Smart Defect Detection in Aero-Engines: Evaluating Transfer Learning with VGG19 and Data-Efficient Image Transformer Models by Samira Mohammadi, Vahid Rahmanian, Sasan Sattarpanah Karganroudi, Mehdi Adda

“…RandomSearchCV was used for hyperparameter optimization, and we selectively froze some layers during training to help better tailor the models to our dataset. …”
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Optimal deep learning based vehicle detection and classification using chaotic equilibrium optimization algorithm in remote sensing imagery by Youseef Alotaibi, Krishnaraj Nagappan, Tamilvizhi Thanarajan, Surendran Rajendran

“…In addition, CEOA based hyperparameter optimizer is designed for the parameter tuning of the ResNet model. …”
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Morphological Analysis and Subtype Detection of Acute Myeloid Leukemia in High-Resolution Blood Smears Using ConvNeXT by Mubarak Taiwo Mustapha, Dilber Uzun Ozsahin

“…Automated AML subtype detection is especially important for underrepresented subtypes to ensure equitable diagnostics; (2) Methods: This study explores the potential of ConvNeXt, an advanced convolutional neural network architecture, for classifying high-resolution peripheral blood smear images into AML subtypes. …”
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Comparative analysis of BERT-based and generative large language models for detecting suicidal ideation: a performance evaluation study by Adonias Caetano de Oliveira, Renato Freitas Bessa, Ariel Soares Teles

“…Abstract: Artificial intelligence can detect suicidal ideation manifestations in texts. …”
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Advanced object detection for smart accessibility: a Yolov10 with marine predator algorithm to aid visually challenged people by Mahir Mohammed Sharif Adam, Hussah Nasser AlEisa, Samah Al Zanin, Radwa Marzouk

“…They face problems with such actions and object detection should be an essential feature they can rely on a regular basis. …”
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Integration of metaheuristic based feature selection with ensemble representation learning models for privacy aware cyberattack detection in IoT environments by M. Karthikeyan, R. Brindha, Maria Manuel Vianny, V. Vaitheeshwaran, Mrinal Bachute, Sanket Mishra, Bibhuti Bhusan Dash

“…The increasing complexity of cyberattacks demands advanced intrusion detection systems (IDS) to defend crucial assets and data. …”
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Maximizing steel slice defect detection: Integrating ResNet101 deep features with SVM via Bayesian optimization by Prabira Kumar Sethy, Laxminarayana Korada, Santi Kumari Behera, Akshay Shirole, Rajat Amat, Aziz Nanthaamornphong

“…To enhance the SVM's performance, Bayesian optimization is employed for hyperparameter tuning. Our method is validated using the ''Severstal: Steel Defect Detection'' dataset from Kaggle, achieving a validation accuracy of 89.1 % and a test accuracy of 90.6 %, with a classification error of 0.10934. …”
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Augmenting cybersecurity through attention based stacked autoencoder with optimization algorithm for detection and mitigation of attacks on IoT assisted networks by Kashi Sai Prasad, E. Laxmi Lydia, M. V. Rajesh, K. Radhika, Janjhyam Venkata Naga Ramesh, N. Neelima, Srinivasa Rao Pokuri

“…Eventually, the hyperparameter tuning of the ASAE method is executed by using pelican optimization algorithm (POA) method. …”
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A hybrid object detection approach for visually impaired persons using pigeon-inspired optimization and deep learning models by Abdullah M. Alashjaee, Hussah Nasser AlEisa, Abdulbasit A. Darem, Radwa Marzouk

“…The bi-directional long short-term memory and multi-head attention (MHA-BiLSTM) approach is utilized to classify the object detection process. Finally, the hyperparameter tuning process is performed using the pigeon-inspired optimization (PIO) approach to advance the classification performance of the MHA-BiLSTM approach. …”
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Enhancing Cybersecurity Through Fusion of Optimization With Deep Wavelet Neural Networks on Denial of Wallet Attack Detection in Serverless Computing by P. Renukadevi, Sibi Amaran, A. Vikram, T. Prabhakara Rao, Mohamad Khairi Ishak

“…Deep learning (DL) models show strong potential in detecting DoW attacks, where attackers disrupt services by exploiting system resources. …”
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Advanced artificial intelligence with federated learning framework for privacy-preserving cyberthreat detection in IoT-assisted sustainable smart cities by Mahmoud Ragab, Ehab Bahaudien Ashary, Bandar M. Alghamdi, Rania Aboalela, Naif Alsaadi, Louai A. Maghrabi, Khalid H. Allehaibi

