Showing papers by "Nebojsa Bacanin published in 2022"

Weight Optimization in Artificial Neural Network Training by Improved Monarch Butterfly Algorithm

Abstract: Artificial neural networks, especially deep neural networks, are the promising and current research domain as they showed great potential in classification and regression tasks. The process of training artificial neural network (weight optimization), as an NP-hard challenge, is typically performed by back-propagation algorithms such as stochastic gradient descent. However, these types of algorithms are susceptible to trapping the local optimum. Recent studies show that, the metaheuristics-based approaches like swarm intelligence can be efficiently utilized in training the artificial neural network. This paper presents an improved version of swarm intelligence and monarch butterfly optimization algorithm for training the feed-forward artificial neural network. Since the basic monarch butterfly optimization suffers from some deficiencies, improved implementation, that enhances exploration ability and intensification–diversification balance, is devised. Proposed method is validated against 8 well-known classification datasets and compared to similar approaches that were tested within the same environment and simulation setup. Obtained results indicate that, the method proposed in this work outperforms other state-of-the-art algorithms that are shown in the recent outstanding computer science literature.

Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection

Abstract: Recent advances in online payment technologies combined with the impact of the COVID-19 global pandemic has led to a significant escalation in the number of online transactions and credit card payments being executed every day. Naturally, there has also been an escalation in credit card frauds, which is having a significant impact on the banking institutions, corporations that issue credit cards, and finally, the vendors and merchants. Consequently, there is an urgent need to implement and establish proper mechanisms that can secure the integrity of online card transactions. The research presented in this paper proposes a hybrid machine learning and swarm metaheuristic approach to address the challenge of credit card fraud detection. The novel, enhanced firefly algorithm, named group search firefly algorithm, was devised and then used to a tune support vector machine, an extreme learning machine, and extreme gradient-boosting machine learning models. Boosted models were tested on the real-world credit card fraud detection dataset, gathered from the transactions of the European credit card users. The original dataset is highly imbalanced; to further analyze the performance of tuned machine learning models, in the second experiment performed for the purpose of this research, the dataset has been expanded by utilizing the synthetic minority over-sampling approach. The performance of the proposed group search firefly metaheuristic was compared with other recent state-of-the-art approaches. Standard machine learning performance indicators have been used for the evaluation, such as the accuracy of the classifier, recall, precision, and area under the curve. The experimental findings clearly demonstrate that the models tuned by the proposed algorithm obtained superior results in comparison to other models hybridized with competitor metaheuristics.

Novel Improved Salp Swarm Algorithm: An Application for Feature Selection

Abstract: We live in a period when smart devices gather a large amount of data from a variety of sensors and it is often the case that decisions are taken based on them in a more or less autonomous manner. Still, many of the inputs do not prove to be essential in the decision-making process; hence, it is of utmost importance to find the means of eliminating the noise and concentrating on the most influential attributes. In this sense, we put forward a method based on the swarm intelligence paradigm for extracting the most important features from several datasets. The thematic of this paper is a novel implementation of an algorithm from the swarm intelligence branch of the machine learning domain for improving feature selection. The combination of machine learning with the metaheuristic approaches has recently created a new branch of artificial intelligence called learnheuristics. This approach benefits both from the capability of feature selection to find the solutions that most impact on accuracy and performance, as well as the well known characteristic of swarm intelligence algorithms to efficiently comb through a large search space of solutions. The latter is used as a wrapper method in feature selection and the improvements are significant. In this paper, a modified version of the salp swarm algorithm for feature selection is proposed. This solution is verified by 21 datasets with the classification model of K-nearest neighborhoods. Furthermore, the performance of the algorithm is compared to the best algorithms with the same test setup resulting in better number of features and classification accuracy for the proposed solution. Therefore, the proposed method tackles feature selection and demonstrates its success with many benchmark datasets.

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

Abstract: There is no doubt that new technology has become one of the crucial parts of most people’s lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

Hybridized sine cosine algorithm with convolutional neural networks dropout regularization application

