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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]

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]

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]

This paper examines the use of mobile phone sensor data to identify transportation mode detection using the K-nearest Neighbor algorithm. The model tries to recognize the walking, still walking, Bus, Train, and Car transportation modes. One of the normalization methods, such as the Min-Max normalization or the Z-Score Normalization, is implemented to pre-process the data. It uses four distance methods such as Euclidian, Manhattan, Chebyshev, and Minkowski as distance calculation mechanisms. Based on the highest accuracy result, the model is selected. The analysis also concluded that the optimal model has the highest accuracy, which has validated the results achieved through extensive normalization methods and the choosing of the most appropriate distance functions. As a result, the outcomes of the research have further indicated the importance of selecting the appropriate normalization techniques and distance functions on the accuracy of the model used in transportation detection. Additionally, the results have also provided critical additional knowledge in the development and formation of Intelligent Transportation Systems (ITS). [ABSTRACT FROM AUTHOR]

Within the domain of robotics and related fields, path planning algorithms have perennially posed a profoundly challenging problem, marked by an ongoing quest for refinement without attaining a universally perfect solution. Among the spectrum of path planning algorithms, A* algorithm stands out as a relatively stable and efficient approach. This paper presents improvements to the conventional A* algorithm by introducing several key modifications. Firstly, it adopts the Euclidean distance metric instead of the Manhattan distance, thereby mitigating the proclivity of the algorithm for diagonal movements. Secondly, the dynamic weight parameterization is introduced, markedly amplifying computational efficiency. In exemplar map scenarios, this augmentation yields a reduction of approximately 50% in program execution time compared to the conventional A* algorithm. Lastly, the integration of Bezier curves serves to optimize path inflections, imparting a smoother trajectory. This approach substitutes discrete grid-based path planning inflection points with continuous curves, obviating the need for abrupt halts and turns in the trajectory of the robot during actual execution. Incorporating the stability and optimality inherited from the traditional A* algorithm, this algorithm enhances program execution speed while reducing inflection points in the route, substituting them with smooth curves. The results identify an improved A* method as an important improvement in path planning, which greatly improves the computational and operational efficiency related to robot motion in production practice. [ABSTRACT FROM AUTHOR]

In this paper, reinforcement learning (RL) algorithms Proximal Policy Optimization (PPO) and Deep Q Networks (DQN) are applied into the setting of autonomous driving research. A highway environment is formalized into a Markov Decision Process, and a simulated highway environment is constructed in compatible with Gymnasium. In this simulated environment, comparisons of PPO and DQN algorithm performance are investigated under different traffic load and vehicle starting position - main road and onramp - in terms of total return gained during an episode of highway driving after training all algorithms for the same number of timesteps. The total return takes driving speed and safety into account to connect with real world driving convention. According to the result, PPO algorithms achieve higher average return and lower standard deviation, indicating their ability to achieve more stable high-quality performance. All trained algorithms are able to drive quickly on the road and avoid other vehicles, demonstrating the potential of reinforcement learning in the application of complete driving automation. [ABSTRACT FROM AUTHOR]

Path planning plays a key role in autonomous robot research. This paper studies in detail three of the various path planning algorithms for autonomous robots, including the A-Star path planning algorithm, unit decomposition, and rapid exploration of random trees (RRT). The A-Star algorithm determines the shortest route between two points in a graph. The principle is to calculate the distance between the starting point and the end point, select the shortest point, and then recalculate the distance and select the shortest route until reaching the target point. RRT can be used for path planning in multi-dimensional, complex spaces. The main principle is to randomly generate points in the map and connect them to the nearest nodes to form Cell decomposition is to divide the plane space into units of different sizes, turn the complex space into a simple space, and use the A-Star path planning algorithm to calculate the shortest path from the starting point to the target point. This article chooses experiments with different formers to analyze the application of various algorithms. Finally, this article concludes that cell decomposition divides simple geometric maps and uses the A-Star algorithm to plan paths, which can make the route on the geometric map more accurate. Different extension algorithms of RRT can be used in scenarios that require high accuracy, high quality, and reasonable computational costs. A-Star is also a suitable choice for quick route planning. This study conducts a comprehensive study of autonomous robot path planning algorithms, which will help future research select the most appropriate algorithm based on specific application requirements. [ABSTRACT FROM AUTHOR]

