This paper proposes a hybrid nearest neighbor system for solving optimal solution in traveling salesman problem (TSP). The hybrid system combines: (1) nearest neighbor approach; (2) crossover operators; and (3) inversion mutation operator. Different crossover operators, specifically the following three: swap, reversion and insertion operators, are employed. The proposed multi-stage system is tested with well-known small dataset Oliver 30 TSP for optimal solution in traveling salesman problem. The system starts in nearest neighbor stage and generates first optimal solution for dataset. That optimal solution is much higher than shortest cycle length, and after crossover stage, the new optimal solution is smaller than first optimal solution. In the next stage, mutation stage, the operator significantly improves the optimal TSP solution. In our research, the optimal solution is 425.26, which is 0.36

This paper proposes a hybrid system for feature selection, allowing the degree of importance of different features to be determined. Different feature selection techniques, specifically the following four: Univariate feature ranking for classification using chi-square tests, Rank features for classification using minimum redundancy maximum relevance, Relief-based feature selection and Constrained greedy k-means with silhouette value ranking method are employed on the three well-known datasets from UCI Machine Learning Repository: Iris, Wine, and Ionosphere. Subsequently, with the variables ordered from highest to lowest importance as a result of each of the four feature selection methods, an iterative clustering process is performed, agglomerating features that are used to calculate accuracy. This accuracy is employed to determine the most relevant features. The best accuracy value for Iris dataset is 96

Population growth has increased the demand for meat and dairy products, making livestock, especially cattle, key to meeting this demand. This has led to an increase in herd size, complicating efficient herd management. To meet this challenge, innovative technologies, such as monitoring collars, have been developed to improve individual animal management.This research work evaluates and compares three unsupervised anomaly detection methods to identify estrus in dairy cows from intensive farms, based on daily activity data recorded by a commercial monitoring collar. Data from two different dairy farms have been used and the results have been compared by evaluating the behavior both individually and at herd level. The results obtained show a good performance of the selected techniques in the individual animal models. Thus, this research demonstrates that these techniques can be very useful tools in farm management, providing valuable information, improving productivity and, consequently, increasing the economic performance of the farm.

Self-Organizing Map (SOM), an unsupervised learning method, is an artificial neural network (ANN) able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more power ability is gained when the SOM-based model is merged with other clustering algorithms, creating hybrid frameworks and architectures. In this paper, hybrid frameworks and architectures are presented to improve the performance of a simple one-layer SOM in different domains such as image classification, assessing environmental pollutants, real-time scheduling, and other real-world problems.

The Vehicle Routing Problem (VRP) is concerned with the optimal design of routes to be used by a fleet of vehicles to serve a set of customers. Usually, VRP is based on a homogeneous fleet size problem which is complex when the vehicle fleet is heterogeneous. In this research, a new hybrid nearest neighbors - tabu search model for homogeneous fleet of vehicle optimization on VRP of logistics distribution is proposed. The proposed hybrid model in routing a homogeneous fleet VRP is tested on a realistic dataset in the Serbian company “Carnex”. The experimental results of this method, in two different scenarios, are compared to the empirical results, demonstrating that the experimental results obtained by the new hybrid nearest neighbors - tabu search model are better than empirical results in the company. Also, experimental results are better for scenario 2, where the starting point for optimization and delivery starts with a customer that has the smallest order.

Optimization, in general, is regarded as the process of finding optimal values for the variables of a given problem in order to minimize or maximize one or more objective function(s). Brain storm optimization (BSO) algorithm solves a complex optimization problem by mimicking the human idea generating process, in which a group of people solves a problem together. The aim of this paper is to present hybrid BSO algorithm solutions in the past 5 years. This study could be divided into two parts: strategies and applications. In the first part, different strategies for the hybrid BSO algorithms intended to improve the various ability of the original BSO algorithm are displayed. In the second part, the real-world applications in the past five years in optimization, prediction and feature selection processes are presented.

The epidemic spread model of infectious diseases is a traditional epidemiological and mathematical problem with crucial practical value. The first transmission of coronavirus to humans started in Wuhan city of China and took the shape of a pandemic called Corona Virus Disease 2019 (COVID-19). Many papers and studies have been written from 2019 until today. The aim of this paper is to present and short-analyse hybrid AI techniques which have been applied: k-nearest neighbours algorithm, Support Vector Machine, Naive Bayes, Random Forest, k-means clustering, hierarchical clustering, fuzzy logic, Logistic Regression, Decision Tree, Artificial Neural Networks, Deep Learning, Convolutional Neural Network to extract significant features and classify various health conditions, detection and diagnoses of COVID-19 patients. The hybrid algorithm overcomes the lack of exploiting individual AI techniques and algorithms; and simultaneously, the obtained statistical results prove the efficiency and robustness compared over other state-of-the-art approaches.

The increasing demands of e-commerce have increased the need for efficient last-mile delivery solutions, presenting logistical challenges such as traffic congestion and high operational costs. This paper explores the application of machine learning (ML) techniques to optimise last-mile delivery, focusing on Q-learning algorithm to solve the Travelling Salesman Problem (TSP). A case study is presented, where modified Q-learning is applied to the TSP, demonstrating its potential to optimise delivery routes and reduce costs. The proposed model is implemented in the MATLAB environment and it is tested on symmetric and asymmetric small and medium-sized instances from TSPLIB as well as on the real-life TSP instances. Experimental results indicate that ML-based model demonstrates better results on average in most instances compared to Basic Q-learning, Double Q-learning, and SARSA algorithms. This paper contributes to the growing body of knowledge on ML applications in logistics, offering insights into future research directions and practical implementations in last-mile delivery.

