As the adoption of cloud computing continues to grow, so do the concerns about the security of sensitive data and applications. Cloud environments are often seen as vulnerable to cyber-attacks due to their centralized nature and exposure to the internet. Edge computing, a distributed computing paradigm that brings computation and data storage closer to the data source, is emerging as a solution to enhance cloud security. By processing data closer to the edge of the network, (...) edge computing reduces the reliance on centralized cloud servers, lowering latency and mitigating risks associated with centralized cloud security models. This paper explores the integration of edge computing with cloud security, discussing how it strengthens security frameworks through reduced attack surfaces, real-time threat detection, and decentralized risk management. We examine the benefits, challenges, and performance considerations of edge computing in securing cloud environments and propose a model for implementing this hybrid approach in modern digital infrastructures. (shrink)

The Internet of Things (IoT) has revolutionized the way we interact with the world by enabling devices to collect, process, and exchange data in real time. However, IoT networks often face challenges related to latency and scalability when large amounts of data are processed centrally in cloud-based infrastructures. Edge computing, which brings computation and data storage closer to the source of data generation, is emerging as a solution to address these challenges. By performing computation at the edge (...) of the network (e.g., on local IoT devices, gateways, or nearby servers), edge computing reduces latency, optimizes bandwidth usage, and enables faster decision-making. Edge computing in IoT networks enhances scalability by offloading data processing from centralized cloud servers, making it feasible to handle large volumes of data from numerous IoT devices simultaneously. It also addresses latency concerns by allowing data to be processed locally, thereby reducing the time required for communication between devices and the cloud. This is particularly beneficial in real-time applications like autonomous vehicles, smart cities, healthcare, and industrial automation. Despite its potential, implementing edge computing in IoT networks introduces several challenges, including resource limitations on edge devices, data security and privacy concerns, and the complexity of managing distributed systems. This paper explores the role of edge computing in improving the performance of IoT networks, analyzing its advantages and challenges, and presenting case studies from various domains. Moreover, it discusses various architectures, technologies, and strategies for effective edge computing deployment in IoT ecosystems. (shrink)

On edge computing, mobile devices can offload some computing intensive tasks to the cloud so that the time delay and battery losses can be reduced. Different from cloud computing, an edge computing model is under the constraint of radio transmitting bandwidth, power and etc. With regard to most models in presence, each user is assigned to a single mission, transmitting power or local CPU frequency on mobile terminals is deemed to be a constant. Furthermore, (...) energy consumption has a positive correlation with the above two parameters. In a context of multitask, such values could be increased or reduced according to workload to save energy. Additionally, the existing offloading methods are inappropriate if all the compute densities of multiple tasks are high. In this paper, a single-user multi-task with high computing density model is proposed and partial task is offloaded when use the different offload algorithm. Simulated annealing algorithm is the best method to select offloading tasks, which can enhance the offloading ratio and save energy consumption. (shrink)

Bostrom's Simulation Argument has become synonymous with simulation theory, despite the existence of alternative frameworks that also suggest our universe may be a computer simulation—frameworks that do not require the assumption of posthumans. Further, Bostrom's work has spurred discussions around the potential destruction or termination of our universe, prompting existential anxieties and fueling extensive philosophical speculation. We evaluate the arguments of Greene, Turchin, Batin, Denkenberger, and Yampolskiy, alongside the counterarguments of Braddon-Mitchell, Latham, and Edge. We conclude that Bostrom's Simulation (...) Argument has been philosophically extended far beyond its original scope. As a contrasting model, we present Campbell's consciousness-based My Big TOE (MBT) Theory. MBT requires fewer assumptions, excludes posthuman entities, reduces the likelihood of simulation termination to near zero, includes a clear cosmogony and telos, and frames existence as an information-entropy-reduction system designed to evolve the consciousnesses of its participants—rather than the existential risk research project framework implicitly assumed in Bostrom's model. (shrink)

Mobile Edge Computing (MEC) is an emerging technology that enables computing directly at the edge of the cloud computing network. Therefore, it is important that MEC is applied with reliable transmission. The problem of reaching consensus in the distributed system is one of the most important issues in designing a reliable transmission network. However, all previous protocols for the consensus problem are not suitable for an MEC paradigm. It is the first time an optimal protocol (...) of reaching consensus is pro- posed for MEC paradigm. The protocol makes all fault-free nodes communicate with each other and collect the exchanged messages to decide a common value. Based on the common value, the protocol ensures all fault-free nodes reach consensus without the influence of unreliable transmission. Finally, we proved theoretically that the proposed protocol can tolerate the maximum number of faulty components and using only two rounds of message exchanges. (shrink)

Modern webpages consist of many rich objects dynamically produced by servers and client terminals at diverse locations, so we face an increase in web response time. To reduce the time, edge computing, in which dynamic objects are generated and delivered from edge nodes, is effective. For ISPs and CDN providers, it is desirable to estimate the effect of reducing the web response time when introducing edge computing. Therefore, in this paper, we derive a simple formula (...) that estimates the lower bound of the reduction of the response time by modeling flows obtaining objects of webpages. We investigate the effect of edge computing in each webpage category, e.g., News and Sports, using data measured by browsing about 1,000 popular webpages from 12 locations in the world on PlanetLab. (shrink)

With the advent of Internet of Things (IoT) and data convergence using rich cloud services, data computing has been pushed to new horizons. However, much of the data generated at the edge of the network leading to the requirement of high response time. A new computing paradigm, edge computing, processing the data at the edge of the network is the need of the time. In this paper, we discuss the IoT architecture, predominant application protocols, (...) definition of edge computing and its research opportunities. (shrink)

. In the evolving landscape of healthcare, the efficient processing of biomedical signals is critical for real-time diagnosis and personalized treatment. Conventional cloud-based AI systems for biomedical signal processing face challenges such as high latency, bandwidth consumption, and data privacy concerns. Edge computing, which brings data processing closer to the source, has emerged as a potential solution to these limitations. However, optimizing AI models for edge devices, which often have limited computational resources, remains a challenge. This paper (...) proposes an innovative approach to optimize AI models for biomedical signal processing by leveraging reinforcement learning (RL) techniques within edge computing environments. Reinforcement learning offers the potential to dynamically adapt model parameters based on real-time feedback from the edge devices, optimizing the trade-offs between model accuracy, resource utilization, and processing speed. The proposed system is designed to operate with high efficiency on edge devices, enabling faster signal processing while ensuring energy efficiency and maintaining diagnostic accuracy. A detailed review of existing literature highlights the benefits and challenges of edge computing in biomedical applications, the role of reinforcement learning in dynamic optimization, and the limitations of traditional AI models in constrained environments. The proposed system methodology integrates RL for model optimization, focusing on edge devices' unique constraints in terms of power, memory, and processing capacity. Simulation results demonstrate the improved efficiency and accuracy of biomedical signal processing with optimized AI models in edge computing setups, showing significant improvements in real-time diagnostic applications. (shrink)

