Papers by Miodrag Zivkovic
Electronics
Developing countries have had numerous obstacles in diagnosing the COVID-19 worldwide pandemic si... more 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 ca...
Scientific Reports
Deep learning has recently been utilized with great success in a large number of diverse applicat... more 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...
7th Conference on the Engineering of Computer Based Systems, 2021
Software development is one of the fastest growing industries today. The defects in software can ... more Software development is one of the fastest growing industries today. The defects in software can be very costly, either in terms of losing money, reputation, or even lives in case of some critical applications. Consequently, there is a high and always increasing demand for software engineers specialized in the branch of software testing. However, traditional software engineering courses pay very little attention to the software testing, and students are entering the market with very little practical testing experience. One way to address this problem is to integrate software testing into the engineering studies through collaborative learning environments and simulators that can help students gain practical experience and learn testing techniques in an interesting way. In this paper, we provide a survey of software testing environments and tools that can help in this process.
Data Intelligence and Cognitive Informatics, 2021
Lecture Notes in Networks and Systems, 2021
A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in th... more A novel type of coronavirus, now known under the acronym COVID-19, was initially discovered in the city of Wuhan, China. Since then, it has spread across the globe and now it is affecting over 210 countries worldwide. The number of confirmed cases is rapidly increasing and has recently reached over 14 million on July 18, 2020, with over 600,000 confirmed deaths. In the research presented within this paper, a new forecasting model to predict the number of confirmed cases of COVID-19 disease is proposed. The model proposed in this paper is a hybrid between machine learning adaptive neuro-fuzzy inference system and enhanced genetic algorithm metaheuristics. The enhanced genetic algorithm is applied to determine the parameters of the adaptive neuro-fuzzy inference system and to enhance the overall quality and performances of the prediction model. Proposed hybrid method was tested by using realistic official dataset on the COVID-19 outbreak in the state of China. In this paper, proposed approach was compared against multiple existing state-of-the-art techniques that were tested in the same environment, on the same datasets. Based on the simulation results and conducted comparative analysis, it is observed that the proposed hybrid approach has outperformed other sophisticated approaches and that it can be used as a tool for other time-series prediction. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Sustainable Cities and Society, 2021
The main objective of this paper is to further improve the current time-series prediction (foreca... more The main objective of this paper is to further improve the current time-series prediction (forecasting) algorithms based on hybrids between machine learning and nature-inspired algorithms. After the recent COVID-19 outbreak, almost all countries were forced to impose strict measures and regulations in order to control the virus spread. Predicting the number of new cases is crucial when evaluating which measures should be implemented. The improved forecasting approach was then used to predict the number of the COVID-19 cases. The proposed prediction model represents a hybridized approach between machine learning, adaptive neuro-fuzzy inference system and enhanced beetle antennae search swarm intelligence metaheuristics. The enhanced beetle antennae search is utilized to determine the parameters of the adaptive neuro-fuzzy inference system and to improve the overall performance of the prediction model. First, an enhanced beetle antennae search algorithm has been implemented that overcomes deficiencies of its original version. The enhanced algorithm was tested and validated against a wider set of benchmark functions and proved that it substantially outperforms original implementation. Afterwards, the proposed hybrid method for COVID-19 cases prediction was then evaluated using the World Health Organization’s official data on the COVID-19 outbreak in China. The proposed method has been compared against several existing state-of-the-art approaches that were tested on the same datasets. The proposed CESBAS-ANFIS achieved R 2 score of 0.9763, which is relatively high when compared to the R 2 value of 0.9645, achieved by FPASSA-ANFIS. To further evaluate the robustness of the proposed method, it has also been validated against two different datasets of weekly influenza confirmed cases in China and the USA. Simulation results and the comparative analysis show that the proposed hybrid method managed to outscore other sophisticated approaches that were tested on the same datasets and proved to be a useful tool for time-series prediction.
