Performance Evaluation Of Networks Research Articles

Performance evaluation of deep neural network on malware detection: visual feature approach.

Nowadays, several malicious applications target computers and mobile users. So, malware detection plays a vital role on the internet so that the device is secure without any malicious activity affecting or gathering the useful content of the user. Researches indicate that the vulnerability of adversarial attacks is more in deep neural networks. When there is a malicious sample in a family, there will not be many changes in the variants, but there will be more signatures. So, a deep learning model, DenseNet was used for detection. The adversarial samples are created by other types of noise, including the Gaussian noise. We added this noise to a subset of malware samples and observed that for Malimg, the modified samples were precisely identified by the DenseNet, and the attack cannot be done. But for BIG2015, we found that there was some marginal decrease in the performance of the classifier, which shows that the model performs well. Further, experiments on the Fast Gradient Sign Method (FGSM) were conducted, and it was observed that a significant decrease in classification accuracy was detected for both datasets. We understand that deep learning models should be robust to adversarial attacks.

Performance evaluation of a recurrent deep neural network optimized by swarm intelligent techniques to model particulate matter

ABSTRACT Atmospheric pollution refers to the presence of substances in the air such as particulate matter (PM) which has a negative impact in population ́s health exposed to it. This makes it a topic of current interest. Since the Metropolitan Zone of the Valley of Mexico’s geographic characteristics do not allow proper ventilation and due to its population’s density a significant quantity of poor air quality events are registered. This paper proposes a methodology to improve the forecasting of PM10 and PM2.5, in largely populated areas, using a recurrent long-term/short-term memory (LSTM) network optimized by the Ant Colony Optimization (ACO) algorithm. The experimental results show an improved performance in reducing the error by around 13.00% in RMSE and 14.82% in MAE using as reference the averaged results obtained by the LSTM deep neural network. Overall, the current study proposes a methodology to be studied in the future to improve different forecasting techniques in real-life applications where there is no need to respond in real time. Implications: This contribution presents a methodology to deal with the highly non-linear modeling of airborne particulate matter (both PM10 and PM2.5). Most linear approaches to this modeling problem are often not accurate enough when dealing with this type of data. In addition, most machine learning methods require extensive training or have problems when dealing with noise embedded in the time-series data. The proposed methodology deals with this data in three stages: preprocessing, modeling, and optimization. In the preprocessing stage, data is acquired and imputed any missing data. This ensures that the modeling process is robust even when there are errors in the acquired data and is invalid, or the data is missing. In the modeling stage, a recurrent deep neural network called LSTM (Long-Short Term Memory) is used, which shows that regardless of the monitoring station and the geographical characteristics of the site, the resulting model shows accurate and robust results. Furthermore, the optimization stage deals with enhancing the capability of the data modeling by using swarm intelligence algorithms (Ant Colony Optimization, in this case). The results presented in this study were compared with other works that presented traditional algorithms, such as multi-layer perceptron, traditional deep neural networks, and common spatiotemporal models, which show the feasibility of the methodology presented in this contribution. Lastly, the advantages of using this methodology are highlighted.

Robustness Enhancement of Neural Networks via Architecture Search with Multi-Objective Evolutionary Optimization

Along with the wide use of deep learning technology, its security issues have drawn much attention over the years. Adversarial examples expose the inherent vulnerability of deep learning models and make it a challenging task to improve their robustness. Model robustness is related not only to its parameters but also to its architecture. This paper proposes a novel robustness enhanced approach for neural networks based on a neural architecture search. First, we randomly sample multiple neural networks to construct the initial population. Second, we utilize the individual networks in the population to fit and update the surrogate models. Third, the population of neural networks is evolved through a multi-objective evolutionary algorithm, where the surrogate models accelerate the performance evaluation of networks. Finally, the second and third steps are performed alternately until a network architecture with high accuracy and robustness is achieved. Experimental results show that the proposed method outperforms some classical artificially designed neural networks and other architecture search algorithms in terms of robustness.

