and stored in an experience replay buffer. The DQN-based technology is built into the SUMO simulation and allows for dynamic traffic signal modifications. Updates to the target network on a regular basis increase stability. Metrics for evaluation include average waiting time, traffic flow efficiency, and congestion levels, which compare favorably to baseline approaches. In conclusion, this study shows the usefulness of DRL in optimizing traffic signal control, providing viable strategies to minimize urban traffic congestion and increase transportation efficiency.

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Fourier single-pixel spectral imaging via local low-rank tensor nuclear norm and deep tensor priors

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Optics Letters 2025年第4期50卷 1281-1284页

作者： Tang, Zixin Hu, Yexun Duo, Chen Yang, Guowei Jiang, Taixiang Guo, Daqing School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Sichuan Province Chengdu China Kash Institute of Electronics and Information Industry Kash China Engineering Research Center of Intelligent Finance Ministry of Education Southwestern University of Finance and Economics China Chongqing University of Education School of Artificial Intelligence China School of Life Science and Technology University of Electronic Science and Technology of China Sichuan Province Chengdu China Chongqing University Industrial Technology Research Institute Department of Science and Technology Development China

The imaging quality of single-pixel spectral imaging (SSI) methods is poor at a low sampling ratio (SR). To tackle this problem, a new Fourier single-pixel spectral imaging (FSSI) technique is proposed. Firstly, we introduce the low-rank tensor nuclear norm (TNN) to characterize the correlation between spectral images. Compared with the conventional method, TNN reconstructs image details better but brings image artifacts simultaneously. Therefore, local low-rank TNN (LTNN) constraint is proposed to ameliorate global ones and to reduce the distortion caused by TNN and low SR. Secondly, to make full use of the spectral information, the proposed constraint is used as the coarse prior, and the deep tensor prior (DTP) is introduced as the fine one to construct the joint priors. Different from the single prior, the joint method can make the two priors benefit and improve each other and further enhance the imaging quality. Finally, an efficient and high-quality SSI technique is achieved by deducing the closed-form solution algorithm. Experimental results show that our method significantly improves the quality of FSSI as much as 7–10 dB when compared to 3DTV at the SR of 5%. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Tensors

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Optimized Reading for ADHD and Dyslexia Users In VR

Optimized Reading for ADHD and Dyslexia Users In VR

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2023 Congress in Computer science, Computer engineering, and Applied computing, CSCE 2023

作者： Hoang, Kyler Deligiannidis, Leonidas School of Engineering Wentworth Institute of Technology BostonMA02115 United States School of Computing and Data Science Wentworth Institute of Technology BostonMA02115 United States

ISBN: (纸本)9798350327595

To combat the rising population of ADHD and dyslexic patients, current advancements in Unity and Oculus Integration are investigated and employed to design a user-friendly reading experience. Unlike physical novels that lack visual aid, our prototype ensures a distraction-free and minimalistic VR environment. It utilizes *** speech recognition to aid ADHD and dyslexic patients in mechanical and comprehensive reading. Users can also change the background color and music to enhance their facilitated experience. This approach allows the user to constantly focus on the text without averting their gaze. We believe that further development of this prototype can create a powerful tool that redefines reading E-books. © 2023 IEEE.

关键词： ADHD Dyslexia Virtual Reality

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Quantifying the Impact of Non-Code Text Block on Java Code Quality: Multi-Model Framework

Quantifying the Impact of Non-Code Text Block on Java Code Q...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sriharsha VVS Pranav B Nair Akarsh S.V Nakka Narmada Peeta Basa Pati Dept. of Computer Science & Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Non-code elements in programming, such as comments, documentation, and whitespace, play a crucial role in improving code readability, maintainability, and collaboration. However, their inclusion introduces overhead affecting code processing tools such as compilers and interpreters and static analyzers. In essence, this study investigates the impact of non-code blocks on the performance metrics of time complexity, space complexity, and execution time. A regression model was used to analyze the proportion of non-code elements in Java code and their effect on the performance metrics. The impact is quantified with the aim of providing empirical insights into how the density of non-code affects performance. Such findings are intended to help Java developers make decisions regarding code structuring and documentation while balancing aspects of maintainability and efficiency. The model's performance is validated by achieving an R2 score of 0.67, which demonstrates an improvement over previous methods and highlights its potential for real-world educational applications.

