Protocol for predicting single- and multiple-dose-dependent gene expression using deep generative learning by Derek E. Bowman, Vishal Panda, Daniel Marri, Omar Kana, Sudin Bhattacharya

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Pillar-X: Integrating Self-Learned Image Features to Improve 3D Object Detection by Mihaly Csontho, Andras Rovid

“…Accurate 3D object detection is essential for robust perception systems in autonomous vehicles. This paper presents Pillar-X, a 3D object recognition framework designed to generate and use self-learned image features. …”
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Deep Learning-Based Medical Ultrasound Image and Video Segmentation Methods: Overview, Frontiers, and Challenges by Xiaolong Xiao, Jianfeng Zhang, Yuan Shao, Jialong Liu, Kaibing Shi, Chunlei He, Dexing Kong

“…The intricate imaging structures, artifacts, and noise present in ultrasound images and videos pose significant challenges for accurate segmentation. …”
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A Novel Model-Based Reinforcement Learning for Online Anomaly Detection in Smart Power Grid by Ling Wang, Yuanzhe Zhu, Wanlin Du, Bo Fu, Chuanxu Wang, Xin Wang

“…The proposed model may be categorized as a general detection method according to the reinforcement learning (RL) architecture for POMDP which can help the learning process based on the award concept. …”
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A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network by Jianzheng Liu, Chunlin Fang, Chao Wu

“…This paper presents a method for recognizing human faces with facial expression. …”
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Why is your paper rejected? Lessons learned from over 5000 rejected transportation papers by Jiaming Wu, Ivan Sanchez-Diaz, Ying Yang, Xiaobo Qu

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MICROTEACHING AS TEACHING STRATEGY OF COMPUTER ASSISTED LANGUAGE LEARNING (CALL) FOR ENGLISH PRE-SERVICE TEACHERS by Badi'atul Azmina, Endang Fauziati, Nur Arifah Drajti

“…In order to facilitate English pre-service teachers to have ability in integrating technology in the classroom, microteaching is one of teaching strategy solutions. This present study examines how microteaching was used as teaching strategy of Computer Assisted Language Learning (CALL) for English pre-service teacher. …”
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Simulation-Driven End-to-End Deep Learning Method for White-Light Interference Topography Reconstruction by Xuan Qi, Yudong Lian, Yulei Wang, Zhiwei Lu

“…Motivated by the application of deep learning in optical metrology, this study presents a novel simulation-driven, end-to-end deep learning approach that significantly advances white-light interference topography reconstruction. …”
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From theory to practice: A systematic literature review of Gamified Flipped Learning in higher education by Giada Marinensi, Matilde Di Lallo, Giancarlo De Matteis, Riccardo Pizolli, Marc Romero Carbonell

“… Gamified Flipped Learning (GFL), a pedagogical approach that combines the methodology of flipped learning with gamification techniques, has gained increasing attention in recent years, particularly within the context of higher education. …”
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Decision-Making Policy for Autonomous Vehicles on Highways Using Deep Reinforcement Learning (DRL) Method by Ali Rizehvandi, Shahram Azadi, Arno Eichberger

“…Automated driving (AD) is a new technology that aims to mitigate traffic accidents and enhance driving efficiency. This study presents a deep reinforcement learning (DRL) method for autonomous vehicles that can safely and efficiently handle highway overtaking scenarios. …”
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A Deep Reinforcement Learning-Based Cooperative Guidance Strategy Under Uncontrollable Velocity Conditions by Hao Cui, Ke Zhang, Minghu Tan, Jingyu Wang

“…We present a novel approach to generating a cooperative guidance strategy using deep reinforcement learning to address the challenge of cooperative multi-missile strikes under uncontrollable velocity conditions. …”
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Nonlinear signal processing, spectral, and fractal based stridor auscultation: A machine learning approach by VIMAL RAJ, A RENJINI, M S SWAPNA, S SREEJYOTHI, S SANKARARAMAN

