Tuning Technique Research Articles

Data-driven topology detector for self-healing strategies in Active Distribution Networks

The integration of distributed energy resources requires the implementation of control and automation functionalities in distribution networks, which allow them to operate in a more flexible, efficient, and reliable way. The operation of these functionalities causes topological changes on the network that must be identified since these affect protection, volt/var control, and state estimation, among others. This paper presents a data-driven topology detector for self-healing strategies in Active Distribution Networks (ADN). This approach uses machine learning (ML) techniques to obtain a trained model as a topology detector. The ML models are integrated into the Intelligent Electronic Device (IED) of ADN so that using the voltage and current signals measured locally determine the network’s topology. A features selection and tuning technique based on a multiverse optimizer are proposed to improve the ML model accuracy. This approach allows it to be implemented in decentralized architectures since each IED detects the system’s topology from local measurements and does not depend on the availability of the communication system. The proposed topology detector was validated on a modified IEEE 123 nodes test feeder considering six topology changes, five DER outages, and five load variations. The results obtained show accuracy values above 96%, which evidences a highlighted potential for real-life applications.

Knowledge transfer for tuning microwave filters with unknown individual differences

Improving the production efficiency of Microwave Filters (MFs) is of great practical significance for constructing modern communication systems. The characteristics of MFs are with various degrees of individual differences caused by materials. The most tuning techniques have to start from scratch for tuning different MFs, which is time-consuming. The knowledge from previous tuning processes can be used in the current tuning due to the relevance of MFs. Motivated by this, a knowledge transfer method for tuning MFs with unknown individual differences is proposed. The main contributions are threefold: 1) An Knowledge Transfer technique for Optimization Tuning (KTOT) is created to tune various MFs efficiently; 2) an Evaluation mechanism of Difference Degree (EDD) is proposed to guide transfer; 3) a Guiding strategy of Knowledge Transfer Intensity (KTIG) in accordance with the difference degree is presented to reuse knowledge reasonably. The high-efficiency of KTOT and the effectiveness of KTIG based on EDD are demonstrated.

Multiobjective Tuning Technique for MPC in Grinding Circuits

We investigate the control challenges in grinding circuits—slow dynamics, long dead times, variable coupling— and the controller tuning challenge, that is, the difficulty in translating operating goals into tuning goals and closed-loop performance. A tuning algorithm for DMC (dynamic matrix control), suitable for the mineral processing industry, is proposed. The tuning problem is posed as a multiobjective optimization problem, in which the tuning goals are directly related to the desired closed-loop performance of process variables. The problem is solved using a compromise optimization, which minimizes the Euclidian distance between a feasible solution and the Utopia solution. Three case studies are presented, which validate the tuning algorithm for DMC in linear and non-linear grinding circuit models. The closed-loop performance obtained with the proposed tuning algorithm is compared to the one obtained through a benchmark tuning technique from the literature. The proposed tuning method has the following features: i) it shapes the closed-loop response according to the goal definitions for linear systems; ii) it requires tailored initial guesses and search spaces to converge to a stabilizing solution in non-linear applications; and iii) it allows the user to specify the desired closed-loop performance behavior in the tuning procedure, allowing the implementation of an adequate controller for each situation.

Tuning of PID controller using genetic algorithm for an electromagnetic semi-active suspension system

Electromagnetic suspension of vehicles has advantage of improved ride comfort and drivability. Electromagnetic levitation has been in use in Maglev trains for smoother transportation. However, application of electromagnetic suspension system was limited by its cost and weight. This paper focuses study on use of an electromagnet and permanent magnet hybrid magnet setup and compares the control method to stabilize the suspension system by genetic algorithm optimization, auto tuning technique and fuzzy logic controller for tuning the Proportional-Integral-Derivative (PID) controller for making the system stable. MATLAB-Simulinkanalysis of hybrid magnetic suspension system show stabilization by reduction of the errors in the closed loop control system. Realization in a real time setup may be possible by varying the magnetic field developed in the electromagnet to control the force of repulsion between the electromagnet and permanent magnet.

An innovative parameter optimization of Spark Streaming based on D3QN with Gaussian process regression.

