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Experimental Evaluation of Spectrum Handoff Management with machine learning algorithms Using Software Defined Radio

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WIRELESS PERSONAL COMMUNICATIONS 2024年第1期138卷 149-170页

作者： Babjan, Patan Rajendran, V. Vels Inst Sci Technol & Adv Studies VISTAS Elect & Commun Engn Chennai India

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by employing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Na & iuml;ve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non-orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyse the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy. The performance of proposed system is compared with the literature and shown a significant improvement that proves the evidence of our findings.

关键词： Spectrum handoff Spectrum sensing Cognitive radio machine learning algorithms Software defined radio NOMA

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Analyzing the parallelization and structural impact of machine learning algorithms in social networks: a simulation-based approach

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2024年第6期101卷 681-701页

作者： Banihashemi, Sepideh Veksler, Keren Abhari, Abdolreza Toronto Metropolitan Univ Dept Comp Sci Toronto ON M5B 2K3 Canada

Analyzing social media networks is crucial for understanding and uncovering common interests and characteristics among users within human societies. In this context, we simulated a simple application of human interaction in social networks, which involves users following others based on text similarity. We then investigated the effects of various machine learning (ML) algorithms employed in the applications to be used as recommendations to decision-making users. A novel agent-based social network simulator called distributed system and multinode processing is developed to assess the parallelization of the ML algorithms (i.e., K-means clustering, cosine similarity, support vector machine, multilayer perceptron) using bag of words (BoW) term frequency-inverse document frequency vectorization by evaluating their performance when executed in parallel across distributed heterogeneous resources. In addition, this simulator compares the effects of BoW with the Doc2Vec model on network structure by observing the differences in detected communities and resulting network graphs when a selected user follows the recommendations produced by an employed algorithm. Three real datasets were used in the experiments: Twitter, Scientific Research Papers, and Retail. This work's contribution is a unique in-house agent-based simulator developed to analyze the impact of common ML algorithms, including supervised and unsupervised learning, on social networks.

关键词： Social network simulation social network analysis machine learning algorithms bag of words (BoW) Doc2Vec

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Applying machine learning algorithms for the Classification of Sleep Disorders

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IEEE ACCESS 2024年 12卷 36110-36121页

作者： Alshammari, Talal Sarheed Univ Hail Coll Comp Sci & Engn Dept Informat & Comp Sci Hail 81481 Saudi Arabia

Sleep disorder classification is crucial in improving human quality of life. Sleep disorders and apnoea can have a significant influence on human health. Sleep-stage classification by experts in the field is an arduous task and is prone to human error. The development of accurate machine learning algorithms (MLAs) for sleep disorder classification requires analysing, monitoring and diagnosing sleep disorders. This paper compares deep learning algorithms and conventional MLAs to classify sleep disorders. This study proposes an optimised method for the Classification of Sleep Disorders and uses the Sleep Health and Lifestyle Dataset publicly available online to evaluate the proposed model. The optimisations were conducted using a genetic algorithm to tune the parameters of different machine learning algorithms. An evaluation and comparison of the proposed algorithm against state-of-the-art machine learning algorithms to classify sleep disorders. The dataset includes 400 rows and 13 columns with various features representing sleep and daily activities. The k-nearest neighbours, support vector machine, decision tree, random forest and artificial neural network (ANN) deep learning algorithms were assessed. The experimental results reveal significant performance differences between the evaluated algorithms. The proposed algorithms obtained a classification accuracy of 83.19%, 92.04%, 88.50%, 91.15% and 92.92%, respectively. The ANN achieved the highest classification accuracy of 92.92%, and its precision, recall and F1-score values on the testing data were 92.01%, 93.80% and 91.93%, respectively. The ANN algorithm that achieved high accuracy than other tested algorithms.

