Analysis of Driver Performance Using Hybrid of Weighted Ensemble Learning Technique and Evolutionary Algorithms

Koohestani, Afsaneh; Abdar, Moloud; Hussain, S. Perwez; Khosravi, Abbas; Nahavandi, Darius; Nahavandi, Saeid; Alizadehsani, Roohallah

doi:10.1007/s13369-020-05115-z

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…Predicting cancer risk as accurately as possible is highly desirable. To this end, Behravan et al [ 121 ] utilized XGBoost [ 122 ] to develop an approach for determining the combination of interacting genetic variants and demographic risk factors leading to maximum accuracy in breast cancer risk prediction. Liu et al [ 123 ] have also utilized the XGBoost method to identify risk factors contributing to breast cancer in menopausal women.…”

Section: Discussionmentioning

confidence: 99%

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Iqbal

Waqas²,

Alizadehsani

et al. 2022

Healthcare

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Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients’ treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

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Section: Discussionmentioning

confidence: 99%

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Iqbal

Waqas²,

Alizadehsani

et al. 2022

Healthcare

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“…Research studies on autonomous cars attempt to narrow the vehicle length, since the driving seat is not required. However, in autonomous convoying with trucks, the vehicle length is a key concern [68] because maneuverability, speed limits on intersections, and overtaking considerations are different.…”

Section: N Consideration Of the Vehicle Lengthmentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

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“…Machine Learning plays a crucial role in extracting useful information in different research domains, for instance, medical data analysis [1][2][3][4], computer vision [5], road accident analysis [6], educational data mining [7], sentiment analysis [8] and many more. Instance reduction is one of the prime pre-processing tasks in machine learning applications.…”

Section: Introductionmentioning

confidence: 99%

A Modified Ant Lion Optimization Method and Its Application for Instance Reduction Problem in Balanced and Imbalanced Data

Bakrawy

Çifçi

Kausar

et al. 2022

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Instance reduction is a pre-processing step devised to improve the task of classification. Instance reduction algorithms search for a reduced set of instances to mitigate the low computational efficiency and high storage requirements. Hence, finding the optimal subset of instances is of utmost importance. Metaheuristic techniques are used to search for the optimal subset of instances as a potential application. Antlion optimization (ALO) is a recent metaheuristic algorithm that simulates antlion’s foraging performance in finding and attacking ants. However, the ALO algorithm suffers from local optima stagnation and slow convergence speed for some optimization problems. In this study, a new modified antlion optimization (MALO) algorithm is recommended to improve the primary ALO performance by adding a new parameter that depends on the step length of each ant while revising the antlion position. Furthermore, the suggested MALO algorithm is adapted to the challenge of instance reduction to obtain better results in terms of many metrics. The results based on twenty-three benchmark functions at 500 iterations and thirteen benchmark functions at 1000 iterations demonstrate that the proposed MALO algorithm escapes the local optima and provides a better convergence rate as compared to the basic ALO algorithm and some well-known and recent optimization algorithms. In addition, the results based on 15 balanced and imbalanced datasets and 18 oversampled imbalanced datasets show that the instance reduction proposed method can statistically outperform the basic ALO algorithm and has strong competitiveness against other comparative algorithms in terms of four performance measures: Accuracy, Balanced Accuracy (BACC), Geometric mean (G-mean), and Area Under the Curve (AUC) in addition to the run time. MALO algorithm results show increment in Accuracy, BACC, G-mean, and AUC rates up to 7%, 3%, 15%, and 9%, respectively, for some datasets over the basic ALO algorithm while keeping less computational time.

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Analysis of Driver Performance Using Hybrid of Weighted Ensemble Learning Technique and Evolutionary Algorithms

Cited by 7 publications

References 58 publications

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Autonomous Convoying: A Survey on Current Research and Development

A Modified Ant Lion Optimization Method and Its Application for Instance Reduction Problem in Balanced and Imbalanced Data

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