International Journal on Recent and Innovation Trends in Computing and Communication
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Evaluating the TOE Framework for Technology Adoption: A Systematic Review of Its Strengths and Limitations
Full text linkThe adoption and decision-making of information technology (IT) remains the cornerstone of organizational innovation and market competitiveness. Various frameworks, such as the Technology Acceptance Model (TAM), the diffusion of innovations (DoI), and the Technology-Organization-Environment (TOE) framework, have been utilized to explain IT adoption decision-making. Among these, the TOE framework stands out for its holistic approach. The TOE framework has demonstrated adaptability across industries and technologies and has been used to examine technological capabilities, organizational readiness, and environmental influences on technology adoption. However, there remains a persistent debate about the TOE framework’s theoretical rigor and contextual applicability to address decision-making about technology adoption. This systematic review critically analyzes the strengths and limitations of the TOE framework while comparing and contrasting it with the DoI and TAM frameworks for technology adoption. This paper identified the gaps, such as the limited consideration of dynamic adoption processes and post-adoption outcomes in the TOE framework. This research synthesizes existing knowledge and critiques the current utility of the framework. It also offers a foundation for its evolution, addressing a significant scholarly need for critical evaluation and innovation in technology adoption studies
Internet of Things (IoT) Adoption in Higher Education Institutions: An Empirical Study in Saudi Arabia Universities
Full text linkThe Internet of Things (IoT) may offer many advantages to academic institutions, but its adoption, like other technologies, may also result in unanticipated risks and the necessity of significant organizational adjustments. This study examines the adoption of IoT by Saudi public and private universities. It targets the students and teachers to measure their intentions and actual behaviors to adopt IoT in academic research. An exhaustive literature review is necessary to create the research hypotheses and classify the anticipated benefits and risks of the Internet of Things (IoT). For the purpose of gaining an understanding of the relationships between university and technology, the study offers a theoretical framework by developing research hypotheses. The study used a quantitative research design by administering the survey questionnaires among the students and teachers of 7 public and private universities in Saudi Arabia. The study received 338 filled responses from the survey questionnaires. The findings showed that perceived usefulness and ease of use significantly and positively influence the intention to adopt IoT. Additionally, perceived ease of use significantly and positively influences perceived usefulness. Finally, the study found that the intention to adopt IoT significantly and positively influences actual user behavior to adopt IoT in academic research. The study recommends that the internet of things (IoT) may then provide universities with a multitude of benefits. It is necessary to make modifications to the organization, its procedures, and its systems to cultivate capabilities and make sure that IoT is compatible with the objectives of academic institution
Advances in Prompt Engineering and Retrieval-Augmented Generation for Scalable AI Systems
Full text linkImmediacy recently become a hot topic of scalable AI system and technologies, due to the rapid development in?AI, especially in NLP. “Noising” Prompt Writing The goal of effective prompt design is to write an input prompt, or set of prompts, that?help encourage LLMs to produce the desired output given the context and in contrast to other output. Approaches like the automatic and flexible prompt generation, few-shot learning, transfer learning to specific domains without?needing to re-train the models below, etc., made the prompts become dominant as an interface in the big model era. Manifesting this aim for fast engineering, retrieval-augmented generation is an instantiation?of the transfer of outside data to instantaneously influence the generation. As opposed to static material from document stores or databases which refines the answer with the latest and most?correct information, classic LLMs condition the answer on massive pre-trained knowledge. The hybridisation of these analogue knowledge sets serves to exploit the strengths of the two, and so?this is a more effective than the previous method of taking each one in isolation. A more efficient and precise AI would be possible by integrating the?two successics so that we have a trustworthy Dialogue system, decision support, and etc. Fast querying strategy, adaptive algorithms, and modular design?for interacting just in time with low intensity calculation are the key technology innovations. However, there are still several challenges that need to be addressed to make prompt-based design more reliable, handle retrieval noise, trade off latency