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136 research outputs found
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Real-time Multimedia Analytics for IoT Applications: Leveraging Machine Learning for Insights
Full text linkThe combination of real-time multimedia analytics and Internet of Things (IoT) applications, along with machine learning techniques, has shown great potential in improving the capabilities of IoT systems. This study investigates the potential of machine learning to gain insights into IoT applications. By thoroughly examining existing literature and analyzing current trends, this study explores essential goals such as improving IoT systems\u27 data processing, decision-making, and security. This study extensively examines the literature on real-time multimedia analytics, machine learning algorithms, and IoT applications using a systematic approach. Doing so aims to provide a comprehensive overview of the field\u27s current state and highlight the main challenges and opportunities. The significant discoveries highlight the impressive capabilities of machine learning algorithms, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), in efficiently handling intricate multimedia data. These algorithms empower organizations to gain real-time insights and make informed decisions. Addressing challenges such as computational constraints, data privacy, and multimodal data integration is crucial for policy implications. This can be achieved through investments in edge computing infrastructure, developing low-power machine learning algorithms, and implementing robust privacy and security measures
Turing Stability and Natural Pattern Formation in the Gray-Scott Reaction-Diffusion System
Full text linkThis paper examines the Gray-Scott model, a coupled system of nonlinear reaction-diffusion equations recognized for its capacity to produce intricate patterns. Our focus is on performing a Turing stability analysis to understand the conditions under which spatially heterogeneous structures emerge. By exploring the dynamics of the model under various parameter regimes, we demonstrate how the resulting patterns closely resemble those found in nature, such as animal coat markings, seashell textures, and chemical oscillations. The sensitivity of the model to its parameters reveals a rich spectrum of behavior, highlighting the profound connection between mathematical models and natural pattern formation
Navigating the AI Landscape: Sectoral Insights on Integration and Impact
Full text linkThis study delves into the varied sentiments and attitudes prevalent across the different sectors related to integrating Artificial intelligence (AI). Understanding how sectors perceive and embrace these changes is crucial for informed decision-making and policy formulation as AI technologies continue to thrive in industries. Artificial intelligence is making waves in 2023 as businesses, consumers, and the government benefit from this technology, promising new opportunities, economic growth, and the transformation of different industries. There was so much propaganda surrounding artificial intelligence based on economic factors such as employment, education, income patterns, housing, and food security, and with time, these issues have been proven true or false. AI will have a broadly beneficial effect on society
Artificial Intelligence in Zero-Knowledge Proofs: Transforming Privacy in Cryptographic Protocols
Full text linkAI and zero-knowledge proofs (ZKPs) may revolutionize cryptographic protocol privacy, as this research shows. The report examines how AI may improve ZKP efficiency, scalability, and security and identifies developing AI-driven privacy-preserving technologies across sectors. The study reviews secondary data from peer-reviewed journals, technical reports, and conference proceedings. Key results show that AI automates proof creation, optimizes verification procedures, and identifies weaknesses, allowing innovative architectures like federated learning mixed with ZKPs for safe, collaborative AI training. The research shows AI\u27s potential to improve privacy in banking, healthcare, and secure identity management. However, concerns about the computational needs of the AI model, explainable systems, and interoperability persist. The policy implications highlight standardization, security framework improvements, and research to solve these shortcomings. The policy should also support openness and accountability in AI-driven cryptography systems to build confidence and acceptance. This paper shows how AI might transform privacy-preserving cryptographic methods and how to overcome their existing limitations to maximize their promise
Dynamic Characteristics of the Closed Soliton Solution and Phase Analysis of the (3+1)-Dimensional Jimbo-Miwa Equation
Full text linkThe main purpose of this paper is to investigate abundant exact traveling wave solutions (TWSs) of the (3+1)-dimensional Jimbo Miwa model utilizing the innovative auxiliary equation technique. By applying this powerful technique, the obtained solutions reveal and elucidate various types of waves, which are essential for comprehensive studies of complex phenomena such as ocean dynamics and other related scientific and engineering areas. The auxiliary equation method has proven successful in yielding new and analytical soliton solutions, including trigonometric functions, rational functions, hyperbolic functions, and exponential functions for the given model. The results of these solutions are represented using 3-D, contour, and combined 2-D graphs, offering a more detailed and insightful visual interpretation. In particular, the velocity effect becomes more comprehensible when analyzing the 2-D plots. This paper also includes further phase plane analysis of the model to examine the solutions\u27 behavior and characteristics. The results of this investigation have been compared with other researchers\u27 findings available in the literature. This technique proves highly effective for various nonlinear models in generating innovative soliton solutions, which are essential in applied science and engineering
Microservices vs. Monoliths: Comparative Analysis for Scalable Software Architecture Design
