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Utilizing machine learning techniques for enhanced water quality monitoring

WATER QUALITY REsearch JOURNAL 2024年第4期59卷 187-204页

作者： Yigit, Gozde Ozsert Baransel, Cesur Gaziantep Univ Dept Comp Engn TR-27410 Gaziantep Turkiye

Water quality is an important issue for environmental health. It directly impacts human well-being, ecosystem sustainability and socioeconomic development. This paper provides an overview for water quality assesment by integrating traditional methods with computational technology. Dimensionality reduction is considered an essential preprocessing step in any data analysis task which can be performed by using either feature selection or feature extraction methods. In this study, we propose an autoencoder-based feature selection method that can be used with both labeled and unlabeled data. It can be implemented with an arbitrary number of hidden layers in the symmetric encoder part of the autoencoder and provides results that compare favorably with the results provided by computationally more expensive methods and also provides a quantitatively ordered rank of features for the features in the dataset. Also, our proposed method for water quality assessment has demonstrated remarkable success in efficiently managing and interpreting complex datasets, offering a promising pathway toward effective environmental stewardship and sustainable water resource management. Through its implementation, we aim to contribute to the preservation and protection of water quality for the benefit of present and future generations.

关键词： autoencoder dimensioanilty reduction feature selection sequential backward search sequential forward search water potability water quality

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MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models

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METHODS 2024年 227卷 37-47页

作者： Kurata, Hiroyuki Harun-Or-Roshid, Md Hasan, Md Mehedi Tsukiyama, Sho Maeda, Kazuhiro Manavalan, Balachandran Kyushu Inst Technol Dept Biosci & Bioinformat 680-4 Kawazu Iizuka Fukuoka 8208502 Japan Louisiana State Univ Sch Vet Med Dept Pathobiol Sci Div Biotechnol & Mol Med Baton Rouge LA 70803 USA Sungkyunkwan Univ Coll Biotechnol & Bioengn Dept Integrat Biotechnol Suwon 16419 South Korea

RNA modification serves as a pivotal component in numerous biological processes. Among the prevalent modifications, 5-methylcytosine (m5C) significantly influences mRNA export, translation efficiency and cell differentiation and are also associated with human diseases, including Alzheimer 's disease, autoimmune disease, cancer, and cardiovascular diseases. Identification of m5C is critically responsible for understanding the RNA modification mechanisms and the epigenetic regulation of associated diseases. However, the large-scale experimental identification of m5C present significant challenges due to labor intensity and time requirements. Several computational tools, using machine learning, have been developed to supplement experimental methods, but identifying these sites lack accuracy and efficiency. In this study, we introduce a new predictor, MLm5C, for precise prediction of m5C sites using sequence data. Briefly, we evaluated eleven RNA sequence -derived features with four basic machine learning algorithms to generate baseline models. From these 44 models, we ranked them based on their performance and subsequently stacked the Top 20 baseline models as the best model, named MLm5C. The MLm5C outperformed the -state-of-the-art predictors. Notably, the optimization of the sequence length surrounding the modification sites significantly improved the prediction performance. MLm5C is an invaluable tool in accelerating the detection of m5C sites within the human genome, thereby facilitating in the characterization of their roles in post -transcriptional regulation.

关键词： RNA 5-methylcytosine sequential forward search Baseline model Sequence analysis Bioinformatics

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Laser-induced fluorescence spectroscopy enhancing pistachio nut quality screening

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FOOD CONTROL 2024年 158卷

作者： Magnus, Indy Abbasi, Fatemeh Thienpont, Hugo Smeesters, Lien Vrije Univ Brussel & Flanders Make Dept Appl Phys & Photon Brussels Photon B PHOT Pleinlaan 2 B-1050 Brussels Belgium

Food safety and quality has become increasingly important in our society, driving the development of novel optical food sensing technologies. Optical sensing technologies have shown good product identification performances, while offering a non-destructive measurement and the possibility for in-line applications. They are, however, currently often limited in their sensitivity and product variability. We therefore pursue the development of a novel pistachio nut screening methodology offering a multi-defect detection, simultaneously detecting shells, tree parts and aflatoxins, by combining fluorescence spectroscopy with advanced chemometrics. Specifically, both one- and two-photon induced fluorescence are investigated, in combination with Linear Discriminant Analysis, Quadratic Discriminant Analysis and K-Nearest Neighbors algorithm, enabling to optimize both the hardware and software parameters. Optimal results were obtained combining the fluorescence spectra using 385 nm excitation with Quadratic Discriminant Analysis, showing a classification accuracy of 99.2% for the healthy pistachio kernels, together with a false positive rate of only 0.8%. This excellent classification accuracy, while considering a multi-defect challenge, is exceeding the state-of-the-art, paving the way towards an improved pistachio screening, benefitting the food processing industry.

