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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%.

Unsatisfactory public services encourage the public to submit complaints/ reports to public service providers to improve their services. However, each complaint/ report submitted varies. Therefore, the first step of the community complaint resolution process is to classify every incoming community complaint. The Ombudsman of The Republic of Indonesia annually receives a minimum of 10,000 complaints with an average of 300-500 reports per province per year, classifies complaints/ community reports to divide them into three classes, namely simple reports, medium reports, and heavy reports. The classification process is carried out using a weight assessment of each complaint/ report using 5 (five) attributes. It becomes a big job if done manually. This impacts the inefficiency of the performance time of complaint management officers. As an alternative solution, in this study, a machine learning method with the Naïve Bayes Classifier algorithm was applied to facilitate the process of automatically classifying complaints/ community reports to be more effective and efficient. The results showed that the classification of complaints/ community reports by applying the Naïve Bayes Classifier algorithm gives a high accuracy value of 92%. In addition, the average precision, recall, and f1-score values, respectively, are 91%, 93%, and 92%.

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.

In recent years, the crack fault is one of the most common faults in the rotor system and it is still a challenge for crack position diagnosis in the hollow shaft rotor system. In this paper, a method based on the Convolutional Neural Network and deep metric learning (CNN-C) is proposed to effectively identify the crack position for a hollow shaft rotor system. Center-loss function is used to enhance the performance of neural network. Main contributions include: Firstly, the dynamic response of the dual-disks hollow shaft rotor system is obtained. The analysis results show that the crack will cause super-harmonic resonance, and the peak value of it is closely related to the position and depth of the crack. In addition, the amplitude near the non-resonant region also has relationship with the crack parameters. Secondly, we proposed an effective crack position diagnosis method which has the highest 99.04% recognition accuracy compared with other algorithms. Then, the influence of penalty factor on CNN-C performance is analyzed, which shows that too high penalty factor will lead to the decline of the neural network performance. Finally, the feature vectors are visualized via t-distributed Stochastic Neighbor Embedding (t-SNE). Naive Bayes classifier (NB) and K-Nearest Neighbor algorithm (KNN) are used to verify the validity of the feature vectors extracted by CNN-C. The results show that NB and KNN have more regular decision boundaries and higher recognition accuracy on the feature vectors data set extracted by CNN-C, indicating that the feature vectors extracted by CNN-C have great intra-class compactness and inter-class separability.

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%.

Child-free is a concept in which a person chooses not to have children or places and situations that are without the presence of a child. Along with the rapid flow of information, this child-free concept began to be discussed virally, especially on Indonesian social media, such as Twitter. Sentiment analysis is the mining of all people’s expressions and views on a phenomenon or product online in the form of text. Through a large sample collection of opinions and expressions, we can capture the voices or views of society, understand the dynamics that are taking place, and even know the extent to which the issue begins to touch aspects of people’s social life. This study aims to conduct sentiment analysis by comparing the performance of two different methods used to classify people’s views in the form of text data crawled tweets from Twitter. The two methods compared are Support Vector Machines (SVM) and Naive Bayes Classifier (NBC). Another purpose of this study is to provide an overview of public sentiment on social media Twitter about the concept of child-free. The results of this study showed that the data experienced an imbalance so to overcome this problem, SMOTE is used, SMOTE managed to increase the sensitivity of the prediction of minor data. The classification method that produces the best prediction on test data using the F1-weighted average criterion is SMOTE-SVM with a value of 60.45%. The opinions that support child-free mostly have to do with parents' unpreparedness to take care of children, while opinions that reject child-free think that it is contrary to religious advice and child-free decisions will make it difficult for old age because no one takes care of them.

