American Journal of Artificial Intelligence and Neural Networks
The American Journal of Artificial Intelligence and Neural Networks is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the fields of artificial intelligence (AI) and neural networks. It serves as a platform for scholars and professionals to share innovative methodologies, applications, and case studies that advance the understanding and practice of these disciplines.
All articles published in the American Journal of Artificial Intelligence and Neural Networks (AJAINN) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license: Authors retain full copyright of their work. Readers are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially. Proper credit must be given to the original author(s) and the source, a link to the license must be provided, and any changes made must be indicated. This open licensing ensures maximum visibility and reusability of research while maintaining author rights.
On-Device Large Language Models and AI Agents for Real-Time Mobile User Experience Optimization
The rapid advancement of artificial intelligence has enabled the deployment of large language models (LLMs) directly on mobile devices, transforming how users interact with their smartphones and tablets. This review examines the current state of on-device large language models (LLMs) and artificial intelligence (AI) agents designed for real-time mobile user experience (UX) optimization. The integration of natural language processing (NLP) capabilities into edge computing environments presents unique opportunities for personalized, privacy-preserving, and responsive mobile applications. This paper synthesizes recent developments in model compression techniques, efficient inference architectures, and AI-driven personalization strategies that enable sophisticated language understanding without cloud dependency. We explore how on-device LLMs facilitate context-aware assistance, predictive text generation, intelligent content recommendation, and adaptive interface design. The review also addresses critical challenges including computational constraints, energy efficiency, model accuracy trade-offs, and real-time performance requirements. By analyzing recent publications from 2019 to 2024, we identify emerging trends in mobile AI deployment, examine the technical innovations that make real-time language processing feasible on resource-constrained devices, and discuss future directions for enhancing mobile UX through intelligent on-device agents. Our findings suggest that the convergence of model optimization techniques and hardware acceleration is creating unprecedented opportunities for delivering sophisticated AI-powered experiences while maintaining user privacy and reducing latency.
Copyright (c) 2025 Wenbin Shang, Zimeng Wang, Boyuan Wang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
AI and Neural Networks in Digital Content Creation and Curation
The rapid development of artificial intelligence (AI) and neural networks has transformed various industries, including digital content creation and curation. These advanced technologies have enabled the automation of tasks that were previously time-consuming and resource-intensive, such as content generation, categorization, and personalization. This article explores the role of AI and neural networks in digital content creation and curation, focusing on their applications in fields like media, entertainment, marketing, and education. We discuss the benefits, challenges, and potential future directions for AI-powered content creation tools, as well as their impact on content consumption and user engagement.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
AI and Neural Networks for Real-Time Translation Services
Real-time translation services are transforming global communication by breaking down language barriers in various sectors, including business, healthcare, and education. Artificial intelligence (AI) and neural networks have significantly advanced the capabilities of real-time translation systems, enabling faster, more accurate, and context-aware translations. This article explores the use of AI and neural networks in real-time translation, focusing on neural machine translation (NMT) models, speech recognition, and multimodal translation systems. Additionally, the article discusses the challenges, benefits, and future directions for AI-powered translation technologies
Enhancing Autonomous Drone Navigation with Neural Networks
Autonomous drones have vast potential for applications in various fields, including delivery, surveillance, search and rescue, and environmental monitoring. Neural networks are playing a crucial role in enhancing drone navigation systems, enabling them to make real-time, context-aware decisions based on sensor data. This article explores how neural networks, particularly deep learning models, are used to improve autonomous drone navigation, including object detection, path planning, obstacle avoidance, and adaptive decision-making. We examine the challenges and opportunities of using neural networks in drone navigation systems, including real-time processing, safety concerns, and the integration of AI models with existing drone hardware.
Using AI to Improve Social Services with Neural Networks
Artificial intelligence (AI) and neural networks are being increasingly applied to improve social services by enhancing decision-making, resource allocation, and service delivery. These technologies have the potential to significantly increase the efficiency and effectiveness of social programs by automating routine tasks, analyzing complex datasets, and providing predictive insights. This article explores the applications of AI and neural networks in the field of social services, focusing on areas such as welfare programs, healthcare access, and public safety. The article also examines the challenges, ethical considerations, and future directions for integrating AI into social service systems.
Deep Learning Models for Image and Voice-Based Authentication Systems
Authentication systems are critical for ensuring the security and privacy of users in digital environments. Traditional authentication methods, such as passwords and PINs, are often vulnerable to security breaches. Recent advancements in deep learning have enabled the development of more secure and efficient image and voice-based authentication systems. This article explores the role of deep learning models, such as convolutional neural networks (CNNs) for image authentication and recurrent neural networks (RNNs) for voice authentication, in enhancing the accuracy and security of biometric authentication systems. We discuss the benefits, challenges, and future directions for these systems in real-world applications.
