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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]

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]

ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. Each sub-system of the stack has recently been improved in order to acquire messaging/telemetry capabilities. This paper describes the whole stack monitoring with the Message Queuing Telemetry Transport (MQTT) protocol and Node-RED, a tool for wiring together hardware/software devices. Finally, this paper shows how Node-RED is used to graphically define metadata aggregation tasks, in a pipelined way, without introducing any single point of failure. [ABSTRACT FROM AUTHOR]

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

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

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

GlideinWMS is a distributed workload manager that has been used in production for many years to provision resources for experiments like CERN's CMS, many Neutrino experiments, and the OSG. Its security model was based mainly on GSI (Grid Security Infrastructure), using X.509 certificate proxies and VOMS (Virtual Organization Membership Service) extensions. Even when other credentials, like SSH keys, were used to authenticate with resources, proxies were also added all the time, to establish the identity of the requestor and the associated memberships or privileges. This single credential was used for everything and was, often implicitly, forwarded wherever needed. The addition of identity and access tokens and the phase-out of GSI forced us to reconsider the security model of GlideinWMS, to handle multiple credentials which can differ in type, technology, and functionality. Both identity tokens and access tokens are supported. GSI proxies even if no more mandatory, are still used, together with various JWT (JSON Web Token) based tokens and other certificates. The functionality of the credentials, defined by issuer, audience, and scope, also differ: a credential can allow access to a computing resource, or can protect the GlideinWMS framework from tampering, or can grant read or write access to storage, can provide an identity for accounting or auditing, or can provide a combination of any the formers. Furthermore, the tools in use do not include automatic forwarding and renewal of the new credentials so credential lifetime and renewal requirements became part of the discussion as well. In this paper, we will present how GlideinWMS was able to change its design and code to respond to all these changes. [ABSTRACT FROM AUTHOR]

The High Luminosity upgrade to the LHC (HL-LHC) is expected to deliver scientific data at the multi-exabyte scale. In order to address this unprecedented data storage challenge, the ATLAS experiment launched the Data Carousel project in 2018. Data Carousel is a tape-driven workflow whereby bulk production campaigns with input data resident on tape are executed by staging and promptly processing a sliding window to disk buffer such that only a small fraction of inputs are pinned on disk at any one time. Data Carousel is now in production for ATLAS in Run3. In this paper, we provide updates on recent Data Carousel R&D projects, including data-on-demand and tape smart writing. Data-on-demand removes from disk data that has not been accessed for a predefined period, when users request them, they will be either staged from tape or recreated by following the original production steps. Tape smart writing employs intelligent algorithms for file placement on tape in order to retrieve data back more efficiently, which is our long term strategy to achieve optimal tape usage in Data Carousel. [ABSTRACT FROM AUTHOR]

The objective of the paper is to use the Neural Network Support Vector Machine (NNSVM) algorithm to compare the prediction accuracy, so as to predict and classify Indian Currency using the parameters taken from the currency data set with VGG19. For each of the group 130 samples, thus for two groups a total of 260 samples were collected for this investigation. Group 1 uses Neural Network Support Vector Machine while group 2 uses VGG 19 and according to the workflow that was followed, Neural Network Support Vector Machine code has been implemented based on the imported data set using anaconda software and Jupyter notebook is launched. The simulation results shows that Neural Network Support Vector Machine algorithm has 95.4% prediction accuracy whereas VGG 19 has 80.1% prediction accuracy. This gives a significance value of 0.0026 that is (p<0.05). The research result is that for Counterfeit recognition, Neural Network Support Vector Machine has better performance than VGG 19 when used to find the prediction accuracy. [ABSTRACT FROM AUTHOR]

Stock checking is a labour-intensive operation in a warehouse. Therefore, it is suggested to automate the task using a robot capable of autonomous navigation. Simultaneous Localization and Mapping (SLAM) is commonly used in robots to achieve autonomous navigation. However, the SLAM navigation controller cannot achieve linear locomotion in the narrow pathways between shelves and maintain a consistent distance to the shelf simultaneously, which is required to obtain an accurate scanning result. This paper aims to resolve this limitation. Shelf Following Transition Algorithm is proposed to achieve near-linear locomotion in a narrow pathway. Several experiments are conducted in real applications. Based on the result, the robot achieved near-linear locomotion with a kp value of 0.001325, a kd value of 0.0035 and an entry angle of 90 degrees. It can be concluded that the requirement for the stock-checking robot is achieved. [ABSTRACT FROM AUTHOR]

