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Medium
2d
268
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AI in Automotive Manufacturing 2025: Driving the Future of Smart Production
- AI in automotive manufacturing 2025 is set to revolutionize production efficiency, quality control, and supply chain optimization.
- Key areas where AI is making a significant impact include smart robotics & automation, predictive maintenance, AI-powered quality control, and supply chain optimization.
- The integration of AI in car manufacturing offers numerous advantages, such as enhanced efficiency, cost savings, improved product quality, sustainability, and workforce safety.
- By 2025, AI is expected to become an integral part of every automotive production process, leading to fully automated factories, AI-driven customization, sustainable manufacturing, and collaboration between AI and humans.
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Medium
2d
59
Image Credit: Medium
Predictive Commerce, Digital Wallets & AI : The Future of Seamless Transactions
- Predictive commerce has evolved from basic data analytics to AI-driven systems with fast computing devices.
- Companies like Amazon, Apple Pay, and other fintech innovations are integrating predictive AI for seamless and automated transactions.
- Digital wallets like Apple Pay, Google Pay, and PayPal store payment information securely, enabling quick, contactless transactions.
- The future of predictive commerce and digital wallets involves seamless and personalized transactions, but privacy concerns need to be addressed.
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Medium
2d
297
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The AI Integration Playbook: Competencies for a Smarter Future
- AI is no longer a futuristic concept — it’s a competitive necessity.
- Strategic Vision for AI Adoption: AI must align with business objectives.
- Technical Proficiency in AI & Automation: AI fluency is key.
- AI integration isn’t about plugging in tools; it’s about building competencies.
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Arxiv
2d
237
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From Occurrence to Consequence: A Comprehensive Data-driven Analysis of Building Fire Risk
- Building fires pose a persistent threat to life, property, and infrastructure.
- A data-driven framework analyzes U.S. fire risks by integrating over one million fire incident reports with various datasets.
- Vulnerable communities face higher fire risks due to socioeconomic disparities and outdated or vacant buildings.
- Targeted interventions, such as mandating fire safety systems and providing subsidies, can enhance fire prevention and protect vulnerable groups.
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Arxiv
2d
276
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Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV
- This study validates hospital admission prediction models initially developed using a small local dataset from a Greek hospital.
- Using the comprehensive MIMIC-IV dataset, five algorithms were evaluated for prediction: Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Random Forest (RF), Recursive Partitioning and Regression Trees (RPART), and Support Vector Machines (SVM Radial).
- Random Forest (RF) demonstrated superior performance, achieving an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.9999, sensitivity of 0.9997, and specificity of 0.9999 when applied to the MIMIC-IV data.
- These findings highlight the robustness of Random Forest in handling complex datasets for admission prediction and provide actionable insights for improving Emergency Department (ED) management strategies.
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Arxiv
2d
0
Image Credit: Arxiv
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis
- Multimodal sentiment analysis is crucial for understanding human emotions across various communication channels.
- A novel framework called Hierarchical Adaptive Expert for Multimodal Sentiment Analysis (HAEMSA) is proposed to address the challenge of effectively integrating modality-shared and modality-specific information.
- HAEMSA utilizes an evolutionary optimization approach, cross-modal knowledge transfer, and multi-task learning to capture both global and local modality representations.
- Extensive experiments demonstrate that HAEMSA outperforms previous methods, achieving improved accuracy and reducing mean absolute error on multiple benchmark datasets.
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Arxiv
2d
102
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Why Representation Engineering Works: A Theoretical and Empirical Study in Vision-Language Models
- Representation Engineering (RepE) is a powerful paradigm for enhancing AI transparency.
- In Vision-Language Models (VLMs), RepE can address challenges related to visual input overriding factual linguistic knowledge.
- Theoretical framework explaining stability of neural activity in VLMs is developed using principal eigenvector.
- This work transforms RepE into a structured theoretical framework, opening new directions for improving AI robustness, fairness, and transparency.
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Arxiv
2d
106
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PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids
- The paper introduces a topology-aware Graph Neural Network (GNN) framework for predicting power system states in electricity grids with high renewable integration.
- The GNN model utilizes a graph-based representation of the power network, capturing both spatial and temporal correlations in system dynamics.