关键词： Fluorescence spectroscopy Two-photon induced fluorescence Pistachio nuts Non-destructive food classification Quadratic discriminant analysis sequential forward search

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Hyperspectral band selection based on triangular factorization

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JOURNAL OF APPLIED REMOTE SENSING 2017年第2期11卷

作者： Zhang, Wenqiang Li, Xiaorun Zhao, Liaoying Zhejiang Univ Dept Elect Engn Hangzhou Zhejiang Peoples R China Hangzhou Dianzi Univ Dept Comp Sci Hangzhou Zhejiang Peoples R China

We proposed an efficient unsupervised band selection method, maximum ellipsoid volume triangular factorization (MEV-TF), which is based on MEV and TF. MEV band selection regards the bands with the maximum determinant of the covariance matrix as the optimal band collection. By adopting TF, MEV-TF replaces the matrix determinant with scalar multiplication and achieves incremental calculation, which decreases the computational cost significantly. MEV-TF tries to select bands with large information and low correlation. Experimental results on different real data verify the efficiency of the proposed method. (C) 2017 Society of PhotoOptical Instrumentation Engineers (SPIE)

关键词： band selection hyperspectral remote sensing triangular factorization sequential forward search

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SUPPORT VECTOR MACHINES COMBINED WITH FEATURE SELECTION FOR DIABETES DIAGNOSIS

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ISTANBUL UNIVERSITY-JOURNAL OF ELECTRICAL AND ELECTRONICS ENGINEERING 2017年第1期17卷 3219-3225页

作者： Patlar Akbulut, Fatma Akan, Aydin Istanbul Univ Dept Biomed Engn Istanbul Turkey Istanbul Univ Dept Elect & Elect Engn Istanbul Turkey

Clinical Decision Support Systems (CDSS) are used as a service software which provides huge support to clinical decision making process where the main properties of a patient are matched to a tangible clinical knowledge. Within this gathered important information about patients, the medical decisions can be made more accurately. In this paper we present a CDSS that uses four physiological parameters of patients such as Pre-prandial Blood Glucose, Postprandial Blood Glucose, Hemoglobin A1C (HbA(1c)) and Glucose in Urine to produce a prediction about the possibility of being diabetic. According to collected reference data provided from hospitals, the disease can be predicted by comparing the input data of patients. If the system cannot procure a prediction about patients' status with these parameters, then the second phase which uses soft computing techniques is put into process with requesting additional data about patients. Our conducted experiments show that the diagnosis can be established in a breeze by getting the patients information with % 80 accuracy. Support Vector Machines were applied to achieve maximum success rate with nine different physiological parameters such as;Pregnancy, glucose, blood pressure, skin fold, insulin, Hemoglobin A1C, body mass index, family tree and age. Four different Kernel Functions are implemented in case studies and classification process is optimized by reducing attributes with feature selection algorithms. This represents an improvement in classification of CDSS, while reducing computational complexity.

关键词： Decision Support System Support Vector Machine sequential forward search Feature Selection

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A Feature Selection Method for Large-Scale Network Traffic Classification Based on Spark

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INFORMATION 2016年第1期7卷 6-6页

作者： Wang, Yong Ke, Wenlong Tao, Xiaoling Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin 541004 Peoples R China Guilin Univ Elect Technol Guangxi Coll & Univ Key Lab Cloud Comp & Complex Guilin 541004 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China

Currently, with the rapid increasing of data scales in network traffic classifications, how to select traffic features efficiently is becoming a big challenge. Although a number of traditional feature selection methods using the Hadoop-MapReduce framework have been proposed, the execution time was still unsatisfactory with numeral iterative computations during the processing. To address this issue, an efficient feature selection method for network traffic based on a new parallel computing framework called Spark is proposed in this paper. In our approach, the complete feature set is firstly preprocessed based on Fisher score, and a sequential forward search strategy is employed for subsets. The optimal feature subset is then selected using the continuous iterations of the Spark computing framework. The implementation demonstrates that, on the precondition of keeping the classification accuracy, our method reduces the time cost of modeling and classification, and improves the execution efficiency of feature selection significantly.

关键词： feature selection Fisher score sequential forward search MapReduce Spark

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Real-time recognition of ball bearing states for the enhancement of precision, quality, efficiency, safety, and automation of manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2014年第5-8期71卷 809-816页

作者： Liu, Tien-I Lee, Junyi Singh, Palvinder Liu, George Natl Taipei Univ Technol Coll Mech & Elect Engn Taipei 106 Taiwan Calif State Univ Sacramento Dept Mech Engn Sacramento CA 95819 USA DMG Mori Seiki Mfg USA Davis CA USA

Real-time recognition of ball bearing states can enhance precision, quality, efficiency, safety, and automation of manufacturing. Three features, including peak amplitude of the frequency domain, percent power, and peak RMS, have been extracted from the radial acceleration of ball bearings. The sequential forward search algorithm has been applied to select the best vibration features. Adaptive neuro fuzzy inference systems (ANFIS) have been used. A 2 x 2 ANFIS using the pi-shaped built-in membership function can distinguish normal bearing states from defective bearings states with 100 % success rate. Furthermore, a 3 x 5 ANFIS can classify ball bearings into six different states with a success rate of over 95 % for diagnostic purpose. In this manner, real-time recognition of ball bearing states for manufacturing can be performed efficiently and effectively.