Machine learning (ML) algorithms are getting unsurpassed exposure as a potential technique for solving and modelling the wear behaviour of polymer matrix composites (PMCs). This paper presents the application of ML algorithms in predicting volume loss of reinforced polytetrafluoroethylene (PTFE) matrix composites. Firstly, the Taguchi L27 was harnessed to generate data set in a regulated way. Then multi linear regression (MLR), support vector regression (SVR), particle swarm optimization (PSO) and Harris Hawk's optimization (HHO) coupled with SVR ML algorithms were developed to accurately predict the volume loss of reinforced PTFE matrix composites. Based on the results achieved, it was found that SVR-HHO ML algorithm predicted the volume loss of reinforced PTFE matrix composites better than the other algorithms with determination coefficient (96 %) and root mean square error of 11 %. The ML algorithms could be used for prediction of volume loss of reinforced PTFE matrix composites and development of new PMCs with specific volume loss resistance. [ABSTRACT FROM AUTHOR]

Power supply is a basic component in robot operation. Particularly with mobile robot, portable batteries are crucial to maintain continuous operation. This has led to the development of an autonomous docking system to overcome the hassle of manually recharging the robot. Presently, there are many well-developed docking systems to achieve independent replenishment of battery power by the robot itself. However, most system aims at achieving success at first attempt. As consideration was not put on the adaptability of the docking system, recharging of mobile robot easily fails upon a slight change in the surrounding environment. This paper thus aims to address this issue through development of a robust docking system that is capable in handling any positional error after a failed docking attempt. In the proposed docking system, a custom object detection model was trained to identify location of charging station and subsequently update the latest coordinates into the mobile robot mapping algorithm. Experiment results illustrate the capability of successfully replenishing battery power with charging station at lateral distance up to 80 centimetres from its initial position. [ABSTRACT FROM AUTHOR]

The proliferation of unmanned aerial vehicles (UAVs) across many sectors is seeing a fast growth trajectory, resulting in heightened congestion inside the airspace. As a result, the need to guarantee flight safety and mitigate the risk of accidents among unmanned aerial vehicles has emerged as a critical concern within the rapidly advancing realm of drone technology. Multi agent reinforcement learning presents a viable methodology for tackling these challenges, since it empowers drones to exhibit enhanced intelligence when operating in intricate surroundings alongside several agents. This article presents an examination of multi-agent reinforcement learning and its utilization in augmenting the safety of unmanned aerial vehicles. In this paper, we provide a pragmatic instantiation of multi-agent reinforcement learning, which encompasses the participation of several agents. The research results presented in this study provide evidence of the algorithm's efficacy in reducing drone collisions in intricate and highly populated settings, resulting in a significant rate of success. [ABSTRACT FROM AUTHOR]

Forensic science often uses reconstruction techniques to aid in their investigation for evidence. Typically documents containing important information are teared into fragments either by hand or by a shredder. Therefore, the benefit of reconstruction of such shredded documents should be immediately understood. This paper focuses on text based vertically strip cut typed documents, where the strips presented may only be fragments from the same document with no extra and no missing fragments. Existing literature in this area shall be reviewed and suitable methods will be tested against Dhivehi text documents to confirm their efficacy and accuracy in the Maldivian context. The general algorithm is segregated into four major phases: fragment pre-processing, fragment featurizing, fragment compatibility calculation and fragment arrangement. The pre-processing stage involves de-noising the image and fixing skew correction issues that may occur when physically scanning thin, long strips. The featurizing stage is used to calculate features which may be helpful in identifying or comparing the images such as the number of empty columns to the left / right of the fragment. The fragment compatibility calculation simply generates a matrix of values such that in row, i and col, j the value in the matrix cell i, j is equivalent to the probability that fragment j should be placed to the right of fragment i. The final arrangement step involves analyzing the fragment compatibility matrix and generating a list of clusters that indicate the correct arrangement of the fragments which when combined in the aforementioned arrangement would result in a near-perfect reconstruction of the original document. The results show that the applicability of tested methods: pixel edge comparisons, Nearest Neighbor arrangement, and Minimum Spanning Tree arrangement still hold true with Dhivehi text-based documents. [ABSTRACT FROM AUTHOR]