- It outperforms baseline approaches, achieving substantial improvements in predictive accuracy with average RMSEs of 0.13 to 0.17 across all predicted variables.
- The results highlight the potential of topology-aware learning for scalable and robust power system forecasting in future grids with high renewable penetration.
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Arxiv
2d
114
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Zero-Shot LLMs in Human-in-the-Loop RL: Replacing Human Feedback for Reward Shaping
- Reinforcement learning often faces challenges with reward misalignment, where agents optimize for given rewards but fail to exhibit the desired behaviors.
- To address these issues, the proposed approach utilizes zero-shot, off-the-shelf large language models (LLMs) for reward shaping in continuous control tasks.
- The LLM-HFBF framework is introduced to identify and correct biases in human feedback while incorporating it into the reward shaping process.
- Empirical experiments demonstrate that the proposed approach reduces reliance on potentially biased human guidance and maintains high reinforcement learning performance.
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Arxiv
2d
118
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A Spatial-temporal Deep Probabilistic Diffusion Model for Reliable Hail Nowcasting with Radar Echo Extrapolation
- A Spatial-temporal Deep Probabilistic Diffusion Model called SteamCast has been introduced for hail nowcasting with radar echo extrapolation.
- The model is trained on historical reanalysis archive from Yan'an Meteorological Bureau in China.
- SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, with high resolution.
- The model delivers competitive results compared to other deep learning-based models for hail nowcasting.
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Arxiv
2d
122
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Uncertainty Weighted Gradients for Model Calibration
- Model calibration is important for accurate predictions in deep neural networks.
- Existing loss functions, like focal loss, fail to achieve optimal calibration performance.
- A new loss framework is proposed, addressing misalignment and uncertainty estimation issues.
- Extensive experiments show the proposed method achieves state-of-the-art performance.
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Arxiv
2d
225
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Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
- This study proposes an oversampling strategy called MGS-GRF for rare event detection in binary classification on tabular data.
- MGS-GRF is designed to handle mixed features (continuous and categorical variables) and exhibits coherence and association properties.
- The method uses a kernel density estimator and locally estimated full-rank covariances to generate continuous features, while categorical features are drawn from the original samples through a generalized random forest.
- Experimental results show that MGS-GRF outperforms other synthetic procedures in terms of predictive performances, as evaluated on both simulated and real-world datasets.
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Arxiv
2d
229
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MoRE-LLM: Mixture of Rule Experts Guided by a Large Language Model
- The paper introduces a method called Mixture of Rule Experts guided by a Large Language Model (MoRE-LLM) to align machine learning models with human domain knowledge.
- MoRE-LLM combines a data-driven black-box model with knowledge extracted from a Large Language Model (LLM) to enable domain knowledge-aligned and transparent predictions.
- The Mixture of Rule Experts (MoRE) generates local rule-based surrogates during training and utilizes them for the classification task, while the LLM enhances domain knowledge alignment and provides context to the rules.
- The proposed method ensures interpretability and is evaluated on various datasets, comparing its performance with interpretable and non-interpretable baselines.
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Arxiv
2d
233
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Reasoning Beyond Limits: Advances and Open Problems for LLMs
- Recent generative reasoning breakthroughs have transformed how large language models (LLMs) tackle complex problems.
- Techniques such as inference-time scaling, reinforcement learning, supervised fine-tuning, and distillation have improved reasoning capabilities of LLM models.
- A comprehensive analysis of the top 27 LLM models released between 2023 and 2025 is presented.
- Key challenges in advancing LLM capabilities, including improving multi-step reasoning, overcoming limitations in chained tasks, and enhancing long-context retrieval are discussed.
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Arxiv
2d
237
Image Credit: Arxiv
RBFleX-NAS: Training-Free Neural Architecture Search Using Radial Basis Function Kernel and Hyperparameter Detection
- RBFleX-NAS is a training-free Neural Architecture Search (NAS) framework that utilizes the Radial Basis Function (RBF) kernel and a detection algorithm.
- Conventional NAS techniques require extensive training for evaluating candidate networks.
- RBFleX-NAS outperforms other training-free NAS methods in terms of top-1 accuracy and search time.
- RBFleX-NAS introduces NAFBee, an extended activation design space, for improved activation function exploration.
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