Computational markers show specific deficits for dyslexia and ADHD in complex learning settings by Yafit Gabay, Lana Jacob, Atil Mansour, Uri Hertz

“…The findings showed that individuals with ADHD and dyslexia exhibited reduced performance across both tasks compared to their controls. …”
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OPTIMIZED CLUSTER-BASED COMMUNICATION IN MWSN USING FUZZY NEURAL NETWORKS AND CROW SEARCH ALGORITHM by S Archana, V Jayapradha

“…Optimizing the performance of Mobile Wireless Sensor Networks (MWSNs) requires efficient cluster formation, Cluster Head (CH) selection, feature selection, and data forwarding strategies to improve communication efficiency and energy usage. …”
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Joint beam hopping and coverage control optimization algorithm for multibeam satellite system by Guoliang XU, Feng TAN, Yongyi RAN, Feng CHEN

“…To improve the performance of multibeam satellite (MBS) systems, a deep reinforcement learning-based algorithm to jointly optimize the beam hopping and coverage control (BHCC) algorithm for MBS was proposed.Firstly, the resource allocation problem in MBS was transformed to a multi-objective optimization problem with the objective maximizing the system throughput and minimizing the packet loss rate of the MBS.Secondly, the MBS environment was characterized as a multi-dimensional matrix, and the objective problem was modelled as a Markov decision process considering stochastic communication requirements.Finally, the objective problem was solved by combining the powerful feature extraction and learning capabilities of deep reinforcement learning.In addition, a single-intelligence polling multiplexing mechanism was proposed to reduce the search space and convergence difficulty and accelerate the training of BHCC.Compared with the genetic algorithm, the simulation results show that BHCC improves the throughput of MBS and reduces the packet loss rate of the system, greedy algorithm, and random algorithm.Besides, BHCC performs better in different communication scenarios compared with a deep reinforcement learning algorithm, which do not consider the adaptive beam coverage.…”
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Advancements in Image Classification: From Machine Learning to Deep Learning by Cheng Haoran

“…The paper also focuses on discussing classic deep learning models such as AlexNet, VGGNet, ResNet and ViT, analyzing their strengths and weaknesses. …”
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An Approach Based on Sum Product Networks for Code Smells Detection by Mostefai Abdelkader

“…The main objective of this paper is to study the performance of a Sum-Product Network as a classifier for code smell detection. …”
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A Novel Active Learning Approach for Improving Classification of Unlabeled Video Based on Deep Learning Techniques by Mohamed Salama

“…We implement this approach by leveraging recent advances in deep neural networks for visual feature extraction and sequence modeling. Our experiments on benchmark datasets demonstrate that our method achieves significant improvements in video classification accuracy with less training data. …”
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Unified Visual-Aware Representations for Data Analytics by Ladislav Peska, Ivana Sixtova, David Hoksza, David Bernhauer, Jakub Lokoc, Tomas Skopal

“…The visual representations serve for data analytics tasks performed by human users as well as serve for universal data representations used in machine learning models for automated tasks. …”
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GaN HEMTs-based compact power factor corrected 96.1% peak efficiency LED driver with gate assisted circuit by Muhammad Faizan, Zhengfang Qian, Hua Zhu, Shuting Fan, Muhammad Zain Yousaf, Mohit Bajaj, Chen Zhichu, Asad Aziz

“…For mitigation of switching oscillations and shoot-through, a novel gate assisted circuit (GAC) is embedding by performing the mathematical analysis and modeling for shoot-through in half bridge configuration. …”
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Hope for OTHERS (Our Tissue Helping Enhance Research & Science): research results from the University of Pittsburgh rapid autopsy program for breast cancer by Alexander Chih-Chieh Chang, Marija Balic, Tanner Bartholow, Rohit Bhargava, Daniel D. Brown, Lauren Brown, Adam Brufsky, Ye Cao, Neil Carleton, Amanda M. Clark, Morgan Cody, Kai Ding, Christopher Deible, Ashuvinee Elangovan, Julia Foldi, Daniel Geisler, Christine Hodgdon, Naomi Howard, Zheqi Li, Jie Bin Liu, Oscar Lopez-Nunez, Dixcy Jaba Sheeba John Mary, Olivia McGinn, Lori Miller, Kanako Mori, Geoffrey Pecar, Nolan Priedigkeit, Shannon Puhalla, Margaret Q. Rosenzweig, Partha Roy, Laura Savariau, Stephanie Walker, Hunter Waltermire, Abdalla M. Wedn, Alan Wells, Megan E. Yates, Jennifer Xavier, Adrian V. Lee, Steffi Oesterreich

“…As of August 2024, we consented 114 patients and performed 37 autopsies, from which we collected 551 unique metastatic frozen tumor samples, 1244 FFPE blocks, 90 longitudinal liquid biopsy samples and developed 14 patient-derived organoid and 8 patient-derived xenograft models. …”
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3D-GloBFP: the first global three-dimensional building footprint dataset by Y. Che, X. Li, X. Liu, X. Liu, Y. Wang, W. Liao, X. Zheng, X. Zhang, X. Xu, Q. Shi, J. Zhu, H. Zhang, H. Zhang, H. Yuan, H. Yuan, Y. Dai, Y. Dai

