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This paper exhibits an adaptation of Campbell Dudek Smith (CDS) algorithm for a flow shop system whose end product consists of four components, each of which is firstly processed in a single-stage with parallel machines, followed by a number of batch processors, and then finally assembled in one of available assembling stations. The first step of the procedure is to compute equivalent processing times of each stage for each job, and then use the equivalent processing times to find the job sequence using the CDS algorithm. After the job sequence found, the final step is to schedule the jobs considering the parallel machines, the batch processors, and the assembling stations. Numerical example shows that this procedure results a reasonably good schedule for the case studied. [ABSTRACT FROM AUTHOR]

The paper describes the combined implementation of the decision block in multiversion systems. The importance of fault-tolerant real-time control systems is proved. The authors describe an approach that makes it possible to increase the reliability parameters of such systems and to guarantee time delays. The approach means the presence of a watchdog timer and a redundant decision block implemented on the FPGA. The authors give an example of the main decision- making block implementation. It executed in a real-time operating system applying the Formula Node and Call Library Function tools in the LabView environment. С. An example of a t/(n-1) decision-making algorithm implemented as an extensible library written in the C language is considered. Also, the implementation of the t/(n-1) decision-making algorithm as a duplicate algorithm implemented on FPGA is considered. An example of software implementation of algorithms is given. The necessary tools are described. [ABSTRACT FROM AUTHOR]

The methodology of functional modeling provides visual and understandable means of describing the domain for a wide range of specialists. The paper proposes a formal language for describing a functional model based on graph theory. Within this language, each functional diagram is represented as a graph with marked edges. The nodes of this graph define the function blocks, the edges correspond to the arrows of the diagram. We develop rules for diagram bounds description with special nodes, the arrows positions using the system of roles, branching arrows as a set of special nodes and multiple edges. The hierarchy which links individual diagrams into a single model, is defined by the relation of decomposition on graphs. We form rules that connect the arrows of the parent and child functional diagrams in correctly constructed functional models. To verify these rules, a functional model verification algorithm has been developed. This algorithm is implemented by means of logic programming using the PROLOG language. The structure of the fact base for describing the relations of decomposition, nodes, and edges of graphs is proposed. A set of predicates is provided to verify the correctness of the functional model description. [ABSTRACT FROM AUTHOR]

This paper is focused on the application of the fractal approach to the problem of preliminary processing of electroencephalogram (EEG) data to detect and remove physical and physiological artifacts. We study the fractal properties of EEG data containing artifacts and present the results of the Hurst exponent estimation obtained by the R/S analysis for various types of artifacts and areas of artifact-free data. We show that the Hurst exponent can be used as an informative feature for algorithms identifying and removing artifacts. [ABSTRACT FROM AUTHOR]

Software defect estimation is an approach aiming to make an estimation of which algorithms can predict defects present in the software. This will help in improving the software quality which indeed can satisfy customers. The process is done during the development life cycle so at an early phase which reduces a lot of time and saves cost. Our model is going to be efficient because it can improve the performances and also give better results by estimation results. The main aim of this paper is to evaluate the capability of machine learning algorithms and find the best category while comparing seven machine learning algorithms within the context of NASA datasets obtained from the public PROMISE repository. The best algorithm is the one with highest accuracy rates. [ABSTRACT FROM AUTHOR]

The development of modern mobile robots is often connected with advanced reliability, especially in undetermined conditions. One widespread reason for robotic mission failures is robot tip-over. In this paper, a method to avoid tip-over is proposed based on the existing tip-over detection methods in application to a mobile robot with two arms. The method has been validated in Matlab/CoppeliaSim co-simulation environment. [ABSTRACT FROM AUTHOR]

The paper considers the algorithm for solving axisymmetric contact problems of thermoelasticity, taking into account the processes of creep. To take into account the contact interaction of bodies, the mortar method was used, the modified symmetric successive upper relaxation method (MSSOR) was used to solve the obtained system of linear equations. For the numerical solution of the problem modeling the creep process, time discretization based on the implicit Euler method was applied, the Newton method was used to linearize the resulting system of equations. An algorithm with automatic step selection based on obtaining an estimate of local error of the method is proposed. The results of the application of the proposed a lgorithm to the demo problem, which simulate thermomechanical processes in the section of the fuel element, which includes from 1 to 10 fuel pellets are presented. [ABSTRACT FROM AUTHOR]

