Acadlore Transactions on AI and Machine Learning最新文献

Enhanced Named Entity Recognition Based on Multi-Feature Fusion Using Dual Graph Neural Networks 基于多特征融合的双图神经网络增强命名实体识别能力

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-04-03 DOI: 10.56578/ataiml030202

Hanzhao Gu, Jialin Ma, Yanran Zhao, Ashim Khadka

引用次数: 0

Advances in Breast Cancer Segmentation: A Comprehensive Review 乳腺癌分类的进展：全面回顾

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-03-21 DOI: 10.56578/ataiml030201

Ayah Abo-El-Rejal, Shehab Eldeen Ayman, Farah Aymen

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Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions 利用带有伪随机仿射函数的新型 Vigenere 方法增强彩色图像加密功能

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-03-14 DOI: 10.56578/ataiml030104

Hamid EL BOURAKKADI, Abdelhakim Chemlal, Hassan Tabti, Mourad Kattass, A. Jarjar, Abdellhamid Benazzi

引用次数: 0

Enhancing Melanoma Skin Cancer Diagnosis Through Transfer Learning: An EfficientNetB0 Approach 通过迁移学习加强黑色素瘤皮肤癌诊断：一种高效的 NetB0 方法

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-03-14 DOI: 10.56578/ataiml030105

Rashmi Ashtagi, Pramila Vasantrao Kharat, Vinaya Sarmalkar, Sridevi Hosmani, Abhijeet R. Patil, Afsha Imran Akkalkot, Adithya Padthe

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Enhanced Real-Time Facial Expression Recognition Using Deep Learning 利用深度学习增强实时面部表情识别能力

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-01-25 DOI: 10.56578/ataiml030103

H. B. U. Haq, Waseem Akram, Muhammad Nauman Irshad, Amna Kosar, Muhammad Abid

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Adaptive Lane Keeping Assistance System with Integrated Driver Intent and Lane Departure Warning 带集成驾驶员意图和车道偏离警告功能的自适应车道保持辅助系统

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-01-22 DOI: 10.56578/ataiml030102

Haigang Wei, Wei Tong, Yueyong Jiang, Jianlu Li, Ramesh Vatambeti

引用次数: 0

Enhanced Pest and Disease Detection in Agriculture Using Deep Learning-Enabled Drones 利用深度学习无人机加强农业病虫害检测

Acadlore Transactions on AI and Machine Learning Pub Date : 2024-01-12 DOI: 10.56578/ataiml030101

Wenqi Li, Xixi Han, Zhibo Lin, Atta-Ur Rahman

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DWT-Based Digital Watermarking for Various Attacks 针对各种攻击的基于 DWT 的数字水印技术

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-11-16 DOI: 10.56578/ataiml020405

Nail Alaoui

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Predictive Modelling of Employee Attrition Using Deep Learning 基于深度学习的员工流失预测模型

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-11-15 DOI: 10.56578/ataiml020404

Dino Michael Quinteros

{"title":"Predictive Modelling of Employee Attrition Using Deep Learning","authors":"Dino Michael Quinteros","doi":"10.56578/ataiml020404","DOIUrl":"https://doi.org/10.56578/ataiml020404","url":null,"abstract":"This investigation delineates an optimised predictive model for employee attrition within a substantial workforce, identifying pertinent models tailored to the specific context of employee and organisational variables. The selection and refinement of the appropriate predictive model serve as cornerstones for enhancements and updates, which are integral to honing the model's precision in prognosticating potential departures. Through meticulous optimisation, the model demonstrates proficiency in pinpointing the pivotal factors contributing to employee turnover and elucidating the interdependencies among salient variables. A suite of 27 general and eight critical variables were scrutinised. Pertinent correlations were unearthed, notably between monthly income and job satisfaction, home-to-work distance and job satisfaction, as well as age with both job satisfaction and performance metrics. Drawing from prior studies in analogous domains, a three-stage analytical methodology encompassing data exploration, model selection, and implementation was employed. The rigorous training of the optimised model encompassed both attrition factors and variable correlations, culminating in predictive outcomes with a precision of 90% and an accuracy of 87%. Implementing the refined model projected that 113 out of 709 employees, equating to 15.93%, were at a heightened risk of exiting the organisation. This quantitative foresight equips stakeholders with a strategic tool for preemptive interventions to mitigate turnover and sustain organisational vitality.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"6 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136229352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparative Analysis of Mortality Predictions from Lassa Fever in Nigeria: A Study Using Count Regression and Machine Learning Methods 尼日利亚拉沙热死亡率预测的比较分析:使用计数回归和机器学习方法的研究

Acadlore Transactions on AI and Machine Learning Pub Date : 2023-11-13 DOI: 10.56578/ataiml020403

Timothy Kayode Samson, Tosin Akingbade, Jesutomi Orija

{"title":"Comparative Analysis of Mortality Predictions from Lassa Fever in Nigeria: A Study Using Count Regression and Machine Learning Methods","authors":"Timothy Kayode Samson, Tosin Akingbade, Jesutomi Orija","doi":"10.56578/ataiml020403","DOIUrl":"https://doi.org/10.56578/ataiml020403","url":null,"abstract":"In Sub-Saharan Africa, particularly in Nigeria, Lassa fever poses a significant infectious disease threat. This investigation employed count regression and machine learning techniques to model mortality rates associated with confirmed Lassa fever cases. Utilizing weekly data from January 7, 2018, to April 2, 2023, provided by the Nigeria Centre for Disease Control (NCDC), an analytical comparison between these methods was conducted. Overdispersion was indicated (p<0.01), prompting the exclusive use of negative binomial and generalized negative binomial regression models. Machine learning algorithms, specifically medium Gaussian support vector machine (MGSVM), ensemble boosted trees, ensemble bagged trees, and Exponential Gaussian Process Regression (GPR), were applied, with 80% of the data allocated for training and the remaining 20% for testing. The efficacy of these methods was evaluated using the coefficients of determination (R²) and the root mean square error (RMSE). Descriptive statistics revealed a total of 30,461 confirmed cases, 4,745 suspected cases, and 772 confirmed fatalities attributable to Lassa fever during the study period. The negative binomial regression model demonstrated superior performance (R²=0.1864, RMSE=4.33) relative to the generalized negative binomial model (R²=0.1915, RMSE=18.2425). However, machine learning algorithms surpassed the count regression models in predictive capability, with ensemble boosted trees emerging as the most effective (R²=0.85, RMSE=1.5994). Analysis also identified the number of confirmed cases as having a significant positive correlation with mortality rates (r=.885, p<0.01). The findings underscore the importance of promoting community hygiene practices, such as preventing rodent intrusion and securing food storage, to mitigate the transmission and consequent fatalities of Lassa fever.","PeriodicalId":292575,"journal":{"name":"Acadlore Transactions on AI and Machine Learning","volume":"44 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0