VM Migration and Resource Management using Meta Heuristic Technique in Cloud Computing Services DOI
Abhijit Kumar, Runumi Devi

Published: April 29, 2023

An increasingly important component in the development of Cloud Computing, an Internet-based technology, is optimization its resources. To make most available resources, cloud data centre models need a resource management strategy. The Bin-Packing issue combinatorial that may be used to efficiently assign virtual machines physical machines. In this study, we present two-stage approach for managing and allocating resources effectively. first step, propose Load Balanced Multi-Dimensional (LBMBP) heuristics (VMs) (PMs or hosts) by taking into account all at their disposal. As indicated second stage, technique identify overload load balance hosts based on anomalies necessary VM migration. CloudSim Plus Simulator simulation results were demonstrate planned work, it was found number operational PMs reduced. Reduced energy use emigration rates due more efficient

Language: Английский

Artificial Intelligence Applied to Human Resources Management: A Bibliometric Analysis DOI

José Javier Galán Hernández,

Gabriel Marín Díaz, José Luis Galdón Salvador

et al.

Lecture notes in networks and systems, Journal Year: 2024, Volume and Issue: unknown, P. 269 - 277

Published: Jan. 1, 2024

Language: Английский

Citations

2
Explainable prediction of node labels in multilayer networks: a case study of turnover prediction in organizations DOI Creative Commons
László Gadár, János Abonyi

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: April 19, 2024

Abstract In real-world classification problems, it is important to build accurate prediction models and provide information that can improve decision-making. Decision-support tools are often based on network models, this article uses encoded by social networks solve the problem of employer turnover. However, understanding factors behind black-box be challenging. Our question was about predictability employee turnover, given from multilayer describes collaborations perceptions assess performance organizations indicate success cooperation. goal develop an procedure, preserve interpretability classification, capture wide variety specific reasons explain positive cases. After a feature engineering, we identified variables with best predictive power using decision trees ranked them their added value considering frequent co-occurrence. We applied Random Forest SMOTE balancing technique for prediction. calculated SHAP values identify contribute most individual predictions. As last step, clustered sample fine-tune explanations quitting due different background factors.

Language: Английский

Citations

2
Selecting the Flexible Last-Mile Delivery Models Using Multicriteria Decision-Making DOI Creative Commons
Mladenka Blagojević, Dragana Šarac, Katarina Mostarac

et al.

PROMET - Traffic&Transportation, Journal Year: 2023, Volume and Issue: 35(5), P. 635 - 654

Published: Oct. 30, 2023

Postal service providers can reorganize the last-mile delivery process within scope of universal and apply some flexible models for organization process. In this paper, question selection Flexible Last-Mile Delivery Models (FLMDM) is treated using multicriteria decision-making. We have identified four different sustainable with an emphasis on number workers. One postal provider from Europe was selected, where proposed FLMDM are tested. The ranked Multiple Criteria Decision Analysis (MCDA) technique. context, MCDA techniques used to make a comparative assessment alternatives. This paper aims find optimal costs in each variant model - workers delivery. obtained results suggest AB as choice Also, ensure complete allocation required technological (the workers), by applying originally proposed, solution (models)

Language: Английский

Citations

4
Uncertainty‐aware nuclear power turbine vibration fault diagnosis method integrating machine learning and heuristic algorithm DOI Creative Commons
Ruirui Zhong, Yixiong Feng, Puyan Li

et al.

IET Collaborative Intelligent Manufacturing, Journal Year: 2024, Volume and Issue: 6(3)

Published: June 7, 2024

Abstract Nuclear power turbine fault diagnosis is an important issue in the field of nuclear safety. The numerous state parameters operation and maintenance turbines are collected, forming a complex high‐dimensional feature space. These spaces contain redundant information, which increases training cost reduces recognition accuracy efficiency model. To address aforementioned challenges, vibration algorithm proposed. First, long short‐term memory‐based denoising autoencoder (LDAE) designed to enhance capability uncertainty awareness. Then, extraction method integrating variational mode decomposition (VMD), L‐cliffs‐based effective selection, sample entropy devised extract latent features from Furthermore, using extreme gradient boosting (XGBoost) as classifier, LDAE‐VMD‐XGBoost model constructed for turbines. Considering impact multiple hyperparameters on performance, pathfinder used optimise hyperparameter settings improve accuracy. Experimental results demonstrate performance proposed improved accurate diagnosis.

Language: Английский

Citations

1
Enhancing Talent Recruitment in Business Intelligence Systems: A Comparative Analysis of Machine Learning Models DOI Creative Commons
Hikmat Al-Quhfa, Ali Mothana, Abdussalam Aljbri

et al.

Analytics, Journal Year: 2024, Volume and Issue: 3(3), P. 297 - 317

Published: July 15, 2024

In the competitive field of business intelligence, optimizing talent recruitment through data-driven methodologies is crucial for better decision-making. This study compares effectiveness various machine learning models to improve accuracy and efficiency. Using data from a major Yemeni organization (2019–2022), we evaluated including K-Nearest Neighbors, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Trees, Random Forest, Gradient Boosting Classifier, AdaBoost Neural Networks. Hyperparameter tuning cross-validation were used optimization. The Forest model achieved highest (92.8%), followed by Networks (92.6%) Classifier (92.5%). These results suggest that advanced models, particularly Networks, can significantly enhance processes in intelligence systems. provides valuable insights recruiters, advocating integration sophisticated techniques acquisition strategies.

