A comprehensive systematic review of intrusion detection systems: emerging techniques, challenges, and future research directions by Arjun Kumar Bose Arnob, Rajarshi Roy Chowdhury, Nusrat Alam Chaiti, Sudipta Saha, Ajoy Roy

“… The role of Intrusion Detection Systems (IDS) in the protection against the increasing variety of cybersecurity threats in complex environments, including the Internet of Things (IoT), cloud computing, and industrial networks. This study evaluates the existing state-of-the-art IDS methodologies using Deep Learning (DL) approaches, and advanced feature engineering techniques. …”
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Future of Alzheimer's detection: Advancing diagnostic accuracy through the integration of qEEG and artificial intelligence by Sahar Rezaei, Farzan Asadirad, Alireza Motamedi, Mohammadsadegh Kamran, Farzaneh Parsa, Haniyeh Samimi, Parna Ghannadikhosh, Mahdi Zahmatyar, Seyed Ali Hosseinzadeh, Hossein Arabi

“…Through systematic analysis of 11 key studies across multiple international databases, we evaluated various AI architectures, including machine learning algorithms and deep learning networks, applied to qEEG data for AD detection. …”
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Unraveling the Drivers of ESG Performance in Chinese Firms: An Explainable Machine-Learning Approach by Hyojin Kim, Myounggu Lee

“…This study develops an explainable artificial intelligence framework using SHAP and permutation feature importance (PFI) methods to predict the ESG performance of Chinese firms. …”
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SS-EMERGE - self-supervised enhancement for multidimension emotion recognition using GNNs for EEG by Chirag Ahuja, Divyashikha Sethia

“…Therefore, this study introduces a hybrid SSL framework: Self-Supervised Enhancement for Multidimension Emotion Recognition using Graph Neural Networks (SS-EMERGE). This model enhances cross-subject EEG-based emotion recognition by incorporating Causal Convolutions for temporal feature extraction, Graph Attention Transformers (GAT) for spatial modelling, and Spectral Embedding for spectral domain analysis. …”
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Advances and Challenges in Deep Learning for Automated Welding Defect Detection: A Technical Survey by Abdulrahim Mohammed, Muhammad Hussain

“…A critical analysis of single-stage and two-stage architectures is conducted to evaluate their ability to address issues like small defect sizes, low image contrast, and diverse defect geometries. …”
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EXPLORING TRANSFER LEARNING AND CONVOLUTIONAL AUTOENCODER FOR EFFECTIVE KITCHEN UTENSILS CLASSIFICATION by Hashim Rosli, Rozniza Ali, Muhamad Suzuri Hitam, Ashanira Mat Deris, Noor Hafhizah Abd Rahim

“…We integrate pre-trained networks into an autoencoder framework to enhance feature extraction and image reconstruction. …”

Osteopontin as a diagnostic and NTZ-response biomarker of multiple sclerosis: a systematic review and meta-analysis by Adela González-Jiménez, Adela González-Jiménez, Elena Urcelay, Elena Urcelay, Laura Espino-Paisán, Laura Espino-Paisán

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A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification by Nagarjun Malagol, Tanuj Rao, Anna Werner, Reinhard Töpfer, Ludger Hausmann

“…Abstract The hairiness of the leaves is an essential morphological feature within the genus Vitis that can serve as a physical barrier. …”
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Structural Attributes Injection Is Better: Exploring General Approach for Radar Image ATR with a Attribute Alignment Adapter by Xiaolin Zhou, Xunzhang Gao, Shuowei Liu, Junjie Han, Xiaolong Su, Jiawei Zhang

“…We evaluated the proposed method using two types of radar image datasets under the conditions of few and sufficient samples. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…Gated recurrent units (GRUs), fully connected neural networks (FCNNs), and convolutional neural networks (CNNs) recorded R2 and MSE values of 0.5971 and 42.11 for CO, 0.7873 and 171.40 for O3, and 0.4954 and 25.17 for SO2, respectively. …”
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Advancing Underwater Vision: A Survey of Deep Learning Models for Underwater Object Recognition and Tracking by Mahmoud Elmezain, Lyes Saad Saoud, Atif Sultan, Mohamed Heshmat, Lakmal Seneviratne, Irfan Hussain

