Spatial Patterns and Characteristics of Urban–Rural Agricultural Landscapes: A Case Study of Bengaluru, India by Jayan Wijesingha, Thomas Astor, Sunil Nautiyal, Michael Wachendorf

“…This study developed a workflow to address this information gap and determine the spatial patterns and characteristics of agricultural landscapes along an urban–rural gradient. …”
Get full text

Comparative study of five-year cervical cancer cause-specific survival prediction models based on SEER data by Yuping Pu, Jundong Liu, Kei Hang Katie Chan

“…Using data from the Surveillance, Epidemiology, and End Results (SEER) program, we applied the Synthetic Minority Over-Sampling Technique to address class imbalance and used stepwise forward selection, feature importance, and permutation importance for feature selection. The Gradient Boosting Survival Analysis (GBSA) model outperformed others with an Inverse Probability of Censoring Weighted Concordance Index of 0.835 and an Integrated Brier Score of 0.120. …”
Get full text

Mussel-inspired thermo-switchable underwater adhesive based on a Janus hydrogel by Hiroya Abe, Daichi Yoshihara, Soichiro Tottori, Matsuhiko Nishizawa

“…The electrode-integrated hydrogel remains on human skin, and electrical signals are continuous over 10 min above the LCST. …”
Get full text

Risks and benefits of artificial intelligence deepfakes: Systematic review and comparison of public attitudes in seven European Countries by Nik Hynek, Beata Gavurova, Matus Kubak

“…This study provides an evidence-based integrated appraisal of artificial intelligence (AI)-generated deepfakes by integrating a cross-disciplinary literature synthesis with original opinion-poll evidence from seven European countries. …”
Get full text

Multi-criteria decision analysis for regional-scale flood susceptibility mapping in Kerala state, India by M. S. Kendagannaswamy, C. K. Roopa, B. S. Harish, M. S. Mukesh

“…This research aims to develop flood susceptibility maps for Kerala using a spatial flood database integrated within the ArcGIS interface. An efficient framework integrating Light Gradient Boosting Machine (LightGBM) learning models with the Analytic Hierarchy Process (AHP) is proposed to address this issue by enhancing flood susceptibility understanding and informed decision-making. …”
Get full text

On-chip graphene photodetectors with a nonvolatile p–i–n homojunction by Ruijuan Tian, Yong Zhang, Yingke Ji, Chen Li, Xianghu Wu, Jianguo Wang, Shuaiwei Jia, Liang Liu, Mingwen Zhang, Yu Zhang, Qiao Zhang, Zhuang Xie, Zhengdong Luo, Duorui Gao, Yan Liu, Jianlin Zhao, Zhipei Sun, Xuetao Gan

Get full text

A hybrid approach for pattern recognition and interpretation in age-related false memory by Noorbakhsh Amiri Golilarz, Elias Hossain, Shahram Rahimi, Hossein Karimi

“…The best-performing model, a modified version of the Light Gradient Boosting Machine (LightGBM), identified nine key features using permutation importance. …”
Get full text

Open Data-Driven 3D Building Models for Micro-Population Mapping in a Data-Limited Setting by Kittisak Maneepong, Ryota Yamanotera, Yuki Akiyama, Hiroyuki Miyazaki, Satoshi Miyazawa, Chiaki Mizutani Akiyama

“…However, many regions lack sufficient resources to acquire and maintain these data, creating challenges in data availability. Our methodology integrates multiple data sources, including aerial imagery, Points of Interest (POIs), and digital elevation models, employing Light Gradient Boosting Machine (LightGBM) and Gradient Boosting Decision Tree (GBDT) to classify building uses and morphological filtration to estimate heights. …”
Get full text

Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…However, SAM’s perturbation is based solely on the gradient of the standard cross-entropy loss. As the model approaches convergence, this gradient diminishes and oscillates, leading to inconsistent perturbation directions. …”
Get full text

Assessing urban renewal opportunities by combining 3D building information and geographic big data by Xin Zhao, Nan Xia, ManChun Li

