Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

“…We first used a U-Net based convolutional neural network, trained and validated using 36 partially annotated whole slide images from 27 patients, to segment vessel structures and tumour regions from which the measurements are taken. …”
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Loss of MEF2C function by enhancer mutation leads to neuronal mitochondria dysfunction and motor deficits in mice by Ali Yousefian-Jazi, Suhyun Kim, Jiyeon Chu, Seung-Hye Choi, Phuong Thi Thanh Nguyen, Uiyeol Park, Min-gyeong Kim, Hongik Hwang, Kyungeun Lee, Yeyun Kim, Seung Jae Hyeon, Hyewhon Rhim, Hannah L. Ryu, Grewo Lim, Thor D. Stein, Kayeong Lim, Hoon Ryu, Junghee Lee

“…Methods Convolutional neural network was used to identify an ALS-associated SNP located in the intronic region of MEF2C (rs304152), residing in a putative enhancer element. …”
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Safety profiles of IDH inhibitors: a pharmacovigilance analysis of the FDA Adverse Event Reporting System (FAERS) database by Ximu Sun, Han Zhou, Yanming Li, Yanhui Luo, Qixiang Guo, Yixin Sun, Chenguang Jia, Bin Wang, Maoquan Qin, Peng Guo

“…Disproportionality analyses including the reporting odds ratio and the Bayesian confidence propagation neural network were performed in data mining to assess IDH inhibitor-relatedAEs. …”
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Embryonic heat conditioning induces paternal heredity of immunological cross- tolerance: coordinative role of CpG DNA methylation and miR-200a regulation by Padma Malini Ravi, Tatiana Kisliouk, Shelly Druyan, Amit Haron, Mark A. Cline, Elizabeth R. Gilbert, Noam Meiri

“…Additionally, analysis of sperm methylation patterns in EHC mature chicks led to identification of genes associated with neuronal development and immune response, indicating potential neural network reorganization. Finally, miR-200a emerges as a regulator potentially involved in mediating the cross-tolerance effect.…”
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Identification of core genes related to exosomes and screening of potential targets in periodontitis using transcriptome profiling at the single-cell level by Wufanbieke Baheti, Diwen Dong, Congcong Li, Xiaotao Chen

“…Subsequently, a core gene-based artificial neural network (ANN) model was built to evaluate the predictive power of core genes for PD. …”
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Response surface methodology and adaptive neuro-fuzzy inference system for adsorption of reactive orange 16 by hydrochar by J. Oliver Paul Nayagam, K. Prasanna

“…This study validated adaptive neuro-fuzzy inference system, an artificial neural network with a fuzzy inference system, using response surface methodology projected experimental run with Box–Behnken method.FINDINGS: The adaptive neuro-fuzzy inference system model is created alongside the response surface methodology model to compare experimental outcomes. …”
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Effects of feature selection and normalization on network intrusion detection by Mubarak Albarka Umar, Zhanfang Chen, Khaled Shuaib, Yan Liu

“…Random forest (RF) models performed better on NSL-KDD and UNSW-NB15 datasets with accuracies of 99.86% and 96.01%, respectively, whereas artificial neural network (ANN) achieved the best accuracy of 95.43% on the CSE–CIC–IDS2018 dataset. …”
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An FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction by Ahmed Hadded, Mossaad Ben Ayed, Shaya A. Alshaya

“…Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency. …”
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Prediksi Detak Jantung Berbasis LSTM pada Raspberry Pi untuk Pemantauan Kesehatan Portabel by Ahmad Foresta Azhar Zen, Eko Sakti Pramukantoro, Kasyful Amron, Viera Wardhani, Putri Annisa Kamila

“…LSTM models are a type of artificial neural network architecture known for their ability to handle sequential data effectively, making them highly suitable for sequential heart rate monitoring and prediction. …”
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A Local Adversarial Attack with a Maximum Aggregated Region Sparseness Strategy for 3D Objects by Ling Zhao, Xun Lv, Lili Zhu, Binyan Luo, Hang Cao, Jiahao Cui, Haifeng Li, Jian Peng

“…The increasing reliance on deep neural network-based object detection models in various applications has raised significant security concerns due to their vulnerability to adversarial attacks. …”
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600 meters to VO2max: Predicting Cardiorespiratory Fitness with an Uphill Run by Kübra Stoican, Regina Oeschger

