Fusion: Practice and Applications

This study addresses the two types of fusion between inflation and economic growth in Uzbekistan. The first is the quantitative relationship between inflation and economic growth, and the second is the marginal relationship between them. The first relationship is based on a simple regression model, while the second analysis is carried out by a threshold regression model. Also, the threshold regression model itself has been analyzed using two methods (TSLS and OLS). The data for the research was covered from 2000 to 2022. Also, the variables used in the analysis were checked for stationarity by the Dickey-Fuller and Phillips-Perron tests. The predictors were included in the study after confirmation of hypothesis tests that were positive. According to the results of the study, the correlation between inflation and economic growth in Uzbekistan is negative. Particularly when inflation is lower than a certain level, economic growth is influenced positively, while it has a negative effect on economic growth when it exceeds a certain level. In general, the study determined the optimal level of inflation for Uzbekistan in terms of its positive impact on economic growth.

The study provides a fusion data analysis to investigate the attitudes and perceptions of legal professionals in Ecuador regarding the effectiveness and fairness of the monitoring procedure, using a questionnaire based on indeterminate Likert scales. By employing Triple Refined Indeterminate Neutrosophic Sets and the Minimum Spanning Tree, responses were analyzed to reveal trends and groupings in opinions. The identification of response clusters suggested marked differences and homogeneous subgroups in perspectives, highlighting specific areas within legislation and judicial procedures that require attention. The threshold used for the Minimum Spanning Tree provided a quantitative view of cohesion and discrepancy, which has significant implications for legislative reform and judicial practice. This innovative approach offers a valuable model for future research, with the potential to influence policy-making and the promotion of legislative reforms based on empirical data.

Intelligent mobile robots move on uncertain grounds, thus requiring good navigation strategies for things like path tracking and obstacle avoidance. This research uses an Omni-drive mobile robot to autonomously approach given objectives in different situations encountered in static and dynamic environments. The paper compares two distinct controllers – fuzzy logic controller and neural network controller- that lead the mobile robot towards its destination without hitting obstacles. These are responsible for adjusting the linear and angular velocities of a mobile robot which makes adaptive navigation possible during real-time. The experimental results have depicted the adaptability of each controller as well as its efficiency especially when dealing with uncertainties involved with the mobile robot navigation system. By systematically evaluating and contrasting them, this study brings out the best performance between Fuzzy Logic and Neural Network Controllers regarding enhancing the autonomy and robustness of Mobile Robots. This research helps to advance knowledge in autonomous systems for practical applications, which will give rise to more efficient navigational techniques for mobile robots; thus, efficient systems that are autonomous become more reliable today. The results show that these controllers are effective in safely steering the robot from its starting point to a specified destination without hitting obstacles.

The Pure Pursuit Algorithm (PPA) is used in this paper to explain how a car with four wheels moves. The MATLAB environment has extensive simulation capabilities that can accurately represent complex robotic behaviors. It was these that were deployed for an extended analysis of the robot’s operational dynamics. In the MATLAB/Simulink framework, waypoints obtained from different algorithms define robot trajectory. An odometer sensor helped to localize the robot thus giving accurate real-time information on its position. After critically evaluating several performance indices, it became clear just how well this control algorithm worked because it smoothly moved the robot from its initial state to its target with almost no oscillations at all. The findings of the simulation confirmed that if an appropriate lookahead distance is selected then the robot can effectively track waypoints and maintain optimal path along a trajectory up until reaching the target point at last

Decision-making based on multiple criteria is common in various contexts, recognized for its high complexity in seeking viable solutions. Computer crimes encompass any act with criminal intent that seeks to cause harm or put at risk a legally protected interest using computer tools. This study aims to determine whether residents of Santo Domingo are aware of computer crimes established in Ecuadorian legislation, employing multicriteria evaluation techniques and the TODIM and PROMETHEE methods. These methodologies are complemented by neutrosophic single-value sets, based on neutrosophic logic, to effectively manage the indeterminate and inconsistent information typical in real-world scenarios. In this way, the utility of these techniques for addressing complex problems in daily life and in various social domains is demonstrated.

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.

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.

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.

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.

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.