Frontiers in Science and Technology (FST) is a peer-reviewed, interdisciplinary journal that aims to publish original research, reviews, and innovations spanning the frontiers of scientific inquiry and technological advancement. It provides a collaborative platform for scholars, engineers, scientists, and innovators to disseminate transformative ideas and breakthrough discoveries. Covering a broad spectrum of domains such as artificial intelligence, sustainable energy, biotechnology, smart materials, and emerging technologies, the journal supports the development of science that directly impacts society and industry. FST maintains a strong commitment to academic excellence, rapid peer review, open access, and global reach, ensuring that high-quality research is accessible to all.
Towards an energy efficient 5G Network: Integrating artificial intelligence with RF circuit design
As 5G networks expand, managing energy consumption has become a significant challenge. This study investigates the integration of Artificial Intelligence (AI) with Radio Frequency (RF) circuit design to enhance energy efficiency without compromising performance. By leveraging machine learning and deep learning algorithms, we optimize RF parameters such as power scaling, beamforming, and fault prediction. Our findings show that AI-driven optimization can reduce power consumption by up to 35%, improve beamforming efficiency by 15%, and reduce system downtimes by 22% through fault prediction. Additionally, dynamic power scaling techniques contribute to a 28% increase in energy efficiency. These results demonstrate the potential of AI to drive significant improvements in 5G infrastructure, offering a path toward more sustainable, high-performance networks. Further advancements in real-time AI optimization can further enhance energy savings, providing a foundation for future energy-efficient 5G networks.
Real-Time Farm Simulation Models for Training and Research Using AI
The integration of artificial intelligence (AI) in real-time farm simulation models offers a transformative approach to enhancing agricultural training and research. Traditional farming methods often rely on static models, limiting the scope for real-world variability and predictive accuracy. This study presents a novel AI-driven farm simulation framework designed to mimic real-time farming conditions, incorporating environmental, crop, and resource management data. By leveraging machine learning algorithms and dynamic data streams, the system predicts crop performance, resource utilization, and potential risks, providing actionable insights to researchers and practitioners. The results demonstrated that the AI-driven simulation achieved a predictive accuracy of 93.4% for crop yield, with a 20% improvement in decision-making efficiency compared to existing static models. Additionally, the system’s interactive interface and real-time updates significantly enhanced user engagement and learning outcomes. The framework also facilitated scenario testing, allowing users to evaluate the impact of various farming strategies under different environmental conditions. This research establishes a scalable and robust platform for improving agricultural education and decision-making, addressing critical challenges in resource optimization and sustainable farming practices. Future work will focus on expanding the system's datasets and incorporating advanced AI techniques to enhance predictive capabilities further
Real time traffic management using convolutional neural networks in smart city IoT frameworks
Rapid urbanization and increasing vehicular density have led to severe traffic congestion, excessive fuel consumption, and increased pollution levels in modern cities. Traditional traffic management systems lack real-time adaptability and often fail to optimize traffic flow effectively, resulting in significant delays and inefficiencies. To address these challenges, this study presents a Convolutional Neural Network (CNN)-based real-time traffic management system integrated into an Internet of Things (IoT) framework. The proposed approach utilizes real-time video surveillance and sensor data, processed by CNN models for vehicle detection, traffic density estimation, and anomaly detection. The system dynamically adjusts traffic signals and optimizes vehicle movement using edge computing to minimize latency. Experimental results demonstrate a 29.4% reduction in congestion, a 35.7% improvement in vehicle detection accuracy compared to traditional systems, and a 21.8% enhancement in traffic signal efficiency. Additionally, fuel consumption and carbon emissions were reduced by 18.2% and 15.9%, respectively. These findings highlight the effectiveness of AI-driven traffic control in improving urban mobility, reducing environmental impact, and enhancing road safety. The integration of CNNs with IoT-based infrastructure provides a scalable and adaptive solution for smart city transportation networks.
Predictive AI Models for Designing Sustainable Crop Insurance Programs
The increasing volatility of climate change and market conditions poses significant challenges to traditional crop insurance programs, which often fail to address the dynamic risks faced by farmers. Predictive AI models offer a transformative solution by leveraging machine learning and data analytics to design sustainable crop insurance programs. This study explores the development and application of predictive models using historical climate, crop yield, and market data to assess risk and recommend optimized insurance premiums. Advanced techniques such as ensemble learning, time-series forecasting, and geospatial analysis are utilized to enhance the accuracy of risk predictions. The results demonstrate that the AI-driven framework achieves a 15% reduction in premium costs for low-risk farmers while increasing loss coverage by 25% compared to traditional methods. The model also enables dynamic adjustments to insurance terms based on real-time weather and market conditions, ensuring greater adaptability and resilience. This research highlights the potential of predictive AI to create equitable, cost-effective, and sustainable crop insurance programs that address the challenges of modern agriculture.
