Automated Detection of Poor-Quality Scintigraphic Images Using Machine Learning by Anil K. Pandey, Akshima Sharma, Param D. Sharma, Chandra S. Bal, Rakesh Kumar

“…Objective In the present study, we have used machine learning algorithm to accomplish the task of automated detection of poor-quality scintigraphic images. We have validated the accuracy of our machine learning algorithm on 99mTc-methyl diphosphonate (99mTc-MDP) bone scan images. …”
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METAHEURISTIC-AI ENHANCED CUSTOM DEEP LEARNING NETWORK OPTIMIZED WITH SAND CAT SWARM ALGORITHM FOR ORAL CANCER DIAGNOSIS by Vinod Kumar Venkatesan, V Sujatha, Audithan Sivaraman, S Durga Devi, Praveen SR Konduri

“…This optimized approach significantly improves the SCSO-CNN system’s performances that indicates the system potential for reliable and early detection of oral cancer in clinical settings.…”
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Improving CNN predictive accuracy in COVID-19 health analytics by Tae-hoon Kim, Asadi Srinivasulu, Ravikumar Chinthaginjala, Dhakshayani J, Xin Zhao, Safia Obaidur Rab, Sivarama Prasad Tera

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Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection by Stepanić Pavle, Dučić Nedeljko, Vidaković Jelena, Baralić Jelena, Popović Marko

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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…In this paper, a method is presented that uses meta-heuristic algorithms such as genetic algorithm, particle swarm optimization, artificial bee colony and gray wolf to find the optimal hyperparameters for the deep learning network and the intrusion detection system is created based on these hyperparameters. …”
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Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks by Simon Pena Pereira, Azarakhsh Rafiee, Stef Lhermitte

“…This study aims to implement a semantic segmentation model that detects PV systems in aerial imagery to explore the impact of area-specific characteristics in the training data and CNN hyperparameters on the performance of a CNN. …”
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Ulnar variance detection from radiographic images using deep learning by Sahar Nooh, Abdelrahim Koura, Mohammed Kayed

“…In this paper, a deep learning-based methodology is used to automatically detect ulnar variance from radiographic images. Advanced Convolutional Neural Networks are exploited instead of traditional manual methods. …”
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Insurance claims estimation and fraud detection with optimized deep learning techniques by P. Anand Kumar, S. Sountharrajan

“…To this extent, it explores the deep learning models like VGG 16 & 19, ResNet 50, and a custom 12 & 15-layer Convolutional Neural Network for accurate estimation of insurance claims and detection of fraud. The proposed work enhanced with Enhanced Hippopotamus Optimization Algorithm (EHOA) combined with a custom 12-layer CNN to optimize the hyperparameters and enhance the performance of the model. …”
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Optimized Machine Learning for the Early Detection of Polycystic Ovary Syndrome in Women by Bharti Panjwani, Jyoti Yadav, Vijay Mohan, Neha Agarwal, Saurabh Agarwal

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DRCO: Dense-Label Refinement and Cross Optimization for Semi-Supervised Object Detection by Yunlong Qin, Yanjun Li, Feifan Ji, Yan Liu, Yu Wang, Ji Xiang

“…In semi-supervised object detection (SSOD), the methods based on dense pseudo-labeling bypass complex post-processing while maintaining competitive performance compared to the methods based on sparse pseudo-labeling. …”
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Enhanced Credit Card Fraud Detection Using Deep Hybrid CLST Model by Madiha Jabeen, Shabana Ramzan, Ali Raza, Norma Latif Fitriyani, Muhammad Syafrudin, Seung Won Lee

“…In the case of hyperparameter tuning, the detection rate is greatly enhanced. …”
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Gripping Success Metric for Robotic Fruit Harvesting by Dasom Seo, Il-Seok Oh

“…In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. …”
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Fault Detection and Classification for Photovoltaic Panel System Using Machine Learning Techniques by Ghalia Nassreddine, Amal El Arid, Mohamad Nassereddine, Obada Al Khatib

“…The results indicated that ensemble methods, particularly XGBoost, excelled in detecting and classifying faults in PV systems, achieving a 99% accuracy rate after hyperparameter adjustments. …”
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Performance Evaluation of Neighbors-Based Learning Methods for Network Intrusion Detection System by Ngoc Thien Nguyen

“…Three classification algorithms are implemented, including k-Nearest Neighbors (k-NN), Radius Nearest Classifier (RNC), and Nearest Centroid Classifier (NCC), with the goal of evaluating their effectiveness in detecting cyber-attacks. Experimental results show that after hyperparameter tuning, the RNC model achieves the highest performance with an accuracy of 83.59%, demonstrating strong potential for building efficient and reliable intrusion detection systems. …”
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Application of VGG16 in Automated Detection of Bone Fractures in X-Ray Images by Resky Adhyaksa, Bedy Purnama

“…The study's findings underscore the potential of VGG16 in improving diagnostic accuracy and reliability in medical imaging, providing a robust tool for fracture detection. Future research should continue exploring hyperparameter optimization to further enhance model performance while balancing computational efficiency.…”
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PELM: A Deep Learning Model for Early Detection of Pneumonia in Chest Radiography by Erdem Yanar, Fırat Hardalaç, Kubilay Ayturan

“…This study introduces PELM (Pneumonia Ensemble Learning Model), a novel deep learning framework for automated pneumonia detection using chest X-ray (CXR) images. The model integrates four high-performing architectures—InceptionV3, VGG16, ResNet50, and Vision Transformer (ViT)—via feature-level concatenation to exploit complementary feature representations. …”
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Hybrid of DSR-GAN and CNN for Alzheimer disease detection based on MRI images by Sarah Oraby, Ahmed Emran, Basel El-Saghir, Saeed Mohsen

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Ensemble based high performance deep learning models for fake news detection by Mohammed E.Almandouh, Mohammed F. Alrahmawy, Mohamed Eisa, Mohamed Elhoseny, A. S. Tolba

“…It significantly aids the global fight against false information by setting the stage for future research to expand fake news detection to multiple languages.…”
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A New Hybrid ConvViT Model for Dangerous Farm Insect Detection by Anil Utku, Mahmut Kaya, Yavuz Canbay

“…This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. …”
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Robust SAR Change Detection Using Hierarchical Clustering With Adaptive Parameter Tuning by Ahidjo Abdoulaye, Alejandro C. Frery, Mingyang Ma, Shaohui Mei

“…The method automatically identifies clusters of varying densities while filtering out noise, ensuring a more precise change detection process. A new hyperparameter optimization strategy is introduced to enhance clustering performance by tuning key parameters based on the silhouette index. …”
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