Current Machine Research Articles

Application of functionally graded graphene nanoplatelets to improve transient vibrations of the car’s roof under mechanical loading: Introducing machine learning algorithm for transient problems

Doubly curved structures under external force due to their applicable structure can be used in the roofs of various structures. Good knowledge about the transient dynamics of this kind of structure under external shock loading is very important in the engineering field. So, in the current work, steady and transient vibrations of composite doubly curved structures under time-dependent external force for the first time are presented. The current structure is reinforced by graphene nanoplatelets with high efficiency to improve the mechanical properties of the system in the thickness direction due to external shock loading in this direction. The peak pulse pressure, the Laplace transform, and some mathematical work to get time-dependent vibration responses of the composite doubly curved structures under external force. With the aid of different pre-processing of machine learning algorithms (Z-Score, Rrobust, Min-Max, and Quantile transformation), the results are predicted. The optimization algorithm, neural network structure, learning rate, mean of square error, and other training data are obtained for the current machine learning algorithm. The outputs of the machine learning method show that instead of simulating the complex structures in various situations with the aid of mathematical simulation, the researchers can use machine learning algorithms to correctly simulate this kind of system in different situations and material properties to obtain optimum conditions for doubly curved structure structures. The findings in the results section suggest that the time-dependent displacement and stress fields of the system are significantly influenced by the geometrical and physical characteristics of the current composite curved structure.

Optimal design and experiments of a novel bobbin thread-hooking mechanism with RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage

Abstract. To solve the problem of low supply of bobbin thread and frequent bobbin changes existing in current embroidery machines, a novel bobbin thread-hooking mechanism with an RRSC (revolute–revolute–spherical–cylindrical) spatial four-bar linkage is proposed which can achieve a large number and continuous supply of bobbin thread. Based on the analysis of the chain stitch formation principle, the design requirements for motion trajectory and posture of the hooking mechanism are proposed. The methods of direction cosine matrix and mechanism identity condition are applied in kinematics modeling and analysis of the RRSC mechanism. The optimization design model and computer-aided software are developed, then the human–computer interaction optimization method is used to obtain optimal solutions to meet working requirements. The physical prototype is machined and a test bench is built. The virtual simulation and prototype high-speed camera kinematics tests are conducted. The results verify the correctness of mechanism design and show good application feasibility of the mechanism.

Log2Graph: A graph convolution neural network based method for insider threat detection

With the advancement of network security equipment, insider threats gradually replace external threats and become a critical contributing factor for cluster security threats. When detecting and combating insider threats, existing methods often concentrate on users’ behavior and analyze logs recording their operations in an information system. Traditional sequence-based method considers temporal relationships for user actions, but cannot represent complex logical relationships well between various entities and different behaviors. Current machine learning-based approaches, such as graph-based methods, can establish connections among log entries but have limitations in terms of complexity and identifying malicious behavior of user’s inherent intention. In this paper, we propose Log2Graph, a novel insider threat detection method based on graph convolution neural network. To achieve efficient anomaly detection, Log2Graph first retrieves logs and corresponding features from log files through feature extraction. Specifically, we use an auxiliary feature of anomaly index to describe the relationship between entities, such as users and hosts, instead of establishing complex connections between them. Second, these logs and features are augmented through a combination of oversampling and downsampling, to prepare for the next-stage supervised learning process. Third, we use three elaborated rules to construct the graph of each user by connecting the logs according to chronological and logical relationships. At last, the dedicated built graph convolution neural network is used to detect insider threats. Our validation and extensive evaluation results confirm that Log2Graph can greatly improve the performance of insider threat detection compared to existing state-of-the-art methods.

