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Neutrosophic Soft n-Topological Spaces: A Framework for Decision-Making Problems

This article introduces the concept of neutrosophic soft n-topological spaces and their application in decision-making problems. Neutrosophic soft sets are used to define the open sets in these spaces, which allow for greater flexibility and uncertainty in the decision-making process. The concept of neutrosophic soft n-homeomorphism is also introduced, which describes the topological equivalence between two neutrosophic soft n-topological spaces. The article provides examples of how neutrosophic soft n-topological spaces can be used in decision-making problems, such as medical diagnosis and stock market analysis. The theory and applications presented in this article provide a valuable tool for dealing with uncertainty in decision-making problems.

groups
Hasan Dadas mail -
Necati Olgun mail
link https://doi.org/10.54216/GJMSA.090202

Volume & Issue

Vol. Volume 9 / Iss. Issue 2

Details open_in_new

A New Proof of Feuerbach’s Theorem

Feuerbach’s theorem on the tangent of the circle of the nine points and the inscribed and exinscribed circle is considered one of the most beautiful theorems in geometry. In this paper, we offer a basic proof of this theorem starting from one of Gh. Buicliu’s ideas [1].

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Ion Patrascu mail -
Florentin Smarandache mail
link https://doi.org/10.54216/PAMDA.020205

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

A review on possible physical meaning of elastic-electromagnetic mathematical equivalences

It is known, despite special theory of relativity has been widely accepted, in our recent draft submitted to this journal it is shown that some experiments have been carried out suggesting superluminal wave propagation, which make Minkowski lightcone not valid anymore. Therefore, it seems worth to reconsider the connection between elastic wave and electromagnetic wave equations, as in their early development. In this paper we will start with Maxwell-Dirac isomorphism, then we will find its connection with elastic wave equations.

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Victor Christianto mail
link https://doi.org/10.54216/JCFA.020201

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Short Communication Communication with Dr Jean de Climont

  Below is a summary of  communication with Dr Jean de Climont a number of weeks ago, as he wrote as follows: “Einstein began his famous 1905 paper by stating that a moving magnet has an electric field around it, in addition to its magnetic field. This is in line with the interpretation of the Faraday's experiment by Maxwell in the Maxwell-Faraday equation. But, this electric field should deviate a cathode ray in addition to the deviation resulting from the magnetic field of a moving magnet. Such an additional deviation has never been observed.”  

groups
Robert N. Boyd mail
link https://doi.org/10.54216/JCFA.020202

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

An update of our Condensed Matter or Superconductor Model of the Solar System based on Eilenberger equation

In the well-known Aharonov-Bohm effect, a charged particle experiences a phase shift as it moves through a region of space where the magnetic field is zero. The presence of a magnetic flux in the area, which influences the wave function of the particle, results in this phase shift. The Aharonov-type interaction, in which the phase shift is caused by a topological flaw in the system, such as a spin texture or a Berry phase, rather than a magnetic field, has attracted increasing interest in recent years. In this regards, in a recent paper, we argued in favour of Gross-Pitaevskii model as a more complete description of both solar system and spiral galaxies, especially taking into account the nature of chirality and vortices in galaxies (see Prespacetime J, 2021, & SMIC, 2020). In this paper, we will discuss shortly a nonlinear cosmology model inspired by analogy between cosmology phenomena and low temperature physics, especially via superconductor / superfluid vortices dynamics. We described: (a) a nonlinear cosmology model based on Navier-Stokes turbulence equations, which then they are connected to superfluid turbulence, and (b) the superfluid turbulence can lead to superfluid quantized vortices, which can be viewed as large scale version of Bohr’s quantization rule, and (c) this superfluid quantized vortice interpretation of Bohr’s rule allow us to predict quantization of planetary orbits in solar system including new possible orbits beyond Pluto. In more specific way. we apply the new model based on Bogoliubov-de Gennes equation correspondence with Bohr-Sommerfeld quantization rules. Then we put forth an argument that from Bohr-Sommerfeld quantization rules we can come up with a model of quantized orbits of planets in our solar system, be it for inner planets and also for Jovian planets. In effect we also tried to explain Sedna’s orbit in the same scheme.

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Victor Christianto mail -
Yunita Umniyati mail
link https://doi.org/10.54216/JCFA.020203

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Exploring Non-Orientable Topology: Deriving the Poincaré Conjecture and possibility of experimental vindication with liquid crystal

This review investigates the potential of non-orientable topology as a fundamental framework for understanding the Poincaré conjecture and its implications across various scientific disciplines. Integrating insights from Dokuchaev (2020), Rapoport, Christianto, Chandra, Smarandache (under review), and other pioneering works, this article explores the theoretical foundations linking non-orientable spaces to resolving the Poincaré conjecture and its broader implications in theoretical physics, geology, cosmology, and biology.

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V. Christianto mail -
F. Smarandache mail
link https://doi.org/10.54216/JCFA.020204

Volume & Issue

Vol. Volume 2 / Iss. Issue 2

Details open_in_new

Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration

This research endeavors to advance the realm of parking space surveillance through a meticulously designed methodology situated within the critical context of urban planning and the dynamic landscape of smart city development. Focused on addressing the challenges posed by escalating urbanization and burgeoning vehicular density, our study introduces a carefully curated dataset comprising images of parking spaces annotated with bounding box masks and occupancy labels. The methodology unfolds across distinct phases, commencing with a comprehensive dataset description that unveils its diversity and intricacies. Feature extraction techniques, harnessing the capabilities of cutting-edge architectures such as AlexNet and ResNet-50, play a pivotal role in enhancing pattern discernment, which is essential for accurate detection. The crux of our approach lies in the integration of Neural Networks with optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the innovative Dipper Throated Optimization (DTO). Results are presented without explicit mention of tables and figures, strategically emphasizing the methodology's effectiveness in enhancing parking space detection accuracy. Notably, Dipper Throated Optimization (DTO) emerges as a key contributor to optimized Neural Network performance, achieving an impressive accuracy of 0.9908. This research contributes significantly to the ongoing discourse on intelligent urban planning and sets a promising trajectory for the future of efficient parking space utilization in modern cities.