关键词： sequential forward search Neuro fuzzy systems

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Determination of the normal boiling point of chemical compounds using a quantitative structure-property relationship strategy: Application to a very large dataset

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FLUID PHASE EQUILIBRIA 2013年 354卷 250-258页

作者： Gharagheizi, Farhad Mirkhani, Seyyed Alireza Ilani-Kashkouli, Poorandokht Mohammadi, Amir H. Ramjugernath, Deresh Richon, Dominique Univ KwaZulu Natal Sch Engn Thermodynam Res Unit ZA-4041 Durban South Africa Islamic Azad Univ Dept Chem Engn Buinzahra Branch Buinzahra Iran IRGCP Paris France Aalto Univ Sch Sci & Technol Dept Biotechnol & Chem Technol Aalto Finland

In this work, the quantitative structure property relationship (QSPR) strategy is applied to predict the normal boiling point (NBP) of pure chemical compounds. In order to propose a comprehensive, reliable, and predictive model, a large dataset of 17,768 pure chemical compounds was exploited. The sequential search mathematical method has been observed to be the only viable search method capable for selection of appropriate model parameters (molecular descriptors) with regard to a data set as large as is used in this study. To develop the model, a three-layer feed forward artificial neural network has been optimized using the Levenberg-Marquardt (LM) optimization strategy. Using this dedicated strategy, satisfactory results were obtained and are quantified by the following statistical parameters: average absolute relative deviations of the predicted properties from existing literature values: 3.2%, and squared correlation coefficient: 0.94. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Normal boiling points QSPR sequential forward search ANN Very large database

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Monitoring and diagnosis of the tapping process for product quality and automated manufacturing

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2013年第5-8期64卷 1169-1175页

作者： Liu, Tien-I Lee, Junyi Liu, George Wu, Zhang Calif State Univ Sacramento Dept Mech Engn Sacramento CA 95819 USA Mori Seiki Mfg Inc Davis CA USA Nanyang Technol Univ Sch Mech & Aerosp Engn Singapore 639798 Singapore

Tapping has been widely used throughout industry, and its proper operation is paramount in ensuring product quality. Therefore, monitoring and diagnosis are needed to detect the tapping process conditions. They are also important for automated manufacturing. In this work, a combination of ten indices of the tapping process was extracted from tapping torque, thrust force, and lateral forces. The sequential forward search (SFS) algorithm has been used to select the best feature sets. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) were used for the monitoring and diagnosis of tapping process. A 3 x 2 ANFIS structure can distinguish normal tapping process from abnormal tapping process with 100 % reliability. The tapping process conditions can be further classified into five categories with over 95 % success rate using a 10 x 2 ANFIS structure for diagnostic purpose. In simple words, monitoring and diagnosis of tapping process can be carried out successfully using SFS and ANFIS.

关键词： sequential forward search Neuro-fuzzy systems

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Identifying a small set of marker genes using minimum expected cost of misclassification

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2012年第1期55卷 51-59页

作者： Huang, Samuel H. Mo, Dengyao Meller, Jarek Wagner, Michael Univ Cincinnati Sch Dynam Syst Cincinnati OH 45221 USA Univ Cincinnati Dept Environm Hlth Cincinnati OH 45267 USA Cincinnati Childrens Hosp Div Biomed Informat Cincinnati OH 45229 USA

Objectives: This paper presents a model independent feature selection approach to identify a small subset of marker genes. Methods and material: An evaluation measure, minimum expected cost of misclassification (MEMC), is used to estimate the discriminative power of a feature subset without building a model. The MECM measure is combined with sequential forward search for feature selection. This approach was applied to a breast cancer profiling problem, with the goal of identifying a small number of marker genes whose expression can be used to predict cancer molecular subtype (p53 gene status). Furthermore, the method was also applied to find a small set of single-nucleotide polymorphisms (SNPs) that can be used to predict molecular phenotype of a different type, namely alleles (genetic variants) of human leukocyte antigen genes that play an important roles in autoimmunity. Results: Two marker genes were identified based on p53 status, which achieved a p-value of 7.53 x 10(-5) (vs. 6 x 10(-4) with 32 genes identified by previous research) in survival analysis. Six SNP loci were identified that achieved a leave-one-out cross-validation accuracy of 92.8% (vs. 90.6% and 89.5% with 18 SNPs selected using chi(2) statistics and information gain, respectively). Conclusion: The MECM-based feature selection approach is capable of identifying a smaller subset of market genes with comparable or even better performance than that obtained using conventional filter methods. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Feature selection Expected cost of misclassification sequential forward search Tag SNPs selection Breast cancer tumor classification

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