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Aperture coupled dielectric resonator antenna optimisation using machine learning techniques

AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS 2022年 154卷

作者： Srivastava, Ayush Gupta, Harshit Dwivedi, Ajay Kumar Penmatsa, Krishna Kanth Varma Ranjan, Pinku Sharma, Anand Motilal Nehru Natl Inst Technol Allahabad Dept Elect & Commun Engn Prayagraj Uttar Pradesh India Atal Bihari Vajpayee Indian Inst Informat Technol Gwalior India Nagarjuna Coll Engn & Technol Dept Elect & Commun Engn Bangalore India SRKR Engn Coll Dept Elect & Commun Engn Bhimavaram Andhra Prades India

In this article, various machine learning (ML) algorithms such as Artificial neural network (ANN), Random Forest, XG boost, K nearest neighbor (KNN), and Knowledge-based neural network (KBNN) are used for efficient optimization of dielectric resonator antenna (DRA). ML models are used to predict the |S11| for a particular set of frequency, resonator height, aperture radius, and resonator radius. Finally, a comparative performance analysis of different ML algorithms has been done with the outcomes of the HFSS EM simulator. Error percentage is in between 1.0% and 7.0% with different ML algorithms. Antenna design is also fabricated and tested. The performance of the fabricated prototype is very close to different ML algorithms and HFSS obtained outcomes.

关键词： Dielectric resonator antenna machine learning algorithm Aperture Coupling ANN KNN KBNN

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Comparing Bag of Words and TF-IDF with different models for hate speech detection from live tweets

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International Journal of Information Technology (Singapore) 2022年第7期14卷 3629-3635页

作者： Akuma, Stephen Lubem, Tyosar Adom, Isaac Terngu Department of Mathematics Computer Science and Statistics Benue State University Makurdi Nigeria

Social media platforms such as Twitter have revolutionized online communication and interactions but often contain components of disdain for its growing user base. This discomforting feed creates instability leading to mental breakdown, and loss of human lives and properties among other results of misuse. Even though the problem posed by the content of social media is obvious, the challenge of detecting hateful content persists. Several algorithms and techniques have been used in the past for detecting hateful content on social media but there is room for improvement. The goal of this paper is to detect hate speech from live tweets on Twitter via a combination of mechanisms. The comparison results of Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW) with machine learning models of Logistic Regression, Naïve Bayes, Decision Tree, and K-Nearest Neighbour (KNN), is used to select the best performing model. This model which is integrated into a web system developed with Twitter Application Programming Interface (API) is used in identifying live tweets which are hateful or not. The outcome of the comparative study presented showed that Decision Tree performed better than the other three models with an accuracy of 92.43% using TF-IDF which gives optimal results compared to BoW. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Bag of Words Hate speech machine learning algorithm Sentiment analysis Social media TF-IDF Twitter

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A Comparative Study Of Binary Class Logistic Regression and Shallow Neural Network For DDoS Attack Prediction

A Comparative Study Of Binary Class Logistic Regression and ...

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SoutheastCon Conference

作者： Tufail, Shahid Batool, Shanzeh Sarwat, Arif, I Florida Int Univ Dept Elect & Comp Engn Miami FL 33199 USA

ISBN: (纸本)9781665406529

In the area of internet security, cybersecurity is a serious subject. Every industry is witnessing thousands of cyberattacks every year. Among the most deadly cyber-attacks are the distributed denial-of-service attack (DDOS) and the False data injection attack (FDIA). In this paper, we performed a comparative study for predicting DDOS attacks using two machine learning algorithms that are logistic regression and shallow neural network(SNN). In logistic regression, we achieved an accuracy of 98.63% and for SNN accuracy we achieved was 99.85%. However, our study shows that the training time was exponentially higher for SNN in comparison to logistic regression.

