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Comparative analysis of nonlinear behavioral models for GaN HEMTs based on machine learning techniques

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2024年第2期37卷

作者： Liu, Bo Cai, Jialin Hangzhou Dianzi Univ Minist Educ Key Lab RF Circuit & Syst Hangzhou Peoples R China

A variety of novel behavioral modeling techniques have been reported to accurately capture the nonlinear characteristics of GaN devices. For the purpose of describing GaN HEMT large-signal behavior, machine learning-based approaches have been proposed. There is, however, a lack of comparison and analysis of their performance with different machine learning techniques in these studies. An examination of machine learning-based large-signal modeling techniques is presented in this article. To develop large-signal models of GaN HEMT, a range of commonly used modeling techniques such as artificial neural networks (ANN), random sample consistency (RANSAC), support vector regression (SVR), Gaussian process regression (GPR), decision trees (DTs), and genetic algorithm-assisted ANN (GA-ANN) are described and employed. Afterwards, integrated modeling techniques such as Bootstrap aggregation (BA), random forests (RF), AdaBoost, and gradient tree boosting (GTB) are reviewed and tested for their capabilities in developing GaN HEMT LSM. The hyperparameters of each built model are modified using the random search optimization (RSO) method, and five-fold cross-validation is used during validation. In order to identify optimal algorithms for nonlinear behavioral modeling of GaN devices, the model prediction results are comprehensively analyzed.

关键词： computational modeling ensemble methods GaN HEMT large-signal modeling machine learning algorithm

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A study of the interaction space of two lactate dehydrogenase isoforms (LDHA and LDHB) and some of their inhibitors using proteochemometrics modeling

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BMC CHEMISTRY 2023年第1期17卷 1-13页

作者： Damavandi, Sedigheh Shiri, Fereshteh Emamjomeh, Abbasali Pirhadi, Somayeh Beyzaei, Hamid Univ Zabol Dept Bioinformat Lab Computat Biotechnol & Bioinformat CBB Lab Zabol Iran Univ Zabol Fac Sci Dept Chem Zabol Iran Univ Zabol Fac Agr Dept Plant Breeding & Biotechnol PBB Zabol Iran Shiraz Univ Med Sci Med & Nat Prod Chem Res Ctr Shiraz Iran

Lactate dehydrogenase (LDH) is a tetramer enzyme that converts pyruvate to lactate reversibly. This enzyme becomes important because it is associated with diseases such as cancers, heart disease, liver problems, and most importantly, corona disease. As a system-based method, proteochemometrics does not require knowledge of the protein's three-dimensional structure, but rather depends on the amino acid sequence and protein descriptors. Here, we applied this methodology to model a set of LDHA and LDHB isoenzyme inhibitors. To implement the proteochemetrics method, the camb package in the R Studio Server programming environment was used. The activity of 312 compounds of LDHA and LDHB isoenzyme inhibitors from the valid Binding DB database was retrieved. The proteochemometrics method was applied to three machine learning algorithms gradient amplification model, random forest, and support vector machine as regression methods to find the best model. Through the combination of different models into an ensemble (greedy and stacking optimization), we explored the possibility of improving the performance of models. For the RF best ensemble model of inhibitors of LDHA and LDHB isoenzymes, and were 0.66 and 0.62, respectively. LDH inhibitory activation is influenced by Morgan fingerprints and topological structure descriptors.

关键词： Proteochemometrics machine learning algorithm Isoenzyme Camb package Morgan fingerprints

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Deep learning for load balancing of SDN-based data center networks

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第7期34卷 e4760-e4760页

作者： Babayigit, Bilal Ulu, Banu Erciyes Univ Fac Engn Comp Engn Dept Kayseri Turkey Erciyes Univ Grad Sch Nat & Appl Sci Comp Engn Dept Kayseri Turkey