A method for improving the quality of restoration of noisy compressed images is proposed. To reduce the amount of transmitted data, it is proposed to use only a subband image after a low-frequency wavelet filter with the highest concentration of signal energy. Taking into account the nature of the statistical relationship between the wavelet coefficients within the same subband and in the same scale, it is proposed to approximate subband images by two-dimensional Markov chains with several states, and bit planes - by Markov chains with two states. To restore a noisy image in the wavelet region, it is proposed to use a recurrent two-dimensional nonlinear filtering algorithm that effectively implements statistical redundancy. The paper presents the results of modeling the proposed method. The superiority of the algorithm of two-dimensional nonlinear filtering in comparison with the wavelet filter is shown when restoring images without compression. The considered method of recovering noisy compressed images retains its efficiency at signal-to-noise ratios up to -6 dB and the compression ratio up to 64 times while reducing the computing resources on the transmitting side of the channel. The proposed approach to restoring noisy compressed images reduces computing resources on the transmitting side of the channel by eliminating the channel encoder. [ABSTRACT FROM AUTHOR]

Optimization problems are interest and common problems that are often encountered in life. Optimization can be applied to solve various problems, for example development, government, business, social, economic and something related to the limitation of resource capacity. The most frequently encountered, optimization is often used to find the best solution, that is maximizing profits or minimizing production costs. One of the optimization problems that often occurs is the knapsack problem. There are several types of knapsack problems, one of which is Modified Bounded Knapsack with Multiple Constraints (MBKMC) problem. In popular mathematical studies, metaheuristic algorithms are very often used to solve optimization problems. In this paper the authors did not only use one algorithm, but implemented two metaheuristic algorithms which were combined into one, namely the Gravitational Search Algorithm (GSA) and the Cat Swarm Optimization (CSO) algorithm. The combined algorithm uses the entire GSA algorithm mechanism which is added with the CSO algorithm seeking mode to become the GSA&sCSO algorithm. The author uses the GSA&sCSO algorithm to solve the MBKMC problem of uncertain coefficient. Based on the results of this research, the GSA&sCSO algorithm produces a better solution (higher profit) than the GSA algorithm and the CSO algorithm and earn a better advantage in accordance with the knapsack capacity. In addition, the uncertain coefficient greatly affects the solution obtained, i.e if there is a change of the coefficient, then the solution also changes. [ABSTRACT FROM AUTHOR]

The Network Design Problem (NDP) is the optimum network expansion planning problem that the branching ought to be involved within the network for obtaining the lowest merit index. This paper aims to apply the branch and bound algorithm to obtain the optimal solution in extend railway (Basrah-faw) project. Numerical experiments show that our algorithm is superior to previous algorithms in terms of both computation time and solution quality. The finding shows that representing the project through a network would facilitate the work of the executing company in calculating the shortest possible time as well as we obtain two paths with lower merit index which gives the flexibility and applicability of our algorithm for the decision maker. [ABSTRACT FROM AUTHOR]

In energy management, providing accurate findings to anticipate electrical load consumption is critical. The goal of this study is to develop methods for predicting electrical load, including artificial intelligence, neural network, ARIMA models, Bayesian models, and regression models. This paper proposes a new support vector regression application with the chaotic algorithms for electrical load forecasting in the southern region (Basra, Maysan, Dhi Qar, Muthanna), When choosing three parameters of a model (SVR) using evolutionary algorithms, you often encounter problems of early convergence, slowly reaching the solution of global optimization or falling into local optimization, and to overcome early local optimization in determining three parameters of the model (SVR) a chaotic algorithm is used. The 1980-2019 electrical load was used as a data set, and the results (CGASVR) were compared with (CPSOSVR) and (CIASVR) to choose the best form of electrical load forecasting where results show that the CGASVR model is more superior and efficient based on MSE, MAE, MAPE and MPE. [ABSTRACT FROM AUTHOR]

In this paper, we will discover the BFGS and compare the result of converging to minimize with the result of L — BFGS. And we discuss how to find an optimal solution of the objective function using the BFGS, and L — BFGS algorithms in Python lets get started. [ABSTRACT FROM AUTHOR]

Cloud computing is among the technologies that have become popular in recent times, due to the increasing demand for their use by individuals, companies and organizations. It offers its resources as an online service upon demand by its users. In order to enhance the trust between the cloud and its users, attention must be paid to addressing the issues that the cloud suffers from, the most outstanding of those are problems relating to the safety and privacy of users' data on the cloud. Encryption is considered one of the successful and efficient ways to raise the security of data kept on the cloud by converting it into data that is incomprehensible to unauthorized persons. In our research paper, we will present a method that provides security and privacy for data stored on the cloud, and this method consists of a combination of two lightweight algorithms (mCrypton and salsa20) to form a new lightweight hybrid algorithm based on the mCrypton algorithm, where stages were added to it and other modifications. Additionally, chaos has been added to generate encryption keys. [ABSTRACT FROM AUTHOR]

