A Brief Review on The Symbolic 2-Plithogenic Number Theory and Algebraic Equations

Nader Mahmoud; Ahmed

doi:https://doi.org/10.54216/GJMSA.050103

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Found 3836 matches for "All Articles"

American Journal of Business and Operations Research | 2023

An Integrated Framework for Dynamic Resource Allocation in Multi-project Environment

This paper proposes an integrated machine learning (ML) framework for dynamic resource allocation in a multi-project environment. The framework utilizes machine learning algorithms to predict future resource demands and identify potential resource shortages. The proposed framework considers various factors such as project priorities, resource availability, and project deadlines to optimize resource allocation decisions. The framework is designed to continuously learn from past resource allocation decisions and improve future resource allocation strategies. The effectiveness of the proposed framework is evaluated through a case study in a real-world multi-project environment. The results show that the framework can significantly improve resource utilization and project completion times while reducing resource waste and cost. Overall, the proposed framework provides a practical solution for dynamic resource allocation in complex multi-project environments.

groups

Mahmoud A. Zaher mail -

Nabil M. Eldakhly mail

link https://doi.org/10.54216/AJBOR.100101

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2023

Enhancing Customer Relationship Management through Sentiment Analysis and Social Media Data Mining

Customer Relationship Management (CRM) is a crucial aspect of modern business that enables companies to maintain healthy relationships with their customers. In today's digital age, customers interact with companies through multiple channels, including social media, email, and phone. Therefore, analyzing customer feedback and sentiment has become increasingly important in understanding their needs and improving the overall customer experience. To this end, this work proposes a new system that applies deep learning for sentiment analysis in a way that improves the performance of CRM by analyzing customer feedback from various sources, companies can gain valuable insights into customer needs and preferences and identify areas for improvement in their products and services. Then, we present a case study of a company that implemented the proposed system in its CRM strategy. The results showed that our system could improve customer satisfaction and retention rates and enable the company to identify and address customer concerns more efficiently.Our approach can be applied as a powerful tool to enable companies to gain valuable insights into customer needs and preferences, identify areas for improvement in their products and services, and develop targeted marketing campaigns and personalized communication strategies.

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Esmeralda Kazia mail -

Bledar Kazia mail

link https://doi.org/10.54216/AJBOR.100102

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2023

The Role of Big Data Analytics in IoT-enabled Green Supply Chain Management: Architecture, challenges, and future perspectives

The integration of the Internet of Things (IoT) and Big Data Analytics (BDA) has brought about a revolution in Green Supply Chain Management (GSCM). In particular, it has enabled the optimization of many aspects of the supply chain (SC), including transportation, inventory management, and customer service. The application of BDA in IoT-enabled GSCM is receiving a lot of attention because it has the capacity to assist businesses become more cost-effective and environmentally sustainable to make more informed decisions. By identifying the inefficiencies in the supply chain and take corrective action. With the advent of the IoT, businesses are now able to get a great deal of information from sensors that are installed in different parts of their SC, including transportation vehicles, warehouses, and factories. This data can be leveraged for a variety of purposes, including optimizing the SC for sustainability and reducing its environmental impact. There are also challenges associated with BDA in IoT-enabled GSCM. The volume of data that needs to be processed presents the biggest obstacles. This requires specialized tools and expertise in data management and analytics. Despite these difficulties, technology has the power to completely alter how firms conduct their operations. This paper presents an overview about BDA in IoT-enabled GSCM. The review highlights the benefits and challenges in adopting BDA in IoT-enabled GSCM, the key technologies involved, and the various applications of BDA in IoT-GSCM. Finally, provides insights into the future directions of research in this area.

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Wafaa A. Saleh mail -

Sherine M. Abdelkader mail -

Heba Rashad mail -

Amal Abdelgawad mail

link https://doi.org/10.54216/AJBOR.100103

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2023

Applying Game Theory Models for Risk Management in Supply Chain Networks

 Supply chain networks are complex systems that involve multiple entities and activities, making them vulnerable to various risks that can negatively impact their performance. Game theory models have been used in various fields to analyze strategic interactions among agents and to make decisions in uncertain environments. This study investigates the application of game theory models for risk management in supply chain networks. Then, we present a framework for applying game theory models for risk management in supply chain networks. Our framework consists of three stages: risk identification, risk analysis, and risk mitigation. We validate the application of the proposed framework using a case study of a supply chain network for a fictional company. The results of the case study demonstrate that game theory models can provide valuable insights into the behavior of supply chain entities in different risk scenarios. The models can also help in identifying optimal strategies for mitigating risks and improving the performance of the supply chain network. The finding  imply that the proposed framework can be used as a guide for practitioners to apply game theory models in their supply chain risk management practices.

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Khyati Chaudhary mail -

Gopal Chaudhary mail -

Manju Khari mail

link https://doi.org/10.54216/AJBOR.100104

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

International Journal of Wireless and Ad Hoc Communication | 2025

Energy-Efficient VLSI Hardware for Edge AI in Image Processing

Artificial intelligence (AI) is becoming more and more necessary for devices, particularly for network edge image processing applications. Building Very-Large-Scale Integration (VLSI) systems that are specifically tuned for low power consumption and enable edge AI techniques for real-time image processing is the aim of this research. One of Edge AI's key characteristics is its ability to process data and make judgements instantly. Edge AI reduces latency by eliminating the need to move massive amounts of data from one location to the cloud. Quick response times are made feasible, which is essential for applications such as industrial automation and autonomous driving. The study will investigate hardware accelerators and approximation computing as efficient approaches to perform image processing algorithms on low-resource edge devices. If all created data were transferred to the cloud, the network infrastructures would be overwhelmed by the exponential growth in linked devices. Edge AI solves this issue by significantly reducing the amount of data that needs to be sent across the network by doing computations locally. This increases the scalability of AI systems and decreases operating costs associated with data transport. By using custom VLSI design, the project aims to achieve significant energy savings over traditional software-based solutions. This will pave the way for edge AI to be widely applied in battery-powered devices for longer battery life and tasks like object and picture identification.

