Volume 11, Issue 1

In this work we discuss the motivation for innovations and need of a teaching tool for the visualization of the natural semantics method of imperative programming languages. We present the rôle of the teaching software, its design, development and use in the teaching process. Our software module is able to visualize the natural semantics evaluation of programs. It serves as a compiler with environment that can visually interpret simple programming language Jane statements and to depict them into a derivation tree that represents the semantic method of natural semantics. A formal definition of programming language Jane used in the teaching of formal semantics and production rules in natural semantics for that language are shown as well. We present, how the presented teaching tool can provide particular visual steps in the process of finding the meaning of well-structured input program and to depict complete natural-semantic representation of an input program.

In this paper an algorithm for detection of nonstandard situations in smart water metering based on machine learning is designed. The main categories for nonstandard situation or anomaly detection and two common methods for anomaly detection are analyzed. The proposed solution needs to fit the requirements for correct, efficient and real-time detection of non-standard situations in actual water consumption with minimal required consumer intervention to its operation. Moreover, a proposal to extend the original hardware solution is described and implemented to accommodate the needs of the detection algorithm. The final implemented and tested solution evaluates anomalies in water consumption for a given time in specific day and month using machine learning with a semi-supervised approach.

Unit under test identification (UUT) is often difficult due to test smells, such as testing multiple UUTs in one test. Because the tests best reflect the current product specification they can be used to comprehend parts of the production code and the relationships between them. Because there is a similar vocabulary between the test and UUT, five NLP techniques were used on the source code of 5 popular Github projects in this paper. The collected results were compared with the manually identified UUTs. The tf-idf model achieved the best accuracy of 22% for a right UUT and 57% with a tolerance up to fifth place of manual identification. These results were obtained after preprocessing input documents with java keywords removal and word split. The tf-idf model achieved the best model training time and the index search takes within 1s per request, so it could be used in an Integrated Development Environment (IDE) as a support tool in the future. At the same time, it has been found that, for document preprocessing, word splitting improves accuracy best and removing java keywords has just a small improvement for tf-idf model results. Removing comments only slightly worsens the accuracy of Natural Language Processing (NLP) models. The best speed provided the word splitting with average 0.3s preprocessing time per all documents in a project.

In this paper, we study a well-known computationally hard problem, called the subgraph isomorphism problem where the goal is for a given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. Numerous algorithms for solving the problem exist in the literature and most of them are based on the backtracking approach. Since straightforward backtracking is usually slow, many algorithmic refinement techniques are used in practical algorithms. The main goal of this paper is to study such refinement techniques and to determine their ability to speed up backtracking algorithms. To do this we use a methodology of experimental algorithmics. We perform an experimental evaluation of the techniques and their combinations and, hence, demonstrate their usefulness in practice.

Java is not only a modern, powerful, and frequently used programming language, but together with Java Virtual Machine it represents a novel dynamic approach of writing and executing computer programs. The fact that Java programs are executed in a controlled environment has several important implications that define the nature of the language and makes it different from the traditional C-like languages. Knowing the detailed differences between the two types of languages and execution environments is a part of the holistic education of a computer engineer. In this paper, we present some behind-the-scene details about the Java Virtual Machine and we show how these details could be used in the educational process to demonstrate the differences and to emphasise the advantages of the dynamic programming approach when compared to the static one. After presenting some information about class files and about the internal structure and operation of the Java Virtual Machine we demonstrate the usage of public domain programs that could be used in the educational process to put these theoretical concepts into practice.

Detecting automorphisms is a natural way to identify redundant information presented in structured data. When such redundancies are detected they can be used for data compression. In this paper we explore two different classes of graphs to capture this intuitive property of automorphisms. Symmetry-compressible graphs are the first class which introduces the basic concepts but use only global symmetries for the compression. In order for this concept to be more practical, we need to use local symmetries. Thus, we extend the basic graph class with Near Symmetry compressible graphs. Furthermore, we develop two algorithms that can be used to compress practical instances and empirically evaluate them on a set of realistic graphs.

This article deals with a high order integration method based on the Taylor series. The paper shows many positive properties of this method on a set of technical initial value problems. These problems can be transformed into the autonomous systems of ordinary differential equations for both linear and nonlinear problems. The MATLAB implementation of the method is compared with state-of-the-art MATLAB solvers.

