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Continuous Sub-prevalent Co-location Pattern Mining 1

4th International Conference on Spatial Data and Intelligence, SpatialDI 2023

作者： Wang, Qilong Chen, Hongmei Wang, Lizhen School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9783031329104

ISBN: (纸本)9783031329098

Spatial co-location pattern represents a subset of spatial features whose instances are frequently located together in space. Sub-prevalent co-location pattern mining discovers patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering spatiotemporal sub-prevalent co-location pattern is important to reveal the spatiotemporal interaction between spatial features and promote the application of patterns. However, the methods for mining spatiotemporal sub-prevalent co-location pattern measure the interestingness of patterns by the frequency of patterns in time slice set, and ignore the duration of patterns which is an important spatiotemproal information in patterns. Thus, this paper presents mining spatiotemporal sub-prevalent co-location pattern by considering the duration and the frequency of patterns. Specifically, a novel pattern, is proposed by defining the continuous sub-prevalent index. Then, an efficient algorithm is designed to mine the proposed patterns by utilizing the anti-monotonicity of continuous sub-prevalent index to prune unpromising patterns. Extensive experiments on synthetic and real datasets verify the practicability of the proposed patterns and the effectiveness of the proposed algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Location

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Efficient multi-target classification for bug priority and resolution time prediction

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Multimedia Tools and Applications 2024年 1-30页

作者： Madaraboina, Satya Narayana Sharma, Sahil Singh, Simranjit Kumar, Vijay Liverpool John Moores University Liverpool United Kingdom School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland Londonderry United Kingdom Department of Information Technology Dr. B R Ambedkar National Institute of Technology Punjab Jalandhar India

Bug triage teams allocate substantial resources to determine the priority and resolution time of reported defects, frequently hampered by a lack of specialized knowledge in the relevant subject. This work presents sophisticated multi-target classification models for the simultaneous prediction of problem priority and resolution time. We investigate transformation strategies, such as Classifier Chains, Binary Relevance, Label Power Sets, and Random k-Label Sets (RAKEL), over various machine learning and deep learning models utilising the Functional API. The models are trained using a newly launched dataset, the Spring JIRA Bug Dataset, available at IEEE ***/documents/spring-jira-bug-dataset, which includes bug summaries from the Jira bug tracking software. Model performance evaluation involves using metrics such as Hamming Loss, Accuracy, Weighted Precision, Weighted Recall, Weighted F1-Score, and MCC. The experimental findings indicate substantial enhancements in prediction accuracy and computing efficiency, showcasing the potential of these models to significantly improve the bug triage process by automating estimations of priority and resolution time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Efficient Inference of Markov Logic Network for Link Prediction 12

Efficient Inference of Markov Logic Network for Link Predict...

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Li, Zhongbin Duan, Liang Yue, Kun Yunnan University School of Information Science and Engineering Kunming China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Kunming China

ISBN: (纸本)9798350340181

Link prediction has achieved great success on ubiquitous graph-based applications, which usually contain multiple types of connections. The heterogeneity of networks introduces complexities in two aspects: representation of multiple types of links, and incorporation of domain knowledge. In this paper, we construct Markov logic network (MLN) to preserve he logical information of graphs for link prediction tasks. By using the graph neural network, we propose a novel inference method in MLNs to guarantee the scalability of link prediction. Experimental results on various datasets show that our proposed method outperforms the state-of-the-art competitors on precision and efficiency. © 2023 IEEE.

关键词： Graph neural networks

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Battery Health Prediction Using Deep Hybrid Learning 3

