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A neural network model for the evolution of learning in changing environments

PLOS COMPUTATIONAL BIOLOGY 2024年第1期20卷 e1011840页

作者： Kozielska, Magdalena Weissing, Franz J. Univ Groningen Groningen Inst Evolutionary Life Sci Groningen Netherlands

learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a strong effect on the evolved learning strategy: a shorter lifespan and a narrow resource distribution reduce the likelihood of learning to evolve. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environmen

关键词： learning machine learning Neural networks Foraging Behavior Evolutionary adaptation Organismal evolution machine learning algorithms

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Deep learning-Based Algorithm for Classification of News Text

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IEEE ACCESS 2024年 12卷 159086-159098页

作者： Yu Li, Xiao Han, Ling Bo Feng Jiang, Zheng Guangdong Ocean Univ Coll Elect & Informat Engn Zhanjiang 524088 Peoples R China Guangxi Minzu Normal Univ Coll Math & Comp Sci Chongzuo 532200 Peoples R China

As online news grows exponentially, hotspot classification is becoming increasingly important. Although traditional machine learning-based text classification methods, such as plain Bayes, support vector machines (SVMs), and classification trees, provide a certain degree of interpretability, they are often incapable of handling complex semantic relations, which frequently results in poor classification accuracy. To address this issue, in this study we introduce a novel deep learning-based text classification (TC) method based on a convolutional neural network (CNN), long short-term memory (LSTM), and an attention mechanism. This method accurately predicts news popularity by combining the feature extraction ability of the CNN, the sequence modeling ability of LSTM, and the weighted summation ability of the attention mechanism. The experimental results demonstrated that, compared to other deep learning models, the proposed method achieved a higher accuracy, more effectively accounted for the context of the text data, and addressed the problem of poor classification accuracy. Along with model selection, feature engineering was also the key to improving the accuracy. Accordingly, we developed a plain Bayesian TC model based on feature extraction, using word embeddings to convert text into richer vector representations. Then we combined the model with the different plain Bayes distributions, proving that the polynomial plain Bayes was the most suitable model for TC. Consequently, we added the feature word classification expressiveness index to improve the term frequency-inverse document frequency (TF-IDF) feature extraction, which produced a classification accuracy of 96%. This demonstrates that the improved model is superior at understanding and classifying text.

关键词： Text categorization Feature extraction Bayes methods Convolutional neural networks Vectors Classification algorithms Accuracy Long short term memory Deep learning machine learning algorithms Convolutional neural network (CNN) naive Bayes news text classification TF-IDF

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Predicting Breast Cancer Using Classical machine learning and Deep learning algorithms

Predicting Breast Cancer Using Classical Machine Learning an...

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Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), International Conference

作者： Vempati Laxmisai Krishna Vasista Katta Sona Jahnavi Pedarla Bhupathi Sahithi Tirumala Kodanda Rama Krishna Rao Kolla Bhanu Prakash Department of CSE Koneru Lakshmiah Education Foundation Vaddeswaram AP India

Cancer is the source of nearly one in six fatalities. There are numerous cancer types, breast cancer being one of them. IT can be easily detected by certain symptoms. Breast cancer affects not only women but also men; it affects around 12.5% of women in Europe, the United States, and 5% of women in India. We can detect the risk of Breast cancer by using some machine learning and deep learning algorithms. The accuracy that was obtained using the machine learning algorithms was good enough that is 92%. Thiswas obtained by the Random Forest Classifier. The other machine learning algorithms were also good, but this got the highest accuracy score. Whereas the deep learning algorithms had significantly higher scores than the machine learning algorithms. The RNN model scored 98%, which was the highest. Breast Cancer Wisconsin (Diagnostic) Set Of data is the set of information that was used.

