Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models by Mohammad Liton Hossain, S. M. Nasif Shams, Saeed Mahmud Ullah

“…These findings underscore the transformative potential of deep learning in enhancing renewable energy forecasting accuracy in developing urban regions, providing critical insights for future energy policy and infrastructure development in Dhaka City.…”
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Hybrid Android Malware Detection and Classification Using Deep Neural Networks by Muhammad Umar Rashid, Shahnawaz Qureshi, Abdullah Abid, Saad Said Alqahtany, Ali Alqazzaz, Mahmood ul Hassan, Mana Saleh Al Reshan, Asadullah Shaikh

“…Abstract This paper presents a deep learning-based framework for Android malware detection that addresses critical limitations in existing methods, particularly in handling obfuscation and scalability under rapid mobile app development cycles. Unlike prior approaches, the proposed system integrates a multi-dimensional analysis of Android permissions, intents, and API calls, enabling robust feature extraction even under reverse engineering constraints. …”
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Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

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Synchronized Survey Scan Approach Allows for Efficient Discrimination of Isomeric and Isobaric Compounds during LC-MS/MS Analyses by Keabetswe Masike, Ntakadzeni Madala

“…Thus, the method was shown to distinguish (by differences in fragmentation patterns) between diCQA and their isobars, caffeoylquinic acid (CQA) glycosides. …”
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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi datasetZenodo by Aparup Roy, Debotosh Bhattacharjee, Ondrej Krejcar

“…However, its large size (∼7 GB) and inherent class imbalance pose significant challenges for machine learning model development. This paper presents a preprocessing framework to enhance VeReMi’s usability and relevance. …”
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A Graph Representation Learning-Based Method for Event Prediction by Xi Zeng, Guangchun Luo, Ke Qin, Pengyi Zheng

“…Predicting events allows for the exploration of the developmental trajectories and summarization of patterns associated with these events. …”
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Client aware adaptive federated learning using UCB-based reinforcement for people re-identification by Dinah Waref, Yomna Alayary, Nadeen Fathallah, Mohamed A. Abd El Ghany, Mohammed A.-M. Salem

“…Finally, we introduce a feature-level attention mechanism focusing on discriminative features for re-identification. …”
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Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data by Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

“…Abstract Microseismic monitoring is crucial for characterizing and assessing fracture systems developed during subsurface operations like geothermal reservoir development, CO2 sequestration, and hydraulic fracturing. …”
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Ensemble Voting for Enhanced Robustness in DarkNet Traffic Detection by Varun Shinde, Kartik Singhal, Ahmad Almogren, Vineet Dhanawat, Vishal Karande, Ateeq Ur Rehman

“…This clearly indicates the strength of ensemble methods in handling a diverse set of patterns and raising the ability to classify, which is an important lesson for the further development of research in machine learning and models.…”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Feature analysis revealed RHOB, DT, NPHI, and Vp as the most significant inputs for predicting HRQI. …”
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CircDock6 drives metabolic dysfunction–associated steatotic liver disease progression in mice and mouse hepatocytes via mmu-let-7g-5p/insulin-like growth factor 1 receptor regulati... by Hongpeng Lu, Xiaoyun Ding, Peifei Li

“…Objective Circular RNAs belong to a category of noncoding RNAs that feature a unique continuous, covalently bonded ring configuration. …”
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Spatiotemporal evolution and driving factors of the synergistic effects of pollution control and carbon reduction in China by Qinggang Meng, Xiaolan Chen, Hui Wang, Wanfang Shen, Peixin Duan, Xinyue Liu

“…The findings indicate that a preliminary model for coordinated development in PCCR has been established. However, regional disparities in PCCR, a persistent and dynamically evolving feature, manifested a spatial pattern of “higher in the east, lower in the west”. …”
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Analysis of the structural changes in domestic consumption of FUEL and energetic resources of Moscow by L. G. Moiseykina, E. S. Darda

“…The formation and development of the fuel and energy complex in Moscow is largely due to the rapidly developing economy of the megapolis – large-scale construction of housing and infrastructure, sustainable population growth entails a constant increase in consumption of fuel and energy resources. …”
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Types and applicability of waterflooding characteristic curves in fractured-cavity carbonate reservoirs: A case study of Tahe Oilfield by ZHENG Lingli, ZHU Bingqian, ZHANG Yuhao, LI Xiaobo, PENG Jiaming, XIAO Wenlian

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T-cell receptor dynamics in digestive system cancers: a multi-layer machine learning approach for tumor diagnosis and staging by Changjin Yuan, Bin Wang, Hong Wang, Fang Wang, Xiangze Li, Ya’nan Zhen

“…Multi-layer machine learning-based diagnostic models were developed by leveraging motif-based feature and deep learning-based feature extraction using ProteinBERT from the 100 most abundant CDR3 sequences per sample. …”
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Adaptive Weighted Diversity Ensemble Learning Approach for Fetal Health Classification on Cardiotocography Data by K. Aditya Shastry, Mohan Sellappa Gounder, T. G. Mohan Kumar, D. U. Karthik, V. Sushma, D. Subashree

“…This research aims to enhance fetal health assessment accuracy by developing a robust model through the integration of advanced ensemble learning techniques with feature selection, scaling, and adaptive weighting mechanisms. …”
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SPECIALIZED MAPPING OF CRUSTAL FAULT ZONES. PART 2: MAIN STAGES AND PROSPECTS by K. Zh. Seminsky

“…On the one side, the method requires time-consuming mass mea­surements and special processing of 'dumb' joints; on the other side, it provides for analyses of abundant jointing data, ensures a high level of detail in mapping of patterns of fault zones, reveals rank subordination of faults and helps to determine other specific features of fractures and faults. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. …”
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sEMG-based gesture recognition using multi-stream adaptive CNNs with integrated residual modules by Yutong Xia, Dawei Qiu, Cheng Zhang, Jing Liu

“…Compared with other deep learning models, MSACNN-RM achieves higher accuracy compared to existing models.DiscussionThe proposed model explores features of sparse sEMG signals by leveraging multi-stream convolution, the combination of adaptive convolution modules and ResNet blocks enhances the model’s ability of extracting crucial gesture features. …”
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Predicting depressive symptoms through social support: a machine learning approach in military populations by Kun-Huang Chen, Pao-Lung Chiu, Ming-Hsuan Chen

“…Subgroup analyses demonstrated similarly high prediction performance, measured by AUPRC, across gender, SES, and future orientation subgroups. Feature importance analyses using the Gini index indicated that different support sources (e.g. leader, peer, senior student) played varying roles across subgroups.Conclusions: Machine learning approaches demonstrate high AUPRC in predicting depressive symptoms and reveal nuanced subgroup patterns in perceived social support needs. …”
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