Drug Efficacy Recommendation System of Glioblastoma (GBM) Using Deep Learning by Sajid Naveed, Mujtaba Husnain, Ali Samad, Amna Ikram, Hina Afreen, Ghulam Gilanie, Najah Alsubaie

“…However, we utilized only 47 genes in our research due to limitations in computer processing speed and memory. A drug efficacy recommendation system was constructed using a deep learning method that combines gene expression, drug Simplified Molecular Input Line Entry System (SMILES), and inhibitory concentration features. …”
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Multisensory Stimulation in Rehabilitation of Dementia: A Systematic Review by Andrea Calderone, Angela Marra, Rosaria De Luca, Desirèe Latella, Francesco Corallo, Angelo Quartarone, Francesco Tomaiuolo, Rocco Salvatore Calabrò

“…<b>Background/Objectives</b>: Dementia leads to cognitive decline, affecting memory, reasoning, and daily activities, often requiring full-time care. …”
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A land-cover-assisted super-resolution model for retrospective reconstruction of MODIS-like NDVI data across the continental United States by blending Landcover300m and GIMMS NDVI3... by Zhicheng Zhang, Zhenhua Xiong, Xuewen Zhou, Kun Xiao, Wei Wu, Qinchuan Xin

“…The model exacts moderate-resolution class features from annual Landcover300m data at the target year, incorporates GIMMS NDVI3g time series to capture seasonal fluctuations, and employs the Long Short-Term Memory (LSTM) method to mitigate sensor differences. …”
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Coordinated Design of Power System Stabilizer and Virtual Inertia Control Using Modified Harris Hawk Optimization for Improving Power System Stability by Mohamad Almas Prakasa, Imam Robandi, Alberto Borghetti, Muhammad Ruswandi Djalal, Waseda Himawari

“…The proposed method uses the modified version of Harris Hawk Optimization with Memory Saving Strategy (HHO-MSS) to find the equilibrium point of global parameters of PSS-VIC through various simulations to ensure scalability. …”
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Enhancing Pan evaporation predictions: Accuracy and uncertainty in hybrid machine learning models by Khabat Khosravi, Aitazaz A. Farooque, Amir Naghibi, Salim Heddam, Ahmad Sharafati, Javad Hatamiafkoueieh, Soroush Abolfathi

“…The models investigated include M5 Prime (M5P), M5Rule (M5R), Kstar, as well as their hybridized versions employing Bagging (BA), the adaptive neuro-fuzzy inference system (ANFIS), ANFIS-GA (genetic algorithm), and long short-term memory (LSTM) networks. A 30-year dataset of monthly meteorological observations (1988–2018) from the Kermanshah synoptic station in Iran served as the basis for this analysis, incorporating variables such as temperature, relative humidity, solar exposure, wind speed, and rainfall. …”
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An Interpretable Model for Salinity Inversion Assessment of the South Bank of the Yellow River Based on Optuna Hyperparameter Optimization and XGBoost by Xia Liu, Yu Hu, Xiang Li, Ruiqi Du, Youzhen Xiang, Fucang Zhang

“…The regression effects of partial least squares regression (PLSR), geographically weighted regression (GWR), long short-term memory networks (LSTM), and extreme gradient boosting (XGBoost) were compared. …”
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Bioinformatics insights into mitochondrial and immune gene regulation in Alzheimer's disease by Tian Meng, Yazhou Zhang, Yuan Ye, Hui Li, Yongsheng He

“…After platform removal, there was a notable increase in the path complexity and time required to reach the target quadrant, suggesting a reduction in spatial memory capacity in AD mice. Moreover, qRT-PCR validation confirmed that the mRNA expression of the five biomarkers was consistent with bioinformatics results. …”
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Enhanced Generation of Human Induced Pluripotent Stem Cells from Peripheral Blood and Using Their Mesoderm Differentiation Ability to Regenerate Infarcted Myocardium by Ju-Young Kim, Han-Mo Yang, Joo-Eun Lee, Mika Jeon, Sang-Bum Bang, Jihye You, Joonoh Kim, Jaewon Lee, Jin Hur, Hyo-Soo Kim

