Biblio

Estimation for obesity levels is always an important topic in medical field since it can provide useful guidance for people that would like to lose weight or keep fit. The article tries to find a model that can predict obesity and provides people with the information of how to avoid overweight. To be more specific, this article applied dimension reduction to the data set to simplify the data and tried to Figure out a most decisive feature of obesity through Principal Component Analysis (PCA) based on the data set. The article also used some machine learning methods like Support Vector Machine (SVM), Decision Tree to do prediction of obesity and wanted to find the major reason of obesity. In addition, the article uses Artificial Neural Network (ANN) to do prediction which has more powerful feature extraction ability to do this. Finally, the article found that family history of obesity is the most decisive feature, and it may because of obesity may be greatly affected by genes or the family eating diet may have great influence. And both ANN and Decision tree’s accuracy of prediction is higher than 90%.

Fashion is the way we present ourselves which mainly focuses on vision, has attracted great interest from computer vision researchers. It is generally used to search fashion products in online shopping malls to know the descriptive information of the product. The main objectives of our paper is to use deep learning (DL) and machine learning (ML) methods to correctly identify and categorize clothing images. In this work, we used ML algorithms (support vector machines (SVM), K-Nearest Neirghbors (KNN), Decision tree (DT), Random Forest (RF)), DL algorithms (Convolutionnal Neurals Network (CNN), AlexNet, GoogleNet, LeNet, LeNet5) and the transfer learning using a pretrained models (VGG16, MobileNet and RestNet50). We trained and tested our models online using google colaboratory with Tensorflow/Keras and Scikit-Learn libraries that support deep learning and machine learning in Python. The main metric used in our study to evaluate the performance of ML and DL algorithms is the accuracy and matrix confusion. The best result for the ML models is obtained with the use of ANN (88.71%) and for the DL models is obtained for the GoogleNet architecture (93.75%). The results obtained showed that the number of epochs and the depth of the network have an effect in obtaining the best results.

In this paper, stock selection strategy design based on machine learning and multi-factor analysis is a research hotspot in quantitative investment field. Four machine learning algorithms including support vector machine, gradient lifting regression, random forest and linear regression are used to predict the rise and fall of stocks by taking stock fundamentals as input variables. The portfolio strategy is constructed on this basis. Finally, the stock selection strategy is further optimized. The empirical results show that the multifactor quantitative stock selection strategy has a good stock selection effect, and yield performance under the support vector machine algorithm is the best. With the increase of the number of factors, there is an inverse relationship between the fitting degree and the yield under various algorithms.

An IDS is a system that helps in detecting any kind of doubtful activity on a computer network. It is capable of identifying suspicious activities at both the levels i.e. locally at the system level and in transit at the network level. Since, the system does not have its own dataset as a result it is inefficient in identifying unknown attacks. In order to overcome this inefficiency, we make use of ML. ML assists in analysing and categorizing attacks on diverse datasets. In this study, the efficacy of eight machine learning algorithms based on KDD CUP99 is assessed. Based on our implementation and analysis, amongst the eight Algorithms considered here, Support Vector Machine (SVM), Random Forest (RF) and Decision Tree (DT) have the highest testing accuracy of which got SVM does have the highest accuracy

This study develops a framework for personalized care to tackle heart disease risk using an at-home system. The machine learning models used to predict heart disease are Logistic Regression, K - Nearest Neighbor, Support Vector Machine, Naive Bayes, Decision Tree, Random Forest and XG Boost. Timely and efficient detection of heart disease plays an important role in health care. It is essential to detect cardiovascular disease (CVD) at the earliest, consult a specialist doctor before the severity of the disease and start medication. The performance of the proposed model was assessed using the Cleveland Heart Disease dataset from the UCI Machine Learning Repository. Compared to all machine learning algorithms, the Random Forest algorithm shows a better performance accuracy score of 90.16%. The best model may evaluate patient fitness rather than routine hospital visits. The proposed work will reduce the burden on hospitals and help hospitals reach only critical patients.