Discontinuity properties largely influence the mechanical behavior of rock mass. Orientation grouping is an important part of rock discontinuity characterization. This paper presents a fast weighted optimal spectral clustering method for orientation grouping of rock discontinuity based on 3D point clouds. The key steps include: (1) point cloud preprocessing with normal vector calculation and sharp point filtering, (2) hemispherical raster point generation based on Fibonacci sequence (3) fast spectral clustering algorithm based on raster point weighting (4) optimal group number selection using CHI validity index. Two benchmark rock slope models are adopted for analysis. The results show that the proposed method can effectively improve the computational efficiency and accuracy of orientation grouping. [ABSTRACT FROM AUTHOR]

This paper introduced a new technique based on the bio-inspired Salp Swarm Algorithm (SSA) that can be used to challenges involving the optimal allocation of Distribution STATic COMpensator (DSTATCOM) in radial distribution systems (RDS). The suggested technique calculates appropriate DSTATCOM placements and sizes utilizing the newly designed Salp Swarm Algorithm. The suggested technique computes load flow utilizing a quick and efficient backward/forward sweep algorithm. The suggested method's goal function is to decrease overall system power losses. The suggested SSA-based optimization strategy in the RDS was demonstrated using a standard IEEE 33 bus system. According to the simulation findings and comparison of the various instances studied, DSTATCOM allocation leads in considerable power loss mitigation with decent voltage profile improvement. [ABSTRACT FROM AUTHOR]

Object detection is one of the most challenging problems in computer vision. Detecting an object from an image or video finds applications in different fields like security, medical, automated vehicle systems, etc. Locating instances from a real-world image requires improved accuracy and low latency. Deep learning (DL) techniques have emerged as a powerful tool for training the system and detecting instances. However, DL-based object detection for real-time applications requires a lot of computational resources. Field Programming Gate Array (FPGA) is found to be more promising to give optimized results for object detection using deep learning. This paper presents a detailed study of various DL-based models using different datasets like MS-COCO, PASCAL VOC, etc for object detection. It also provides good insight for selecting an algorithm for an object detection application with FPGA architecture. The study shows that the advancement in software and hardware technologies enables accurate and efficient detection systems for a wide range of applications. [ABSTRACT FROM AUTHOR]

The process of gleaning important facts and insights from vast amounts of unstructured textual data is called text mining, sometimes referred to as text analytics. In today's data-driven business climate, text analytics is becoming increasingly significant. A key component of text mining is term-weighting, which aids in locating significant keywords or phrases in a text document. Text mining algorithms can more precisely identify and analyze documents by giving these phrases weights, which can result in important insights and knowledge discovery. The study article thoroughly analyses several methods put out in the literature to help computers detect language and text. It covers the mathematical underpinnings of term weighting schemes, categorizes them broadly into supervised and statistical methods, and provides examples from both groups. The Vector-Space Model and its variations, which are at the core of many other techniques covered in the paper, are given special emphasis in this article. The significant research highlights the necessity for machines to effectively understand language and material without engaging in plagiarism or other immoral activities. Overall, the study lays the framework for further research in this field by offering a thorough overview of contemporary language and text recognition methods. [ABSTRACT FROM AUTHOR]

The travelling salesperson problem (TSP) is the most studied problem in optimization and computer science. A more challenging version of the Travelling Salesperson Problem (TSP) is the Multiple Travelling Salesperson Problem (MTSP). The goal is to find the shortest path, which is the shortest distance for each salesperson to travel from the depot to each city and return to depot. It is a NP hard problem and has various applications in routing and scheduling. There are various algorithms to solve MTSP such as exact solution method, bioinspired algorithms like genetic ant colony, evolutionary. In this paper we review exact algorithm and bioinspired algorithm. [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]