关键词： Deep learning machine learning algorithms Computational modeling Europe Forestry Prediction algorithms Breast cancer

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Predicting EURO Games Using an Ensemble Technique Involving Genetic algorithms and machine learning

Predicting EURO Games Using an Ensemble Technique Involving ...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arisoa S. Randrianasolo School of Information Technology and Computing Abilene Christian University Abilene TX USA

This paper summarizes our first attempt to use an ensemble technique to predict soccer games. We chose to represent the games by converting the difference of the teams' statistics into the values -1,0, and 1. This conversion was done using tolerance values that “zero-out” some of the differences. We used a genetic algorithm to find the tolerance values. 101 tolerance vectors produced by the genetic algorithm were used to create game representations on which various machine learning algorithms were trained to create an ensemble technique. Our approach had an accuracy of 70% predicting the Women's Euro 2022 and an accuracy of 67% predicting the men's Euro 2020.

关键词： machine learning algorithms Conferences Games machine learning Prediction algorithms Genetic algorithms Sports

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Quantum machine learning Revolution in Healthcare: A Systematic Review of Emerging Perspectives and Applications

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IEEE ACCESS 2024年 12卷 11423-11450页

作者： Ullah, Ubaid Garcia-Zapirain, Begonya Univ Deusto eVida Res Grp Bilbao 48007 Spain

Quantum computing (QC) stands apart from traditional computing systems by employing revolutionary techniques for processing information. It leverages the power of quantum bits (qubits) and harnesses the unique properties exhibited by subatomic particles, such as superposition, entanglement, and interference. These quantum phenomena enable quantum computers to operate on an entirely different level, exponentially surpassing the computational capabilities of classical computers. By manipulating qubits and capitalising on their quantum states, QC holds the promise of solving complex problems that are currently intractable in the case of traditional computers. The potential impact of QC extends beyond its computational power and reaches into various critical sectors, including healthcare. Scientists and engineers are working diligently to overcome various challenges and limitations associated with QC technology. These include issues related to qubit stability, error correction, scalability, and noise reduction. In such a scenario, our proposed work provides a concise summary of the most recent state of the art based on articles published between 2018 and 2023 in the healthcare domain. Additionally, the approach follows the necessary guidelines for conducting a systematic literature review. This includes utilising research questions and evaluating the quality of the articles using specific metrics. Initially, a total of 2,038 records were acquired from multiple databases, with 468 duplicate records and 1,053 records unrelated to healthcare subsequently excluded. A further 258, 68, and 39 records were eliminated based on title, abstract, and full-text criteria, respectively. Ultimately, the remaining 49 articles were subject to evaluation, thus providing a brief overview of the recent literature and contributing to existing knowledge and comprehension of Quantum machine learning (QML) algorithms and their applications in the healthcare sector. This analysis establishes a

关键词： Medical services machine learning Quantum computing machine learning algorithms Systematics Computers Databases quantum machine learning algorithms healthcare systematic review

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Experimental comparison and optimal machine learning technique for predicting the thermo-hydraulic performance of Low-GWP refrigerants (R1234yf, R290, and R13I1/R290) during evaporation in plate heat exchanger

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CASE STUDIES IN THERMAL ENGINEERING 2024年 64卷

作者： Prabakaran, Rajendran Mohanraj, Thangamuthu Dhamodharan, Palanisamy Kim, Sung Chul Yeungnam Univ Sch Mech Engn 280 Daehak Ro Gyongsan 712749 Gyeongbuk South Korea Amrita Vishwa Vidyapeetham Amrita Sch Engn Dept Mech Engn Coimbatore India

