Waste heat recovery cycles integration into a net-Zero emission solar-thermal multi-generation system; Techno-economic analysis and ANN-MOPSO optimization by Pradeep Kumar Singh, Ali Basem, Rebwar Nasir Dara, Mohamed Shaban, Sarminah Samad, Raymond Ghandour, Ahmad Almadhor, Samah G. Babiker, Iskandar Shernazarov, Ibrahim A. Alsayer

“…To optimize the system's performance, an artificial neural network is integrated with a multi-objective particle swarm optimization algorithm to reduce computational time from approximately 16 h to 4 min. …”
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BMNet: Enhancing Deepfake Detection Through BiLSTM and Multi-Head Self-Attention Mechanism by Demao Xiong, Zhan Wen, Cheng Zhang, Dehao Ren, Wenzao Li

“…When forgery techniques can generate highly realistic videos, traditional convolutional neural network (CNN)-based detection models often struggle to capture subtle forgery features and temporal dependencies. …”
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An Automatic Emergency Braking Model considering Driver’s Intention Recognition of the Front Vehicle by Wei Yang, Jiajun Liu, Kaixia Zhou, Zhiwei Zhang, Xiaolei Qu

“…Therefore, we propose a driver’s intention recognition model for the front vehicle, which is based on the backpropagation (BP) neural network and hidden Markov model (HMM). The brake pedal, accelerator pedal, and vehicle speed data are used as the input of the proposed BP-HMM model to recognize the driver’s intention, which includes uniform driving, normal braking, and emergency braking. …”
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A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations by Beste Ustubioglu, Gul Tahaoglu, Arda Ustubioglu, Guzin Ulutas, Muhammed Kilic

“…Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. …”
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Large-scale mapping of plastic-mulched land from Sentinel-2 using an index-feature-spatial-attention fused deep learning model by Lizhen Lu, Yunci Xu, Xinyu Huang, Hankui K. Zhang, Yuqi Du

“…In this paper, we demonstrated a large-scale PML mapping using Sentinel-2 data by combining the PML domain knowledge and the deep Convolutional Neural Network (CNN). We developed a dual-branch Index-Feature-Spatial-Attention fused Deep Learning Model (IFSA_DLM) for effectively acquiring and fusing multi-scale discriminative features and thus for accurately detecting PML. …”
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Application of Multiattention Mechanism in Power System Branch Parameter Identification by Zhiwei Wang, Liguo Weng, Min Lu, Jun Liu, Lingling Pan

“…To overcome these limitations, we propose a novel multitask Graph Transformer Network (GTN), which combines a graph neural network and a multiattention mechanism to construct our model. …”
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Clinical feasibility of deep learning-driven magnetic resonance angiography collateral map in acute anterior circulation ischemic stroke by Ye Jin Jeon, Hong Gee Roh, Sumin Jung, Hyun Yang, Hee Jong Ki, Jeong Jin Park, Taek-Jun Lee, Na Il Shin, Ji Sung Lee, Jin Tae Kwak, Hyun Jeong Kim

“…We employed a 3D multitask regression and ordinal regression deep neural network, called as 3D-MROD-Net, to generate DL-driven MRA collateral maps. …”
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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data by Ísak Valsson, Matthew T. Warren, Charlotte M. Deane, Aniket Magarkar, Garrett M. Morris, Philip C. Biggin

“…Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). …”
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Normalized difference vegetation index prediction using reservoir computing and pretrained language models by John Olamofe, Ram Ray, Xishuang Dong, Lijun Qian

“…Using MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m SIN Grid V061 dataset, we designed and implemented Reservoir Computing (RC) models and transformer-based models including pretrained language model, and compared the prediction performance of these models to traditional machine learning and deep learning methods such as Nonlinear Regression, Decision Tree, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) network, and DLinear. …”
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Visibility Enhancement of Lesion Regions in Chest X-Ray Images With Image Fidelity Preservation by Ryoichi Ishikawa, Tomohisa Yuzawa, Taiki Fukiage, Masataka Kagesawa, Toru Watsuji, Takeshi Oishi

“…The proposed method predicts the image processing parameters that enhance the lesion signals via the inference neural network. The framework consists of an X-ray image enhancer and an enhanced model predictor for reference. …”
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Adaptive CNN Ensemble for Complex Multispectral Image Analysis by Syed Muslim Jameel, Manzoor Ahmed Hashmani, Mobashar Rehman, Arif Budiman

