Canalization of gene expression and domain shifts in the Drosophila blastoderm by dynamical attractors. by Manu, Svetlana Surkova, Alexander V Spirov, Vitaly V Gursky, Hilde Janssens, Ah-Ram Kim, Ovidiu Radulescu, Carlos E Vanario-Alonso, David H Sharp, Maria Samsonova, John Reinitz

“…Our analysis shows that the complex phenomena of canalization and pattern formation in the Drosophila blastoderm can be understood in terms of the qualitative features of the dynamical system. …”
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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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Improving internet of vehicles research: A systematic preprocessing framework for the VeReMi datasetZenodo by Aparup Roy, Debotosh Bhattacharjee, Ondrej Krejcar

“…This preprocessing pipeline effectively maintains data integrity and preserves the representativeness of malicious patterns. The optimized dataset is well-suited for ITS and IoV applications, such as anomaly detection and network security, underscoring the crucial role of preprocessing in overcoming real-world constraints and enhancing model performance.…”
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Preparation of Porous F-WO3/TiO2 Films with Visible-Light Photocatalytic Activity by Microarc Oxidation by Chung-Wei Yeh, Kee-Rong Wu, Chung-Hsuang Hung, Hao-Cheng Chang, Chuan-Jen Hsu

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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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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. …”
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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

“…The model enhances reservoir characterization by capturing complex lithological patterns, supporting better zonation and well placement. …”
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Classification Model for Bot-IoT Attack Detection Using Correlation and Analysis of Variance by Firgiawan Faira, Dandy Pramana Hostiadi

“…To address this imbalance, an innovative data aggregation technique was applied, effectively reducing similar patterns and trends. This approach resulted in a balanced dataset consisting of 8 attack activity points and 80 normal activity points. …”
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Cross-Modal Complementarity Learning for Fish Feeding Intensity Recognition via Audio–Visual Fusion by Jian Li, Yanan Wei, Wenkai Ma, Tan Wang

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Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification by Aswan Aswan, Eva Yulia Puspaningrum, Billy Eden William Asrul

“…The results demonstrate that selecting an optimal activation function substantially enhances convolutional neural network classification of complex batik patterns. The findings offer practical guidance for development of resource aware batik identification systems in support of cultural digitization and education initiatives.…”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

“…The CNN model is adjusted for different convolutional layers and fully connected layers to identify patterns and features in brain tumor pictures using an enhanced salp swarm algorithm (SSA) with kernel extreme learning machine (KELM). …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…These datasets serve two functions: improving illness prediction and examining large data reservoirs to identify previously unknown disease patterns. Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. …”
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TableBorderNet: A Table Border Extraction Network Considering Topological Regularity by Jing Yang, Shengqiang Zhou, Xialing Li, Yuchun Huang, Honglin Jiang

“…Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. …”
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Bangla Character Detection Using Enhanced YOLOv11 Models: A Deep Learning Approach by Mahbuba Aktar, Nur Islam, Chaoyu Yang

“…A key limitation lies in the absence of robust detection frameworks capable of accommodating the script’s complex visual patterns and nuances. To address this gap, we propose an enhanced object detection model based on the YOLOv11 architecture, incorporating a ResNet50 backbone for improved feature extraction. …”
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A Comprehensive Review of EEG-Based Seizure Detection Techniques by Goldwyn Sudhakar Jebaraj, Konguvel Elango

“…Despite considerable progress in EEG signal acquisition, preprocessing methodologies, and the application of machine learning algorithms, significant challenges persist due to the inherent complexity and inter-subject variability observed in seizure patterns. This review aims to provide guidance for new researchers in this field, starting with the fundamental aspects of epilepsy, encompassing its diverse clinical presentations (syndromes), associated health conditions (comorbidities), and characteristic EEG manifestations. …”
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Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures by Weijie Zhang, Veronika Belcheva, Tatiana Ermakova

“…The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. …”
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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

“…The ARIMA(2,1,2) and SARIMA(2,1,2)(1,1,1,24) models captured basic temporal patterns but struggled with non-linear dynamics, achieving R2 scores of − 0.0008 and − 0.1104, respectively. …”
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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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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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