A good ecological environment is crucial to attracting talents, cultivating talents, retaining talents and making talents fully effective. This study provides a solution to the current mainstream problem of how to deal with excellent employee turnover in advance, so as to promote the sustainable and harmonious human resources ecological environment of enterprises with a shortage of talents.This study obtains open data sets and conducts data preprocessing, model construction and model optimization, and describes a set of enterprise employee turnover prediction models based on RapidMiner workflow. The data preprocessing is completed with the help of the data statistical analysis software IBM SPSS Statistic and RapidMiner.Statistical charts, scatter plots and boxplots for analysis are generated to realize data visualization analysis. Machine learning, model application, performance vector, and cross-validation through RapidMiner's multiple operators and workflows. Model design algorithms include support vector machines, naive Bayes, decision trees, and neural networks. Comparing the performance parameters of the algorithm model from the four aspects of accuracy, precision, recall and F1-score. It is concluded that the performance of the decision tree algorithm model is the highest. The performance evaluation results confirm the effectiveness of this model in sustainable exploring of enterprise employee turnover prediction in human resource management.

With the development of technology, mobile phones are an indispensable part of human life. Factors such as brand, internal memory, wifi, battery power, camera and availability of 4G are now modifying consumers' decisions on buying mobile phones. But people fail to link those factors with the price of mobile phones; in this case, this paper is aimed to figure out the problem by using machine learning algorithms like Support Vector Machine, Decision Tree, K Nearest Neighbors and Naive Bayes to train the mobile phone dataset before making predictions of the price level. We used appropriate algorithms to predict smartphone prices based on accuracy, precision, recall and F1 score. This not only helps customers have a better choice on the mobile phone but also gives advice to businesses selling mobile phones that the way to set reasonable prices with the different features they offer. This idea of predicting prices level will give support to customers to choose mobile phones wisely in the future. The result illustrates that among the 4 classifiers, SVM returns to the most desirable performance with 94.8% of accuracy, 97.3 of F1 score (without feature selection) and 95.5% of accuracy, 97.7% of F1 score (with feature selection).

Nowadays, the MOBA game is the game type with the most audiences and players around the world. Recently, the League of Legends has become an official sport as an e-sport among 37 events in the 2022 Asia Games held in Hangzhou. As the development in the e-sport, analytical skills are also involved in this field. The topic of this research is to use the machine learning approach to analyze the data of the League of Legends and make a prediction about the result of the game. In this research, the method of machine learning is applied to the dataset which records the first 10 minutes in diamond-ranked games. Several popular machine learning (AdaBoost, GradientBoost, RandomForest, ExtraTree, SVM, Naïve Bayes, KNN, LogisticRegression, and DecisionTree) are applied to test the performance by cross-validation. Then several algorithms that outperform others are selected to make a voting classifier to predict the game result. The accuracy of the voting classifier is 72.68%.

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic.

Android malware is continuously evolving at an alarming rate due to the growing vulnerabilities. This demands more effective malware detection methods. This paper presents DynaMalDroid, a dynamic analysis-based framework to detect malicious applications in the Android platform. The proposed framework contains three modules: dynamic analysis, feature engineering, and detection. We utilized the well-known CICMalDroid2020 dataset, and the system calls of apps are extracted through dynamic analysis. We trained our proposed model to recognize malware by selecting features obtained through the feature engineering module. Further, with these selected features, the detection module applies different Machine Learning classifiers like Random Forest, Decision Tree, Logistic Regression, Support Vector Machine, Naïve-Bayes, K-Nearest Neighbour, and AdaBoost, to recognize whether an application is malicious or not. The experiments have shown that several classifiers have demonstrated excellent performance and have an accuracy of up to 99%. The models with Support Vector Machine and AdaBoost classifiers have provided better detection accuracy of 99.3% and 99.5%, respectively.

Artificial intelligence creation comes into fashion and has brought unprecedented challenges to intellectual property law. In order to study the viewpoints of AI creation copyright ownership from professionals in different institutions, taking the papers of AI creation on CNKI from 2016 to 2021, we applied orthogonal design and analysis of variance method to construct the dataset. A kernel-SVM classifier with different kernel methods in addition to some shallow machine learning classifiers are selected in analyzing and predicting the copyright ownership of AI creation. Support vector machine (svm) is widely used in statistics and the performance of SVM method is closely related to the choice of the kernel function. SVM with RBF kernel surpasses the other seven kernel-SVM classifiers and five shallow classifier, although the accuracy provided by all of them was not satisfactory. Various performance metrics such as accuracy, F1-score are used to evaluate the performance of KSVM and other classifiers. The purpose of this study is to explore the overall viewpoints of AI creation copyright ownership, investigate the influence of different features on the final copyright ownership and predict the most likely viewpoint in the future. And it will encourage investors, researchers and promote intellectual property protection in China.

