Journal of Algorithms and Computation
Volume:56 Issue: 2, Dec 2023

A data fabric architecture ensures that different types of data can be quickly and easily integrated, accessed, transformed, and managed. All users involved with data fabric technology, directly or indirectly, have access to appropriate information in real-time, from the correct location and in the proper manner. As data storage and access have become increasingly common, there are growing concerns about data security. In this paper, we present a new and more efficient data fabric architecture for providing secure communication between the data store and the customer, utilizing Shamir's Secret Sharing (SSS) and secure Attribute-Based Encryption (ABE). We call this architecture Data Fabric equipped with Attribute-Based Authentication and Access Control (DFAAA). It offers security features such as perfect forward secrecy, untraceability, anonymity, mutual authentication, and availability. We verify these features using both informal and formal techniques. The results demonstrate that our architecture not only provides greater security compared to other architecture.

Early detection of gastrointestinal cancer remains a major challenge, particularly in identifying cancerous regions at their initial stages. Anatomical landmarks are crucial for guiding physicians during endoscopic screenings, with accurate localization enhancing diagnostic precision. This study proposes a deep learning approach using convolutional neural networks (CNNs) to detect and localize anatomical landmarks in endoscopic video frames from 40 patients at Firoozgar Hospital, Tehran. Pre-processed frames were annotated with bounding boxes to highlight regions of interest. The CNN model achieved 97.0% accuracy for landmark detection and classification and an MSE of 0.004 for bounding box regression, showing promise for assisting early diagnosis.

This study presents an innovative approach to real-time facial expression analysis using a guided module-based convolutional neural network. The proposed methodology simultaneously detects emotions, age, and gender with high accuracy, achieving 95.1% for seven facial emotions. The research contributes to various fields, including healthcare, security, and human-computer interaction. A custom real-time dataset encompassing diverse age groups was created to enhance the model's efficiency. The study conducted an ablation analysis to optimize the architecture's effectiveness. Quantitative and qualitative results demonstrate superior performance compared to existing methods across multiple datasets. The proposed approach outperforms six state-of-the-art models in accurately detecting emotions based on age and gender in real-time scenarios. This research advances the development of explainable deep-learning models for emotion recognition, addressing challenges posed by specialized datasets and facilitating more sophisticated systems for real-time human interaction analysis.

A number $\alpha$ has a representation with respect to the numbers $\alpha_1,...,\alpha_n$, if there exist the non-negativeintegers $\lambda_1,... ,\lambda_n$ such that $\alpha=\lambda_1\alpha_1+...+\lambda_n \alpha_n$. The largest natural number that does not have a representation with respect to the numbers $\alpha_1,...,\alpha_n$ is called the Frobenius number and is denoted by the symbol$g(\alpha_1,...,\alpha_n)$. In this paper, we present a new algorithm to calculate the Frobenius number. Also we present the sequential form of the new algorithm. A number $\alpha$ has a representation with respect to the numbers $\alpha_1,...,\alpha_n$, if there exist the non-negativeintegers $\lambda_1,... ,\lambda_n$ such that $\alpha=\lambda_1\alpha_1+...+\lambda_n \alpha_n$.The largest natural number that does not have a representation with respect to the numbers $\alpha_1,...,\alpha_n$ is called theFrobenius number and is denoted by the symbol $g(\alpha_1,...,\alpha_n)$. In this paper, we present a new algorithm to calculate theFrobenius number. Also we present the sequential form of the new algorithm.

As organizations increasingly depend on large-scale data for strategic decision-making, managing data warehouses has become a complex and resource-intensive challenge. This paper introduces DataBay, a unified platform designed to automate the entire data warehouse lifecycle, from data ingestion and transformation to real-time processing, monitoring, and ensuring data quality. DataBay leverages Avro for data serialization, providing optimal throughput and storage efficiency. Additionally, its automated data pipeline orchestration, along with built-in data quality checks, enhances the reliability and accuracy of insights derived from the data. The platform’s architecture is highly scalable, supporting enterprise-level datasets and adapting to evolving business needs. Through its seamless integration and flexibility, DataBay helps businesses make timely, data-driven decisions and enables continuous optimization of data workflows. This paper discusses the platform’s architecture, its implementation in real-world industry settings, and the significant business value it delivers by enhancing operational efficiency and empowering data-driven decision-making across organizations.