Nowadays, Spark Streaming, a computing framework based on Spark, is widely used to process streaming data such as social media data, IoT sensor data or web logs. Due to the extensive utilization of streaming media data analysis, performance optimization for Spark Streaming has gradually developed into a popular research topic. Several methods for enhancing Spark Streaming's performance include task scheduling, resource allocation and data skew optimization, which primarily focus on how to manually tune the parameter configuration. However, it is indeed very challenging and inefficient to adjust more than 200 parameters by means of continuous debugging. In this paper, we propose an improved dueling double deep Q-network (DQN) technique for parameter tuning, which can significantly improve the performance of Spark Streaming. This approach fuses reinforcement learning and Gaussian process regression to cut down on the number of iterations and speed convergence dramatically. The experimental results demonstrate that the performance of the dueling double DQN method with Gaussian process regression can be enhanced by up to 30.24%.

On the Development of New Tuning and Inter-Coupling Techniques Using Ferroelectric Materials in the Detection of Dark Matter Axions

Tuning is an essential requirement for the search of dark matter axions employing haloscopes since its mass is not known yet to the scientific community. At the present day, most haloscope tuning systems are based on mechanical devices which can lead to failures due to the complexity of the environment in which they are used. However, the electronic tuning making use of ferroelectric materials can provide a path that is less vulnerable to mechanical failures and thus complements and expands current tuning systems. In this work, we present and design a novel technique for using the ferroelectric Potassium Tantalate (KTaO3 or KTO) material as a tuning element in haloscopes based on coupled microwave cavities. In this line, the structures used in the Relic Axion Detector Exploratory Setup (RADES) group are based on several cavities that are connected by metallic irises, which act as interresonator coupling elements. In this article, we also show how to use these KTaO3 films as interresonator couplings between cavities, instead of inductive or capacitive metallic windows used in the past. These two techniques represent a crucial upgrade over the current systems employed in the dark matter axions community, achieving a tuning range of 2.23% which represents a major improvement as compared to previous works (< 0.1%) for the same class of tuning systems. The theoretical and simulated results shown in this work demonstrate the interest of the novel techniques proposed for the incorporation of this kind of ferroelectric media in multicavity resonant haloscopes in the search for dark matter axions.

Erythemato-Squamous Disease Detection using Best Optimized Estimators of ANN

Medical area focused on automating skin cancer detection after the pandemic era of "Monkey Pox". Previous works proposed ANN mechanisms to classify the type of skin cancer. However, all those models implement layers of ANN with standard estimator components like hidden layers implemented using the ReLu activation function, several neurons are generally a power of two and others, but these values are not always perfect. Few researchers implemented optimization techniques for tuning the estimators of A.I. algorithms, but all those mechanisms require more resources and don't guarantee the best values for each estimator. The proposed method analyzes all the essential estimators of every possible neural network layer. Then it applies a modified version of Bayesian optimization because it avoids the disadvantages of Grid and Random optimization techniques. It picks the best estimator by using the conditional probability of naive Bayesian for every combination.

Tunable Coaxial Bandpass Filters Based on Inset Resonators

A new class of compact, high- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> , tunable coaxial filter is presented in this article based on a novel inset resonator concept. The tuning concept is based on the displacement of movable resonators inside a properly modified metallic housing which features wide tuning capabilities and stable high <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -factor performance with minimum variation throughout the tuning window. Various prototypes are designed and implemented to demonstrate and validate the proposed concept. A single tunable inset resonator is first designed and measured showing distinctive results of a 43% tuning range, stable high- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of 4100% ± 4%, spurious-free band up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.8\times f_{0}$ </tex-math></inline-formula> , and volume-saving up to 50% when compared with the conventional combline and half-wavelength structures. The design procedure for constant absolute bandwidth (CABW) tunable filters is then presented, and two different tunable inset filters are designed and implemented. First, a manually tunable four-pole filter is demonstrated with the merits of a wide 39.3% tuning range, while maintaining a constant bandwidth of 116 MHz ± 6% and a stable high- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> of 1820% ± 6%. Next, an automatically tunable third-order inset filter is designed and measured using high-accuracy piezomotors. Similarly, the measured results exhibit a wide 1.3-GHz tuning range from 2.65 to 3.95 GHz with a stable insertion loss that is less than 0.35 dB, a return loss that is better than 15 dB, and a good spurious performance up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.8\times f_{0}$ </tex-math></inline-formula> . To our own knowledge, the proposed tuning technique and tunable components represent state-of-the-art tuning range and stable high- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> with minimal variation when compared with similar loaded-waveguide designs.

