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Data mining for healthcare is an interdisciplinary field of study that originated in database statistics and is useful in examining the effectiveness of medical therapies. Machine learning and data visualization Diabetes-related heart disease is a kind of heart disease that affects diabetics. Diabetes is a chronic condition that occurs when the pancreas fails to produce enough insulin or when the body fails to properly use the insulin that is produced. Heart disease, often known as cardiovascular disease, refers to a set of conditions that affect the heart or blood vessels. Despite the fact that various data mining classification algorithms exist for predicting heart disease, there is inadequate data for predicting heart disease in a diabetic individual. Because the decision tree model consistently beat the naive Bayes and support vector machine models, we fine-tuned it for best performance in forecasting the likelihood of heart disease in diabetes individuals.

This paper is a unique effort to resolve the scaling issues of machine learning using the multi-node environment of Big Data. For this purpose, it incorporates the concept of machine learning with big data. Machine learning is a branch of Artificial Intelligence which trains the computers to learn without being explicitly programmed. Machine learning works on the development of computer programs and software that work according to the input dataset. In machine learning, classification is used to identify the class of instances. In this the category or class or a group provides the new observation to which it belongs on the basis of the training dataset. Training datasets contain those instances whose class is already known. An algorithm that implements classification is known as a classifier. The main aim of this paper is to evaluate how different classifiers work on two different datasets, one is a real time dataset of MP Election nomination (2018), while the other is a standard Breast Cancer dataset, released by University of Wisconsin (1995). There are various classifiers available like Decision Trees, K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Naive Bayes etc. Different classifiers work differently on different datasets, giving different accuracy according to the kind and the size of the dataset. So we have used different classifiers for our work. When we were applying machine learning classifier on a real-time dataset, i.e. MPSEC dataset, Accuracy goes down. Similarly, these classifiers apply to Standard Breast Cancer dataset then results show outstanding accuracy. This research work shows the exciting patterns to study the case of a real-time and standard dataset using different machine learning classifiers with a combination of big data. This research work also incorporates the concept of parallelization using a multi-nodes environment which is able to deal with scalability issues.

One of the chronic diseases is Diabetes, Diabetes is a metabolic disorder category caused by continued high levels of blood sugar. It is regarded as one of the most deadly diseases in the world. If accurate early prediction is possible, Diabetes severity and risk factor can be significantly lowered. Machine learning has become more popular in the medical community as a result of its ascent, and in the field of diseases in particular. In this paper, we propose a model that can predict where the patient has or hasn't have diabetes. Our model is based on the prediction precision of certain powerful machine learning (ML) algorithms based on different measures such as precision, recall, and F1-measure. The Pima Indian Diabetes (PIDD) dataset has been used, that can predict diabetic onset based on diagnostics manner. The results we obtained using Logistic Regression (LR), Naive Bayes (NB), and K-nearest Neighbor (KNN) algorithms were 94%, 79%, and 69% respectively. The results show that LR is more efficient at predicting diabetes Compared to other algorithms.

Distributed Denial of service (DDoS) attacks is an enormous threat to today’s cyber world, cyber networks are compromised by the attackers to distribute attacks in a large volume by denying the service to legitimate users. The toughest and challenging task in today’s network and network security engineers is to identify compromised traffic (attacked) and legitimate (normal) traffic. The main goal of the paper is to detect DDos attacks using classification algorithms. To achieve the goal the proposed system uses attacking tool to initiate attacks using Loic attacking tool with the data set extracted from open source tool Wireshark and transferring the dataset to apache Spark for detection analysis. The system also uses Apache spark machine learning algorithms (MLib), classification algorithms to classify the dataset. We use Naive Bayes, KNN and Random forest classification algorithms to classify normal traffic and attacked traffic. Our system is capable of detecting attacks with respect to any traffic protocols ICMP, TCP, or UDP. The accuracy of detection is compared on three classification algorithms and noted that random forest gives the accuracy of 96.75%.

Objective The initial principal task of a Brain-Computer Interfacing (BCI) research is to extract the best feature set from a raw EEG (Electroencephalogram) signal so that it can be used for the classification of two or multiple different events. The main goal of the paper is to develop a comparative analysis among different feature extraction techniques and classification algorithms. Materials and methods In this present investigation, four different methodologies have been adopted to classify the recorded MI (motor imagery) EEG signal, and their comparative study has been reported. Haar Wavelet Energy (HWE), Band Power, Cross-correlation, and Spectral Entropy (SE) based Cross-correlation feature extraction techniques have been considered to obtain the necessary features set from the raw EEG signals. Four different machine learning algorithms, viz. LDA (Linear Discriminant Analysis), QDA (Quadratic Discriminant Analysis), Naive Bayes, and Decision Tree, have been used to classify the features. Results The best average classification accuracies are 92.50%, 93.12%, 72.26%, and 98.71% using the four methods. Further, these results have been compared with some recent existing methods. Conclusion The comparative results indicate a significant accuracy level performance improvement of the proposed methods with respect to the existing one. Hence, this presented work can guide to select the best feature extraction method and the classifier algorithm for MI-based EEG signals.

The unprecedented growth of mobile technology has generated an increase in malware and raised concerns over malware threats. Different approaches have been adopted to overcome the malware attacks yet this spread is still increasing. To combat this issue, this study proposes an Android malware detection system based on permission features using Bayesian classification. The permission features were extracted via the static analysis technique. The 10,000 samples for the judgement were obtained from AndroZoo and Drebin databases. The experiment was then conducted using two algorithms for feature selection: information gain and chi-square. The best accuracy rate of detection of permission features achieved was 91.1%.