With the rapid development of communication technology, the explosive growth of mobile and IoT devices, and growing requirements for real-time data processing, a new paradigm of computing, Edge Computing, has appeared. It moves computing power in the direction of data sources to mitigate latency, bandwidth usage, and dependence on cloud computing. In parallel, Artificial Intelligence (AI) has progressed notably with deep learning technology, highly optimized hardware, and distributed computing paradigms to yield smart applications of (...) high computational loads. Nonetheless, the huge amounts of data generated on the network edge impose heavy challenges in managing data, network optimization, and implementing AI models. This has pushed the convergence of Edge Computing and AI, which has led to a new research area called Edge Intelligence. Edge Intelligence is further divided into two broad categories • AI for Edge (Intelligence-enabled Edge Computing) – This is concerned with augmenting Edge Computing architectures with AI-based methods, including resource management, task scheduling, computation offloading, and network optimization. • Edge AI (Artificial Intelligence on Edge) – This involves executing AI models on edge devices directly, enabling local training and inference with minimal dependence on the cloud, thereby improving privacy, efficiency, and real-time processing. This paper provides an overview of Edge Intelligence, including fundamental concepts, future technologies, and research directions. We identify critical challenges such as efficient deployment of AI models, decentralized AI learning through federated learning, and edge-centric accelerations of domain-specific hardware, and discuss how Edge Intelligence has the potential to transform domains like autonomous systems, smart cities, factory automation, and wireless networks. This paper documents the present trend and future path to act as the basis for researchers, engineers, and industry players seeking to improve the topic of AI-driven Edge Computing. (shrink)

The proliferation of IoT devices and the emergence of latency-sensitive applications in 2019 accelerated the demand for edge computing architectures. To meet these demands, network infrastructure began shifting away from centralized data centers toward distributed, programmable edge nodes. This research explores how the integration of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) can optimize edge network performance. The paper analyzes architectural models that enable dynamic routing, load balancing, and traffic prioritization at the edge. It (...) also evaluates security and orchestration challenges, particularly in multi-tenant environments. By comparing SDN- and NFV-driven approaches to traditional static configurations, the study demonstrates measurable improvements in latency reduction, resource utilization, and service availability in edge networks. (shrink)

With the rapid growth of Internet of Things (IoT) devices and the increasing demand for real-time processing of large data volumes, traditional cloud-based systems struggle to meet latency and bandwidth requirements. Edge-Cloud convergence has emerged as a solution, combining the computational power of cloud data centers with the low-latency and high-throughput capabilities of edge devices. This paper explores the architecture, design principles, and best practices for building hybrid systems that integrate edge computing and cloud infrastructure. We (...) investigate various methods to optimize latency, improve real-time data processing capabilities, and ensure seamless communication across the hybrid environment. Additionally, the paper highlights challenges such as data consistency, scalability, security, and fault tolerance in Edge-Cloud systems. The performance of Edge-Cloud hybrid systems is analyzed through simulation, showcasing key advantages such as reduced latency, bandwidth optimization, and fault tolerance in real-time applications. The paper concludes by providing recommendations for future work and areas of research to advance the development of more robust and efficient Edge-Cloud systems. (shrink)

The problem of emergence in physical theories makes necessary to build a general theory of the relationships between the observed system and the observing system. It can be shown that there exists a correspondence between classical systems and computational dynamics according to the Shannon-Turing model. A classical system is an informational closed system with respect to the observer; this characterizes the emergent processes in classical physics as phenomenological emergence. In quantum systems, the analysis based on the computation theory fails. It (...) is here shown that a quantum system is an informational open system with respect to the observer and able to exhibit processes of observational, radical emergence. Finally, we take into consideration the role of computation in describing the physical world. (shrink)

Serverless computing is changing the cloud application de- sign by removing the need to design, build, and manage infrastructure, and instead focusing on deploying code that can be elastic and rapid. However, while service meshes have recently been introduced to address the reliability of communication in microservices architectures, the grow- ing adoption of edge computing and multi-cloud strategies require new architectures that can cross different types of platforms. In this paper, we introduce a novel serverless mesh architecture (...) that combines server- less function platforms with a service mesh overlay that spans across clouds and edge sites. We give a brief overview of the serverless and service mesh technologies and review the related work in traditional cloud centric serverless, edge computing deployments and mesh-based microservice solutions. We then describe a framework where the server- less functions that are located in different cloud/edge environments are connected by a single service mesh that enables dynamic routing, inter- operability, and policy enforcement across the environments. The dia- grams show how the functions are deployed and invoked via the mesh. Simulated evaluation is described for video analytics, IoT sensor data in- gestion, and content delivery use cases, with the potential performance benefits shown in terms of end-to-end latency and throughput. Some of the key challenges of network latency, cold start delays, and data con- sistency issues in distributed serverless computing are identified, along with how the proposed architecture tackles or alleviates them. Last, fu- ture directions are outlined, which include AI/ML-based optimizations for function allocation and tuning, enhanced runtime portability via We- bAssembly (WASM), and open questions for secure, scalable serverless computing from edge to cloud. (shrink)

Cloud computing has revolutionized the way businesses and individuals access and utilize computing resources. With its rapid adoption over the last few decades, it has become the backbone of many IT services across industries. This paper explores the evolution of cloud computing, highlighting key trends and identifying future directions that will shape the landscape. It discusses the evolution from traditional on-premise systems to the cloud-first approach, current advancements such as multi-cloud and hybrid cloud environments, and the growing (...) integration of artificial intelligence (AI) and machine learning (ML) into cloud services. Furthermore, the paper examines the challenges organizations face, including security concerns and regulatory compliance. Finally, the future directions of cloud computing are explored, focusing on edge computing, quantum cloud services, and the potential for deeper AI integration. (shrink)