Evaluating Translation Tools: Google Translate, Bing Translator, and Bing AI on Arabic Colloquialisms

This study examines the advancements in AI-driven machine translation, specifically focusing on the accurate translation of Arabic colloquial expressions. It aims to assess the progress made by Large Language Models, such as Bing AI Chat, compared to traditional machine translation systems. By focusing on colloquial expressions, this research aims to shed light on the challenges and opportunities for improvement in machine translation systems, particularly when dealing with the complexities of translating informal Arabic utterances. Building upon At-tall’s 2019 thesis, which compared Google Translate and human translators, the study employs the same Arabic sentences as a test dataset, allowing for a direct comparison between 2019 translations and those produced by current machine translation tools. The findings indicate limited improvement in Google Translate since 2019, with Bing Translator exhibiting a similar level of translation accuracy. In contrast, Bing AI Chat consistently outperformed the other systems, showcasing the potential of Large Language Model machine translation. Notably, Bing AI Chat provided interpretations and valuable comments on the tested Arabic phrases, demonstrating a deeper understanding of the intended meaning. This study contributes significantly to the field of machine translation by providing evidence of the potential of Large Language Model systems in producing more accurate Arabic-English translations. It emphasizes the advantage of Large Language Models in dealing with non-standard Arabic expressions, encouraging further exploration of Large Language Model-powered approaches in machine translation. The findings offer a promising pathway towards achieving more accurate and expressive translations across diverse languages and cultures.

Do AI models produce better weather forecasts than physics-based models? A quantitative evaluation case study of Storm Ciarán

There has been huge recent interest in the potential of making operational weather forecasts using machine learning techniques. As they become a part of the weather forecasting toolbox, there is a pressing need to understand how well current machine learning models can simulate high-impact weather events. We compare short to medium-range forecasts of Storm Ciarán, a European windstorm that caused sixteen deaths and extensive damage in Northern Europe, made by machine learning and numerical weather prediction models. The four machine learning models considered (FourCastNet, Pangu-Weather, GraphCast and FourCastNet-v2) produce forecasts that accurately capture the synoptic-scale structure of the cyclone including the position of the cloud head, shape of the warm sector and location of the warm conveyor belt jet, and the large-scale dynamical drivers important for the rapid storm development such as the position of the storm relative to the upper-level jet exit. However, their ability to resolve the more detailed structures important for issuing weather warnings is more mixed. All of the machine learning models underestimate the peak amplitude of winds associated with the storm, only some machine learning models resolve the warm core seclusion and none of the machine learning models capture the sharp bent-back warm frontal gradient. Our study shows there is a great deal about the performance and properties of machine learning weather forecasts that can be derived from case studies of high-impact weather events such as Storm Ciarán.

DeFiTrust: A transformer-based framework for scam DeFi token detection using event logs and sentiment analysis

Decentralized finance (DeFi) token scams are rising, leading to significant losses for investors and users. Hence, it is vital to identify malicious DeFi tokens accurately and early to protect investor funds. Current Machine Learning models that detect scam tokens rely entirely on DeFi token transactional behavior and have overlooked the public opinion that can be derived from news and social media posts, which can provide helpful information for identifying scam tokens. Further, existing frameworks to detect scam tokens fail to explain why certain tokens are identified as scams, hindering stakeholder confidence in such systems. This paper proposes a new transformer-based framework called “DeFiTrust” to identify malicious DeFi tokens using transaction events and social media posts. DeFiTrust has two components: event log processing and sentiment analysis. The former creates a feature vector representing temporal variations in the event logs of a token. The latter analyzes social media posts to determine public opinion about the token. A fully-connected neural network then processes both pieces of information to determine whether the token is legitimate or a scam. We evaluated the proposed model against existing Machine Learning-based models using real-world data. The results demonstrate that DeFiTrust outperforms existing approaches and detects scam tokens earlier. Explanations generated using Integrated Gradients reveal that DeFiTrust identifies scam tokens based on valid reasons, enhancing stakeholder trust in the proposed model.

An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm.

