Journal articles: 'ML algorithm'

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Sharaff, Aakanksha, and Naresh Kumar Nagwani. "ML-EC2." International Journal of Web-Based Learning and Teaching Technologies 15, no. 2 (April 2020): 19–33. http://dx.doi.org/10.4018/ijwltt.2020040102.

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A multi-label variant of email classification named ML-EC2 (multi-label email classification using clustering) has been proposed in this work. ML-EC2 is a hybrid algorithm based on text clustering, text classification, frequent-term calculation (based on latent dirichlet allocation), and taxonomic term-mapping technique. It is an example of classification using text clustering technique. It studies the problem where each email cluster represents a single class label while it is associated with set of cluster labels. It is multi-label text-clustering-based classification algorithm in which an email cluster can be mapped to more than one email category when cluster label matches with more than one category term. The algorithm will be helpful when there is a vague idea of topic. The performance parameters Entropy and Davies-Bouldin Index are used to evaluate the designed algorithm.

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JAY, C. B., G. BELLÈ, and E. MOGGI. "Functorial ML." Journal of Functional Programming 8, no. 6 (November 1998): 573–619. http://dx.doi.org/10.1017/s0956796898003128.

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We present an extension of the Hindley–Milner type system that supports a generous class of type constructors called functors, and provide a parametrically polymorphic algorithm for their mapping, i.e. for applying a function to each datum appearing in a value of constructed type. The algorithm comes from shape theory, which provides a uniform method for locating data within a shape. The resulting system is Church–Rosser and strongly normalizing, and supports type inference. Several different semantics are possible, which affects the choice of constants in the language, and are used to illustrate the relationship to polytypic programming.

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Nutipalli, Preeti. "Model Construction Using ML for Prediction of Student Placement." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2213–19. http://dx.doi.org/10.22214/ijraset.2022.44273.

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Abstract: “Model construction using ML for prediction of student placement” aims to predict the placement of a student using various performance metrics on the Machine Learning algorithms. Early prediction makes the institutional growth as well as the student to get placed. It helps the student to prepare all the company requirements at early stage and monitors the student performance. Existed work was done on the algorithms like Logistic Regression (LR), Support Vector Machine (SVM), Naive Bayes. In the proposed work to predict the student placement considered dataset and applied data preprocessing to make the data easier to train the model for prediction using Decision Tree (DT) and XG Boost along with the existing algorithms. Accuracies are calculated using different performance metrics like Accuracy and F1-score, Precision, Recall. The algorithm that worked with the best accuracy is SVM with 91%, and the LR and DT algorithms got 88% accuracy whereas Naïve Bayes got 86% and then the XG Boost stood last with an accuracy of 84%. We are able to make a decision which algorithm is better than other algorithms. Higher accuracy algorithm is mostly preferred to predict the student performance.

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Garduno, Edgar, and Gabor T. Herman. "Superiorization of the ML-EM Algorithm." IEEE Transactions on Nuclear Science 61, no. 1 (February 2014): 162–72. http://dx.doi.org/10.1109/tns.2013.2283529.

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Wang, Peng, Weijia He, Fan Guo, Xuefang He, and Jiajun Huang. "An improved atomic search algorithm for optimization and application in ML DOA estimation of vector hydrophone array." AIMS Mathematics 7, no. 4 (2022): 5563–93. http://dx.doi.org/10.3934/math.2022308.

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<abstract><p>The atom search optimization (ASO) algorithm has the characteristics of fewer parameters and better performance than the traditional intelligent optimization algorithms, but it is found that ASO may easily fall into local optimum and its accuracy is not higher. Therefore, based on the idea of speed update in particle swarm optimization (PSO), an improved atomic search optimization (IASO) algorithm is proposed in this paper. Compared with traditional ASO, IASO has a faster convergence speed and higher precision for 23 benchmark functions. IASO algorithm has been successfully applied to maximum likelihood (ML) estimator for the direction of arrival (DOA), under the conditions of the different number of signal sources, different signal-to-noise ratio (SNR) and different population size, the simulation results show that ML estimator with IASO algorithum has faster convergence speed, fewer iterations and lower root mean square error (RMSE) than ML estimator with ASO, sine cosine algorithm (SCA), genetic algorithm (GA) and particle swarm optimization (PSO). Therefore, the proposed algorithm holds great potential for not only guaranteeing the estimation accuracy but also greatly reducing the computational complexity of multidimensional nonlinear optimization of ML estimator.</p></abstract>

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Choubey, Shubham. "Diabetes Prediction Using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (June 30, 2023): 4209–12. http://dx.doi.org/10.22214/ijraset.2023.54415.

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Abstract: The goal of this research is to create a machine learning algorithm-based system that is effective in detecting diabetes with high accuracy. Machine learning approaches have the potential to develop into trustworthy tools for diabetes diagnosis by utilising data analytics and pattern identification. Utilising feature selection techniques, the most pertinent elements that significantly influence diabetes prediction are found. Implemented and assessed using performance metrics including accuracy, recall, precision, and F1 Score are various machine learning algorithms, such as K-Nearest Neighbour, Logistic Regression, Random Forest, Support Vector Machine (SVM), and Decision Tree. The suggested technique works better than conventional methods, providing a more automated and effective method of diabetes detection. It could transform diabetes diagnosis, enhance patient outcomes, and enable individualised treatment plans.

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Chen, Haihua, Shibao Li, Jianhang Liu, Yiqing Zhou, and Masakiyo Suzuki. "Efficient AM Algorithms for Stochastic ML Estimation of DOA." International Journal of Antennas and Propagation 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4926496.