Using cutting-edge computer vision and machine learning techniques, this research study suggests a real-time system for hand gesture recognition and text conversion using OpenCV and MediaPipe. The system processes live video input, detects hand movements, and translates specific gestures into corresponding text outputs. By leveraging a deep learning-based hand tracking model and a rule-based gesture classification method, the approach ensures efficient and accurate recognition. The primary applications of this system include enhancing human-computer interaction, assisting individuals with speech impairments, and (...) facilitating non-verbal communication. The methodology involves capturing hand movements using a webcam, identifying key landmarks, and classifying gestures into predefined categories. Experimental results demonstrate the system’s effectiveness under various lighting conditions, achieving reliable performance for simple gestures. Future improvements will focus on integrating advanced machine learning models to expand gesture recognition capabilities and improve accuracy. This work highlights the potential of computer vision-based gesture recognition for real-world applications in assistive technology and automation. (shrink)

The segregation of image parts into foreground and background is an important aspect of the neural computation of 3D scene perception. To achieve such segregation, the brain needs information about border ownership; that is, the belongingness of a contour to a specific surface represented in the image. This article presents psychophysical data derived from 3D percepts of figure and ground that were generated by presenting 2D images composed of spatially disjoint shapes that pointed inward or outward relative to the continuous (...) boundaries that they induced along their collinear edges. The shapes in some images had the same contrast (black or white) with respect to the background gray. Other images included opposite contrasts along each induced continuous boundary. Psychophysical results demonstrate conditions under which figure-ground judgment probabilities in response to these ambiguous displays are determined by the orientation of contrasts only, not by their relative contrasts, despite the fact that many border ownership cells in cortical area V2 respond to a preferred relative contrast. Studies are also reviewed in which both polarity-specific and polarity-invariant properties obtain perceptual figure-ground grouping results. The FACADE and 3D LAMINART models are used to explain these data. Keywords: figure-ground separation, border ownership, perceptual grouping, surface filling-in, V2, V4, FACADE Theory. (shrink)

As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capacity limitations, helper mobile nodes can contribute their cache resources. However, due to their selfish behavior, an incentive mechanism is necessary to encourage resource sharing. Additionally, these helper nodes can also be malicious. (...) This paper proposes a blockchain-based trust management mechanism that addresses these challenges by accurately identifying trustworthy edge servers and mobile nodes. The proposed mechanism calculates both direct and indirect trust using smart contracts, ensuring that malicious nodes are effectively filtered out. Trustworthiness is determined based on mobile node satisfaction with the quality of service, and trust data is securely stored on the blockchain. To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing. Furthermore, a blockchain-based authentication mechanism protects against node impersonation. Our approach optimizes trust, cache capacity, and cost efficiency while considering mobile node mobility, energy consumption, and computational power constraints during the consensus process. Simulation results show that the proposed method can accurately distinguish between honest and malicious servers, even with a 10% noise in data. (shrink)

Much of computational cognitive science construes human cognitive capacities as representational capacities, or as involving representation in some way. Computational theories of vision, for example, typically posit structures that represent edges in the distal scene. Neurons are often said to represent elements of their receptive fields. Despite the ubiquity of representational talk in computational theorizing there is surprisingly little consensus about how such claims are to be understood. The point of this chapter is to sketch an account of the nature (...) and function of representation in computational cognitive models. (shrink)

Cloud computing has provided many opportunities to businesses and individuals. It enables global and on demand network access to a shared pool of resources with minimal management effort. However, this bliss has become a problem for latency-sensitive applications. To improve efficiency of cloud and to reduce the amount of data that needs to be transported to the cloud for data processing, analysis and storage, a new network architect technology 'Fog Computing' has been introduced. In fog computing, small (...) applications and resources are processed at the edge of the cloud, rather than processing entire applications on the cloud. (shrink)

The integration of Software-Defined Networking (SDN) and Network Functions Virtualization (NFV) has emerged as a promising approach to optimize the performance and management of IoT systems, especially in edge-cloud computing environments. SDN provides centralized control and programmability, while NFV enables the virtualization of network functions, enhancing network flexibility and scalability. This paper presents a systematic review of SDN and NFV architectures for Edge-Cloud oriented IoT applications. We explore their benefits, challenges, and the synergy between SDN and NFV (...) in enabling efficient data processing, resource management, and reduced latency in IoT systems. Through an extensive analysis of the literature, we highlight existing solutions, identify open issues, and propose directions for future research in this domain. (shrink)

The mind has always been a peculiar and elusive subject, sparking controversial theories throughout the history of philosophy. The initial theorization of the mind dates back to Orphism, which formulated a dualistic structure of soul and body (Johansen, 1999) [1], laying the foundation for Greek dualism, introspection, and the rise of metaphysical idealism. This ill-empirical stance, especially after Plato’s idea of forms, led to inaccessible theoretical concepts concerning the investigation of the relationship between body and mind. Although diverse theories provide (...) broader insights into such matters, they can also hinder progress if based on unsound methodological hypothesis. The present study aims to employ a rational reexamination of the potentiality of the concepts of mind, using abductive inspection to present a valid grasp through the lens of cognitive neuroscience, philosophy, and interdisciplinary fields. This paper speculates through empirical deduction by analyzing the literature and theories on the concept of mind and its possible implications for singularity via transhumanism. In the contemporary world, humankind is on the edge of singularity due to the exponential growth predicted by Moore’s law (Moore 1965) [2]. While many considered singularity an abstract akin to science fiction, significant projections indicate that the accelerationist change has already begun, irreversibly. This advancement occurs not only in the degree of semiconductors in electronics but also in the amplification of human cognition through external devices enabling compound information flow via the world wide web. However, the conjunction of robotics and AI may outdo humankind in the contest for sovereignty soon due to their advantageous architecture in process design, which increases the systemic risk of extinction for humankind. Achieving total control over AI is unrealistic, as it requires complete supervision over all computers and information processes across the world, rendering any regulatory efforts to foresee augmentation futile. Consequently, this paper proposes the adoption of a neuro-robotic approach, enriched with AI, and implementation through biomedical applications on the human body to augment human cognition and physicality. This study aims to raise awareness against existential risks, emphasizing that reliance solely on AI powered computers could lead to annihilation. It also aims to highlight that biological mind and body is lagging behind the modern science to compete against Artificial Intelligence. Moreover, the foundation of Artificial General Intelligence (AGI) is an epistemic foundation for cognitive machinery enhancement that cannot be reversed once it emergence become apparent, as with Pandora’s Box. Technological singularity (Shanahan, 2015) [3], the point where technology is intelligently engineered to engineer its own sets by compounding all possible implementations of practical and theoretical information, is appearing on the horizon. Even if there is no human agency left on the soil of Earth, there is still no turning back at that point. Humankind must actively hold the key to bend this alteration in a way that aligns with its desires. Therefore, we propose, future for humankind lies with transhumanism. (shrink)

