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2D-RNA-coupling numbers:: A new computational chemistry approach to link secondary structure topology with biological function

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JOURNAL OF COMPUTATIONAL CHEMISTRY 2007年第6期28卷 1049-1056页

作者： Gonzalez-Diaz, Humberto Agueero-Chapin, Guillermin Varona, Javier Molina, Reinaldo Delogu, Giovanna Santana, Lourdes Uriarte, Eugenio Podda, Gianni Univ Cagliari Dipartimento Farmaco Chim Tecnol I-09124 Cagliari Italy Univ Nacl Autonoma Mexico Biomed Unit FES Istacala Tlalnepantla 54090 DF Mexico Univ Porto Fac Ciencias REQUIMTE P-4169007 Oporto Portugal

Methods for prediction of proteins, DNA, or RNA function and mapping it onto sequence often rely on bioinformatics alignment approach instead of chemical structure. Consequently, it is interesting to develop computational chemistry approaches based on molecular descriptors. In this sense, many researchers used sequence-coupling numbers and our group extended them to 2D proteins representations. However, no coupling numbers have been reported for 2D-RNA topology graphs, which are highly branched and contain useful information. Here, we use a computational chemistry scheme: (a) transforming sequences into RNA secondary structures, (b) defining and calculating new 2D-RNA-coupling numbers, (c) seek a structure-function model, and (d) map biological function onto the folded RNA. We studied as example 1-aminocyclopropane-1-carboxylic acid (ACC) oxidases known as ACO, which control fruit ripening having importance for biotechnology industry. First, we calculated tau(k)(2D-RNA) values to a set of 90-folded RNAs, including 28 transcripts of ACO and control sequences. Afterwards, we compared the classification performance of 10 different classifiers implemented in the software WEKA. In particular, the logistic equation ACO = 23.8 . tau(1)(2D-RNA) + 41.4 predicts ACOs with 98.9%, 98.0%, and 97.8% of accuracy in training, leave-one-out and 10-fold cross-validation, respectively. Afterwards, with this equation we predict ACO function to a sequence isolated in this work from Coffea arabica (GenBank accession DQ218452). The tau(1)(2D-RNA) also favorably compare with other descriptors. This equation allows us to map the codification of ACO activity on different mRNA topology features. The present computational-chemistry approach is general and could be extended to connect RNA secondary structure topology to other functions. (C) 2007 Wiley Periodicals, Inc.

关键词： RNA secondary structure molecular descriptors sequence-function relationships coupling numbers linear classifiers machine learning algorithms

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Weighted Outlier Detection of High-Dimensional Categorical Data Using Feature Grouping

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2020年第11期50卷 4295-4308页

作者： Li, Junli Zhang, Jifu Pang, Ning Qin, Xiao Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Jinzhong Univ Sch Informat Technol & Engn Jinzhong 030619 Peoples R China Auburn Univ Samuel Ginn Coll Engn Dept Comp Sci & Software Engn Auburn AL 36849 USA

We propose a weighted outlier mining method called WATCH to identify outliers in high-dimensional categorical datasets. WATCH is composed of two distinctive modules: 1) feature grouping by the virtue of correlation measurement among features and 2) outlier mining by assigning scores to objects in each feature groups. At the heart of WATCH is the feature grouping module, which groups an array of features into multiple groups to discover various aspects of feature patterns in each group. The outlier mining module detects outliers from high-dimensional categorical datasets. Except for the number of outliers specified by users, WATCH is conducive to bypassing the optimization of any user-given parameter. We implement and evaluate WATCH using synthetic and real-world datasets. Our experimental results show that WATCH is a promising and practical algorithm to detect outliers in high-dimensional categorical datasets, because WATCH achieves high performance in terms of precision, efficiency, and interpretability.