Previous studies on water quality assessment for watershed management have predominantly focused on specific seasonal or annual average values, rather than considering water quality variations based on flow fluctuations. It is crucial to identify the water quality characteristics within a watershed by incorporating flow conditions to establish a customized watershed management approach over different time periods. In this study, a vulnerability analysis was conducted to attain the target water quality (TWQ) in 22 watersheds within the Nakdong River system in South Korea. Additionally, factor analysis (FA) was employed to analyze the characteristics of water quality fluctuations in relation to flow conditions. The FA results categorized the pollution source characteristics of the 22 watersheds into various types, indicating the need for specific pollution source management strategies. These findings enabled an initial decision-making process regarding which water pollution sources to prioritize based on flow conditions. Moreover, detailed analyses of pollution sources were performed for watersheds, where achieving TWQ was challenging. Subsequently, a data-based prediction model was developed using the naïve Bayes classification model to determine the likelihood of achieving TWQ. As a result, this study proposes a technique for water quality management in watersheds by introducing a water quality excess probability model, which employs data-based analysis instead of traditional numerical modeling for watershed water quality assessment and proactive prediction. The study discusses the potential of various data-based tools to reduce development and analysis time, providing a powerful alternative to physical-based models that require extensive input data and are time-consuming. To advance future studies, the establishment of comprehensive water environment big data, improvement of real-time monitoring systems within watersheds, and advancements in spatial and temporal observation technologies are emphasized as essential for the development of an advanced watershed management system.

Essential oils are valuable in various industries, but their easy adulteration can cause adverse health effects. Electronic nasal sensors offer a solution for adulteration detection. This article proposes a new system for characterising essential oils based on low-cost sensor networks and machine learning techniques. The sensors used belong to the MQ family (MQ-2, MQ-3, MQ-4, MQ-5, MQ-6, MQ-7, and MQ-8). Six essential oils were used, including Cistus ladanifer, Pinus pinaster, and Cistus ladanifer oil adulterated with Pinus pinaster, Melaleuca alternifolia, tea tree, and red fruits. A total of up to 7100 measurements were included, with more than 118 h of measurements of 33 different parameters. These data were used to train and compare five machine learning algorithms: discriminant analysis, support vector machine, k-nearest neighbours, neural network, and naive Bayesian when the data were used individually or when hourly mean values were included. To evaluate the performance of the included machine learning algorithms, accuracy, precision, recall, and F1-score were considered. The study found that using k-nearest neighbours, accuracy, recall, F1-score, and precision values were 1, 0.99, 0.99, and 1, respectively. The accuracy reached 100% with k-nearest neighbours using only 2 parameters for averaged data or 15 parameters for individual data.

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.

Tokopedia adalah e-commerce dengan pengunjung situs web terbanyak, mencapai 157 juta kunjungan pada kuartal pertama tahun 2022. Meskipun menempati urutan pertama dan bintang 5 menerima nilai paling banyak, itu pasti ada yang memberikan komentar negatif. Untuk membuktikan analisis dan pengujian itu maka memerlukan metode analisis dan klasifikasi komentar pelanggan dalam beberapa sentimen positif dan negatif. Analisis ini menggunakan metode Naïve Bayes Classifier terhadap komentar pelanggan tentang aplikasi Tokopedia di Playstore. Performa yang dihasilkan dari hasil percobaan yang dilakukan oleh rapid evaluator pada 143 data pengujian menghasilkan nilai akurasi 95,10%, dengan class recall: 91,46% (positive class: Positive) sedangkan class precision: 100.00% (positive class: Positive) dan nilai AUC: 0.999 (positive class: Positive)

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.

Computational systems that process multiple affective states may benefit from explicitly considering the interaction between the states to enhance their recognition performance. This work proposes the combination of a multi-label classifier, Circular Classifier Chain (CCC), with a multimodal classifier, Fusion using a Semi-Naive Bayesian classifier (FSNBC), to include explicitly the dependencies between multiple affective states during the automatic recognition process. This combination of classifiers is applied to a virtual rehabilitation context of post-stroke patients. We collected data from post-stroke patients, which include finger pressure, hand movements, and facial expressions during ten longitudinal sessions. Videos of the sessions were labelled by clinicians to recognize four states: tiredness, anxiety, pain, and engagement. Each state was modelled by the FSNBC receiving the information of finger pressure, hand movements, and facial expressions. The four FSNBCs were linked in the CCC to exploit the dependency relationships between the states. The convergence of CCC was reached by 5 iterations at most for all the patients. Results (ROC AUC)) of CCC with the FSNBC are over $0.940 \pm 0.045$ ( $mean \pm std.\,deviation$ ) for the four states. Relationships of mutual exclusion between engagement and all the other states and co-occurrences between pain and anxiety were detected and discussed.

