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检索条件"机构=Data Science Program Computer Science Department School of Computer Science"

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AutoEDA: Iterative data Focusing and Exploratory Analysis Based on Attribute Frequency

AutoEDA: Iterative Data Focusing and Exploratory Analysis Ba...

引用

IEEE International Conference on Systems, Man and Cybernetics

作者： Tong Wu Song Wang Xin Peng Department of Computer Science School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The paper proposes an automated data exploration and analysis method based on Attribute Frequency Statistical Feature Ratio (AFSFR). It integrates AutoVis and data Preprocessing Methods to design and develop AutoEDA-Segment. Addressing the Concentrate on Field Sequences (CFS) problem in data exploration and analysis, this study employs various classification models and combines AFSFR with field type and the Elbow Inflection Point (EIP) of index features to design a field type identification and field value assessment method. For evaluating the effectiveness of focus analysis, the approach provides clustering visualization effects and an analysis scheme based on Field Type Search Tree (FST) and cluster comparison profiles, using a custom CFS approach. Additionally, to enhance the value of focused subset analysis data, the approach introduces a Parallel Coordinates-Based data Filter (PCF), forming an EDA feedback loop to achieve Iterative Exploratory data Analysis for User-inferred Cognition (IEDA-UC). Finally, we engaged graduate students with varying levels of experience in visualization research for collaboration and discussion, validating the effectiveness and feasibility of the approach using structured data from Kaggle.

关键词： Feedback loop Design methodology data preprocessing data visualization Focusing Filtering algorithms Iterative algorithms data models Indexes Elbow

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IoT-Enabled WBAN and Machine Learning for Emotion Recognition 4

IoT-Enabled WBAN and Machine Learning for Emotion Recognitio...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Mathur, Hritesh Panwar, Harsh Karki, Rahul Jangra, Vivek Yadav, Ramnaresh Kapoor, Anjali Amity University Department of Information Technology Noida India School of Computer Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350387490

All industries, including computer science and health care and customer service are in dire need of efficient recognition of human feelings. This work is a script for a novel approach to affect recognition which involves both vocal emotion analysis and face emotion detection. Voice sentiment analysis project used machine learning technique to identify the prospects of emotional content in voice data. The model also assists in the classification of emotions with reference to the speech emotional states based on added features of MFCC and chroma. On the other hand, face emotion recognition project by applying deep learning algorithms identifies emotions by face expressions. With the help of convolutional, neural networks (CNNs) determining the happiness, sadness, and anger it analyses the facial features correctly. Combining all of these results in a coherent system that allows for the recognition of feelings through voice and or facial signals. Consequently, as demonstrated in the experimental results provided hereinafter, the accuracy of the presented system in correctly identifying human emotions is significantly higher than when using single models on their own. Therefore this combination creates space for other uses such as healthcare marketing among other fields hence improving user experience while at the same time providing unforeseen analysis of human conduct during an interaction with intelligent devices. © 2024 IEEE.

关键词： Convolutional neural networks

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Progressive Stepwise Diffusion Model with Dual Decoders for Semi-Supervised Medical Image Segmentation

Progressive Stepwise Diffusion Model with Dual Decoders for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Xiaolin Lin, Jingchun Chen, Jiacheng Ma, Xiao Chen, Bingzhi Lu, Guangming South China Normal University School of Software Foshan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798350386226

Semi-supervised medical image segmentation tasks aim to harness the potential of vast amounts of unlabeled data using a limited amount of annotated data. Denoising Diffusion Probabilistic Models, which have achieved significant success in image generation, are gradually being explored for their potential in semantic image segmentation. However, their application in semi-supervised medical image segmentation is still in its early stages. Initially, due to the high randomness of diffusion models, the pseudo-labels generated during the early training phase may mislead the processing of unlabeled data. Additionally, the use of fixed-time steps for random sampling during training limits the ability of the model to learn effective denoising functions at an early stage. To address these issues, we propose an innovative framework named Progressive Stepwise Diffusion Network with Dual Decoders (PSDD) for semi-supervised medical image segmentation. This framework incorporates an additional normal decoder into the denoising diffusion encoder-decoder structure to provide more accurate labels and employs a Progressive Incremental Step strategy to gradually train the model for longer generation processes. Evaluated on two 2D colon polyp segmentation datasets and a 3D Left Atrium dataset, the experimental results demonstrate significant performance improvements over current advanced methods, thereby validating the effectiveness and potential of this framework in handling complex semi-supervised learning scenarios. © 2024 IEEE.

关键词： Semantic Segmentation

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Commonsense for Zero-Shot Natural Language Video Localization 38

Commonsense for Zero-Shot Natural Language Video Localizatio...

