Accuracy and robustness evaluation of deep learning algorithms in facial recognition systems by Jing Zhang, Ningyu Hu

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Correlation Analysis and Prediction of the Physical and Mechanical Properties of Coastal Soft Soil in the Jiangdong New District, Haikou, China by Yongchang Yang, Xinying Song, Shuai Zhang, Jun Hu, Ming Ruan, Dongling Zeng, Han Luo, Jiangsi Wang, Zhixin Wang

“…By conducting a correlation analysis of various physical properties of soil and utilizing the random forest algorithm, we developed a predictive model for the compressibility and shear strength of coastal soft soil. …”
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Prediction and reliability analysis of rigid pipeline response in soft soil using improved particle swarm neural network by Laifu Song, Shun Zhang, Jun Wang, Hongtao Fu, Jiayu Cai

“…Abstract Pipelines in soft soil are prone to deformation and failure under traffic loads. …”
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HarSoNet: a two-stage point cloud registration method integrating soft and hard matching by Qiongdan Huang, Jiapeng Wang, Jiejing Han, Shilin Kang

“…To overcome these challenges, this study introduces HarSoNet, a two-stage Hard-to-Soft Network designed for end-to-end point cloud registration. …”
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Image enhancement for detection of underwater moulted crabs in greenhouse soft-shell crab farming using deep learning by Mohammad Affaiq Bin Aini, Siow Hoo Leong, Yueh Tiam Yong, Beng Yong Lee, Xiaomin Zhao, Sharifah Raina Manaf, Firdaus Abdullah, Heng Yen Khong

“…In soft-shell crab farming, collecting the moulted crabs within a narrow window of one hour is crucial to meet the quality of high-grade paper shell crab. …”
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Algorithm for blind separation of PCMA based on CHASE decoding by Jian DU, Ke-xian GONG, Hua PENG

“…The results of soft output of SOVA-PSP were sorted based on reliability in the algorithm, the mixed symbols that had low reliability in the sorted results were reconstructed. …”
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Network intrusion detection based on improved KNN algorithm by Hongsheng Bao, Jie Gao

“…Therefore, a new three-branch decision soft increment K-nearest neighbor algorithm is proposed, representing the class cluster as an interval set. …”
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Computer-Aided Facial Soft Tissue Reconstruction with Computer Vision: A Modern Approach to Identifying Unknown Individuals by Svenja Preuß, Sven Becker, Jasmin Rosenfelder, Dirk Labudde

“…Facial soft tissue reconstruction is an important tool in forensic investigations, especially when conventional identification methods are unsuccessful. …”
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REASONING FOR THE USE OF TEMPORO-MANDIBULAR DISORDER DIAGNOSTIC ALGORITHMS by V. Makeyev, U. Telishevska, A. Kucher

“…The degree of necessity and sequence of application of methods of radial investigation of temporo-mandibular disorders is taken into account using these algorithms. The algorithm based on organ principle unites information of different radial investigation methods in detecting structural disorders in soft tissue and bone tissue elements of the joint. …”
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Optimization of Z-fuzzy soft $\beta$-covering based fuzzy rough sets and their application to multiple attribute group decision making by S. Pavithra, A. Manimaran

“…These relations form new fuzzy soft $\beta$ covering based fuzzy rough set models. …”
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Strategic Perturb and Observe Algorithm for Partial Shading Conditions by Muhammad Mateen Afzal Awan

“…Tracking the MPP requires electronic circuits govern by MPP tracking (MPPT) algorithms. The most simple, cheap, and softly implementable MPPT algorithm is “Perturb and Observe (P&O)”. …”
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Real-Time Detection and Localization of Force on a Capacitive Elastomeric Sensor Array Using Image Processing and Machine Learning by Peter Werner Egger, Gidugu Lakshmi Srinivas, Mathias Brandstötter

“…Soft and flexible capacitive tactile sensors are vital in prosthetics, wearable health monitoring, and soft robotics applications. …”
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Discrete Sizing Optimization of Steel Structures Using Modified Fireworks Algorithm by Navid Seraji, Mehdi Babaei

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Masterpiece Optimization Algorithm: A New Method for Solving Engineering Problems by Mohammad Reza Ghasemi, Nader Haji Aghajanpour, Hamed Ghohani Arab

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Structural Damage Identification Using Novel Objective Functions and Metaheuristic Algorithms by Moeinoddin Rokhsati, Mohamad Mohamadi Dehcheshmeh, Gholamreza Ghodrati Amiri, Morteza Raissi Dehkordi

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Four Machine Learning Algorithms for Biometrics Fusion: A Comparative Study by I. G. Damousis, S. Argyropoulos

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Damage Detection of Steel Roof Systems Using Planet Optimization Algorithm by Thanh Sang-To, Van The Huy-Nguyen, Samir Khatir, The Vi-Huynh, Hoang Le-Minh, Thanh Cuong-Le

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Minimizing the Active Power Losses and Retaining the Voltage Profile of the Distribution System Using Soft Computing Techniques with DG Source by P. Sundararaman, E. Mohan, S. V. Aswin Kumer, Sridhar Udayakumar, Abdissa Fekadu Moti

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Artificial Neural Network Modeling of NixMnxOx based Thermistor for Predicative Synthesis and Characterization by T.D. Dongale, K.G. Kharade, N.B. Mullani, G.M. Naik, R.K. Kamat

“…Thus, we demonstrate exploitation of modeling, simulation and soft computational approaches for predicting the best suitable chemical composition and thus establish the synergy between the materials science and soft computing paradigm.…”
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Non-Destructive Detection of Pomegranate Blackheart Disease via Near-Infrared Spectroscopy and Soft X-ray Imaging Systems by Rongke Nie, Xingyi Huang, Xiaoyu Tian, Shanshan Yu, Chunxia Dai, Xiaorui Zhang, Qin Fang

“…This study established discriminative models for blackheart disease severity in pomegranates by using near-infrared (NIR) spectroscopy and soft X-ray imaging techniques. The results showed that the optimal NIR-based discriminative model, constructed with a Random Forest (RF) algorithm based on spectra preprocessed by the second-derivative (D2) denoising and a Competitive Adaptive Reweighted Sampling (CARS) algorithm, achieved a prediction set accuracy of 86.00%; the optimal soft X-ray imaging-based discriminative model, built with an RF algorithm using textural features extracted from images preprocessed by median filtering and a Contrast-Limited Adaptive Histogram Equalization (CLAHE) algorithm combined with gray-level co-occurrence matrix (GLCM) and gray-gradient co-occurrence matrix (GGCM) algorithms, reached a prediction set accuracy of 93.10%. …”
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