This paper explores the correlation between mental calculation performance and the frequency of using written algorithms in mental calculation tasks. Mental calculation is a mathematical tool used in everyday life situations during and after our formal education. After presenting an overview of the professional literature on this topic, the paper will present calculation methods and show how represented they are in the Curriculum. For the empirical part of the research, a total of 233 Croatian students aged 10 to 22 years were tested and interviewed. The previously mentioned correlation was then analyzed. An overview of the interview results will be presented as well. It was found that school mathematics does not always contribute to the development and flexibility in using mental calculation strategies because of the student preference for acquired written algorithms. Definitely, recommendation is shifting the focus from written calculation and procedures to the mental, discussing the associated strategies and different concepts of number. In this way, formal education could contribute to what students really need later on, in both private and professional situations in which they may find themselves on a daily basis. [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 amount of available scientific literature is increasing, and studies have proposed various methods for evaluating document-document similarity in order to cluster or classify documents for science mapping and knowledge discovery. In this paper, we propose hybrid methods for bibliographic coupling (BC) and linear evaluation of text or content similarity: We combined BC with BM25, Cosine, and PMRA to compare their performances with single methods in paper recommendation tasks using TREC Genomics Track 2005datasets. For paper recommendation, BC and text-based methods complement each other, and hybrid methods were better than single methods. The combinations of BC with BM25 and BC with Cosine performed better than BC with PMRA. The performances were best when the weights of BM25, Cosine, and PMRA were 0.025, 0.2, and 0.2, respectively, in hybrid methods. For paper recommendation, the combinations of BC with text-based methods were better than BC or text-based methods used alone. The choice of method should depend on the actual data and research needs. In the future, the underlying reasons for the differences in performance and the specific part or type of information they complement in text clustering or recommendation need to be examined. [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]

Trust is a factor that is becoming more prominent in human robot interaction research. Only few approaches so far tackle the challenge of data-driven trust assessment. In this paper, we present a data set consisting of motion tracking data from an industrial human robot collaboration task. The data is collected during a trust manipulation experiment that has been designed to elicit different trust levels in the participants. Additionally, participants filled out a standard trust questionnaire. The data set allows for developing and testing data-driven trust assessment algorithms. [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]

Insurance fraud, especially automobile insurance fraud, is a common fraud topic which causes significant financial losses for insurance companies. Dishonest claims impose a significant financial strain on insurance companies which influence on the entire industry. Because of this, insurers are constantly seeking for more effective detection systems to get beyond the limitations of traditional techniques; therefore, building a reliable and effective fraud detection model is crucial to maintaining the insurance providers' financial stability and reputation. Our research paper major objective is to use machine learning techniques to develop a reliable system for detecting car insurance fraud. This study intends to examine multiple supervised machine learning algorithms using classification method, and assess their efficacy, to ultimately select the most accurate model with significant accuracy for identifying fraudulent insurance claims. [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]

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

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

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

Two-wheeler vehicles are a major form of personal transport in the world and specially India, a substantial number of 2 wheelers on roads have made it difficult to monitor them correctly. This has led to carelessness from the drivers and increase in the number of accidents that can be prevented and minimized. One of the protective gears-helmet is not always worn by riders and it is tough for the traffic police to catch these violators, due to sheer size. Our project involves using the YOLO (You Only Look Once) algorithm to detect whether the riders as well as pillions are wearing the helmets so that they can be caught, and their vehicle details be sent for further processing to the traffic police. Along with the 2 wheelers, helmets are also required at construction sites, and we plan to apply the same. This paper proposes a framework for identification of motorcyclists without helmets. The application, with the help of YOLOv5 (You Only Look Once) and rigorous training gives us an accuracy of 87% true positives detection. [ABSTRACT FROM AUTHOR]

The development of aberrant cell proliferation in the lungs is a problematic condition that has the potential to result in death. On the list of diseases that most frequently result in mortality, lung cancer takes first place. The early stages of lung cancer are notoriously difficult to diagnose due to the fact that cancer cells with dimensions less than very small are notoriously difficult to spot by imaging. If the cell abnormalities are discovered in the early stages, it will be possible to begin therapy sooner, which will result in an improved chance of the patient surviving the illness. Several different image processing strategies can be utilized in the diagnostic phase of patient care to help spot signs of disease. In this paper, classification of Lung Cancer from chest X-ray images has been done using optimized Convolutional Neural Network (OCNN) and Ant Lion Optimization (ALO) algorithm. In pre-processing step, the contrast of all images are enhanced using Histogram Equalization (HE) method and the noises are removed from all images using Median Filtering. After the pre-processing step, feature extraction is performed using Gray Level Spatial Dependence (GLSD) to extract the statistical features. The feature vector is then trained and classified using OCNN-ALO algorithm. The ALO algorithm is used to optimize the hyper parameters of CNN layers. It classifies the lung images into normal and lung tumor affected. Performance results have indicated that OCNN-ALO attains the superior performance with 95.15% accuracy, 85.43% sensitivity, 93.4% specificity and 76.43% F1-score. [ABSTRACT FROM AUTHOR]