关键词： Distributed Denial of Service DDOS Attacks DOS Attacks Cyber Attacks Networks Data Packets Logistic Regression machine learning algorithm Multi Layer Perceptron

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Improved short-term prediction of significant wave height by decomposing deterministic and stochastic components

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RENEWABLE ENERGY 2021年 177卷 743-758页

作者： Huang, Weinan Dong, Sheng Ocean Univ China Coll Engn Qingdao 266100 Peoples R China

Significant wave height prediction for the following hours is a necessity for the planning and operation of wave energy devices. For a site-specific and short-term prediction, classical numerical wave forecasting methods may not be justified as exhaustive climatological data and huge computational power are needed. In this paper, a combination of a decomposition approach and long short-term memory network was presented to forecast the significant wave heights. An improved version of complete ensemble empirical mode decomposition algorithm and recurrence quantification analysis were applied to separate the original time series into deterministic and stochastic components. Each decomposed series was forecasted by the long short-term memory network and the final predicted significant wave heights were obtained by integrating the deterministic and stochastic predictions. Wave data measured at three buoy stations along the eastern coast of the United States were utilized to verify the hybrid model. The performance of the proposed method in three different wave height ranges was evaluated. The results suggested that the hybrid model outperformed the stand-alone long short-term memory network adjusted on the unseparated signal;in particular, for longer lead times and larger wave heights. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： machine learning algorithm Decomposition technique Deterministic component Stochastic component

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Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm

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APPLIED MICROBIOLOGY AND BIOTECHNOLOGY 2021年第12期105卷 5201-5212页

作者： Hesami, Mohsen Jones, Andrew Maxwell Phineas Univ Guelph Dept Plant Agr Gosling Res Inst Plant Preservat Guelph ON N1G 2W1 Canada

Plant callus is generally considered to be a mass of undifferentiated cells and can be used for secondary metabolite production, physiological studies, and plant transformation/regeneration. However, there are several types of callus with different morphological and developmental characteristics and not all are suitable for all applications. Callogenesis is a multivariable developmental process affected by several intrinsic and extrinsic factors, but the most important driver is plant growth regulator (PGRs) levels and type. Since callogenesis is a non-linear process influenced by many different factors, robust computational methods such as machine learning algorithms have great potential to model, predict, and optimize callus growth and development. The current study was conducted to evaluate the effect of PGRs on callus morphology in drug-type Cannabis sativa to maximize callus growth and promote embryogenic callus production. For this aim, random forest (RF) and support vector machine (SVM) were applied in conjunction with image processing to model and predict callus morphological and physical traits. The results showed that SVM was more accurate than RF. In order to find the optimal level of PGRs for optimizing callus growth and development, the SVM was linked to a genetic algorithm (GA). To confirm the reliability of SVM-GA, the optimized-predicted outcomes were tested in a validation experiment that revealed SVM-GA was able to accurately model and optimize the system. Moreover, our results showed that there is a significant correlation between embryogenic callus production and the true density of callus.

关键词： Embryogenesis Indirect organogenesis machine learning algorithm Plant tissue culture Secondary metabolite production

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Optimization of Using Multiple machine learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study

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JMIR Formative Research 2024年 8卷 e47803页

作者： Chuang, Beau Bo-Sheng Yang, Albert C. School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Digital Medicine and Smart Healthcare Research Center National Yang Ming Chiao Tung University Taipei Taiwan Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan

Background: Atrial fibrillation (AF) represents a hazardous cardiac arrhythmia that significantly elevates the risk of stroke and heart failure. Despite its severity, its diagnosis largely relies on the proficiency of health care professionals. At present, the real-time identification of paroxysmal AF is hindered by the lack of automated techniques. Consequently, a highly effective machine learning algorithm specifically designed for AF detection could offer substantial clinical benefits. We hypothesized that machine learning algorithms have the potential to identify and extract features of AF with a high degree of accuracy, given the intricate and distinctive patterns present in electrocardiogram (ECG) recordings of AF. Objective: This study aims to develop a clinically valuable machine learning algorithm that can accurately detect AF and compare different leads’ performances of AF detection. Methods: We used 12-lead ECG recordings sourced from the 2020 PhysioNet Challenge data sets. The Welch method was used to extract power spectral features of the 12-lead ECGs within a frequency range of 0.083 to 24.92 Hz. Subsequently, various machine learning techniques were evaluated and optimized to classify sinus rhythm (SR) and AF based on these power spectral features. Furthermore, we compared the effects of different frequency subbands and different lead selections on machine learning performances. Results: The light gradient boosting machine (LightGBM) was found to be the most effective in classifying AF and SR, achieving an average F₁-score of 0.988 across all ECG leads. Among the frequency subbands, the 0.083 to 4.92 Hz range yielded the highest F₁-score of 0.985. In interlead comparisons, aVR had the highest performance (F₁=0.993), with minimal differences observed between leads. Conclusions: In conclusion, this study successfully used machine learning methodologies, particularly the LightGBM model, to differentiate SR and AF based on

