Sajeeda Shikalgar

Bio: Sajeeda Shikalgar is an academic researcher from Maharashtra Institute of Technology. The author has contributed to research in topics: Ontology Inference Layer & Web query classification. The author has an hindex of 2, co-authored 5 publications receiving 14 citations.

Prediction of Stock Market Indices by Artificial Neural Networks Using Forecasting Algorithms

Abstract: Application of artificially intelligent methods for predictions is a fairly old area, although it is also the one in which there is always room for improvement in performance and in consistency, given the escalating nature of information and the varying efficacy of prediction logics. A hybrid of simple statistical methods coupled with intelligent computing (here artificial neural networks) is most likely to yield the closest prediction values with modest error rates. We propose to build an analytical and predictive model for estimating the stock market indices. This model can guide any kind of a user with or without experience in the stock market to make profitable investments. The forecasting done is by way of three statistical algorithms and an adaptive, intelligent algorithm, thus making the process fairly robust. Training and testing the neural network will be done with two-month stock market index values for some of the companies listed with the Bombay Stock Exchange. A comparative result of the four algorithms is calculated, and the one with best precision is suggested to the user with a sale/buy/hold answer.

An Internet of Things (IOT) based monitoring system for efficient milk distribution

Abstract: Milk distribution and safety is of high concern as it involves the health of 90% of our society. Our project, milk distribution monitoring system targets the cold chain maintenance and milk spoilage avoidance. The system is based on Internet of Things and data mining. Traditionally milk is supplied in cans with minimum monitoring which may result in milk getting spoiled before its use specially during transportation at any point of time which causes vendors to know about milk spoilage only after the milk has been spoiled completely. To overcome these problems first data mining technique is employed to discover the routing plans so as to generate case-based routing plans for the drivers. Then the existing sensor based system will measure the pH value of milk and determine its quality. The system will direct to nearest milk booth with the highest proximity by using routing technique where data center serves as a cloud server to calculate the costs of a finding the nearest milk booth request, and these costs will be frequently updated by considering the location of van and the total number of milk booth in each areas whose data is accessible any time in the network. This routing algorithm will be used to offer a solution of finding an available milk booth if the PH value deviates from 6.7. This is implemented by a system prototype with wireless access in an open-source physical computing platform based on Ardiuno and using a smart phone that provides the communication and user interface for both the control system and the vehicles to verify the feasibility of the system. It will also give an alert SMS to vehicle driver so that collection of milk can be faster without more spoilage. This way it will maintain good quality and cost effective distribution of milk.

Rule extraction for detection and prevention of asthma attacks

Abstract: This paper solves the problem of prevention of asthma attacks, and also it can predict a future asthma attack based on prevalent asthma triggers. The system forms an opinion of the everyday condition of the user, for example, if the user is doing fine today but might not be fine tomorrow. With the help of neural networks, it is possible to predict precisely and correct the user habits. A user who is not an asthma patient but thinks he may have the disease can also use this system. Monthly reports are generated of every user stating whether the patient's condition has worsened or got better.

A cross platform mobile expert system for agriculture task scheduling

Abstract: In this paper, we explain a cross platform mobile expert system for agriculture task scheduling, which will be developed to help Indian farmers. In spite of so many reforms and initiatives introduced by the government of India in the past decades, the quality of information provided to the marginalized farmer is uneven. Dangerous issues that infect Indian farming at hand are the data shortage and high risk because of the volatile nature of the factors involved, like, natural weather change. This paper introduces an advisory, information and financial system available on mobiles, which is designed for farmers to help them stay on track, avoid troubles, and receive all the latest and updated information, government schemes and strategies related to the field of agriculture. Information System has tools forecasting schedule that creates a systematic schedule for farmers for crop cultivation based on the weather predictions. This proposed idea is to ensure that the farmer plan their resources properly and also suggest a sequence of tactical decisions throughout a production cycle. The advisory system will enable its users to receive real-time and interactive advices and alerts on crop. Alerts would include phase of the plantation, weather conditions, diseases and nutrition. Farmers will also receive regular weather bulletins to support on-farm decision-making. Finance related tools, helps the farmer to keep track and control his financial income and expenses. It helps the farmer to get better control of his financial condition, productivity. In turn it will help him to increase profitability and fulfill their long-term goals. This paper is a start of a complete solution for addressing Indian farmer's needs, which would help them grow systematically.

