An Optimized Routing Algorithm for Internet of Vehicle (IoV) Environment

Ravi Shankar

doi:https://doi.org/10.54216/JISIoT.170115

International Journal of Neutrosophic Science | 2025

Connection between Legendre Polynomials and classes of Bi-Bazilevic Functions Defined by Borel Distribution and Ruscheweyh Operator

This paper introduces two new classes of bi-Bazilevic and bi-univalent functions that are defined using Borel distribution and Ruscheweyh operator, which also associated with Legendre polynomials and modified Sig-moid function within the open unit disk D. This paper explores the characteristics and behaviors of these functions, we find estimates for the modulus of the initial Taylor series coefficients a2 and a3 for functions within our newly defined classes and some of their various subclasses. Moreover, this paper explores the classical Fekete-SzegÖ functional problem concerning functions f that are classified within our specific classes. Additionally, we obtain the classical Fekete-SzegÖ inequalities of functions belonging to these classes and some of their various subclasses.

groups

Waleed Al-Rawashdeh mail

link https://doi.org/10.54216/IJNS.260317

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

International Journal of Neutrosophic Science | 2025

Numerical Solution of Fuzzy Second Kind Fredholm Integral Equations in Double Parametric Form of Fuzzy Number

In this paper, two numerical methods that are method of successive approximations and Fredholm’s first fundamental theorem are developed, reformatted, and applied to solve fuzzy second kind Fredholm integral equations with a separable kernel. The fuzziness in the equations is represented utilizing convex normalized triangular fuzzy numbers, which are based on a single and double parametric form of fuzzy numbers. A comparative analysis study between the proposed schemes are discussed through numerical experiment. It was found that Fredholm's first fundamental theorem is more efficient and effective than method of successive approximations. Furthermore, the double parametric form of fuzzy number is a general and more reliable than single parametric form since it reduced the computational cost and provides more certain fuzzy cases.

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Hamzeh Zureigat mail

link https://doi.org/10.54216/IJNS.260318

Volume & Issue

Vol. Volume 26 / Iss. Issue 3

Details open_in_new

Fusion: Practice and Applications | 2025

Cyber-Physical Systems and Networking Technologies: The Impact of Data Integration on Economic Security

This study delves into the relationship between cyber-physical systems (CPS) and economic security, with particular emphasis on how networking technologies facilitate more efficient data integration. It investigates how CPS adoption is reshaping national economies by influencing productivity levels, altering labor market structures, and introducing new cybersecurity challenges. Employing a hybrid research design that merges cross-sectional data evaluation with expert consultations, the research offers a comprehensive view of the implications of CPS implementation on sectoral productivity, employment trends, and macroeconomic resilience.CPS are positioned in the study as strategic innovations powered by data intelligence, underlining both their promising opportunities and associated threats. The findings support the development of informed policy measures that aim to enhance benefits while reducing potential risks. Ultimately, the work contributes to the evolving discourse on CPS by offering a balanced analysis of their socio-economic impacts and outlining actionable recommendations for decision-makers and industry stakeholders to capitalize on CPS innovations effectively.

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Rimma Yunusova mail -

Roman Pantin mail

link https://doi.org/10.54216/FPA.200101

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Lossless Compression without Coding and Decoding using Arabic Ligature Characters Unicode

Data compression technologies play a big role in various areas where efficient data storage and transmission are essential. Data compression is the science of reducing redundant data to a compact form, which used to safely store files or information. On the other side, Unicode is a global standard for the representation of text and symbols in computers. The basic elements of the Unicode standard are code points, which represent a specific symbol. Unicode provides a unified way to map and manage these points to ensure consistent representation and interpretation of text data across different systems, platforms, and languages. This paper proposes a method to compress texts in Arabic, based on Unicode ligatures, which typically join characters together. This method replaces two or more Unicode Arabic ligature characters with a single Unicode Arabic ligature based on their appearance in the Arabic text file, eliminating the need for coding or decoding. The size of the original and output text files has been compared to show the percentage of compression. The selected dataset: Modern Standard Arabic text involves Arabic news, and Classical Arabic text involves Arabic Holy and Honorific text collected from Kaggle. The percentage of compression depends on the frequency of ligature characters in Arabic documents. Unfortunately, the results were not promising, as the method was only able to compress the file to a very small percentage (6.71 %and 12.82 %, respectively, for Arabic news and Arabic Holy text). We think that the proposed method can be improved by using a hybrid technique of text compression in the future; in addition, consider other properties of Arabic Unicode. Programming can express competency concepts in a well-defined mathematical model for a particular.

