Volume 27, Issue 2

Despite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.

Internet of Things (IoT) has been a most important research area for almost a decade now, where a huge network of billions or trillions of “things” communicating with one another is facing many technical and application challenges. Although there are many uncertainties about its security and privacy, the literature presents different techniques to handle the security issues and challenges in order to develop a well-defined security architecture. This paper reviews 50 research papers that are related to the security of IoT. The security techniques were classified with respect to time consumption, energy consumption, power consumption, lightweight property, reliability, robustness, and smart applicability. Also, the security techniques were analyzed based on the considered attacks, application, utilized simulation tool, security model, and attributes. The objective of the survey is focused on the security loopholes arising out of the information exchange technologies used in IoT. Finally, the important research issues are addressed for the researchers to find the way for further research in the security of IoT. The survey signifies that multilevel and mutual authentication based on attribute-based profile modeling bring more security for access control and authentication.

Neutrosophy (neutrosophic logic) is used to represent uncertain, indeterminate, and inconsistent information available in the real world. This article proposes a method to provide more sensitivity and precision to indeterminacy, by classifying the indeterminate concept/value into two based on membership: one as indeterminacy leaning towards truth membership and the other as indeterminacy leaning towards false membership. This paper introduces a modified form of a neutrosophic set, called Double-Valued Neutrosophic Set (DVNS), which has these two distinct indeterminate values. Its related properties and axioms are defined and illustrated in this paper. An important role is played by clustering in several fields of research in the form of data mining, pattern recognition, and machine learning. DVNS is better equipped at dealing with indeterminate and inconsistent information, with more accuracy, than the Single-Valued Neutrosophic Set, which fuzzy sets and intuitionistic fuzzy sets are incapable of. A generalised distance measure between DVNSs and the related distance matrix is defined, based on which a clustering algorithm is constructed. This article proposes a Double-Valued Neutrosophic Minimum Spanning Tree (DVN-MST) clustering algorithm, to cluster the data represented by double-valued neutrosophic information. Illustrative examples are given to demonstrate the applications and effectiveness of this clustering algorithm. A comparative study of the DVN-MST clustering algorithm with other clustering algorithms like Single-Valued Neutrosophic Minimum Spanning Tree, Intuitionistic Fuzzy Minimum Spanning Tree, and Fuzzy Minimum Spanning Tree is carried out.

This paper focuses on the problem of quantifying how certain words in a text affect, positively or negatively, some numeric signal. These words can lead to important decisions for significant applications such as E-commerce. For example, consider the corpus of real-estate classifieds, which we developed as a case study. Each classified has a description of a real-estate property, along with simple features such as the location and the number of bedrooms. The problem then is to identify which keywords influence the price of the property. Such identification is complicated due to the existence of simple features (numeric and nominal attributes) that also affect the price. In this research, we propose a two-stage regression model to solve this problem. To assess our contribution, we analyze, as a case study, four corpora of real-estate classifieds. The analysis shows that our model predicts the price of a real-estate unit more accurately using the accompanying text, compared to the prediction relying only on simple features. We also demonstrate the capability of our model to annotate (automatically) words that affect the price positively or negatively.

In the area of modern intelligent systems, the retrieval process of video objects is still a challenging task because objects are usually affected by object confusion, similar appearance among objects, different posing, small size of objects, and interactions among multiple objects. In order to overcome these challenges, the video object is retrieved based on the trajectory points of the multiple-motion objects. However, if an object is in an occlusion situation, the calculation of trajectory points from the objects is considerably altered. In order to overcome the above challenges, we have proposed a technique of query-specific distance and hybrid tracking model for video object retrieval. To verify the performance of the proposed method, five videos were collected from the CAVIAR dataset. Then, the proposed tracking process was applied with these five videos and the performance was analysed based on various parameters, such as precision, recall, and f-measure. From the results, we can prove that the proposed hybrid model attained a higher f-measure of 76.7% compared to that of other existing tracking models, such as the nearest neighbourhood algorithmic model and spatial-exponential weighted moving average model.

