Detection Error Rate Research Articles

Landmark Localization for Cephalometric Analysis Using Multiscale Image Patch-Based Graph Convolutional Networks.

Accurate and robust cephalometric image analysis plays an essential role in orthodontic diagnosis, treatment assessment and surgical planning. This paper proposes a novel landmark localization method for cephalometric analysis using multiscale image patch-based graph convolutional networks. In detail, image patches with the same size are hierarchically sampled from the Gaussian pyramid to well preserve multiscale context information. We combine local appearance and shape information into spatialized features with an attention module to enrich node representations in graph. The spatial relationships of landmarks are built with the incorporation of three-layer graph convolutional networks, and multiple landmarks are simultaneously updated and moved toward the targets in a cascaded coarse-to-fine process. Quantitative results obtained on publicly available cephalometric X-ray images have exhibited superior performance compared with other state-of-the-art methods in terms of mean radial error and successful detection rate within various precision ranges. Our approach performs significantly better especially in the clinically accepted range of 2 mm and this makes it suitable in cephalometric analysis and orthognathic surgery.

Algorithm of overfitting avoidance in CNN based on maximum pooled and weight decay

Abstract This paper aims to eradicate the poor performance of the convolutional neural network (CNN) for intelligent analysis and detection in samples. Moreover, to avoid overfitting of the CNN model during the training process, an algorithm is proposed for the fusion of maximum pooled and weight decay. Firstly, the maximum pooled method for the pooling layer is explored after mask processing to reduce the number of irrelevant neurons. Secondly, when updating the neuron weight parameters, the weight decay is introduced to further cut down complexity in model training. The experimental comparison shows that the overfitting avoidance algorithm can reduce the detection error rate by more than 10% in image detection than other methods, and it has better generalisation.

Performance of a Convolutional Neural Network- Based Artificial Intelligence Algorithm for Automatic Cephalometric Landmark Detection.

The aim of this study is to develop an artificial intelligence model to detect cephalometric landmark automatically en- abling the automatic analysis of cephalometric radiographs which have a very important place in dental practice and is used routinely in the diagnosis and treatment of dental and skeletal disorders. In this study, 1620 lateral cephalograms were obtained and 21 landmarks were included. The coordinates of all landmarks in the 1620 films were obtained to establish a labeled data set: 1360 were used as a training set, 140 as a validation set, and 180 as a testing set. A convolutional neural network-based artificial intelligence algorithm for automatic cephalometric landmark detection was developed. Mean radial error and success detection rate within the range of 2 mm, 2.5 mm, 3 mm, and 4 mm were used to eval- uate the performance of the model. Presented artificial intelligence system (CranioCatch, Eskişehir, Turkey) could detect 21 anatomic landmarks in a lateral ceph- alometric radiograph. The highest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the sella point as 98.3, 99.4, 99.4, and 99.4, respectively. The mean radial error ± standard deviation value of the sella point was found as 0.616 ± 0.43. The lowest success detection rate scores of 2 mm, 2.5 mm, 3 mm, and 4 mm were obtained from the Gonion point as 48.3, 62.8, 73.9, and 87.2, respectively. The mean radial error ± standard deviation value of Gonion point was found as 8.304 ± 2.98. Although the success of the automatic landmark detection using the developed artificial intelligence model was not in- sufficient for clinical use, artificial intelligence-based cephalometric analysis systems seem promising to cephalometric analysis which provides a basis for diagnosis, treatment planning, and following-up in clinical orthodontics practice.

Development and validation of a checklist for use with automatically generated radiotherapy plans.

