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Cloud computing has become a popular approach for processing data and running computationally expensive services on a pay-as-you-go basis. Due to the ever-increasing requirement for cloud-based apps, appropriately allocating resources according to user requests while meeting service-level agreements between customers and service providers has become increasingly complex. An efficient and versatile resource allocation method is required to properly deploy these assets and meet user needs. The technique of distributing resources has become more arduous as user demand has increased. One of the key areas of research experts is how to design optimal solutions for this approach. In this paper, a literature review on proposed dynamic resource allocation approaches is introduced. [ABSTRACT FROM AUTHOR]

In this paper we present an application of two Computational Intelligence Algorithms, namely Particle Swarm Optimization (PSO) Algorithm and Differential Evolution (DE) for finding and optimal solution to two optimization problems that occur in a paper industry. The first problem deals with the economic optimization of a hypothetical but realistic Kraft pulping process and in the second problem we have considered the optimization of Boiler load allocation problem. Both the problems form an integral part of paper making process. The simulation results show the efficiency and time effectiveness of DE and PSO. [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]

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]

The software testing process is not only important but also a complex stage SDLC (software development life cycle) process. For estimating the activities of the system in the software testing stage testers implement test scenarios as input for the system under test (SUT). For improving the superiority of the system and also finding faults implementing test-cases shows an energetic role. Our research work target towards implementation of test cases automatically by combinational Unified Modeling Language (UML) diagram so that it can display higher effectiveness in starting phase of the system development procedure of model-based testing. This research uses UML diagrams like Use Case Diagram (UCD), Activity Diagram (AD), and a combination of both generates System Testing Graph (STG). The combination of these two diagrams has been proved as the well-suited diagrams to implement test cases from the earlier combination graphs. Our technique suggested in this research paper is Uniformed Graph Traversing (UGT) Algorithm that is combined with Level Order Traversal (LOT) Algorithm to implement predictable test scenarios. In this research paper, we prove that the UGT algorithm is useful to implement test scenarios, and also these test scenarios are providing precise outcomes; each vertex existing in the test scenarios includes some situation. In this proposed approach we also disclose faults for example decision fault, fault of dependency, interaction fault, and loop fault which results in assessment effort reduction. The implemented test scenarios are directed to one requests controlling tool which can be in contradiction of the requests. It also protects time period and work and also, raises the value of implemented test scenarios, thus enhances the complete routine in a testing phase. [ABSTRACT FROM AUTHOR]

Infrared (IR) spectral data are always influenced by undesired random and systematic variations. As such, preprocessing of spectral data is normally required before chemometric modeling. Two most widely used pre-processing techniques, i.e. scatter-correction methods and spectral derivatives, were used to pre-process 150 IR spectral data of paper. The algorithms investigated in this preliminary study are Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), Savitzky-Golay (SG) and Gap-Segment (GS). The visual examination of the clustering among three studied varieties of paper, i.e. IK Yellow, One Paper and Save Pack, is accomplished via Principal Component Analysis (PCA). Overall, separation of the three varieties of paper is greatly enhanced after pre-processing. The most significant improvement is obtained with pre-processing via 1st derivative using SG algorithms. [ABSTRACT FROM AUTHOR]

Knowledge management system (KMS) is a method that can help companies manage knowledge assets within the company. A KMS in addition to being able to manage knowledge assets is also expected to be safe from unauthorized parties, easily accessible when needed and become a place for sharing knowledge for employees in the company. In its application, the documentation of knowledge which is the result of employee training at PT X still uses paper. So, when the company wants to implement knowledge sharing, it will be hampered because it is difficult to distribute paper documents to all employees. Based on this, the authors designed a website-based KMS. The KMS design in the modeling uses UML and the implementation uses the php and MySQL programming languages with the help of the Laravel framework and the SAW algorithm. This KMS is equipped with features that make it easier for the Company to implement knowledge sharing. The results of this KMS design, produce a system that has been tested using black box testing resulting in a 100% tested success percentage and user acceptance test testing that is tested on four assessment criteria with an average user satisfaction percentage of 82% and includes good criteria. [ABSTRACT FROM AUTHOR]