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Chandraman M. mail -

Santhiyakumari N. mail -

Saravanan V. mail -

Shanmugasundaram P. mail -

Arun A. mail

link https://doi.org/10.54216/IJWAC.090201

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2023

Comparison of Epilepsy Induced by Ischemic Hypoxic Brain Injury and Hypoglycemic Brain Injury using Multilevel Fusion of Data Features

The study aims to investigate the similarities and differences in the brain damage caused by Hypoxia-Ischemia (HI), Hypoglycemia, and Epilepsy. Hypoglycemia poses a significant challenge in improving glycemic regulation for insulin-treated patients, while HI brain disease in neonates is associated with low oxygen levels. The study examines the possibility of using a combination of medical data and Electroencephalography (EEG) measurements to predict outcomes over a two-year period. The study employs a multilevel fusion of data features to enhance the accuracy of the predictions. Therefore this paper suggests a hybridized classification model for Hypoxia-Ischemia and Hypoglycemia, Epilepsy brain injury (HCM-BI). A Support Vector Machine is applied with clinical details to define the Hypoxia-Ischemia outcomes of each infant. The newborn babies are assessed every two years again to know the neural development results. A selection of four attributes is derived from the Electroencephalography records, and SVM does not get conclusions regarding the classification of diseases. The final feature extraction of the EEG signal is optimized by the Bayesian Neural Network (BNN) to get the clear health condition of Hypoglycemia and Epilepsy patients. Through monitoring and assessing physical effects resulting from Electroencephalography,  The Bayesian Neural Network (BNN) is used to extract the test samples with the most log data and to report hypoglycemia and epilepsy patients non-invasively. The experimental findings demonstrate that the suggested strategy improves accuracy by 95.05% and reduces the error rate to 0.41 when comparing diseases.

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Sameer Kadem mail -

Noor Sami mail -

Ahmed Elaraby mail -

Shahad Alyousif mail -

Mohammed Jalil mail -

M. Altaee mail -

Muntather Almusawi mail -

Ismaeel, A. Ghany mail -

Ali Kamil Kareem mail -

Massila Kamalrudin mail -

Adnan Allwi ftaiet mail

link https://doi.org/10.54216/FPA.100106

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Fusion: Practice and Applications | 2023

Intelligent Multilevel Fusion System for Wireless Sensor Network Virtualization Using Deep Reinforcement Learning in Education

wireless sensor networks (WSN) in ubiquitous learning environments to enhance teaching and learning quality. WSNs can serve as a learner-to-context interface, enabling learners to interact with the learning environment while collecting contextual information. With the help of WSN virtualization technology, learners can leverage different virtualized characteristics of the state-of-the-art WSN and engage with the ubiquitous learning paradigm to gain knowledge and skills. The report examines the current state of WSN virtualization and its potential for sharing in this context. Research concerns are discussed in-depth, and an in-depth overview of the current state of the art is provided. This paper presents the fundamentals of WSN virtualization and argues for its usefulness. By allowing learners to learn while on the go in an environment that interests them, gadgets and embedded computers work together to keep students connected to their learning environment. Recent years have seen an increase in interest in deep reinforcement learning technologies. Despite the availability of several internet resources for researching this field, it might be challenging for those just getting started to design effective teaching systems for autonomous vehicles. This article offers a model for a highly effective and interactive ubiquitous learning environment system based on ubiquitous computing technology. An educational system based on deep reinforcement learning and system development is developed in this project using the WSNV-ES method. The web-based system that has been designed can do the following: settings for reinforcing student success, learning scripts to run, and the learning state to monitor are described.

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Shahad Al-yousif mail -

Aws Nabeel mail -

Waleed K. Ibrahim mail -

Mustafa Musa Jaber mail -

Mohammed Hasan Ali mail -

M. jaber mail -

Asaad Shakir Hameed mail -

Ahmed Hussein Al-khayyat mail -

Ahmed F. Omer mail -

Nuridawati Mustafa mail -

Kadim A. Jabbar mail -

A. Abd Ali Abbood mail

link https://doi.org/10.54216/FPA.100107

Volume & Issue

Vol. Volume 10 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

A Review on Symbolic 2-Plithogenic Algebraic Structures

The objective of this paper is to give a good review about the 2-plithogenic algebraic structures. Three kinds of algebraic structures will be revisited and discussed, symbolic 2-plithogenic rings, symbolic 2-plithogenic vector spaces, and 2-plithogenic modules.

groups

Nader Mahmoud Taffach mail -

Ahmed Hatip mail

link https://doi.org/10.54216/GJMSA.050101

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023

Comparison of Some Entropy Measures for Non-Central Fisher Probability Distribution

In this paper, many entropy measures of noncentral Fisher distribution were driven including Shannon, Renyi, Sharma, Havrda, Arimoto and Tsallis. A comparison between these entropies was made according to distribution’s shift parameter, distribution’s degrees of freedom, shape parameter and truncation parameter. The entropy that had less relative loss was said to be better than the other. There were significant differences according to all studied parameters except the shift parameter and we found that the best entropy of the mentioned entropies for noncentral Fisher distribution was Renyi entropy.

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Mazeat Koreny mail -

Mohamed Bisher Zeina mail -

Shaza Zubeadah mail

link https://doi.org/10.54216/GJMSA.050102

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Galoitica: Journal of Mathematical Structures and Applications | 2023