An exponential growth in the literature in general and the medical literature in particular raises a need for effective intelligent analysis strategies and tools to provide valuable insights to researchers about the current evolving literature. While existing applications provide more specific approaches to the problem, such as focusing on particular genome or protein information, in this paper, the proposed application provides effective and detailed analysis of PubMed. The developed tool, named KoExPubMed, follows a more generalized and holistic way by taking into consideration different types of information such as authors, countries, genes, and the interactions between them. The developed application consists of four main components; (1) keyword search and ID extraction, (2) PubMed article information and abstract retrieval, (3) country and address extraction, and (4) gene information extraction. In addition to the fundamental components, the tool provides a variety of visualization options for showing the extracted information and the related associations, including line charts for densities and countries, chord charts for collaborations of authors, network graphs for the genes mentioned together, bubble charts for gene frequencies, etc. By addressing the need for a generalized data mining tool, we propose a comprehensive application which is capable of employing data mining and machine learning techniques to extract from PubMed knowledge valuable to researchers and practitioners who are interested in closely investigating the achievements of others.

Molecular subtyping is a method of separating tumor clusters in a cancer type with common features according to molecular data and classification models. Genome datasets are taken from many different people and some genetic material, more precisely genetic markers, are obtained to predict the presence of a disease. In addition, breast cancer occurs due to mutation or modification observed in cells. miRNAs and lncRNAs take participation in cell cycle, regulation, and even chromatic inhibition of cell. For example, miRNAs function in cell cycle regulation as the degradation of mRNAs. Therefore, the aim of this work is to investigate the roles of miRNAs and lncRNAs in prognosis and characterizing the subtypes of Breast Cancer.

The aim of this work is to employ face recognition for creating learning profiles of the analysed persons who are students in this study. Generating education profiles will help experts in the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), which is a serious problem in children. Children with ADHD often have the ability and potential to learn. However, it may be difficult to reveal their capabilities and skills. Accordingly, a suffering child may have a hard time succeeding in real life when he/she is ignored and expected to mix with other children. The unrealized gap and deficiency may lead to other problems and more complicated situation with unpredictable consequences. Thanks to the system developed in this study, and the like, which will help in diagnosing the ADHD disease, and hence suffering individuals will be able to recognize their deficiencies, understand their ability to learn and adapt when approached differently in a way which suits his/her situation. This personalized handling of infected students will be an excellent guide to advance their potential and integration within the society carefully and smoothly. The system analyzes the face of a student to inspire his/her emotional state. The reported test results demonstrate how the system works well and produces high accuracy under a variety of severe conditions such as skewed angle, less illumination, accessories etc.

The development in technology has led to the generation of huge amounts of data from various sources, including biological data, social networking data, etc. Accordingly, social network analysis has received considerable attention with the availability of more raw datasets which could be realized using a network structure. Most of the datasets can be represented as a social network which is a graph consisting of actors having relationships. Many tools exist for social network analysis inspired to extract knowledge from the networks. NetDriller has been developed as a social network extraction, manipulation and analysis tool to cover the lack that exists in other tools. It is capable of constructing social networks from raw data by employing a variety of data mining and machine learning techniques. In this paper, we describe an extend version of NetDriller, which has some new essential functions, including social network construction using data collection from Twitter, DBLP and IEEE. We also added (1) a new chart for viewing the network property and metrics, and (2) new graph manipulation techniques using GUI to keep the tool up to date with the huge volume of networks and the different types of raw data available on the web.