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The estimation of direction-of-arrival (DOA) of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML) is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM) algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM) algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM) using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.

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Lee, J. H., H. J. Kwon, and Y. K. Jin. "Numerically Efficient Implementation of JADE ML Algorithm." Journal of Electromagnetic Waves and Applications 22, no. 11-12 (January 2008): 1693–704. http://dx.doi.org/10.1163/156939308786390256.

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Mansour, Mohammad M. "A Near-ML MIMO Subspace Detection Algorithm." IEEE Signal Processing Letters 22, no. 4 (April 2015): 408–12. http://dx.doi.org/10.1109/lsp.2014.2357991.

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Pachouly, Shikha. "Student General Performance Prediction Using ML Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 7201–9. http://dx.doi.org/10.22214/ijraset.2023.53398.

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Abstract: We will start to examine and categorise the data once we have obtained the required information using our surveys. This plan involves locating data patterns and trends that can be leveraged to create powerful machine learning models. The accuracy, recall, and precision of these models will then be tested to ensure that they are reliable and effective. They will then be developed using a variety of techniques. This would require meticulous attention to detail when analysing datasets as well as a thorough understanding of the advantages and disadvantages of various machine learning techniques. Our ultimate goal is to develop machine learning (ML) models that accurately predict events using the data we have collected, providing insightful knowledge about our area of interest. We are confident that we can achieve this goal and significantly advance the fields of machine learning and data analysis by carefully planning and carrying out our work. For example, this technique could be used to predict how well students in a particular school district will perform academically. By collecting information on factors such as their emotional state and extracurricular activities in addition to more conventional data like grades and test results, we could develop ML models that precisely forecast which children are at risk of falling behind and how to support them. Which will enable us to identify the variables influencing students' success or contributing to their poor performance. This could make it easier for teachers to provide more individualised support for students and improve their overall academic performance

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Hassan, Danish Ul, Abid Qadir, and Basit Hassan. "Iris Classification with Supervised ML using Algorithm of KNN in JavaScript." International Journal of Artificial Intelligence & Mathematical Sciences 1, no. 2 (January 31, 2023): 12–15. http://dx.doi.org/10.58921/ijaims.v1i2.37.

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The Machine learning is what we have to predict the data of unseen nature and gives us predicted results. There are number different machine learning algorithms for prediction and here we will use the supervised machine learning algorithm k-nearest neighbors (KNN) which is used for both the classification and regression analysis problems. In this paper we are predicting the Iris. Iris species are of three types and here we are going to predict that using k-nearest neighbors (KNN) algorithm model with the help of JavaScript.

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Cho, Minseon, and Donghyun Kang. "ML-CLOCK: Efficient Page Cache Algorithm Based on Perceptron-Based Neural Network." Electronics 10, no. 20 (October 14, 2021): 2503. http://dx.doi.org/10.3390/electronics10202503.

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Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.

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Cho, Minseon, and Donghyun Kang. "ML-CLOCK: Efficient Page Cache Algorithm Based on Perceptron-Based Neural Network." Electronics 10, no. 20 (October 14, 2021): 2503. http://dx.doi.org/10.3390/electronics10202503.

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Today, research trends clearly confirm the fact that machine learning technologies open up new opportunities in various computing environments, such as Internet of Things, mobile, and enterprise. Unfortunately, the prior efforts rarely focused on designing system-level input/output stacks (e.g., page cache, file system, block input/output, and storage devices). In this paper, we propose a new page replacement algorithm, called ML-CLOCK, that embeds single-layer perceptron neural network algorithms to enable an intelligent eviction policy. In addition, ML-CLOCK employs preference rules that consider the features of the underlying storage media (e.g., asymmetric read and write costs and efficient write patterns). For evaluation, we implemented a prototype of ML-CLOCK based on trace-driven simulation and compared it with the traditional four replacement algorithms and one flash-friendly algorithm. Our experimental results on the trace-driven environments clearly confirm that ML-CLOCK can improve the hit ratio by up to 72% and reduces the elapsed time by up to 2.16x compared with least frequently used replacement algorithms.

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Jadhav, Sairaj. "Phishing Website Detector using ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5509–14. http://dx.doi.org/10.22214/ijraset.2023.52872.

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Abstract: With the proliferation of mobile devices in recent years, there is a tendency to move almost all real-world activities to the cyber world. Although this makes our daily life easier, it violates many security rules due to the anonymous nature of the Internet. Phishing attacks are the easiest way to get sensitive information from innocent users. Phishers aim to obtain sensitive information such as usernames, passwords, and bank account information. Cybersecurity professionals are looking for reliable and consistent detection methods to detect phishing websites. This project deals with machine learning technology to detect phishing URLs by extracting and analyzing various features of legitimate and phishing URLs. Decision trees, random forests, and support vector machine algorithms are used to identify phishing websites in a two-step process of first visualizing and extracting features of URLs using python libraries and then training them into a model using Gradient Classifier Algorithm to predict real-time phishing websites

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Deng, Zhi An. "An Improved WLAN Positioning Algorithm Based on RSS Variation over Physical Space." Applied Mechanics and Materials 651-653 (September 2014): 395–99. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.395.