To evaluate the performance of the classic machine learning algorithms and the effectiveness of various features, the iterative algorithms (i.e., support vector machine (SVM), and least-squares SVM (LS-SVM)) and non-iterative algorithms (i.e., random forest (RF) and naive bayes (NB)) for six feature schemes were performed to classify the ECG recordings. The ECG recordings were initially filtered with a 0.1 Hz − 12 Hz band pass filter. Then 80 features, including 48 time domain, 18 frequency domain, 12 time–frequency and two principle component analysis (PCA) features, were extracted to construct six feature schemes. The RF, SVM, LS-SVM and NB were employed to discern a binary-classification task (i.e., normal and AF ECG recordings) and a tri- classification task (i.e., the normal, AF and ST change ECG recordings) for the six feature schemes. The results revealed that time domain, frequency domain features and PCA features can provide relatively reliable feature combinations to the RF and SVM. In addition, the RF yielded the highest F1-scores (0.8908 and 0.7535) for the binary-classification task and the tri-classification task than the SVM, LS-SVM and NB.

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.

This study encompasses the proposal of a novel hybrid-security model that incorporates the benefits of both the centralised data mining system as well the distributed data mining system. The hybrid model provides two levels of security. The first security level perturbs the individual datasets by transforming it into a non-understandable form using the four-dimensional rotation transformation, and the second security level helps in performing secure distributed data mining using the ratio of secure summation protocol. The trivial data mining techniques, such as k-means clustering technique and naive Bayes classification technique, verifies the efficiency and accuracy of the hybrid security model. The hybrid security model provides security to sensitive data without compromising the quality of the data. The accuracy obtained in classification task and clustering task using naive Bayes and k-means technique is high when different datasets in a privacy-enhanced distributed data mining environment verify the working of the hybrid security model.

— The goal of this project is to build a recommendation model which categories users according to their interests, partner preference, and other characteristics using machine learning techniques like the Naive Bayes Classifier (NBC). The initial step is to gather user information, such as profiles and preferences. Then, to extract pertinent features, this data is preprocessed using NLP techniques like tokenization, stemming, and stop-word removal. The technology then classifies users based on their attributes using machine learning techniques like NBC. An interest- and preference-based label is given to each user profile in the labelled dataset of user profiles on which NBC models are trained. The NBC model can be used to predict the labels of new users based on their attributes once it has been trained. After that, the programme suggests suitable partners for the user.

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.

It is believed that online disease risk assessment system has great potential to alleviate the medical treatment problems for the future smart city and communities, as it can excavate disease risk factors from a large number of patient features, provide diagnostic references for doctors, and save medical treatment time for patients. However, due to the sensitivity of medical data, the flourish of online disease risk assessment service still faces severe challenges including information privacy and security. In this paper, based on the naive bayesian classification, we propose an efficient and privacy-preserving disease risk assessment scheme over multi-outsourced vertical datasets, named CARER. With CARER, the e-healthcare provider can securely train a disease risk predication model over vertically distributed medical data from multiple medical centers(i.e.,hospitals), and provide privacy-preservin disease risk predication services for users(i.e.,patients and doctors). During the model training and disease risk prediction phases, all sensitive data are operated over ciphertexts without decryption. Therefore, the private information of medical centers, e-healthcare provider, and users can be well protected. Detailed security analysis shows that CARER can resist various known security threats. In addition, we evaluate the performance of CARER with real medical datasets, and the results demonstrate that CARER is efficient.

Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting the adversarial attack, self-driving car. As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation to the true posterior. Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output. A common accurate solution is to use Monte Carlo (MC) integration. However, it needs to maintain a large number of samples, and evaluate the model repeatedly, and average multiple model outputs. In many real-world cases, this is computationally prohibitive. In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by the model posterior with a parameterized model and in an amortized fashion. The aim is to approximate the predictive uncertainty of a specific Bayesian model, meanwhile alleviating the heavy workload of MC integration at testing time. The proposed method is universally applicable to Bayesian classification models that allow for posterior sampling. Theoretically, we show that the idea of amortization incurs no additional costs on approximation performance. Empirical results validate the strong practical performance of our approach.

