The cloud computing is the development of distributed computing, parallel computing and grid computing, or defined as the commercial implementation of these computer science concepts. One of the fundamental issues in this environment is related to task scheduling. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. A good task scheduler should adapt its scheduling strategy to the changing environment and the types of tasks. This paper proposes a cloud task scheduling policy based on Load Balancing Ant Colony Optimization (LBACO) algorithm. The main contribution of our work is to balance the entire system load while trying to minimizing the make span of a given tasks set. The new scheduling strategy was simulated using the CloudSim toolkit package. Experiments results showed the proposed LBACO algorithm outperformed FCFS (First Come First Serve) and the basic ACO (Ant Colony Optimization).

Cloud Task Scheduling Based on Load Balancing Ant Colony Optimization

https://cyberleninka.org/article/n/468779.pdf

A review of metaheuristic scheduling techniques in cloud computing

Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Based on the summary, we also highlight some future research directions in this topic.

Applications of deep learning in stock market prediction: recent progress

This has been wonderful support for the University and I very much appreciate your role and others at JSC in giving us good advice and direction. As you can see from the table of contents, the program has covered a very broad range of topics. The 15 technical reports are those which were directly supported by the grant. Note also. that 13 M.Sc. and 11 Ph.D. students have also been directly involved in this effort. Finally, four staff members (Tesar, Tosunoglu, Hooper, and Freeman) have beer. involved and participated in the direction of the research.

University of Texas at Austin의 연구 현황

Cluster-based routing protocols in wireless sensor networks: A survey based on methodology

Security indices are the main tools for evaluation of the status of financial markets. Moreover, a main part of the economy of any country is constituted of investment in stock markets. Therefore, investors could maximize the return of investment if it becomes possible to predict the future trend of stock market with appropriate methods. The nonlinearity and nonstationarity of financial series make their prediction complicated. This study seeks to evaluate the prediction power of machine‐learning models in a stock market. The data used in this study include the daily close price data of iShares MSCI United Kingdom exchange‐traded fund from January 2015 to June 2018. The prediction process is done through four models of machine‐learning algorithms. The results indicate that the deep learning method is better in prediction than the other methods, and the support vector regression method is in the next rank with respect to neural network and random forest methods with less error.

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Limitation in the number of characters in microblogging systems, such as Twitter, forces users to use various terms for the same meaning, object, or concept. Sometimes the same term is used in a shorter form (e.g. #friend and #frnd) in a tweet. This problem makes finding similarities between such tags and their corresponding tweets harder. The classical text mining methods cannot be used efficiently in the short tweets. Thus tweets similarity and subsequently tag recommendation, as one of the problems in microblogging social networks, needs a new method with higher efficiency. In this paper we have defined a new semantic based method to find similarities among short messages. We have modeled each short message as a semantic vector which can be used along with any similarity method such as cosine similarity. Then we evaluated the accuracy of the new semantic similarity based tag recommendation system using various semantic based algorithms and compare their results. The semantic based algorithms used are: Shortest Path, Wu a Palmer, Lin, JiangConrath, Resnik, Lesk, LeacockChodorow, and Hirst-StOnge. Results are evaluated using 8396744 real English tweets and show around 6 times improvement in accuracy over normal TF-IDF.

Microblogging Hash Tag Recommendation System Based on Semantic TF-IDF: Twitter Use Case

Certificate authorities (CAs) are the main components of PKI that enable us for providing basic security services in wired networks and Internet. But, we cannot use centralized CAs, in mobile ad hoc networks (MANETs). So, many efforts have been made to adapt CA to the special characteristics of MANETs and new concepts such as distributed CAs (DCAs) have been proposed that distribute the functionality of CA between MANET nodes. In this article, we study various proposed DCA schemes for MANET and then classify these schemes according to their internal structures and techniques. Finally, we propose the characteristics of an ideal DCA system that can be used to verify the completeness of any DCA scheme. This classification and taxonomy identify the weakness and constraints of each scheme, and are very important for designing more secure, scalable, and high performance DCA systems for MANETs and other networks.

/pdf/a-survey-and-taxonomy-of-distributed-certificate-authorities-2c72c09jen.pdf

A survey and taxonomy of distributed certificate authorities in mobile ad hoc networks

Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

Based on data types, four learning methods have been presented to extract patterns from data: supervised, semi-supervised, unsupervised, and reinforcement. Regarding machine learning, labeled data are very hard to access, although unlabeled data are usually collected and accessed easily. On the other hand, in most projects, most of the data are unlabeled but some data are labeled. Therefore, semi-supervised learning is more practical and useful for solving most of the problems. Different semi-supervised learning models have been introduced such as iterative learning (self-training), generative models, graph-based methods, and vector-based techniques. In addition, deep neural networks are used to extract data features using a multilayer model. Various models of this method have been presented to deal with semi-supervised data such as deep generative, virtual adversarial, and Ladder models. In semi-supervised learning, labeled data can contribute significantly to accurate pattern extraction. Thus, they can result in better convergence by having greater effects on models. The aim of this paper was to analyze the available models of semi-supervised learning with an approach to deep learning. A research solution for future studies is to benefit from memory to increase such an effect. Memory-based neural networks are new models of neural networks which can be used in this area.

Jamshid Bagherzadeh

Papers

Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms

Microblogging Hash Tag Recommendation System Based on Semantic TF-IDF: Twitter Use Case

A survey and taxonomy of distributed certificate authorities in mobile ad hoc networks

Semantic knowledge LDA with topic vector for recommending hashtags: Twitter use case

A review of various semi-supervised learning models with a deep learning and memory approach