Big Data Analysis and Computing Visions
Volume:3 Issue: 4, Dec 2023

There are numerous and various methods for solving the Multi-Attribute Decision-Making (MADM) problems in the literature, such as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Elimination and Choice Expressing Reality (ELECTRE), Analytic Hierarchy Process (AHP), etc. We have explored Support Vector Machine (SVM) as an efficient method for solving MADM problems. The SVM technique was proposed for classifying data at first. At the same time, in the current research, this popular method will be used to sort the preference alternatives in a MADM problem with interval data. The accuracy of the proposed technique will be compared with a popular extended method for interval data, say interval TOPSIS. Numerical experiments showed that admissible results can be obtained by the new method.

Forest fires represent a significant threat to natural ecosystems and human lives, necessitating early detection and rapid response for effective mitigation. In recent years, the Internet of Things (IoT) has emerged as a promising technology for forest fire detection. IoT-based solutions leverage Wireless Sensor Networks (WSNs), which consist of sensor nodes equipped with various sensors, data processing capabilities, and wireless communication, all powered by batteries. Energy efficiency is a critical consideration for WSNs, as they lack the luxury of periodic recharging. This paper explores the utilization of IoT-enabled WSNs in forest fire detection, with a specific focus on the sensor nodes' ability to monitor environmental parameters such as temperature, pressure, and humidity, as well as chemical indicators including Carbon Monoxide, Carbon Dioxide, and Nitrogen Dioxide. The self-healing and self-organizing characteristics of IoT sensor networks enhance their reliability and robustness in remote forested areas. ZigBee, based on IEEE 802.15.4, is a wireless technology that has gained prominence due to its low-cost, battery-powered applications and suitability for low data rates and short-range communications. This paper highlights the advancements, challenges, and potential applications of IoT-enabled WSNs for forest fire detection, underscoring the expanding possibilities enabled by the rapid development of the IoT. It emphasizes the growing research interest in IoT sensor networks and their potential deployment in various domains. The insights provided aim to contribute to ongoing efforts in developing efficient forest fire detection systems, ultimately enhancing the safety and preservation of our natural environment.

In recent times, we have witnessed an extraordinary surge in technological advancements. The rise in transportation, driven by various factors such as schools, offices, factories, and more, has become increasingly apparent. The extensive use of personal vehicles contributes significantly to the escalation of transportation demands, resulting in pollution and a rapid surge in traffic congestion, especially in urban areas. To curb this issue, it is imperative to implement effective techniques. This paper explores the contemporary approach to traffic monitoring systems and highlights the indispensable role of wireless sensor networks in addressing these challenges.

In any economy, it is essential to monitor the rate of population change closely. Governments employ various strategies and programs to regulate population growth since different population growth rates have distinct economic consequences. This paper reveals a global trend of reduced desire to have children, with variations across countries. The paper aims to predict the population growth rate in England by employing Artificial Neural Networks (ANN) in combination with various meta-heuristic algorithms, including the Sparrow Search Algorithm (SSA). The selection of SSA and other algorithms is based on factors such as accuracy and computational efficiency. A set of 18 economic indicators serves as input variables, and a Genetic Algorithm (GA) is used for feature selection. The data used for analysis spans the most recent ten years and is presented on a monthly basis. The results indicate that SSA exhibits the lowest prediction errors for the population growth rate among the applied algorithms in this paper. The primary contribution of this study lies in the application of hybrid algorithms that combine SSA-ANN with other algorithms, such as LA. The paper also emphasizes the inclusion of influential and impactful indices as input variables to enhance prediction accuracy.

This paper introduces a novel approach to stakeholder prioritization by employing Data Envelopment Analysis (DEA). The study utilizes five key criteria-stakeholder power, urgency, legitimacy, level of involvement, and project impact-to quantitatively evaluate stakeholder efficiency. DEA models tailored for each stakeholder are applied, yielding a comprehensive prioritization depicted in numerical form. The methodology showcased in this research harnesses the versatility of DEA in stakeholder analysis. The resulting prioritization aids project managers in strategic decision-making, offering valuable insights to enhance project success and minimize negative impacts. This paper contributes a practical and effective tool for project managers seeking to optimize stakeholder engagement and project outcomes.

Sub-attribute-valued sets are occasionally viewed as more significant in real-life circumstances than a single set of attributes. The current models that deal with ambiguity and uncertainty, or soft sets, are insufficient to address such situations. To adequately fit the current models for multi-attributive sets, the hypersoft set, an extension of the soft set, has been developed. The multi-argument approximate function takes the place of the soft sets' approximate function. Many academics have recently focused on convexity in uncertain environments or soft and fuzzy structures. This paper examines the traditional concepts of -convex and -concave sets in a hypersoft set context, discussing their fundamental inclusive features and set-theoretic operations. Furthermore, traditional notions of first and second senses for convexity are applied to suggested convex structures to provide more broadly applicable outcomes for ambiguous situations.