Enhancing early lung cancer detection with MobileNet: A comprehensive transfer learning approach by Raquel Ochoa-Ornelas, Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Sofia Uribe-Toscano

“…This study investigates the application of MobileNetV2, a state-of-the-art, lightweight convolutional neural network, for the accurate classification of lung adenocarcinoma (LAC), benign lung tissue (BLT), and lung squamous cell carcinoma (LUSC). …”
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ANN-based software cost estimation with input from COCOMO: CANN model by Chaudhry Hamza Rashid, Imran Shafi, Bilal Hassan Ahmed Khattak, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

“…This research aims to identify the factors that influence the software effort estimation using the constructive cost model (COCOMO), and artificial neural networks (ANN) model by introducing a novel cost estimation approach, COCOMO-ANN (CANN), utilizing a partially connected neural network (PCNN) with inputs derived from calibrated values of the COCOMO model. …”
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Automatic History Matching for Adjusting Permeability Field of Fractured Basement Reservoir Simulation Model Using Seismic, Well Log, and Production Data by Le Ngoc Son, Nguyen The Duc, Sumihiko Murata, Phan Ngoc Trung

“…After that, a feed-forward artificial neural network (ANN) model trained by the back-propagation algorithm of the relationship between initial permeability with seismic attributes and geomechanical properties of their grid cell values is developed. …”
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Deciphering Design of Aggregation‐Induced Emission Materials by Data Interpretation by Junyi Gong, Ziwei Deng, Huilin Xie, Zijie Qiu, Zheng Zhao, Ben Zhong Tang

“…Furthermore, a conditional variational autoencoder and integrated gradient analysis are employed to examine the trained neural network model, thereby gaining insights into the relationship between the structural features encapsulated in the fingerprints and the macroscopic photophysical properties. …”
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Recent Developments in Heavy Metals Detection: Modified Electrodes, Pretreatment Methods, Prediction Models and Algorithms by Yujie Shi, Shijie Zhang, Hang Zhou, Yue Dong, Gang Liu, Wenshuai Ye, Renjie He, Guo Zhao

“…To address these issues, two potential solutions have been proposed: the development of advanced algorithms (such as machine learning (ML), back-propagation neural network (BPNN), support vector machines (SVM), random forests (RF), etc.) for signal processing and the use of pretreatment methods (such as Fenton oxidation (FO), ozone oxidation, and photochemical oxidation) to suppress such interferences. …”
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Combining machine learning algorithms for bridging gaps in GRACE and GRACE Follow-On missions using ERA5-Land reanalysis by Jaydeo K. Dharpure, Ian M. Howat, Saurabh Kaushik, Bryan G. Mark

“…Unlike previous studies, we use a combination of Machine Learning (ML) methods—Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Deep Neural Network (DNN), and Stacked Long-Short Term Memory (SLSTM)—to identify and efficiently bridge the gap between GRACE and GFO by using the best-performing ML model to estimate TWSA at each grid cell. …”
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Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques by Prince Odame, Maxwell Mawube Ahiamadzor, Nana Kwaku Baah Derkyi, Kofi Agyekum Boateng, Kelvin Sarfo‐Acheampong, Eric Tutu Tchao, Andrew Selasi Agbemenu, Henry Nunoo‐Mensah, Dorothy Araba Yakoba Agyapong, Jerry John Kponyo

“…The approaches used included Contrast Limited Adaptive Histogram Equalization (CLAHE) with machine and deep learning models, Discrete Wavelet Transformations (DWT) with a novel Gated Wavelet Convolutional Neural Network (CNN) model, and FixCaps, an improved version of Capsule Networks utilizing Convolutional Block Attention Module (CBAM) to reduce spatial information loss. …”
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Analysis of tensile properties in tempered martensite steels with different cementite particle size distributions by Kengo Sawai, Keiya Sugiura, Toshio Ogawa, Ta-Te Chen, Fei Sun, Yoshitaka Adachi

“…We succeeded in developing image-based regression models with high accuracy using a convolutional neural network (CNN). Moreover, gradient-weighted class activation mapping (Grad-CAM) suggested that fine cementite particles and coarse and spheroidal cementite particles are the dominant factors for tensile strength and total elongation, respectively.…”
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Physical-aware model accuracy estimation for protein complex using deep learning method by Haodong Wang, Meng Sun, Lei Xie, Dong Liu, Guijun Zhang

