Neural networks in oncourology by M. P. Korchagin, A. V. Govorov, A. O. Vasilyev, I. O. Gritskov, D. Yu. Pushkar

“…Accurate and early diagnosis of malignancies is a key challenge in oncology. Neural networks can analyse a wide range of medical data and identify relationships between qualitative and quantitative features. …”
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An Effective Feature Extraction Method for Tomato Leafminer - Tuta Absoluta (Meyrick) (Lepidoptera: Gelechiidae) Classification by Tahsin Uygun, Serhat Kiliçarslan, Cemil Közkurt, Mehmet Metin Ozguven

“…Using a hybrid approach, features were extracted through Convolutional Neural Networks (CNNs) with transfer learning and classified using traditional machine learning techniques. …”
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A Comparative Analysis of Artificial Intelligence Techniques for Single Open-Circuit Fault Detection in a Packed E-Cell Inverter by Bushra Masri, Hiba Al Sheikh, Nabil Karami, Hadi Y. Kanaan, Nazih Moubayed

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Diagnosis of array antennas based on near-field data using Faster R-CNN by Boguang Yang, Yulun Wei, Jixiang Shi, Tao Hong, Liangyu Li, Kai-Da Xu

“…Four different excitation states are trained using the same neural network to compare the effects of excitation faults on feature extraction and classification accuracy. …”
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Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration by Isuru Udayangani Hewapathirana

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Deep-m6Am: a deep learning model for identifying N6, 2′-O-Dimethyladenosine (m6Am) sites using hybrid features by Islam Uddin, Salman A. AlQahtani, Sumaiya Noor, Salman Khan

“…Finally, a multilayer deep neural network (DNN) is used as a classification algorithm for identifying m6Am sites. …”
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Underwater image enhancement using hybrid transformers and evolutionary particle swarm optimization by Ajay Kumar, Gagandeep Berar, Manmohan Sharma, Sakshi, Ajit Noonia, Gunjan Verma

“…The HTN-PSO framework combines the strengths of convolutional neural networks and transformer models to effectively capture low-level features and model long-range dependencies. …”
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Research on multi class pests identification and detection based on fusion attention mechanism with Mask-RCNN-CBAM by Xingwang Wang, Xingwang Wang, Xingwang Wang, Can Hu, Xufeng Wang, Hainie Zha, Xueyong Chen, Shanshan Yuan, Jing Zhang, Jianfeng Liao, Zhangying Ye

“…The framework combines three innovations: (1) a CBAM attention mechanism to amplify pest features while suppressing background noise; (2) a feature-enhanced pyramid network (FPN) for multi-scale feature fusion, enhancing small pest recognition; and (3) a dual-channel downsampling module to minimize detail loss during feature propagation. …”
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Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…The NSynth database was used for training and evaluation. Visual heatmap analysis and statistical metrics, including Difference Mean, KL Divergence, JS Divergence, and Earth Mover’s Distance, were utilized to assess feature importance and model interpretability.ResultsOur findings highlight the strengths and limitations of each spectrogram type in capturing distinctive features of different instruments. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

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Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network by Haohua Zheng, Jianchen Zhang, Heying Li, Guangxia Wang, Jianzhong Guo, Jiayao Wang

“…To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where the feature masking method is initially utilized to assess the significance of road features. …”
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Integrating particle swarm optimization with backtracking search optimization feature extraction with two-dimensional convolutional neural network and attention-based stacked bidir... by Jyotirmayee Rautaray, Sangram Panigrahi, Ajit Kumar Nayak

“…This study introduces an improvised Particle Swarm Optimization with Backtracking Search Optimization (PSOBSA) designed for feature extraction. For classification purpose, it recommends two-dimensional convolutional neural network (2D CNN) along with an attention-based stacked bidirectional long short-term memory (ABS-BiLSTM) model to generate new summarized sentences by analyzing entire sentences. …”
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Neuro-Evolution of Augmenting Topologies for Dynamic Scheduling of Flexible Job Shop Problem by Jian Huang, Yarong Chen, Jabir Mumtaz, Liuyan Zhong

“…Employing the entropy weight method, a fitness function for multiobjective optimization is formulated, facilitating the enhancement of the neural network’s structural and nodal parameters through genetic algorithms. …”
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Predicting the Evolution of Lung Squamous Cell Carcinoma In Situ Using Computational Pathology by Alon Vigdorovits, Gheorghe-Emilian Olteanu, Ovidiu Tica, Andrei Pascalau, Monica Boros, Ovidiu Pop

“…We used this dataset to train two models: a pathomics-based ridge classifier trained on 80 principal components derived from almost 2000 extracted features and a deep convolutional neural network with a modified ResNet18 architecture. …”
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Deep Complex Gated Recurrent Networks-Based IoT Network Intrusion Detection Systems by Engy El-Shafeiy, Walaa M. Elsayed, Haitham Elwahsh, Maazen Alsabaan, Mohamed I. Ibrahem, Gamal Farouk Elhady

“…Furthermore, DCGR_IoT harnesses complex gated recurrent networks (CGRNs) to construct multidimensional feature subsets, enabling a more detailed spatial representation of network traffic and facilitating the extraction of critical features that are essential for intrusion detection. …”
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Feature fusion with attributed deepwalk for protein–protein interaction prediction by Mei-Yuan Cao, Suhaila Zainudin, Kauthar Mohd Daud

“…This study proposes FFADW (Feature Fusion Method with Attributed DeepWalk), a novel approach that integrates sequence and network features using a weighted fusion strategy controlled by an adjustable α parameter. …”
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Learning Feature Fusion in Deep Learning-Based Object Detector by Ehtesham Hassan, Yasser Khalil, Imtiaz Ahmad

“…Our hypothesis is to reinforce these features with handcrafted features by learning the optimal fusion during network training. …”
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ACFM: Adaptive Channel Feature Matching for Pedestrian Re-Identification by Zhengcai Lu, Zhengwei Tian

“…The multi-branch structure is designed to handle both global and local features, enabling the network to comprehensively capture and integrate image information. …”
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Innovative approaches to English pronunciation instruction in ESL contexts: integration of multi-sensor detection and advanced algorithmic feedback by Li Ping, Ning Tao

“…The approach employs multi-sensor detection methods for precise data collection, preprocessing techniques such as pre-emphasis, normalization, framing, windowing, and endpoint detection to ensure high-quality speech signals. Feature extraction focuses on key attributes of pronunciation, which are then fused through a feedback neural network for comprehensive evaluation. …”
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Infant Type Ia Supernovae from the KMTNet. I. Multicolor Evolution and Populations by Yuan Qi Ni, Dae-Sik Moon, Maria R. Drout, Youngdae Lee, Patrick Sandoval, Jeehye Shin, Hong Soo Park, Sang Chul Kim, Kyuseok Oh

“…We conduct a systematic analysis of the early multiband light curves and colors of 19 Type Ia supernovae (SNe) from the Korea Microlensing Telescope Network SN Program, including 16 previously unpublished events. …”
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