The networking industry is currently undergoing a steady trend of softwarization. Yet, network engineers suffer from the lack of software development tools that support programming of new protocols. We are creating a cost analysis tool for the P4 programming language, that automatically verifies whether the developed program meets soft deadline requirements imposed by the network. In this paper, we present an approach to estimate the average execution time of P4 program based on control flow graphs. Our approach takes into consideration that many of the parts of P4 are implementation-defined: required information can be added in through incremental refinement, while missing information is handled by falling back to less precise defaults. We illustrate application of this approach to a P4 protocol in two case studies: we use it to examine the effect of a compiler optimization in the deparse stage, and to show how it enables cost modelling complex lookup table implementations. Finally, we assess future research tasks to be completed before the tool is ready for real-world usage.

Legged robots have great potential to travel across various types of terrain. Their many degrees of freedom enable them to navigate through difficult terrains, narrow spaces or various obstacles and they can move even after losing a leg. However, legged robots mostly move quite slowly. This paper deals with the design and construction of an omni-directional seven degrees of freedom hexapod ( i.e. , six-legged) robot, which is equipped with omnidirectional wheels (two degrees of freedom are used, one for turning the wheel and one for the wheel itself) usable on flat terrain to increase travel speed and an additional coxa joint that makes the robot more robust when climbing inclined terrains. This unique combination of omnidirectional wheels and additional coxa joint makes the robot not only much faster but also more robust in rough terrains and allows the robot to ride inclined terrains up to 40 degrees and remain statically stable in slopes up to 50 degrees. The robot is controlled by a terrain adaptive movement controller which adjusts the movement speed and the gait of the robot according to terrain conditions.

The main goal of this contribution is to present the current developmental state of FRIMAN – the graphical development environment designed to support the teaching process of the object-oriented paradigm. FRIMAN project has two main purposes: 1. simplifying the understanding of the basics of the object-oriented programming for JAVA language beginners, 2. teaching students of applied informatics to collaborate in bigger project development. Therefore, an application called FRIMAN has been developed at the Faculty of Management Science and Informatics at the University of Žilina. This project is developed by students of Master degrees under the leaderships of experienced software developers. The suggested system consists of several modules. In this paper, we focus on the description of selected modules and their current functionality as well as description of future plans for this project and brief description of FRIMAN development process. Attention is paid to a module for class management and a graphical code editor, which enables the creation of method bodies using flow diagrams without the necessity of programming language syntax knowledge. Based on good evaluation by the development team preparing changes in the high school education process, the current application is planned to be applied in practice.

Recent advances in the field of web technologies, including the increasing support of virtual reality hardware, have allowed for shared virtual environments, reachable by just entering a URL in a browser. One contemporary solution that provides such a shared virtual reality is LIRKIS Global Collaborative Virtual Environments (LIRKIS G-CVE). It is a web-based software system, built on top of the A-Frame and Networked-Aframe frameworks. This paper describes LIRKIS G-CVE and introduces its two original components. The first one is the Smart-Client Interface, which turns smart devices, such as smartphones and tablets, into input devices. The advantage of this component over the standard way of user input is demonstrated by a series of experiments. The second component is the Enhanced Client Access layer, which provides access to positions and orientations of clients that share a virtual environment. The layer also stores a history of connected clients and provides limited control over the clients. The paper also outlines an ongoing experiment aimed at an evaluation of LIRKIS G-CVE in the area of virtual prototype testing.

Static analysis is an essential way to find code smells and bugs. It checks the source code without execution and no test cases are required, therefore its cost is lower than testing. Moreover, static analysis can help in software engineering comprehensively, since static analysis can be used for the validation of code conventions, for measuring software complexity and for executing code refactorings as well. Symbolic execution is a static analysis method where the variables ( e.g. input data) are interpreted with symbolic values. Clang Static Analyzer is a powerful symbolic execution engine based on the Clang compiler infrastructure that can be used with C, C++ and Objective-C. Validation of resources’ usage ( e.g. files, memory) requires finite state automata (FSA) for modeling the state of resource ( e.g. locked or acquired resource). In this paper, we argue for an approach in which automata are in-use during symbolic execution. The generic automaton can be customized for different resources. We present our domain-specific language to define automata in terms of syntactic and semantic rules. We have developed a tool for this approach which parses the automaton and generates Clang Static Analyzer checker that can be used in the symbolic execution engine. We show an example automaton in our domain-specific language and the usage of generated checker.