The paper investigates images of textures with a defect, characterized by a sufficiently prominent structure. A procedure for detecting a defect in a textured image using an intellectual procedure for assessing the similarity of different parts of the tested image scanned with a sliding window is proposed. It is assumed that an area of the image that contains a defect in whole or in part will be the least similar to the other areas considered. To assess the similarity of images in the paper, the previously proposed measures are used, built on the basis of the values of the components and the magnitude of the gradient, considered as a sample from a random variable with the Weibull distribution. The effectiveness of the proposed methods is demonstrated by experimental investigation. [ABSTRACT FROM AUTHOR]

Data Mining is one of the greatest critical aspects of automatic disease diagnosis and disease prediction. It includes data mining algorithms and techniques to study medicinal data. Recently, liver disorders have frequently increased and liver illnesses have become one of the greatest deadly diseases in different countries. In this paper, we collected 534 instances of Iraqi liver patients from Baqubah Teaching Hospital. This collected dataset includes several elements such as Age, Total Bilirubin, Direct Bilirubin, Gender, Aspartate Aminotransferase, Total Proteins, and Albumin and Globulin ratio. This paper explores the early prediction of liver disease using various data mining algorithms. The main aim of this paper is to compute the performance of many data mining techniques and compare their performance with the performance of The Indian Liver Patient Dataset. We applied three classification algorithms and parameters like accuracy, precision, recall, and f-measure were investigated to evaluate the performance of these classifiers. [ABSTRACT FROM AUTHOR]

In this paper, the proposed system to control the robotic by using an adaptive controller and using the artificial neural network with optimized PID by using the PSO is used to regulate the motion of a robot. The problems of reverse kinematic is solved by using a proposed method which is important to determine the robotic arm joints angle values , when traced in a different path. The D-H approach Devavit – Hartenberg can be used to solve the Forward Kinematics problem. The dynamic model was computedusing the Lagrange model, which is a mathematical model. Computing a dynamic model was a crucial step in designing a robotics control. The proposed adaptive controller, which is based on a PSO-optimized artificial neural network, is utilized to increase system response. In this paper designing a GUI by using MATLAB to compute the inverse and forward kinematics and to compute the trajectory planning. Derived the inverse kinematic and the forward kinematic by traditional methods is complicated, by applying the proposed method is an easier and fast way. The main problem in the dynamic model was the non-linearity, so by using the proposed method, this method will selects optimal parameters of the PID controller that overcome the plant non-linearity. The performance of the system show good response when using the proposed method. Rom the simulation results the overshot approaches to the zero the raising time reduced to 0.11 for the first joint1, the best settling time is 0.24 in the first joint, in joint 3 the delay time was reduced to the 0.1. Based on these findings, the suggested method outperforms other standard methods such as PID controller in terms of system performance. [ABSTRACT FROM AUTHOR]

An algorithm for constructing super-large semantic networks has been developed in current work. Algorithm was tested using the "Cosmos" category of the Internet encyclopedia "Wikipedia" as an example. During the implementation, a parser for the syntax analysis of Wikipedia pages was developed. A graph based on list of articles and categories was formed. On the basis of the obtained graph analysis, algorithms for finding domains of high connectivity in a graph were proposed and tested. Algorithms for constructing a domain based on the number of links and the number of articles in the current subject area is considered. The shortcomings of these algorithms are shown and explained, an algorithm is developed on their joint use. The possibility of applying a combined algorithm for obtaining the final domain is shown. The problem of instability of the received domain was discovered when starting an algorithm from two neighboring vertices related to the domain. [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]

This paper presents the comparison ofselective harmonic elimination in a cascaded h-bridge multilevel (CHBMLI) inverter using PSO and TLBO optimization algorithms. The inverters are employed in a variety of applications, including high-voltage dc transmission, variable drive systems, and electrical vehicles. The inverter's output includes harmonics. PSO and TLBO algorithm is used in this paper To find out the best switching angles for a CHBMLI selective harmonic elimination. In order to get the appropriate fundamental component and get rid of low-order harmonics, this project solves nonlinear equations. These algorithms can produce the most effective triggering pulses to minimize Overall Harmonic Distortion(THD) and Selected Harmonics. If the harmonics are removed, the inverter's output will be improved. MATLAB is used to implement and compared both optimization techniques usingthe seven-level inverter. [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]