Breast cancer is one of the most often diagnosed cancers in women, and identifying breast cancer histological images is an essential challenge in automated pathology analysis. According to research, the global BrC is around 12% of all cancer cases. Furthermore, around 25% of women suffer from BrC. Consequently, the prediction of BrC depends critically on the quick and precise processing of imaging data. The primary reason deep learning models are used in breast cancer detection is that they can produce findings more quickly and accurately than current machine learning-based techniques. Using a BreakHis dataset, we demonstrated in this work the viability of automatically identifying and classifying BrC. The first stage is pre-processing, which employs an Adaptive Switching Modified Decision Based Unsymmetrical Trimmed Median Filter (ASMDBUTMF) to remove high-density noise. After the image has been pre-processed, it is segmented using the Thresholding Level set approach. Next, we propose a hybrid chaotic sand cat optimization technique, together with the Remora Optimization Algorithm (ROA) for feature selection. The suggested strategy facilitates the acquisition of precise functionality attributes, hence simplifying the detection procedure. Additionally, it aids in resolving problems pertaining to global optimization. Following the selection, the best characteristics proceed to the categorization procedure. A DL classifier called the Conditional Variation Autoencoder is used to discriminate between cancerous and benign tumors while categorizing them. Consequently, a classification accuracy of 99.4%, Precision of 99.2%, Recall of 99.1%, F- score of 99%, Specificity of 99.14%, FDR of 0.54, FNR of 0.001, FPR of 0.002, MCC of 0.98 and NPV of 0.99 were obtained using the proposed approach. Furthermore, compared to other research using the current BreakHis dataset, the results of our research are more desirable.

Interpretable intelligent fault diagnosis strategy for fixed-wing UAV elevator fault diagnosis based on improved cross entropy loss

The current popular machine learning-based fault diagnosis methods make it difficult to explain the diagnostic results, leading to low user trust in such diagnostic techniques. In this regard, this paper explores the study of the interpretability of intelligent fault diagnosis algorithms using the elevator of a fixed-wing unmanned aerial vehicle (UAV) as a diagnostic object. The Transformer model combines excellent modeling capability and efficient sequence data processing, is chosen to mine fault signal features to guarantee accurate diagnosis. Among the proposed interpretable fault diagnosis models, the local interpretable model-agnostic explanations (LIME) model is used to provide explicit interpretability for the decision-making process of the diagnosis model. In addition, a loss function called RDCE (reinforced diagnostic cross-entropy) Loss is designed to minimize the negative impact of different sample sizes for different fault types on the diagnostic performance. This loss function is designed to weigh the various types of faults to speed up the convergence of the model and improve the diagnostic accuracy. By comparing the proposed diagnostic strategy with other commonly used machine learning models, including long short term memory and recurrent neural network (RNN), the average diagnostic accuracy of the proposed diagnostic strategy is 99.97%, significantly better than that of the comparison algorithms. At the same time, this paper provides an in-depth interpretable analysis of the diagnostic process of the Transformer. The diagnostic process of the Transformer model gives the reasons for the diagnostic results from the point of view of the kind of features processed by the model. Based on this, the diagnostic model is simplified. After streamlining the number of features from 40 to 24 according to their importance, the diagnostic accuracy of the model is improved by 0.26%, and the diagnostic efficiency is improved. In addition, the proposed diagnostic strategy also shows significant advantages in terms of noise robustness.

Design and Fabrication of Chaff Cutter Machine

India is an agricultural nation with the majority of its population residing in rural areas. The rural landscape is predominantly occupied by agricultural activities, including crop production and poultry farming by using of chaff cutter machines. The conventional cutter machines are unsafe design resulted in severe injuries and needed more workforces. Taking into consideration the limitations of the current conventional cutter machine, this project has been undertaken to develop and manufacture a chaff cutter machine, aiming to mitigate the potential for serious injuries and enhance operational efficiency. First of all, the work of design of the chaff cutter machine will be developed and analyzed. The new chaff cutter machine will be tried out for cutting various types of animal feed like dry straw, grass, corn straw, wheat stalk, etc. We'll also check how cost-effective the new machines are compared to the old-style ones and newer models. This paper talks about how we made and tested a chaff cutter machine in India. We'll also look at how much it costs to use the new chaff cutting machine compared to the old ones.