A dimensional reduction algorithm is applied to an intelligent classification model with the purpose of improving the efficiency and accuracy. The proposed classification model, used to distinguish the operating mode: Hard- and Soft-Switching, is presented and an analysis of the synchronized rectified step-down converter is done. With the aim of improving the accuracy and reducing the computational cost of the model, three different methods for dimensional reduction are applied to the input dataset of the model: self-organizing maps, principal component analysis and correlation matrix. The obtained results show how the number of variable is highly reduced and the performance of the classification model is boosted: the results manifest an improve in the accuracy and efficiency of the classification.

Headache disorders can be considered as the predominant neurological condition. In the field of neurological diseases, migraine was estimated to cost a total of euro27 billion per year for the loss through reduced work productivity in the European Community. Medical data and information in turn provide knowledge based on which physicians make scientific decisions for diagnosis and treatments. It is, therefore, very useful to create diagnostic tools to help physicians make better decisions. This paper is focused on a new hybrid clustering system combining analytic hierarchy process and weighted fuzzy c-means clustering method for diagnosing children with primary headache disorder. The proposed three-stage hybrid diagnosing system is tested on data set collected from hospitalized children in the Clinical Centre of Vojvodina, Novi Sad, Serbia.

The travelling salesman problem (TSP) belongs to the class of NP-hard problems, in which an optimal solution to the problem cannot be obtained within a reasonable computational time for large-sized problems. To address TSP, we propose a hybrid algorithm, called GA-TCTIA-LBSA, in which a genetic algorithm (GA), tour construction and tour improvement algorithms (TCTIAs) and a list-based simulated annealing (LBSA) algorithm are used. The TCTIAs are introduced to generate a first population, and after that, a search is continued with the GA. The problem of premature convergence of the GA to local optimum is tackled by a method called social disaster technique. Afterwards, the LBSA is applied to generate a new population based on one of two proposed operators called packing and judgement day. The proposed algorithm is implemented in the MATLAB environment, and its two variants, called GA-TCTIA-LBSA packing and GA-TCTIA-LBSA judgement day, are tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed GA-TCTIA-LBSAs offer promising results, particularly for small-sized instances.

To create an efficient electricity system that includes renewable energy generation, able to respond correctly to the variability of power generation, it is essential to establish future production. For this reason, in this work, the performance of different regression models when predicting the energy produced by a small wind turbine based on meteorological variables is compared, seeking the best predictions. Four methods are evaluated: polynomial, bayesian, support vector machine, and artificial neural network. Several metrics are used to compare the models, such as Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Median Absolute Error and Coefficient of Determination, along with hypothesis testing.

Transport is the backbone of the economy aiming to move people and goods efficiently. The gross domestic product is collected from vehicle taxes, energy taxes, and taxes on fuel. On the other side, 25

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human -level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.

Intense growth of e-commerce over the last years has been recognized to have a strong impact on logistics and transportation, and particularly on different modes of last-mile deliveries in urban areas. Simultaneously, many European regulations have imposed strict environmental limitations in terms of decreased air and noise pollution in urban areas. In such circumstances, logistics and transportation sector needs to offer more efficient and more environmental ways to deliver goods. In this paper, we consider electric vehicles (EV) and different concepts of EVs, like unmanned aerial vehicles, e-scooters, and e-bikes as appropriate solutions for last-mile deliveries from the environmental perspective and lifecycle costs.

The volume of data available has risen significantly in recent years due to advancements in data gathering techniques in different fields. The collected data in many domains are typically of high dimensionality, making it impossible to select an optimum range of features. There are many existing research papers that discuss feature selection process used by metaheuristic algorithm. One of them is brain storm optimisation (BSO) algorithm, which is relatively new swarm intelligence algorithm that mimics the brainstorming process in which a group of people solve a problem together. The aim of this paper is to present and analyse hybrid BSO algorithm solutions combined with other metaheuristic algorithms in feature selection process. The hybrid BSO algorithm overcomes the lack of exploitation in the original BSO algorithm; and simultaneously, the obtained statistical results prove the efficiency and robustness over other state-of-the-art approaches.

The Internet of Things (IoT) systems rapidly expand and offer a wide range of services in diverse environments. However, due to the vast assortment of these systems, ensuring their security has become an increasingly significant challenge. The rise of malware such as Mirai or Dark Nexus clearly indicates the increasing number of attacks targeting IoT systems. Currently, the Constrained Application Protocol, known as CoAP, is one of the most commonly used protocols in the application layer of IoT networks. However, this protocol is vulnerable to Denial of Service (DoS) attacks. In this context, this research presents a hybrid system, based on supervised classification techniques, for detecting DoS attacks on IoT networks over CoAP protocol. For the validation of the system, a dataset including network traffic in an IoT network that has suffered DoS attacks has been used.

A discrete symbiotic organisms search (DSOS) and list-based simulated annealing (LBSA) are new metaheuristic search algorithms used for solving different complex optimization problems. DSOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival, while LBSA simplify parameter tuning of the simulated annealing algorithm. In this paper, we propose a hybrid algorithm, named DSOS-LBSA, to solve the well-known traveling salesman problem (TSP) which belongs to the class of NP-hard problems. Additionally, an arbitrary insertion algorithm is introduced to produce organisms in the initial ecosystem. The proposed DSOS-LBSA is implemented in the MATLAB environment and it is tested on symmetric and asymmetric instances from TSPLIB. The overall results demonstrate that the proposed DSOS-LBSA offers promising results, particularly for small-size symmetric instances and large-size asymmetric instances.