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Nowadays, BLDC motors have become important motors for their many applications. Some of these applications require precise position and speed control, such as paper and textile mills, robots and CNC machines, etc. These and other applications require an accurate tracking trajectory of the movement. In this paper, design a cascade P-PI controller to control the position of the BLDC motor. This cascade controller consists of three controllers, one for current control, one for speed control, and the last for position control. In addition, the DC-link current can be used in block current control. Parameter values of these three controllers are calculated and extracted using the two new methods which are: SSO and GWO. The simulation result proves the power of the cascade P-PI controllers to reject disturbances, in addition, the results showed SSO Algorithm is better than the GWO algorithm. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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]

The selection of material for any product plays a critical role in every manufacturing processes. Choosing a suitable material among several alternatives is a challenge. A careful investigation must be conducted into the material selection process, as any failure that occurs in the material will end up in the failure of the entire product. The quality and standard of the product are weighed more than the financial aspect of any component. In this aspect, in this paper, proper material shock absorber coil spring material is chosen using the DEAR Algorithm in a novel way. A shock absorber coil spring is considered as one of the most important components in the suspension system of any vehicle. Suitable material must be selected, otherwise it will lead to the fatigue failure of the vehicle. The best performance from the alternative material is to be found by subjecting the coil spring to fatigue without any considerations over the financial aspect. To avoid any complications, material selection is a complex process, and to that end, a systematic method called the DEAR Algorithm is used in this work. Using this Multi-Objective Optimization (MOO) technique, ASTM grade A228 is picked as the suitable material for the shock absorber coil spring without providing weightage to any specific attribute. The results are justified using reports from analysis along with analytical calculations and confirmed by the average validation method corresponding to the desired characteristics. The DEAR algorithm is applied over the analysis report once again in a novel way. The similarity of all the findings demonstrates the DEAR algorithm's superior efficiency and accuracy. [ABSTRACT FROM AUTHOR]

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

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 aim of this paper is mining serendipitous drug usage to validate and generate drug repositioning hypotheses from social media. Materials and Methods: Two machine learning algorithms svm with sample size=12 and adaboost algorithm with sample size=12. Results: The support vector machine algorithm has shown more accuracy of (96. 66%) in reducing the false positive rates when compared with Adaboost algorithm accuracy(84.6%). The pre-test was calculated with a g-power value = 80% and threshold 0. 05% confidence interval of 95% mean and standard deviation by using the G-power tool. t is found that the svm algorithm has more accuracy percentage when compared with the Adaboost algorithm. [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]

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

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

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

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

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

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

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

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

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

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

One ongoing issue with text data files is the cost of data transmission across networks. The problem is related to the high cost of sending data at high rates. To solve this problem, data compression must be used to reduce the size of transmitted data. The idea is to utilize smaller compressed files; it turns out that utilizing smaller compressed files is more cost-effective and quicker than using larger uncompressed files. In general, compression lowers file sizes, increasing the efficiency of storage and permitting faster internet transfers. In this paper, four text files, each varying in size (5, 25, 50, and 100 kB), are employed in this paper. Two compression algorithms are used namely Lempel-Ziv-Welch (LZW) and Arithmetic Coding (ARC). The effectiveness of LZW compression algorithms and the ARC algorithm has been examined in terms of mathematical performances using MATLAB R2022a. Also, an algorithm has been proposed by combining the LZW and ARC algorithms, demonstrating better performance as compared to each algorithm on its own. The evaluation included the metrics of Compression Ratio (CR), Compression Factor (CF), Bitrate (BR), and Saving Ratio (SR) metrics. On the other hand, at 100 kB the proposed algorithms achieve of 2.747, while 1.834, and 2.207 for both ARC and LZW in term of CR. The CF of the proposed algorithm was 0.364, outperforming both ARC (0.545) and LZW (0.453). The BR values were 2.219, 4.362, and 3.642 for the proposed algorithm, ARC, and LZW, respectively. The SR for the proposed algorithm was 63.59% which is higher than ARC at 45.47% and LZW at 54.68%. [ABSTRACT FROM AUTHOR]

Polar Codes are a new class of capacity-reaching codes that have low decoding complexity and excessive reliability. They are primarily based on the concept of channel polarization, which makes a few bits inside the codeword more reliable and a few much less reliable because the code duration increases. This examination comprehensively evaluates how effective Belief Propagation (BP) and Belief Propagation with List (BPL) Decoding algorithms are in the Binary Erasure Channel (BEC) setup. The fundamental purpose is to recognize how nicely these algorithms deal with errors and enhance the reliability of verbal exchange structures. Different code lengths and list sizes are examined to see how they affect the Block Error Rate (BLER). This paper considers 4 codes studied and carried out in the usage of MATLAB lengths: N=256, 512, 1024, and 2048, and varies the Energy per bit to Noise power (Eb/No) from 1 dB to a 3.5 dB with list sizes eight and sixteen. The experiments show how the BP and BPL algorithms perform in coping with erasure errors and ensuring reliable deciphering. The BLER figures provide an in-depth description and comparison of the BP and BPL interpreting algorithms. The analysis highlights the promising blunder-correction talents of each BP and BPL algorithm in the BEC context. As the code length increases, the capability to correct errors also improves, showing that these algorithms can scale effectively. The addition of list decoding in BPL similarly enhances blunder correction by offering multiple possible answers. These examine the effect of Eb/No stages on the performance of BP and BPL deciphering, displaying that better Eb/No stages enhance accuracy but also increase the erasure chance. This paper discusses the trade-offs between accuracy and complexity for BP and BPL deciphering, presenting insights into their performance traits and optimization. [ABSTRACT FROM AUTHOR]