关键词： atrial fibrillation atrial fibrillation detection clinical outcome detection digital health electrocardiogram electrocardiography leads light gradient boosting machine machine learning machine learning algorithm power spectral density real-time

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Identification of novel potential biomarkers using bulk RNA and single cells to build a neural network model for diagnosis of liver cancer

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DISCOVER ONCOLOGY 2025年第1期16卷 1-17页

作者： Gao, Yingzheng Chen, Jiahao Du, Weidong Zhejiang Chinese Med Univ Sch Clin Med 1 Hangzhou 310006 Peoples R China Chinese Med Univ Zhejiang Prov Hosp Tradit Chinese Med Affiliated Hosp Zhejiang 1 Hangzhou 310006 Peoples R China

BackgroundAs a common cancer, liver cancer imposes an unacceptable burden on patients, but its underlying molecular mechanisms are still not fully understood. Therefore, there is an urgent need to potential biomarkers and diagnostic models for liver *** this study, transcriptome and single-cell datasets related to liver cancer were downloaded from the UCSC Xena database and the Mendeley database, and differential analysis and weighted gene co-expression network analysis were used to find differentially expressed genes related to liver cancer. We used multiple machine algorithms to find hub genes related to liver cancer, and constructed new artificial neural network models based on their transcriptome expression patterns to assist in the diagnosis of liver cancer. Subsequently, we conducted survival analysis and immune infiltration analysis to explore the correlation between hub genes and immune cells, and used single-cell data to verify hub genes related to liver *** study identified MARCO, KCNN2, NTS, TERT and SFRP4 as central genes associated with liver cancer, and constructed a new artificial neural network model for molecular diagnosis of liver cancer. The diagnostic performance of the training cohort and the validation cohort was good, with the areas under the ROC curves of 1.000 and 0.986, respectively. Immune infiltration analysis determined that these central genes were closely associated with different types of immune cells. The results of immunohistochemistry and the results at the single cell level were consistent with those at the transcriptome level, and also showed obvious differences between different cell types in liver cancer and healthy *** study identified MARCO, KCNN2, NTS, TERT, and SFRP4 from multiple dimensions and highlighted their key roles in the diagnosis and treatment of liver cancer from multiple dimensions, providing promising biomarkers for the diagnosis of liver cancer.

关键词： Liver cancer Molecular marker machine learning algorithm Artificial neural network Single cell analysis

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Extraction of Photovoltaic Plants Using machine learning Methods: A Case Study of the Pilot Energy City of Golmud, China

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REMOTE SENSING 2022年第11期14卷

作者： Chen, Zhenghang Kang, Yawen Sun, Zhongxiao Wu, Feng Zhang, Qian China Agr Univ Coll Land Sci & Technol Beijing 100083 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China

Solar energy is an abundant, clean, and renewable source that can mitigate global climate change, environmental pollution, and energy shortage. However, comprehensive datasets and efficient identification models for the spatial distribution of photovoltaic (PV) plants locally and globally over time remain limited. In the present study, a model that combines original spectral features, PV extraction indexes, and terrain features for the identification of PV plants is established based on the pilot energy city Golmud in China, which covers 71,298.7 km(2) and has the highest density of PV plants in the world. High-performance machine learning algorithms were integrated with PV plant extraction models, and performances of the XGBoost, random forest (RF), and support vector machine (SVM) algorithms were compared. According to results from the investigations, the XGBoost produced the highest accuracy (OA = 99.65%, F1score = 0.9631) using Landsat 8 OLI imagery. The total area occupied by PV plants in Golmud City in 2020 was 10,715.85 ha based on the optimum model. The model also revealed that the area covered by the PV plant park in the east of Golmud City increased by approximately 10% from 2018 (5344.2 ha) to 2020 (5879.34 ha). The proposed approach in this study is one of the first attempts to identify time-series large-scale PV plants based on a pixel-based machine learning algorithm with medium-resolution free images in an efficient way. The study also confirmed the effectiveness of combining original spectral features, PV extraction indexes, and terrain features for the identification of PV plants. It will shed light on larger- and longer-scale identification of PV plants around the world and the evaluation of the associated dynamics of PV plants.

