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Computational Performance Evaluation (ComPE), International Conference on

作者： Chaitanya Kaul Neeraj Sharma Amity School of Engineering and Technology Amity University Gurugram India

ISBN: (纸本)9781665436571

This research article is based on the ensemble approach of different supervised machine learning algorithms to identify the early stages of breast cancer problems. The World Health Organization (WHO) approved that existence of the breast tumor is high for the women in developing countries and it is one of the significant research issues in current scenario in the real world. In this research article researcher used the 30 features to extract and predict accurate prediction on breast cancer using ensemble approach of supervised machine learning algorithms. It is a great challenge in designing a machine learning model to evaluate the performance of the classification of breast tumor. Implementing an efficient classification methodology will support in resolving the complications in analyzing breast cancer. This proposed model employs four machine learning (ML) algorithms Decision tree classifiers, Random Forest KNN, and support vector machine (SVM) and found support vector machine (SVM) which given the high accuracy of 0.976688 among them for the categorization of breast tumor in women. This classification includes the two levels of disease as benign or malignant. The researcher also used the other parameters and evaluated this predictive model using Precision, Recall and F1-Score. The data analysis report is proved that this predictive model is having 98% accuracy level to predict the cancer at early stages in women.

关键词： Support vector machines Performance evaluation machine learning algorithms Breast tumors Predictive models Feature extraction Breast cancer

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Analysis of machine learning algorithms for Crop Mapping on Satellite Image Data

Analysis of Machine Learning Algorithms for Crop Mapping on ...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Vineet Saxena Rakesh Kumar Dwivedi Anil Kumar College of Computing Sciences and Information Technology Teerthanker Mahaveer University Moradabad India College of Computing Sciences and Information Technology Indian Institute of Remote Sensing ISRO

ISBN: (纸本)9781665439718

Crop classification is main area of our planet for understanding the agricultural cover. Studies via satellite imagery are often limited to public data with low revisit rates and/or coarse spatial resolution. However, a recent surge in satellite data from new-aerospace companies provides daily imagery with relatively high spatial resolution. With high revisit rates in satellite image capture enable the incorporation of temporal information into crop classification schemes. With high cadence temporal information just now becoming available, there is plenty of room to explore the data and methods for classification [60].Crop mapping methodology is used for the monitoring of various crop types. These methodology is depend on a large space of satellite imagery and different time series data values which is use in supervised classifiers such as Support Vector machines (SVMs) and Random Forest (RF)[1]. These classifiers are applied at three unique degrees of crop terminology order and compare the result with accuracy and execution time. SVM gives ideal execution and demonstrates essentially better than RF for the least level of the classification. The significance of information factors such as Near Infrared (NIR), vegetation red-edge, and Short-Wave Infrared (SWIR) multispectral groups, and the Normalized Difference Vegetation (NDVI) and Plant Senescence Reflectance (PSRI) are used during cutting edge crop phenology stages and crop mapping [2].

关键词： Earth Satellites machine learning algorithms Crops Vegetation mapping Pansharpening Image capture

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Non Invasive Stress Detection Method Based on Discrete Wavelet Transform and machine learning algorithms

Non Invasive Stress Detection Method Based on Discrete Wavel...

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Computer Applications & Industrial Electronics (ISCAIE), IEEE Symposium on

作者： Hunain Altaf S Noorjannah Ibrahim Rashidah Funke Olanrewaju International Islamic University Malaysia

ISBN: (纸本)9781665403382;9781665447898

Stress can lead to many serious health problems as it can cause long-term chemical changes if our body is exposed to it for a lengthened period. Thus, it is important to develop a stress detection system to provide early warning for individuals. This is a study about the stress detection in students during their real time activities using the more convenient wearable smartwatch (Raqib) with the complete access to the raw ECG data. We have proposed a Discrete Wavelet Transform (DWT) method to preprocess the raw data along with the use of symlet4 filter to generate 4 levels of Daubechies coefficients. This study identifies 9 different time as well frequency-based features that show significant dependence on stress or simply directly related to stress. We have applied 7 different classifiers to our ECG data features obtained from 30 students and Naïve Bayes classifier produces the highest accuracy of 96.67% under 10-fold cross validation procedure.

关键词： Time-frequency analysis machine learning algorithms Databases Wearable computers Transforms Electrocardiography Real-time systems

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Road Accident Severity Prediction — A Comparative Analysis of machine learning algorithms

Road Accident Severity Prediction — A Comparative Analysis ...