With the advent of cloud computing, novel methods of system design have emerged. Cloud providers now offer computing power and storage as a service, allowing customers to outsource computations more extensively than ever before. As sensitive data migrates to the cloud, ensuring privacy and security becomes increasingly critical for users. Fully Homomorphic Encryption (FHE) presents a contemporary approach to address these concerns. As far as my knowledge extends, this appears to be the initial academic publication focused on FHE. The article employs not only to explore a systematic analysis of fully homomorphic encryption but also to assess its practical implementation feasibility with novel techniques. Various papers on FHE have been aimed at enhancing efficiency and reducing noise through innovative techniques. A novel algorithm has been introduced to enhance the security of data transfers. The final thematic modeling study of this paper ensures that all discussed articles center around cloud computing and FHE. [ABSTRACT FROM AUTHOR]

Automation in purchase process is todays need in every sector and specially for purchasing daily need products. This paper reviews the current trends in automation and precision of billing procedures. In this paper, an Intelligent Billing System that makes use of object detection algorithms are studied. The primary objective is to compare and contrast two well-known object detection algorithms:"Convolutional Neural Networks (CNN) and Cascade Region-based Convolutional Neural Networks (Cascade R-CNN)". The CNN and Cascade R-CNN are used in an object detection framework that is based on the "You Only Look Once (YOLO) model".The algorithms, advantages, limitations and their level of accuracy evaluated for the CNN and Cascade R-CNN algorithms' when combined with the YOLO model. [ABSTRACT FROM AUTHOR]

Aiming at the problem of poor music source files and sound quality in the process of AI music creation, this paper proposes an automatic text generation algorithm to assist the analysis of AI music creation. Firstly, for the text generation task of independent multi-source syntactic structure diagram data, the relationship between multi-source input documents is modeled from semantic association and syntactic dependence, so as to realize the generation of final music writing text. Secondly, considering the problem of difficult to locate related work in massive music texts. Finally, the actual effect of AI music is comprehensively judged. The results show that this paper proposes a text automatic generation algorithm, which can optimize the multimodal encoder, make overall judgment on the internal data, network data, and graph structure of music text, and improve the encoding rate of information and semantics. Therefore, the text automatic generation algorithm can control the music unit, identify the characteristics of multiple modes in music creation, and improve the effect of AI music creation. [ABSTRACT FROM AUTHOR]

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

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]

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

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

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 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]

Simple linear models, have manifest spectacular execution to form document representations in recent past. In this paper, we put forward the conception of containers by experiments and theoretical affirmations. We perceive that the charter repository and the chronicle matrix indisputably unable to entirely crammed beyond the canister and will no longer have some well-formed and syntactic statistics on exceedingly huge narrative datum. We also advance a well-organized proposition for document delineation, by using clustering algorithms to split up a repository container into various sub-containers and initiate the correlation in the middle of sub-containers. We moreover dispute the possessions of both types of clustering algorithms, DVEM-K means and DVEM-Random, on vast content datasets by tenderness inquiry and topic ranking endeavours. Set side by side to simple even models, the outcomes manifest that our model give-rise to finest document depictions for document-eminent division correspondence tasks. Our perpectives can also be set forth to another models based on neural webbings, such as convolutional semantic networks, repetitive interconnected networks and productive antagonistic networks, in superintend or semi-supervised settings. [ABSTRACT FROM AUTHOR]

Mobile cloud technology is currently playing a vital role in the field of mobile communication. Frequently new updates are coming in the field of image processing, online games, Natural language processing and other application using mobile and cloud. On mobile phones, the execution of sophisticated software and Apps might result in poor performance in terms of battery consumption and response time. Therefore a new method is required to utilize the benefits of mobile cloud by using computation offloading technique. In this paper we proposed EDOMAC method for execution of Picasa and Mathdroid application using cloudlet environment. We have computed response time and energy consumption for above mentioned applications based on mobile and cloud. At the end compared results with POMAC and M-POMAC. [ABSTRACT FROM AUTHOR]

Cloud computing promises computing surroundings wherein distinct sources are distributed as a carrier to the consumer or numerous stakeholders over the internet network. Task allocation is a crucial and maximum crucial part of the environment of cloud computing. The scheduling of tasks specifically focuses to decorate the optimized usage of sources and as a result discountsthe mission of the entire time. Scheduling of a task is to disseminate a particular task to precise assets at a selected time. Exceptional strategies were proposed to clear up the hurdles of assignment scheduling. Project scheduling enhances the green optimization of useful resources/assets and obtained a reduced response. This paper discusses diverse cloud computing scheduling algorithms. [ABSTRACT FROM AUTHOR]

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

The paper considers math models and algorithms for modeling the propagation of an acoustic signal of side scan sonar. A mathematical model is presented for seabed generating in order to simulate the antennas signals and hydroacoustic echograms. Mathematical models for calculating receiving antenna signals are described. The advantages of the approach include the relatively low computational complexity of applied algorithms (in comparison with the solution of the wave equation with boundary conditions), as well as the possibility of parallelizing computational processes on several threads and computers. The presented results are applicable for sonar signal algorithms debugging, object detection methods developing and other tasks related to hydroacoustic images simulation. Further work is seen in the implementation of described approach as part of a modeling complex for simulating hydroacoustic signals in real time. [ABSTRACT FROM AUTHOR]

In recent years, the internet of things is prevalent and widely used. The new problem with IoT is security, which needs to be considered carefully because of the technology heterogeneity. These threats can affect IoT performance; therefore, it is necessary for effective monitoring. This paper examines several machine learning methods in intrusion detection systems that possibly run on IoT. Random Forests and Decision Tree are employed in this study for performance comparison. The experimental results show that the Random Forest and Decision tree algorithms application produces good performance with a faster response time and possible running on IoT. [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]

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

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

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