An improved Pi-Sigma neural network using error feedback for time series prediction

Time series prediction grabs much attention because of its effect on the vast range of real-life applications. Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that decrease the performance of the model. Higher Order Neural Network (HONN) using recurrent feedback appeared as a powerful technique in the domain of time series prediction and it has the ability to expand the input space, making them more efficient for solving complex problems and perform high learning abilities in time series prediction. This study proposed a model called improved Pi-Sigma Neural Network using Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback. PSNN-EF uses backpropagation gradient descent algorithm for training purpose and is tested with physical time series signals of humidity, evaporation and wind direction datasets that are collected from Malaysian Meteorological Department (MMD). The prediction result is compared with Jordan Pi-Sigma Neural Network (JPSN) and the ordinary PSNN. The results clearly showed that the PSNN-EF significantly improved the computational efficiency of the training process and has been developed to produce more realistic and acceptable results. The average improvement of the proposed model on evaporation dataset is 2.06%, humidity is 7.45% and wind is 3.51% as compared to other models. The benefit of using error feedback is that it generates more accurate and promising results of prediction. Therefore, from the performance of the proposed method, it is noticed that PSNN-EF can provide better solution to JPSN for one-step-ahead prediction of those three datasets

A comprehensive survey on pi-sigma neural network for time series prediction

Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks

SPILLOVER EFFECT OF MEAN AND VOLATILITY IN ALTERNATIVE INVESTMENTS: STUDY FOR PAKISTANI FUND MANAGERS

The main purpose of the study is to find out the hedging capabilities between alternative assets (Gold, Crude oil, Currencies, MSCI Global, and Mutual Funds) and stock returns of Pakistan Stock Exchange (PSX). ARCH (1,1) and and GARCH (1,1) models are applied to determine the mean and spillover effect between alternative assets returns and PSX index returns. The results of the study reveal that volatility spillover exists in all alternative investments except MSCI Global Index because MSCI indicates insignificant results. Therofre, it is better opportunity for fund managers to invest in MSCI Global or Emerging Index as it will provide more hedging opportunities. Keywords: Alternative Investments, Mean volatility, Volatility Spillover, ARCH (1,1),  GARCH (1,1), Gold, MSCI Global Index,  Crude oil, Mutual Funds, Dollar , KSE&nbsp

A Comprehensive Survey on Pi-Sigma Neural Network for Time Series Prediction

Prediction of time series grabs received much attention because of its effect on the vast range of real life applications. This paper presents a survey of time series applications using Higher Order Neural Network (HONN) model. The basic motivation behind using HONN is the ability to expand the input space, to solve complex problems it becomes more efficient and perform high learning abilities of the time series forecasting. Pi-Sigma Neural Network (PSNN) includes indirectly the capabilities of higher order networks using product cells as the output units and less number of weights. The goal of this research is to present the reader awareness about PSNN for time series prediction, to highlight some benefits and challenges using PSNN. Possible fields of PSNN applications in comparison with existing methods are presented and future directions are also explored in advantage with the properties of error feedback and recurrent networks

Comparative analysis of TF-IDF and loglikelihood method for keywords extraction of twitter data

Twitter has become the foremost standard of social media in today’s world. Over 335 million users are online monthly, and near about 80% are accessing it through their mobiles. Further, Twitter is now supporting 35+ which enhance its usage too much. It facilitates people having different languages. Near about 21% of the total users are from US and 79% of total users are outside of US. A tweet is restricted to a hundred and forty characters; hence it contains such information which is more concise and much valuable. Due to its usage, it is estimated that five hundred million tweets are sent per day by different categories of people including teacher, students, celebrities, officers, musician, etc. So, there is a huge amount of data that is increasing on a daily basis that need to be categorized. The important key feature is to find the keywords in the huge data that is helpful for identifying a twitter for classification. For this purpose, Term Frequency-Inverse Document Frequency (TF-IDF) and Loglikelihood methods are chosen for keywords extracted from the music field and perform a comparative analysis on both results. In the end, relevance is performed from 5 users so that finally we can take a decision to make assumption on the basis of experiments that which method is best. This analysis is much valuable because it gives a more accurate estimation which method’s results are more reliable

Optical Dromions for Spatiotemporal Fractional Nonlinear System in Quantum Mechanics