A Low Gain Variation LC-VCO With Mutual Inductive Tuning for K VCO Linearity Compensation

A high-linearity LC-voltage-controlled oscillator (LC-VCO) with a mutual coupled inductive tuning technique is proposed to reduce VCO-gain ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K_{\mathrm {vco}}$ </tex-math></inline-formula> ) variation induced by varactors in finite linear region. The oscillator is fabricated in a standard 180-nm CMOS process occupying 0.88 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times0.9$ </tex-math></inline-formula> mm and consuming 3.6 mW from mixed 0.9/1.8 V supply voltage. With the variable effective inductance of the resonant tank, the proposed topology achieves a low gain variation of less than 6.1%, possessing a linear-tuning control voltage range from 0.5 to 1.8 V and operating from 3.40 to 4.04 GHz. The phase noise is measured to be −108.59 dBc/Hz at 1 MHz offset with a carrier frequency of 4.04 GHz, and the figure of merit (FoM) is −173.8 dBc/Hz.

Improved wolf swarm optimization with deep-learning-based movement analysis and self-regulated human activity recognition

&lt;abstract&gt; &lt;p&gt;A wide variety of applications like patient monitoring, rehabilitation sensing, sports and senior surveillance require a considerable amount of knowledge in recognizing physical activities of a person captured using sensors. The goal of human activity recognition is to identify human activities from a collection of observations based on the behavior of subjects and the surrounding circumstances. Movement is examined in psychology, biomechanics, artificial intelligence and neuroscience. To be specific, the availability of pervasive devices and the low cost to record movements with machine learning (ML) techniques for the automatic and quantitative analysis of movement have resulted in the growth of systems for rehabilitation monitoring, user authentication and medical diagnosis. The self-regulated detection of human activities from time-series smartphone sensor datasets is a growing study area in intelligent and smart healthcare. Deep learning (DL) techniques have shown enhancements compared to conventional ML methods in many fields, which include human activity recognition (HAR). This paper presents an improved wolf swarm optimization with deep learning based movement analysis and self-regulated human activity recognition (IWSODL-MAHAR) technique. The IWSODL-MAHAR method aimed to recognize various kinds of human activities. Since high dimensionality poses a major issue in HAR, the IWSO algorithm is applied as a dimensionality reduction technique. In addition, the IWSODL-MAHAR technique uses a hybrid DL model for activity recognition. To further improve the recognition performance, a Nadam optimizer is applied as a hyperparameter tuning technique. The experimental evaluation of the IWSODL-MAHAR approach is assessed on benchmark activity recognition data. The experimental outcomes outlined the supremacy of the IWSODL-MAHAR algorithm compared to recent models.&lt;/p&gt; &lt;/abstract&gt;

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which identifies seven distinct types of faults. At the same time, the chicken swarm optimization (CSO) algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process, showing the novelty of the work. To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm, a comprehensive set of simulations are applied. The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30% and TPR of 93.09%.

A Robust Tuned Random Forest Classifier Using Randomized Grid Search to Predict Coronary Artery Diseases

Coronary artery disease (CAD) is one of the most authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue. The breakdown of coronary cardiovascular disease is one of the principal sources of death all over the world. Cardiovascular deterioration is a challenge, especially in youthful and rural countries where there is an absence of human-trained professionals. Since heart diseases happen without apparent signs, high-level detection is desirable. This paper proposed a robust and tuned random forest model using the randomized grid search technique to predict CAD. The proposed framework increases the ability of CAD predictions by tracking down risk pointers and learning the confusing joint efforts between them. Nowadays, the healthcare industry has a lot of data but needs to gain more knowledge. Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease (HD). We evaluated the proposed framework over two public databases, Cleveland and Framingham datasets. The datasets were pre-processed by using a cleaning technique, a normalization technique, and an outlier detection technique. Secondly, the principal component analysis (PCA) algorithm was utilized to lessen the feature dimensionality of the two datasets. Finally, we used a hyperparameter tuning technique, randomized grid search, to tune a random forest (RF) machine learning (ML) model. The randomized grid search selected the best parameters and got the ideal CAD analysis. The proposed framework was evaluated and compared with traditional classifiers. Our proposed framework’s accuracy, sensitivity, precision, specificity, and f1-score were 100%. The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.

