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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 29 Documents
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Optimization of Organizational Performance by Utilization of AI for Strategic Management Insights Loso Judijanto; Asfahani Asfahani; Syamsul Muqorrobin; Nova Krisnawati
Journal of Artificial Intelligence and Development Vol. 1 No. 2 (2022): AI Development
Publisher : Edujavare Publishing

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Abstract

In response to the complexities of modern markets, organizations are increasingly turning to Artificial Intelligence (AI) as a transformative tool for enhancing strategic decision-making and organizational performance. This study investigates the impact of AI utilization on optimizing organizational performance within strategic management. A qualitative research method was employed, utilizing semi-structured interviews to gather insights from key organizational informants. The findings highlight the significant role of organizational culture, leadership support, and collaborative approaches in maximizing AI's potential for agility, informed decision-making, and competitive advantage. The analysis contributes to a deeper understanding of AI's implications for strategic management and underscores the importance of holistic approaches in leveraging AI for sustained performance enhancement.
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.
Enhancing Stock Price Prediction through Attention-BiLSTM and Investor Sentiment Analysis Kangming Xu; Biswajit Purkayastha
Journal of Artificial Intelligence and Development Vol. 3 No. 2 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

The change of stock price is the focus of investors in the stock market, so stock price trend prediction has always been a hot topic in quantitative investment research. Traditional machine learning prediction model is difficult to deal with nonlinear, high frequency and high noise stock price time series, which makes the prediction accuracy of stock price trend low. In order to improve the forecasting accuracy, the temporal characteristics of stock price data are studied. A bidirectional long short-term memory neural network combining empirical mode decomposition (EMD), investor sentiment and attention mechanism is proposed to predict the rise and fall of stock prices. First, the empirical mode decomposition algorithm is used to extract the characteristics of stock price time series on different time scales, and the investor complex index of the text from the close of the last trading day to the opening of the next trading day is extracted by constructing the all-inclusive sentiment dictionary.The realization of a stock price trend prediction model based on Attention-BiLSTM involves combining the Bidirectional Long Short-Term Memory (BiLSTM) network with an attention mechanism. The BiLSTM processes data points from both past and future for better context understanding, while the attention mechanism selectively focuses on crucial information, improving the model's predictive accuracy in capturing and utilizing patterns in stock price movements. This sophisticated approach enhances the model's ability to forecast stock trends effectively.
Integrating Artificial Intelligence with KMV Models for Comprehensive Credit Risk Assessment Kangming Xu; Vishal Jangir
Journal of Artificial Intelligence and Development Vol. 3 No. 2 (2024): AI Development
Publisher : Edujavare Publishing

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Abstract

With the continuous development of artificial intelligence and various new intelligent algorithm technologies, the business contacts between various institutions within financial enterprises are gradually increasing, and traditional financial risk management can no longer adapt to the current status quo in the era of big data. The lack of information sharing among institutions can reduce the efficiency of financial management and adversely affect the operation of enterprises. At present, financial credit risk mainly includes credit risk, market risk and operational risk. Credit risk relates to the possibility that a borrower will not be able to repay loans or debts on time, market risk covers potential losses caused by market volatility, price changes and adverse events, while operational risk includes risks such as internal operational errors, technical failures and fraud, which may adversely affect the normal operations and financial condition of a financial institution. These risk factors need to be integrated and managed in the financial sector to ensure financial stability and customer trust. Therefore, this paper aims to establish a KMV financial credit risk model, continuously strengthen the internal risk management of enterprises, achieve management modeling and a good KMV algorithm mechanism, and realize the cooperation and stickiness between customers and enterprises, so as to avoid unnecessary financial risks
Data Security Analysis in AI Systems: Risks and Protection Strategies in the Digital Era Judijanto, Loso
Journal of Artificial Intelligence and Development Vol. 2 No. 1 (2023): AI Development
Publisher : Edujavare Publishing

