Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning by Zhaoya Gong, Chenglong Wang, Bin Liu, Binbo Li, Wei Tu, Yuting Chen, Zhicheng Deng, Pengjun Zhao

“…Understanding dynamics of urban land-use is crucial for comprehending urban spaces and evaluating planning strategies. A range of data-driven models based on the representation learning of multiple data sources have focused on extracting spatially explicit characteristics at the feature level for urban function inference. …”
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Integration of LSTM networks with gradient boosting machines (GBM) for assessing heating and cooling load requirements in building energy efficiency by Reenu Batra, Shakti Arora, Mayank Mohan Sharma, Sonu Rana, Kanishka Raheja, Abeer Saber, Mohd Asif Shah

“…Due to rising demand for energy-efficient buildings, advanced predictive models are needed to evaluate heating and cooling load requirements. This research presents a unified strategy that blends LSTM networks and GBM to improve building energy load estimates’ precision and reliability. …”
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Learning physics and temporal dependencies: real-time modeling of water distribution systems via Kolmogorov–Arnold attention networks by Zekun Zou, Zhihong Long, Gang Xu, Raziyeh Farmani, Tingchao Yu, Shipeng Chu

“…To address these challenges, this study proposes the Kolmogorov–Arnold Attention Network for the real-time modeling of WDSs (KANSA), which combines Kolmogorov–Arnold Networks with attention mechanisms to extract temporal dependency features through bidirectional spatiotemporal processing. …”
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Papayafreshnet: a hybrid deep learning framework for non-destructive freshness classification of papayas using convolutional and transformer networks by Ayan Sar, Tanupriya Choudhury, Tanmay Sarkar, Ketan Kotecha

“…The architecture integrates the advantages of Convolutional Neural Networks (CNNs) with sophisticated Transformer-based feature extraction and attention methods. …”
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Sustainable crop recommendation system using seasonally adaptive recursive spectral convolutional neural network for responsible agricultural production by Gopinath Selvaraj, Sakthivel Kuppusamy, Menaka Aswathanarayanan

“…The model incorporates feature subsets derived from high-impact factors through minimal Redundancy and Maximum Weight (mRmW) and Recursive Fisher Score Feature Selection (RFSFS), eliminating irrelevant data and enhancing accuracy. …”
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A Novel Dangerous Goods Detection Network Based on Multi-Layer Attention Mechanism in X-Ray Baggage Images by Xu Yang, Ting Lan, Yili Xu

“…In the network, a multilevel refinement (MR) module is designed to improve multi-level feature representation, enabling the network to capture varied goods sizes and overlapping structures effectively. …”
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Adversarial learning network for multi-source change detection in built-up areas: integrating VHR RGB and multispectral imagery by Amel Oubara, Falin Wu, Guoxin Qu, Reza Maleki, Gongliu Yang

“…The discriminator network of the proposed network evaluates the authenticity of the generated CM, refining feature differentiation between changed and unchanged regions. …”
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Energy-Efficient Multi-Hop Routing Protocol for Tag-to-Tag Communication in Passive RFID Networks Using Reinforcement Learning by Guowei Guo, Zeli Xi, Zhixin Lu, Ximei Zhan, Xinsen Yang, Peisong Li

“…In this paper, we propose a novel multi-hop routing protocol tailored for passive RFID tag-to-tag networks utilizing a reinforcement learning method. …”
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A Novel Similarity Score for Link Prediction Approach Using Financial Transaction Networks and Firms’ Attribute by Aparajita Bose, Byunghoon Kim

“…Existing studies often overlook important features such as the direction of transactions between firms, the hierarchical nature of transaction networks, and the significance of node attributes, thereby hindering accurate link prediction. …”
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A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection by Murat Sarıateş, Erdal Özbay

“…Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. …”
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Conv1D-GRU-Self Attention: An Efficient Deep Learning Framework for Detecting Intrusions in Wireless Sensor Networks by Kenan Honore Robacky Mbongo, Kanwal Ahmed, Orken Mamyrbayev, Guanghui Wang, Fang Zuo, Ainur Akhmediyarova, Nurzhan Mukazhanov, Assem Ayapbergenova