Optimizing human sleep patterns using AI-Driven Insights from Wearable data Abd behavioral analysis
Sleep optimization is highly important for an individual's general well-being; however, many traditional methods that are used in improving sleep patterns lack personalization. This research explores the feasibility of AI-based insights obtained through wearable data and behavioral analysis for optimizing human sleep patterns. We used a dataset of sleep metrics from 5,000 participants over six months to analyze sleep duration, efficiency, and disturbances using machine learning models, including Random Forest and Long Short-Term Memory (LSTM) networks. Feature selection techniques identified key determinants, such as heart rate variability, movement patterns, and sleep latency. Results indicate that AI-driven models improved sleep efficiency predictions by 23% compared to conventional heuristics. Personalized sleep recommendations reduced sleep onset latency by an average of 14 minutes and increased deep sleep duration by 18%. Compared to self-reported improvements, AI-assisted insights showed a 30% higher accuracy in predicting sleep disturbances. These findings highlight the potential of integrating AI and wearable technology for personalized sleep enhancement strategies. Future work will focus on real-time feedback mechanisms and expanding datasets to diverse demographics.
Neuromorphic computing architectures for enhanced signal processing in autonomous systems
The increasing complexity of urban traffic management demands innovative solutions to enhance traffic flow, minimize congestion, and improve safety. Traditional methods often fall short in handling real-time traffic prediction and signal optimization. This study proposes a hybrid approach that combines Convolutional Neural Networks (CNNs) with the Internet of Things (IoT) framework to predict traffic flow and optimize traffic signals dynamically in smart cities. Data from IoT sensors installed across urban locations is fed into a CNN model, which forecasts traffic patterns and adjusts signal timings accordingly. Our findings show that the CNN-IoT model achieves 92.5% accuracy in traffic flow prediction, reduces congestion by 18%, and enhances signal efficiency by 15%. The system is capable of adapting to unforeseen traffic conditions, demonstrating significant improvements over traditional methods. This study highlights the potential of combining deep learning and IoT for real-time traffic management and sets the stage for scaling such systems in larger urban settings for further validation
Microbial Soil Health Management Systems Using AI
Soil health plays a pivotal role in sustainable agriculture, influencing crop productivity and ecosystem stability. However, traditional methods of soil management often lack the precision and adaptability needed to address the dynamic nature of soil environments. This study proposes an AI-driven microbial soil health management system designed to optimize soil conditions through the analysis of microbial diversity, nutrient availability, and environmental factors. The system utilizes machine learning algorithms, such as Random Forest (RF), Support Vector Machines (SVM), and Deep Learning (DL) models, to predict microbial activity and identify patterns in soil health based on real-time data from IoT sensors and remote sensing technology. In a case study involving various agricultural ecosystems, the system demonstrated a 25-30% improvement in soil health prediction accuracy compared to conventional methods. Additionally, the model successfully identified critical microbial species linked to soil fertility, enhancing nutrient cycling and promoting soil resilience. This research highlights the potential of AI in advancing microbial soil health management, offering a scalable, data-driven solution that enhances soil sustainability, optimizes agricultural productivity, and supports eco-friendly farming practices
Hybrid quantum computing and deep learning approaches for enhaving wireless communication security
The rapid expansion of wireless communication networks has introduced significant security challenges, particularly with the rise of sophisticated cyberattacks. Traditional cryptographic methods often struggle to balance security and computational efficiency, necessitating innovative approaches. In this study, we propose a hybrid quantum computing and deep learning framework to enhance wireless communication security. The quantum computing component leverages quantum key distribution (QKD) to establish theoretically unbreakable encryption, while deep learning models—trained on adversarial attack datasets—enhance intrusion detection and anomaly recognition. Experimental results demonstrate that our hybrid approach improves attack detection accuracy to 98.7%, surpassing conventional machine learning models by 5.2%. The quantum-enhanced cryptographic layer reduces key exchange vulnerabilities, leading to a 40% improvement in resistance against man-in-the-middle attacks compared to classical schemes. Our findings underscore the potential of integrating quantum computing with deep learning for robust, scalable, and future-proof wireless security solutions
Hybrid AI-Electronic systems for Real-Time Edge Processing in IoT networks
The rapid expansion of the Internet of Things (IoT) has driven the need for real-time, low-latency data processing at the edge. Traditional cloud-based architectures suffer from high transmission delays, increased energy consumption, and privacy concerns, limiting their efficiency in time-sensitive applications. To address these challenges, this paper proposes a Hybrid AI-Electronic System for Real-Time Edge Processing in IoT Networks, integrating artificial intelligence (AI) with electronic edge computing to enhance performance, efficiency, and decision-making capabilities. The proposed system utilizes lightweight AI models optimized for edge devices, reducing computational overhead while maintaining accuracy. A hybrid architecture combining neuromorphic computing, FPGA accelerators, and energy-efficient processors ensures rapid inference and adaptive learning at the edge. Experiments conducted on real-world IoT datasets demonstrate a 34% improvement in processing speed, a 22% reduction in energy consumption, and 15% higher inference accuracy compared to conventional cloud-based approaches. These results highlight the feasibility and effectiveness of hybrid AI-electronic systems in enabling scalable, real-time, and intelligent IoT networks. Future research will focus on enhancing security, improving model adaptability, and integrating self-learning mechanisms to further optimize performance.