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Abstract

This research focuses on the analysis of data security risks in Artificial Intelligence (AI) systems, particularly in the context of the growing challenges posed by the digital era. With the increasing reliance on AI for processing sensitive data, vulnerabilities such as adversarial attacks, privacy violations, and data breaches have become significant concerns. The primary objective of this study is to identify these risks, evaluate existing protection strategies, and propose effective solutions to enhance data security in AI systems. A mixed-methods approach was employed, combining a comprehensive literature review with qualitative and quantitative data collection, including case studies, expert interviews, and statistical analysis of AI security incidents. The results revealed that while traditional security measures like encryption and access control are essential, they are insufficient to address the unique risks posed by AI technologies. Emerging techniques such as federated learning, differential privacy, and adversarial training were found to offer promising solutions but face challenges in terms of implementation and model accuracy. The research concluded that a holistic approach, integrating both traditional cybersecurity practices and AI-specific strategies, is necessary to safeguard sensitive data in AI systems. This study contributes to the field by offering practical insights into current AI security issues and proposing recommendations for improving data protection mechanisms. Future research should focus on enhancing the scalability and efficiency of these protection strategies to ensure their effective application in diverse real-world AI systems.
The Role of Natural Language Processing (NLP) in Advancing Language Learning Technology in Educational Platforms Judijanto, Loso
Journal of Artificial Intelligence and Development Vol. 2 No. 2 (2023): AI Deevelopment
Publisher : Edujavare Publishing

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Abstract

This research explores the role of Natural Language Processing (NLP) in enhancing language learning technologies within educational platforms. With the increasing reliance on digital tools for language education, NLP technologies offer promising solutions for personalizing learning, providing real-time feedback, and improving learner engagement. The study aims to investigate how NLP is applied in language learning platforms, identify its benefits and challenges, and explore its potential for further development. Using a qualitative approach, the research includes case studies of popular language learning platforms such as Duolingo and Babbel, and interviews with educators, language learners, and platform developers. Thematic analysis was employed to examine the data, identifying key themes such as personalized learning, learner engagement, and conversational simulation. The findings indicate that NLP significantly enhances personalized learning experiences by adapting content to individual learner needs and providing immediate feedback, which improves learner retention and motivation. However, challenges related to the system's ability to capture linguistic nuances and regional variations, as well as concerns about data privacy, were also identified. This study concludes that while NLP has great potential to transform language education, there are still limitations that need to be addressed. Future research should focus on refining NLP algorithms to handle complex language structures and cultural contexts, as well as addressing ethical concerns regarding data security. The study contributes valuable insights for educators, developers, and policymakers looking to integrate NLP into language learning platforms.
Optimization of Deep Learning Algorithms for Medical Image Detection in Cloud Computing-Based Health Applications Putri, Desfita Eka; Prayudani, Santi; Sitopu, Joni Wilson
Journal of Artificial Intelligence and Development Vol. 4 No. 1 (2025): Journal of Artificial Intelligence and Development
Publisher : Edujavare Publishing

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Abstract

The integration of deep learning into cloud-based healthcare systems has opened new frontiers in medical image analysis, enabling faster, more accurate, and accessible diagnostics. However, the high computational demands of conventional deep learning models pose significant challenges for deployment in cloud environments, especially in latency-sensitive and resource-limited settings. This study aims to optimize deep learning algorithms to enhance their efficiency and scalability for medical image detection within cloud computing infrastructures. A quantitative research approach was employed, involving algorithmic optimization techniques such as pruning, quantization, transfer learning, and federated learning. The models were tested using benchmark medical image datasets and deployed in a simulated cloud environment to evaluate performance metrics such as accuracy, inference time, resource usage, and privacy compliance. Results showed that optimized models, particularly EfficientNet with pruning and quantization, achieved high diagnostic accuracy (up to 91.7%) while significantly reducing computational overhead. Federated learning proved effective in maintaining data privacy with minimal loss in accuracy. The findings suggest that lightweight, secure, and fast deep learning models can be realistically integrated into cloud-based healthcare applications. This study contributes a framework for efficient and scalable AI deployment in clinical settings, particularly in underserved or remote areas.

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