Anomaly Detection and Business Process Orchestration for Low-Code Platform in Power System Based on Deep-Cross Model With Data Balancing by Jin Huang, Ming Sheng Xu, Chang Ming Zhu, Guo An Wang

“…Third, we implement a hierarchical feature interaction mechanism that enables both explicit and implicit learning of high-order patterns, resulting in a 35% improvement in anomaly pattern recognition over traditional deep learning approaches. …”
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Research into the Application of ResNet in Soil: A Review by Wenjie Wu, Lijuan Huo, Gaiqiang Yang, Xin Liu, Hongxia Li

“…ResNet outperforms traditional methods by effectively mitigating the vanishing gradient problem, enabling deeper network training, enhancing feature extraction, and improving accuracy in complex pattern recognition tasks. …”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
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Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

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MRI Delta Radiomics to Track Early Changes in Tumor Following Radiation: Application in Glioblastoma Mouse Model by Mohammed S. Alshuhri, Haitham F. Al-Mubarak, Abdulrahman Qaisi, Ahmad A. Alhulail, Abdullah G. M. AlMansour, Yahia Madkhali, Sahal Alotaibi, Manal Aljuhani, Othman I. Alomair, A. Almudayni, F. Alablani

“…A machine learning model was developed to classify irradiated tumors based on delta radiomic features, and statistical analyses were conducted to evaluate feature feasibility, stability, and predictive performance. …”
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sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules by Yutong Xia, Dawei Qiu, Cheng Zhang, Jing Liu

“…Compared with other deep learning models, MSACNN-RM achieves higher accuracy compared to existing models.DiscussionThe proposed model explores features of sparse sEMG signals by leveraging multi-stream convolution, the combination of adaptive convolution modules and ResNet blocks enhances the model’s ability of extracting crucial gesture features. …”
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Informal Settlements Extraction and Fuzzy Comprehensive Evaluation of Habitat Environment Quality Based on Multi-Source Data by Zanxian Yang, Fei Yang, Yuanjing Xiang, Haiyi Yang, Chunnuan Deng, Liang Hong, Zhongchang Sun

“…The study demonstrates that: (1) texture features from the gray level co-occurrence matrix (GLCM) significantly improved the classification of informal settlements, with the random forest classification model achieving a kappa coefficient above 0.77, an overall accuracy exceeding 0.89, and F1 scores above 0.90; (2) informal settlements exhibited two primary development patterns: gradual expansion near formal residential areas and dependence on natural resources such as farmland, forests, and water bodies; (3) economic vitality emerged as a critical factor in improving the living environment, while social, natural, and residential conditions remained relatively stable; (4) the proportion of highly suitable and moderately suitable areas increased from 65.62% to 65.92%, although the overall improvement in informal settlements remained slow. …”
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Spatial Correlation Network Characteristics of Comprehensive Transportation Green Efficiency in China by Qifei Ma, Sujuan Li, Zhenchao Zhang

“…Accurately characterizing the structural features of the spatial correlation network of comprehensive transportation green efficiency (CTGE) is essential for achieving balanced regional transportation development and eliminating regional disparities. …”
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An optimal weighting-based hybrid classifier for Children's congenital heart diseases signal processing by Morteza Ebrahimpour, Mehdi Khashei

“…The main objective of the proposed optimal weighting-based CNN-LSTM-SVM (OCLS) hybrid classifier is to simultaneously leverage the unique advantages of CNN in feature extraction from input signals, LSTM in modeling the sequential patterns of signals, SVM in classifying regular patterns, and especially the proposed weighting algorithm to optimally integrate the outputs of these components. …”
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Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. …”
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Leveraging LSTM and ensemble classifiers for enhanced food waste classification by Khalaf Alsalem

“…The proposed feature extraction approach detects temporal patterns in feature data, improving the robustness of decisions and making this method suitable for waste classification applications. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. …”
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Characteristics and accumulation mode of volcanic rock reservoir of the Fengcheng Formation in the southern of Mahu Sag, Junggar Basin by Xincai YOU, Qi YAN, Mengyao LI, Wenlong DANG, Gang GAO, Xiaobing JIAO, Jun WU, Zhiyuan AN, Lingli ZHU, Yu BAI

“…Objective To clarify the oil and gas accumulation patterns and main controlling factors of the thin weathered crust volcanic rock reservoir at the top of Member 2 of the Fengcheng Formation in the southern Mahu Sag(Manan Sag). …”
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The Evolution of the Idiolect over the Lifetime: A Quantitative and Qualitative Study of French 19th Century Literature by Olga Seminck, Philippe Gambette, Dominique Legallois, Thierry Poibeau

“…Using the Corpus for Idiolectal Research (CIDRE) that contains the dated works of 11 prolific 19th century French fiction writers, we propose new methods to identify, quantify and describe the grammatical-stylistic changes that take place using lexico-morphosyntactic patterns, also called motifs. To examine the strength of the chronological signal of change, we developed a method to calculate if a distance matrix of literary works contains a stronger chronological signal than expected by chance. …”
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Minimising Security Deviations in Software-Defined Networks Using Deep Learning by mohammed alghaloom

“…A comprehensive data analysis was conducted, beginning with feature identification and dimensionality reduction using the Gain Information method to filter out redundant features, thereby improving model performance. …”
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Research on Methods for the Recognition of Ship Lights and the Autonomous Determination of the Types of Approaching Vessels by Xiangyu Gao, Yuelin Zhao

“…Secondly, 23 spatio-semantic feature indicators are established to encode ship light patterns, and a multi-viewing angle dataset is constructed. …”
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Low-hit frequency-hopping communication systems for power Internet of things random access by Shu DU, Mei MA, Bo ZHAO, Qi ZENG, Xing LIU

“…The communication network is an essential component of the data acquisition and information transmission in power Internet of things (IoT).To meet the development requirements of multiple power service in the future, the wireless communication technique with flexible-access and high scalability is one of the development directions for power IoT.Due to the features of large-scale access, random access time, high security and reliability for information transmission of power IoT, a frequency-hopping (FH) technique with low-hit rate for random access was proposed.The construction algorithm of such FH pattern was based upon the combination and shift operation of conventional FH sequences.By the theoretical arithmetic and numerical simulation, the properties of the proposed FH pattern and the error-rate of the FH-based power IoT were evaluated.The analysis reveals that the new class of FH sequences and the system can meet the requirements of large-scale access and highly reliable communication for the power IoT, which has good application prospects.…”
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Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism. by Meshari Alazmi, Nasir Ayub

“…Initially, data is passed through preprocessing to address outliers and missing values. In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. …”
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Pathway-like Activation of 3D Neuronal Constructs with an Optical Interface by Saeed Omidi, Yevgeny Berdichevsky

“…Responses of the network to the stimulation possessed features of neuronal population code, including separability by input pattern and mixed selectivity of individual neurons. …”
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Self-organizing dynamic research based on phase coherence graph autoencoders: Analysis of brain metastable states across the lifespan by Hao Guo, Yu-Xuan Liu, Yao Li, Qi-Li Guo, Zhi-Peng Hao, Yan-Li Yang, Jing Wei

“…The leading eigenvector dynamics analysis (LEiDA) method significantly reduces the dimensionality of data and is widely used to capture the temporal trajectory characteristics of transient functional patterns, i.e., metastable brain states. However, LEiDA's linear dimensionality reduction of high-dimensional raw brain data may overlook non-linear information and lose some relationships between features. …”
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