RaNet: a residual attention network for accurate prostate segmentation in T2-weighted MRI by Muhammad Arshad, Chengliang Wang, Muhammad Wajeeh Us Sima, Jamshed Ali Shaikh, Salem Alkhalaf, Fahad Alturise

“…The encoder leverages residual connections to extract hierarchical features, capturing both fine-grained details and multi-scale patterns in the prostate. The MSAF enhances segmentation by dynamically focusing on key regions, refining feature selection and minimizing errors, while the FFM optimizes the handling of spatial hierarchies and varying object sizes, improving boundary delineation. …”
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Convolutional Neural Network Based Vehicle Classification in Adverse Illuminous Conditions for Intelligent Transportation Systems by Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki-Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan

“…In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. …”
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Grafting enhances drought stress tolerance by regulating the proteome and targeted gene regulatory networks in tomato by Pritam Paramguru Mahapatra, Pritam Paramguru Mahapatra, Dong Won Bae, Michitaka Notaguchi, Sowbiya Muneer

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Securing Industrial IoT Environments: A Fuzzy Graph Attention Network for Robust Intrusion Detection by Safa Ben Atitallah, Maha Driss, Wadii Boulila, Anis Koubaa

“…Conventional machine learning methods and typical Graph Neural Networks (GNNs) often struggle to capture the complexity and uncertainty in IIoT network traffic, which hampers their effectiveness in detecting intrusions. …”
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LMSOE-Net: lightweight multi-scale small object enhancement network for UAV aerial images by Zhixing Ma, Peidong Luo, Xiaole Shen

“…This upgrade strengthens the network’s ability to capture fine details and complex patterns, improving multi-scale feature extraction without a significant increase in parameters. …”
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Investigating the Complexity of Multidimensional Symptom Experiences in Patients With Cancer: Systematic Review of the Network Analysis Approach by Vincent Richard, Allison Gilbert, Emanuela Pizzolla, Giovanni Briganti

“…Among these, 3 evaluated the longitudinal changes in symptom networks across chemotherapy cycles, and 1 evaluated changes during radiotherapy. …”
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Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes by Richard Lupat, Rashindrie Perera, Sherene Loi, Jason Li

“…We evaluated our use of Autoencoder against other dimensionality reduction techniques and demonstrated its superiority in learning patterns associated with breast cancer subtypes. …”
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Multi-Modal Fused-Attention Network for Depression Level Recognition Based on Enhanced Audiovisual Cues by Yihan Zhou, Xiaokang Yu, Zixi Huang, Feierdun Palati, Zeyu Zhao, Zihan He, Yuan Feng, Yuxi Luo

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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. …”
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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection by Muhammad Shafiq, J. Kavitha, Dhruva R. Rinku, N. K. Senthil Kumar, Kamal Poon, Amar Y. Jaffar, V. Saravanan

“…Based on features, the sorted-out data gets evaluated through a GRU-LSTM (Gated Recurrent Unit - Long Short-Term Memory) network to identify the state of the infant as usual and suggestive of hypoglycemia—blood glucose below 70 mg/dL, pale complexion, profuse perspiration. …”
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Weighted explainable federated learning for privacy-preserving and scalable energy optimization in autonomous vehicular networks by Muhammad Saleem, Ali Arishi, Muhammad Sajid Farooq, M.A. Khan, Khan M. Adnan

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BCTDNet: Building Change-Type Detection Networks with the Segment Anything Model in Remote Sensing Images by Wei Zhang, Jinsong Li, Shuaipeng Wang, Jianhua Wan

“…We first construct a dual-feature interaction encoder that employs SAM to extract image features, which are then refined through trainable multi-scale adapters for learning architectural structures and semantic patterns. Moreover, an interactive attention module bridges SAM with a Convolutional Neural Network, enabling seamless interaction between fine-grained structural information and deep semantic features. …”
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COMPARATIVE ANALYSIS OF TIME SERIES FORECASTING MODELS USING ARIMA AND NEURAL NETWORK AUTOREGRESSION METHODS by Melina Melina, Sukono Sukono, Herlina Napitupulu, Norizan Mohamed, Yulison Herry Chrisnanto, Asep ID Hadiana, Valentina Adimurti Kusumaningtyas, Ulya Nabilla

