ML Based Social Media Data Emotion Analyzer and Sentiment Classifier with Enriched Preprocessor by Jayamalini Kothandan, Ponnavaikko Murugesan

“…This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.…”
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Smart Organization of Imbalanced Traffic Datasets for Long-Term Traffic Forecasting by Mustafa M. Kara, H. Irem Turkmen, M. Amac Guvensan

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Cloud-based real-time enhancement for disease prediction using Confluent Cloud, Apache Kafka, feature optimization, and explainable artificial intelligence by Abdulaziz AlMohimeed

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A non-invasive computer-aided personalized diagnosis system for Osteopenia and Osteoporosis by Hadeel Osama El-Sisi, Fatma El-Zahraa Ahmed El-Gamal, Noha Ahmed Hikal

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Accompanying study of the development process towards a smart city strategy-with a particular focus on social change by Christian SCHACHTNER, Nadine BAUMANN

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Classifying breast cancer and fibroadenoma tissue biopsies from paraffined stain-free slides by fractal biomarkers in Fourier Ptychographic Microscopy by Vittorio Bianco, Marika Valentino, Daniele Pirone, Lisa Miccio, Pasquale Memmolo, Valentina Brancato, Luigi Coppola, Giovanni Smaldone, Massimiliano D’Aiuto, Gennaro Mossetti, Marco Salvatore, Pietro Ferraro

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Analyzing Diagnostic Reasoning of Vision–Language Models via Zero-Shot Chain-of-Thought Prompting in Medical Visual Question Answering by Fatema Tuj Johora Faria, Laith H. Baniata, Ahyoung Choi, Sangwoo Kang

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Model-Based RL Decision-Making for UAVs Operating in GNSS-Denied, Degraded Visibility Conditions with Limited Sensor Capabilities by Sebastien Boiteau, Fernando Vanegas, Julian Galvez-Serna, Felipe Gonzalez

“…The proposed system represents a step forward in UAV autonomy for critical applications, with potential extensions to unknown and fully stochastic environments.…”
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Multi-Scenario Remote Sensing Image Forgery Detection Based on Transformer and Model Fusion by Jinmiao Zhao, Zelin Shi, Chuang Yu, Yunpeng Liu

“…On the other hand, we construct a combined learning rate optimization strategy that first uses step degeneration and then cosine annealing, which reduces the risk of the network falling into local optima. …”
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Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer by Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi, Lill-Tove Rasmussen Busund, Stig Manfred Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas Karsten Kilvær

“…The pipeline's computational advances, particularly in reducing processing time, and clinical relevance demonstrate a step forward in computational pathology.…”
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Ensemble Computational Intelligent for Insomnia Sleep Stage Detection via the Sleep ECG Signal by Pragati Tripathi, M. A. Ansari, Tapan Kumar Gandhi, Rajat Mehrotra, Md Belal Bin Heyat, Faijan Akhtar, Chiagoziem C. Ukwuoma, Abdullah Y. Muaad, Yasser M. Kadah, Mugahed A. Al-Antari, Jian Ping Li

“…Accurate detection of insomnia disorder is a crucial step for mental disease analysis in the early stages. …”
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A multi-label deep residual shrinkage network for high-density surface electromyography decomposition in real-time by Jinting Ma, Lifen Wang, Renxiang Wu, Naiwen Zhang, Jing Wei, Jianjun Li, Qiuyuan Li, Lihai Tan, Guanglin Li, Naifu Jiang, Guo Dan

“…These networks were trained and tested on various window sizes and step sizes. Results With the shortest window size (20 data points) and step size (10 data points), ML-DRSNet significantly outperformed both ML-DCNN (0.86 ± 0.18 vs. 0.71 ± 0.24, P < 0.001) and MT-DCNN (0.86 ± 0.18 vs. 0.66 ± 0.16, P < 0.001) in decomposition precision. …”
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Compound dataset and custom code for deep generative multi-target compound design by Thomas Blaschke, Jürgen Bajorath

“…Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available. Limitations & next steps: MT-CPD design via deep learning is still at its conceptual stages. …”
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Enhanced HoVerNet Optimization for Precise Nuclei Segmentation in Diffuse Large B-Cell Lymphoma by Gei Ki Tang, Chee Chin Lim, Faezahtul Arbaeyah Hussain, Qi Wei Oung, Aidy Irman Yajid, Sumayyah Mohammad Azmi, Yen Fook Chong

“…<b>Methods</b>: A dataset of 122 CMYC-stained whole slide images (WSIs) was used. Pre-processing steps, including stain normalization and patch extraction, were applied to improve input consistency. …”
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MISTIC: a novel approach for metastasis classification in Italian electronic health records using transformers by Livia Lilli, Mario Santoro, Valeria Masiello, Stefano Patarnello, Luca Tagliaferri, Fabio Marazzi, Nikola Dino Capocchiano

“…Conclusions MISTIC achieves high performance in the Italian metastasis classification task, outperforming rule-based systems, zero-shot BERT models, and LLMs. Its few-shot learning setup offers a computationally efficient alternative to large-scale models, while its segmentation and topic analysis steps enhance explainability by explicitly linking predictions to key textual elements. …”
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Vehicle detection and recognition approach in smart surveillance system: A comparative analysis by Stephanie Ness

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Research on Hybrid Architecture Neural Networks for Time Series Prediction by Fujin Zhuang, Xiao Chen, Punyaphol Horata, Khamron Sunat

“…Therefore, the AdamW optimizer with an adaptive learning rate adjustment strategy was adopted for model training. …”
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Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA by Yang Sen, Wang Hengdi, Cui Yongcun, Li Chang, Tang Yuanchao

“…Firstly, the optimization step size of SSA is adaptively changed according to the fittness function value and the number of iterations. …”
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Deploying UAV-based detection of bridge structural deterioration with pilgrimage walk optimization-lite for computer vision by Jui-Sheng Chou, Chi-Yun Liu, Pin-Jun Guo

“…This advancement enhances the sustainability and safety of bridge infrastructure, representing a substantial step forward in infrastructure management and UAV applications.…”
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Hybrid intelligence systems for reliable automation: advancing knowledge work and autonomous operations with scalable AI architectures by Allan Grosvenor, Anton Zemlyansky, Abdul Wahab, Kyrylo Bohachov, Aras Dogan, Dwyer Deighan

“…Planning: Reinforcement learning, world model-based reinforcement learning, and model predictive control orchestrate adaptive workflows tailored to user requests or live system demands.ResultsThe system’s capabilities are demonstrated in two mission-critical applications: Space Domain Awareness, Satellite Behavior Detection: A graph-based JEPA paired with multi-agent reinforcement learning enables near real-time anomaly detection across 15,000 on-orbit objects, delivering a precision-recall score of 0.98. …”
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