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This paper describes in detail the method and algorithm for calculating the numerical integration of non-stationary two-dimensional systems of Navier-Stokes equations for a compressible gas using high-order implicit (explicit) difference schemes, i.e., the Beam-Warming difference scheme. A number of mathematical transformations, such as non-dimensionalization of coordinates and physical parameters, transforming the shape of the channel into a square one, as well as thickening the integration steps with large gradients of unknowns, which make it possible to bring problems to a model one for solving similar problems. In addition to the study, as convergence in the number of calculated points, the dependence of the number of iterations on the Courant number and comparison of the results obtained by different proposed models for calculating the effective viscosity, as well as a number of numerical studies as co-occurrence, temperature inhomogeneities, non-design, the ratio of the sizes of the inlet slots of cocurrent flows and the length of the channel to the height of the main flow and the degree of expansion of the channel to the process of mixing and distribution of flows in the channel. It was revealed at what ratios of the initial parameters of the cocurrent flows and the linear dimensions of the channel a recirculation zone is formed. For a channel of constant cross section at large ratios of velocities (72, 46) and temperature (2, 33), as well as small ratios of the channel length to the height of the inlet slot of the main flow (5, 31), even at the same jet pressures in the initial section, the recirculation zone occupies more than 55% of the input section, and the length along the longitudinal coordinate reaches 20 cm. [ABSTRACT FROM AUTHOR]

Feature selection is an important technique in building prediction systems. In various searches, the judgment of the relevancy of a feature is often calculated using all the instances of the considered sample. However, when the dataset size grows, some of the instances are not useful for weighting the features. In this paper, we rank features according to a fitness function that relies on the relevancy using all instances and on the relevancy using a maximum number of significant instances (which really contribute to the feature positive relevancy with the target variable). The relevancy is based on preordonnances theory where the instances are expressed in pairs. The proposed algorithm can be mainly divided into three steps, namely, (a) eliminating all features that are in disagree with the target feature, (b) Finding the best subset of instances, to each feature, that maximize the relevancy and which the cardinal tends to the cardinal of instances in the original dataset, and (c) Ranking features. The second step is defined by dividing the dataset (instances of each feature) into several consistent regions by fuzzy clustering. Then, performing GA-based instances selection independently within each cluster. Finally, aggregating of the partial results by the ensemble voting. Experimental results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Uncertainties, parameters variations and external disturbances may affect highly the task of lane-keeping, lane-changing and path-tacking autonomous vehicles. This paper presents a novel variable gains second-order sliding mode algorithm for en-hanced path tracking performance of autonomous vehicles subject to uncertainties and disturbances. The used algorithm is a modified super-twisting with both nonlinear and linear correction terms. Hence, the proposed method doesn't suffer from the chattering phenomenon, ensures high order precision and guarantees both the robustness against perturbations and the convergence in finite-time during the reaching phase. To evaluate the efficiency of the developed approach, a double lane-changing scenario is considered in simulation for an uncertain autonomous vehicle and the results are compared with the first-order sliding mode. [ABSTRACT FROM AUTHOR]

The concept of web of things (WoT) is to connect anything in the physical world and then display them in the World Wide Web, that is the major objective, but that mean information storage or transfer that led to thinking how can protected this information from attacks, this can happen by protected information by encryption it. Cryptography and chaos inextricably linked because of many features. In this paper uses a novel model for the encryption of information via WoT, we suggest HIGHT and Simon as a block cipher algorithm using for lightweight encryption by make a new hybrid between them. Also use chaotic system to take key from it; moreover, we use other operations to arrive the best result. [ABSTRACT FROM AUTHOR]

Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. The goal of optimization of PP is to find a path that is close to or optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOO present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. This paper the problem of PP is investigated with path length, security and smoothness objectives for autonomous mobile robots in dynamic environment with moving target. The proposed approach based on Bat algorithm (BA). The proposed solution is tested in a number of simulations with varying scenarios. The simulation results show that multi objective can be achieved efficiently in a dynamic environment which proves its effectiveness. [ABSTRACT FROM AUTHOR]

During pandemic circumstance world, the size of data is exploded like rocket speed. At the same time, there is a need to process it and investigate the enormous volumes of information. The company and research tool is Hadoop and is most broadly utilized in information serious applications. The Hadoop default processing nature is homogeneous. The limit of enormous measure of data in Hadoop is finished utilizing Hadoop Distributed File System. The performance is degraded when it applied to heterogeneous environment. Hadoop mainly uses 2 components viz HDFS used for storage purpose and MapReduce used for programming. In this paper we introduce Optimal Data Placement Algorithm (ODPA). [ABSTRACT FROM AUTHOR]

