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Data acquisition problem on large distributed wireless sensor networks (WSNs) is considered as a challenge in the growth of Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted much attention of researchers. An open question of this combination is how to integrate these techniques effectively for specific tasks. On the other hand, CS data reconstruction process is considered as one of the CS challenges because it requires to recover N data from only M measurement where M<

Medical Cyber-Physical Systems (MCPS) are complex, location-aware, networked systems of medical devices that can be used as a piece of the healing center to give the best medical care to patients. Hence, they integrate human, cyber, and physical elements. Since MCPSs are life-critical and context-aware, they are significant to the healthcare industry, which is prone to data breaches and cyber-attacks. As an emerging research area, MCPS faces several challenges with respect to system reliability, assurance, autonomy and security, and privacy. In this paper, we initially examine the state-of-the-arts of MCPS over the last few decades (1998–2020) and subsequently propose a new framework considering security/privacy for MPCS that incorporates several models that depict various domains of security. An interaction between various models followed with a qualitative assessment of the framework has been carried out to present a detailed description of the proposed framework. It is useful in various healthcare industries like health care services, manufacturing, pharmaceuticals, etc. that utilize smart devices. Additionally, the framework may be applied to enhance security in the Internet of Things (IoT) environment. It may be also useful to deploy efficient workflow operations for patients under the consideration framework. The framework will also lay out the foundation for implementing cybersecurity infrastructures in many healthcare applications.

Recommender systems are becoming increasingly important in every aspect of life for the diverse needs of users. One of the main goals of the recommender system is to make decisions based on criteria. It is thus important to have a reasonable solution that is consistent with user requirements and characteristics of the stored data. This paper proposes a novel recommendation method based on the resonance relationship of user criteria with Choquet Operation for building a decision-making model. It has been evaluated on the multirecsys tool based on R language. Outputs from the proposed model are effective and reliable through the experiments. It can be applied in appropriate contexts to improve efficiency and minimize the limitations of the current recommender systems.

Collaborative tagging is a useful and effective way for classifying items with respect to search, sharing information so that users can be tagged via online social networking. This article proposes a novel recommender system for collaborative tagging in which the genre interestingness measure and gradual decay are utilized with diffusion similarity. The comparison has been done on the benchmark recommender system datasets namely MovieLens, Amazon datasets against the existing approaches such as collaborative filtering based on tagging using E-FCM, and E-GK clustering algorithms, hybrid recommender systems based on tagging using GA and collaborative tagging using incremental clustering with trust. The experimental results ensure that the proposed approach achieves maximum prediction accuracy ratio of 9.25% for average of various splits data of 100 users, which is higher than the existing approaches obtained only prediction accuracy of 5.76%.

Hospital cost analysis (HCA) becomes a key topic and forefront of politics, social welfare and medical discourse. HCA includes a wide range of expenses; yet the foremost attention relates to the money expense in which hospital managers would like to draw a figure of incomes in the past and future. Based on the HCA results, they can develop many plans for improving hospital’s service quality and investing in potential healthcare services in order to deliver better services with lower costs. Machine learning methods are often opted for prediction in HCA. In this paper, we propose a new method for HCA that uses genetic algorithm (GA) and artificial neural network (ANN). Operators of GA are used to boost up calculation to get optimal weights in the forward propagation of ANN. Experiments on a real database of Hanoi Medical University Hospital (HMUH) including calculus of kidney and ureter inpatients show that the new method achieves better accuracy than the relevant ones including linear regression, K-nearest neighbors (KNN), ANN and deep learning. The mean squared error of the proposed model gets the lowest value (0.00360), compared to those of deep learning, KNN and linear regression which are 0.00901, 0.01205 and 0.01718 respectively.

This article proposes a new method to overcome the sparse data problem of the collaborative filtering models (CF models) by considering the homologous relationship between users or items calculated on contextual attributes when we build the CF models. In the traditional CF models, the results are built only based on data from the user’s ratings for items. The results of the proposed models are calculated on two factors: (1) the similar factors based on rating values; (2) the similar factors based on contextual attributes. The findings from the experimentation on two datasets DePaulMovie and InCarMusic, show that the proposed models have higher accuracy than the traditional CF models.

This paper proposed a novel first-order single-valued neutrosophic hesitant fuzzy time series (SVNHFTS) forecasting model. Our aim is to improve the previously proposed neutrosophic time series (NTS) model by incorporating the degree of the hesitancy using single-valued neutrosophic hesitant fuzzy set (SVNHFS) model instead of single-valued neutrosophic set (SVNS). Our paper's novelty is that we incorporate an algorithm that automatically converts the crisp dataset into the neutrosophic set that eliminates the need for experts' input or opinions in determining the membership in each of the partitioned neutrosophic set. We also incorporate Markov Chain algorithm in the de-neutrosophication process to include the weightage of the repeating neutrosophic logical relationships (NLRs). Our paper's significant contribution is to add to the existing body of knowledge related to fuzzy time series (FTS) by developing a new FTS model based on SVNHFS, one of the improved version of fuzzy sets, since this area of research is still relatively underdeveloped. To determine our proposed model's capability, we apply our proposed SVNHFTS model to three real datasets while also comparing the result to the other FTS models based on improved versions of fuzzy sets. Our datasets include benchmark enrollment data of University of Alabama, IDX Composite (Indonesian composite stock index), and MERVAL index (Argentinian composite stock index). The result shows that our proposed SVNHFTS model outperforms most of the other FTS models in terms of AFE and RMSE, especially the previously proposed NTS model.

