Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis and latent class analysis, to identify clustering patterns of diet, physical activity and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity.

The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review

Fundamentals of statistical signal processing: Estimation theory: by Steven M. KAY; Prentice Hall signal processing series; Prentice Hall; Englewood Cliffs, NJ, USA; 1993; xii + 595 pp.; $65; ISBN: 0-13-345711-7

Artificial bee colony (ABC) algorithm inspired by the foraging behaviour of the honey bees is one of the most popular swarm intelligence based optimization techniques. Quick artificial bee colony (qABC) is a new version of ABC algorithm which models the behaviour of onlooker bees more accurately and improves the performance of standard ABC in terms of local search ability. In this study, the qABC method is described and its performance is analysed depending on the neighbourhood radius, on a set of benchmark problems. And also some analyses about the effect of the parameter limit and colony size on qABC optimization are carried out. Moreover, the performance of qABC is compared with the state of art algorithms' performances.

A quick artificial bee colony (qABC) algorithm and its performance on optimization problems

A survey on applications and variants of the cuckoo search algorithm

This work addresses the target tracking problem that makes use of combined measurements, namely received signal strength (RSS) and angle of arrival (AoA). By linearizing the measurement models and incorporating the prior knowledge obtained from target state transition model, we show that the application of the Kalman filter (KF) to the considered tracking problem is straightforward. Then, an extension of the linearization approach to the case where the target transmit power is not known is introduced and applied to the measurement model to obtain an estimate of the transmit power. By taking advantage of this estimated value, we show that the proposed KF algorithm can easily be generalized to the case of unknown transmit power. Our simulation results confirm the efficacy of the proposed algorithms in comparison with the existing one, as well as the robustness of the proposed approach to not knowing the transmit power. Finally, the supremacy of using the Bayesian approach in comparison with the classical one which disregards the prior knowledge information is also validated through computer simulations.

Kalman filter for target tracking using coupled RSS and AoA measurements

Forecasting climate and the development of the environment have been essential in recent days since there has been a drastic change in nature. Weather forecasting plays a significant role in decision-making in traffic management, tourism planning, crop cultivation in agriculture, and warning the people nearby the seaside about the climate situation. It is used to reduce accidents and congestion, mainly based on climate conditions such as rainfall, air condition, and other environmental factors. Accurate weather prediction models are required by meteorological scientists. The previous studies have shown complexity in terms of model building, and computation, and based on theory-driven and rely on time and space. This drawback can be easily solved using the machine learning technique with the time series data. This paper proposes the state-of-art deep learning model Long Short-Term Memory (LSTM) and the Transductive Long Short-Term Memory (T-LSTM) model. The model is evaluated using the evaluation metrics root mean squared error, loss, and mean absolute error. The experiments are carried out on HHWD and Jena Climate datasets. The dataset comprises 14 weather forecasting features including humidity, temperature, etc. The T-LSTM method performs better than other methodologies, producing 98.2% accuracy in forecasting the weather. This proposed hybrid T-LSTM method provides a robust solution for the hydrological variables. 

DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)

Digital images have revolutionized work in numerous scientific fields such as healthcare, astronomy, biology, agriculture as well as in every day life. One of the frequent tasks in applications with digital images is image classification which is a very challenging task. Major progress was made when convolution neural networks were introduced. The use of CNN has produced significant improvements in applications that require image classification. With today’s technology, it is relatively simple to implement and use CNNs but in order to obtain the best possible results it is necessary to find the optimal architecture and hyperparameters for every single task. Due to a large number of hyperparameters, it is difficult to find the optimal configuration and there is no deterministic way to do it. In this early stage of CNN development, the common method of tuning CNN is by guessing and estimating, known as the guestimating method. Since this is a hard optimization problem, there is a chance to apply an optimization metaheuristic. There are several studies that have applied different optimization methods for tuning CNN hyperparameters have applied. One promising approach is the application of swarm intelligence algorithms. In this paper, a brief review of the CNN hyperparameters tuning will be presented and discussed.

Convolutional Neural Networks Hyperparameters Tuning

The concepts of smart agriculture, with the aim of highly automated industrial mass production leaning towards self-farming, can be scaled down to the level of small farms and homesteads, with the use of more affordable electronic components and open-source software. The backbone of smart agriculture, in both cases, is the Internet of Things (IoT). Single-board computers (SBCs) such as a Raspberry Pi, working under Linux or Windows IoT operating systems, make affordable platform for smart devices with modular architecture, suitable for automation of various tasks by using machine learning (ML), artificial intelligence (AI) and computer vision (CV). Similarly, the Arduino microcontroller enables the building of nodes in the IoT network, capable of reading various physical values, wirelessly sending them to other computers for processing and furthermore, controlling electronic elements and machines in the physical world based on the received data. This review gives a limited overview of currently available technologies for smart automation of industrial agricultural production and of alternative, smaller-scale projects applicable in homesteads, based on Arduino and Raspberry Pi hardware, as well as a draft proposal of an integrated homestead automation system based on the IoT.

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IoT as a Backbone of Intelligent Homestead Automation

The most challenging task in the machine learning domain is optimizing the hyperparameters in convolutional neural networks. This task is representative of NP-hard problems, and consequently, it is not possible to solve it by applying standard deterministic approaches in an acceptable amount of time. Additionally, convolutional neural networks’ hyperparameters must be optimized for each particular problem, as there is no solution that fits all possible applications. Swarm intelligence metaheuristics have been established as efficient optimizers, and this paper proposes the enhanced sine cosine algorithm to address the task of hyperparameters optimization. The experiments conducted in this research were executed with the CIFAR-10 benchmark dataset. The experimental results were analyzed and validated against other proven metaheuristics approaches, and it can be concluded that the presented enhanced sine cosine approach outperformed other methods included in this research.

Nebojsa Bacanin

Papers

Kalman filter for target tracking using coupled RSS and AoA measurements

DWFH: An improved data-driven deep weather forecasting hybrid model using Transductive Long Short Term Memory (T-LSTM)

Convolutional Neural Networks Hyperparameters Tuning

IoT as a Backbone of Intelligent Homestead Automation

Convolutional Neural Networks Hyperparameters Optimization Using Sine Cosine Algorithm