This paper provide a general overview on different kinds of mining categorical covid-19 data Using Chi-Square and Logistic Regression Algorithms. Our study methodology are categorized into three steps, the first one is data preprocessing & feature extraction approaches, the second is feature selection by using chi-square approach,and finally classification by using Logistic Regression Algorithm approaches. Some techniques are illustrated in details while the others with general explanation. In this research we used two datasets (dataset1 & dataset2) in order to test and train our program. The final result after applying chi-square and logistic regression algorihm to the aforementioned data set , the category of each input case will be determines by the likelihood of belonging to one of the two categories in the data set. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the major problems facing drivers all over the world, especially in the urban road network, where lost time and fuel consumption are a major concern. Traffic Signal Control (TSC) can be self-regulating and adaptive to changes in traffic conditions using Vehicle Dedicated Network (VANET) technology and intelligent control algorithm as VANET technology provides comprehensive information on vehicles approaching intersections. Scheduling algorithm for traffic lights in VANET scenario shows low performance with large coverage area. This is, most probably, because an intersection is considered to be isolated under a small coverage area. As the area increases, especially in large cities, such a consideration is not suitable. Hence, a scheduling algorithm for traffic lights in a VANET scenario is investigated and developed in this paper under different coverage areas. The main objects here are to alleviate the problem of traffic congestion and shorten vehicle wait times at an intersection in Ramadi city as a case study. Instead of using a scheduling algorithm with a fixed maximum green time, an adaptive time is proposed according to coverage area. The orders of vehicles that have arrived or will arrive at the intersection make maximum green time adaptive. The developed algorithm is simulated for different coverage areas and interesting results are shown in detail. The results showed the superiority of the developed scheduling algorithm over the traditional system used in the intersection, and this algorithm is suitable for non-isolated intersections because even outgoing vehicles from an intersection can be taken into consideration. [ABSTRACT FROM AUTHOR]

In the domain of network security, there is a never-ending quest for cyber-attacks that might disrupt a network. Furthermore, hostile actions in the network are rapidly increasing because of the unanticipated inception and rising use of the Internet. It is important to build a reliable intrusion detection system (IDS) to resist unwanted access to network resources, Information protection, and network intrusion detection. Deep learning has recently acquired popularity because of the possibilities it has for machine learning. As a result, Deep Learning algorithms are being used in a variety of domains, including pattern recognition and categorization. Intrusion detection analysis used data from security event monitoring to assess the network's status. Several traditional machine-learning algorithms for intrusion detection have been proposed, however detection performance and accuracy must be improved. This paper examines the many methods that have been used to classify network traffic in real-time. We selected to experiment with various methods on UNSW-NB15 datasets to find the optimum way for real-time intrusion detection. [ABSTRACT FROM AUTHOR]

A hash function may use a block cipher algorithm as its basic structure to improve the efficiency and cost of hardware and software implementation. The SIMON, SPECK, and SIMECK algorithms are a family of lightweight block ciphers designed for implementation on devices with constrained resources. In this paper, we examine the robustness of the Davies-Meyer scheme using the SIMON-32, SPECK-32, and SIMECK-32 algorithms as the basic structure using fixed-point attacks. The result obtained is that the Davies-Meyer scheme based on SIMON, SPECK, and SIMECK algorithms shows the vulnerability to fixed-point attacks. [ABSTRACT FROM AUTHOR]

Numerous researchers have applied nature-inspired population-based metaheuristics for solving optimization problems including data clustering. However, the issues of premature convergence and slow convergence rate can still occur when these promising search methods are applied to complex and large data-clustering problems, including the evaporation-rate based water cycle algorithm, WCAER. In this paper, a recently proposed hybrid version of WCAER in conjunction with a local search method named Hookes and Jeeves method was further tested to perform data clustering for large dataset. The proposed hybrid algorithm is experimented on the gas turbine emission data that contains 36733 instances of 11 sensor measures aggregated over one hour, from a gas turbine in Turkey, available from the UCI machine-learning repository. The simulation results confirm the superiority of the hybrid method as an efficient and reliable algorithm to solve gas turbine clustering problem, in comparison to the original evaporation-rate based water cycle algorithm, in terms of solution quality as well as computational performance formulated from two applied objective functions namely the Euclidean distance and the Davies-Bouldin index. Thus, the outcome of the study generally provides some performance evaluation of WCAER for large dataset when applied in cluster analysis, a valuable data analysis and data mining technique. It is hoped that other metaheuristics could be applied to the 5 years span large dataset for performance comparison with other further considerations such as using various suitable objective functions and cluster validity indices. [ABSTRACT FROM AUTHOR]

