A Review On Some Neutrosophic Algebraic Linear Structures

Malath F.

doi:https://doi.org/10.54216/IJNS.140204

International Journal of Neutrosophic Science | 2025

Predictive Modeling of Financial Crisis Using Advanced Classification Models Powered by Neutrosophic Fusion of Rough Set Theory

Neutrosophic set (NS) and logic are powerful mathematical approaches for managing different uncertainties. Amongst different approaches for examining NS statistics, rough set theory (RST) offers a valuable instrument in the domain of NS statistics, and masses of researchers have been motivated by NS combination of RST. Recently, there have been no wide-ranging statistics and literature reviews of the universal RST and its applications. The Financial Crisis Prediction mechanism leverages cutting-edge computation methods to predict possible disruptions or economic downturns. By investigating past fiscal information, marketplace gauges, and macroeconomic features, the typical recognizes primary caution indications of imminent disasters. This practical method helps financial institutions, policymakers, and investors in applying pre-emptive procedures to alleviate fiscal marketplaces and threats. In this paper, we develop a Financial Crisis Prediction Model using Neutrosophic Fusion of Rough Set Theory (FCPM-NFRST) methodology. The suggested FCPM-NFRST method for financial crises incorporates numerous forward-thinking systems to improve predictive performance. It is initiated by the Firefly Algorithm (FFA) based feature selection to detect the fittest fiscal gauges. Consequently, the Neutrosophic Fusion of RST (NFRST) is exploited for strong cataloguing and successful management of vagueness and roughness in economic information. Lastly, the Whale Optimization Algorithm (WOA) is exploited for parameter fine-tuning, enhancing the system's accuracy. Investigational study displays that the FCPM-NFRST ensemble mechanism is more robust and superior than its complements. Accordingly, this study powerfully suggests that the suggested FCPM-NFRST method is very competitive than conventional and other existing algorithms.

groups

Elvir Akhmetshin mail -

Ilyos Abdullayev mail -

Hafis Hajiyev mail -

Emil Hajiyev mail

link https://doi.org/10.54216/IJNS.250211

Volume & Issue

Vol. Volume 25 / Iss. Issue 2

Details open_in_new

International Journal of BIM and Engineering Science | 2022

BIM-based Sakeholder Information Exchange (IE) During the Planning Phase in Smart Construction Megaprojects (SCMPs)

Purpose – Information plays a significant role in managing construction projects. The architecture, engineering and construction (AEC) industry encounter a massive information exchange (IE) challenge. This study aims to develop a BIM-based stakeholder information exchange (IE) workflow scheme during the planning phase in smart construction megaprojects (SCMPs) that faces a massive IE challenge, especially during the COVID-19 pandemic. Design/methodology/approach – To accomplish the above stated goal, a research approach including a literature analysis, case studies, and survey questions was developed. Based on the aforementioned, the study created a BIM-based IE workflow to simplify the implementation of IM in SCMPs. Findings – This study has yielded an extensive insight into the types of information exchange, difficulties, and ways to its hand over. In the context of CMPs, The research conceptualised BIM&SM synergy and proposed IE Workflow strategy during the planning phase in MCPs. However, IE needs to be planned from the beginning of the process, agreed upon between different parties, tested, and verified. Research limitations – The scope of this research is limited to the SCMPs during the planning phase. Practical implications – This study contributes to the developing body of knowledge addressing the application of BIM& IE synergy during the planning phase in SCMPs. The outcomes of this research will be beneficial for clients, contractors, and project managers, when taking into account in future plans. Originality/value – This study provides contributes to understanding information flow during the project planning phase and how to control it properly. Generally, the deliverables of this study could be utilized by professionals engageded in BIM and SM practices on SCMPs to enlightens and enhance information exchange and the utilization of the produced information throughout the entire process.

groups

Ayman Mashali mail -

Ahmed El tantawi mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

International Journal of BIM and Engineering Science | 2022

Submitting BIM to the Educational Plan for the Faculty of Architecture According to NARS and ARS Standards