Federated Learning Systems for Enhancing AI in Precision Agriculture
Federated learning (FL) has emerged as a promising solution for enhancing AI applications in precision agriculture, particularly in data-sensitive environments. By enabling machine learning models to be trained on decentralized data across multiple devices or farms, FL addresses concerns related to data privacy, security, and the need for large, centralized datasets. This study explores the potential of federated learning systems in optimizing agricultural processes, such as crop yield prediction, soil health monitoring, and pest detection, by leveraging diverse, real-time data from sensors and IoT devices spread across various farms. The key advantage of federated learning is its ability to train robust AI models without the need for raw data to leave the local farms, thus ensuring data privacy while still benefiting from collaborative learning. Our analysis demonstrates that FL can improve the accuracy of predictions and decisions in precision agriculture while reducing the communication overhead and increasing the scalability of AI solutions. Results from simulated farm scenarios show that FL-based models perform comparably to traditional centralized models, with the added benefit of greater data privacy. This work highlights the potential of federated learning as a scalable and secure framework for advancing AI-driven innovations in precision agriculture, offering a new pathway for sustainable and data-efficient farming practices.
Cross layer optimization in AI powered cognitive radio networks for dynamic spectrum access
The rapid growth of wireless communication, therefore, has led to spectrum scarcity, creating a need for efficient spectrum utilization. Cognitive Radio Networks offer solutions in the form of DSA. However, optimization of spectrum usage remains one of the great challenges because of the dynamic and uncertain nature of the environment in which wireless communications take place. The purpose of this paper is to propose an AI-powered cross-layer optimization framework to enhance DSA in CRNs. The proposed methodology integrates Machine Learning (ML) and Reinforcement Learning (RL) in optimizing spectrum sensing at the physical layer and resource allocation at the MAC layer and interference management at the network layer. The scheme automatically adapts to the changes in spectrum conditions, thus reducing interference and increasing throughput. Simulation results demonstrate that the AI-driven approach enhances spectrum efficiency by 30%, reduces interference by 20%, and increases network throughput by 15% as compared to traditional methods. This work demonstrates that AI-enabled cross-layer optimization in CRNs can significantly improve spectrum utilization, making networks more adaptive and efficient.
Block Chain-Enabled Traceable Agriculture with AI Synergy
Agriculture faces significant challenges, including inefficiencies in supply chain transparency, food safety, and sustainability. Traditional methods often lack real-time data tracking, leading to increased waste and reduced consumer trust. This study implements a blockchain-enabled traceability system combined with Artificial Intelligence (AI) to enhance agricultural practices. We deployed IoT sensors to collect data on soil conditions, crop health, and environmental factors, which were recorded on a blockchain for immutable record-keeping. AI algorithms analyzed this data to optimize resource allocation and predict yield outcomes. The integration of blockchain and AI resulted in a 30% reduction in resource usage (water and fertilizers) and a 25% increase in crop yields over a six-month period. Additionally, consumer trust improved, with a 40% increase in product traceability ratings based on surveys conducted with over 1,000 participants. The findings underscore the potential of blockchain and AI synergy in revolutionizing agricultural practices, providing a sustainable model that enhances efficiency, transparency, and consumer confidence. By adopting this integrated approach, the agricultural sector can significantly mitigate its environmental impact while ensuring food security.
