Current Issue : April-June Volume : 2026 Issue Number : 2 Articles : 5 Articles
As the information society advances swiftly, individuals and corporations are producing vast quantities of data daily. Cloud computing presents considerable strengths in storing and applying this data. Yet, challenges related to data security and privacy within cloud computing are obstructing its continued expansion. To guarantee data confidentiality, data owners (DOs) employ conventional cryptographic techniques to encrypt information prior to delegating it to cloud servers. However, this makes efficient search difficult to achieve. Searchable encryption (SE) can effectively alleviate this dilemma. However, most existing SE schemes have not fully considered spelling errors and semantic extension of keywords. At the same time, users’ personalized characteristics are not considered in the search process, and personalized retrieval services cannot be supported on encrypted data. The study designs an efficient and intelligent personalized search (EIPS) scheme based on user’s interest, which can intelligently conduct multikeyword precise search and fuzzy semantic search based on user’s interest model, and return accurate top-k search results. Our contribution consists of three aspects. First, this scheme combines precise search, fuzzy search, semantic expansion, and personalized search technology to realize intelligent personalized multikeyword search. Second, the use of vector cross matching and short-circuit matching effectively improves retrieval efficiency. Third, considering the protection of data privacy, a hybrid cloud server architecture was employed. Specifically, the user interest model (UIM) is stored on a private cloud server (PRCS), and the sorting of search results is also completed on the PRCS. This setting not only ensures the security of user data and computing operations but also reduces the burden on users. The security analysis results indicate that EIPS can ensure the privacy of data and users. The experimental results also show that this scheme has high efficiency while providing personalized search results for users....
This paper evaluates key issues in cloud computing and introduces a novel model, known as sky computing, to address these challenges. Cloud computing, a transformative technology, has played a critical role in reshaping modern operations—especially following the COVID-19 pandemic, when many human activities shifted to technology-driven platforms. It offers multiple service models, including Software as a Service, Hardware as a Service, Desktop as a Service, Backup as a Service, and Network as a Service, each tailored to user requirements. However, the rapid expansion of cloud-based technologies and interconnected systems has intensified infrastructure and scalability challenges. Sky computing, or the “cloud of clouds,” emerges as an advanced layer above traditional cloud models, enabling dynamically provisioned, distributed domains built over multiple serial clouds. Its core capability lies in offering variable computing capacity and storage resources with dynamic, real-time support, providing a robust and unified platform by integrating diverse cloud resources. This paper reviews related technologies, summarizes prior research on sky computing, and discusses its structural design. Furthermore, it examines the limitations of current cloud computing frameworks and highlights how sky computing could overcome these barriers, positioning it as a pivotal architecture for the future of distributed computing....
This article introduces the main characteristics of PyMossFit, a software for Mössbauer spectra fitting. It is explained how each aspect of the code works. Based on the Lmfit Python package, it is a robust data fitting tool. Designed to run through Jupyter Notebook in the Google Colab cloud, it also allows one to work via multiple devices and operating systems. In addition, it allows the fitting procedure to be performed collaboratively among researchers. The software performs the folding of raw data with a discrete Fourier transform. Data smoothing is available with the use of a Savitzky–Golay algorithm. Moreover, a Knearest neighbor algorithm enables users to determine the present phases by matching the correlations of hyperfine parameters from a local database....
Traditional relational databases have been widely used for many years and have been the go-to choice for the industry. However, the emergence of NoSQL databases coincided with the increasing popularity of the Internet, social networks, and cloud computing. Data migration has become a crucial topic, due to the rapid growth of applications and the ever-expanding amount of data being collected. This has led to a shift from relational database management systems (RDBMS) to NoSQL databases, also known as not only SQL. This article compares the characteristics of both database architectures, SQL and NoSQL, focusing on aspects such as scalability and performance, flexibility, and security. Additionally, the role of AI in streamlining the migration process will be explored....
The Internet of Medical Things (IoMT) with edge computing provides opportunities for the rapid growth and development of a smart healthcare system (SHM). It consists of wearable sensors, physical objects, and electronic devices that collect health data, perform local processing, and later forward it to a cloud platform for further analysis. Most existing approaches focus on diagnosing health conditions and reporting them to medical experts for personalized treatment. However, they overlook the need to provide dynamic approaches to address the unpredictable nature of the healthcare system, which relies on public infrastructure that all connected devices can access. Furthermore, the rapid processing of health data on constrained devices often leads to uneven load distribution and affects the system’s responsiveness in critical circumstances. Our research study proposes a model based on AI-driven and edge computing technologies to provide a lightweight and innovative healthcare system. It enhances the learning capabilities of the system and efficiently detects network anomalies in a distributed IoMT network, without incurring additional overhead on a bounded system. The proposed model is verified and tested through simulations using synthetic data, and the obtained results prove its efficacy in terms of energy consumption by 53%, latency by 46%, packet loss rate by 52%, network throughput by 56%, and overhead by 48% than related solutions....
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