Automatic Seizure Detection Using Modified CNN Architecture and Activation Layer

An epileptology expert must visually inspect the EEG to identify abnormal neural activity, which is time-consuming and subject to human errors. The capability of convolution neural networks (CNN) to extract visuospatial features and learn from these discriminative features makes them useful for this task. This paper presents seizure classification based on long-term EEGs using CNN. After filtering, the scalogram is plotted using a 1-second window each. A recently published dataset (TUSZ v1.5.2) was used for the performance evaluation of various CNN-based deep neural networks. The best accuracy obtained for GoogLeNet and AlexNet is 95.88%, and 95.79% respectively with 50 epochs and 32 mini-batch sizes by using the SWISH activation function. The proposed hybrid architecture (AG86) for epoch 50 with mini-batch size 32 has shown the best testing results in terms of accuracy (94.98%) as compared to the SqueezeNet (93.19%), GoogLeNet (92.65%), and AlexNet (94.44%). Similar performance was observed using metrics specificity, sensitivity, Mathew correlation coefficient (MCC), and F1 score. A general inference based on evaluation can be drawn as the proposed hybrid architecture (AG86) showed better test results compared to pre-trained CNN models. Moreover, by replacing ReLU with the SWISH activation function, the performance of AlexNet and GoogLeNet improved.

Evaluation of artificial neural network performance for classification of potato plants infected with potato virus Y using spectral data on multiple varieties and genotypes

Potato virus Y (Potyviridae, PVY) is a plant virus that poses a significant threat to potato producers on a global basis. The pathogen has disrupted seed potato supplies and negatively impacted yield and quality of commercial potato crops. The potato industry currently manages PVY infection levels via insecticide applications, regional seed certification programs that rely on field scouting to visually assess individual plants for infection status, and destructive and costly tissue sampling coupled with laboratory assays. Despite these efforts, PVY continues to confound potato industry stakeholders resulting in economic harm. Remote sensing and machine learning provide for the development of new tools to more accurately detect and spatially quantify PVY-infected plants versus the current state of the art. However, there is a need to understand how the occurrence of many different potato varieties impact the dynamics of developing models to detect potato plants impacted with PVY and their potential effectiveness. This study evaluates classification modelling outcomes using spectral datasets collected in different temporal and spatial environments (greenhouse and a production field) on multiple potato varieties consisting of labelled instances of plants infected with PVY and those not infected with the virus. A modelling framework was developed to support iterative modelling runs using artificial neural network (ANN) architectures configured as binary classifiers to develop sample populations to support statistical analysis on model performance using specific spectral subsets. When using spectral data to detect PVY-infected plants, ANN models achieved the highest mean accuracy of 0.894 on a single variety. Conversely, the same ANN model architecture only achieved a mean accuracy of 0.575 on a spectral data set representing 29 potato breeding lines. Additionally, statistical analysis indicates spectral regions including the red edge, near infrared and shortwave infrared contain more important spectral features for the ANN classifier introduced in this research.

QoS Performance Evaluation of IoT-based Virtual Private Network for UAV Video

Unmanned Aerial Vehicles (UAVs) are popular and are used for surveillance and safety purposes, thus often include live video streaming. An attacker with knowledge of the flight controller’s settings can intercept the camera footage/stream. A virtual private network (VPN) connection is considered as one of the solutions in preventing unauthorised interception, but with a cost. In providing end-to-end encryption connection, the VPN connection can slow the wireless connection speed and, thus, impact the real-time streaming. This article describes the implementation of the IoT-based live video streaming system with VPN and analyses the live streaming performance based on quality of service (QoS) measurements. Comparative analysis between video streaming with and without VPN is presented.

Physico-chemical/mechanical properties of treated groundnut shell fibre; Response surface methodology and artificial neural network performance evaluation and optimisation

The difficulties associated with forecasting mechanical behaviours of fibres based on the theoretical method is a big threat to precise design when dealing with complex variables; hence, the need to evaluate the fibres' properties using artificial intelligence techniques to minimise error. This study predicted the effect of acetylation process parameters (treatment time and concentration) on the mechanical properties (elongation at break and tensile strength) of groundnut shell fibre (GSF) using Response surface methodology (RSM) and Artificial neural network (ANN). The GSF was processed, dried and treated with NaOH varying the time and concentration of the treatment and then acetylated. The characterisation results from the physicochemical analysis, SEM and FTIR indicated that acetylation treatment removed excess lignin, hemicellulose and cellulose from GSF and improve its bonding characteristics. Assessment of the models through the coefficient of determination (R2) and mean square error (MSE) indicates that ANN for elongation at break (R2 =0.9999, MSE=2.769E-13) and tensile strength (R2 =0.99997, MSE=3.5146E-18) is superior to the RSM model for elongation at break (R2 =0.9264, MSE 1.59) and for tensile strength (R2 =0.9326, MSE=7.71) in predicting mechanical properties of GSF. Numerical optimisation revealed that optimal values of 3 h and treatment concentration of 3% gave a maximum percentage elongation at break of 10.583% and 135.045 MPa for tensile strength. This result shows that acetylation treatment enhanced the physicochemical/mechanical properties of GSF thereby making it suitable for composite processing.