Botnet is regarded as one of the most sophisticated vulnerability threats nowadays. A large portion of network traffic is dominated by Botnets. Botnets are conglomeration of trade PCs (Bots) which are remotely controlled by their originator (BotMaster) under a Command and-Control (C&C) foundation. They are the keys to several Internet assaults like spams, Distributed Denial of Service Attacks (DDoS), rebate distortions, malwares and phishing. To over the problem of DDoS attack, various machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) (K-means, X-means etc.) were proposed. With the increasing popularity of Machine Learning in the field of Computer Security, it will be a remarkable accomplishment to carry out performance assessment of the machine learning methods given a common platform. This could assist developers in choosing a suitable method for their case studies and assist them in further research. This paper performed an experimental analysis of the machine learning methods for Botnet DDoS attack detection. The evaluation is done on the UNBS-NB 15 and KDD99 which are well-known publicity datasets for Botnet DDoS attack detection. Machine learning methods typically Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT), and Unsupervised Learning (USML) are investigated for Accuracy, False Alarm Rate (FAR), Sensitivity, Specificity, False positive rate (FPR), AUC, and Matthews correlation coefficient (MCC) of datasets. Performance of KDD99 dataset has been experimentally shown to be better as compared to the UNBS-NB 15 dataset. This validation is significant in computer security and other related fields.

In cloud computing environment, cross-cloud live migration of virtual machines (VMs) is a major concern in these days. Cloud computing provides the users with huge, versatile and on-demand access to a bulk of customizable and configurable registered physical devices or things. It helps organizations or enterprises to share data efficiently by privately owned cloud or by the third-party servers. This type of sharing of bulky data through cloud is more efficient and reliable. In an enterprise environment, one of the essential capabilities of cloud infrastructure is VM migration. VM live migration basically involves the transference of instances that includes the operating system, runtime memory pages and active CPU states from source hub to the destination hub. In this paper, we have discussed on resource allocation algorithm which performs better in utilization of CPU, time and memory. Our proposed algorithm deals with the effective utilization of unoccupied memory, and we have also measured VM memory stack flow of total memory for cloud computing architecture.

Software maintainability predicts changes or failures that may occur in software after it has been deployed. Since it deals with the degree to which an application may be understood, repaired, or enhanced, it also takes into account the overall cost of the project. In the past, several measures have been taken into account for predicting metrics that influence software maintainability. However, deep learning is yet to be explored for the same. In this paper, we perform deep learning for software maintainability metrics' prediction on a large number of datasets. Unlike the previous research works, we have relied on large datasets from 299 software and subsequently applied various metrics and functions to the same; 29 object-oriented metrics have been considered along with their impact on software maintainability of open source software. Several metrics have been analyzed and descriptive statistics of these metrics have been pointed out. The proposed long short term memory has been evaluated using measures, such as mean absolute error, root mean square error and accuracy. Five machine learning algorithms, namely, ridge regression with variable selection, decision tree, quantile regression forest, support vector machine, and principal component analysis have been applied to the original datasets, as well as, to the refined datasets. It was found that this paper provides results in the form of metrics that may be used in the prediction of software maintenance and the proposed deep learning model outperforms all of the other methods that were considered. Furthermore, the results of experiment affirm the efficiency of the proposed deep learning model for software maintainability prediction.

This paper proposes a multi-criteria decision making method called the neutrosophic data analytic hierarchy process (NDAHP) for the single-valued neutrosophic set (SVNS). This method is an extension of the neutrosophic analytic hierarchy process (NAHP) designed to handle actual datasets which consist of crisp values. The proposed NDAHP method uses an objective weighting mechanism whereas all other existing versions of the AHP, fuzzy AHP, and other fuzzy based AHP method in literature such as the NAHP and picture fuzzy AHP uses a subjective weighting mechanism to arrive at the decision. This makes the proposed NDAHP method effective as the weightage of the criteria which forms the input of the evaluation matrix are determined in an objective manner using actual data collected for the problem, and hence will not change according to the opinions of different decision makers which are subjective. The proposed NDAHP method is applied to a multi-criteria decision making problem related to the ranking of the financial performance of five public listed petrochemical companies trading in the main board of the Kuala Lumpur Stock Exchange (KLSE). Actual dataset of 15 financial indices for the five petrochemical companies for 2017 obtained from Yahoo! Finance was used in this paper. Following this, a brief comparative study is conducted to evaluate the performance of our NDAHP algorithm against the results of other existing SVNS-based decision making methods in the literature. The results are compared against actual results obtained from KLSE. To further verify the rankings obtained through each method, the Spearman and Pearson ranking tests are carried out on each of the decision making methods that are studied. It is proved that NDAHP produces the most accurate results, and this was further verified from the results of the Spearman and Pearson ranking tests.

A large community of research has been developed in recent years to analyze social media and social networks, with the aim of understanding, discovering insights, and exploiting the available information. The focus has shifted from conventional polarity classification to contemporary application-oriented fine-grained aspects such as, emotions, sarcasm, stance, rumor, and hate speech detection in the user-generated content. Detecting a sarcastic tone in natural language hinders the performance of sentiment analysis tasks. The majority of the studies on automatic sarcasm detection emphasize on the use of lexical, syntactic, or pragmatic features that are often unequivocally expressed through figurative literary devices such as words, emoticons, and exclamation marks. In this paper, we propose a deep learning model called sAtt-BLSTM convNet that is based on the hybrid of soft attention-based bidirectional long short-term memory (sAtt-BLSTM) and convolution neural network (convNet) applying global vectors for word representation (GLoVe) for building semantic word embeddings. In addition to the feature maps generated by the sAtt-BLSTM, punctuation-based auxiliary features are also merged into the convNet. The robustness of the proposed model is investigated using balanced (tweets from benchmark SemEval 2015 Task 11) and unbalanced (approximately 40000 random tweets using the Sarcasm Detector tool with 15000 sarcastic and 25000 non-sarcastic messages) datasets. An experimental study using the training- and test-set accuracy metrics is performed to compare the proposed deep neural model with convNet, LSTM, and bidirectional LSTM with/without attention and it is observed that the novel sAtt-BLSTM convNet model outperforms others with a superior sarcasm-classification accuracy of 97.87% for the Twitter dataset and 93.71% for the random-tweet dataset.