Deep Learning for Real-Time Monitoring of Industrial Systems
Real-time monitoring of industrial systems is critical for ensuring operational efficiency, minimizing downtime, and maintaining safety. Deep learning techniques have proven to be highly effective in analyzing complex industrial data, enabling predictive maintenance, fault detection, and process optimization. This article explores the application of deep learning models in real-time monitoring of industrial systems, highlighting their potential to improve productivity and reduce operational risks. It also addresses the challenges, benefits, and future directions for implementing deep learning in industrial environments.
Neural Networks for Predictive Modeling in Disaster Management
Neural networks have shown great potential in predictive modeling for disaster management. By analyzing large and complex datasets, these AI models can provide accurate forecasts of disaster events, helping authorities to make timely decisions for effective response and mitigation. This article explores the use of neural networks, particularly deep learning techniques, in predicting natural disasters such as earthquakes, floods, hurricanes, and wildfires. We discuss various applications, including early warning systems, resource allocation, and evacuation planning, as well as the challenges and ethical considerations in the use of AI in disaster management.
Neural Networks for Financial Risk Assessment in Investment Strategies
Financial risk assessment plays a critical role in investment strategies, as it helps investors make informed decisions by identifying and mitigating potential risks. Neural networks have emerged as powerful tools in financial risk assessment, leveraging complex data patterns to predict market trends, identify risks, and optimize investment portfolios. This article explores the applications of neural networks in financial risk assessment, focusing on their use in market prediction, credit scoring, and portfolio optimization. It also discusses the challenges, limitations, and future directions for using neural networks in financial decision-making.
Neural Networks for Supply Chain Demand Forecasting
Accurate demand forecasting is critical for optimizing supply chain operations, ensuring that products are available when needed while minimizing excess inventory. Neural networks, particularly deep learning models, have shown great promise in improving the accuracy of demand forecasting by analyzing large datasets and identifying complex patterns. This article explores the application of neural networks in supply chain demand forecasting, focusing on key techniques such as recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs). We discuss the benefits and challenges of using neural networks for demand forecasting, as well as their potential to transform supply chain management.
AI-Powered Personal Assistants: The Future of Customer Interaction
AI-powered personal assistants are rapidly becoming an integral part of customer interaction across various industries, offering personalized, efficient, and automated solutions. These assistants utilize advanced machine learning, natural language processing, and speech recognition technologies to understand and respond to customer queries. This article explores the evolution of AI-powered personal assistants, their applications in customer service, and their potential to transform industries such as retail, healthcare, and finance. Additionally, the article discusses the challenges, ethical considerations, and future directions for AI-powered personal assistants in enhancing customer experience
AI-Driven Decision Support for Autonomous Vehicles
Autonomous vehicles (AVs) represent a paradigm shift in transportation, offering the potential to increase safety, reduce traffic, and enhance mobility. AI-driven decision support systems play a crucial role in enabling AVs to make real-time, context-aware decisions. This article explores the application of AI techniques such as machine learning, computer vision, and reinforcement learning in decision-making for AVs. We examine the key challenges in developing decision support systems for AVs, including safety, interpretability, and ethical concerns, and discuss the future directions for these technologies in autonomous driving.
Real-Time AI-Based Fraud Detection Systems with Neural Networks
Fraud detection is a critical component of financial systems, as fraudulent activities continue to evolve in sophistication and scale. Artificial intelligence (AI) and neural networks have emerged as powerful tools for real-time fraud detection, enabling systems to analyze vast amounts of data and identify suspicious activities with high accuracy. This article explores the role of AI and neural networks in developing real-time fraud detection systems, highlighting their applications, challenges, and future directions. It focuses on how deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are being utilized to enhance fraud detection across various industries
AI-Powered Neural Networks for Music Composition and Generation
The application of artificial intelligence (AI) in music composition and generation has gained significant interest in recent years. Neural networks, particularly deep learning techniques, are now being used to create original compositions, remix existing music, and even generate new sounds. This article explores the role of AI-powered neural networks in music generation, focusing on key methods such as recurrent neural networks (RNNs), generative adversarial networks (GANs), and transformers. We discuss the potential of these technologies to revolutionize music creation, as well as their implications for the music industry, musicians, and listeners..
Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-nd/4.0
Deep Learning in Predicting Health Outcomes and Disease Progression
Deep learning has emerged as a transformative technology in healthcare, offering significant potential to predict health outcomes and disease progression. By analyzing large volumes of complex patient data, including medical imaging, genetic information, and electronic health records, deep learning models can uncover patterns and make predictions about disease development. This article explores the role of deep learning in predicting health outcomes, with a focus on disease progression, early diagnosis, and personalized treatment strategies. It also discusses the challenges and future directions for implementing deep learning models in clinical practice.
AI and Neural Networks in Predicting Natural Disasters
Predicting natural disasters such as earthquakes, hurricanes, and floods is critical for disaster preparedness and response. Artificial intelligence (AI), particularly neural networks, has shown significant potential in improving the accuracy and timeliness of disaster prediction models. This article explores the application of AI and neural networks in predicting natural disasters, focusing on their ability to analyze complex datasets and recognize patterns that are indicative of impending disasters. We examine the use of deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in disaster prediction, and discuss the challenges and ethical considerations associated with their deployment.
Artificial Intelligence and Neural Networks for Sustainable Urban Development
Sustainable urban development is a critical aspect of addressing global challenges such as climate change, rapid urbanization, and resource management. Artificial intelligence (AI) and neural networks have the potential to significantly enhance the efficiency, livability, and resilience of urban environments. This article explores the applications of AI and neural networks in sustainable urban development, focusing on energy management, transportation optimization, and smart city infrastructure. It also discusses the challenges and future directions for integrating AI-driven solutions into urban planning and development.
Exploring the Role of Neural Networks in Predicting Weather Patterns
Weather forecasting is a complex and critical task in meteorology, requiring the analysis of vast amounts of data. Recent advancements in deep learning, specifically neural networks, have shown great promise in enhancing the accuracy of weather prediction models. This article explores the role of neural networks in predicting weather patterns, focusing on applications such as temperature forecasting, precipitation prediction, and severe weather event detection. We examine how neural networks, including deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are transforming traditional weather forecasting methods.
Deep Neural Networks for Human Activity Recognition and Monitoring
Human Activity Recognition (HAR) and monitoring have become increasingly important in various fields, including healthcare, fitness, and smart environments. Deep neural networks (DNNs) have shown great promise in accurately classifying human activities from sensor data, enabling real-time monitoring of individuals' movements and behaviors. This article explores the application of DNNs in human activity recognition, with a focus on sensor-based data, model architectures, and real-time monitoring systems. It also discusses the challenges and future directions of HAR systems, emphasizing the role of DNNs in advancing personalized health monitoring and smart environments. Human Activity Recognition (HAR) and monitoring have become increasingly important in various fields, including healthcare, fitness, and smart environments. Deep neural networks (DNNs) have shown great promise in accurately classifying human activities from sensor data, enabling real-time monitoring of individuals' movements and behaviors. This article explores the application of DNNs in human activity recognition, with a focus on sensor-based data, model architectures, and real-time monitoring systems. It also discusses the challenges and future directions of HAR systems, emphasizing the role of DNNs in advancing personalized health monitoring and smart environments.
AI in Robotics: Neural Networks for Autonomous Robot Behavior
The integration of artificial intelligence (AI) and robotics has revolutionized the development of autonomous robotic systems. Neural networks, particularly deep learning techniques, are a key component in enabling robots to perform complex tasks with minimal human intervention. This article explores the role of neural networks in autonomous robot behavior, including decision-making, perception, navigation, and task execution. We discuss how neural networks enable robots to learn from their environments, improve their functionality over time, and perform tasks autonomously in various applications, such as manufacturing, healthcare, and service industries.
Neural Networks for Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) are revolutionizing modern transportation infrastructure by using advanced technologies to improve the efficiency, safety, and sustainability of transportation networks. Neural networks, particularly deep learning models, are playing a crucial role in enhancing ITS by providing capabilities for traffic prediction, autonomous driving, route optimization, and incident detection. This article explores the applications of neural networks in ITS, focusing on their impact on traffic management, autonomous vehicles, and real-time decision-making systems. It also discusses the challenges, future trends, and ethical considerations associated with the integration of neural networks in transportation systems.
Neural Networks for Real-Time Sports Analytics and Performance Prediction
Neural networks are increasingly being applied in sports analytics to provide real-time insights into player performance, team strategies, and game outcomes. These AI-driven models are capable of processing large datasets from various sources, including player statistics, game footage, and biometric data, to predict performance and provide valuable decision support. This article explores the role of neural networks in real-time sports analytics, focusing on their applications in performance prediction, injury prevention, and tactical analysis. We also discuss the challenges and future opportunities for using AI to enhance sports performance and provide actionable insights for coaches, players, and analysts.