The real world has been changed into a virtual one as a result of the pandemic scenario. Cloud computing plays a major role for storing and computing the data using remote computing infrastructure for solving the day to day activities. The LB Techniques are used in the cloud computing process. LB techniques distributing the system's load among its various nodes in order to maximise resource efficiency and job response time. The major issues in load balancing are Geographical Distributed system, Single point of failure (SPoF), live Migration of instance, Heterogeneous device, Storage device Management, Scale-out/scale-in and Complexity of Algorithm. Load balancing ensures that each node in the network shortens reaction times, reduces expenses, and boosts performance. This paper address the various cloud computing balancing algorithm survey. [ABSTRACT FROM AUTHOR]

Today's modern world is being driven towards automation and as well gathering any type of information from every nook and corner of the world which is possible through one of the technology named Internet of Things(IoT). IoT is not just a single technology but it is a combination of various technologies like embedded systems, wireless sensor networks, cloud computing etc. Basic IoT network consists of various layers; one among them is the device layer which is the base layer responsible for gathering the data from physical world through the sensors. Various sensors can be deployed into an area depending upon the type of application it is used for. Various homogenous sensors can be made into clusters due to which it would be beneficiary in terms of bandwidth and power consumption, number of internet connections required, fault tolerance, longevity of devices etc. This paper presents a comparison between various algorithms used to cluster the sensors at device level. [ABSTRACT FROM AUTHOR]

By using thoracic electrical bioimpedance (TEB) analysis stroke volume calculated for sudden cardiac arrests. In this paper proposed robust normalized diffusion least power algorithm (RNDLPA) is proposed to get high resolution components. In clinical scenarios, TEB components are contaminated with various artifact components, which interrupts the identification of several features in stroke intensity. The number of multiplications is also one of the key elements for monitoring in health care applications. Hence by proposed RNDLPA algorithm it reduces computational complexity, stability is improved and also steady state error rate is reduced. To eliminate noise components various adaptive cancelers are presented and they are compared with proposed algorithm. Further to show performance improvement signal to noise ratio (SNR) improvement is taken into consideration it gets better values 14.3594 dB and 12.3551 dB for respiratory noise and muscle noise respectively when compared to existed techniques. [ABSTRACT FROM AUTHOR]

Xilinx System Generator (XSG) integrates with MATLAB Simulink to build a complex system through Xilinx and Simulink blocks. This paper presents a hardware implementation of various image processing techniques to modify and extract the pixel information from various input images in run time. Complex coding strategies are eradicated with integrated system. The intricacy in structural design is reduced to a huge extent using Xilinx System generator. The reconfigurable attribute of Field Programmable Gate Array (FPGA) bags the massive advantages in image processing like co simulating with selective pixel and verified on FPGA. Image processing algorithms explored in methodical manner by performing diverse operations like edge detection, and color enhancement, complement, contrast stretching etc. The proposed image processing techniques (IPT) intended for various applications. Dissimilar image processing techniques are processed by FPGA and displayed on Video Graphics Array (VGA). Image processing system is designed in XSG, hardware description language (HDL) like VHDL & Verilog code is generated by processing system by system generator token. Design is implemented on Artix-7 and targeted on Basys3 reconfigurable hardware (XC7A35T-1-CPG236C). [ABSTRACT FROM AUTHOR]

We live in times where technology is advancing at an unprecedent rate. From online communication, shopping, data sharing, cashless payments to transmitting sensitive information through internet, everything is being done online. These online methods of data transfer are always vulnerable to online phishing and frauds. Therefore, it becomes very imperative to devise ways for safe and secure transmission of data. To keep up this, new and secure cryptographic schemes are the need of the modern age. In this paper, the author presents a new improvised encryption technique which makes modifications in the traditional approach of the Caesar cipher by combining it with extended Hill cipher. Additionally, it makes use of the Linear congruential method to generate the randomized secret key. [ABSTRACT FROM AUTHOR]

The paper considers the main methods of testing a software product at the development stage. The methods are compared with typical architectures of software products. The optimization problem of the testing process is solved on the basis of the algorithm development that presents the process under consideration at a more detailed level. The optimization of the algorithm is presented on business processes by BPMN 2.0 notation. [ABSTRACT FROM AUTHOR]

The article presents the software implementation of the bioinspired ant colony algorithm, which is used to form the composition of the multiversion software modules. It is shown that the reliability of programs and performance indicators of multiversion software significantly depend on the results of the design stage, at which a decision is made on the formation of the structure of the program complex and the choice of the composition of multiversion software modules. The solution of these problems is supported by the developed software application. The application is focused on the user, who sets all the parameters of the multiversions. Within the framework of the bioinspired approach, the paper presents the main functions implemented in a software package using ant colony algorithms. [ABSTRACT FROM AUTHOR]

Active Contour Model is the most original algorithm among the class of Contour Finding methods. Its capability is tested against wound parameter detection given image data. The performance is less satisfying, in general, few perimeters are found correctly, but the rest are far from ideal. However, through some tuning by adding an extra pre-processing step, the result can be improved. Image Interpolation is used to achieve this improvement. This paper reports how significant the improvement is. [ABSTRACT FROM AUTHOR]