“…Our approach integrated multisource remote-sensing features and building morphology features to develop height estimation models using the extreme gradient boosting (XGBoost) regression method across diverse global regions. …”
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Probabilistic Attention Map: A Probabilistic Attention Mechanism for Convolutional Neural Networks by Yifeng Liu, Jing Tian

“…The experimental results show that the proposed probabilistic attention mechanism effectively boosts image classification accuracy performance across various CNN backbone models, outperforming both baseline and other attention mechanisms.…”
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Age-related cognitive decline and associations with sex, education and apolipoprotein E genotype across ethnocultural groups and geographic regions: a collaborative cohort study. by Darren M Lipnicki, John D Crawford, Rajib Dutta, Anbupalam Thalamuthu, Nicole A Kochan, Gavin Andrews, M Fernanda Lima-Costa, Erico Castro-Costa, Carol Brayne, Fiona E Matthews, Blossom C M Stephan, Richard B Lipton, Mindy J Katz, Karen Ritchie, Jacqueline Scali, Marie-Laure Ancelin, Nikolaos Scarmeas, Mary Yannakoulia, Efthimios Dardiotis, Linda C W Lam, Candy H Y Wong, Ada W T Fung, Antonio Guaita, Roberta Vaccaro, Annalisa Davin, Ki Woong Kim, Ji Won Han, Tae Hui Kim, Kaarin J Anstey, Nicolas Cherbuin, Peter Butterworth, Marcia Scazufca, Shuzo Kumagai, Sanmei Chen, Kenji Narazaki, Tze Pin Ng, Qi Gao, Simone Reppermund, Henry Brodaty, Antonio Lobo, Raúl Lopez-Anton, Javier Santabárbara, Perminder S Sachdev, Cohort Studies of Memory in an International Consortium (COSMIC)

“…Our primary goal was to investigate how rates of age-related decline in cognitive test performance varied among international cohort studies of cognitive aging. …”
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AI-Powered Stroke Diagnosis System: Methodological Framework and Implementation by Marta Narigina, Agris Vindecs, Dušanka Bošković, Yuri Merkuryev, Andrejs Romanovs

“…Our approach involves rigorous data preprocessing, feature engineering, and ensemble techniques to optimize the predictive performance. …”
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Reconstitution of T cell‐mediated immunity by umbilical cord‐derived mesenchymal stem cells in ulcerative colitis by Xiaoying Luo, Jieping Deng, Xiaoke Jiang, Jun Mi, Yangqiu Bai, Huimin Zhang, Yalong Li, Min Liu, Conghui Cai, Pengju Li, Huanrong Huang, Yueping Xu, Yiwen Qin, Yang Mi, Hui Ding, Zhiyu Yang, Yue Wu, Zhenjuan Li, Ling Lan, Lida Zhang, Li Wang, Guobing Chen, Han Yue, Oscar Junhong Luo, Bingyong Zhang

“…Dextran sulphate sodium (DSS)‐induced colitis mouse models with or without UMSC injection were used to delineate colon inflammation and T cell function. …”
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Self-Correlation Network With Triple Contrastive Learning for Hyperspectral Image Classification With Noisy Labels by Kwabena Sarpong, Mohammad Awrangjeb, Md. Saiful Islam, Islam Helmy

“…Data quality is essential for training deep learning models, and recently, the challenge of noisy labels in hyperspectral image (HSI) classification has attracted considerable attention. …”
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slendr: a framework for spatio-temporal population genomic simulations on geographic landscapes by Petr, Martin, Haller, Benjamin C., Ralph, Peter L., Racimo, Fernando

“…Additionally, slendr can simulate data from traditional, non-spatial models, either with SLiM or using an alternative built-in coalescent msprime back end. …”
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Phenotyping, genome‐wide dissection, and prediction of maize root architecture for temperate adaptability by Weijun Guo, Fanhua Wang, Jianyue Lv, Jia Yu, Yue Wu, Hada Wuriyanghan, Liang Le, Li Pu

“…Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. …”
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Behavioral Classification of Sequential Neural Activity Using Time Varying Recurrent Neural Networks by Yongxu Zhang, Catalin Mitelut, David J. Arpin, David Vaillancourt, Timothy Murphy, Shreya Saxena

“…Recurrent neural networks are common models for sequence data. However, standard recurrent neural networks are not able to handle data with temporal distributional shifts to guarantee robust classification across time. …”
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Cache Assignment for a Flexible Mobile User in Wireless Heterogeneous Networks by Mohammad Hossein Amerimehr, Parisa Eslami, Nahid Amani, sara Efazati

“…Moreover, in the system model, the interference and the mobility of users are considered. …”
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ORM1 Mediates Ln-IgG-Induced Podocyte Damage and Autophagy via the AMPK/mTOR Signaling by Jie Chen, Libin Zou, Lu Liu, Chunfeng Wu, Mi Hu

“…Podocyte damage is a central feature of lupus nephritis (LN), making the identification of potential therapeutic targets to prevent podocyte injury and improve treatment outcomes essential. …”
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