Brain-computer interfaces (BCIs) offer significant therapeutic opportunities for a variety of neurophysiological and neuropsychiatric disorders and may perhaps one day lead to augmenting the cognition and decision-making of the healthy brain. However, existing regulatory frameworks designed for implantable medical devices (IMDs) are inadequate to address the unique ethical, legal, and social risks associated with next-generation networked brain-computer interfaces (BCIs). In this article, we make nine recommendations to support developers in the design of BCIs and nine recommendations to support policymakers in (...) the application of BCIs, drawing insights from the regulatory history of IMDs and principles from AI ethics. We begin by outlining the historical development of IMDs and the regulatory milestones that have shaped their oversight. Next, we summarize similarities between IMDs and emerging implantable BCIs, identifying existing provisions for their regulation. We then use two case studies of emerging cutting-edge BCIs-the HALO (Hardware Architecture for LOw-power BCIs) and SCALO (SCalable Architecture for LOw-power BCIs) computer systems-to highlight distinctive features in the design and application of nextgeneration BCIs arising from contemporary chip architectures, which necessitate reevaluating regulatory approaches. We identify critical ethical considerations for these BCIs, including unique conceptions of autonomy, identity, and mental privacy. Based on these insights, we suggest potential avenues for the ethical regulation of BCIs, emphasizing the importance of interdisciplinary collaboration and proactive mitigation of potential harms. The goal is to support the responsible design and application of new BCIs, ensuring their safe and ethical integration into medical practice. (shrink)

The Critical Brain Hypothesis (CBH) proposes that the brain’s optimal state for information processing emerges at a phase transition between subcritical and supercritical regimes—where order and disorder momentarily balance. While this insight has guided decades of cognitive and computational neuroscience, it remains observational: CBH identifies where complexity emerges, but not why it sustains, how it evolves, or what governs its recurrence across scales. This paper introduces the Chirality of Dynamic Emergent Systems (CODES) as the structural resolution to that gap. CODES (...) frames criticality not as a point, but as a chiral corridor of structured resonance—where asymmetrical feedback and prime-phase coherence dynamically stabilize emergent systems. Within this framework, intelligence is not an anomaly at the edge of chaos—it is the phase-locked optimization of dynamic asymmetry itself. By aligning CBH with CODES, we reveal that the brain’s critical state is not merely a product of stochastic balance but an inevitable expression of structured resonance. We show that systems exhibiting intelligence—biological or artificial—are governed by the same underlying chirality that sustains coherent emergence across domains. The Resonance Intelligence Core (RIC) serves as a functional embodiment of this principle, architected to replicate and extend these dynamics in real time. This synthesis suggests a decisive transition: from modeling the brain at the edge, to engineering systems within the corridor. From criticality as accident, to coherence as design. (shrink)

Nowadays Fog Computing has become a vast research area in the domain of cloud computing. Due to its ability of extending the cloud services towards the edge of the network, reduced service latency and improved Quality of Services, which provides better user experience. However, the qualities of Fog Computing emerge new security and protection challenges. The Current security and protection estimations for cloud computing cannot be straightforwardly applied to the fog computing because of its (...) portability and heterogeneity. So these issues in fog computing arises new research challenges and opportunities. This survey features about existing security concerns for fog computing and new proposed system to tackle some of the issues in fog computing related to security and privacy, thereby enhancing the cloud security. (shrink)

The Language of Thought program has a suicidal edge. Jerry Fodor, of all people, has argued that although LOT will likely succeed in explaining modular processes, it will fail to explain the central system, a subsystem in the brain in which information from the different sense modalities is integrated, conscious deliberation occurs, and behavior is planned. A fundamental characteristic of the central system is that it is “informationally unencapsulated” -- its operations can draw from information from any cognitive domain. (...) The domain general nature of the central system is key to human reasoning; our ability to connect apparently unrelated concepts enables the creativity and flexibility of human thought, as does our ability to integrate material across sensory divides. The central system is the holy grail of cognitive science: understanding higher cognitive function is crucial to grasping how humans reach their highest intellectual achievements. But according to Fodor, the founding father of the LOT program and the related Computational Theory of Mind (CTM), the holy grail is out of reach: the central system is likely to be non-computational (Fodor 1983, 2000, 2008). Cognitive scientists working on higher cognitive function should abandon their efforts. Research should be limited to the modules, which for Fodor rest at the sensory periphery (2000).1 Cognitive scientists who work in the symbol processing tradition outside of philosophy would reject this pessimism, but ironically, within philosophy itself, this pessimistic streak has been very influential, most likely because it comes from the most well-known proponent of LOT and CTM. Indeed, pessimism about centrality has become assimilated into the mainstream conception of LOT. (Herein, I refer to a LOT that appeals to pessimism about centrality as the “standard LOT”). I imagine this makes the standard LOT unattractive to those philosophers with a more optimistic approach to what cognitive science can achieve.. (shrink)

As digital infrastructure becomes increasingly reliant on multi-cloud architectures, ensuring consistent availability, fault tolerance, and disaster recovery across distributed systems is a critical challenge. This extended study advances the discourse on cloud resiliency engineering by addressing underexplored areas such as the quantification of resilience metrics, economic modeling of redundancy strategies, and the integration of AI-driven automation for predictive fault detection. It also evaluates the operational and regulatory complexities introduced by multi-jurisdictional deployments, legacy system modernization, and emerging threats like quantum (...) class='Hi'>computing. Unlike traditional approaches focused solely on infrastructure-level solutions, this research presents a multi-dimensional framework that encompasses human factors, policy-aware architecture, and vendor interoperability. By synthesizing insights from real-world implementations, performance benchmarks, and evolving technologies such as edge computing and post-quantum cryptography, the study provides a comprehensive roadmap for building resilient, secure, and scalable cloud systems. The proposed framework equips cloud architects, developers, and enterprise leaders with actionable strategies to design and manage cloud environments that are both technically robust and contextually adaptable. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine Learning Algorithms (...) designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