One of the measurements for evaluating time series forecasting performances is the mean square error (MSE). This paper proposes an algorithm to find the smallest MSE. To capture the four components of the time series data (namely, seasonal variation, trend variation, cyclical variation, and random variation), the exponential smoothing method was used. This method uses three factors for smoothing where the data are the smoothing factor, 0 < α < 1, the trend smoothing factor, 0 < γ < 1, and the seasonal change smoothing factor, 0 < β < 1. First, all possible combination values of smoothing factors will be generated to one decimal digit. After that, the smallest MSE of those combinations will be determined by using an algorithm and observing their convergence pattern. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modified mean-variance portfolio selection procedure using the proximal gradient method. This novel selection procedure has the advantage of encouraging sparsity in the entire portfolio usingl0−norm while requiring only O(n) storage. The proximal gradient method (PSG) introduces a multiple damping gradient (MDG) method to reduce the computation time, with Lipschitz constant serving as the step size, and iterative thresholding (IHT) as the proximal method to induce the sparsity of the portfolio. To find the optimal portfolio, an efficient algorithm is developed. The performance of the proposed selection procedure is illustrated by its application to real-life data set. It is shown that the proposed portfolio performs consistently better in comparison with other methods. [ABSTRACT FROM AUTHOR]

The paper is devoted to the modernization of the previously developed algorithm for detecting pulses in geoacoustic signals against a background of noise. The algorithm is implemented as a computer program. Geoacoustic signals are recorded in Mikizha Lake in Kamchatka using a combined receiver. The receiver simultaneously records acoustic pressure and three orthogonal components of its gradient at a point in the medium. From the registered signals, an input data series is formed in various ways for further processing by the program. The paper proposes a new way of forming the input series based on the Umov–Poynting vector. The improved algorithm detects hundreds of times more pulses in highly noisy signals. This increases the accuracy of determining the direction to their sources. Detailed histograms of the spatial location of geoacoustic emission sources are constructed. Note that the algorithm can be applied to pulsed acoustic signals only if they are registered by a combined receiver, i.e, the values of pressure and its gradient at the point of the medium under study must be known simultaneously. [ABSTRACT FROM AUTHOR]

Palm oil is an important commodity for Indonesia. Therefore, oil palm cultivation is a supporter of the success of oil palm commodities in producing products. The harvesting process is an activity that must be considered because it affects the production and quality of oil palm fresh fruit bunches. For this reason, the sorting of oil palm fresh fruit bunches is very important. This paper tries to classify the maturity level of oil palm fresh fruit bunches through image management using the Linear Discriminant Analysis (LDA) algorithm. LDA algorithm is an approach used to classify data into several classes. The steps taken in this paper are starting with the transformation of the color space from RGB images to L*a*b images, then image segmentation with thresholding and feature extraction with HSV. Image transformation to HSV aims to make it easier to classify image characteristics based on hue and saturation. Furthermore, the LDA algorithm will perform a dispersion matrix analysis in order to obtain optimal projections to enter into spaces to look for patterns that can be separated. This paper finds that the classification of oil palm fresh fruit bunches image processing using the LDA algorithm produces an accuracy rate of 85%. [ABSTRACT FROM AUTHOR]

This paper introduce the comprehensive overview of the Arithmetic Optimizer Algorithm (AOA). Laith Abualigah et.al introduced novel meta-heuristic approached of AOA by arithmetically modelled and executed to implement the optimization developments in an extensive array of spaces. The review included the application and variants of Arithmetic Optimization Algorithm to solve complicated engineering problems. To showcase its applicability the performance of AOA is checked by the Laith Abualigah et.al on 29 benchmark functions and actual world engineering design problems. This review paper also given the idea how AOA has been evaluated by the analysis of enactment, convergence performances and the computational involvedness. This paper also review all the experimental results of AOA which are tested on the different uni-modal and multimodal benchmarks functions and also review the usefulness of AOA for solving the challenging engineering optimization problems compared with others optimization algorithms. This paper also covers all the parameters values that has been used for the comparative algorithms. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become an easy target. As there are numerous devices, management is hard and security problems arise. There are various encryption algorithms to provide the security of data on an IoT device. One of the encryption algorithms that is used for IoT devices is the AES algorithm. Although the AES algorithm is not a lightweight algorithm, it can effectively be used to protect the data of IoT devices. In this paper, a Bluetooth Low Energy (BLE) module is used as an IoT device. The AES algorithm is used to encrypt the data that is generated on the BLE device. There are two options for encryption: hardware-based encryption and software-based one. The aim of the paper is to show the hardware implementation efficiency of the AES algorithm on an IoT device by comparing it with software implementation. As a result, the hardware implementation of the AES algorithm is efficient when using the proper hardware infrastructure. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) has become an easy target. As there are numerous devices, management is hard and security problems arise. There are various encryption algorithms to provide the security of data on an IoT device. One of the encryption algorithms that is used for IoT devices is the AES algorithm. Although the AES algorithm is not a lightweight algorithm, it can effectively be used to protect the data of IoT devices. In this paper, a Bluetooth Low Energy (BLE) module is used as an IoT device. The AES algorithm is used to encrypt the data that is generated on the BLE device. There are two options for encryption: hardware-based encryption and software-based one. The aim of the paper is to show the hardware implementation efficiency of the AES algorithm on an IoT device by comparing it with software implementation. As a result, the hardware implementation of the AES algorithm is efficient when using the proper hardware infrastructure. [ABSTRACT FROM AUTHOR]