Deep learning has been proved to be one of the most effective method in feature encoding for different intelligent applications such as video based human action recognition. However, its non-convex optimization mechanism leads large memory consumption, which hinders its deployment on the smart embedded systems with limited computation resources. To overcome this challenge, we propose a novel deep model compression technique for smart embedded systems, which realizes both the memory size reduction and inference complexity decrease within a small drop of accuracy. First, we propose an improved naive Bayes inference based channel parameter pruning to obtain a sparse model with higher accuracy. Then, to improve the inference efficiency, the improved Tucker decomposition method is proposed, where an improved genetic algorithm is used to optimize the Tucker ranks. Finally, to elevate effectiveness of our proposed method, extensive experiments are conducted. The experimental results show that our method can achieve the state-of-the-art performance compared with existing methods in terms of accuracy, parameter compression and floating point operations reduction.

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naive Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.

Non-response is unavoidable in any disaggregate-level survey efforts where information is sought from individuals. There is a strong need to understand the contributing factors of non-response behavior in freight surveys as there is very little information on what sort of establishments respond positively to survey inquiries. To meet this research gap, this paper analyzes the non-response behavior in an establishment-based freight survey (EBFS) conducted in Kerala, India. The analysis involves usage of five prominent classifiers: classification trees, random forests, Naive Bayes classifier, logistic regression and K nearest neighbors. A closer look at the results revealed that the industrial classes handling commodities with high value density (machinery, electronics and electrical equipment, other manufacturing products, textiles) exhibit high non-response probability for freight surveys. This may be explained in terms of the limited logistic information shared by establishments handling high valued products due to less attention given to logistics strategies. Another reason could be that the amount of capital and opportunity costs tied up with these commodities are high, and therefore, their information tend to be more proprietary in nature. The analysis also reveals that the location of establishment plays a modest, yet, noticeable effect on non-response behavior. The establishments from cities located closer to Ports with high per-capita income are less likely to respond to survey requests, possibly due to the higher opportunity cost for time and increased concerns of intrusion into privacy. Finally, the comparison of ROC curves suggests that KNN algorithm is most suitable for modelling non-response behavior. These models are expected to be useful for developing sample weighting schemes and targeted incentive strategies that can improve the state of practice of freight data collection in middle-income countries like India. The study findings are expected to be useful for developing more elaborate and dynamically responsive survey designs for EBFS, in which sample recruitment strategies can be adapted real time during data collection. This study also informs policymakers that the apparent trends in non-response, if not arrested, are likely to weaken the validity of inferences drawn from estimates based on EBFS and undermine, perhaps fatally, the potential of EBFS to guide facility planning and policy interventions.

This article introduces ILA4: A new algorithm designed to handle datasets with missing values. ILA4 is inspired by a series of ILA algorithms which also handle missing data with further enhancements. ILA4 is applied to datasets with varying completeness and also compared to other, known approaches for handling datasets with missing values. In the majority of cases, ILA4 produced favorable performance that is on a par with many established approaches for treating missing values including algorithms that are based on the Most Common Value (MCV), the Most Common Value Restricted to a Concept (MCVRC), and those that utilize the Delete strategy. ILA4 was also compared with three known algorithms namely: Logistic Regression, Naive Bayes, and Random Forest; the accuracy obtained by ILA4 is comparable or better than the best results obtained from these three algorithms.

Copy–Move Forgery Detection (CMFD) helps to detect copied and pasted areas in one image. It plays a crucial role in legal evidence, forensic investigation, defence, and many more places. In the proposed CMFD method, a two-step identification of forgery is presented. In step one, the suspected image will be classified into either one of two classes that are forged or authentic. Step two is carried out​ only if the suspected is classified as forged, then forged location will be identified using the block-matching procedure. Initially, the suspected image is decomposed into different orientations using Steerable Pyramid Transform (SPT); Grey Level Co-occurrence Matrix (GLCM) features are extracted from each orientation. These features are used to train Optimized Support Vector Machine (OSVM) as well as to classify. If the suspected image is categorized into forged, then the suspected grey image is converted into overlapping blocks, and from each block, GLCM features are extracted. The proper similarity threshold value and distance threshold value can locate the forged region using GLCM block features. The performance of the proposed method is tested using standard datasets CoMoFoD and CASIA Datasets. The proposed CMFD approach results are consistent, even the forged image suffered from attacks like JPEG compression, scaling, and rotation. The OSVM classifier is showing superiority over the Optimized Naive Bayes Classifier (ONBC), Extreme Learning Machine (ELM) and Support Vector Machine (SVM).

Social media is an indispensable necessity for modern life. As a result, it is full of people’s opinions, emotions, ideas, and attitudes, whether positive or negative. This abundance of views creates many opportunities for applying sentiment analysis to the education sector, which reflects how countries and cultures develop. In this research, a real-world Twitter dataset was collected, containing approximately 8144 tweets related to Qassim University, Saudi Arabia. The main aim of this experimental study was to explore the possibility of using a one-way analysis of variance (ANOVA) as a feature selection method to considerably reduce the number of features when classifying opinions conveyed through Arabic tweets. The primary motivation for this research was that no previous studies had examined one-way ANOVA comprehensively to tackle the curse of dimensionality and to enhance classification performance in sentiment analysis for Arabic tweets. Therefore, various experiments were conducted to investigate the effects of one-way ANOVA and to select important features concerning the performance of different supervised machine learning classifiers. Support Vector Machine and Naive Bayes achieved the best results with one-way ANOVA as compared to the baseline experimental results in the collected dataset. Furthermore, the differences between all results have been statistically analyzed in this study. As further evidence, one-way ANOVA with Support Vector Machine represented an excellent combination across different Arabic benchmark datasets, with its results outperforming other studies.