Nowadays, society is growing and crowded, the construction of automatic smart waste sorter machine utilizing the intelligent sensors is important and necessary. To build this system, trash classification from trash images is an important issue in computer vision to be addressed for integrating into sensors. Therefore, this study proposes a robust model using deep neural networks to classify trash automatically which can be applied in smart waste sorter machines. Firstly, we collect the VN-trash dataset that consists of 5904 images belonging to three different classes including Organic, Inorganic and Medical wastes from Vietnam. Next, this study develops a deep neural network model for trash classification named DNN-TC which is an improvement of ResNext model to improve the predictive performance. Finally, the experiments are conducted to compare the performances of DNN-TC and the state-of-the-art methods for trash classification on VN-trash dataset as well as Trashnet dataset to show the effectiveness of the proposed model. The experimental results indicate that DNN-TC yields 94% and 98% in terms of accuracy for Trashnet and VN-trash datasets respectively and thus it outperforms the state-of-the-art methods for trash classification on both experimental datasets.

Assessment of code smell for predicting software change proneness is essential to ensure its significance in the area of software quality. While multiple studies have been conducted in this regard, the number of systems studied and the methods used in this paper are quite different, thus, causing confusion for understanding the best methodology. The objective of this paper is to approve the effect of code smell on the change inclination of a specific class in a product framework. This is the novelty and surplus of this work against the others. Furthermore, this paper aims to validate code smell for predicting class change proneness to find an error in the prediction of change proneness using code smell. Six typical machine learning algorithms (Naive Bayes Classifier, Multilayer Perceptron, LogitBoost, Bagging, Random Forest, and Decision Tree) have been used to predict change proneness using code smell from a set of 8200 Java classes spanning 14 software systems. The experimental results suggest that code smell is indeed a powerful predictor of class change proneness with multilayer perceptron being the most effective technique. The sensitivity and specificity values for all the models are well over 70% with a few exceptions.

Maritime Border Collision is one of the vital concerns in coastal states since the maritime boundaries of any two countries cannot be identified easily during fishing. Maritime domain awareness and the border line control are the essential requirement which happens via recognition, and observing of boats inside their country boundary. It is necessary to identify the maritime border and alert the fisherman during the fishing. In this paper, we propose an Automatic Identification System (AIS) which can protect fishermen by notifying the country’s border. If they are nearing towards the International Maritime Border Line (IMBL), an alert will be sent to coast guards via VHF set. Using the inbuilt GPS, AIS can find the location and transmits to the embedded systems, which gathers the recent position by comparing autonomy and longitudinal values with the existing assessment. The proposed system is validated under a case study in the maritime border between India and Sri Lanka, which is identified as Gulf of Mannar. It has been revealed that fishermen can aware that they are about to near the nautical border by means of visual and audio alert. Then, protectors in the coast preserve support and afford supplementary assist to those fishermen. This system also provides collision avoidance by using AIS/ ultrasonic sensors. It has better performance than the relevant methods such as RF (Charan et al. 2016), ECDIS (Vanparia and Ghodasara, International Journal of Computer Applications & Information Technology, 1:58–64, 2014), Android (Kumar et al. 2016), GSM and GPS (Sivagnanam et al., International Journal of Innovative Research in Advanced Engineering (IJIRAE), 2:124–132, 2015).

Complex fuzzy theory has strong practical background in many important applications, especially in decision-making support systems. Recently, the Mamdani Complex Fuzzy Inference System (M-CFIS) has been introduced as an effective tool for handling events that are not restricted to only values of a given time point but also include all values within certain time intervals (i.e., the phase term). In such decision-making problems, the complex fuzzy theory allows us to observe both the amplitude and phase values of an event, thus resulting in better performance. However, one of the limitations of the existing M-CFIS is the rule base that may be redundant to a specific dataset. In order to handle the problem, we propose a new Mamdani Complex Fuzzy Inference System with Rule Reduction Using Complex Fuzzy Measures in Granular Computing called M-CFIS-R. Several fuzzy similarity measures such as Complex Fuzzy Cosine Similarity Measure (CFCSM), Complex Fuzzy Dice Similarity Measure (CFDSM), and Complex Fuzzy Jaccard Similarity Measure (CFJSM) together with their weighted versions are proposed. Those measures are integrated into the M-CFIS-R system by the idea of granular computing such that only important and dominant rules are being kept in the system. The difference and advantage of M-CFIS-R against M-CFIS is the usage of the training process in which the rule base is repeatedly changed toward the original base set until the performance is better. By doing so, the new rule base in M-CFIS-R would improve the performance of the whole system. Experiments on various decision-making datasets demonstrate that the proposed M-CFIS-R performs better than M-CFIS.

Unmanned aerial vehicles (UAVs)/drones have become very popular in recent years as they are widely used in several domains. They are widely used in both military and civilian applications such as aerial photography, entertainment, search and rescue missions, reconnaissance, traffic monitoring, and logistics. Typically, UAVs are operated from a controller or a ground control station (GCS) with the help of different communication protocols such as MAVLink, UranusLink, UAVCAN. These communication protocols are used to exchange messages. The messages contain considerable information about the UAV and certain control commands sent from GCS to UAV or UAV to GCS. Though these protocols provide better communication along with secure aspects, however, mostly there is no subtle mechanism for securing these messages and are prone to many security attacks such as man-in-the-middle (MITM) attack, denial-of-services (DoS) attack, packet data injection attack, and eavesdropping. This can result in serious consequences, for instance, crash land of a military or civilian UAV, steal important data of a military operation, false injection of reports in a reconnaissance or search and rescue operation, and many more. So, there is a need for a secure communication protocol which can ensure the required security standard sets for communication of UAVs. This survey presents the applications, general architecture, attacks on UAVs, and an insight into the security issues of UAV’s communication protocols and proposes a new secure communication protocol for the stated issues of UAVs.