This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features. [ABSTRACT FROM AUTHOR]

The numerical performance of an algorithm for the inversion of a generalization of the regularized incomplete beta function (the noncentral beta distribution, Bp,q(x, y)), is discussed in this paper. The algorithm uses as starting point the asymptotic approximations obtained in [1] for the inversion problem: find y in the equation Bp,q(x, y) = z, where p, q, x, z are given. The asymptotic approximations are combined with Newton iterations in the resulting algorithm. [ABSTRACT FROM AUTHOR]

This paper presents methods for software licenses & patents issuance & validation using the peer-to-peer block chain technology and leveraging the properties of non-fungible token in order to state proof of ownership of the software licenses and patents. Further, the paper illustrates the implementation of different hashing algorithms to secure the confidential and non-public facing data associated with software licenses and patents and a mechanism to do validation of the ownership and authenticity of software licenses and patents on public block chain. By tokenization of these license and patent documents the intellectual property creators will have the capability to only reveal the data to the owners of their choice or whom they can trust to reveal the original data of these documents and can provide only ownership to the other holders of the non-fungible token. The paper will also illustrate brief comparisons between the gas requirements of different on-chain hashing algorithms and then using them while minting the non-fungible tokens for the licenses and patents. [ABSTRACT FROM AUTHOR]

The term optimization refers to find the best possible solution. Gradient computation and Nature evolution rule are the two main bases of almost all optimization algorithms. Single objective optimization problems deal with optimization of one objective function whereas multi-objective problems deal with two or more objective functions to be optimized. Many algorithms have been developed till now for the optimization of multi-objective problems. In this paper, we will review and compare two multi-objective evolutionary algorithms SPEA2 (Strength Pareto Evolutionary algorithm) and PAES (Pareto Archived Evolution strategy). Evolutionary algorithms use fitness function, selection, crossover, and mutation procedures to generate a new population. SPEA2 algorithm uses the population set as well as an external set known as an archive to select and generate the next population members. PAES uses a local search approach to generate new solutions and maintains an archive to refer to previously generated solutions and thereby find the dominance ranking of current and newly generated solutions. Four sets of problems (DTLZ1, DTLZ2, ZDT1, ZDT2) will be solved using these algorithms and the results of these algorithms on these test problems will be compared in this paper. [ABSTRACT FROM AUTHOR]

S.L. Sobolev [1] firstly posed the problem of finding an extremal function for an interpolation formula and calculating the norm of the error functional in the space W 2 m . In this paper, an extremal function of the interpolation formula was found in explicit form in the Sobolev space W 2 m , functions for which the generalized derivatives of order m are square integrable. In the present paper, we consider the problem of finding the norm of the error functional for interposional formulas of Hermite type in the space of S.L. Sobolev W ˜ 2 (m) (T1). [ABSTRACT FROM AUTHOR]

This paper analyzed the feature selection for reducing the number of input variables when developing a predictive model. Boruta Algorithm is using in this paper as a wrapper around a Random Forest classification algorithm. Boruta algorithm is one of the algorithms used to determine the significant variables (feature selection) in a classification model in the machine learning approach, as supervised learning. Our results show that on the German Credit Data from the UCI Machine Learning with 20 variables, feature selection using Boruta Algorithm with Python Programming obtains 4 significant features. [ABSTRACT FROM AUTHOR]

Proper measurement is essential to ensure a good performance of a machine to produce a high-quality product. To have a good measurement, the first thing to consider is the datum since it is used primarily for manufacturing and inspection setups. However, the common datum as defined by ASME and ISO somehow is not practical to be used for a rotating machine tool. Thus, this paper focuses to define an appropriate datum for a 3D simulated surface by including a possible source of variation or error that may exist. To achieve the objective of this paper, surface reversal measurement is used as the main approach along with aid of the correlation function in the image-processing tool. Development of the surface reversal algorithm begins as two simulated surfaces are created and undergo surface correlation to achieve the best match point between both surfaces. Then, after one of the surfaces is translated to the desired position, surface subtraction is conducted to determine the inclined plane (real datum) of the simulated surface. The algorithm ends with the validation process through surface characterization by comparing the Sz value. As a result, both surfaces show different Sz values although both have the same surface topography and result for A minus with the real datum (LSPAB) providing less error on the surface topography data compared to A-minus with AB. This evaluation proves that an appropriate datum is necessary for every measurement so that the result obtained is accurate. Moreover, the objective to improvise the datum definition is successfully achieved since the error found along with the datum has been removed. [ABSTRACT FROM AUTHOR]