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An improved maximum likelihood (ML) positioning algorithm in WLAN is proposed. In contrast to the previous ML and weighted k-nearest neighbor (WKNN) algorithm, additional information about RSS variation over physical space is deployed in the proposed algorithm. The RSS variation information is integrated to modify the Gaussian kernel width of the ML algorithm. Besides, the mean value for Gaussian likelihood function is modified by mean RSS value of the physical adjacent reference points. Experiments are carried in a real WLAN indoor environment and the well-known ML algorithm and weighted k-nearest neighbor (WKNN) algorithm are also compared. Experimental results show that the improved ML algorithm performs best and obtains 13.0 percent improvement than compared algorithms in the sense of mean positioning error.

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Wang, Jian Guo, Gang Yi Hu, and Chun Meng Jiang. "System Identification of Underwater Vehicles with ML Algorithm." Applied Mechanics and Materials 455 (November 2013): 366–71. http://dx.doi.org/10.4028/www.scientific.net/amm.455.366.

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Maximum-likelihood (ML) and its relaxation algorithm are discussed which are used to identify the mathematics model of an Underwater Vehicle (UV). With trial data of zigzag tests, the hydrodynamic derivatives of the UV were estimated, and the better astringency of the relaxation algorithm can be acquired from the contrast between the two methods. A simulation environment based on these parameters is established to verify the validity and effect of these methods. The result shows the model is credible and the methods are very useful for the research of maneuverability and intelligent control of underwater vehicles.

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Zhu, Xiaoyan, Chenzhen Ying, Jiayin Wang, Jiaxuan Li, Xin Lai, and Guangtao Wang. "Ensemble of ML-KNN for classification algorithm recommendation." Knowledge-Based Systems 221 (June 2021): 106933. http://dx.doi.org/10.1016/j.knosys.2021.106933.

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Pan, Wen, ZhanJun Jiang, ZhengFeng Du, Yan Wang, and XiaoHu You. "Analysis of a reduced-ML algorithm in BLAST." Science in China Series F: Information Sciences 51, no. 12 (August 9, 2008): 2094–100. http://dx.doi.org/10.1007/s11432-008-0129-7.

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Djurović, Igor. "Quasi ML algorithm for 2-D PPS estimation." Multidimensional Systems and Signal Processing 28, no. 2 (July 4, 2015): 371–87. http://dx.doi.org/10.1007/s11045-015-0344-5.

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Mao, Jia Li, Hong Ying Jin, Ming Dong Li, and Jia Li. "Ml-KNN Algorithm Based on Frequent Item Sets." Applied Mechanics and Materials 380-384 (August 2013): 1533–37. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1533.

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In order to solve the problem of ignoring the correlation between class labels, this paper describes a new method for multi-label classification based on the frequent item sets to classify an unseen instance on the basis of its k nearest neighbors ( MLFI-KNN). For each unseen instance, MLFI-KNN takes its k-nearest neighbors in the training set and counts the number of occurrences of each label in this neighborhood, and then utilizes the FP-growth algorithm to obtain the frequent item sets between the labels that these neighboring instances include, in order to determine the predicted label set. Experiments on benchmark dataset demonstrate the effectiveness of the proposed approach as compared to some existing well-known methods.

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Nagwani, Naresh Kumar, and Shrish Verma. "ML-CLUBAS: A Multi Label Bug Classification Algorithm." Journal of Software Engineering and Applications 05, no. 12 (2012): 983–90. http://dx.doi.org/10.4236/jsea.2012.512113.

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Zheng, Yu, Lutao Liu, and Xudong Yang. "SPICE-ML Algorithm for Direction-of-Arrival Estimation." Sensors 20, no. 1 (December 24, 2019): 119. http://dx.doi.org/10.3390/s20010119.

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Sparse iterative covariance-based estimation, an iterative direction-of-arrival approach based on covariance fitting criterion, can simultaneously estimate the angle and power of incident signal. However, the signal power estimated by sparse iterative covariance-based estimation approach is inaccurate, and the estimation performance is limited to direction grid. To solve the problem above, an algorithm combing the sparse iterative covariance-based estimation approach and maximum likelihood estimation is proposed. The signal power estimated by sparse iterative covariance-based estimation approach is corrected by a new iterative process based on the asymptotically minimum variance criterion. In addition, a refinement procedure is derived by minimizing a maximum likelihood function to overcome the estimation accuracy limitation imposed by direction grid. Simulation results verify the effectiveness of the proposed algorithm. Compared with sparse iterative covariance-based estimation approach, the proposed algorithm can achieve more accurate signal power and improved estimation performance.

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Dai, Dongrui, Changran Geng, and Xiaobin Tang. "Machine Learning-based reconstruction of single and double radiation source distribution using plastic scintillation optical fiber." Journal of Instrumentation 18, no. 08 (August 1, 2023): T08005. http://dx.doi.org/10.1088/1748-0221/18/08/t08005.

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Abstract In this study, we propose a novel method for reconstructing the radiation distribution of a one-dimensional radioactive source using Machine Learning (ML) algorithms and plastic scintillation optical fiber. The wavelength spectrum unfolding technique is used to estimate the source position accurately. We compare the accuracy and time efficiency of three different algorithms, namely, Generalized reduced gradient (GRG), Maximum likelihood expectation maximization (MLEM), and ML, in the single-source and dual-source cases. Our results demonstrate that MLEM algorithm has a shorter reconstruction time with comparable accuracy of position and intensity compared to GRG algorithm for single-source case. For dual-source case, ML algorithm provides real-time estimation of position and intensity with acceptable errors, while GRG algorithm has a larger error in intensity estimation and longer computation time. Our proposed ML algorithms offer useful guidance for practical applications in radiation source location.

