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Default Prediction on Commercial Credit Big data Using Graph-based Variable Clustering 17

Default Prediction on Commercial Credit Big Data Using Graph...

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17th IEEE international conference on Semantic computing (ICSC)

作者： Boyapati, Mallika Aygun, Ramazan Kennesaw State Univ Sch Data Sci & Analyt Kennesaw GA 30144 USA Kennesaw State Univ Dept Comp Sci Kennesaw GA 30144 USA

ISBN: (纸本)9781665482639

Credit-lending organizations have resorted to the use of machine learning (ML) algorithms in the recent past to predict the probability of the default of a business. Explainability of the decisions made by the traditional statistical algorithms like Logit models brings transparency to every stakeholder involved in the process. On the other hand, machine learning models like XGBoost and Neural Nets have achieved better accuracy scores, but their decisions are not easily comprehensible. In this paper, we propose a graph based variable clustering (GVC) method as a filter based approach to select prominent features while retaining as much variance as possible. Our experiments show that our GVC approach is not only almost 40 times faster than the existing variable clustering methods but retains retains 5% more variance than the existing *** feature set from GVC approach has performed better with an increase of 6% accuracy on an average. The predictions on the feature set from GVC were 98% accurate using XGBoost algorithm.

关键词： Graph Variable Clustering Credit Default Prediction

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A Review of Multi-Agent Path Finding algorithms 11

A Review of Multi-Agent Path Finding Algorithms

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11th international conference on Information Systems and computing Technology, ISCTech 2023

作者： Wu, Mengdie Yan, Wenyao Hasan, Haslin Jamaluddin, Robiatul A'Dawiah Xi'an Innovation College of Yan'an University School of Data Science and Engineering Xi'an China Infrastructure University Kuala Lumpur Faculty of Creative Media and Innovative Technology De Centrum City Malaysia Infrastructure University Kuala Lumpur Faculty of Business Information and Human Sciences De Centrum City Malaysia

ISBN: (纸本)9798350342406

In the context of Industry 4.0, Autonomous Guided Vehicles (AGVs) and other types of Multi-Agent System (MAS) have been widely used in large-scale warehouses and unmanned factories. Coordination of AGVs in shared space is drawing increasing attention of researchers, especially in the field of Multi-Agent Path Finding (MAPF). The MAPF problem has been studied a lot, and there are many kinds of solutions. Not all kinds of MAPF solvers can be applied in the field of AGV. Few literature focused on which MAPF solutions are suitable for AGV navigation. This review categorized existing MAPF algorithms into five branches, Conflict-Based Search (CBS) algorithms, Rule-Based algorithms, Priority-Based algorithms, Numerical Optimization-based algorithms and Learning-based algorithms. Furthermore, we evaluate these algorithms in terms of their optimality, lifelong property, scalability and computational cost. Finally, we analyzed which MAPF solvers are suitable for AGV navigation. © 2023 IEEE.

关键词： Optimization

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2023 10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

2023 10th IEEE Uttar Pradesh Section International Conferenc...

引用

10th IEEE Uttar Pradesh Section international conference on Electrical, Electronics and Computer Engineering, UPCON 2023

ISBN: (纸本)9798350382471

The proceedings contain 329 papers. The topics discussed include: systematic cognitive computing framework application using medical information processing;comparative analysis of epileptic seizure prediction: exploring diverse pre-processing techniques and machine learning models;analyzing textual data for mental health assessment: natural language processing for depression and anxiety;assessing water desalination in the United Arab Emirates: an overview;selection of cutting fluid for sustainable design for manufacturing;ESDPCOS: effectiveness of combined CNN and handcrafted features for ovarian cyst detection in PCOS patients using ultrasound images;analyzing the effect of edge computing on real-time data processing and latency reduction;text recognition in images and converting recognized text to speech – image processing;and comparative study of proposed linear regression algorithm to Scikit-Learn algorithm.

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Joint Task Scheduling and Container Image Caching in Edge computing 19

Joint Task Scheduling and Container Image Caching in Edge Co...