The emerging growth of edge devices and the expansion of IoT systems produced the demand to enhance their security algorithms to run on diverse applications and appliances. Lightweight cipher algorithms are mainly designed for IoT-constrained devices. Thus, due to the new services and applications of IoT that require extensive networks and devices, the need for efficient lightweight cipher algorithm implementations is a critical issue. In this paper, an enhanced cipher LED-128 algorithm based on Galois Fields (GF) operations is presented. The mathematical model of the LED algorithm simply requires a slight execution time for encryption-decryption processing. The Enhanced LED algorithm is applied with 64 bits of block plaintext and 128 bits of the key size which turn out 64 bits block size of ciphertext. The proposed algorithm is presented to advance the randomness of cipher text by enhancing the key schedule based on GF operations. The developed key schedule is enhanced to generate a robust key for the proposed algorithm. The NIST statistical tests have been examined and experimented for the proposed enhanced algorithm by utilizing diverse keys with distinct cipher text, which passed the randomness for each test. The proposed algorithm demonstrated a high confusion and diffusion based on the correlation coefficient analysis. The executed time for the encryption and decryption processes of the proposed enhanced LED algorithm is concentrated at minimum compared to the original LED-128 cipher algorithm. Hence, the proposed enhanced algorithm can be implemented for diverse IoT systems and can reach the trade-off between cost and performance as an efficient lightweight algorithm. The results of the enhanced algorithm are benchmarked with the current LED 64/128 algorithms and presented significant improvements in the performance compared to original design of the LED cipher algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to apply two variants of the hybrid optimization algorithm, namely Chaotic Honey Badger Algorithm (CHBA) and Group Teaching Optimization Algorithm (GTOA) to solve the Uncapacitated Facility Location Problem (UFLP). We enhance using chaotic maps on the HBA to strengthen the search process and hybridization with GTOA leads to a fast exploration process in finding the best solution. There are two variants of hybrid CHBA and GTOA applied. In the first variant, GTOA is applied to determine the initial population of CHBA, then for the second variant, CHBA is used to determine the initial population of GTOA. The performance of these two hybrid variants is used to solve the Uncapacitated Facility Location Problem (UFLP). There were three types of data for UFLP such as small data, medium data, and large data. Furthermore, by comparing the best results it was concluded that hybrid variant GTOA-CHBA has better solution than CHBA-GTOA. The results of the hybrid were compared with other algorithms such as Cuckoo Search (CS), Flower Pollination Algorithm (FPA), and Teaching Learning Based Optimization (TLBO). It was concluded that the average of hybrid GTOA-CHBA better than each algorithm. In terms of best and PRDmin, the hybrid GTOA-CHBA on several data has good results from the other algorithms. [ABSTRACT FROM AUTHOR]

Three contributions are presented in this paper. Designing two cascade controllers to control the position of two brushless DC motors (BLDC) working together at the same time is the first contribution. The second is using a sliding mode speed synchronization controller to connect these motors and fine-tune the precise synchronization of their movement on the axes. In the third contribution, the best parameter values for the two cascade controllers and the synchronization controller are extracted using the salp swarm algorithm (SSA) and the objective function Integral Time Absolute Error (ITAE) to produce the most accurate results. the results were compared by using this algorithm with manual tuning. The results of the simulation revealed that the tuning that was done by the salp swarm algorithm (SSA) was superior to the manual tuning in terms of performance and overshoot as well as any deviation in tracking the path to reach the intended position. note that the drop in velocity (0.11 rad/sec) at a time of 15 seconds because of the load causes deviations (0.343 rad for BLDC 1 and 0.3496 rad for BLDC 2) For manual tuning. while the drop in velocity is very small (0.0037 rad/sec) at the same time causes deviations very small (0.003 rad for BLDC 1 and 0.0052 rad for BLDC 2) for second tuning. [ABSTRACT FROM AUTHOR]

An algorithm for modeling of diffusion, thermal conductivity and viscosity is presented in the paper. Its purpose is demonstration of the transport phenomena in fluids and some basic results in statistical physics in the physics education. The realization of the algorithm as computer program is simple permitting its application by students with only basic knowledge in programming. At the same time the results from the model describe correctly the studied phenomena. [ABSTRACT FROM AUTHOR]

