Abstract
Meta-heuristics have been applied for a long time to the Travelling Salesman Problem (TSP) but information is still lacking in the determination of the parameters with the best performance. This paper examines the impact of the Simulated Annealing (SA) and Discrete Artificial Bee Colony (DABC) parameters in the TSP. One special consideration of this paper is how the Neighborhood Structure (NS) interact with the other parameters and impacts the performance of the meta-heuristics. NS performance has been the topic of much research, with NS proposed for the best-known problems, which seem to imply that the NS influences the performance of meta-heuristics, more that other parameters. Moreover, a comparative analysis of distinct meta-heuristics is carried out to demonstrate a non-proportional increase in the performance of the NS.

Abstract

Abstract
The exponential growth of data in the digital age has led to a significant demand for innovative approaches to assess data in a manner that is both effective and efficient. Machine Learning as a Service (MLaaS) is a category of services that offers considerable potential for organisations to extract valuable insights from their data while reducing the requirement for heavy technical expertise. This article explores the use of MLaaS within the realm of marketing applications. In this study, we provide a comprehensive analysis of MLaaS implementations and their benefits within the domain of marketing. Furthermore, we present a platform that possesses the capability to be customised and expanded to address marketing's unique requirements. Three modules are introduced: Churn Prediction, One-2-One Product Recommendation, and Send Frequency Prediction. When applied to marketing, the proposed MLaaS system exhibits considerable promise for use in applications such as automated detection of client churn prior to its occurrence, individualised product recommendations, and send time optimisation. Our study revealed that AI-driven campaigns can improve both the Open Rate and Click Rate. This approach has the potential to enhance customer engagement and retention for businesses while enabling well-informed decisions by leveraging insights derived from consumer data. This work contributes to the existing body of research on MLaaS in marketing and offers practical insights for businesses seeking to utilise this approach to enhance their competitive edge in the contemporary data-oriented marketplace.

Abstract
This paper presents several case studies that show that it is possible to use clean energy to produce electricity, we have environmental benefits and benefits for the management of the electrical transmission network. In this case wind energy are used.For this work, software was developed in Matlab for the model we developed and the results of this were compared with the results obtained by the simulator Power World.To make the decision to replace generators fossil generators with wind generators, Local Marginal Prices (LMP) were used. Some case studies were created using a model system, with the objective of evaluating the benefits of this allocation based on the LMP.The test network presented in this paper is a 9 Bus network. However, the developed software was also tested on an IEEE 30 Bus network. © 2023 IEEE.

Abstract
Nowadays, decision making is one of the most important and influential aspects of everyday life, and the application of metaheuristics and heuristics facilitates the process. Thus, this paper presents a performance analysis of the combination of constructive heuristics used to generate initial solutions for metaheuristics applied to scheduling problems. Namely, Nawaz, Enscore, and Ham Heuristic (NEH), Palmer Heuristic and Campbell, Dudek, and Smith Heuristic (CDS) with Cuckoo Search, Firefly Algorithm and Simulated Annealing. The aim is to compare the performance of these combinations to analyse the efficiency, effectiveness and robustness of each. All combinations were analysed in an in-depth computational study and then subjected to a statistical study to support an accurate analysis of the results. The results of the analysis show that the Firefly Algorithm associated with NEH, despite having a high runtime, performs better than the other combinations. However, the best effectiveness-efficiency ratio corresponds to SA-Palmer and SA-CDS. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
The intrinsic challenges of contemporary marketing encourage discovering new approaches to engage and retain customers effectively. As the main channels of interactions between customers and brands pivot between the physical and the digital world, analyzing the outcome behavioral patterns must be achieved dynamically with the stimulus performed in both poles. This systematic review investigates the collaborative impact of adopting multidisciplinary fields of Affective Computing to evaluate current marketing strategies, upholding the process of using multimodal information from consumers to perform and integrate Sentiment Analysis tasks. The adjusted representation of modalities such as textual, visual, audio, or even psychological indicators enables prospecting a more precise assessment of the advantages and disadvantages of the proposed technique, glimpsing future applications of Multimodal Artificial Intelligence in Marketing. Embracing the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as the research method to be applied, this article warrants a rigorous and sequential identification and interpretation of the synergies between the latest studies about affective computing and marketing. Furthermore, the robustness of the procedure is deepened in knowledge-gathering concerning the current state of Affective Computing in the Marketing area, their technical practices, ethical and legal considerations, and the potential upcoming applications, anticipating insights for the ongoing work of marketers and researchers.