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]

In multimedia content, text play major role for transmission and it is crucial to protect text data while transmitting over network. This can be achieved by text encryption algorithm. However, the method relies on many resources that increase the processing unit time, memory, and battery power. There are so many different techniques should be used to protect confidential text from unauthorized access. This paper summarizes on the existing works which used different techniques for text encryption by using deep learning algorithms. Combination of these approaches helps to analyze different algorithms for different text, accelerates the processing time while maintaining accurate preservation and retrieval. [ABSTRACT FROM AUTHOR]

The paper explores how the daily lives of people with vision impairments are changed by artificial intelligence. They suffer a great deal in circumstances they are not aware of. When they go alone in town, people are worried about their safety. The overall aim of the system is to provide low-cost navigation assistance to blind people that give a sense of artificial vision by informing people of the artificial intelligence environment of objects. An ultrasound sensor is used to detect the distance between objects to the blind person to guide voice and vibration, which can be heard and felt by the blind person. The software can help identify objects in the world by using the voice command, conduct text analysis and recognize the document's text on paper. It can be an important way for blind people to communicate and encourage blind people to live independently. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms show outstanding performance when they are applied to optimization problems with a few variables, i.e. problems with less than a hundred continuous variables. Large-scale global optimization with continuous variables is still a challenging task for a wide range of evolutionary algorithms. Their performance decreases when the number of variables increases because the search space grows exponentially. Classic evolutionary algorithms cannot find a good solution using the allocated resources. A cooperative coevolution approach is a good tool for increasing the performance of an optimizer in solving high-dimensional problems. The approach splits the objective vector into a predefined number of parts (subcomponents), and each part is optimized by its optimizer. This paper makes an effort to solve the problem of selecting the number of subcomponents. The paper represents a novel metaheuristic for solving optimization problems with a huge number of continuous variables. The suggested approach is based on the self-adaptive cooperation of algorithms and the cooperative coevolution approach. Each algorithm has a unique number of subcomponents. The metaheuristic automatically allocates resources between algorithms during the optimization process. Algorithms optimize the same population one by one. The proposed metaheuristic is titled COSACC, coordination of self-adaptive cooperative coevolution algorithms. We have evaluated the proposed algorithm on fifteen problems from the IEEE LSGO CEC'2013 benchmark. The study demonstrates that COSACC outperforms in average cooperative coevolution algorithms with the static number of subcomponents. Wilcoxon test has proven the results of numerical experiments. We have tested COSACC performance with other state-of-the-art metaheuristics, COSACC is a competitive approach. [ABSTRACT FROM AUTHOR]

In a wireless network like ad-hoc, IEEE 802.11 Distributed Coordinated Function (DCF) is used for computation of idle channel that is predominantly built on Carrier Sensing Manifold Access Mechanism through Collision Avoidance (CSMA-CA) approach. While transmission of data among multiple stations at the same time, contention occurs and resolving the issue of contention Back-off (BO) algorithm is used and assigned on a contention window (CW).However, the current approaches for addressing the contention issue do not decrement the issue explicitly which gave rise to numerous issues and this paper provides an optimal solution to these issues. This paper proposes a Fuzzy and game theory-based on back off algorithm to multi-traffic classes, the proposed algorithm is compared with the efficient adaptive MAC protocol that is based on attributes which include but not limited to packet delivery ration, residual energy, packet drop along with throughput to upsurge the utility function, saturation throughput. [ABSTRACT FROM AUTHOR]