A malicious insider threat is more vulnerable to an organization. It is necessary to detect the malicious insider because of its huge impact to an organization. The occurrence of a malicious insider threat is less but quite destructive. So, the major focus of this paper is to detect the malicious insider threat in an organization. The traditional insider threat detection algorithm is not suitable for real time insider threat detection. A supervised learning-based anomaly detection technique is used to classify, predict and detect the malicious and non-malicious activity based on highest level of anomaly score. In this paper, a framework is proposed to detect the malicious insider threat using supervised learning-based anomaly detection. It is used to detect the malicious insider threat activity using One-Class Support Vector Machine (OCSVM). The experimental results shows that the proposed framework using OCSVM performs well and detects the malicious insider who obtain huge anomaly score than a normal user.

An Industrial Control System (ICS) is essential in monitoring and controlling critical infrastructures such as safety and security. Internet of Things (IoT) in ICSs allows cyber-criminals to utilize systems' vulnerabilities towards deploying cyber-attacks. To distinguish risks and keep an eye on malicious activity in networking systems, An Intrusion Detection System (IDS) is essential. IDS shall be used by system admins to identify unwanted accesses by attackers in various industries. It is now a necessary component of each organization's security governance. The main objective of this intended work is to establish a deep learning-depended intrusion detection system that can quickly identify intrusions and other unwanted behaviors that have the potential to interfere with networking systems. The work in this paper uses One Hot encoder for preprocessing and the Auto encoder for feature extraction. On KDD99 CUP, a data - set for network intruding, we categorize the normal and abnormal data applying a Deep Convolutional Neural Network (DCNN), a deep learning-based methodology. The experimental findings demonstrate that, in comparison with SVM linear Kernel model, SVM RBF Kernel model, the suggested deep learning model operates better.

Without security, any network system loses its efficiency, reliability, and resilience. With the huge integration of the ICT capabilities, the Electric Vehicle (EV) as a transportation form in cities is becoming more and more affordable and able to reply to citizen and environmental expectations. However, the EV vulnerability to cyber-attacks is increasing which intensifies its negative impact on societies. This paper targets the cybersecurity issues for Connected Electric Vehicles (CEVs) in parking lots where a peer-to-peer(P2P) energy transaction system is launched. A False Data Injection Attack (FDIA) on the electricity price signal is considered and a Machine Learning/SVM classification protocol is used to detect and extract the right values. Simulation results are conducted to prove the effectiveness of this proposed model.

To reduce the false negative rate in power data anomaly detection, enhance the overall detection accuracy and reliability, and create a more stable data detection environment, this paper designs a power big data anomaly detection method based on improved support vector machine technology. The abnormal features are extracted in advance, combined with the changes of power data, the multi-target anomaly detection nodes are laid, and on this basis, the improved support vector machine anomaly detection model is constructed. The anomaly detection is realized by combining the normalization processing of the equivalent vector. The final test results show that compared with the traditional clustering algorithm big data anomaly detection test group and the traditional multi-domain feature extraction big data anomaly detection test group, the final false negative rate of the improved support vector machine big data exception detection test group designed in this paper is only 2.04, which shows that the effect of the anomaly detection method is better. It is more accurate and reliable for testing in a complex power environment and has practical application value.

With the variety of cloud services, the cloud service provider delivers the machine learning service, which is used in many applications, including risk assessment, product recommen-dation, and image recognition. The cloud service provider initiates a protocol for the classification service to enable the data owners to request an evaluation of their data. The owners may not entirely rely on the cloud environment as the third parties manage it. However, protecting data privacy while sharing it is a significant challenge. A novel privacy-preserving model is proposed, which is based on differential privacy and machine learning approaches. The proposed model allows the various data owners for storage, sharing, and utilization in the cloud environment. The experiments are conducted on Blood transfusion service center, Phoneme, and Wilt datasets to lay down the proposed model's efficiency in accuracy, precision, recall, and Fl-score terms. The results exhibit that the proposed model specifies high accuracy, precision, recall, and Fl-score up to 97.72%, 98.04%, 97.72%, and 98.80%, respectively.