“…Finally, these features are fed into a fused network architecture employing equivalent graph neural network and ResNet network to estimate residue-wise model accuracy. …”
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Single-shot super-resolved fringe projection profilometry (SSSR-FPP): 100,000 frames-per-second 3D imaging with deep learning by Bowen Wang, Wenwu Chen, Jiaming Qian, Shijie Feng, Qian Chen, Chao Zuo

“…SSSR-FPP uses only one pair of low signal-to-noise ratio (SNR), low-resolution, and pixelated fringe patterns as input, while the high-resolution unwrapped phase and fringe orders can be deciphered with a specific trained deep neural network. Our approach exploits the significant speed gain achieved by reducing the imaging window of conventional high-speed cameras, while “regenerating” the lost spatial resolution through deep learning. …”
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Context aware machine learning techniques for brain tumor classification and detection – A review by Usman Amjad, Asif Raza, Muhammad Fahad, Doaa Farid, Adnan Akhunzada, Muhammad Abubakar, Hira Beenish

“…Specifically, it focuses on multi-modalities of Magnetic Resonance Imaging (MRI) and histopathology, utilizing Convolutional Neural Networks (CNN) for image processing and analysis. …”
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Machine learning assisted prediction with data driven robust optimization: Machining process modeling of hard part turning of DC53 for tooling applications supporting semiconductor... by Mehdi Tlija, Muhammad Sana, Anamta Khan, Sana Hassan, Muhammad Umar Farooq

“…Multiple artificial neural network (ANN) architectures are generated to accurately model the non-linearity of the process for better prediction of key characteristics. …”
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Boosting Cyberattack Detection Using Binary Metaheuristics With Deep Learning on Cyber-Physical System Environment by Alanoud Al Mazroa, Fahad R. Albogamy, Mohamad Khairi Ishak, Samih M. Mostafa

“…Cyberattack detection employing deep learning (DL) contains training neural networks to identify patterns indicative of malicious actions within system logs or network traffic, allowing positive classification and mitigation of cyber-attacks. …”
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Research on Operation Characteristics and Safety Risk Forecast of Bus Driven by Multisource Forewarning Data by Shejun Deng, Hongru Yu, Caoye Lu

“…To prevent and control public transport safety accidents in advance and guide the safety management and decision-making optimization of public transport vehicles, based on the forewarning and other multisource data of public transport vehicles in Zhenjiang, holographic portraits of public transport safety operation characteristics are constructed from the perspectives of time, space, and driver factors, and a prediction model of fatigue driving and driving risk of bus drivers based on BP neural network is constructed. Finally, model checking and virtual simulation experiments are carried out. …”
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Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network by Mujtaba Husnain, Malik Muhammad Saad Missen, Shahzad Mumtaz, Dost Muhammad Khan, Mickäel Coustaty, Muhammad Muzzamil Luqman, Jean-Marc Ogier, Hizbullah Khattak, Sikandar Ali, Ali Samad

“…We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. …”
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Testing General Relativity Using Large-scale Structure Photometric Redshift Surveys and the Cosmic Microwave Background Lensing Effect by Shang Li, Jun-Qing Xia

“…In this formulation, we reconstruct the growth rate of structure, fσ _8 ( z ), using the artificial neural network method, while simultaneously utilizing model-independent constraints on the parameter bσ _8 ( z ), directly obtained from the DES collaboration. …”
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Multi-Temporal Image Fusion-Based Shallow-Water Bathymetry Inversion Method Using Active and Passive Satellite Remote Sensing Data by Jie Li, Zhipeng Dong, Lubin Chen, Qiuhua Tang, Jiaoyu Hao, Yujie Zhang

“…A backpropagation (BP) neural network model is then used to incorporate the initial multispectral information of Sentinel-2 data at each bathymetric point and its surrounding area during the training process. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. …”
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Short-term solar irradiance forecasting using deep learning models by Syed Saad Ahmed, Chang Wei Bin, Nisar Humaira, Riaz Hannan Naseem, Yeap Kim Ho, Zaber Nursaida Mohamad

“…The data for Penang, Malaysia is used in this research. A Dense Neural Network (DNN) with 32 units achieved a validation MAE of 21.33 and MSE of 1343.68 in the 6th fold. …”
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Design of an Improved Method for Visual Rendering in the Metaverse Using CIEM and MSRANet by Janapati Venkata Krishna, Priyanka Singh, Regonda Nagaraju, Setti Vidya Sagar Appaji, Attuluri Uday Kiran, K. Spandana

“…Finally, BEER, standing for Bioinspired Energy-Efficient Rendering, borrows from the energy-efficient way of visual processing in the human brain through a spiking neural network that reduces energy consumption by 35% without image quality degradation. …”
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