2014 IEEE Global Engineering Education Conference (EDUCON), 2014
In this paper we describe a virtual 3D lab created using Open Wonderland to enable simultaneous e... more In this paper we describe a virtual 3D lab created using Open Wonderland to enable simultaneous execution of different computer science simulator modules in a collaborative, immersive workspace. Simulators were divided into functional modules that can be reused and combined for different teaching topics based on statistics of their usage. We discuss some important issues in our platform setup, describe our learning environment and logging module we developed in order to track system usage. Preliminary usability testing confirms the efficiency of this approach.
2013 21st Telecommunications Forum Telfor (TELFOR), 2013
In this work we describe how a wireless sensor network simulator was integrated into a 3D environ... more In this work we describe how a wireless sensor network simulator was integrated into a 3D environment for use in multi-lingual teaching environment. It has been known that simulators are very useful as supplement to a physical equipment in the classroom; the idea in creating internationalized environment was to help Libyan students studying in Serbia overcome the language barrier and organize virtual sessions according to their learning profiles and cultural habits, which is not possible in a traditional classroom setup.
Algorithms for Intelligent Systems, 2022
PeerJ Computer Science
The research proposed in this article presents a novel improved version of the widely adopted fir... more 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 net...
Mathematics
Recent advances in online payment technologies combined with the impact of the COVID-19 global pa... more 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 th...
Sensors
There are many machine learning approaches available and commonly used today, however, the extrem... more 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 appro...
Proceedings of the International Scientific Conference - Sinteza 2017, 2017
Wireless sensor networks usually consist of a large number of sensor nodes forming a network. Sen... more Wireless sensor networks usually consist of a large number of sensor nodes forming a network. Sensor nodes are miniature autonomous devices with very limited resources, capable of collecting and processing data from the surrounding area, and transmitting this data wirelessly over short distances via radio transmitters. The range of wireless sensor networks applications varies from the environment monitoring and detecting forest fires, climate changes monitoring, tele-health monitoring, detecting toxic fumes in factories, to smart sensor systems in the car. With the emerging technology of Internet of Things, the importance and number of applications of wireless sensor networks are increasing every day. Wireless sensor networks are one of the most important parts of the whole Internet of Things concept. The main idea of Internet of Things is to provide smart world, where every device has built-in intelligence, and is connected to other devices in the environment. As such, Internet of Things basically integrates the world of information with real devices, and enables us to have immediate access to this information. Wireless sensor networks provide aspect of surveillance, physical phenomena detection, environment monitoring and remote access to Internet of Things. This paper surveys the current state of the art of wireless sensor networks and Internet of Things, presents challenges of integrating two technologies together, and gives an overview of the new applications of wireless sensor networks in the scope of Internet of Things.
Computers, Materials & Continua, 2022
Computerized tomography (CT) scans and X-rays play an important role in the diagnosis of COVID-19... more 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 proposed MWOA with 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.
Neural Computing and Applications, 2022
Edge computing is a novel technology, which is closely related to the concept of Internet of Thin... more Edge computing is a novel technology, which is closely related to the concept of Internet of Things. This technology brings computing resources closer to the location where they are consumed by end-users-to the edge of the cloud. In this way, response time is shortened and lower network bandwidth is utilized. Workflow scheduling must be addressed to accomplish these goals. In this paper, we propose an enhanced firefly algorithm adapted for tackling workflow scheduling challenges in a cloud-edge environment. Our proposed approach overcomes observed deficiencies of original firefly metaheuristics by incorporating genetic operators and quasi-reflection-based learning procedure. First, we have validated the proposed improved algorithm on 10 modern standard benchmark instances and compared its performance with original and other improved state-of-the-art metaheuristics. Secondly, we have performed simulations for a workflow scheduling problem with two objectives-cost and makespan. We performed comparative analysis with other state-of-the-art approaches that were tested under the same experimental conditions. Algorithm proposed in this paper exhibits significant enhancements over the original firefly algorithm and other outstanding metaheuristics in terms of convergence speed and results' quality. Based on the output of conducted simulations, the proposed improved firefly algorithm obtains prominent results and managed to establish improvement in solving workflow scheduling in cloud-edge by reducing makespan and cost compared to other approaches.