and quality and use?it responsibly to mitigate bias and disinformation. Decentralised retrieval for better privacy and scalability, and multimodal retrieval and generation that could self-optimise using reinforcement learning, are some of?the interesting directions to explore in the future. We also?illustrate the interplay between these two relatively new developments in AI system design: retrieval-augmented generation and blitz engineering. In it you will find benchmarking?performance, current trends, and best practices that elevate AI from static information to dynamic knowledge through responsive, context-aware agents. By building on these previous AI breakthroughs, AI systems can?unlock more real-world use-cases, providing experiences that are more personalised, transparent, and grounded in reality
Skin Disease Classification Using Multi-Model Optimization and Augmentation
Full text linkSkin diseases affect millions globally, posing screening challenges due to complex lesion characteristics and limited access to medical expertise. Traditional screening methods are time consuming, often requiring extensive laboratory testing. Deep learning and machine learning techniques have gained significant traction in recent years, serving as powerful tools in tackling complex problems, particularly in areas requiring substantial prior knowledge, such as biomedicine. With the challenge of inadequate medical resources, these methods have found impactful applications in disease screening, emerging as a pivotal research focus on dermatology. This project aims to develop an automated skin disease screening system using machine learning and deep learning techniques. The system is designed to accurately identify skin diseases, enhance early detection, address existing challenges in screening and ensure accessibility and affordability for all. This provides a concise review of the classification of skin diseases, leveraging Convolutional Neural Networks (CNN) and K-Nearest Neighbors (KNN) to analyze skin lesion characteristics and evaluate imaging technologies. By exploring the strengths of CNNs due to its high performance in image classification and feature extraction. KNN providing evidence by identifying similar images, making it an explainable AI model. This study presents an Evidence based screening system a virtual dermatology platform leveraging cutting-edge artificial intelligence and deep learning techniques for efficient skin disease classification. Using pre-trained models like GoogleNet, EfficientNet, ResNet, DenseNet, MobileNet and achieving a classification accuracy of 97% through EfficientNet. significantly reducing screening time and cost. The proposed system optimizes preprocessing, transfer learning, model training and cross-validation, significantly improving accuracy. The results highlight AI's potential to revolutionize dermatological screening, reducing costs and improving early detection
Revolutionizing Big Data: Scalable Pipelines and the Power of Data Lakehouse Architecture
No full textThis paper studies how the Data Lakehouse architecture has the potential to change data analysis because it combines the most useful elements of data lakes and data warehouses into a single, scalable and cost-effective system. This looks at parts of the Lakehouse system, including open storage standards, extra data layers and engines that use ACID principles and points out why it is important to have scalable data pipelines. A comparison of warehouses, lakes and lake houses proves that lake houses are better equipped to handle different types of data tasks. By showing how finance, healthcare and retail use data lake houses to do complex analytics and machine learning with large data, this paper demonstrates how these systems enable organizations to avoid the common limits faced with traditional infrastructure. It also explains the tools and technologies involved in making Lakehouse work—for instance, Apache Spark, Delta Lake, Apache Airflow and Databricks and it looks at topics for further study, like live data, AI-friendly orchestration and data settings that are both safe and easy to use together. All of these insights explain how data pipelines and Lakehouse systems play an important role in the future of big data
A Unified Framework for Digital Delivery: Transition Strategies from Legacy to Cloud-Native Systems
Full text linkIn this essay, the paradigm changes in cloud migration strategies—from lift-and-shift to full cloud-native transformation—is examined. By examining technology elements, organisational factors, and architectural patterns, the paper offers a comprehensive framework for comprehending and deploying cloud native infrastructure. The demands of contemporary analytics workloads, real-time processing needs, and the exponential expansion of data volumes are becoming more and more difficult for legacy data warehouse systems to handle, despite their decades of dependability. Cloud-native tactics are revolutionising how businesses link heterogeneous systems, manage processes, and provide uniform user experiences. In order to address the particular security needs of cloud-native systems, this paper looks at a range of privacy-enhancing