Path planning is a key problem that needs to be studied in the field of automated guided vehicles. Its ultimate goal is to accomplish the movement of the AGV from the initial target point to the final target point. Although many mature algorithms can achieve this goal. In practical applications, problems of dynamic obstacle avoidance and global movement combination still exist due to the complexity and dynamics of the application scenarios. The map needs real-time control of the position, posture, and kinematic parameters of Automated Guided Vehicles to solve this problem. Based on these issues, this paper combines global path planning with local path planning algorithms to achieve algorithm fusion. Then, it constructs a warehouse scene in Gazebo and simulates the operation of the AGV. Finally, it compares the accuracy of the fusion algorithms, obstacle avoidance techniques, and search speed. Choose the fusion algorithm of the A* hybrid algorithm and the TEB algorithm as the optimal solution for the shortest and smoothest path in practice. Compared to traditional algorithms, there is a 220%-615.8% improvement in time. With the help of this algorithm, intelligent warehouse operations may operate more effectively and the risk of accidents can be decreased. [ABSTRACT FROM AUTHOR]

Presently, path-planning algorithms play an important role in unmanned robots. In coverage of these algorithms, scientists try to seek some ways to improve those traditional algorithms. For example, A* was invented to solve Dijkstra's problem, of searching for targets slowly, using a heuristic function. Although these algorithms have been significantly improved, these changes still cannot adapt to special environments, such as amphibious environments and complex traffic environments. In this paper, four conventional algorithms are analyzed to test them in a similar traffic map to find their weakness and merits. First, we propose to design a map with different extents of traffic congestion on different roads. Then, four algorithms, A*, Probabilistic Roadmap Method, Artificial Potential Field, and Rapidly Exploring Random Tree series, are applied in this map to search for a road from the initial point to the goal point. Finally, all results are recorded and compared with other algorithms to investigate their effectiveness and disadvantages. Experiments with randomly generated paths in the map are conducted to demonstrate the proposed approach. The experimental results are concluded in the end. The A* algorithm is a suitable choice when solving the shortest path problems. The RRT series can search for a road frequently. For the APF, it is confirmed that it has a significant usage in selecting a path with less congestion and the PRM method can complete the same task as well with a certain possibility of failure in path planning. This study is of great importance for discovering the directions of future research. [ABSTRACT FROM AUTHOR]

Chromatic Dispersion (CD) compensation in optical fiber Offset Quadrature Amplitude Modulation based Filter Bank Multicarrier (FBMC-OQAM) systems is the most used today, especially on terrestrial Long-haul optical fiber type. As any other related system, FBMC-OQAM have a lot of strength and weakness points that should take in account, therefore, focus on studying data transmission over optical cables with FBMC-OQAM modulation for long distance and improve data access by reducing the noise on phase and have less dispersion on the transmitted light. To get a high spectral efficiency keep the subcarriers number sufficiently high, so will work on 256 subcarriers with cable length up to 3000 km. To get less complexity on an algorithm chose to use, and therefore compensate some of the dispersion at the receiver, must not set the subcarriers number at too high level. In parallel, know that the fiber dispersion or the propagation distance increases or the number of subcarriers decreases, the CD tolerance of single-tap equalization is reduced, so in this paper, propose to use the frequency sampling multi-tap equalizer for improving the CD compensation. Furthermore, will derive a new simple phase noise (PN) estimation algorithm which can be seen as an adaptive maximum likelihood (AML) algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a way how to automatically find and set an optimal configuration for a linear system solver during a transient analysis in order to reduce computation time. Our approach is based on biologically inspired algorithms which are able to find a reasonable configuration in tens of trials, i.e., tens of timesteps in terminology of the transient analysis. This work shows a measurement within which we compared several optimization algorithms and explored their time overhead. We focus on parallel multiphysical solvers for engineering simulations developed for high-performance computing. Our experiment includes one such solver based on Finite Element Tearing and Interconnection and 4 test cases, but our solution is modular, thus, it can be connected to other similar solvers. [ABSTRACT FROM AUTHOR]