Control of the BLDC Motor Using Ant Colony Optimization Algorithm for Tuning PID Parameters

A key component of industrial applications is the DC motor. Hence, due to their superior features and performance, Brushless DC (BLDC) motors are more suitable for fractional kilowatt motor’s applications. Albeit, for the purpose of controlling the speed of Brushless DC motor easily, it is quite difficult for obtaining the best controlling performance through the use of the conventional approaches of tuning. In order to search the Proportional – Integral - Derivative (PID) tuning parameters optimally for the different controllers taken into consideration, the use of modern bio-inspired metaheuristic technique called Ant Colony Optimization (ACO) algorithm is employed. This paper particularly discusses and presents on the tunning parameters of . The performance of traditional controller and novel approach is compared, analyzed and presented. Brushless DC motor is buildup in MATLAB and the usage, importance, efficiency and strength of the proposed approach is validated against traditional tunning methods. The obtained result shows better performance of the proposed system with the aid of the proposed controllers for different speed trajectories of the drive when compared with that of the classical Proportional – Integral - Derivative (PID) controllers. Ant Colony Optimization (ACO) seems to be the one of the most effective tuning techniques of Proportional – Integral - Derivative (PID) controllers. This research has significant impact on modern control applications.

Low-Delay Analog Joint Source-Channel Coding With Deep Learning

We consider the design of low-delay joint source-channel coding (JSCC) schemes for the transmission of discrete-time analog sources over noisy channels based on deep neural networks. The design problem is addressed as optimization of an autoencoder model, and several scenarios are discussed. For point-to-point communication of independent and identically distributed (i.i.d) Gaussian sources and Gauss-Markov sources over additive-white Gaussian noise (AWGN) channels, the encoder and decoder are constructed using recurrent neural networks (RNNs). With minimum prior knowledge used for design, the performance of these RNNs-based models is optimized using fine tuning techniques during training. Sinusoidal representation networks (SIRENs)-based models are proposed and optimized for three JSCC problems namely, transmitting multivariate Gaussian sources over AWGN channels, transmitting i.i.d Gaussian sources with side information at the decoder, and for communicating correlated sources over orthogonal Gaussian channels. We show that these deep learning-based JSCC schemes perform comparably or better than state-of-the-art (SOTA) traditional schemes. The proposed scheme can extend flexibly to different pairs of source and channel dimensions. Moreover, the spontaneously learned encoder mappings exhibit structured patterns that are interpretable.

Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets.

Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time-consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model's performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.

An explainable intelligent algorithm for the multiple performance prediction of cement-based grouting materials

Cement-based grouting material, distinguished by excellent fluidity and high strength, is widely used in the field of construction reinforcement, and anchoring of restraints. Owing to the high-performance requirements and complicated influencing factors of grouting material, the design of the mixing proportion has been challenging. This study proposes a machine learning (ML) based algorithmic framework integrating prediction, interpretation, and automatic hyperparameter tuning to identify the complex potential relationships between the mixing proportion parameters on the compressive strength and fluidity of cement-based grouting materials. The 442 compressive strength data and 217 fluidity data derived from both published literatures and laboratory experiments were collected to build a dataset for demonstrating the predictive performance of the ML models. The results indicated that the hyperparameter tuning technique via Bayesian Optimisation (BO) can significantly improve the time efficiency compared to grid search, reducing time consumption from 8000 s to 197 s with comparable accuracy. The optimal prediction results were obtained based on the XGBoost model with R2 = 0.93, RMSE = 7.37 for compressive strength, and R2 = 0.92, RMSE = 16.33 for fluidity. The SHapley Additive exPlainations (SHAP) is introduced to interpret the evaluation results and the influence of the various mix factors on grouting material from both global(model) and local(instance) perspectives. The suggested model can be seen as a function of influential input variables that help engineers conduct a rapid assessment and then in turn to optimize the design of the mixture proportion.

Photodetection Properties of MoS2, WS2 and MoxW1-xS2 Heterostructure: A Comparative Study.

