Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling by Jing Zhang, Tingyi Tan, Yuhao Jiang, Congming Tan, Liangliang Hu, Daowen Xiong, Yikang Ding, Guowei Huang, Junjie Qin, Yin Tian

“…Therefore, identifying working memory load is an essential area of research. Deep learning models have demonstrated remarkable potential in identifying the intensity of working memory load. …”
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Segmentation of Laser Marks of Diabetic Retinopathy in the Fundus Photographs Using Lightweight U-Net by Yukang Jiang, Jianying Pan, Ming Yuan, Yanhe Shen, Jin Zhu, Yishen Wang, Yewei Li, Ke Zhang, Qingyun Yu, Huirui Xie, Huiting Li, Xueqin Wang, Yan Luo

“…In this study, we develop a deep learning algorithm based on the lightweight U-Net to segment laser marks from the color fundus photos, which could help indicate a stage or providing valuable auxiliary information for the care of DR patients. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Transformers are a promising deep learning approach for automated medical image segmentation. …”
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BananaImageBD: A comprehensive banana image dataset for classification of banana varieties and detection of ripeness stages in BangladeshMendeley Data by Md Hasanul Ferdaus, Rizvee Hassan Prito, Ahmed Abdal Shafi Rasel, Masud Ahmed, Md. Jahid Hassan Saykot, Shanjida Sultan Shanta, Sonali Akter, Ankan Chandra Das, Mohammad Manzurul Islam, Mahamudul Hasan, Md Sawkat Ali

“…Machine Learning (ML) and Deep Learning (DL) models can be trained on this dataset to accurately categorize banana varieties and determine their ripeness stages. …”
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Comparison of the Efficacy of Artificial Intelligence-Powered Software in Crown Design: An In Vitro Study by Ziqiong Wu, Chengqi Zhang, Xinjian Ye, Yuwei Dai, Jing Zhao, Wuyuan Zhao, Yuanna Zheng

“…AI-powered software requires further research and extensive deep learning to improve the morphological accuracy and stability of the crown design.…”
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Monitoring Moso bamboo (Phyllostachys pubescens) forests damage caused by Pantana phyllostachysae Chao considering phenological differences between on-year and off-year using UAV h... by Anqi He, Zhanghua Xu, Yifan Li, Bin Li, Xuying Huang, Huafeng Zhang, Xiaoyu Guo, Zenglu Li

“…The results demonstrate that classical machine learning and deep learning models can effectively detect P. phyllostachysae damage, with the 1D-CNN algorithm achieving the best performance. …”
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Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System by Xiayue Xu, Boxiang Yun, Yumin Zhao, Ling Jin, Yanning Zong, Guanzhen Yu, Chuanliang Zhao, Kai Fan, Xiaolin Zhang, Shiwang Tan, Zimu Zhang, Yan Wang, Qingli Li, Shaoqing Yu

“…Using Deep Snake, U-Net, and Att-Res2-UNet, we developed a nasal neoplastic detection network based on endoscopic images. After deep learning, the optimal network was selected as the initialization model and trained to optimize the SiamMask online tracking algorithm. …”
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Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network by Hans Juwiantho, Esther Irawati Setiawan, Joan Santoso, Mauridhi Hery Purnomo

“…Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. Metode deep learning dengan algoritme Deep Convolutional Neural Network ditawarkan untuk menjawab permasalahan tersebut dengan melakukan operasi convolution menggunakan filter sebesar ukuran window untuk mendapatkan fitur berdasarkan urutan kata. …”
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A novel early stage drip irrigation system cost estimation model based on management and environmental variables by Masoud Pourgholam-Amiji, Khaled Ahmadaali, Abdolmajid Liaghat

“…Then, different machine learning models such as Multivariate Linear Regression, Support Vector Regression, Artificial Neural Networks, Gene Expression Programming, Genetic Algorithms, Deep Learning, and Decision Trees, were used to estimate the costs of each of the of the aforementioned sections. …”
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Characterizing immune biomarkers and effector CD8+ T-cell exhaustion in pancreatic adenocarcinoma via single-cell RNA sequencing profiling by Rawaa AlChalabi, Raghda Makia, Semaa A. Shaban, Ahmed AbdulJabbar Suleiman

“…Virtual screening using a deep learning framework, GNINA, explored the inhibitory features of the anti-inflammatory drugs oxaprozin and celecoxib on IL7R. …”
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Positional embeddings and zero-shot learning using BERT for molecular-property prediction by Medard Edmund Mswahili, JunHa Hwang, Jagath C. Rajapakse, Kyuri Jo, Young-Seob Jeong