Autonomous Farming Robots for Real-Time Weed Detection and Removal using YOLOv8
The increasing demand for sustainable agriculture has driven the development of autonomous farming solutions for precise weed management. Traditional weed control methods, including manual removal and chemical herbicides, are labor-intensive, environmentally harmful, and economically inefficient. This study proposes an autonomous farming robot equipped with YOLOv8 (You Only Look Once, version 8) for real-time weed detection and removal. The system integrates high-resolution cameras, deep learning-based image processing, and a robotic arm with an adaptive end-effector to eliminate weeds efficiently. The YOLOv8 model, trained on a dataset of 50,000 images, achieved an mAP@50 of 92.4%, demonstrating superior performance compared to existing state-of-the-art detection models. The robot, tested across various crop fields, achieved an average weed removal accuracy of 89.7%, reducing herbicide usage by 67% while increasing yield potential by 15%. Compared to manual weeding, the system improved operational efficiency by 58%. These findings highlight the potential of AI-driven robotic systems in enhancing agricultural productivity, minimizing chemical dependency, and promoting eco-friendly farming practices. Future work will focus on multi-weed classification, real-time adaptation to diverse field conditions, and energy-efficient navigation to further optimize performance.
Augmented Reality Applications in Vertical Farming Powered by AI
The integration of augmented reality (AR) with artificial intelligence (AI) is revolutionizing vertical farming by enhancing visualization, efficiency, and decision-making processes. This synergy addresses critical challenges in vertical farming, such as resource optimization, crop monitoring, and real-time decision support. AR applications provide immersive, data-driven visualizations of crop growth metrics, environmental conditions, and resource utilization, enabling farmers to interact with and monitor their farms remotely. AI algorithms analyze sensor data, predict crop yields, and optimize resource usage, facilitating precise control over irrigation, lighting, and nutrient delivery systems. Key findings reveal a 20% improvement in resource efficiency and a 15% increase in crop yields in experimental vertical farms equipped with AR-AI systems. Moreover, real-time anomaly detection through AI-powered AR interfaces significantly reduced response times to potential crop health issues by 30%. Comparative analyses with conventional vertical farms demonstrate the superior adaptability and operational efficiency of this integrated approach. This paper highlights how AR, powered by AI, can transform vertical farming into a more sustainable and scalable solution for urban agriculture, catering to the growing global demand for fresh produce. Future developments could focus on enhancing AR interface usability and integrating blockchain for transparent supply chain management.
An efficient approach to Data clustering using the K-Means algorithm in Big data analytics
With the exponential growth of data, efficient clustering techniques are essential for extracting meaningful patterns in Big Data Analytics. The K-Means algorithm is widely used due to its simplicity and scalability. However, its performance is often hindered by high-dimensional data, initialization sensitivity, and computational complexity. This study proposes an optimized K-Means clustering approach that integrates an improved centroid initialization method and parallel processing to enhance efficiency in Big Data environments. The proposed method was evaluated using real-world datasets such as KDD Cup and UCI Machine Learning Repository, with data sizes ranging from 10GB to 100GB. Experimental results demonstrate a 30% reduction in execution time and a 15% improvement in clustering accuracy compared to traditional K-Means. The optimized approach also shows a 20% lower convergence time, making it suitable for large-scale applications. In conclusion, the enhanced K-Means algorithm significantly improves clustering performance in Big Data settings. The combination of advanced initialization and parallel computing ensures better scalability and accuracy, making it a viable solution for real-time analytics. Future work will focus on extending this approach to handle streaming data and non-Euclidean spaces.
AI-Powered Optimization of Robotic Swarms for Agricultural Automation
Agricultural automation using robotic swarms is a promising solution to address the challenges of labor shortages, resource inefficiency, and increased demand for sustainable food production. However, optimizing the coordination, scalability, and performance of these robotic swarms remains a complex problem due to diverse environmental conditions and task requirements. This study leverages AI-powered optimization techniques, including reinforcement learning and swarm intelligence algorithms, to enhance the efficiency and adaptability of robotic swarms for agricultural tasks such as precision planting, weeding, and harvesting. The proposed methodology integrates real-time data analytics with adaptive task allocation, enabling dynamic swarm reconfiguration based on field conditions. Simulation results indicate a 25% increase in task efficiency and a 30% reduction in energy consumption compared to traditional rule-based approaches. Additionally, field trials demonstrated a significant improvement in crop yield prediction accuracy (R² = 0.92) and a 20% reduction in resource wastage. The findings underscore the potential of AI-optimized robotic swarms to revolutionize agricultural automation by improving productivity and sustainability. Future research will focus on integrating IoT and edge computing for real-time swarm management, aiming to further enhance scalability and operational resilience
AI-Driven Innovations in Agroforestry for Sustainable Land Use
Agroforestry, a synergistic land-use system combining agricultural and forestry practices, is pivotal for sustainable land management and climate resilience. Recent advancements in Artificial Intelligence (AI) have revolutionized agroforestry by enabling data-driven decision-making, optimizing resource allocation, and enhancing ecosystem services. This study explores AI-driven innovations in agroforestry, focusing on machine learning models, remote sensing, and predictive analytics for biodiversity conservation, carbon sequestration, and yield optimization. Key methodologies include the application of deep learning for species classification, geospatial analysis for land suitability mapping, and reinforcement learning for adaptive management strategies. Results demonstrate a 25% increase in yield prediction accuracy and a 30% improvement in land-use efficiency using AI tools compared to traditional approaches. Unexpected findings include the identification of microclimatic conditions as a critical determinant of species compatibility, highlighting the importance of localized data in AI models. This study underscores the transformative potential of AI in agroforestry, offering scalable solutions to enhance sustainability and productivity. Future research should focus on integrating AI with indigenous knowledge systems and addressing ethical considerations in AI deployment for equitable and inclusive land-use practices.
