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International Conference on Emerging Trends in Networks and computer Communications (ETNCC)

作者： Hassan Ali M. Hamza Amin Attique Ur Rehman Sabeen Javaid Tahir Muhammad Ali Azka Mir Department of Software Engineering University of Sialkot Sialkot Pakistan Department of Computer Science GULF University for Sciences and Technology Mubarak Al-Abdullah Kuwait

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

The increasing prevalence of stress among university students has raised concern about its impact on academic performance and overall well-being. This conference paper explores the applications of machine learning algorithms to predict and analyze the student's stress level. A comprehensive dataset encompassing various stress-related factors, including academic workload, social interactions, and lifestyle, was collected from Kaggle which consists of 1100 instances and 19 attributes. Our methodology places a premium on human connection, integration of qualitative data to refine the accuracy of stress predictions. By seamlessly merging the capability of machine learning with an empathetic understanding of the human experience, our approach strives to pave the way for a more holistic and personalized educational ecosystem. The insights derived from this study aspire to guide educators, administrators, and policy makers in crafting nurturing environments that empower students to excel both academically and emotionally. Embarking on a journey to unravel the myriad factors influencing students' stress, we consider academic pressures, social dynamics, and personal experiences. Harnessing machine learning algorithms such as decision trees, neural networks, we navigate expansive student populations. Acknowledging the nuanced nature of stress, our study integrates a compassionate approach with cutting-edge technology. The study employs state-of-the-art machine learning techniques using various classification algorithms, to build predictive models for assessing stress levels out of which random forest gives 97.45% accuracy.

关键词： Machine learning algorithms Accuracy Computational modeling Heuristic algorithms Anxiety disorders Education Human factors Predictive models Random forests Tuning

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Intelligent analysis for software data:research and applications

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Frontiers of Information Technology & Electronic engineering 2022年第5期23卷 661-663页

作者： Tao ZHANG Xiaobing SUN Zibin ZHENG Ge LI School of Computer Science and Engineering Macao University of Science and TechnologyMacaoChina School of Information Engineering Yangzhou UniversityYangzhou 225127China School of Software Engineering Sun Yat-sen UniversityZhuhai 519082China School of Computer Science Peking UniversityBeijing 100871China

Over the last few decades,software has been one of the primary drivers of economic growth in the *** life depends on reliable software;therefore,the software production process(i.e.,software design,development,testing,and maintenance)becomes one of the most important factors to ensure the quality of *** the production process,large amounts of software data(e.g.,source code,bug reports,logs,and user reviews)are generated.

关键词： software bug testing

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Emotion Based Signal Enhancement Through Multisensory Integration Using Machine Learning

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computers, Materials & Continua 2022年第6期71卷 5911-5931页

作者： Muhammad Adnan Khan Sagheer Abbas Ali Raza Faheem Khan T.Whangbo Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Progress in understanding multisensory integration in human have suggested researchers that the integration may result into the enhancement or depression of incoming *** is evident based on different psychological and behavioral experiments that stimuli coming from different perceptual modalities at the same time or from the same place,the signal having more strength under the influence of emotions effects the response *** research inmultisensory integration has not studied the effect of emotions despite its significance and natural influence in multisensory enhancement or ***,there is a need to integrate the emotional state of the agent with incoming stimuli for signal enhancement or *** this study,two different neural network-based learning algorithms have been employed to learn the impact of emotions on signal enhancement or *** was observed that the performance of a proposed system for multisensory integration increases when emotion features were present during enhancement or depression of multisensory signals.

关键词： Multisensory integration sensory enhancement depression emotions

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Rate of Convergence of an Over-Parametrized Convolutional Neural Network Image Classifier Learned by Gradient Descent

Rate of Convergence of an Over-Parametrized Convolutional Ne...

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IEEE International Symposium on Information Theory

作者： Michael Kohler Benjamin Walter Adam Krzyżak Fachbereich Mathematik Technische Universität Darmstadt Darmstadt Germany Department of Computer Science and Software Engineering Concordia University Montreal Quebec Canada

ISBN: (数字)9798350382846

ISBN: (纸本)9798350382853

Image classifiers based on over-parametrized deep convolutional neural networks with an average-pooling are proposed. The weights of the network are learned by gradient descent. We present the bound on the rate of convergence of the difference between the expected misclassification risk of the plug-in classifier and the Bayes risk. The obtained rate of convergence is independent of image dimension under appropriate constraints on the image distribution.

