Task-Driven Real-World Super-Resolution of Document Scans by Maciej Zyrek, Tomasz Tarasiewicz, Jakub Sadel, Aleksandra Krzywon, Michal Kawulok

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Diagnostic System of Power Oil Transformer in Accordance with Thermal Parameters by D. I. Zalizny, O. G. Shirokov

“…The paper proposes a device and algorithms for early detection of slowly developing faults in a power transformer that cause its abnormal heating. …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Compared to the moth-flame optimization (MFO) algorithm and traditional regularization methods, for both noise-free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. …”
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Pathogenetic mechanisms of nephropathy formation in type 1 diabetes mellitus by L. V. Kutsenko, I. V. Zorin, A. A. Vyalkova

“…It is necessary to search for available markers of the early stage of kidney damage and to create an algorithm for the formation of risk groups for diabetic nephropathy.…”
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SIGNAL FILTERING FOR SUDDEN SENSOR FAILURES BASED ON RECURRENT LEAST SQUARES METHOD by V. M. Artemiev, A. O. Naumov, L. L. Kokhan

“…It allows to design simple filtering algorithms with failure detection and to realize filter structure reorganization providing filter functionality.…”
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Multiparameter extrapolation and deflation methods for solving equation systems by A. J. Hughes Hallett

“…Most models in economics and the applied sciences are solved by first order iterative techniques, usually those based on the Gauss-Seidel algorithm. This paper examines the convergence of multiparameter extrapolations (accelerations) of first order iterations, as an improved approximation to the Newton method for solving arbitrary nonlinear equation systems. …”
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A cosine similarity-based method for improving the accuracy of GIS discharge spectrum recognition by CHEN Xiaoxin, ZHOU Tonghao, LIU Jiangming, DAI Pengfei, LIU Yanqi, LI Wendong, ZHANG Guanjun

“…The study of effective feature extraction and optimization algorithms is crucial for improving the accuracy of partial discharge （PD） detection in gas-insulated switchgear （GIS）. …”
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Special Issue on Contemporary Research Studies in Operations Research, Business Analytics, and Business Intelligence by Viswanath Kumar Ganesan, S. Vinodh, Malolan Sundararaman, M. Vimala Rani, M. Mathirajan

“…Through empirical and statistical analysis, this study identifies top-performing algorithms. A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests: This paper introduces a hybrid framework combining Hierarchical Temporal Memory and Sequential Probability Ratio Test for real-time data drift detection and anomaly identification. …”
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Fortifying IoT Infrastructure Using Machine Learning for DDoS Attack within Distributed Computing-based Routing in Networks by Sharaf Aldeen Abdulkadhum Abbas, Abdullahi Abdu Ibrahim

“…In addition, Algorithms perform quality control to determine the optimal hyperplane for the given data, Find neighboring data points and preserve the structure of the tree. …”
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Monitoring Pine Wilt Disease Using High-Resolution Satellite Remote Sensing at the Single-Tree Scale with Integrated Self-Attention by Wenhao Lv, Junhao Zhao, Jixia Huang

“…This study introduces several advanced self-attention algorithms into the task of satellite-based monitoring of pine wilt disease to enhance detection performance. …”
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Exploring the comprehensive integration of artificial intelligence in optimizing HVAC system operations: A review and future outlook by Shengze Lu, Shiyu Zhou, Yan Ding, Moon Keun Kim, Bin Yang, Zhe Tian, Jiying Liu

“…The main features of the AI technology are first introduced. The main algorithms of supervised learning, reinforcement learning, and deep learning are then analyzed in the fields of air conditioning operation optimization, energy consumption prediction and control, indoor environmental protection, and fault detection and diagnosis. …”
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A proficient approach for the classification of Alzheimer’s disease using a hybridization of machine learning and deep learning by Hafiz Ahmed Raza, Shahab U. Ansari, Kamran Javed, Muhammad Hanif, Saeed Mian Qaisar, Usman Haider, Paweł Pławiak, Iffat Maab