Classification algorithms predict the class membership of an unknown record. Methods such as logistic regression or the naïve Bayes algorithm produce a score related to the likelihood that a record belongs to a particular class. A cutoff threshold is then defined to delineate the prediction of one class over another. This paper derives analytic results for the selection of an optimal cutoff threshold for a classification algorithm that is used to inform a two-action decision in the cases of risk aversion and risk neutrality. The results provide insight to how the optimal cutoff thresholds relate to the associated costs and the sensitivity and specificity of the algorithm for both the risk neutral and risk averse decision makers. The optimal risk averse threshold is not reliably above or below the optimal risk neutral threshold, but the relation depends on the parameters of a particular application. The results further show the risk averse optimal threshold is insensitive to the size of the data set or the magnitude of the costs, but instead is sensitive to the proportion of positive records in the data and the ratio of costs. Numeric examples and sensitivity analysis derive further insight. Results show the percent value gap from a misspecified risk attitude increases as the specificity of the classification algorithm decreases.

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.

This paper focuses on the design of the optimal or near-optimal detector resorting to extreme eigenvalues. A general framework for detector design involving model-driven and data-driven approaches is introduced. Specifically, the extreme eigenvalues based likelihood ratio test (LRT) is derived via the model-driven approach. Merging the model-driven and data-driven approaches, the Naive Bayesian detector is proposed based on the extreme eigenvalues, which converts the design of test statistic into a two-class decision boundary construction problem, and a solution is provided by the Naive Bayesian classifier. To render the detectors more practical, two near-optimal detectors called α-sum and α-product of maximum and minimum eigenvalues (α-SMME, α-PMME) are further designed, in which α is a weight coefficient. Furthermore, the theoretical performance analysis of the α-SMME and α-PMME algorithms is provided, and the optimal weight selection is further obtained by solving an optimization problem under the Neyman-Pearson criterion. Finally, simulation experiments demonstrate that the proposed detectors achieve performance improvements over the state-of-the-art detectors using extreme eigenvalues, and almost coincide with the detection performance of the LRT detector.

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.

Quality of Service (QoS) is well acknowledged as a decisive means for ascertaining the performance of third-party Web services. QoS has high uncertainty in complex and dynamic network environments. QoS monitoring is considered as one of the most effective techniques to detect QoS violations at runtime. However, existing QoS monitoring approaches only consider single QoS attribute and do not provide a promising solution for comprehensively monitoring multivariate QoS attributes. To overcome this problem, a novel QoS monitoring approach, named M-BSRM (Multivariate BayeSian Runtime Monitoring), is proposed. First, M-BSRM adopts the point mutual information theory to initialize the weights of different environmental impact factors and solves the problem of uneven distribution between classes brought by traditional algorithms. Second, each single QoS attribute is integrated with user preference using the information fusion theory. Finally, a Bayesian classifier is used to comprehensively evaluate multivariate QoS attributes at runtime. The experimental results on both the real-world and simulated data sets show that M-BSRM is more effective, practical and efficient than the other approaches.

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.

Spread-F (SF) is one of the most important types of the ionospheric irregularities as it causes ionospheric scintillation which can severely affect the performance and reliability of communication, navigation, and radar systems. The ionosonde provides the most effective and economical way to study the ionosphere and SF. However, the manual identification of SF from an ionogram is boring and hard work. To automatically identify SF on the ionogram and extend the study of SF into the middle and low latitudes of East Asia, this paper presents a statistical analysis of SF in this region, based on the naïve Bayesian classifier. The results showed that the accuracy of automatic identification reached up to 97% on both the validation datasets and test datasets composed of Mohe, I-Cheon, Jeju, Wuhan, and Sanya ionograms, suggesting that it is a promising way to automatically identify SF on ionograms. Based on the classification results, the statistical analysis shows that SF has a complicated morphology in the middle and low latitudes of East Asia. Specifically, there is a peak of occurrence of SF in the summer in I-Cheon, Jeju, Sanya, and Wuhan; however, the Mohe station has the highest occurrence rate of SF in December. The different seasonal variations of SF might be due to the different geographic local conditions, such as the inland-coastal differences and formation mechanism differences at these latitudes. Moreover, SF occurs more easily in the post-midnight hours when compared with the pre-midnight period in these stations, which is consistent with the previous results. Furthermore, this paper extracts the frequency SF (FSF) index and range SF (RSF) index to characterize the features of SF. The results shows that the most intense FSF/RSF appeared in the height range of 220–300 km/1–7 MHz in these stations, although there are different magnitude extensions on different season in these regions. In particular, strong spread-F (SSF) reached its maximum at the equinox at Sanya, confirming the frequent SSF occurrence at the equinox at the equator and low latitudes. These results would be helpful for understanding the characteristics of SF in East Asia.

