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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]

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]

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]

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

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

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

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

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

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

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

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

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

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]

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

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

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

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

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

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

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

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

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

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

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

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

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