Full text linkThis research compares monolithic versus microservices architectures for scalable software design. The study reviews the literature on both designs\u27 scalability, development agility, fault isolation, operational complexity, and performance. The results show that monolithic structures are simple and efficient for small applications but struggle with scaling. Microservices provide scalability and flexibility, enabling autonomous scaling and quick development cycles, but they complicate inter-service communication and system integration. Policy implications imply that enterprises should develop explicit architectural governance to choose and deploy software architectures based on application complexity, scalability needs, and team competence. Team training and strong infrastructure are needed to handle microservices\u27 complexity. Software design supports present needs and future development by connecting architectural decisions with strategic goals
Ultra-Reliable Low-Latency Communication (URLLC) in 5G Networks: Enabling Mission-Critical Applications
Full text linkIn 5G networks, Ultra-Reliable Low-Latency Communication (URLLC) is a critical technology that supports mission-critical applications in various industry sectors. This study investigates the importance, difficulties, and policy consequences of URLLC deployment. A thorough assessment of the literature, an analysis of the underlying technologies, performance evaluation methods, and the effect of URLLC on mission-critical systems are all part of the process. Principal discoveries underscore the revolutionary possibilities of URLLC in healthcare, transportation, industrial automation, and public safety. However, they also point to obstacles to technological optimization, spectrum distribution, security, and interoperability. The significance of proactive steps in spectrum policy, regulatory frameworks, data protection, and research funding is highlighted by policy implications as they aid in successfully implementing URLLC. Stakeholders may fully realize the potential of URLLC to build a connected world where incredibly dependable communication enables people, companies, and communities to prosper and innovate in the digital era by tackling these issues and accepting policy actions
Evaluating Current Techniques for Detecting Vulnerabilities in Ethereum Smart Contracts
Full text linkEthereum intelligent contract security must be guaranteed since these decentralized apps oversee large-scale financial transactions independently. To strengthen the dependability and credibility of Ethereum smart contracts, this paper assesses existing methods for finding weaknesses in them. The primary goals are to evaluate how well hybrid approaches, formal verification, dynamic analysis, and static analysis find vulnerabilities. Methodologically, a thorough assessment of available resources and instruments was carried out to evaluate the advantages and disadvantages of each approach. Important discoveries show that although static analysis covers a large area, it ignores runtime-specific problems and produces false positives. While highly effective in finding runtime vulnerabilities, dynamic analysis is resource-intensive. High assurance is provided by formal verification, although it is complex and resource-intensive. Hybrid approaches combine several approaches to provide a well-rounded strategy but must be used carefully. The policy implications emphasize that to limit risks effectively, it is crucial to embrace multifaceted security techniques, set explicit norms, and promote easily accessible verification tools. This research advances our knowledge of smart contract security and guides policymakers and developers on securing blockchain applications
GenAI-Augmented Data Analytics in Screening and Monitoring of Cervical and Breast Cancer: A Novel Approach to Precision Oncology
Full text linkThis research examines how Generative Artificial Intelligence (GenAI) might improve cervical and breast cancer screening and monitoring data analytics to improve diagnosis accuracy and patient care in precision oncology. We evaluate literature and secondary data to show how GenAI technologies, including improved imaging analysis, genetic data integration, and predictive modeling, might improve early diagnosis and patient care. Significant results show that GenAI improves imaging analysis and genetic insights to personalize treatment approaches, enhancing diagnostic efficiency. However, model interpretability, data bias, and resource restrictions prevented broad deployment. The paper underlines the need for legislative frameworks that support explainable AI, safe data-sharing protocols, and inclusive datasets to guarantee different groups have fair access to GenAI applications. These issues must be addressed for GenAI to enhance cancer treatment, improve patient outcomes, and create a more equitable healthcare system. This study adds to the discussion on AI and oncology and highlights GenAI\u27s potential to enhance precision cancer treatment
AI-Driven Data Engineering for Real-Time Public Health Surveillance and Early Outbreak Detection
Full text linkThis research examines AI-driven data engineering in real-time public health monitoring and early epidemic detection to improve outbreak response speed, accuracy, and effectiveness. The study investigates frameworks and technologies that use electronic health records, social media, and environmental sensors via secondary data review. AI increases epidemic detection and response via sophisticated data integration and analysis, but data quality discrepancies, model interpretability, and privacy problems persist. The research also finds that resource constraints, especially in low-income areas, hinder the broad use of these technologies. Policy implications include standardizing data frameworks to improve integration, establishing AI transparency rules, and strengthening privacy safeguards to retain public confidence. We advocate investing in scalable, cloud-based infrastructures to access AI-driven surveillance technologies equally. Addressing these difficulties will strengthen public health systems\u27 resilience and reactivity to new health risks, improving global health security