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This article describes a method for evolvement predicting of an emergency zone on the example of a forest fire using neural networks and applying the Hurst exponent to increase the accuracy and reduce the computational complexity of the algorithm. The inputs in this method are a set of environment state maps and the fire characteristics. The lack of a sufficient number of maps and a large amount of available data has a negative effect on the forecast accuracy and the overall system performance. It proposed to divide the fire area into sectors and perform the forecast by sectors with an accuracy that depends on the sector behavior characteristics. The sector behavior characteristic in this paper presented by the persistence analysis of the sector vectors, and be replaced by any other indicator. Persistence indicates trend stability in the behavior of the time series and, accordingly, the forecast reliability. In the case of low forecast reliability, the sector is iteratively divided into segments, which are analyzed and characterized. In this case, forecasting carried out for each sector and segment, which generally increases overall forecast accuracy. The paper presents the key mathematical calculations and the results of experimental studies. [ABSTRACT FROM AUTHOR]

Huge number of nodes of sensor make wireless sensor networks (WSNs) that not just gather data associated with various happenings but also submit those events to central station hop-by-hop. Data submitted through nodes of neighbor may be extra as well as duplicate. In addition, generated data number in wide WSNs might be a lot to be processed in sink. The most essential variable for gathering WSNs data is optimum energy usage to integrate also gather data. The accuracy of the information is usually assessed by detecting the error of the data contained in the CHs. This process helps increase the integrity and reliability of the WSN application. Basically, data identification is a classification issue. Support Vector Machine (SVM) is one of the most common classification approaches used in this paper to create an identification model. The SVM classification parameters will also be optimized by the Eurasian oystercatcher optimizer algorithm (EOO). The simulation results show that the proposed method is better than the basic paper in terms of 5 specific criteria. The proposed method has improved by about 1.02% compared to the other methods. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

Based on nonsubsampled contourlet transform (NSCT) and modified pulse coupled neural network (MPCNN), the paper proposes an effective method of image fusion. Firstly, the paper decomposes the source image into the low-frequency components and high-frequency components using NSCT, and then processes the low-frequency components by regional statistical fusion rules. For high-frequency components, the paper calculates the spatial frequency (SF), which is input into MPCNN model to get relevant coefficients according to the fire-mapping image of MPCNN. At last, the paper restructures the final image by inverse transformation of low-frequency and high-frequency components. Compared with the wavelet transformation (WT) and the traditional NSCT algorithm, experimental results indicate that the method proposed in this paper achieves an improvement both in human visual perception and objective evaluation. It indicates that the method is effective, practical and good performance. [ABSTRACT FROM AUTHOR]

Most of the mobile robot path planning is estimated to reach its predetermined aim through the shortest path and avoiding the obstacles. This paper is a survey on path planning algorithms of various current research and existing system of Unmanned Ground Vehicles (UGV) where their challenging issues to be intelligent autonomous robot. The focuses are some short reviews on individual papers for UGV in the known environment. Methods and algorithms in path planning for the autonomous robot had been discussed. From the reviews, we obtained that the algorithms proposed are appropriate for some cases such as single or multiple obstacles, static or movement obstacle and optimal shortest path. This paper also describes some pros and cons for every reviewed paper toward algorithms improvement for further work. [ABSTRACT FROM AUTHOR]

The paper presents the main components of a methodological approach to the creation of an apparatus for analyzing the reliability of fuel and energy supply to consumers in conditions of negative impacts on the energy sector operation. The authors characterize the main factors affecting the scale of the implementation of threats to the reliable functioning of power systems and the energy complex as a whole. The paper describes algorithms for analyzing the impact of negative impacts on the reliability of fuel and energy supply to consumers, algorithms for forming lists of critical objects of energy systems, as well as algorithms for choosing measures to minimize the consequences of negative disturbances in the energy sector. [ABSTRACT FROM AUTHOR]

In this paper we construct one type of de bruijn sequence. First, we modify the prefer-one and prefer-zero algorithm to build one cycle. The other cycles are constructed by modified pure circulating register. Finally, we use cycle joining method to get the full length of the sequence. [ABSTRACT FROM AUTHOR]

This paper discusses the application of Data Envelopment Analysis based Ranking (DEAR) algorithm in selection of materials for the rollers which moves across for cleaning the solar panel automatically. Selecting suitable materials for roller used in the developed automated solar panel cleansing system, indeed, the selection of material for any part in a design process requires substantial knowledge in the process. Even having required knowledge in the domain, it is often requiring some guidance or reference for providing the justification after selecting the materials for the product. In this sense, in this work, the DEAR algorithm one the multi criterion decision making algorithm is used as guiding algorithm to identify the suitable candidate material for the rollers. Algorithm helps in selecting the material which considered to be as an effective material from the group of available materials which are suitable for adapting as the materials for rollers. For the algorithm to work, the various properties like weight, friction coefficient, cost which are considered as important properties for roller to work efficiently were presented to the algorithm and based on the same, the algorithm suggested the best material for the rollers. [ABSTRACT FROM AUTHOR]