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Li, Yanping, and Liyong Zhang. "A Novel OFDM Timing Synchronization Algorithm Based on Stochastic Approximation and ML Algorithm." Information Technology Journal 11, no. 8 (July 15, 2012): 1138–40. http://dx.doi.org/10.3923/itj.2012.1138.1140.

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Suryadibrata, Alethea, and Julio Christian Young. "Visualisasi Algoritma sebagai Sarana Pembelajaran K-Means Clustering." Ultimatics : Jurnal Teknik Informatika 12, no. 1 (July 2, 2020): 25–29. http://dx.doi.org/10.31937/ti.v12i1.1523.

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Algorithm Visualization (AV) is often used in computer science to represents how an algorithm works. Educators believe that visualization can help students to learn difficult algorithms. In this paper, we put our interest in visualizing one of Machine Learning (ML) algorithms. ML algorithms are used in various fields. Some of the algorithms are used to classify, predict, or cluster data. Unfortunately, many students find that ML algorithms are hard to learn since some of these algorithms include complicated mathematical equations. We hope this research can help computer science students to understand K-Means Clustering in an easier way.

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Murarka, Utsav, Kinjal Banerjee, Tuhin Malik, and Constança Providência. "The neutron star outer crust equation of state: a machine learning approach." Journal of Cosmology and Astroparticle Physics 2022, no. 01 (January 1, 2022): 045. http://dx.doi.org/10.1088/1475-7516/2022/01/045.

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Abstract Constructing the outer crust of the neutron stars requires the knowledge of the Binding Energy (BE) of the atomic nuclei. Although the BE of a lot of the nuclei is experimentally determined and can be obtained from the AME data table, for the others we need to depend on theoretical models. There exist a lot of physical theories to predict the BE, each with its own strengths and weaknesses. In this paper we apply Machine Learning (ML) algorithms on AME2016 data set to predict the Binding Energy of atomic nuclei. The novel feature of our work is that it is model independent. We do not assume or use any nuclear physics model but use only ML algorithms directly on the AME2016 data set. Our results are further refined by using another ML algorithm to train the errors of the first algorithm, and repeating this process iteratively. Our best algorithm gives σrms ∼ 0.58 MeV for Binding Energy on randomized testing sets. This is comparable to all physics models or ML improved physics models studied in literature till date. Using the predictions of our Machine Learning algorithm, we construct the outer crust equation of state (EoS) of a neutron star and show that our model is comparable to existing models. This work also demonstrates the use of various ML algorithms and a detailed analysis on how we arrived at our best algorithm. It will help the physics community in understanding how to choose an ML algorithm which would be suited for their data set. Our algorithms and best fit model is also made publicly available for the use of the community.

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Jiang, Ming-Xin, Min Li, and Hong-Yu Wang. "Visual Object Tracking Based on 2DPCA and ML." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/404978.

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We present a novel visual object tracking algorithm based on two-dimensional principal component analysis (2DPCA) and maximum likelihood estimation (MLE). Firstly, we introduce regularization into the 2DPCA reconstruction and develop an iterative algorithm to represent an object by 2DPCA bases. Secondly, the model of sparsity constrained MLE is established. Abnormal pixels in the samples will be assigned with low weights to reduce their effects on the tracking algorithm. The object tracking results are obtained by using Bayesian maximum a posteriori (MAP) probability estimation. Finally, to further reduce tracking drift, we employ a template update strategy which combines incremental subspace learning and the error matrix. This strategy adapts the template to the appearance change of the target and reduces the influence of the occluded target template as well. Compared with other popular methods, our method reduces the computational complexity and is very robust to abnormal changes. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm achieves more favorable performance than several state-of-the-art methods.

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Taha, Azza A. "ML-Style Multi-Abstraction Calculus with Type Inference Algorithm." Journal of Computer Science 15, no. 5 (May 1, 2019): 745–57. http://dx.doi.org/10.3844/jcssp.2019.745.757.

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Simón, A., P. Ferrario, and A. Izmaylov. "Event reconstruction in NEXT using the ML-EM algorithm." Nuclear and Particle Physics Proceedings 273-275 (April 2016): 2624–26. http://dx.doi.org/10.1016/j.nuclphysbps.2015.10.010.

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Pouchol, Camille, and Olivier Verdier. "The ML–EM algorithm in continuum: sparse measure solutions." Inverse Problems 36, no. 3 (February 12, 2020): 035013. http://dx.doi.org/10.1088/1361-6420/ab6d55.

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Liu, Chuanhai. "ML Estimation of the MultivariatetDistribution and the EM Algorithm." Journal of Multivariate Analysis 63, no. 2 (November 1997): 296–312. http://dx.doi.org/10.1006/jmva.1997.1703.

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Kishimoto, Akihiro, Djallel Bouneffouf, Radu Marinescu, Parikshit Ram, Ambrish Rawat, Martin Wistuba, Paulito Palmes, and Adi Botea. "Bandit Limited Discrepancy Search and Application to Machine Learning Pipeline Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 9 (June 28, 2022): 10228–37. http://dx.doi.org/10.1609/aaai.v36i9.21263.

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Optimizing a machine learning (ML) pipeline has been an important topic of AI and ML. Despite recent progress, pipeline optimization remains a challenging problem, due to potentially many combinations to consider as well as slow training and validation. We present the BLDS algorithm for optimized algorithm selection (ML operations) in a fixed ML pipeline structure. BLDS performs multi-fidelity optimization for selecting ML algorithms trained with smaller computational overhead, while controlling its pipeline search based on multi-armed bandit and limited discrepancy search. Our experiments on well-known classification benchmarks show that BLDS is superior to competing algorithms. We also combine BLDS with hyperparameter optimization, empirically showing the advantage of BLDS.

