Deep Learning in the Fast Lane: A Survey on Advanced Intrusion Detection Systems for Intelligent Vehicle Networks by Mohammed Almehdhar, Abdullatif Albaseer, Muhammad Asif Khan, Mohamed Abdallah, Hamid Menouar, Saif Al-Kuwari, Ala Al-Fuqaha

“…Our systematic review covers a range of AI algorithms, including traditional ML, and advanced neural network models, such as Transformers, illustrating their effectiveness in IDS applications within IVNs. …”
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A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity by Ryan L'Abbate, Anthony D'Onofrio, Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao

“…Compared to state-of-the-art approaches, co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. …”
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Implementation of Process-Based and Data-Driven Models for Early Prediction of Construction Time by Silvana Petruseva, Valentina Zileska-Pancovska, Diana Car-Pušić

“…Five hybrid models have been developed, and the most accurate one was the BTC-GRNN model, which uses Bromilow’s time-cost (BTC) model as a process-based model and the general regression neural network (GRNN) as a data-driven model. For evaluating the quality of the models, the 10-fold cross-validation method has been used. …”
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DC-BiLSTM-CNN Algorithm for Sentiment Analysis of Chinese Product Reviews by Yuanfang Dong, Xiaofei Li, Meiling He, Jun Li

“…The algorithm constructs two channels, transforming text into both character and word vectors and inputting them into Bidirectional Long Short-Term Memory (BiLSTM), and Convolutional Neural Network (CNN) models. The combination of these channels facilitates a more comprehensive feature extraction from reviews. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…This paper presents a novel hybrid model of DL models combined a Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for binary breast cancer classification on two datasets available at the Kaggle repository. …”
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Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques by Samitha Vidhanaarachchi, Janaka L. Wijekoon, W. A. Shanaka P. Abeysiriwardhana, Malitha Wijesundara

“…This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. …”
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The Significant Effects of Threshold Selection for Advancing Nitrogen Use Efficiency in Whole Genome of Bread Wheat by Mohammad Bahman Sadeqi, Agim Ballvora, Said Dadshani, Md. Nurealam Siddiqui, Mohammad Kamruzzaman, Ahossi Patrice Koua, Jens Léon

“…By incorporating the neural network algorithm, it is possible to improve the reliability of FDR threshold and increase the probability of identifying true genetic associations while minimizing the risk of false positives in GWAS results.…”
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Embedded Rough-Neck Helmholtz Resonator Low-Frequency Acoustic Attenuator by Xianming Sun, Tao Yu, Lipeng Wang, Yunshu Lu, Changzheng Chen

“…A back-propagation (BP) neural network models and predicts how structural parameters impact the acoustic transmission coefficient, elucidating the effects of geometric variations. …”
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Identification of Spectrally Similar Materials From Multispectral Imagery Based on Condition Number of Matrix by Maozhi Wang, Shu-Hua Chen, Jun Feng, Wenxi Xu, Daming Wang

“…The results for a case study to identify water, ice, snow, shadow, and other materials from Landsat 8 OLI data indicate that SF-CNM can identify the materials specified by the given samples successfully and accurately and that SF-CNM significantly outperforms those of spectral angle mapper algorithm, Mahalanobis classifier, maximum likelihood, and artificial neural network, and produces the performance similar to, even slightly better than that of support vector machine.…”
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Pre-trained artificial intelligence-aided analysis of nanoparticles using the segment anything model by Gabriel A. A. Monteiro, Bruno A. A. Monteiro, Jefersson A. dos Santos, Alexander Wittemann

“…The automated segmentation of whole particles, as well as their individual subdivisions, is investigated using the Segment Anything Model, which is based on a pre-trained neural network. The subdivisions of the particles are organized into sets, which presents a novel approach in this field. …”
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Identification and Characterization of Novel Perivascular Adventitial Cells in the Whole Mount Mesenteric Branch Artery Using Immunofluorescent Staining and Scanning Confocal Micro... by Chandra Somasundaram, Rahul K. Nath, Richard D. Bukoski, Debra I. Diz