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Artificial Intelligence and Internet of Things (GCAIoT), IEEE Global Conference

作者： Sumbal Malik Hesham El Sayed Manzoor Ahmed Khan Muhammad Jalal Khan UAE University Al Ain Abu Dhabi United Arab Emirates UAE University

ISBN: (纸本)9781665438421

Crash severity prediction models enable various agencies to predict the severity of a crash to gain insights into the factors that affect or are associated with crash severity. One of the potential ways to predict the crash severity is to leverage machine learning (ML) algorithms. With the help of accident data, ML algorithms find hidden patterns to predict whether the severity of the crash is fatal, serious, or slight. In this research, we develop a prediction framework and implemented six different machine learning algorithms, namely: Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, Bagging, and AdaBoost to predict the severity of the crash. Experimental results procured for the crash dataset published by the UK shows that Random Forest, Decision Tree, and Bagging significantly outperformed other algorithms in terms of all performance metrics. Furthermore, we analyze the huge; traffic data and extract insightful crash patterns to figure out the significant factors that have a clear effect on road accidents and provide beneficial suggestions regarding this issue. We strongly believe that the proposed prediction framework and the extracted pattern analysis would be helpful in improving the traffic safety system and assist the road authorities to establish proactive strategies to prevent traffic accidents.

关键词： machine learning algorithms Road accidents Roads Prediction algorithms Computer crashes Safety Random forests

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Applications of machine learning algorithms on the compressive strength of laterite blocks made with metakaolin-based geopolymer and sugarcane molasses

Waste Management Bulletin

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Waste Management Bulletin 2025年第3期3卷

作者： Sinkhonde, David Mirindi, Derrick Dabakuyo, Ismael Bezabih, Tajebe Mashava, Destine Mirindi, Frederic Department of Civil and Construction Engineering Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Mechanical Engineering Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Economics African Economic Research Consortium (AERC) Nairobi Kenya

To refine the process of anticipating the structural integrity of laterite block components, the use of machine learning (ML) algorithms is required. This study initiates an exploration into forecasting the compressive strength of laterite blocks infused with metakaolin-based geopolymer (MKG) and sugarcane molasses (SM), utilizing machine learning techniques such as artificial neural networks (ANN), random forests (RF), decision trees (DT), and support vector machines (SVM). The models were developed using four input values, including the MKG, SM, laterite soil, and water, with compressive strength as the output. Results show that for all the models, the majority of the data points lie within the error lines range of −20 % and +20 %. Using the Taylor diagram model, the results demonstrate that the SVM (train) model achieves the highest performance in predicting the compressive strength of laterite blocks, with a correlation coefficient of 0.99 and the lowest root mean square error (RMSE) of 0.139. The correlation coefficient values (R) for training and testing algorithm models ranged between 0.65 and 0.99, implying that all models fairly predict the compressive strength of laterite blocks containing MKG and SM. The RF model emerges as an important model for generalization across training and testing phases, with R values of 0.9828 and 0.789, respectively. SHapley Additive exPlanations (SHAP) analysis assesses the model's explainability behavior. According to a SHAP-based feature importance study, age (85.33 %) and water content (17.87 %) are critical components that may improve compressive strength compared to MKG (8.60 %) and SM (6.74 %), respectively. This study not only assists in comprehending the essential parameters necessary for making well-informed decisions but also opens exciting possibilities for the application of ML in fostering sustainable construction practices. © 2025 The Authors

关键词： Laterite blocks machine learning algorithms Metakaolin-based geopolymer Sugarcane molasses Sustainable construction

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5G Laboratory for Checking machine learning algorithms

5G Laboratory for Checking Machine Learning Algorithms

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International Conference on Radio Electronics & Info Communications (UkrMiCo)

作者： Maksim Iavich Lela Mirtskhulava Giorgi Iashvili Larysa Globa School of technology Caucasus University Tbilisi Georgia Iv. Javakhishvili Tbilisi State University Tbilisi Georgia “Igor Sikorsky Kyiv Polytechnic Institute” National Technical University of Ukraine Kyiv Ukraine

The telecommunications industry is undergoing a major transformation towards 5G networks to meet existing and expected requirements. The provision of basic services provided by 5G requires new technologies of data storage and processing. With the introduction of these technologies, we will face challenges related to new 5G cybersecurity systems and their operation. Researchers from around the world are involved in the 5G security assessment process. The reviewed materials show that 5G still has security issues. Notably, in a 5G security study, the researchers identified weaknesses in the system that allow malicious code to be integrated into the system, Therefore, it is necessary to determine new architectures for 5G and next generation 6G networks in order to set new AI / ML based techniques, that should provide high security level. Even in the absence of a 5G network, it is necessary to check the design of the security function of 5G cellular networks. Usually the scientists refer to problem of not having the opportunity to check the received results. For this purpose, the paper proposes to test 5G security systems using a simulated 5G library before testing in a real 5G hardware in the lab. Attack datasets are required to validate the system. Below is depicted a design methodology for a modeled 5G lab using 4G sim card raspberry PI modems and a server. Smart IDS (Intrusion Detection System) test results are reviewed. The tests were carried out in the proposed analog laboratory.

