Enhancing security in 6G-enabled wireless sensor networks for smart cities: a multi-deep learning intrusion detection approach by Waqar Khan, Muhammad Usama, Muhammad Shahbaz Khan, Oumaima Saidani, Hussam Al Hamadi, Noha Alnazzawi, Mohammed S. Alshehri, Jawad Ahmad

“…This hybrid approach captures spatial, temporal, and contextual patterns in network traffic, improving detection accuracy against botnets, denial-of-service (DoS) attacks, and reconnaissance threats.Results and discussionTo validate the proposed framework, we employ the Kitsune and 5G-NIDD datasets, which provide intrusion detection scenarios relevant to IoT-based and non-IP traffic environments. …”
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An Optimized Hybrid Framework Based on Long-Short Term Memory Neural Networks and Fourier SynchroSqueezed Transform for Photovoltaic Power Forecasting by Samer Rajah, Francisco J. Munoz, Alejandro Rodriguez

“…The model integrates the Fourier SynchroSqueezed Transform with the Long Short-Term Memory network to enhance the identification of very short-term patterns in photovoltaic power production. …”
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From improvements in accessibility to the impact on territorial cohesion: the spatial approach by Marcin Stępniak, Piotr Rosik

“…The fact that analyses conducted in the national and international dimension yielded opposite results supports the presented approach of a multidimensional evaluation of transport network development.…”
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Evaluating Time Series Models for Monthly Rainfall Forecasting in Arid Regions: Insights from Tamanghasset (1953–2021), Southern Algeria by Ballah Abderrahmane, Morad Chahid, Mourad Aqnouy, Adam M. Milewski, Benaabidate Lahcen

“…This is particularly important in arid regions like Tamanghasset, where precipitation is the primary driver of agricultural viability and water resource management. This study evaluates the performance of several time series models for monthly rainfall prediction, including the autoregressive integrated moving average (ARIMA), Exponential Smoothing State Space Model (ETS), Seasonal and Trend decomposition using Loess with ETS (STL-ETS), Trigonometric Box–Cox transform with ARMA errors, Trend and Seasonal components (TBATS), and neural network autoregressive (NNAR) models. …”
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Advancing Marine Surveillance: A Hybrid Approach of Physics Infused Neural Network for Enhanced Vessel Tracking Using Automatic Identification System Data by Tasmiah Haque, Md Asif Bin Syed, Srinjoy Das, Imtiaz Ahmed

“…This paper introduces a novel approach that leverages Long Short-Term Memory (LSTM), a type of recurrent neural network, renowned for its proficiency in recognizing patterns over extended periods. …”
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Landslide Susceptibility Prediction Based on a CNN–LSTM–SAM–Attention Hybrid Model by Honggang Wu, Jiabi Niu, Yongqiang Li, Yinsheng Wang, Daohong Qiu

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O PAPEL DAS INSTITUIÇÕES NO SUPORTE A INDIVÍDUOS NO DESENVOLVIMENTO DE INOVAÇÕES: by Camila Alves Damásio, Sergio Evangelista Silva, Alana Deusilan Sester Pereira

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PlantDeepMeth: A Deep Learning Model for Predicting DNA Methylation States in Plants by Zhongwei Guo, Wenyuan Fan, Chengcheng Cai, Kang Zhang, Xilin Hou, Ying Li, Feng Cheng

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Real-Time Co-Simulation Implementation for Voltage and Frequency Regulation in Standalone AC Microgrid with Communication Network Performance Analysis across Traffic Variations by Ola Ali, Osama A. Mohammed

“…This study employs a real-time co-simulation testbed setup that integrates OPAL-RT and network simulator (ns-3), supporting a rigorous evaluation of the interplay between the communication networks and control within the MG. …”
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Evaluating Water Levels From the Surface Water and Ocean Topography (SWOT) Mission in a Hyper‐Tidal Coastal and Estuarine Environment by I. D. Lichtman, P. S. Bell, C. Gommenginger, C. Banks, F. M. Calafat, J. Brown, S. D. P. Williams

“…Here, this novel data set is evaluated in the hyper‐tidal coastal‐estuarine environment of the Bristol Channel‐Severn Estuary. …”
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Spatiotemporal data modeling and prediction algorithms in intelligent management systems by Xin Cao, Chunxiao Mei, Zhiyong Song, Hao Li, Jingtao Chang, Zhihao Feng