Skin cancer is one of the most common and dangerous cancer that exists worldwide. Malignant melanoma is one of the most dangerous skin cancer types has a high mortality rate. An estimated 196,060 melanoma cases will be diagnosed in 2020 in the USA. Many computerized techniques are presented in the past to diagnose skin lesions, but they are still failing to achieve significant accuracy. To improve the existing accuracy, we proposed a hierarchical framework based on two-dimensional superpixels and deep learning. First, we enhance the contrast of original dermoscopy images by fusing local and global enhanced images. The entire enhanced images are utilized in the next step to segmentation skin lesions using three-step superpixel lesion segmentation. The segmented lesions are mapped over the whole enhanced dermoscopy images and obtained only segmented color images. Then, a deep learning model (ResNet-50) is applied to these mapped images and learned features through transfer learning. The extracted features are further optimized using an improved grasshopper optimization algorithm, which is later classified through the Naïve Bayes classifier. The proposed hierarchical method has been evaluated on three datasets (Ph2, ISBI2016, and HAM1000), consisting of three, two, and seven skin cancer classes. On these datasets, our method achieved an accuracy of 95.40%, 91.1%, and 85.50%, respectively. The results show that this method can be helpful for the classification of skin cancer with improved accuracy.

Homomorphic encryption (HE) schemes, such as fully homomorphic encryption (FHE), support a number of useful computations on ciphertext in a broad range of applications, such as e-voting, private information retrieval, cloud security, and privacy protection. While FHE schemes do not require any interaction during computation, the key limitations are large ciphertext expansion and inefficiency. Thus, to overcome these limitations, we develop a novel cryptographic tool, MAS-Encryption (MASE), to support real-value input and secure computation on the multiply-add structure. The multiply-add structures exist in many important protocols, such as classifiers and outsourced protocols, and we will explain how MASE can be used to protect the privacy of these protocols, using two case study examples. Specifically, the first case study example is the privacy-preserving Naive Bayes classifier that can achieve minimal Bayes risk, and the other example is the privacy-preserving support vector machine. We prove that the constructed classifiers are secure and evaluate their performance using real-world datasets. Experiments show that our proposed MASE scheme and MASE based classifiers are efficient, in the sense that we achieve an optimal tradeoff between computation efficiency and communication interactions. Thus, we avoid the inefficiency of FHE based paradigm.

Women worldwide are afflicted by breast cancer, a widely occuring health issue that causes a large number of deaths. This paper aims to review and present several approaches to identify breast cancers using ML algorithms and Biosensors. The objective is to investigate the application of multiple algorithms based on Machine Learning approach and biosensors for early breast cancer detection. Biosensors and machine learning are needed to identify cancers based on microscopic images, that is why automation is needed. ML aims to make computers capable of self-learning. Rather than relying on explicit pre-programmed rules and models, it is based on identifying patterns in observed data and building models to predict outcomes. We have compared and analyzed various types of algorithms like fuzzy ELM-RBF, SVM, SVR, RVM, Naive Bayes, K-NN, DT, ANN, BPNN and Random forest over varieties of databases like images digitized from fine needle aspirations (FNAs) of breast masses, Scanned film mammography, Breast infrared images, MR Images, Data collected by using the blood analyses, and Histopathology image samples. The result is compared on performance metric elements like accuracy, precision and recall. Further, we have used Biosensors to determine the presence of a specific biological analyte by transforming the cellular constituents of proteins, DNA, or RNA into electrical signals that can be detected and analyzed. Here, we have compared detection of different types of analyte like HER2, miRNA 21, miRNA 155, MCF-7 cells, DNA, BRCA1, BRCA2, human tears and saliva using different types of biosensors like FET, Electrochemical, Sandwich electrochemical, etc. Several biosensors use a different type of specification which is also discussed. The result of which is analysed on the basis of detection limit, linear ranges and response time. Different studies and related articles were ultimately reviewed and analyzed systematically and the ones published from 2010 to 2021 were considered. Biosensors and machine learning both have the potential to detect breast cancer in a fast and effective way.

In this study, a high accident risk prediction model is developed to analyze traffic accident data, and identify priority intersections for improvement. A database of the traffic accidents was organized and analyzed, and an intersection accident risk prediction model based on different mechanical learning methods was created to estimate the possible high accident risk locations for traffic management departments to use in planning countermeasures to reduce accident risk. Using Bayes' theorem to identify environmental variables at intersections that affect accident risk levels, this study found that road width, speed limit and roadside markings are the significant risk factors for traffic accidents. Meanwhile, Naive Bayes, Decision tree C4.5, Bayesian Network, Multilayer perceptron (MLP), Deep Neural Networks (DNN), Deep Belief Network (DBN) and Convolutional Neural Network (CNN) were used to develop an accident risk prediction model. This model can also identify the key factors that affect the occurrence of high-risk intersections, and provide traffic management departments with a better basis for decision-making for intersection improvement. Using the same environmental characteristics as high-risk intersections for model inputs to estimate the degree of risk that may occur in the future, which can be used to prevent traffic accidents in the future. Moreover, it also can be used as a reference for future intersection design and environmental improvements.