Building Information Modeling (BIM) has imposed itself as a powerful engineering and technological tool over time. It's even become mandatory in some countries (UK) and is gradually gaining more and more awareness worldwide. Although that, BIM education is still a new concept even in some countries that are already implementing it in their various engineering and construction projects. This research aims to conclude with an educational plan and curricula for the faculty of architecture that contains BIM as the core of it. The methodology used in this research is the online-structured questionnaire, distributed to students and staff of the faculty of architecture at Al-Baath University which is the case study of this research. Architecture faculty undergraduates and graduates with different degrees were surveyed by an online-structured questionnaire, and the results of the questionnaire were gathered and analyzed using google forms. This study concludes with the proposed modified plan and curricula for the previously mentioned faculty in the light of the theoretical study, questionnaire results, and similar experiences around the world. This new plan is expected to prepare a new generation of architects who are High-tech qualified and fully aware of BIM and its general ideas, which makes it easier for these architects to emerge within the job market and fulfill AEC firms' requirements of course this would also help to promote the university's reputation and help to spread BIM education among other local universities and also to other engineering competencies.

groups

Eisa Al Hammoud mail -

Sonia Ahmed mail

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

Volume & Issue

Vol. Volume 5 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

Hybrid Neural Networks in Generic Biometric System: A Survey

There are numerous definitions of hybrid systems that vary from one another in terms of the methods suggested. In general, hybrid systems can be characterised as combining two or more distinct approaches to create a fusion system that depends on the merged approaches. Sequential, auxiliary, and embedded hybrid systems are different types of hybrid systems. In general, in order to use biometrics, there must be a way to record the chosen distinguishing attribute. Preprocessing is then utilised to enhance the input. Then, for processing and storage, the most distinguishing features are extracted, encoded, and added to a suitable representation template. By comparing query inputs to templates that have been stored, it is possible to identify the subject under examination. Biometric systems have been employed by numerous industries.

groups

M. Y. Shams mail

link https://doi.org/10.54216/JAIM.010102

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics |

Improving the Regression of Communities and Crime Using Ensemble of Machine Learning Models

 The term” crime prevention” refers to a group of initiatives that work with people, communities, businesses, non-governmental organizations, and all levels of government to address the numerous social and environmental risk factors for crime, disorder, and victimization in communities. In this paper, the authors proposed various regression model for the prediction of communities and crime including decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The communities and crime dataset are used for training and evaluation the proposed model. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.

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Hamzah A. Alsayadi mail -

Nima Khodadadi mail -

Sunil Kumar mail

link https://doi.org/10.54216/JAIM.010103

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Journal of Artificial Intelligence and Metaheuristics | 2022

New Approach of Estimating Sarcasm based on the percentage of happiness of facial Expression using Fuzzy Inference System

Generally, the process of detecting micro expressions takes significant importance because all these expressions reflect the hidden emotions even when the person tried to conceal them. In this paper, a new approach has been proposed to estimate the percentage of sarcasm based on the detected degree of happiness of facial expression using fuzzy inference system. Five regions in a face (right/left brows, right/left eyes, and mouth) are considered to determine some active distances from the detected outline points of these regions. The membership functions in the proposed fuzzy inference system are used as a first step to determine the degree of happiness expression based mainly on the computed distances and then another membership function is used to estimate the percentage of sarcasm according the outcomes of the membership functions in the first step. The proposed approach is validated using some face images which are collected from the SMIC, SAMM, and CAS(ME)2 standard datasets.

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Louloua M. AL-Saedi mail -

Methaq Talib Gaata mail -

Mostafa Abotaleb mail -

Hussein Alkattan mail

link https://doi.org/10.54216/JAIM.010104

Volume & Issue

Vol. Volume 1 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Image Classification Based On CNN: A Survey

