Optimal Content Delivery Research Articles

Personalized Learning Style Detection And Pathway Optimization Using Hybrid Machine Learning Approaches

The demand for personalized learning in education has led to the need for adaptive systems that cater to diverse student needs and learning styles. This paper proposes a hybrid framework that combines learning style detection with dynamic pathway optimization to enhance student engagement and outcomes. By leveraging data-driven techniques, the framework identifies individual preferences and adapts educational content accordingly, ensuring an effective and tailored learning experience. Through illustrative scenarios, we demonstrate how this approach can accommodate various learning styles, such as visual, auditory, and kinesthetic, while optimizing content delivery to improve satisfaction and performance. This work highlights the potential for scalable and adaptable solutions in e-learning platforms, offering significant benefits across diverse educational and training contexts. The proposed framework lays a foundation for future research in personalized education systems. Keywords: Artificial Intelligence (AI), Machine Learning in Education, Personalized Learning, Learning Style Detection, Student Segmentation, Clustering Techniques, Reinforcement Learning.

Sustainability using Artificial Intelligence: Transformative Solutions in the IT Sector

Sustainability has become a critical priority for businesses and governments as we address environmental changes such as climate change, resource depletion, waste management, and water level increases. This paper investigates the transformative role of artificial intelligence (AI) in sustainable product development, highlighting how advancements in AI technologies are reshaping traditional practices. Traditional product development processes often relied on resource-intensive methods that generated significant waste and offered limited adaptability to sustainability goals. As a cornerstone of the digital economy, the IT sector faces increasing pressure to address its environmental impact, particularly in areas like server management, network optimization, and data transmission. This paper explores the transformative role of Artificial Intelligence (AI) in driving sustainable practices within IT operations. AI-powered solutions, such as dynamic workload management, predictive maintenance, and traffic optimization, enable energy-efficient server and network operations, significantly reducing power consumption. Advanced AI-driven algorithms facilitate data compression, prioritize transmission, and optimize content delivery, minimizing carbon emissions from data-intensive activities. Additionally, AI supports renewable energy integration, tracks carbon footprints, and provides actionable insights for developing sustainable software and IT infrastructure. These innovative applications of AI enhance operational efficiency and contribute to global sustainability goals by reducing the environmental footprint of IT systems. This research highlights the potential of AI to create a greener IT landscape while maintaining the sector’s growth and performance imperatives.

Personalization at Scale: Big Data Technologies in Digital Content Delivery and Their Effect on Viewer Experience

This article examines the transformative impact of big data technologies on the digital content delivery industry, focusing on their application in enhancing customer experience through video streaming services. Through a comprehensive case study of a leading provider, we investigate the implementation of Hadoop and AWS Big Data product suites to optimize content delivery, personalize user recommendations, and enable dynamic ad insertion. Our findings demonstrate significant improvements in service reliability, viewer engagement, and advertising effectiveness. The article reveals how Big Data analytics facilitate real-time content optimization and personalized user experiences, increasing viewer retention and satisfaction. Furthermore, we explore the challenges and limitations encountered during implementation, providing insights into the practical applications of Big Data in the evolving landscape of digital media consumption. This article contributes to the growing body of literature on Big Data applications in media and entertainment, offering valuable insights for practitioners and researchers in digital content delivery.

Learning-based joint recommendation, caching, and transmission optimization for cooperative edge video caching in Internet of Vehicles

In an era dominated by multimedia information, achieving efficient video transmission in the Internet of Vehicles (IoV) is crucial because of the inherent bandwidth constraints and network volatility within vehicular environments. In this paper, we propose a cooperative edge video caching framework designed to enhance video delivery efficiency in IoV by integrating joint recommendation, caching, and transmission optimization. Leveraging deep reinforcement learning with the discrete soft actor–critic algorithm, our methodology dynamically adapts to fluctuating network conditions and diverse user preferences, aiming to optimize content delivery efficiency and quality of experience. The proposed approach combines recommendation and caching strategies with transmission optimization to provide a comprehensive solution for high-performance video services. Extensive simulation results demonstrate that our framework significantly outperforms traditional baseline methods, achieving superior outcomes in terms of service utility, delivery rate, and delay reduction. These results highlight the robust potential of our solution to facilitate seamless and high-quality video experiences in the complex and dynamic landscape of vehicular networks, advancing the capabilities of IoV content delivery.

