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GA-XGBoost: A Comprehensive Approach to Bank Customer Churn Prediction

This research addresses the challenge of customer churn in the banking sector using Genetic Algorithm eXtreme Gradient Boosting (GA-XGBoost). The study emphasizes the significance of techniques like SMOTEENN in handling data imbalances and introduces the SHAP interpretation framework for model interpretability. The optimized GA-XGBoost model proves effective in predicting customer churn, offering valuable insights for proactive customer retention strategies in the dynamic banking landscape.

18 Dec 2023

Hybrid RidgeGAN: Predicting Transportation Indices in Small and Medium-Sized Indian Cities

Researchers introduced a hybrid Ridge Generative Adversarial Network (RidgeGAN) model to predict road network density in small and medium-sized Indian cities under the Integrated Development of Small and Medium Towns (IDSMT) project. Integrating City Generative Adversarial Network (CityGAN) and Kernel Ridge Regression (KRR), the model successfully generated realistic urban patterns, aiding urban planners in optimizing layouts for efficient transportation infrastructure development.

18 Dec 2023

RefCap: Advancing Image Captioning through User-Defined Object Relationships

The RefCap model pioneers visual-linguistic multi-modality in image captioning, incorporating user-specified object keywords. Comprising Visual Grounding, Referent Object Selection, and Image Captioning modules, the model demonstrates efficacy in producing tailored captions aligned with users' specific interests, validated across datasets like RefCOCO and COCO captioning.

18 Dec 2023

Swin-APT: Enhancing Semantic Segmentation in Intelligent Transportation Systems

Researchers introduced Swin-APT, a deep learning-based model for semantic segmentation and object detection in Intelligent Transportation Systems (ITSs). The model, incorporating a Swin-Transformer-based lightweight network and a multiscale adapter network, demonstrated superior performance in road segmentation and marking detection tasks, outperforming existing models on various datasets, including achieving a remarkable 91.2% mIoU on the BDD100K dataset.

18 Dec 2023

Intelligent Manufacturing in Plastic Injection Molding Using Artificial Neural Network and Genetic Algorithms

Researchers proposed a hybrid optimization approach, combining Artificial Neural Network (ANN) and Genetic Algorithm (GA), to enhance plastic injection molding. Addressing quality, production efficiency, and sustainability, the method demonstrated effectiveness in achieving global multi-objective optimization, providing a valuable tool for smart, sustainable, and economically efficient production processes.

15 Dec 2023

LlamaGuard: A Robust LLM-Based Model for Safety-Focused Human-AI Conversations

LlamaGuard, a safety-focused LLM model, employs a robust safety risk taxonomy for content moderation in human-AI conversations. Leveraging fine-tuning and instruction-following frameworks, it excels in adaptability, outperforming existing tools on internal and public datasets. LlamaGuard's versatility positions it as a strong baseline for content moderation, showcasing superior overall performance and efficiency in handling diverse taxonomies with minimal retraining efforts.

15 Dec 2023

Spatial Variation-Dependent Verification for Enhanced Handwriting Identification using Artificial Intelligence

Researchers from Nanjing University of Science and Technology present a novel scheme, Spatial Variation-Dependent Verification (SVV), utilizing convolutional neural networks and textural features for handwriting identification and verification. The scheme outperforms existing methods, achieving 95.587% accuracy, providing a robust solution for secure handwriting recognition and authentication in diverse applications, including security, forensics, banking, education, and healthcare.

15 Dec 2023

CYBERSECEVAL: Benchmarking Cybersecurity Risks in Large Language Models for Secure Code Generation

The CYBERSECEVAL benchmark addresses cybersecurity risks in Large Language Models (LLMs) used for coding support. The evaluation, involving seven models, highlights significant concerns, revealing a 30% occurrence of insecure code suggestions and a 53% compliance rate in aiding cyberattacks. This benchmark emphasizes the critical need to integrate security considerations in LLM development, providing a robust framework for ongoing research to enhance AI safety in the context of evolving LLM usage.

