Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images by M. Eliazer, Sibi Amaran, K. Sreekumar, A. Vikram, Gyanendra Prasad Joshi, Woong Cho

“…Finally, the enhanced coati optimization algorithm (ECOA) method is implemented for the hyperparameter tuning of the KELM method, which results in a higher classification process. …”
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PatchCTG: A Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring by M. Jaleed Khan, Manu Vatish, Gabriel Davis Jones

“…However, it is primarily designed to rule out adverse outcomes rather than detect them, resulting in a high specificity (90.7%) but low sensitivity (18.2%) in identifying fetal distress. …”
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Sentiment analysis for deepfake X posts using novel transfer learning based word embedding and hybrid LGR approach by Madiha Khalid, Muhammad Faheem Mushtaq, Urooj Akram, Mejdl Safran, Sultan Alfarhood, Imran Ashraf

“…The ML models are fine-tuned with extensive hyperparameter tuning to enhance performance and efficiency. …”
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Deep convolutional fuzzy neural networks with stork optimization on chronic cardiovascular disease monitoring for pervasive healthcare services by Nuzaiha Mohamed, Reem Lafi Almutairi, Sayda Abdelrahim, Randa Alharbi, Fahad M. Alhomayani, Amer Alsulami, Salem Alkhalaf

“…The main goal of the DCFNN-SOCVDC method is to detect and classify CVD in the healthcare environment. …”
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Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO by Jian Tiong Lim, Achnaf Habibullah, Eddie Yin Kwee Ng

“…Subsequently, an unconventional method of detecting alarms by using conformal prediction were also proposed, resulting in a significantly reduced number of alarms. …”
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Implementing a deep learning model for defect classification in Thai Arabica green coffee beans by Sujitra Arwatchananukul, Dan Xu, Phasit Charoenkwan, Sai Aung Moon, Rattapon Saengrayap

“…This research developed a classification model based on a Convolutional Neural Network to detect 17 types of defects in green coffee beans. …”
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Breast cancer ultrasound image segmentation using improved 3DUnet++ by Saba Hesaraki, Abdul Sajid Mohammed, Mehrshad Eisaei, Ramin Mousa

“…In this research, we presented 3D structures based on UNet, ResUNet, and UNet++ for the automatic detection of cancer in ABUS volume to speed up examination while providing high detection sensitivity with low false positives (FPs). …”
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Design of Block-Scrambling-Based privacy protection mechanism in healthcare using fusion of transfer learning models with Hippopotamus optimization algorithm by Ghada Moh. Samir Elhessewi, Mohammed Yahya Alzahrani, Mohammad Alamgeer, Abdulbasit A. Darem, Da’ad Albalawneh, Mohammed Alqahtani, Mutasim Al Sadig, Sultan Alanazi

“…To optimize the CVAE model performance, the hippopotamus optimization (HO) model is utilized for hyperparameter tuning to ensure that the optimum hyperparameters are chosen for enhanced accuracy. …”
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Improved chaotic fruit fly optimization with elman neural network based data-driven approach on product reviews classification by Mohammed Alghamdi, Najla I. Al-shathry, Yahia Said, Hasan Beyari, Shaymaa Sorour, Khalid Nazim Abdul Sattar, Fouad Shoie Alallah, Abdulrhman Alshareef

“…The major goal of the ENLPPR-ICFFO method is to detect and classify the sentiments existing in product ratings. …”
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Mitigating malicious denial of wallet attack using attribute reduction with deep learning approach for serverless computing on next generation applications by Amal K. Alkhalifa, Mohammed Aljebreen, Rakan Alanazi, Nazir Ahmad, Sultan Alahmari, Othman Alrusaini, Ali Alqazzaz, Hassan Alkhiri

“…The primary purpose of the MMDoWA-ARDL approach is to propose a novel framework that effectively detects and mitigates malicious attacks in serverless environments using an advanced deep-learning model. …”
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Construction and validation of HBV-ACLF bacterial infection diagnosis model based on machine learning by Neng Wang, Shuai Tao, Liang Chen

