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Vehicle detection using improved region convolution neural network for accident prevention in smart roads

PATTERN RECOGNITION LETTERS 2022年 158卷 42-47页

作者： Djenouri, Youcef Belhadi, Asma Srivastava, Gautam Djenouri, Djamel Lin, Jerry Chun-Wei SINTEF Digital Math & Cybernet Oslo Norway Kristiania Univ Coll Dept Technol Oslo Norway Brandon Univ Dept Math & Comp Sci Brandon MB Canada Univ West England Comp Sci Res Ctr Dept Comp Sci & Creat Technol Bristol Glos England Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci Bergen Norway China Med Univ Res Ctr Interneural Comp Taichung Taiwan

This paper explores the vehicle detection problem and introduces an improved regional convolution neural network. The vehicle data (set of images) is first collected, from which the noise (set of outlier images) is removed using the SIFT extractor. The region convolution neural network is then used to detect the vehicles. We propose a new hyper-parameters optimization model based on evolutionary computation that can be used to tune parameters of the deep learning framework. The proposed solution was tested using the well-known boxy vehicle detection data , which contains more than 20 0,0 0 0 vehicle images and 1,990,0 0 0 annotated vehicles. The results are very promising and show superiority over many current state-of-the-art solutions in terms of runtime and accuracy performances. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Deep learning Vehicle detection Region convolution neural network hyper-parameters optimization Deep learning Vehicle detection Region convolution neural network hyper-parameters optimization

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Predicting maritime accident consequence scenarios for emergency response decisions using optimization-based decision tree approach

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MARITIME POLICY & MANAGEMENT 2023年第1期50卷 19-41页

作者： Li, Baode Lu, Jing Lu, Han Li, Jing Dalian Maritime Univ Transportat Engn Coll Dalian Peoples R China

Emergency response decision-making for maritime accidents needs to consider the possible consequences and scenarios of an accident to develop an effective emergency response strategy to reduce the severity of the accident. This paper proposes a novel machine learning-based methodology for predicting accident scenarios and analysing its factors to assist emergency response decision-making from an emergency rescue perspective. Specifically, the accident data used are collected from maritime accident investigation reports, and then two types of decision tree (DT) algorithms, classification and regression tree (CART) and random forest (RF), are used to develop scenario prediction models for three accident consequences including ship damage, casualty, and environmental damage. The hyper-parameters of these two DT algorithms are optimized using two state-of-the-art optimization algorithms, namely random search (RS) and Bayesian optimization (BO), respectively, aiming to obtain the prediction model with the highest accuracy. Experimental results reveal that BO-RF algorithm produces the best accuracy as compared to others. In addition, an analysis of feature importance shows that the number of people involved in an accident is the most important driving factor affecting the final accident scenario. Finally, decision rules are generated from the obtained optimal prediction model, which can provide decision support for emergency response decisions.

关键词： Maritime accidents emergency response decision tree algorithms hyper-parameters optimization decision rule generation

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A Combining Forecasting Method Based on Seasonal Unit Root Test and Support Vector Regression 7th

A Combining Forecasting Method Based on Seasonal Unit Root T...

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7th International Conference on Human Centered Computing (HCC)

作者： Gu, Songyuan Qin, Yuanyuan Lu, Anwen China Elect Technol Grp Corp Beijing Peoples R China China Acad Elect & Informat Technol Beijing Peoples R China North China Elect Power Univ Baoding Peoples R China

ISBN: (纸本)9783031237409;9783031237416

For catching and processing seasonality in seasonal time series, a combining forecastingmethod based on seasonal unit root test, i.e., the Dickey-Hasza-Fuller (DHF) test and support vector regression (SVR) is proposed, which is denoted as DHF-SVR method. The DHF-SVR method employs DHF test to identify seasonality in series and utilizes seasonal differencing operator to process the seasonal time series;for solving the difficulty of adaptive selection of maximum lag order, a SVR hyper-parameters tuning method based on a genetic algorithm (GA) with real-integer hybrid encoding is proposed. The experimental comparison demonstrates that the proposed DHF-SVR method could improve the forecasting performance in comparison with the comparative methods.