15 Dec 2023

Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios: A Comprehensive Review

This article critically reviews the challenges and advancements in intelligent vehicle safety within complex multi-vehicle interactions. Addressing data collection methods, vehicle interaction dynamics, and risk evaluation techniques, the study categorizes risk assessment into state inference-based and trajectory prediction-based methods. It underscores the need for deeper analysis of multi-vehicle behaviors and emphasizes the advantages and limitations of existing risk assessment approaches.

15 Dec 2023

Improved Deep Reinforcement Learning for Collision-Free Routing in Vehicular Ad-Hoc Networks

Researchers propose an Improved Deep Reinforcement Learning (IDRL) approach to optimize routing paths and reduce control overhead in Vehicular Ad-Hoc Networks (VANETs). The IDRL routing technique leverages deep reinforcement learning to dynamically determine routes based on transmission capacity and vehicular data, resulting in reduced latency, improved data reliability, and increased packet delivery ratio (PDR) compared to existing routing techniques.

15 Dec 2023

Pearl: A Versatile Reinforcement Learning Agent for Real-World Challenges

Researchers introduce Pearl, a meticulously designed RL agent software addressing challenges like delayed rewards, partial observability, and safety constraints. Pearl's modular design facilitates efficient learning in sequential decision-making, supporting offline learning, exploration, and safety considerations within a unified framework. Through extensive benchmarking, Pearl showcases superior performance across diverse tasks, establishing itself as a production-ready solution with a broad range of applications in real-world scenarios.

14 Dec 2023

Automated Feline Pain Assessment: Deep Learning Predicts Facial Landmarks and Grimace Scale Scores

Researchers employ deep neural networks and machine learning to predict facial landmarks and pain scores in cats using the Feline Grimace Scale. The study demonstrates advanced CNN models accurately predicting facial landmarks and an XGBoost model achieving high accuracy in discerning painful and non-painful cats. This breakthrough paves the way for an automated smartphone application, addressing the challenge of non-verbal pain assessment in felines and marking a significant advancement in veterinary care.

14 Dec 2023

SZU-EmoDage: A Novel AI-Synthesized Facial Dataset for Cross-Cultural Emotion and Age Studies

Researchers from China introduce the SZU-EmoDage dataset, a pioneering facial dataset crafted with StyleGAN, featuring Chinese individuals of diverse ages and expressions. This innovative dataset, validated for authenticity by human raters, surpasses existing ones, offering applications in cross-cultural emotion studies and advancements in facial perception technology. The study emphasizes the dataset's value in exploring cognitive processes, detecting disorders, and enhancing technologies like face recognition and animation.

14 Dec 2023

Enhancing Biomedical Named Entity Recognition with Dictionary-Based Matching Graph Network

Researchers present a novel approach, the Dictionary-Based Matching Graph Network (DBGN), for Biomedical Named Entity Recognition (BioNER). By incorporating biomedical dictionaries and utilizing BiLSTM and BioBERT encoders, DBGN outperforms existing models across various biomedical datasets, demonstrating significant advancements in entity recognition with improved efficiency.

14 Dec 2023

LogEI: Addressing Numerical Challenges in Bayesian Optimization

Researchers tackled the numerical limitations of Expected Improvement (EI) in Bayesian optimization. Introducing LogEI functions, they demonstrated enhanced numerical stability while surpassing traditional methods, offering promising avenues for more efficient optimization strategies. The study emphasized LogEI's advantages in handling vanishing gradients and complex optimization landscapes, proposing it as a superior alternative in Bayesian optimization scenarios.

14 Dec 2023

Double Camouflage Encryption of QR Codes for Secure UAV Landing

This paper introduces a double camouflage encryption method for QR codes in UAV landing scenarios to ensure information security. Utilizing a private key and carrier image, the QR code undergoes double camouflage encryption, modulating a public key based on ghost imaging principles to generate ciphertext. The decryption process involves extracting the camouflage image with the public key and decrypting the QR code with the private key.