关键词： Feature extraction Anomaly detection Correlation machine learning algorithms Clustering algorithms Entropy Categorical data feature grouping feature relation feature weighting outlier detection

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Design of an Intrusion Detection Model for IoT-Enabled Smart Home

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IEEE ACCESS 2023年 11卷 52509-52526页

作者： Rani, Deepti Gill, Nasib Singh Gulia, Preeti Arena, Fabio Pau, Giovanni Maharshi Dayanand Univ Dept Comp Sci & Applicat Rohtak 124001 Haryana India Kore Univ Enna Fac Engn & Architecture I-94100 Enna Italy

machine learning (ML) provides effective solutions to develop efficient intrusion detection system (IDS) for various environments. In the present paper, a diversified study of various ensemble machine learning (ML) algorithms has been carried out to propose design of an effective and time-efficient IDS for Internet of Things (IoT) enabled environment. In this paper, data captured from network traffic and real-time sensors of the IoT-enabled smart environment has been analyzed to classify and predict various types of network attacks. The performance of Logistic Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting machine classifiers have been benchmarked using an open-source largely imbalanced dataset 'DS2OS' that consists of 'normal' and 'anomalous' network traffic. An intrusion detection model "LGB-IDS" has been proposed using the LGBM library of ML after validating its superiority over other algorithms using ensemble techniques and on the basis of majority voting. The performance of the proposed intrusion detection system is suitably validated using certain performance metrics of machine learning such as train and test accuracy, time efficiency, error-rate, true-positive rate (TPR), and false-negative rate (FNR). The experimental results reveal that XGB and LGBM have almost equal accuracy, but the time efficiency of LGBM is much better than RF, and XGB classifiers. The main objective of the present paper is to propose a design of an efficient intrusion detection model with high accuracy, better time efficiency, and reduced false alarm rate. The experimental results show that the proposed model achieves an accuracy of 99.92% and the time efficiency comes to be much higher than other prevalent algorithms-based models. The threat detection rate is greater than 90% and less than 100%. Time complexity of LGBM is also very much low as compared to other ML algorithms.

关键词： Intrusion detection Classification algorithms Internet of Things machine learning Telecommunication traffic machine learning algorithms Boosting machine learning classification algorithms ensemble classifiers gradient boosting algorithms light gradient boosting machines (LGBM) and intrusion detection systems (IDS)

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Indoor Intelligent Fingerprint-Based Localization: Principles, Approaches and Challenges

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2020年第4期22卷 2634-2657页

作者： Zhu, Xiaoqiang Qu, Wenyu Qiu, Tie Zhao, Laiping Atiquzzaman, Mohammed Wu, Dapeng Oliver Tianjin Univ Sch Comp Sci & Technol Coll Intelligence & Comp Tianjin 300350 Peoples R China Tianjin Univ Tianjin Key Lab Adv Networking Tianjin 300350 Peoples R China Univ Oklahoma Sch Comp Sci Norman OK 73019 USA Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

With the rapid development of Internet of Things (IoT) technology, location-based services have been widely applied in the construction of smart cities. Satellite-based location services have been utilized in outdoor environments, but they are not suitable for indoor technology due to the absence of global positioning system (GPS) signal. Therefore, many indoor localization technologies and systems have emerged by utilizing many other signals. In particular, fingerprinting localization has recently garnered attention because its promising performance. In this work, we aim to study recent indoor localization technologies and systems based on various fingerprints, which use machine learning and intelligent algorithms. We also present the architecture of intelligent localization. The development of indoor localization technology should have the ability of self-adaptation and self-learning in the future. And the architecture shows how to make localization become more "smart" by advanced techniques. The state-of-the-art localization systems' working principles are summarized and compared in terms of their localization accuracy, latency, energy consumption, complexity, and robustness. We also discuss the challenges of existing indoor localization technologies, potential solutions to these challenges, and possible improvement measures.

关键词： Wireless communication Global Positioning System machine learning algorithms Communication system security Prediction algorithms Support vector machines Wireless sensor networks Internet of Things intelligent localization fingerprint machine learning

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Ensemble Pruning via Quadratic Margin Maximization

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IEEE ACCESS 2021年 9卷 48931-48951页

作者： Martinez, Waldyn G. Miami Univ Dept Informat Syst & Analyt Oxford OH 45056 USA

Ensemble models refer to methods that combine a typically large number of weak learners into a stronger composite model. The output of an ensemble method is the result of fitting a base-learning algorithm to a given data set, and obtaining diverse answers by re-weighting the observations or by re-sampling them using a given probabilistic selection. A key challenge of using ensembles in large-scale multidimensional data lies in the complexity and the computational burden associated with them. The models created by ensembles are often difficult, if not impossible, to interpret and their implementation requires more computational power than individual learning algorithms. Recent research effort in the field has concentrated on reducing ensemble size, while maintaining predictive accuracy. We propose a method to prune an ensemble solution by optimizing its margin distribution, while increasing its diversity. The proposed algorithm results in an ensemble that uses only a fraction of the original weak learners, with generally improved estimated generalization performance. We analyze and test our method on both synthetic and real data sets. The analysis shows that the proposed method compares favorably to the original ensemble solutions and to other existing ensemble pruning methodologies.