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19th international conference on Mobility, Sensing and Networking (MSN)

作者： Mou, Fangyi Tang, Zhiging Lou, Jiong Guo, Jianxiong Wang, Wenhua Wang, Tian BNU HKBU United Int Coll Guangdong Key Lab AI & Multi Modal Data Proc Xiangzhou Zhuhai Peoples R China Beijing Normal Univ Inst Artificial Intelligence & Future Networks Beijing Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Yunnan Key Lab Software Engn Kunming Yunnan Peoples R China

ISBN: (纸本)9798350358261;9798350358278

In Edge computing (EC), containers have been increasingly used to deploy applications to provide mobile users services. Each container must run based on a container image file that exists locally. However, it has been conspicuously neglected by existing work that effective task scheduling combined with dynamic container image caching is a promising way to reduce the container image download time with the limited bandwidth resource of edge nodes. To fill in such gaps, in this paper, we propose novel joint Task Scheduling and Image Caching (TSIC) algorithms, specifically: 1) We consider the joint task scheduling and image caching problem and formulate it as a Markov Decision Process (MDP), taking the communication delay, waiting delay, and computation delay into consideration;2) To solve the MDP problem, a TSIC algorithm based on deep reinforcement learning is proposed with the customized state and action spaces and combined with an adaptive caching update algorithm. 3) A real container system is implemented to validate our algorithms. The experiments show that our strategy outperforms the existing baseline approaches by 23% and 35% on average in terms of total delay and waiting delay, respectively.

关键词： Task scheduling image caching container edge computing

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Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare 43

Multi-Site Clinical Federated Learning using Recursive and A...

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43rd IEEE international conference on Distributed computing Systems (ICDCS)

作者： Yun, Won Joon Kim, Samuel Kim, Joongheon Korea Univ Sch Elect Engn Seoul South Korea Cipherome Inc San Jose CA USA

ISBN: (纸本)9798350339864

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach.

关键词： Federated Learning Clinical data Language Model

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Research on intelligent decision-making mechanism of the elderly care system based on fuzzy neural network 8

Research on intelligent decision-making mechanism of the eld...

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8th international conference on Intelligent computing and Signal processing, ICSP 2023

作者： Tang, Lijun Chongqing Vocational Institute of Engineering Big Data and Internet of Things School Chongqing China

ISBN: (纸本)9798350302455

In order to solve the problem that the existing detection mechanism only using the fall sensors easily leads to the elderly care system receiving false fall help signals, an intelligent decision-making mechanism based on a fuzzy neural network is proposed. After receiving the fall help signal reported by the person label, the mechanism first reads the sensor data collected by the person label at that moment, then standardizes the sensor data, and finally inputs the standardized sensor data into the fuzzy neural network model to analyze and judge the authenticity of the fall help signal. The results show that the intelligent decision-making mechanism based on the fuzzy neural network can effectively eliminate the false fall help-seeking signal caused by the false judgment of the fall sensor, and the judgment accuracy of the fall help-seeking signal can reach 97. 5%. © 2023 IEEE.

关键词： Fuzzy neural networks

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The 5G Edge computing using Deep Reinforcement Learning for data processing 3

The 5G Edge Computing using Deep Reinforcement Learning for ...

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3rd international conference on Smart Generation computing, Communication and Networking, SMART GENCON 2023

作者： Nagpal, Manish Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

Due to the absence of methods and dedicated equipment for underground 5G applications, data processing latency is significant in coal mines and transmission efficiency is low. To combat this problem, scientists are investigating how to improve underground 5G communication through edge caching and automatically allocating resources based on the actual situation in coal mines, using a technology called mobile edge processing (MEC), which is built on 5G wireless base stations. Based on experimental data, it can be concluded that the median delay of all local execution techniques is 30 ms, whereas the mean delay of completed jobs is 15 ms, provided no modifications are made to the present base station's reported power or transmission power. Compared to other MEC execution techniques, this one has the lowest average time, by 37.5%. A single base stations has the potential to achieve throughputs of 1 Gbps on the uplink and 1.5 Gbps on the downlink simultaneously. Our approach has the potential to considerably enhance the efficiency with which resources are allocated in mining 5G communication networks. © 2023 IEEE.

关键词： 5G Deep Reinforcement Learning DQN MEC

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Sentimental Analysis on Twitter data using Supervised algorithms 7

Sentimental Analysis on Twitter Data using Supervised Algori...