Perancangan Sistem Pakar Untuk Menilai Kinerja Irigasi

Irrigation is a supporting infrastructure for water distribution in agriculture. Its role is vital, especially for agricultural areas which are not rain-fed, meaning that to irrigate agricultural areas the irrigation infrastructure is needed. It is important for all parties involved in agriculture and irrigation management institutions to find out and ensure the condition and performance of an irrigation network, whether its function is still as planned or even vice versa. So far, the performance evaluation of irrigation networks is still carried out conventionally and only reaches a form of Maintenance Operation report, not specific to its performance. So based on these conditions, this research will build an expert system that can be used to assess the performance of irrigation networks. The design begins by determining the logic of assessment that will be used as a way of thinking from the expert system that will be built. The indicators used by the system to assess irrigation performance are as stated in the regulation of the Ministry of Public Works and Public Housing No. 12 of 2015. After designing the expert system, the next step is to simulate it using the data available in the operation and maintenance reports of some irrigation networks that are used as samples. Then the simulation results are analyzed so that it can be known whether the system can work as planned or not. The system simulation results show a mismatch between the two indicators namely the performance of infrastructure and water services. While other assessment indicators have logical results.

Multi-Location Emulation of a Process-Based Salinity Model Using Machine Learning

Advances in machine-learning techniques can serve practical water management needs such as salinity level estimation. This study explores machine learning, particularly deep-learning techniques in developing computer emulators for a commonly used process model, the Delta Simulation Model II (DSM2), used for salinity estimation in California’s Sacramento-San Joaquin Delta (Delta). We apply historical daily input data to DSM2 and corresponding salinity simulations at 28 study locations from 1990 to 2019 to train two machine-learning models: a multi-layer perceptron (MLP) and Long-Short-Term Memory (LSTM) networks in a multi-task learning framework. We assess sensitivity of both networks to the amount of antecedent input information (memory) and training data to determine appropriate memory size and training data length. We evaluate network performance according to several statistical metrics as well as visual inspection. The study further investigates two additional networks, the Gated Recurrent Unit (GRU) and Residual Network (ResNet) in salinity modeling, and compares their efficacy against MLP and LSTM. Our results demonstrate strong performance of the four neural network models over the study period, achieving absolute bias below 4%, plus near-perfect correlation coefficients and Nash–Sutcliffe efficiency coefficients. The high complexity LSTM shows slight performance edge. We further show that deeper and wider versions of MLP and LSTM yield only marginal benefit over their baseline counterparts. We also examined issues related to potential overfitting by the proposed models, training data selection strategies, and analytical and practical implications. Overall, this new study indicates that machine-learning-based emulators can efficiently emulate DSM2 in salinity simulation. They exhibit strong potential to supplement DSM2 in salinity modeling and help guide water resource planning and management practices for the Delta region.

Mathematical Analysis and Performance Evaluation of the Cell-Free mMIMO Networks Based on Cognitive Relays

The cell-Free massive multiple input multiple output “mMIMO” networks can provide a satisfied performance for the fifth generation “5G” systems and beyond even there are shadowed users or indoor ones. The performance of the cell-Free mMIMO systems was mathematically analyzed and simulated in a lot of previous works. However, this work aims to provide a complete mathematical model and simulation for a cell-Free mMIMO system that applies relays which operate with the cognitive radio theory. The proposed work can provide novel contributions on two axes. Firstly, the relays can improve the coverage performance of a cell-Free mMIMO network especially at shadowed users, indoor users, and the cell-edge ones. Secondly, the cognitive radio can mitigate the interference in a cell-Free mMIMO network. The interference mitigation results in performance enhancement. Therefore, the cell-Free mMIMO network, based on cognitive relays, is studied. The proposed system is mathematically analyzed and simulated. The spectral efficiency “SE” and signal to interference plus noise ratio “SINR” is deduced. They are given in a closed form formula. It can be concluded that the cognitive relays can improve; SINR, SE, and Energy Efficiency “EE” of a cell-Free mMIMO system.

