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<xml><records><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed Yamni</style></author><author><style face="normal" font="default" size="100%">Achraf Daoui</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Osama Alfarraj</style></author><author><style face="normal" font="default" size="100%">Ahmed El-Latif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Hardware-Accelerated Approach to Chaotic Image Encryption: LTB Map and FPGA Implementation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3432008</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mengfan Li</style></author><author><style face="normal" font="default" size="100%">Jian Zhang</style></author><author><style face="normal" font="default" size="100%">Roohallah Alizadehsani</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Multi-Channel Advertising Budget Allocation Using Reinforcement Learning and an Improved Differential Evolution Algorithm</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3429359</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Achraf Daoui</style></author><author><style face="normal" font="default" size="100%">Mohamed Yamni</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Osama Alfarraj</style></author><author><style face="normal" font="default" size="100%">Ahmed El-Latif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A New Chaotic Memristor-Based Cryptosystem for Secure Bio-Signal Transmission on Low-Cost Hardware</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3408136</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Liu Yang</style></author><author><style face="normal" font="default" size="100%">Jiang Guofan</style></author><author><style face="normal" font="default" size="100%">Zhang Yixin</style></author><author><style face="normal" font="default" size="100%">Wei Qianze</style></author><author><style face="normal" font="default" size="100%">Zhang Jian</style></author><author><style face="normal" font="default" size="100%">Roohallah Alizadehsani</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Reinforcement Learning Approach Combined With Scope Loss Function for Crime Prediction on Twitter (X)</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3473296</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ismail Lamaakal</style></author><author><style face="normal" font="default" size="100%">Ibrahim Ouahbi</style></author><author><style face="normal" font="default" size="100%">Khalid Makkaoui</style></author><author><style face="normal" font="default" size="100%">Yassine Maleh</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Fahad Alblehai</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A TinyDL Model for Gesture-Based Air Handwriting Arabic Numbers and Simple Arabic Letters Recognition</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3406631</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahmood Sotoodeh</style></author><author><style face="normal" font="default" size="100%">Ali Kohan</style></author><author><style face="normal" font="default" size="100%">Mohamad Roshanzamir</style></author><author><style face="normal" font="default" size="100%">Senthil Jagatheesaperumal</style></author><author><style face="normal" font="default" size="100%">Mohammad Qazani</style></author><author><style face="normal" font="default" size="100%">Javad Joloudari</style></author><author><style face="normal" font="default" size="100%">Roohallah Alizadehsani</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Comparison Between State-of-the-Art Color Local Binary Pattern-Based Descriptors for Image Retrieval</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3486754</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Soner Kiziloluk</style></author><author><style face="normal" font="default" size="100%">Eser Sert</style></author><author><style face="normal" font="default" size="100%">Mohamed Hammad</style></author><author><style face="normal" font="default" size="100%">Ryszard Tadeusiewicz</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EO-CNN: Equilibrium Optimization-Based hyperparameter tuning for enhanced pneumonia and COVID-19 detection using AlexNet and DarkNet19</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Biocybernetics and Biomedical Engineering</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0208521624000470</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.bbe.2024.06.006</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">44</style></volume><abstract><style face="normal" font="default" size="100%">Convolutional neural networks (CNN) have been increasingly popular in image categorization in recent years. Hyperparameter optimization is a critical stage in enhancing the effectiveness of CNNs and achieving better results. Properly tuning hyperparameters allows the model to exhibit improved performance and facilitates faster learning. Misconfigured hyperparameters can prolong the training time or lead to the model not learning at all. Manually tuning hyperparameters is a time-consuming and challenging process. Automatically adjusting hyperparameters helps save time and resources. This study aims to propose an approach that shows higher classification performance than unoptimized convolutional neural network models, even at low epoch values, by automatically optimizing the hyperparameters of AlexNet and DarkNet19 with equilibrium optimization, the newest metaheuristic algorithm. In this respect, the proposed approach optimizes the number and size of filters in the first five convolutional layers in AlexNet and DarkNet19 using an equilibrium optimization algorithm. To evaluate the efficacy of the suggested method, experimental analyses were conducted on the pneumonia and COVID-19 datasets. An important advantage of this approach is its ability to accurately classify medical images. The testing process suggests that utilizing the proposed approach to optimize hyperparameters for AlexNet and DarkNet19 led to a 7% and 4.07% improvement, respectively, in image classification accuracy compared to non-optimized versions of the same networks. Furthermore, the approach displayed superior classification performance even in a few epochs compared to AlexNet, ShuffleNet, DarkNet19, GoogleNet, MobileNet-V2, VGG-16, VGG-19, ResNet18, and Inceptionv3. As a result, automatic tuning of the hyperparameters of AlexNet and DarkNet-19 with EO enabled the performance of these two models to increase significantly.</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Farhad Banoori</style></author><author><style face="normal" font="default" size="100%">Nouman Ijaz</style></author><author><style face="normal" font="default" size="100%">Jinglun Shi</style></author><author><style face="normal" font="default" size="100%">Khalid Khan</style></author><author><style face="normal" font="default" size="100%">Xiongying Liu</style></author><author><style face="normal" font="default" size="100%">Sadique Ahmad</style></author><author><style face="normal" font="default" size="100%">Allam Prakash</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Mohamed Hammad</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Few-Shot Bioacoustics Event Detection Using Transductive Inference With Data Augmentation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Sensors Letters</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/LSENS.2024.3363021</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">8</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mahdieh Moghadam</style></author><author><style face="normal" font="default" size="100%">Zabih Moghadam</style></author><author><style face="normal" font="default" size="100%">Mohammad Qazani</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Roohallah