Both the problem of class imbalance in datasets and parameter selection of Support Vector Machine (SVM) are crucial to predict software defects. However, there is no one working to solve these problems synchronously at present. To tackle this problem, a hybrid multi‐objective cuckoo search under‐sampled software defect prediction model based on SVM (HMOCS‐US‐SVM) is proposed to solve synchronously above two problems. Firstly, a hybrid multi‐objective cuckoo search with dynamical local search (HMOCS) is utilized to select synchronously the non‐defective sampling and optimize the parameters of SVM. Then, three under‐sampled methods for decision region range are proposed to select the non‐defective modules. In the simulation, the three indicators, including the false positive rate (pf), the probability of detection (pd), and G‐mean, are employed to measure the performance of the proposed algorithm. In addition, eight datasets from Promise database are selected to verify the proposed software defect predication model. Comparing with the result of eight prediction models, the proposed method comes into effect on solving software defect prediction problem.

An under‐sampled software defect prediction method based on hybrid multi‐objective cuckoo search

Previous personalized DTV recommendation systems focus only on viewers' historical viewing records or demographic data. This study proposes a new recommending mechanism from a user oriented perspective. The recommending mechanism is based on user properties such as Activities, Interests, Moods, Experiences, and Demographic information--AIMED. The AIMED data is fed into a neural network model to predict TV viewers' program preferences. Evaluation results indicate that the AIMED model significantly increases recommendation accuracy and decreases prediction errors compared to the conventional model.

AIMED- A Personalized TV Recommendation System

We study the problem of representing and recommending products for grocery shopping. We carefully investigate grocery transaction data and observe three important patterns: products within the same basket complement each other in terms of functionality (complementarity); users tend to purchase products that match their preferences (compatibility); and a significant fraction of users repeatedly purchase the same products over time (loyalty). Unlike conventional e-commerce settings, complementarity and loyalty are particularly predominant in the grocery shopping domain. This motivates a new representation learning approach to leverage complementarity and compatibility holistically, as well as a new recommendation approach to explicitly account for users' 'must-buy' purchases in addition to their overall preferences and needs. Doing so not only improves product classification and recommendation performance on both public and proprietary transaction data covering various grocery store types, but also reveals interesting findings about the relationships between preferences, necessity, and loyalty in consumer purchases.

Representing and Recommending Shopping Baskets with Complementarity, Compatibility and Loyalty

Modern health information systems can generate several exabytes of patient data, the so called "Health Big Data", per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of Health Big Data as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

Health big data analytics : current perspectives, challenges and potential solutions

With the development of social networks, the impact of rumor propagation on human lives is more and more significant. Due to the change of propagation mode, traditional rumor propagation models designed for word-of-mouth process may not be suitable for describing the rumor spreading on social networks. To overcome this shortcoming, we carefully analyze the mechanisms of rumor propagation and the topological properties of large-scale social networks, then propose a novel model based on the physical theory. In this model, heat energy calculation formula and Metropolis rule are introduced to formalize this problem and the amount of heat energy is used to measure a rumor’s impact on a network. Finally, we conduct track experiments to show the evolution of rumor propagation, make comparison experiments to contrast the proposed model with the traditional models, and perform simulation experiments to study the dynamics of rumor spreading. The experiments show that (1) the rumor propagation simulated by our model goes through three stages: rapid growth, fluctuant persistence and slow decline; (2) individuals could spread a rumor repeatedly, which leads to the rumor’s resurgence; (3) rumor propagation is greatly influenced by a rumor’s attraction, the initial rumormonger and the sending probability.

Energy Model for Rumor Propagation on Social Networks. Physica A: Statistical Mechanics and its Applications

The rampant propagation of rumors in online social networks leads to potential damage to society. This phenomenon has attracted significant attention to the researches on faster rumor detection. Most recent works on rumor detection adapted the neural network-based deep learning approaches because of its high success rate. Though neural network-based models have high success rates, these models are less efficient in finding rumors at the earliest. Such models require huge training data for better and accurate results. Unfortunately, the data available in the early stages of rumor propagation are sparse in nature. This nature makes rumor detection using neural networks a complex solution, therefore rendering less efficiency for early stage rumors. To address this issue, we have proposed a certainty-factor-based convolutional neural network (CNN) approach to efficiently classify events as rumor or not by leveraging the inherent features of the set of information in spite of data sparsity. The certainty-factor-based activation function requires a minimum number of training data to obtain a generalization. In the proposed approach, two parallel CNNs are employed for the rumor event classification task that efficiently utilizes the inherent features of information, such as temporal, content, and propagation features. A decision tree combines the outputs of both CNNs and provides the classification output. This rumor event classification approach is then compared with recent and well-known state-of-the-art rumor classification approaches, and the results prove that the proposed approach detects rumor efficiently and rapidly with minimal inputs compared to other approaches.

Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks

Aim of this article is to propose a text preprocessing model for sentiment analysis (SA) over twitter posts with the help of Natural Language processing (NLP) techniques. Discussions and investments on health-related chatter in social media keep on increasing day by day. Capturing the actual intention of the tweeps (twitter users) is challenging. Twitter posts consist of Text. It needs to be cleaned before analyzing and we should reduce the dimensionality problem and execution time. Text preprocessing plays an important role in analyzing health-related tweets. We gained 5.4% more accurate results after performing text preprocessing and overall accuracy of 84.85% after classifying the tweets using LASSO approach.

Novel Text Preprocessing Framework for Sentiment Analysis

A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.

Scientific collaboration plays a vital role in generating novel ideas and innovative research progress among the researchers. Similarly, news diffusion also has an important role among the research communities. Though the collaboration networks have made an impact in scientific activities and attracted the attention of scientific communities, no work so far analyzed the cause which can determine the future research publications. The objective of this paper is to study influence of news trends on scientific collaboration and research publications. For this purpose, we have collected the top technological news trends and applied the LDA model to identify top research keywords from the articles. The results show that the news trends play a significant role on scientific collaborations and innovative research progress. It is found that the researchers identify their research gap, make future collaborations and does interdisciplinary research. Our results highlight the important role of diffusion of news, which influence the young researchers to generate novel ideas and tend to collaborate more with different scientific communities.

Analyzing the impact of news trends on research publications and scientific collaboration networks

With almost all educated individuals having access to computing cum communication gadgets like mobiles, tablets, PCs, and laptops, online social networks (OSNs) have become the default means of networking among majority of individuals. OSNs have become an inseparable part of daily lives attracting more than one third of the current world population. Majority of the users enjoy the entertainment aspects of OSNs like gaming with friends from different geographic locations. Social gaming has spawned a whole new sub culture which helps users to discover and build connections with other users. Game development companies constantly try to publicise and attract new users using OSNs to enhance their revenues. In this context, we propose a new firefly inspired strategy to spread and disseminate games in OSNs and assist the gaming companies to decrease the acceptance-discontinuance anomaly. Collective behaviour in online social network is closely related to swarm intelligence techniques. We have also proposed a rewarding and efficient Firefly inspired QoS-based priority pricing model that will attract more users to play online games while using online social networks, thereby, enhancing the profits of the service providers and game developers.

L. D. Dhinesh Babu

Papers

Earlier detection of rumors in online social networks using certainty-factor-based convolutional neural networks

Novel Text Preprocessing Framework for Sentiment Analysis

A hybrid of whale optimization and late acceptance hill climbing based imputation to enhance classification performance in electronic health records.

Analyzing the impact of news trends on research publications and scientific collaboration networks

A firefly inspired game dissemination and QoS-based priority pricing strategy for online social network games