Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

“…The second method extracts deep features from the breast images capturing high-level predictive information using ResNet-50 and MobileNet pre-trained convolution neural networks (CNNs). …”
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Green Apple Detection Method Based on Multidimensional Feature Extraction Network Model and Transformer Module by Wei Ji, Kelong Zhai, Bo Xu, Jiawen Wu

“…To enhance the fast and accurate detection of pollution-free green apples for food safety, this paper uses the DETR network as a framework to propose a new method for pollution-free green apple detection based on a multidimensional feature extraction network and Transformer module. …”
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Machine-Learning-Based Biomechanical Feature Analysis for Orthopedic Patient Classification with Disc Hernia and Spondylolisthesis by Daniel Nasef, Demarcus Nasef, Viola Sawiris, Peter Girgis, Milan Toma

“…The classification is based on six key biomechanical features of the pelvis and lumbar spine. …”
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ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif, Nasrin Shabani

“…Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. …”
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Composite Tor traffic features extraction method of webpage in actual network flow based on SDN by Hongping YAN, Qiang ZHOU, Shihao WANG, Wang YAO, Liukun HE, Liangmin WANG

“…Website fingerprinting (WF) methods for Tor webpage traffic are often based on the separated Tor traffic or even the separated Tor webpage traffic.However, distinguishing Tor traffic from the original traffic of the actual network and Tor webpage traffic from the Tor traffic costs amount of computation, which is more difficult than the WF attack itself.According to the current architecture of the Internet and the characteristics of network traffic converging to regional central nodes, the bi-directional statistical feature (BSF) was proposed for distinguishing Tor traffic through the intra-domain global perspective provided by the SDN structure of the central node and the node information disclosed by the Tor network.Furthermore, a hidden feature extraction method for Web traffic based on lifted structure fingerprinting (LSF) was proposed, and a composited Tor-webpage-identification traffic feature (CTTF) was proposed based on BSF and LSF deep features.For solving the problem of traffic training data scarcity, a traffic data augmentation method based on translation was proposed, which made the augmented traffic data as consistent as the Tor traffic data captured in the real working environment.The experimental results show that the identification rate based on CTTF can be improved by about 4% compared with using only the original data features.When there is less training data, the classification accuracy is improved more obvious after using the traffic data augmentation method, and the false positive rate can be effectively reduced.…”
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Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection by Sunil Kaushik, Akashdeep Bhardwaj, Ahmad Almogren, Salil bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen, Habib Hamam

“…In order to address this issue, this study presents a unique feature selection algorithm based on basic statistical methods and a lightweight intrusion detection system. …”
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The best angle correction of basketball shooting based on the fusion of time series features and dual CNN by Meicai Xiao

“…However, the current method is limited by the variability of the shape base, ignoring dynamic features and visual information, and there are some problems in the process of feature extraction and correction of related actions. …”
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Preoperative prediction of recurrence risk factors in operable cervical cancer based on clinical-radiomics features by Xue Du, Xue Du, Chunbao Chen, Lu Yang, Yu Cui, Min Li

“…ObjectiveTo investigate the value of preoperative prediction of risk factors for recurrence of operable cervical cancer based on the radiomics features of biparametric magnetic resonance imaging (bp-MRI) combined with clinical features.MethodA retrospective collection of cervical cancer cases undergoing radical hysterectomy + pelvic and/or para-aortic lymph node dissection at the Affiliated Hospital of North Sichuan Medical College was conducted. …”
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Identification of gastroenteropancreatic neuroendocrine tumor patients with high liver tumor burden based on clinicopathological features by Nuerailaguli Jumai, Luohai Chen, Xiaoxuan Lin, Qiao He, Man Liu, Yuan Lin, Yanji Luo, Yu Wang, Min-hu Chen, Xiangsong Zhang, Zhirong Zeng, Ning Zhang