and trust-centric tools and strategies. In particular, a range of solutions are discussed, including cloud-native endpoint security solutions for guaranteeing trust and resilience in dynamic contexts, runtime protection platforms for real-time threat detection and responses, and service mesh technologies for secure service-to-service communication. To improve trust and transparency in cloud-native security, the significance of threat detection and response systems, cloud-native security information and event management (SIEM) solutions, and network security are also discussed. To guarantee comprehensive security in a cloud-native architecture, we also provide an extensive case study that illustrates how security measures are implemented across many levels, including application, network, infrastructure, security, and compliance. Organisations may strengthen the security posture of their cloud-native implementations by looking at these privacy-enhancing techniques and technologies. This will lower risks and guarantee the reliability of their data and apps in the dynamic ecosystem of today's digital world
Digital Transformation in Local Governance: A Blockchain Framework for Barangay Awitan
Full text linkInformation is one of the most important pieces of data to process and store, but some difficulties can be faced if the processing of documents still uses a manual process. Barangay Awitan in Labo, Camarines Norte, Philippines, faces challenges like delays, errors, and disorganized information. The researchers aim to automate the services of Barangay Awitan and integrate blockchain technology into their processes. To achieve the goal of the study, the researchers conducted an interview to determine the needs of Barangay Awitan for automation and a case study to determine the previous concept of automation. The researchers also used Feature-Driven Development (FDD) under the agile methodology, which is valuable for the development of the system. Finally, the researchers successfully developed a system framework to be implemented for Barangay Awitan, enhancing administrative operations, data management, communication, service delivery, and ensuring secure community information
A new Dynamic Routing Approach for Software Defined Network
Full text linkIntroduces a new dynamic routing approach tailored for Software Defined Network (SDN) that takes advantages of the programmability and centralized control inherent SDN architectures. Traditional routing protocols often struggles often to adapt to dynamic network conditions, leading to suboptimal performance and resource utilization. In contrast the objective of the paper is to proposed approach uses real time network information collected by the SDN controller to dynamic adjust routing decisions and dynamic routing algorithms for software define networks in wide area network (SDN-WAN), provide a new approach; By employing a combination of machine learning algorithm and network speed back mechanism. Using the approach optimizes routing paths based on factors such as link utilization and quality of service requirements. The shortest feasible path (SOFP) is an adaptation of the shortest feasible path algorithm that uses a statistical technique from the OpenFlow interface. The goal of the SFOP algorithm is to efficiently use SDN-WAN resources by determining the best route from source to destination. Overall, the dynamic routing approach provides a promising solution to efficiently manage network traffic in SDN. Paving the way for more adaptative and responsive networking infrastructure
Smart Conferencing Rooms: A Comprehensive Approach to AI-Driven Gesture and Virtual Interaction
Full text linkThe “Smart Conferencing Rooms” project is aimed at changing the interaction of users in educational and healthcare facilities through the use of artificial intelligence in hand gesture recognition and virtual communication technologies. By embedding Artificial Intelligent Hand Gesture Recognition and Virtual Communication Technologies to enhance the user interaction in education and health care these are the objectives of the “Smart Conferencing Rooms”. The goal is to create new AI/ML models for hand detection, fingertip tracking and air writing allowing for such a realization of interactions as drawing on the midair, typing, etc. , or solving mathematical problems. Further, it aims at improving more real-time interaction through the virtual chat system with better face and gesture recognition than Google Meet but with more functions. This cross-platform solution is asserted to enhance the qualities of communications and enhance collaboration and connectivity, which will be highly beneficial to these sectors.
This work is inspired from progress made in the field of object tracking which is one of the core issues of computer vision and it entails the ability to recognize and find objects like hand gestures in successive frames. It is suitable for those applications such as automatic surveillance and video recognition. By analyzing gestures the system can translate them into text which will especially be so beneficial to the deaf group of people because it helps them communicate effectively using gestures