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Fault Tolerance Exploration and SDN Implementation for de Bruijn Topology based on betweenness Coefficient

By Artem Volokyta Heorhii Loutskii Oleksandr Honcharenko Oleksii Cherevatenko Volodymyr Rusinov Yurii Kulakov Serhii Tsybulia

DOI: https://doi.org/10.5815/ijcnis.2024.01.08, Pub. Date: 8 Feb. 2024

This article considers the method of analyze potentially vulnerable places during development of topology for fault-tolerant systems based on using betweenness coefficient. Parameters of different topological organizations using De Bruijn code transformation are observed. This method, assessing the risk for possible faults, is proposed for other topological organizations that are analyzed for their fault tolerance and to predict the consequences of simultaneous faults on more significant fragments of this topology.

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Teaching Partial Order Relations: A Programming Approach

By Dayou Jiang

DOI: https://doi.org/10.5815/ijeme.2024.01.03, Pub. Date: 8 Feb. 2024

This paper investigates teaching methods that leverage programming techniques to strengthen the understanding of partial ordering relations. Partial orders are vital in diverse domains, such as mathematics and economics. A comprehensive teaching framework is presented in this paper, incorporating standard programming languages to instruct partial order relations effectively. The approach integrates theoretical concepts, practical illustrations, and interactive programming exercises to enhance students' comprehension and application of partial order relations. Furthermore, the evaluation of teaching effectiveness and potential implications for computer science and mathematics education are discussed.

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Generation of Images from Text Using AI

By Nimesh Yadav Aryan Sinha Mohit Jain Aman Agrawal Sofia Francis

DOI: https://doi.org/10.5815/ijem.2024.01.03, Pub. Date: 8 Feb. 2024

Reading the words can be confusing, and it may be hard to picture what is happening. There are some circumstances where words can be misunderstood. It's much simpler to recognize text if it's displayed as an image. The use of visuals is proven to increase viewership and retention.
Synthesizing realistic images automatically is a challenging undertaking, and even the most advanced artificial intelligence and machine learning algorithm has trouble meeting this standard. GANs (Generative Adversarial Networks) are just one example of a powerful neural network architecture that has shown promising results in recent years. Existing text-to-image methods can generate examples that generally reflect the meaning of the provided descriptions, but they often lack the necessary details and colorful object elements.
The primary objective of our research was to explore diverse architectural methodologies with the intention of facilitating the generation of visual representations from textual descriptions. By delving into this investigation, we aimed to discover and examine various approaches that could effectively support the creation of visuals that accurately depict the content and context provided within written narratives. Our aim was to unlock new possibilities in the realm of visual storytelling by establishing a strong connection between language and imagery through innovative architectural techniques.

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Advances in Medical Imaging: Using Convolutional Neural Networks for White Blood Cell Identification

By Ishwari Singh Rajput Sonam Tyagi Aditya Gupta Vibha Jain

DOI: https://doi.org/10.5815/ijigsp.2024.01.08, Pub. Date: 8 Feb. 2024

White blood cells (WBC) perform a vital function within the immune system by actively protecting the body from a wide range of diseases and foreign substances. Diverse types of WBCs exist, including neutrophils, lymphocytes, eosinophils, and monocytes, each possessing distinct roles within the immune response. Neutrophils are typically the initial immune cells to mobilize in response to infections and inflammation, exhibiting a rapid and robust reaction. Conversely, lymphocytes play a pivotal role in the recognition and targeted elimination of pathogens. Nevertheless, identifying and classifying WBCs poses significant challenges and demands considerable time, even for seasoned medical practitioners. The process of manual classification is frequently characterized by subjectivity and is susceptible to errors, thereby potentially compromising the precision of both diagnosis and treatment. In response to this challenge, scholars have devised deep learning methodologies that can automate the process of WBC classification, thereby enhancing its precision. This study employs a convolutional neural network (CNN) to classify WBCs based on imaging data. The CNN underwent training using a substantial dataset comprising body cell images. This training facilitated the acquisition of discerning characteristics specific to various WBC types, thereby enabling accurate classification. The methodology was evaluated within a simulated environment, yielding encouraging outcomes. The approach that was proposed successfully achieved an average accuracy rate of 98.33% in the classification of WBCs. This outcome serves as evidence of deep learning techniques enhancing the speed and accuracy of WBC classification.