In this research, we introduce a model to detect inconsistent & anomalous samples in tabular labeled datasets which are used in machine learning classification tasks, frequently. Our model, abbreviated as the ClaCO (Classes vs. Communities: SNA for Outlier Detection), first converts tabular data with labels into an attributed and labeled undirected network graph. Following the enrichment of the graph, it analyses the edge structure of the individual egonets, in terms of the class and community belongings, by introducing a new SNA metric named as ‘the Consistency Score of a Node - CSoN’. Through an exhaustive analysis of the ego network of a node, CSoN tries to exhibit consistency of a node by examining the similarity of its immediate neighbors in terms of shared class and/or shared community belongings. To prove the efficiency of the proposed ClaCO, we employed it as a subsidiary method for detecting anomalous samples in the train part in the traditional ML classification task. With the help of this new consistency score, the least CSoN scored set of nodes flagged as outliers and removed from the training dataset, and remaining part fed into the ML model to see the effect on classification performance with the ‘whole’ dataset through competing outlier detection methods. We have shown this outlier detection model as an efficient method since it improves classification performance both on the whole dataset and reduced datasets with competing outlier detection methods, over several known both real-life and synthetic datasets.

Nowadays, with the rapid increase in popularity of online social networks (OSNs), these platforms are realized as ideal places for spammers. Unfortunately, these spammers can easily publish malicious content, advertise phishing scams by taking advantage of OSNs. Therefore, effective identification and filtering of spam tweets will be beneficial to both OSNs and users. However, it is becoming increasingly difficult to check and eliminate spam tweets due to this great flow of posts. Motivated by these observations, in this paper we propose an approach for the detection of spam tweets using machine learning and effective preprocessing techniques. The approach proposes the advantages of the preprocessing and which of these preprocessing techniques are the most effective. To compare these techniques UtkML Twitter spam dataset is used in testing. After the most effective methods determined, the detection accuracy of the spam tweets will be better optimized by combining them. We have evaluated our solution with four different machine learning algorithms namely - Naïve Bayes Classifier, Neural Network, Logistic Regression and Support Vector Machine. With SVM Classifier, we are able to achieve an accuracy of 93.02%. Experimental results show that our approach can improve the performance of spam tweet classification effectively.

The unprecedented growth in technology has increased the importance of the required information security that is still hard to be reached. Recently, network and web application attacks have occurred frequently, causing confidential data to be stolen by the available vulnerabilities in the systems and the most prominent is in the form of open ports. This causes the CIA (Confidentiality Integrity and Availability) Triad Model to break. Penetration testing is one of the key techniques used in real life to accurately detect the possible threats and potential attacks against the system, and the first step for hackers to conduct attacks is information collection. In this paper, we present a useful schema for the active information-gathering phase that can be used during penetration testing and by system administrators. It will be the first feature of a security engine going to be implemented. The work involves an automated API-based IP and port scanner, service-version enumerator, and vulnerability detection system. This scheme is based on the Network Mapper (Nmap) to collect the information with high accuracy depending on the provided rules in our schema. Besides, the work has been implemented as a RESTful-API server, aiming at easy integration for real-life cases and allowing administrators to scan and secure their networks more quickly and easily. The effectiveness and efficiency of this technique has been proved by the various test cases applied considering different scenarios from the real world. The average time of scanning a server and detecting the vulnerabilities is 2.2 minutes. Regardless of the number of vulnerabilities, the increase in time for each open port is just about 12 seconds.

With the growth of social media, Twitter has become one of the most popularly used microblogging communication platforms between people. Due to the wide preference of Twitter, popular issues in public, events like local or global news and daily life stories can immediately publish on Twitter. Thus, a substantial number of hot topics are created by Twitter users in real-time. These topics can exhibit every incident of everyday life. Therefore, detection of hot topics can be used in many applications such as observing public judgment, product recommendation, and incidence detection. In this paper, we propose a method for detecting Twitter hot topics and evaluate the effect of multi-relations such as retweets and hashtags on hot topics. The dataset was generated by fetching tweets for a certain time and location by using GetOldTweets3 API. Then using the LDA topic modeling algorithm the hot topics were identified for each multi relation. Finally, the effect of each relation is described by using the coherence scores)

Human action recognition has recently started to find its way into applications in different applications. Accordingly, human action recognition methods are becoming increasingly important in our daily life. They are used for different purposes such as automation, security, surveillance, health, smart home systems, and customer behaviour prediction, among others. Though have more systems with methods provides a rich pool of choices, it is important to well understand the performance of these systems and their success rates in recognizing the right activities in order to decide on the most appropriate system for the current application domain. This survey tackles this issue by analyzing and commenting on the available human action recognition systems and methods.