PurposeTo develop a checklist that improves the rate of error detection during the plan review of automatically generated radiotherapy plans.MethodsA custom checklist was developed using guidance from American Association of Physicists in Medicine task groups 275 and 315 and the results of a failure modes and effects analysis of the Radiation Planning Assistant (RPA), an automated contouring and treatment planning tool. The preliminary checklist contained 90 review items for each automatically generated plan. In the first study, eight physicists were recruited from our institution who were familiar with the RPA. Each physicist reviewed 10 artificial intelligence‐generated resident treatment plans from the RPA for safety and plan quality, five of which contained errors. Physicists performed plan checks, recorded errors, and rated each plan's clinical acceptability. Following a 2‐week break, physicists reviewed 10 additional plans with a similar distribution of errors using our customized checklist. Participants then provided feedback on the usability of the checklist and it was modified accordingly. In a second study, this process was repeated with 14 senior medical physics residents who were randomly assigned to checklist or no checklist for their reviews. Each reviewed 10 plans, five of which contained errors, and completed the corresponding survey.ResultsIn the first study, the checklist significantly improved the rate of error detection from 3.4 ± 1.1 to 4.4 ± 0.74 errors per participant without and with the checklist, respectively (p = 0.02). Error detection increased by 20% when the custom checklist was utilized. In the second study, 2.9 ± 0.84 and 3.5 ± 0.84 errors per participant were detected without and with the revised checklist, respectively (p = 0.08). Despite the lack of statistical significance for this cohort, error detection increased by 18% when the checklist was utilized.ConclusionOur results indicate that the use of a customized checklist when reviewing automated treatment plans will result in improved patient safety.

An Automatic Error Correction Method for English Composition Grammar Based on Multilayer Perceptron

In order to improve the timeliness of English grammar error correction and the recall rate of English grammar error correction, this paper proposes an automatic error correction method for English composition grammar based on a multilayer perceptron. On the basis of preprocessing the English composition corpus data, this paper extracts the grammatical features in the English composition corpus and constructs a grammatical feature set. We take the feature set as the input information of the multilayer perceptron and realize feature classification through network learning and training. The grammatical error items in the English composition are detected according to the similarity, and the error correction is completed by setting the penalty parameter and reducing the deviation parameter. The experimental results show that the syntax error detection time of this method is less than 6 minutes, the recall rate is higher than 90%, and the detection error rate is lower than 6%. The method improves the timeliness of grammatical error correction and improves the efficiency of error correction.

Evaluation Method of Financial Internal Control in Colleges and Universities Based on Artificial Intelligence Technology

The internal financial control of colleges and universities is the main tool to restrain the behavior of financial managers. With the improvement of the economic operation efficiency of colleges and universities, the application effect of internal control management system is poor. Therefore, the evaluation method of the university financial internal control based on artificial intelligence technology is proposed. First put forward the artificial intelligence technology in the application of financial internal control in colleges and universities, and then construct the financial internal organizational structure, on this basis, to meet the enormous workload processing requirements, the creation of financial internal control evaluation in colleges and universities, the chain at the same time use of financial system of general function, the university financial internal control evaluation data output to the financial system. The data acquisition program is used to obtain the relevant data in the evaluation report so as to realize the evaluation of internal financial control in colleges and universities under the artificial intelligence technology. The experimental results show that the evaluation acceleration is faster with the increase of the evaluation time of the key elements. The evaluation effect is the best; the closest to the ideal value of 0.01, and the error detection rate curve is relatively stable, with good effect.

Abstract 2741: A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells

Abstract Previous studies have shown that approximately 40% of estrogen receptor positive (ER+) breast cancer (BC) patients harbor immune signaling defects in their blood at diagnosis that predicts overall survival. The aim of this work is to apply communication theory and signal processing methods to model cytokine-mediated signaling errors in immune cells in the peripheral blood. Our approach uses communication theory concepts to develop a communication system model consisting of a transmitter, receiver, and channel, applied to the JAK/STAT signaling pathway and tested using multi-color flow cytometry analysis of peripheral blood mononuclear cells stimulated with IFNγ, IL-2, IL-6, IL-4, IL-10, IL-12, or TGFβ in samples from 2 BC patients and 4 healthy controls. We use our model to estimate the detection error rate when one of the 7 cytokines is being transduced through the JAK/STAT signaling pathway, but its signal is being received on multiple phosphorylated signal transducers and activators of transcription (pSTAT) molecules. In addition, we extend the measured error rate value to an error rate curve by varying the signal-to-noise ratio available to the receiver in our system model. Our results show a trend towards higher detection error rates in immune cells from BC patients as compared to healthy controls, which may indicate altered immune signaling and incorrect cell decisions in these patients. Also, considering different cell subtypes, our results show two orders of magnitude increase in error rate in CD4+ memory T cells compared to CD4+ naïve T cells in BC patients. With further development, we believe that our communication theory model and quantification of optimal signal detection in cells will provide a better understanding into how immune cells can accurately “decode” information to respond properly to various signals and how the immune signaling is altered in BC patients, causing better or worse outcomes. The benefit of our novel approach is that it integrates all the transmitted (cytokines) and received signals (pSTATs) into a single communications framework that can be efficiently analyzed together. Citation Format: Adina Matache, Aleksandra Karolak, Sergio Branciamore, Andrei Rodin, Peter P. Lee, Russell C. Rockne. A communication theory framework for modeling cytokine-mediated signaling in healthy and breast cancer derived peripheral blood immune cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2741.