Adaptive Caching Strategies for IoV Based on LTE Signal Quality Analysis

The Internet of Vehicles (IoV) enhances vehicular communication, promising better connectivity and user experiences. However, maintaining stable data delivery in dynamic vehicular environments is challenging. This paper introduces Mob-Cach, a novel mobility-aware cache management strategy that leverages real-time vehicular mobility information. Our preliminary study on LTE signal quality shows that mobility patterns significantly impact signal degradation. Based on these findings, we developed a dynamic caching framework that adapts to real-time mobility data, optimizing content delivery and network performance. Simulations demonstrate significant improvements in cache hit rates, reduced latency, and overall network efficiency. This research lays a foundation for future advancements in IoV networks, addressing the challenges of mobile and dynamic network topologies.

Cloud-Based Solutions for Video Streaming and Big Data Testing

In the era of digital transformation, cloud-based solutions have revolutionized the video streaming industry and big data testing methodologies. As consumer demand for high-quality, on-demand video content surges, traditional on-premises infrastructure often falls short in meeting scalability, performance, and cost-efficiency requirements. Cloud-based platforms, with their scalable resources and flexibility, have become essential in addressing these challenges. This paper explores the benefits and implementation strategies of cloud-based solutions for video streaming and big data testing, focusing on how these technologies enhance performance, scalability, and cost-effectiveness Video streaming services require a robust infrastructure capable of handling large volumes of data and delivering seamless user experiences across diverse devices and network conditions. Cloud-based video streaming solutions offer several advantages, including the ability to dynamically scale resources based on user demand, optimize content delivery through content delivery networks (CDNs), and provide a global reach without the need for extensive physical infrastructure. By leveraging cloud services, streaming platforms can reduce latency, enhance video quality, and improve overall user satisfaction. Traditional testing methods often struggle to cope with the complexity and volume of big data. Cloud-based testing platforms provide scalable environments where testers can simulate various data scenarios, execute performance tests, and analyze results with greater efficiency. The cloud's elasticity allows for on-demand provisioning of testing resources, making it easier to handle the dynamic nature of big data and conduct comprehensive testing without investing in expensive hardware.

User-Centric Algorithms: Sculpting the Future of Adaptive Video Streaming

This paper explores the transformative potential of user-centric algorithms in shaping the future landscape of adaptive video streaming. Traditional streaming methods, though effective, often lack the ability to dynamically adapt to individual user preferences. We argue for a paradigm shift towards user-centricity, where algorithms are designed to consider user behavior, preferences, and feedback to optimize content delivery. By examining the impact of personalized streaming experiences on viewer satisfaction and engagement, we present a compelling case for the integration of machine learning and artificial intelligence in adaptive video streaming. Through real-world examples and case studies, we showcase the efficacy of user-centric algorithms in sculpting a more tailored and immersive streaming environment. This paper provides insights into the challenges, considerations, and future trends surrounding user-centric adaptive video streaming, highlighting its potential to redefine the viewer experience and set new standards for digital content delivery.

Deep Adversarial Reinforcement Learning based Incentive Mechanism for Content Delivery in D2D-Enabled Mobile Networks

With the explosive growth of mobile devices at the edge of mobile networks, the amount of content that needs to be transmitted is exploded. As it takes resources (cache, bandwidth, and power) for the edge server and nearby devices to deliver content, the incentive mechanism with the optimal pricing and content delivery strategy needs to be studied without relying on the knowledge of network parameters in a competitive environment. In this paper, built on the framework of content delivery with the Stackelberg game, we have proposed a robust deep reinforcement learning method based on adversarial training to learn price strategy for lack of knowledge of private information. Specifically, the Stackelberg game is constructed to describe the interaction between different service providers and service demanders, and a deep adversarial reinforcement learning method is proposed to approximate the opponent’s decision in the worst case, where the price strategy can be trained end-to-end in the competitive environment. The simulation results show that the proposed algorithm can form an effective price strategy and improve the utility of service providers.