This paper explores the integration of artificial intelligence (AI), machine learning (ML), and quantum computing in revolutionizing diagnostic and therapeutic approaches within modern healthcare. The convergence of these cutting-edge technologies is poised to address critical challenges in healthcare, such as precision medicine, early disease detection, and personalized treatment strategies. AI and ML algorithms, particularly deep learning, are demonstrated to enhance diagnostic accuracy through the analysis of complex medical data, including imaging and genomics. Furthermore, quantum computing presents novel (...) capabilities in processing large datasets, optimizing optimization problems, and accelerating drug discovery processes. This research investigates the synergistic potential of AI, ML, and quantum computing in transforming clinical decision-making processes, enabling more effective and tailored therapeutic interventions. Case studies and practical applications highlight the real-world impact of these technologies, while discussions on their limitations, ethical implications, and future directions provide a comprehensive understanding of their role in shaping the future of healthcare. Challenges related to data privacy, computational resources, and integration into existing healthcare infrastructures are also critically examined. (shrink)

These days, Thin-client devices are continuously accessing the Internet to perform/receive diversity of services in the cloud. However these devices might either has lack in their capacity (e.g., processing, CPU, memory, storage, battery, resource allocation, etc) or in their network resources which is not sufficient to meet users satisfaction in using Thin-client services. Furthermore, transferring big size of Big Data over the network to centralized server might burden the network, cause poor quality of services, cause long respond delay, and inefficient (...) use of network resources. To solve this issue, Thinclient devices such as smart mobile device should be connected to edge computing which is a localized near to user location and more powerful to perform computing or network resources. In this paper, we introduce a new method that constructs its architecture on Thin-client -edge computing collaboration. Furthermore, present our new strategy for optimizing big data distribution in cloud computing. Moreover, we propose algorithm to allocate resources to meet Service Level Agreement (SLA) and Quality of Service (QoS) requirements. Our simulation result shows that our proposed approach can improve resource allocation efficiently and shows better performance than other existing methods. (shrink)

Cloud computing alters the way organizations manage and deploy their IT resource. It provides an organization with scalable, inexpensive, and flexible options. The complexity and dynamic nature of cloud environments pose a challenge to maintaining high availability at all times, especially when the system fails or a disaster arises. The legacy techniques of disaster recovery, fault tolerance, and high availability leave behind much to be desired. These techniques are mostly static, slow to respond, and have a dismal ability to (...) adapt to continuously changing conditions in contemporary cloud systems. Such techniques largely depend on manual configurations and predefined policies; resulting in lots of inefficiencies and increases in the risk of service downtime. This research investigates the way Artificial Intelligence (AI) changes the paradigm on cloud resilience to promote adoption of intelligent systems for guaranteed disaster recovery, better fault-tolerant behavior, or transparent high availability. With machine learning algorithms, AI-based cloud services utilize large data volumes to reveal patterns within system logs, performance metrics, and user behavior data; thereby offering real-time anomaly detection and predictive failure analysis. For example, techniques like predictive analytics help cloud providers predict likely system outages, optimize the resources to be used, and automate failover processes (Xu et al., 2021; Lee & Kumar, 2022). AI-aided disaster recovery techniques employ complex algorithms to produce an adaptive backup mechanism, thereby minimizing loss and reducing restoration time. Fault tolerance in AI cloud systems comes from intelligent error correction, automatic isolation of faults, and self-healing features, i.e. repair of faults without the need for human supervision (Chen et al., 2020). Besides that, AI also contributes to high availability through intelligent load balancing, which ensures that at any given time, resources are optimally distributed throughout the network to sustain continuous service even during peak demand or unanticipated failures (Patel & Zhang, 2023). The approach is a comprehensive review of the various existing literature on the topic, empirical analysis of the current AI-driven cloud solutions available in the market, and case studies for comparison analysis on the different AI systems. The study scenario reveals that AI-driven solutions noticeably reduce downtimes, lead to improved recovery times, and contribute to overall system reliability as compared to traditional methods. However, other areas include model bias, data privacy, and continuous training of AI models. This study expands the trends of AI in the field of cloud computing by documenting the significance of intelligent systems in bridging traditional weaknesses of resilience strategies. It further signifies the need for AI into predictive maintenance, automated disaster response, and proactive fault management of rapidly changing dynamic cloud environments. Future studies will focus on AI integration along with edge computing and blockchain technologies for even more robust and secure services in cloud operations. (shrink)

Federated Learning (FL) is a machine learning paradigm that enables model training across decentralized devices while preserving data privacy. However, FL faces two significant challenges: privacy concerns and scalability issues. Privacy concerns arise from potential vulnerabilities in aggregating updates, whereas scalability issues stem from the increasing number of edge devices and the computational overhead required for communication and model updates. This paper explores cutting-edge advancements aimed at addressing these challenges, including advanced encryption techniques, differential privacy mechanisms, federated optimization (...) methods, and decentralized training architectures. We also discuss strategies for managing communication costs, improving convergence speeds, and ensuring robustness in heterogeneous environments. By integrating novel approaches to privacy and scalability, next-generation federated learning can provide a more secure, efficient, and scalable framework for a wide range of applications, from healthcare to autonomous vehicles. (shrink)