Corpus-based Research on English-Chinese Translation Teaching Combining Vocabulary Learning and Practice

As the global economy is developing, real time translation among major influential languages such as English, Chinese has become a necessity. Translation from English to Chinese is difficult since Chinese has a different grammar and ambiguous word boundaries. Furthermore, there are issues with the current Chinese-English machine translation, including difficult-to-understand extended sentences and inaccurate word translation. To address this drawback, the methodology for more precisely categorizing vocabulary in both English and Chinese is described in this research. The data are collected from UM-Corpus news dataset. Then, using SEGBOT: Neural Text Segmentation method, English words are segmented from the sentences available on the dataset. Afterward, the data are fed to pre-processing. In pre-processing segment; words missing in translation from the news dataset are eliminated and enhances the input data utilizing Iterated Square-Root Cubature Kalman Filter. The outcome from the pre-processing data is transferred to the Pyramidal Convolution Shuffle Attention Neural Network (PCSANN). The word order, grammatical structure, consistent style and smooth flow for English to Chinese translation are successfully classified by using PCSANN. The Waterwheel Plant Algorithm (WWPA) is used to optimize the weight parameter of PCSANN. The proposed PCSANN-WWPA is applied in python working platform. Performance metrics, like accuracy, precision, F1-score, and recall are examined to compute proposed method. The gained results of the proposed PCSANN-WWPA method attains higher accuracy of 16.65%, 18.85%, and 17.89%, higher sensitivity of 16.34%, 12.23%, and 18.54% and higher precision of 14.89%, 16.89%, and 18.23%. The proposed ECTT-PCSANN-WWPA method is compared with the existing methods such as ECTT-RNN, ECTT-DNN, and ECTT-MLPNN models respectively.

Distributed Systems for Machine Learning in Cloud Computing: A Review of Scalable and Efficient Training and Inference

Traditional computer systems have been pushed to their limits as a result of the exponential rise of data and the rising complexity of machine learning (ML) models. As a result of its on-demand scalability and resource agility, cloud computing has emerged as the platform of choice for training and deploying large-scale machine learning models. However, in order to make good use of cloud resources for machine learning, it is necessary to make use of distributed systems. These systems are responsible for coordinating computations over several nodes in order to manage the demanding workloads. The purpose of this paper is to investigate the realm of distributed systems for machine learning in cloud computing, with a particular emphasis on training and inference that is both scalable and efficient. During the discussion on the need of distributed systems in machine learning, it was made clear why conventional single-machine techniques are not enough for the requirements of current machine learning and how distributed systems might help solve these difficulties. Scalability and Efficiency Considerations were reviewed in relation to the primary elements that contribute to the effectiveness of a distributed system for machine learning. These elements include task partitioning, communication overhead, fault tolerance, and resource optimization that were discussed. In the context of cloud computing, the purpose of this review research is to provide a complete overview of the fascinating topic of distributed systems for machine learning. In order to successfully traverse the intricate and ever-changing world of cloud-based machine learning, it provides vital insights and information.

Characterization of Oscillatory Response of Light-Weight Wind Turbine Rotors under Controlled Gust Pulses

Given the industry-wide trend of continual increases in the size of utility-scale wind turbines, a point will come where reductions will need to be made in terms of the weight of the turbine’s blades to ensure they can be as long as needed without sacrificing structural stability. One such technique that may be considered is to decrease the material used for the shell and spar cap. While this will solve the weight issue, it creates a new one entirely—less material for the shell and spar cap will in turn create blades that are more flexible than what is currently used. This article aims to investigate how the oscillatory response of light-weight wind turbine rotors is affected by these flexibility changes. The object of our study is the Sandia National Lab National Rotor Testbed (SNL-NRT) wind turbine, which the authors investigated in the course of a research project supported by SNL. Using a reduced-order characterization (ROC) technique based on controlled gust pulses, introduced by the authors in a previous work, the aeroelastic dynamics of the NRT’s original baseline blade design and several of its flexible variations were studied via numerical simulations employing the CODEF multiphysics suite. Results for this characterization are presented and analyzed, including a generalization of the ROC of the SNL-NRT oscillatory dynamics to larger machines with geometrical similarity. The latter will prove to be valuable in terms of extrapolating results from the present investigation and other ongoing studies to the scale of current and future commercial machines.