Language: Английский

Citations

1
A Critical Review on Metaheuristic Algorithms based Multi-Criteria Decision-Making Approaches and Applications DOI

Rishabh Rishabh,

Kedar Nath Das

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 2, 2024

Language: Английский

Citations

1
An Incremental Optimization Algorithm for Efficient Verification of Graph Transformation Systems DOI Creative Commons
Faranak Nejati, Nor Asilah Wati Abdul Hamid, Sina Zangbari Koohi

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 75748 - 75760

Published: Jan. 1, 2023

This paper proposes an incremental optimization framework for verifying graph transformation systems to overcome the state space explosion (SSE). SSE refers exponential growth of number possible states in a system during its verification. The maps verification problem search and incrementally generates space. generated increments can still be significant size, thus we use Raccoon Optimization Algorithm (ROA), non-exhaustively through ROA selects sequences with higher potential having deadlock increments, which prevents ensures that memory capacity is not exceeded. However, there possibility migration method lead loss diversity population, reducing algorithm's ability explore new regions To address this issue, propose ROA, called Improved (IROA), preserves population reduces execution time risk getting stuck local optima. Our approach evaluated using Groove simulation tool compared other relevant meta-heuristic algorithms terms computation consumption. experimental results show IROA outperforms both consumption, total efficiency 1.043 1.02, respectively, demonstrating effectiveness massive spaces without facing reasonable time.

Language: Английский

Citations

2
Integrated Machine Learning Algorithms and MCDM Techniques in Optimal Ranking of Battery Electric Vehicles DOI Creative Commons
Sudha Murthy,

Deepak F.X. Edwin,

M. Nivetha

et al.

E3S Web of Conferences, Journal Year: 2023, Volume and Issue: 405, P. 02005 - 02005

Published: Jan. 1, 2023

The automobile industries across the world of this present age are streamlining manufacture battery electric vehicles (BEV) as a step towards creating pollution free environment. BEVs used an alternate strategy to alleviate carbon emission at global level. As environmental conservation is one long standing sustainable 1f ?developmental goals it need hour make paradigm shift from fossil fuels renewable energy sources, same time also gives rise decision-making problem on making optimal choice vehicles. In paper decision based ten alternative and eleven criteria considered earlier works Faith Ecer. new ranking method multi-criteria MCRAT(Multiple Criteria Ranking by Alternative Trace) together with three different criterion weight computing methods AHP(Analytical Hierarchy Process) ,CRITIC (CRiteria Importance Through Intercriteria Correlation) & MEREC (MEthod Removal Effects Criteria). results obtained compared validated using random forest machine learning algorithm. This research work conjoins algorithms decisions Battery integrated approach yields will certainly create rooms in approaches coming days.

Language: Английский

Citations

2
Object Detection in Cybersecurity DOI
Stones Dalitso Chindipha

Advances in information security, privacy, and ethics book series, Journal Year: 2024, Volume and Issue: unknown, P. 175 - 196

Published: Feb. 23, 2024

With the increase in malware attacks, need for automated detection cybersecurity has become more important. Traditional methods of detection, such as signature-based and heuristic analysis, are becoming less effective detecting advanced evasive malware. It potential to drastically improve malware, well reduce manual efforts required scanning flagging malicious activity. This chapter also examines advantages limitations challenges associated with deploying object cybersecurity, its reliance on labeled data, false positive rates, evasion. Finally, review presents future research directions needed make technique reliable useful professionals. provides a comparison results obtained by these techniques traditional methods, emphasizing

Language: Английский

Citations

0
Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach DOI Creative Commons
Wen‐Chin Chen,

An-Xuan Ngo,

Hui-Pin Chang

et al.

Mathematics, Journal Year: 2024, Volume and Issue: 12(13), P. 2094 - 2094

Published: July 3, 2024

This research proposes a hybrid multi-criteria decision-making (MCDM) framework for workforce recruitment in Taiwan’s electronics manufacturing companies, an area with limited research. First, comprehensive review of existing literature and interviews industry experts were conducted to compile list criteria sub-criteria relevant selection industry. The Fuzzy Delphi Method (FDM) was then applied identify retain the most critical while eliminating less important ones. Next, Interpretive Structural Modelling (ISM) used calculate interdependencies among identified factors. Finally, based on these relationships, Analytic Network Process (FANP) employed relative importance weights sub-criteria. These rank criteria, identifying ones aiding decision-making. findings indicate that proposed method provides structured assessable model making informed decisions recruitment, particularly challenging environment industry, which faces shortage skilled labor. presents three primary contributions: development systematic technique using FDM, establishment consistent relations decision-makers ISM, proposal application employing FANP appropriate hiring new employees. study highlights work attitude, adaptability environment, ability as major criteria. It also emphasizes discipline compliance, positive adherence health safety protocols top selection.

Language: Английский

Citations

0