After a voice control system transforms audio input into a natural language sentence, its main purpose is to map this sentence to a specific action in the API (application programming interface) that should be performed. This mapping is usually specified after the API is already designed. In this paper, we show how an API can be designed with voice control in mind, which makes this mapping natural. The classes, methods, and parameters in the source code are named and typed according to the terms expected in the natural language commands. When this is insufficient, annotations (attribute-oriented programming) are used to define synonyms, string-to-object maps, or other properties. We also describe the mapping process and present a preliminary implementation called VCMapper. In its evaluation on a third-party dataset, it was successfully used to map all the sentences, while a large portion of the mapping was performed using only naming and typing conventions.

Technological advances and increasing customer need for highly customized products have triggered a fourth industrial revolution. A digital revolution in the manufacturing industry is enforced by introducing smart devices and knowledge bases to form intelligent manufacturing information systems. One of the goals of the digital revolution is to allow flexibility of smart factories by automating shop floor changes based on the changes in input production processes and ordered products. In order to make this possible, a formal language to describe production processes is needed, together with a code generator for its models and an engine to execute the code on smart devices. Existing process modeling languages are not usually tailored to model production processes, especially if models are needed for automatic code generation. In this paper we propose a research on Industry 4.0 manufacturing using a Domain-Specific Modeling Language (DSML) within a Model-Driven Software Development (MDSD) approach to model production processes. The models would be used to generate instructions to smart devices and human workers, and gather a feedback from them during the process execution. A pilot comparative analysis of three modeling languages that are commonly used for process modeling is given with the goal of identifying supported modeling concepts, good practices and usage patterns.

Despite the modern boom in technology, we are still faced with the fact that people write texts without diacritics. There are two main reasons for this. The first, historical reason stems from the past when the use of diacritics was troublesome and people would write text without them. The second one is the speed - typing without diacritics is usually faster. Text without diacritics is easy to understand for people, but for some types of documents, missing diacritics can cause a problem. This is also an issue when computers process such text. In this paper, we propose an algorithm based on word n-grams (a contiguous sequence of n words) that can restore diacritics of text written in the Slovak language. We also compare and evaluate our results with other algorithms developed for Slovak text.

This paper presents a strategic approach for implementing a Smart Pedestrian Network (SPN) navigation System that is geared towards promoting sustainable mobility. The SPN system is being designed to reach multiple market segments by providing information on suitable walking routes aiming to satisfy potential users’ needs, but also multiple stakeholder demands. The paper presents the main objectives of the SPN system as well as its system structure, application features, functions, and relevant data sources. The proposed strategic implementation framework deems necessary for scaling up so that the SPN system is successfully implemented in a variety of urban conditions. The framework emphasizes the element of adaptability, so that SPN can be adjusted where and when necessary, to deal with a variety of contexts and specific sustainable mobility issues, depending on the particular municipality conditions. The proposed framework combines elements of both the waterfall and agile software development methods, as well as, aspects of Open Innovation, Customer Involvement and Co-Creation taking a multiple stakeholder approach.

This paper investigates the transmission performance of 16-channel DWDM (Dense Wavelength Division Multiplexing) system with two complex Optical Differential Quadrature Phase Shift Keying modulator configurations using 2 LiNb MZM (Mach-Zehnder Modulator) and 3 LiNb MZM. The link performance is evaluated for 100 Gbps data rate per channel in a total 750 km single mode fiber link. The perfomance is analyzed in terms of forward power, reflected power and bit-error rate of the received signal. From the simulation results we prove, that the link performance can be improved by adopting the high efficiency optical modulation.

Information and Communication Technology (ICT), and recent advancements in Computer Science can serve as a catalyst for promoting sustainable means of transport. Through ICT applications, active mobility can be promoted and established as a viable transport mode. This can be achieved by providing relevant information for fostering social capital and promoting physical activity, thus contributing to a higher quality of life. Further, active mobility can greatly contribute to reducing air pollution and improving health status. For this purpose, the implementation of a Smart Pedestrian Network (SPN) information system is proposed. Such an implementation requires the collaboration of various stakeholders including the public, local authorities and local businesses. To convince stake-holders of the viability of implementing SPN, the benefits of active mobility should be clear. This paper proposes a framework to quantify active mobility benefits so that stake-holders can assess the investment that can be realized from implementing SPN. The proposed framework makes use of quantifying benefits in various market conditions. The benefits are shown to be significant and very much in favor of investing in technology and implementing the envisioned SPN system.

