Deep Learning-Based Brain Tumor Segmentation in MRI Images: A MobileNetV2-DeepLabV3+ Approach by Farhad Abedinzadeh Torghabeh, Seyyed Abed Hosseini

“…We aim to achieve accurate localization and delineation of tumor regions across different axial views.Material and Methods: The dataset used in this study consists of 3064 T1-weighted contrast-enhanced MRI images obtained from patients diagnosed with glioma, meningioma, and pituitary tumors. …”
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Personalizing Seizure Detection for Individual Patients by Optimal Selection of EEG Signals by Rosanna Ferrara, Martino Giaquinto, Gennaro Percannella, Leonardo Rundo, Alessia Saggese

“…The tailored channel selection boosts detection accuracy and ensures robust performance across different seizure types while reducing the computational burden typical of multi-electrode systems. …”
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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation by Sitara Afzal, Haseeb Ali Khan, Jong Weon Lee

“…We developed a custom dataset comprising over 28,000 images of 48 different plant species at various growth stages, captured under diverse lighting conditions and camera settings. …”
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Detection of Violent Scenes in Cartoon Movies Using a Deep Learning Approach by Noreen Fayyaz Khan, Sareer Ul Amin, Zahoor Jan, Changhui Yan

“…A significant contribution of this study is the meticulous categorization of violent scenes into four distinct types, allowing for further investigation into the diverse effects of different violence categories. Furthermore, the study introduces an innovative approach by integrating a dense layer into the sequential model to enhance final classification. …”
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ECG Signal Analysis for Detection and Diagnosis of Post-Traumatic Stress Disorder: Leveraging Deep Learning and Machine Learning Techniques by Parisa Ebrahimpour Moghaddam Tasouj, Gökhan Soysal, Osman Eroğul, Sinan Yetkin

“…In parallel, statistical features were extracted directly from the ECG signals and used in traditional machine learning (ML) classifiers for performance comparison. Four different segment lengths (5 s, 10 s, 15 s, and 20 s) were tested to assess their effect on classification accuracy. …”
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Severity classification and disposition prediction using ensemble learning for home-based patient management with adequate decision making by Amjad El Khatib, Chaima Ben Abdallah, Ahmed Nait Sidi Moh

“…Comparative analysis of different models provided valuable insights into performance tradeoffs, reinforcing the potential of ensemble learning for home-based patient assessment.…”
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Quantitative Assessment of Bolt Looseness in Beam–Column Joints Using SH-Typed Guided Waves and Deep Neural Network by Ru Zhang, Xiaodong Sui, Yuanfeng Duan, Yaozhi Luo, Yi Fang, Rui Miao

“…The dispersion properties of the I-shaped steel beam were analyzed using the semi-analytical finite element method, and a mode weight coefficient was presented to clarify the mode distribution under different types of external loads. Two pairs of transducers arranged on the same side of the bolt-connected region were utilized to obtain the directly incoming and end-reflected wave packets from four wave propagation paths. …”
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A Deep Learning Framework for Chronic Kidney Disease stage classification by Gayathri Hegde M, P Deepa Shenoy, Venugopal KR, Arvind Canchi

“…To evaluate the proposed method, eight DL models — Feedforward Neural Network, Recurrent Neural Network, Deep Neural Network, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU) and Bidirectional GRU were trained on selected features using different FS methods, as well as complete dataset. …”
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EnGCI: enhancing GPCR-compound interaction prediction via large molecular models and KAN network by Weihao Liu, Xiaoli Li, Bo Hang, Pu Wang

“…Each module leverages different sources of multimodal information, and their fusion enhances the overall accuracy of GPCR-compound interaction (GCI) prediction. …”
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DSGRec: dual-path selection graph for multimodal recommendation by Zihao Liu, Wen Qu

“…We introduce independent contrastive learning tasks for the auxiliary signals, enabling DSGRec to explore the mechanisms behind feature embeddings from different perspectives. This approach ensures that each auxiliary module aligns with the user-item interaction view independently, calibrating its contribution based on historical interactions. …”
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Real-Time Common Rust Maize Leaf Disease Severity Identification and Pesticide Dose Recommendation Using Deep Neural Network by Zemzem Mohammed Megersa, Abebe Belay Adege, Faizur Rashid

“…The agricultural experts verified the accuracy of the recommendation system across different stages of the disease, and the system demonstrated 100% accuracy. …”
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Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN by Juan Lv, Zhaowei Wang, Jianxiao Ma

“…The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. …”
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RTL-Net: real-time lightweight Urban traffic object detection algorithm by Zhiqing Cui, Jiahao Yuan, Haibin Xu, Yamei Wei, Zhenglong Ding

“…Firstly, a Powerable-IoU (PIoU) loss function was introduced to make the algorithm more suitable for different scales of targets and reduce false detection. …”
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Research on a Joint Extraction Method of Track Circuit Entities and Relations Integrating Global Pointer and Tensor Learning by Yanrui Chen, Guangwu Chen, Peng Li

“…First, a multi-layer dilate gated convolutional neural network with residual connections is used to extract key features and fuse the weighted information from the 12 different semantic layers of the RoBERTa-wwm-ext model, fully exploiting the performance of each encoding layer. …”
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A hybrid machine learning model with attention mechanism and multidimensional multivariate feature coding for essential gene prediction by Wu Yan, Fu Yu, Li Tan, Li Mengshan, Xie Xiaojun, Zhou Weihong, Sheng Sheng, Wang Jun, Wu Fu-an

“…In the model, GCN was used to extract feature encoding information from the visualized graphics of gene sequences and the attention mechanism was combined with Bi-LSTM to assess the importance of each feature in gene sequences and analyze the influences of different feature encoding methods and data imbalance. …”
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Construction and scheduling optimization of renewable energy consumption forecasting system for twisted tire porcelain manufacturing industry based on deep learning by Fangrong Liao, Runlin Ran, Jiayi Zhang

“…The experimental results show that the proposed pattern classification algorithm can realize the automatic classification of different patterns of twisted tire porcelain with high accuracy, which saves workforce, improves efficiency and meets the needs of industrial intelligence.…”
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High-Quality Sample Generation for Power System Transient Stability Assessment Based on Data-Driven Methods by Baoqin Li, Pengfei Fan, Qixin Chen, Rong Li, Kaijun Lin

“…Firstly, the representative topologies provided by the system operator are clustered into four different categories by density-based spatial clustering of applications with noise (DBSCAN). …”
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Cloud-edge collaborative data anomaly detection in industrial sensor networks. by Tao Yang, Xuefeng Jiang, Wei Li, Peiyu Liu, Jinming Wang, Weijie Hao, Qiang Yang

“…Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guaranteeing the system's operational reliability and security. …”
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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…Therefore, a total of 800 channelized reservoirs were generated from four TIs, which have different channel directions to consider geological uncertainty. …”
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Wavelet Transform-Based 3D Landscape Design and Optimization for Digital Cities by Yang Chen, Xiaolin Wang, Chang Zhang

“…Firstly, for the wavelet change denoising problem, an effective denoising algorithm for natural noise and abnormal noise is proposed by combining convolutional neural network and wavelet transform. The algorithm extracts mixed feature information of local long path and local short path based on the information retention module, and decomposes the information by combining wavelet transform, inputs the different components obtained from the decomposition into the network for training, and removes the noise by subsequent feature screening of the network structure. …”
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