Internet of Things and Deep Learning for Citizen Security: A Systematic Literature Review on Violence and Crime by Chrisbel Simisterra-Batallas, Pablo Pico-Valencia, Jaime Sayago-Heredia, Xavier Quiñónez-Ku

“…This study conducts a systematic literature review following the PRISMA framework and the guidelines of Kitchenham and Charters to analyze the application of Internet of Things (IoT) technologies and deep learning models in monitoring violent actions and criminal activities in smart cities. …”
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Landscape layout characteristics and evaluation of smart buildings based on deep learning algorithms by Li Zhang

“…Based on the landscape layout of intelligent buildings, this study proposes an evaluation framework based on a deep learning algorithm, aiming at improving the quality and efficiency of landscape design through intelligent means. …”
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Equivariant spherical CNNs for accurate fiber orientation distribution estimation in neonatal diffusion MRI with reduced acquisition time by Haykel Snoussi, Davood Karimi

“…In this study, we propose a rotationally equivariant Spherical Convolutional Neural Network (sCNN) framework tailored for neonatal dMRI. …”
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Real-time Jordanian license plate recognition using deep learning by Salah Alghyaline

“…This paper aims to develop an accurate ALPR for Jordanian LPs. Two-stage Convolutional Neural Networks (CNNs) are used in the proposed approach, the CNNs are based on the YOLO3 framework. …”
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A Deep Learning based Hybrid Model for Maternal Health Risk Detection and Multifaceted Emotion Analysis in Social Networks by Geethanjali R., Valarmathi A.

“…Our study addresses this challenge by introducing the maternal health risk factor detection using deep learning approach (MHRFD-DLA), a novel framework that integrates convolutional neural networks, long short-term memory networks, and attention mechanisms. …”
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Automated Crack Width Measurement in 3D Models: A Photogrammetric Approach with Image Selection by Huseyin Yasin Ozturk, Emanuele Zappa

“…By synthesizing photogrammetry and a convolutional neural network (CNN), the framework eliminates subjectivity in inspections, enhances safety by reducing manual intervention, and provides engineers with dimensionally accurate data for maintenance decisions.…”
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Multi-Time Scale Scenario Generation for Source–Load Modeling Through Temporal Generative Adversarial Networks by Liang Ma, Shigong Jiang, Yi Song, Chenyi Si, Xiaohan Li

“…To address these issues, this paper proposes a multi-time scale source–load scenario generation method based on temporal convolutional networks and multi-head attention mechanisms within a temporal generative adversarial network framework. …”
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Hierarchical Information-guided robotic grasp detection by Zeyao Hou, Yueran Zhao, Yutao Jin, Chao Yang, Zongyu He, Xiaoyan Chen

“…GraspFormer features a unique Encoder-Decoder framework that incorporates a Grasp Transformer Block designed to model long-range dependencies while avoiding background interference. …”
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Research and Optimization of White Blood Cell Classification Methods Based on Deep Learning and Fourier Ptychographic Microscopy by Mingjing Li, Junshuai Wang, Shu Fang, Le Yang, Xinyang Liu, Haijiao Yun, Xiaoli Wang, Qingyu Du, Ziqing Han

“…To address these limitations, this paper proposes an enhanced WBC classification algorithm, CCE-YOLOv7, which is built upon the YOLOv7 framework. The proposed method introduces four key innovations to enhance detection accuracy and model efficiency: (1) A novel Conv2Former (Convolutional Transformer) backbone was designed to combine the local pattern extraction capability of convolutional neural networks (CNNs) with the global contextual reasoning of transformers, thereby improving the expressiveness of feature representation. (2) The CARAFE (Content-Aware ReAssembly of Features) upsampling operator was adopted to replace conventional interpolation methods, thereby enhancing the spatial resolution and semantic richness of feature maps. (3) An Efficient Multi-scale Attention (EMA) module was introduced to refine multi-scale feature fusion, enabling the model to better focus on spatially relevant features critical for WBC classification. (4) Soft-NMS (Soft Non-Maximum Suppression) was used instead of traditional NMS to better preserve true positives in densely packed or overlapping cell scenarios, thereby reducing false positives and false negatives. …”
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Improved leaf area index reconstruction in heavily cloudy areas: A novel deep learning approach for SAR-Optical fusion integrating spatiotemporal features by Mingqi Li, Pengxin Wang, Kevin Tansey, Fengwei Guo, Ji Zhou

