Some Properties of α^g-Closed Sets in Fuzzy Neutrosophic Topology

Hajar Y.; Fatimah M.; Ghada

doi:https://doi.org/10.54216/IJNS.240102

Journal of Neutrosophic and Fuzzy Systems | 2023

On a Two-Fold Algebra Based on the Standard Fuzzy Number Theoretical System

This paper is dedicated to studying the algebraic structure generated from the fusion of two-fold algebras with the standard fuzzy number theoretical system, where the novel fuzzy algebraic structure will be defined with a well-defined binary operation, and then its substructures will be studied concerning the corresponding operation such as hyper/under ideals, and two-fold fuzzy homomorphisms. Also, many examples will be illustrated to clarify the validity of our work.

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Mohammad Abobala mail

link https://doi.org/10.54216/JNFS.070202

Volume & Issue

Vol. Volume 7 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Optimizing H.266/VVC Intra Coding with a Genetic Algorithm: Balancing Speed and Quality

The growing need for high-definition video material requires improvements in video encoding systems that maximize encoding performance while simultaneously improving compression efficiency. This paper presents a novel genetic algorithm-based intra-coding optimization method for the H.266/Versatile Video Coding (VVC) standard. One of the biggest problems in video compression is finding the ideal balance between encoding speed and video quality, which is what our approach aims to solve. Our suggested method makes use of the strong search capabilities of the evolutionary algorithm to choose the best Multi-Type Tree (MTT) partitions and coding tools from the wide range of possibilities present in H.266/VVC. The wellness assessment work that guides this choice method combines criteria for perceptual appraisal of video quality and measures for coding productivity appraisal.

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Murooj Khalid I. Ibraheem mail -

Alexander V. Dvorkovich mail

link https://doi.org/10.54216/FPA.150201

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Journal of Cognitive Human-Computer Interaction | 2023

Object Detection Using Deep Learning

Object recognition is an important task in computer vision that involves identifying the objects such as digital images or videos. This research paper provides a comprehensive review of the different techniques and applications of object recognition. The paper first discusses the basic concepts of object recognition, including feature extraction and matching, classification, and detection. Next, the paper reviews the different techniques for object recognition, such as template matching, PCA-based recognition, and deep learning-based recognition. The paper then presents an overview of the different applications of object recognition, including image and video classification, object tracking, face recognition, and autonomous driving. Finally, the paper ends with a discussion of the difficulties and likely new paths for object recognition.

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S. Vimala mail -

Vibu Krishnan S. mail -

Mathan Raj Kumar mail -

Ashok Kumar mail -

M. Janakiraman mail

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

Volume & Issue

Vol. Volume 6 / Iss. Issue 1

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Fusion: Practice and Applications | 2024

AMR-XAI-DWT: Age-Related Macular Regenerated Classification using X-AI with Dual Tree CWT

Age-related macular degeneration (AMD) is the leading cause of permanent vision loss, and drusen is an early clinical sign in the progression of AMD. Early detection is key since that's when treatment is most effective. The eyes of someone with AMD need to be checked often. Ophthalmologists may detect illness by looking at a color picture of the fundus taken using a fundus camera. Ophthalmologists need a system to help them diagnose illness since the global elderly population is growing rapidly and there are not enough specialists to go around. Since drusen vary in size, form, degree of convergence, and texture, it is challenging to detect and locate them in a color retinal picture. Therefore, it is difficult to develop a Modified Continual Learning (MCL) classifier for identifying drusen. To begin, we use X-AI (Explainable Artificial Intelligence) in tandem with one of the Dual Tree Complex Wavelet Transform models to create captions summarizing the symptoms of the retinal pictures throughout all of the different stages of diabetic retinopathy. An Adaptive Neuro Fuzzy Inference System (ANFIS) is constructed using all nine of the pre-trained modules. The nine image caption models are evaluated using a variety of metrics to determine their relative strengths and weaknesses. After compiling the data and comparing it to many existing models, the best photo captioning model is selected. A graphical user interface was also made available for rapid analysis and data screening in bulk. The results demonstrated the system's potential to aid ophthalmologists in the early detection of ARMD symptoms and the severity level in a shorter amount of time.

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Naresh Kumar mail -

Gunikhan Sonowal mail -

V. Balaji mail

link https://doi.org/10.54216/FPA.150202

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

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Fusion: Practice and Applications | 2024

Predicting Loop Vectorization through Machine Learning Algorithms

Automatic vectorization is often utilized to improve the speed of compute-intensive programs on current CPUs. However, there is enormous space for improvement in present compiler auto-vectorization capabilities. Execution with optimizing code on these resource-controlled strategies is essential for both energy and performance efficiency. While vectorization suggests major performance developments, conventional compiler auto-vectorization techniques often fail. This study investigated the prospective of machine learning algorithms to enhance vectorization. The study proposes an ensemble learning method by employing Random Forest (RF), Feedforward Neural Network (FNN), and Support Vector Machine (SVM) algorithms to estimate the effectiveness of vectorization over Trimaran Single-Value Code (TSVC) loops. Unlike existing methods that depend on static program features, we leverage dynamic features removed from hardware counter-events to build efficient and robust machine learning models. Our approach aims to improve the performance of e-business microcontroller platforms while identifying profitable vectorization opportunities. We assess our method using a benchmark group of 155 loops with two commonly used compilers (GCC and Clang). The results demonstrated high accuracy in predicting vectorization benefits in e-business applications.