Medical diagnosis is a procedure for the investigation of a person’s symptoms on the basis of disease. This problem has been investigated and applied to personal healthcare systems in medicine. The relevant methods have limitations regarding neutrosophication, deneutrosophication, similarity measures, correlation coefficients, distance measure, and patients’ history. In this paper, we propose a novel neutrosophic recommender system for medical diagnosis based on algebraic neutrosophic measures. Specifically, a single-criterion neutrosophic recommender system (SC-NRS) and a multi-criteria neutrosophic recommender system (MC-NRS) accompanied by algebraic operations such as union, complement and intersection are proposed. Several types of similarity measures based on the algebraic operations and their theoretic properties are investigated. A prediction formula and a new forecast algorithm using the proposed algebraic similarity measures are designed. The proposed method is experimentally validated on some benchmark medical datasets against the relevant ones namely ICSM, DSM, CARE and CFMD. The experiments demonstrate that the proposed method has better Mean Square Error (MSE) than the other algorithms. Besides, there is no large increase in computational time taken by the proposed method and other algorithms. Experiments by various cases of parameters suggest that the MSE values remain almost the same for each dataset when randomly changing the values of parameters in all the medical datasets. Lastly, the strength of all the algorithms is analyzed through ANOVA one-way test and Kruskal-Wallis test. The proposed method has better accuracy than the related algorithms. Experimental results support the advantage and superiority of the proposed method.

Some new similarity measures for link prediction based on fuzzy and neutrosophic environments are proposed. It aims to determine possible association between two objects in a social network represented by a graph including nodes and edges. It is widely used in various domains such as in the co-authorship network and protein-interaction systems. Similarity measure is an important tool for such the determination. Herein, some new fuzzy and neutrosophic measures are proposed accompanied with mathematical properties. The validation on the co-authorship network datasets demonstrates the efficiency of the proposed method.

In this paper, a new method for handling the Multi-Input Multi-Output Student Academic Performance Prediction (MIMO SAPP) problem is proposed. The MIMO SAPP aims to predict the future performance of a student after being enrolled into a university. The existing methods have limitations of using a parameter set and an unsuitable training strategy. Thus, the new method called MANFIS-S (Multi Adaptive Neuro-Fuzzy Inference System with Representative Sets) uses multiple parameter sets and a special learning strategy to resolve those weaknesses. Specifically, the idea of multiple parameter sets is to approximate the MANFIS-S model with many meaningful parameters to ensure the performance of system. This is regarded as the representative problem, which is mathematically formulated and theoretically validated. The idea for the special learning strategy is to use global and local training. In global training, a random parameter set is trained from the first to the last record of the database. Each time of training results in a set of parameters. Global training will rectify and achieve a meaningful subset of parameters by the last training process. In local training, there are 2 types of parameters in MANFIS-S namely premise and consequent that are trained by the gradient descent and Particle Swarm Optimization in a hybrid way. Lastly, for a new record in the testing set, Fuzzy K-Nearest Neighbor is used to find which group it belongs to. The proposed MANFIS-S model is experimentally validated against ANFIS, MANFIS, OneR and Random Tree in a benchmark student performance dataset from UCI, a real student performance dataset from VNU University of Science, Vietnam, and 3 educational datasets taken from KDD Cup. The experiments demonstrated the superiority of MANFIS-S over the related algorithms in term of accuracy.

In this paper, we investigate the privacy issue that is remained in the spatial association rule mining (SARM). The main aim of SARM is to calculate the relationship among attributes according to their geographic locations. However, the major problem of the distributed data mining is the privacy and security issues, which require executing global results without disclosing private information by third parties. The problem of privacy preservation for SARM in distributed environments is controlled by the proposed algorithm, which is able to extract association among different numbers of attributes in geo-graphical distributed database with high privacy. The proposed algorithm is validated in term of data utility rate, efficiency and privacy preservation against the existing algorithms. It has been revealed that this algorithm decreases the execution time, memory requirements, and privacy failure rate when the size of database increases within the geographically distributed database environment.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link Association rule mining (ARM) aims to find out association rules that satisfy predefined minimum support and confidence from a given database. However, in many cases ARM generates extremely large number of association rules, which are impossible for end users to comprehend or validate, thereby limiting the usefulness of data mining results. In this paper, we propose a new mining algorithm based on Animal Migration Optimization (AMO), called ARM-AMO, to reduce the number of association rules. It is based on the idea that rules which are not of high support and unnecessary are deleted from the data. Firstly, Apriori algorithm is applied to generate frequent itemsets and association rules. Then, AMO is used to reduce the number of association rules with a new fitness function that incorporates frequent rules. It is observed from the experiments that, in comparison with the other relevant techniques, ARM-AMO greatly reduces the computational time for frequent item set generation, memory for association rule generation, and the number of rules generated.

Intuitionistic fuzzy sets (IFSs) are successful to handle the uncertain situations of data. Distance measures of IFSs are important in the evaluation of IFSs relationships. In this paper, we analyzed the disadvantages of existing distance measures of IFSs and proposed a new distance measure called H-max of IFSs. We continued to point out some new results on intuitionistic t-norms and intuitionistic t-conorms and evaluated distance measure between two IFSs which are basically structured from these operations. Further, we combined the classification of t-representable intuitionistic fuzzy t-norms and t-conorms with the proposed distance measure to study some interesting properties. Moreover, we studied De Morgan triplets of IFSs based on the proposed distance measure. Finally, we applied the proposed distance measure to medical diagnosis problem examples and experimental validation on real-world datasets to check the applicability and effectiveness.