Comparison of different machine learning models: Linear model, forest and SVM

Over the past century, machine learning has developed rapidly with the fast evolution of AI. As high-performance computing (HPC) has popularized over the past few decades, huge differences have been made in the field of machine learning, from expert systems to deep learning algorithms nowadays. This study summarizes some well-known models and compares three of the current popular machine learning models: the Linear Model, the Forest Model, and the Supporting Vector Machine (SVM), which aims to give and clearer view of those models in several aspects. Forest and SVM are more powerful in performance than the Linear Model due to their ability to deal with more types and more complex relationships and patterns. However, Forest and SVM have a higher complexity than the Linear Model and are less interpretable. Through the comparison of the three models, the basic model and algorithms will be better understood, the appropriate model will be selected faster when solving the problem, saving training time and space, and effectively saving resources and costs.

Quantum Computing in Machine Learning - The Future of Quantum Computing

In order to solve challenging optimization issues, analyze data effectively, and improve the capabilities of current machine learning algorithms, quantum computing has the potential to revolutionize the area of machine learning. The proposed work examines the fundamental ideas and methods of quantum computing—including quantum gates, quantum circuits, and quantum algorithms—as they relate to machine learning in this abstract. Various quantum computing applications in machine learning, including quantum neural networks, quantum support vector machines, and conventional methods influenced by quantum mechanics are also discussed. A review of state-of-the-art in quantum computing for machine learning, including recent advancements in quantum hardware and software has been done and the future prospects of this fascinating area has been examined.

High performance artificial visual perception and recognition with a plasmon-enhanced 2D material neural network

The development of neuromorphic visual systems has recently gained momentum due to their potential in areas such as autonomous vehicles and robotics. However, current machine visual systems based on silicon technology usually contain photosensor arrays, format conversion, memory and processing modules. As a result, the redundant data shuttling between each unit, resulting in large latency and high-power consumption, seriously limits the performance of neuromorphic vision chips. Here, we demonstrate an artificial neural network (ANN) architecture based on an integrated 2D MoS2/Ag nanograting phototransistor array, which can simultaneously sense, pre-process and recognize optical images without latency. The pre-processing function of the device under photoelectric synergy ensures considerable improvement of efficiency and accuracy of subsequent image recognition. The comprehensive performance of the proof-of-concept device demonstrates great potential for machine vision applications in terms of large dynamic range (180 dB), high speed (500 ns) and low energy consumption per spike (2.4 × 10−17 J).

Mathematical model of discharge and charge in rechargeable polymer based lithium-ion battery

Shepherd’s model is considered as an improved and simple to-apply battery dynamic model. The experimental nominal parameters of five different capacities of Lithium-ion Polymer battery samples are established from the manufacturer’s datasheets. The discharge and charge dynamics of the battery sample with the highest capacity, 3100 mAh are examined and validated. The clarity to derive the dynamic model parameters from the battery datasheet is a fascinating attribute of this model. The experimental database is utilized to study its dynamic feature and subsequently the dynamic battery model, which describes the relationship between the state of charge, the open circuit voltage and ambient temperature. The model is implemented and simulated by using MATLAB/Simulink tools. The current charge-discharge equations describe the optimized electrochemical activities with sustainable long cycle duration. The temperature model brings under control the aging effect, which plays down on the energy capacity of the battery. A steady load of 5 A is linked to the battery and used in a detail simulation of a direct current machine in determination of load effect. The compatibility of these equations and simulations implies that Shepherd’s model is promising to provide guidelines for the analysis of discharge and charge in rechargeable polymer based lithium-ion batteries. The results show that the model can precisely illustrate the dynamic behavior of the batteries.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Analysis of the Causes of Crooked Sugarcane Crepe Shafts in Sugar Factories