Computer vision is one of the fields of computer science that is one of the most powerful and persuasive types of artificial intelligence. It is similar to the human vision system, as it enables computers to recognize and process objects in pictures and videos in the same way as humans do. Computer vision technology has rapidly evolved in many fields and contributed to solving many problems, as computer vision contributed to self-driving cars, and cars were able to understand their surroundings. The cameras record video from different angles around the car, then a computer vision system gets images from the video, and then processes the images in real-time to find roadside ends, detect other cars, and read traffic lights, pedestrians, and objects. Computer vision also contributed to facial recognition; this technology enables computers to match images of people’s faces to their identities. which these algorithms detect facial features in images and then compare them with databases. Computer vision also play important role in Healthcare, in which algorithms can help automate tasks such as detecting Breast cancer, finding symptoms in x-ray, cancerous moles in skin images, and MRI scans. Computer vision also contributed to many fields such as image classification, object discovery, motion recognition, subject tracking, and medicine. The rapid development of artificial intelligence is making machine learning more important in his field of research. Use algorithms to find out every bit of data and predict the outcome. This has become an important key to unlocking the door to AI. If we had looked to deep learning concept, we find deep learning is a subset of machine learning, algorithms inspired by structure and function of the human brain called artificial neural networks, learn from large amounts of data. Deep learning algorithm perform a task repeatedly, each time tweak it a little to improve the outcome. So, the development of computer vision was due to deep learning. Now we'll take a tour around the convolution neural networks, let us say that convolutional neural networks are one of the most powerful supervised deep learning models (abbreviated as CNN or ConvNet). This name "convolutional" is a token from a mathematical linear operation between matrixes called convolution. CNN structure can be used in a variety of real-world problems including, computer vision, image recognition, natural language processing (NLP), anomaly detection, video analysis, drug discovery, recommender systems, health risk assessment, and time-series forecasting. If we look at convolutional neural networks, we see that CNN are similar to normal neural networks, the only difference between CNN and ANN is that CNNs are used in the field of pattern recognition within images mainly. This allows us to encode the features of an image into the structure, making the network more suitable for image-focused tasks, with reducing the parameters required to set-up the model. One of the advantages of CNN that it has an excellent performance in machine learning problems. So, we will use CNN as a classifier for image classification. So, the objective of this paper is that we will talk in detail about image classification in the following sections.

groups

Ahmed A. Elngar mail -

Mohamed Arafa mail -

Amar Fathy mail -

Basma Moustafa mail -

Omar Mahmoud mail -

Mohamed Shaban mail -

Nehal Fawzy mail

link https://doi.org/10.54216/JCIM.060102

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

Journal of Cybersecurity and Information Management | 2021

Explaining feature detection Mechanisms: A Survey

Feature detection, description and matching are essential components of various computer vision applications; thus, they have received a considerable attention in the last decades. Several feature detectors and descriptors have been proposed in the literature with a variety of deﬁnitions for what kind of points in an image is potentially interesting (i.e., a distinctive attribute). This chapter introduces basic notation and mathematical concepts for detecting and describing image features. Then, it discusses properties of perfect features and gives an overview of various existing detection and description methods. Furthermore, it explains some approaches to feature matching. Finally, the chapter discusses the most used techniques for performance evaluation of detection algorithms.

groups

Ahmed A. Elngar mail -

Mohamed Arafa mail -

Mustafa Marouf mail -

Mahmoud Ahmed mail -

Nehal Fawzy mail

link https://doi.org/10.54216/JCIM.060103

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2021

A Survey on Machine Learning Techniques for Supply Chain Management

Machine learning arose from the increasing ability of machines to handle large amounts of data over the last two decades, and some machines could also identify hidden patterns and complicated associations that humans couldn't, allowing them to make rational and precise decisions, especially for disruptive and discontinuous data. In several areas of decision-making, machines could produce more reliable outcomes than humans and have already begun to replace them. Machine learning, which is widely recognized as a breakthrough technology, has recently made significant progress in improving supply chain management processes and efficiency. From planning to delivery, machine learning may be applied at different stages of the supply chain management process. Machine learning types are supervised, unsupervised, reinforcement. Each type has many tools which are discussed below in detail. This paper presents a detailed survey on machine learning techniques for supply chain management including supply chain and supply chain management interpretation, machine learning definition, its types, and some algorithms that belong to it.

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Amal F.Abd El-Gawad mail -

Shereen Zaki mail -

Esraa Kamal mail

link https://doi.org/10.54216/AJBOR.020103

Volume & Issue

Vol. Volume 2 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2021