Layered transition metals dichalcogenides such as MoS2 and WS2 have shown a tunable bandgap, making them highly desirable for optoelectronic applications. Here, we report on one-step chemical vapor deposited MoS2, WS2 and MoxW1-xS2 heterostructures incorporated into photoconductive devices to be examined and compared in view of their use as potential photodetectors. Vertically aligned MoS2 nanosheets and horizontally stacked WS2 layers, and their heterostructure form MoxW1-xS2, exhibit direct and indirect bandgap, respectively. To analyze these structures, various characterization methods were used to elucidate their properties including Raman spectroscopy, X-ray diffraction, X-ray photoelectron spectrometry and high-resolution transmission electron microscopy. While all the investigated samples show a photoresponse in a broad wavelength range between 400 nm and 700 nm, the vertical MoS2 nanosheets sample exhibits the highest performances at a low bias voltage of 5 V. Our findings demonstrate a responsivity and a specific detectivity of 47.4 mA W-1 and 1.4 × 1011 Jones, respectively, achieved by MoxW1-xS2. This study offers insights into the use of a facile elaboration technique for tuning the performance of MoxW1-xS2 heterostructure-based photodetectors.

Geometric Tuning for Enhanced Moisture-Driven Electricity Generation Enabled by Graphene-Oxide Flakes

In this work, we formulate water-based graphene oxide (GO) inks to fabricate moisture energy generators (MEGs) while a two-fold geometric tuning is proposed to encourage enhanced performance. Two GO-based structures with distinctly different thicknesses were prepared as the moisture absorbing layer: a GO-pellet (GOP) and a thinner GO-film (GOF). The effect of electrical contacts’ configuration on the MEG’s output voltage (Vo) was evaluated as a second geometric tunning approach by varying the surface area of the contacts and their orientation with respect to the GO plane, i.e., horizontal or vertical. GOF-based devices that employed a horizontal contacts’ configuration demonstrated champion Vo values (~350 mV) and the fastest response to humidity (3 min required to reach maximum Vo when the relative humidity, or RH, was increased). In GOP devices with horizontal point-like contacts, Vo is inversely related to the contacts’ distance, with a maximum Vo of ~205 mV achieved at a ~1 mm contacts’ distance. GOP-based MEGs with point-like contacts placed vertically to the GO-plane yielded a higher Vo value (~285 mV), while the humidity response time was 15 min. Replacing these contacts with large area electrodes in GOP devices resulted in devices with a slower response to humidity (~30 min) due to a smaller exposed GO surface area. These geometric tuning techniques allowed for the investigation of the optimum device configuration towards efficient moisture-based energy generation with a fast response.

Synthesis of Recursive Control Barrier Functions

Abstract This paper deals with the enforcement of safety constraints using control barrier functions (CBFs). We focus on dynamic systems that have high relative degree and where the output needs to be differentiated multiple times to obtain an explicit dependence on the control input. As the system approaches operating points that are close to the safety boundary, the CBF shrinks the allowed control action space. To quantify this shrinkage, we propose practical proxy metrics to capture the volume of the CBFs action space. We exploit these proxy metrics to derive sum-of-squares optimization formulations that synthesize CBFs with high-order degree, in particular their extended class K functions. We apply this tuning technique to synthesize CBFs that avoid vehicle collisions in an adaptive cruise control problem. Simulation results demonstrate the ability of our tuning approach to reduce loss of control performance when the system operates close to the safety boundary.

Partitioning based multi-persistence model for multi-paradigm database

In the era of Internet of things and social media huge amount of data are generated day by day from numerous sources. The problem for each and every organization is to tackle with the volume, variety and velocity of data. This data is commonly referred to as “Big Data”. Most of the recent surveys provide the tools and techniques to deal with pace of data. In past decades traditional databases have shown tremendous growth in consistency, durability and isolation of data. But in current trends of data analytics, traditional database are fertile to deal with the volume, variety and velocity of database. In this paper, initially comparison is drawn to check the drawbacks of the existing system. To cope with the problems in existing system a partitioning-based technique is proposed and implemented using NoSQL Database. The final goal of this work is to identify the problems faced in traditional system and accordingly a partitioning based techniques are designed which will optimize the performance of query operation in terms of size and query time. To check the prediction accuracy around the regression line error estimation is used in the scenario. The measurement of speed difference between these proposed scenarios is depending on error estimation. Higher estimation is equal to higher performance. In this work the performance of MongoDB tuning technique is increased approximately by 65% as compare to the performance with default configuration of MongoDB. By using these techniques, the optimization of the system using MongoDB is increased compare to the Neo4j, Oracle 11g and traditional database. The final result shows that MongoDB performs best, followed by and Oracle 11g, Neo4j.