“…Discussion/Conclusion These results suggest that our short, high-intensity field test, when combined with a neural network model, can provide accurate predictions of VO2max. …”
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Evaluating CNN Architectures for the Automated Detection and Grading of Modic Changes in MRI: A Comparative Study by Li‐peng Xing, Gang Liu, Hao‐chen Zhang, Lei Wang, Shan Zhu, Man Du La Hua Bao, Yan‐ni Wang, Chao Chen, Zhi Wang, Xin‐yu Liu, Shuai Zhang, Qiang Yang

“…This study developed and investigated the performance of convolutional neural network (CNN) in detecting and grading MCs based on their maximum vertical extent. …”
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Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images by Zhaojiang Yan, Chong Fang, Kaishan Song, Xiangyu Wang, Zhidan Wen, Yingxin Shang, Hui Tao, Yunfeng Lyu

“…This study compared and evaluated 6 commonly used machine learning models, including extreme gradient boosting (XGBoost), support vector regression (SVR), backpropagation neural network (BP), gradient boosting decision tree (GBDT), random forest (RF), and categorical boosting (CatBoost). …”
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Comparative Analysis of Tillage Indices and Machine Learning Algorithms for Maize Residue Cover Prediction by Jian Li, Kewen Shao, Jia Du, Kaishan Song, Weilin Yu, Zhengwei Liang, Weijian Zhang, Jie Qin, Kaizeng Zhuo, Cangming Zhang, Yu Han, Yiwei Zhang, Bingrun Sui

“…MRC estimation models were built using six machine learning algorithms, including back propagation neural network (BPNN), random forest (RF), support vector regression (SVR), extreme gradient boosting (XGBoost), Stacking1, and Stacking2. …”
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ViTAU: Facial paralysis recognition and analysis based on vision transformer and facial action units by Jia GAO, Wenhao CAI, Junli ZHAO, Fuqing DUAN

“…These maps are then processed through a pyramid convolutional neural network interpreter to generate heatmaps. By optimizing the mean squared error between the predicted and actual heatmaps, we can effectively identify the affected paralysis areas. …”
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Fine particulate matter concentrations forecasting using long short-term memory network and meteorological inputs by T. Istiana, B. Kurniawan, S. Soekirno, A. Wihono, D.E. Nuryanto, B.A. Pertala, A. Sopaheluwakan

“…This study introduces the long short-term memory deep learning model and contrasts it with the one-dimensional convolution neural network as well as their hybrid counterpart. The dataset is split into 80 percent training and 20 percent testing data. …”
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Intelligent prediction method of virtual network function resource capacity for polymorphic network service slicing by Julong LAN, Di ZHU, Dan LI

“…For the extraction of temporal features, the temporal dependencies of the input data are perceived through the information transfer between the units via gated recurrent units.Then,based on the mapping relationship between the data flow sequence and the number of VNF instances, the feedforward neural network performs data dimension transformation and finally outputs the VNF resource demand prediction results. …”
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors by Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou

“…Residual convolutional neural network (ResNet) technology was used to extract image features. …”
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Peningkatan Performa Pengelompokan Siswa Berdasarkan Aktivitas Belajar pada Media Pembelajaran Digital Menggunakan Metode Adaptive Moving Self-Organizing Maps by Onky Prasetyo, Ahmad Afif Supianto, Syaiful Anam, Hilman Ferdinandus Pardede, Vicky Zilvan, R. Budiarianto Suryo Kusumo

“…One of the most frequently used clustering methods is Self-Organizing Maps (SOM), SOM is a neural network method to maintain data topology when multidimensional input data is converted into output data with lower dimensions. …”
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An optimized approach for predicting water quality features and a performance evaluation for mapping surface water potential zones based on Discriminant Analysis (DA), Geographical... by Abhijeet Das

“…Again, this research used a strong methodology by incorporating Machine learning (ML) algorithms, such as: Artificial Neural Network (ANN), Gaussian Process Regression (GPR), Support Vector Machine (SVM), and Linear Regression Model (LRM), were applied to forecast and confirm the quality of the water. …”
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