“…Finally, experiments were conducted using wireless network datasets to evaluate the performance of the model. …”
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Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework by Liang Yu, Yuanshen Hong, Hua Lin, Xu Jiang, Ziming Song

“…The experimental results demonstrate that the BP neural network model incorporating multi-scale features outperforms traditional BP neural network models and other control models in several evaluation metrics. …”
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Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature by Assmaa Abd-Elmonem, Zill E Shams, Mariam Imtiaz, Kashif Ali, Sohail Ahmad, Wasim Jamshed, Fayza Abdel Aziz ElSeabee, Neissrien Alhubieshi, Syed M. Hussain, Hijaz Ahmad

“…Finally, an artificial intelligence-based analysis is presented that correlates our research methodology with a Neural Fitting app. This neural network app is leveraged to enrich the accuracy of the present numerical technique and ensures a robust evaluation of the thermal signature of nanofluid flow within the pipe exposed to sunlight-induced heating.…”
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Evaluation method and application for in-situ stress in No. 8+9 coal seam, southern Shenfu block, northeastern margin of Ordos Basin by Jiawei WU, Wei TANG, Yanhe ZHU, Cunwu WANG, Yongjing TIAN, Jingyu ZI, Jianghao YANG, Xian SHI

“…The direction and magnitude of the present in-situ stress influence the propagation of hydraulic fractures in coal seams, making it a key geological parameter for coalbed methane (CBM) well network deployment and fracturing design. Accurate evaluation of the present in-situ stress direction and magnitude in coal seams is crucial for CBM exploration and development. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…Synthetic Minority Oversampling Technique was used to address class imbalance. Semantic network analysis was conducted to visualize co-occurrence patterns and conceptual clustering. …”
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Development of an MRI based artificial intelligence model for the identification of underlying atrial fibrillation after ischemic stroke: a multicenter proof-of-concept analysisRes... by Zijie Zhang, Yang Ding, Kaibin Lin, Wenli Ban, Luyue Ding, Yudong Sun, Chuanliang Fu, Yihang Ren, Can Han, Xue Zhang, Xiaoer Wei, Shundong Hu, Yuwu Zhao, Li Cao, Jun Wang, Saman Nazarian, Ying Cao, Lan Zheng, Min Zhang, Jianliang Fu, Jingbo Li, Xiang Han, Dahong Qian, Dong Huang

“…Interpretation: Our work suggested a potential association between brain ischemic lesion pattern on MR images and underlying AF. Furthermore, with additional validation, the AI model we developed may serve as a rapid screening tool for AF in clinical practice of stroke units. …”
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An Association Between Product Discount, Rating and Customer Service: An Empirical Study by Adeel Ansari, Seema Ansari, Ayesha Ghayas

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A Real-World Case Study Towards Net Zero: EV Charger and Heat Pump Integration in End-User Residential Distribution Networks by Thet Paing Tun, Oguzhan Ceylan, Ioana Pisica

“…This study evaluates the impact of heat pump and electric vehicle (EV) penetration on a UK residential distribution network, considering the highest coincident electricity demand and worst weather conditions recorded over the past decade. …”
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Mapping, intensities and future prediction of land use/land cover dynamics using google earth engine and CA- artificial neural network model. by Maysoon A A Osman, Elfatih M Abdel-Rahman, Joshua Orungo Onono, Lydia A Olaka, Muna M Elhag, Marian Adan, Henri E Z Tonnang

“…Therefore, the aims of the current study were to (1) evaluate LULC changes in the Gedaref state, Sudan for the past thirty years (1988-2018) using Landsat imageries and the random forest classifier, (2) determine the underlying dynamics that caused the changes in the landscape structure using intensity analysis, and (3) predict future LULC outlook for the years 2028 and 2048 using cellular automata-artificial neural network (CA-ANN). …”
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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data by Nazish Ashfaq, Zeeshan Aziz, Muhammad Hassan Khan, Muhammad Adeel Nisar, Adnan Khalid

“…The model exhibits robust generalization across complex activity patterns and sensor configurations, underscoring the significance of multi-modal integration in advancing HAR systems. …”
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