In recent times, employing machine learning techniques (MLTs) to forecast the evaporation/ condensation performance of refrigerants, namely the heat transfer coefficient (HTC) and frictional pressure drop (FPD), has become increasingly significant. In the current investigation, an experimental comparison of the evaporation HTC and FPD of R1234yf, R290, and R13I1/R290 was explored in an offset strip fin-plate heat exchanger. Following this, six MLTs-support vector regressor (SVR), multilayer perceptron regressor (MLPR), gradient boosting regressor (GBR), AdaBoost regressor (ABR), ridge regressor (RR), and K-nearest neighbors regressor (KNNR)- were proposed to forecast the HTC and FPD of these refrigerants. The comparative experimental analysis revealed that the evaporation-HTC of R290 was 27.8-76.2 % and 46.2-73.8 % higher than that of R1234yf and R13I1/R290, respectively, while FPD declined by up to 74.8 % and 57.2 %, respectively. Under the same working conditions, the first transition from nucleation to convective boiling occurred in the order of R1234yf, R13I1/290, and R290. In addition, nucleate boiling dominated at lower vapor quality, while convective boiling prevailed at higher vapor quality. A total of 336 experimental data points corresponding to various test conditions from existing studies and current experiments were considered for the MLT prediction analysis. According to the results, GBR (without any enhancement approaches) was the best approach for forecasting the FPD and HTC, with mean absolute errors (MAE) of 0.125 % and 0.131 %, respectively. Additionally, to enhance the forecasting efficiency of the MLTs, feature selection, analysis of principal components, and hyperparameter tuning were employed. According to feature importance, mass flux, reduced pressure, saturation temperature, heat flux, and entry vapor quality were identified as the most influential parameters on the FPD and HTC. Ultimately, the most accurate predictions of HTC and FPD, with

关键词： Evaporation characteristics machine learning algorithms Support vector regression multilayer perceptron regressor Extreme gradient boosting AdaBoost regressor

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machine learning models and dimensionality reduction for improving the Android malware detection

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2616-e2616页

作者： Moran, Pablo Robles-Gomez, Antonio Duque, Andres Tobarra, Llanos Pastor-Vargas, Rafael Univ Nacl Educ Distancia Dept Sistemas Comunicac & Control Madrid Spain Univ Nacl Educ Distancia Dept Lenguajes & Sistemas Informat Madrid Spain

Today, a great number of attack opportunities for cybercriminals arise in Android, since it is one of the most used operating systems for many mobile applications. Hence, it is very important to anticipate these situations. To minimize this problem, the analysis of malware search applications is based on machine learning algorithms. Our work uses as a starting point the features proposed by the DREBIN project, which today constitutes a key reference in the literature, being the largest public Android malware dataset with labeled families. The authors only employ the support vector machine to determine whether a sample is malware or not. This work first proposes a new efficient dimensionality reduction of features, as well as the application of several supervised machine learning algorithms for prediction purposes. Predictive models based on Random Forest are found to achieve the most promising results. They can detect an average of 91.72% malware samples, with a very low false positive rate of 0.13%, and using only 5,000 features. This is just over 9% of the total number of features of DREBIN. It achieves an accuracy of 99.52%, a total precision of 96.91%, as well as a macro average F1-score of 96.99%.

关键词： machine learning algorithms Random Forest Supervised feature selection techniques Feature filtering techniques Predictive goodness metrics

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Optimized Intrusion Detection for IoMT Networks with Tree-Based machine learning and Filter-Based Feature Selection

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SENSORS 2024年第17期24卷 5712页

作者： Balhareth, Ghaida Ilyas, Mohammad Florida Atlantic Univ Dept Elect Engn & Comp Sci 777 Glades Rd Boca Raton FL 33431 USA