“…Secondly, an adaptive convolutional neural network (CNN) ensemble framework is proposed and evaluated for a new spectral band adaptation. …”
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Machine learning-based forecasting of ground surface settlement induced by metro shield tunneling construction by Qiankun Wang, Chuxiong Shen, Chao Tang, Zeng Guo, Fangqi Wu, Wenyi Yang

“…On this basis, the Particle Swarm Optimization (PSO) algorithm is employed to optimize a Back Propagation Neural Network(BPNN) for the subsequent prediction of ground surface settlement. …”
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Automated Detection of Macular Diseases by Optical Coherence Tomography and Artificial Intelligence Machine Learning of Optical Coherence Tomography Images by Soichiro Kuwayama, Yuji Ayatsuka, Daisuke Yanagisono, Takaki Uta, Hideaki Usui, Aki Kato, Noriaki Takase, Yuichiro Ogura, Tsutomu Yasukawa

“…The remaining 100 images were used to evaluate the trained convolutional neural network (CNN) model. Results. Automated disease detection showed that the first candidate disease corresponded to the doctor’s decision in 83 (83%) images and the second candidate disease in seven (7%) images. …”
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BA-ATEMNet: Bayesian Learning and Multi-Head Self-Attention for Theoretical Denoising of Airborne Transient Electromagnetic Signals by Weijie Wang, Xuben Wang, Xiaodong Yu, Debiao Luo, Xinyue Liu, Kai Yang, Wen Yang, Xiaolan Yang, Ke Hu, Wenyi Hu

“…The incorporation of a multi-head self-attention mechanism significantly enhances the feature extraction capabilities of the convolutional neural network, allowing for improved differentiation between signal and noise. …”
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Understanding Confusion: A Case Study of Training a Machine Model to Predict and Interpret Consensus From Volunteer Labels by Ramanakumar Sankar, Kameswara Mantha, Cooper Nesmith, Lucy Fortson, Shawn Brueshaber, Candice Hansen-Koharcheck, Glenn Orton

“…In this paper, we explore using a neural network to interpret volunteer confusion across the dataset, to increase the purity of the downstream analysis. …”
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Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model by Rajesh Doss, Jayabrabu Ramakrishnan, S. Kavitha, S. Ramkumar, G. Charlyn Pushpa Latha, Kiran Ramaswamy

“…The proposed model is composed of a pretrained deep transfer learning model called ShuffleNet-v2 with convolutional neural network (CNN) architecture. This ShuffleNet-v2-CNN performs the defects identification and classification process following the workflow of data preprocessing, data augmentation, feature extraction, and classification. …”
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Enhancing depression recognition through a mixed expert model by integrating speaker-related and emotion-related features by Weitong Guo, Qian He, Ziyu Lin, Xiaolong Bu, Ziyang Wang, Dong Li, Hongwu Yang

“…Our approach begins with a Time Delay Neural Network to pre-train a speaker-related feature extractor using a large-scale speaker recognition dataset while simultaneously pre-training a speaker’s emotion-related feature extractor with a speech emotion dataset. …”
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Multi-Scale Building Load Forecasting Without Relying on Weather Forecast Data: A Temporal Convolutional Network, Long Short-Term Memory Network, and Self-Attention Mechanism Appro... by Lanqian Yang, Jinmin Guo, Huili Tian, Min Liu, Chang Huang, Yang Cai

“…The reconstructed features are then input into the long short-term memory (LSTM) neural network to achieve the extraction of load time features. …”
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Efficient and accurate methodologies for MCS-based probabilistic analysis of tunnel face stability by Bin Li, Yong-Kai Shen, Yuan-Sheng Lan

“…The second strategy uses a previously established training dataset to construct an ensemble of metamodels to classify the MCS samples. Backpropagation Neural Network (BP) is utilized to construct a regression metamodel to predict the safety factors of each sample; the samples that are predicted to be near the limit state will be further evaluated by an adaptive classification metamodel constructed by the combination of K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). …”
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Comparing Cross-Subject Performance on Human Activities Recognition Using Learning Models by Zhe Yang, Mengjie Qu, Yun Pan, Ruohong Huan

“…In this paper, we evaluate three traditional machine learning methods and five deep neural network architectures under the same metrics on three popular HAR datasets: mHealth, PAMAP2, and UCIDSADS. …”
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