INTELLIGENT ACQUISITION METHOD OF HERBACEOUS FLOWERS IMAGE BASED ON THEME CRAWLER, DEEP LEARNING AND GAME THEORY

In order to obtain a large amount of training data in a short time, flower classification is carried out through image processing and deep learning algorithms based on game theory and optimization. The Python web crawler technology is used to write the image and short video crawler programs based on the Chinese name of herbal flowers, and the target detection model is used to screen the flower image on the basis of the static frame of the segmented video, so as to improve the speed and accuracy of image acquisition. The result show that the use of theme crawler technology can obtain the image of herbaceous flowers effectively; target detection can greatly improve the image utilization, the number of samples can be increased by 3~10 times, and the average error detection rate is only 3.62%; the GAN (GenerativeAdversarial Network) is a deep learning model based on game theory. GAN model can generate realistic flower pictures, which provides a new research idea to solve the problem of lack of agriculture data set at present, and shows the feasibility of intelligent data collection method for herbaceous flowers.

Covert Millimeter-Wave Communication: Design Strategies and Performance Analysis

In this paper, we investigate covert communication over millimeter-wave (mmWave) frequencies. In particular, a mmWave transmitter, referred to as Alice, attempts to reliably communicate to a receiver, referred to as Bob, while hiding the existence of communication from a warden, referred to as Willie. In this regard, operating over the mmWave bands not only increases the covertness thanks to directional beams, but also increases the transmission data rates given much more available bandwidths and enables ultra-low form factor transceivers due to the lower wavelengths used compared to the conventional radio frequency (RF) counterpart. We first assume that the transmitter Alice employs two independent antenna arrays in which one of the arrays is to form a directive beam for data transmission to Bob. The other antenna array is used by Alice to generate another beam toward Willie as a jamming signal while changing the transmit power independently across the transmission blocks in order to achieve the desired covertness. For this dual-beam setup, we characterize Willie’s detection error rate with the optimal detector and the closed-form of its expected value from Alice’s perspective. We then derive the closed-form expression for the outage probability of the Alice-Bob link, which enables characterizing the optimal covert rate that can be achieved using the proposed setup. We further obtain tractable forms for the ergodic capacity of the Alice-Bob link involving only one-dimensional integrals that can be computed in closed forms for most ranges of the channel parameters. Finally, we highlight how the results can be extended to more practical scenarios, particularly to the cases where perfect information about the location of the passive warden is not available. Our results demonstrate the advantages of covert mmWave communication compared to the RF counterpart. The research in this paper is the first analytical attempt in exploring covert communication using mmWave systems.

Assessment of Statistical Process Control Based DVH Action Levels for Systematic Multi-Leaf Collimator Errors in Cervical Cancer RapidArc Plans.

