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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Miyandoab, Sevil Zanjani Rahnamayan, Shahryar Bidgoli, Azam Asilian Ebrahimi, Sevda Makrehchi, Masoud Lab Department of Electrical Computer and Software Engineering Ontario Tech University OshawaON Canada Brock University Department of Engineering St. CatharinesON Canada Wilfrid Laurier University Faculty of Science WaterlooON Canada

ISBN: (纸本)9798350308365

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly en-hancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from sev-eral prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmen-tation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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A 3D Designed Portable Programmable Device Using Gas Sensors for Air Quality Checking and Predicting the Concentration of Oxygen in Coal Mining Areas 5th

A 3D Designed Portable Programmable Device Using Gas Sensors...

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5th International Conference on Soft Computing and Signal Processing, ICSCSP 2022

作者： Aslamiya, M. Saleena, T.S. Haque, A. K. M. Bahalul Ilyas, P. Muhamed Department of Computer Science Al Jamia Arts and Science College Perinthalmanna India Department of Computer Science Sullamussalam Science College Areekode India Software Engineering LENS LUT University Lappeenranta Finland Sullamussalam Science College Areekode673639 India

ISBN: (纸本)9789811986680

Air pollution has become one of the most dreadful threats to the whole living thing. There are numerous global researches giving lots of attention to the impact of air pollution on health and the environment. One of the issues faced by the coal mining workers is the reduction in the concentration of oxygen due to the increased emission of methane in mining areas. The proposed system is an Arduino based 3D designed portable programmable system with the capability of measuring air quality in the atmosphere using MQ4, MQ7, MQ135 gas sensors, finding the levels of pollutant gasses, and predicting the oxygen level especially in coal mining areas. Increase in methane causes a reduction in oxygen. Based on the sensing values, the system produces appropriate results and gives alerts to the users of the device. A 3D case designed using AutoCAD software envelopes the system, it makes the system more user-friendly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coal combustion

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SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Narrative Overlap Summaries at Scale

SEM-F1: an Automatic Way for Semantic Evaluation of Multi-Na...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

Recent work has introduced an important yet relatively under-explored NLP task called Semantic Overlap Summarization (SOS) that entails generating a summary from multiple alternative narratives which conveys the common information provided by those narratives. Previous work also published a benchmark dataset for this task by collecting 2, 925 alternative narrative pairs from the web and manually annotating 411 different reference summaries by engaging human annotators. In this paper, we exclusively focus on the automated evaluation of the SOS task using the benchmark dataset. More specifically, we first use the popular ROUGE metric from text-summarization literature and conduct a systematic study to evaluate the SOS task. Our experiments discover that ROUGE is not suitable for this novel task and therefore, we propose a new sentence-level precision-recall style automated evaluation metric, called SEM-F1 (Semantic F1). It is inspired by the benefits of the sentence-wise annotation technique using overlap labels reported by the previous work. Our experiments show that the proposed SEM-F1 metric yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Semantic Overlap Summarization among Multiple Alternative Narratives: an Exploratory Study 29

Semantic Overlap Summarization among Multiple Alternative Na...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

In this paper, we introduce an important yet relatively unexplored NLP task called Semantic Overlap Summarization (SOS), which entails generating a single summary from multiple alternative narratives which can convey the common information provided by those narratives. As no benchmark dataset is readily available for this task, we created one by collecting 2, 925 alternative narrative pairs from the web and then, went through the tedious process of manually creating 411 different reference summaries by engaging human annotators. As a way to evaluate this novel task, we first conducted a systematic study by borrowing the popular ROUGE metric from text-summarization literature and discovered that ROUGE is not suitable for our task. Subsequently, we conducted further human annotations to create 200 document-level and 1, 518 sentence-level ground-truth overlap labels. Our experiments show that the sentence-wise annotation technique with three overlap labels, i.e., {Absent (A), Partially-Present (PP), and Present (P)}, yields a higher correlation with human judgment and higher inter-rater agreement compared to the ROUGE metric. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Overbooking-Enabled Task Scheduling and Resource Allocation in Mobile Edge Computing Environments