Virtual, augmented and mixed reality (VR, AR and MR) infiltrated not only gaming, industry, engineering, live events, entertainment, real estate, retail, military, etc., but as surveys indicate, also healthcare and education. In all these areas there is a lack of software development experts for VR, AR and MR to meet the needs of practice. Therefore, our intention at the Department of Computer Science, Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Slovakia, is to focus on the education and enlightenment of these areas. The aim of this article is to show the role of interactivity in different VR applications and its impact on users in three different areas: gaming, healthcare and education. In the case of one application of Arachnophobia, we also present the results of the research using a questionnaire.

In this paper we study the question of parallelization of a variant of Branch-and-Bound method for solving of the subset sum problem which is a special case of the Boolean knapsack problem. The following natural approach to the solution of this question is considered. At the first stage one of the processors (control processor) performs some number of algorithm steps of solving a given problem with generating some number of subproblems of the problem. In the second stage the generated subproblems are sent to other processors for solving (one subproblem per processor). Processors solve completely the received subproblems and return their solutions to the control processor which chooses the optimal solution of the initial problem from these solutions. For this approach we define formally a model of parallel computing (frontal parallelization scheme) and the notion of complexity of the frontal scheme. We study the asymptotic behavior of the complexity of the frontal scheme for two special cases of the subset sum problem.

The paper proposes the concept of a weak Berge equilibrium. Unlike the Berge equilibrium, the moral basis of this equilibrium is the Hippocratic Oath “First do no harm”. On the other hand, any Berge equilibrium is a weak Berge equilibrium. But, there are weak Berge equilibria, which are not the Berge equilibria. The properties of the weak Berge equilibrium have been investigated. The existence of the weak Berge equilibrium in mixed strategies has been established for finite games. The weak Berge equilibria for finite three-person non-cooperative games are computed.

A numerical method for investigating k -coverings of a convex bounded set with circles of two given radii is proposed. Cases with constraints on the distances between the covering circle centers are considered. An algorithm for finding an approximate number of such circles and the arrangement of their centers is described. For certain specific cases, approximate lower bounds of the density of the k -covering of the given domain are found. We use either 0–1 linear programming or general integer linear programming models. Numerical results demonstrating the effectiveness of the proposed methods are presented.

The authors consider a continuous dynamic approximating model of a gas fields group and, on its basis, set maximum and minimum issues. The tasks proposed for research are optimal control problems with mixed constraints with free-final-time and moving right end. We analytically solve the rapid-action problem. The central mathematical apparatus is Pontryagin maximum principle in Arrow form, using Lagrange multipliers. The theoretically obtained results of the analysis are of particular interest.

Ultrasonography is an extensively used medical imaging technique for multiple reasons. It works on the basic theory of echoes from the tissues under consideration. However, the occurrence of signal dependent noise such as speckle destroys utility of ultrasound images. Speckle noise is subject to the composition of image tissue and parameters of image. It reduces the effectiveness of many image processing steps and decreases human perception of fine details form ultrasound images. In many medical image processing methods, despeckling is used as the preprocessing step before segmentation and feature extraction. Many speckle reduction filters are proposed but while combining many techniques some speckle diagnostic information should be preserved. Removal of speckle noise from ultrasound image by preserving edges and added features is a great challenging task in ultrasound image restoration. This paper aims at a comprehensive description and comparison of reduction of speckle noise of ultrasound fibroid image. Many filters are applied on ultrasound scanned images and the performance is marked in terms of some statistical measures. Even though several despeckling filters are there for speckle reduction, all are not good for ultrasound scanned images. A comparison of quality measures such as mean square error, peak signal-to-noise ratio, and signal-to-noise ratio is done in ultrasound images in despeckling.