BackgroundIn the patient-specific quality assurance (QA), DVH is a critical clinically relevant parameter that is finally used to determine the safety and effectiveness of radiotherapy. However, a consensus on DVH-based action levels has not been reached yet. The aim of this study is to explore reasonable DVH-based action levels and optimal DVH metrics in detecting systematic MLC errors for cervical cancer RapidArc plans.MethodsIn this study, a total of 148 cervical cancer RapidArc plans were selected and measured with COMPASS 3D dosimetry system. Firstly, the patient-specific QA results of 110 RapidArc plans were retrospectively reviewed. Then, DVH-based action limits (AL) and tolerance limits (TL) were obtained by statistical process control. Secondly, systematic MLC errors were introduced in 20 RapidArc plans, generating 380 modified plans. Then, the dose difference (%DE) in DVH metrics between modified plans and original plans was extracted from measurement results. After that, the linear regression model was used to investigate the detection limits of DVH-based action levels between %DE and systematic MLC errors. Finally, a total of 180 test plans (including 162 error-introduced plans and 18 original plans) were prepared for validation. The error detection rate of DVH-based action levels was compared in different DVH metrics of 180 test plans.ResultsA linear correlation was found between systematic MLC errors and %DE in all DVH metrics. Based on linear regression model, the systematic MLC errors between -0.94 mm and 0.88 mm could be caught by the TL of PTV95 ([-1.54%, 1.51%]), and the systematic MLC errors between -1.00 mm and 0.80 mm could also be caught by the TL of PTVmean ([-2.06%, 0.38%]). In the validation, for original plans, PTV95 showed the minimum error detection rate of 5.56%. For error-introduced plans with systematic MLC errors more than 1mm, PTVmean showed the maximum error detection rate of 88.89%, and then was followed by PTV95 (86.67%). All the TL of DVH metrics showed a poor error detection rate in identifying error-induced plans with systematic MLC errors less than 1mm.ConclusionIn 3D quality assurance of cervical cancer RapidArc plans, process-based tolerance limits showed greater advantages in distinguishing plans introduced with systematic MLC errors more than 1mm, and reasonable DVH-based action levels can be acquired through statistical process control. During DVH-based verification, main focus should be on the DVH metrics of target volume. OARs in low-dose regions were found to have a relatively higher dose sensitivity to smaller systematic MLC errors, but may be accompanied with higher false error detection rate.

Assessing initial plan check efficacy using TG 275 failure modes and incident reporting.

Plan checks are important components of a robust quality assurance (QA) program. Recently, the American Association of Physicists in Medicine (AAPM) published two reports concerning plan and chart checking, Task Group (TG) 275 and Medical Physics Practice Guideline (MPPG) 11.A. The purpose of the current study was to crosswalk initial plan check failure modes revealed in TG 275 against our institutional QA program and local incident reporting data. Ten physicists reviewed 46 high‐risk failure modes reported in Table S1.A.i of the TG 275 report. The committee identified steps in our planning process which sufficiently checked each failure mode. Failure modes that were not covered were noted for follow‐up. A multidisciplinary committee reviewed the narratives of 1599 locally‐reported incidents in our Radiation Oncology Incident Learning System (ROILS) database and categorized each into the high‐risk TG 275 failure modes. We found that over half of the 46 high‐risk failure modes, six of which were top‐ten failure modes, were covered in part by daily contouring peer‐review rounds, upstream of the traditional initial plan check. Five failure modes were not adequately covered, three of which concerned pregnancy, pacemakers, and prior dose. Of the 1599 incidents analyzed, 710 were germane to the initial plan check, 23.4% of which concerned missing pregnancy attestations. Most, however, were caught prior to CT simulation (98.8%). Physics review and initial plan check were the least efficacious checks, with error detection rates of 31.8% and 31.3%, respectively, for some failure modes. Our QA process that includes daily contouring rounds resulted in increased upstream error detection. This work has led to several initiatives in the department, including increased automation and enhancement of several policies and procedures. With TG 275 and MPPG 11.A as a guide, we strongly recommend that departments consider an internal chart checking policy and procedure review.

Optimal Number and Location of Sensors for Structural Damage Detection using the Theory of Geometrical Viewpoint and Parameter Subset Selection Method

The recorded responses at predefined sensor placements are used as input to solve an inverse structural damage detection problem. The error rate that noise causes from the recorded responses of the sensors is a significant issue in damage detection methods. Therefore, an optimal number and location of sensors is a goal to achieve the lowest error rate in structural damage detection. To overcome this problem, an algorithm (GVPSS) based on a Geometrical Viewpoint (GV) of optimal sensor placement and Parameter Subset Selection (PSS) method is proposed. The goal of the GVPSS algorithm is to minimize the effect of noise on damage detection problem. Therefore, the fitness function based on error in damage detection is minimized by GVPSS. In this method, the degrees of freedom are arranged to place sensors using a fitness function based on GV theory. Then, the optimal number and location of sensors are found on these arranged the degrees of freedom using the objective function. The efficiency of the proposed method is studied in a 52-bar dome structure under static and dynamic loadings. In the examples, damages are detected in two states: 1) using responses recorded at all DOFs, 2) using responses recorded at the optimal number and location of sensors obtained by GVPSS. The results showed that the damage detection error in state 2 is approximately equal to the error in state 1. Therefore, the GVPSS have the high performance to find the optimal number and location of sensors for structural damage detection.