With the development of new communication technologies, the amount of data transmission has increased gradually. To satisfy this increasing computing resource demand effectively, the number of data center networks (DCNs), which are structures composed of servers connected with well-organized-switches, has increased worldwide. However, traditional switches do not efficiently satisfy the needs of DCNs. In recent years, an emerging networking architecture software-defined network (SDN) has been proposed to manage the DCNs to control network switches and to deploy new network protocols. However, the main challenge in DCNs is to balance the load among servers. One potential solution for this challenge is to use machine learning (ML) techniques to tackle network transmission demand. A recent successful ML technique is deep learning (DL) which makes prediction, classification, and decisions by handling large amounts of data. Although DL has drawn increasing attention in many research fields, its applications to networking problems are scarce. In this paper, a DL technique is proposed for the load-balancing of SDN-based DCNs. To train the DL network, the variable load values among links are used. The response time for load balancing of the DL technique is compared with those of different ML algorithms, such as an artificial neural network (ANN), support vector machine (SVM), and logistic regression (LR). The experimental results reveal that the response-time results of ANN and DL are lower than those of the SVM and LR algorithms. Also, DL accuracy is higher than ANN accuracy. As a result, DL is very efficient for the load balancing of SDN-based DCNs.

关键词： data center networks deep learning load balancing machine learning algorithm software‐ defined network

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Transfer learning Based Recurrent Neural Network algorithm for Linguistic Analysis

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION PROCESSING 2021年第3期20卷 1–16页

作者： Jiang, Peipei Chen, Liailun Wang, Min-Feng West Anhui Univ Fac Foreign Languages Luan 237012 Anhui Peoples R China Wuhan Tech Coll Commun Sch Publ Teaching & Practice Wuhan 430065 Peoples R China Zheng Zhou Sheng Da Univ Econ Business & Manageme Sch Arts & Law Zhengzhou 451191 Peoples R China

Each language is a system of understanding and skills that allows language users to interact, express thoughts, hypotheses, feelings, wishes, and all that needs to be expressed. Linguistics is the research of these structures in all respects: the composition, usage, and sociology of language, in particular, are the core of linguistics. machine learning is the research area that allows machines to learn without being specifically scheduled. In linguistics, the design of writing is understood to be a foundation for many distinct company apps and probably the most useful if incorporated with machine learning methods. Research shows that besides text tagging and algorithm training, there are major problems in the field of Big Data. This article provides a collaborative effort (transfer learning integrated into Recurrent Neural Network) to analyze the distinct kinds of writing between the language's linear and non-computational sides, and to enhance granularity. The outcome demonstrates stronger incorporation of granularity into the language from both sides. Comparative results of machine learning algorithms are used to determine the best way to analyze and interpret the structure of the language.

关键词： Linguistics machine learning algorithm text analytics part-of-speech sentence analysis

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machine learning ensemble with image processing for pest identification and classification in field crops

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NEURAL COMPUTING & APPLICATIONS 2021年第13期33卷 7491-7504页

作者： Kasinathan, Thenmozhi Uyyala, Srinivasulu Reddy Natl Inst Technol Tiruchirappalli Dept Comp Applicat Ctr Excellence Artificial Intelligence Machine Learning & Data Analyt Lab Tiruchirappalli 620015 Tamil Nadu India

In agriculture field, yield loss is a major problem due to attack of various insects in field crops. Traditional insect identification and classification methods are time-consuming and require entomologist experts. Early information about the attack of insects helps farmers to control the crop damage to improve the productivity and reduce the use of pesticides. This research work focuses on the classification of crop insects by applying machine vision and knowledge-based techniques with image processing by using different feature descriptors including texture, color, shape, histogram of oriented gradients (HOG) and global image descriptor (GIST). A combination of all these features was used in the classification of insects. In this research, several machine learning algorithms including both base classifiers and ensemble classifiers were applied for three different insect datasets and the performances of classification results were evaluated by majority voting. Naive bayes (NB), support vector machine (SVM), K-nearest-neighbor (KNN) and multi-layer perceptron (MLP) were used as base classifiers. Ensemble classifiers include random forest (RF), bagging and XGBoost were utilized;10-fold cross-validation test was conducted to achieve a better classification and identification of insects. The experimental results showed that the classification accuracy is improved by majority voting with ensemble classifiers in the combination of texture, color, shape, HOG and GIST features.