A Model Approach to Build Basic Ontology

Abstract: As todays world grows with the technology on the other hand it seems to be small with the World Wide Web. With the use of Internet more and more information can be search from the web. When Users fires a query they want relevancy in obtained results. In general, search engines perform the ranking of web pages in an offline mode, which is after the web pages have been retrieved and stored in the database. But most of the time this method does not provide relevant results as most of the search engines were using some ranking algorithms like page Rank, HITS, SALSA and Hilltop. Where these algorithms does not always provides the results based on the semantic web. So a concept of Ontology is been introduced in search engines to get more meaningful and relevant results with respect to the users query.Ontologies are used to capture knowledge about some domain of interest. Ontology describes the concepts in the domain and also the relationships that hold between those concepts. Different ontology languages provide different facilities. The most recent development in standard ontology languages is OWL (Ontology Web Language) from the World Wide Web Consortium. OWL makes it possible to describe concept to its full extent and enables the search engines to provide accurate results to the user.

An efficient equilibrium optimizer with support vector regression for stock market prediction

Abstract: A hybridized method that relies on using the support vector regression (SVR) method with equilibrium optimizer (EO) is proposed to foresee the closing prices of Egyptian Exchange (EGX). Three indices are modeled and employed: EGX 30, EGX 30 capped, and EGX 50 EWI. The efficiency of using the technical indicators and statistical measures in the forecasting process is evaluated. The proposed EO-SVR-based forecasting model is adopted and evaluated using mean absolute percentage error, average, standard deviation, best fit, worst fit, and CPU time. Also, it is compared with recently developed metaheuristic optimization algorithms published in the literature such as whale optimization algorithm, salp swarm algorithm, Harris Hawks optimization, gray wolf optimizer, Henry gas solubility optimization, Barnacles mating optimizer, Manta ray foraging optimization, and slime mold algorithm. The proposed EO-SVR model got better results than other the counterparts, and EO-SVR is considered the optimal model according to its superior outcomes. Moreover, there is no need to use technical indicators and statistical measures as their effect is not noticeable.

Assess deep learning models for Egyptian exchange prediction using nonlinear artificial neural networks

Abstract: Financial analysis of the stock market using the historical data is the exigent demand in business and academia. This work explores the efficiency of three deep learning (Dl) techniques, namely Bayesian regularization (BE), Levenberg–Marquardt (lM), and scaled conjugate gradient (SCG), for training nonlinear autoregressive artificial neural networks (NARX) for predicting specifically the closing price of the Egyptian Stock Exchange indices (EGX-30, EGX-30-Capped, EGX-50-EWI, EGX-70, EGX-100, and NIlE). An empirical comparison is established among the experimented prediction models considering all techniques for the time horizon of 1 day, 3 days, 5 days, 7 days, 5 days and 30 days in advance, applying on all the datasets used in this study. For performance evaluation, statistical measures such as mean squared error (MSE) and correlation R are used. From the simulation result, it can be clearly suggested that BR outperforms other models for short-term prediction especially for 3 days ahead. On the other hand, lM generates better prediction accuracy than BR- and SCG-based models for long-term prediction, especially for 7-day prediction.

An Internet of Things (IOT) based Smart Parking Routing System for Smart Cities

Abstract: Recently, the number of cars on the road has been growing due to the increase in car manufacturing in parallel with customer services provided to help the new driver to buy cars at affordable prices. On the other hand, we find that the infrastructure of big cities cannot support this number of cars and with the disorganization of parking places in the city this problem will lead us to have serious problem in the city which involves the increase of drivers requests to find the nearest parking places to avoid traffic congestion in those areas. In parallel today we talk about the concept of smart cities and how can we use the evolution of the Internet of Things (IoT) to improve the quality of the smart city. Several efforts have been made on the Internet of Things to improve the reliability and the productivity of public infrastructure. Many problems have been handled and controlled by the IoT such as vehicle traffic congestion, road safety and the inefficient use of car parking spaces. This work introduces a novel technique based on a distributed cloud architecture of IoT to manage the parking systems combined with a distributed swarm intelligence technique using the Ant System algorithm to improve the process of finding the nearest car parking in the minimum time based on the state of traffic on this road. This prototype will help drivers to find the nearest car parking and improve the exploitation of the available car parking in the city.