This paper deals with an unfavorable phenomenon called the ponding effect. This phenomenon can cause an increase in local deformations on a tensile surface and therefore endanger the integrity of the structure in case of overcoming the tensile strength of the membrane material. A searching algorithm has been developed in order to analyze and prevent occurrence of this phenomenon. An improvement of the algorithm was made so that only an expected amount of precipitation would be considered during the calculation. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of image deblurring. A suitable discretization scheme for a particular nonlinear time-dependent partial differential equation of parabolic type is experimented. The method is implemented by reversing the arrow of time in order to damp diffusion. Only one step is enough to reconstruct the edges of a corrupted picture affected by average blur. Thus, the procedure turns out to be extremely efficient. [ABSTRACT FROM AUTHOR]

In our research, we are focusing on the development of iterative algorithms for solving contact problems emerging in the analysis of steel structures bolt connections. Despite the project is focusing mainly on the development of efficient numerical algorithms, several supplementary problems have to be solved. In our research, we decided to utilize widely-used popular commercial software Ansys to provide the data for our algorithms implemented in Matlab as well as for the comparison of results. This paper presents experiences with our newly developed data interface. [ABSTRACT FROM AUTHOR]

In the paper, the BFGS algorithm is applied to minimization of the functional in the problem of approximating the gravity anomalies by the fields of material segments and the fields of point sources. The selected fields were compared at different heights. Efficiency of the algorithm is demonstrated on model examples. Fields from the three-dimensional sources were selected as selected ones. [ABSTRACT FROM AUTHOR]

In Handwritten Character Recognition (HCR), interest in feature extraction has been on the increase with the abundance of algorithms derived to increase the accuracy of classification. In this paper, a metaheuristic approach for feature extraction technique in HCR based on Crow Search Algorithm (CSA) was proposed. Freeman Chain Code (FCC) was used as data representation. The main problem in representing a character using FCC is that the results of the extractions depend on the starting points that affected the route length of chain code. To solve this problem, the metaheuristic approach via CSA was proposed to find the shortest route length and minimum computational time for HCR. The performance measurements of the proposed CS-FCC extraction algorithm are the route lengths and computation times. The experiments on the algorithms are performed based on the chain code representation derived from established previous works of Center of Excellence for Document Analysis and Recognition (CEDAR) dataset which consists of 126 upper-case letter characters. Based on the result, the proposed CS-FCC obtained 1880.28 in term of route length and only needs 1.10 second to solve the whole character images. [ABSTRACT FROM AUTHOR]

In this paper, we proposed the Low light image enhancement using a single scale retinex algorithm. The main aim is to improve the clarity of low light images. By considering the real scenes, the dynamic range of digital camera is small, hence contrast correction is required to reproduce the information of images in darker regions. The proposed method, by using single scale retinex algorithm RGB color components are extorted and modified by applying logarithm and converted into HSV. For enhancing this component, the gaussian surround function is convoluted to it and then scaled version of components and the convoluted one is added to the original image. Then contrast stretching is performed to increase quality of image. This is implemented with MATLAB. [ABSTRACT FROM AUTHOR]

A Blockchain is a decentralized database that stores data that all network participants can access. The central authority is no longer in charge of maintaining this public database by running a cryptographic protocol with designated nodes. Bitcoin is presently the latest object within the Blockchain, and the Bitcoin node can confirm the transaction content and package deal it into the block. Through the underlying consensus agreement, the blockchain ensures the consistency of the information. These consensus algorithms are unique because the set of rules protection assumptions differs from the actual requirements. This paper provides information and compares various blockchain consensus algorithms, classifies advancement based entirely on blockchain consensus, and highlights the benefits. [ABSTRACT FROM AUTHOR]