A Neural Network Model for Business Performance Management Based on Random Matrix Theory

This paper uses random matrix theory to construct a neural network model for business performance management. The random sample covariance matrix of the random monitoring matrix is constructed, and the maximum eigenvalue and the minimum eigenvalue of the sample covariance matrix are solved. The ratio of eigenvalues is used to construct the eigenvalue detection index and determine the eigenvalue index detection threshold algorithm to judge the abnormal state of enterprise operation. The data of 66 listed Internet finance companies are selected, normalized, and correlation tested, and the index weights of each level are obtained using hierarchical analysis to derive the expected output of the BP neural network. Finally, the constructed BP neural network performance evaluation model is used for network training and simulation analysis, in which 192 data of 48 companies in the last four years are selected as training samples and 8 companies in the last four years are used as test samples to analyze the simulation output results. Using the original data instead of the main factor to join the BP neural network model, after two systematic optimizations, the final model with high accuracy, low mean square error, and low average error was formed. When applying the newly added data for testing, an accuracy of 95.98% was achieved for the ranking prediction, and the average deviation of 0.0021 points for the score prediction, which fully reflected the feasibility and adaptability of the model.

Accelerated cardiac T1 mapping with recurrent networks and cyclic, model-based loss.

Using the spin-lattice relaxation time (T1) as a biomarker, the myocardium can be quantitatively characterized using cardiac T1 mapping. The modified Look-Locker inversion (MOLLI) recovery sequences have become the standard clinical method for cardiac T1 mapping. However, the MOLLI sequences require an 11-heartbeat breath-hold that can be difficult for subjects, particularly during exercise or pharmacologically induced stress. Although shorter cardiac T1 mapping sequences have been proposed, these methods suffer from reduced precision. As such, there is an unmet need for accelerated cardiac T1 mapping. To accelerate cardiac T1 mapping MOLLI sequences by using neural networks to estimate T1 maps using a reduced number of T1-weighted images and their corresponding inversion times. In this retrospective study, 911 pre-contrast T1 mapping datasets from 202 subjects (128 males, 56 ± 15 years; 74 females, 54 ± 17 years) and 574 T1 mapping post-contrast datasets from 193 subjects (122 males, 57 ± 15 years; 71 females, 54 ± 17 years) were acquired using the MOLLI-5(3)3 sequence and the MOLLI-4(1)3(1)2 sequence, respectively. All acquisition protocols used similar scan parameters: , , and , gadoteridol (ProHance, Bracco Diagnostics) dose . A bidirectional multilayered long short-term memory (LSTM) network with fully connected output and cyclic model-based loss was used to estimate T1 maps from the first three T1-weighted images and their corresponding inversion times for pre- and post-contrast T1 mapping. The performance of the proposed architecture was compared to the three-parameter T1 recovery model using the same reduction of the number of T1-weighted images and inversion times. Reference T1 maps were generated from the scanner using the full MOLLI sequences and the three-parameter T1 recovery model. Correlation and Bland-Altman plots were used to evaluate network performance in which each point represents averaged regions of interest in the myocardium corresponding to the standard American Heart Association 16-segment model. The precision of the network was examined using consecutively repeated scans. Stress and rest pre-contrast MOLLI studies as well as various disease test cases, including amyloidosis, hypertrophic cardiomyopathy, and sarcoidosis were also examined. Paired t-tests were used to determine statistical significance with . Our proposed network demonstrated similar T1 estimations to the standard MOLLI sequences (pre-contrast: vs. with ; post-contrast: vs. with ). The precision of standard MOLLI sequences was well preserved with the proposed network architecture ( vs. ). Network-generated T1 reactivities are similar to stress and rest pre-contrast MOLLI studies ( vs. with ). Amyloidosis T1 maps generated using the proposed network are also similar to the reference T1 maps (pre-contrast: vs. with ; post-contrast: vs. with ). A bidirectional multilayered LSTM network with fully connected output and cyclic model-based loss was used to generate high-quality pre- and post-contrast T1 maps using the first three T1-weighted images and their corresponding inversion times. This work demonstrates that combining deep learning with cardiac T1 mapping can potentially accelerate standard MOLLI sequences from 11 to 3 heartbeats.