Shear strength of the soil is an important engineering parameter used in the design and audit of geo-technical structures. In this research, we aim to investigate and compare the performance of four machine learning methods, Particle Swarm Optimization - Adaptive Network based Fuzzy Inference System (PANFIS), Genetic Algorithm - Adaptive Network based Fuzzy Inference System (GANFIS), Support Vector Regression (SVR), and Artificial Neural Networks (ANN), for predicting the strength of soft soils. For this purpose, case studies of 188 plastic clay soil samples collected from two major projects, Nhat Tan and Cua Dai bridges in Viet Nam have been used for generating training and testing datasets for constructing and validating the models. Validation and comparison of the models have been carried out using RMSE, and R. The results show that the PANFIS has the highest prediction capability (RMSE = 0.038 and R = 0.601), followed by the GANFIS (RMSE = 0.04 and R = 0.569), SVR (RMSE = 0.044 and R = 0.549), and ANN (RMSE = 0.059 and R = 0.49). It can be concluded that out of four models the PANFIS indicates as a promising technique for prediction of the strength of soft soils.

Background Computerized medical diagnosis systems from X-Ray images are of great interest to physicians for accurate decision making of possible diseases and treatments. Subclinical disease has no recognizable clinical findings, thus it is desirable to segment the dental X-Ray image into groups and then use soft computing methods to check the possibility of whether or not any disease occurs therein. Methods The current work proposed a novel framework called Dental Diagnosis System (DDS) for dental diagnosis based on the hybrid approach of segmentation, classification and decision making. It utilized the best dental image segmentation method based on semi-supervised fuzzy clustering for the segmentation task. A new graph-based clustering algorithm called APC+ for the classification task was proposed. A new decision making procedure was designed to determine the final disease from a group of diseases found from the segments. Results The proposed DDS was modeled under the real dental case of Hanoi Medical University, Vietnam including 87 dental images of five popular diseases, namely: root fracture, incluse teeth, decay, missing teeth, and resorption of periodontal bone. The DDS accuracy is 92.74% which is superior to the other methods namely fuzzy inference system (89.67%), fuzzy k-nearest neighbor (80.05%), prim spanning tree (58.46%), kruskal spanning tree (58.46%), and affinity propagation clustering (90.01%). Conclusion Empirical results established that superior performance of the DDS to other related methods the findings of the achieved results can assist dental clinicians in their professional work.

Link prediction in online social networks is used to determine new interactions among its members which are likely to occur in the future. Link prediction in the co-authorship network has been rega...

In this paper, we provide a state of-the-art survey over Bitcoin related technologies and sum up various challenges. Bitcoin is the first and most prevalent decentralized crypto-currency to date. It is decentralized peer-to-peer digital currency in which coins are produced by an appropriated set of excavators and exchange are communicated by means of a peer-to-peer organize. While Bitcoin gives some level of secrecy by urging clients to have any number of irregular looking Bitcoin addresses, late research demonstrates that this level of obscurity is fairly low. This urges clients to associate with the Bitcoin arrange through anonymizers like Tor and propels advancement of default Tor usefulness for prevalent versatile customers. A low-asset aggressor can increase full control of data streams between all clients who utilized Bitcoin over Tor. Specifically, the aggressor can connect together client’s exchanges paying little respect to pen names, control which Bitcoin squares and exchanges are handed-off to the client and can defer or dispose of client’s exchanges and pieces. Bitcoins have risen as a conceivable competitor to regular monetary forms, yet other crypto-monetary forms have similarly showed up as competitors to the Bitcoin currency. The extending business sector of crypto-monetary forms now includes capital comparable to 1010 US Dollars, furnishing the scholarly community with a bizarre chance to examine the development of significant worth. Bitcoin is an absolutely online virtual currency, unbaked by either physical wares or sovereign commitment; rather, it depends on a mix of cryptographic security and a peer-to-peer protocol for seeing settlements. Understanding Bitcoin related technologies and challenges would help maximize its usage in community.

Display Omitted We considered the problem of clustering 3D Wireless Sensor Network.A mathematical model considering 3D energy functions was proposed.A novel fuzzy clustering scheme was presented to determine clustering results.Experimental validation was performed on real 3D terrains.The results revealed superiority of the proposed method vs. the related ones. In this paper, we consider the sensor-energy optimization in 3D Wireless Sensor Networks (WSNs), which determines an optimal topology of sensors to prolong the network lifetime and reduce the energy expenditure. A new mathematical model for clustering in 3D WSN, considering energy consumption, constraints of communication and a 3D energy function, is presented. Using the Lagrange multiplier method, solutions of the model consisting of cluster centres and the membership matrix are computed and used in the new algorithm, called FCM-3 WSN. Experimental validation on real 3D datasets demonstrates that FCM-3 WSN outperforms the relevant methods, namely, Low-Energy Adaptive Clustering Hierarchy (LEACH), Centralized LEACH (LEACH-C), Single-hop Clustering and Energy-Efficient Protocol (SCEEP), Hybrid-Low Energy Adaptive Clustering Hierarchy (H-LEACH), K-Means and Fuzzy C-Means (FCM).

In this paper, we propose a novel semi-supervised fuzzy clustering algorithm with spatial constraints for dental segmentation from X-ray images. The detailed contributions include: i) Formulating the spatial features of a dental X-ray image in a dental feature database; ii) Modeling the dental segmentation problem in the form of semi-supervised fuzzy clustering with spatial constraints; iii) Solving the model by the Lagrange multiplier method; iv) Determining the additional information for clustering process by mixing optimal results of Fuzzy C-Means with spatial constraints; v) Proposing a novel Semi-Supervised Fuzzy Clustering algorithm with Spatial Constraints (SSFC-SC) that combines those processes for dental segmentation. The new algorithm is validated on a real dataset from Hanoi Medical University, Vietnam including 56 dental images. The experimental results reveal that the proposed work has better accuracy than the original semi-supervised fuzzy clustering and other relevant methods. We also suggest the most appropriate values of parameters that should be opted for the algorithm.