With increasing number of applications of Unmanned Aerial Vehicles (UAVs) in civilian sectors, enthusiasts are finding ways to build their own UAV which can be used for jobs that are too dull, dirty or dangerous for humans. Design of UAVs may vary according to their respective mission and payload requirements. The algorithm asks for the specific requirements of the user such as payload weight & volumetric dimensions, maximum speed at which UAV is supposed to fly etc. The process includes aerodynamic analysis of numerous airfoils and selecting the optimum one, calculating wing-span required with the selected airfoil cross section to attain required lift, reducing drag, maintaining stability of aircraft etc. This process is iterative and can take lot of time. Here arose a need to make this process automatic with the help of computer by developing an algorithm which can save lot of time for the people who have to work from scratch in order to make an UAV. In this research paper an effort is made to establish a computer-based algorithm for design and development of a flying wing UAV using MATLAB. [ABSTRACT FROM AUTHOR]

The paper discusses the methods of inverse problems solutions of gas and plasma diagnostics problems using tomographic algorithms, in conditions of limited data. Due to restrictions in the original data, conventional computed tomography algorithms cease to work, and their modifications are required. The partial absence of the angular data can be approximately compensated for by extrapolation of such data in the sinogram space, applying modifications of tomography algorithms. In particular, these algorithms should be complemented by a priori information and regularization procedures. In a computational experiment, a comparison of the errors of the reconstruction of models with weak refraction in the cases of parallel straight lines and curvilinear trajectories of penetrating radiation are made. [ABSTRACT FROM AUTHOR]

In this paper, a new technique was used to solve a type of integrative fractal equations after an update was made on the Levenberg–Marquardt training algorithm for winding the user in the neural network under suitable conditions for choosing an appropriate value for a parameter η k = (ρ − 1) | | J k T E k | | , J (w k) = E ′ (w k) is the Jacobian, ρ ∈ [0,1] is a constant. We proved that the method converges superlinearly and quadratically to a solution of the nonlinear equations under suitable conditions, and this convergence led to a rapid convergence of results and reduce the error value after a comparison between the proposed method and the standard method. [ABSTRACT FROM AUTHOR]

Many research paid much attention to attain robot automation for implementation in several industrial applications. In order to improve the robot guidance, the methods of robot path planning are modified. This paper proposed the hybridization of the artificial potential field and firefly algorithm for path planning of the robot in the dynamic environment. The artificial potential field is applied to analyze the environment and provide the guiding force. The firefly algorithm is applied to select the best next movement of the robot within a specific distance. The essential function of the firefly algorithm is to select randomly potential field forces and determine the best next point. The firefly algorithm depends on the iterative update of the total potential for the environment that contains the dynamic obstacles. The collision check function is used to avoid select the point from the distance of dynamic obstacle influence. The proposed method is applied to find the robot path in the tested environment consists of moving obstacles only. The demonstrated results of applied the hybridization method represent the efficiency of the proposed method performance in finding the robot path in the dynamic environment with minimum cost and avoiding all the dynamic obstacles. And the hybridization method guarantee finds the shortest path in the case of various environments' complexity with free-collision with dynamic obstacles. [ABSTRACT FROM AUTHOR]

Combine steganography techniques and the public key cryptosystem is new line to protect the transferring information. This paper presents a new method to hide the data into an image using RSA cryptosystem. Proposed algorithm is clarified in detail through the overall scenario and through concrete examples. The experiments on the proposed algorithm demon strated that it is a simple and efficient technique with a high level of security for transferring information. Accordingly, this proposed algorithm can be considered as a hybrid of steganography techniques and the public key cryptosystem and it can be used effectively for this purpose. [ABSTRACT FROM AUTHOR]

Currently, the quality management of production processes is not only an integral part of the enterprise management system, but also largely determines the ability to meet the needs of consumers with minimal production costs. In this regard, the paper considers one of the modern methods of quality management, based on the probabilistic assessment of the qualitative completion of the stages of the process under study. This approach takes into account the sequential dependence of the production stages, as well as the joint impact of measures aimed at effective process management. At the same time, the costs of the activities carried out must be minimized. The subject of this paper is a mathematical problem of operational management of multi-stage production processes with consistently interrelated stages based on a probabilistic approach to quality assessment. An algorithm for solving the problem and its software implementation in the form of an application is presented. The software product is acceptable for practical use and allows you to optimally choose the measures of influence on consistently interrelated stages of the process. [ABSTRACT FROM AUTHOR]