The electroencephalogram (EEG) has been commonly used to identify epileptic seizures, but identification of seizures from EEG remains a challenging task that requires qualified neurophysiologists. It is important to detect seizures in real time, which can be achieved in an internet of things (IoT)-based cloud platform to alert patients of impending seizures. Therefore, in this study, we propose a new model to recognize seizure states (e.g., ictal, preictal, interictal) from EEG in the IoT framework to monitor patients remotely. The proposed model uses an efficient hybrid genetic whale optimization algorithm (GWOA) based on naive Bayes (NB-GWOA) for feature selection, and an adaptive extreme learning machine (ELM) based on a differential evolutionary (DE) algorithm (DEELM) for classification. In the NB-GWOA method, the genetic algorithm serves to enhance the exploitation of the whale optimization algorithm in the search of the optimal solutions, while the naive Bayes method is used to determine a fitness function to assess every agent in the search space. GWOA has strong robustness and is capable of finding the best solutions in less than five iterations, so it is suitable for selecting discriminative features from a huge number of neurofeatures obtained from EEG. Further, the classification model is constructed based on ELM, which uses the DE algorithm for a fast and efficient learning solution. Results show that the proposed NB-GWOA-DEELM model can avoid over- and under-fitting and can provide better and more accurate performance in classifying seizure states from EEG than its competitors.

Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Image analysis demonstrated the difficulty of processing the captured image under variant illumination in natural environment and with textured objects of non-ideal geometric shapes. However, this is subjected to certain consideration settings and image-processing algorithms. The study presents an automatic method for identifying and recognising the pineapple’s crown images in the designated plot using image processing and further counts the detected images using machine learning classifiers namely artificial neural network (ANN), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision trees (DT) and k-nearest neighbours (KNN). The high spatial-resolution aerial images were pre-processed and segmented, and its extracted features were analysed according to shape, colour and texture for recognising the pineapple crown before classifying it as fruit or non-fruit. Feature fusion using one-way analysis of variance (ANOVA) was incorporated in this study to optimise the performance of machine learning classifier. The algorithm was quantitatively analysed and validated for performance via accuracy, specificity, sensitivity and precision. The detection for the pineapple’s crown images with ANN-GDX classification has demonstrated best performance fruit counting with accuracy of 94.4% and has thus demonstrated clear potential application of an effective RGB images analysis for the pineapple industry.

Nowadays, many anti-phishing systems are being developed to identify phishing contents in online communication systems. Despite the availability of myriads anti-phishing systems, phishing continues unabated due to inadequate detection of a zero-day attack, superfluous computational overhead and high false rates. Although Machine Learning approaches have achieved promising accuracy rate, the choice and the performance of the feature vector limit their effective detection. In this work, an enhanced machine learning-based predictive model is proposed to improve the efficiency of anti-phishing schemes. The predictive model consists of Feature Selection Module which is used for the construction of an effective feature vector. These features are extracted from the URL, webpage properties and webpage behaviour using the incremental component-based system to present the resultant feature vector to the predictive model. The proposed system uses Support Vector Machine and Naive Bayes which have been trained on a 15-dimensional feature set. The experiments were based on datasets consisting of 2541 phishing instances and 2500 benign instances. Using 10-fold cross-validation, the experimental results indicate a remarkable performance with 0.04% False Positive and 99.96% accuracy for both SVM and NB predictive models.

The rapid development of Information-Centric Wireless Sensor Network (ICWSN) has solved the challenges of information transmission and processing caused by the accelerated growth of wearable devices and the wide deployment of the Internet of Things (IoT) recently. The privacy security is also a growing problem. The existing works use earphones, covert and user-friendly wearable devices, for user authentication. However, some of the earphone-based authentication solutions need to customize special earphones, which are not universal. Other solutions use microphones and speakers of earphones for authentication, which are susceptible to changes in the auricle’s internal environment, resulting in a decline in performance. To solve this problem, a new authentication solution based on the existing commercial earphones is proposed to authenticate a user by tapping on the earphone rhythmically. This rhythmic tap behaviour causes a change of the signal waveform of the built-in accelerometer in the earphone. Based on this, we design a pipeline to authenticate the user’s identity. We first design an event detection algorithm to segment the tap signal accurately. Then we use the global features calculated based on event detection algorithm and local features extracted from the Convolutional Neural Network (CNN) for building an authentication model using the Naive Bayes (NB) classifier. Finally, 20 users are recruited to evaluate the experiment, and the recognition accuracy reaches 98%. Moreover, we extend the experiment to prove that it has a good performance against the different attacks and is robust in different scenarios.

Social media is one of the technological developments that has contributed greatly in making it easy for us to communicate and socialize, one of which is using Twitter social media. Twitter in this study is used as a data source to analyze tweets discussing KRI Nanggala 402. Analysis of KRI Nanggala 402 twitter sentiment is used to see the tendency of public responses to the sinking of the KRI Nanggala 402 submarine whether to give positive or negative opinions. This Sentiment analysis uses the Naïve Bayes Classifier method, which is a classification method. The first research stage is crawling, processing, classification, and evaluation. The classification stage is carried out after the processing phase, where the classification results tend to be positive or negative, using the Naïve Bayes Classifier method. The accuracy of the system in the Sentiment analysis of the KRI Nanggala 402 tweet is 73.00%.

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied. The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naive bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms.

A growing body of literature has emerged that demonstrates the potential of neurogaming platforms for inter- facing with well-known video games. With the recent convergence of advances in consumer electronics, ubiquitous computing, and wearable sensor technologies real-time monitoring of neurocognitive and affective states can be studied in an objective manner. Whilst establishing the optimal relation among frequency bands, task engagement, and arousal states is a goal of neurogaming, a standardized method has yet to be established. Herein we aimed to test classifiers within the same context, group of participants, feature extraction methods, and protocol. Given the emphasis upon neurogaming, a commercial-grade electroencephalographic (EEG; Emotiv EPOC) headset was used to collect signals from 30 participants. The EEG data was then filtered to get separate frequency bands to train cognitive-affective classifiers with three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Results revealed that the NB classifier was the most robust classifier for identifying negative (e.g., character death) game-based events. The identification of general gameplay events is best identified using kNN and the Beta band. Results from this study suggest that a combination of classifiers is preferable over selection of a single classifier.

