Fusion: Practice and Applications

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2692-4048ISSN (Online) 2770-0070ISSN (Print)

Fusion Data Management and Modeling Techniques in Power Quality Compensation Using SAPF

Jessica N. Castillo , Guido G. Carrillo , Luigi O. Freire , Javier Culqui

The development of transportation today encompasses a broad range of technological applications that occasionally present new challenges arising from difficulties that require solutions. The article analyzes the difficulties in electric trains concerning the compensation of electric power quality in a traction system using a parallel active power filter (SAPF). From the literature review of several studies, the test distribution system in a distribution network for an electric train system is analyzed, with a variable load and harmonic content. The estimation and control technique used in the SAPF to compensate for the harmonic content and reduce the reactive power at the output of a traction substation is described. A data fusion management strategy is employed in the analyses, demonstrating the system's effectiveness.

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Doi: https://doi.org/10.54216/FPA.160201

Vol. 16 Issue. 2 PP. 08-21, (2024)

Integrating Machine Learning Models for Enhanced Soil Organic Carbon Estimation: A Multi-Model Fusion Approach

Bryan Barragán-Pazmiño , Angel Ordóñez Echeverría , Magdy Echeverría Guadalupe , Theofilos Toulkeridis

Machine learning approaches are utilized to identify patterns in behavior and generate predictions across various applications. The objective of this work is to create a highly efficient model for accurately measuring and analyzing the levels of soil organic carbon (SOC) in the Chambo river sub-basin, which is situated in the province of Chimborazo. The model evaluation entails the application of diverse machine learning algorithms and approaches to determine the most efficient regression model. Regression models are improved using techniques such as Artificial Neural Networks, Support Vector Machines, and Decision Trees. The Resilient Backpropagation method yields the most precise model, as it accounts for a greater proportion of the variability in SOC content for the test data. This aligns with the findings from the training data, demonstrating a relatively low mean absolute error and a processing time that is approximately 400 times faster than that of the Multilayer Perceptron algorithm. The evaluation of estimating models is an objective procedure that considers not only the findings and precise metrics derived from the model's design, but also other relevant elements. The effectiveness of the Random Forest approach, specifically the quantile regression forests technique, has been established for estimating SOC contents in the Chambo river sub-basin data.

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Doi: https://doi.org/10.54216/FPA.160202

Vol. 16 Issue. 2 PP. 22-31, (2024)

Fusion of Forensic Analysis of Mobile Devices: Integrating Multi-Criteria Decision Methods and Case Study Insights

Jorge B. Rubio Peñaherrera , Kevin Mauricio T. Diaz , Adam Marks

This study employed a Multi-Criteria Decision Analysis (MCDM) approach, utilizing the DEMATEL and TOPSIS methodologies, to assess the effectiveness of forensic tools designed for mobile devices, with a specific emphasis on Android and iOS platforms. The investigation evaluated technologies used for collecting, retrieving, and validating data in the Cyber Forensic Field Triage paradigm, with a focus on rapidly identifying and interpreting digital evidence. The study incorporated several factors and expert preferences, concluding that the Android Triage and Andriller tools were the most efficient.

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Doi: https://doi.org/10.54216/FPA.160203

Vol. 16 Issue. 2 PP. 32-42, (2024)

SOM and Hybrid Filtering: Pioneering Next-Gen Movie Recommendations in the Entertainment Industry

