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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 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]

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]

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 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 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 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]

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]

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]

Absolute value detectors implement a widely used spike-detection algorism called absolute value detection. 4-bit absolute value detectors are extensively used in computer storage and acquiring neural signals. As a basic logic component, there's a rising demand for low energy cost and high working frequency. This paper performed a series of optimizations to the conventional design. A traditional absolute value finder is composed of an absolute value finder using ripple-carry adder and multiplexer, and a comparator. In this paper, both parts are redesigned with static-CMOS logic and transmission gate logic (TGL) to shorten critical path. After the circuit topology is determined, two methods are used to control delay and power consumption: sizing logic gates and adjusting supply voltage. A technique called logic effort is used to derive the delay of critical path. By using a linear programming solver, three sets of parameters are found, in which 38.59-unit delay, 52.75-unit energy cost is a balanced design, with the lowest EDP (Energy delay product). [ABSTRACT FROM AUTHOR]

In order to make the auxiliary training ball picking robot pick up the ball efficiently, the path planning of the ball picking robot is needed. The traditional A-star algorithm can be applied to project the trajectory of the mobile robot under the motion model, but there are problems of general obstacle avoidance and insufficient accuracy of obstacle avoidance, based on the characteristics of iteration of the colony algorithm to improve the accuracy of the algorithm. This paper proposes an approach to make the A* algorithm iterative to improve the ability of the algorithm to perform path planning. Compared with the separate A* and colony algorithms, the algorithm in this paper is more accurate and efficient in handling complex routes. It is also paired with object recognition and grasping functions to achieve the function of identifying basketballs, grasping them and transporting them back to the players for the ball pickup robot. In the final Matlab simulation experiments, the effectiveness of target recognition, object grasping, and optimized path recognition is verified. Among them, the optimization algorithm of A* shortens the length of the path by 28% after 40 iterations. [ABSTRACT FROM AUTHOR]

The field of robotics research has been well developed with the combination of computers and machinery. With the advancement of artificial intelligence, mobile robots are also attracting more attention today, and path planning is a crucial foundational technology for mobile robots to complete transportation requirements and reach pertinent target areas. In an efficiency-conscious society, the single-goal planning of transmission is gradually failing to meet the needs of enterprises and factories for efficient operations, path planning that can simultaneously plan the optimal methods and reach many target points is increasingly replacing the conventional single-goal path planning. However, there are more factors to be considered in the real complex environment to face various complex road conditions, and for this reason, various single or hybrid algorithms are being optimized and solved for this kind of problem. This paper summarizes the main methods of path planning to simulate the scale of obstacles and environmental scene maps in various conditions, focusing on several basic algorithms and their hybrid algorithms for solving multi-objective path planning problems in global and local path planning, as well as their improvements and innovations on the basic algorithms. This paper's primary idea is to divide the multi-path planning process into various components and substitute each part into a suitable algorithm and model to solve it separately to accomplish the task of reaching multiple target points efficiently. [ABSTRACT FROM AUTHOR]

In their highest grade, brain tumors are the most widespread and dangerous diseases with a very short life span. Therefore, early automatic brain tumor detection is required to lower the fatality rate. Due to this, MRI is a commonly used imaging technology for diagnosis; however, it is practically impossible to do manual segmentation of the volume of data generated by MRI promptly. This paper is intended to analyze the suitable tools, techniques and language for automatic detection of Brain Tumor. From the nature of the problem, it is quite evident that it requires high precision of accuracy in detecting such a deadly disease in a very short period and if possible, in real-time, for a large number of datasets will be required not only to train the algorithm but also for its testing. Spark is an open-source platform to deal with lots of data. Spark's API, PySpark is coupled with Python language to enable the developers to develop a python script for the Spark processing engine. Deep learning algorithms are the most useful and appropriate for such types of tasks where a large amount of data is involved and requires high precision training and testing of the models and the algorithms. In this paper, we have discussed the selection of the tools, techniques and language for the implementation of the model for detecting the brain tumor. Tools like Google Colab and PySpark have been explored in this paper. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to improve the accuracy for automatic classification of meal waste from innovative trash data with the help of image processing. There are 2572 images for the classification of meal waste were used for this paper. The images are labeled as "Cardboard", "Plastic", "Paper", "Metal", "Glass", "Trash" and there are 20 number of images have been used for RF classifier taken as first set of machine learning algorithm and is compared with SVM algorithm taken as second set of machine learning algorithm With a g-power value of 80%, the revolutionary garbage data images, a threshold of 0.05%, a confidence interval of 95%, and a standard deviation, these photographs were gathered from various web sources. When compared to the SVM method, which had an accuracy of 61.45%, the proposed system's accuracy was enhanced to 84.81%, with a significant value of 0.001 (p 0.05) with a 95% confidence interval. This study found the meal waste from trash using the RF is significantly better than SVM algorithm. [ABSTRACT FROM AUTHOR]

With the spread of the Internet and a large number of network users, the problem of congestion on the network has arisen. This paper presents a strategy to reduce network congestion using clustering algorithms, K-means, and shortest path algorithms. The paper aims to solve the problems of congestion control, access to quality of service, reduce time wasted when sending and receiving network users, take advantage of network devices, and provide quality of service without data loss. The results of the proposed system simulation showed its effectiveness and accuracy through the simulation of the system. [ABSTRACT FROM AUTHOR]

One of the profound things that human being has ventured is to demystify the natural process that is happening in nature. Nature inspires technology to solve real-time problems. There are various nature-inspired techniques based on insects that are used in population-based or ecology-based nature-inspired algorithms. In this paper, the nature-inspired Bat Algorithm, which is based on the echolocation behavior of bats, is discussed in detail. The Bat Algorithm has various hybrids which are used in numerous applications; all these hybrids are reviewed and discussed in this paper. Systematic analysis is conducted using Scopus and WoS databases to analyze the research trend for these hybrid algorithms, study its variants and application domains, and predict the future for these nature-inspired algorithms. Numerous experiments based upon year-wise analysis, correlation of related terms, article growth, etc. are conducted and described in this paper. [ABSTRACT FROM AUTHOR]

Abnormality location frameworks conveyed for checking in oil and gas ventures are generally WSN based frameworks or SCADA frameworks which all experience the ill effects of significant impediments. WSN based frameworks are not homogenous or contradictory frameworks. They need facilitated correspondence and straightforwardness among districts and cycles. Then again, SCADA frameworks are costly, unyielding, not versatile, and furnish information with a long postponement. In this paper, a novel IoT based engineering is proposed for Oil and gas businesses to make information assortment from associated objects as straightforward, secure, hearty, solid and speedy. This paper audits and examinations recurrence and outcomes of disappointment rates for inward and outside consumption, human activity and normal powers are dissected and the normal disappointment rate for every disappointment instrument in oil pipeline transport frameworks. Verifiable information on results of the unplanned loss of regulation of inland pipelines are evaluated. To stay away from this event, designs for the most part do pressure transient investigation in the water powered plan period of pipeline network frameworks. Demonstrating and reproduction of homeless people in pipelines is an adequate and savvy technique for evaluating this issue and discovering specialized arrangements. Qualities and restrictions of different discovery and control procedures with some down to earth models are examined. [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]