The next generation of E-Government and healthcare has the potential to increase the more intelligent governance with improvements in transparency, accountability, efficiency, and effectiveness. It enables organizations to use the benefits of information via big data analysis to settle the difficulties effectively. Big Data has emerged which plays a significant role in many sectors around the world. Global trends in taking advantage of the benefits from big data are considered with an overview of the US, European Union, and several developing countries. To deeply understand the utilization of big data in several domains, this study has presented a brief survey of key concepts (such as IoT-enabled data, blockchain-enabled data, and intelligent systems data) to deeply understand the utilization of big data in several domains. Our analysis sets out also the similarities and differences in these concepts. We have also surveyed state-of-the-art technologies including cloud computing, multi-cloud, webservice, and microservice which are used to exploit potential benefits of big data analytics. Furthermore, some typical big data frameworks are surveyed and a big data framework for E-Government is also proposed. Open research questions and challenges are highlighted (for researchers and developers) following our review. Our goal in presenting the novel concepts presented in this article is to promote creative ideas in the research endeavor to perform efficaciously next-generation E-Government in the context of Industry 4.0.

Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.

Task scheduling and load balancing are a concern for service providers in the cloud computing environment. The problem of scheduling tasks and balancing loads in a cloud is categorized under an NP-hard problem. Thus, it needs an efficient load scheduling algorithm that not only allocates the tasks onto appropriate VMs but also maintains the trade-off amidst VMs. It should keep an equilibrium among VMs in a way that reduces the makespan while maximizing the utilization of resources and throughput. In response to it, the authors propose a load balancing algorithm inspired by the mimicking behavior of a flock of birds, which is called the Bird Swarm Optimization Load Balancing (BSO-LB) algorithm that considers tasks as birds and VMs as destination food patches. In the considered cloud simulation environment, tasks are assumed to be independent and non-preemptive. To evaluate the efficacy of the proposed algorithm under real workloads, the authors consider a dataset (GoCJ) logged by Goggle in 2018 for the execution of cloudlets. The proposed algorithm aims to enhance the overall system performance by reducing response time and keeping the whole system balanced. The authors have integrated the binary variant of the BSO algorithm with the load balancing method. The proposed technique is analyzed and compared with other existing load balancing algorithms such as MAX-MIN, RASA, Improved PSO, and other scheduling algorithms as FCFS, SJF, and RR. The experimental results show that the proposed method outperforms when being compared with the different algorithms mentioned above. It is noteworthy that the proposed approach illustrates an improvement in resource utilization and reduces the makespan of tasks.

Geographical routing is an area of interest for wireless sensor networks because of its scalability and local decision making capability. Usually, geographical routing protocols rely on greedy approach and suffer from the void node problem (VNP). This paper presents an angular three-dimensional routing protocol (A3DR) for wireless sensor networks. In A3DR, the next hop is selected from nodes available in solid angle decided dynamically based on network density, i.e. , larger solid angle for sparse networks while a smaller angle for dense one. The delay based contention mechanism has been proposed to select a suitable forwarding node among nodes lying in a particular solid angle. A3DR has a provision to adaptively tune the solid angle to resolve the void node problem. Further, the proposed protocol A3DR will take care of traffic congestion problem suffered by beaconless routing protocol. The A3DR tackle the concave void problem by allowing conditional backtracking to find another suitable path and prevent looping. Additionally, A3DR address overhear problem by utilizing the concept of request-to-forward and clear-to-forward for the nodes lying outside the solid angle 60°. The performance of proposed A3DR is compared with existing protocols by implementing it on the INET framework under OMNET++ simulator. The simulation results indicate that A3DR achieves a high packet reception rate while maximizing the residual energy and minimizing traffic overhead.

Lung cancer is considered to be one of the deadly diseases that threaten the survival of human beings. It is a challenging task to identify lung cancer in its early stage from the medical images because of the ambiguity in the lung regions. This paper proposes a new architecture to detect lung cancer obtained from the CT images. The proposed architecture has a three-stream network to extract the manual and automated features from the images. Among these three streams, automated feature extraction as well as the classification is done using residual deep neural network and custom deep neural network. Whereas the manual features are the handcrafted features obtained using high and low-frequency sub-bands in the frequency domain that are classified using a Support Vector Machine Classifier. This makes the architecture robust enough to capture all the important features required to classify lung cancer from the input image. Hence, there is no chance of missing feature information. Finally, all the obtained prediction scores are combined by weighted based fusion. The experimental results show 98.2% classification accuracy which is relatively higher in comparison to other existing methods.