“…Our study showed a new method to isolate human heart-derived stem cells from human peripheral blood and to generate iPSCs efficiently. Due to epigenetic memory, these CiMS-iPSCs easily differentiated into cardiovascular lineage cells, resulting in improved efficiency in vivo. …”
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Understanding ICU Nursing Knowledge, Perceived Barriers, and Facilitators of Sepsis Recognition and Management: A Cross-Sectional Study by Katherine A. Kissel, MN, RN, Karla D. Krewulak, PhD, Thérèse G. Poulin, BSc, Ken Kuljit S. Parhar, MD, MSc, Daniel J. Niven, MD, PhD, Vanessa M. Doiron, MScHQ, RN, Kirsten M. Fiest, PhD

“…Fewer nurses agreed it was easy to identify priority sepsis interventions based on order urgency (n = 53, 53%, TDF domain Memory, Attention, and Decision Processes). Reoccurring barriers and facilitators to sepsis recognition and management were commonly identified across the TDF domains of Knowledge, Skills, Environmental Context and Resources, and Social Influences, including competency deficit (with facilitators including support from colleagues), workload or staffing, and equipment or resource availability. …”
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Effects of acupuncture at the Taichong (LIV3) and Hegu (LI4) points on functional connectivity with the retrosplenial cortex in patients with Alzheimer’s disease by Junkai Wang, Xinyue Bai, Xiaojun Chen, Shui Liu, Mengmeng Sun, Kuncheng Li, Yunsong Zheng, Zhiqun Wang

“…The retrosplenial cortex (RSC) is one of the earliest brain regions affected in AD, and changes in its functional connectivity (FC) are reported to underlie disease-associated memory impairment. The aim of this study was to examine the effect of acupuncture on FC with the RSC in patients with AD.MethodsDemographic data, neuropsychological assessments, and resting-state functional magnetic resonance imaging (fMRI) data were collected from 14 AD patients and 14 normal controls (NCs) matched by age, sex, and educational level at baseline. …”
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Sleep quality and cognitive functioning among Chinese older adults living in the US: a mixed-effects model analysis by Fengyan Tang, Yuyang Zhu, Dasuni Jayawardena, Guoping Jin, Yanping Jiang

“…Cognitive functioning was assessed using a battery of tests, including the Chinese Mini-Mental State Examination (C_MMSE), the immediate and delayed recall of the East Boston Memory Test, the Digit Span Backwards assessment, and the Symbol Digit Modalities Test. …”
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Synaptic transistor implementing potentiation or depression via field distribution modulation of dual dielectric layers by single-polarity pulsed voltage stimuli by Jieun Kim, Jung Wook Lim, Chohyeon Park, Chaerin Yu, Jaehee Lee

“…This study designs, fabricates, and characterizes a dual-gate dielectric synaptic device that utilizes interfacial charge traps to mimic long-term potentiation (LTP) and long-term depression (LTD), which are essential for learning and memory. The device is fabricated using a standard complementary metal-oxide-semiconductor process and is composed of a TiO2 channel deposited over a dual dielectric of SiOx on Al2O3. …”
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Regional Ionospheric Parameter Estimation by Assimilating the LSTM Trained Results Into the SAMI2 Model by Jeong‐Heon Kim, Young‐Sil Kwak, Yong Ha Kim, Su‐In Moon, Se‐Heon Jeong, Jong Yeon Yun

“…The NN model was constructed from the data set of Jeju ionosonde (33.43°N, 126.30°E) for the period of 1 January 2011 to 31 December 2015 by using the long‐short term memory (LSTM) algorithm. The NN model provides 24‐hr prediction of the peak density (NmF2) and peak height (hmF2) of the F2 layer over Jeju. …”
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A recurrent YOLOv8-based framework for event-based object detection by Diego A. Silva, Kamilya Smagulova, Ahmed Elsheikh, Mohammed E. Fouda, Ahmed M. Eltawil