A performance evaluation of convolution neural networks for kinship discernment: An application in digital forensics

Kinship Verification from facial images is known to have attracted major attention since time immemorial. Identifying the underlying patterns that exist between images and analysing the relationship hidden between them have enabled the multitudes of applications to utilize kinship relationships. This work serves as a study on the amount of influence that hereditary features can exert on the families tied together by lineage in identifying the relationship prevailing between them and whether it holds true or not. The approach employed involves detecting the relationship existing between the provided facial images using Siamese Network, which comprises two identical convolutional neural networks that share common weight values. A difference vector is computed from this Siamese CNN, which is then fed into a network of fully connected linear layers. This extended layer will determine whether the two individuals in the input images are related to each other or not.

Experimental network performance evaluation for human-robot interaction collision detection using cameras

A practical solution to human-robot collision detection using devices commonly found in workplaces, such as 2D cameras, requires thorough planning and evaluation of network restrictions that may deny timely access to and process important context data collected by IoT devices. In this study, we evaluate the behavior of the AMQP and MQTT application protocols for camera image transmission. We examine the packet overhead for each protocol when streaming video signals. Also, we evaluate the impact of transmission delay on the total decision time starting from the moment the camera captures an image to the moment it is decided if there is a robot-human collision or not. Finally, we also evaluated an open-source platform to emulate wireless Mininet-wifi, seeking to understand the level of influence it would have on the results. The results show that transmission overhead represents as much as 80% of the total decision time and that the AMQP protocol takes around 5% less transmission time than MQTT. The results also show that the use of hardware accelerators such as a GPU increases by 37 times the number of detections. We found that the size of the image to be transmitted and wireless communications did not influence the results for our scenario. In addition, we also noticed that the use of emulation through Mininet-wifi does not negatively influence the behavior of the experiments.

Analysis and Optimization for Large-Scale LoRa Networks: Throughput Fairness and Scalability

LoRa networks are pivotally enabling Long Range connectivity to low-cost and power-constrained user equipments (UEs) in a wide area, whereas a critical issue is to effectively allocate wireless resources to support potentially massive UEs while resolving the prominent near–far fairness issue, which is challenging due to the lack of tractable analytical model and the practical requirement for low-complexity and low-overhead design. Leveraging on stochastic geometry, especially the Poisson rain model, we derive (semi-) closed-form formulas for the aggregate interference distribution, packet success probability, and hence, system throughput in both single-cell and multicell setups with frequency reuse, by accounting for channel fading, random UE distribution, partial packet overlapping, and/or multi-gateway (GW) packet reception. The analytical formulas require only average channel statistics and spatial UE distribution, which enable tractable network performance evaluation and incubate our proposed iterative balancing (IB) method that quickly yields high-level policies of joint spreading factor (SF) allocation, power control, and duty-cycle adjustment for gauging the average max–min UE throughput or supported UE density with rate requirements. Numerical results validate the analytical formulas and the effectiveness of our proposed optimization scheme, which greatly alleviate the near–far fairness issue and reduces the spatial power consumption, while significantly improving the cell-edge throughput as well as the spatial (sum) throughput for the majority of UEs, by adapting to the UE/GW densities.

Performance evaluation and energy optimization of CM3A cooperative WBAN

This work presents performance evaluation and energy optimization of a cooperative Wireless body area network (WBAN). Link metrics like Bit Error Rate (BER), Signal-to-Noise ratio (SNR), spectral efficiency (SE), and outage probability have been investigated to evaluate the performance of non-cooperative and cooperative WBAN at different frequency bands supporting body-centric communication (Ultra-wideband (UWB), Worldwide Industrial, Scientific and Medical (W-ISM) band, US-ISM (U-ISM) band and Wireless Mobile Telemetry Services (WMTS)). Evaluation reveals that cooperative communication shows better performance by exhibiting improved SNR and SE and lower BER and outage probability compared to non-cooperative communication. A critical comparison of Amplify and forward (A&F) cooperation has been performed with decode and forward (D&F) cooperation. Results reveal that the D&F scheme performs better with increased SNR and SE and lower levels of BER and outage probability in comparison to the A&F scheme by 43.74%, 1.64%, 0.15%, and 99%, respectively. W-MTS with the highest SNR and SE and lowest BER and outage probability outperforms the other frequency bands under both non-cooperative and cooperative scenarios. Further, the threshold distance has been evaluated using the Eigenvalues to optimize the transmission energy for the D&F scenario to enhance the performance of on-body WBAN resulting in an average decrease of 3.8% in the transmitted energy, with UWB showing a maximum reduction of 5.25% thus making the network more energy efficient.