In practical dentistry, dentists use their experience to examine dental X-ray images and to derive symptoms from patients for concluding possible diseases. This method is based solely on the own dentists' experience. Dental diagnosis from X-Ray images is proposed to support for dentists in their decision making. This paper presents an application of consultant system for dental diagnosis from X-Ray images based on fuzzy rule. Fuzzy rule was applied in many applications and has important role in computational intelligence, data mining, machine learning, etc. Based on a dental X-ray image dataset, we use Fuzzy C-Means to classify them into clusters and construct the rule set. Fuzzy Inference System is then used to evaluate the rules by three validity indices. These rules accompanied with symptoms from patients help dentists in diagnosing dental diseases. This method is implemented and experimentally validated on the real dataset of Hanoi Medical University Hospital, Vietnam against the related algorithms.

Fuzzy clustering is a useful segmentation tool which has been widely used in many applications in real life problems such as in pattern recognition, recommender systems, forecasting, etc. Fuzzy clustering algorithm on picture fuzzy set (FC-PFS) is an advanced fuzzy clustering algorithm constructed on the basis of picture fuzzy set with the appearance of three membership degrees namely the positive, the neutral and the refusal degrees combined within an entropy component in the objective function to handle the problem of incomplete modeling in fuzzy clustering. A disadvantage of FC-PFS is its capability to handle complex data which include mix data type (categorical and numerical data) and distinct structured data. In this paper, we propose a novel picture fuzzy clustering algorithm for complex data called PFCA-CD that deals with both mix data type and distinct data structures. The idea of this method is the modification of FC-PFS, using a new measurement for categorical attributes, multiple centers of one cluster and an evolutionary strategy – particle swarm optimization. Experiments indicate that the proposed algorithm results in better clustering quality than others through clustering validity indices.

Artificial intelligence has been applied in various fields including medicine. Cancer is one of the most common and dangerous diseases that need to be diagnosed for the best treatment. Herein, we introduce a new model using complex fuzzy inference system (CFIS) for colorectal cancer diagnosis. The time series characteristic of colorectal disease is signification in examination, diagnosis, and treatment. The model is implemented on a colonography dataset to evaluate the performance. The obtained results show that it gets high quality in comparison with some other compared methods.

A diagnosing heart disease is significant for researchers investigated to develop applications for improvement of an expert system in health care. The aim of this paper is to design an expert system in diagnoses using deep learning neural networks and fuzzy rules for physicians to make the right decisions in diagnosing heart disease. Based on the historical data of patients, our proposed approach is to quantify uncertain information represented in fuzzy rules in the Knowledge Base for diagnosing heart disease domain. The proposed approach helps doctors to make the right decision in the diagnosing heart disease. All doctors’ preferences can be updated to the Knowledge Base which can significantly improve the quality of heart disease diagnoses. The experimental results show that the proposed system is capable of diagnosing heart disease with a high confidence of accuracy in decision-making as compared to conventional neural networks.

The framework of the T-spherical fuzzy set is a recent development in fuzzy set theory that can describe imprecise events using four types of membership grades with no restrictions. The purpose of this manuscript is to point out the limitations of the existing intuitionistic fuzzy Einstein averaging and geometric operators and to develop some improved Einstein aggregation operators. To do so, first some new operational laws were developed for T-spherical fuzzy sets and their properties were investigated. Based on these new operations, two types of Einstein aggregation operators are proposed namely the Einstein interactive averaging aggregation operators and the Einstein interactive geometric aggregation operators. The properties of the newly developed aggregation operators were then investigated and verified. The T-spherical fuzzy aggregation operators were then applied to a multi-attribute decision making (MADM) problem related to the degree of pollution of five major cities in China. Actual datasets sourced from the UCI Machine Learning Repository were used for this purpose. A detailed study was done to determine the most and least polluted city for different perceptions for different situations. Several compliance tests were then outlined to test and verify the accuracy of the results obtained via our proposed decision-making algorithm. It was proved that the results obtained via our proposed decision-making algorithm was fully compliant with all the tests that were outlined, thereby confirming the accuracy of the results obtained via our proposed method.

Customer retention is invariably the top priority of all consumer businesses, and certainly it is one of the most critical challenges as well. Identifying and gaining insights into the most probable cause of churn can save from five to ten times in terms of cost for the company compared with finding new customers. Therefore, this study introduces a full-fledged geodemographic segmentation model, assessing it, testing it, and deriving insights from it. A bank dataset consisting 11,000 instances, which consists of 10,000 instances for training and 10,000 instances for testing, with 14 attributes, has been used, and the likelihood of a person staying with the bank or leaving the bank is computed with the help of logistic regression. Base on the proposed model, insights are drawn and recommendations are provided. Stepwise logistic regression methods, namely, backward elimination method, forward selection method, and bidirectional model are constructed and contrasted to choose the best among them. Future forecasting of the models has been done by using cumulative accuracy profile (CAP) curve analysis.

This work focuses on models selection in a multi-model air quality ensemble system. The models are operational long-range transport and dispersion models used for the real-time simulation of pollutant dispersion or the accidental release of radioactive nuclides in the atmosphere. In this context, a methodology based on temporal hierarchical agglomeration is introduced. It uses fuzzy similarity relations combined by a transitive consensus matrix. The methodology is adopted for individuating a subset of models that best characterize the predicted atmospheric pollutants from the ETEX-1 experiment and discard redundant information.