This paper provides a modelling algorithm for renewable energy based distributed generators with the goal of reducing power loss and improving voltage profiles in unbalanced distribution networks. Recently, the utilization of Distributed Generation (DG) technologies in the power system is gaining the utmost importance owing to its potential benefits. The combining of the distribution system with DG put forwards a cost - effective as well as benefits to the clients. In any case the scanty of DGs might not assure the enhancement in terms of system performance. Hence In the distribution system appropriate DG units' placement plays vital role. In this paper, the optimal DG placement problem will be discussed as a constrained nonlinear optimization problem for voltage profile enhancement and power loss minimization. As a novelty the result of Lion Algorithm (LA) compared with the supervised firefly algorithm and Bat algorithm. The voltage profile and power loss reduction in the feeder system with DGs is evaluated for two test cases: case 1(DG supplying real power alone (P), and case 2 (DG supplying reactive power alone (Q). The proposed algorithms are evaluated on the IEEE 33 and IEEE 69 node feeders in a MATLAB environment. Several case studies have been conducted, and the results of the subsequent discussions illustrate the effectiveness of the presented algorithms. [ABSTRACT FROM AUTHOR]

The 0/1 Knapsack Problem (KP) is one of the problems in optimization where a set of items with given benefit and weights.The aim is to select a subset of the items in order to maximize the total benefit without exceeding the knapsack capacity. Nowadays, it becomes a big dilemma due to the explosion in the amount of datasets. In this paper, P(KD)P algorithm has been proposed and performed. For effectively calculating the KM matrix using multi- core system, an improved approach of the DP_KP algorithm has been considered. The proposed scheduling and partitioning approaches have accomplished a significant enhancement to the overall performance. Executions were implemented on corei7 with (8 cores). It outperforms sequential KP_DP more than 16-fold speedup and (KD)P more than 5-fold speedup. Its efficiency reaches 0.316, 0.0829 and 0.0979 over the KP_DP, (KD)P, and P(KD)P respectively. [ABSTRACT FROM AUTHOR]

Automated diagnostic systems have become increasingly popular in the healthcare industry in recent years. In recent years, these systems have gained popularity for their diagnostic benefits. The operator-dependent existence of medical imaging systems can be minimized with this method, and the diagnostic process can be made more repeatable. In addition, it aids in the improvement of diagnostic accuracy. This effectively works with features that cannot be obtained by visual analysis or intuitive examinations (such as computational and statistical features). In this paper, we propose a new algorithm called "Optimized Weighted KNNI algorithm" (OWKNNI). In this research paper, we have addressed the topic of migration. This paper discusses the problem of dimensionality reduction and the use of feature selection algorithms, which is one of the most important tasks in feature engineering. The solution is given sorting out the problem is proposed as an algorithm. [ABSTRACT FROM AUTHOR]

In the era of machine intelligence, dynamic gesture recognition is assumed to be a difficult and challenging problem because it involves the human element. Moreover, the gestures formed for a particular problem vary from person to person. Therefore, the accuracy of classification sometimes becomes inappropriate. Also, gesture recognition is affected by lag in detection; however, a negative lag is assumed to be favorable. In this paper, an attempt has been made to control the system volume through hand gesture recognition which may help in the remote operation of a device and differently-abled people. The YOLO algorithm will deep learning approach is used in this paper, which detects hand movement and its direction. Based on the direction of hand movement, the volume can be increased or decreased. [ABSTRACT FROM AUTHOR]

Recommender systems are information filtering systems that forecast user-item ratings, primarily drawing insights from extensive datasets to make personalized recommendations. Movie recommendation systems offer a mechanism for helping users connect with others who share similar interests. In many cases, traditional recommender systems struggle with accuracy when dealing with sparse data. This study focuses on addressing the issue of data sparsity and seeks to remedy it by implementing singular value decomposition collaborative filtering within a web-based movie recommendation system. In this paper, we introduce a movie recommendation system that primarily utilizes singular value decomposition (SVD) and cosine similarity to generate a list of recommendations. We improve the model by implementing a factorization approach, reducing complexity by minimizing parameters. Furthermore, our approach incorporates movie content information into the calculation of item similarities. Our proposed algorithm recommends a personalized list of the top N movies to users based on their reviewed preferences. Additionally, it includes a graphical representation of proportion of movies already watched by the user and those suggested to them. [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]

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]

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]

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]