关键词： Sleep apnea Classification algorithms machine learning algorithms Feature extraction Deep learning Brain modeling Radio frequency Artificial neural networks Support vector machines Random forests Optimization methods deep learning classification sleep disorder genetic algorithm

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Temperature Prediction for Electric Vehicles Using machine learning algorithms

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2024年第6期60卷 9251-9259页

作者： Kishore, Shradha Bharti, Sonam Kumari Anand, Priyadarshi Srivastav, Dishant Sonali, Shubham Birla Inst Technol Dept Elect & Elect Engn Patna Off Campus Patna 800014 India

The development of precise, efficient, and cost-effective temperature prediction algorithms to maintain the ambient charging and operational environment of electric vehicles (EVs) has gained significance in recent times. machine learning algorithms are widely used for prediction and decision-making processes due to the growing presence of optimized statistical models based on training datasets, that are far superior to conventional computational statistical methods. In this work, a temperature dataset has been generated in real-time from an IoT and temperature sensor-based hardware, clipped-on the casing of a vehicle, pre-possessed and fitted into optimized models so that future values of temperature can be accurately predicted using different supervised regression machine learning algorithms. The predicted temperatures have been compared with the actual recorded temperatures and a statistical error analysis has been done to compare the results based on the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Square (R-2) and adjusted R-2 Score. The advantage of this methodology is that it is independent of system parameters, and that any future values of critically high temperature may be predicted before they occur, so that drivers can be alerted and take corrective measures before actual damage takes place. This methodology may be applied to prevent incidents of fire in EV batteries, energy wastage due to high temperatures during charging or normal running, and to enhance passenger comfort.

关键词： Temperature sensors Temperature measurement machine learning algorithms Predictive models Monitoring Batteries Prediction algorithms Regression machine learning temperature prediction random forest r-square

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The application and comparison between machine learning algorithms in cooperative spectrum sensing

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第33期83卷 78697页

作者： Hu, Bin Liu, Yuxiang Zhai, Mingxi Wang, Aoxiang Univ Durham Dept Elect & Informat Engn Durham DH1 4FL England Wuhan Britain China Sch Wuhan 430022 Peoples R China Shenzhen Sr High Sch Shenzhen 518040 Peoples R China Wuhan Univ Technol Dept Elect & Elect Engn Wuhan 430070 Peoples R China

The world is progressing at a rapid rate, and with continuous advancements in technology, the need for cooperative spectrum sensing has evolved. Cooperative spectrum sensing is an approach used to enhance detection performance, wherein secondary users collaborate with each other to sense the spectrum and identify spectrum holes. As technology improves over time, the spectrum becomes increasingly allocated to primary users. The role of cooperative spectrum sensing in cognitive radio fulfills the essential requirement of protecting primary users from harmful interference. The progressive evolution of technology has led to a reduction in available spectrum, prompting the emergence of the concepts of cognitive radio and cooperative spectrum sensing. To begin with, this paper introduces the fundamental application of cooperative spectrum sensing algorithms, encompassing primary and secondary users' models and an energy model. Subsequently, three machine learning algorithms, namely K-means, support vector machine, and logistic regression, are explained, and their schematics are presented in detail. The proposed model uses supervised and unsupervised learning techniques to develop a cooperative spectrum sensing framework. It compares the performance of three machine learning algorithms, K-means clustering, logistics regression and support vector machine. The comparison is based on accuracy, recall and precision metrics, and the results show that the K-means clustering algorithm has better performance than the other two algorithms. The findings highlight the superiority of the K-means clustering algorithm over logistic regression and support vector machine.

关键词： Cognitive radio networks Cooperative spectrum sensing machine learning algorithms Support vector machine Logical regression k-means Accuracy Recall Precision

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Modeling specific capacitance of carbon nanotube-based supercapacitor electrodes by machine learning algorithms

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PHYSICA SCRIPTA 2024年第6期99卷 066011-066011页

作者： Tawfik, Wael Z. Abdel-Wahab, Mohamed Sh Lee, June Key Al-Enizi, Abdullah M. Youssef, Rasha Y. Beni Suef Univ Fac Sci Phys Dept Bani Suwayf 62511 Egypt Beni Suef Univ Fac Postgrad Studies Adv Sci Mat Sci & Nanotechnol Dept Bani Suwayf 62511 Egypt Chonnam Natl Univ Dept Mat Sci & Engn Gwangju 61186 South Korea King Saud Univ Coll Sci Dept Chem POB 2455 Riyadh 11451 Saudi Arabia Suez Univ Fac Comp & Informat Dept Informat Syst Suez 43512 Egypt