This research paper addresses the complexity of understanding the critical concept of process scheduling in operating systems and proposes an innovative approach to teaching it using an AR-based game. We have developed a game that leverages AR technology to virtualize the process scheduling concept, using practical examples of different users with varying arrival, and burst times. To simplify the explanation, we have utilizedthe First Come First Serve (FCFS) scheduling algorithm in the game. The game development was carried out using the Unity game engine and Vuforia software development kit, which provides robust support for AR game development. The AR-based game offers an immersive and engaging learning experience, providing aneffective means of making complex concepts like process scheduling more accessible to a wider audience. [ABSTRACT FROM AUTHOR]

Conventionally, a six-step inverter used in Active Power Filter (APF) to mitigate the harmonics in power system because of its simplicity and easy to control. However, six-step inverter only operates in low voltage rating for APF and additional transformer is essential to operate in high voltage rating of APF. There are three types of Multilevel Inverter (MLI) namely Cascaded H-Bridge, Neutral Point Diode Clamped (NPC) and Flying Capacitor (FC). Among the types of Multilevel Inverter (MLI), only the Cascaded H-Bridge (CHB MLI) can be used to replace the conventional six-step inverter due to its capability to work in high voltage rating, less used power device and easy to control. This paper evaluates a three-phase three-wire shunt APF based on six-step conventional inverter and Nine Level CHB MLI using Indirect Current Control (ICC) schemes. MATLAB/Simulink tools are used to model and simulate both of algorithms, consisting of three main parts namely harmonic extraction algorithm, DC link algorithm and switching algorithm. In MATLAB/Simulink, the model was created and validated the Total Harmonic Distortion (THD) line current with different frequency modulation (mf) values. According to the findings, the increasing of frequency modulation will produce a good result of THD line current. The THD line current produced by both algorithms is less than 5%, which is the permitted value as stated in IEEE 519. [ABSTRACT FROM AUTHOR]

This paper discusses the application of the Content-Based Image Retrieval (CBIR) system in image classification for various types of batik in the region. The aim is to detect and recognize certain types of batik with high accuracy, as well as develop a digital image-based security system for various types of batik with various motifs, patterns, and colors. In this research, a typical South Sulawesi batik type, namely Lontara motif batik, was used as a sample. The methods used to perform feature extraction and classification are the Gray Level Co-Occurrence Matrix (GLCM) and Multilayer Perceptron (MLP). The use of GLCM is equipped with the Angular Second Moment (ASM) feature with contrast, correlation, energy, and homogeneity parameters, so that the image data extraction results will be more accurate. Apart from that, the combination with the Multi-Layer Perceptron (MLP) algorithm will make it easier for the system to carry out classification because MLP uses three training functions, namely Levenberg-Marquardt (LM), Variable Learning Rate Backpropagation (GDX), and Scaled Conjugate Gradient (SCG), which produce high accuracy values at smaller epochs. The best accuracy value obtained in the test results was 88.89%. The combination of the GLCM-MLP method can be used as a solution to accurately detect various batik motifs. [ABSTRACT FROM AUTHOR]

Removing the specular reflection of an image by using a dark channel prior algorithm is analyzed in this paper. This method is gives to provide solution to the Eproblem in specular highlight images of information loss. It involves mainly three steps. First, the image is passed through a dark channel prior algorithm, which will give the atmospheric light component. Second, with the help of the atmospheric light component by using the L1 norm function remove the reflection component from the original image, then adjust the halo artifacts in the image by comparing adjacent pixels. Third, the image is passed through the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to improve brightness, color, sharpness. This method is most effective when compared to the existing methods. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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