In this paper, we propose a novel fuzzy inference system on picture fuzzy set called picture inference system (PIS) to enhance inference performance of the traditional fuzzy inference system. In PIS, the positive, neutral and negative degrees of the picture fuzzy set are computed using the membership graph that is the combination of three Gaussian functions with a common center and different widths expressing a visual view of degrees. Then, the positive and negative defuzzification values, synthesized from three degrees of the picture fuzzy set, are used to generate crisp outputs. Learning in PIS including training centers, widths, scales and defuzzification parameters is also discussed. The system is adapted for all architectures such as the Mamdani, the Sugeno and the Tsukamoto fuzzy inferences. Experimental results on benchmark UCI Machine Learning Repository datasets and an example in control theory - the Lorenz system are examined to verify the advantages of PIS.

Data are getting larger, and most of them are necessary for our businesses. Rapid explosion of data brings us a number of challenges relating to its complexity and how the most important knowledge can be captured in reasonable time. Fuzzy C-means (FCM)—one of the most efficient clustering algorithms which have been widely used in pattern recognition, data compression, image segmentation, computer vision and many other fields—also faces the problem of processing large datasets. In this paper, we propose some novel hybrid clustering algorithms based on incremental clustering and initial selection to tune up FCM for the Big Data problem. The first algorithm determines meshes of rectangle covering data points as the representatives, while the second one considers data points that have high influence to others as the representatives. The representatives are then clustered by FCM, and the new centers are selected as initial ones for clustering of the dataset. Theoretical analyses of the new algorithms including comparison of quality of solutions when clustering the representatives set versus the entire set are examined. The experimental results on both simulated and real datasets show that total computational time of the new methods including time of finding representatives and clustering is faster than those of other relevant algorithms. The validation on clustering quality is also examined. The findings of this paper have great impact and significance to researches in the fields of soft computing and Big Data processing. It is obvious that computing methodologies nowadays are facing with huge amount of diverse and complex data structures. Speed of processing is the main priority when considering effectiveness of a specific method. The findings demonstrated practical algorithms and investigated their characteristics that could be referenced by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

We presented a novel automatic picture fuzzy clustering method called AFC-PFS.It automatically determined the most suitable number of clusters for a dataset.Picture Composite Cardinality was used for best matching of the number of clusters.AFC-PFS created both the most suitable number of clusters and related solutions.It produced closest numbers of clusters to real ones. Fuzzy clustering plays an important role in pattern recognition and knowledge discovery. Recently, there has been a great interest of developing fuzzy clustering algorithms on advanced fuzzy sets such as Picture Fuzzy Clustering (FC-PFS) which is an extension of Fuzzy C-Means on Picture Fuzzy Set. A major disadvantage of FC-PFS is how to define a prior number of clusters before clustering. Because each dataset has distinctive features and distributions of patterns, determining such the number for a clustering algorithm would result in good quality. In this paper, we propose a method called Automatic Picture Fuzzy Clustering (AFC-PFS) for determining the most suitable number of clusters for FC-PFS. It is a hybrid method between Particle Swarm Optimization (PSO) and FC-PFS where combined solutions consisting of the number of clusters and equivalent clustering centers and membership matrices are packed and optimized in PSO. A new term namely Picture Composite Cardinality is also given to determine a suitable number of clusters. AFC-PFS is empirically validated on benchmark datasets of UCI Machine Learning Repository by different clustering quality indices. The results show that AFC-PFS has better performance than the relevant methods.

In this paper, we extend the basic distance measure in picture fuzzy sets to the new measures called generalized picture distance measures and picture association measures. Some properties of these measures are examined. An application to clustering problem is given to illustrate applicability of the proposed works.

Display Omitted We focused on the clustering problem in picture fuzzy sets.A generalized picture distance measure was proposed.A novel hierarchical picture clustering method based on the measure was presented.Its clustering quality is better than those of relevant clustering algorithms.A clustering system for picture fuzzy sets was designed. Picture fuzzy set (PFS), which is a generalization of traditional fuzzy set and intuitionistic fuzzy set, shows great promises of better adaptation to many practical problems in pattern recognition, artificial life, robotic, expert and knowledge-based systems than existing types of fuzzy sets. An emerging research trend in PFS is development of clustering algorithms which can exploit and investigate hidden knowledge from a mass of datasets. Distance measure is one of the most important tools in clustering that determine the degree of relationship between two objects. In this paper, we propose a generalized picture distance measure and integrate it to a novel hierarchical picture fuzzy clustering method called Hierarchical Picture Clustering (HPC). Experimental results show that the clustering quality of the proposed algorithm is better than those of the relevant ones.

Weather nowcasting comprises the detailed description of the current weather along with forecasts obtained by extrapolation for very short-range period of zero to six hours ahead. It is particularly useful when forecasting complicated processes such as rainfall, clouds, and rapidly developing or changing storms. This plays an important role for daily activities like working, traveling, daily planning, flying, etc. Weather forecast can be solved by latest radar, satellite or observational data. However, the main challenges associated with nowcasting are the flawed characterization of transitions between different meteorological structures. In this paper, we propose two novel hybrid forecast methods based on picture fuzzy clustering for weather nowcasting. The first method named as PFC-STAR uses a combination of picture fuzzy clustering and spatiotemporal regression. The second one named as PFC-PFR integrates picture fuzzy clustering with picture fuzzy rule. Those methods are equipped with advanced training processes which enhance the accuracy of predicted outputs. The experiments indicate that the proposed methods are better than the relevant ones for weather nowcasting.