The paper describes our program implementation of Global Sequence alignments of DNA sequences using the Needleman-Wunsch algorithm. This modified Neeedlenan-Wunsch algorithm is designed to align multiple sequences in parallel with OpenMP. The algorithm supports different sequence sizes by scanning and equalizing them simultaneously. The results of our experiment are obtained by comparing Sars-CoV-2 fasta files in a versatile, speedy and convenient manner. The program is adapted for optimal performance when aligning Sars-CoV-2 sequences. We analyze program hotspots, multithreading execution and potential bottlenecks using the Intel vTune Profiler. [ABSTRACT FROM AUTHOR]

The mosaic image is an essential process for merging several images, especially for very high-resolution satellite images, as its width swath is small. The main issue of mosaic images is the geometric accuracy between the images. This paper proposed a sub-pixel geometric evaluation to assess the geometric shift between the images. It is an essential step for image registration. Image registration is a process to determine a geometric shift involving two or more images with the same object but different acquisition dates, viewpoints, and sensors. The basic idea of image registration in the frequency domain is estimating the shift between the test image and the reference image. In this study, we used the phase cross-correlation function to determine the geometric shift between two different acquisition Pleiades images from each polygon. Phase correlation is based on the translation property of the Fourier Transform. It transforms the displacement of two correlated images in the spatial domain into a phase difference in the frequency domain. The phase correlation algorithm calculates a phase difference map containing a single peak. The peak location is proportional to the value of the shift between the two images. Several Pleiades images were used in the experiment. The image processing level is an ortho-image, which is already pan-sharped. As a result, the highest geometric shift is on Polygon 2 based on the R-value, and the lowest error value is in polygon 1. The experiments also showed that the most significant error value is in the blue band, and the lowest is in the red band. The average geometric shift between the two Pleiades data is 1.187 pixels, with the most shift located in the blue band with a 1.282 pixels shift, and the Root Mean Square Error (RMSE) is 1.8. [ABSTRACT FROM AUTHOR]

Multispectral optical remote sensing images are often contaminated by haze and cirrus, especially for tropical regions such as Indonesia. This will affect the image quality and reduce the accuracy of the interpretation of the existing data. A number of methods for performing haze detection and haze removal have been developed by experts. However, haze detection and removal still remain one of the most important challenges for the correction of multispectral optical data. Haze detection and removal are also needed, especially in producing cloud-free mosaic images. A method to perform haze removal on SPOT 6/7 images using the haze index algorithm is presented in this paper. The haze removal method is developed based on the reflectance values of the blue and the red bands of SPOT 6/7 imagery. A new algorithm with new coefficient is proposed to remove the haze on SPOT 6/7 imagery. Haze removal is done by using haze index algorithm and haze threshold limit value. The experimental result demonstrate that the new coefficient used in the haze index algorithm give better result in performing haze removal on SPOT 6/7 imagery. [ABSTRACT FROM AUTHOR]

Football is a famous sport played around the world that involves various actions from defending to attacking the ball to score goals. Various rules like half-side, No hands, Throw-ins Direct and Indirect kicks, etc. are followed. In a game, players might get yellow-cards and red-cards which serves as warning and elimination from that particular game. There are-many factors like shots, possession, pass accuracy, yellow and red cards, etc. that decides which team wins in a match. In the data that we worked on,the Rating is calculated from the parameters which are mentioned. Machine learning regression algorithms have been applied to predict the Rating feature. The goal of this paper is to know which regression algorithm performs better to predict the Rating variable. Evaluation metrics like Root mean square error (RMSE), Mean absolute error (MAE), and R square (R2) are used to identify the best algorithm among the applied algorithms. [ABSTRACT FROM AUTHOR]

Cloud computing has always been an area of interest for the researchers and the industry oriented professionals. The cloud computing has its primary concern to deliver better performance with low energy consumption and least SLA violations. To rescue the rising challenges in the cloud computing, virtualization is an important process when the Physical Machines (PMs) are expected to work at a higher speed. This paper represents two co-operative algorithms: Cosine Elastic Clustered Scheduling Algorithm (CESCA) and Threshold-Modified Best Fit Algorithm (T-MBFD) for the VM selection and reallocation. CESCA clusters the VMs to be migrated based on three attributes namely CPU-Utilization (CPU), Associated RAM (AsRAM) and Bandwidth Utilization (BU) and the co-relation between them using cosine similarity. CESCA also prioritize the created clusters for the allocation. T-MBFD uses a logarithmic scale to neutralize the priority and the density of the created cluster to finally set the VM to its best suitable PM. The evaluation of the proposed algorithm is done using power consumption and total number of migrations. [ABSTRACT FROM AUTHOR]

Micro-expressions are unintended, impulsive, and fleeting facial expressions that have potential of disclosing the true feelings that people frequently strive to conceal. Experts have been paying increasingly close attention in recent years to the significance of the micro-expression as it contains abundant psychological information whose recognition has potential application value in the fields of lie detection, criminal investigation, neuromarketing, psychotherapy, teaching and learning. This paper analyzes the prominent applications of Micro-expression recognition and basic differences between two forms of facial expression (Macro-expression and Micro-expression) are also highlighted. A thorough analysis of mainstream recognition algorithms is also provided by elucidating each of their benefits and limitations. The results of this survey present readers with knowledge of Micro-expression recognition and potential application as well as related algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we studied the cyclicity parameter of dihedral group D2n and we introduced algorithm to compute these parameters. [ABSTRACT FROM AUTHOR]