The impact of ChatGPT on blended learning: Current trends and future research directions

Designing sustainable and scalable educational systems is a challenge. Artificial Intelligence (AI) offers promising solutions to enhance the effectiveness and sustainability of blended learning systems. This research paper focuses on the integration of the Chat Generative Pre-trained Transformer (ChatGPT), with a blended learning system. The objectives of this study are to investigate the potential of AI techniques in enhancing the sustainability of educational systems, explore the use of ChatGPT to personalize the learning experience and improve engagement, and propose a model for sustainable learning that incorporates AI. The study aims to contribute to the body of knowledge on AI applications for sustainable education, identify best practices for integrating AI in education, and provide insights for policymakers and educators on the benefits of AI in education delivery. The study emphasizes the significance of AI in sustainable education by addressing personalized learning and educational accessibility. By automating administrative tasks and optimizing content delivery, AI can enhance educational accessibility and promote inclusive and equitable education. The study’s findings highlight the potential benefits of integrating AI chatbots like ChatGPT into education. Such benefits include promoting student engagement, motivation, and self-directed learning through immediate feedback and assistance. The research provides valuable guidance for educators, policymakers, and instructional designers who seek to effectively leverage AI technology in education. In conclusion, the study recommends directions for future research in order to maximize the benefits of integrating ChatGPT into learning systems. Positive results have been observed, including improved learning outcomes, enhanced student engagement, and personalized learning experiences. Through advancing the utilization of AI tools like ChatGPT, blended learning systems can be made more sustainable, efficient, and accessible for learners worldwide.

Proactive Caching in D2D Assisted Multitier Cellular Network.

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption.

Delay-Aware Content Delivery With Deep Reinforcement Learning in Internet of Vehicles

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm.

Light forwarding based optimal CCN content delivery: a case study in metropolitan area network

Content Centric Networking (CCN) has been introduced in recent years to effectively deliver the named content objects rather than relying on the traditional host-to-host communication model, in which a significant and non-negligible characteristic of CCN is in-network caching. In this paper, we stand on the shoulders of in-network caching to propose the light forwarding based approach in order to optimize CCN content delivery, i.e., the process of data routing irrespective of interest routing. At first, we specially devise an additional packet type which has no data entity at the same time the caching/delivery of data is the granularity of packet. Then, we present a generic content delivery scheme based on packet-level caching and new designed packets; in addition, we make a case study in Metropolitan Area Network (MAN) due to some realistic constraints. Finally, we implement the proposed light forwarding based content delivery scheme and compare it with three existing schemes, and the experimental results reveal that it has more efficient performance optimization, such as network load, delivery delay and energy saving.

An optimized content delivery approach based on demand–supply theory in disruption-tolerant networks

Disruption-tolerance networks (DTNs) are suitable for applications that may lack continuous network connectivity. Examples of such applications include coupon distribution, crisis relief, traffic notification, and broadcasting news from a website. Generally, these contents have a temporal constraint, so that their value will be decreased over time. DTNs have to utilize mobile relay nodes to transmit messages from the sender to the destination. These relay nodes often have selfish behavior, leading to a lack of cooperation. To improve the overall routing functionality, one must motivate relay nodes to share their resources. Thus, different incentives and rewarding mechanisms must be devised to encourage cooperation. We believe that microeconomics theories are appropriate mathematical tools to model the interactions between the DTN nodes. In microeconomics, buyers aim at maximizing their utility concerning their budget constraints. In this paper, the demand–supply theory is deployed to mitigate nodes’ selfishness and to create incentives among them. Each user can receive multiple sub-messages each of which containing special benefits for his/her. In this way, nodes are motivated to forward messages, which in turn leads to greater profitability for them and maximizing the social welfare of the society. The simulation of the proposed algorithm illustrates its superiority in terms of significant criteria such as delivery ratio, end-to-end delay, number of dropped messages, buffering time, number of hops, overhead ratio, etc.

Enhancing Strabismus Education through Interactive Learning Multimedia

The objective of our project was to create an interactive online educational module that integrates biomechanics of the eye with its underlying anatomy to allow for a comprehensive understanding of pediatric strabismus and its clinical detection to prevent amblyopia.Amblyopia is the leading cause of monocular vision loss in the pediatric population, affecting 3–5% of children. Untreated, it leads to irreversible visual impairment in an otherwise structurally normal eye and can have significant effects on quality of life that impact self‐esteem and restrict career opportunities due to loss of stereopsis. Majority of cases are caused by strabismus, a condition resulting in the ocular misalignment of the eye. Early intervention significantly improves vision outcomes and psychosocial well‐being of the patient, however requires prompt and skilled detection. The pediatric eye examination can be inherently challenging, particularly considering the low emphasis on ophthalmology teaching in medical school curricula. Primary care residents and undergraduate medical student trainees often report low levels of comfort performing screening eye examinations and managing common pediatric ophthalmological presentations. Integration of open educational resources such as interactive clinical skills modules can effectively advance ophthalmology education and promote prompt detection of pediatric strabismus, with the goal of mitigating serious ophthalmic complications and improving overall patient care.To effectively address these educational challenges, we have developed an innovative online module that correlates ocular anatomy to biomechanical mechanisms, and provides practical guidelines to clinical examination. The module focuses on the pediatric eye examination, evaluating for amblyopia & strabismus, and was created through an international collaboration with experts in anatomy, education, and neuro‐ophthalmology to optimize content delivery and integrate clinical relevance. It offers a variety of interactive activities to engage student learning and concludes with a set of virtual cases entailing simulated clinical examination to assess acquired knowledge. Critical feedback will be collected using a student experience survey at the beginning and end of the module to solicit response regarding effectiveness of content delivery, preference of educational media, and comfort conducting a strabismus clinical examination.This international effort aims to significantly improve confidence performing clinical examination, guide prompt amblyopia detection in the pediatric population, and prevent development of its irrevocable complications.