关键词： Transformers Planning Bayes methods Convolutional neural networks Convergence Information theory

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Network Coverage Improvement during Natural Disaster using Self-Organizing Maps

Network Coverage Improvement during Natural Disaster using S...

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Electrical, computer, Communications and Mechatronics engineering (ICECCME), International Conference on

作者： Nam Hoang Prathyusha Devabhakthini Raj Mani Shukla Suman Bhunia Department of Computer Science and Software Engineering Miami University Oxford Ohio Computing and Information Science Anglia Ruskin University Cambridge UK

This paper presents an emergency response management system to tackle the problem of the absence of network connectivity during the time of a natural disaster. Network connectivity is often enabled by the base stations on the ground. However, during the time of the disaster, the connectivity is disrupted due to the base station being damaged. During such scenarios, the Unmanned Aerial Vehicles (UAV) based stations could help in partially providing the network connectivity and help in the rescue operations. But, the UAVs need to be quickly deployed and placed at a suitable location based on the population coverage and base stations being impacted due to the disruptions. In this paper, we propose the Self Organizing Map (SOM) based optimal UAV deployment to enhance the network coverage, and increase the percentage of people having network access. In contrast to other Artificial Intelligence-based approaches, like Deep Neural Networks, our method does not require to be heavily trained using train and the test dataset.

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Hausdorff point convolution with geometric priors

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science China(Information sciences) 2021年第11期64卷 67-79页

作者： Liqiang LIN Pengdi HUANG Fuyou XUE Kai XU Daniel COHEN-OR Hui HUANG College of Computer Science and Software Engineering Shenzhen University School of Computer Science National University of Defense Technology

Developing point convolution for irregular point clouds to extract deep features remains challenging. Current methods evaluate the response by computing point set distances which account only for the spatial alignment between two point sets, but not quite for their underlying shapes. Without a shapeaware response, it is hard to characterize the 3 D geometry of a point cloud efficiently with a compact set of kernels. In this paper, we advocate the use of modified Hausdorff distance as a shape-aware distance measure for calculating point convolutional responses. The technique we present, coined Hausdorff point convolution(HPC), is shape-aware. We show that HPC constitutes a powerful point feature learning with a rather compact set of only four types of geometric priors as kernels. We further develop an HPC-based deep neural network(HPC-DNN). Task-specific learning can be achieved by tuning the network weights for combining the shortest distances between the input and the kernel point sets. We also realize hierarchical feature learning by designing a multi-kernel HPC for multi-scale feature encoding. Extensive experiments demonstrate that HPC-DNN outperforms strong point convolution baselines(e.g., KPConv), achieving 2.8%m Io U performance boost on S3 DIS and 1.5% on Semantic KITTI for semantic segmentation task.

关键词： point convolution Hausdorff distance geometric prior deep neural network

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Time-enhanced Dynamic Graph Network for Next POI Recommendation

Time-enhanced Dynamic Graph Network for Next POI Recommendat...

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Internet of Things and Smart Cities (ISITSC), International Symposium on

作者： Zhao Liu Wei Liu Junyang Chen School of Artificial Intelligence Sun Yat-sen University Zhuhai China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331505516

ISBN: (纸本)9798331505523

Predicting the next Point-of-Interest (POI) is crucial for location-based services. In this paper, we propose the Time-enhanced Sequence Prediction Model (TSPM) to improve the accuracy of next POI recommendations by incorporating temporal information and dynamic graph *** approach utilizes a Time-enhanced Sequence-based Dynamic Graph (TSDG) that captures both temporal transitions of POIs and sequential dependencies in user behavior. By embedding temporal information directly into the graph structure, TSPM effectively models user movements. We further enhance POI embeddings using knowledge graph techniques and Eigenmap to preserve the topological properties of the *** proposed model integrates these enriched embeddings into a Time-aware Recurrent Neural Network (TiRNN) to capture the influence of past check-ins across different time intervals. Experiments on real-world datasets demonstrate that TSPM significantly outperforms existing methods in prediction accuracy.