The emerging global SDN technology for improving network traffic and quality of service can be achieved through routing algorithms that rely on even distribution of traffic according to network loads. Various network load balancing algorithms which improve QoS are proposed. To find the most suitable mechanism for improving the routing mechanism and QoS is needed a comparative analysis. This paper presents a comparative evaluation process of existing algorithms as well as proposed algorithms by the authors. [ABSTRACT FROM AUTHOR]

This paper is based on the OVATION control system for cascade superheated temperature model of the control system design, intended to imitate the actual production of thermal power plant superheated temperature control system, so that we can preliminary understand the design of the OVATION system and the control method of large delay controlled system. Because the control of superheated temperature model has the characteristics of unidirectional heating, large inertia, large delay and time-varying, it is particularly important to establish accurate model and determine the relevant PID control parameters by mathematical method for this course design. Furthermore, OVATION system, as a more advanced control system in the world today, has many advantages (this paper will introduce), this system is also used by most of the domestic power plants, for our future development also has certain advantages, learning and application of this system is particularly important, this paper will I/O measurement points. Establishment, the establishment of logical configuration (SAMA diagram), the establishment of screen configuration and other necessary operations are described in detail. Finally, several algorithms (cascade control, ordinary Smith prediction and improved Smith prediction) are compared. At the same time, the shortcomings and improvement direction of this algorithm are also proposed. [ABSTRACT FROM AUTHOR]

The paper deals with the one-time pad symmetric secure algorithm, called OSA. The method involves a double-memory technique in order to improve the security aspects. In particular, the paper proposes a key-stream generator for the OSA algorithm. Furthermore, security analysis and the results of the experimental verification of OSA are presented. The conducted investigations were focused on a security analysis of the proposed key-stream generator. [ABSTRACT FROM AUTHOR]

In order to solve the ill-posed problem in super-resolution image reconstruction, this paper proposes an adaptive regularization way use sparse representation. We build a new L1/2 non-convex optimization model and apply reweighted L2 Norm for the adaptive algorithm in this paper. Experimental results show the significant effect in denoising and preserving edge details. It outperforms some traditional methods in the value of peak signal to noise ratio and structural similarity [ABSTRACT FROM AUTHOR]

Storage is the key link of modern logistics. Warehouse environment monitoring is an important part of storage safety management. To meet the storage requirements of different materials, guarantee their quality in the greatest extent, which has great significance. In the warehouse environment monitoring, the most important parameters are air temperature and relative humidity. In this paper, a design of warehouse multipoint temperature and humidity monitoring system based on King view, which realizes the multipoint temperature and humidity data real-time acquisition, monitoring and storage in warehouse by using temperature and humidity sensor. Also, this paper will take the bulk grain warehouse as an example and based on the data collected in real-time monitoring, giving the corresponding expert advice that combined with the corresponding algorithm, providing theoretical guidance to control the temperature and humidity in grain warehouse. [ABSTRACT FROM AUTHOR]

Research on task assignment and scheduling problems began in the 1960's, and has become a popular research topic in the past few decades. In this paper, the theoretical graph application using bi-partitioning technique is presented to assign a number of tasks onto two processors. This paper considered a directed-weighted cyclic task graph. Combining the technique of reduction and bi-partitioning led to an efficient graph-mapping concept. The developed simulation model found that the proposed techniques and algorithms are easy to be implemented and minimized the computational time. [ABSTRACT FROM AUTHOR]

Diabetes is referred to a group of metabolic disorder that is caused by irregular amount of insulin produced in body. There are two types of diabetes in major clinical cases that is type 1 diabetes (T1D) and type 2 diabetes (T2D). Most of the cases reported is T2D and this is caused by a number of people that are unaware of their possibility in having T2D and lack of knowledge regarding its risks. It is important to perform an early diagnosis of T2D to ensure that people will become more aware and also reduce the chances of them getting high risk or complications such as stroke, blindness and damage to body and nerves. To do a classification task, a tool namely machine learning will be used. This tool is used by researcher to analyze a large amount of data to obtain a data pattern. This pattern then will be interpreted to get a knowledge that can help in decision making process. For the medical diagnosis, there are two things that is important which is accuracy and comprehensibility. Ant Colony Optimization (ACO) is a meta-heuristic method that is branch of swarm intelligence and it has been used to solve many complex problems and generate comprehensible result. Hence, this paper will introduce the new proposed algorithm that will hybrid between ACO with Gini Index. Gini Index is a heuristic measure that is widely used in Classification and Regression Trees (CART). The proposed algorithm was execute using the BlueJ application that is written in Java language. The aim of this paper is to investigate the performance of the new proposed algorithm in terms of accuracy and computation time. Experimental results on Pima Indian Diabetes data set show that the proposed algorithm can improve the predictive accuracy and computation time compared to the original ACO. In addition, it also leads to a smaller number of rules and terms. [ABSTRACT FROM AUTHOR]