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Huda Ragheb Kadhim mail -

Rand Abdulwahid Albeer mail -

Dhamyaa A. Nasrawi mail -

Huda Hallawi mail -

Muthanna Medin Nasser mail -

Ibrahim Haider Jabbar mail -

Burhan Karar Abbas mail

link https://doi.org/10.54216/FPA.200102

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Assessing Emotional Intelligence among Employees in the Private Hospitality Sector: An Analytical Hierarchy Process (AHP) approach

The hospitality industry is rapidly evolving, with intense competition among organizations striving to attract and retain customers. One of the key factors influencing customer satisfaction and loyalty is the emotional intelligence of employees. Higher emotional intelligence fosters positive behavior, which enhances customer experience and engagement. This study aims to identify and prioritize the most critical factors and sub-factors of emotional intelligence in the private hospitality sector. Data for this research has been collected from hospitality businesses in the Lucknow region. The prioritization process is carried out using the Analytical Hierarchy Process (AHP), a widely used multi-criteria decision-making (MCDM) technique. The rankings derived from AHP provide valuable insights into the key attributes of emotional intelligence that employees should focus on for professional growth. By understanding these priorities, hospitality employees can enhance their emotional intelligence, leading to improved customer interactions, better teamwork, and overall organizational success.

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Sadia Nawaz mail -

Shujauddin Khan mail -

Jamal Abdul Nasir mail

link https://doi.org/10.54216/FPA.200103

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

Systematic Review of VLC-Based NOMA Using Machine Learning Algorithms

Visible light communication (VLC) integrated with nonorthogonal multiple access (NOMA) is a promising technique to meet the increasing demand for high capacity, energy-efficient communication in forthcoming 6G networks. This work thoroughly evaluates VLC-NOMA systems and emphasizes the incorporation of machine learning (ML) approaches to improve spectrum efficiency, the bit error rate, and resource allocation. A technique based on Preferred Reporting Items for Systematic Reviews and Meta-analyses produced 244 records, among which 45 were selected for comprehensive study. The review identified obstacles, including scalability, computational complexity, and insufficient experimental validation. A comparative examination elucidated the strengths and limits of machine learning methodologies, including machine learning, deep neural networks, and federated learning, in addressing these difficulties. The study identified key research gaps, proposed future directions, and emphasized the need for hybrid optimization techniques, lightweight machine learning models, and real-world implementations. The findings contribute to the development of robust, scalable VLC-NOMA systems for 6G applications.

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Ayah A. Hameed mail -

Lwaa F. Abdulameer mail -

Heba M. Fadhil mail

link https://doi.org/10.54216/FPA.200104

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2025

The Adoption of Artificial Intelligence for Higher Education Sustainability

Business executives and scholars maintain that Artificial Intelligence (AI) is positioned alongside pivotal human inventions and advancements such as fire, electricity, and the incandescent light bulb. By harnessing AI technologies, academic institutions can augment pedagogical approaches, elevate the caliber of education, and furnish learners with novel avenues to cultivate their proficiencies and competencies. However, on the contrary, the implementation of AI in higher education has provoked deliberations regarding whether institutions ought to prohibit its utilization entirely or promote its integration to enhance educational outcomes. Nevertheless, despite the escalating acknowledgment of AI's importance in the educational sphere, there needs to be more thorough exploration concerning its adoption and comprehending its impacts. Data was collected from 300 respondents to fill this gap by building on the 'Unified Theory of Acceptance and Use of Technology' (UTAUT) model. We empirically contribute to the existing literature by clarifying the fundamental factors that affect the adoption of AI within higher education, in addition to scrutinizing the consequences of AI on knowledge acquisition. Moreover, we elucidate the moderating effects of workload and temporal limitations. The findings provide substantial insights relevant to the incorporation of AI for knowledge acquisition in higher education and are anticipated to provoke further scholarly discussion.