Abstract: Deep learning has recently been utilized with great success in a large number of diverse application domains, such as visual and face recognition, natural language processing, speech recognition, and handwriting identification. Convolutional neural networks, that belong to the deep learning models, are a subtype of artificial neural networks, which are inspired by the complex structure of the human brain and are often used for image classification tasks. One of the biggest challenges in all deep neural networks is the overfitting issue, which happens when the model performs well on the training data, but fails to make accurate predictions for the new data that is fed into the model. Several regularization methods have been introduced to prevent the overfitting problem. In the research presented in this manuscript, the overfitting challenge was tackled by selecting a proper value for the regularization parameter dropout by utilizing a swarm intelligence approach. Notwithstanding that the swarm algorithms have already been successfully applied to this domain, according to the available literature survey, their potential is still not fully investigated. Finding the optimal value of dropout is a challenging and time-consuming task if it is performed manually. Therefore, this research proposes an automated framework based on the hybridized sine cosine algorithm for tackling this major deep learning issue. The first experiment was conducted over four benchmark datasets: MNIST, CIFAR10, Semeion, and UPS, while the second experiment was performed on the brain tumor magnetic resonance imaging classification task. The obtained experimental results are compared to those generated by several similar approaches. The overall experimental results indicate that the proposed method outperforms other state-of-the-art methods included in the comparative analysis in terms of classification error and accuracy.

Feature Selection by Hybrid Brain Storm Optimization Algorithm for COVID-19 Classification

Abstract: A large number of features lead to very high-dimensional data. The feature selection method reduces the dimension of data, increases the performance of prediction, and reduces the computation time. Feature selection is the process of selecting the optimal set of input features from a given data set in order to reduce the noise in data and keep the relevant features. The optimal feature subset contains all useful and relevant features and excludes any irrelevant feature that allows machine learning models to understand better and differentiate efficiently the patterns in data sets. In this article, we propose a binary hybrid metaheuristic-based algorithm for selecting the optimal feature subset. Concretely, the brain storm optimization algorithm is hybridized by the firefly algorithm and adopted as a wrapper method for feature selection problems on classification data sets. The proposed algorithm is evaluated on 21 data sets and compared with 11 metaheuristic algorithms. In addition, the proposed method is adopted for the coronavirus disease data set. The obtained experimental results substantiate the robustness of the proposed hybrid algorithm. It efficiently reduces and selects the feature subset and at the same time results in higher classification accuracy than other methods in the literature.

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Abstract: The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

Energy Efficient Clustering in Wireless Sensor Networks by Opposition-Based Initialization Bat Algorithm

Abstract: Wireless sensor networks belong to the group of technologies that enabled emerging and fast developing of other novel technologies such as cloud computing, environmental and air pollution monitoring, and health applications. One important challenge that must be solved for any wireless sensor network is energy-efficient clustering, that is categorized as NP-hard problem. This led to a great number of novel clustering algorithms, that emerged with sole purpose to establish the proper balance in energy consumption between the sensors, and to enhance the efficiency and lifetime of the network itself. In this manuscript, a modified version of the bat algorithm, that belongs to a group of nature-inspired swarm intelligence metaheuristics, is proposed. Devised algorithm was utilized to tackle the energy-efficient clustering problems. Performance of proposed improved bat metaheuristics has been validated by conducting a comparative analysis with its original version, and also with other metaheuristics approaches that were tested for the same problem. Obtained results from conducted experiments suggest that the proposed method’s performance is superior, and that it could bring valuable results in the other domains of use as well.

COVID-19 Severity Prediction Using Enhanced Whale with Salp Swarm Feature Classification

Abstract: Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19 and pneumonia. On the basis of the image analysis results of chest CT and X-rays, the severity of lung infection is monitored using a tool. Many researchers have done in diagnosis of lung infection in an accurate and efficient takes lot of time and inefficient. To overcome these issues, our proposed study implements four cascaded stages. First, for pre-processing, a mean filter is used. Second, texture feature extraction uses principal component analysis (PCA). Third, a modified whale optimization algorithm is used (MWOA) for a feature selection algorithm. The severity of lung infection is detected on the basis of age group. Fourth, image classification is done by using the proposedMWOAwith the salp swarm algorithm (MWOA-SSA). MWOA-SSA has an accuracy of 97%, whereas PCA and MWOA have accuracies of 81% and 86%. The sensitivity rate of the MWOA-SSA algorithm is better that of than PCA (84.4%) and MWOA (95.2%). MWOA-SSA outperforms other algorithms with a specificity of 97.8%. This proposed method improves the effective classification of lung affected images from large datasets. © 2022 Tech Science Press. All rights reserved.