关键词： PV plant machine learning algorithm Landsat 8 OLI images XGBoost

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Application of ANFIS-PSO Hybrid algorithm for Predicting the Dimensions of the Downstream Scour Hole of Ski-Jump Spillways

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF CIVIL ENGINEERING 2021年第3期45卷 1845-1859页

作者： Nou, M. Rashki Ghaleh Zolghadr, M. Bajestan, M. Shafai Azamathulla, H. M. Univ Sistan & Baluchestan Hydraul Struct Zahedan Iran Jahrom Univ Dept Water Sci Engn Jahrom Iran Shahid Chamran Univ Ahvaz Dept Hydraul Struct Ahwaz Iran Univ West Indies Dept Civil Engn Trinidad Trinidad Tobago

Ski-jump spillway (SJS) is one of the energy-dissipating structures, passing the flood through the dams. In these structures, the downstream bed is scoured due to the collision of the high energy jet. It is essential to obtain information on the scour-hole dimensions to enhance the safety of the dam and related structures. The machine learning and data mining methods are efficient in predicting hydraulic phenomena. In this study, the geometric properties of the scour-hole including the hole expansion (width), maximum depth of the scour and its distance from the end edge of the spillway were predicted using a model by combining the adaptive neuro-fuzzy inference system (ANFIS) and the particle swarm optimization (PSO) algorithm. The results showed that the developed ANFIS-PSO model obtained maximum scour depth with a precision ofR(2) = 0.94 and RMSE = 0.565, scour hole width with precision ofR(2) = 0.95 and RMSE = 1.153 and its distance from the end edge of the SJS with an accuracy ofR(2) = 0.96 and RMSE = 1.809. The evaluation and comparison of the simulation results indicated the superiority of the proposed model over other methods.

关键词： Energy-dissipating structures machine learning algorithm Scouring hole Data mining Optimization technique

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Prediction of heat waves over Pakistan using support vector machine algorithm in the context of climate change

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2021年第7期35卷 1335-1353页

作者： Khan, Najeebullah Shahid, Shamsuddin Ismail, Tarmizi Bin Behlil, Farida Lasbela Univ Agr Water & Marine Sci LUAWMS Fac Engn Sci & Technol Uthal 90150 Balochistan Pakistan Univ Teknol Malaysia UTM Fac Engn Sch Civil Engn Johor Baharu 81310 Malaysia Sardar Bahadur Khan Womens Univ Fac Basic Sci Dept Chem Quetta 87300 Balochistan Pakistan

Many efficient forecasting models have been found to fail or show low skill due to the changes in the predictor-predictand relationship with the changes in global climate. An attempt has been taken to develop a climate change resilient heatwave prediction model using machine learning (ML) algorithms known as Support Vector machines (SVM), random forest and artificial neural network. The National Centres for Environmental Prediction/National Centre for Atmospheric Research reanalysis data of ocean-atmospheric variables were used as the predictors of ML models for forecasting the number of heatwave days (HWDs) in the summer of Pakistan. An SVM based recursive feature elimination method was used to select the skilful predictors. The ML models were developed by considering a moving window of 29 years with a time step of 5 years to incorporate the changes in the relation of HWDs with its predictors due to climate change. The result showed changes in the relationship of HWDs with all the ocean-atmospheric variables considered in this study as probable predictors, which indicates the necessity of forward-rolling approach proposed in this study for the development of climate change resilient forecasting model. The relative performance of ML showed the higher capability of SVM to predict HWDs with an %NRMSE of 36, R-2 of 0.87, md score of 0.76 and an rSD of 0.88 during the validation period. The result revealed the potential of SVM model to be used for reliable forecasting of heatwaves in the context of climate change.

关键词： Heatwaves Forecasting Climate change machine learning algorithm Robust prediction model

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