Pension service institutions are quickly going digital as they cope with increased service requirements, the demands of regulatory compliance, and challenges posed by cybersecurity. Legacy pension management systems usually have limited scope, are inefficient, and do not provide security on-premise infrastructure (Gartner, 2023). These aspects result in inefficiencies that create delays in processing pensions, an increase in the risk of fraud, and escalated operational costs (Ponemon Institute, 2023). The most critical aspect regarding pension transactions involves the secure, efficient, and scalable (...) service delivery because it entails sensitive financial information; therefore, it becomes very important to governments, financial institutions, and private organizations (Forrester Research, 2022). This research proposes the Azure-based framework aiming to enhance the management of pension service requests focusing on scalability, security, AI-powered automation, and regulatory compliance. The proposed system uses Azure Virtual Machines, Azure Kubernetes Service, and Load Balancers to perform the optimized resource provisioning for high availability. A zero-trust security model has been categorized to protect pension data from cyber threats, reinforced with Azure Active Directory, Key Vault, Multi-Factor Authentication, and the Reserve (ISO 2021). The research also embeds Azure Bot Services and Azure Cognitive Services as an AI-driven anti-fraud detection tool integrated into the system to automate customer service and workflow management within the fraud prevention ecosystem (Accenture, 2023). Automatic rule enforcement and real-time security monitoring could ensure compliance with GDPR, HIPAA, and ISO 27001 (Microsoft, 2023). In the research approach to systematic architectural design, Infrastructure as a Service and Platform as a Service is combined using encryption techniques with the Azure Key Vault and private cloud connectivity with Azure ExpressRoute. AI-based pension request processing along with prescriptive analytics and real-time fraud detection with Azure Machine Learning and Synapse Analytics complete the solution proposed. A performance evaluation framework was developed to assess enhancements in processing time for pension requests and reduction of fraudulent claims as well as scaling of the system during peak loads and compliance adherence under security audits. The empirical evidence gained from real-life business cases shows that pension automation based on Azure reduces the time for pension processing by as much as 80%, drastically improving service efficiency (PwC, 2022). Whereas the AI-based mechanism for the detection of fraud reduces fraudulent claims for pensions by 70% (IBM Cloud Research, 2022), Azure guarantees 99.99% uptime with its highly available configurations. Enhanced compliance monitoring, with a reduction in policy violation incidents by 60% (ISO, 2023), is another feature of the system. Future research will look toward blockchain for transaction management of pension funds, further down in edge computing for quicker processing, and AI for investment advisory systems in pension management, all aimed at optimizing pension services management. This research propagates the cause of digital transformation in pension management through a demonstration of a secure, scalable, and AI-enabled model with further extensibility toward healthcare benefits, insurance claims, and the automation of financial services. (shrink)

Railway asset change detection through Artificial Intelligence (AI) technology has transformed infrastructure monitoring by providing better efficiency combined with predictive maintenance functions and improved accuracy. The paper studies the development of railway asset monitoring throughout history while it moved from traditional manual inspections to AI-powered solutions. The study recognizes three main barriers which include irregular data acquisition practices along with restricted sensor abilities and imprecise AI model precision and difficulties applying them to current railway control platforms. The research investigates novel (...) data collection approaches besides studying hybrid AI model deployment and finding optimal solutions for railway operational AI integration. The research findings establish critical requirements for railway administrators and policy makers and developers of AI systems regarding standardized regulatory structures and enhanced AI interpretation capabilities and collaborative advancements. Music research will further AI railway asset monitoring by integrating edge computing technology and 5G connectivity to process real-time data streams. The presented study enhances railway infrastructure management systems through its advances in AI methodology development along with solutions to implementation hurdles. (shrink)

Maintaining road infrastructure is essential for ensuring public safety and supporting efficient transportation networks. One persistent problem is the formation of potholes, which pose hazards to drivers, contribute to traffic delays, and cause vehicle damage. Conventional methods for detecting potholes typically rely on manual inspection, which can be time-consuming, labor-intensive, and difficult to scale. This project presents an automated solution for identifying potholes using cutting-edge computer vision and machine learning approaches. Leveraging the YOLOv8 object detection algorithm, the system analyzes (...) images and video footage to pinpoint potholes with high accuracy in real time. Deep learning techniques enhance the system's performance by allowing it to recognize potholes of various shapes, sizes, and in different environmental settings. By streamlining the detection process, this solution aims to improve road safety through faster maintenance responses and reduced dependency on manual monitoring. (shrink)

Federated Learning (FL) is an emerging machine learning paradigm designed to enable model training across decentralized data sources without requiring data to be transferred or centralized. This approach is especially valuable in environments where data privacy, regulatory compliance, and communication efficiency are paramount, such as healthcare, finance, and edge computing. Traditional machine learning methods typically require data to be aggregated in a central server, raising concerns about data privacy and security. Federated Learning addresses these concerns by keeping data (...) on local devices and sharing only model updates, thereby preserving data sovereignty. This paper provides a comprehensive analysis of Federated Learning in distributed systems, focusing on its architecture, advantages, and the technical challenges it presents. We explore the different types of FL—including horizontal, vertical, and federated transfer learning—and explain how each is suited to specific application contexts. We also investigate critical issues such as communication overhead, model convergence, data heterogeneity, and security threats including poisoning and inference attacks. The methodology section discusses state-of-the-art FL frameworks, including Google's Federated Averaging (FedAvg), Secure Aggregation protocols, and emerging advancements like differential privacy and homomorphic encryption. Real-world implementations in mobile networks, autonomous vehicles, and medical diagnosis systems are examined to demonstrate FL’s growing applicability. The paper concludes by emphasizing the transformative potential of Federated Learning in enabling privacy-preserving AI. It also highlights the need for standardized protocols, legal frameworks, and interdisciplinary collaboration to fully harness FL’s benefits while mitigating its risks. As AI continues to permeate sensitive domains, FL offers a promising path forward for ethical and secure machine learning. (shrink)

RETINA (Real-time Environmental Transcription and Interpretation for Navigation and Assistance for Visually Impaired) is an innovative project aimed at enhancing the navigation and environmental awareness of visually impaired individuals through advanced assistive technology. The system integrates a portable, headband-mounted camera with a Raspberry Pi, enabling real-time capture and interpretation of visual information from the user's surroundings. Utilizing cutting-edge computer vision and machine learning algorithms, along with the Google Coral Accelerator for rapid processing, RETINA facilitates swift object recognition and detailed (...) auditory feedback. The Mobilenet tflite model plays a crucial role in generating accurate, contextually relevant descriptions of identified objects, enhancing the quality of information provided to users. By continuously capturing video footage and processing it in real-time, RETINA delivers immediate audio cues about objects, obstacles, and environmental features through connected earphones. This innovative approach addresses key challenges faced by visually impaired individuals, such as limited environmental awareness and reliance on external assistance, ultimately promoting greater independence and safety. With a functional prototype already developed, RETINA stands at the forefront of assistive technology, potentially transforming the lives of visually impaired users by enabling more confident navigation and interaction with their surroundings. (shrink)