“…However, ARIMA may struggle to capture complex non-linear patterns in non-stationary data. Instead, NNAR can handle non-stationary data more effectively by modeling the complex non-linear relationships between input and output variables. …”
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A Blockchain-Based Federated Forest for SDN-Enabled In-Vehicle Network Intrusion Detection System by Ibrahim Aliyu, Marco Carlo Feliciano, Selinde Van Engelenburg, Dong Ok Kim, Chang Gyoon Lim

“…In-vehicle communication systems are usually managed by controller area networks (CAN). By broadcasting packets to their bus, the CAN facilitates the interaction between Electronic Control Units (ECU) that coordinate, monitor and control internal vehicle components. …”
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Computational analysis of Annona muricata phytochemicals for targeted modulation of endocrine networks in polycystic ovary syndrome by Babatunji Emmanuel Oyinloye, Oluwaseun Emmanuel Agboola, Aderonke Moyosola Ayeni, Oluwatoyin Mary Oyinloye, Samuel Sunday Agboola, Olajumoke Tolulope Idowu, Makhosazana Siduduzile Mathenjwa-Goqo, Olutunmise Victoria Owolabi, Bolajoko Idiat Ogunyinka, Foluso Oluwagbemiga Osunsanmi, Basiru Olaitan Ajiboye, Olaposi Idowu Omotuyi

“…Detailed structural mapping of emodin and coclaurin uncovered conserved non-covalent interaction patterns, notably hydrogen bonding networks, facilitating the ligands’ competitive receptivity and deep projection into dysfunctionally upregulated pockets. …”
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Development of an Artificial Neural Network-Based Image Retrieval System for Lung Disease Classification and Identification by Atul Pratap Singh, Ajeet Singh, Amit Kumar, Himanshu Agarwal, Sapna Yadav, Mohit Gupta

“…Leveraging the capabilities of ANNs, specifically convolutional neural networks (CNNs), the system aims to capture intricate patterns and features within images that are often imperceptible to human observers. …”
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Insights into gait performance in Parkinson's disease via latent features of deep graph neural networks by Jiecheng Wu, Jiecheng Wu, Ning Su, Xinjin Li, Xinjin Li, Chao Yao, Jipeng Zhang, Xucheng Zhang, Wei Sun

“…Using ST-GCN, we extracted spatial and temporal patterns from these five states directly from the data, thereby enhancing the accuracy of gait parameter calculation. …”
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The Performance of an ML-Based Weigh-in-Motion System in the Context of a Network Arch Bridge Structural Specificity by Dawid Piotrowski, Marcin Jasiński, Artur Nowoświat, Piotr Łaziński, Stefan Pradelok

“…In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. …”
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SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing by Wangdan Liao, Fei Chen, Fei Chen, Changyue Liu, Weidong Wang, Weidong Wang, Weidong Wang, Hongyun Liu, Hongyun Liu

“…Notably, we achieved a top-1 accuracy of 81.23% on ImageNet-1K using the directly trained Large SpikeAtConv, which is a state-of-the-art result in the field of SNN.DiscussionOur evaluations on standard image classification benchmarks indicate that the proposed architecture narrows the performance gap with traditional neural networks, providing insights into the design of more efficient and capable neuromorphic computing systems.…”
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An Evaluation and Implementation of Rule-Based Home Energy Management System Using the Rete Algorithm by Tomoya Kawakami, Naotaka Fujita, Tomoki Yoshihisa, Masahiko Tsukamoto

“…The Rete algorithm is a typical pattern matching algorithm for IF-THEN rules. Currently, we have proposed a rule-based Home Energy Management System (HEMS) using the Rete algorithm. …”
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