This article describes a method for evolvement predicting of an emergency zone on the example of a forest fire using neural networks and applying the Hurst exponent to increase the accuracy and reduce the computational complexity of the algorithm. The inputs in this method are a set of environment state maps and the fire characteristics. The lack of a sufficient number of maps and a large amount of available data has a negative effect on the forecast accuracy and the overall system performance. It proposed to divide the fire area into sectors and perform the forecast by sectors with an accuracy that depends on the sector behavior characteristics. The sector behavior characteristic in this paper presented by the persistence analysis of the sector vectors, and be replaced by any other indicator. Persistence indicates trend stability in the behavior of the time series and, accordingly, the forecast reliability. In the case of low forecast reliability, the sector is iteratively divided into segments, which are analyzed and characterized. In this case, forecasting carried out for each sector and segment, which generally increases overall forecast accuracy. The paper presents the key mathematical calculations and the results of experimental studies. [ABSTRACT FROM AUTHOR]

This paper presents the developed algorithms for a tool environment for the design of complex information systems based on automata models using the algebra of high-dimensional vectors. The paper raises the problem of the little knowledge of methods of coding complex systems in the format of automata based on the algebra of hyperdimensional vectors. Methods of formalized description of systems in the form of hyperdimensional vectors and description of algorithms for transformation of formalized description using the algebra of hyperdimensional vectors are introduced. The features, advantages and disadvantages of modeling automata models using hyperdimensional vectors are listed. [ABSTRACT FROM AUTHOR]

Image stitching is a promising method for developing longer digital x-ray radiographs without sacrificing image resolution. This paper presents our effort on developing the image stitching method based on the SIFT (Scale Invariant Feature Transform) algorithm and the template matching method. Digital radiographic images generally have different contrast and brightness values that need to be adjusted in the image stitching procedure. In this paper, research was carried out related to the effect of differences in contrast values on image stitching results. The reference image, which is a thorax image, is cut into two parts and one of its part is changed the contrast values, to then be merged and stitched into one single complete image. The process of stitching images that has been carried out uses the Scale Invariant Feature Transform (SIFT) method and key point matching. The results of the stitched image are then analyzed using digital image correlation (DIC) values. The results of digital correlation coefficient value obtained from the DIC analysis is close to 1 for all variations in contrast. This shows that the image stitching result is similar and almost identical to the reference image. Therefore, with the method used, it can be obtained a longer image stitching result even though it has differences in contrast. [ABSTRACT FROM AUTHOR]

A prototype portable tomographic system for microwave imaging is presented in this paper as non-invasive monitoring to preserve cultural heritage work of art. Initial simulations of the system configuration have been carried out to validate the system architecture. The most significative outputs of these simulations are presented and discussed in the paper. Hence, a first lab microwave tomographic set-up has been developed and tested. The outcomes of this preliminary experiments have driven the development of an optimized portable device for in-situ measurements. A custom-made 3D- printed structure has been built to support the antenna elements of the systems and enclose the target being tested. Transmission measurements between pairs of antennas have been acquired through a vector network analyzer connected to a modular switching matrix, and the data generated have been inverted by an opensource reconstruction algorithm. The results of the tests are here reported. They clearly show the capability of the systems to detect metallic intrusion in heritage samples. [ABSTRACT FROM AUTHOR]

In this paper we introduce an algorithm to do the computation of near lower and near upper approximations in groups with respect to the normal subgroups. We consider dihedral group D6 and abelian group ℤ36. We implement the algorithm in SageMath. Since if we compute manually, the possibility of miscalculation is quite large, the algorithm is become very helpful for computing to show the zero mistake. [ABSTRACT FROM AUTHOR]

In this paper we construct one type of de bruijn sequence. First, we modify the prefer-one and prefer-zero algorithm to build one cycle. The other cycles are constructed by modified pure circulating register. Finally, we use cycle joining method to get the full length of the sequence. [ABSTRACT FROM AUTHOR]

