Applied Linguistics. Reflections on an evolving discipline with a focus on publications in the journal TEANGA, The Journal of the Irish Association for Applied Linguistics, from 20... by Jennifer Bruen

“…Limited evidence was found of some of the more niche and emerging areas in the broader field of applied linguistics such as social justice, raciolinguistics, artificial intelligence, ethics in linguistics research and multilingual sustainability. …”
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Therapeutic Effect of Intense Pulsed Light (IPL) Combined with Meibomian Gland Expression (MGX) on Meibomian Gland Dysfunction (MGD) by Shanshan Wei, Xiaotong Ren, Yuexin Wang, Yilin Chou, Xuemin Li

“…Additionally, an artificial intelligence automated software program was applied in our study for corneal nerve analysis. …”
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Securing Urban Landscape: Cybersecurity Mechanisms for Resilient Smart Cities by Qiang Lyu, Sujuan Liu, Zhouyuan Shang

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A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats by Hazem M. Kotb, Tarek Gaber, Salem AlJanah, Hossam M. Zawbaa, Mohammed Alkhathami

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A Multi-Granularity Features Representation and Dimensionality Reduction Network for Website Fingerprinting by Yaojun Ding, Bingxuan Hu

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Efficiency and safety of automated label cleaning on multimodal retinal images by Tian Lin, Meng Wang, Aidi Lin, Xiaoting Mai, Huiyu Liang, Yih-Chung Tham, Haoyu Chen

“…Abstract Label noise is a common and important issue that would affect the model’s performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. …”
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Resource-based view theory and its applications in supply chain management: A systematic literature review by Robert Agwot Komakech, Thomas Ogoro Ombati, Reuben Wambua Kikwatha, Michael Githii Wainaina

“…The findings revealed that emerging trends like integrating advanced technologies like Blockchain, Artificial Intelligence and the Internet of Things are identified as strategic resources that redefine competitive landscapes by enhancing transparency, responsiveness and responsiveness within supply chains. …”
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Level-Up Learning with CIGLE Framework: Enhancing Learning Through Interactive, Game-Based Collaboration for Effective Problem Solving by Ismat Zahra, Mai Neo, Soon Hin Hew

“…The shift to technology-oriented learning environments including Digital games, Artificial Intelligence (AI), Augmented Reality (AR)/Virtual Reality (VR) tools necessitated innovative solutions to maintain and enhance educational quality. …”
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Model Selection from Multiple Model Families in Species Distribution Modeling Using Minimum Message Length by Zihao Wen, David L. Dowe

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Tailored Channel Pruning: Achieve Targeted Model Complexity Through Adaptive Sparsity Regularization by Suwoong Lee, Yunho Jeon, Seungjae Lee, Junmo Kim

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Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space by Bojun Liu, Jordan G. Boysen, Ilona Christy Unarta, Xuefeng Du, Yixuan Li, Xuhui Huang

“…Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). TS-DAR offers an end-to-end pipeline that can simultaneously detect all transition states between multiple free minima from MD simulations using the regularized hyperspherical embeddings in latent space. …”
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From data to diagnosis: leveraging deep learning in IoT-based healthcare by Miracle A. Atianashie, Chukwuma Chinaza Adaobi

“…Ethical and legal considerations are also critical as the deployment of artificial intelligence in healthcare necessitates careful consideration of patient consent and data security. …”
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Non-Volatile Reconfigurable Optical Digital Diffractive Neural Network Based on Phase Change Material by Qiaomu Hu, Jingyu Zhao, Chu Wu, Rui Zeng, Xiaobing Zhou, Shuang Zheng, Minming Zhang

“…Thanks to its reconfigurability and low energy supply, the digital optical diffractive neural network holds great potential to facilitate a programmable and low-power-consumption photonic processor for optical-artificial-intelligence.…”
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Hierarchical proxy consensus optimization for IoV based on blockchain and trust value by Baoqin ZHAI, Jian WANG, Lei HAN, Jiqiang LIU, Jiahao HE, Tianhao LIU

“…With the rapid development of Internet of vehicles, 5G and artificial intelligence technologies, intelligent transportation has become the development trend of transportation technology.As a vehicle-vehicle and vehicle-road information interaction platform, the Internet of vehicles is the basic support platform for intelligent traffic information sharing and processing.At the same time, the security of Internet of vehicles has attracted much attention, especially data security which may cause user privacy leakage.The blockchain technology has become a solution, but it still faces new challenges in efficiency, security and other aspects.With the increase of vehicle nodes and information, how to efficiently achieve information consensus in high-speed vehicle moving environment has become a key problem.Then a bottom-up RSU (road side unit) chain consensus protocol was proposed based on blockchain and trust value.Several typical consensus structures were compared, and bottom-up two-layer consensus structure was adopted according to the actual scenarios of the Internet of vehicles.Moreover, a group leader node election algorithm was proposed which is based on node participation, work completion and message value.The system security was ensured by assigning trust value to each vehicle.Following the consensus structure and algorithm work mentioned above, the specific process of the protocol was comprehensively described, which was divided into six steps: region division, group leader node selection, local consensus, leader primary node selection, global consensus, and intra-domain broadcast.Then the experiments were analyzed from four aspects: security, communication complexity, consensus algorithm delay and fault tolerance rate.Experiments showed that, compared with other schemes, the proposed protocol can effectively reduce communication complexity and shorten consensus delay under the condition of resisting conspiracy attack, witch attack and other attacks.On the premise of security, the protocol improves fault tolerance rate and enables more nodes to participate in information sharing to satisfy the requirements of Internet of vehicles scenarios.…”
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Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network by Hindriyanto Purnomo, Tad Gonsalves, Evangs Mailoa, Fian Julio Santoso, Muhammad Rizky Pribadi

“…Deep learning is an artificial intelligence technique that has been used for various tasks. …”
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Detection of Diabetic Retinopathy Using a Multi-Decision Inception-ResNet-Blended Hybrid Model by Santosh Kumar Henge, Nikhil Reddy Viraati, Musaed Alhussein, Ajay Shriram Kushwaha, Khursheed Aurangzeb, Ravleen Singh

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Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects by Naveed Ur Rehman Ahmed, Afzal Badshah, Hanan Adeel, Ayesha Tajammul, Ali Daud, Tariq Alsahfi

“…In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. …”
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The Effect of Hypertension Duration and the Age of Onset on CV Risk Factors Expression in Perimenopausal Women by Ljiljana Trtica Majnarić, Ivo Martinović, Šefket Šabanović, Stjepan Rudan, František Babič, Thomas Wittlinger

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Hybrid quantum neural networks show strongly reduced need for free parameters in entity matching by Lukas Bischof, Stefan Teodoropol, Rudolf M. Füchslin, Kurt Stockinger

“…Hence, entity matching, i.e. the process of linking data about a given entity gathered from multiple data sets, is a major problem in artificial intelligence with applications in science and industry. …”
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Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia by Nahla F. Omran, Sara F. Abd-el Ghany, Hager Saleh, Abdelmgeid A. Ali, Abdu Gumaei, Mabrook Al-Rakhami

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