Hybrid CNN and XGBoost Model Tuned by Modified Arithmetic Optimization Algorithm for COVID-19 Early Diagnostics from X-ray Images

Abstract: Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic since its emergence. One of the most important ways to control the spread of this disease begins with early detection, which allows that isolation and treatment could perhaps be started. According to recent results, chest X-ray scans provide important information about the onset of the infection, and this information may be evaluated so that diagnosis and treatment can begin sooner. This is where artificial intelligence collides with skilled clinicians’ diagnostic abilities. The suggested study’s goal is to make a contribution to battling the worldwide epidemic by using a simple convolutional neural network (CNN) model to construct an automated image analysis framework for recognizing COVID-19 afflicted chest X-ray data. To improve classification accuracy, fully connected layers of simple CNN were replaced by the efficient extreme gradient boosting (XGBoost) classifier, which is used to categorize extracted features by the convolutional layers. Additionally, a hybrid version of the arithmetic optimization algorithm (AOA), which is also developed to facilitate proposed research, is used to tune XGBoost hyperparameters for COVID-19 chest X-ray images. Reported experimental data showed that this approach outperforms other state-of-the-art methods, including other cutting-edge metaheuristics algorithms, that were tested in the same framework. For validation purposes, a balanced X-ray images dataset with 12,000 observations, belonging to normal, COVID-19 and viral pneumonia classes, was used. The proposed method, where XGBoost was tuned by introduced hybrid AOA, showed superior performance, achieving a classification accuracy of approximately 99.39% and weighted average precision, recall and F1-score of 0.993889, 0.993887 and 0.993887, respectively.

Wireless Sensor Networks Localization by Improved Whale Optimization Algorithm

Abstract: Wireless sensor networks that are composed of a finite number of spatially distributed autonomous sensors are widely used in different areas with many potential applications. However, in order to be implemented efficiently, especially in poorly accessible terrains, localization challenge should be addressed. Localization refers to determining the unknown target nodes positions by using information about location of anchor nodes, based on different measurements, such as the time and the angle of arrival, time difference of arrival, and so on. This task is considered to be NP-hard by its nature and cannot be addressed with traditional deterministic approaches. In this research we have proposed the improved implementation of swarm intelligence approach, whale optimization algorithm, to address localization challenge in wireless sensor networks. Observed drawbacks of original whale optimization algorithm are overcome in enhanced implementation by incorporating quasi-reflected-based learning algorithm. Proposed metaheuristics is tested using the same network topology and experimental conditions as other advanced metaheuristics which results are published in the most recent computer science literature. Based on simulation results, devised algorithm manages to establish lower localization error than the basic whale optimization algorithm, as well as other outstanding metaheuristics.

Feature Selection Using Grey Wolf Optimization with Random Differential Grouping

Abstract: Big data are regarded as a tremendous technology for processing a huge variety of data in a short time and with a large storage capacity. The user’s access over the internet creates massive data processing over the internet. Big data require an intelligent feature selection model by addressing huge varieties of data. Traditional feature selection techniques are only applicable to simple data mining. Intelligent techniques are needed in big data processing and machine learning for an efficient classification. Major feature selection algorithms read the input features as they are. Then, the features are preprocessed and classified. Here, an algorithm does not consider the relatedness. During feature selection, all features are misread as outputs. Accordingly, a less optimal solution is achieved. In our proposed research, we focus on the feature selection by using supervised learning techniques called grey wolf optimization (GWO) with decomposed random differential grouping (DrnDG-GWO). First, decomposition of features into subsets based on relatedness in variables is performed. Random differential grouping is performed using a fitness value of two variables. Now, every subset is regarded as a population in GWO techniques. The combination of supervised machine learning with swarm intelligence techniques produces best feature optimization results in this research. Once the features are optimized, we classify using advanced kNN process for accurate data classification. The result of DrnDG-GWO is compared with those of the standard GWO and GWO with PSO for feature selection to compare the efficiency of the proposed algorithm. The accuracy and time complexity of the proposed algorithm are 98% and 5 s, which are better than the existing techniques.

Dimensionality Reduction Using Hybrid Brainstorm Optimization Algorithm

Abstract: In this work, a swarm intelligence-based algorithm, brainstorm optimization, is proposed for reducing dimensionality (feature selection) in datasets that are used for classification. Dimensionality reduction is a well-known and widely used technique in analyzing big data. Its role is to reduce the number of features in high-dimensional datasets and to keep only those that contain useful and rich information. This results in better understanding and interpretation of data, higher accuracy, and boosting the training process of machine learning method used for classification. After extracting features from the dataset, it should be decided which subset of features will be used in the training process. Since, in high-dimensional datasets many features exist, this problem is categorized as NP hard and it is necessary to employ metaheuristics for its solving. For tackling this issue, a binary hybrid brainstorm optimization metaheuristics that overcome the drawbacks of original algorithm, is proposed. For performance evaluation, 21 datasets are used. The comparative analysis is made between the proposed approach and the original brainstorm optimization algorithm, as well as with nine other metaheuristics adopted for feature selection. Experimental results prove the robustness of proposed method, since it is capable to reduce the number of features by simultaneously achieving better classification accuracy than other methods taken for comparative analysis.