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Ammar Emad, Azeez, and Bondarenko Yulia Valentinovna. "Algorithm for CPU resource allocator case study and compering between ordinary and ML Algorithms." IOP Conference Series: Materials Science and Engineering 928 (November 19, 2020): 032064. http://dx.doi.org/10.1088/1757-899x/928/3/032064.

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Krishnan, S., S. K. Aruna, Karthick Kanagarathinam, and Ellappan Venugopal. "Identification of Dry Bean Varieties Based on Multiple Attributes Using CatBoost Machine Learning Algorithm." Scientific Programming 2023 (April 21, 2023): 1–21. http://dx.doi.org/10.1155/2023/2556066.

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Dry beans are the most widely grown edible legume crop worldwide, with high genetic diversity. Crop production is strongly influenced by seed quality. So, seed classification is important for both marketing and production because it helps build sustainable farming systems. The major contribution of this research is to develop a multiclass classification model using machine learning (ML) algorithms to classify the seven varieties of dry beans. The balanced dataset was created using the random undersampling method to avoid classification bias of ML algorithms towards the majority group caused by the unbalanced multiclass dataset. The dataset from the UCI ML repository is utilised for developing the multiclass classification model, and the dataset includes the features of seven distinct varieties of dried beans. To address the skewness of the dataset, a Box-Cox transformation (BCT) was performed on the dataset’s attributes. The 22 ML classification algorithms have been applied to the balanced and preprocessed dataset to identify the best ML algorithm. The ML algorithm results have been validated with a 10-fold cross-validation approach, and during validation, the CatBoost ML algorithm achieved the highest overall mean accuracy of 93.8 percent, with a range of 92.05 percent to 95.35 percent.

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Gao, Jing Peng, Dan Feng Zhao, and Chao Qun Wu. "SAGE-ML Joint Estimation and Detection Algorithm in MIMO-OFDM Systems." Applied Mechanics and Materials 389 (August 2013): 494–500. http://dx.doi.org/10.4028/www.scientific.net/amm.389.494.

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In order to improve the decoding performance of MIMO-OFDM system in the case of the channel state information is not accurate enough, a new algorithm is proposed, which combines SAGE algorithm, DFT-LS channel estimation and maximum likelihood detection algorithm. The algorithm utilizes joint iterative technology to achieve channel estimation and decoding effect, thereby enhances the reliability of the system. Theoretical study and simulation results show that the proposed algorithm can track the channel change correctly without increasing the system overhead, and the convergence speed is accelerated. Besides, the performance is superior to the commonly used joint detection algorithm. Moreover, comparing with the ideal channel estimation under the maximum likelihood detection algorithm, the new proposed algorithm only has a loss of 0.5dB with the same bit error rate.

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Alshifa, S. "Face Mask and Social Distancing Detection Using ML Technique." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3218–22. http://dx.doi.org/10.22214/ijraset.2021.37021.

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Detecting Mask and Social Distance is our main motive in this project.Face detection plays important roles in detecting face mask. Face detection means detecting or searching for a face in an image or video. For face and mask detection we use viola jones algorithm or Haar cascade algorithm using Open CV. For social distancing we use YOLO algorithm. We have created a system which detect the face and then, it will detect nose and mouth to confirm that the person wear mask or not.

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Sun, Bingbing, and Tariq Alkhalifah. "ML-descent: An optimization algorithm for full-waveform inversion using machine learning." GEOPHYSICS 85, no. 6 (October 21, 2020): R477—R492. http://dx.doi.org/10.1190/geo2019-0641.1.

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Full-waveform inversion (FWI) is a nonlinear optimization problem, and a typical optimization algorithm such as the nonlinear conjugate gradient or limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) would iteratively update the model mainly along the gradient-descent direction of the misfit function or a slight modification of it. Based on the concept of meta-learning, rather than using a hand-designed optimization algorithm, we have trained the machine (represented by a neural network) to learn an optimization algorithm, entitled the “ML-descent,” and apply it in FWI. Using a recurrent neural network (RNN), we use the gradient of the misfit function as the input, and the hidden states in the RNN incorporate the history information of the gradient similar to an LBFGS algorithm. However, unlike the fixed form of the LBFGS algorithm, the machine-learning (ML) version evolves in response to the gradient. The loss function for training is formulated as a weighted summation of the L2 norm of the data residuals in the original inverse problem. As with any well-defined nonlinear inverse problem, the optimization can be locally approximated by a linear convex problem; thus, to accelerate the training, we train the neural network by minimizing randomly generated quadratic functions instead of performing time-consuming FWIs. To further improve the accuracy and robustness, we use a variational autoencoder that projects and represents the model in latent space. We use the Marmousi and the overthrust examples to demonstrate that the ML-descent method shows faster convergence and outperforms conventional optimization algorithms. The energy in the deeper part of the models can be recovered by the ML-descent even when the pseudoinverse of the Hessian is not incorporated in the FWI update.

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Ambavkar, Om, Prathmesh Bharti, Amit Chaurasiya, Roshan Chauhan, and Mahalaxmi Palinje. "Review on IDS based on ML Algorithms." International Journal for Research in Applied Science and Engineering Technology 10, no. 11 (November 30, 2022): 169–74. http://dx.doi.org/10.22214/ijraset.2022.47284.