This paper is aimed at efficiently distributing workload between the Fog Layer and the Cloud Network and then optimizing resource allocation in cloud networks to ensure better utilization and quick response time of the resources available to the end user. We have employed a Dead-line aware scheme to migrate the data between cloud and Fog networks based on data profiling and then used K-Means clustering and Service-request prediction model to allocate the resources efficiently to all requests. To substantiate our model, we have used iFogSim, which is an extension of the CloudSim simulator. The results clearly show that when an optimized network is used the Quality of Service parameters exhibit better efficiency and output.

Nowadays, in real-world applications, the dimensions of data are generated dynamically, and the traditional batch feature selection methods are not suitable for streaming data. So, online streaming feature selection methods gained more attention but the existing methods had demerits like low classification accuracy, fails to avoid redundant and irrelevant features, and a higher number of features selected. In this paper, we propose a parallel online feature selection method using multiple sliding-windows and fuzzy fast-mRMR feature selection analysis, which is used for selecting minimum redundant and maximum relevant features, and also overcomes the drawbacks of existing online streaming feature selection methods. To increase the performance speed of the proposed method parallel processing is used. To evaluate the performance of the proposed online feature selection method k-NN, SVM, and Decision Tree Classifiers are used and compared against the state-of-the-art online feature selection methods. Evaluation metrics like Accuracy, Precision, Recall, F1-Score are used on benchmark datasets for performance analysis. From the experimental analysis, it is proved that the proposed method has achieved more than 95% accuracy for most of the datasets and performs well over other existing online streaming feature selection methods and also, overcomes the drawbacks of the existing methods.

Advances in communication technology have significantly altered our knowledge about telephone networks. The remarkable expansion in communication is obvious in numerous aspects of life of life; the transmission of information has never been easier. Wireless communication has also had a remarkable development, and these facilities have become more reliable and easily accessible. This study, through a descriptive approach in accordance with earlier studies, aims to explain how to increase the capacity of wireless data delivery in addition to other important challenges that face the enhancement of these services in remote areas. This study highlights the challenges that accompany the adoption of wireless networks in remote areas: particularly signal strength. This is highly linked to the specific features of rural areas in terms of living habits in separated nodes rather than in high-density population centers areas.

In this work we have proposed a model for Citizen Profiling. It uses veillance (Surveillance and Sousveillance) for data acquisition. For representation of Citizen Profile Temporal Knowledge Graph has been used through which we can answer semantic queries. Previously, most of the work lacks representation of Citizen Profile and have used surveillance for data acquisition. Our contribution is towards enriching the data acquisition process by adding sousveillance mechanism and facilitating semantic queries through representation of Citizen Profiles using Temporal Knowledge Graphs. Our proposed solution is storage efficient as we have only stored data logs for Citizen Profiling instead of storing images, audio, and video for profiling purposes. Our proposed system can be extended to Smart City, Smart Traffic Management, Workplace profiling etc. Agent based mechanism can be used for data acquisition where each Citizen has its own agent. Another improvement can be to incorporate a decentralized version of database for maintaining Citizen profile.

With the high incidence of the dengue epidemic in developing countries, it is crucial to understand its dynamics from a holistic perspective. This paper analyzes different types of antecedents from a cybernetics perspective using a structural modelling approach. The novelty of this paper is twofold. First, it analyzes antecedents that may be social, institutional, environmental, or economic in nature. Since this type of study has not been done in the context of the dengue epidemic modelling, this paper offers a fresh perspective on this topic. Second, the paper pioneers the use of fuzzy multiple attribute decision making (F-MADM) approaches for the modelling of epidemic antecedents. As such, the paper has provided an avenue for the cross-fertilization of knowledge between scholars working in soft computing and epidemiological modelling domains.

The advancement in computer science technology has led to some serious concerns about the piracy and copyright of digital content. Digital watermarking technique is widely used for copyright protection and other similar applications. In this paper, a technique for digital watermarking based on Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), and Grasshopper Optimization Algorithm (GOA) is proposed. The method computes the DWT of the cover image to obtain the sub-components of the image. The subcomponent is converted to frequency domain using DCT. The challenge is to find the optimal scaling factor to be used for watermarking. The authors have designed a GOA based technique that finds the optimized scaling factor and the coefficient for embedding the watermark. GOA makes the watermark undetectable and is invisible in the cover image. The watermark image is embedded in the cover image using these coefficients. The extraction of watermark from the cover image is done by using inverse DCT and DWT. The proposed method is compared with the other state of the art methods. The effectiveness of the proposed method is computed using Peak Signal to Noise Ratio (PSNR), Normalized Cross Correlation (NCC) and Image Fidelity (IF). The proposed method outperforms the other methods and can be effectively used for practical digital watermarking.