Cloud infrastructure is the "backbone" technology of the internet era. Ever-growing scale of cloud network concerns the sustainability of centralized cloud. Fog computing aims to decentralizes cloud computing distributing computational processes across network devices, offers a potential solution. If application are distributed effectively in available nodes, network resource usage can be reduce while reducing latency by existing closer to data sources and end points. However, the lack of standardization of fog networks, researcher must list its evaluation setups to contextualize placement algorithm's potential abilities. Thus, selecting an evaluation environment is crucial in researching fog application placement problem. There are several reviews about evaluation environment, but there is a lack of information regarding important aspect placement problem such as algorithm type, placement objective and performance metric available in each evaluation environment. In this paper we discuss the capabilities of several evaluation environments such as iFogSim, YAFS, MATLAB, Python, real world testbed, solver, and other evaluation environments. We surveyed usage trend per year, algorithm type usage, and performance metric that can be produced by each evaluation environment. This survey aims to help future researchers on fog application placement problems to identify evaluation environment based on selected algorithm type and objective metrics. [ABSTRACT FROM AUTHOR]

Widespread use of high-resolution satellite images may be found in many fields and applications. Its ability to record the earth's surface in more detail benefits regional spatial management, natural resources and disaster monitoring, and several other fields. The smaller coverage is unfortunate if areas are not visible due to cloud contamination. The presence of clouds can reduce or even eliminate the information in the image. Cloud detection on high-resolution images is a challenge because this image only has four channels: blue, green, red, and NIR. While the thermal channel, which is often used to detect the presence of clouds, is not owned by this image. This study proposes a method to identify clouds in high-resolution satellite imagery based on this limitation. The cloud detection software in this paper uses U-Net version 1.0. This software can be executed on a server or personal computer (PC). The model applied to the Dice coefficient and IoU to know how the segmentation model performs. The results of this cloud detection process are cloud detection raster data. This software generates the percentage of clouds in an image as a.txt file. The dice model is recommended for the cloud detection method based on the accuracy assessment. Users can utilize these results, especially in overcoming cloud constraints on high-resolution satellite imagery. This software is expected to fulfill the needs of remote-sensing data users. [ABSTRACT FROM AUTHOR]

The quick development of technology not only makes life easier but also raises several security concerns, so cyber security has become very important and vital research area, rather an inevitable part of computer system. Still, various research being done on the development of effective intrusion detection system (IDS). An IDS is one of the suspicious network activities. An IDS is used to identify many types of malicious actions that can undermine a computer system's protection and confidence. Recently, ensemble algorithms are applied in IDS in order to identify and classify the security threats. In this paper author intends to do a brief review of various Ensemble learning Algorithms in ML, which are most frequently used in IDS for several applications; with specific interest in dataset and metric. This work provides broad study and investigation on current literature, the gap for improving and creating efficient IDS can be determined. [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]

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]

Data Mining and Analytics have transformed the way business is conducted. Prominent techniques of Data Mining include Clustering, Classification and Association Rule Mining. There are a lot of algorithms for cluster analysis. But all algorithms have some limitations especially for high dimensional dataset. In this paper modified the k-mean algorithm for better cluster accuracy and minimized the computational time processing the dataset for student academic performance. Students undergo immense stress and pressure to outscore their competitors. Higher and Technical Education has many challenges owing to voluminous data and various dimensions for acquisition and processing. Various Clustering Algorithms provide partition accuracy, computational time, lead time but lack visual assessment post partition. This paper proposes a variant of k-Means algorithm using comparative criteria of Partition accuracy (PA) parameter and computational time. [ABSTRACT FROM AUTHOR]