“…Abstract Objective To develop and validate a novel diagnostic model for detecting bacterial infections in patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF) using advanced machine learning algorithms. …”
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Understanding Human Papillomavirus Vaccination Hesitancy in Japan Using Social Media: Content Analysis by Junyu Liu, Qian Niu, Momoko Nagai-Tanima, Tomoki Aoyama

“…Misinformation within opposed-stance tweets was detected using LLMs. Furthermore, we analyzed the relationship between attitudes toward HPV and COVID-19 vaccines through logic analysis. …”
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Machine learning-based identification of proteomic markers in colorectal cancer using UK Biobank data by Swarnima Kollampallath Radhakrishnan, Dipanwita Nath, Dominic Russ, Dominic Russ, Dominic Russ, Laura Bravo Merodio, Laura Bravo Merodio, Laura Bravo Merodio, Priyani Lad, Folakemi Kola Daisi, Animesh Acharjee, Animesh Acharjee, Animesh Acharjee

“…Incidence and mortality are predicted to rise globally during the next several decades. When detected early, colorectal cancer is treatable with surgery and medications. …”
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Classifying active and inactive states of growing rabbits from accelerometer data using machine learning algorithms by Mónica Mora, Lucile Riaboff, Ingrid David, Juan Pablo Sánchez, Miriam Piles

“…In this study, the objective was to automatically detect rabbit activity/inactivity states from accelerometer data in growing rabbits. …”
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Statistics and behavior of clinically significant extra-pulmonary vein atrial fibrillation sources: machine-learning-enhanced electrographic flow mapping in persistent atrial fibri... by Peter Ruppersberg, Steven Castellano, Philip Haeusser, Kostiantyn Ahapov, Melissa H. Kong, Stefan G. Spitzer, Stefan G. Spitzer, Georg Nölker, Andreas Rillig, Tamas Szili-Torok

“…Notably, the majority of significant sources were not continuously active; however, when these sources switched “ON,” the spatial variability of AF cycle lengths in the respective atrium decreased by more than 50%, suggesting an entraining effect.ConclusionsBy systematically optimizing the EGF Model's hyperparameters based on clinical outcomes, we reliably detect and target key AF sources that, when ablated, improve procedural success. …”
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Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal. by John H Tay, Arthur Kocher, Sebastian Duchene

“…Using microbial molecular sequence data sets and simulation experiments, we assess the impact of the tree prior and its hyperparameters on the accuracy of temporal signal detection. …”
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Ensemble EfficientNet: a novel technique for identification, classification and prediction of diabetic retinopathy by Nilesh Shelke, Rachna Somkunwar, Amit Pimpalkar, Sudhanshu Maurya, Sharda Chhabria

“…Diabetic retinopathy (D.R.) is a leading cause of blindness worldwide, making early detection and accurate classification essential for preserving vision. …”
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Adaptive malware identification via integrated SimCLR and GRU networks by Faisal S. Alsubaei, Abdulwahab Ali Almazroi, Walid Said Atwa, Abdulaleem Ali Almazroi, Nasir Ayub, N. Z. Jhanjhi

“…Abstract Malware has become a big issue for digital infrastructure with the growing complexity and frequency of intrusions; it usually avoids conventional detection systems via obfuscation and dynamic behaviour patterns. …”
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Automatic melanoma and non-melanoma skin cancer diagnosis using advanced adaptive fine-tuned convolution neural networks by Muhammad Amir Khan, Tehseen Mazhar, Muhammad Danish Ali, Umar Farooq Khattak, Tariq Shahzad, Mamoon M. Saeed, Habib Hamam

“…Abstract Skin Cancer is an extensive and possibly dangerous disorder that requires early detection for effective treatment. Add specific global statistics on skin cancer prevalence and mortality to emphasize the importance of early detection. …”
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Depression Classification From Tweets Using Small Deep Transfer Learning Language Models by Muhammad Rizwan, Muhammad Faheem Mushtaq, Urooj Akram, Arif Mehmood, Imran Ashraf, Benjamin Sahelices

“…Depression detection from social media texts such as Tweets or Facebook comments could be very beneficial as early detection of depression may even avoid extreme consequences of long-term depression i.e. suicide. …”
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