Determination of lithofacies is one of the most important steps for reservoir characterization. Well log curves are not always sufficient to determine lithology as some times the signals are similar for different lithologies. We believe that the sequence of sedimentary patterns that follows the general geologic rules can be essential to help this disambiguation. This work aims to present a computational system based on deep recurrent neural networks (RNNs) as an effective method to automatically identify lithofacies patterns from well logs. We show that bidirectional long-short-term memory (BiLSTM) RNNs can learn long-term dependencies between time steps of sequence data improving the context available to facies classification. We validated our method by applying it to a real case study from Rio Bonito Formation (Parana Basin, Brazil) and the proposed method is compared with XGBoost, Random Forest, Naive Bayes, and support vector machine (SVM) learning approaches. The results indicate that the performance of the proposed method for lithology identification is higher when compared with these other methods.

The methods of Bayesian predictive inference and variation index are vital in the constant false alarm rate (CFAR) detection, which adaptively predicts and evaluates the background level to achieve target detection. However, in complex scenarios, the interference effect can lead to imprecise prediction and complex calculations of the background level, which results in severely degraded CFAR performance. To solve the above situation, a robust variation index CFAR detector based on Bayesian interference control (BVI-CFAR) is proposed. The clutter range profile (CRP) is equally divided and the decision-making selection is optimized to dynamically evaluate the background clutter level in this algorithm; meanwhile, the clutter level will feedback control the former. Then, based on the Bayesian interference control theory, the Bayesian classification interference control idea is proposed to simultaneously predict inference multiple targets in the background. Therefore, the anti-interference ability of the detector is improved while reducing the computational complexity. The false alarm rate and decision expression are given, and the detection is extended when the segmentation and interference are arbitrary. Both the simulation results and the field test results verify the favorable performance of the algorithm in complex scenarios.

Type 2 Diabetes is a fast-growing, chronic metabolic disorder due to imbalanced insulin activity. As lots of people are suffering from it, access to proper treatment is necessary to control the problem. Most patients are unaware of health complexity, symptoms and risk factors before diabetes. The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with a high accuracy, in order to identify and treat diabetes patients at an early age. Our training and test dataset is an accumulation of 9483 diabetes patients’ information. The training dataset is large enough to negate overfitting and provide for highly accurate test performance. We use performance measures such as accuracy and precision to find out the best algorithm deep ANN which outperforms with 95.14% accuracy among all other tested machine learning classifiers. We hope our high performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naive Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.

BACKGROUND The heterogeneity of B-type natriuretic peptide (BNP) levels among individuals with heart failure and preserved ejection fraction (HFpEF) makes predicting the development of cardiac events difficult. This study aimed at creating high-performance Naive Bayes (NB) classifiers, beyond BNP, to predict the development of cardiac events over a 3-year period in individual outpatients with HFpEF.Methods and Results:We retrospectively enrolled 234 outpatients with HFpEF who were followed up for 3 years. Parameters with a coefficient of association ≥0.1 for cardiac events were applied as features of classifiers. We used the step forward method to find a high-performance model with the maximum area under the receiver operating characteristics curve (AUC). A 10-fold cross-validation method was used to validate the generalization performance of the classifiers. The mean kappa statistics, AUC, sensitivity, specificity, and accuracy were evaluated and compared between classifiers learning multiple factors and only the BNP. Kappa statistics, AUC, and sensitivity were significantly higher for NB classifiers learning 13 features than for those learning only BNP (0.69±0.14 vs. 0.54±0.12 P=0.024, 0.94±0.03 vs. 0.84±0.05 P

Motivation Bioinformatic tools capable of annotating, rapidly and reproducibly, large, targeted lipidomic datasets are limited. Specifically, few programs enable high-throughput peak assessment of liquid chromatography–electrospray ionization tandem mass spectrometry data acquired in either selected or multiple reaction monitoring modes. Results We present here Bayesian Annotations for Targeted Lipidomics, a Gaussian naïve Bayes classifier for targeted lipidomics that annotates peak identities according to eight features related to retention time, intensity, and peak shape. Lipid identification is achieved by modeling distributions of these eight input features across biological conditions and maximizing the joint posterior probabilities of all peak identities at a given transition. When applied to sphingolipid and glycerophosphocholine selected reaction monitoring datasets, we demonstrate over 95% of all peaks are rapidly and correctly identified. Availability and implementation BATL software is freely accessible online at https://complimet.ca/batl/ and is compatible with Safari, Firefox, Chrome and Edge. Supplementary information Supplementary data are available at Bioinformatics online.

Background Persistent infection with high-risk types Human Papillomavirus could cause diseases including cervical cancers and oropharyngeal cancers. Nonetheless, so far there is no effective pharmacotherapy for treating the infection from high-risk HPV types, and hence it remains to be a severe threat to health of female. Methods In light of drug repositioning strategy, we trained and benchmarked multiple machine learning predictive models so as to predict potential effective antiviral drugs for HPV infection in this work. Based on antiviral-target interaction dataset, we generated high dimension feature set of drug-target interaction pairs and used the dataset to train and construct machine learning predictive models. Results Through optimizing models, measuring models’ predictive performance using 182 pairs of antiviral-target interaction dataset which were all approved by United States Food and Drug Administration, and benchmarking different models’ predictive performance, we identified the optimized Support Vector Machine and K-Nearest Neighbor classifier with high precision score were the best two predictors (0.80 and 0.85 respectively) amongst classifiers of Support Vector Machine, Random forest, Adaboost, Naive Bayes, K-Nearest Neighbors, and Logistic regression classifier. We applied these two predictors together and successfully predicted 58 pairs of antiviral-HPV protein interactions from 846 pairs of antiviral-HPV protein associations. Conclusions Our work provided good drug candidates for anti-HPV drug discovery. So far as we know, we are the first one to conduct such HPV-oriented computational drug repositioning study.

