Development and evaluation of machine learning models for premixed flame classification in different hydrogen-natural gas proportions using images and audio by Pedro Narvaez, Alejandro Lopez, Jousef E. Karam, Alejandro Restrepo, Andrés A. Amell

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Botnets’ similarity analysis based on communication features and D-S evidence theory by ZANG Tian-ning1, YUN Xiao-chun1, ZHANG Yong-zheng2, MEN Chao-guang1, CUI Xiang2

“…A potential hidden relationship may exist among different zombie groups.A method to analyze the relationship among botnets was proposed based on the communication activities.The method extracted several communication fea-tures of botnet,including the number of flows per hour,the number of packets per flow,the number of flows per IP and the packet payloads.It defined similarity statistical functions of the communication features,and built the analysis model of botnets relationship based on the advanced dempster-shafer（D-S） evidence theory to synthetically evaluate the simi-larities between different zombie groups.The experiments were conducted using several botnet traces.The results show that the method is valid and efficient,even in the case of encrypted botnet communication messages.Moreover,the ideal processing results is achieved by applying our method to analyze the data captured from the security monitoring platform of computer network,as well as compare with similar work.…”
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SPECIFIC STAGE-DEPENDENT FEATURES OF CYTOKINE CASCADE IN THE PATIENTS WITH BRONCHIAL ASTHMA by L. V. Ryabova, A. V. Zurochka

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An Indoor Scene Classification Method for Service Robot Based on CNN Feature by Shaopeng Liu, Guohui Tian

“…To solve this problem, an indoor scene classification method is proposed in this paper, which utilizes CNN feature of scene images to generate scene category features to classify scenes by a novel feature matching algorithm. …”
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Advancing EEG based stress detection using spiking neural networks and convolutional spiking neural networks by Aaditya Joshi, Paramveer Singh Matharu, Lokesh Malviya, Manoj Kumar, Akshay Jadhav

“…We apply Discrete Wavelet Transform (DWT) for feature extraction and evaluate CSNN performance on the Physionet EEG dataset, benchmarking it against traditional deep learning and machine learning methods. …”
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Thyroid disease classification using generative adversarial networks and Kolmogorov-Arnold network for three-class classification by Aysel Topşir, Ferdi Güler, Ecesu Çetin, Mehmet Furkan Burak, Melih Ağraz

“…This study introduces an advanced machine learning approach that integrates generative adversarial networks (GANs) for data augmentation and Kolmogorov-Arnold networks (KANs) for classification. …”
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Differentiating biomarker features and familial characteristics of B-SNIP psychosis Biotypes by David A. Parker, Rebekah L. Trotti, Jennifer E. McDowell, Sarah K. Keedy, Matcheri S. Keshavan, Godfrey D. Pearlson, Elliot S. Gershon, Elena I. Ivleva, Ling-Yu Huang, Kodiak Sauer, S. Kristian Hill, John A. Sweeney, Carol A. Tamminga, Brett A. Clementz

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Comparative analysis of convolutional neural networks and traditional machine learning models for IVF live birth prediction: a retrospective analysis of 48514 IVF cycles and an eva... by Yu Liu, Yi Wang, Kai Huang, Hao Shi, Hang Xin, Shanjun Dai, Jinhao Liu, Xinhong Yang, Jianyuan Song, Fuli Zhang, Yihong Guo

“…The study also explores the feasibility of deploying such models in resource-limited clinical settings.DesignRetrospective cohort study based on EMR data using five models: CNN, Naïve Bayes, Random Forest, Decision Tree, and Feedforward Neural Network. Feature importance and model interpretability were evaluated using SHAP.SettingFirst Hospital of Zhengzhou University.Population48,514 fresh IVF cycles from August 2009 to May 2018.MethodsPreprocessed EMR data were used to train and evaluate five classification models predicting live birth outcomes. …”
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An efficient supervised framework for music mood recognition using autoencoder‐based optimised support vector regression model by Gaurav Agarwal, Hari Om

