Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

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Balancing signature variance between local and global minima/maxima: Restricted maximum likelihood (REML) classification and the search for plagioclimax by N. Manspeizer, A. Karnieli

“…Entropy (a measure of disorder) and emptiness (a proxy for fragmentation) measures were designed into bin tables and examined relative to the variance in the spectral signatures. A restricted maximum likelihood (REML) classifier that relies on limiting variance was chosen to identify local maximum clusters (the unique plagioclimax classes), and the five classification results were compared. …”
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ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias by Hongzhen Cui, Shenhui Ning, Shichao Wang, Wei Zhang, Yunfeng Peng

“…Specifically, preprocessing steps such as sample balancing and variance sorting effectively optimized the feature distribution and significantly enhanced the model’s classification performance. …”
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Mean-Variance optimal portfolio selection integrated with support vector and fuzzy support vector machines by Simrandeep Kaur, Arti Singh, Abha Aggarwal

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SEM Deep Learning Multiclass Noise Level Classification With Data Augmentation by Kai Liang Lew, Kok Swee Sim, Shing Chiang Tan

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A lightweight spatiotemporal classification framework for tree species with entropy-based change resistance filter using satellite imagery by Biao Zhang, Zhichao Wang, Boyi Liang, Liguo Dong, Zebang Feng, Mingyang He, Zhongke Feng

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A comprehensive review of machine learning and deep learning techniques for intraclass variability breast cancer recognition by Kashif Khan, Suryanti Awang, Mohammed Ahmed Talab, Hasan Kahtan

Subjects: “…Intraclass variance…”
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Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…The typical standard classification of length difference (ulnar variance) is divided into three major types: positive ulnar variance, negative ulnar variance, and neutral ulnar variance. …”
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Machine learning with analysis-of-variance-based method for identifying rice varieties by Nabin Kumar Naik, M. Venkata Subbarao, Prabira Kumar Sethy, Santi Kumari Behera, Gyana Ranjan Panigrahi

“…Using a combination of 12 morphological features, four shape features, and 90 color features obtained from five different color spaces, 106 features were extracted from the images. An analysis of variance (ANOVA) was employed to select high-rank features, which were then fed to a support vector machine (SVM) for classification. …”
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Uncertainty quantification from ensemble variance scaling laws in deep neural networks by Ibrahim Elsharkawy, Benjamin Hooberman, Yonatan Kahn

“…We compute the mean $\mu_{\mathcal{L}}$ and variance $\sigma_{\mathcal{L}}^2$ of the test loss $\mathcal{L}$ for an ensemble of multi-layer perceptrons with neural tangent kernel initialization in the infinite-width limit, and compare empirically to the results from finite-width networks for three example tasks: MNIST classification, CIFAR classification and calorimeter energy regression. …”
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IG-FIQA: Improving Classifiability-Based Face Image Quality Assessment Through Intra-Class Variance Guidance by Minsoo Kim, Gi Pyo Nam, Haksub Kim, Haesol Park, Ig-Jae Kim

“…In the realm of face image quality assessment (FIQA), methods based on sample relative classification have shown impressive performance. However, the quality scores used as pseudo-labels assigned from images of classes with low intra-class variance could be unrelated to the actual quality in such methods. …”
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A prototype-based rockburst types and risk prediction algorithm considering intra-class variance and inter-class distance of microseismic data by Xiufeng Zhang, Guoying Li, Yang Chen, Hao Wang, Haikuan Zhang, Haitao Li, Weisheng Du, Xiao Li, Xuewei Xu, Yuze He

“…The prediction and classification of rockburst risk based on microseismic data is the premise of preventing rockbursts during deep mine excavation. …”
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The Classification of Sharia Assets and Performance of Financial Portfolio by Sarfaraz Ahmed Bhutto, Saifullah Shaikh, Hussain Amar, Qamar Abbas Mangi

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Neuro-evolutionary models for imbalanced classification problems by Israa Al-Badarneh, Maria Habib, Ibrahim Aljarah, Hossam Faris

“…On the other hand, pragmatically, abundant real-world problems suffer from the imbalance problem, where the distribution of data varies considerably among classes resulting in more training biases and variances which degrades the performance of the learning algorithm. …”
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Flood Image Classification using Convolutional Neural Networks by Olusogo Julius Adetunji, Ibrahim Adepoju Adeyanju, Adebimpe Omolayo Esan, Adedayo Aladejobi Sobowale Sobowale

“…Important parameters such as standard deviation and variance were incorporated in the parameters tuned CNN model that performed flood images feature extraction and classification for better predictive performance. …”
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Multidimensional time series classification with multiple attention mechanism by Chen Liu, Zihan Wei, Lixin Zhou, Ying Shao

“…Within multidimensional time series data, features pertinent to classification exhibit variance in their positional distribution along the entirety of the sequence. …”
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Research on learning achievement classification based on machine learning. by Jianwei Dong, Ruishuang Sun, Zhipeng Yan, Meilun Shi, Xinyu Bi

“…And different feature combinations and data augmentation techniques were used to evaluate the performance of multiple models in classification tasks. In addition, we also checked the synthetic data's effectiveness with variance homogeneity and P-values, and studied how the oversampling rate affects actual classification results. …”
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Application of Neural Networks to the Classification of Pancreatic Intraductal Proliferative Lesions by Krzysztof Okoń, Romana Tomaszewska, Krystyna Nowak, Jerzy Stachura

“…The aim of the study was to test applycability of neural networks to classification of pancreatic intraductal proliferative lesions basing on nuclear features, especially chromatin texture. …”
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Clouds Height Classification Using Texture Analysis of Meteosat Images by Baghdad Science Journal

“…In the present work, pattern recognition is carried out by the contrast and relative variance of clouds. The K-mean clustering process is then applied to classify the cloud type; also, texture analysis being adopted to extract the textural features and using them in cloud classification process. …”
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Deep Metric Learning-Based Classification for Pavement Distress Images by Yuhui Li, Jiaqi Wang, Bo Lü, Hang Yang, Xiaotian Wu

“…This study proposes a deep metric learning-based pavement distress classification method to address critical limitations in conventional approaches, including their dependency on large training datasets and inability to incrementally learn new categories. …”
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