关键词： Time series forecasting Seasonal unit root test Support vector regression hyper-parameters optimization

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Machine learning tuning by diversity oriented firefly metaheuristics for Industry 4.0

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EXPERT SYSTEMS 2024年第2期41卷 e13293-e13293页

作者： Jovanovic, Luka Bacanin, Nebojsa Zivkovic, Miodrag Antonijevic, Milos Jovanovic, Bojan Sretenovic, Marija Bogicevic Strumberger, Ivana Singidunum Univ Fac Informat & Comp Danijelova 32 Belgrade 11000 Serbia Fac Org Sci Dept Informat Technol Jove Il 154 Belgrade 11000 Serbia

The progress of Industrial Revolution 4.0 has been supported by recent advances in several domains, and one of the main contributors is the Internet of Things. Smart factories and healthcare have both benefited in terms of leveraged quality of service and productivity rate. However, there is always a trade-off and some of the largest concerns include security, intrusion, and failure detection, due to high dependence on the Internet of Things devices. To overcome these and other challenges, artificial intelligence, especially machine learning algorithms, are employed for fault prediction, intrusion detection, computer-aided diagnostics, and so forth. However, efficiency of machine learning models heavily depend on feature selection, predetermined values of hyper-parameters and training to deliver a desired result. This paper proposes a swarm intelligence-based approach to tune the machine learning models. A novel version of the firefly algorithm, that overcomes known deficiencies of original method by employing diversification-based mechanism, has been proposed and applied to both feature selection and hyper-parameter optimization of two machine learning models-XGBoost and extreme learning machine. The proposed approach has been tested on four real-world Industry 4.0 data sets, namely distributed transformer monitoring, elderly fall prediction, BoT-IoT, and UNSW-NB 15. Achieved results have been compared to the results of eight other cutting-edge metaheuristics, that have been implemented and tested under the same conditions. The experimental outcomes strongly indicate that the proposed approach significantly outperformed all other competitor metaheuristics in terms of convergence speed and results' quality measured with standard metrics-accuracy, precision, recall, and f1-score.

关键词： artificial intelligence feature selection firefly algorithm hyper-parameters optimization metaheuristics optimization swarm intelligence

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NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient's symptoms

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NONLINEAR DYNAMICS 2021年第2期106卷 1453-1475页

作者： Soui, Makram Mansouri, Nesrine Alhamad, Raed Kessentini, Marouane Ghedira, Khaled Saudi Elect Univ Coll Comp & Informat Riyadh Saudi Arabia Univ Gabes Gabes Tunisia Univ Michigan Dearborn MI 48128 USA Private Higher Sch Engn & Technol Ariana Tunisia

Nowadays, humanity is facing one of the most dangerous pandemics known as COVID-19. Due to its high inter-person contagiousness, COVID-19 is rapidly spreading across the world. Positive patients are often suffering from different symptoms that can vary from mild to severe including cough, fever, sore throat, and body aches. In more dire cases, infected patients can experience severe symptoms that can cause breathing difficulties which lead to stern organ failure and die. The medical corps all over the world are overloaded because of the exponentially myriad number of contagions. Therefore, screening for the disease becomes overwrought with the limited tools of test. Additionally, test results may take a long time to acquire, leaving behind a higher potential for the prevalence of the virus among other individuals by the patients. To reduce the chances of infection, we suggest a prediction model that distinguishes the infected COVID-19 cases based on clinical symptoms and features. This model can be helpful for citizens to catch their infection without the need for visiting the hospital. Also, it helps the medical staff in triaging patients in case of a deficiency of medical amenities. In this paper, we use the non-dominated sorting genetic algorithm (NSGA-II) to select the interesting features by finding the best trade-offs between two conflicting objectives: minimizing the number of features and maximizing the weights of selected features. Then, a classification phase is conducted using an AdaBoost classifier. The proposed model is evaluated using two different datasets. To maximize results, we performed a natural selection of hyper-parameters of the classifier using the genetic algorithm. The obtained results prove the efficiency of NSGA-II as a feature selection algorithm combined with AdaBoost classifier. It exhibits higher classification results that outperformed the existing methods.

关键词： COVID-19 prediction Machine learning Feature selection AdaBoost NSGA-II hyper-parameters optimization

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hyper-parameter optimization in the context of Smart Manufacturing: a Systematic Literature Review

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Procedia Computer Science 2024年 232卷 804-812页

作者： Maria Chernigovskaya Abdulrahman Nahhas Andrey Kharitonov Klaus Turowski Magdeburg Research and Competence Cluster for Very Large Business Applications Faculty of computer science Otto von Guericke University of Magdeburg Universitätsplatz 2 Magdeburg 39106 Germany

Smart manufacturing involves the use of a variety of automation solutions, such as robotics, machines with embedded software, and advanced sensors collecting vast quantities of data. Efficient control of a complex composition of such solutions, as well as the analysis of the collected data, are essential for improving the efficiency of the production processes and decision-making. Data analysis and process optimization are enabled through the application of state-of-the-art optimization and machine learning algorithms. However, the efficient use of these algorithms often depends on the careful selection of the parameters, which on its own is a process that requires a high degree of expertise and time. Therefore, another class of algorithms can be applied that is designed to discover the optimal parameter configuration given the specific nature of the manufacturing process and the used algorithm. In this work, we systematically analyze the published literature to discover which parameter selection techniques are used in the context of Industry 4.0, for which processes, and how these benefit from automated parameter selection. Within our literature review, we discover nine relevant publications, most of which concentrate on parameter selection for machine learning algorithms through various numerical optimization and metaheuristic techniques.