“…The proposed model employs a unique combination of machine learning and deep learning approaches to optimize the precision and accuracy of the detection and classification of AD. The proposed approach surpassed multi-modal machine learning algorithms in accuracy, precision, and F-measure performance measures. …”
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DRAFTS: A Deep-learning-based Radio Fast Transient Search Pipeline by Yong-Kun Zhang, Di Li, Yi Feng, Chao-Wei Tsai, Pei Wang, Chen-Hui Niu, Hua-Xi Chen, Yu-Hao Zhu

“…Traditional search algorithms are often inefficient, time-consuming, and generate a high number of false positives. …”
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Body roundness index, visceral adiposity index, and metabolic score for visceral fat in predicting new-onset atrial fibrillation: a UK Biobank cohort study by Yi ZHENG, Lei LIU, Xinyu ZHENG, Tong LIU, Xiaoping LI

“…Monitoring and managing BRI may be important in the early detection and intervention of AF.…”
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Transceiver Design for an FDA-MIMO Radar and MIMO Communication Spectral Coexistence System by Qihang XU, Lan LAN, Guisheng LIAO, Kewei WANG, Tongxing ZHENG

“…In addition, mainlobe deceptive interferences pose a serious threat to radar target detection. To address these issues, we devise a Frequency Diverse Array Multiple-Input Multiple-Output (FDA-MIMO) radar and MIMO communication spectral coexistence system and propose a radar-centric joint transceiver design scheme. …”
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An Investigation of Suicidal Ideation from Social Media Using Machine Learning Approach by Soumyabrata Saha, Suparna Dasgupta, Adnan Anam, Rahul Saha, Sudarshan Nath, Surajit Dutta

“…The machine learning algorithms showed high accuracy, precision, recall, and F1-score in detecting suicide patterns on social media data whereas SVM has the highest performance with an accuracy of 0.886.       …”
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Application of graph theory in liver research: A review by Xumei Hu, Longyu Sun, Rencheng Zheng, Xueqin Xia, Meng Liu, Weibo Chen, Xinyu Zhang, Chengyan Wang

“…This has allowed significant advancements in the detection, treatment, and control of liver disorders. …”
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The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…Evaluation was performed using established feature detection algorithms, such as SIFT and SURF, applied to multiple underwater datasets capturing diverse imaging conditions. …”
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DIO-SLAM: A Dynamic RGB-D SLAM Method Combining Instance Segmentation and Optical Flow by Lang He, Shiyun Li, Junting Qiu, Chenhaomin Zhang

“…Feature points from moving objects can negatively impact the accuracy of Visual Simultaneous Localization and Mapping (VSLAM) algorithms, while detection or semantic segmentation-based VSLAM approaches often fail to accurately determine the true motion state of objects. …”
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A systematic approach to study the effects of acquisition parameters and biological factors on computerized mammography analysis using ex vivo human tissue: A protocol description. by Nicole Hernández, Tomppa Pakarinen, Annukka Salminen, Santiago Laguna Castro, Ulla Karhunen-Enckell, Markus Hannula, Ritva Heljasvaara, Jari Hyttinen, Katriina Joensuu, Otto Jokelainen, Arja Jukkola, Sanna-Maria Karppinen, Auni Lindgren, Eero Lääperi, Emilia Peuhu, Taina Pihlajaniemi, Renata Prunskaite-Hyyryläinen, Kirsi Rilla, Pekka Ruusuvuori, Leena Latonen, Teemu Tolonen, Masi Valkonen, Mira Valkonen, Miska Vuorlaakso, Said Pertuz, Irina Rinta-Kiikka, Otso Arponen

“…<h4>Background</h4>Mammography is the most common imaging modality for the detection of breast cancer. Artificial intelligence algorithms for mammography analysis have shown promising performance for breast cancer risk assessment and lesion detection and classification; however, these models often fail the test of external validation. …”
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