Carbon nanotubes (CNTs) have emerged as promising materials for supercapacitors (SCs) due to their unique properties and exceptional electrical conductivity. These cylindrical structures composed of carbon atoms offer several advantages for SC electrode applications. The electrochemical performance of CNT-based electrodes is strongly influenced by factors such as surface area, pore structure, and ID/IG ratio. However, the lack of a credible physical model capable of accurately predicting the performance of SCs based on these physicochemical properties of CNTs poses a challenge. In this study, we propose the utilization of a data-driven approach employing various models including a gradient boosting regression (GBR), Bayesian regression (BR), ridge regression (RR), and stochastic gradient descent (SGD) model to predict the performance of SCs with CNT electrodes based on the microstructural properties of the electrode material and electrochemical operational parameters. The developed GBR model demonstrates its feasibility by achieving a low root mean square error (RMSE) value of approximately 36.31 for the prediction of specific capacitance for test split. Additionally, a sensitivity analysis was conducted to investigate the influence of independent input parameters on a single output parameter, specifically the specific capacitance. This analysis provides insights into the relative importance and impact of various input parameters on the specific capacitance of CNT-based SCs.

关键词： modeling approaches capacitance prediction carbon nanotubes supercapacitors electrodes machine learning algorithms

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Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024年 356卷 120560页

作者： Zhang, Maomao Tan, Shukui Liang, Jinshui Zhang, Cheng Chen, Enqing Huazhong Univ Sci & Technol Coll Publ Adm Wuhan 430079 Peoples R China Dalian Ocean Univ Coll Marine Technol & Environm Dalian 116023 Peoples R China Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430079 Peoples R China Wuhan Donghu Univ Sch Educ & Foreign Languages Wuhan 430212 Peoples R China

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km(2) in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20( degrees)C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 C-degrees is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

关键词： Urban land expansion Thermal environment Sustainable development machine learning algorithms China

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Predicting the risk of a high proportion of three/multiple pronuclei (3PN/ MPN) zygotes in individual IVF cycles using comparative machine learning algorithms

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EUROPEAN JOURNAL OF OBSTETRICS & GYNECOLOGY AND REPRODUCTIVE BIOLOGY 2025年 306卷 139-146页

作者： Hong, Pingping Lu, Yaxin Li, Haiyang Liu, Zifeng Ou, Jianpin Li, Tao Shu, Yimin Sun Yat Sen Univ Affiliated Hosp 3 Reprod Med Ctr 6 East Longkou Rd Guangzhou 510630 Peoples R China Sun Yat Sen Univ Affiliated Hosp 3 Ctr Big Data & Artificial Intelligence 600 Tianhe Rd Guangzhou Guangdong Peoples R China Univ Kansas Med Ctr Ctr Adv Reprod Med Dept Obstet & Gynecol Overland Pk KS 66211 USA

Background: The majority of machine learning applications in assisted reproduction have been focused on predicting the likelihood of pregnancy. In the present study, we aim to investigate which machine learning models are most effective in predicting the occurrence of a high proportion (>30 %) of 3PN/MPN zygotes in individual IVF cycles. Methods: Eight machine learning algorithms were trained and compared, including the AdaBoost and Gaussian NB. Data from IVF cycles carried out from September 2015 to September 2019 were used as a training set. Cycle data from October 2019 to June 2020 were used as a validation set to verify the training model. Cycles with a 3PN/MPN zygote proportion higher than 30 % were classified as high 3PN/MPN zygote proportion cycles. Results: The AdaBoost algorithm was the best model for model construction and external validation. In both the training and validation sets, age, basal FSH, FSH and E2 level on the day of Gonadotrophin (GN) stimulation, and FSH and LH levels on the day of HCG were statistically higher in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %;AFC, AMH, E2 level on HCG day and total number of oocytes were lower in patients with 3PN/MPN > 30 % than in patients with 3PN/MPN <= 30 %. The top five predictors were the number of oocytes retrieved, age, male factor infertility, AFC, and total days of GN stimulation. Conclusion: By applying a suitable machine learning algorithm, we can potentially predict the risk of a high proportion of 3PN/MPN zygotes in individual IVF cycles before insemination and avoid polyspermy fertilization by ICSI fertilization method.