14 Dec 2023

GLanCE: Revolutionizing Global Land Cover Estimation with Cloud Computing and Machine Learning

Researchers employed cutting-edge cloud computing and machine learning on Google Earth Engine to create a vast global land cover training dataset. This meticulous resource spans nearly four decades, encompassing diverse biogeographic regions and addressing challenges in existing global datasets. The GLanCE dataset's validation process, utilizing sophisticated machine learning techniques, ensures data accuracy while highlighting the complexities and challenges in distinguishing specific land cover categories even at a 30-meter spatial resolution.

13 Dec 2023

Enhancing IoT Security: Evaluating Tree-Based Machine Learning Algorithms for Botnet Detection

This article delves into bolstering Internet of Things (IoT) security, specifically countering botnet attacks that jeopardize IoT ecosystems. Employing tree-based algorithms, including Decision Trees, Random Forest, and boosting techniques, the researchers conduct a thorough empirical analysis, highlighting Random Forest's standout multi-class classification accuracy and superior computational efficiency.

13 Dec 2023

Homomorphic Encryption and Dynamic Sparse Attention for Advancing Privacy in Dialogue Models

Researchers present a groundbreaking privacy-preserving dialogue model framework, integrating Fully Homomorphic Encryption (FHE) with dynamic sparse attention (DSA). This innovative approach enhances efficiency and accuracy in dialogue systems while prioritizing user privacy. Experimental analyses demonstrate significant improvements in precision, recall, accuracy, and latency, positioning the proposed framework as a powerful solution for secure natural language processing tasks in the information era.

13 Dec 2023

Audiobox: Advancing Controllable Audio Generation with Unified Models

Researchers from Meta present Audiobox, a novel model integrating flow-matching techniques for controllable and versatile audio generation. Audiobox demonstrates unprecedented controllability across various audio modalities, such as speech and sound, addressing limitations in existing generative models. The proposed Joint-CLAP evaluation metric correlates strongly with human judgment, showcasing Audiobox's potential for transformative applications in podcasting, movies, ads, and audiobooks.

13 Dec 2023

AI News

GA-XGBoost: A Comprehensive Approach to Bank Customer Churn Prediction

Hybrid RidgeGAN: Predicting Transportation Indices in Small and Medium-Sized Indian Cities

RefCap: Advancing Image Captioning through User-Defined Object Relationships

Swin-APT: Enhancing Semantic Segmentation in Intelligent Transportation Systems

Intelligent Manufacturing in Plastic Injection Molding Using Artificial Neural Network and Genetic Algorithms

LlamaGuard: A Robust LLM-Based Model for Safety-Focused Human-AI Conversations

Spatial Variation-Dependent Verification for Enhanced Handwriting Identification using Artificial Intelligence

CYBERSECEVAL: Benchmarking Cybersecurity Risks in Large Language Models for Secure Code Generation

Intelligent Vehicle Driving Risk Assessment in Multi-Vehicle Interaction Scenarios: A Comprehensive Review

Improved Deep Reinforcement Learning for Collision-Free Routing in Vehicular Ad-Hoc Networks

Pearl: A Versatile Reinforcement Learning Agent for Real-World Challenges

Automated Feline Pain Assessment: Deep Learning Predicts Facial Landmarks and Grimace Scale Scores

SZU-EmoDage: A Novel AI-Synthesized Facial Dataset for Cross-Cultural Emotion and Age Studies

Enhancing Biomedical Named Entity Recognition with Dictionary-Based Matching Graph Network

LogEI: Addressing Numerical Challenges in Bayesian Optimization

Double Camouflage Encryption of QR Codes for Secure UAV Landing

GLanCE: Revolutionizing Global Land Cover Estimation with Cloud Computing and Machine Learning

Enhancing IoT Security: Evaluating Tree-Based Machine Learning Algorithms for Botnet Detection

Homomorphic Encryption and Dynamic Sparse Attention for Advancing Privacy in Dialogue Models

Audiobox: Advancing Controllable Audio Generation with Unified Models

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