关键词： Crops Ensemble classification Image processing Insect classification machine learning algorithm Majority voting

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Assessment of landslide susceptibility using DBSCAN-AHD and LD-EV methods

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Journal of Mountain Science 2022年第1期19卷 184-197页

作者： MAO Yi-min MWAKAPESA Deborah S LI Yi-can XU Kai-bin NANEHKARAN Yaser A ZHANG Mao-sheng School of Information Engineering Jiangxi University of Science and TechnologyGanzhou 341000China Key Laboratory for Geo-hazards in Loess Area Xi’an 710054China Northwest Nonferrous Geological Mining Group Limited Company Xi’an 710054China

Landslide susceptibility assessment plays a vital role in understanding landslide information in advance and taking preventive as well as control measures. The need to initially specify the number of clusters, difficulty in handling noise and quantifying rainfall data, limits the application of traditional clustering models in landslide susceptibility assessment which then lowers their performance accuracy. Thus, to overcome these limitations, this study proposed an improved clustering algorithm titled the DBSCAN-AHD algorithm, which combines the traditional DBSCAN(Density Based Spatial Clustering of Application with Noise) algorithm and an Adaptive Hausdorff Distance(AHD) for landslide susceptibility modeling. Firstly, AHD was introduced to the traditional DBSCAN to quantify rainfall. Then,the DBSCAN-AHD grouped the mapping units with similar topology and geology characteristics into subclasses without specifying the number of clusters in advance for its ability to handle noise in the ***, the LD-EV(landslide density or eigenvalues) approach was introduced to obtain the susceptibility levels using K-means algorithm. Finally,to verify the model’s performance, statistical indices and the area under the curve(AUC) were applied and compared to the traditional DBSCAN, KPSO, Kmeans and Hierarchical Clustering algorithms,whereby, the proposed model outperformed the others. Also, the obtained susceptibility map can provide references in taking relevant preventive and control measures.

关键词： Landslide susceptibility DBSCAN machine learning algorithm Uncertain data Clustering algorithm

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Fine-tune BERT based on machine learning Models For Sentiment Analysis

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Procedia Computer Science 2024年 246卷 2390-2399页

作者： Nadia Smairi Houda Abadlia Hajer Brahim Wided Lejouad Chaari National School of Computer Science LARIA UR22ES01 Campus Universitaire Manouba 2010 Tunisia College of Science Department of Computer Science and Information in Majmaah University Az Zulf 15941 Saudi Arabia

Sentiments Analysis is a technique applied to examine emotions expressed by the users of social networks where they massively convey their opinions and permanently share their feelings. Therefore, its main objective is to determine the emotional attitude of a discourse by classifying it into different categories as positive, negative or neutral. However, in some research works, the authors failed to accurately specify the sentiments from the context due to the varying text lengths and the presence of equivocal emotional cues. This paper sets forth the deployment and assessment of the capabilities of applying machine learning sentiment analysis techniques using a publicly available IMDB dataset. Notably, this dataset encompasses numerous instances of irony and sarcasm. Lately, the Bidirectional Encoder Representations from Transformers model (BERT) has showcased its efficacy in the domain of text sentiment analysis classification. However, there remains a scope for enhancing the accuracy of SA. In the current study, BERT, and Word2vec are utilized to extract contextual sentences embeddings, while Support Vector machine (SVM) is used to classify the emotions of the social networks users. To enhance SVM accuracy, the genetic algorithm is integrated to optimize its hyper-parameters. The comparative study revealed that the developed technique applied on IMDB dataset outperforms other baseline machine learning techniques.

关键词： Emotion classification BERT transformer machine learning algorithm SVM Genetic algorithm

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Multi-model deep learning system for screening human monkeypox using skin images

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EXPERT SYSTEMS 2024年第10期41卷

作者： Gupta, Kapil Bajaj, Varun Jain, Deepak Kumar Hussain, Amir UPES Sch Comp Sci Dehra Dun Uttrakhand India MANIT Dept Elect & Commun Engn Bhopal Madhya Pradesh India Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian Peoples R China Edinburgh Napier Univ Ctr AI & Robot Edinburgh Scotland

