Computer-aided diagnosis of Haematologic disorders detection based on spatial feature learning networks using blood cell images by Jamal Alsamri, Hamed Alqahtani, Ali M. Al-Sharafi, Abdulbasit A. Darem, Khalid Nazim, Abdul Sattar, Menwa Alshammeri, Ahmad A. Alzahrani, Marwa Obayya

“…Currently, numerous physical methods exist to evaluate and forecast blood cancer utilizing the microscopic health information of white blood cell (WBC) images that are stable for prediction and cause many deaths. Machine learning (ML) and deep learning (DL) have aided the classification and collection of patterns in data, foremost in the growth of AI methods employed in numerous haematology fields. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

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Similarity Measures of Pythagorean Neutrosophic Sets with Dependent Neutrosophic Components Between T and F by Mohana Krıshnaswamy, Jansi Rajan

“…Clustering plays an important role in data mining, pattern recognition and machine learning. This paper proposes Pythagorean neutrosophic clustering methods based on similarity measures between Pythagorean neutrosophic sets with T and F are dependent neutrosophic components [PN-Set]. …”
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Dementia Scale Score Classification Based on Daily Activities Using Multiple Sensors by Akira Minamisawa, Shogo Okada, Ken Inoue, Mami Noguchi

“…The experimental results show that a maximum accuracy of 0.871 was obtained with a linear support vector machine (SVM) model by fusing the door, location, and sleep features and by clustering activity patterns using the X-means algorithm.…”
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A novel Probabilistic Bi-Level Teaching–Learning-Based Optimization (P-BTLBO) algorithm for hybrid feature extraction and multi-class brain tumor classification using ResNet-50 and... by Mahananda Malkauthekar, Avinash Gulve, Ratnadeep Deshmukh

“…Experimental findings indicate that the P-BTLBO algorithm surpasses conventional optimization methods, including TLBO and PSO, regarding classification accuracy, feature subset size, and computational efficiency. …”
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Detecting and Analyzing Botnet Nodes via Advanced Graph Representation Learning Tools by Alfredo Cuzzocrea, Abderraouf Hafsaoui, Carmine Gallo

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BCAST IDS: A Novel Network Intrusion Detection System for Broadcast Networks by Javier Gombao

“…A modern approach to enhancing the capabilities of NIDSs is the use of machine learning (ML) algorithms that predict attacks based on data. …”
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Klasifikasi Metode Data Mining untuk Prediksi Kelulusan Tepat Waktu Mahasiswa dengan Algoritma Naïve Bayes, Random Forest, Support Vector Machine (SVM) dan Artificial Neural Nerwor... by Satrio Junaidi, Rani Valicia Anggela, Delsi Kariman

“…The results of this study were obtained with the best algorithm accuracy in the support vector machine (SVM) algorithm is 0.94. …”
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Disulfide bond-related gene signature development for bladder cancer prognosis prediction and immune microenvironment characterization by Hua Huang, Haiyan Shao, Yifan Wang, Lili Ge

“…By integrating data from TCGA and GEO cohorts, we developed a Disulfide-Related Prognostic Signature (DRPS) using ten machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) elucidated the cell subtype-specific expression patterns of disulfide bond regulatory genes, while immune microenvironment and drug sensitivity analyses validated its clinical translational potential. qRT-PCR experiments confirmed differential expression patterns of core genes in bladder cancer cell lines. …”
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Regulatory T cells and matrix-producing cancer associated fibroblasts contribute on the immune resistance and progression of prognosis related tumor subtypes in ccRCC by Chao Zhang, Yisu Song, Xiaobo Cui, Yina Wang, Jiang Liu, Zhouji Shen

“…By analyzing public single cell RNA-sequencing and bulk RNA-sequencing data with the Scissor algorithm, we have identified three distinct prognosis related cancer cell subtypes which play an indispensable role on tumor metastasis, immune response and proliferation respectively. …”
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Unsupervised learning analysis on the proteomes of Zika virus by Edgar E. Lara-Ramírez, Gildardo Rivera, Amanda Alejandra Oliva-Hernández, Virgilio Bocanegra-Garcia, Jesús Adrián López, Xianwu Guo

“…Results The four UL algorithms revealed specific host and geographical clustering patterns for ZIKV. …”
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Exploring the VAK model to predict student learning styles based on learning activity by Ahmed Rashad Sayed, Mohamed Helmy Khafagy, Mostafa Ali, Marwa Hussien Mohamed

“…To accomplish this goal, we have proposed an integrated system which encompasses the use of machine learning (ML) algorithms. This hybrid model is aimed at linking various activities to VAK model of learning and hence place students in their various class learning preferences derived from their activities and the patterns created during the learning processes. …”
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Flexible imputation toolkit for electronic health records by Alireza Vafaei Sadr, Jiang Li, Wenke Hwang, Mohammed Yeasin, Ming Wang, Harold Lehmann, Ramin Zand, Vida Abedi

“…It benchmarks the performance of ten existing machine learning imputation algorithms against Flexible on real-world EHR datasets containing laboratory measurements. …”
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Long-Range Wide Area Network Intrusion Detection at the Edge by Gonçalo Esteves, Filipe Fidalgo, Nuno Cruz, José Simão

“…This paper proposes the implementation of machine learning algorithms, specifically the K-Nearest Neighbours (KNN) algorithm, within an Intrusion Detection System (IDS) for LoRaWAN networks. …”
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MF-ShipNet: a multi-feature weighted fusion and PCA-SVM model for ship detection in remote sensing images by Jianfeng Li, Yibing Yang, Liutong Yang, Yang Zhao, Qinghua Luo, Chenxu Wang

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Development of an artificial intelligence system for the forecasting of infectious diseases by A. A. Kuzin, R. I. Glushakov, S. A. Parfenov, K. V. Sapozhnikov, A. A. Lazarev

“…Employing machine learning algorithms, AI systems can rapidly analyze a large amount of data, extract specific disease patterns, and screen for the most efficient AI instruments in relation to specific tasks, thus contributing to prevention, diagnostics, and treatment of infectious diseases in the context of personalized medicine. …”
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Evaluating textural descriptors for automated image classification of stony reefs in turbid temperate waters by Saulė Medelytė, Yuri Rzhanov, Andrius Šiaulys, Kim Lowell

“…Among these, the MRELBP (Median Robust Extended Local Binary Pattern) algorithm achieved the highest overall performance. …”
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A Comparative Evaluation of Texture Features for Semantic Segmentation of Breast Histopathological Images by R. Rashmi, Keerthana Prasad, Chethana Babu K. Udupa, V. Shwetha

“…Support Vector Machine and Multi Layer Perceptron algorithms are trained to perform pixelwise classification. …”
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Intelligent regulation for cryptocurrency anti-money laundering: models, methods, and applications by ZHANG Yifei, YUAN Yong, YANG Dong, WANG Fei-Yue

“…The main models, methods, and application patterns in AML regulatory research were summarized from multiple dimensions, including traditional machine learning, deep learning, ensemble learning, graph analysis, and heuristic approaches. …”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…In this study, we develop a systematic methodology with multivariate models and machine learning algorithms to (i) model complex patterns of relationships or multi-phenotypic differences between the thermal environment and thermoregulatory, hormonal, biochemical, hematological and productive responses; and (ii) identify potential associations among biological relationships that may underlie shared and specific phenotypic patterns of adaptive responses. …”
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