This paper discusses on the applications of non-traditional method. There is different non-conventional optimization are reviewed to solve the optimization problems. In this survey, the methods we are going to review are Krill Herd Algorithm (KHA), Pattern Search Algorithm (PSA), Sal Swarm Algorithm (SSA) and Gradient Based Algorithm (GBA). These methods are approach to find the optimization methods can solve the linear and non-linear optimization problems and results the global values. These methods are broadly reviewed. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are a massive number of tiny nodes installed in harsh and hostile environments to monitor and gather specific data. WSNs installed to support bridge structures, healthcare, robotics, environmental monitoring, etc. WSNs can be used to monitor the pressure, temperature, sabotage of transmission lines, and leak detection. WSNs are vulnerable to different attacks. Cryptographic algorithms have a good role in information security for wireless sensor networks. Accordingly, various cryptographic algorithms provide security in networks. Cryptography has two techniques: the symmetric-key technique and the asymmetric-key technique. But there are still some problems. For example, to store the key across all nodes in the network, the symmetric-key technique needs more storage, while the asymmetric-key technique takes more computation time. This paper proposed a hybrid security algorithm that provides a high-security level with high-speed encryption and decryption using symmetric-key and asymmetric-key cryptographic techniques to avoid computation and memory overheads. It gives three cryptographic primitives, confidentiality, integrity, and authentication. The concept is to use the Blowfish to provide encryption, Message Digest 5 (MD5) for integrity, and Elliptic Curve Diffie Hellman (ECDH) for authentication. The proposed algorithm obtains the best results overall using two computers (A and B) compared to several other algorithms in terms of ciphertext size, encryption time, decryption time, and throughput. By using computer A and the plaintext size is 609 Bytes, the proposed algorithm has 87.9% ciphertext size compared to HA, 19.6% encryption time compared to HCA, and 41.1% decryption time compared to HCA. [ABSTRACT FROM AUTHOR]

The millimeter-wave (mmWave) technology suffers from several issues such as blockage by solid material and absorption by oxygen or rain. These issues cause degradation in TCP performance, where many packets are dropped. Retransmission for the packet drops will be required to receive the whole segments of data. This paper focuses on the side effects of congestion and link errors over TCP performance in the mmWave environment. This study proposes two mechanisms to enhance the conventional TCP Congestion Control Algorithm (CCA) through Additive Increase/Multiplicative Decrease (AIMD) approach. Firstly, the proposed method exploits the Additive Increase (AI) phase to stay in a stable state as long as possible. Secondly, the proposed method uses a Multiplicative Decrease (MD) phase to prevent the congestion window (cwnd) to be reduced. The proposed TCP enhancement will be compared with the default TCP-CCA. The evaluation of the proposed TCP enhancement is conducted in ns-3. The simulation results confirm that the proposed method refines cwnd up to 14% of in comparison with default TCP. [ABSTRACT FROM AUTHOR]

In this paper we present formulations of flow laws and evolutive laws in non-smooth plasticity. This formulations are suitable for non-smooth problems in plasticity theory. The tools of convex analysis is adopted in order to properly treat non-differentiable functions. Within this framework generalized forms of flow laws and complimentarity conditions are expressed. The treatment is particularly useful for variational formulations and for the development of computational algorithms in non-smooth plasticity problems. [ABSTRACT FROM AUTHOR]

For the past few years, the Third Generation Partnership Project (3GPP) has introduced the Long-Term Evolution (LTE) as a critical answer to supporting the users' demand for a faster data connection. Nevertheless, another problem arises as the users move from one location to another; they will face a service interruption. As a result, switching from one base station to another base station is required. This process is known as handover. The LTE handover can be done using two algorithms: the A3RSRP and A2A4RSRQ. Both algorithms are associated with two Handover Control Parameters (HCPs). Earlier, many studies have been conducted to evaluate the handover algorithms' impact on the LTE network of Handover Control Parameters (HCPs). However, the studies solely focused on the Quality of Service (QoS), thus neglecting the Quality of Experience (QoE). Therefore, this paper presents an empirical analysis of the impact of the Handover Control Parameters based on user trajectories in terms of the QoS (Throughput and delay) and QoE (time to complete file download). The study found that the A2A4RSRQ algorithm performed better than the A3RSRP algorithm once the mobile user moved in a range of Angle-1 and Angle-2 directions. On the other side, different Handover Control Parameters should be assigned based on user trajectories to obtain good handover performances. [ABSTRACT FROM AUTHOR]

Mobile robotics is the discipline of robotics that deals with moveable robot systems that can work in an unfamiliar environment or terrain. It is mostly utilized in navigation and exploration research, with applications in autonomous guided vehicles. A mobile robot's path planning is critical for determining the optimal path from source to destination while avoiding collisions with obstacles if present in the path. This paper employs the modified E-Bug algorithm to find out an approximate optimized path for a mobile robot using a simple range sensor in terms of certain parameters. The near optimum path for navigating a mobile robot from a start point to a destination point in the presence of obstacles is determined by detecting 'sudden points' using an ultrasonic range sensor fixed on the Activity-Bot mobile robot to avoid any barriers in its path. Furthermore, the efficiency of the modified E-Bug algorithm in a real-world situation is verified using basic integrated development environment (IDE) software by considering the dimensions of the robot rather than treating it as a point. The developed program in C language has been successfully tested for a variety of workspace layouts, and it will aid process engineers in path planning for industrial applications. [ABSTRACT FROM AUTHOR]