Alizadehsani</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3450970</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed Fartitchou</style></author><author><style face="normal" font="default" size="100%">Ismail Lamaakal</style></author><author><style face="normal" font="default" size="100%">Yassine Maleh</style></author><author><style face="normal" font="default" size="100%">Khalid Makkaoui</style></author><author><style face="normal" font="default" size="100%">Zakaria Allali</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Fahad Alblehai</style></author><author><style face="normal" font="default" size="100%">Ahmed El-Latif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">IOTASDN: IOTA 2.0 Smart Contracts for Securing Software-Defined Networking Ecosystem</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Sensors</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/1424-8220/24/17/5716</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.3390/s24175716</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">24</style></volume><abstract><style face="normal" font="default" size="100%">Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Furqan Alam</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Ahmed Almaghthawi</style></author><author><style face="normal" font="default" size="100%">Mohammad Qazani</style></author><author><style face="normal" font="default" size="100%">Sanat Mohanty</style></author><author><style face="normal" font="default" size="100%">and Alizadehsani</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">NeuroHAR: A Neuroevolutionary Method for Human Activity Recognition (HAR) for Health Monitoring</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3441108</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Achraf Daoui</style></author><author><style face="normal" font="default" size="100%">Mohamed Yamni</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Fahad Alblehai</style></author><author><style face="normal" font="default" size="100%">Ahmed El-Latif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Integer Hahn Transform for Real-Time Lossless Image Authentication in IoT: A 32-Bit Microcontroller Implementation</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3457690</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mohamed Yamni</style></author><author><style face="normal" font="default" size="100%">Achraf Daoui</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Osama Alfarraj</style></author><author><style face="normal" font="default" size="100%">Ahmed El-Latif</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel Integer Reversible Charlier Transform for Image Reversible Data Hiding Application</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3427357</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Pradipta Sasmal</style></author><author><style face="normal" font="default" size="100%">Vanshali Sharma</style></author><author><style face="normal" font="default" size="100%">Allam Prakash</style></author><author><style face="normal" font="default" size="100%">M.K. Bhuyan</style></author><author><style face="normal" font="default" size="100%">Kiran Patro</style></author><author><style face="normal" font="default" size="100%">Nagwan Samee</style></author><author><style face="normal" font="default" size="100%">Hayam Alamro</style></author><author><style face="normal" font="default" size="100%">Yuji Iwahori</style></author><author><style face="normal" font="default" size="100%">Ryszard Tadeusiewicz</style></author><author><style face="normal" font="default" size="100%">U. Rajendra Acharya</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Information Sciences</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.sciencedirect.com/science/article/pii/S0020025523016195</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">https://doi.org/10.1016/j.ins.2023.120033</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">658</style></volume><abstract><style face="normal" font="default" size="100%">Early and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabeled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Ghazal Rahimzadeh</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Shady Mohamed</style></author><author><style face="normal" font="default" size="100%">Kathleen Lacy</style></author><author><style face="normal" font="default" size="100%">Darius Nahavandi</style></author><author><style face="normal" font="default" size="100%">Ryszard Tadeusiewicz</style></author><author><style face="normal" font="default" size="100%">Houshyar Asadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Significance of Physiological Signal Thresholds in the Early Diagnosis of Simulator Sickness</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">IEEE Access</style></secondary-title><electronic-resource-num><style face="normal" font="default" size="100%">10.1109/ACCESS.2024.3467920</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Bibcite" version="8.x">Drupal-Bibcite</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hamidreza Ashayeri</style></author><author><style face="normal" font="default" size="100%">Navid Sobhi</style></author><author><style face="normal" font="default" size="100%">Paweł Pławiak</style></author><author><style face="normal" font="default" size="100%">Siamak Pedrammehr</style></author><author><style face="normal" font="default" size="100%">Roohallah Alizadehsani</style></author><author><style face="normal" font="default" size="100%">Ali Jafarizadeh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transfer Learning in Cancer Genetics, Mutation Detection, Gene Expression Analysis, and Syndrome Recognition</style></title></titles><keywords/><dates><year><style face="normal" font="default" size="100%">2024</style></year></dates><secondary-title><style face="normal" font="default" size="100%">Cancers</style></secondary-title><urls><style face="normal" font="default" size="100%">https://www.mdpi.com/2072-6694/16/11/2138</style></urls><electronic-resource-num><style face="normal" font="default" size="100%">10.3390/cancers16112138</style></electronic-resource-num><volume><style face="normal" font="default" size="100%">16</style></volume><abstract><style face="normal" font="default" size="100%">Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), has revolutionized medical research, facilitating advancements in drug discovery and cancer diagnosis. ML identifies patterns in data, while DL employs neural networks for intricate processing. Predictive modeling challenges, such as data labeling, are addressed by transfer learning (TL), leveraging pre-existing models for faster training. TL shows potential in genetic research, improving tasks like gene expression analysis, mutation detection, genetic syndrome recognition, and genotype–phenotype association. This review explores the role of TL in overcoming challenges in mutation detection, genetic syndrome detection, gene expression, or phenotype–genotype association. TL has shown effectiveness in various aspects of genetic research. TL enhances the accuracy and efficiency of mutation detection, aiding in the identification of genetic abnormalities. TL can improve the diagnostic accuracy of syndrome-related genetic patterns. Moreover, TL plays a crucial role in gene expression analysis in order to accurately predict gene expression levels and their interactions. Additionally, TL enhances phenotype–genotype association studies by leveraging pre-trained models. In conclusion, TL enhances AI efficiency by improving mutation prediction, gene expression analysis, and genetic syndrome detection. Future studies should focus on increasing domain similarities, expanding databases, and incorporating clinical data for better predictions.</style></abstract><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>
