Neural networks demonstrate great potential for discovering non-linear relationships
in time-series and extrapolating from them. Results of forecasting using financial data are
particularly good [LapFar87, Schone90, ChaMeh92]. In contrast, traditional statistical
methods are restrictive as they try to express these non-linear relationships as linear models.
This thesis investigates the use of the Backpropagation neural model for time-series
forecasting. In general, neural forecasting research [Hinton87] can be approached in three
ways: research into, the weight space, into the physical representation of inputs, and into the
learning algorithms. A new method to enhance input representations to a neural network,
referred to as model sNx, has been developed. It has been studied alongside a traditional
method in model N. The two methods reduce the unprocessed network inputs to a value
between 0 and 1. Unlike the method in model N, the variants of model sNx, sN1 and sN2,
accentuate the contracted input value by different magnitudes. This different approach to
data reduction exploits the characteristics of neural extrapolation to achieve better forecasts.
The feasibility of the principle of model sNx has been shown in forecasting the direction of
the FFSE-100 Index.
The experimental strategy involved optimisation procedures using one data set and
the application of the optimal network from each model to make forecasts on different data
sets with similar and dissimilar patterns to the first.
A Neural Forecasting System (NFS) has been developed as a vehicle for the research.
The NFS offers historical and live simulations, and supports: a data alignment facility for
standardising data files with non-uniform sampling times and volumes, and merging them
into a spreadsheet; a parameter specification table for specifications of neural and system
control parameter values; a pattern specification language for specification of input pattern
formation using one or more time-series, and loading to a configured network; a snapshot
facility for re-construction of a partially trained network to continue or extend a training
session, or re-construction of a trained network to forecast for live tests; and a log facility for
recording experimental results.
Using the NFS, specific pattern features selected from major market trends have been
investigated [Pring8O]: triple-top ('three peaks'), double-top ('two peaks'), narrow
band ('modulating'), bull ('rising') and recovery ('U-turn'). Initially, the triple-top pattern
was used in the N model to select between the logarithmic or linear data form for presenting
raw input data. The selected linear method was then used in models sN1, sN2 and N for
network optimisations. Experiments undertaken used networks of permutations of sizes of
input nodes (I), hidden nodes (H), and tolerance value. Selections were made for: the best
method, by value, direction, or value and direction, for measuring prediction accuracy; the best configuration function, H - I 4), with 4) equal to 0.9, 2 or 3; and the better of sN1 and
sN2. The evaluation parameters were, among others, the prediction accuracy (%), the
weighted return (%), the Relative Threshold Prediction Index (RTPI) indicator, the forecast
error margins. The RTPI was developed to filter out networks forecasting above a minimum
prediction accuracy with a credit in the weighted return (%). Two optimal networks, one
representing model sNx and one N were selected and then tested on the double-top, narrow
band, bull and recovery patterns.
This thesis made the following research conthbutions.
• A new method in model sNx capable of more consistent and accurate predictions.
• The new RTPI neural forecasting indicator.
• A method to forecast during the consolidation ('non-diversifying') trend which most
traditional methods are not good at.
• A set of improvements for more effective neural forecasting systems.

Neural networks for financial forecasting

With the rapid growth of information technology, the amount of unstructured text data in digital libraries is rapidly increased and has become a big challenge in analyzing, organizing and how to classify text automatically in E-research repository to get the benefit from them is the cornerstone. The manual categorization of text documents requires a lot of financial, human resources for management. In order to get so, topic modeling are used to classify documents. This paper addresses a comparison study on scientific unstructured text document classification (e-books) based on the full text where applying the most popular topic modeling approach (LDA, LSA) to cluster the words into a set of topics as important keywords for classification. Our dataset consists of (300) books contain about 23 million words based on full text. In the used topic models (LSA, LDA) each word in the corpus of vocabulary is connected with one or more topics with a probability, as estimated by the model. Many (LDA, LSA) models were built with different values of coherence and pick the one that produces the highest coherence value. The result of this paper showed that LDA has better results than LSA and the best results obtained from the LDA method was ( 0.592179 ) of coherence value when the number of topics was 20 while the LSA coherence value was (0.5773026) when the number of topics was 10.

/pdf/lsa-lda-topic-modeling-classification-comparison-study-on-e-25vqce58ya.pdf

LSA & LDA topic modeling classification: comparison study on e-books

Machine learning is an artificial intelligence method of discovering knowledge for making intelligent decisions. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in machine learning, main technologies in Big Data, and some applications of machine learning in Big Data. Challenges of machine learning applications in Big Data are discussed. Some new methods and technology progress of machine learning in Big Data are also presented.