Battery Health Prediction Using Deep Hybrid Learning

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9798350322842

The type and condition of the road the vehicle travels;how long the battery is used for are just a few of the variables that affect the battery life of electric cars (EVs). These elements might affect the battery's lifespan by causing it to drain more quickly than usual. By understanding the various factors that impact battery life, work can be done to mitigate these issues and extend the useful life of the battery. To find the best circumstances for increasing the battery's longevity, this research paper is concentrated on examining the impacts of road conditions on battery life. The previous article classified routes with an accuracy of 83.56% based on battery health while arriving at the destination [1]. This article examines EV routed and categorises them on the basis of the number of brakes applied upon arrival at the destination. By doing so, it aims to develop recommendations for drivers on how to optimize their driving habits to extend the battery's lifespan. Observations indicate that bad routes generally involve a lot more brakes as compared to good routes. Vehicle speed has a negative correlation with heater power in a bad route and a positive correlation in a good route. Battery voltage and Battery state- of-charge have opposing correlations in both categories. In this research, a prediction of the route category was attempted by using Deep Hybrid Learning to integrate three pre-trained models., VGGI6, Resnet50 and VGG19 with two boosting algorithms:- XGBoost and AdaBoost. The accuracy of the six deep hybrid learning models in predicting the category of these routes are as follows:-0.87625, 0.84875, 0.80125, 0.7925, 0.8425 and 0.855. © 2023 IEEE.

关键词： Deep learning

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ScaDLES: Scalable Deep Learning over Streaming data at the Edge

ScaDLES: Scalable Deep Learning over Streaming data at the E...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Tyagi, Sahil Swany, Martin Computing and Engineering Indiana University Luddy School of Informatics Department of Intelligent Systems Engineering BloomingtonIN United States

ISBN: (纸本)9781665480451

Distributed deep learning (DDL) training systems are designed for cloud and data-center environments that assumes homogeneous compute resources, high network bandwidth, sufficient memory and storage, a s w ell a s independent and identically distributed (IID) data across all nodes. However, these assumptions don't necessarily apply on the edge, especially when training neural networks on streaming data in an online manner. computing on the edge suffers from both systems and statistical heterogeneity. systems heterogeneity is attributed to differences in compute resources and bandwidth specific to each device, while statistical heterogeneity comes from unbalanced and skewed data on the edge. Different streaming-rates among devices can be another source of heterogeneity when dealing with streaming data. If the streaming rate is lower than training batch-size, device needs to wait until enough samples have streamed in before performing a single iteration of stochastic gradient descent (SGD). Thus, low-volume streams act like stragglers slowing down devices with high-volume streams in synchronous training. On the other hand, data can accumulate quickly in the buffer if the streaming rate is too high and the devices can't train at line-rate. In this paper, we introduce ScaDLES to efficiently train on streaming data at the edge in an online fashion, while also addressing the challenges of limited bandwidth and training with non-IID data. We empirically show that ScaDLES converges up to 3.29× faster compared to conventional distributed SGD. © 2022 IEEE.

关键词： Digital storage

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Structural Entropy Based Graph Node Classification 12

Structural Entropy Based Graph Node Classification

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12th IEEE Global Conference on Consumer Electronics, GCCE 2023

作者： Zhang, Jun Yue, Kun School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9798350340181

Node classification is to predict the labels of the unlabeled nodes in a graph, which is useful for various applications of social network and biological information analysis. To measure the uncertainty of structural information, we propose the structural entropy theory based method for graph node classification. First, we calculate the structural entropy of different graph structures, since the smaller the structural entropy, the less the uncertainty of the structural relationship in the graph. Then, we use the minimal structural entropy to determine the uncertainty of graphical structures. Finally, the nodes with similar structural relationships in the graph are classified. Experimental results show that our method outperforms some state-of-the-art competitors. © 2023 IEEE.

关键词： Classification (of information)

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Music Recommendation based on Facial Expressions using Data Augmentation 3

Music Recommendation based on Facial Expressions using Data ...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Kapoor, Tanisha Ganguly, Arnaja Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

Emotion recognition from facial expressions plays a pivotal role in human-computer interaction, psychology, marketing, and various other domains. This research paper presents a comprehensive study and implementation of an automated emotion recognition system based on deep learning techniques. The project addresses the challenge of accurately classifying a wide range of emotions from facial images in real-time. Leveraging Convolutional Neural Networks (CNNs) and modern deep learning approaches, the system demonstrates high accuracy in identifying emotions, including happiness, sadness, anger, and more. This project used the image dataset FER-2013 for the mood prediction portion of the project, and Spotify music data to predict emotions for music recommendation. Both the datasets utilized are freely available on Kaggle. The proposed system can achieve an accuracy of 91.23% in predicting the users' emotion based on their facial expressions. © 2023 IEEE.