The work described in this paper illustrates how social media is a valuable source of data which may be processed for informative knowledge discovery which may help in better decision making. We concentrate on Twitter as the source for the data to be processed. In particular, we extracted and captured tweets written in Turkish. We analyzed tweets online and real-time to determine most recent trending events, their location and time. The outcome may help predicting next hot events to be broadcasted in the news. It may also raise alert and warn people related to upcoming or ongoing disaster or an event which should be avoided, e.g., traffic jam, terror attacks, earthquake, flood, storm, fire, etc. To achieve this, a tweet may be labeled with more than one event. Named entity recognition combined with multinomial naive Bayes and stochastic gradient descent have been integrated in the process. The reported 95% success rate demonstrate the applicability and effectiveness of the proposed approach.

Chronic diseases such as heart and blood vessels are considered among the most common and serious reasons of mortality in the world. In Europe alone, over four million deaths a year (45% of all deaths) are caused by heart diseases [1]. In addition, chronic diseases are responsible for 70 % of United States deaths, and account for more than 75% of annual United States medical care cost [2]. For instance, Cardio Vascular Diseases (CVD) are considered the main cause for around 14.3% of total deaths in Denmark5, and it is the main cause for over 45% of the total death in Lebanon6. It is too costly to keep CVD patients under control locally within the vicinity of a healthcare unit. Thus, researchers recently started to realize the need for automated monitoring in health systems that are expected to reduce the overall death rate and cost associated with monitoring of patients. However, a general monitoring health system will not cover all diseases at once. Therefore, there is a pushing necessity for monitoring health systems which are dedicated to specific health cases. To contribute to the ongoing efforts, this work develops an automated system which could be customized for various chronic diseases. A mobile application based solution is proposed. Further, the work concentrates on CVD by conducting a survey in Lebanon to investigate the acceptance and awareness of ECG for remote monitoring of patients. The results are promising and reflect how specialists are aware of the need to utilize the rapid development in technology combined with the widespread usage of mobile phone which may be used as the main device to guarantee 24/7 communication link for ECG. Adopting ECG in the healthcare system will allow for capturing some valuable data which could guide the development of a recommendation system. This will issue necessary alerts to specialists and guidance to patients and their careers so that specialists could attend to the case on timely basis and patients with their careers could follow the recommendations to keep the case under control until the specialist becomes available. Finally, a secure forum based communication system will be developed to allow patients to share their experience and specialists to provide consultancy and guidance on demand.

Rapid development and wide usage of social networking applications have enabled large amounts of valuable data which can be analyzed for various reasons by companies, governments, non-profit organizations such as UN. This paper presents an evolutionary approach for detecting communities in social networks. We formulated a genetic algorithm that does not require the number of communities as input and is able to detect communities effectively in a very fast way. The performance of the proposed method is compared to its counterparts in order to show that good results can be generated. Additionally, we have done experiments using Newman's Spectral Clustering Method as a pre-processing step and it gave much better results.

Acquired immune deficiency syndrome (AIDS) is the last and the most life-threatening phase of Human Immunodeficiency Virus (HIV) disease. HIV attacks and heavily affects the immune system of the body which remains unable to resist the disease. HIV uses white blood cells to replicate itself and spreads everywhere in the body. The lifecycle of HIV disease, especially the replication stage must be prominently understood in order to develop effective drugs for treatment. HIV-1 protease enzyme is in charge of cleaving an amino acid octamer into peptides which are used to create proteins by virus. It should be scrutinized properly since it is a potential target to tightly bind drugs to protease for blocking the virus action at an early stage before cell infection. It is very critical to induce a model and predict cleavage of HIV-1 protease on octamers. Several machine learning approaches have been applied for predicting and profiling cleavage rules. However, we propose a novel general approach that can also be applied on different domains. It basically utilizes social network analysis and data mining techniques for classification. This method yet presents promising results that are comparable with existing machine learning methods, besides it gives the opportunity to validate the results obtained by using other techniques from social network analysis perspective. We have used the HIV-1 protease cleavage data set from UCI machine learning repository and demonstrated the effectiveness of our proposed method by comparing it with decision tree, Naive-Bayes and k-nearest neighbor methods.

