Deep Neural Network-Based Detection of Modulated Jamming in Free-Space Optical Systems: Theory and Performance Under Atmospheric Fading by Manav R. Bhatnagar

“…The DNN operates on a composite feature vector comprising raw signal samples, spectral content, energy statistics, and higher-order distributional descriptors, enabling robust detection under both modulated and persistent jamming scenarios. …”
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Mixed effect gradient boosting for high-dimensional longitudinal data by Oyebayo Ridwan Olaniran, Saidat Fehintola Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Nada MohammedSaeed Alharbi

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Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications by Zvinodashe Revesai, Okuthe P. Kogeda

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The Influence of the Amplitude Spectrum Correction in the HFCC Parametrization on the Quality of Speech Signal Frame Classification by Stanislaw GMYREK, Robert HOSSA, Ryszard MAKOWSKI

“…The aim of the correction was to narrow the GMM distributions, which, according to detection theory, reduces the classification errors. The results obtained confirm the effectiveness of the proposed method.…”
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Efficient and accurate determination of the degree of substitution of cellulose acetate using ATR-FTIR spectroscopy and machine learning by Frank Rhein, Timo Sehn, Michael A. R. Meier

“…By applying a n-best feature selection algorithm based on the F-statistic of the Pearson correlation coefficient, several relevant areas were identified and the optimized model achieved an improved MAE of 0.052. …”
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EXPERIENCE OF USING DIGITAL SYSTEMS FOR DIAGNOSTICS OF HYPERTROPHIC SKIN SCARS OF FACE by D.S. Avetikov, O.P. Bukhanchenko, I.O. Ivanytsky, N.A. Sokolova, I.V. Boyko

“…Despite significant pathogenetic and morphological differences of scarring, some of their types often have clinically similar features, resulting in a significant number of diagnostic errors. …”
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End‐To‐End Deep Learning Temperature Prediction Algorithms of a Phase Change Materials From Experimental Photos by Mohammad Hassan Ranjbar, Kobra Gharali, Artie Ng

“…Comparison of the networks shows that MobileNets based Weights IV – Deep Neural Network (WIV‐ DNN) detects the temperature at different locations of the PCM successfully with an average error of less than 0.9% during the whole melting process in 0.03 s. …”
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Leveraging Deep Learning for Fault Detection and Localization in Distributed Systems by Debolina Ghosh, Jay Prakash Singh

“…According to experimental results, CNN performs best overall on the HDFS dataset, with an Mean Squared Error (MSE) of 0.00002 and an Coefficient of Determination (R2 Score) Score of 0.996. …”
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Accurate Needle Localization in the Image Frames of Ultrasound Videos by Mohammad I. Daoud, Samira Khraiwesh, Rami Alazrai, Mostafa Z. Ali, Adnan Zayadeen, Sahar Qaadan, Rafiq Ibrahim Alhaddad

“…The first phase aims to extract features that quantify the speckle variations associated with needle insertion, the edges that match the needle orientation, and the pixel intensity statistics of the ultrasound image. …”
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Improved Surface Electromyogram-Based Hand–Wrist Force Estimation Using Deep Neural Networks and Cross-Joint Transfer Learning by Haopeng Wang, He Wang, Chenyun Dai, Xinming Huang, Edward A. Clancy

“…A convolutional neural network with a 391 ms sliding window fine-tuned using 20 s of training data had an error of 6.03 ± 0.49% maximum voluntary torque, which is statistically lower than both our multilayer perceptron model with TL and a linear regression model using 40 s of training data. …”
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TBM Net Advance Rate Prediction Model Based on Ridge Regression Analysis by SHI Jian, ZHANG Shilin, FAN Zuosong, KONG Desen