Perkembangan Twitter di Indonesia sebagai platform media sosial yang digemari masyarakat, yang menunjukkan Indonesia negara pengguna Twitter terbanyak kelima di dunia pada Januari 2022. Seiring berkembangnya tren saat ini, memaksa pemerintah Indonesia untuk melakukan berbagai cara lebih banyak berinteraksi dengan masyarakat, salah satunya dengan penggunaan Twitter. Memanfaatkan media social Twitter, pemerintah akan mendapatkan berbagai informasi yang sedang kontroversial di kalangan masyarakat. Seperti dilaksanakannya kegiatan vaksinasi oleh pemerintah Indonesia, banyak masyarakat menilai kontroversial dalam kegiatan vaksinasi ini dan banyak kalangan di masyarakat yang memberikan berbagai pendapatnya di media sosial. Melalui Twitter salah satunya, masyarakat berbagi postingan opini masing-masing tentang vaksinasi ini. Tahapan analisis pada pengujian ini akan membandingkan dua algoritma klasifikasi yakni Naive Bayes Classifier (NBC) dengan Decision Tree dengan menggunakan perhitungan Confusion Matrix untuk mengukur kinerja pada kedua algoritma tersebut. Hasil tes pengujian akhir disajikan dalam bentuk nilai akurasi, presisi dan recall. Perbandingan kedua algoritma klasifikasi Naïve Bayes dengan Decision Tree hasil yang diperoleh memiliki perbedaan tingkat akurasi yang berbeda. Algoritma Naïve Bayes Classifier mendapat hasil akurasi 93.96 % dan nilai presisi 91% dengan nilai recall 98%, sedangkan pada algoritma Decision Tree menghasilkan nilai akurasi sebesar 88.64 %, dengan nilai presisi 91% dan nilai recall 94%. Hasil ini didasarkan pada perhitungan Confusion Matrix. Kemudian bisa disimpulkan jika algoritma NBC penelitian ini lebih akurat dibanding algoritma Decision Tree.

Procurement of the COVID-19 vaccination has led to diverse opinions among Indonesian people on Twitter. Sentiment analysis on Twitter can be carried out to find out public opinion, especially among Twitter users. The data was used in the form of tweets with the topic of the COVID-19 vaccine using the keywords covid 19 vaccine, covid vaccine, AstraZeneca, Sinovac, Moderna, Pfizer, Novavax and Sinopharm. analysis of the performance of the Naive Bayes Classifier, Support Vector Machine and K-Nearest Neighbors algorithms to determine the results of the accuracy level between the algorithms. The highest classification test is using the Support Vector Machine with an accuracy rate of 0.701. The results of the comparison of algorithms tested using tweet data on the topic of the COVID-19 vaccine found that the Support Vector Machine was better than the Naive Bayes Classifier and K-Nearest Neighbors. From the classification test carried out using COVID-19 vaccine tweet data with 2500 data. The amount of data after going through the data processing process is 1052 data. Neutral sentiment results in as many as 645 positive sentiments as many as 250 and negative sentiments as many as 157.

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.

Toddlers are among the most vulnerable groups to nutritional problems when viewed from the point of view of health and nutrition problems, while at this time they are experiencing a cycle of relatively rapid growth and development. .7% is quite high where the number of births is relatively large. Researchers try to classify 10 toddlers using WEKA to find out whether they have nutritional disorders or are normal by using 5 attributes as system input and a class namely nutrition which divides this class into 4 namely bad, less, good and more with the amount of training data 219 data then data compared with the actual nutritional conditions and obtained an accuracy of 60% and an error of 40% with these results it can be concluded that the accuracy is not too good. Based on this, it is hoped that the results of this classification can help further research in classifying the nutrition of children under five.

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.