In this paper, a new method of conjugate gradient methods is proposed that is used in minimizing functions to solve nonlinear fuzzy equations. This method is called hybrid conjugate gradient method, where it is hybridized using convex structures, The properties of global convergence and descent have been proven using some mathematical hypotheses, The numerical results and the figures showed a clear superiority of the proposed algorithm compared to the FR and KH algorithms for solving nonlinear fuzzy equations. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are susceptible to various attacks due to node deployment in hard and hostile environments. In order to mitigate the impact of those offensives, the security of the data in WSNs depends heavily on cryptographic techniques. As a result, different cryptographic methods ensure network security. Symmetric and asymmetric are the two methods of cryptography. However, there are still some issues. For example, the symmetric method needs more storage to distribute the key to all nodes in the network, while the asymmetric method needs more computation. In this paper, a hybrid cryptographic algorithm is proposed. It combines high security with fast decryption and encryption via the use of symmetric and asymmetric cryptographic methods to minimize memory overhead and computation. It enables the use of three cryptographic objectives: integrity, confidentiality, and authentication. The idea is to encrypt using Rivest Cipher 4 (RC4) and Blowfish, ensure integrity using Message Digest 5 (MD5), and authenticate using Elliptic Curve Diffie Hellman (ECDH). A Raspberry Pi and two PCs are used to check the performance of the proposed algorithm. It is better than many other existing algorithms in terms of throughput, ciphertext size, encryption time, and decryption time. By using a Raspberry Pi and a 184,162 B plaintext size, the proposed algorithm achieves a reduction in ciphertext size of 37% when compared to an algorithm named HA, a reduction in encryption time of 88.5% when compared to an algorithm named HCA, a reduction in decryption time of 39.5% when compared to HCA, and boosts the throughput by 866% compared to HCA. [ABSTRACT FROM AUTHOR]

The improving of a company's earnings is considered as one of the important Challenges that facing these companies in terms of the amounts of their manufactured products. Therefore, it is very essential to determine which units of production that gives the reasonable profit. In this paper, data Mining (DM) and decision tree were used to define the products quantities of the Iraqi carpet company in the year 2016 that led to the acceptable profit using decision tree algorithm. This said algorithm is determined the product amounts that can gave the required profit namely; low permeable carpet, high embroidered blanket and high woven blanket with their quantities138, 1172, and 5025 respectively. In order to check the validity of the acceptable profit, these products were tested by the Naive Bayes algorithm which check their probabilities as probability of P (yes) and P (no) got 0.0208 and 0 respectively. [ABSTRACT FROM AUTHOR]

Because of its simplicity and ease of implementation, the Nave Bayes algorithm is widely employed in text categorization. Multivariate Bernoulli and multinomial models are the most common event models utilized by Nave Bayes for text categorization. A huge number of research use the multinomial model plus Laplace smoothing simply because they believe It outperforms the multivariate model under practically all circumstances. This paper examines Nave Bayes event models and smoothing approaches from a new view in order to throw some light on this generally held belief. To further understand the differences between Nave Bayes events models, their classification performance is examined on datasets in English and Turkish. The findings of our comprehensive trials reveal that the multinomial model does not always perform better. When paired with a proper smoothing strategy, a multivariate Bernoulli model, on the other hand, can perform well under a variety of size criteria for training data. [ABSTRACT FROM AUTHOR]

With the evolution of cloud computing, the dominant virtualization technology for data centers has shifted from virtual machine to container. Despite its success, Cloud Computing falls short of several Internet of Things requirements (IoT). Edge Computing appears to be a complement to the Cloud, filling gaps in the IoT scene. By 2025, it is expected that 75 percent of the generated data will be processed in locations other than cloud servers or datacenters. As a result, the number of edge computing nodes is expected to grow rapidly. Among others, Kubernetes was created with the goal of automatically deploying and managing cloud applications created using container runtime techniques such as Docker. Although Kubernetes is designed for cloud computing environments, it does not have adequate system support for edge computing. To use Kubernetes in edge computing environments, edge computing nodes must be managed in a more efficient manner. The Hybrid Round-Robin multi-level queue scheduling algorithm (HRRMLQ) is proposed and implemented in this paper to schedule containers to a single board computer (SBC) cluster based on their priorities. HRRMLQ uses a hybridization of round robin and multilevel queue scheduling algorithms to categorize IoT apps as high or low priority. HRRMLQ classifies IoT apps as either high or low priority. To alleviate the starvation problem that traditional multilevel queues suffer from, HRRMLQ assigned 4 quantum to the high priority queue and 2 quantum to the low priority queue. Experiments show that HRRMLQ can schedule high priority pods first while also reducing the starvation problem that multilevel queues may experience. [ABSTRACT FROM AUTHOR]