关键词： Dimensionality reduction Java Codes Reviews Transfer learning Documentation Robustness Integrated circuit modeling Time complexity Testing

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Systematic Literature Review on Sentiment Analysis in Airline Industry

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SN Computer science 2025年第1期6卷 1-26页

作者： Hasib, Khan Md Naseem, Usman Keya, Ashfia Jannat Maitra, Shithi Mithu, Kamruzzaman Alam, Md. Golam Rabiul Department of Computer Science and Engineering Brac University Mohakhali Dhaka 1212 Bangladesh Department of Computer Science and Engineering Bangladesh University of Business and Technology Mirpur Dhaka 1216 Bangladesh School of Computing Macquarie University Sydney 2109 NSW Australia Unilever BD Ltd. Shanta Forum Dhaka 1208 Bangladesh Department of Computer Science and Engineering United International University United City Dhaka 1212 Bangladesh

The significance of sentiment analysis lies in its ability to identify key areas of customer satisfaction and dissatisfaction, revealing patterns that can guide enhancements in airline services and strategic decision-making. Additionally, the identification of topics within airline passengers’ opinions across various flight classes is crucial, achieved through topic modeling. By analyzing these opinions, we can discern passengers’ priorities and expectations, which are essential for delivering superior service. This survey converges the sparse implications and provides a dense, comprehensive understanding of the state of the art through a Systematic Literature Review (SLR). Our SLR breaks down the research process into three distinct phases: Planning (defining research questions and protocol), Conducting (identifying and assessing relevant studies), and Document Review (synthesizing findings). We analyze 60 papers on sentiment analysis and topic modeling to examine the application and effectiveness of machine learning and deep learning methods in the airline industry. Our study highlights critical tasks in airline sentiment analysis, including multilingual sentiment analysis, and provides an overview of popular datasets, their fundamental properties, and the machine learning and deep learning models applied to them. The existing literature is critically examined to uncover both strengths and weaknesses, yielding additional insights into the prevailing research paradigms. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Airline industries Customer feedback analysis Machine learning Sentiment analysis Systematic literature review Topic modeling

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Glaucoma Insights: a Visual Comparative Analysis of Fundus Images Across Diverse Datasets

Glaucoma Insights: a Visual Comparative Analysis of Fundus I...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Chunduri Yoshitha G Vishwa Prakashini Neha S Aiswariya Milan K Dept. of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Glaucoma is a major global health issue that can lead to irreversible vision loss if untreated. Early diagnosis and continuous monitoring are crucial to mitigating its impacts. Fundus images are necessary to evaluate, diagnose glaucoma and offer an in-depth perception of retinal structures and features indicative of the condition. The project aims to compare the features of fundus images from three widely recognized datasets, they are REFUGE, ODIR, and DRONIS-DB focusing on analyzing critical metrics such as pixel intensity, brightness, image channel analysis, edge mean, and saturation means. Advanced visualizations are employed to ensure a comprehensive analysis of the features. The main objective of the work is to provide a structured data-driven approach for feature extraction and visualization, enhancing our understanding. This project contributes to advancing diagnostic precision, supporting clinical decision-making, and enabling researchers to explore innovative solutions in ophthalmology by bridging the gap between medical imaging data and actionable insights.

关键词： Glaucoma Visualization Decision making Data visualization Feature extraction Smart systems Retina Ophthalmology Biomedical imaging Resistance heating

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Super-Resolution of Diffusion-Weighted Images via TDI-Conditioned Diffusion Model 15th

Super-Resolution of Diffusion-Weighted Images via TDI-Cond...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ma, Jiquan Teng, Yujun Chen, Geng Jiang, Haotian Zhang, Kai Liu, Feihong Rekik, Islem Shen, Dinggang School of Computer Science and Technology Heilongjiang University Harbin China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Biomedical Engineering and State Key Laboratory of Advanced Medical Materials and Devices ShanghaiTech University Shanghai China School of Information Science and Technology Northwest University Xi’an China BASIRA Lab Imperial-X and Department of Computing Imperial College London London United Kingdom

ISBN: (纸本)9783031869198

Diffusion-Weighted Imaging (DWI) is a significant technique for studying white matter. However, it suffers from low-resolution obstacles in clinical settings. Post-acquisition Super-Resolution (SR) can enhance the resolution of DWIs and has gained increasing research interest in recent years. An advanced generative model, the Diffusion Model (DM), exhibits particularly promising performance in image SR. However, effective conditions are required to bootstrap the DM for DWI SR. To this end, we proposed the first DM-based DWI SR model with two effective conditions based on low-solution DWIs and Track Density Imaging (TDI) maps, which possess rich high-resolution prior knowledge Additionally, we consider another condition based on features from low-resolution DWIs. These two conditions are integrated into our model, which comprises three components: DWI Resolution Enhancer (DRE), DWI Feature Extractor (DFE), and TDI Feature Extractor (TFE). DRE combines low-resolution DWI features from DFE with TDI features from TFE to progressively generate high-resolution DWIs. We performed extensive experiments on DWIs of normal subjects from human connectome projects and patients with Parkinson’s disease. The results demonstrate that our model outperforms existing DWI SR models, both qualitatively and quantitatively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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