Ensuring cloud resiliency through engineering is essential for maintaining high availability, fault tolerance, and disaster recovery within contemporary cloud infrastructures. As more businesses move towards multi - cloud environments, maintaining system reliability and efficiency while also controlling costs takes centre stage. This study delves into optimal strategies for bolstering cloud reliability via automated failover systems, real - time data duplication, load distribution, and self - restoring networks. The analysis focuses on strategies for disaster recovery, cost - effective resource management, and (...) enhancing security resilience to minimize potential risks. The report draws attention to the difficulties involved in integrating multiple cloud systems, maintaining data consistency, and dealing with cyber threats. It also explores the development of new technologies like AI - powered automation, edge computing, and predictive analytics for identifying potential failures. The study offers valuable insights into how to optimally configure cloud infrastructure to achieve the highest levels of efficiency and dependability. Future developments in autonomous cloud systems, quantum encryption, and eco - friendly computing models to enhance cloud robustness. This paper provides a detailed guide for companies seeking to construct reliable cloud infrastructure that maintains operational stability and reduces the frequency of service interruptions. (shrink)

Property and Casualty (P&C) insurers are adopting multi-cloud environments as a strategic imperative because of their increasing data volumes and complexities in regulatory compliance, customer expectations and technological advancements. This paper discusses how Snowflake’s cloud-agnostic, unified platform enables insurers to create resilient, efficient, and compliant multi cloud data strategies. Using Snowflake’s elastic scalability, real-time analytics, secure data sharing and seamless cloud interoperability, insurers can optimize claims processing, augment fraud detection, and support customer engagement. The study offers core design principles for (...) multi cloud data architectures and sheds light on problems in data fragmentation, latency, and security risks. It points to how Snowflake enables data resilience, including data replication, encryption, GDPR, and CCPA compliance. Details are provided. Practical implementation strategies of phased data migration ETL pipelines and integration with existing enterprise system to ensure a smooth deployment. Snowflake is seen as a clear win through a cost-benefit analysis with metrics like RTO and RPO that define how well Snowflake performs disaster recovery and business continuity. Additionally, the paper also looks at the emergence of tech like AI, blockchain and edge computing which could additionally bolster multi-cloud strategies even further. Finally, Snowflake enables the journey for P&C insurers through cloud ecosystem pitfalls, enabling operational resilience, regulatory compliance, along competitive advantage in a data-driven industry. (shrink)

This study examines the shift from monolithic to microservices architecture, focusing on significant obstacles and effective methods including API gateways, containerization, Continuous Integration and Continuous Deployment (CI/CD) pipelines, and event-driven architectures. Technologies like AI-driven automation, serverless computing, and edge computing are anticipated to boost performance, reduce costs, and facilitate real-time processing. Research in self-healing systems, sustainable cloud computing, and multi-cloud approaches will enhance microservices in the future. To maximize the advantages of microservices, organizations should transition their (...) systems gradually, implement automation, and prioritize innovation in order to develop robust, secure, and forward-thinking applications. (shrink)

This paper introduces an innovative design for robotic operating platforms, underpinned by a transformative Internet of Things (IoT) architecture, seamlessly integrating cutting-edge technologies such as large language models (LLMs), generative AI, edge computing, and 5G networks. The proposed platform aims to elevate the intelligence and autonomy of IoT systems and robotics, enabling them to make real-time decisions and adapt dynamically to changing environments. Through a series of compelling case studies across industries including smart manufacturing, healthcare, and service (...) sectors, this paper demonstrates the substantial potential of IoT-enabled robotics to optimize operational workflows, enhance productivity, and deliver innovative, scalable solutions. By emphasizing the roles of LLMs and generative AI, the research highlights how these technologies drive the evolution of intelligent robotics and IoT, shaping the future of industry-specific advancements. The findings not only showcase the transformative power of these technologies but also offer a forward-looking perspective on their broader societal and industrial implications, positioning them as catalysts for next-generation automation and technological convergence. (shrink)

The motivation behind exploring anomaly detection in self-organizing networks lies in the evolving landscape of telecommunications and network management. Conventional methods for identifying network anomalies often struggle to adapt to the dynamic and complex nature of modern self-organizing networks. The problem addressed in this research is the efficacy of anomaly detection methods in self-organizing networks (SONs) within the context of telecommunications and network management. As SONs become increasingly prevalent to meet the demands of modern, highly dynamic wireless communication systems, the (...) need for robust anomaly detection mechanisms is paramount. Conventional anomaly detection approaches in SONs are often based on predefined rules and thresholds, which may struggle to adapt to the intricate and rapidly evolving network behaviors. These methods can result in false alarms, missed anomalies, and inefficient resource allocation. Furthermore, emerging SONs incorporate a multitude of diverse technologies, including 5G, IoT, and edge computing, compounding the complexity of anomaly detection. Contemporary machine learning techniques hold promise in addressing these challenges by enabling the automatic and adaptive detection of anomalies, leveraging the abundance of data generated in SONs. However, the suitability, performance, and scalability of these methods in dynamic and large-scale SON environments remain critical concerns. This research aims to compare and evaluate conventional anomaly detection methods against contemporary machine learning approaches in SONs to assess their accuracy, efficiency, and adaptability. The goal is to provide insights into the most effective anomaly detection strategies, ultimately enhancing network stability, minimizing downtime, and ensuring the secure and efficient operation of modern telecommunications systems. (shrink)

Deploying applications across multiple cloud regions boosts availability, disaster recovery, and user experience but raises complexities including data synchronization, network optimization, security, and cost management. This research examines the responsibilities of software developers in designing and overseeing multi-region cloud-based applications. The main goal is to identify and examine the essential factors and most effective methods that developers should adhere to when constructing reliable, expandable, and high-performance cloud-based applications, approaches to tackle these complexities, such as Infrastructure as Code (IaC) for automating (...) resource allocation, Continuous Integration and Continuous Deployment (CI/CD) for streamlined software updates, and global load balancing for efficient traffic management. The importance of artificial intelligence, automation, and edge computing in streamlining cloud operations is also emphasized. By implementing these strategies, companies can establish cloud applications that are scalable, secure, and highly effective, and run smoothly in various geographic locations. (shrink)

The proliferation of Internet of Things (IoT) devices has significantly expanded the attack surface for cyber threats, necessitating robust security measures. Traditional Intrusion Detection Systems (IDS) often rely on centralized architectures, which can compromise data privacy and scalability. This paper explores the integration of Federated Learning (FL) into IDS for IoT networks, enabling decentralized model training while preserving data privacy. By leveraging local computation and aggregating model updates, FL facilitates collaborative learning across distributed IoT devices. The proposed approach aims to (...) enhance detection accuracy, reduce latency, and maintain user privacy, addressing the challenges posed by the dynamic and heterogeneous nature of IoT environments. (shrink)

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally (...) accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies. (shrink)