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38th AAAI Conference on Artificial Intelligence, AAAI 2024

作者： Holla, Meghana Lourentzou, Ismini Department of Computer Science Virginia Tech United States School of Information Sciences University of Illinois Urbana-Champaign United States

ISBN: (纸本)1577358872

Zero-shot Natural Language-Video Localization (NLVL) methods have exhibited promising results in training NLVL models exclusively with raw video data by dynamically generating video segments and pseudo-query annotations. However, existing pseudo-queries often lack grounding in the source video, resulting in unstructured and disjointed content. In this paper, we investigate the effectiveness of commonsense reasoning in zero-shot NLVL. Specifically, we present CORONET, a zero-shot NLVL framework that leverages commonsense to bridge the gap between videos and generated pseudo-queries via a commonsense enhancement module. CORONET employs Graph Convolution Networks (GCN) to encode commonsense information extracted from a knowledge graph, conditioned on the video, and cross-attention mechanisms to enhance the encoded video and pseudo-query representations prior to localization. Through empirical evaluations on two benchmark datasets, we demonstrate that CORONET surpasses both zero-shot and weakly supervised baselines, achieving improvements up to 32.13% across various recall thresholds and up to 6.33% in mIoU. These results underscore the significance of leveraging commonsense reasoning for zero-shot NLVL. Copyright © 2024, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Knowledge graph

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Predicting Land Values: A Socio-Economic Analysis using Machine Learning 9

Predicting Land Values: A Socio-Economic Analysis using Mach...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Kanchana, R. Fernandez, F. Mary Harin Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350377972

This research study explores the dynamics of land value predictions in Hyderabad's Ranga Reddy district, focusing on the interplay of various socio-economic factors and urban development trends. As one of the fastest-growing regions in India, understanding the drivers of land value fluctuations in Ranga Reddy district is essential for stakeholders, policymakers, and investors. Drawing upon principles of urban economics and real estate analysis, the research considers factors such as economic growth, infrastructure development, government policies, urbanization trends, market demand and supply dynamics, and locationspecific attributes. Through a comprehensive analysis of these factors, the study aims to provide insights into the future trajectory of land values in Ranga Reddy district, aiding in informed decision-making for real estate investments, urban planning initiatives, and policy formulation. By synthesizing empirical data, expert opinions, and theoretical frameworks, the research contributes to the broader understanding of land value dynamics in rapidly urbanizing regions, offering valuable implications for sustainable development and economic growth strategies. © 2024 IEEE.

关键词： Adversarial machine learning

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Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehensive Study using XGBoost Classifier and Feature Selection Techniques 2

Analyzing the Impact of COVID-19 on Liver Cancer: A Comprehe...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Nair, Rekha R. Babu, Tina Kishore, S. Vilashini, V. Department of Computer Science and Engineering Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte University Karnataka India Department of Computer Applications School of Engineering Dayananda Sagar University Karnataka Bengaluru India

Healthcare systems around the world have faced challenges due to the Coronavirus disease (COVID-19) epidemic, which has taken resources and attention away from long-term diseases like liver cancer. To identify the effect of COVID-19 on liver cancer, proposed an effective research work with Extreme Gradient Boosting (XGBoost) classifier. The identification of the most suitable feature is executed through the utilization of SelectKBest, Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) techniques. A comparative analysis is carried out to ascertain the more precise feature selection, employing Decision Tree, K-Nearest Neighbors (KNN), CatBoost, LightGBM, and XGBoost algorithms. Further, the proposed work undertakes an analysis of the impact of COVID-19 on liver cancer, encompassing both the pandemic and pre-pandemic periods. In comparison to other feature selection techniques and models, SelectKBest feature selection with Xgboost classifier provided an accuracy of 0.92. The proposed research work provides valuable insights to healthcare professionals and policy makers, providing a better understanding of the challenges faced by liver cancer patients during the pandemic. By meticulously exploring feature selection techniques and employing advanced machine learning models, this study quantifiably demonstrates the effectiveness of the proposed methodology in addressing the impact of COVID-19 on liver cancer, contributing to a more comprehensive understanding of this critical issue. © 2024 Elsevier B.V.. All rights reserved.

关键词： Principal component analysis

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On the Deployment and Operation of Correlated data-Intensive vNF-SCs in Inter-DC EONs 32

On the Deployment and Operation of Correlated Data-Intensive...