关键词： Boosting Training Radio frequency Prediction algorithms Vegetation machine learning algorithms Forestry Ensemble learning ensemble thinning quadratic programming ensemble margins

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Design and Fault Diagnosis of Induction Motor Using ML-Based algorithms for EV Application

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IEEE ACCESS 2023年 11卷 34186-34197页

作者： Aishwarya, M. Brisilla, R. M. Vellore Inst Technol VIT Sch Elect Engn Vellore 632014 India

The need for alternate transportation is driven by the increased fossil fuel cost and the adverse effects of climatic change. Electric vehicles (EVs) are the best option as they have less carbon footprint and reduced dependency on fossil fuels. Prodigious efforts to enhance the efficiency of EVs resulted in the development of highly efficient three-phase induction motors. Difficulties in designing highly efficient induction motors (IM) with high torque and power factors hindered the success of EV applications. Hence, our aim is to diagnosis fault in the designed IM under variable load conditions. The proposed EV motor is designed for 415V, 50Hz, and 5HP output power rating using ANSYS RMxprt simulation software. A fault detection strategy is also implemented with various machine learning (ML) techniques like Support Vector machine (SVM), K-nearest neighbors (k-NN), ML perceptron (MLP), Random Forest (RF), Decision Tree (DT), Gradient boosting (GB), Extreme Gradient Boosting (XGBoost), and Deep learning (DL) for both healthy and faulty conditions. Short Circuit (SC), High Resistance connection (HRC), and Open-Phase circuit (OPC) are considered as faulty states for this study. Motor performance with variable load for all the states healthy and faulty are evaluated through machine learning.

关键词： Induction motor electric vehicle motor design material ANSYS machine learning algorithms

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Distributed Reservoir Computing with Sparse Readouts

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2016年第4期11卷 59-70页

作者： Scardapane, Simone Panella, Massimo Comminiello, Danilo Hussain, Amir Uncini, Aurelio Univ Roma La Sapienza Dept Informat Engn Elect & Telecommun DIET Rome Italy Univ Stirling Sch Nat Sci Stirling FK9 4LA Scotland

In a network of agents, a widespread problem is the need to estimate a common underlying function starting from locally distributed measurements. Real-world scenarios may not allow the presence of centralized fusion centers, requiring the development of distributed, message-passing implementations of the standard machine learning training algorithms. In this paper, we are concerned with the distributed training of a particular class of recurrent neural networks, namely echo state networks ( ESNs). In the centralized case, ESNs have received considerable attention, due to the fact that they can be trained with standard linear regression routines. Based on this observation, in our previous work we have introduced a decentralized algorithm, framed in the distributed optimization field, in order to train an ESN. In this paper, we focus on an additional sparsity property of the output layer of ESNs, allowing for very efficient implementations of the resulting networks. In order to evaluate the proposed algorithm, we test it on two well-known-prediction benchmarks, namely the Mackey-Glass chaotic time series and the 10th order nonlinear auto regressive moving average ( NARMA) system. [GRAPHICS] .

关键词： Regression analysis Training data Algorithm design and analysis machine learning algorithms Linear regression Optimization Recurrent neural networks

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Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI-Explainable Artificial Intelligence

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第8期18卷 5127-5137页

作者： Machlev, Ram Perl, Michael Belikov, Juri Levy, Kfir Yehuda Levron, Yoash Technion Israel Inst Technol Andrew & Erna Viterbi Fac Elect & Comp Engn IL-3200003 Haifa Israel Tallinn Univ Technol Dept Software Sci EE-12618 Tallinn Estonia

