Developing an Automatic Asbestos Detection Method Based on a Convolutional Neural Network and Support Vector Machine by Tomohito Matsuo, Mitsuteru Takimoto, Suzuyo Tanaka, Ayami Futamura, Hikari Shimadera, Akira Kondo

“…The findings of this study provide valuable insights into effectively detecting asbestos fibers.…”
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Enhancing pancreatic cancer detection in CT images through secretary wolf bird optimization and deep learning by Sandhya Mekala, Phani Kumar S

“…Finally, pancreatic tumor detection is performed by SeWBO_Efficient DenseNet. …”
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ADCGNet: Attention-based dual channel Gabor network towards efficient detection and classification of electrocardiogram images by Joseph Roger Arhin, Xiaoling Zhang, Kenneth Coker, Isaac Osei Agyemang, Wisdom Kwame Attipoe, Francis Sam, Isaac Adjei-Mensah, Emmanuel Agyei

“…Specifically, ADCGNet achieved an accuracy of 99.17%, 98.98% in precision, a recall of 98.87%, an F1-score of 98.82% and AUC, 98.75% with optimal hyperparameters. Further, a GRAD-CAM visualization of activated areas on the test samples gives graphical insight into the performance of ADCGNet. …”
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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. …”
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Wavelet-Based ensembled intelligent technique for a better quality of fault detection and classification in AC microgrids by Nityananda Giri, Pravati Nayak, Ranjan Kumar Mallick, Sairam Mishra, Aymen Flah, Habib Kraiem, Lukas Prokop, Mohammad Kanan

“…This research introduces a novel intelligent protection technique for microgrids, leveraging Discrete Wavelet Transform (DWT) and an Ensemble Bagged Decision Tree (EBDT) classifier for precise fault detection and classification across various scenarios. …”
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Detection and classification of hypertensive retinopathy based on retinal image analysis using a deep learning approach by Bambang Krismono Triwijoyo, Ahmat Adil, Muhammad Zulfikri

“…These gap factors make the research on hypertensive retinopathy urgent since it requires an early detection model to improve treatment accuracy and prevent heart attacks and strokes before they happen. …”
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Double-layer stacking optimization for electricity theft detection considering data incompleteness and intra-class imbalance by Leijiao Ge, Jingjing Li, Tianshuo Du, Luyang Hou

“…Electricity theft detection is crucial for reducing non-technical losses in electric power enterprises and ensuring fairness in electric power transactions. …”
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Unmanned Aerial Vehicle-Based Hyperspectral Imaging for Potato Virus Y Detection: Machine Learning Insights by Siddat B. Nesar, Paul W. Nugent, Nina K. Zidack, Bradley M. Whitaker

“…In this proof-of-concept study, we propose the use of unmanned aerial vehicles (UAVs) integrated with hyperspectral cameras, including a downwelling irradiance sensor, to detect the PVY in commercial growers’ fields. We used a 400–1000 nm visible and near-infrared (Vis-NIR) hyperspectral camera and trained several standard machine learning and deep learning models with optimized hyperparameters on a curated dataset. …”
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A deep dive into artificial intelligence with enhanced optimization-based security breach detection in internet of health things enabled smart city environment by S Jayanthi, Sodagudi Suhasini, N. Sharmili, E. Laxmi Lydia, V. Shwetha, Bibhuti Bhusan Dash, Mrinal Bachute

“…So, improving an intrusion detection system (IDS) for attack identification and detection using the IoHT method is fundamentally necessary. …”
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Voice analysis and deep learning for detecting mental disorders in pregnant women: a cross-sectional study by Hikaru Ooba, Jota Maki, Hisashi Masuyama

