STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent by James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar

“…To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. …”
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Few-shot Remote Sensing Imagery Recognition with Compositionality Inductive Bias in Hierarchical Representation Space by Shichao Zhou, Zhuowei Wang, Zekai Zhang, Wenzheng Wang, Yingrui Zhao, Yunpu Zhang

“…Different from the naive data-driven strategies mentioned above, we alternatively devote to delicate feature modeling by constraining the mapping behavior of deep neural networks. …”
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Modeling of Interactive Teaching and Learning System for Students Based on Artificial Intelligence by Jiaming Liu, Han Zou

“…In order to improve the interactive effect of modern teaching, this paper combines the artificial intelligence interactive technology to model the teaching interactive process and designs and analyzes the system static test of the infrared feature threshold online monitoring system. …”
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Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model by Yanjiao Chen

“…This method not only considers the prior knowledge of feature words, word frequency, and number of documents but also fuses the meaning of the feature words into the hidden Markov classification model. …”
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Robust Photovoltaic Power Forecasting Model Under Complex Meteorological Conditions by Yuxiang Guo, Qiang Han, Tan Li, Huichu Fu, Meng Liang, Siwei Zhang

“…Based on VMD-PCA refined feature extraction, the iTransformer is then employed to perform continuous active power prediction across time steps, leveraging its strength in modeling long-range temporal dependencies under complex meteorological conditions. …”
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Nonparametric Estimation of Dynamic Value-at-Risk: Multifunctional GARCH Model Case by Zouaoui Chikr-Elmezouar, Ali Laksaci, Ibrahim M. Almanjahie, Fatimah Alshahrani

“…Value-at-Risk (VaR) estimation using the GARCH model is an important topic in financial data analysis. …”
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Adaptive CoCoLasso for High-Dimensional Measurement Error Models by Qin Yu

“…While the Adaptive CoCoLasso estimator performs comparably or is slightly outperformed by certain methods, such as Hard, in reducing the number of incorrectly identified covariates, its strength lies in offering a more favorable trade-off between prediction accuracy and sparse modeling.…”
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Lightweight malicious domain name detection model based on separable convolution by Luhui YANG, Huiwen BAI, Guangjie LIU, Yuewei DAI

“…The application of artificial intelligence in the detection of malicious domain names needs to consider both accuracy and calculation speed,which can make it closer to the actual application.Based on the above considerations,a lightweight malicious domain name detection model based on separable convolution was proposed.The model uses a separable convolution structure.It first applies depthwise convolution on every input channel,and then performs pointwise convolution on all output channels.This can effectively reduce the parameters of convolution process without impacting the effectiveness of convolution feature extraction,and realize faster convolution process while keeping high accuracy.To improve the detection accuracy considering the imbalance of the number and difficulty of positive and negative samples,a focal loss function was introduced in the training process of the model.The proposed algorithm was compared with three typical deep-learning-based detection models on a public data set.Experimental results denote that the proposed algorithm achieves detection accuracy close to the state-of-the-art model,and can significantly improve model inference speed on CPU.…”
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Impact of Developer Queries on the Effectiveness of Conversational Large Language Models in Programming by Viktor Taneski, Sašo Karakatič, Patrik Rek, Gregor Jošt

“…Rather than comparing different models, it focuses on how students interact with LLM tools to isolate the impact of query type on coding success. …”
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Prediction Model of Late Fetal Growth Restriction with Machine Learning Algorithms by Seon Ui Lee, Sae Kyung Choi, Yun Sung Jo, Jeong Ha Wie, Jae Eun Shin, Yeon Hee Kim, Kicheol Kil, Hyun Sun Ko

“…The simplified prediction model performed similarly to the original model. …”
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Assessment of Toxicity and Therapeutic Effects of Goose Bone in a Rat Model by Md. Yousuf Ali, Alamgir Kabir, Shahad Saif Khandker, Tareq Hossan, Md. Salman Shakil, Md. Aminul Islam, Md. Ashraful Hasan, Md. Sakib Hossen, Siew Hua Gan, Md. Ibrahim Khalil