Multi-Swarm Algorithm for Extreme Learning Machine Optimization

Abstract: There are many machine learning approaches available and commonly used today, however, the extreme learning machine is appraised as one of the fastest and, additionally, relatively efficient models. Its main benefit is that it is very fast, which makes it suitable for integration within products that require models taking rapid decisions. Nevertheless, despite their large potential, they have not yet been exploited enough, according to the recent literature. Extreme learning machines still face several challenges that need to be addressed. The most significant downside is that the performance of the model heavily depends on the allocated weights and biases within the hidden layer. Finding its appropriate values for practical tasks represents an NP-hard continuous optimization challenge. Research proposed in this study focuses on determining optimal or near optimal weights and biases in the hidden layer for specific tasks. To address this task, a multi-swarm hybrid optimization approach has been proposed, based on three swarm intelligence meta-heuristics, namely the artificial bee colony, the firefly algorithm and the sine–cosine algorithm. The proposed method has been thoroughly validated on seven well-known classification benchmark datasets, and obtained results are compared to other already existing similar cutting-edge approaches from the recent literature. The simulation results point out that the suggested multi-swarm technique is capable to obtain better generalization performance than the rest of the approaches included in the comparative analysis in terms of accuracy, precision, recall, and f1-score indicators. Moreover, to prove that combining two algorithms is not as effective as joining three approaches, additional hybrids generated by pairing, each, two methods employed in the proposed multi-swarm approach, were also implemented and validated against four challenging datasets. The findings from these experiments also prove superior performance of the proposed multi-swarm algorithm. Sample code from devised ELM tuning framework is available on the GitHub.

Multi-Step Crude Oil Price Prediction Based on LSTM Approach Tuned by Salp Swarm Algorithm with Disputation Operator

Abstract: The economic model derived from the supply and demand of crude oil prices is a significant component that measures economic development and sustainability. Therefore, it is essential to mitigate crude oil price volatility risks by establishing models that will effectively predict prices. A promising approach is the application of long short-term memory artificial neural networks for time-series forecasting. However, their ability to tackle complex time series is limited. Therefore, a decomposition-forecasting approach is taken. Furthermore, machine learning model accuracy is highly dependent on hyper-parameter settings. Therefore, in this paper, a modified version of the salp swarm algorithm is tasked with determining satisfying parameters of the long short-term memory model to improve the performance and accuracy of the prediction algorithm. The proposed approach is validated on real-world West Texas Intermediate (WTI) crude oil price data throughout two types of experiments, one with the original time series and one with the decomposed series after applying variation mode decomposition. In both cases, models were adjusted to conduct one, three, and five-steps ahead predictions. According to the findings of comparative analysis with contemporary metaheuristics, it was concluded that the proposed hybrid approach is promising for crude oil price forecasting, outscoring all competitors.

Novel Harris Hawks Optimization and Deep Neural Network Approach for Intrusion Detection

Abstract: Intrusion detection systems attempt to identify assaults while they occur or after they have occurred and they detect abnormal behavior in a network of computer systems in order to identify whether the activity is hostile or unlawful, allowing a response to the violation. Intrusion detection systems gather network traffic data from a specific location on the network or computer system and utilize it to safeguard hardware and software assets against malicious attacks. These systems employ high-dimensional datasets with a high number of redundant and irrelevant features and a large number of samples. One of the most significant challenges from this domain is the analysis and classification of such a vast amount of heterogeneous data. The utilization of machine learning models is necessary. The method proposed in this paper represents a hybrid approach between recently devised yet well-known, harris hawks optimization metaheuristics and deep neural network machine learning model. Since the basic harris hawks optimization exhibits some deficiencies, its improved version is used for dimensionality reduction, followed by the classification executed by the deep neural network model. Proposed approach is tested against well-known NSL-KDD and KDD Cup 99 Kaggle datasets. Comparative analysis with other similar methods proved the robustness of the presented technique when metrics like accuracy, precision, recall, F1-score are taken into account.