“…The EfficientFormer V2-L model, pretrained on ImageNet, was employed with transfer learning for classification. The hyperparameters were optimized using Optuna, and an ensemble learning approach was used for the final predictions. …”
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Leveraging explainable artificial intelligence for early detection and mitigation of cyber threat in large-scale network environments by G. Nalinipriya, S. Rama Sree, K. Radhika, E. Laxmi Lydia, Faten Khalid Karim, Mohamad Khairi Ishak, Samih M. Mostafa

“…Machine learning (ML) plays a crucial role in cybersecurity by making malware detection more scalable, efficient, and automated, reducing reliance on conventional human intervention methods. …”
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An exploratory analysis of longitudinal artificial intelligence for cognitive fatigue detection using neurophysiological based biosignal data by Sameer Nooh, Mahmoud Ragab, Rania Aboalela, Abdullah AL-Malaise AL-Ghamdi, Omar A. Abdulkader, Ghadah Alghamdi

“…The graph convolutional autoencoder (GCA) classifier is employed to classify cognitive fatigue detection. Finally, the multi-objective hippopotamus optimization (MOHO) method is utilized for parameter tuning, optimizing the model’s hyperparameters to enhance overall detection accuracy. …”
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DESIGN OF AN IMPROVED MODEL FOR CARDIOVASCULAR DISEASE DETECTION USING DEEP CANONICAL CORRELATION ANALYSIS AND BIOINSPIRED OPTIMIZATION by Prakash Chandra Sahoo, Binod Kumar Pattanayak, Rajani Kanta Mohanty, Ayasa Kanta Mohanty

“…Cardio Vascular Diseases (CVDs) are one of the most prevalent causes of death in the world and require an appropriate early detection method that could satisfactorily integrate diversified patient data available in today's healthcare. …”
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Comparing Models and Performance Metrics for Lung Cancer Prediction using Machine Learning Approaches. by Ruqiya, Noman Khan, Saira Khan

“…This research focuses on utilizing Machine-Learning (ML) to detect early-stage lung cancer, aiming to address these major public health concerns by using ML to help develop more efficient early detection techniques. …”
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An ultrasonic-AI hybrid approach for predicting void defects in concrete-filled steel tubes via enhanced XGBoost with Bayesian optimization by Shuai Wan, Shipan Li, Zheng Chen, Yunchao Tang

“…Based on 3600 ultrasonic measurement samples, an Extreme Gradient Boosting (XGBoost) model was enhanced through oversampling and hyperparameter optimization via Bayesian optimization (BO-XGBoost). …”
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Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images by V. Padmavathi, Kavitha Ganesan

“…The framework employs the Grey Wolf Optimizer (GWO) for hyperparameter tuning and Particle Swarm Optimization (PSO) for feature selection, leveraging the ViT model’s self-attention mechanism to extract global and local image features crucial for severity classification. …”
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Development of an efficient method for object detection and localization in 3D space using RGBD cameras for autonomous systems by Nataliya Boyko

“…The work presents an efficient algorithm for object detection, orientation estimation, and isometric positioning in 3D space using RGBD camera data. …”
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A Machine-Learning-Based Approach for the Detection and Mitigation of Distributed Denial-of-Service Attacks in Internet of Things Environments by Sebastián Berríos, Sebastián Garcia, Pamela Hermosilla, Héctor Allende-Cid

“…Additionally, a web-based dashboard was developed to visualize detection outcomes, facilitating real-time monitoring. …”
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A Hybrid Machine Learning Framework for Early Fault Detection in Power Transformers Using PSO and DMO Algorithms by Mohammed Alenezi, Fatih Anayi, Michael Packianather, Mokhtar Shouran

“…The early detection of faults in power transformers is crucial for ensuring operational reliability and minimizing system disruptions. …”
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An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments by Mimouna Abdullah Alkhonaini

“…This paper presents a Fox Optimizer-Based Feature Selection with Deep Learning for Securing Cyberattack Detection (FOFSDL-SCD) model. This paper aims to analyze cybersecurity-driven approaches for enhancing IoT networks’ resilience and threat detection capabilities using advanced techniques. …”
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