A Machine Learning Approach for the Detection of QRS Complexes in Electrocardiogram (ECG) Using Discrete Wavelet Transform (DWT) Algorithm.

This study describes a modified approach for the detection of cardiac abnormalities and QRS complexes using machine learning and support vector machine (SVM) classifiers. The suggested technique overtakes prevailing approaches in terms of both sensitivity and specificity, with 0.45 percent detection error rate for cardiac irregularities. Moreover, the vector machine classifiers validated the proposed method's superiority by accurately categorising four ECG beat types: normal, LBBBs, RBBBs, and Paced beat. The technique had 96.67 percent accuracy in MLP-BP and 98.39 percent accuracy in support of vector machine classifiers. The results imply that the SVM classifier can play an important role in the analysis of cardiac abnormalities. Furthermore, the SVM classifier also categorises ECG beats using DWT characteristics collected from ECG signals.

Tai Chi Movement Recognition Method Based on Deep Learning Algorithm

The current action recognition method has good effect when applied to static recognition, but, when applied to dynamic action sequence recognition, the temporal and spatial feature segmentation is too dependent on sample template, resulting in low recognition accuracy. To address the inadequacies of standard movement detection techniques in the application of comparable domains, a deep learning algorithm is utilised to recognise Tai Chi Chuan motions. For Tai Chi Chuan movement human body skeleton framework, add image depth parameter is added, and OpenPose model is utilised to estimate joint point coordinates. The ST-GCN deep learning model was created to recognise Tai Chi Chuan motions by extracting movement features from the spatiotemporal trajectory of human joints during Tai Chi Chuan movements. Instance test results show that rate of using the deep learning algorithm of gesture recognition is 89.22%, with significantly lower error detection rate, which is good for Tai chi chuan movement recognition effect.

English Pronunciation Calibration Model Based on Multimodal Acoustic Sensor

In recent years, with the increasing frequency of international exchanges, people have gradually realized that language is a tool of communication and communication, and language learning should attach importance to oral teaching. However, in traditional classrooms, one of the problems faced by oral teaching is the mismatch of the teacher-student ratio: a teacher has to deal with dozens of students, one-on-one oral teaching and pronunciation guidance is impossible, and it is also affected by the teachers and the environment constraints. Therefore, the research on how to efficiently automate pronunciation training is becoming more and more popular. Many phonemes in English have different facial visual features, especially vowels. Almost all of them can be distinguished by the roundness and tightness of the lips in appearance. In order to give full play to the role of lip features in oral pronunciation error detection, this paper proposes a multimodal feature fusion model based on lip angle features. The model interpolates the lip features constructed based on the opening and closing angles and combines audio and video in time series. Feature alignment and fusion and feature learning and classification are realized through the two-way LSTM SOFTMAX layer, and finally, end-to-end pronunciation error detection is realized through CTC. It is verified on the GRID audio and video corpus after phoneme conversion and the self-built multimodal test set. The experimental results show that the model has a higher false pronunciation recognition rate than the traditional single-modal acoustic error detection model. The increase in error detection rate is more obvious. Verification by the audio and video corpus with white noise was added, and the proposed model has better noise immunity than the traditional acoustic model.

A Target Corner Detection Algorithm Based on the Fusion of FAST and Harris

Corner detection is a common method to obtain image features, and the detection effect influences the performance of matching and tracking directly. A FAST-Harris fusion corner detection algorithm is proposed to improve the shortcomings of the Harris algorithm, such as the low detection accuracy and low positioning accuracy, and a corner detection fusion model is established. First, the detected target image is padded, and then the FAST algorithm is used with a 25% reduced contrast points to achieve fast capture roughly; in this way, a candidate corner set is obtained. Then, screening the candidate corner is set one by one by calculating the response function of the Harris with Scharr operator to achieve capture accurately. Finally, the real corners are obtained using SAD for nonmaximum suppression. The positioning error, error detection rate, robustness, and running time of corner detection are obtained by the PyCharm platform. Compared with Harris, the error detection rate and localization error of the algorithm are reduced by 16.89% and 42.04%, respectively. Compared with 8 popular corner detection algorithms, the error detection rate and localization error of the algorithm in this paper are the lowest, which are 24.60% and 1.42 pixels. The robust performance in lossy JPEG compression is the best, with 17.37% shorter running time than Harris algorithm. The method in this paper can be used in scenarios such as autonomous driving and image search services.