The emergence of Industry 4.0 leads to the latest technology, and blockchain is one of them. Blockchain guarantees transparency, decentralization and protection of a transaction. It revolutionizes industries where entrust and security have vital significance. In the blockchain network, data is recorded in the form of blocks. The first block of the blockchain network is acknowledged as Genesis-Block. The rest of the blocks are added to the blockchain in chronological order so that participants entities can keep an eye on digital transactions deprived of the interference of third parties or central authorities. Transactions on the blockchain network require a set of rules and regulations. These rules are known as consensus algorithms. The consensus algorithm is a procedure employed for common agreement, validation, verification, and confirmation of transactions in a blockchain network. Examples of popular consensus algorithms are Proof-of-Work (PoW), Proof-of-Burn (PoB), Proof-of- Stake (BoS), Directed-Acyclic-Graph (DAG), Practical-Byzantine-Fault-Tolerance (PBFT), Proof-of-Elapsed-Time (PoET), etc. Initially, the paper introduces the evolution and categorization of blockchain networks. Subsequently, a brief discussion on available consensus algorithms and the inclusion process of a node in blockchain networks. Furthermore, the paper compares existing blockchain consensus algorithms based on various significant parameters such as scalability, transaction rate, tolerated power, etc. [ABSTRACT FROM AUTHOR]

The aim of this paper is to create/proposea new hybrid root finding algorithm to solve the given transcendental equations. The algorithm proposed in this paper is built on the trigonometrical algorithm using hyperbolic tangentfunction to find a root. Couples of numerical examples and one sample computations are presented to explain the proposed algorithms, efficiency and accuracy. Implementation of the proposed algorithms is presented in a mathematical software tool Maple. [ABSTRACT FROM AUTHOR]

Mobile robots are implemented in various fields for diverse applications. Mobile robots with wheeled, legged, and hybrid types are used in industrial and service sectors. Obstacle avoidance algorithms play an important role in mobile robot navigations. In this paper, comparative study of various hybrid path planning algorithms for avoiding static obstacles are carried out for determining the most efficient path planning algorithm for the given task. A modified Probablistic Road Map (PRM) approach is combined with variants of A star algorithm to create different hybrid path planning algorithms in this paper. The nodes and connections are created using PRM approach where as the optimum path is constructed with variants of A star algorithm. This paper compares the computational time, algorithm size, memory, path optimality, etc. The proposed hybrid algorithms are compared and evaluated by simulating the obstacle avoidance by the mobile robot in a structured environment. [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]

A new swarm optimization algorithm known as the sparrow search algorithm (SSA) consider in this work. It fascinated a lot of consideration due to its extremely good attributes. However, it has some pitfalls like minimum global search ability, falling into local optima etc which were found by many researchers. In this paper,first time sparrow search algorithm (SSA) is tested on welded beam design (WBD) optimization problem. Welded beam design (WBD) optimization problem is an engineering optimization problem that has been solved by various metaheuristic algorithms such as SiC-PSO, neuromorphic optimization (NSO) technique etc. Obtained results of SSA were compared with othertechniques such as GWO, PSO, TLBO and it is found that although it works very well on WBD problem with regards to convergence speed, in relation to optimality, convergence accuracy and stability SSA gave the results which were obtained from other algorithms. So, SSA needs to be modified for future work. [ABSTRACT FROM AUTHOR]

In this paper, we study the combinatorial set of pieces formed when slicing a disk with a given number of straight cuts. The cardinality function of this combinatorial set is described by the central polygonal numbers. Using the obtained cardinality function, we construct the corresponding AND/OR tree structure and determine a bijection between the set of variants of the AND/OR tree and the considered combinatorial set. In addition, we develop ranking and unranking algorithms for the combinatorial set. [ABSTRACT FROM AUTHOR]

Digital technologies as a means of managing activities have penetrated almost all aspects of modern human life. As a rule, they are based on a system of algorithms. Even operation of neural networks is actually based on algorithms, they are formed and improved at the stage of training the network, and are used in the process of its operation (the stages of training and operation can be partially combined). In other words, neural networks also relate to control by means of algorithms, but the algorithm is created in the process of training the neural network and cannot always be restored by the network developers. To some extent, an exception can be the systems based on the so-called fuzzy mathematics: the theory of fuzzy sets, fuzzy logic. Use of digital systems for working with information and management requires a qualitatively higher level of information formalization than formalization intended for human use. The aim of the paper is to formalize the terms of "activity goal", "activity plan", "activity strategy" oriented to their application in digital control systems. [ABSTRACT FROM AUTHOR]