Finger vein(s) based biometrics is another way to deal with individual's distinguishing proof and has recently received much consideration. The strategy in light of low-level components, like the dark surface of finger vein is taken as standard. However, it is typically looked with numerous difficulties that involves affectability to noise and low neighbourhood consistency. Generally finger vein recognition in view of abnormal state highlights the portrayal that has ended up being a promising method to successfully defeat the above restrictions and enhance the framework execution. This research work proposes finger vein-based recognition technique making use of Hybrid BM3D Filter along with grouped sparse representation for image denoising and feature selection (Local Binary Pattern – LBP, Scale Invariant Feature Transform – SIFT) to evaluate features, key-points and perform recognition. The experimental results on two open databases of finger vein, i.e. , HKPU and SDU show that the proposed method has enhanced the overall performance of finger vein pattern recognition system compared with other existing methods.

This paper presents a review of state-of-the-art security frameworks for IoT applications. It aims to find out what alternatives have been proposed to guide IoT application developers in the implementation of security measures through all development phases. In this literature review, we identified 21 security frameworks, and we analyzed them from IoT application domains addressed and IoT elements protected. We find four application domains: generic, smart cities, smart car/VANET, and smart infrastructures. Concerning elements protected, we analyzed the frameworks through protected application resources and we also consider security properties in this paper. Our two principal findings are: (i) Even though there are a wide variety of security frameworks, we did not find a proposal that addresses all the layers of an IoT application (device, network, service and application) and all development phases (analysis, design, implementation, testing, deployment, and maintenance), (ii) Addressing security from the design phase allows IoT developers to have a broader perspective of the system, avoiding massive changes to be made in later stages, saving costs and time. This gap and concerns enable various research on security by design and secure development to be carried out, and proposed frameworks to address the identified problems.

Segmentation of brain image should be done accurately as it can help to predict deadly brain tumor disease so that it can be possible to control the malicious segments of brain image if known beforehand. The accuracy of the brain tumor analysis can be enhanced through the brain tumor segmentation procedure. Earlier DCNN models do not consider the weights as of learning instances which may decrease accuracy levels of the segmentation procedure. Considering the above point, we have suggested a framework for optimizing the network parameters such as weight and bias vector of DCNN models using swarm intelligent based algorithms like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). The simulation results reveals that the WOA optimized DCNN segmentation model is outperformed than other three optimization based DCNN models i.e. , GA-DCNN, PSO-DCNN, GWO-DCNN.

Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.

The information transmitted in IoT is susceptible to affect the user’s privacy, and hence the information ought to be transmitted securely. The conventional method to assure integrity, confidentiality, and non-repudiation is to first sign the message and then encrypt it. Signcryption is a technique where the signature and the encryption are performed in a single round. The current Signcryption system uses traditional cryptographic approaches that are overloaded for IoT, as it consists of resource-constrained devices and uses the weak session key to encrypt the data. We propose a hybrid Signcryption scheme that employs PRESENT, a lightweight block cipher algorithm to encrypt the data, and the session key is encrypted by ECC. The time taken to signcrypt the proposed Signcryption is better when compared to current Signcryption techniques, as it deploys lightweight cryptography techniques that are devoted to resource-constrained devices.

In the last decade, many researchers applied shallow and deep networks for human activity recognition (HAR). Currently, the trending research line in HAR is applying deep learning to extract features and classify activities from raw data. However, we observed that, authors of previous studies have not performed an efficient hyperparameter search on their artificial neural network (shallow or deep)-based classifier. Therefore, in this article, we demonstrate the effect of the random and Bayesian parameter search on a shallow neural network using five HAR databases. The result of this work shows that a shallow neural network with correct parameter optimization can achieve similar or even better recognition accuracy than the previous best deep classifier(s) on all databases. In addition, we draw conclusions about the advantages and disadvantages of the two hyperparameter search techniques according to the results.