The Recommender System (RS) is an efficient tool for decision makers that assists in the selection of appropriate items according to their preferences and interests. This system has been applied to various domains to personalize applications by recommending items such as books, movies, songs, restaurants, news articles and jokes, among others. An important issue for the RS that has greatly captured the attention of researchers is the new user cold-start problem, which occurs when there is a new user that has been registered to the system and no prior rating of this user is found in the rating table. In this paper, we first present a classification that divides the relevant studies addressing the new user cold-start problem into three major groups and summarize their advantages and disadvantages in a tabular format. Next, some typical algorithms of these groups, such as MIPFGWC-CS, NHSM, FARAMS and HU–FCF, are described. Finally, these algorithms are implemented and validated on some benchmark RS datasets under various settings of the new user cold start. The experimental results indicate that NHSM achieves better accuracy and computational time than the relevant methods.

Fuzzy clustering plays an important role in data-mining, especially in decision making, pattern recognition, etc. There have been many approaches to improve fuzzy clustering performance and quality when it was first introduced by Bezdek. Recently, an approach related to data with sub-information has been most concerned. The idea of this approach combines the advantages of fuzzy C-means method with the benefits of additional information so-called the semi-supervised fuzzy clustering algorithms (SSFC). Through this report, a series of typical SSFC algorithms are presented in brief to give an overview of this approach.

Dental X-ray image segmentation has an important role in practical dentistry and is widely used in the discovery of odontological diseases, tooth archeology and in automated dental identification systems. Enhancing the accuracy of dental segmentation is the main focus of researchers, involving various machine learning methods to be applied in order to gain the best performance. However, most of the currently used methods are facing problems of threshold, curve functions, choosing suitable parameters and detecting common boundaries among clusters. In this paper, we will present a new semi-supervised fuzzy clustering algorithm named as SSFC-FS based on Interactive Fuzzy Satisficing for the dental X-ray image segmentation problem. Firstly, features of a dental X-Ray image are modeled into a spatial objective function, which are then to be integrated into a new semi-supervised fuzzy clustering model. Secondly, the Interactive Fuzzy Satisficing method, which is considered as a useful tool to solve linear and nonlinear multi-objective problems in mixed fuzzy-stochastic environment, is applied to get the cluster centers and the membership matrix of the model. Thirdly, theoretically validation of the solutions including the convergence rate, bounds of parameters, and the comparison with solutions of other relevant methods is performed. Lastly, a new semi-supervised fuzzy clustering algorithm that uses an iterative strategy from the formulae of solutions is designed. This new algorithm was experimentally validated and compared with the relevant ones in terms of clustering quality on a real dataset including 56 dental X-ray images in the period 2014–2015 of Hanoi Medial University, Vietnam. The results revealed that the new algorithm has better clustering quality than other methods such as Fuzzy C-Means, Otsu, eSFCM, SSCMOO, FMMBIS and another version of SSFC-FS with the local Lagrange method named SSFC-SC. We also suggest the most appropriate values of parameters for the new algorithm.

We concentrated on the dental X-ray image segmentation problem.A new framework combining Otsu, FCM and semi-supervised fuzzy clustering was shown.It was tested on real datasets from Hanoi Medical University in terms of accuracy.The new framework has better performance than the relevant methods.Suggestions on means and variances of the criteria of the new framework were made. Dental X-ray image segmentation (DXIS) is an indispensable process in practical dentistry for diagnosis of periodontitis diseases from an X-ray image. It has been said that DXIS is one of the most important and necessary steps to analyze dental images in order to get valuable information for medical diagnosis support systems and other recognition tools. Specialized data mining methods for DXIS have been investigated to achieve high accuracy of segmentation. However, traditional image processing and clustering algorithms often meet challenges in determining parameters or common boundaries of teeth samples. It was shown that performance of a clustering algorithm is enhanced when additional information provided by users is attached to inputs of the algorithm. In this paper, we propose a new cooperative scheme that applies semi-supervised fuzzy clustering algorithms to DXIS. Specifically, the Otsu method is used to remove the Background area from an X-ray dental image. Then, the FCM algorithm is chosen to remove the Dental Structure area from the results of the previous steps. Finally, Semi-supervised Entropy regularized Fuzzy Clustering algorithm (eSFCM) is opted to clarify and improve the results based on the optimal result from the previous clustering method. The proposed framework is evaluated on a real collection of dental X-ray image datasets from Hanoi Medical University, Vietnam. Experimental results have revealed that clustering quality of the cooperative framework is better than those of the relevant ones. The findings of this paper have great impact and significance to researches in the fields of medical science and expert systems. It has been the fact that medical diagnosis is often an experienced and case-based process which requests long time practicing in real patients. In many situations, young clinicians do not have chance for such the practice so that it is necessary to utilize a computerized medical diagnosis system which could simulate medical processes from previous real evidences. By learning from those cases, clinicians would improve their experience and responses for later ones. In the view of expert systems, this paper made uses of knowledge-based algorithms for a practical application. This shows the advantages of such the algorithm in the conjunction domain between expert systems and medical informatics. The findings also suggested the most appropriate configuration of the algorithm and parameters for this problem that could be reused by other researchers in similar applications. The usefulness and significance of this research are clearly demonstrated within the extent of real-life applications.

In this paper, we propose a novel multiple fuzzy clustering method based on internal clustering validation measures with gradient descent. Firstly, some single fuzzy clustering algorithms such as Fuzzy C-Means, Kernel Fuzzy C-Means and Gustafson–Kessel are used to construct similarity matrixes for each partition. Secondly, those similarity matrixes are aggregated into a final one by means of the direct sum of weighted vectors where the values of weights are determined by internal clustering validation measures. Finally, final membership matrix is calculated by minimizing the sum of square errors through the gradient descent method. The proposed approach has been validated in terms of clustering quality on UCI Machine Learning Repository datasets. Experimental results show that the proposed approach’s performance is better than those of other ensemble and standalone methods.