“…A Conv1D extracts spatial features from network traffic, GRU captures temporal dependencies, and Self-Attention emphasizes critical sequence components, collectively enhancing detection of subtle and complex intrusion patterns. …”
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Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening by Bo Wang, Muhammad Junaid, Wenjin Li

“…Taking cGAS and kRAS proteins as examples, through rigorous data screening and feature extraction, the study constructed multiple supervised learning models containing traditional machine learning models, and deep learning models like graph convolutional networks. …”
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DS Net: A Dual-Coded Segmentation Network Leveraging Large Model Prior Knowledge for Intelligent Landslide Extraction by Xiao Wang, Dongsheng Zhong, Chenghao Liu, Xiaochuan Song, Luting Xu, Yue Deng, Shaoda Li

“…An innovative Dual-Coded Segmentation Network (DS Net), which realizes dynamic alignment and deep fusion of local details and global context, image features and domain knowledge through the multi-attention mechanism of Prior Knowledge Integration (PKI) module and Cross-Feature Aggregation (CFA) module, significantly improves the landslide detection accuracy and reliability. …”
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A robust Parkinson’s disease detection model based on time-varying synaptic efficacy function in spiking neural network by Priya Das, Sarita Nanda, Ganapati Panda, Sujata Dash, Amel Ksibi, Shrooq Alsenan, Wided Bouchelligua, Saurav Mallik

“…To evaluate the performance of SEFRON, 2 publicly available standard datasets, namely (1) UCI: Oxford Parkinson’s Disease Detection Dataset and (2) UCI: Parkinson Dataset with replicated acoustic features are used. …”
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IoT-Driven Intelligent Energy Management: Leveraging Smart Monitoring Applications and Artificial Neural Networks (ANN) for Sustainable Practices by Azza Mohamed, Ibrahim Ismail, Mohammed AlDaraawi

“…The application allows for continuous energy monitoring via modern IoT devices and wireless sensor networks, while ANN-based prediction models evaluate consumption data to dynamically optimize energy use and reduce environmental effect. …”
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The application of natural language processing technology in hospital network information management systems: Potential for improving diagnostic accuracy and efficiency by Shiyong Wang, Hong Luo

“…Bag of Words (BoW) is used to extract the featured data. Method: Reports are divided among 70 % training and 30 % test sets for NLP model evaluation. …”
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A novel unified Inception-U-Net hybrid gravitational optimization model (UIGO) incorporating automated medical image segmentation and feature selection for liver tumor detection by Tathagat Banerjee, Davinder Paul Singh, Pawandeep Kour, Debabrata Swain, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…This new deep learning model merges U-Net and Inception networks, incorporating advanced feature selection and optimization strategies. …”
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Introducing a Novel Fast Neighbourhood Component Analysis–Deep Neural Network Model for Enhanced Driver Drowsiness Detection by Sama Hussein Al-Gburi, Kanar Alaa Al-Sammak, Ion Marghescu, Claudia Cristina Oprea, Ana-Maria Claudia Drăgulinescu, George Suciu, Khattab M. Ali Alheeti, Nayef A. M. Alduais, Nawar Alaa Hussein Al-Sammak

“…FNCA is employed to optimize feature representation, effectively highlighting critical features for drowsiness detection, which are then analysed using a DNN to achieve high accuracy in recognizing signs of driver fatigue. …”
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PINN ME: A Physics-informed Neural Network Framework for Accurate Milne–Eddington Inversions of Solar Magnetic Fields by Robert Jarolim, Momchil E. Molnar, Benoit Tremblay, Rebecca Centeno, Matthias Rempel

“…In this study, we present a novel approach for spectropolarimetric inversions based on physics-informed neural networks to infer the photospheric magnetic field under the Milne–Eddington approximation (PINN ME). …”
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A New Pallet-Positioning Method Based on a Lightweight Component Segmentation Network for AGV Toward Intelligent Warehousing by Bin Wu, Shijie Wang, Yi Lu, Yang Yi, Di Jiang, Mengmeng Qiao

“…Due to the lack of a public pallet image segmentation dataset, the network was tested using a custom-made dataset. The results show that by extracting intermediate-, low-, and high-level features from dual-branch input images and integrating them to construct multi-scale images, precise segmentation of various types of pallets can be achieved with limited annotated images. …”
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