关键词： hyper-parameters optimization Smart Manufacturing Industry 4.0 Automation

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A High-Robust Displacement Prediction Model for Super-High Arch Dams Integrating Wavelet De-Noising and Improved Random Forest

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WATER 2023年第7期15卷 1271页

作者： Gu, Chongshi Wu, Binqing Chen, Yijun Hohai Univ State Key Lab Hydrol Water Resources & Hydraul En Nanjing 210098 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China Natl Engn Res Ctr Water Resources Efficient Utili Nanjing 210098 Peoples R China

We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth.

关键词： dam safety monitoring displacement prediction model wavelet de-noising processing improved random forest hyper-parameters optimization

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Multiple Convolutional Neural Networks for Diabetic Retinopathy Classification 9

Multiple Convolutional Neural Networks for Diabetic Retinopa...

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9th IEEE International Conference on e-Health and Bioengineering (EHB)

作者： Schweisthal, Brigitte Lascu, Mihaela Politehn Univ Timisoara Fac Elect Telecommun & Informat Technol Timisoara Romania

ISBN: (纸本)9781665440004

Diabetic retinopathy (DR) is a complication of diabetes, caused by high blood sugar levels damaging the retina blood vessels, and can lead to vision loss if it is not treated or detected in initial phases. Convolutional neural networks (CNNs) were used to classify multiple DR images on different stages for the early diagnosis purposes. Improving the accuracy and precision of examination, developing the classification of cases of diabetic retinopathy by severity are essential steps in treating diabetic retinopathy and other eye diseases.

关键词： diabetic retinopathy deep learning convolutional neural network hyper-parameters optimization classification

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Automated feature engineering for prediction of victories in online computer games

Automated feature engineering for prediction of victories in...

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9th IEEE International Conference on Big Data (IEEE BigData)

作者： Ruta, Dymitr Cen, Ling Liu, Ming Quang Hieu Vu Khalifa Univ EBTIC Abu Dhabi U Arab Emirates Zalora Singapore Singapore

ISBN: (纸本)9781665439022

An accurate evaluation of player-win likelihoods during online games is critical for controlling the attractiveness and immersion of the gameplay, especially if played against an AI bot. Predicting game victories is a very challenging problem heavily depends on the complexity and the stage of the game. With the multitude of ways and formats that the players, the gameplay, individual state and history can be encoded and utilized by machine learning (ML) models, it is fascinating to take part in the IEEE Big Data 2021 Cup and explore which data representations, feature engineering methods and prediction models work better than others and what levels of predictability they can achieve in the specific use case of predicting victories in Tactical Troops: Anthracite Shift online computer game. Our explorations throughout the game and a high 4th place in the Cup's final indicate that a careful and comprehensive feature engineering methodology numerically capturing the last state of the game paired with the variants of the gradient boosting models offer a robust and competitive solution for this challenge.

关键词： Feature engineering gradient boosting convolutional neural networks hyper-parameters optimization

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An Improved Arabic Sentiment Analysis Approach using Optimized Multinomial Nave Bayes Classifier

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第8期13卷 90-98页

作者： Alsanad, Ahmed King Saud Univ Dept Informat Syst Artificial Intelligence Chair Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia

sentiment analysis has emerged during the last decade as a computational process on Arabic texts for extracting people's attitudes toward targeted objects or their feelings and emotions regarding targeted events. Sentiment analysis (SA) using machine learning (ML) methods has become an important research task for developing various text-based applications. Among different ML classifiers, multinomial Naive Bayes (MNNB) classifier is widely used for documents classification due to its ability for performing statistical analysis of text contents. It significantly simplifies textual-data classification and offers an alternative to heavy ML-based semantic analysis methods. However, the MNNB classifier has a number of hyper-parameters affects the classification task of texts and controls the decision boundary of the model itself. In this paper, an optimized MNNB classifier-based approach is proposed for improving Arabic sentiment analysis. A number of experiments are conducted on large sets of Arabic tweets to evaluate the proposed approach. The optimized MNNB classifier is trained on three datasets and tested on a different separated test set to show the performance of developed approach. The experimental results on the test set revealed that the optimized MNNB classifier of proposed approach outperforms the traditional MNNB classifier and other baseline classifiers. The accuracy rate of the optimization approach is increased by 1.6% compared with using the default values of the classifier's hyper-parameters.

关键词： Machine learning Arabic sentiment analysis optimized multinomial Na?ve Bayes (MNNB) classifier hyper-parameters optimization

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