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A Novel Approach to Customer Segmentation for Optimal Clustering Accuracy

By Hammed Mudasiru Soyemi Jumoke

DOI: https://doi.org/10.5815/ijieeb.2024.01.03, Pub. Date: 8 Feb. 2024

Customer segmentation is not only limited to the identification of user groups but searching and determining the attitude of individual customer groups toward a particular product or service aside helping organization in developing better marketing strategies. Many studies have proposed different techniques for customer segmentation, but some of these studies failed to examine individual customer’s needs in the cluster. In a customer segmentation, when customers are grouped into various cluster based on their common needs, there may be customers that have other needs that differ from the general needs of the group. In a situation where the needs of individual were not captured, organizations may find it difficult to control the rendering of their services. The aim of this study is to extract the individual customer’ needs to enhance organizations’ services that meet the needs of customers, as well as increase organization profits. This study, therefore, proposes the use of an associative rules mining algorithm augmented with assignment optimization to properly examine the needs of individual customers in the group. This approach enhances the cross-segmentation of customers for better marketing strategies and the assignment technique also improved the segmentation processing speed. The degree of accuracy of the system developed was tested with about 9,500 customers’ dataset that was obtained from goggle multi category online store dataset. Both customer transaction history dataset and customer purchasing behavior dataset were obtained for segmentation which achieved 94.5% customer segmentation accuracy. The implementation was done using Python programming language.

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Comparative Study: Performance of MVC Frameworks on RDBMS

By M. H. Rahman M. Naderuzzaman M. A. Kashem B. M. Salahuddin Z. Mahmud

DOI: https://doi.org/10.5815/ijitcs.2024.01.03, Pub. Date: 8 Feb. 2024

The regular utilization of web-based applications is crucial in our everyday life. The Model View Controller (MVC) architecture serves as a structured programming design that developers utilize to create user interfaces. This pattern is commonly applied by application software developers to construct web-based applications. The use of a MVC framework of PHP Scripting language is often essential for application software development. There is a significant argument regarding the most suitable PHP MVC such as Codeigniter & Laravel and Phalcon frameworks since not all frameworks cater to everyone's needs. It's a fact that not all MVC frameworks are created equal and different frameworks can be combined for specific scenarios. Selecting the appropriate MVC framework can pose a challenge at times. In this context, our paper focuses on conducting a comparative analysis of different PHP frameworks. The widely used PHP MVC frameworks are picked to compare the performance on basic Operation of Relational databases and different type of Application software to calculate execution time. In this experiment a large (Big Data) dataset was used. The Mean values of insert operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 149.99, 145.48 and PostgreSQL database`s 48.259, 49.39, 45.87 respectively. The Mean values of Update operation in MySQL database of Codeigniter, Laravel, Phalcon were 149.64, 158.39, 207.82 and PostgreSQL database`s 48.24, 49.39, 46.64 respectively. The Mean values of Select operation in MySQL database of Codeigniter, Laravel, Phalcon were 1.60, 3.23, 0.98 and PostgreSQL database`s 1.95, 4.57, 2.36 respectively. The Mean values of Delete operation in MySQL database of Codeigniter, Laravel, Phalcon were 150.27, 156.99, 149.63 and PostgreSQL database`s 42.95, 48.25, 42.07 respectively. The findings from our experiment can be advantageous for web application developers to choose proper MVC frameworks with their integrated development environment (IDE). This result will be helpful for small, medium & large-scale organization in choosing the appropriate PHP Framework.

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AI-powered Predictive Model for Stroke and Diabetes Diagnostic

By Ngoc-Bich Le Thi-Thu-Hien Pham Sy-Hoang Nguyen Nhat-Minh Nguyen Tan-Nhu Nguyen

DOI: https://doi.org/10.5815/ijisa.2024.01.03, Pub. Date: 8 Feb. 2024

Research efforts in the prediction of stroke and diabetes prioritize early detection in order to enhance patient outcomes. To achieve this, a variety of methodologies are integrated. Existing studies, on the other hand, are marred by imbalanced datasets, lack of diversity in their datasets, potential bias, and inadequate model comparisons; these flaws underscore the necessity for more comprehensive and inclusive research methodologies. This paper provides a thorough assessment of machine learning algorithms in the context of early detection and diagnosis of stroke and diabetes. The research employed widely used algorithms, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), and XGBoost Classifier, to examine medical data and derive significant findings. The XGBoost Classifier demonstrated superior performance, with an outstanding accuracy, precision, recall, and F1-score of 87.5%. The comparative examination of the algorithms indicated that the Decision Tree, Random Forest, and XGBoost classifiers consistently exhibited strong performance across all measures. The models demonstrated impressive discrimination capabilities, with the XGBoost Classifier and Random Forest reaching accuracy rates of roughly 87.5% and 86.5% respectively. The Decision Tree Classifier exhibited notable performance, with an accuracy rate of 83%. The overall accuracy of the models was evident in the F1-score, a metric that incorporates recall and precision, where the XGBoost model exhibited a marginal improvement of 2% over the Random Forest and Decision Tree models, and 4.25 percent over the last two. The aforementioned results underscore the effectiveness of the XGBoost Classifier, which will be employed as a predictive model in this study, alongside the Random Forest and Decision Tree models, for the accurate identification of stroke and diabetes. Furthermore, combining datasets improves model performance by utilizing relative features. This integrated dataset improves the model's efficiency and creates a resilient and comprehensive prediction model, improving healthcare outcomes. The findings of this research make a valuable contribution to the advancement of AI-driven diagnostic systems, hence enhancing the quality of healthcare decision-making.