“…Abstract Recently, advancements in cheminformatics such as representation learning for chemical structures, deep learning (DL) for property prediction, data-driven discovery, and optimization of chemical data handling, have led to increased demands for handling chemical simplified molecular input line entry system (SMILES) data, particularly in text analysis tasks. …”
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Pathological and radiological assessment of benign breast lesions with BIRADS IVc/V subtypes. should we repeat the biopsy? by Wesam Rjoop, Anwar Rjoop, Alia Almohtaseb, Lama Bataineh, Zeina Nser Joubi, Maha Gharaibeh, Abdalrahman Al-Qwabah, Yousef Alasheh, Ismail Matalka

“…There is a need for continuous research to improve the diagnosis and treatment of breast lesions and reduce false-positive rates by incorporating other methodologies such as sonoelastography and incorporating deep learning and artificial intelligence in the decision-making to eliminate unnecessary procedures.…”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…Recent advances in deep learning (DL) models have shown promise in overcoming potential challenges in identifying Arabic dialects. …”
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The Impact of Artificial Intelligence on Healthcare: A Comprehensive Review of Advancements in Diagnostics, Treatment, and Operational Efficiency by Md. Faiyazuddin, Syed Jalal Q. Rahman, Gaurav Anand, Reyaz Kausar Siddiqui, Rachana Mehta, Mahalaqua Nazli Khatib, Shilpa Gaidhane, Quazi Syed Zahiruddin, Arif Hussain, Ranjit Sah

“…The study aims to describe AI in healthcare, including important technologies like robotics, machine learning (ML), deep learning (DL), and natural language processing (NLP), and to investigate how these technologies are used in patient interaction, predictive analytics, and remote monitoring. …”
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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen by Mark Hutchinson, Jeffrey A. Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J. Gray, Gilad Kaplan

“…In this study, DeepAb, a deep learning model for predicting antibody Fv structure directly from sequence, was used in conjunction with single-point experimental deep mutational scanning (DMS) enrichment data to design 200 potentially optimized variants of an anti-hen egg lysozyme (HEL) antibody. …”
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BeatProfiler: Multimodal In Vitro Analysis of Cardiac Function Enables Machine Learning Classification of Diseases and Drugs by Youngbin Kim, Kunlun Wang, Roberta I. Lock, Trevor R. Nash, Sharon Fleischer, Bryan Z. Wang, Barry M. Fine, Gordana Vunjak-Novakovic

“…To accurately classify between these biological signals, we apply feature-based ML and deep learning models (temporal convolutional-bidirectional long short-term memory model or TCN-BiLSTM). …”
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Dynamic selectout and voting-based federated learning for enhanced medical image analysis by Saeed Iqbal, Adnan N Qureshi, Musaed Alhussein, Khursheed Aurangzeb, Atif Mahmood, Saaidal Razalli Bin Azzuhri

“…When these agents work together in the system, it is imperative to tackle the complexities of distributed deep learning. We suggest a novel approach that uses a voting mechanism and dynamic SelectOut inside the FL framework to address these problems. …”
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Remote-sensing-based forest canopy height mapping: some models are useful, but might they provide us with even more insights when combined? by N. Besic, N. Picard, C. Vega, J.-D. Bontemps, L. Hertzog, J.-P. Renaud, J.-P. Renaud, F. Fogel, M. Schwartz, A. Pellissier-Tanon, G. Destouet, F. Mortier, F. Mortier, M. Planells-Rodriguez, P. Ciais

“…<p>The development of high-resolution mapping models for forest attributes based on remote sensing data combined with machine or deep learning techniques has become a prominent topic in the field of forest observation and monitoring. …”
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Machine Learning-Based Alzheimer’s Disease Stage Diagnosis Utilizing Blood Gene Expression and Clinical Data: A Comparative Investigation by Manash Sarma, Subarna Chatterjee

“…For the clinical study data, eight of the most effective biomarkers were selected using SFBS. A deep learning (DL) classifier was used to identify the stages—cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer’s disease (AD)/dementia. …”
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Application of a methodological framework for the development and multicenter validation of reliable artificial intelligence in embryo evaluation by D. Gilboa, Akhil Garg, M. Shapiro, M. Meseguer, Y. Amar, N. Lustgarten, N. Desai, T. Shavit, V. Silva, A. Papatheodorou, A. Chatziparasidou, S. Angras, J. H. Lee, L. Thiel, C. L. Curchoe, Y. Tauber, D. S. Seidman

“…Results The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. …”
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