A Chaotic Oppositional Whale Optimisation Algorithm with Firefly Search for Medical Diagnostics

Abstract: There is a growing interest in the study development of artificial intelligence and machine learning, especially regarding the support vector machine pattern classification method. This study proposes an enhanced implementation of the well-known whale optimisation algorithm, which combines chaotic and opposition-based learning strategies, which is adopted for hyper-parameter optimisation and feature selection machine learning challenges. The whale optimisation algorithm is a relatively recent addition to the group of swarm intelligence algorithms commonly used for optimisation. The Proposed improved whale optimisation algorithm was first tested for standard unconstrained CEC2017 benchmark suite and it was later adapted for simultaneous feature selection and support vector machine hyper-parameter tuning and validated for medical diagnostics by using breast cancer, diabetes, and erythemato-squamous dataset. The performance of the proposed model is compared with multiple competitive support vector machine models boosted with other metaheuristics, including another improved whale optimisation approach, particle swarm optimisation algorithm, bacterial foraging optimisation algorithms, and genetic algorithms. Results of the simulation show that the proposed model outperforms other competitors concerning the performance of classification and the selected subset feature size.

A Novel Multiswarm Firefly Algorithm: An Application for Plant Classification

Abstract: Areas of swarm intelligence and machine learning are constantly evolving, recently attracting even more researchers world-wide. This stems from the no free lunch which states that universal approach that could render satisfying results for all practical challenges does not exist. Therefore, in this research a novel multi-swarm firefly algorithm, that tries to address flaws of original firefly metaheuristics, is proposed. Devised algorithm is applied to interesting and important practical challenge of plants classification, as part of the hybrid framework between machine learning and optimization metaheuristics. For this purpose, a set of 1,000 random images of healthy leaves, from one public repository, is retrieved for the following plants: apple, cherry, pepper and tomato. Hybrid framework includes pre-processing, constructing bag of features and classification steps. After pre-processing, a bag of features is constructed by utilizing well-known scale-invariant feature transform algorithm, K-means-based vocabulary generation and histogram. Such images are then categorized with support vector machine classifier. However, to obtain satisfying results for a particular dataset, the support vector machines hyper-parameters’ need to be tuned and in the proposed research multi-swarm firefly algorithm is employed to determine optimal (sub-optimal) hyper-parameters’ values for this practical challenge. Comparative analysis with the basic firefly metaheuristics and other well-known swarm intelligence algorithms was conducted to assess the performance of the proposed method in terms of precision, recall, F-score for this multi-class classification challenge. Obtained results show significant performance improvements of devised method over the original firefly algorithm and also better metrics than other state-of-the-art techniques in the majority of cases.

Application of Natural Language Processing and Machine Learning Boosted with Swarm Intelligence for Spam Email Filtering

Abstract: Spam represents a genuine irritation for email users, since it often disturbs them during their work or free time. Machine learning approaches are commonly utilized as the engine of spam detection solutions, as they are efficient and usually exhibit a high degree of classification accuracy. Nevertheless, it sometimes happens that good messages are labeled as spam and, more often, some spam emails enter into the inbox as good ones. This manuscript proposes a novel email spam detection approach by combining machine learning models with an enhanced sine cosine swarm intelligence algorithm to counter the deficiencies of the existing techniques. The introduced novel sine cosine was adopted for training logistic regression and for tuning XGBoost models as part of the hybrid machine learning-metaheuristics framework. The developed framework has been validated on two public high-dimensional spam benchmark datasets (CSDMC2010 and TurkishEmail), and the extensive experiments conducted have shown that the model successfully deals with high-degree data. The comparative analysis with other cutting-edge spam detection models, also based on metaheuristics, has shown that the proposed hybrid method obtains superior performance in terms of accuracy, precision, recall, f1 score, and other relevant classification metrics. Additionally, the empirically established superiority of the proposed method is validated using rigid statistical tests.

Deep learning model for deep fake face recognition and detection

Abstract: Deep Learning is an effective technique and used in various fields of natural language processing, computer vision, image processing and machine vision. Deep fakes uses deep learning technique to synthesis and manipulate image of a person in which human beings cannot distinguish the fake one. By using generative adversarial neural networks (GAN) deep fakes are generated which may threaten the public. Detecting deep fake image content plays a vital role. Many research works have been done in detection of deep fakes in image manipulation. The main issues in the existing techniques are inaccurate, consumption time is high. In this work we implement detecting of deep fake face image analysis using deep learning technique of fisherface using Local Binary Pattern Histogram (FF-LBPH). Fisherface algorithm is used to recognize the face by reduction of the dimension in the face space using LBPH. Then apply DBN with RBM for deep fake detection classifier. The public data sets used in this work are FFHQ, 100K-Faces DFFD, CASIA-WebFace.

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

Abstract: 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.

IoT as a Backbone of Intelligent Homestead Automation

Abstract: 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.