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32nd Wireless and Optical Communications Conference, WOCC 2023

作者： Zhu, Zuqing Zhang, Liang Jabbari, Bijan School of Information Science and Technology University of Science and Technology of China China George Mason University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350337150

In this work, we study the problem of deploying and operating correlated data-intensive vNF-SCs in inter-datacenter elastic optical networks. Requiring for a set of correlated data-intensive vNF-SCs, the service completion time (SCT) of a network service is related to the maximum branch completion time of those vNF-SC branches, making the correlation awareness of importance for the problem optimization. Being aware of it, we propose a dynamic programming based optimization scheme for the deployment and operation of single vNF-SC branch and two correlation-aware service provisioning algorithms to minimize the average SCT of network services. Simulation results verified that the proposed algorithms can effectively reduce the average SCT of network services compared with a benchmark algorithm that ignores the importance of correlation awareness. © 2023 IEEE.

关键词： Dynamic programming

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Important and Difficult Topics in CS2: An Expert Consensus via Delphi Study

Important and Difficult Topics in CS2: An Expert Consensus v...

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Wittie, Lea Kurdia, Anastasia Huggard, Meriel Nguyen, Nguyen Department of Computer Science Bucknell University LewisburgPA17837 United States Department of Computer Science Tulane University New OrleansLA70118 United States School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Bucknell University LewisburgPA17837 United States

Almost every computer science program contains two semester-long introductory courses, usually named computer science 1 (CS1) and computer science 2 (CS2). They have been a mandatory element of the ACM Computing Curriculum for nearly fifty years and are likely to remain so for many years to come. While there seems to be a broad agreement on the key elements of CS1, the curriculum for CS2 can vary significantly between institutions. What material should in fact be included in CS2? Ideally, an educator would want to cover the topics that students need to master in order to successfully learn other topics further in the course and in the subsequent courses in the curriculum (important topics). They would also want to concentrate on topics that students are likely to struggle with and take a longer time to understand (difficult topics). This paper details the process and results of a multi-year international study that examined the topics of difficulty and importance in CS2 using Delphi method (an iterative process for reaching consensus among a group of subject experts that allows the participants to reconsider their opinions based on the anonymized responses of the other experts in the group provided at preceding iterations). We present four topic sets aiming to inform the choice of topics for designing a CS2 course or exam. The first set contains the topics selected based on importance and indicates what topics should be included in a CS2 course or its textbook. The second and third sets of topics are based on both importance and difficulty and as such would be a guide for the creation of exams and concept inventories. The fourth set contains the topics that none of the faculty experts marked as either important or difficult, making them candidates for delegation to a different course, exclusion, or leaving them for self-study. We also provide a comparison with other published topic sets for CS2. © American Society for Engineering Education, 2023.

关键词： Curricula

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Efficient Clustered Mean Forecasting for Time Series with Head-Based Aggregation 5th

Efficient Clustered Mean Forecasting for Time Series with He...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Anand, Piyush Ramesh, T. Agarwal, Trapty Preethi, D. Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Luknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Green Clustered mean Forecasting (ECMF) for Time series is a novel forecasting approach that is capable of efficaciously aggregating the underlying additives of a time collection through combining the top-primarily based Aggregation (HBA) technique alongside the mean Forecasting (MF) method. ECMF may be utilized to detect and become aware of companies of time collection intently associated with every other, therefore taking into account extra correct and reliable forecasts. By way of counting on HBA to discover any patterns in the information and then imposing MF to generate forecasts from the ones styles, ECMF can offer more accurate and reliable forecasts than the prevailing strategies. Moreover, this technique is computationally green, due to the hierarchical shape of the data, which reduces the quantity of forecasting experiments wanted. The ensuing forecasts are also easier to examine, as they may be generated from a standard head-based totally version. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Investigating Time Series Clustering Algorithms for Hyper Spectral Image Analysis 5th

Investigating Time Series Clustering Algorithms for Hyper Sp...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Mahalakshmi, R. Agarwal, Trapty Kudari, Jayashree M. Mehra, Ritika School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Time collection clustering algorithms (TSCAs) are of increasing significance within the evaluation and interpretation of hyper spectral image (HSI) facts. TSCAs are being used to become aware of spatiotemporal styles of spectral characteristics in HSI records by identifying areas of interest (ROIs) which can be associated with physical techniques and mapping the ones ROIs to clusters. One-of-a-kind clustering algorithms may be used to analyze HSI statistics and tease out diffused spatiotemporal modifications. This paper investigates the software of two exceptional TSCAs, particularly okay-method clustering and Self-Organizing Maps (SOMs) to HSI *** SOM set of rules changed into additionally capable of phase the hyperspectral facts into meaningful clusters primarily based on their spectral traits. The accuracy of the SOM results was also assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self organizing maps

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