Cloud users and service providers are the leading players in the cloud computing environment. This environment comprises data centers, hosts, agents and virtual machines. The cloud user's application of varied loads is leased on the provider's resources. Scientific applications are large-scale complex workflow problems that demand more computing power. The cloud fulfills the workflow requirements of huge availability and increased computational power. One of the most crucial issues of cloud computing is scheduling tasks for the system's effective functioning. This paper reviews several existing task-scheduling techniques based on diverse metrics. This work will help the investigators to gain a better understanding of task scheduling techniques. In order to boost an algorithm's performance, a few strategies are offered. [ABSTRACT FROM AUTHOR]

Recent developments in wireless networking and communication have demonstrated improvements in many areas of our lives at a fast pace. Yet still the wireless industry is still not where it wants to be despite these advancements in technology, but have nonetheless accomplished a lot in such a short time and are continually working on improvements and new innovations. Because wireless devices use batteries with a finite amount of power, energy efficiency in these networks becomes a concern. In this paper, we present a design of energy aware on-demand routing protocol (EAORP), a new energy efficient algorithm that aims to overcome the shortcomings of DSR and provide a scalable traffic based and energy aware routing algorithm which aims to address energy issues in DSR by making it more aware and sensitive to nodes' energy, traffic loads, and transmission power management. [ABSTRACT FROM AUTHOR]

In Wireless sensor networks (WSN) tiny sensor nodes are deployed in remote locations to collect information like temperature, moisture level, water level etc in the selected area. Information collected by sensor nodes is highly sensitive and vulnerable to different security attacks including Denial of Service (DoS) attack. Due to resource constraints providing security to the WSN is a difficult task. Successful authentication schemes used in wired networks are not suitable for WSN. Most of the available authentication schemes use more computations for encryption and decryption. This paper proposes a novel token-based authentication algorithm to provide security against Denial of Service attacks. The proposed algorithm has three different phases namely Registration phase, Authentication phase and New token creation phase to preserve user anonymity and provide secure authentication against DoS attacks. A simple private key based cryptography algorithm with minimal computation has been used for encryption and decryption which makes this algorithm more suitable for WSN. [ABSTRACT FROM AUTHOR]

This paper describes an novel classification approach of EEG signals for the detection of epileptic seizures using K-Nearest Neighbour and Artificial Neural Networks. : The total of 1372 samples are collected from the UCI repository which contain 15 attributes. The epilepsy detection is carried out with the help of two groups where group 1 is K Nearest Neighbour and group 2 is Artificial Neural Networks. K Nearest Neighbour Achieved 97% respectively compared to 88% by Artificial Neural Networks. The obtained significant value is (p<0.05). We can conclude that K-Nearest Neighbor has significantly greater accuracy when compared with the Artificial Neural Networks. [ABSTRACT FROM AUTHOR]

To predict the accuracy percentage of Short Message Services (SMS) spam detection using machine learning classifiers. Two ensemble learning algorithms named random forest algorithm and naive bayes are applied to data. The algorithms have been implemented and tested over a dataset which consists of 5574 records. Ensemble learning methods combined several models trained with a given learning algorithm to improve accuracy. After performing the experiment as result shows mean accuracy of 89.75 % by using random forest and compared naive bayes algorithm mean accuracy is 88.05% for SMS spam detection. There is a statistically significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. This paper is intended to implement Innovative Classification for prediction of SMS spam detection. The comparison results shows that the naive bayes algorithm has appeared to be better performance than Random Forest Algorithm. [ABSTRACT FROM AUTHOR]

There is enormous amount of data is available on the internet today in the agricultural domain. About the history of agricultural produce, type of seeds, the fertilizers used, Industrial agriculture, monoculture, agronomy, information on agrochemicals. Plants being grouped into Angiosperms, gymnosperms, perineal, non-perineal etc. In such situation the need for Information retrieval systems is high as the Questionnaires posted by the end user has to be rightly understood, and to provide accurate results with the available colossal data is at its peak. Multiclass classification of agricultural Pomegranate Questionnaire. This paper intends to discuss on the different types of algorithms that can be employed in the evaluation of the Pomegranate Questionnaire raised by the end user. Farmers, being the end user can request in the form of Questions regarding the name of the fruit, details such as the growing climatic conditions required, vitamins contents and mineral components crop growing fertilizers, suitable crop rotation techniques taking into consideration of climate, humidity, temperature, the soil of the region where he is residing etc. It proposes the different types of algorithms can be used for multi class question classification having classes i.e. Name, Descriptive, Numeric, Location and Entity: Other. [ABSTRACT FROM AUTHOR]