Investment decision is difficult to be determined as the investors need to earn highest possible return in generating profit and protecting capital. Mathematical model which can provide the ranking and proportion of the investment alternatives will benefit investors as selection guideline. This paper aims to investigate in ranking and allocating proportion of asset funds using fuzzy preference relation and weighting algorithm. The evaluation of asset fund is expressed in trapezoidal fuzzy number. The data used for this study is 30 asset funds that listed in the Bursa Malaysia. This study found that 14 asset funds are suggested based on ranking and proportion allocation obtained from this model. As a conclusion, this study can assist investors in making decision in terms of ranking and allocating asset funds. [ABSTRACT FROM AUTHOR]

Various machine learning algorithms are developed with an aim to create precise and trustworthy models and extract knowledge from data sources. Deep expertise in the field of machine learning is required for the challenging task of choosing the right algorithms for a specific dataset. There is no single algorithm that outperforms all others across all applications and different datasets. The difficulty of choosing an appropriate algorithm for a specific task in specific domain is related to the properties of the dataset. Properties of the dataset are measured through meta-features. Meta-features describe task and can provide explanation how one machine learning approach outperforms other algorithms on a given dataset. Learning about the effectiveness of learning algorithms, or meta-learning was developed to deal with this issue. Focus is required because previous research papers have not successfully identified meta-features in particular domains. In this research, we have evaluated various meta-feature characterization methodologies and have concentrated on basic meta-features. Business domain data is in the focus of this paper. We computed basic (general) meta-features and illustrated several use cases for their applications. [ABSTRACT FROM AUTHOR]

The sine and cosine algorithm has become a widely researched swarm optimization method in recent years due to its simplicity and effectiveness. Based on the advantages, the study in this paper delves deeper into the key parameters that influence the performance of the algorithm, and has implemented modifications such as integrating the reverse learning algorithm and adding elite opposition solution to create the modified Sine and Cosine Algorithm (the modified SCA). Furthermore, by combining the fuzzy k-nearest neighbor method with the modified SCA, the study simulates numeric datasets with two or multiple classes, and analyzes the results. The accuracy rate (ACC) achieved by the modified SCA FKNN in this paper is compared to other models, with data comparison results and tables presented for each. The modified SCA FKNN proposed in this paper has obvious advantages on accuracy rate(ACC). [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is an essential component in modern games. With AI, players can feel the challenges in the game and the game will feel more real. AI has several branches, one of which is path-finding. Pathfinding is a way of finding the shortest path between two points. The main problem in path-finding is how to perform a path-finding accurately and requires fewer computational resources (CPU and memory). This paper describes the results of research that tested the A* and Hierarchical Pathfinding A* algorithms using the Unity 3D platform and the C# programming language. The graph or space to be used is a graph with 8 branch nodes. While the benchmarks used are the path generated and the processing time of an algorithm. This paper results in a conclusion that the path generated by the A* algorithm is shorter than the Hierarchical Pathfinding A* algorithm. The number of paths processed for the A* algorithm is more than the Hierarchical Pathfinding A* algorithm. The total execution time of the A* Algorithm is smaller than that of the A* Hierarchical Pathfinding Algorithm. The Hierarchical Pathfinding A* algorithm experiences spikes in execution time more often than the A* algorithm. However, if the total spike time, the Hierarchical Algorithm is less than the A* Algorithm. [ABSTRACT FROM AUTHOR]