Multiple Cloud Compute providers can be managed together as a Federated Cloud. Such an agglomeration introduces challenges for effective resource allocation and load balancing. To address such issues, multiple papers have been written describing various algorithms for resource allocation. This paper thus compares and contrasts various algorithms proposed. A Federated Cloud environment is usually organized as a many providers having multiple datacenters (DCs), each with multiple hosts, and each host having multiple VMs. We compare the various algorithms on the basis of execution time (time-till-response), latency (time between task scheduling and VM allocation), and load (resource utilization). [ABSTRACT FROM AUTHOR]

In this paper, two new hybrid algorithms are proposed to solve global optimization problems. The first algorithm combines the sooty tern optimization algorithm with the arithmetic optimization algorithm, and the second algorithm combines the sooty tern optimization algorithm with the conjugate gradient method. The sooty tern optimization algorithm is a metaheuristic algorithm proposed by Dhiman and Kaur in 2019 Sooty migration and attack behaviors seabirds in nature were the primary inspiration for this algorithm. In this paper, this algorithm was hybridized using the arithmetic optimization algori thm and the conjugate gradient method, and the two algorithms were measured by applying them to 10 functions, and the results of the hybrid algorithms were very good compared to the original algorithm. [ABSTRACT FROM AUTHOR]

Algorithms are increasingly being used and recognized for their ability to improve the performance of diagnostic tools such as contemporary electrocardiogram (ECG). For instance, evidence from previous studies reveals that QRS enhancement and detection algorithms have enabled the ECG device to measure and classify heartbeat more accurately. Based on the review of the previous works on QRS detection in ECG, this paper examines the key components of the ECG, QRS detection features, the different techniques used for developing QRS enhancement and detection algorithms as well as the criteria for evaluating their performance. [ABSTRACT FROM AUTHOR]

As the Internet of Things (IoT) becomes more widely used, the danger of information leakage, the danger of information leakage, data leakage and theft of the internet of things is continuous increasing, as the communication channel for data transfer is open to the public. A considerable portion of this data includes sensitive and personal information for the user and the company as well. Because of the openness, distributed nature, and lack of control over the entire IoT environment, the number of attack vectors for bad users is significant. End-users must trust the system for the IoT to become a viable service platform. Consequently, in critical infrastructures like smart home, smart city, smart healthcare, and smart industry, information security and privacy are the major concern in the Internet of Things. In this paper, we present an information concealment strategy using steganography to protect communication in critical IoT infrastructure. RGB photographs are employed as the information carrier. Deep layers of low-distortion image channels in the least significant bit (LSB) are used to hide the data to be used as a data marker. Both mathematically and experimentally, we evaluate our method. We display mathematically that the striker's analysis cannot expect the current information. The proposed strategy outperformed previous strategies in terms of imperceptibility and capacity, as well as resilience to steganalysis attacks like histogram analysis, as demonstrated experimentally. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of safe data from novel HMAC cloud administration with respect to low time consumption in Database Management. In order to foresee the information in time consumption of the Database Management in cloud administration, we tested the HMAC encryption Algorithm with a test size of =20 and the IDEA encryption calculation (IEA) with a test size of =20. HMAC encryption was used to reduce the time needed to recover data from more secure distributed storage. When comparing HMAC and IDEA encryption calculations, HMAC has a higher utilisation rate (66.80 percent) (62.97 percent). In general, HMAC encryption calculation outperforms IDEA encryption calculation (p 0.05, 2-tailed). In data set administration frameworks, a novel HMAC encryption computation helps reduce the possibility of data recovery. [ABSTRACT FROM AUTHOR]

The loss of RFID tags in cattle is a multi-faceted problem involving economic, logistical and animal health factors. This challenge requires innovative solutions that combine technology, data analytics and practicality. This paper proposes an extended algorithmic approach to investigate a method to reset lost RFID tags in cows and thus improve the efficiency of livestock management systems. The intricacies of RFID technology, the importance of livestock management in modern agriculture, and various factors that cause tag loss are studied. However, the focus of our discussion is on presenting an algorithmic solution that not only detects missing tags, but also simplifies the tag reset process and uses resources efficiently. [ABSTRACT FROM AUTHOR]