Impact of an Anatomy Themed Snapchat Account on Learner Utilization, Satisfaction, and Assessment Outcomes

Social media has been established as an effective method of rapidly disseminating information to a large audience and is appealing to anatomy educators that are looking for ways to provide review opportunities beyond lectures. Snapchat is unique in the social media landscape due to the ephemeral nature of the content posted, which disappears after 24 hours. Snapchat users have reported paying closer attention to content due to its ephemeral nature. There is a need to determine learner utilization and satisfaction of an anatomy themed Snapchat account and if the potential for closer attention results in improved short‐ and long‐term assessment outcomes. To address this need, an anatomy themed Snapchat account was created and used to deliver a series of blood flow drawing summaries during the first‐year clinical anatomy course of an entry‐level doctor of physical therapy program. Viewing statistics were collected, a user satisfaction survey was distributed, and learner performance on a blood flow question was assessed immediately at the end of the unit of the course and retention assessed 12 months later. Learner performance was also compared with a previous cohort (control group) that took the course during the year prior to the development of the Snapchat account using an independent t‐test. The Snapchat account was followed by 56 of the 67 (84%) learners in the course. Five Snapchat stories were created that consisted of an average of 23 snaps per story (range: 12–36). The average percent of followers that viewed each story was 70% and of these learners, the percent that completed viewing of all snaps in a story was 87%. Twenty screen shots were taken across the 114 snaps distributed. The satisfaction survey was completed by 66 of the 67 (99%) learners in the Snapchat group. Learners rated Snapchat as quite useful in reviewing blood flow drawings (4 out of 5 Likert‐scale), as having extremely accurate content (5 out of 5), being quite helpful in preparing for blood flow questions on the exam (4 out of 5), and were quite interested in having more course content summarized using the account (4 out of 5). Strengths of the account included: easily accessible, additional exposure beyond lecture, and content on a platform already being used. Improvements suggested included: maintaining content after 24 hours, beginning use of the account earlier in the course, and providing more content. Learner performance on a blood flow question at the end of the unit was identical between the Snapchat group (100% correct) and the control group (100% correct). One year later, the assessment scores were similar (P = 0.17) between the Snapchat (89.0 ± 14.1% correct) and the control groups (86.7 ± 14.1%). These results demonstrated that an anatomy themed Snapchat account had a large following and was extensively viewed by learners. The user survey revealed learners were satisfied with the usefulness, accuracy, and ability of the account to prepare them for the assessment. There were no significant improvements observed in either short‐ or long term assessment outcomes. This initial work showed that learners were receptive to reviewing anatomy concepts using Snapchat and there remains opportunity to determine how to optimize content delivery that results in significantly improved learning outcomes.Screen shot of a Snapchat story reviewing a drawing of the arterial supply to the lower limb.Figure 1

Applications of Natural Language Techniques to Enhance Curricular Coherence

Abstract An effective and highly efficient curriculum is the foundation of an optimal content delivery to students and may lead to the success of the academic program. Ensuring the effectiveness of curricular processes goes beyond the successful design of individual courses and it is subject to careful planning in accordance to appropriate policies and procedures that enable a high level of curriculum coherence. Such curriculum can guarantee that students are presented with a complete set of interrelated topics related to the area of study that can enable students to connect topics in individual courses and apply them in professional settings. The goal of this article is to evaluate the curriculum coherence of an Information Science program using ontologies and natural language processing techniques. Curricular coherence is evaluated using a possibilistic approach which evaluates academic gaps and overlaps that exist in the core courses offered in the degree. The use of ontologies enables us to perform a qualitative study of curricular coherence at different levels of ontological abstraction using mereotopological principles. The knowledge inferred from this process can be used at the universities to optimize their course offering and content for better academic content.