Twitter is known to be one of the familiar social networking platform these days, among many others, with a lot of user engagement. This microblogging site encourages social interactions, allowing users to stay up to date on the latest news and events and share them with others in real time. Tweets are limited to 280 characters and is allowed to include links to related websites and tools. With a platform having such wide reach, it is prone to be targeted negatively and spams are one way to do it. Spammers use this platform to display malicious content that is inappropriate and harmful to users worldwide. Machine Learning uses various approaches that can be used to detect spam and overcome it. However, with the advent of recent technologies it has been observed that the properties of tweets vary overtime making it difficult to detect spam leading to the "Twitter Spam Drift" problem. This paper reviews the papers published since 2018 that have focused on the spam drift problem and gives a comparative analysis of the different algorithms that are utilized on the various data sets to tackle such a problem. [ABSTRACT FROM AUTHOR]

Aim: Cloud computing has become one of the most popular storage and computing technologies. The aim of this paper is to provide a secure and cost effective platform for the employee management system. Materials and Methods: This study contains a total of 2 groups, the El Gamal encryption algorithm is analysed in the group 1 (434 samples), and the advanced encryption standard algorithm is analysed in the group 2 (434 samples). The accuracy and efficiency of each of the models are compared. Result: This paper is to improve the efficiency of the algorithm used in the employee management system. The proposed model appears to be efficient and fastest than the existing algorithm and the mean accuracy of detection is ±1SD with power value of 80%. Conclusion: The final outcome of the existing system, El Gamal encryption algorithm is compared with the outcome of the advanced encryption standard algorithm, and the proposed model proved to be significantly higher efficiency(1.3 times faster) than the existing model. [ABSTRACT FROM AUTHOR]

In the world of modern era we find many techniques for enhancement of reliability in distribution networks and meeting the consumer demand by provision of quality and incessant power supply. Amidst the various techniques, in this study, we opt for demand response (DR) and charging/discharging of electrical vehicles (EV's) are evaluated to ameliorating reliability for radially distributed power system by adopting teaching learning based optimization (TLBO) algorithm. This analysis is considered due to the contribution of technologies like are DR and EV's for reducing the usage of conventional sources and enhancing the air quality. Here in this paper we are adopting reliability evaluation by considering energy expected not supplied (EENS) and loss of load expected (LOLE)where the characteristics of distribution network contains load and voltage profile. This paper provides a new optimization approach for finding the optimal solution. [ABSTRACT FROM AUTHOR]

This paper focuses on finding the best classification algorithm model in the case study of student performance prediction and comparing the algorithm performance before and after using the normalization method. To achieve this goal, this study uses data mining classification techniques to analyse student performance at Vocational High School in 2020-2021. The steps of the research carried out include: data collection, data pre-processing, build algorithm models without using normalization and with using normalization, and final step are comparing algorithm performance before and after using normalization. The algorithms that will be used include: Random Forest, Decision Tree, Logistic Regression, SVM, Naive Bayes, and KNN. While the normalization methods used are Standard Scaler, Min-Max Scaler, and Robust Scaler. The result of this research is that the normalization method is able to significantly increase the accuracy of the model. Based on the tests and evaluations carried out, the normalization method using the Min-Max Scaler has the biggest impact in improving the overall model performance and the algorithm with the best performance is Random Forest. This paper reviews the effect of the normalization method to improve algorithm performance in predicting student performance, where based on previous research no one has used the normalization method to gain accuracy of the model which actually has a considerable impact on gaining accuracy of the model. [ABSTRACT FROM AUTHOR]

The paper examines the application of systematic and risk-based approaches to problem solving in the agro-industrial complex, which generally prompted research in the field of various model-based methodologies. Of particular interest to this study is the Quality by Design initiative in the agro-industrial complex. Motivated by their need, this paper reports some of the contributions to analysing the existence and availability of the experimental plan. The analysis of the feasibility and adaptation of the nested sampling algorithm for the probabilistic characteristics of the design space is considered. There is described an initial adaptation of the nested sampling algorithm, common for large Bayesian calculations, for the probabilistic characterization of spatial design, which is a key work for practitioners in the agro-industrial complex. The choice-based approach has been found to be effective with the optimization approach, allowing practitioners to take advantage of the choice-based approach with other methods. A step-by-step technology is given for complex and one-time implementations of the original nested sample for spatial design, which further reduces the computational load, thereby allowing solutions of more complex problems. Design-centring methodology is shown as an alternative coding method for methodological choice, providing the practitioner with a convenient format for communicating with process operators. A particular attention is paid to several pronounced problems that arise in the special design of experiments when there are various levels of model uncertainty, which are often encountered in the early stages of model development. The paper presents the results of the study obtained by developing optimal experimental queries in the presence of restrictions. [ABSTRACT FROM AUTHOR]