Improving efficiency of multi-level fast multi-pole algorithm (MLFMA) on distributed and parallel systems has been vastly studied, specially for GPUs. Unlike the far-field computation, acceleration of near-field computation in MLFMA algorithm on GPUs was of less concern in the literature, however there are some solutions that exploited special specifications of GPU’s memory. This article proposes data replication for P2P operator and uses analytical performance models to determine its optimality criteria. By modelling the speedup, we found that making threads independence by creating redundancy in the data makes the algorithm for lower dense problems nearly 13 times faster than non-redundant mode.

In this paper we investigate the pair difference cordial labelingbehaviour of diamond ladder graph,lattitude ladder, octopus graph,pagodagraph, planter graph and semi jahangir graph. Prime labeling behaviour of plantergraph, duplication of planter graph, fusion of planter graph,switching of plantergraph, joining of two copies of planter graph were studied by A.Edward samueland S.Kalaivani[4]. Dafik, Riniatul Nur Wahidah hve been examined the RainbowAntimagic coloring of special graphs like volcano, sandat graph, sunflower,octpus,semijahangir [3]. Prihandini,R M., at.el have been studied the elegant labeling ofshackle graphs and diamond ladder graphs [16]. Classical meanness of some graphssuch as one-side step graph,double-sided step graph,grid,slanding ladder,diamondladder,lattitude ladder was studied by Alanazi et. al [1]. In [17] Yeni Susanti et.al studied the edge odd geaceful labeling behaviour of prism, antiprism, cartesianproduct graphs. The notion of pair diference cordial labeling of a graph was introduced in [7].

Music serves as a universal medium for expressing emotions and entertainment. With advancements in technology and artificial intelligence, online systems for sharing and recommending music have grown, but a gap remains in location-based, emotion-aware music suggestions. This article proposes a novel system that combines location and emotional context to recommend music. Users can attach songs tied to specific locations and memories on a map, enabling intelligent suggestions for others. The system’s hybrid design incorporates user feedback, distinguishing it from existing models. Challenges like incomplete databases and limited user preference data at launch were addressed during implementation. This project aims to enhance music sharing by integrating emotional and geographical contexts, offering a personalized and interactive experience for users.

The evolving educational landscape requires innovative teaching methods to enhance learning and accommodate diverse styles. Technologies like artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are transforming education by improving student performance and engagement. This research introduces an Artificial Intelligent Personal Recommender System Algorithm (AIPRS) that focuses on individual learning styles and interests to recommend customized learning programs. Unlike traditional methods, AIPRS personalizes education by analyzing learners' backgrounds and preferences, utilizing AI, ML, and IoT. This tailored approach marks a significant shift from conventional systems, emphasizing the importance of individual learning styles to improve the overall educational experience. The study highlights the potential of personalized education, offering a solution to efficiently deliver knowledge and enhance student satisfaction.

Solving a linear system of equations is needed in many different applications and there exist many different techniques to solve such a system with no need to compute inverse matrix, as a costly and not stable computation. But the challenge is that in some other applications such as 3D prints, the goal is exactly computing the inverse of a matrix. In this paper, an optimization model equivalent to inverse matrix is introduced and an effective algorithm based on steepest-descent and Barzilai-Borwein step length is suggested. We also used conjugate gradient instead, to provide better numerical results. Finally, we used the Metropolis-Hastings algorithm to accelerate the convergence rate. A key point is that even a random step length is working for global convergence. Numerical results look promising based on stability and accuracy.