Advanced machine learning techniques have recently demonstrated outstanding performance when applied to power quality disturbance (PQD) classification. Nevertheless, a possible problem is that power experts may find it hard to trust the results of such algorithms, if they do not fully understand the reasons for their outputs. In this light, this article suggests a method that explains the outputs of PQD classifiers, using explainable artificial intelligence (XAI). The method operates as follows: first, various XAI techniques and classifiers are combined and scored based on their explanations during the validation step. Then, the best combination of classifier and XAI technique for each disturbance is used on the testing set, such that the classifier outputs are more transparent. To accomplish these steps, a definition for the correct explanation in PQD is given. Also, to determine the quality of an explanation for a certain output, we propose an evaluation process to measure the explainability score for each XAI technique and classifier. By means of this approach, the PQD classifier outputs are optimized to be both accurate and easy to understand, allowing experts to make informed and trustworthy decisions.

关键词： Convolution Power quality machine learning algorithms Classification algorithms Power measurement Measurement Kernel Convolutional neural network (CNN) deep-learning (DL) energy evaluation metrics explainable artificial intelligence (XAI) power quality disturbances (PQDs) power

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Effectual Energy Consumption and User Comfort Optimization Based on Dynamic User Set Parameters in Electric Vehicles

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 178-189页

作者： Fayaz, Muhammad Khan, Junaid Bilal, Muhammad Univ Cent Asia Sch Arts & Sci Dept Comp Sci Naryn 722900 Kyrgyzstan Chungnam Natl Univ Dept Environm & IT Engn Daejeon 34134 South Korea Univ Lancaster Sch Comp & Commun Lancaster LA1 4WA England

The reduction of energy usage and the enhancement of comfort indices within electric vehicles stand as key areas of focus for modern research. Numerous approaches have been devised to address these concerns, yet each of these models exhibit certain constraints;some effectively reduce energy usage yet do so at the cost of comfort, whereas others succeed in elevating comfort at the price of increased energy consumption. Our research introduces a comprehensive model that leverages optimization, machine learning, data smoothing, and control protocols to defeat these challenges. The purpose of the proposed model is two folds: firstly, to reduce energy use, and secondly, to enhance the comfort of the user. Our proposed model comprises three main modules: the smoothing, the optimization, and control modules. In the smoothing module, the alpha-beta filter, the simplest and most effective filter, has been used to remove noise and smooth the data. The optimization module is segmented into two additional modules: the FA-GA module and the support vector machine module. The purpose of the support vector machine in the optimization module is to make the system fully automatic and dynamic by setting user parameters in the objective function of the FA-GA module. Within the control module, Mamdani fuzzy logic is employed to deliver the appropriate energy to the respective actuators. The proposed technique is evaluated against several proven methods, with results suggesting that our approach achieves commendable results in comparison to similar algorithms.

关键词： Optimization Energy consumption Smoothing methods Genetic algorithms machine learning algorithms Electric vehicles Petroleum Energy user comfort optimization electric vehicles control algorithm machine learning

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Resetting Weight Vectors in MOEA/D for Multiobjective Optimization Problems With Discontinuous Pareto Front

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第9期52卷 9770-9783页

作者： Zhang, Chunjiang Gao, Liang Li, Xinyu Shen, Weiming Zhou, Jiajun Tan, Kay Chen Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430078 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vectors (RWVs) for MOEA/D. When the RWV mechanism is triggered, a classic data clustering algorithm DBSCAN is used to categorize current solutions into several parts. A classic statistical method called principal component analysis (PCA) is used to determine the ideal number of solutions in each part of PF. Thereafter, PCA is used again for each part of PF separately and virtual targeted solutions are generated by linear interpolation methods. Then, the new weight vectors are reset according to the interrelationship between the optimal solutions and the weight vectors under the Tchebycheff decomposition framework. Finally, taking advantage of the current obtained solutions, the new solutions in the decision space are updated via a linear interpolation method. Numerical experiments show that the proposed MOEA/D-RWV can achieve good results for bi-objective and tri-objective optimization problems with discontinuous PF. In addition, the test on a recently proposed MaF benchmark suite demonstrates that MOEA/D-RWV also works for some problems with other complicated characteristics.

关键词： Optimization Principal component analysis Evolutionary computation Clustering algorithms Heuristic algorithms machine learning algorithms machine learning DBSCAN multiobjective evolutionary algorithm based on decomposition (MOEA D) multiobjective evolutionary algorithm (MOEA) principal component analysis (PCA) weight vectors

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