The paper describes selection of the processor architecture for the external control block implementation in assembler. This block is used to control the operation of the main control system and improve the reliability of the system as a whole. A scheme for the considered method implementation with an external control unit on CPU instead of FPGA is proposed. The development of software for it is realized in assembler. Analysis of the architectures' peculiarities that are currently available on the market is carried out. They are x86 and x86-64 for CISC. ARM v8, ARM64, RISC-V, SPARC, and LEON are for RISC. The paper performs translation of decision-making algorithms in multiversion environments from the C++ language into the assembler of the necessary architectures. The size of the resulting code is analyzed. The authors made conclusions on the basis of the obtained results. It is preferable to select a CISC architecture, since it simplifies and speeds up a software development process in the case when it is required to develop a code in assembler. As for the scheme selection for the control system implementation with an external control unit, the authors prove that it is quite acceptable to apply a CPU for the external unit instead of the initially proposed FPGA. [ABSTRACT FROM AUTHOR]

Nature Inspired Computing Algorithms are the best solutions for solving complex problems around us. The objective of our paper use Ant Colony Optimization (ACO) to optimize the shortest route between various departments located in university campus. The Internet of Things (IoT) technologies by navigation of mobile application to find shortest path located by online graphical form in a SR University campus. A graph is generated by MATLAB and the simulation results showing information of shortest route of source to destination & neighbour information. The working procedure of ACO is edge(departments) between vertices(paths) then maintain the pheromone level at that edge should be higher. As per pheromone level is high on that path, other ants also follow this path, or otherwise omitted the edge. The paper focus on Internet of Things (IoT) based ACO Optimized intelligent system for mobile phone application that students and visitors found nearest location within shortest time selective locations within SR University campus. For that save time and flexibility to find short route in a reasonable amount of time while using NIC based algorithm. [ABSTRACT FROM AUTHOR]

The goal to promote human limits is for Artificial Intelligence (AI). It takes a posture on public administrations, represents the increasing availability of regaining clinical data and the rapid creation of intelligent strategies. The need to stress the need to use AI in the fight against the COVID-19 crisis. The paper outlines the main role played by Ai technologies in this unprecedented war and introduces a survey of AI methods used for multiple purposes in the fight against the outbreak of COVID-19. This paper also explains how the body temperature and coughing of the incoming person are assessed and whether the incoming person has not a protective facial mask. Should either of the above tests disqualify the participant, an alarming device invokes the local officials; the entrant may otherwise enter the premises after his/her hand has been sanitized. [ABSTRACT FROM AUTHOR]

Information security plays a crucial role in data protection. All our data is secured and protected by cryptographic algorithms. This paper aims to consider the performance of cryptographic algorithms such as AES (Advanced Encryption Standard), ElGamal, RSA (Rivest Shamir Adelman), and Hybrid Cryptography and further compare them. We have done a comparison on parameters like the CPU execution time, memory space, and encryption-decryption time for different key sizes and file sizes. [ABSTRACT FROM AUTHOR]

The paper presents a separated staggered phase field methodology for modelling of brittle fracture. The basic relations of the phase field formulation are derived, along with decomposition of the strain energy. In separated scheme, displacement model is computed separately from the phase field model in concurrent fashion. The transfer of variables among models, loading imposition and incrementation are governed by the head routine. A convergence criterion has been suggested, which is based on the norm-2 of the phase field variable. The framework has been implemented into FE software Abaqus and Fortran via user subroutines. The proposed framework has been verified on the standard benchmark problems. The solutions obtained have been compared to the other staggered schemes found in the literature. It has been presented that separated scheme provides correct solutions and requires shorter computational time. [ABSTRACT FROM AUTHOR]