Slime mould Algorithm (SMA) is modern meta–heuristic algorithm that is depended on the mechanism of swarming of slimes. SMA considers one of the good algorithms in the field of intelligence optimization. In this paper we proposed a new binary Slime mould Algorithm to depend on transformation function which converts the continuous space to discrete space. The purpose of create this algorithm is to use in the feature selections. The new algorithm used with different dataset and compared with different algorithm. The results of numerical experimental showed that the proposed algorithm have a good performance compared with other algorithm. [ABSTRACT FROM AUTHOR]

One of the central algorithmic graph theory problems is the minimum dominating set (MDS) problem. The MDS has various implementations particularly in graph mining. Various fields such as engineering, physics, social and biological sciences are widely used graph theory.Within the most powerful naturally algorithms are inspired for optimization. The firefly algorithm (FFA) FFA had been successfully used for solving many problems in several fields. This paper introduces FFA for solving the MDS problem. FFA is a general purpose algorithm; however, the problem has to be presented in an appropriate manner to convenient the presented algorithm steps. Hence, binary presentation in this study has been implemented for the MDS problem. Overall, the effectiveness of the presented algorithm experimental results demonstrated over the MDS problem. [ABSTRACT FROM AUTHOR]

This paper discusses the application of Data Envelopment Analysis based Ranking (DEAR) algorithm in selection of materials for the rollers which moves across for cleaning the solar panel automatically. Selecting suitable materials for roller used in the developed automated solar panel cleansing system, indeed, the selection of material for any part in a design process requires substantial knowledge in the process. Even having required knowledge in the domain, it is often requiring some guidance or reference for providing the justification after selecting the materials for the product. In this sense, in this work, the DEAR algorithm one the multi criterion decision making algorithm is used as guiding algorithm to identify the suitable candidate material for the rollers. Algorithm helps in selecting the material which considered to be as an effective material from the group of available materials which are suitable for adapting as the materials for rollers. For the algorithm to work, the various properties like weight, friction coefficient, cost which are considered as important properties for roller to work efficiently were presented to the algorithm and based on the same, the algorithm suggested the best material for the rollers. [ABSTRACT FROM AUTHOR]

In modern systems of automated architectural and construction design, the method of creating three-dimensional models of objects under development is widely used. This method allows to visualize the object, determine various geometric object parameters, calculate its strength characteristics, solve issues related to production technology, etc. In most up-to-date graphics systems, standard solid-state objects, such as a parallelepiped, a cylinder, a ball, etc., are used as input data when creating three-dimensional models. At the same time, when creating complex models consisting of standard bodies, it is nec-essary to determine the type of object, its position and geometric parameters. The paper considers algorithms for recognizing some standard solids and determining their basic geometric parameters. The algorithms are implemented using ObjectARX technology for the AutoCAD system. The algorithms developed in the article can be applied in the problems of designing various objects, using technologies for creating three-dimensional models. [ABSTRACT FROM AUTHOR]

In the last decade, a scientific workflow has become the dominant trend to enable specialists to accelerate scientific developments in various areas. The scientific workflow requires high computing, therefore, cloud computing is exploited, as it provides the computing required for scientific workflow with a good cost. The frequent use of scientific workflows by researchers in cloud computing stimulates the need to find mechanisms to manage scientific workflow, providing resources, and scheduling in a way that achieves fairness for all users and meets the required Quality of Service (QoS). In a cloud computing environment, scheduling scientific workflows is an NP-hard problem. A lot of work has been done to resolve the decision problem of scheduling workflows, provision of resources, or both. In this survey, many papers related to scientific workflow algorithms of provisioning and scheduling in a cloud computing system are reviewed. We investigate and classify numerous related articles in the field. The objectives of each study, the application domains, and tools that were used are highlighted. [ABSTRACT FROM AUTHOR]

AES as a block cipher considers as one of the most important security applications that provides confidentiality through encryption. However, AES cryptosystem is under analyzing by cryptanalysts using the computing technologies that have rapidly progressed. Therefore, the possibility of breaking AES algorithm might be accelerated in anytime. Consequently, this paper improves the AES security against a possible future attack. It proposes a model that has the ability to encrypt data with block size 256-bit and a key size of 256-bits. In addition, it also uses S-box of the serpent algorithm to be replaced the original S-box of the AES cryptosystem. The implementation of the proposed idea shows that the time requirements is acceptable even if the shifting algorithm is applied in two directions on row and column. Moreover, the randomness of the modified algorithm remains high. [ABSTRACT FROM AUTHOR]