A Distributed Interdomain Control System for Information-Centric Content Delivery

The Internet, the de facto platform for large-scale content distribution, suffers from two issues that limit its manageability, efficiency, and evolution. First, the IP-based Internet is host-centric and agnostic to the content being delivered and, second, the tight coupling of the control and data planes restrict its manageability, and subsequently the possibility to create dynamic alternative paths for efficient content delivery. Here, we present the CURLING system that leverages the emerging Information-Centric Networking paradigm for enabling cost-efficient Internet-scale content delivery by exploiting multicasting and in-network caching. Following the software-defined networking concept that decouples the control and data planes, CURLING adopts an interdomain hop-by-hop content resolution mechanism that allows network operators to dynamically enforce/change their network policies in locating content sources and optimizing content delivery paths. Content publishers and consumers may also control content access according to their preferences. Based on both analytical modeling and simulations using real domain-level Internet subtopologies, we demonstrate how CURLING supports efficient Internet-scale content delivery without the necessity for radical changes to the current Internet.

Direct push–pull or assisted push–pull? Toward optimal video content delivery using shared storage-based cloud CDN (SS-CCDN)

Video content delivery networks face many challenges such as scalability, quality of service and flexibility. Video suppliers address them through CDN. Cloud computing and Video content Delivery as a Service (VDaaS) plays a key role in improving the content delivery standard and makes the work of content providers, easier. By hosting video contents in the cloud, the content delivery costs are minimized and the overall content delivery performance enhanced by optimization of cloud CDN. Cost optimization of the cloud-based content delivery network requires a focus on delay or throughput, the overall performance and content delivery. The content placement and content access, the QoS and the QoE in CDN can be improved by enhancing the video content delivery performance. In this paper, a unique model for video content delivery, cloud-based is developed, titled as shared storage-based cloud CDN (SS-CCDN) to achieve the objective. This design optimizes through algorithms, the effective placement of video data and dynamic update of video data. For analysis, GA, PSO, and ACO algorithms are used. The proposed model uses direct and assisted push–pull content delivery schemes for cost-efficient content delivery. The low-cost VDaaS model reduces the storage cost, keeps the latency and the traffic cost. Experimental results validate that this model, with regard to storage, traffic, and latency generate higher performance with lower price and satisfy the QoS and QoE aspects in content delivery.

Towards an improved heuristic genetic algorithm for static content delivery in cloud storage

A key challenge in computer networking is how to organize network topology effectively among a large number of servers in the cloud storage system. In a cloud environment, the topology, which is different from the underlying topology, may be established in any form at any potential edge peers. The cloud content delivery network (CDN) always faces problems of complex distributed path creation, cache update, load balancing, etc. To address the problem as a static content delivery, we propose an Improved Heuristic Genetic Algorithm for Static Content Delivery in Cloud Storage (IHGA-SCDCS) based on a resource management model and cost model. The static content delivery in cloud storage is abstracted into mathematical model for set solving problem, which is then solved by an improved Genetic Algorithm (GA). Finally, the optimal solution is reduced to an optimal content delivery program. The simulation experiment, based on CloudSim, shows that IHGA-SCDCS can effectively obtain optimal solution while reducing delivery cost.

Optimal Cooperative Content Caching and Delivery Policy for Heterogeneous Cellular Networks

To address the explosively growing demand for mobile data services in the 5th generation (5G) mobile communication system, it is important to develop efficient content caching and distribution techniques, aiming at significantly reducing redundant data transmissions and improving content delivery efficiency. In heterogeneous cellular network (HetNet), which has been deemed as a promising architectural technique for 5G, caching some popular content items at femto base-stations (FBSs) and even at user equipment (UE) can be exploited to alleviate the burden of backhaul and to reduce the costly transmissions from the macro base-stations to UEs. In this paper, we develop the optimal cooperative content caching and delivery policy, for which FBSs and UEs are all engaged in local content caching. We formulate the cooperative content caching problem as an integer-linear programming problem, and use hierarchical primal-dual decomposition method to decouple the problem into two level optimization problems, which are solved by using the subgradient method. Furthermore, we design the optimal content delivery policy, which is formulated as an unbalanced assignment problem and solved by using Hungarian algorithm. Numerical results have shown that the proposed cooperative content caching and delivery policy can significantly improve content delivery performance in comparison with existing caching strategies.