Kidney is one of the significant organs in the human body which performs filtering out blood, balances fluid, removes the waste, maintains the level of electrolytes and hormone levels. So, any disorder or dysfunction in kidney needs to be detected on time in order to preserve life. Segmentation on kidney tumor in medical field is a critical task and many conventional methods have been employed for early prediction of kidney abnormalities but with limitations such as high cost, extended time for computation and analysis with huge amount of data. Due to all such problems, the prediction rate and accuracy has reduced considerably. In order to overcome the challenges, Artificial Intelligence (AI) technology has penetrated into the field of medicine particularly in the renal department. The evolution of AI in kidney therapies improve the process of diagnosis through several Machine Learning (ML) and Deep Learning (DL) algorithms. It has the capability of improving and influencing on the status with its capacity of learning from the massive data and apply them accordingly to differentiate on the circumstances. The storage of larger data and segmentation with AI assistance are highly helpful for the analysis of occurrence of the disease. AI algorithms have predicted the severity of tumor stages with effective accuracies. Hence, this paper provides a critical review of different AI based algorithms being used in the kidney tumor prognostication. Its numerous benefits in field of segmentation have been researched from the existing works and provides an insight on the contribution of AI in the kidney disease prediction. [ABSTRACT FROM AUTHOR]

Blood sugar levels rise as a result of impaired glucose homeostasis, which is a feature of Diabetes Mellitus (DM). Coronary artery disease, nerve damage, diabetic retinal disease, kidney failure, and sex disorders are just a few of the consequences that can result from DM. DM has to be diagnosed early to minimize its effects and those of its consequences. One technique for automatically detecting DM based on the prior data is the clustering algorithm. However, the K-mean Clustering method is easy to be implemented, has fast computational time and adapted easily. However, the centroids of K-means are initialized randomly that causes to be stuck in local optima. For this reason, the robust centroids initialization on the K-means clustering method are necessary to handle this problem. This paper uses swarm intelligence algorithm for this goal since it is able to obtain solution near the global optima, is robust and easy to be implemented. As a result, this study proposes an improved K-means clustering-based technique for diagnosing DM. The K-means is improved by using robust centroid initialization-based swarm intelligence algorithm. The swarm intelligence algorithm which is used is Particle Swarm Optimization. By using experiments, the proposed method is confirmed better than the original K-means clustering in diagnosing DM. [ABSTRACT FROM AUTHOR]

This paper describes an approach to improving the process of resource redistribution in a coevolutionary algorithm using a fuzzy logic system. Two bases of fuzzy rules are proposed for setting the size of the fine and the social card for the losing coevolution algorithms. The effectiveness of the developed fuzzy system was studied in comparison with the usual co-evolutionary algorithm without integration of fuzzy control procedures. [ABSTRACT FROM AUTHOR]

The paper presents an analysis and review of sources considering temperature-dynamic characteristics as performance indicators of agricultural automotive and tractor equipment, the impact of energy efficiency of automotive equipment on the temperature and dynamic characteristics of cooling systems, an analysis of the impact of performance indicators of automotive equipment on the efficiency of a block cooling system. An improved scheme of a block-modular cooling system and a method for determining its main indicators during operation for functional units of automotive equipment using innovative materials are proposed. A complex method was used, including an analysis of the state of the issue, theoretical studies of the operating parameters of the polymer radiator of the cooling system with the inclusion of thermal, aerodynamic and hydrodynamic calculations, both in steady and unsteady modes of operation of the MTZ-82 tractor engine. At the same time, analytical and numerical methods, a set of mathematical models and graphic visualization of solutions using the Excel, KOMPAS-3D, Statistica software packages were used. An improved theory of temperature-dynamic characteristics of automotive equipment is used in the cooling system of the MTZ-82 tractor equipped with a polymer radiator, while combined options for using both liquid and oil-liquid engine cooling are possible. [ABSTRACT FROM AUTHOR]