Member selection to form an effective collaboration new product development (Co-NPD) team is crucial for a successful NPD. Existing researches on member selection mostly focus on the individual attributes of candidates. However, under the background of collaboration, knowledge complementarity and collaboration performance among candidates are important but overlooked. In this paper, we propose a multi-objective optimization model for member selection of a Co-NPD team, considering comprehensively the individual knowledge competence, knowledge complementarity, and collaboration performance. Then, to solve the model, an improved adaptive genetic algorithm (IAGA) is developed. Finally, a real case is provided to illustrate the application of the model, and the IAGA is implemented to select the desired team members for optimal team composition. Additionally, the standard generic algorithm and particle swarm optimization are used to compare with the IAGA to further verify the effectiveness of the IAGA.

Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

Several users use metasearch engines directly or indirectly to access and gather data from more than one data source. The effectiveness of a metasearch engine is majorly determined by the quality of the results it returns in response to user queries. The rank aggregation methods that have been proposed until now exploit a very limited set of parameters, such as total number of used resources and the rankings they achieved from each individual resource. In this paper, we use the fuzzy-bat to merge the score computation module effectively. Initially, we give a query to different search engines we use and the top n list from each search engine is chosen for further processing our technique. We then merge the top n list based on unique links, and we do some parameter calculations such as title-based calculation, snippet-based calculation, content-based calculation, address-based calculation, link-based calculation, uniform resource locator-based calculation, and co-occurrence-based calculation. We give the solutions of the calculations with the user given the ranking of links to the fuzzy-bat to train the system. The system then ranks and merges the links we obtain from different search engines for the query we give.

Data mining techniques support numerous applications of intelligent transportation systems (ITSs). This paper critically reviews various data mining techniques for achieving trip planning in ITSs. The literature review starts with the discussion on the contributions of descriptive and predictive mining techniques in ITSs, and later continues on the contributions of the clustering techniques. Being the largely used approach, the use of cluster analysis in ITSs is assessed. However, big data analysis is risky with clustering methods. Thus, evolutionary computational algorithms are used for data mining. Though unsupervised clustering models are widely used, drawbacks such as selection of optimal number of clustering points, defining termination criterion, and lack of objective function also occur. Eventually, various drawbacks of evolutionary computational algorithm are also addressed in this paper.

In this paper, we are going to present the multiclass medical image content-based image retrieval (CBIR) system based on classification and clustering. Images are segmented using hill climbing-based segmentation (HCBS) based on the extracted visual features. In the improved HCBS technique, a clustering that is based on kernel-based fuzzy C -means is employed. In the next step, features like color, texture, edge density, region area, and visual words from the segmented images are extracted. The visual word can be extracted by using the clustering techniques. This visual word represents the uniqueness of the medical image, and it is used for better classification. Then, the image can be classified by using an optimal classifier artificial neural network based on the firefly algorithm. This classification leads to filtering out the irrelevant images from the database and reduces the search space for further retrieval process. In the second stage, the relevant images are extracted from the reduced database based on the similarity measure. The proposed CBIR technique is assessed by querying different images, and the retrieval efficiency is estimated by determining the precision-recall values for the retrieval results.

Railway freight transportation is an important part of the national economy. Accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision making of railways. After analyzing the application status of generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model by using improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of GRNN, and then the optimal spread is used for forecasting in GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared with the results of GRNN, a higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method.

To deal with the lack of accuracy and generalization ability in some single models, grain output models were built with lots of relevant data, based on the powerful non-linear reflection of the back-propagation (BP) neural network. Three kinds of grain output models were built and took advantage of – particle swarm optimization algorithm, mind evolutionary algorithm, and genetic algorithm – to optimize the BP neural network. By the use of data fusion algorithm, the outcomes of different models can be modified and fused together, and the combination-predicted outcome can be obtained finally. Taking advantage of this combination model to predict the total grain output of China, the results showed that the total grain output in 2015 was a bit larger than the actual value of about 0.0115%. It was much more accurate than the three single models. The experimental results verify the feasibility and validity of the combination model.