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Abstract: Intrusion detection is one of the challenging problems encountered by the modern network security industry. The developing pace of digital assaults on framework networks as of late compounds the protection and security of PC foundation and PCs. Intrusion Detection and Prevention systems are transforming into a critical part of PC organizations and network safety. Various approaches have been proposed to determine the most effective features and hence enhance the efficiency of intrusion detection systems, the methods include, machine learning-based (ML), Bayesian based algorithm, Random Forest, SVM, Decision Tree. This paper presents an intensive survey on different examination articles that utilized single, hybrid and ensemble classification algorithms. The outcomes measurements, weaknesses and datasets involved by the concentrated on articles in the advancement of IDS were looked at. A future heading for potential explores is likewise given. The paper addressed latest research papers written from the use of machine learning classifiers in intrusion detection systems.

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Takkar, Sakshi, Aman Singh, and Babita Pandey. "Application of Machine Learning Algorithms to a Well Defined Clinical Problem: Liver Disease." International Journal of E-Health and Medical Communications 8, no. 4 (October 2017): 38–60. http://dx.doi.org/10.4018/ijehmc.2017100103.

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Liver diseases represent a major health burden worldwide. Machine learning (ML) algorithms have been extensively used to diagnose liver disease. This study accordingly aims to employ various individual and integrated ML algorithms on distinct liver disease datasets for evaluating the diagnostic performances, to integrate dimensionality reduction method with the ML algorithms for analyzing variation in results, to find the best classification model and to analyze the merits and demerits of these algorithms. KNN and PCA-KNN emerged to be the top individual and integrated models. The study also concluded that one specific algorithm can't show best results for all types of datasets and integrated models not always perform better than the individuals. It is observed that no algorithm is perfect and performance of an algorithm totally depends on the dataset type and structure, its number of observations, its dimensions and the decision boundary.

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Wang, Biao, Chengxi Wu, Yunan Zhu, Mingliang Zhang, Hanqiong Li, and Wei Zhang. "Ship Radiated Noise Recognition Technology Based on ML-DS Decision Fusion." Computational Intelligence and Neuroscience 2021 (October 7, 2021): 1–14. http://dx.doi.org/10.1155/2021/8901565.

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Ship radiated noise is an important information source of underwater acoustic targets, and it is of great significance to the identification and classification of ship targets. However, there are a lot of interference noises in the water, which leads to the reduction of the model recognition rate. Therefore, the recognition results of radiated noise targets are severely affected. This paper proposes a machine learning Dempster–Shafer (ML-DS) decision fusion method. The algorithm combines the recognition results of machine learning and deep learning. It uses evidence-based decision-making theory to realize feature fusion under different neural network classifiers and improve the accuracy of judgment. First, deep learning algorithms are used to classify two-dimensional spectrogram features and one-dimensional amplitude features extracted from CNN and LSTM networks. The machine learning algorithm SVM is used to classify the chromaticity characteristics of radiated noise. Then, according to the classification results of different classifiers, a basic probability assignment model (BPA) was designed to fuse the recognition results of the classifiers. Finally, according to the classification characteristics of machine learning and deep learning, combined with the decision-making of D-S evidence theory of different times, the decision-making fusion of radiated noise is realized. The results of the experiment show that the two fusions of deep learning combined with one fusion of machine learning can significantly improve the recognition results of low signal-to-noise ratio (SNR) datasets. The lowest fusion recognition result can reach 76.01%, and the average fusion recognition rate can reach 94.92%. Compared with the traditional single feature recognition algorithm, the recognition accuracy is greatly improved. Compared with the traditional one-step fusion algorithm, it can effectively integrate the recognition results of heterogeneous data and heterogeneous networks. The identification method based on ML-DS proposed in this paper can be applied in the field of ship radiated noise identification.

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Berka, Petr, and Ivan Bruha. "Empirical Comparison of Various Discretization Procedures." International Journal of Pattern Recognition and Artificial Intelligence 12, no. 07 (November 1998): 1017–32. http://dx.doi.org/10.1142/s0218001498000567.

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The genuine symbolic machine learning (ML) algorithms are capable of processing symbolic, categorial data only. However, real-world problems, e.g. in medicine or finance, involve both symbolic and numerical attributes. Therefore, there is an important issue of ML to discretize (categorize) numerical attributes. There exist quite a few discretization procedures in the ML field. This paper describes two newer algorithms for categorization (discretization) of numerical attributes. The first one is implemented in the KEX (Knowledge EXplorer) as its preprocessing procedure. Its idea is to discretize the numerical attributes in such a way that the resulting categorization corresponds to KEX knowledge acquisition algorithm. Since the categorization for KEX is done "off-line" before using the KEX machine learning algorithm, it can be used as a preprocessing step for other machine learning algorithms, too. The other discretization procedure is implemented in CN4, a large extension of the well-known CN2 machine learning algorithm. The range of numerical attributes is divided into intervals that may form a complex generated by the algorithm as a part of the class description. Experimental results show a comparison of performance of KEX and CN4 on some well-known ML databases. To make the comparison more exhibitory, we also used the discretization procedure of the MLC++ library. Other ML algorithms such as ID3 and C4.5 were run under our experiments, too. Then, the results are compared and discussed.

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Kaur, Karamjeet. "Eye Facial Gesture System Controller Based on AI ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 5063–68. http://dx.doi.org/10.22214/ijraset.2023.52854.

