Fusion: Practice and Applications

In this study, the issue of criminogenic factors in the Lizarzaburu parish of Riobamba-Ecuador is addressed, an area marked by a notable increase in crime. Recognizing the complexity of these factors and the need for an integrated approach for their analysis, the use of Ordered Weighted Averaging (OWA) operators for information fusion is proposed, aiming to create a composite indicator that allows for a holistic and accurate measure of criminality in the area. The implementation of OWA operators facilitates effective weighting of these factors, resulting in the creation of a composite indicator that more faithfully reflects the criminogenic dynamics of Lizarzaburu. This study not only provides a valuable tool for diagnosing crime in urban areas but also establishes a methodological foundation for future research and intervention policies in the field of public security.

The present study aims to analyze the situation of educational inequality in Ecuadorian rural students during the study period, by identifying the barriers and factors that affect their access to equitable and quality education. The results showed a marked digital educational gap between rural and urban students, aggravated by the lack of access to technological resources and connectivity in rural areas. The transition to virtual education during the pandemic exacerbated these inequalities, making it difficult for rural students to learn and limiting their integration into the education system. In conclusion, the need to implement comprehensive public policies to address these inequalities and promote educational equity is highlighted, considering the evaluations and weightings carried out through the MULTIMOORA method. For this, an investment in educational infrastructure, the provision of devices, and teacher training in educational technologies are required to improve access to quality education in rural areas. With this, the path towards a fairer and more inclusive future for all Ecuadorians is sought.

One of the most dangerous natural disasters, which causes massive damage all over the world, is flash floods. Therefore, the assessment of flash floods disasters is considered increasingly urgent and important. The widely used techniques for studying and analyzing the causes and impact of natural hazards are multi-criteria techniques. Several researchers used traditional multi-criteria decision-making techniques in the estimation process of flash floods problems as the analytical hierarchy process, decision making trial and evaluation laboratory and analytic network process. The main disadvantage of these traditional models is the incapability of simulating and reflecting uncertain human thoughts. Since neutrosophic logic has a great ability for simulating human’s thoughts and increase the flexibility of expert's preferences in real world problems, we applied it in this study. There are different locations in Egypt that are at a serious risk of flooding, especially in Upper Egypt. Asyut has suffered from frequent flash floods, with some flood events that lead to mortality, damages, and economic losses in the last decades. The intensity of floods in Egypt varies from year to year, according to several climatic and hydrological variables. This study focuses on using a Neutrosophic Decision making trial and evaluation laboratory (N-DEMATEL) technique with remotely sensed data and geographical information system (GIS) for producing a flash floods hazard map. The N-DEMATEL technique is applied to determine the weights of various factors that related to flash flooding, including elevation, slope, topographic wetness index, distance from the stream, flow accumulation, aspect, flow direction, soil, land cover, watershed, curvature, drainage density , total population , population density and precipitation. The obtained weight of selected criteria used then to produce the flood hazard map (FHM) using a raster calculator tool in geographic information system.

Given the increasing importance of technology in meeting human needs, its utilization has become crucial. In contemporary democracies, where public trust in governments is declining and elections play a pivotal role, the widespread adoption of technology has led to new challenges. Elections hold significant importance as they determine the future leaders of countries or organizations. However, certain computerized voting systems have faced criticism for their lack of transparency. Establishing public trust in the government is a formidable task due to the lack of transparency and susceptibility to exploitation in existing voting procedures. Both traditional and current digital voting systems are ineffective due to their vulnerabilities. The main objective is to address issues in conventional and electronic voting systems, including errors and unfairness that may arise during the voting process. Integrating blockchain technology into the electoral process can ensure fair elections and reduce unfair practices. The computerized voting methods do not meet the necessary standards for widespread usage, and the physical voting systems also face numerous issues. This underscores the importance of finding a solution to protect the democratic principles of citizens. By offering a fast and secure voting method, this system has the potential to bring about a revolutionary change in the electoral process. It could lead to higher voter participation and more accurate election results. The proposed approach presents a framework for digital voting using blockchain technology, eliminating the need for physical polling locations. Our suggested design incorporates adaptable consensus algorithms to support a scalable blockchain. Smart contracts ensure secure interactions between users and the network during transaction execution. The security aspects of the blockchain-based voting mechanism have also been addressed, including the use of cryptographic hashes for transaction encryption and prevention of 51% attacks. Furthermore, blockchain technology has been utilized to establish transaction systems throughout the voting process. Performance studies of the proposed system demonstrate its feasibility for deployment in large populations.

Demand forecasting is a crucial aspect of managing the supply chain, as it helps companies optimize inventory levels and minimize expenses related to inventory shortages. In recent years, machine learning (ML) algorithms have gained popularity for demand forecasting, as they can handle large and complex datasets and provide accurate predictions. Precise demand prediction for car brands is vital for companies to minimize costs and prevent inventory shortages. The demand for distributing cars is a critical component of inventory management. However, estimating demand for new car sales is difficult due to its continuous nature. To address this challenge, a study was conducted to train, test, and compare the performance of five machine learning algorithms (Random Forest, Multiple Linear Regression, k-Nearest Neighbors, Extreme Gradient Boosting, and Support Vector Machine) using a benchmark dataset. Among all the experiments, the Support Vector Machine algorithm achieved the highest accuracy score of 71.42%. Moreover, Multiple Linear Regression performed well, with an accuracy score of 66.66%. On the other hand, the Extreme Gradient Boosting algorithm had the lowest accuracy score of 42.85%. All experiments used a train-test split of 75/25.

In this paper, to protect software-defined networks (SDN) from various ARP attacks, we implement a three-dimensional algorithm (TDA). The main objective of TDA is to limit the methods by which attackers can breach SDN privacy and to prevent the three main types of ARP attacks, such as ARP flooding, ARP spoofing, and ARP broadcasting. This work discusses the three different ARP attack types, which are broken down into five different scenarios, and how the proposed solution detects and mitigates each one. We simulated the five attack scenarios by creating five Python scripts utilizing the Scapy library. And then we applied an efficient TDA to restrict the five scenarios of ARP attacks more efficiently and faster than existing methods. TDA provides the Ryu controller with a modified module to detect and mitigate these types of attacks, using a three-dimensional secure channel to analyze incoming ARP packets, which works as a filter that analyzes and filters incoming ARP packets from malicious ones, and then giving the controller the choice to forward or drop the packet. To simulate our investigation and apply our proposed solution, we used a Mininet emulator. To evaluate TDA, we calculated the delay times, accuracy controller's throughput, bandwidth, and other metrics. The results that we showed after applying TDA 100 times on our test scenarios indicate that the accuracy is 99.9% for the three stages and that the detection and mitigation times are very short compared to the existing solutions, which are that the minimum detection time is only from 0.1ms to 3.6ms, and the minimum mitigation time is only from 0.3ms to 2.9ms. We evaluated our algorithm by other important metrics such as controller bandwidth, which ranged from 18 GB/sec to 17.7 GB/sec in the cases before and after the attack and 16.5GB/sec in the case of attack; controller throughput, which recorded 1.72GB/sec in the case under the attack and reached 2.11GB/sec in the case after defense; and CPU utilization, which recorded 30.4% during the attack and reduced to 0.3% after mitigation. These metrics proved that our algorithm achieved the highest efficiency compared to other work in this field.