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7th international conference on computing Methodologies and Communication, ICCMC 2023

作者： Susmitha, C. Nikhil, L. Akhil, L. Kavitha, M. Reddy, V. Surya Narayana Shailaja, K. Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of CSE VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India Department of CSE Vasavi College of Engineering Ibrahimbag Hyderabad India

ISBN: (纸本)9781665464086

Sentimental Analytics is the phenomenon used to show the feelings of different people, emotions, opinions. It is also referred as the opinion meaning, which identifies the emotional tone behind a body of text The Mechanism gives whether the result is positive, negative, or neutral. It gives finer results, to keep track of users, and tells whether the tweet is uplifted or down lifted by the user's content. Machine learning can be analyzed by using various analysis algorithms to provide a precise value. Machine learning algorithms play a major role in various domains, and one of them includes data analysis. In this work, machine learning techniques are used to predict the Twitter analysis. Machine Learning (ML) algorithms such as Support Vector Machine (SVM), Logistic Regression (LR), and Naive Bayes (NB) algorithm are used for implementation. The performance of the implemented algorithm is analyzed in the terms of accuracy, precision, recall, F1-score on the tweets taken from the social media. © 2023 IEEE.

关键词： Support vector machines

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An Improved Scheduling Algorithm Based on Jellyfish Search for Measurement data Process System

An Improved Scheduling Algorithm Based on Jellyfish Search f...

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2023 international conference on Computer Science and Automation Technology, CSAT 2023

作者： Fang, Bin Zhu, Shi Yu, Qi Li, Li Li, Yijia Lou, Jiayuan Electric Power Company Limited Information and Communication Branch of State Grid Hunan Changsha China Nanjing Nari Information and Communication Technology Nanjing City China School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9798350371437

Parallel computing of real-time data is an effective solution to the problem of timely analysis with the rapid increase of real-time data in today's power grid. Currently, most data parallel processing methods do not consider the characteristics of the real-time power measurement data to improve efficiency. This paper proposes an improved jellyfish search method for data process task scheduling, which considers time window segmentation based on data arrival characteristic. The method proposed in this paper can improve the processing speed of realtime power measurement data with resources reduction. © 2023 IEEE.

关键词： data handling

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HiSec: Towards Cyber Threat Correlation and Discovery Based on Hierarchical Graph Neural Networks 22

HiSec: Towards Cyber Threat Correlation and Discovery Based ...

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IEEE 22nd international conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigdataSE conference / CSE conference / EUC conference / ISCI conference

作者： Xu, Liwen Lin, Xiang Li, Jianhua Bai, Min Wang, Liejun Shanghai Jiao Tong Univ Inst Cyber Sci & Technol Shanghai Peoples R China Qianxin Technol Grp Co LTD Qianxin Threat Intelligence Ctr Beijing Peoples R China

ISBN: (纸本)9798350381993;9798350382006

A multi-step targeted cyberattack refers to a sophisticated, systematic, and persistent form of attack aiming to compromise the security and integrity of a complex network system. These attacks exhibit spatial and temporal correlations in their execution sequences. However, conventional anomaly detection methods, which often focus on single facets such as network traffic or host behavior, lack the capacity to correlate and validate these steps. To address this deficiency, we introduce HiSec, a cyber threat correlation and discovery framework that leverages dynamic graph modeling and hierarchical graph neural networks. HiSec enhances the modeling of complex network systems and the analysis of spatio-temporal characteristics of multi-step targeted cyberattacks. Specifically, we introduce a novel dynamic graph modeling algorithm that employs overlapping samplers and sliding windows to establish long-term correlations among system activities. Aided by graph attention networks and the Transformer, HiSec uniquely exploits the spatio-temporal correlated edge feature representation, a capability inaccessible to traditional algorithms. Evaluation results reveal that HiSec surpasses existing benchmarks in unsupervised detection, achieving a high degree of accuracy (93.10%) and recall (94.77%). When deployed in our intranet for two rounds of evaluation, HiSec demonstrated remarkable efficiency, taking only 0.15 seconds to model 10,000 system activities occurring within approximately an hour.

关键词： Complex Network System Multi-source Heterogeneous Cybersecurity data Graph Modeling Hierarchical Graph Neural Network Edge Feature Embedding Multi-step Targeted Cyberattacks

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