In physics, mathematics, and other disciplines, new integrable equations have been found using the P-test. Novel insights and discoveries in several domains have resulted from this. Whether a solution is oscillatory, decaying, or expanding exponentially can be observed by using the AEM approach. In this work, we examined the integrability of the triple nonlinear fractional Schrödinger equation (TNFSE) via the Painlevé test (P-test) and a number of optical solitary wave solutions such as bright dromions (solitons), hyperbolic, singular, periodic, domain wall, doubly periodic, trigonometric, dark singular, plane-wave solution, combined optical solitons, rational solutions, etc., via the auxiliary equation mapping (AEM) technique. In mathematical physics and in engineering sciences, this equation plays a very important role. Moreover, the graphical representation (3D, 2D, and contour) of the obtained optical solitary-wave solutions will facilitate the understanding of the physical phenomenon of this system. The computational work and conclusions indicate that the suggested approaches are efficient and productive

Depression Classification From Tweets Using Small Deep Transfer Learning Language Models

Depression detection from social media texts such as Tweets or Facebook comments could be very beneficial as early detection of depression may even avoid extreme consequences of long-term depression i.e. suicide. In this study, depression intensity classification is performed using a labeled Twitter dataset. Further, this study makes a detailed performance evaluation of four transformer-based pre-trained small language models, particularly those having less than 15 million tunable parameters i.e. Electra Small Generator (ESG), Electra Small Discriminator (ESD), XtremeDistil-L6 (XDL) and Albert Base V2 (ABV) for classification of depression intensity using Tweets. The models are fine-tuned to get the best performance by applying different hyperparameters. The models are tested by classification of depression intensity of labeled tweets for three label classes i.e. ‘severe’, ‘moderate’, and ‘mild’ by downstream fine-tuning the parameters. Evaluation metrics such as accuracy, F1, precision, recall, and specificity are calculated to evaluate the performance of the models. Comparative analysis of these models is also done with a moderately larger model i.e. DistilBert which has 67 million tunable parameters for the same task with the same experimental settings. Results indicate that ESG outperforms all other models including DistilBert due to its better deep contextualized text representation as it gets the best F1 score of 89% with comparatively less training time. Further optimization of ESG is also proposed to make it suitable for low-powered devices. This study helps to achieve better classification performance of depression detection as well as to choose the best language model in terms of performance and less training time for Twitter-related downstream NLP tasks.This work was supported in part by the Department of Informatics, University of Valladolid, Spain; in part by the Spanish Ministry of Economy and Competitiveness through Feder Funds under Grant TEC2017-84321-C4-2-R; in part by MINECO/AEI/ERDF (EU) under Grant PID2019-105660RB-C21 / AEI / 10.13039/501100011033; in part by the Aragón Government under Grant T58_20R research group; and in part by the Construyendo Europa desde Aragón under Grant ERDF 2014-202

Presence of laxative and antidiarrheal activities in Periploca aphylla: A Saudi medicinal plant.

Periploca aphylla (Famly; aselepiadaceae) in notive to Saudi Araba and is used as purgative. The aim of this study was to investingation the gut modulatiory effect of the aqueau

Investigating the effect of Aspergillus niger inoculated press mud (biofertilizer) on the potential of enhancing maize (Zea mays. L) yield, potassium use efficiency and potassium agronomic efficiency

Globally field application of mineral potassium (K) fertilizer has grown, followed by reduced K use efficiency (KUE) and K agronomic efficiency (KAE) which ultimately leads to environmental pollution and economic loss. The soils of Pakistan have a low K level due to a higher proportion is present in an unavailable form. The objective of the current study was to isolate efficient plant growth-promoting fungus to sustainably manage huge burden of sugar industry waste press mud into a productive biofertilizer. K from biofertilizer was then evaluated in different treatments for maize biological yield, grain yield, harvest index (HI), K uptake in different maize parts, KUE and KAE in comparison to mineral fertilizer (MF). The efficiency of treatment was measured on higher KUE and KAE. In-vitro studies revealed that A. niger PM-4 was found to solubilize phosphate (389 ug/ml) and zinc (115 ug/ml) from insoluble tri-calcium phosphate and zinc oxide, respectively, at a wider temperature and pH range. The strain was also found to inhibit the production of aflatoxins and its inoculation into press mud produced non-phytotoxic and mature biofertilizer with germination index 96.5%. Bio-augmentation of press mud with A. niger shortens maturity period with improved nutrient contents. Higher grain yield and harvest index of maize were achieved with a higher amount of incorporated K from mineral and biofertilizer T5(100%Org+50%MF) than any other treatment. However, higher KUE and KAE were found in the following order: T6 > T5 > T2 > T3 > T4 > T1, demonstrating the integrated and balanced use of K from mineral and biofertilizer without threatening the environment