Mobile Computing and Sustainable Informatics, 2021
Artificial neural networks, especially deep neural networks, are the promising and current resear... more 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 backpropagation 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.
Proceedings of the International Scientific Conference - Sinteza 2021, 2021
Wireless sensor networks can be found almost everywhere. They consist of small devices, known as ... more Wireless sensor networks can be found almost everywhere. They consist of small devices, known as sensor nodes, which are arranged in the desired environment. They collect information and send it to the client through a network gateway using a routing protocol. These networks have many good characteristics like quality of service (QoS), reliability, low power consumption, flexibility, scalability, etc. Despite these good characteristics, many problems and challenges are plaguing these networks. Challenges depend on the type of network, and each network has its problems. Connecting wireless sensor networks to the Internet of Things also leads to new challenges. Security is one of the main problems plaguing these networks, but it is also one of the most difficult challenges due to network limitations. In addition to safety, the biggest challenge is still consuming energy because it is not always possible to charge the battery, so the sensor nodes die. Another more serious challenge is the real-time transmission. Traditional wireless sensor networks do not work in real-time, and even if they need to work in real-time, they avoid it. The Internet of Things works in real-time, so it needs real-time wireless sensor networks.
7th Conference on the Engineering of Computer Based Systems, 2021
Autonomous vehicles are constantly collecting information from their environment, employing reaso... more Autonomous vehicles are constantly collecting information from their environment, employing reasonably sophisticated cameras and sensors. Applying new software on those vehicles should aim at correcting human errors and gaining new knowledge from all road users. The discussion of autonomous vehicles regarding software from various perspectives, including different knowledge branches such as law, social, economic, design, and ethics. Autonomous vehicles present new challenges with interesting problems in several scientific fields, which are gradually being successfully solved. Social dilemmas, moral and ethical issues show different approaches. There is still an unsolvable problem of decision-making when it comes to the involvement of the ”trolley” problem. Autonomous vehicle software should within the future have flexibility to reconstruct collisions in order that the logic, ethics, and moral algorithm applied may be analyzed. This demands the existence of very good software and hardware technology that can record all the data during a collision. This paper will cover security concerns and current challenges for developing software in the field of autonomous vehicles.
Mathematics, 2021
Swarm intelligence techniques have been created to respond to theoretical and practical global op... more Swarm intelligence techniques have been created to respond to theoretical and practical global optimization problems. This paper puts forward an enhanced version of the firefly algorithm that corrects the acknowledged drawbacks of the original method, by an explicit exploration mechanism and a chaotic local search strategy. The resulting augmented approach was theoretically tested on two sets of bound-constrained benchmark functions from the CEC suites and practically validated for automatically selecting the optimal dropout rate for the regularization of deep neural networks. Despite their successful applications in a wide spectrum of different fields, one important problem that deep learning algorithms face is overfitting. The traditional way of preventing overfitting is to apply regularization; the first option in this sense is the choice of an adequate value for the dropout parameter. In order to demonstrate its ability in finding an optimal dropout rate, the boosted version of ...
Computer Systems Science and Engineering, 2022
Breast cancer has become the second leading cause of death among women worldwide. In India, a wom... more Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid wavelet filter for accurate image enhancement. The main objective of enhancement is to produce quality images for detecting cancer sections in images. Image enhancement is the main step where the quality of the input image is improved to detect cancer masses. In this study, we use a combination of two filters, namely, Gabor and Legendre. The edges are detected using the Canny detector to smoothen the images. High-quality enhanced image is obtained through the Gabor-Legendre filter (GLFIL) process. Further image is used by classification algorithm. Animal migration optimization with neural network is implemented for classifying the image. The output is compared to existing filter techniques. Ultimately, the accuracy achieved by the proposed technique is 98%, which is higher than existing algorithms.
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Papers by Miodrag Zivkovic