“…However, conventional data sources often fall short in encompassing diverse urban characteristics in the evaluation process, such as urban three-dimensional (3D) building information and the intensity of human activities. To address this gap, this study integrated 3D building data and geographic data to create a comprehensive set of 28 indicators spanning four dimensions: natural environmental conditions, land use, socio-economic factors, and building conditions. …”
Get full text

ALL diagnosis: can efficiency and transparency coexist? An explainble deep learning approach by Dost Muhammad, Muhammad Salman, Ayse Keles, Malika Bendechache

“…To address the opacity inherent in Deep learning (DL) models, the framework integrates advanced XAI techniques, including Gradient-weighted Class Activation Mapping (Grad-CAM), Class Activation Mapping (CAM), Local Interpretable Model-Agnostic Explanations (LIME), and Integrated Gradients (IG), providing transparent and explainable insights into model predictions. …”
Get full text

Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…This study suggested the potential of ToxCast bioassays and machine learning models in predicting DART potential, offering a promising approach to address data-gap in consumer product safety.…”
Get full text

Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. …”
Get full text

Diversity, functionality, and stability: shaping ecosystem multifunctionality in the successional sequences of alpine meadows and alpine steppes on the Qinghai-Tibet Plateau by Xin Jin, Abby Deng, Yuejun Fan, Kun Ma, Yangan Zhao, Yingcheng Wang, Kaifu Zheng, Xueli Zhou, Guangxin Lu

“…However, these efforts have not thoroughly explored how different successional stages affect key ecological parameters, such as species and functional diversity, stability, and ecosystem multifunctionality, which are fundamental to ecosystem resilience and adaptability. Given this gap, we systematically investigate variations in vegetation diversity, functional diversity, and the often-overlooked dimension of community stability across the successional gradient from alpine meadows to alpine steppes. …”
Get full text

Modeling Hydrologically Mediated Hot Moments of Transient Anomalous Diffusion in Aquifers Using an Impulsive Fractional‐Derivative Equation by Yong Zhang, Xiaoting Liu, Dawei Lei, Maosheng Yin, HongGuang Sun, Zhilin Guo, Hongbin Zhan

“…To bridge this knowledge gap, we propose an innovative model termed “the impulsive, tempered fractional advection‐dispersion equation” (IT‐fADE) to simulate HM‐HMs of TAD. …”
Get full text

Chi2 weighted ensemble: A multi-layer ensemble approach for skin lesion classification using a novel framework - optimized RegNet synergy with Attention-Triplet. by Anwar Hossain Efat

“…A significant gap in current research is the lack of techniques for optimal weight allocation in model predictions. …”
Get full text

Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators. by Girmaw Abebe Tadesse, Laura Ferguson, Caleb Robinson, Shiphrah Kuria, Herbert Wanyonyi, Samuel Murage, Samuel Mburu, Rahul Dodhia, Juan M Lavista Ferres, Bistra Dilkina

“…We aim to address the existing gap in decision-makers' ability to develop and utilize malnutrition forecasting capabilities for timely interventions. …”
Get full text

Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

“…The STFT's ability to reveal cyclical trends in Ethereum's price provides valuable insights for the ANFIS model, leading to more precise predictions and addressing a notable gap in cryptocurrency research. Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.…”
Get full text

A Framework to Predict the Quality of a Video for Popularity on Social Media by Abqa Javed, Nimra Abid, Muhammad Shoaib, Muhammad Farrukh Shahzad, Fahad Sabah, Raheem Sarwar

“…Four machine learning models—random forest, stochastic gradient descent classifier (SGDC), gradient boosting, and XGBoost—were evaluated for classification. …”
Get full text

Detection of kidney bean leaf spot disease based on a hybrid deep learning model by Yiwei Wang, Qianyu Wang, Yue Su, Binghan Jing, Meichen Feng

“…To address these challenges, this study constructs the first-ever kidney bean leaf spot disease (KBLD) dataset, filling a significant gap in the field. Based on this dataset, a novel hybrid deep learning model framework is proposed, which integrates deep learning models (EfficientNet-B7, MobileNetV3, ResNet50, and VGG16) for feature extraction with machine learning algorithms (Logistic Regression, Random Forest, AdaBoost, and Stochastic Gradient Boosting) for classification. …”
Get full text