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Intelligent Automation & Soft Computing 2023年第7期37卷 1-16页

作者： Jixun Gao Bingyi Hu Jialei Liu Huaichen Wang Quanzhen Huang Yuanyuan Zhao School of Computer Henan University of EngineeringZhengzhou451191China School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo454000China School of Computer Hubei University of Arts and ScienceXiangyang441053China School of Software Engineering Anyang Normal UniversityAnyang455000China School of Computer and Information Engineering Henan Normal UniversityXinxiang453003China The Publicity Department Zhengzhou University of TechnologyZhengzhou450044China

Mobile Edge Computing(MEC)is proposed to solve the needs of Inter-net of Things(IoT)users for high resource utilization,high reliability and low latency of service ***,the backup virtual machine is idle when its primary virtual machine is running normally,which will waste *** the backup virtual machine under the above circumstances can effectively improve resource ***,these virtual machines are deployed into slots randomly,and then some tasks with cooperative relationship are off-loaded to virtual machines for *** deployment locations have different resource utilization and average service response *** want toﬁnd a balanced solution that minimizes the average service response time of the IoT application while maximizing resource *** this paper,we propose a task scheduler and exploit a Task Deployment Algorithm(TDA)to obtain an optimal virtual machine deployment ***,the simulation results show that the TDA can signiﬁcantly increase the resource utilization of the system,while redu-cing the average service response time of the application by comparing TDA with the other two classical *** experimental results conﬁrm that the perfor-mance of TDA is better than that of other two methods.

关键词： Mobile edge computing overbooking resource utilization service response time task deployment algorithm

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Handling class imbalance problem in software maintainability prediction:an empirical investigation

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Frontiers of computer science 2022年第4期16卷 5-18页

作者： Ruchika Malhotra Kusum Lata Discipline of Software Engineering Department of Computer Science&EngineeringDelhi Technological UniversityDelhi110042India University School of Management and Entrepreneurship East Delhi CampusDelhi Technological UniversityDelhi110095India

As the complexity of software systems is increasing;software maintenance is becoming a challenge for software *** prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and high-quality *** research of software engineering predictive modeling,various software maintainability prediction(SMP)models are evolved to forecast *** develop a maintainability prediction model,software practitioners may come across situations in which classes or modules requiring high maintainability effort are far less than those requiring low maintainability *** condition gives rise to a class imbalance problem(CIP).In this situation,the minority classes’prediction,i.e.,the classes demanding high maintainability effort,is a ***,in this direction,this study investigates three techniques for handling the CIP on ten open-source software to predict software *** empirical investigation supports the use of resampling with replacement technique(RR)for treating CIP and develop useful models for SMP.

关键词： sofiware maintenance software maintainability imbalanced learning

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An Integrated Machine Learning Framework for Effective Classification of Water

An Integrated Machine Learning Framework for Effective Class...

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2023 International Conference on Energy, Power, Environment, Control, and Computing, ICEPECC 2023

作者： Aleem, Isha Ur Rehman, Attique Javaid, Sabeen Ali, Tahir Muhammad University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Computer Science Mubarak Al-Abdullah Kuwait

ISBN: (纸本)9781665476010

Water is an essential need for all living things. The problem people are facing especially in urban areas is the diseases that have been caused by water which is mainly Dengue or malaria because there are many metals such as ammonium aluminum silver and other bacterial or viral things present in water. The first step toward a healthy lifestyle is to drink purified water. In this paper, the methodology that has been used here is to detect whether the water people are using for drinking purposes is safe enough to use or not in the data set and the specific methodology is binomial type as yes or no, 912 for positive states and 7084 negative states which means that 0.8864 for 0 and 0.114 for 1. The ratio for negative is far higher than the positive one. We tested the model in two ways first with simple feature extraction, smote Upsampling and with vote ensemble. In smote Upsampling accuracy with the random forest is 88.21 % (highest). The classification error of random forest is 11.7% and with the highest rate is 94.73% which is recall and specificity is 81.71% which is the lowest in the random forest whereas with vote ensemble the combinations of two algorithms have been used and the highest accuracy is from naive Bayes and KNN and the accuracy from them is 96.00% with the classification error of 6% only its precision rate is higher which is 98.89% and lowest specificity of 75.00%. © 2023 IEEE.