关键词： CNN Emotion recognition Facial expression analysis FER-2013 Music Recommendation

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Predicting the Impact of Road Conditions on Battery Health Via Transfer Learning 3

Predicting the Impact of Road Conditions on Battery Health V...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Nair, Rahul Anil Suresh, Dev Karan Rajeswari, D. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665462167

Battery life of EV (electric vehicles) depends on various factors. These factors include temperature of the battery, duration of use or the type and condition of the road traversed by the vehicle. This may cause the battery to discharge faster than it used to. Understanding what may cause the battery life to shorten can help us alleviate the given problem and can preserve the battery for longer time. This research focuses on the effects road conditions have on battery life to be able to recommend the favourable conditions to extend it. It proposes to find the routes taken by the vehicles and classifying them based on their battery health upon reaching the destination. In this paper, an attempt has been made to predict the category of the routes by employing Transfer learning via a pre-trained model VGG-16. Observations indicate that paths which have more turns and curves are generally worse for battery life as compared to straight and narrow paths. Another model which is an artificial neural network has been developed to predict the based on the average vehicle speed and the distance covered by the vehicle. The VGG-16 model predicts the data with which it is validated with an accuracy of 84.29% and the testing data with an accuracy of 83.56%. The artificial neural network model predicts the category of the data with an accuracy of 88.02%. © 2023 IEEE.

关键词： Deep learning

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Colour Correction and Detail Enhancement in Underwater Images using Hybrid Real-ESRGAN 3

Colour Correction and Detail Enhancement in Underwater Image...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Chandar, Vijay Nair, Vishnu G Rajeswari, D. SRM Institute of Science and Technology Kattankulathur School of Computing College of Engineering and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798350343632

The motive of underwater image enhancement is to improve and enhance the quality of photographs taken underwater. Various traditonal image processing techniques and deep learning models have been developed to work on this task. But most often the deep learning models and conventional methods of image processing work in silos, leading to subpar results. This research proposes a strategy that utilizes a combination of both approaches to solve this issue. Initially, traditional image processing techniques like colour correction and image stretching are used to enhance the colour of the images. The enhanced images are then fed into a custom Real-ESRGAN model that has been fine-tuned on an underwater image dataset to optimize performance in this context. This hybrid approach has the potential to outperform the models utilizing either traditional or deep learning methods in isolation by leveraging the strengths of both. In addition, batch processing is used in the pipeline to efficiently handle multiple images at the same time. This effectively addresses the scalability problem that arises frequently in image enhancement tasks. With eye-catching visuals, the proposed methodology achieves a Peak Signal-to-Noise Ratio (PSNR) of 23.66 and a Mean Squared Error (MSE) of 0.28, suggesting excellent image restoration and noise reduction. The qualitative assessments show that the improved image graphs have better colour, contrast, and clarity than the originals. © 2023 IEEE.

关键词： Batch Processing Image Enhancement Image Stretching MSI PSNR Real-ESRGAN

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An Input Space Modeling Method for Software Testing Considering Timing 23

An Input Space Modeling Method for Software Testing Consider...

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23rd IEEE International Conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Zhang, Bo J., Li Z., Fan Y., Wang School of Reliability and Systems Engineering Beihang University Beijing China Tianjin University Computer Science and Technology Division of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350359398

Specification based testing is a common method in software testing. Specification based testing is divided into behavior-based testing, such as finite state machine method;and input-based testing, such as random testing, combination testing, decision table testing, equivalence class testing, boundary value testing method and so on. At present, there are many modeling methods for behavior-based testing and test cases can be generated automatically through these models. However, for these input-based testing methods, test cases are generated manually by searching for parameters and parameter values, which is too inefficient. To solve this problem, we want to improve the efficiency of input-based testing methods by automatically generating test cases. Therefore, this paper presents a software input space model that can represent parameter values and constraint relationships as the basis of automatic generation of test cases. Some safety-critical software tests need to consider the input timing of parameters, so we also express the input timing of parameters in the input space model. Then we propose the process of modeling the input space, which is finally expressed in the form of a directed graph. The values of parameters and the input timing of parameters are expressed on the edges of the directed graph, and the constraints between parameters are described by the way of node splitting, and all valid test sequences are represented by the path traversing the graph. Finally, the proposed input space modeling method is applied to a real test project to demonstrate the feasibility of the method. © 2023 IEEE.

关键词： Software testing

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