“…The experimental results are evaluated and compared with the existing classifiers including SVR, deep belief network (DBN) and Recurrent neural network (RNN). …”
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Challenges and Perspectives in Interpretable Music Auto-Tagging Using Perceptual Features by Vassilis Lyberatos, Spyridon Kantarelis, Edmund Dervakos, Giorgos Stamou

“…We developed a pipeline incorporating three types of information extraction procedures: 1) symbolic knowledge, 2) auxiliary deep neural networks, and 3) signal processing, to extract perceptual features of audio files, which were then used to train an explainable machine learning model to predict tags. …”
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Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment by Neha Tarigopula, Sandrine Tornay, Ozge Mercanoglu Sincan, Richard Bowden, Mathew Magimai.-Doss

“…Existing methods rely on handcrafted skeleton-based features for hand movement within a KL-HMM framework to identify errors in manual components. …”
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ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection by Bin Li, Jie Li, Mingyu Jia

“…Many existing intrusion detection studies often fail to fully extract the spatial features of network traffic and make reasonable use of temporal features. …”
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User-Independent Activity Recognition via Three-Stage GA-Based Feature Selection by Theresia Ratih Dewi Saputri, Adil Mehmood Khan, Seok-Won Lee

“…The proposed system uses simple time domain features with a single neural network and a three-stage genetic algorithm-based feature selection method for accurate user-independent activity recognition. …”
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Impact of agricultural industry transformation based on deep learning model evaluation and metaheuristic algorithms under dual carbon strategy by Xuan Zhao, Weiyun Tang, Qiuyan Liu, Hongtao Cao, Fei Chen

“…A hybrid deep learning model—combining Convolutional Neural Networks and Long Short-Term Memory networks—is developed to evaluate the effects of agricultural industry transformation. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

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Dense Neural Network Classification Model for Software Defined Network for Fine Grained Traffic Routing and Flow Analysis by Dr. Khaliq Ahmed, Maheen Danish, Asif Raza

“…Fine-grained traffic classification cannot be underestimated in SDN, given the wide gamut that runs applications over SDN networks. The DDNN model handles diverse types of traffic with a lot of efficiency through extracting deep features from complex datasets, hence increasing the capability of SDN to meet the ever-evolving demands in networks. …”
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A Method for Fault Localization in Distribution Networks with High Proportions of Distributed Generation Based on Graph Convolutional Networks by Xiping Ma, Wenxi Zhen, Haodong Ren, Guangru Zhang, Kai Zhang, Haiying Dong

“…By abstracting busbars and lines into graph structure nodes and edges, GCN captures spatial coupling relationships between nodes, using key electrical quantities such as node voltage magnitude, current magnitude, power, and phase angle as input features to construct a fault localization model. A multi-type fault dataset is generated using the Matpower toolbox, and model training is evaluated using K-fold cross-validation. …”
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MEFSR-GAN: A Multi-Exposure Feedback and Super-Resolution Multitask Network via Generative Adversarial Networks by Sibo Yu, Kun Wu, Guang Zhang, Wanhong Yan, Xiaodong Wang, Chen Tao

“…MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). …”
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Distinct network patterns emerge from Cartesian and XOR epistasis models: a comparative network science analysis by Zhendong Sha, Philip J. Freda, Priyanka Bhandary, Attri Ghosh, Nicholas Matsumoto, Jason H. Moore, Ting Hu

“…Additionally, the XOR network features triangle network motifs, indicative of higher-order epistatic interactions. …”
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Distinguishable IQ Feature Representation for Domain-Adaptation Learning of WiFi Device Fingerprints by Abdurrahman Elmaghbub, Bechir Hamdaoui

“…By accurately capturing device hardware impairments while suppressing irrelevant domain information, <monospace>EPS</monospace> offers improved feature selection for DL models in RFFP. Our experimental evaluation demonstrates the effectiveness of the integration of <monospace>EPS</monospace> representation with a Convolution Neural Network (CNN) model, termed <monospace>EPS-CNN</monospace>, achieving over 99% testing accuracy in same-day/channel/location evaluations and 93% accuracy in cross-day evaluations, outperforming the traditional IQ representation. …”
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