In this paper, logical controllers, their scope of application as a type of intelligent control and classification were considered. Precise logic controllers with a step-time algorithm for fuzzification of input parameters were also considered as an attempt to eliminate the main disadvantages of logic controllers with an anytime-algorithm. A logical scheme was given that implements the step-time algorithm for fuzzification of a continuous physical quantity and an analysis of expressions was carried out to increase the speed of the fuzzifier and controller with the step-time algorithm, respectively, due to the development of a system of production rules to a rule whose antecedent at the current time is equal to a logical unit, from which it is concluded that the speed of precise logical controllers with step-time algorithm has increased due to the lack of a search procedure in the system of production rules, rules with an antecedent, equal at a given time to a logical unit. Although in case of an unsuccessful arrangement of the rules in the fuzzifier production system (the rule with the highest response frequency is located at the end of the production system), the increase in the speed of the controller may be insignificant. [ABSTRACT FROM AUTHOR]

This paper solves the optimal reactive power dispatch (ORPD) problem applying sine-cosine optimization algorithm. The main intention of the ORPD problem is to reduce the real power loss in the transmission lines and maintain voltage magnitude at an acceptable level in the system network while satisfying the set of specified operational constraints. The sine-cosine algorithm is a powerful population formed algorithm for solving the optimization problems. The sine-cosine algorithm generates a number of initial candidate solutions, and progresses towards the optimal solution. The proposed method is applied to standard IEEE-14 and 30 bus systems. The simulation results compare favorably with other reported results in the literature. [ABSTRACT FROM AUTHOR]

Given the importance of transportation in logistics operations, the vehicle routing problem (VRP) is critical to logistics practice. The Green Vehicle Routing Problem (GVRP) is a variation of the VRP that focuses on issues related to sustainable transportation. GVRP's goal is to balance the economy and environmental costs by developing effective solutions to environmental and financial concerns. The purpose of this paper is to propose a Salp Swarm Algorithm (SSA) to solve the GVRP. Using the benchmark test instance E-n23-k3, a numerical experiment is carried out to test the proposed algorithm. The results show that the SSA outperforms the Route Splitting Tabu Search (RS-TS). On average, the total cost is reduced by 1.41%. Furthermore, we show that the use of various modes of transportation affects fuel consumption, emission costs, and vehicle usage costs. [ABSTRACT FROM AUTHOR]

Activity recognition offers a wide range of applications in various industrial processes and healthcare. This work proposes an approach to collect data from a spherical coordinate system using smartphones, then extract the highly efficient features using advanced preprocessing. The paper also proposes an algorithm to recognize activity using various ensemble machine-learning approaches based on extracted features. These approaches are evaluated under various combinations of features to analyze the accuracy, sensitivity, specificity, and training time. Experimental results reveal that weighted KNN performs best among all models by achieving 96.2% accuracy with 12 features. On the other hand, Bagged tree ensemble classifiers perform better than subspace KNN ensemble classifiers with an accuracy of 95.3% using 12 features. [ABSTRACT FROM AUTHOR]

The paper analyzes the influence of the adaptive mechatronic system parameters on the process of stabilizing the step of moving materials during automatic assembly. Dependences of variances and mathematical expectations of the displacement step are obtained depending on the parameters of the system under a random external action. Numerical simulation of the operation of the algorithms used is carried out, recommendations are given for the optimal choice of system parameters. [ABSTRACT FROM AUTHOR]

In the present paper algorithms for constructing a finite element mesh of compound domains and minimizing the ribbon width of the system of resolving equations by the finite element method (FEM) are considered. A technology for constructing a finite element mesh by connecting elementary subdomains, a semi-automatic method for partitioning a two-dimensional multiply connected domain with a curvilinear boundary, and ordering the node numbers of a finite element mesh is described. In this case, the algorithms allow, by ordering the numbering of nodes, to determine such a sequence in which the ribbon width of non-zero coefficients of the system of resolving equations by the FEM will be locally minimal. [ABSTRACT FROM AUTHOR]

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]