关键词： Three/multiple pronuclei (3PN/MPN) zygotes machine learning algorithms AdaBoost algorithm IVF Abnormal fertilization

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Automated classification of hand-woven and machine-woven carpets based on morphological features using machine learning algorithms

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JOURNAL OF THE TEXTILE INSTITUTE 2024年第12期115卷 2650-2659页

作者： Isik, Melike Ozulku, Burcu Kursun, Ramazan Taspinar, Yavuz Selim Cinar, Ilkay Yasin, Elham Tahsin Koklu, Murat Selcuk Univ Dept Technol Selcuklu Turkiye Selcuk Univ Guneysinir Vocat Sch Guneysinir Turkiye Selcuk Univ Doganhisar Vocat Sch Doganhisar Turkiye Selcuk Univ Dept Comp Engn Selcuklu Turkiye Selcuk Univ Grad Sch Nat & Appl Sci Selcuklu Turkiye

As a cultural heritage, hand-woven carpets engender trust and admiration in individuals who recognize their authenticity. It is the expertise of experts who determine whether a carpet is hand-woven or machine-woven based on authenticity criteria. A total of 48 morphological features were extracted by three carpet experts from 359 handwoven and machine woven carpets. machine-learning algorithms were used to classify the extracted features. With an accuracy of 96.66%, the ANN algorithm achieved the best classification performance. Afterward, 28 morphological features were selected with the highest gain ratios and reclassified. Based on the selected features, SVM (Support Vector machine) achieved the best classification accuracy of 96.66%. A carpet expert performed classification using the morphological features extracted by machine learning algorithms to evaluate the classification results obtained through machine learning algorithms. When 48 morphological features were used, the accuracy was 98.61%, and when 28 morphological features were used, the accuracy was 97.77%. Based on the results, artificial intelligence techniques are suitable for detecting morphological features in hand-woven and machine-woven carpets and for automatically classifying them.

关键词： Hand-woven carpet machine-woven carpet carpet classification machine learning algorithms morphological features of carpets

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Species distribution modeling of Malva neglecta Wallr. weed using ten different machine learning algorithms: An approach to site-specific weed management (SSWM)

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EUROPEAN JOURNAL OF AGRONOMY 2025年 167卷

作者： Dastres, Emran Esmaeili, Hassan Edalat, Mohsen Shahid Beheshti Univ Med Plants & Drugs Res Inst Dept Agr Tehran *** Iran Shiraz Univ Sch Agr Plant Prod & Genet Dept Shiraz Iran

The integration of artificial intelligence (AI) and machine learning algorithms (MLAs) has transformed ecological modeling and precision agriculture by improving the prediction of plant species distribution. This advancement is particularly significant in agricultural practices like weed management, where precision and sustainability are critical for enhancing farming efficiency and environmental conservation. The study conducted in Fars province, Iran was aimed to apply species distribution modeling (SDM) to site-specific weed management (SSWM) in rapeseed (Brassica napus L.) fields. The focus was on improving precision in control of the weed Malva neglecta, using advanced machine learning algorithms. The study employed machine learning algorithms, including Random Forest (RF), Boosted Regression Trees (BRT), Support Vector machine (SVM), Flexible Discriminant Analysis (FDA), Mixture Discriminant Analysis (MDA), k-Nearest Neighbors (KNN), Generalized Additive Model (GAM), Generalized Boosting Model (GBM), Classification and Regression Trees (CART), and Multivariate Adaptive Regression Splines (MARS) to predict the distribution of M. neglecta. Geographic Information Systems (GIS) were used to create raster maps from eleven environmental factors for spatial analysis. A multi-collinearity test using the variance inflation factor (VIF) ensured model robustness, and factor importance was analyzed with Boruta, RF, Generalized Linear Model (GLM), and Partial Least Squares (PLS) algorithms. The study found that machine learning algorithms, particularly RF, CART, MARS, and BRT, provided highly accurate predictions of M. neglecta distribution. The analysis identified mean annual rainfall, slope, and silt percentage as key environmental factors influencing the distribution of M. neglecta. The successful application of these algorithms in predicting weed distribution demonstrates their potential for improving ecological modeling. The study highlights the effectiveness of SDM i

关键词： Data usage technique Environmental factors Spatial modeling machine learning algorithms Malva neglecta Weed distribution

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