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Soliman Aljarboa mail -

Abdulatif Alabdulatif mail -

Makhmoor Bashir mail

link https://doi.org/10.54216/FPA.200105

Volume & Issue

Vol. Volume 20 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

A Novel IoT based Wavelet and PCA Approach for Improved Glaucoma Classification Using Retinal Images

The proposed research implements a new 3D-block-based alpha-rooting enhancement method, which uses PCA classification for detecting glaucoma. The use of Euclidean distance in current image enhancement methods tends to lose important structural details that result in incorrect classification outcomes. The proposed method executes block-matching and grouping operations to locate equivalent 3D patterns before using adaptive alpha-rooting adjustment, which automatically controls contrast throughout optic disc and optic cup regions. Following enhancement processing an additional polishing stage optimizes these results for classification purposes. The classification of enhanced images takes place using PCA and its wavelet variants to extract important retinal features. The proposed system utilizes both ACRIMA dataset and real-world hospital images to show better classification achievements than CLAHE-based enhancement while validating its effectiveness. The experimental outcome demonstrated both high accuracy and reduced time consumption when using biorthogonal DWT with (2D) ²-PCA for classification. The proposed method offers a time-effective hardware-oriented solution for automatic glaucoma detection by combining conventional statistical techniques with deep learning-based classification approaches. The method provides clinical facilities with a dependable standard for glaucoma identification and diagnosis improvement. The Proposed 3D block-based adaptive alpha rooting method achieves a total accuracy level of 95.1%. The U-net model achieves 91.0% accuracy while CNN reaches 90.3% and RF delivers 87.1%. At the same time, SVM provides 86.3% accuracy while PCA returns 85.2% and DWT reaches 84.2% and KNN establishes 81.2% accuracy.

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Vivek Jain mail -

H. Shree Kumar mail -

Hemant Sharma mail -

R. Kiran Kumar mail -

Chandrasekaran Raja mail -

Krishna Kishore Thota mail

link https://doi.org/10.54216/JISIoT.170113

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

Hybrid Compressive Sensing based Secure Medical Image Compression and Reconstruction in Telemedicine Application

The transmission of complex medical images in telemedicine applications poses significant challenges. An effective hybrid compressed sensing and encryption framework is proposed for enabling efficient MRI compression and secure transmission in telemedicine applications. Firstly, a fuzzy-logic-based image enhancement is pressed. Then an optimized chaotic sequence generation scheme is formulated based on image characteristics to achieve compression robustness and security of the compression process. In addition, the proposed framework uses a lightweight public key encryption method to speed up encryption and decryption time. Our experimental results demonstrate the effectiveness of the proposed system on various metrics, including PSNR, SSIM, correlation coefficient, and processing time. The system consistently achieved high SSIM scores (0.96 to 1.0) and maintained low algorithm processing time, validating its efficiency in high-quality reconstruction.

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Shilpa A. N. mail -

Santosh Kumar G. mail -

Veena C. S. mail

link https://doi.org/10.54216/JISIoT.170114

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new

Journal of Intelligent Systems and Internet of Things | 2025

An Optimized Routing Algorithm for Internet of Vehicle (IoV) Environment

Internet of Vehicles (IoV) is the later application of VANET and is the fusion of the Internet and IoT. With the advancement in innovation, individuals are investigating a traﬃc environment wherever they would have the extreme cooperation with their environment including other vehicles. The Internet of Vehicles (IoV) was created so that vehicles can communicate with each other in an infrastructure environment. The prerequisite is to form a more secure trip in an IoV environment with the least delay and high packet delivery rate. This guarantees that all information is received with negligible delay to maintain a strategic distance from any mishap. This paper presents a new position-based routing algorithm called Position-Based Connectivity Aware Routing (PBCAR) for IoV that covers sparse and coarse regions of vehicles. It takes advantage of the Internet and street format to progress the execution of routing in IoV. The PBCAR algorithm uses a GPS real-time chasing system to find traffic information for forming position-based paths from the source node to the destination node. The PBCAR algorithm has been simulated using SUMO and Network Simulator and compared with AODV and GPSR. The results show that the PBCAR algorithm obtains exceptional results considering the several simulation parameters.

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Ravi Shankar Shukla mail

link https://doi.org/10.54216/JISIoT.170115

Volume & Issue

Vol. Volume 17 / Iss. Issue 1

Details open_in_new