Network performance evaluation of smart distribution systems using smart meters with TCP/IP communication protocol

The escalating significance of system reliability and resilience is transforming the planning and operation of contemporary distribution systems. To accomplish self-healing against power outages, advanced emerging technologies and devices such as smart meters are being deployed in the distribution systems. This work attempts to evaluate the performance of the smart distribution networks, where the communication protocol TCP/IP integrated with smart meters plays an essential responsibility. TCP/IP as a communication protocol has been used to send the data to the real world from simulation and receive it back into the simulation without losing data. Two-level control architecture has been proposed. The model has been designed using MATLAB/SIMULINK. To achieve it, the model consists of two LV distribution networks falling under one substation. Thus, two local controllers and one master controller have been modeled. Different single-phase loads (like domestic loads, colony loads having several houses, VIPs loads, shop loads, hospital loads, school loads, college loads, etc.) have been modeled as an amalgamation that has constant impedance and constant power loads. To simulate the functionalities of controllers, different case scenarios such as overcurrent protection, power theft, no shutdown of essential loads (like hospitals, VIPs), available power loss, and phase unbalancing in 42 residential loads altogether in two different areas have been studied and validated with real-time simulator through OPAL-RT.

DDoS attack detection in Internet of Things using recurrent neural network

The Internet of Things (IoT) is an emerging communication paradigm due to its wide range of applications. For IoT, distributed denial of service (DDoS) attacks are becoming increasingly widespread, and the solutions to combat these attacks are in increased demand. The significant contributions of this paper include offering a novel algorithm named DALCNN (Detecting Attack using Live Capture Neural Network) for detecting DDoS attacks in IoT using the concept of recurrent neural network and implementation of a Software-defined-Network (SDN) using OpenDayLight platform. Furthermore, a three-tier architecture is proposed to classify and detect DDoS attacks. The algorithm classifies the kind of attack using a novel activation function and the machine/deep learning concepts. The proposed classifier is tested on 177 instances. The Simulation was carried out using the tools such as Mininet, Wireshark to generate the DDoS attack and accurately detect the different types of DDoS attacks in the network. The simulation results reveal that the values of benchmark parameters – accuracy, true positive rate, false-positive rate, precision, recall, F-measure, and ROC-area are: 99.98%, 0.999, 0.01, 0.999, 0.999, 0.999, and 0.999, respectively. Apart from it, the network performance evaluation of the various open-source controllers’ including Floodlight, Ryu, ONOS, and the OpenDayLight based on the performance metrics – Throughput, Latency, and Aggregate Controller performance as well was carried out. The Simulation results reveal that the proposed algorithm performs efficiently compared to the other existing algorithms. The proposed OpenDayLight controller performs way better than the other open-source controllers.

Secrecy performance evaluation of cooperative vehicular relay networks against location‐uncertain eavesdroppers

This letter is concerned with the physical layer security of the cooperative vehicular relay network where a source vehicle communicates with a destination vehicle via both a direct link in the presence of a location-uncertain eavesdropping vehicle and an indirect link with the help of an amplify-and-forward relay vehicle. Based on the double-Rayleigh fading model for all links, an approximate analytical expression of the average secrecy capacity of the considered system is derived. This analysis on the average secrecy capacity is shown to be validated by Monte-Carlo simulations and numerical computations. The simulation and numerical results also demonstrate the effects of the uncertainty of the eavesdropper's location and the relay's location on the average secrecy capacity.

Evaluation and Prediction of Train Communication Network Performance

The performance of train communication network (TCN) is one of the most important factors to ensure the safety of train operation, and there is a lack of research for the TCN performance currently. To master the state of TCN, a combined method to evaluate and predict the TCN performance is proposed in the paper. First, we select appropriate evaluation indexes and determine their comprehensive weights. Then, the evaluation model of TCN performance is established based on a multidimensional normal cloud model combined with weights. According to the evaluation results, the artificial neural network (ANN) is used to predict the TCN performance to realize the real-time evaluation. Finally, the effectiveness of the evaluation and prediction model of TCN performance is verified by the simulation result, which is obtained from the simulation platform of TCN. The results show that service quality for TCN has been effectively improved, which provides a theoretical reference for evaluation and prediction of TCN performance.