“…Recent advancements in deep learning (DL) have demonstrated remarkable success in overcoming these challenges by enabling robust feature extraction, image enhancement, and object recognition. …”
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Learning Deceptive Strategies in Adversarial Settings: A Two-Player Game with Asymmetric Information by Sai Krishna Reddy Mareddy, Dipankar Maity

“…We develop a suite of progressively complex grid-based environments featuring dynamic goals, fake targets, and navigational obstacles. …”
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Sentiment Analysis of ChatGPT on Indonesian Text using Hybrid CNN and Bi-LSTM by Vincentius Riandaru Prasetyo, Mohammad Farid Naufal, Kevin Wijaya

“…This study explores sentiment analysis on Indonesian text using a hybrid deep learning approach that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM). …”
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A lightweight framework to secure IoT devices with limited resources in cloud environments by Vivek Kumar Pandey, Dinesh Sahu, Shiv Prakash, Rajkumar Singh Rathore, Pratibha Dixit, Iryna Hunko

“…In terms of memory, they also use only 12.5 MB in it and evaluated on benchmark datasets including NSL-KDD and Bot-IoT, it gives an accuracy of 98.2% and 97.9%, respectively, and less than 1% false positives, thereby giving up to 6.8% accuracy over some traditional models such as SVM and Neural Networks and up to 78% less energy. …”
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Enhancing Photovoltaic Panel Segmentation in Remote Sensing Imagery: A Comparative Study of Attention-Integrated UNet Models by M. Q. Alkhatib, M. Al-Saad, N. Aburaed, M. S. Zitouni, S. Almansoori, H. Al-Ahmad

“…Using the high-resolution PV01 dataset, which includes UAV-captured rooftop PV samples, we evaluate the impact of four distinct attention modules: Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation Networks (SE-Net), Efficient Channel Attention (ECA-Net), and Coordinate Attention (CA) on segmentation performance. …”
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OpenABM-Covid19-An agent-based model for non-pharmaceutical interventions against COVID-19 including contact tracing. by Robert Hinch, William J M Probert, Anel Nurtay, Michelle Kendall, Chris Wymant, Matthew Hall, Katrina Lythgoe, Ana Bulas Cruz, Lele Zhao, Andrea Stewart, Luca Ferretti, Daniel Montero, James Warren, Nicole Mather, Matthew Abueg, Neo Wu, Olivier Legat, Katie Bentley, Thomas Mead, Kelvin Van-Vuuren, Dylan Feldner-Busztin, Tommaso Ristori, Anthony Finkelstein, David G Bonsall, Lucie Abeler-Dörner, Christophe Fraser

“…OpenABM-Covid19 can evaluate non-pharmaceutical interventions, including both manual and digital contact tracing, and vaccination programmes. …”
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Counterfactual Thinking in Tourette’s Syndrome: A Study Using Three Measures by Stefano Zago, Adriana Delli Ponti, Silvia Mastroianni, Federica Solca, Emanuele Tomasini, Barbara Poletti, Silvia Inglese, Giuseppe Sartori, Mauro Porta

“…It is a pervasive cognitive feature in everyday life and it is closely related to decision-making, planning, problem-solving, and experience-driven learning—cognitive processes that involve wide neuronal networks in which prefrontal lobes play a fundamental role. …”
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Prediction of coronary heart disease based on klotho levels using machine learning by Yuan Yao, Ying Zhao, Haifeng Li, Yanlin Han, Yue Wu, Renwei Guo, Mingfeng Ma, Lixia Bu

“…We randomly assigned the dataset of the National Health and Nutrition Examination Survey (NHANES) 2007–2016 to training and test sets at a ratio of 70:30. We evaluated the ability of five models constructed using logistic regression, neural networks, random forest, support vector machine, and eXtreme Gradient Boosting to predict CHD. …”
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Integrating radiomics, artificial intelligence, and molecular signatures in bone and soft tissue tumors: advances in diagnosis and prognostication by Guochao Li, Peipei Feng, Yayun Lin, Peng Liang

“…This systematic review evaluates the integration of radiomics, artificial intelligence (AI), and molecular signatures for diagnosing and prognosticating bone and soft tissue tumors (BSTTs). …”
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Transcriptome signature for multiple biotic and abiotic stress in barley (Hordeum vulgare L.) identifies using machine learning approach by Bahman Panahi

“…Machine learning models, specifically Random Forest and C4.5, were optimized and evaluated using a 10-fold cross-validation approach. …”
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