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Abstract: This paper presents a ground-breaking real-time algorithm for accurately detecting eye blinks in video sequences captured using standard cameras. The algorithm utilizes advanced landmark detectors trained on diverse datasets to ensure robustness against variations in head orientation, illumination, and facial expressions. By employing precise landmark detection techniques, the algorithm estimates the level of eye opening using the eye aspect ratio (EAR) in each frame. An SVM classifier analyses the patterns of EAR values within a short temporal window to identify eye blinks. Comparative evaluations on popular datasets demonstrate the superior performance of the proposed algorithm compared to existing methods. Additionally, the research explores the utilization of eye movements for controlling computer programs, evaluating four distinct approaches in a user-friendly photo viewer. The evaluation process considers various factors such as component sizes, execution time, unintended selections, and gesture repetitions. User experiments reveal that component sizes of 200px provide a convenient and efficient means of application control. The gaze-based method and gestures based on joining points receive positive feedback and exhibit satisfactory performance. This paper contributes significantly to the field of eye-based interaction by introducing an innovative blink detection algorithm and conducting a comprehensive evaluation of eye movement approaches for application control.

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Yingquan Wu and D. A. Pados. "An adaptive two-stage algorithm for ML and sub-ML decoding of binary linear block codes." IEEE Transactions on Information Theory 49, no. 1 (January 2003): 261–69. http://dx.doi.org/10.1109/tit.2002.806127.

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Bahraini, Masoud S., Ahmad B. Rad, and Mohammad Bozorg. "SLAM in Dynamic Environments: A Deep Learning Approach for Moving Object Tracking Using ML-RANSAC Algorithm." Sensors 19, no. 17 (August 26, 2019): 3699. http://dx.doi.org/10.3390/s19173699.

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The important problem of Simultaneous Localization and Mapping (SLAM) in dynamic environments is less studied than the counterpart problem in static settings. In this paper, we present a solution for the feature-based SLAM problem in dynamic environments. We propose an algorithm that integrates SLAM with multi-target tracking (SLAMMTT) using a robust feature-tracking algorithm for dynamic environments. A novel implementation of RANdomSAmple Consensus (RANSAC) method referred to as multilevel-RANSAC (ML-RANSAC) within the Extended Kalman Filter (EKF) framework is applied for multi-target tracking (MTT). We also apply machine learning to detect features from the input data and to distinguish moving from stationary objects. The data stream from LIDAR and vision sensors are fused in real-time to detect objects and depth information. A practical experiment is designed to verify the performance of the algorithm in a dynamic environment. The unique feature of this algorithm is its ability to maintain tracking of features even when the observations are intermittent whereby many reported algorithms fail in such situations. Experimental validation indicates that the algorithm is able to perform consistent estimates in a fast and robust manner suggesting its feasibility for real-time applications.

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Yang, Kaikai, Sheng Hong, Qi Zhu, and Yanheng Ye. "Maximum Likelihood Angle-Range Estimation for Monostatic FDA-MIMO Radar with Extended Range Ambiguity Using Subarrays." International Journal of Antennas and Propagation 2020 (September 8, 2020): 1–10. http://dx.doi.org/10.1155/2020/4601208.

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In this paper, we consider the joint angle-range estimation in monostatic FDA-MIMO radar. The transmit subarrays are first utilized to expand the range ambiguity, and the maximum likelihood estimation (MLE) algorithm is first proposed to improve the estimation performance. The range ambiguity is a serious problem in monostatic FDA-MIMO radar, which can reduce the detection range of targets. To extend the unambiguous range, we propose to divide the transmitting array into subarrays. Then, within the unambiguous range, the maximum likelihood (ML) algorithm is proposed to estimate the angle and range with high accuracy and high resolution. In the ML algorithm, the joint angle-range estimation problem becomes a high-dimensional search problem; thus, it is computationally expensive. To reduce the computation load, the alternating projection ML (AP-ML) algorithm is proposed by transforming the high-dimensional search into a series of one-dimensional search iteratively. With the proposed AP-ML algorithm, the angle and range are automatically paired. Simulation results show that transmitting subarray can extend the range ambiguity of monostatic FDA-MIMO radar and obtain a lower cramer-rao low bound (CRLB) for range estimation. Moreover, the proposed AP-ML algorithm is superior over the traditional estimation algorithms in terms of the estimation accuracy and resolution.

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Bhide, Abhishek, Dnyaneshvar Ghodake, Ashish Jamle, Salman Shaikh, and Prof S. R. Bhujbal. "Predictive Machine Maintenance Using Tiny ML." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (April 30, 2023): 4252–55. http://dx.doi.org/10.22214/ijraset.2023.51254.

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Abstract: Anomaly detection (AD) is detection of pattern in data in expected behaviour. In an industrial environment, any equipment and system that breaks down are affecting productivity. Therefore, Tiny Machine Learning (Tiny ML) is introduced to address this problem. Tiny ML undergo anomaly detection to detect if any equipment did not act expected behaviour and notify the user if an anomaly detection has been detected. Anomaly detection is an unsupervised learning algorithm. It has aim to identify the patterns of data that do not follow the expected behaviour. Tiny Machine Learning (Tiny), a rapidly evolving edge computing concept that links embedded systems (hardware and software) and machine learning, with purpose of realizing ultra-low-power or low-cost and efficiency and privacy also brings machine learning inference to battery-powered intelligent devices. By using TensorFlow Lite Micro, the Tiny ML can be trained to undergo anomaly detection. How is the machine learning process has exported for TensorFlow, then TensorFlow and final TensorFlow L Micro order to upload the machine learning algorithm with Tiny ML. The paper highlight the state the art of the current work on Tiny Machine Learning.

