MFHG-DDI: An Enhanced Hybrid Graph Method Leveraging Multiple Features for Predicting Drug–Drug Interactions by Xiang Li, Xiangmin Ji, Chengzhen Xu, Jie Hou, Xiaoyu Zhao, Guodong Peng

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The Possible Role of Plant-Based Bars Consumption in CKD Geriatric Patients by Giulia Marrone, Silvia Urciuoli, Manuela Di Lauro, Kevin Cornali, Claudia Masci, Manfredi Tesauro, Pamela Vignolini, Annalisa Noce

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Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture by Gopalakrishnan Nagaraj, Dakshinamurthy Sungeetha, Mohit Tiwari, Vandana Ahuja, Ajit Kumar Varma, Pankaj Agarwal

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Hybrid Dual-Input Model for Respiratory Sound Classification With Mel Spectrogram and Waveform by Fan Wang, Jiacheng Gao, Ying Wang, Guoheng Huang, Xiaochen Yuan

“…Respiratory sounds serve as early indicators of lung diseases. The development of computer-aided classification systems has become a key enabler for timely diagnosis and treatment. …”
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FetalMovNet: A Novel Deep Learning Model Based on Attention Mechanism for Fetal Movement Classification in US by Musa Turkan, Emre Dandil, Furkan Erturk Urfali, Mehmet Korkmaz

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Code Obfuscation: A Comprehensive Approach to Detection, Classification, and Ethical Challenges by Tomer Raitsis, Yossi Elgazari, Guy E. Toibin, Yotam Lurie, Shlomo Mark, Oded Margalit

“…Code obfuscation has become an essential practice in modern software development, designed to make source or machine code challenging for both humans and computers to comprehend. It plays a crucial role in cybersecurity by protecting intellectual property, safeguarding trade secrets, and preventing unauthorized access or reverse engineering. …”
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A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence by Emrah Aslan, Yildirim Ozupak, Feyyaz Alpsalaz, Zakaria M. S. Elbarbary

“…The study presents a detailed comparison of different classification algorithms and evaluates their performance using metrics such as accuracy, recall, and F1 score. …”
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Prediction of mung bean production using machine learning algorithms by Azanu Mirolgn Mequanenit, Aleka Melese Ayalew, Ayodeji Olalekan Salau, Eyerusalem Alebachew Nibret, Million Meshesha

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Real-time anti-sleep alert algorithm to prevent road accidents to ensure road safety by Abhishek Kumar Pathak, Ankit Kumar Singh, Pankaj Kumar, Vimal Bhatia, Vimal Bhatia, Vimal Bhatia, Ondrej Krejcar, Ondrej Krejcar

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Effects of Changing Consumer Demand on Distributed Generation and Grid Optimization by Meisam Mahdavi, Amir Bagheri, Alireza Soleimani, Anna Pinnarelli, Francisco Jurado, Augustine Awaafo

“…Considering load variability in DG placement within reconfigurable networks adds considerable computational complexity, resulting in extended processing times. …”
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A Hybrid Method for Implicit Intention Inference Based on Punished-Weighted Naïve Bayes by Zheng Gao, Shiqian Wu, Zhonghua Wan, Sos Agaian

“…An intention inference engine is developed by combining the human prior knowledge, and the data-driven class-specific attribute weighted naïve Bayes model. Computer simulations: (i) verify the contribution of each critical component of the proposed model, (ii) evaluate the inference accuracy of the proposed model, and (iii) show that the proposed method is superior to state-of-the-art intention inference methods in terms of accuracy.…”
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Butterfly-like phase shift: a novel gauge for critical coupling of add–drop resonator by Mahdi Bahadoran, Preecha Yupapin

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Improving synergistic drug combination prediction with signature-based gene expression features in oncology by Mozhgan Mozaffarilegha, Sajjad Gharaghani

“…We compared their performance with that of conventional drug signatures and chemical structure-based descriptors.Results:Our results demonstrate that models incorporating DRS features consistently outperform traditional approaches across all evaluated algorithms. Validation on independent datasets, including ALMANAC, O’Neil, OncologyScreen, and DrugCombDB, confirms the robustness and generalizability of the proposed framework.DiscussionThese findings emphasize the importance of integrating resistance-informed transcriptomic features into computational models. …”
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Variant configuration of the arterial circle of Willis by Beryl S. Ominde, Mamerhi T. Enaohwo, Joice E. Ikubor, Sonia U. Chukwujindu, Ogheneyole Jeremiah, Orovwoghene F. Omoro, Patrick S. Igbigbi

“…Materials and Methods: Using a cross-sectional design, we analyzed 200 archived computed tomographic brain images (104 males and 96 females) from the imaging department of a hospital in Delta State, Nigeria. …”
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An AHP-DEA Approach of the Bike-Sharing Spots Selection Problem in the Free-Floating Bike-Sharing System by Minjiao Cheng, Wenchao Wei

“…Specifically, a hyperplane adjusting model is developed to adjust the infeasible results. In the computational study, we evaluate 36 current parking spots in three regions in Beijing, China, to verify the rationality of the combined approach and put forward some managerial suggestions for this pilot project in Beijing, China.…”
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Nanoparticle Delivery in Microvascular after Cerebral Ischemia: A Simulation Study by Peiqian Chen, Bing Dong, Weiwu Yao

“…After building the mesh of microvascular, computational fluid dynamics is used to simulate blood flow and nanoparticle delivery. …”
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Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. by Yingjing Yan, Defu Zhang

“…Then We use a cascaded framework to increase the accuracy of segmentation results. We evaluate our network on the NIH pancreas dataset and measure the segmentation accuracy by the Dice similarity coefficient (DSC). …”
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The EU’s Education Policy Response to the Covid-19 Pandemic: A Discourse and Content Analysis by Vasileios Symeonidis, Denis Francesconi, Evi Agostini

“…Data were collected through a combination of discourse analysis and computer-assisted content analysis, which was applied to official EU education policy documents published in 2020. …”
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Research on information extraction methods for historical classics under the threshold of digital humanities by Lifan HAN, Zijing JI, Zirui CHEN, Xin WANG

“…Digital humanities aims to use modern computer network technology to help traditional humanities research.Classical Chinese historical books are the important basis for historical research and learning, but since their writing language is classical Chinese, it is quite different from the vernacular Chinese in grammar and meaning, so it is not easy to read and understand.In view of the above problems, the solution to extract entities and relations in historical books based on pre-trained models was proposed to obtain the rich information contained in historical texts effectively.The model usedmulti-level pre-training tasks instead of BERT's original pre-training tasks to fully capture semantic information.And the model added some structures such as convolutional layers and sentence-level aggregations on the basis of the BERT model to optimize the generated word representation further.Then, in view of the scarcity of classical Chinese annotation data, a crowdsourcing system for the task of labeling historical classics was constructed, high-quality, large-scale entity and relation data was obtained and the classical Chinese knowledge extraction dataset was constructed.So it helped to evaluate the performance of the model and fine-tune the model.Experiments on the dataset constructed in this paper and on the GulianNER dataset demonstrated the effectiveness of the model proposed in this paper.…”
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The Role of Artificial Intelligence in Enhancing Diabetic Retinopathy Lesion Detection: A Review by Lakshmi K Santhiya, Sargunam B

“…Therefore, deep learning algorithms have been used for the early diagnosis of DR in recent years, and medical image analysis utilising machine learning has demonstrated to be effective in evaluating retinal fundus images. This review's objective is to go over the numerous Deep learning techniques for automated computer-aided analysis of microaneurysms, haemorrhages, and exudates were also addressed, along with a knowledge gap in DR identification. …”
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