关键词： Adaptation models Accuracy Recurrent neural networks Smart cities Knowledge graphs Predictive models User experience Real-time systems Trajectory Internet of Things

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PhoGAD: Graph-based Anomaly Behavior Detection with Persistent Homology Optimization

arXiv

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arXiv 2024年

作者： Yuan, Ziqi Zhou, Haoyi Chen, Tianyu Li, Jianxin School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China School of Software Beihang University Beijing China

A multitude of toxic online behaviors, ranging from network attacks to anonymous traffic and spam, have severely disrupted the smooth operation of networks. Due to the inherent sender-receiver nature of network behaviors, graph-based frameworks are commonly used for detecting anomalous behaviors. However, in real-world scenarios, the boundary between normal and anomalous behaviors tends to be ambiguous. The local heterophily of graphs interferes with the detection, and existing methods based on nodes or edges introduce unwanted noise into representation results, thereby impacting the effectiveness of detection. To address these issues, we propose PhoGAD, a graph-based anomaly detection framework. PhoGAD leverages persistent homology optimization to clarify behavioral boundaries. Building upon this, the weights of adjacent edges are designed to mitigate the effects of local heterophily. Subsequently, to tackle the noise problem, we conduct a formal analysis and propose a disentangled representation-based explicit embedding method, ultimately achieving anomaly behavior detection. Experiments on intrusion, traffic, and spam datasets verify that PhoGAD has surpassed the performance of state-of-the-art (SOTA) frameworks in detection efficacy. Notably, PhoGAD demonstrates robust detection even with diminished anomaly proportions, highlighting its applicability to real-world scenarios. The analysis of persistent homology demonstrates its effectiveness in capturing the topological structure formed by normal edge features. Additionally, ablation experiments validate the effectiveness of the innovative mechanisms integrated within PhoGAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Anomaly detection

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AN ALGORITHM AND COMPUTATION TO VERIFY LEGENDRE’S CONJECTURE UP TO 3.33 · 1013

arXiv

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arXiv 2024年

作者： Sorenson, Jonathan Webster, Jonathan Computer Science and Software Engineering Department Butler University IndianapolisIN United States Mathematical Sciences Department Butler University IndianapolisIN United States

We state a general purpose algorithm for quickly finding primes in evenly divided sub-intervals. Legendre’s conjecture claims that for every positive integer n, there exists a prime between n2 and (n+1)2. Oppermann’s conjecture subsumes Legendre’s conjecture by claiming there are primes between n2 and n(n+1) and also between n(n+1) and (n+1)2. Using Cramér’s conjecture as the basis for a heuristic run-time analysis, we show that our algorithm can verify Oppermann’s conjecture, and hence also Legendre’s conjecture, for all n ≤ N in time O(N log N log log N) and space NO(1/ log log N). We implemented a parallel version of our algorithm and improved the empirical verification of Oppermann’s conjecture from the previous N = 2 · 109 up to N = 3.33 · 1013, so we were finding 27 digit primes. The computation ran for about half a year on four Intel Xeon Phi 7210 processors using a total of 256 *** Codes 11A41, 11Y16, 11Y55, 68Q25 Copyright © 2024, The Authors. All rights reserved.

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LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT Image Segmentation

LDE-UNet: A Novel Model for Rapid COVID-19 Diagnosis via CT ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： QiSong Wang JiaRou Wu YingZhuo Wang HuiLi Qu Shanling Nie Hai Yang Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Faculty of Engineering The University of Sydney Australia

The COVID-19 pandemic has had a profound impact on human society. It has highlighted the need for faster diagnostic methods. Research has shown that combining semantic segmentation with traditional medical approaches can significantly accelerate the process. To address this, leveraging COVID-19 CT images, our team designs a revolutionary semantic segmentation model called Level of Detail Enhancement U-Net (LDE-UNet), which shows the lesion area on CT images. By introducing the LDE block, the model has the unique advantage of overcoming the loss of data details during the downsampling process by emphasizing and transmitting details at the same level. Our SOTA model outperforms the second-best model by at least 0.7% in the most critical indicator precision. Compared with other models, LDE-UNet’s strong reliability determines its ability to be used in the medical field to accelerate the localization and division of lesion areas on CT images by professional doctors, thus completing patient diagnosis faster. In addition, we also propose a standardized method for processing medical images.

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