Deep Learning Techniques for Real-Time Video Processing and Analysis
Real-time video processing and analysis have become critical components in various applications, including security surveillance, autonomous vehicles, healthcare, and entertainment. Deep learning techniques, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, have significantly improved the performance of video processing systems. This article explores the role of deep learning in real-time video analysis, focusing on applications such as object detection, motion tracking, event recognition, and video classification. We examine the challenges involved in processing large video data in real-time and the potential solutions deep learning offers to address these challenges.
AI-Powered Facial Recognition in Security Systems
Facial recognition technology, powered by artificial intelligence (AI), is rapidly becoming a key component of modern security systems. AI-driven facial recognition systems can analyze and match facial features with a high degree of accuracy, enabling enhanced surveillance, access control, and identification capabilities. This article explores the role of AI-powered facial recognition in security systems, highlighting its applications in various industries, the challenges it faces, and the future directions of this technology. It also discusses the ethical and privacy concerns associated with facial recognition and its potential impact on society.
Predicting Financial Market Movements with Deep Learning Techniques
The prediction of financial market movements is a challenging task due to the volatility and complexity of market data. Deep learning techniques, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs), have shown great promise in predicting market trends by analyzing large datasets and capturing hidden patterns. This article explores the application of deep learning in financial market prediction, focusing on techniques such as RNNs, LSTMs, and CNNs for analyzing time-series data and financial indicators. Additionally, the article examines the challenges and opportunities of using deep learning in the financial industry, including issues related to data quality, model interpretability, and risk management.
Enhancing AI-Based Speech Recognition Systems for Multilingual Support
Speech recognition systems have made significant advancements in recent years, driven by artificial intelligence (AI) and machine learning algorithms. However, most systems remain predominantly focused on a single language or a limited set of languages. This article explores the challenges and opportunities in enhancing AI-based speech recognition systems to support multiple languages, with a focus on developing models that can recognize and process speech across diverse linguistic contexts. We discuss the role of neural networks in multilingual speech recognition, examine strategies for improving system accuracy and adaptability, and consider the societal implications of making speech recognition accessible to a broader range of languages.
The Role of Deep Learning in Augmented Reality Applications
Augmented Reality (AR) has gained significant attention in recent years due to its ability to blend digital content with the real world in real-time. Deep learning has played a key role in advancing AR applications by providing more accurate object recognition, scene understanding, and real-time data processing. This article explores the role of deep learning in AR, focusing on its applications in real-time object tracking, facial recognition, spatial mapping, and natural language processing. We also examine how deep learning enables improved user experiences and performance in various AR fields, including gaming, healthcare, education, and manufacturing.
The Future of Neural Networks in Real-Time Healthcare Monitoring
Real-time healthcare monitoring has become a crucial component of modern healthcare systems, providing continuous data collection and analysis to improve patient outcomes. Neural networks, particularly deep learning models, have shown great potential in enhancing real-time monitoring by analyzing vast amounts of patient data, detecting anomalies, and enabling early interventions. This article explores the role of neural networks in real-time healthcare monitoring, focusing on their applications in personalized health tracking, disease prediction, and decision support systems. It also discusses the challenges and future directions for integrating neural networks into real-time healthcare systems.
Enhancing Facial Recognition Systems with Deep Neural Networks
Facial recognition systems have evolved significantly over the past decade, driven by advances in deep neural networks (DNNs). This article explores how deep learning models, particularly convolutional neural networks (CNNs), are enhancing facial recognition systems by improving accuracy, speed, and robustness. We examine the architecture of deep neural networks used in facial recognition, including CNNs and deep face models, and discuss their application in various domains, such as security, healthcare, and marketing. Additionally, the article addresses challenges, ethical concerns, and future trends in the development and deployment of facial recognition technologies.
AI-Driven Smart Grid Optimization for Sustainable Energy Use
The integration of artificial intelligence (AI) into smart grids offers a powerful approach to optimizing energy distribution, enhancing efficiency, and supporting sustainable energy use. AI-driven systems can process vast amounts of real-time data from sensors, meters, and renewable energy sources, enabling grid operators to optimize energy flow, predict demand, and prevent outages. This article explores the role of AI in smart grid optimization, focusing on how machine learning algorithms, predictive analytics, and real-time decision-making are transforming energy systems. We also discuss the challenges and future opportunities of implementing AI technologies in smart grids to promote sustainability and reduce carbon footprints.