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Automated credit assessment framework using ETL process and machine learning. 使用 ETL 流程和机器学习的自动信用评估框架。
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-31 DOI: 10.1007/s11334-022-00522-x
Neepa Biswas, Anindita Sarkar Mondal, Ari Kusumastuti, Swati Saha, Kartick Chandra Mondal

In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.

在当前的商业环境下,通过商业智能(BI)对企业数据进行实时分析,对于支持运营活动和做出任何战略决策都至关重要。自动化 ETL(抽取、转换和加载)流程可确保近乎实时地将数据摄入数据仓库,并通过基于实时数据的 BI 流程产生洞察力。在本文中,我们重点讨论了基于机器学习方法的金融领域信用风险自动评估。基于机器学习的分类技术可以为数据分类提供一个自我调节的过程。建立自动化信贷决策系统有助于贷款机构管理风险、提高运营效率并符合监管机构的要求。本文根据《巴塞尔 II 新资本协议》的标准,采用逻辑回归和神经网络分类方法进行信用风险评估。本文采用《巴塞尔 II 新资本协议》标准来计算预期损失。建立机器学习模型所需的数据整合是通过自动 ETL 流程完成的。我们通过评估这一新的信用风险评估方法,结束了这项研究工作。
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引用次数: 0
Comparative exploration of CNN model and transfer learning on fire image dataset 火灾图像数据集上CNN模型与迁移学习的比较研究
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-27 DOI: 10.1007/s11334-022-00521-y
Sudip Suklabaidya, Indrani Das
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引用次数: 0
A low-cost hybrid handwritten Devanagari character classifier 一个低成本的混合手写德文字符分类器
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-23 DOI: 10.1007/s11334-022-00518-7
Jayati Mukherjee, Sneha Mishra, Arjit Tomar, Vikas Kumar
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引用次数: 1
Emotion recognition from lyrical text of Hindi songs 从印地语歌曲抒情文本看情感识别
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-22 DOI: 10.1007/s11334-022-00520-z
Sourish Dhar, Vishal Gour, Arnab Paul
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引用次数: 1
Smart automation in manufacturing process using industrial internet of things (IIoT) architecture 利用工业物联网(IIoT)架构实现制造过程的智能自动化
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-12 DOI: 10.1007/s11334-022-00504-z
Jasjeet Singh, C. Banerjee, S. Pandey
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引用次数: 4
A hybrid cellular automata-based model for leakage detection in smart drip irrigation water pipeline structure using IoT sensors 基于混合元胞自动机的物联网传感器智能滴灌管道结构泄漏检测模型
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-12 DOI: 10.1007/s11334-022-00503-0
A. Banerjee, C. Banerjee
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引用次数: 0
Mental health issues assessment using tools during COVID-19 pandemic. 在 COVID-19 大流行期间使用工具评估心理健康问题。
IF 1.1 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-12 DOI: 10.1007/s11334-022-00510-1
Hamnah Rao, Meenu Gupta, Parul Agarwal, Surbhi Bhatia, Rajat Bhardwaj

COVID-19 has brought distress among people as pandemic has impacted the globe not only economically or physically, but also psychologically by degrading their mental health. Several research were done in the past which tried to capture these issues but post-covid situation needs to be critically handled and analyzed so that corrective measures for cure and support can be taken. The current work is an attempt to observe the mental health issues (anxiety and depression) that occurred during the lockdown by combining a few pre-designed questionnaires. The online survey included 244 respondents (females = 126, males = 118) and when we thoroughly examined gender, age group, and occupational activity as three main factors, the results showed that female students aged 21-35 were affected more than male students of the same age group. In this study, we used a 4-item Geriatric Depression Scale (GDS-4) as a depression screening instrument and discovered that 225 out of total respondents were depressed. Using the Generalized Anxiety Disorder (GAD-7), a self-administered anxiety tool, we found 103 responders with mild, 87 with moderate, 12 with severe, and 42 with no anxiety symptoms. Patient Health Questionnaire (PHQ-9) showed the symptoms of mental disorders where 68 individuals had mild, 85 had moderate, 37 had moderately severe, 12 had severe, and 42 had no symptoms. With the help of multiple linear regression analysis, demographic data were evaluated, and later results were compared between GDS-4, GAD-7, and PHQ-9 using correlation coefficients. This will help practitioners and individuals to focus on their physiological health and adopt diagnostic measures.