“…The energy envelope of the PSD plot of ST shows three peaks labelled as A (256 Hz), B (369 Hz), and C (540 Hz), of which A alone is present in BR at 265 Hz. The appearance of B and C in the PSD plot of ST is due to the obstructions in the trachea and upper airways caused by lesions. …”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

“…Although deep learning methods have achieved better results than traditional trajectory prediction algorithms, there are still problems such as information loss, interaction and uncertainty difficulties in modelling, and lack of interpretability of predictions when implementing multimodal high-precision prediction for autonomous vehicles in heterogeneous, highly dynamic and complex changing environments.The newly developed Transformer's long-range modelling capability and parallel computing ability make it a great success not only in the field of natural language processing, but also in solving the above problems when extended to the task of multimodal trajectory prediction for autonomous driving.Based on this, the aim of this paper is to provide a comprehensive summary and review of past deep neural network-based approaches, in particular the Transformer-based approach.The advantages of Transformer over traditional sequential network, graphical neural network and generative model were also analyzed and classified in relation to existing challenges, simultaneously.Transformer models can be better applied to multimodal trajectory prediction tasks, and that such models have better generalisation and interpretability.Finally, the future directions of multimodal trajectory prediction were presented.…”
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From Convolutional Networks to Vision Transformers: Evolution of Deep Learning in Agricultural Pest and Disease Identification by Mengyao Zhang, Chaofan Liu, Zihan Li, Baoquan Yin

“…In this paper, we systematically present the application of traditional machine learning methods in pest and disease identification and their limitations, and focus on the research progress of deep learning methods, covering three mainstream architectures: convolutional neural network (CNN), Vision Transformer model and CNN–Transformer hybrid model. …”
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Does text generation improve learning from expository text? A conceptual replication attempt by Julia Schindler, Tobias Richter

“…Abstract The aim of the present study was to test the replicability of the text generation effect for learning with expository texts while systematically varying contextual factors that—based on extant literature—can be assumed to affect the occurrence and magnitude of the text generation effect. …”
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A Hybrid Deep Learning Model for Link Dynamic Vehicle Count Forecasting with Bayesian Optimization by Chunguang He, Dianhai Wang, Yi Yu, Zhengyi Cai

“…The link dynamic vehicle count is a spatial variable that measures the traffic state of road sections, which reflects the actual traffic demand. This paper presents a hybrid deep learning method that combines the gated recurrent unit (GRU) neural network model with automatic hyperparameter tuning based on Bayesian optimization (BO) and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model. …”
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Crafting math minds: A bibliometric odyssey into innovative didactical designs for learning (2006-2023) by Dadan Dasari, Ilham Muhammad, Dadang Juandi

“…The study also identified the emergence of new themes in Didactical Design research, such as the study of specific mathematical competencies and the integration of technology in design, e-learning, augmented reality, and STEM. Several implications are presented as helpful information for scientists and stakeholders.…”
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Motivation in foreign language learning: a look at type of school environment as a contextual variable by Pavičić Takać Višnja, Berka Nives

“…The studies have been rooted in different theories and methodologies, (most notably those advanced by Gardner and Dörnyei and their respective associates) that have given precedence to a number of variables assumed to play an important role in understanding the phenomenon of FLL motivation. The present study is set between the macroperspective of the social-psychological period–by giving a general view of second language motivation–and the situation-specific period–by taking into consideration the immediate learning context. …”
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A Fast Logdet Divergence Based Metric Learning Algorithm for Large Data Sets Classification by Jiangyuan Mei, Jian Hou, Jicheng Chen, Hamid Reza Karimi

“…In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. …”
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Overcoming Data Scarcity: Guiding Citation Function Classification With Prompt-Based Few-Shot Learning by Krittin Chatrinan, Thanapon Noraset, Suppawong Tuarob

“…To mitigate this challenge, we propose a meta-learning strategy that utilizes prompt learning with pre-trained language models, also known as prompt-based tuning, for the task of few-shot learning in citation function classification. …”
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