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Esraa H. Alwan mail

link https://doi.org/10.54216/FPA.150203

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Performance Prediction Data Mining System for Disabled Students Using Machine Learning

This study aims to explore the educational achievements of individuals aged 21 to 38, specifically examining the differences between those with disabilities and those without. The research delves into the realm of Online Learning Platforms, which are recognized for offering extensive online courses that cater to both educational institutions and individual learners. Additionally, the study investigates Collaboration and Communication Platforms, which are designed to enhance interaction and cooperation among students and educators through various tools like discussion forums, chats, and shared workspaces. Adaptive Learning Platforms: Employing advanced algorithms and data analytics, this study used a dataset covering the UK from July 2013 to June 2020 to examine the highest skill levels of these two different groups. The data set, originally in Excel format, was carefully organized and structured for analytical purposes. The approach included the use of Python libraries such as NumPy for numerical calculations, and Matplotlib for visualization and proposed integration in a cloud-based system. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. It is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis. Furthermore, the proposed integration of this analytical setup into a cloud-based system underscores the study's forward-thinking approach, aiming to leverage the scalability, accessibility, and collaborative potential of cloud computing. This integration promises to streamline the data analysis process, facilitating real-time data processing and enabling a dynamic exploration of the dataset. The study's methodology is underpinned by sophisticated data analysis techniques, utilizing Python libraries such as NumPy, renowned for its efficiency in handling complex numerical calculations, and Matplotlib, which offers powerful visualization tools that are instrumental in elucidating the trends and patterns within the data. This analytical framework is not only robust but also versatile, accommodating the integration of additional Python libraries such as Pandas for data manipulation and SciPy for more advanced scientific computations, thereby enhancing the depth and breadth of the analysis.

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Kumar Pradyot Dubey mail -

Narendra Kumar Gupta mail -

Aditi Sharma mail

link https://doi.org/10.54216/FPA.150204

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Fusion: Practice and Applications | 2024

Quantum Convolutional Neural Network for Image Classification

In the field of image processing, a well-known model is the Convolutional Neural Network, or CNN. The unique benefit that sets this model apart is its exceptional ability to use the correlation information included in the data. Even with their amazing accomplishment, conventional CNNs could have trouble improving further in terms of generalization, accuracy, and computing economy. However, it could be challenging to train CNN correctly and process information quickly if the model or data dimensions are too large. This is since it will cause the data processing to lag.  The Quantum Convolutional Neural Network, or QCNN for short, is a novel proposed quantum solution that might either enhance the functionality of an existing learning model or solve a problem requiring the combination of quantum computing with CNN. To highlight the flexibility and versatility of quantum circuits in improving feature extraction capabilities, this paper compares deep quantum circuit architecture designed for image-based tasks with classical Convolutional Neural Networks (CNNs) and a novel quantum circuit architecture. The covidx-cxr4 dataset was used to train quantum-CNN models, and their results were compared against those of other models. The results show that when paired with innovative feature extraction methods, the suggested deep Quantum Convolutional Neural Network (QCNN) outperformed the conventional CNN in terms of processing speed and recognition accuracy. Even though it required more processing time, QCNN outperformed CNN in terms of recognition accuracy. When training on the covidx-cxr4 dataset, this dominance becomes much more apparent, demonstrating how deeper quantum computing has the potential to completely transform image classification problems.

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Mohammed Yousif mail -

Belal Al-Khateeb mail

link https://doi.org/10.54216/FPA.150205

Volume & Issue

Vol. Volume 15 / Iss. Issue 2

Details open_in_new

Pure Mathematics for Theoretical Computer Science | 2024

Using A Semi-Parametric Regression Model to Estimate A Function of The Quantity of Date Production In Iraq

The semi-parametric referral model is one of the important developments that used the analysis of their independent effect on other variables, which produces prediction issues. It is known that the semi-parametric referral model combines direct referral models, whose variables are linear, with analmic conversion models, whose variables are non-linear. In this, it was done. The research presents the production function of the quantity of dates, which is affected by the multiplicities. Some of them control parameters such as heart rate and settings of fruiting palm trees, and some of them behave nonlinearly, such as humidity, temperature, wind, and others, and we take the variable to determine the air temperature. It has been observed that the mean square error of the semi-parametric regression model is less than the mean square error of the parametric regression model, which assumes that all variables behave linearly. This proves the validity of the results in the case of using sample sizes for a set of data generated through simulation experiments, which showed that the regression models are semi-parametric.

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Kareem K. Aazer mail

link https://doi.org/10.54216/PMTCS.030104

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

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International Journal of Neutrosophic Science | 2024

Partner Sets for Generalizations of MultiNeutrosophic Sets

Fuzzy sets and their various generalizations, especially neutrosophic and multineutrosophic sets, have had an essential imprint in other scientific, engineering, and applied fields. This came from the characteristics of membership functions that determine the extent to which members belong to their sets, which is an important criterion. Hence, the idea of building the optimal membership function for fuzzy set using the arithmetic mean, we called this set by partner sets of a neutrosophic set of -type.

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Tuqa A. H. Al-Tamimi mail -

Luay A. A. Al-Swidi mail -

Ali H. M. Al-Obaidi mail

link https://doi.org/10.54216/IJNS.240101

Volume & Issue

Vol. Volume 24 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2024