关键词： Machine learning

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Learning to Generate Overlap Summaries through Noisy Synthetic Data

Learning to Generate Overlap Summaries through Noisy Synthet...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Bansal, Naman Akter, Mousumi Karmaker, Shubhra Kanti Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

Semantic Overlap Summarization (SOS) is a novel and relatively under-explored seq-to-seq task which entails summarizing common information from multiple alternate narratives. One of the major challenges for solving this task is the lack of existing datasets for supervised training. To address this challenge, we propose a novel data augmentation technique, which allows us to create large amount of synthetic data for training a seq-to-seq model that can perform the SOS task. Through extensive experiments using narratives from the news domain, we show that the models fine-tuned using the synthetic dataset provide significant performance improvements over the pre-trained vanilla summarization techniques and are close to the models fine-tuned on the golden training data;which essentially demonstrates the effectiveness of out proposed data augmentation technique for training seq-to-seq models on the SOS task. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Comparative Performance Evaluation of Random Forest, Extreme Gradient Boosting and Linear Regression Algorithms Using Nigeria’s Gross Domestic Products 6th

Comparative Performance Evaluation of Random Forest, Extreme...

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6th EAI International Conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2023

作者： Adewale, M.D. Ebem, D.U. Awodele, O. Azeta, A. Aggrey, E.M. Okechalu, E.A. Olayanju, K.A. Owolabi, A.F. Oju, J. Ubadike, O.C. Otu, G.A. Muhammed, U.I. Oluyide, O.P. African Centre of Excellence on Technology Enhanced Learning National Open University of Nigeria Abuja Nigeria Department of Computer Science University of Nigeria Nsukka Nigeria Department of Computer Science Babcock University Ogun Ilishan-Remo Nigeria Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia

ISBN: (纸本)9783031518485

Statistical methods like linear regression analysis are frequently used to create predictive analytic models. However, these methods have limitations that may affect the accuracy of the models. Using a typical dataset, this study seeks to accomplish two main goals. First, we fitted three predictive models, including linear regression analysis and two ensemble machine learning algorithms: Random Forest Regressor and Extreme Gradient Boosting Regressor. Secondly, we compared the performance of the models using a 5-fold cross-validation technique. The Random Forest Regressor outperformed the other models, with a Mean Absolute Error (MAE) of 10.138, Mean Square Error (MSE) of 139.729, Mean Absolute Percentage Error (MAPE) of 0.071, Root Mean Square Error (RMSE) of 11.821, and Normalised Mean Square Error (NMSE) of 13.782. These results suggest that the Random Forest Regressor is optimal for developing predictive models with similar datasets. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Machine learning

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A High Availability Microservices Architecture Implementation using Saga and Backup Mechanism 10

A High Availability Microservices Architecture Implementatio...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Lee, Wen-Tin Song, Ping-Yi Tsai, Ming-Kai Wu, Meng-Hsien Ma, Shang-Pin National Kaohsiung Normal University Department of Software Engineering and Management Kaohsiung Taiwan National Taiwan Ocean University Department of Computer Science and Information Engineering Keelung Taiwan

ISBN: (纸本)9798350304770

In recent years, the proliferation of complex applications has led to the emergence of microservices architecture as the preferred approach for developing largescale applications. Consequently, numerous design patterns for microservice communication have evolved, with service orchestration gaining widespread acceptance as a standard solution in recent *** study delves into design patterns for microservice data consistency and develops the service orchestration library, Anser-Saga. This library enables the creation of backups and restart points of distributed transaction states, ensuring eventual data consistency between microservice endpoints. Furthermore, it guarantees that our orchestrator can store, compensate for, and restart the orchestration process in case of an unexpected failure, ensuring transaction integrity. We conducted experiments involving actual microservice invocations to evaluate the availability and performance of microservices. The findings revealed that the Anser-Saga library demonstrated superior performance compared to the DTM solution. © 2023 IEEE.

关键词： Architecture

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