pdf/machine-learning-in-big-data-3gm8xm07qx.pdf

Machine Learning in Big Data

Recommender system (RS) is a unique type of information clarification system that anticipates the user's evaluation of items from a large pool based on the expectations of a single stakeholder. The proposed system is highly useful for getting expected meaning suggestions and guidance for choosing the proper product using artificial intelligence and IoT (Internet of Things) such as chatbot. The current proposed technique makes it easier for stakeholders to make context-based decisions that are optimal rather than reactive, such as which product to buy, news classification based on high filtering views, highly recommended wanted music to choose, and desired product to choose. Recommendation systems are a critical tool for obtaining verified information and making accurate decisions. As a result, operational efficiency would skyrocket, and the risk to the company that uses a recommender system would plummet. This proposed solution can be used in a variety of applications such as commercial hotels OYO and other hotels, hospitals (GYAN), public administrative applications banks HDFC, and ICICI to address potential questions on the spot using intelligence computing as a recommendation system. The existing RS is considering a few factors such as buying records, classification or clustering items, and user's geographic location. Collaborative filtering algorithms (CFAs) are much more common approaches for cooperating to mesh the respective documents they retrieved from the historical data. CFAs are distinguished in plenty of features that are uncommon from other algorithms. In this existing system classification, precision and efficiency and error rate are statistical measurements that need to be enhanced according to the current need to fit for global requirements. The proposed work deals with enhancing accuracy levels of text reviews with the recommender system while interacting by the numerous users for their domains. The authors implemented the recommender system using a user-based CF method and presented the significance of collaborative filtering on the movie domain with a recommender system. This whole experiment has been implanted using the RapidMiner Java-based tool. Results have been compared with existing algorithms to differentiate the efficiency of the current proposed approach.

https://downloads.hindawi.com/journals/misy/2022/2854741.pdf

Rating-Based Recommender System Based on Textual Reviews Using IoT Smart Devices

Abstract Natural language processing (NLP) refers to the field of study that focuses on the interactions between human language and computers. It has recently gained much attention for analyzing human language computationally and has spread its applications for various tasks such as machine translation, information extraction, summarization, question answering, and others. With the rapid growth of cloud computing services, merging NLP in the cloud is a significant benefit. It allows researchers to conduct NLP-related experiments on large amounts of data handled by big data techniques while harnessing the cloud’s vast, on-demand computing power. However, it has not sufficiently spread its tools and applications as a service in the cloud and there is little literature available that discusses the scope of interdisciplinary work. NLP, cloud Computing, and big data are vast domains and contain their challenges and potentials. By overcoming those challenges and integrating these fields, great potential for NLP and its applications can be unleashed. This paper presents a survey of NLP in cloud computing with a key focus on the comparison of cloud-based NLP services, challenges of NLP and big data while emphasizing the necessity of viable cloud-based NLP services. In the first part of this paper, an overview of NLP is presented by discussing different levels of NLP and components of natural language generation (NLG), followed by the applications of NLP. In the second part, the concept of cloud computing is discussed that highlights the architectural layers and deployment models of cloud computing and cloud-hosted NLP services. In the third part, the field of big data in the cloud is discussed with an emphasis on NLP. Furthermore, information extraction via NLP techniques within big data is introduced. 

/pdf/nlp-based-platform-as-a-service-a-brief-review-1zmqa1kw.pdf

NLP-based platform as a service: a brief review

Performance Analysis of Topic Modeling Algorithms for News Articles

Recommender systems that can suggest the user when to buy and sell stocks can be of immense help to those who wish to trade in stocks but are constrained by their limited knowledge of stock market dynamics. Traditionally, the trading recommendations have been generated on the basis of technical analysis. However, recent research in the field indicates that soft computing/data mining based recommender systems are also capable of generating profitable trading recommendations. An attempt has been made in this study to generate novel classifier based stock trading recommender systems that employ historical stock price data and technical indicators as input features. Moreover, there have been very few studies on the effectiveness recommender systems in the context of India, the world’s sixth largest economy and home to one of the world’s largest stock exchanges: the Bombay Stock Exchange (BSE). This study presents an empirical evaluation the effectiveness of five single classifier and six ensemble classifier based recommender systems on a total of 293 stocks drawn from the BSE. Recommender system performance for each stock is evaluated based on classification accuracy and eight economic performance measures. Results indicate that the proposed approach can indeed be used successfully for generating profitable trading recommendations.

Classifier Based Stock Trading Recommender Systems for Indian stocks: An Empirical Evaluation

January-March 2016 533 JCPS Volume 9 Issue 1 Forecasting short-term stock prices using sentiment analysis and Artificial Neural Networks Binoy B Nair*, Kumar PN, Sidharth R Prasad, Lopa M Singh, Vijayalakshmi K, Sai Ganesh R, Reshma J Dept. of Electronics and Communications Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India. Dept. of Computer Science and Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Amrita University, India *Corresponding author E-mail: b_binoy@cb.amrita.edu ABSTRACT Short-term fluctuations in stock prices are generally considered to be extremely difficult to predict, primarily due to their nonlinear nature. The authors believe that one of the reasons for such seemingly unpredictable fluctuations is the type of sentiment prevailing amongst traders at that point in time. An attempt has been made in this study to forecast the stock returns using the sentiments expressed on social media and Artificial Neural Networks. The proposed system is validated on stocks drawn from the Indian stock markets. Results indicate that the proposed technique can indeed be successfully used for short-term forecasting of stock prices.

Forecasting Short-Term Stock Prices using Sentiment Analysis and Artificial Neural Networks

Analysis and Performance of Collaborative Filtering and Classification Algorithms

Big Data Analytics: A Supervised Approach for Sentiment Classification Using Mahout-An Illustration

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