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A Multispectral Feature Selection Method Based on a Dual-Attention Network for the Accurate Estimation of Fractional Vegetation Cover in Winter Wheat by Runzhi Yang, Shanshan Li, Bing Zhang, Quanjun Jiao, Dailiang Peng, Songlin Yang, Ruyi Yu

“…In the second step, the importance of Sentinel-2 multispectral bands was converted from the hyperspectral band importance identified in the previous stage, and subsequently ranked accordingly. Based on the feature ranking results, multispectral simulated data translated from hyperspectral simulated data were used for CNN training, and multispectral feature selection was conducted based on FVC accuracy. …”
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Implementation of Extreme Learning Machine Based on HSV Color Features for Marine Animal Image Classification by Dzil Hidayati, Yuliana Pertiwi, Agung Ramadhanu

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Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning by Umar Subhan Malhi, Junfeng Zhou, Abdur Rasool, Shahbaz Siddeeq

“…In fashion e-commerce, predicting item compatibility using visual features remains a significant challenge. Current recommendation systems often struggle to incorporate high-dimensional visual data into graph-based learning models effectively. …”
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Attention-enhanced and integrated deep learning approach for fishing vessel classification based on multiple features by Xin Cheng, Jintao Wang, Xinjun Chen, Fan Zhang

“…Finally, the feature vector was fed into an ensemble model of a two-dimensional bidirectional long short-term memory network and a convolutional neural network with an attention mechanism for training, and the prediction results were obtained through a fully connected layer. …”
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Possibility to increase the speeds of express and high-speed trains by turnouts and connecting tracks by A. M. Brzhezovskiy, B. E. Glyuzberg, M. I. Titarenko, I. E. Il’In, V. S. Viktorov, V. N. Tulushev

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High-Dimensional Projected Clustering for Learner Competency Analysis in Medical Training Programs by Sandhya Harikumar, C. S. Jayamohan Pillai, V. Vani Chithra, Raghu Raman, Mr Kaimal, Kai-Yu Tang, Prema Nedungadi

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Application of BERT-GCN Model Based on Strong Link Relation Graph in Water Use Enterprise Classification by Junhong Xiang, Baoxian Zheng, Chenkai Cai, Shuiping Yao, Shang Gao

“…First, we constructed a co-word relation graph based on the typical industry characteristics keywords extracted by the <i>TF-IDF</i> and extracted co-word relation features using a graph convolutional network (GCN). …”
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Lightweight Indoor Positioning System Based on Multiple Self-Learning Features and Key Frame Classification by C. Wang, K. Bi, B. Zhao, M. Li, Y. Chen, S. Tao, J. Yang

“…In the preprocessing stage, image information is collected for the entire indoor environment, and a key-frame recognizer is trained based on the image information. Simultaneously, an environmental feature information database is established. …”
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XGBoost models based on non imaging features for the prediction of mild cognitive impairment in older adults by Miguel A. Fernández-Blázquez, José M. Ruiz-Sánchez de León, Rubén Sanz-Blasco, Emilio Verche, Marina Ávila-Villanueva, María José Gil-Moreno, Mercedes Montenegro-Peña, Carmen Terrón, Cristina Fernández-García, Jaime Gómez-Ramírez

“…The aim of this study is to develop and validate machine learning (ML) models based on non-imaging features to predict the risk of MCI conversion in cognitively healthy older adults over a three-year period. …”
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Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer’s disease progression by Sepehr Aghajanian, Fateme Mohammadifard, Ida Mohammadi, Shahryar Rajai Firouzabadi, Ali Baradaran Bagheri, Elham Moases Ghaffary, Omid Mirmosayyeb

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EmoBERTa–CNN: Hybrid Deep Learning Approach Capturing Global Semantics and Local Features for Enhanced Emotion Recognition in Conversational Settings by Mingfeng Zhang, Aihe Yu, Xuanyu Sheng, Jisun Park, Jongtae Rhee, Kyungeun Cho

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