Current Approaches to Vaccine Safety Using Observational Data: A Rationale for the EUMAEUS (Evaluating Use of Methods for Adverse Events Under Surveillance-for Vaccines) Study Design.

Post-marketing vaccine safety surveillance aims to detect adverse events following immunization in a population. Whether certain methods of surveillance are more precise and unbiased in generating safety signals is unclear. Here, we synthesized information from existing literature to provide an overview of the strengths, weaknesses, and clinical applications of epidemiologic and analytical methods used in vaccine monitoring, focusing on cohort, case-control and self-controlled designs. These designs are proposed to be evaluated in the EUMAEUS (Evaluating Use of Methods for Adverse Event Under Surveillance–for vaccines) study because of their widespread use and potential utility. Over the past decades, there have been an increasing number of epidemiological study designs used for vaccine safety surveillance. While traditional cohort and case-control study designs remain widely used, newer, novel designs such as the self-controlled case series and self-controlled risk intervals have been developed. Each study design comes with its strengths and limitations, and the most appropriate study design will depend on availability of resources, access to records, number and distribution of cases, and availability of population coverage data. Several assumptions have to be made while using the various study designs, and while the goal is to mitigate any biases, violations of these assumptions are often still present to varying degrees. In our review, we discussed some of the potential biases (i.e., selection bias, misclassification bias and confounding bias), and ways to mitigate them. While the types of epidemiological study designs are well established, a comprehensive comparison of the analytical aspects (including method evaluation and performance metrics) of these study designs are relatively less well studied. We summarized the literature, reporting on two simulation studies, which compared the detection time, empirical power, error rate and risk estimate bias across the above-mentioned study designs. While these simulation studies provided insights on the analytic performance of each of the study designs, its applicability to real-world data remains unclear. To bridge that gap, we provided the rationale of the EUMAEUS study, with a brief description of the study design; and how the use of real-world multi-database networks can provide insights into better methods evaluation and vaccine safety surveillance.

Multi-Feature Fusion Based Deepfake Face Forgery Video Detection

With the rapid development of deep learning, generating realistic fake face videos is becoming easier. It is common to make fake news, network pornography, extortion and other related illegal events using deep forgery. In order to attenuate the harm of deep forgery face video, researchers proposed many detection methods based on the tampering traces introduced by deep forgery. However, these methods generally have poor cross-database detection performance. Therefore, this paper proposes a multi-feature fusion detection method to improve the generalization ability of the detector. This method combines feature information of face video in the spatial domain, frequency domain, Pattern of Local Gravitational Force (PLGF) and time domain and effectively reduces the average error rate of span detection while ensuring good detection effect in the library.

A Transformation of Oral English Teaching Method in the Network Environment

In order to effectually improve the application effect of the existing oral English teaching methods, this paper studies the conversion of effective oral English teaching methods under the network environment, aiming at the application problems, such as the flexibility of the current education platform. We build an effective oral English teaching platform which is comprised of oral English perception processing module, oral English pronunciation acquisition program module, oral English pronunciation preprocessing module, and oral English pronunciation error detection modeling module. We establish the information technology teaching objectives of the effective oral English teaching model on this basis. This research work is focused on the study of the output driven input-facilitated college oral English teaching model in oral English classroom. It is performed to realize the transformation of effective oral English teaching methods under the network environment. The experimental results show that the transformation effect of the effective teaching method of oral English, under the designed network environment, is better as compared to the existing work. The error detection rate in the proposed methodology reaches up to 98.6%, which is significantly higher than the results obtained by researchers in their existing work. The proposed method has obvious application advantages.

A New Multi-Level Semi-Supervised Learning Approach for Network Intrusion Detection System Based on the ‘GOA’

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical [Formula: see text]-means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN’s accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously.