NeuroFuzzXAI: A Hybrid Artificial Intelligence Framework for Epileptic Seizure Detection by Flavia COSTI, Emanuel COVACI, Ovidiu ROMAN, Darian ONCHIS

“…This hybrid approach enhances interpretability while maintaining predictive accuracy, reducing computational complexity, and mitigating overfitting. Results: Compared to a standard deep learning model utilizing all features, our hybrid approach improved classification performance by 12%. …”
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Quote-and-Reply in WeChat: A Conversation Analytic Study by Yajing Yang

“…Findings reveal how Q&R transforms digital communication by mitigating challenges of multi-party chats and asynchronous interactions. …”
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Enhancing fairness in disease prediction by optimizing multiple domain adversarial networks. by Bin Li, Xiaoqian Jiang, Kai Zhang, Arif O Harmanci, Bradley Malin, Hongchang Gao, Xinghua Shi, Alzheimer’s Disease Neuroimaging Initiative

“…However, current approaches struggle to simultaneously mitigate biases induced by multiple sensitive features in biomedical data. …”
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Enhancing smart contract security: Leveraging pre‐trained language models for advanced vulnerability detection by Fei He, Fei Li, Peili Liang

“…This method leverages the BERT pre‐trained model to discern semantic features from contract opcodes, which are then refined using a Bidirectional Long Short‐Term Memory Network (BiLSTM) and augmented by an attention mechanism that prioritizes critical features. …”
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Ultrawideband Non-Line-of-Sight Classification Using Transformer-Convolutional Neural Networks by Hae-Ji Hwang, Seon-Geun Jeong, Won-Joo Hwang

“…Accurate identification of non-line-of-sight channels is essential for mitigating localization errors. While recent deep learning methods, such as convolutional neural networks and long short-term memory models, have been applied to classify non-line-of-sight scenarios from raw channel impulse response data, they often struggle with capturing long-range dependencies, efficiently extracting spatial and temporal features, resulting in suboptimal classification performance. …”
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The Study of the Possibilities of Using SOC Based on Free and Open Source Software by Andrii Yefimenko, Mykhailo Honcharov

“…The study identifies each of the components of such a Security Operations Center, describes their place and interaction in the process of detecting, analyzing and mitigating the consequences of cyber attacks. The competitiveness of such an Security Operations Center is analyzed, its advantages, disadvantages and development prospects are determined. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

“…These high-dimensional features are then segmented, and enhanced channel and spatial features are obtained via attention mechanisms, effectively mitigating background interference and intra-class ambiguity. …”
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Acceptability of an AI-enabled family module in a mobile app for enhanced diabetes management: Patient and family perspectives by Sungwon Yoon, Rena Lau, Yu Heng Kwan, Huiyi Liu, Razeena Sahrin, Jie Kie Phang, Yichi Zhang, Nicholas Graves, Lian Leng Low

“…Objective To explore the acceptability of family support through an AI-enabled mobile app and identify preferences for its novel family module features among patients with type 2 diabetes (T2DM) and family members. …”
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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

“…This unsupervised technique enables the mining of discriminative features from the skin cancer images, effectively mitigating overfitting concerns and letting the model adjust structural features of skin cancer images, facilitating effective detection of skin cancer features. …”
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An Effective Detection Approach for Phishing URL Using ResMLP by S. Remya, Manu J. Pillai, Kajal K. Nair, Somula Rama Subbareddy, Yong Yun Cho

“…Our results demonstrate superior accuracy, precision, F1 Score, and recall, showcasing its effectiveness in mitigating phishing threats. Utilizing a residual pipeline made up of convolutional and inverted residual blocks, we start our method by identifying similar URL features and sentiments. …”
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Enhancing Model Robustness in Noisy Environments: Unlocking Advanced Mono-Channel Speech Enhancement With Cooperative Learning and Transformer Networks by Wei Hu, Yan Wu

“…Additionally, we introduce a Gaussian-Weighted Progressive Structure (GWPS) to compensate for any lost detailed features in deeper network layers, thereby enhancing the fidelity of the enhanced speech signal and mitigating information loss. …”
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FCN-PD: An Advanced Deep Learning Framework for Parkinson’s Disease Diagnosis Using MRI Data by Manal Alrawis, Farah Mohammad, Saad Al-Ahmadi, Jalal Al-Muhtadi

“…This architecture enables the model to effectively represent hierarchical features and handle high-dimensional MRI data while mitigating issues such as overfitting and feature redundancy. …”
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Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting by Sheng Li, Min Wang, Minghang Shi, Jiafeng Wang, Ran Cao

“…Finally, by embedding gradient highway units and adding skip connection, CloudPredRNN++ is constructed into a hierarchical recursive structure, mitigating the gradient vanishing and enhancing the uniform modeling of temporal–spatial features. …”
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Spiking Residual ShuffleNet-Based Intrusion Detection in IoT Environment by Sneha Leela Jacob, H. Parveen Sultana

“…After feature selection, the bootstrapping technique is used to augment the features in the DA phase. …”
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A complex network perspective on spatiotemporal evolution of extreme precipitation over the middle and lower reaches of the Yangtze river by Ziyi Hu, Aixia Feng, Changgui Gu, Peng Zhao, Qiguang Wang

“…Abstract With the increasing frequency and intensity of extreme precipitation events globally, it is imperative to study the characteristics of extreme precipitation for disaster prevention and mitigation. Traditional statistical analyses to study extreme precipitation often tend to separate temporal and spatial features, and spatial features are often studied based on complex networks, with few studies conducted from a combined temporal and spatial perspective. …”
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Optimising Cyclist Road-Safety Scenarios Through Angle-of-View Analysis Using Buffer and GIS Mapping Techniques by Zahra Yaghoobloo, Giuseppina Pappalardo, Michele Mangiameli

“…It integrates cyclists’ behavioural data with the urban environmental features encountered, such as intersections, road design, and traffic controls. …”
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Sustainable Risk Management in Green Banking by Jelena Vapa Tankosić, Nemanja Lekić, Borjana Mirjanić, Snežana Lekić, Bojan Vapa

“…Particular attention is given to the integration of ESG (Environmental, Social, and Governance) criteria into risk management processes, as well as to tools for identifying and mitigating environmental and regulatory risks. The findings highlight the importance of improving the regulatory framework and developing new methodologies for risk management tailored to the unique features of green banking. …”
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A REVIEW OF SUBSISTENCE FARMERS’ INDIGENOUS KNOWLEDGE OF SEASONS AND ITS IMPORTANCE IN CLIMATE CHANGE ADAPTATION by Shuan N.M., Sejabaledi A.R.

“…Research from Africa and other regions supports existing literature, offering essential insights into seasonal variations, their effects on rain-fed agricultural productivity, and the measures for adaptation and mitigation. The review details small-scale farmers' understanding of the four seasons (summer, winter, spring, and fall), including their corresponding months, distinctive features, and relevant agricultural practices. …”
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Cable fault detection based on enhanced spread spectrum time domain reflectometry by Xinwen Ma, Zhendong Yin

“…This approach removes the incident signal component from the acquired waveform, thereby mitigating its interference with the reflected signal and enhancing reflective characteristics. …”
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ICRL: independent causality representation learning for domain generalization by Liwen Xu, Yuxuan Shao

“…Nevertheless, ensuring the independence of features when incorporating causal models is often overlooked, leading to spurious causal relationships. …”
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