The Internet of Medical Things (IoMTs) is a network of connected medical equipment such as pacemakers, prosthetics, and smartwatches. Utilizing the IoMT-based system, a huge amount of data is generated, offering experts a valuable resource for tasks such as prediction, real-time monitoring, and diagnosis. To do so, the patient's health data must be transferred to database storage for processing because of the limitations of the storage and computation capabilities of IoMT devices. Consequently, concerns regarding security and privacy can arise due to the limited control over the transmitted information and reliance on wireless transmission, which leaves the network vulnerable to several kinds of attacks. Motivated by this, in this study, we aim to build and improve an efficient intrusion detection system (IDS) for IoMT networks. The proposed IDS leverages tree-based machine learning classifiers combined with filter-based feature selection techniques to enhance detection accuracy and efficiency. The proposed model is used for monitoring and identifying unauthorized or malicious activities within medical devices and networks. To optimize performance and minimize computation costs, we utilize Mutual Information (MI) and XGBoost as filter-based feature selection methods. Then, to reduce the number of the chosen features selected, we apply a mathematical set (intersection) to extract the common features. The proposed method can detect intruders while data are being transferred, allowing for the accurate and efficient analysis of healthcare data at the network's edge. The system's performance is assessed using the CICIDS2017 dataset. We evaluate the proposed model in terms of accuracy, F1 score, recall, precision, true positive rate, and false positive rate. The proposed model achieves 98.79% accuracy and a low false alarm rate 0.007 FAR on the CICIDS2017 dataset according to the experimental results. While this study focuses on binary classification for intrusion detecti

关键词： machine learning algorithms feature selection intrusion detection system (IDS) IoMT SoT XGBoost CatBoost

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Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第11期196卷 1-22页

作者： Sankalpa, J. K. S. Rathnayaka, A. M. R. W. S. D. Ishani, P. G. N. Liyanaarachchi, L. A. T. S. Gayan, M. W. H. Wijesuriya, W. Karunaratne, S. Rubber Res Inst Sri Lanka Agalawatta 12200 Sri Lanka CSIRO Agr & Food Clunies Ross St Black Mt ACT 2601 Australia

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District of Sri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available. Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

关键词： Grid search cross-validation Google Earth Engine Kegalle District of Sri Lanka machine learning algorithms

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Real-Time Classification of Vehicles Using machine learning Algorithm on the Extensive Dataset

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IEEE ACCESS 2024年 12卷 98338-98351页

作者： Pemila, M. Pongiannan, R. K. Narayanamoorthi, R. Sweelem, Emad A. Hendawi, Essam Abu El-Sebah, Mohamed I. SRM Inst Sci & Technol Dept Elect & Elect Engn Kattankulathur 603203 India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Kattankulathur 603203 India Elect Res Inst Dept PV Cells Cairo 12622 Egypt Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia Elect Res Inst Dept Power Elect & Energy Convers Cairo 12622 Egypt

Vehicle classification (VC) is a prominent research domain within image processing and machine learning (ML) for identifying vehicle volumes and traffic rule violations. In developed countries, nearly 40% of daily accidents are fatal, while in developing countries, the figure rises to 70%. Traditionally, vehicle detection and classification have been performed manually by experts, which is difficult, time-consuming, and prone to errors. Furthermore, incorrect detection and classification can result in hazardous situations. This highlights the need for more reliable techniques to identify and classify vehicles accurately and practically. In existing applications, numerous automated methods have been proposed. However, employing deep and machine learning algorithms on complex datasets of vehicle images has failed to achieve accuracy in various climate conditions and has been time-consuming. This paper presents an accurate, robust, real-time system to classify vehicles from onsite roads. The proposed system utilizes a random wavelet transform for pre-processing, edge and region-based segmentation for feature extraction, an embedded method for feature selection, and the XGBoost algorithm for VC. The proposed work classifies vehicles under complex weather, illumination, color, and occlusion conditions over 10 datasets, including a novel dataset named SRM2KTR, containing 75,436 vehicle images on an FPGA platform. The results show 98.81% accuracy, outperforming the state-of-the-art (98%). The system was demonstrated with four different classifiers, classifying images in 0.16 ns with an average accuracy of 97.79%. The system exhibits high accuracy, rapid identification time, and robustness in practical use.

关键词： Feature extraction Accuracy Task analysis machine learning algorithms Reluctance motors Image edge detection Classification algorithms Gradient methods Boosting Vehicle classification machine learning eXtreme gradient boost algorithm

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