关键词： machine learning algorithms 5G mobile communication Memory Modems Malware Telecommunications Security

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Classification of Swallowing Foods Using machine learning algorithms

Classification of Swallowing Foods Using Machine Learning Al...

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

作者： Ji Hyun Lim Petar M. Djuric Milutin Stanacevic Stony Brook University Stony Brook USA

ISBN: (纸本)9781665442329

There are limits in assessing healthy and abnormal swallowing by Videofluoroscopic swallowing study. Classification of accelerometric swallowing signals is much more efficient method to judge healthy swallowing. However, these methods have developed mostly with dual axis accelerometric signals and classifying two-class problems. This study is to examine classification methods with multi-class three-axis accelerometric signals. Swallowing signals of five foods are classified with both supervised learning algorithm and unsupervised learning algorithm. Three-axis signals denoised by 10-level discrete wavelet transform with soft thresholding before feature calculation. The result confirmed that classification with support vector machine and K-nearest neighbor can predict with 90% accuracy. However, Classification with fuzzy c-mean clustering produce low purity and normalized mutual information.

关键词： Support vector machines machine learning algorithms Supervised learning Clustering algorithms Transforms Prediction algorithms Classification algorithms

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Performance Analysis of machine learning algorithms for Disease Prediction

Performance Analysis of Machine Learning Algorithms for Dise...

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Grace Hopper Celebration India (GHCI)

作者： B Hema Priya C Chaitra K. Viswavardhan Reddy RV College of Engineering Bengaluru

With the recent technological advances in microelectronics, wireless communication, machine learning (ML), and decision-making process, Wireless Body Area Network (WBAN) has become the most promising technology. As we all know that we are in global pandemic due to Covid-19 situation now, hence, there is a demand occurring in health care services and continuous monitoring. Moreover, prediction of abnormalities at an early stage will be crucial for a person in diagnosis. Hence, in this paper we have developed and compared the performance of three machine learning algorithms such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN), and Random Forest (RF). Each algorithm is tested with datasets of 100, 200, 500 & 1000 users respectively. Further, threshold values have been identified by consulting with doctors for accurate disease prediction based on the vital signals collected by various sensors. The three algorithms used are based on supervised learning, where the output is predicted based on the training of the developed classifier. From the results, it is observed that the accuracy in disease prediction using RF is 0.99 & outperformed when compared with state of the art for datasets of 1000 users.

关键词： Wireless communication Radio frequency Training Wireless sensor networks machine learning algorithms Medical services Vegetation

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Diabetes Disease Prediction Using machine learning algorithms

Diabetes Disease Prediction Using Machine Learning Algorithm...

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IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)

作者： Arwatki Chen Lyngdoh Nurul Amin Choudhury Soumen Moulik National Institute of Technology Meghalaya Shillong India

ISBN: (数字)9781728142456

ISBN: (纸本)9781728142463

This paper deals with the prediction of Diabetes Disease by performing an analysis of five supervised machine learning algorithms, i.e. K-Nearest Neighbors, Naïve Baye, Decision Tree Classifier, Random Forest and Support Vector machine. Further, by incorporating all the present risk factors of the dataset, we have observed a stable accuracy after classifying and performing cross-validation. We managed to achieve a stable and highest accuracy of 76% with KNN classifier and remaining all other classifiers also give a stable accuracy of above 70%. We analyzed why specific machine learning classifiers do not yield stable and good accuracy by visualizing the training and testing accuracy and examining model overfitting and model underfitting. The main goal of this paper is to find the most optimal results in terms of accuracy and computational time for Diabetes disease prediction.

关键词： Training Analytical models Visualization machine learning algorithms Computational modeling Diabetes Diseases

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Application of machine learning algorithms for Power transformer Internal faults identification

Application of Machine learning algorithms for Power transfo...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Sobhana obulareddy Surya Kalavathi Munagala JNTUA Ananthapuramu A.P India JNTUH Hyderabad Telangana India

Power transformer insulation failure is one of the key concerns for the effective operating state of transformers. The breakdown of insulation due to exposure of systemic faults for a long- duration lead to inter-turn short circuit of windings, discs, and movement of winding in the radial and axial directions are considered here for evaluation. The analysis with traditional methods needs expertise which may not generate comprehensive results. This paper describes an approach to extract significant features of impulse test responses of the transformer using continuous wavelet transform in the time-frequency domain and extracted data is balanced with random sampling. The performance of three types of classification algorithms is compared to classify (or) predict the fault current data. The comprehensive results of training accuracy indicate that the ensemble algorithm has been performed best among the other classifiers and the prediction rate of the ensemble classifier is represented by the probability curve and the prediction accuracy of the classifier algorithms was presented based on the true positive rate.

关键词： Fault diagnosis Time-frequency analysis machine learning algorithms Windings Prediction algorithms Feature extraction Classification algorithms

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