Nowadays, everyone is looking for solutions to send messages in a secure manner. There are two ways to do that-either one delivers the message himself/herself to the receiver or one wraps the message in a way that no delivery partner can access it. In today's fast-paced world, we are all left with the second option of delivering the message through a delivery partner. We can do so through 'Cryptography' which is a technique that ensures secure delivery of our messages. In this paper, we are going to use the 'Stirling Transform' and 'Additive Group' under Modulo 27 to secure and guard a secret message from the ones we do not intend to share the message. In order to make message secure message, various algorithm have been created. [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]

Recent research had shown that conventional AR-IoT applications only specialize in the localized area for industrial monitoring. One of the biggest challenges in the application of AR technology in the industry is the absence of the tools and skills to do so. Therefore, this paper introduces a programming algorithm for the development of an off-site AR-monitoring for a material handling process. A case study was conducted on a simple process using a conveyor system to sort out coloured cubes into respective buckets using TSC3200 colour sensor and ESP32 microprocessor. The CAD design of the conveyor was re-designed and imported into Unity to create the virtual 3D visualization of the machine. The simulation is made in sync with the movements of the real machine, through the communication between smart sensors and microcontroller, ESP32 using Uduino concept. The connection is cloud-based with the use of MQTT to communicate between different devices. From the fusion of these concept, a programming algorithm was created. The AR-Simulasi Apps allows user to access the virtual version of the process in real-time remotely, in Augmented Reality environment. The AR 3D visualization was superimposed in real world using AR marker. Therefore, the relationships among accuracy, augmented simulation error and object interaction were investigated. The results of the numerical simulation indicate that the AR-IoT 3D simulation would be a great potential for the current AR-IoT monitoring applications used by industries. In which it shall enhances user interaction for better viewing and perception between human and machine for off-site monitoring. [ABSTRACT FROM AUTHOR]

With industry development, suburbanization, and population growth, the load demand is constantly increasing year by year. Excessive load demand has greatly strained the distribution network (DN), resulting in increased power losses due to the high-power flow. Therefore, it becomes very important to minimize power losses at the DN to maximize the efficiency of the distribution companies. Network reconfiguration is one of the effective methods used by distribution companies to minimize losses in the DN. This paper proposes Mayfly Algorithm (MA) to solve network reconfiguration problems and minimize the DN's power loss. MA is inspired by the social behavior of the mayflies, especially their mating process. IEEE-33 bus DN has been used to demonstrate the effectiveness of the proposed method. The obtained simulation results compared with other methods in the literature and comparison showed that the proposed MA provides better quality solutions than other methods. [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]

A large part of the surveillance video is considered unnecessary to the user and it is considered redundant. Therefore, this work focuses on scenes in which a certain car appears in the surveillance video, while other scenes are neglected. In this paper, Viola-Jones is used to detect the car plate, SVM is employed to improve performance, and PNN is utilized for classification. The experimental results of the proposed system are more efficient as compared with other research papers such as Viola-Jones accuracy is (97%), SVM accuracy is (99%), and accuracy of the car plate identification is (94.8%). [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]

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]

Huge number of nodes of sensor make wireless sensor networks (WSNs) that not just gather data associated with various happenings but also submit those events to central station hop-by-hop. Data submitted through nodes of neighbor may be extra as well as duplicate. In addition, generated data number in wide WSNs might be a lot to be processed in sink. The most essential variable for gathering WSNs data is optimum energy usage to integrate also gather data. The accuracy of the information is usually assessed by detecting the error of the data contained in the CHs. This process helps increase the integrity and reliability of the WSN application. Basically, data identification is a classification issue. Support Vector Machine (SVM) is one of the most common classification approaches used in this paper to create an identification model. The SVM classification parameters will also be optimized by the Eurasian oystercatcher optimizer algorithm (EOO). The simulation results show that the proposed method is better than the basic paper in terms of 5 specific criteria. The proposed method has improved by about 1.02% compared to the other methods. [ABSTRACT FROM AUTHOR]