Insider threats are one of the most challenging and growing security threats which the government agencies, organizations, and institutions face. In such scenarios, malicious (red) activities are performed by the authorized individuals within the company. Because of which, an insider threat has become a taxing and difficult task to identify among other attacks. Along with other monitoring parameters; email logs play a vital role in many research areas such as stalking Insider Threat involving Collaborating Traitors, Textual Analysis, and Social Media exploration. This paper presents a semi-supervised machine learning framework which embraces the pre-processing and classification techniques together for unlabeled dataset i.e. emails. Enron Corporation dataset has been used for experiments and TWOS for evaluation of the proposed framework. Initially, dataset is transformed into vector form using Term Frequency–Inverse Document Frequency (TF–IDF). Thereafter, K-Means is used to classify emails based on message content. Finally, Machine Learning algorithm Decision Tree (DT) is applied to classify the malicious activities. The proposed framework has also been tested with other algorithms such as Logistic Regression (LR), Naive Bayes (NB), KNN, Support Vector Machine (SVM), Random Forest (RF) and Neural Network (NN). However, Decision Tree (DT) combined with pre-processing steps has given the desired results with 99.96% Accuracy and 0.994 AUC for identification of malicious content.

Background Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence. Method/results All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naive Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology. Conclusion In the years to come, deep learning–based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.

Management of medical actions carried out in handling patients who are ODP (people under monitoring), OTG (asymptomatic people), PDP (patient under monitoring) and positive Covid-19 patients is carried out based on assumptions, such as self-isolation, hospitalization, or special treatments in the ICU (Intensive Care Unit) room. The condition of the body in each patient is different, a patient may have same symptoms but the treatment is different, especially in elderly patients. Many problems occur in determining medical action because the patient's body condition is different. Therefore, it needs to be appointed as a research. The research method used in this study was Nive Bayes algorithm with supporting application Rapid Miner. It was applied to carry out the process of testing on patient data as much as 500 data, 25 variables or patient symptoms and 3 outputs as a form of medical action. Based on the results of the analysis carried out in this study, prediction of medical actions for ODP, PDP, OTG and positive Covid-19 patients were obtained by comparing training data with testing data using Rapid Miner application. It resulted that an accuracy rate of 76.00% was obtained

Projects suspensions are between the most insistent tasks confronted by the construction field accredited to the sector’s difficulty and its essential delay risk foundations’ interdependence. Machine learning provides a perfect group of techniques, which can attack those complex systems. The study aimed to recognize and progress a wellorganized predictive data tool to examine and learn from delay sources depend on preceding data of construction projects by using decision trees and naïve Bayesian classification algorithms. An intensive review of available data has been conducted to explore the real reasons and causes of construction project delays. The results show that the postponement of delay of interim payments is at the forefront of delay factors caused by the employer’s decision. Even the least one is to leave the job site caused by the contractor’s second part of the contract, the repeated unjustified stopping of the work at the site, without permission or notice from the client’s representatives. The developed model was applied to about 97 projects and used as a prediction model. The decision tree model shows higher accuracy in the prediction.

Failure caused by corrosion in industries are the major cause of breakdown maintenance. Acoustic emission during the accelerated corrosion testing is a reliable method for corrosion detection, however, classification of these acoustic emission signals by machine learning techniques is still in its infancy. Proposed approach uses a hybrid technique that combines the detection of corrosion through acoustic emission signals from accelerated corrosion testing with machine learning techniques to accurately predict the corrosion severity levels. Laboratory based experimentation setup was established for accelerated corrosion testing of mild steel samples for different time spans and mass loss of samples were recorded. Acoustic emission signals were acquired at high frequency sampling rate with Sound Well AE sensor, NI Elvis kit and NI Labview software. AE mean, AE RMS, AE energy, and kurtosis were selected as distinct features as they represent a linear relationship with the corrosion process. For multi-class problem, five Corrosion severity levels have been made based on mass loss occurred during accelerated corrosion testing for which Naive Bayes, BP-NN and RBF-NN showed accuracy of 90.4%, 94.57%, and 100% respectively.

An expert system is a computer program designed to solve problems in a domain that has human expertise. The knowledge built into the system is usually obtained from experts in the field. Based on this knowledge, an expert system can replicate the thinking process of the human experts and make logical deductions accordingly. Malaria and Typhoid are major health challenge in our society today (Nigeria), its symptoms can lead to other illness which include prolonged fever, fatigue, headaches, nausea, abdominal pain and constipation or diarrhea. People in endemic areas are at risk of contracting both infections concurrently. According to the world malaria report 2011, there were about 216 million cases of malaria and typhoid and estimated 655,000 deaths in 2010. (WHO report, 2011). The main challenging issue confronting the healthcare is lack of quality of service at minimal cost implying from diagnosing to predicting patients correctly. This issue can sometimes lead to an unfortunate clinical decision that can result in devastating consequences that are unacceptable. Although many studies were carried out by different researchers in the medical domain using various data techniques. In this research work, an efficient expert system that diagnoses patients with malaria and typhoid was developed. A secondary data was collected from university of Maiduguri teaching hospital for the period of four years which ranges from 2017 to 2020. The work explored the potential benefits of proposing a new model for prediction and diagnosis of malaria and typhoid using symptoms. The model adopted the Naive bayes and was implemented using the python. The system diagnoses a patient in real time (within 30 minutes) without necessarily visiting the laboratory for a test. Three algorithms were used these are, Support vector machine, Artificial neural network and Naïve bayes. From our finding, it is observed that Naïve bayes and support vector machine give the best result which is 100% in terms of accuracy of diagnosis. Keywords: Diagnosis, Prediction, Expert System, Typhoid, Malaria