AI-Driven Adaptive intrusion detection system for IOT networks: Enhancing cybersecurity through deep learning techniques
AI-augmented metabolomics is revolutionizing precision medicine by enabling comprehensive analysis of metabolic pathways, biomarker discovery, and personalized therapeutic strategies. Traditional metabolomics approaches face challenges related to data complexity, variability, and integration with multi-omics datasets. Artificial intelligence (AI) techniques, including machine learning and deep learning, enhance metabolite identification, pathway analysis, and disease classification with improved accuracy and efficiency. This study explores AI-driven advancements in metabolomics, demonstrating an increase in biomarker prediction accuracy by 25–30% compared to conventional methods. Furthermore, AI-based models enable real-time data interpretation, accelerating drug discovery and metabolic disorder diagnosis. The integration of AI with high-throughput mass spectrometry and nuclear magnetic resonance spectroscopy enhances data processing capabilities, making precision medicine more accessible and effective. Future developments in AI-powered metabolomics hold promise for transforming disease diagnostics, therapeutic monitoring, and personalized healthcare strategies.
AI Techniques for Enhancing Disease Resistance Pathways in Crops
Enhancing disease resistance in crops is critical for ensuring global food security amid the growing threat of plant pathogens and climate change. Traditional breeding methods, while effective, are often time-consuming and limited by genetic resources. This study explores the application of artificial intelligence (AI) techniques to optimize and accelerate the identification of resistance pathways in crops. We employed machine learning models, including Random Forest and Gradient Boosting, for feature selection and prediction, integrated with multi-omics data to identify key genes and regulatory networks involved in disease resistance. Our methods demonstrated a significant improvement in the accuracy of resistance pathway predictions, achieving a mean accuracy of 92.4%, compared to 85.7% using traditional statistical models. Furthermore, the application of neural networks facilitated the identification of novel gene interactions, reducing the analysis time by 35%. Comparative results across multiple crops, including wheat and rice, showed enhanced disease resistance indices, improving by an average of 18.6% post-implementation of AI-optimized strategies. In conclusion, this research highlights the transformative potential of AI in advancing crop resilience, offering a scalable, data-driven approach for sustainable agriculture. Future studies should focus on integrating these findings into breeding programs to achieve practical applications.
AI Solutions for Personalized Farmer Advisory Platforms abstract
Personalized advisory platforms are critical for empowering farmers with tailored insights to enhance productivity and sustainability. Traditional advisory services often lack scalability, real-time insights, and context-specific recommendations, limiting their impact. This study explores the integration of Artificial Intelligence (AI) in developing personalized farmer advisory platforms to address these challenges. By leveraging machine learning models, the platform delivers data-driven recommendations on crop selection, pest management, irrigation schedules, and market trends based on real-time environmental, geographic, and socioeconomic data. The proposed system was evaluated in a pilot program with 1,000 farmers across diverse agro-climatic zones, resulting in a 25% increase in crop yield and a 30% reduction in water and fertilizer usage. The AI-powered pest management module achieved an 89% accuracy in pest detection, significantly reducing crop losses. Furthermore, 92% of farmers reported improved decision-making and satisfaction with the personalized advice. These findings demonstrate the transformative potential of AI in delivering localized, timely, and actionable insights, fostering sustainable farming practices and economic growth. Future developments will focus on integrating multilingual support, enhancing predictive accuracy, and expanding the platform to underserved farming communities globally.