COVID-19 给人们带来了痛苦,因为该流行病不仅在经济上或身体上对全球造成了影响,还在心理上损害了人们的精神健康。过去曾做过一些研究,试图捕捉这些问题,但对 COVID 后的情况需要进行批判性的处理和分析,以便采取正确的治疗和支持措施。目前的工作是通过结合一些预先设计的问卷,尝试观察在封锁期间出现的心理健康问题(焦虑和抑郁)。在线调查包括 244 名受访者(女性 = 126 人,男性 = 118 人),当我们深入研究性别、年龄组和职业活动这三个主要因素时,结果显示 21-35 岁的女学生比同年龄组的男学生受到的影响更大。在这项研究中,我们使用了 4 项老年抑郁量表(GDS-4)作为抑郁筛查工具,发现受访者中有 225 人患有抑郁症。我们使用自制的焦虑工具 "广泛性焦虑症(GAD-7)",发现 103 名受访者有轻度焦虑症状,87 名受访者有中度焦虑症状,12 名受访者有重度焦虑症状,42 名受访者没有焦虑症状。患者健康问卷(PHQ-9)显示,68 人有轻度精神障碍症状,85 人有中度精神障碍症状,37 人有中重度精神障碍症状,12 人有重度精神障碍症状,42 人无精神障碍症状。在多元线性回归分析的帮助下,对人口统计学数据进行了评估,随后使用相关系数对 GDS-4、GAD-7 和 PHQ-9 的结果进行了比较。这将有助于医生和个人关注生理健康并采取诊断措施。
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引用次数: 0
LowEST: a low resource semantic text summarization method for big data LowEST:一种低资源的大数据语义文本摘要方法
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-09 DOI: 10.1007/s11334-022-00514-x
Sufal Das
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引用次数: 0
Opinion classification at subtopic level from COVID vaccination-related tweets. 根据 COVID 疫苗接种相关推文进行子话题层面的意见分类。
IF 1.1 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-09 DOI: 10.1007/s11334-022-00516-9
Mrinmoy Sadhukhan, Pramita Bhattacherjee, Tamal Mondal, Sudakshina Dasgupta, Indrajit Bhattacharya

Coronavirus disease 2019 (Covid-19) is a contiguous disease which affected a large volume of population with a high mortality rate across the globe. For dealing with the recent spread of COVID-19, one of the prime measures was to vaccinate people in full extent. People across the globe have diverse opinion regarding the vaccination process, its side effect and effectiveness. Such opinions get located into different micro-blogging sites including twitter. Opinion mining through analyzing public sentiments of such micro-blogs is a common method for detection of public responses. This paper focuses on classifying the public opinions expressed related to COVID-19 vaccination at sub topic level. The procedure tries to find out different keywords regarding positive, negative and neutral sentences. From those keywords, different related query set was constructed using Rocchio query expansion algorithm for positive, negative and neutral sentiments. Later Extended query set is used to form subtopic using LDA algorithm to identify the nature of the tweets. The proposed LDA model came across with 0.56 coherence score with twenty subtopics, which is fair enough to classify the tweets in different classes. This trained model is finally used to classify the tweets in real time with Apache Kafka framework regarding different subtopic based on positive, negative or neutral sentiment.

冠状病毒病 2019(Covid-19)是一种传染性疾病,在全球范围内影响了大量人口,死亡率很高。为应对近期 COVID-19 的传播,首要措施之一是为人们全面接种疫苗。全球各地的人们对疫苗接种过程、其副作用和效果有着不同的看法。这些意见会在包括 twitter 在内的不同微博网站上传播。通过分析此类微博的公众情绪来进行意见挖掘是检测公众反应的常用方法。本文主要从子话题层面对与 COVID-19 疫苗接种相关的公众意见进行分类。该程序试图找出有关正面、负面和中性句子的不同关键词。从这些关键词中,使用 Rocchio 查询扩展算法为正面、负面和中性情绪构建了不同的相关查询集。之后,扩展查询集使用 LDA 算法形成子主题,以识别推文的性质。所提出的 LDA 模型与 20 个子主题的一致性得分为 0.56,足以将推文分为不同的类别。这个训练有素的模型最终被用于使用 Apache Kafka 框架根据正面、负面或中性情绪对不同子主题的推文进行实时分类。
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引用次数: 0
A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing 一种基于蚁群优化和禁忌搜索的云计算混合多资源负载平衡方法
IF 1.2 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Pub Date : 2022-12-08 DOI: 10.1007/s11334-022-00508-9
Jyotsna P. Gabhane, Sunil Pathak, N. Thakare
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引用次数: 4
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