“…In summary, CGRP, and NCAM-containing neural cells in the perivascular adventitia also express palladin and CaSR, and coexpress Gap-43 which may participate in response to stress/injury and vasodilator mechanisms as part of a perivascular sensory neural network.…”
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Regional Boundary Control of Traffic Network Based on MFD and FR-PID by Xin Yang, Juncheng Chen, Mantun Yan, Zhao He, Ziyan Qin, Jiandong Zhao

“…In order to effectively alleviate the traffic congestion of the regional road network, aiming at the problem of lack of accurate OD data of the road network, a regional boundary control method of the traffic network based on fuzzy RBF neural network PID (FR-PID) is proposed by combining the theory of macroscopic fundamental diagram (MFD). …”
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Indoor Positioning System in Learning Approach Experiments by Dodo Zaenal Abidin, Siti Nurmaini, Erwin, Errissya Rasywir, Yovi Pratama

“…The test was conducted with a deep learning approach using a deep neural network (DNN) algorithm. The DNN method can estimate the actual space and get better position results, whereas machine learning methods such as the DNN algorithm can handle more effectively large data and produce more accurate data. …”
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Examining the relationships between patients’ multimorbidity trajectories and prognostic outcomes after the initial hip fracture by Jessica Qiuhua Sheng, Da Xu, Paul Jen-Hwa Hu, Liang Li, Ting-Shuo Huang

“…We then leverage the discovered relationships to develop a cross-attention neural network method for estimating patients’ post-fracture prognoses and demonstrate its predictive utilities relative to several prevalent machine leaning methods. …”
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Efficiency-House Optimization to Widen the Operation Range of the Double-Suction Centrifugal Pump by Wenjie Wang, Majeed Koranteng Osman, Ji Pei, Shouqi Yuan, Jian Cao, Fareed Konadu Osman

“…A two-layer feedforward artificial neural network (ANN) and the Kriging model were combine based on a hybrid approximate model and solved with swarm intelligence for global best parameters that would maximize the pump efficiency. …”
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Model-Oriented Training of Coordinators of the Decentralized Control System of Technological Facilities With Resource Interaction by Volodymyr M. Dubovoi, Maria S. Yukhimchuk, Viacheslav V. Kovtun, Krzysztof R. Grochla

“…Conducted experimental studies of the proposed method of training neural network coordinators, implemented on Python TensorFlow, showed greater effectiveness of Collaborative Federated Learning compared to independent training of coordinators or direct transfer of learning outcomes between coordinators.…”
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Tackling the Problem of Distributional Shifts: Correcting Misspecified, High-dimensional Data-driven Priors for Inverse Problems by Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur

“…., in a trained deep neural network) as priors is emerging as an appealing alternative to simple parametric priors in a variety of inverse problems. …”
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Two-stage deep reinforcement learning method for agile optical satellite scheduling problem by Zheng Liu, Wei Xiong, Zhuoya Jia, Chi Han

“…Next, a decomposition strategy decomposes the executable task sequence into multiple sub-sequences in the observation scheduling stage, and the observation scheduling process of these sub-sequences is modeled as a concatenated Markov decision process. A neural network is designed as the observation scheduling network to determine observation actions for the sequenced tasks, which is well trained by the soft actor-critic algorithm. …”
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Experimental and analytical study on axial behaviour of square corrugated concrete filled single and double skin tube stub columns by Aya Mohsen Handousa, Mohamed Abdellatief, Fikry Abdo Salem, Nabil Mahmoud, Mohamed Ghannam

“…Furthermore, the study proposed two machine-learning models, namely Artificial Neural Network (ANN) and Gaussian Process Regression (GPR), to estimate the ultimate compressive strength of square CFDST columns. …”
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Improving Road Semantic Segmentation Using Generative Adversarial Network by Arnick Abdollahi, Biswajeet Pradhan, Gaurav Sharma, Khairul Nizam Abdul Maulud, Abdullah Alamri

“…Road network extraction from remotely sensed imagery has become a powerful tool for updating geospatial databases, owing to the success of convolutional neural network (CNN) based deep learning semantic segmentation techniques combined with the high-resolution imagery that modern remote sensing provides. …”
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