Multisensor-Weighted Fusion Algorithm Based on Improved AHP for Aircraft Fire Detection by Rui Wang, Yahui Li, Hui Sun, Kaixin Yang

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Ion-Pair Complex of Trimethoprim with Alizarin Yellow and its Determination and Extraction by LLE and DLLME by Araf I. Jabbar, Mohammed Z. Thani, Ali I. Khaleel

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Integrating Proteomics and GWAS to Identify Key Tissues and Genes Underlying Human Complex Diseases by Chao Xue, Miao Zhou

“…Background: The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. …”
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Fault Detection of the Power System Based on the Chaotic Neural Network and Wavelet Transform by Zuoxun Wang, Liqiang Xu

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Comprehensive influence evaluation algorithm of complex network nodes based on global-local attributes by Weijin JIANG, Ying YANG, Tiantian LUO, Wenying ZHOU, En LI, Xiaowei ZHANG

“…Mining key nodes in the network plays a great role in the evolution of information dissemination, virus marketing, and public opinion control, etc.The identification of key nodes can effectively help to control network attacks, detect financial risks, suppress the spread of viruses diseases and rumors, and prevent terrorist attacks.In order to break through the limitations of existing node influence assessment methods with high algorithmic complexity and low accuracy, as well as one-sided perspective of assessing the intrinsic action mechanism of evaluation metrics, a comprehensive influence (CI) assessment algorithm for identifying critical nodes was proposed, which simultaneously processes the local and global topology of the network to perform node importance.The global attributes in the algorithm consider the information entropy of neighboring nodes and the shortest distance nodes between nodes to represent the local attributes of nodes, and the weight ratio of global and local attributes was adjusted by a parameter.By using the SIR (susceptible infected recovered) model and Kendall correlation coefficient as evaluation criteria, experimental analysis on real-world networks of different scales shows that the proposed method is superior to some well-known heuristic algorithms such as betweenness centrality (BC), closeness centrality (CC), gravity index centrality(GIC), and global structure model (GSM), and has better ranking monotonicity, more stable metric results, more adaptable to network topologies, and is applicable to most of the real networks with different structure of real networks.…”
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Insights into the Silver Camphorimine Complexes Interactions with DNA Based on Cyclic Voltammetry and Docking Studies by Joana P. Costa, Gonçalo C. Justino, Fernanda Marques, M. Fernanda N. N. Carvalho

“…The formation of a light grey product adherent to the Pt electrode in the case of {Ag(OH)} and {Ag₂(µ-O)} complexes further corroborates the interaction of the complexes with CT-DNA detected by CV. …”
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Multitask semantic change detection guided by spatiotemporal semantic interaction by Yinqing Wang, Liangjun Zhao, Yueming Hu, Hui Dai, Yuanyang Zhang

“…Abstract Semantic Change Detection (SCD) aims to accurately identify the change areas and their categories in dual-time images, which is more complex and challenging than traditional binary change detection tasks. …”
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Enhanced U-Net++ for Improved Semantic Segmentation in Landslide Detection by Meng Tang, Yuelin He, Muhammed Aslam, Edore Akpokodje, Syeda Fizzah Jilani

“…Landslide detection and segmentation are critical for disaster risk assessment and management. …”
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Leveraging Deep Learning for Fault Detection and Localization in Distributed Systems by Debolina Ghosh, Jay Prakash Singh

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Machine Learning Detection of Melting Layers From Radar Observations by Yan Xie, Fraser King, Claire Pettersen, Mark Flanner

“…Compared to a traditional detection method, the U‐Net model increases the Probability of Detection by 57% and improves the mean Dice‐Sørensen coefficient from 0.69 to 0.91. …”
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Research on Fire Detection of Cotton Picker Based on Improved Algorithm by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song

“…The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. …”
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Damage Localization of Piles Based on Complex Continuous Wavelet Transform: Numerical Example and Experimental Verification by Wenting Zheng, Sifan Wang, Chengxu Lin, Xianying Yu, Jingliang Liu

“…A new signal processing method called complex continuous wavelet transform (CCWT) is introduced in this paper to localize pile damage because it clearly reveals inherent characteristics of response signals. …”
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Machine Learning Techniques for Enhanced Intrusion Detection in IoT Security by Hanadi Hakami, Muhammad Faheem, Majid Bashir Ahmad

“…This research introduces a new model that leverages machine learning (ML) and deep learning (DL) to enhance detection effectiveness and ensure reliability. The approach optimizes data preprocessing by integrating SMOTE for effective data balancing and Pearson’s Correlation Coefficient (PCC) for feature selection. …”
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Detecting microcephaly and macrocephaly from ultrasound images using artificial intelligence by Abraham Keffale Mengistu, Bayou Tilahun Assaye, Addisu Baye Flatie, Zewdie Mossie

“…Objective This study aims to develop a fetal head abnormality detection model from ultrasound images via deep learning. …”
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From Misinformation to Insight: Machine Learning Strategies for Fake News Detection by Despoina Mouratidis, Andreas Kanavos, Katia Kermanidis

“…Through extensive experimentation across multiple datasets, our results demonstrate that BERT-based models consistently achieve superior performance, significantly improving detection accuracy in complex misinformation scenarios. …”
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Artificial intelligence-driven predictive framework for early detection of still birth by Sarah A. Alzakari, Asma Aldrees, Muhammad Umer, Lucia Cascone, Nisreen Innab, Imran Ashraf

“…Predictive modeling is becoming increasingly popular in the context of early disease detection. The use of machine learning approaches for predictive modeling can help early detection of diseases thereby enabling medical experts to appropriate medical treatments. …”
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Complexation reaction with Sm(III): a facile spectrophotometric quantification of daunorubicin in pharmaceutical preparation and biological fluids by Basima A. A. Saleem

“…The proposed method demonstrated a significant linear relationship between absorbance and daunorubicin concentration in the range of 1–48 µg/mL, with a correlation coefficient of 0.9974. Detection limits were established at 0.0025 and 0.0250 µg/mL. …”
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A triple protein-based ELISA for differential detection of ASFV antibodies by Shuai Zhang, Yuzhu Zuo, Wenyuan Gu, Yunhuan Zhao, Ying Liu, Jinghui Fan, Jinghui Fan

“…As no vaccines or effective drugs are available to aid prevention and control, early detection is crucial. The emergence of the low-virulence ASFV strain not expressing CD2v/MGFs (ASFVΔCD2v/ΔMGFs) has been identified domestically and internationally and has even become an epidemic in China, resulting in a complex epidemic. …”
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Improved FastICA algorithm for data optimization processing in intrusion detection by Ye DU, dan ZHANGYa, hong LIMei, wei ZHANGDa

“…The algorithm was introduced concretely, meanwhile the time complexity was analyzed in detail. The experiment shows that the method has the advantages of less times of iteration and fast speed of convergence, which can effectively decrease the losses of data and increase the efficiency of data optimization in in ion detection.…”
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Unpacking Sarcasm: A Contextual and Transformer-Based Approach for Improved Detection by Parul Dubey, Pushkar Dubey, Pitshou N. Bokoro

“…Performance evaluation is based on accuracy, F1 score, and the Jaccard coefficient, ensuring a comprehensive assessment. Experimental results demonstrate that RoBERTa achieves 98.5% accuracy with metadata, while DistilBERT offers a 1.74x speedup, highlighting the trade-off between accuracy and computational efficiency for real-world sarcasm detection applications.…”
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