PurposeHuman monkeypox (MPX) is a viral infection that transmits between individuals via direct contact with animals, bodily fluids, respiratory droplets, and contaminated objects like bedding. Traditional manual screening for the MPX infection is a time-consuming process prone to human error. Therefore, a computer-aided MPX screening approach utilizing skin lesion images to enhance clinical performance and alleviate the workload of healthcare providers is needed. The primary objective of this work is to devise an expert system that accurately classifies MPX images for the automatic detection of MPX *** work presents a multi-modal deep learning system through the fusion of convolutional neural network (CNN) and machine learning algorithms, which effectively and autonomously detect MPX-infected subjects using skin lesion images. The proposed framework, termed MPXCN-Net is developed by fusing deep features of three pre-trained CNNs: MobileNetV2, DarkNet19, and ResNet18. Three classifiers-K-nearest neighbour, support vector machine (SVM), and ensemble classifier-with various kernel functions, are used to identify infected patients. To validate the efficacy of our proposed system, we employ a publicly accessible MPX skin lesion *** amalgamating features extracted from all three CNNs and utilizing the medium Gaussian kernel of the SVM classifier, our proposed system achieves an outstanding average classification accuracy of 90.4%.ConclusionsDeveloped MPXCN-Net is suitable for testing with a large diversified dataset before being used in clinical settings.

关键词： biomedical image classification convolutional neural network machine learning algorithm monkeypox infection

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machine learning-driven prediction of phosphorus adsorption capacity of biochar: Insights for adsorbent design and process optimization

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 369卷 122405页

作者： Lyu, Huafei Xu, Ziming Zhong, Jian Gao, Wenhao Liu, Jingxin Duan, Ming Wuhan Text Univ Sch Environm Engn Wuhan 430073 Peoples R China Chinese Acad Sci Inst Hydrobiol Key Lab Breeding Biotechnol & Sustainable Aquacult Wuhan 430072 Peoples R China Wuhan Text Univ Engn Res Ctr Clean Prod Text Dyeing & Printing Minist Educ Wuhan 430073 Peoples R China

Phosphorus (P) pollution in aquatic environments poses significant environmental challenges, necessitating the development of effective remediation strategies, and biochar has emerged as a promising adsorbent for P removal at the cost of extensive research resources worldwide. In this study, a machine learning approach was proposed to simulate and predict the performance of biochar in removing P from water. A dataset consisting of 190 types of biochar was compiled from literature, encompassing various variables including biochar characteristics, water quality parameters, and operating conditions. Subsequently, the random forest and CatBoost algorithms were fine-tuned to establish a predictive model for P adsorption capacity. The results demonstrated that the optimized CatBoost model exhibited high prediction accuracy with an R2 value of 0.9573, and biochar dosage, initial P concentration in water, and C content in biochar were identified as the predominant factors. Furthermore, partial dependence analysis was employed to examine the impact of individual variables and interactions between two features, providing valuable insights for adsorbent design and operating condition optimization. This work presented a comprehensive framework for applying a machine learning approach to address environmental issues and provided a valuable tool for advancing the design and implementation of biochar-based water treatment systems.

关键词： Phosphorus removal from water Biochar machine learning algorithm Feature importance Prediction and optimization

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Collusion Strategy Investigation and Detection for Generation Units in Electricity Market Using Supervised learning Paradigm

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IEEE SYSTEMS JOURNAL 2021年第1期15卷 146-157页

作者： Razmi, Peyman Buygi, Majid Oloomi Esmalifalak, Mohammad Ferdowsi Univ Mashhad Fac Engn Mashhad *** Razavi Khorasan Iran McMaster Univ Hamilton ON L8S 4L8 Canada

In an oligopoly market, producers compete together to seize the electricity market share. Since they cannot obtain their desired profits through fair competition, they may collude to set their bid prices illegally higher than the oligopoly level. Manipulation and increasing market price decrease social welfare and then market efficiency. This article intends to provide independent system operators (ISOs) with a tool to analyze day-ahead market data so as to identify generator units who intend to exercise collusion and raise the market prices. Toward this goal, all possible collusion and competition scenarios are simulated and then the generated data are used to train a supervised learning algorithm. By applying the proposed approach to the IEEE 57 and 30 bus test systems, the efficiency of the proposed approach was assessed. Furthermore, it is demonstrated how colluding generators choose between maximizing their colluded profit and reducing the risk of being detected by ISO. The results show machine learning is capable of identifying colluding companies with accuracy of 95%. Also, it was rightly obvious that the closer the bidding price of companies is to competitive level, the more downward the efficiency of the machine is.

关键词： Electricity supply industry machine learning algorithms Generators machine learning Companies Supervised learning Oligopoly Collusion equilibrium point machine learning algorithm nonoptimal strategy

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