Churn prediction is a customer relationship process that predicts for customers who are at the brink of transferring all the business to competitor. It is predicted by modeling customer behaviors in order to extract patterns. An acquaintance of a customer is more costly than retainment of an existing customer. Churn predictions shed light on members about to leave the service and support promotion activities. These attempts are utilized to avoid subscription cancellation of existing customers. Nowadays, telecommunication companies take churn prediction very serious. They strive for monitoring customers in the business by using various applications in systematic approach. Our study is based on leading internet service providing company, Turksat Satellite Communications and Cable TV Operations Company's customer behavior analysis. It is the leading internet service provider of Turkey operating in telecommunications sector. We have created a two-phase solution utilizing data mining techniques. These are time series clustering and classification techniques.

Multi objective clustering is one focused area of multi objective optimization. Multi objective optimization attracted many researchers in several areas over a decade. Utilizing multi objective clustering mainly considers multiple objectives simultaneously and results with several natural clustering solutions. Obtained result set suggests different point of views for solving the clustering problem. This paper assumes all potential solutions belong to different experts and in overall, ensemble of solutions finally has been utilized for finding the final natural clustering. We have tested on categorical, further on mixed credit card dataset with different objectives, and compared them against single objective clustering result in terms of purity.

Recommendation systems are one of the research areas studied intensively in the last decades and several solutions have been elicited for problems in different domains for recommending. Recommendation may differ as content, collaborative filtering or both. Other than known challenges in collaborative filtering techniques, accuracy and computational cost at a large scale data are still at saliency. In this paper we proposed an approach by utilizing matrix value factorization for predicting rating i by user j with the sub matrix as k-most similar items specific to user i for all users who rated them all. In an attempt, previously predicted values are used for subsequent predictions. In order to investigate the accuracy of neighborhood methods we applied our method on Netflix Prize [1]. We have considered both items and users relationships on Netflix dataset for predicting movie ratings. We have conducted several experiments.

In parallel to rapid growth of internet technologies, security becomes more critical in various real life applications such as e-finance, e-health, and e-government. These applications strictly require data authentication mechanisms. To address this essential issue, we grasp the idea of client based authenticity for interactive web technologies. We proposed a novel client based web form signing and checking with XML data structure method. Our method specifically uses XML structure for the involvement of data exchange between web applications. Our method curbs the DoS (Denial of Service) attacks for protection of the server. In order to illustrate our ideas, we adapted our digital signature mechanism on health related forms with two commonly used web browsers.

There are many activities that people prefer/opt out attending and these events are announced for attracting people. An intelligent recommendation system can be used in a social networking site in order to recommend people according to content and collaboration assessment. This study is an effort to recommend events to users within a social networking site. It can be any networking environment. We have used social environment that has been designed as a facebook application. Our application has also been integrated with several web sites. System collects event data from several related web sites either by using web services or web scraping. It also permits users rating events they have attended or planned. Given the social network between people, system tries to recommend upcoming events to users. For this purpose a combination of content based and collaborative filtering has been used. We have also taken geographical location info and social concept of an event.

This paper addresses the development of a plug and run wrapper to incorporate fuzziness into VRXQuery, the querying facility of VIREX which is a user-friendly system for transforming and querying relational data as XML. Our basic argument is not to force the underlying XML data to incorporate fuzziness. Rather, fuzziness is smoothly supported in a novel plug and run manner via a wrapper. Either the user specifies the membership functions for the elements/attributes to be queried as fuzzy, or multi-objective genetic algorithm is used to automatically decide on and optimize the membership functions. The interface of VIREX has been expanded to allow specifying queries with fuzziness. Then, queries expressed in VRXQuery empowered with fuzziness are translated into corresponding XQuery code, which is run on the underlying XML and the returned result is translated into a fuzzy representation; translation into SQL is also possible. The user is given the choice to display the result either as colored- text or in graphical format.