Ensuring high data availability while realizing privacy protection is a research hotspot in the field of privacy-preserving data publishing. In view of the instability of data availability in the existing differential privacy high-dimensional data publishing methods based on Bayesian networks, this paper proposes an improved MEPrivBayes privacy-preserving data publishing method, which is mainly improved from two aspects. Firstly, in view of the structural instability caused by the random selection of Bayesian first nodes, this paper proposes a method of first node selection and Bayesian network construction based on the Maximum Information Coefficient Matrix. Then, this paper proposes a privacy budget elastic allocation algorithm: on the basis of pre-setting differential privacy budget coefficients for all branch nodes and all leaf nodes in Bayesian network, the influence of branch nodes on their child nodes and the average correlation degree between leaf nodes and all other nodes are calculated, then get a privacy budget strategy. The SVM multi-classifier is constructed with privacy preserving data as training data set, and the original data set is used as input to evaluate the prediction accuracy in this paper. The experimental results show that the MEPrivBayes method proposed in this paper has higher data availability than the classical PrivBayes method. Especially when the privacy budget is small (noise is large), the availability of the data published by MEPrivBayes decreases less.

One of the major threats in the cyber security and networking world is a Distributed Denial of Service (DDoS) attack. With massive development in Science and Technology, the privacy and security of various organizations are concerned. Computer Intrusion and DDoS attacks have always been a significant issue in networked environments. DDoS attacks result in non-availability of services to the end-users. It interrupts regular traffic flow and causes a flood of flooded packets, causing the system to crash. This research presents a Machine Learning-based DDoS attack detection system to overcome this challenge. For the training and testing purpose, we have used the NSL-KDD Dataset. Logistic Regression Classifier, Support Vector Machine, K Nearest Neighbour, and Decision Tree Classifier are examples of machine learning algorithms which we have used to train our model. The accuracy gained are 90.4, 90.36, 89.15 and 82.28 respectively. We have added a feature called BOTNET Prevention, which scans for Phishing URLs and prevents a healthy device from being a part of the botnet.

Missing values are an unavoidable problem for classification tasks of machine learning in medical data. With the rapid development of the medical system, large scale medical data is increasing. Missing values increase the difficulty of mining hidden but useful information in these medical datasets. Deletion and imputation methods are the most popular methods for dealing with missing values. Existing studies ignored to compare and discuss the deletion and imputation methods of missing values under the row missing rate and the total missing rate. Meanwhile, they rarely used experiment data sets that are mixed-type and large scale. In this work, medical data sets of various sizes and mixed-type are used. At the same time, performance differences of deletion and imputation methods are compared under the MCAR (Missing Completely At Random) mechanism in the baseline task using LR (Linear Regression) and SVM (Support Vector Machine) classifiers for classification with the same row and total missing rates. Experimental results show that under the MCAR missing mechanism, the performance of two types of processing methods is related to the size of datasets and missing rates. As the increasing of missing rate, the performance of two types for processing missing values decreases, but the deletion method decreases faster, and the imputation methods based on machine learning have more stable and better classification performance on average. In addition, small data sets are easily affected by processing methods of missing values.

Cognitive radio (CR) networks are an emerging and promising technology to improve the utilization of vacant bands. In CR networks, security is a very noteworthy domain. Two threatening attacks are primary user emulation (PUE) and spectrum sensing data falsification (SSDF). A PUE attacker mimics the primary user signals to deceive the legitimate secondary users. The SSDF attacker falsifies its observations to misguide the fusion center to make a wrong decision about the status of the primary user. In this paper, we propose a scheme based on clustering the secondary users to counter SSDF attacks. Our focus is on detecting and classifying each cluster as reliable or unreliable. We introduce two different methods using an artificial neural network (ANN) for both methods and five more classifiers such as support vector machine (SVM), random forest (RF), K-nearest neighbors (KNN), logistic regression (LR), and decision tree (DR) for the second one to achieve this goal. Moreover, we consider deterministic and stochastic scenarios with white Gaussian noise (WGN) for attack strategy. Results demonstrate that our method outperforms a recently suggested scheme.