Vehicular communication is the communication between the vehicles to provide intelligent transportation systems (ITSs) services to the end users. It is the most advance and emerging wireless technology in ad hoc network. On the other hand, construction of roads has a great impact in forwarding the data to the destination. As vehicles are moving with high speeds, the architecture of roads can change the performance of routing and data forwarding in the vehicular ad hoc network (VANET). If the construction of the roads in a city area is planned with intelligent junctions, flyovers, multilane, etc., then the performance of the system increases. In this paper, we have analyzed the impact of road elements like intersections, flyovers, multilane, buildings, hills, etc., on VANET routing and find solutions for the problems related to the performance of the system. We also simulate the impact of these elements in VANET routing and analyzed the performance using OMNeT++ network simulator and SUMO traffic simulator. The performance is studied by comparing standard GSR and GPSR position-based routing protocols.

Wireless sensor networks (WSNs) have grown widely due to their application in various domains, such as surveillance, healthcare, telecommunication, etc. In WSNs, there is a necessity to design energy-efficient algorithms for different purposes. Load balancing of gateways in cluster-based WSNs is necessary to maximize the lifetime of a network. Shuffled frog leaping algorithm (SFLA) is a popular heuristic algorithm that incorporates a deterministic approach. Performance of any heuristic algorithm depends on its exploration and exploitation capability. The main contribution of this article is an enhanced SFLA with improved local search capability. Three strategies are tested to enhance the local search capability of SFLA to improve the load balancing of gateways in WSNs. The first proposed approach is deterministic in which the participation of the global best solution in information exchange is increased. The next two variations reduces the deterministic approach in the local search component of SFLA by introducing probability-based selection of frogs for information exchange. All three strategies improved the success of local search. Second contribution of article is increased lifetime of gateways in WSNs with a novel energy-biased load reduction phase introduced after the information exchange step. The proposed algorithm is tested with 15 datasets of varying areas of deployment, number of sensors and number of gateways. Proposed ESFLA-RW variation shows significant improvement over other variations in terms of successful local explorations, best fitness values, average fitness values and convergence rate for all datasets. Obtained results of proposed ESFLA-RW are significantly better in terms of network energy consumption, load balancing, first gateway die and network life. The proposed variations are tested to check the effect of various algorithm-specific parameters namely frog population size, probability of information exchange and probability of energy-biased load reduction phase. Higher population size and probabilities give better solutions and convergence rate.

Detection of plant disease has a crucial role in better understanding the economy of India in terms of agricultural productivity. Early recognition and categorization of diseases in plants are very crucial as it can adversely affect the growth and development of species. Numerous machine learning methods like SVM (support vector machine), random forest, KNN ( k -nearest neighbor), Naïve Bayes, decision tree, etc., have been exploited for recognition, discovery, and categorization of plant diseases; however, the advancement of machine learning by DL (deep learning) is supposed to possess tremendous potential in enhancing the accuracy. This paper proposed a model comprising of Auto-Color Correlogram as image filter and DL as classifiers with different activation functions for plant disease. This proposed model is implemented on four different datasets to solve binary and multiclass subcategories of plant diseases. Using the proposed model, results achieved are better, obtaining 99.4% accuracy and 99.9% sensitivity for binary class and 99.2% accuracy for multiclass. It is proven that the proposed model outperforms other approaches, namely LibSVM, SMO (sequential minimal optimization), and DL with activation function softmax and softsign in terms of F -measure, recall, MCC (Matthews correlation coefficient), specificity and sensitivity.

The main aim of this article is to explore the real-life problem-solving potential of the proposed Lévy flight-based chaotic gravitational search algorithm (LCGSA) for the minimization of engineering design variables of speed reducer design (SRD), three bar truss design (TBTD), and hydrodynamic thrust bearing design (HTBD) problems. In LCGSA, the diversification of the search space is carried out by Lévy flight distribution. Simultaneously, chaotic maps have been utilized for the intensification of the candidate solutions towards the global optimum. Moreover, the penalty function method has been used to deal with the non-linear and fractional design constraints. The investigation of experimental outcomes has been performed through various performance metrics like statistical measures, run time analysis, convergence rate, and box plot analysis. Moreover, statistical verification of experimental results is carried out using a signed Wilcoxon rank-sum test. Furthermore, eleven heuristic algorithms were employed for comparative analysis of the simulation results. The simulation outcomes clearly show that LCGSA provides better values for TBTD and HTBD benchmarks than standard GSA and most of the competing algorithms. Besides, all the participating algorithms, including LCGSA, have the same results for the SRD problem. On the qualitative side, LCGSA has successfully resolved entrapment in local minima and convergence issues of standard GSA.