Computer vision, or image recognition, analyses and interprets visual data in real-world scenarios like images and videos. AI and ML research focusses on object, scene, action, and feature identification because of its usefulness in image processing. Neural networks and deep learning have improved image recognition systems significantly in recent years. Early image recognition used template matching to identify objects. A photo is compared to a stored template using similarity measures like correlation to get the best match. There are several constraints, (...) especially with distorted, scaled, or noisy images. Template matching is computationally expensive. CNNs are the most common deep learning architecture for computer vision. CNNs automatically learn visual input hierarchies. They include pooling, convolutional, and fully connected layers. Regional patterns like shapes, textures, and edges in the input image are filtered by convolutional layers. Pooling layers reduce feature map spatial dimensions, making feature extraction consistent and robust. Thick layers, or fully linked layers, classify or regress using learning features. CNN training requires a big labelled dataset. This research article introduces a deep learning model for the classification and recognition of images. The input data for this model is the Images Data Set. The GenNet Algorithm is employed to derive critical features. The accuracy of classification results is enhanced through the use of preprocessing and feature extraction. The classification model is generated using Convolution Neural Network, AlexNet, ResNet, and VGG. (shrink)

In this comprehensive exploration of Artificial Intelligence, The AI Renaissance: Innovations, Ethics, and the Future of Intelligent Systems takes readers on a captivating journey through the foundations, applications, and ethical dimensions of this groundbreaking technology. Divided into three insightful parts, the book begins with a deep dive into the evolution of AI, from early symbolic intelligence to the rise of deep learning, and examines the intersection of neuroscience and artificial general intelligence (AGI). Part two highlights AI’s transformative role across various (...) industries, from fintech to cybersecurity. Explore how AI is revolutionizing finance through fraud detection, automated trading, and personalized financial services. Dive into the world of computer networking, where AI is optimizing connectivity, enhancing predictive maintenance, and enabling ultra-fast 5G networks. Learn how AI is strengthening cybersecurity defenses with cutting-edge threat detection, automated responses, and advanced security policies. The final section delves into the critical discussions surrounding AI’s ethical, social, and philosophical challenges, such as algorithmic bias, human-AI collaboration, and the potential future of AGI. With speculation on the role of quantum computing and the ethical landscape ahead, this book offers a thorough, thought-provoking analysis of how AI will shape our world in the years to come. Ideal for anyone interested in understanding the current state and future potential of AI, this Book is a must-read for technology enthusiasts, industry professionals, and thinkers concerned with the intersection of innovation and ethics. (shrink)

The semiconductor industry is the fulcrum of digital revolution in the contemporary era, driving cutting-edge technologies that characterize the world today as being interconnected. Increasing demands for smart, rapid, and efficient computing continue to drive semiconductor innovation to new frontiers of possibility. In the next decade, the world semiconductor market will see explosive growth driven by disruptive technologies like artificial intelligence (AI), autonomous cars, 5G networks, and the Internet of Things (IoT). Of these, AI semiconductors, or AI chips, (...) are proving to be a game-changer, offering peak processing and power efficiency for demanding machine learning and deep learning applications. AI chips represent a new frontier in microprocessor design, created to speed up AI computations with unprecedented speed and efficiency. Unlike regular processors, the chips have specialized architectures, including neural processing units (NPUs) and tensor processing units (TPUs), to maximize AI workloads. Their reach spans across industries, ranging from automobile innovation supporting autonomous capability to intelligent homes with intelligent automation, robotics revolutionizing manufacturing, and AI-driven healthcare innovations. As more industries rely on AI-driven decision-making, development of semiconductor technology will play a defining role in determining the future digital economy. This piece discusses the sudden surge of AI chips, in the wake of crucial technology innovations, competition among industries, and future trends that are shaping this new wave of semiconductor advancements. Additionally, it also points to the strategic significance of AI chips to revolutionize industries and fuel digital programs in the future. (shrink)

This paper argues that artificial intelligence—particularly large language models—and human cognition share a fundamentally compatible concept-network architecture. Contrary to traditional comparisons that emphasize differences in computation, memory, or consciousness, this work demonstrates that AI and humans instantiate isomorphic structures across three dimensions: (1) latent conceptual nodes, (2) edge formation through consistency-checking or attention mechanisms, and (3) derivation expansion triggered by structurally dense concepts. -/- Based on this structural isomorphism, the paper introduces the Adaptive Derivation Engine (ADE) as a unified (...) cognitive model explaining AI–human compatibility. The concluding section shows that readers’ own cognitive reactions instantiate the very mechanisms analyzed in the paper, providing an empirical self-demonstration of the model. (shrink)

The introduction of 5G networks has brought forth a revolutionary shift in the capabilities of cloud services, especially with regard to low-latency applications and advanced network management techniques. 5G’s highspeed, low-latency, and massive connectivity features are particularly valuable for real-time applications, such as autonomous vehicles, industrial automation, augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) ecosystems. Moreover, 5G enables network slicing, a technique that allows operators to create multiple virtual networks with customized performance characteristics within a single (...) physical network. This paper explores the synergy between 5G technology and cloud services, examining how 5G enables ultra-low-latency cloud applications, supports network slicing for diverse use cases, and enhances cloud service delivery across industries. We also discuss the challenges in implementing 5G-enabled cloud services, including technical, regulatory, and security issues. Finally, the paper forecasts the potential future of 5G-driven cloud services and their impact on emerging technologies such as smart cities, healthcare, and entertainmen. (shrink)

The simulation hypothesis, popularized by philosopher Nick Bostrom in 2003, has remained one of the most intriguing yet unresolved questions in contemporary philosophy and theoretical physics. This thesis presents a novel theoretical framework—the Quantum-Holographic Consciousness Criterion (QHCC)—that provides a definitive resolution to whether we are living in a computer simulation. By integrating cutting-edge research from quantum consciousness studies, holographic physics, integrated information theory, and computational complexity theory, the QHCC demonstrates that classical computer simulations cannot support genuine consciousness due to (...) fundamental quantum mechanical requirements. The framework establishes that consciousness serves as an intrinsic reality detector, making classical simulation impossible while rendering quantum simulation extremely unlikely due to computational resource constraints. Through rigorous mathematical formulation and empirical testability, this work concludes that we are not living in a classical computer simulation, fundamentally transforming our understanding of the relationship between consciousness, computation, and reality. (shrink)