Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model by Jiapeng Liu, Yi Liu, Qiuping Jiang

“…On the other hand, based on this newly constructed OD-UIUA dataset, we train a deep UIUA network (DeepUIUA) that can automatically and accurately predict UIUS. To the best of our knowledge, this is the first benchmark dataset for UIUA and also the first model focusing on the specific UIUA problem. …”
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Jointing sparse recovery estimation algorithm of underwater acoustic channels with long time delay spread by Yue-hai ZHOU, Xiu-ling CAO, Dong-sheng CHEN, Feng TONG

“…A method was proposed which exploiting the joint correlation of sparse multipath structure between adjacent data blocks to deal with the estimation of long time delay channels under the framework of distributed compressed sensing (DCS).Thus the large time delay underwater acoustic channels can be jointly reconstructed by the simultaneous orthogonal matching t (SOMP)algorithm to fa-cilitate the system overhead reduction and estimation ance improvement. …”
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Mod Tanh‐Activated Physical Neural Network MPPT Control Algorithm for Varying Irradiance Conditions by Khuong Nguyen‐Vinh, Surender Rangaraju, Michal Jasinski

“…The proposed method incorporates beta‐distributed point estimation technique for mismatch factor correction and a Buck‐Boost converter with a feedback control using the Chinese Remainder Theorem – Puzzle Optimization Algorithm‐tuned PID controller. Simulations demonstrate an efficiency improvement of 98.42%, with a 4.54 dB reduction in total harmonic distortion and faster convergence compared to traditional methods such as ANN and LSTM. …”
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Coal and gas outburst prediction based on data augmentation and neuroevolution. by Wenbing Shi, Ji Huang, Gaoming Yang, Shuzhi Su, Shexiang Jiang

“…This paper proposes a CGO risk prediction method based on data augmentation and a neuroevolution algorithm, denoted as ANEAT. …”
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Multiclass Sparse Bayesian Regression for fMRI-Based Prediction by Vincent Michel, Evelyn Eger, Christine Keribin, Bertrand Thirion

“…However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. …”
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Comparative analysis and selection of the geometry of the microphone array based on MEMS microphones for sound localisation by Andrii Riabko, Tetiana Vakaliuk, Oksana Zaika, Roman Kukharchuk, Yuriy Smorzhevsky

“…The goal is to determine the most effective array architecture and beamforming algorithms to achieve compactness, accuracy, and balanced omnidirectional coverage. …”
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Dynamic Optimization of Xylitol Production Using Legendre-Based Control Parameterization by Eugenia Gutiérrez, Marianela Noriega, Cecilia Fernández, Nadia Pantano, Leandro Rodriguez, Gustavo Scaglia

“…The proposed technique employs Legendre polynomials to parameterize two control actions (the feeding rates of glucose and xylose), and it uses a hybrid optimization algorithm combining Monte Carlo sampling with genetic algorithms for coefficient selection. …”
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Localization algorithm for large-scale wireless sensor networks based on FCMTSR-support vector machine by Fang Zhu, Junfang Wei

“…Finally, the limitation of the proposed localization algorithm is discussed and future work is present.…”
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A Non-Rigid Three-Dimensional Image Reconstruction Algorithm Based on Deformable Shape Reliability by Haiying Chen, Syed Atif Moqurrab

“…Most reconstruction algorithms for non-rigid three-dimensional (3D) images assume that non-rigidity can be represented as a linear combination of a fixed number of rigid bases. …”
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Frequency minimum inertia calculation of complex power systems based on an improved simulated annealing algorithm by Qiang Zhang, Qi Jia, Tingqi Zhang, Hui Zeng, Chao Wang, Wansong Liu

“…It is also suggested that more uncertainty factors can be considered in the future to improve the model and algorithm and enhance the practicality and adaptability.…”
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Cloud Model-Based Adaptive Time-Series Information Granulation Algorithm and Its Similarity Measurement by Hailan Chen, Xuedong Gao, Qi Wu, Ruojin Huang

“…Taking IGV as the granulation target for time series, an adaptive information granulation algorithm for time series (CMAIG) is proposed, which can transform a time series into a granular time series consisting of several normal clouds without pre-specifying the number of information granules, achieving efficient dimensionality reduction. …”
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Impact of stain variation and color normalization for prognostic predictions in pathology by Siyu Lin, Haowen Zhou, Mark Watson, Ramaswamy Govindan, Richard J. Cote, Changhuei Yang

“…This highlights the need to develop an entirely new way to process and collect consistent microscopy images from histologic slides that can be used to both train and allow for the general application of predictive DNN algorithms.…”
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Optimizing Deep Learning Models with Improved BWO for TEC Prediction by Yi Chen, Haijun Liu, Weifeng Shan, Yuan Yao, Lili Xing, Haoran Wang, Kunpeng Zhang

“…The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. …”
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Co-folding, the future of docking – prediction of allosteric and orthosteric ligands by Eva Nittinger, Özge Yoluk, Alessandro Tibo, Gustav Olanders, Christian Tyrchan

“…Recently, computational biology has seen a breakthrough with deep learning algorithms capable of predicting protein structures based on amino acid sequences. …”
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Protein structure prediction via deep learning: an in-depth review by Yajie Meng, Zhuang Zhang, Chang Zhou, Xianfang Tang, Xinrong Hu, Geng Tian, Jialiang Yang, Yuhua Yao, Yuhua Yao, Yuhua Yao

“…The application of deep learning algorithms in protein structure prediction has greatly influenced drug discovery and development. …”
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Prognostic tools or clinical predictions: Which are better in palliative care? by P Stone, V Vickerstaff, A Kalpakidou, C Todd, J Griffiths, V Keeley, K Spencer, P Buckle, D Finlay, R Z Omar

“…PPI was significantly (p<0.001) worse than CPS at predicting which patients would survive for 3 or 6 weeks. …”
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Development of time to event prediction models using federated learning by Rasmus Rask Kragh Jørgensen, Jonas Faartoft Jensen, Tarec El-Galaly, Martin Bøgsted, Rasmus Froberg Brøndum, Mikkel Runason Simonsen, Lasse Hjort Jakobsen

“…Method We propose two methods for training time-to-event prediction models based on distributed data, relying on FL algorithms, for time-to-event prediction models. …”
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Predicting the generalization of computer aided detection (CADe) models for colonoscopy by Joel Shor, Carson McNeil, Yotam Intrator, Joseph R. Ledsam, Hiro-o Yamano, Daisuke Tsurumaru, Hiroki Kayama, Atsushi Hamabe, Koji Ando, Mitsuhiko Ota, Haruei Ogino, Hiroshi Nakase, Kaho Kobayashi, Masaaki Miyo, Eiji Oki, Ichiro Takemasa, Ehud Rivlin, Roman Goldenberg

“…Abstract Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. …”
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Crop Yield Prediction: Data Structure and Ai-Powered Methods by V. K. Kalichkin, K. Yu. Maksimovich, O. A. Aleshchenko, V. V. Aleshchenko

“…(Research purpose) The aim of this study is to analyze data structures and compare machine learning and deep learning algorithms used in used in crop yield prediction. (Materials and methods) Using a convergent approach and applying methods of cognitive and semantic analysis, the authors examined the subject area of artificial intelligence applications in crop yield prediction. …”
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Predicting Quality of Multimedia Experience Using Electrocardiogram and Respiration Signals by Sowmya Vijayakumar, Ronan Flynn, Peter Corcoran, Niall Murray

“…We compare the performances of several machine learning (ML) and deep learning (DL) models in predicting QoE subjective scores from electrocardiograms and respiration signals. …”
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