“…ReYOLOv8 incorporates a low-latency, memory-efficient method for encoding event data called Volume of Ternary Event Images (VTEI) and introduces a novel data augmentation technique based on Random Polarity Suppression (RPS) optimized for event-based sensors and tailored to leverage the unique attributes of event data. …”
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Klasifikasi Teks Hadis Bukhari Terjemahan Indonesia Menggunakan Recurrent Convolutional Neural Network (CRNN) by Muhammad Yuslan Abu Bakar, Adiwijaya Adiwijaya

“…While Recurrent Neural Networks are used as a classification method by carrying out capabilities that can be used as very long contextual information specifically on sequential data such as text data by relying on the ‘memory’ it has. This research presents several classification results using deep learning, where the best accuracy results are given by the Convolutional Recurrent Neural Network (CRNN), which is equal to 80.79%. …”
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Solar Energy Forecasting Framework Using Prophet Based Machine Learning Model: An Opportunity to Explore Solar Energy Potential in Muscat Oman by Mazhar Baloch, Mohamed Shaik Honnurvali, Adnan Kabbani, Touqeer Ahmed, Sohaib Tahir Chauhdary, Muhammad Salman Saeed

“…In this research, different models, named Linear Regression (LR), Support Vector Machine (SVR), KNN Regressor, Decision Forest Regressor, XGBoost Regressor, Neural Network (NN), Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), Random Forest Regressor, Categorical Boosting (CatBoost), Deep Autoregressive (DeepAR), and Facebook Prophet, are trained and tested under both identical features and a training–testing ratio. …”
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Development of improved deep learning models for multi-step ahead forecasting of daily river water temperature by Mehdi Gheisari, Jana Shafi, Saeed Kosari, Samaneh Amanabadi, Saeid Mehdizadeh, Christian Fernandez Campusano, Hemn Barzan Abdalla

“…This study addresses the limited use of signal decomposition in hybrid WT prediction models by proposing three methods: namely ensemble empirical mode decomposition (EEMD) on AdaBoost, long short-term memory (LSTM), and gated recurrent unit (GRU). These models integrate ensemble empirical mode decomposition (EEMD) with machine learning techniques for forecasting WT across multiple time horizons (one, three, and five days). …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…The 3D-ResNet component utilizes 3D convolutions and residual connections for pattern recognition, complemented by Bidirectional Long Short-Term Memory (BiLSTM) for enhanced long-term dependency learning by processing data arrangements in forward and backward directions. …”
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Multisource Accident Datasets-Driven Deep Learning-Based Traffic Accident Portrait for Accident Reasoning by Chun-Hao Wang, Yue-Tian-Si Ji, Li Ruan, Joshua Luhwago, Yin-Xuan Saw, Sokhey Kim, Tao Ruan, Li-Min Xiao, Rui-Jue Zhou

“…Our multisource accident datasets-driven deep learning model is composed of the following three submodels: (1) the structured data accident model using our accident feature-driven bidirectional long short-term memory (Bi-LSTM) and accident feature-driven bidirectional conditional random field (Bi-CRF) model to extract labels, (2) the unstructured traffic accident data model using our accident feature-driven piecewise convolutional neural network (PCNN) model to identify the extracted labels, and (3) the semistructured traffic accident data processing model. …”
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Analysis of argument structure constructions in the large language model BERT by Pegah Ramezani, Pegah Ramezani, Achim Schilling, Achim Schilling, Patrick Krauss, Patrick Krauss

“…In this study, we investigate the processing and representation of Argument Structure Constructions (ASCs) in the BERT language model, extending previous analyses conducted with Long Short-Term Memory (LSTM) networks. We utilized a custom GPT-4 generated dataset comprising 2000 sentences, evenly distributed among four ASC types: transitive, ditransitive, caused-motion, and resultative constructions. …”
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