Saurabh Sharma , Ghanshyam Prasad Dubey , Harish Kumar Shakya , Aditi Sharma

In an age where digital connectivity is increasingly shaping entertainment content, personalized movie recommendations play a pivotal role in enhancing user satisfaction and engagement. This research introduces an innovative approach utilizing Enhanced Self-Organizing Maps (SOM) to streamline movie selection processes. Self-Organizing Maps (SOMs), a type of unsupervised neural network architecture, are particularly adept at discerning intricate data patterns, making them valuable assets in recommendation systems. The methodology outlined in this paper commences with gathering user-movie interaction data, including user feedback and movie characteristics, which is standardized to ensure consistency before model training. Leveraging its adaptable learning rate and neighborhood function, the Enhanced SOM effectively identifies subtle data nuances. Personalized movie suggestions are then generated by exploiting the Enhanced SOM's capacity to identify similar users and films. Integration of hybrid filtering techniques enriches recommendation quality, blending collaborative filtering algorithms, which leverage user-item interactions, with content-based filtering, which utilizes movie attributes such as genres and descriptions. This amalgamation results in suggestions that harmoniously combine diverse filtering methodologies. The proposed solution's efficacy is rigorously evaluated by comparing suggestion accuracy and user satisfaction against predefined benchmarks. Extensive real-world dataset testing corroborates the effectiveness of the Enhanced SOM-based movie recommendation approach. Furthermore, the system offers flexibility through options for parameter adjustment, grid size variations, and neighborhood function modifications to further refine recommendation accuracy. Collectively, these elements underscore the efficacy of the proposed method in furnishing tailored movie recommendations. When coupled with hybrid filtering techniques, the implementation of Enhanced SOMs emerges as a reliable model for content platforms seeking to enhance user experiences by delivering precise movie recommendations, coupled with scalability and adaptability.

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Doi: https://doi.org/10.54216/FPA.160204

Vol. 16 Issue. 2 PP. 43-62, (2024)

Employing Deep Learning Techniques for the Identification and Assessment of Skin Cancer

Sowmya Koneru , Pappula Madhavi , Krishna Kishore Thota , Janjhyam V. Naga Ramesh , Venkata Nagaraju Thatha , S. Phani Praveen

These days, skin cancer is a prominent cause of death for people. Skin cancer is the name given to the abnormal development of skin cells that are exposed to the sun. These skin cells can develop anywhere on the human body. The majority of malignancies are treatable in the early stages. Thus, early detection of skin cancer is anticipated in order to preserve patient life. With cutting edge innovation, it is possible to detect skin cancer early on. Here, we provide a novel framework for the recognition of dermo duplication pictures that makes use of a neighbouring descriptor encoding method and deep learning technique. Specifically, the deep representations of a rescaled dermo duplication image that were initially removed through training an extraordinarily deep residual neural network on a big dataset of normal images. Subsequently, the neighbourhood deep descriptors are obtained by request-less visual measurement highlights, which rely on fisher vector encoding to create an international image representation. Lastly, a convolution neural network (CNN) was utilised to orchestrate melanoma images employing the Fisher vector encoded depictions. This proposed technique can give more discriminative parts to oversee huge contrasts inside melanoma classes and little varieties among melanoma and non-melanoma classes with least readiness information.

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Doi: https://doi.org/10.54216/FPA.160205

Vol. 16 Issue. 2 PP. 63-85, (2024)

Classification of Monkeypox Using Greylag Goose Optimization (GGO) Algorithm

Ahmed Eslam , Mohamed G. Abdelfattah , El-Sayed M. El-Kenawy , Hossam El-Din Moustafa

After the COVID-19 epidemic, public health awareness increased. A skin viral disease known as monkeypox sparked an emergency alert, leading to numerous reports of infections across numerous European countries. Common symptoms of this disease are fever, high temperatures, and water-filled blisters. This paper presents one of the recent algorithms based on a metaheuristic framework. To improve the performance of monkeypox classification, we introduce the GGO algorithm. Firstly, we employ four pre-trained models (AlexNet, GoogleNet, Resnet-50, and VGG-19) to extract the most common features of monkeypox skin image disease (MSID). Then, we reduce the number of extracted features to select the most distinguishing features for the disease. We make it by using GGO in binary form, which has an average fitness of 0.60068 and a best fitness of 0.50248. Lastly, we apply various optimization algorithms, including the (WWPA) waterwheel plant algorithm, the (DTO) Boosted Dipper Throated Optimization, the (PSO) particle swarm optimizer, the (WAO) whale optimization algorithm, the (GWO) gray wolf optimizer, the (FA) firefly algorithm, and the GGO algorithm, all based on the Convolution Neural Network (CNN), to achieve the best performance. Best Performance is indicated in accuracy and sensitivity; it reached 0.9919 and 0.9895 by GGO. A rigorous statistical analysis test was applied to confirm the validity of our findings. We applied Analysis of Variance ANOVA, and Wilcoxon signed tests, and the results indicated that the value of p was less than 0.005, which strongly supports our hypothesis.  

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Doi: https://doi.org/10.54216/FPA.160206

Vol. 16 Issue. 2 PP. 86-107, (2024)