This paper aims to systematically examine the literature of machine learning for the period of 1968~2017 to identify and analyze the research trends. A list of journals from well-established publishers ScienceDirect, Springer, JMLR, IEEE (approximately 23,365 journal articles) related to machine learning is used to prepare a content collection. To the best of our information, it is the first effort to comprehend the trend analysis in machine learning research with topic models: Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and LDA with Coherent Model (LDA_CM). The LDA_CM topic model gives the highest topic coherence amongst all topic models under consideration. This study provides a scientific ground that helps to overcome the subjectivity of collective opinion. The Mann-Kendall test is used to understand the trend of the topics. Our findings provide indicative of paradigmatic shifts in research methodology of significant patterns of topical prominence and the evolving research areas. It is used to highlight the evolution regarding the previous and recent trends in research topics in the area of machine learning. Understanding such an intellectual structure and future trends will assist the researchers to adopt the divergent developments of this research in one place. This paper analyzes the overall trends of the machine learning research since 1968, based on the latent topics identified in the period of 2007~2017 that may be helpful to the researchers exploring the recommended areas and publish their research articles.

Requirements prioritization is a most important activity to rank the requirements as per their priority of order .It is a crucial phase of requirement engineering in software development process. In this research introduced a MCDM model for requirements prioritization. To select a best supplier firm of washing machine three important criteria are used. In this proposed model investigation for requirements prioritization, a case study adopted from Ozcan et al using LOG FAHP (Logarithmic fuzzy analytic hierarchy process) and ANN (Artificial Neural Network) based model to choose the best supplier firm granting the highest client satisfaction among all technical aspects. The test was conducted on MATLAB software and result evaluated on fuzzy comparison matrix with three supplier selection criteria based on FAHP and LOGANFIS that shows the decision making outcome for requirements prioritization is better than existing approaches with higher priority.

The load scheduling is one of the prime concerns for the computation of tasks in a virtual distributed environment. Many meta-heuristic swarm based optimization methods have been developed for scheduling the load in cloud computing environment. These swarm intelligence based algorithms like PSO play a key role in determining the scheduling of the cloudlets on the VMs in the datacenter. Gravitational Search algorithm based on law of gravity schedules the load in an effective manner. Its potential has not been utilized in cloud for load scheduling. This paper proposes a linear improved gravitational search algorithm in Cloud (LIGSA-C). This presents a new linear gravitational function and cost evaluation function for cloudlets using gravitational search approach in cloud. The results are computed by particles for scheduling 10 cloudlets on 8 VMs in the cloud. The detailed analysis of the result is performed. This paper states that LIGSA-C outperforms the existing algorithms like GSA and PSO for minimized cost.

Requirements prioritization is an essential component of software release planning and requirement engineering. In requirement engineering the requirements are arranged as per their priority using prioritization techniques to develop high-quality software’s. It also helps to the decision makers for making good decisions about, which set of requirements should be executed first. In any software development industry a ‘software project’ may have a larger number of requirements and then it is very difficult to prioritize such type of larger number of requirements as per their priority when stakeholder’s priorities are in the form of linguistic variables. This paper presents a comparative analysis of existing seven techniques based on various aspects like: scale of prioritization, scalability, time complexity, easy to use, accuracy, and decision making, etc. It was found from literature survey none of the techniques can be considered as the best one. These techniques undergo from a number of drawbacks like: time complexity, lack of scalability, Negative degree of membership function, inconsistency ratio, rank updates during requirement development, and conflicts among stakeholders. This paper proposed a model called ‘ANN Fuzzy AHP model’ for requirements prioritization that will overcome these limitations and drawbacks. In the investigation of this proposed model, a case study is implemented. Ozcan et al [31] using a FAHP (Fuzzy AHP) with ANN based technique to choose the best supplier based on the multiple criteria. The examination on ANN with FAHP is performed on MATLAB software and outcome evaluated by fuzzy pair-wise comparison matrix with three supplier selection criteria states that the requirements prioritization outcome is better from existing techniques.with higher priority.

Topic modeling techniques have been primarily being used to mine the topics from text corpora. These techniques reveal the hidden thematic structure in a collection of documents and facilitate to build up new ways to browse, search and summarize large archive of texts. A topic is a group of words that frequently occur together. A topic modeling can connect words with similar meanings and make a distinction between uses of words with several meanings. Here we present a survey on journey of topic modeling techniques comprising Latent Dirichlet Allocation (LDA) and non-LDA based techniques and the reason for classify the techniques into LDA and non-LDA is that LDA has ruled the topic modeling techniques since its inception. We have used the three hierarchical classification criteria’s for classifying topic models that include LDA and non-LDA based, bag-of-words or sequence-of-words approach and unsupervised or supervised learning for our survey. Purpose of this survey is to explore the topic modeling techniques since Singular Value Decomposition (SVD) topic model to the latest topic models in deep learning. Also, provide the brief summary of current probabilistic topic models as well as a motivation for future research.

Most of the protocols for enhancing the lifetime of wireless sensor networks (WSNs) are of a homogeneous nature in which all sensors have equal amount of energy level. In this paper, we study the effect of heterogeneity on the homogeneous protocols. The ALBPS and ADEEPS are the two important homogeneous protocols. We incorporate heterogeneity to these protocols, which consists of 2-level, 3-level and multi-level heterogeneity. We simulate and compare the performance of the ALBPS and ADEEPS protocols in homogeneous and heterogeneous environment. The simulation results indicate that heterogeneous protocols prolong the network lifetime as compared to the homogeneous protocols. Furthermore, as the level of heterogeneity increases, the lifetime of the network also increases.

In this paper, we propose a network model with energy heterogeneity. This model is general enough in the sense that it can describe 1-level, 2-level, and 3-level heterogeneity. The proposed model is characterized by a parameter whose lower and upper bounds are determined. For 1-level heterogeneity, the value of parameter is zero and, for 2-level heterogeneity, its value is (√5-1)/2. For 3-level of heterogeneity, the value of parameter varies between its lower bound and upper bound. The lower bound is determined from the energy levels of different node types, whereas the upper bound is given by (√5-1)/2. As value of parameter decreases from upper bound towards the lower bound, the network lifetime increases. Furthermore, as the level of heterogeneity increases, the network lifetime increases.