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The Journal of Artificial Intelligence and Development
Published by Edujavare Publishing
ISSN : -     EISSN : 30317428     DOI : https://doi.org/10.xxx/
Core Subject : Science, Education, Social,
Computer Science & IT Environmental Science Mathematics Social Sciences Other
The Journal of Artificial Intelligence and Development (e-ISSN: 3031-7428) is dedicated to the rapid dissemination of important research results to the global artificial intelligence (AI) community. The journal’s scope encompasses all areas of AI, including agents and multi-agent systems, automated reasoning, constraint processing and search, knowledge representation, machine learning, natural language, planning and scheduling, robotics and vision, and uncertainty in AI.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 4 Documents
 1 
Search results for , issue "Vol. 3 No. 1 (2024): AI Development" : 4 Documents clear
The Role of Artificial Intelligence in the Development of Digital Era Educational Progress Baso Intang Sappaile; Arnes Yuli Vandika; Much Deiniatur; Nuridayanti Nuridayanti; Opan Arifudin
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

In recent years, the transformative impact of artificial intelligence (AI) on educational progress in the digital era has been revealed. This research aims to explain how AI can revolutionize the educational landscape, driving inclusivity, equality, and innovation. This research method uses a qualitative approach and a multilateral approach which includes theoretical analysis and empirical findings. This research explains the role of AI in personalized learning, accessibility, educator empowerment, and ethical considerations. These findings reveal the potential for AI to enhance the learning experience by providing customized content, encouraging inclusivity through flexible learning modalities, and empowering educators with actionable insights. However, ethical issues such as data privacy and algorithmic bias require careful consideration. The study concludes with recommendations for future research, advocating further exploration of the efficacy of AI in addressing specific educational challenges and interdisciplinary collaboration to inform the responsible integration of AI in education, ultimately paving the way for a more equitable and innovative educational landscape in the digital world. era.
Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores Yadong Shi; Fu Shang; Zeqiu Xu; Shuwen Zhou
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

This paper presents a novel approach to recommendation systems by integrating emotion analysis from user reviews with deep learning techniques. We propose an Emotion-Driven Deep Learning Recommendation System (ED-DLRS) that mines user preferences and predicts scores by leveraging both the semantic content and emotional context of reviews. Our framework incorporates a dual-perspective emotion modeling strategy, considering both global emotion influence across the user base and localized emotional patterns of individual users. We introduce a deep neural network architecture that effectively fuses these emotion representations with latent user and item features. Extensive experiments on real-world datasets demonstrate that ED-DLRS significantly outperforms state-of-the-art recommendation methods, particularly in addressing the cold-start problem and data sparsity issues. Our results show an average improvement of 12% in prediction accuracy and a 15% increase in recommendation relevance compared to baseline models. Furthermore, we provide insights into the impact of different types of emotions on recommendation quality and user satisfaction. This work opens new avenues for emotion-aware, personalized recommendation systems that can enhance user experience in e-commerce and content delivery platforms
Personalized UI Layout Generation using Deep Learning: An Adaptive Interface Design Approach for Enhanced User Experience Xiaoan Zhan; Yang Xu; Yingchia Liu
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

This study presents a new approach to personalized UI design using deep learning techniques to improve user experience through interface customization. We propose a hybrid VAE-GAN architecture combining variational autoencoders and generative adversarial networks to create coherent and user-specific UI layouts. The system includes user-friendly electronic models that capture personal preferences and behaviors, enabling real-time personalization of interactions. Our methodology leverages large-scale UI design datasets, and user interaction logs to train and evaluate the model. Experimental results demonstrate significant improvements in layout quality, personalization accuracy, and user satisfaction compared to existing approaches. A customer research study with 200 participants from different cultures proves the effectiveness of the personalization model in real situations. The system achieves a personalization accuracy of 0.89 ± 0.03 and a transfer speed of 1.2s ± 0.1s, the most efficient state-of-the-art UI personalization system. In addition, we discuss the theoretical implications of our approach to UI/UX design principles, potential business applications, and ethical considerations around AI-driven identity. This research contributes to advancing adaptive interface design and opens up new ways to integrate deep learning with UI/UX processes
A Personalized Causal Inference Framework for Media Effectiveness Using Hierarchical Bayesian Market Mix Models Xin Ni; Yitian Zhang; Yanli Pu; Ming Wei; Qi Lou
Journal of Artificial Intelligence and Development Vol. 3 No. 1 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

This study presents a novel framework for personalized causal inference in media effectiveness using Hierarchical Bayesian Market Mix Models (ABM). The proposed approach integrates individual-level data with aggregate market information to estimate personalized media effects while addressing the challenges of data sparsity and high dimensionality. By combining the identity layer and the optimization process in a Bayesian hierarchical model, the model captures heterogeneity across consumers and provides robust predictions of individual causality. Affect different media. The framework is used for e-commerce business data, which includes 500,000 customers across 50 markets in 24 months. The model shows better prediction performance than the integrated business model, with a 30.4% reduction in RMSE. Empirical results reveal significant heterogeneity in media effectiveness across channels and consumer segments. Email marketing emerges as the most effective channel on average, followed by TV advertising, digital display ads, and social media engagements. Sensitivity analyses and robustness checks, including alternative prior specifications and placebo tests, support the validity of the estimated causal effects. The findings provide valuable insights for media planning and marketing strategy, highlighting the importance of tailored budget allocation and campaign design approaches. This research contributes to the growing body of literature on personalized marketing analytics and offers a powerful tool for estimating individualized media effects in complex marketing environments.

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