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Ahmed Mohamed Zaki mail -
S. K. Towfek mail -
Weiguo Gee mail -
Wang Zhang mail -
Marwa Adel Soliman mail
link https://doi.org/10.54216/JAIM.060202

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Evaluating the Efficacy of Deep Learning Architectures in Predicting Traffic Patterns for Smart City Development

Smart city development necessitates the implementation of effective traffic management strategies. In this vein, various deep learning architectures, including VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet, are employed to predict diverse traffic patterns extracted from a comprehensive dataset. Evaluating performance metrics such as accuracy, sensitivity, and specificity reveals discernible variations among models, with ResNet-50 and AlexNet demonstrating superior predictive capabilities. Descriptive statistics and statistical analyses, including ANOVA and the Wilcoxon Signed Rank Test, provide nuanced insights into model differences and significance. The findings bear significant implications for urban planners and policymakers transforming cities into intelligent ecosystems, offering valuable insights for informed decision-making in innovative city development. Improved traffic predictions enhance daily commuting experiences and contribute to the informed development of sustainable urban infrastructure, aligning seamlessly with the ongoing evolution of smart cities toward a more connected and efficient future. Notably, AlexNet exhibits a significant accuracy of 0.931780366 in the context of traffic pattern prediction.

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Mohamed Ahmed Kandel mail -
Faris H. Rizk mail -
Lima Hongou mail -
Ahmed Mohamed Zaki mail -
Hakan Khan mail -
El-Sayed M. El-Kenawy mail
link https://doi.org/10.54216/JAIM.060203

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection

Traffic detection is critical in ensuring road safety and efficient traffic management, demanding deploying accurate and practical algorithms. This research explores the fusion of Convolutional Neural Networks (CNNs) and the Waterwheel Plant Algorithm to augment global traffic detection capabilities, utilizing a diverse dataset primarily collected from Turkey. A comprehensive evaluation of prominent CNN architectures, such as VGG19Net, AlexNet, ResNet-50, GoogLeNet, and a generic CNN, underscores substantial efficacy, with the CNN achieving an accuracy of 92.14%. Introducing the Waterwheel Plant Algorithm (WWPA) further enhances performance, as exemplified by the hybrid WWPA-CNN model, exhibiting an impressive accuracy of 97.28%. These findings highlight the promising synergies between traditional optimization algorithms and advanced neural networks, showcasing the potential for innovative developments in traffic monitoring systems and broader applications within computer vision. The statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, robustly underscore the significance of this integrated approach. As the research contributes to the evolution of traffic monitoring systems, these insights provide a solid foundation for advancements in the field, fostering innovation and shaping the future landscape of computer vision applications.

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Faris H. Rizk mail -
Sofia Arkhstan mail -
Ahmed Mohamed Zaki mail -
Mohamed Ahmed Kandel mail -
S. K. Towfek mail
link https://doi.org/10.54216/JAIM.060204

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new

BER-XGBoost: Pothole Detection based on Feature Extraction and Optimized XGBoost using BER Metaheuristic Algorithm

Within the realm of intelligent transportation systems, the imperative challenge of pothole detection assumes a pivotal role in ensuring road safety and upholding infrastructure integrity. This research undertaking meticulously navigates the intricacies of automated pothole detection, employing a nuanced and multifaceted approach. The dataset, comprising over 300 meticulously labeled images of roads with and without potholes, constitutes the cornerstone of our investigation. By leveraging the robust GoogLeNet for feature extraction and orchestrating the optimization of XGBoost through the Al-Biruni Earth Radius Metaheuristic Algorithm, our proposed methodology exhibits a commendable efficacy in discerning road anomalies. The outcomes elucidate the efficacy of the implemented strategies, with BER-XGBoost emerging as a preeminent performer, achieving an accuracy rate of 96.01%. This model not only attains superior accuracy but also manifests a comprehensive array of metrics, including sensitivity, specificity, positive predictive value, negative predictive value, and F-score. Rigorous statistical analyses, encompassing ANOVA and the Wilcoxon Signed Rank Test, furnish empirical substantiation of the consequential nature of our methodologies. In conclusion, this study not only contributes practical insights to the pertinent field but also stimulates pivotal inquiries regarding the ramifications of optimization strategies and the intricate role played by feature extraction in the domain of automated pothole detection. This research propels the ceaseless evolution of intelligent systems, effectively bridging the chasm between technological progressions and real-world applications, thereby augmenting road safety and fortifying infrastructure management.

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Mark Emad S. Abdelmalak mail -
Khaled Sh. Gaber mail -
Mariam Abdallah Ahmed mail -
Najaad OubeBlika mail -
Ahmed Mohamed Zaki mail -
Marwa M. Eid mail
link https://doi.org/10.54216/JAIM.060205

Volume & Issue

Vol. Volume 6 / Iss. Issue 2

Details open_in_new