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An Unorthodox Trapdoor Function

By Awnon Bhowmik

DOI: https://doi.org/10.5815/ijmsc.2024.01.04, Pub. Date: 8 Feb. 2024

At the bedrock of cryptosystems lie trapdoor functions, serving as the fundamental building blocks that determine the security and efficacy of encryption mechanisms. These functions operate as one-way transformations, demonstrating an inherent asymmetry: they are designed to be easily computable in one direction, while proving computationally challenging, if not infeasible, in the opposite direction. This paper contributes to the evolving landscape of cryptographic research by introducing a novel trapdoor function, offering a fresh perspective on the intricate balance between computational efficiency and security in cryptographic protocols.
The primary objective of this paper is to present and scrutinize the proposed trapdoor function, delving into a comprehensive analysis that unveils both its strengths and weaknesses. By subjecting the function to rigorous examination, we aim to shed light on its robustness as well as potential vulnerabilities, contributing valuable insights to the broader cryptographic community. Understanding the intricacies of this new trapdoor function is essential for assessing its viability in practical applications, particularly in securing sensitive information in real-world scenarios.
Moreover, this paper does not shy away from addressing the pragmatic challenges associated with deploying the proposed trapdoor function at scale. A thorough discussion unfolds, highlighting the potential hurdles and limitations when attempting to integrate this function into large-scale environments. Considering the practicality and scalability of cryptographic solutions is pivotal, and our analysis strives to provide a clear understanding of the circumstances under which the proposed trapdoor function may encounter obstacles in widespread implementation.
In essence, this paper contributes to the ongoing discourse surrounding trapdoor functions by introducing a new entrant into the cryptographic arena. By meticulously exploring its attributes, strengths, and limitations, we aim to foster a deeper understanding of the intricate interplay between cryptographic theory and real-world applicability.

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Analyzing Students’ Performance Using Fuzzy Logic and Hierarchical Linear Regression

By Dao Thi Thanh Loan Nguyen Duy Tho Nguyen Huu Nghia Vu Dinh Chien Tran Anh Tuan

DOI: https://doi.org/10.5815/ijmecs.2024.01.01, Pub. Date: 8 Feb. 2024

Due to the COVID-19 situation, all activities, including education, were shifted to online platforms. Consequently, instructors encountered increased challenges in evaluating students. In traditional assessment methods, instructors often face ambiguous cases when evaluating students’ competencies. Recent research has focused on the effectiveness of fuzzy logic in assessing students’ competencies, considering the presence of uncertain factors or multiple variables. Additionally, demographic characteristics, which can potentially influence students’ performance, are not typically utilized as inputs in the fuzzy logic method. Therefore, analyzing students’ performance by incorporating these factors is crucial in suggesting adjustments to teaching and learning strategies. In this study, we employ a combination of fuzzy logic and hierarchical linear regression to analyze students’ performance. The experiment involved 318 students from various programs and showed that the hybrid approach assessed students’ performance with greater nuance and adaptability when compared to a traditional method. Moreover, the findings in this study revealed the following: 1) There are differences in students’ performance between traditional and fuzzy evaluation methods; 2) The learning method is an impact on students’ fuzzy grades; 3) Students studying online do not perform better than those studying onsite. These findings suggest that instructors and educators should explore effective strategies being fair and suitable in assessment and learning.

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A Novel Approach by Integrating Dynamic Network Selection and Security Measures to improve Seamless Connectivity in Ubiquitous Networks

By Prasanna Kumar G. Shankaraiah N. Rajashekar M B Sudeep J Shruthi B S Darshini Y Manasa K B

DOI: https://doi.org/10.5815/ijwmt.2024.01.03, Pub. Date: 8 Feb. 2024

Researchers have developed an innovative approach to ensure seamless connectivity in ubiquitous networks with limited or irregular network coverage. The proposed method leverages advanced network technologies and protocols to seamlessly establish and maintain network connections across various environments. It integrates multiple wireless communication technologies and dynamic network selection algorithms, overcoming issues like poor reliability, limited scalability, and security problems. Compared to existing solutions, the method exhibits improved connection handover efficiency, network throughput, and end-to-end delay. Considering user mobility, network availability, and quality of service needs, it makes informed decisions about the most suitable network connections. The proposed method is expected to significantly impact the development of future ubiquitous networking solutions.

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