“…Next, the stacked sparse auto-encoder (SSAE) classifier is employed for detecting cyberthreats. Eventually, the walrus optimization algorithm (WOA) is used for hyperparameter tuning to improve the parameters of the SSAE approach and achieve optimal performance. …”
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Optimal Fuzzy Deep Neural Networks-Based Plant Disease Detection and Classification on UAV-Based Remote Sensed Data by M. Pajany, S. Venkatraman, U. Sakthi, M. Sujatha, Mohamad Khairi Ishak

“…Besides, the OFDNN-PDDC technique utilizes a fuzzy restricted Boltzmann machine (FRBM) model to detect plant diseases. Finally, the hyperparameter selection of the OFDNN-PDDC technique is performed by the tent chaotic salp swarm algorithm (TCSSA) model. …”
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Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms by S. Jayanthi, Swathi Sowmya Bavirthi, P. Murali, K. Vijaya Kumar, Hend Khalid Alkahtani, Mohamad Khairi Ishak, Samih M. Mostafa

“…Various methods are presented for attack detection and prevention. However, artificial intelligence (AI)-based Machine learning (ML) and deep learning (DL) methodologies are highly effective for detecting DDoS attacks in cybersecurity. …”
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Internet of things driven object detection framework for consumer product monitoring using deep transfer learning and hippopotamus optimization by Amnah Alshahrani, Mukhtar Ghaleb, Hany Mahgoub, Achraf Ben Miled, Nojood O. Aljehane, Mohammed Yahya Alzahrani, Hasan Beyari, Sultan Alanazi

“…It utilizes advanced holography to create virtual projections in real-time environments. Object detection (OD) is the most significant and challenging problem in computer vision (CV). …”
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Deployable Deep Learning for Cross-Domain Plant Leaf Disease Detection via Ensemble Learning, Knowledge Distillation, and Quantization by Mohammad Junayed Hasan, Suvodeep Mazumdar, Sifat Momen

“…We propose a unified optimization approach integrating ensemble learning, knowledge distillation, and quantization across 24 deep learning architectures for edge-compatible disease detection. Strategic data augmentation and ADASYN-based balancing mitigate the severe 75:1 class imbalance, while systematic hyperparameter tuning optimizes model configurations. …”
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Nondestructive detection of sweet potato leaf curl virus using 3D laser imaging combined with deep learning by Yican Yang, Nuwan K. Wijewardane, Tyler J. Slonecki, Phillip A. Wadl, Sharon A. Andreason, Jingdao Chen, Lorin Harvey

“…Therefore, there is a need to establish alternative, point-of-care techniques for SPLCV detection. The goal of this study was to investigate the potential of using 3D point cloud data and deep learning to detect SPLCV. …”
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Machine Learning Framework for Early Detection of Chronic Kidney Disease Stages Using Optimized Estimated Glomerular Filtration Rate by Samit Kumar Ghosh, Namareq Widatalla, Ahsan H. Khandoker

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Talent identification in soccer using a one-class support vector machine by Jauhiainen S., Äyrämö S., Forsman H., Kauppi J-P.

“…Therefore, we approach talent identification as an anomaly detection problem. We trained a nonlinear one-class support vector machine (one-class SVM) on a dataset (N=951) collected from 14-year-old junior soccer players to detect potential future elite players. …”
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Efficient traffic sign recognition using YOLO for intelligent transport systems by Cong Wang, Bin Zheng, Chenxing Li

“…Three key innovations are introduced: (1) k-means++ clustering for anchor box optimization, achieving a 77.55% average IoU (vs. 75.95% for traditional k-means) to enhance small-target detection; (2) comprehensive comparative analysis of YOLOv5 variants (s/m/x), revealing precision-speed trade-offs (99.3–99.5% mAP@0.5 vs. 32–45 ms inference time) for deployment flexibility; and (3) systematic hyperparameter tuning to maximize robustness across diverse scenarios. …”
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Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations by Zohaib Khurshid, MRes, FDTFEd, FHEA, Maria Waqas, PhD, Shehzad Hasan, PhD, Shakeel Kazmi, PhD, Muhammad Faheemuddin, FCPS

“…Methods: An orthopantomography dataset has been used to train several models employing various object detection architectures, hyperparameters, and training techniques. …”
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