“…The aim of the present study was to evaluate the subacute toxicity of goose bone in a rat model by investigating some hematological and biochemical parameters in rats. …”
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The Prognosis Prediction Model for Endometrial Cancer Based on DNA Methylation Signature by Ran Ran, Ming Wang, Jinwei Miao

“…The univariate Cox regression analysis and the LASSO regression analysis were subsequently used to identify prognosis‐related methylation regulators and construct a risk model. Gene functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular feature analysis were performed in different subgroups. …”
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Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy by Hua Chen, Kehui Mei, Yuan Zhou, Nan Wang, Guangxing Cai

“…Thus, the feature dimensionality of the dataset is reduced and the generalization ability of the model is improved. …”
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A Hybrid RBF-PSO Framework for Real-Time Temperature Field Prediction and Hydration Heat Parameter Inversion in Mass Concrete Structures by Shi Zheng, Lifen Lin, Wufeng Mao, Yanhong Wang, Jinsong Liu, Yili Yuan

“…The hybrid F₃, incorporating Dynamic Time Warping (DTW) for elastic time alignment and feature penalties for engineering-critical metrics, achieved superior performance with a 74% reduction in the prediction error (mean MAE = 1.0 °C) and <2% parameter identification errors, resolving the phase mismatches inherent in F₂ and avoiding F₁’s prohibitive computational costs (498 FEM calls). …”
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Hyperspectral Imaging Combined with Deep Learning for the Early Detection of Strawberry Leaf Gray Mold Disease by Yunmeng Ou, Jingyi Yan, Zhiyan Liang, Baohua Zhang

“…The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. …”
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EEG Emotion Recognition Using AttGraph: A Multi-Dimensional Attention-Based Dynamic Graph Convolutional Network by Shuai Zhang, Chengxi Chu, Xin Zhang, Xiu Zhang

“…Methods: To address these challenges, this paper proposes a multi-dimensional attention-based dynamic graph convolutional neural network (AttGraph) model. The model delves into the impact of different EEG features on emotion recognition by evaluating their sensitivity to emotional changes, providing richer and more accurate feature information. …”
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Ensemble Learning for Spatial Modeling of Icing Fields from Multi-Source Remote Sensing Data by Shaohui Zhou, Zhiqiu Gao, Bo Gong, Hourong Zhang, Haipeng Zhang, Jinqiang He, Xingya Xi

“…Additionally, we employed the SHAP model to provide a physical interpretation of the stacking model, confirming the independence of selected features. …”
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Prediction of Rice Chlorophyll Index (CHI) Using Nighttime Multi-Source Spectral Data by Cong Liu, Lin Wang, Xuetong Fu, Junzhe Zhang, Ran Wang, Xiaofeng Wang, Nan Chai, Longfeng Guan, Qingshan Chen, Zhongchen Zhang

“…Among the individual sensor types, MS data achieved relatively high accuracy at certain stages, while models based on RGB and ChlF features exhibited weaker performance and lower prediction stability. …”
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PF2N: Periodicity–Frequency Fusion Network for Multi-Instrument Music Transcription by Taehyeon Kim, Man-Je Kim, Chang Wook Ahn

“…Unlike handcrafted alignments in classical approaches, the PF2N performs data-driven fusion using a learnable joint feature extractor. …”
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A Multi-Objective Bio-Inspired Optimization for Voice Disorders Detection: A Comparative Study by Maria Habib, Victor Vicente-Palacios, Pablo García-Sánchez

“…The results reveal that NSGA-II with the MLP algorithm attained the best convergence and performance. Further, the conformal prediction is leveraged to quantify uncertainty in the feature-selected models, ensuring statistically valid confidence intervals for predictions.…”
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