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

The performance assessment of students is the most important work as this is directly related to the future of the students. This research paper focuses on structural study, analysis and identification of non academic characteristics and data mining techniques, specifically classification and clustering. These are required for designing a model which collectively gives performance assessment in the form of academic and non-academic characteristics. These characteristics need to be classified separately on the basis of different subcategories in each type of characteristics and data collected from the questionnaire from the stakeholders of this model students, teachers and parents. Identifying and using classification and clustering algorithms suitable to the structure of this assessment leads to required output of the research. [ABSTRACT FROM AUTHOR]

Simulation-driven optimization problems often require large computational resources and as such are often solved with algorithms which rely on surrogates, namely computationally cheaper mathematical approximations of the simulation. A common approach is to use a surrogate in conjunction with an evolutionary algorithm to seek an optimum based on the surrogate's predictions. In this setup the mechanics of the evolutionary operators are unrelated to the surrogate and do not benefit from the information it accumulates during the search. As such this paper proposes new EA operators in which surrogates are intrinsically incorporated. The proposed recombination operator combines local surrogates with an SQP search and the mutation operator uses a global surrogate based on nearest-neighbour distances. Performance analysis based on well-established test functions shows the effectiveness of the proposed implementations. [ABSTRACT FROM AUTHOR]

Quantum computers are here, and the search for applications and use of these allow us to overcome the limits that today's hardware information processing gives us is constantly going on. Quantum machine learning is one of the many emerging fields that use quantum computers to process information. In this paper, we present a method and a set of experiments where we see the potential and capacity of the Noisy intermediate-scale quantum hardware for the execution of different models having as the basis in some of them the quantum algorithm corresponding to the Quantum Fourier Transform. With this, we demonstrate the effectiveness of how this algorithm can enhance the performance of quantum computations in quantum machine learning models in near-term applications. We used the systems offered by IBM Quantum and the cross-platform Python library for quantum differentiable programming Pennylane by Xanadu Quantum Technologies Inc. [ABSTRACT FROM AUTHOR]

This paper studies mathematical algorithms for delivery maximum power network observability using synchronized measurements. This problem is an intrinsic extension of the minimization of phasor measurement units (PMUs) by considering the least cost number of these devices around the power network and maximizing the reliability for better performance of power electrical networks. Optimization properties are investigated and used to lie a Βranch-and-Βound algorithm (BBA) jointly with Successive Quadratic Programming (SQP) and Interior Point Methods (IPMs) to find optimality. Τo evaluate optimal solutions with maximum measurement redundancy, solution algorithms such as BBA, SQP, IPM are used. The nonlinear algorithms mainly rely on local search procedure as the convergence indicator to optimality, whereas BBA is implemented to build a binary-tree to find an optimal solution. Thus, it is reasonable and necessary to compare nonlinear algorithms with BBA to show their difference and their efficiency convergence properties towards global optimality whereas the maximum observability is preserved. Moreover, the polyhedron being constructed for the implementation of the BBA scheme is transformed into a polytope solved by a Semi-Define-Programming approach to find appropriate optimal solutions for further optimization study. Numerical studies show significant improvement about the maximum observability over existing optimization schemes already published in the recent bibliography. [ABSTRACT FROM AUTHOR]

In this paper, an approach to recognize hole features automatically from a 3-dimension solid model is proposed. The proposed algorithm is developed to improve the existing Computer Aided Inspection Planning (CAIP) platform. STEP data is utilized as raw data. An algorithm is designed to analyze and calculate the number of face bonds. Hole features are defined according to face bond and cylindrical surfaces type. A software called HECTOR (Hole Inspector) has been developed based on the proposed algorithm. The experiment result shows that HECTOR has accurately recognized cylindrical features from 3D solid model. In addition, a number of cylindrical features, features type, features dimension, features coordinates, and features direction are also successfully retrieved from the 3D solid model. [ABSTRACT FROM AUTHOR]

IoT and its effect in sharing information has been a huge mutation for Internet; increasing development and use of internet in healthcare departments requires more optimized performance in the speed of sharing and publishing information and energy consumption. An important aspect in this field is routing optimization in IoT; we introduce a new idea, which is a combination of two algorithms: PSO optimization algorithm (particle swarm optimization) and GA algorithm (genetic algorithm) and it is used for routing optimization and latency reduction during sending information between IoT groups in an IoT healthcare system. [ABSTRACT FROM AUTHOR]

This paper focus on the requirements of load balancing algorithms for essential and efficient operations in the cloud computing technologies. The load balancing approaches is to enhance the performance of the system and fulfil the client expectation for better services and better results. Our purpose is to provide the potential information about the requirement of load balancing in the cloud computing environments, moreover analysis and identification of the challenges of load balancing algorithms and to provide an approachable information for future enhancements. [ABSTRACT FROM AUTHOR]