Halodoc is a telemedicine-based healthcare application that connects patients with health practitioners such as doctors, pharmacies, and laboratories. There are some comments from halodoc users, both positive and negative comments. This indicates the public's concern for the Halodoc application so it is necessary to analyze the sentiment or comments that appear on the Halodoc application service, especially during the COVID-19 pandemic in order for Halodoc application services to be better. The Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms are used to analyze the public sentiment of Halodoc's telemedicine service application users. The negative category sentiment classification result was 12.33%, while the positive category sentiment was 87.67% from 5,687 reviews which means that the positive review sentiment is more than the negative review sentiment. The accuracy performance of the Naive Bayes Classifier Algorithm resulted in an accuracy rate of 87.77% with an AUC value of 57.11% and a G-Mean of 40.08%, while svm algorithm with KERNEL RBF had an accuracy value of 86.1% with an AUC value of 60.149% and a G-Mean value of 49.311%. Based on the accuracy value of the model can be known SVM Kernel RBF model better than NBC on classifying the review of user sentiment of halodoc telemedicine service

Purpose: Halal tourism or muslim friendly tourism has big potential for the tourism industry in Indonesia. According to Cresent Rating, the world’s leading authority on halal-friendly travel, one of the indicators for halal tourism is the availability of choices for halal foods. To support halal tourism, unfortunately, not all restaurants around the tourism object or in the city where the tourism object is located have labels or information that makes people know about halal food in the restaurant easily.Methods/Study design/approach: The data in this research was obtained from online media such as Google Maps, TripAdvisor, and Zoomato. The data consists of 870 data with the classification of halal food restaurants and 590 data with the reverse classification. Machine learning methods were chosen as classifiers. Some of them were Naive Bayes, Support Vector Machine, and K-Nearest Neighbor. Result/Findings: The result from this research shows that the proposed method achieved an accuracy of 95,9% for Support Vector Machine, 93,8% for Multinomial Naive Bayes, and 91% for K-Nearest Neighbor. In the future, our result will be to support the halal tourism environment in terms of technology. Novelty/Originality/Value: In this study, we utilize restaurant reviews done by visitors to get information about the classification of halal food restaurants.

The rapid deployment of the Internet of Things (IoT) devices have led to the development of innovative information services, unavailable a few years ago. To provide these services, IoT devices connect and communicate using networks like Bluetooth, Wi-Fi, and Ethernet. This full-stack connection of the IoT devices has introduced a grand security challenge. This paper presents an IoT security framework to protect smart infrastructures from cyber attacks. This IoT security framework is applied to Bluetooth protocol and IoT sensors networks. For the Bluetooth protocol, the intrusion detection system (IDS) uses n-grams to extract temporal and spatial features of Bluetooth communication. The Bluetooth IDS has a precision of 99.6% and a recall of 99.6% using classification technique like Ripper algorithm and Decision Tree (C4.5). We also used AdaBoost, support vector machine (SVM), Naive Bayes, and Bagging algorithm for intrusion detection. The Sensor IDS uses discrete wavelet transform (DWT) to extract spatial and temporal features characteristics of the observed signal. Using the detailed coefficients of Biorthogonal DWT, Daubechies DWT, Coiflets DWT, Discrete Meyer DWT, Reverse Biorthogonal DWT, Symlets DWT, we present the results for detecting attacks with One-Class SVM, Local Outlier Factor, and Elliptic Envelope. The attacks used in our evaluation include Denial of Service Attacks, Impersonation Attacks, Random Signal Attacks, and Replay Attacks on temperature sensors. The One-Class SVM performed the best when compared with the results of other machine learning techniques.

Purpose : Cervical cancer is one of the most common types of cancer that kills women worldwide. One way for early detection of cervical cancer risk is by looking at human behavior determinants. Detection of cervical cancer based on behavior determinants has been researched before using Naive Bayes and Logistic Regression but has never using ADALINE Neural Network. Methods : In this paper, ADALINE proposes to detect early cervical cancer based on the behavior on the UCI dataset. The data used are 72 data, consisting of 21 cervical cancer patients and 51 non-cervical cancer patients. The dataset is divided 70% for training data and 30% for testing data. The learning parameters used are maximum epoch, learning rate, and MSE. Result: MSE generated from ADALINE training process is 0.02 using a learning rate of 0.006 with a maximum epoch of 19. Twenty-two test data obtained an accuracy of 95.5%, and overall data got an accuracy value of 97.2%. Novelty : One alternative method for early detection of cervical cancer based on behavior is ADALINE Neural Network.

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

The human liver is one of the major organs in the body and liver disease can cause many problems in human live. Due to the increase in liver disease, various data mining techniques are proposed by the researchers to predict the liver disease. These techniques are improving day by day in order to predict and diagnose the liver disease in human. In this paper, real-world liver disease dataset is incorporated for diagnosing liver disease in human body. For this purpose, feature selection models are used to select a number of features that best are the most important feature to diagnose the liver disease. After selecting features and splitting data for training and testing, different classification algorithms in terms of naïve Bayes, supervised vector machine, decision tree, k near neighbor and logistic regression models to diagnose the liver disease in human body. The results are cross-validated by tenfold cross validation methods and achieve an accuracy as good as 93%.