Practical work is a form of education and vocational implementation that is followed by students so that students can work directly in the business world as well as industry or fabrication. Practical work aims to prepare students to become productive human beings and can immediately work in accordance with their respective fields, students can also feel the atmosphere of production and can help deal with some problems- problems experienced by Engineering professionals inside the factory. Therefore, in welding, knowledge must accompany practice, in more detail it can be said that the design of building construction and machines with welded joints, must also be planned about welding methods. This method of inspection, welding material, and type of weld to be used, based on the function of the building parts or machines designed. Based on the definition of DIN (Deutch Industrie Normen) Galvanized Welded Broken Products is the best way to assemble or connect constructions and products made from iron. This welding method is specifically performed for galvanized materials. The iron welding process requires special preparation and skills. Based on the results of fieldwork practices that have been carried out in CV. Sumber Agung Widodo, The process of making panel tables is carried out into several stages, namely the hollow iron cutting, 45- degree galvanized angle cutting, iron plate cutting, elbow iron cutting, splicing by welding, frame painting, and mounting a series of panels to the finished panel table. The materials used are hollow iron, iron plate, elbow iron, iron paint, wheels. The tools used are ac current welding machines, grinders and elbow rulers. Stage Highlights : Practical work enhances students' readiness for the industrial environment. Welding methods must align with building construction and machine design. Fieldwork at CV. ABC involves various stages, including cutting, welding, painting, and assembly. Keywords : Practical work, Welding methods, Galvanized Welded Products, CV. ABC, Iron welding process

Analysis of Weld Joint Strength on Galvanized Material Using Rb-26 Electrode

Practical work is a form of education and vocational implementation that is followed by students so that students can work directly in the business world as well as industry or fabrication. Practical work aims to prepare students to become productive human beings and can immediately work in accordance with their respective fields, students can also feel the atmosphere of production and can help deal with some problems- problems experienced by Engineering professionals inside the factory. Therefore, in welding, knowledge must accompany practice, in more detail it can be said that the design of building construction and machines with welded joints, must also be planned about welding methods. This method of inspection, welding material, and type of weld to be used, based on the function of the building parts or machines designed. Based on the definition of DIN (Deutch Industrie Normen) Galvanized Welded Broken Products is the best way to assemble or connect constructions and products made from iron. This welding method is specifically performed for galvanized materials. The iron welding process requires special preparation and skills. Based on the results of fieldwork practices that have been carried out in CV. Sumber Agung Widodo, The process of making panel tables is carried out into several stages, namely the hollow iron cutting, 45-degree galvanized angle cutting, iron plate cutting, elbow iron cutting, splicing by welding, frame painting, and mounting a series of panels to the finished panel table. The materials used are hollow iron, iron plate, elbow iron, iron paint, wheels. The tools used are ac current welding machines, grinders and elbow rulers. Stage

Dynamic multi-label feature selection algorithm based on label importance and label correlation

Multi-label distribution is a popular direction in current machine learning research and is relevant to many practical problems. In multi-label learning, samples are usually described by high-dimensional features, many of which are redundant or invalid. This paper proposes a multi-label static feature selection algorithm to solve the problems caused by high-dimensional features of multi-label learning samples. This algorithm is based on label importance and label relevance, and improves the neighborhood rough set model. One reason for using neighborhood rough sets is that feature selection using neighborhood rough sets does not require any prior knowledge of the feature space structure. Another reason is that it does not destroy the neighborhood and order structure of the data when processing multi-label data. The method of mutual information is used to achieve the extension from single labels to multiple labels in the multi-label neighborhood; through this method, the label importance and label relevance of multi-label data are connected. In addition, in the multi-label task scenario, features may be interdependent and interrelated, and features often arrive incrementally or can be extracted continuously; we call these flow features. Traditional static feature selection algorithms do not handle flow features well. Therefore, this paper proposes a dynamic feature selection algorithm for flow features, which is based on previous static feature selection algorithms. The proposed static and dynamic algorithms have been tested on a multi-label learning task set and the experimental results show the effectiveness of both algorithms.