In the present paper a new idea has been proposed that involve a low-cost encryption/decryption algorithm called (LiNA cipher) that manipulate data with small devices of limited resources. LiNA works under the concept of symmetric block cipher with 64-bit size for data block and with ciphering key of 80-bit for the secret key. The proposal cipher looped with up to 12-rounds to increase data security and the complexity of decryption against the crypto-analysts. The LiNA structure is a product and iterated lightweight cipher with substitution and permutation network design. LiNA cipher implemented and designed by a series of logical and mathematical methods that reduced the gate equivalent (GE) efficiently to match the IoT embedded devices. The experimental results of LiNA cipher after the comparison to the other lightweight block ciphers shows an outstanding execution in the terms of performance and efficiency, especially, with low amount of (GE) that estimated by 1408 GE. [ABSTRACT FROM AUTHOR]

The Advanced Encryption Standard (AES) algorithm is a symmetric encryption algorithm which is much faster, secure and consumes less power when compared to the previous cryptographic algorithms like DES and RSA. It can be implemented in software as well as hardware. The paper compares the advantages and disadvantages of various implementation of AES algorithm and proposes the design of standard cells and macro cells for Application-Specific Integrated Circuit (ASIC) of AES. A total of 212 standard cells and 20405 instances were designed and implemented using cadence virtuoso. The cells were fully custom designed using 45 nm technology with minimum channel length of 50 nm. The total number of cells in proposed implementation was lesser than other ASIC implementations. There was a significant percentage of decrease in area, power and timing constraints when compared to GPDK045_SC. [ABSTRACT FROM AUTHOR]

In this paper, we will solve ordinary differential equations of fractional order by homotopy analytical method (HAM), We will also improve HAM by using the sine cosine algorithm with Simpson's 3/8 integral rule, where the SCA using to adjusts and modifies the parameter ℎ and picks its best value, then Simpson's 3/8 integral rule will solve the final series of (HAM-SCA) to obtain a best approximate solution than the previous methods. The proposed method (HAM-SCA-SIMP) yields a solution characterized by reliability and efficiency corresponding to HAM and the HAM-SCA hybrid method by calculating the maximum absolute error MAE and the mean square error MSE. [ABSTRACT FROM AUTHOR]

This manuscript introduces a valid strategy to prove the optimization problem is the NP_Hardness problem. First of all, K-cluster is one of the most important NP-hardness problems of combinatorial optimization problems. Secondly, a problem is hard if it cannot be solved by a feasible (polynomial time) algorithm; i.e. the hard problem cannot be solved in particular. Furthermore, the strategy of this paper is to use a technique to show the problem is NP-Hard. Finally, the problem is NP-Hard if every problem from NP can be reduced to that which is means reductions proven. [ABSTRACT FROM AUTHOR]

Chaos-based cryptographic algorithm has proposed to build reliable image encryption techniques in recent years. This paper. Presents a partial encryption method for grey images focused on α (the critical region) that determined by the contribution of a of significance bit plane's level. The method using Henon Chaos Map and DNA coding in image encryption. Using henon map gives the algorithm wide range of randomness distribution bits, the DNA provides high capacity in transmitting information and enhance computational speed. The efficacy of the proposal algorithm security was tested using histogram analysis, correlation test, entropy test, NPCR, UACI. [ABSTRACT FROM AUTHOR]

The 0/1 Knapsack Problem (KP) is one of the problems in optimization where a set of items with given benefit and weights.The aim is to select a subset of the items in order to maximize the total benefit without exceeding the knapsack capacity. Nowadays, it becomes a big dilemma due to the explosion in the amount of datasets. In this paper, P(KD)P algorithm has been proposed and performed. For effectively calculating the KM matrix using multi- core system, an improved approach of the DP_KP algorithm has been considered. The proposed scheduling and partitioning approaches have accomplished a significant enhancement to the overall performance. Executions were implemented on corei7 with (8 cores). It outperforms sequential KP_DP more than 16-fold speedup and (KD)P more than 5-fold speedup. Its efficiency reaches 0.316, 0.0829 and 0.0979 over the KP_DP, (KD)P, and P(KD)P respectively. [ABSTRACT FROM AUTHOR]

The job sequencing technique is used to determine the best order in which jobs should be processed on different machines. In this paper, we propose a novel method for determining the best job sequence. Optimal sequence computed using the proposed algorithm, minimizes the total elapsed and idle time. Numerical illustration is also provided. [ABSTRACT FROM AUTHOR]

In a present world, some of the incident has occurred on without the any prior information. In this case our entire human society (survival, health etc.) and industrial peoples are affected in many parameters. For particularly transportation is most important need of day to day life. So, in this paper we consider the above situation and to analyze the optimization range value of neutrosophic transportation problem. In this article intends to develop the algorithm of transportation problem on neutrosophic environment. An example is included. [ABSTRACT FROM AUTHOR]