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Raji, Ismail Damilola, Habeeb Bello-Salau, Ime Jarlath Umoh, Adeiza James Onumanyi, Mutiu Adesina Adegboye, and Ahmed Tijani Salawudeen. "Simple Deterministic Selection-Based Genetic Algorithm for Hyperparameter Tuning of Machine Learning Models." Applied Sciences 12, no. 3 (January 24, 2022): 1186. http://dx.doi.org/10.3390/app12031186.

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Hyperparameter tuning is a critical function necessary for the effective deployment of most machine learning (ML) algorithms. It is used to find the optimal hyperparameter settings of an ML algorithm in order to improve its overall output performance. To this effect, several optimization strategies have been studied for fine-tuning the hyperparameters of many ML algorithms, especially in the absence of model-specific information. However, because most ML training procedures need a significant amount of computational time and memory, it is frequently necessary to build an optimization technique that converges within a small number of fitness evaluations. As a result, a simple deterministic selection genetic algorithm (SDSGA) is proposed in this article. The SDSGA was realized by ensuring that both chromosomes and their accompanying fitness values in the original genetic algorithm are selected in an elitist-like way. We assessed the SDSGA over a variety of mathematical test functions. It was then used to optimize the hyperparameters of two well-known machine learning models, namely, the convolutional neural network (CNN) and the random forest (RF) algorithm, with application on the MNIST and UCI classification datasets. The SDSGA’s efficiency was compared to that of the Bayesian Optimization (BO) and three other popular metaheuristic optimization algorithms (MOAs), namely, the genetic algorithm (GA), particle swarm optimization (PSO) and biogeography-based optimization (BBO) algorithms. The results obtained reveal that the SDSGA performed better than the other MOAs in solving 11 of the 17 known benchmark functions considered in our study. While optimizing the hyperparameters of the two ML models, it performed marginally better in terms of accuracy than the other methods while taking less time to compute.

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Anderson, Connor J., Daniel Heins, Keith C. Pelletier, and Joseph F. Knight. "Improving Machine Learning Classifications of Phragmites australis Using Object-Based Image Analysis." Remote Sensing 15, no. 4 (February 10, 2023): 989. http://dx.doi.org/10.3390/rs15040989.

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Uncrewed aircraft systems (UASs) are a popular tool when surveilling for invasive alien plants due to their high spatial and temporal resolution. This study investigated the efficacy of a UAS equipped with a three-band (i.e., red, green, blue; RGB) sensor to identify invasive Phragmites australis in multiple Minnesota wetlands using object-based image analysis (OBIA) and machine learning (ML) algorithms: artificial neural network (ANN), random forest (RF), and support vector machine (SVM). The addition of a post-ML classification OBIA workflow was tested to determine if ML classifications can be improved using OBIA techniques. Results from each ML algorithm were compared across study sites both with and without the post-ML OBIA workflow. ANN was identified as the best classifier when not incorporating a post-ML OBIA workflow with a classification accuracy of 88%. Each of the three ML algorithms achieved a classification accuracy of 91% when incorporating the post-ML OBIA workflow. Results from this study suggest that a post-ML OBIA workflow can increase the ability of ML algorithms to accurately identify invasive Phragmites australis and should be used when possible. Additionally, the decision of which ML algorithm to use for Phragmites mapping becomes less critical with the addition of a post-ML OBIA workflow.

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Obuseh, Marian, Denny Yu, and Poching DeLaurentis. "Detecting Unusual Intravenous Infusion Alerting Patterns with Machine Learning Algorithms." Biomedical Instrumentation & Technology 56, no. 2 (April 1, 2022): 58–70. http://dx.doi.org/10.2345/1943-5967-56.2.58.

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Abstract Objective To detect unusual infusion alerting patterns using machine learning (ML) algorithms as a first step to advance safer inpatient intravenous administration of high-alert medications. Materials and Methods We used one year of detailed propofol infusion data from a hospital. Interpretable and clinically relevant variables were feature engineered, and data points were aggregated per calendar day. A univariate (maximum times-limit) moving range (mr) control chart was used to simulate clinicians' common approach to identifying unusual infusion alerting patterns. Three different unsupervised multivariate ML-based anomaly detection algorithms (Local Outlier Factor, Isolation Forest, and k-Nearest Neighbors) were used for the same purpose. Results from the control chart and ML algorithms were compared. Results The propofol data had 3,300 infusion alerts, 92% of which were generated during the day shift and seven of which had a times-limit greater than 10. The mr-chart identified 15 alert pattern anomalies. Different thresholds were set to include the top 15 anomalies from each ML algorithm. A total of 31 unique ML anomalies were grouped and ranked by agreeability. All algorithms agreed on 10% of the anomalies, and at least two algorithms agreed on 36%. Each algorithm detected one specific anomaly that the mr-chart did not detect. The anomaly represented a day with 71 propofol alerts (half of which were overridden) generated at an average rate of 1.06 per infusion, whereas the moving alert rate for the week was 0.35 per infusion. Discussion These findings show that ML-based algorithms are more robust than control charts in detecting unusual alerting patterns. However, we recommend using a combination of algorithms, as multiple algorithms serve a benchmarking function and allow researchers to focus on data points with the highest algorithm agreeability. Conclusion Unsupervised ML algorithms can assist clinicians in identifying unusual alert patterns as a first step toward achieving safer infusion practices.

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Silva-Aravena, Fabián, Hugo Núñez Delafuente, Jimmy H. Gutiérrez-Bahamondes, and Jenny Morales. "A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making." Cancers 15, no. 9 (April 25, 2023): 2443. http://dx.doi.org/10.3390/cancers15092443.

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Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

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Journal articles on the topic 'ML algorithm'

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