“…Firstly, the two-dimensional Convolutional Neural Network-Transformer (2D CNN-Transformer) is applied to bridge SAR and optical data. …”
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Temporal–Spatial Partial Differential Equation Modeling of Land Cover Dynamics via Satellite Image Time Series and Sparse Regression by Ming Kang, Zheng Zhang, Zhitao Zhao, Keli Shi, Junfang Zhao, Ping Tang

“…By integrating temporal and spatial differential terms, the TS-PDE framework captures the intricate interactivity of these factors, overcoming the limitations of traditional pixel-wise prediction methods. …”
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Scalable AI-driven air quality forecasting and classification for public health applications by Mohammad Wasil Jalali, Bahir Saidi, Habibullah Farahmand, Mohammad Aref Rezvan Panah, Eda Nur Saruhan

“…Methods We designed a hybrid AI framework that combines ensemble machine learning models such as Random Forest and XGBoost with deep learning architectures including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and the Transformer-based Time Series Mixer (TSMixer). …”
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Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images by Junyang Xie, Yan Li, Hao Wu, Ziwei Wu, Ruina Zhao, Anqi Lin, Marcos Adami, Guoqiang Li, Jian Zhang

“…In addition, comparisons with other methods further validated the effectiveness of this framework in mapping crop planting structure.…”
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Multimodal Pain Recognition in Postoperative Patients: Machine Learning Approach by Ajan Subramanian, Rui Cao, Emad Kasaeyan Naeini, Seyed Amir Hossein Aqajari, Thomas D Hughes, Michael-David Calderon, Kai Zheng, Nikil Dutt, Pasi Liljeberg, Sanna Salanterä, Ariana M Nelson, Amir M Rahmani

“…ConclusionsThis study presents a novel, multimodal machine learning framework for objective pain recognition in postoperative patients. …”
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A Hybrid Brain Tumor Classification Using FL With FedAvg and FedProx for Privacy and Robustness Across Heterogeneous Data Sources by N. Sivakumar, Ahmad Raza Khan, Syed Umar, R. N. Ravikumar, I. Bremnavas, Munindra Lunagaria, Krunal Vaghela, Ghanshyam G. Tejani, Sunil Kumar Sharma

“…This framework combines Federated Averaging (FedAvg) and Federated Proximal (FedProx) to train Convolutional Neural Networks (CNN) on data hosted by multiple clients. …”
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Federated learning for privacy-enhanced mental health prediction with multimodal data integration by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…This study addresses these challenges by utilising a multimodal dataset comprising physiological signals (heart rate variability, sleep patterns) and behavioural data (online activity, social media interactions). The proposed framework incorporates a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network within a federated learning environment, ensuring that raw user data remains decentralised and privacy is preserved using differential privacy and encryption techniques. …”
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Alzheimer’s Disease Detection in Various Brain Anatomies Based on Optimized Vision Transformer by Faisal Mehmood, Asif Mehmood, Taeg Keun Whangbo

“…This work contributes a robust optimizer-centric framework that enhances training efficiency and diagnostic accuracy for automated Alzheimer’s disease detection.…”
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Integrating principal component analysis, fuzzy inference systems, and advanced neural networks for enhanced estuarine water quality assessment by Richard O. Usang, Bamidele I. Olu-Owolabi, Kayode O. Adebowale

“…Study focus: The research develops a comprehensive framework for assessing estuarine water quality by integrating Principal Component Analysis (PCA), Fuzzy Inference Systems (FIS), and advanced neural network models, specifically Long Short-Term Memory (LSTM) and a hybrid LSTM-Convolutional Neural Network (CNN). …”
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Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea. by Injae Seo, Minkyoung Kim, Jong Wook Kim, Beakcheol Jang

“…To address these challenges, we propose a novel framework consisting of four key components: (1) a hybrid Convolutional Neural Network-Long Short-Term Memory mechanism designed to capture complex patterns in CPI data, enhancing estimation accuracy; (2) multivariate inputs that incorporate CPI component indices alongside auxiliary variables for richer contextual information; (3) data augmentation through linear interpolation to convert monthly data into daily data, optimizing it for highly parametrized deep learning models; and (4) sentiment index derived from Korean CPI-related news articles, providing insights into external factors influencing CPI fluctuations. …”
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OFPoint: Real-Time Keypoint Detection for Optical Flow Tracking in Visual Odometry by Yifei Wang, Libo Sun, Wenhu Qin

“…However, mainstream CNN-based detectors rely on the “joint detection and descriptor” framework to realize matching, making them incompatible with optical flow tracking. …”
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