“…The absolute prediction error of the ridge regression prediction model is within 5 mm/min, which meets the requirements of engineering prediction.…”
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Predicting carbon dioxide emissions using deep learning and Ninja metaheuristic optimization algorithm by Anis Ben Ghorbal, Azedine Grine, Ibrahim Elbatal, Ehab M. Almetwally, Marwa M. Eid, El-Sayed M. El-Kenawy

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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…In the modern era of computers, numerous disciplines are witnessing the development of massive data volumes. Statistics are important in healthcare engineering because they provide insights into various diseases and match patient data. …”
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Misdiagnosis and analysis of clinical characteristics in patients with giant cystic pheochromocytoma/paraganglioma by Yue Zhang, Bo Zhou

“…Unfortunately, prior case reports have shown that giant cystic PPGLs are highly susceptible to diagnostic errors. Therefore, this study aimed to explore giant cystic PPGLs by comparing them with non-cystic PPGLs, defining the clinical features of the affected patients, and analyzing the characteristics of misdiagnosis and mistreatment associated with PPGLs. …”
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Early Diagnosis of Alzheimer’s Disease Using Adaptive Neuro K-Means Clustering Technique by Karan Kumar, Shweta Agrawal, Isha Suwalka, Celestine Iwendi, Cresantus N. Biamba

“…It achieved a mean accuracy of 99.8%, reducing the Mean Squared Error (MSE) from 2.3 to 0.44 and improving the Discriminative Overlap Index (DOI) and Tissue Clarity (TC) values to 0.435105 and 0.282381, respectively. …”
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Galaxy Morphological Classification with Zernike Moments and Machine Learning Approaches by Hamed Ghaderi, Nasibe Alipour, Hossein Safari

“…The uniqueness due to the orthogonality and completeness of Zernike polynomials, reconstruction of the original images with minimum errors, invariances (rotation, translation, and scaling), different block structures, and discriminant decision boundaries of ZMs’ probability density functions for different order numbers indicate the capability of ZMs in describing galaxy features. …”
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Thai Morning Glory Price Forecasting Using Deep Learning by Kanokwan Waeodi, Laor Boongasame, Karanrat Thammarak

“…The findings indicate that stepwise feature selection minimizes prediction errors and improves MSE, RMSE, MAPE, and MAE. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

“…The Random Forest model achieved the highest accuracy (67.2%) and demonstrated the best overall performance with a 0.41 Kappa statistic and the lowest mean absolute error (0.59). …”
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STRUCTURAL SYNTHESIS OF NAVIGATION SUPPORT OF TRIAD INTEGRATED NAVIGATION SYSTEM ON THE BASIS OF INERTIAL AND SATELLITE TECHNOLOGIES by V. S. Maryukhnenko, V. V. Erokhin

“…Based on optimal filtering theory methods the integrated navigation data processing algorithm, which is an extended Kalman filter, is synthesized. A distinctive feature of the algorithm is that the variables of the state vector which are to be measured, are the errors in the determination of appropriate navigation and timing parameters. …”
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Variation within and between digital pathology and light microscopy for the diagnosis of histopathology slides: blinded crossover comparison study by David RJ Snead, Ayesha S Azam, Jenny Thirlwall, Peter Kimani, Louise Hiller, Adam Bickers, Clinton Boyd, David Boyle, David Clark, Ian Ellis, Kishore Gopalakrishnan, Mohammad Ilyas, Paul Kelly, Maurice Loughrey, Desley Neil, Emad Rakha, Ian SD Roberts, Shatrughan Sah, Maria Soares, YeeWah Tsang, Manuel Salto-Tellez, Helen Higgins, Donna Howe, Abigail Takyi, Yan Chen, Agnieszka Ignatowicz, Jason Madan, Henry Nwankwo, George Partridge, Janet Dunn

“…Due to smaller sample size, for renal, the margin of error was 3.1%. Statistical analysis An intra-pathologist agreement was estimated by computing percentage LM versus DP CMC using a random-effects (RE) logistic regression model with crossed RE terms for pathologist and case. …”
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