PURPOSE To examine the usefulness of machine learning to predict prognosis in cervical cancer using clinical and radiomic features of 2-deoxy-2-[18F]fluoro-D-glucose (18F-FDG) positron emission tomography/computed tomography (CT) (18F-FDG-PET/CT). METHODS This retrospective study included 50 cervical cancer patients who underwent 18F-FDG-PET/CT before treatment. Four clinical (age, histology, stage, and treatment) and 41 18F-FDG-PET-based radiomic features were ranked and a subset of useful features for association with disease progression was selected based on decrease of the Gini impurity. Six machine learning algorithms (random forest, neural network, k-nearest neighbors, naive Bayes, logistic regression, and support vector machine) were compared using the areas under the receiver operating characteristic curve (AUC). Progression-free survival (PFS) was assessed using Cox regression analysis. RESULTS The five top predictors of disease progression were: stage, surface area, metabolic tumor volume, gray-level run length non-uniformity (GLRLM_RLNU), and gray-level non-uniformity for run (GLRLM_GLNU). The naive Bayes model was the best-performing classifier for predicting disease progression (AUC = 0.872, accuracy = 0.780, F1 score = 0.781, precision = 0.788, and recall = 0.780). In the naive Bayes model, 5-year PFS was significantly higher in predicted non-progression than predicted progression (80.1% vs. 9.1%, p

BACKGROUND Several groups have previously developed logistic regression models for predicting delayed graft function (DGF). In this study, we used an automated machine learning (ML) modeling pipeline to generate and optimize DGF prediction models en masse. METHODS Deceased donor renal transplants at our institution from 2010 to 2018 were included. Input data consisted of 21 donor features from United Network for Organ Sharing. A training set composed of ~50%/50% split in DGF-positive and DGF-negative cases was used to generate 400 869 models. Each model was based on 1 of 7 ML algorithms (gradient boosting machine, k-nearest neighbor, logistic regression, neural network, naive Bayes, random forest, support vector machine) with various combinations of feature sets and hyperparameter values. Performance of each model was based on a separate secondary test dataset and assessed by common statistical metrics. RESULTS The best performing models were based on neural network algorithms, with the highest area under the receiver operating characteristic curve of 0.7595. This model used 10 out of the original 21 donor features, including age, height, weight, ethnicity, serum creatinine, blood urea nitrogen, hypertension history, donation after cardiac death status, cause of death, and cold ischemia time. With the same donor data, the highest area under the receiver operating characteristic curve for logistic regression models was 0.7484, using all donor features. CONCLUSIONS Our automated en masse ML modeling approach was able to rapidly generate ML models for DGF prediction. The performance of the ML models was comparable with classic logistic regression models.

The early detection of pulmonary nodules using computer-aided diagnosis (CAD) systems is very essential in reducing mortality rates of lung cancer. In this paper, we propose a new deep learning approach to improve the classification accuracy of pulmonary nodules in computed tomography (CT) images. Our proposed CNN-5CL (convolutional neural network with 5 convolutional layers) approach uses an 11-layer convolutional neural network (with 5 convolutional layers) for automatic feature extraction and classification. The proposed method is evaluated using LIDC/IDRI images. The proposed method is implemented in the Python platform, and the performance is evaluated with metrics such as accuracy, sensitivity, specificity, and receiver operating characteristics (ROC). The results show that the proposed method achieves accuracy, sensitivity, specificity, and area under the roc curve (AUC) of 98.88%, 99.62%, 93.73%, and 0.928, respectively. The proposed approach outperforms various other methods such as Naïve Bayes, K-nearest neighbor, support vector machine, adaptive neuro fuzzy inference system methods, and also other deep learning-based approaches.

In today’s world using data mining and classification is considered to be one of the most important techniques, as today’s world is full of data that is generated by various sources. However, extracting useful knowledge out of this data is the real challenge, and this paper conquers this challenge by using machine learning algorithms to use data for classifiers to draw meaningful results. The aim of this research paper is to design a model to detect diabetes in patients with high accuracy. Therefore, this research paper using five different algorithms for different machine learning classification includes, Decision Tree, Support Vector Machine (SVM), Random Forest, Naive Bayes, and K- Nearest Neighbor (K-NN), the purpose of this approach is to predict diabetes at an early stage. Finally, we have compared the performance of these algorithms, concluding that K-NN algorithm is a better accuracy (81.16%), followed by the Naive Bayes algorithm (76.06%).

Intrusion detection system is a significant security mechanism that monitors network traffic to assist prevents unwanted access to network resources. Effective intrusion detection is an important issue for defending networks against potential intrusions. In this paper, a new intrusion detection strategy is proposed. The recommended intrusion detection strategy is divided into three steps: (i) Preparing step, (ii) Feature selection step, and (iii) Classification step. Preparing step gathers and analyzes network traffic in readiness for training and testing. Feature selection step aims to choose the significant features for detecting intrusion attacks form preparing step. It comprises of two successive feature selection modules, which are; quick selection module and precise selection module. Precise selection module deploys genetic algorithm as a wrapper method, whereas quick selection module relies on filter. Based on the most effective features identified by feature selection step, the classification step seeks to detect intrusion attacks with the least amount of time penalty. It contains two phases: prioritized naive bayes phase and distance encouragement phase, which avoids the problems of naive bayes classifiers. The presented intrusion detection strategy beats other previous approaches using the NSL-KDD dataset, according to the experimental tests. Intrusion detection strategy provides the highest accuracy, precision, recall and F1-measure with values equal to 97.6%, 98.24%, 98.14%, and 98.11% respectively with minimum time penalty.