In this cyber era, the number of cybercrime problems grows significantly, impacting network communication security. Some factors have been identified, such as malware. It is a malicious code attack that is harmful. On the other hand, a botnet can exploit malware to threaten whole computer networks. Therefore, it needs to be handled appropriately. Several botnet activity detection models have been developed using a classification approach in previous studies. However, it has not been analyzed about selecting features to be used in the learning process of the classification algorithm. In fact, the number and selection of features implemented can affect the detection accuracy of the classification algorithm. This paper proposes an analysis technique for determining the number and selection of features developed based on previous research. It aims to obtain the analysis of using features. The experiment has been conducted using several classification algorithms, namely Decision tree, k-NN, Naïve Bayes, Random Forest, and Support Vector Machine (SVM). The results show that taking a certain number of features increases the detection accuracy. Compared with previous studies, the results obtained show that the average detection accuracy of 98.34% using four features has the highest value from the previous study, 97.46% using 11 features. These results indicate that the selection of the correct number and features affects the performance of the botnet detection model.

The internet has developed and transformed the world dramatically in recent years, which has resulted in several cyberattacks. Cybersecurity is one of society’s most serious challenge, costing millions of dollars every year. The research presented here will look into this area, focusing on malware that can establish botnets, and in particular, detecting connections made by infected workstations connecting with the attacker’s machine. In recent years, the frequency of network security incidents has risen dramatically. Botnets have previously been widely used by attackers to carry out a variety of malicious activities, such as compromising machines to monitor their activities by installing a keylogger or sniffing traffic, launching Distributed Denial of Service (DDOS) attacks, stealing the identity of the machine or credentials, and even exfiltrating data from the user’s computer. Botnet detection is still a work in progress because no one approach exists that can detect a botnet’s whole ecosystem. A detailed analysis of a botnet, discuss numerous parameter’s result of detection methods related to botnet attacks, as well as existing work of botnet identification in field of machine learning are discuss here. This paper focuses on the comparative analysis of various classifier based on design of botnet detection technique which are able to detect P2P botnet using machine learning classifier.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is a widely used technology to distinguish real users and automated users such as bots. However, the advance of AI technologies weakens many CAPTCHA tests and can induce security concerns. In this paper, we propose a user-friendly text-based CAPTCHA generation method named Robust Text CAPTCHA (RTC). At the first stage, the foregrounds and backgrounds are constructed with font and background images respectively sampled from font and image libraries, and they are then synthesized into identifiable pseudo adversarial CAPTCHAs. At the second stage, we utilize a highly transferable adversarial attack designed for text CAPTCHAs to better obstruct CAPTCHA solvers. Our experiments cover comprehensive models including shallow models such as KNN, SVM and random forest, as well as various deep neural networks and OCR models. Experiments show that our CAPTCHAs have a failure rate lower than one millionth in general and high usability. They are also robust against various defensive techniques that attackers may employ, including adversarially trained CAPTCHA solvers and solvers trained with collected RTCs using manual annotation. Codes available at https://github.com/RulinShao/RTC.

The big data platform based on cloud computing realizes the storage, analysis and processing of massive data, and provides users with more efficient, accurate and intelligent Internet services. Combined with the characteristics of college teaching resource sharing platform based on cloud computing mode, the multi-faceted security defense strategy of the platform is studied from security management, security inspection and technical means. In the detection module, the optimization of the support vector machine is realized, the detection period is determined, the DDoS data traffic characteristics are extracted, and the source ID blacklist is established; the triggering of the defense mechanism in the defense module, the construction of the forwarder forwarding queue and the forwarder forwarding capability are realized. Reallocation.

To detect human behaviour and measure accuracy of classification rate. Materials and Methods: A novel deep belief network with sample size 10 and support vector machine with sample size of 10. It was iterated at different times predicting the accuracy percentage of human behaviour. Results: Human behaviour detection utilizing novel deep belief network 87.9% accuracy compared with support vector machine 87.0% accuracy. Deep belief networks seem to perform essentially better compared to support vector machines \$(\textbackslashmathrmp=0.55)(\textbackslashtextPiˆ0.05)\$. The deep belief algorithm in computer vision appears to perform significantly better than the support vector machine algorithm. Conclusion: Within this human behaviour detection novel deep belief network has more precision than support vector machine.