scholarly journals Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning

2021 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Tajeddine Benbarrad ◽  
Marouane Salhaoui ◽  
Soukaina Bakhat Kenitar ◽  
Mounir Arioua
Keyword(s):  
Machine Learning ◽  
Machine Vision ◽  
Process Improvement ◽  
Machine Learning Algorithms ◽  
Innovative Technology ◽  
Manufacturing Operations ◽  
Proposed Model ◽  
Product Classification ◽  
Smart Factories ◽  
Ensure Product Quality

Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques.

scholarly journals Prediction of Pest Insect Appearance Using Sensors and Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4846
Author(s):  
Dušan Marković ◽  
Dejan Vujičić ◽  
Snežana Tanasković ◽  
Borislav Đorđević ◽  
Siniša Ranđić ◽  
...  
Keyword(s):  
Machine Learning ◽  
Relative Humidity ◽  
Weather Conditions ◽  
Daily Basis ◽  
Machine Learning Algorithms ◽  
Lower Percentage ◽  
Timely Manner ◽  
Proposed Model ◽  
Set Up ◽  
Accuracy Of Prediction

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect’s appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields.

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

scholarly journals Machine Learning, Ethics, and Change Management: A Data-Driven Approach to Improving Hospital Observation Unit Operations

2021 ◽  
Author(s):  
Dessislava Pachamanova ◽  
Vera Tilson ◽  
Keely Dwyer-Matzky
Keyword(s):  
Machine Learning ◽  
Change Management ◽  
Process Improvement ◽  
Operations Management ◽  
Model Performance ◽  
Machine Learning Algorithms ◽  
Business Analytics ◽  
Observation Unit ◽  
Improvement Project ◽  
Data Driven Approach

This case discusses a process improvement project aimed at maximizing the use of hospital capacity as the flu season looms. Dr. Erin Kelly heads the observation unit and turns to predictive models to improve the assignment of patients to her unit. The case covers three major themes: (1) data analytics life cycle and interface of predictive and prescriptive analytics in the context of process improvement, (2) design and ethical application of machine learning models, and (3) effecting organizational change to operationalize the findings of the analysis. Realistic data, R code, and Excel models are provided. The rich context of the case allows for discussing change management in a healthcare organization, analytics problem framing and model mapping, service process capacity analysis and Little’s law, data summaries and visualizations, interpretable machine learning algorithms, evaluations of predictive model performance, algorithmic bias, and dealing with dirty data. The case is appropriate for use in courses in machine learning, business analytics, operations management, and operations research, both at the advanced undergraduate level and at the master’s/MBA level.

scholarly journals An Insider Data Leakage Detection Using One-Hot Encoding, Synthetic Minority Oversampling and Machine Learning Techniques

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1258
Author(s):  
Taher Al-Shehari ◽  
Rakan A. Alsowail
Keyword(s):  
Machine Learning ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Sensitive Period ◽  
Insider Threat ◽  
Leakage Detection ◽  
Insider Threats ◽  
Insider Attack ◽  
Proposed Model

Insider threats are malicious acts that can be carried out by an authorized employee within an organization. Insider threats represent a major cybersecurity challenge for private and public organizations, as an insider attack can cause extensive damage to organization assets much more than external attacks. Most existing approaches in the field of insider threat focused on detecting general insider attack scenarios. However, insider attacks can be carried out in different ways, and the most dangerous one is a data leakage attack that can be executed by a malicious insider before his/her leaving an organization. This paper proposes a machine learning-based model for detecting such serious insider threat incidents. The proposed model addresses the possible bias of detection results that can occur due to an inappropriate encoding process by employing the feature scaling and one-hot encoding techniques. Furthermore, the imbalance issue of the utilized dataset is also addressed utilizing the synthetic minority oversampling technique (SMOTE). Well known machine learning algorithms are employed to detect the most accurate classifier that can detect data leakage events executed by malicious insiders during the sensitive period before they leave an organization. We provide a proof of concept for our model by applying it on CMU-CERT Insider Threat Dataset and comparing its performance with the ground truth. The experimental results show that our model detects insider data leakage events with an AUC-ROC value of 0.99, outperforming the existing approaches that are validated on the same dataset. The proposed model provides effective methods to address possible bias and class imbalance issues for the aim of devising an effective insider data leakage detection system.

Adaptive Ensemble Multi-Agent Based Intrusion Detection Model

2010 ◽  
pp. 36-48 ◽  
Author(s):  
Tarek Helmy
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Agent Based ◽  
Detection Model ◽  
Detection Analysis ◽  
Proposed Model ◽  
Multi Agent

The system that monitors the events occurring in a computer system or a network and analyzes the events for sign of intrusions is known as intrusion detection system. The performance of the intrusion detection system can be improved by combing anomaly and misuse analysis. This chapter proposes an ensemble multi-agent-based intrusion detection model. The proposed model combines anomaly, misuse, and host-based detection analysis. The agents in the proposed model use rules to check for intrusions, and adopt machine learning algorithms to recognize unknown actions, to update or create new rules automatically. Each agent in the proposed model encapsulates a specific classification technique, and gives its belief about any packet event in the network. These agents collaborate to determine the decision about any event, have the ability to generalize, and to detect novel attacks. Empirical results indicate that the proposed model is efficient, and outperforms other intrusion detection models.

scholarly journals Keystroke dynamics Based Technique to Enhance the Security in Smart Devices

2021 ◽  
Vol 4 (1) ◽  
pp. 14
Author(s):  
Farman Pirzado ◽  
Shahzad Memon ◽  
Lachman Das Dhomeja Dhomeja ◽  
Awais Ahmed
Keyword(s):  
Machine Learning ◽  
User Authentication ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Smart Devices ◽  
Mobile Banking ◽  
Alternative Technique ◽  
Keystroke Dynamics ◽  
Data Set ◽  
Proposed Model

Nowadays, smart devices have become a part of ourlives, hold our data, and are used for sensitive transactions likeinternet banking, mobile banking, etc. Therefore, it is crucial tosecure the data in these smart devices from theft or misplacement.The majority of the devices are secured with password/PINbaseduser authentication methods, which are already proveda less secure or easily guessable user authentication method.An alternative technique for securing smart devices is keystrokedynamics. Keystroke dynamics (KSD) is behavioral biometrics,which uses a natural typing pattern unique in every individualand difficult to fake or replicates that pattern. This paperproposes a user authentication model based on KSD as an additionalsecurity method for increasing the smart devices’ securitylevel. In order to analyze the proposed model, an android-basedapplication has been implemented for collecting data from fakeand genuine users. Six machine learning algorithms have beentested on the collected data set to study their suitability for usein the keystroke dynamics-based authentication model.

scholarly journals Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

Sensors ◽  
2022 ◽  
Vol 22 (2) ◽  
pp. 461
Author(s):  
Mujeeb Ur Rehman ◽  
Arslan Shafique ◽  
Kashif Hesham Khan ◽  
Sohail Khalid ◽  
Abdullah Alhumaidi Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Medical Records ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
X Ray ◽  
Non Invasive ◽  
Proposed Model ◽  
Pneumonia Diagnosis ◽  
Better Than

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

scholarly journals Detection of Online Fake News Using Blending Ensemble Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Arvin Hansrajh ◽  
Timothy T. Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Fake News ◽  
Learning Models ◽  
Linear Discriminant ◽  
Proposed Model ◽  
Machine Learning Models

The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.

Sign in / Sign up

Export Citation Format

Share Document