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Enhanced Stock Market Prediction Using Hybrid LSTM Ensemble
Stock market value prediction is the activity of predicting future market values so as to increase gain and profit. It aids in forming important financial decisions which help make smart and informed investments. The challenges in stock market predictions come due to the high volatility of the market due to current and past performances. The slightest variation in current news, trend or performance will impact the market drastically. Existing models fall short in computation cost and time, thereby making them less reliable for large datasets on a real-time basis. Studies have shown that a hybrid model performs better than a stand-alone model. Ensemble models tend to give improved results in terms of accuracy and computational efficiency. This study is focused on creating a better yielding model in terms of stock market value prediction using technical analysis, and it is done by creating an ensemble of long short-term memory (LSTM) model. It analyzes the results of individual LSTM models in predicting stock prices and creates an ensemble model in an effort to improve the overall performance of the prediction. The proposed model is evaluated on real-world data of 4 companies from Yahoo Finance. The study has shown that the ensemble has performed better than the stacked LSTM model by the following percentages: 21.86% for the Tesla dataset, 22.87% for the Amazon dataset, 4.09% for Nifty Bank and 20.94% for the Tata dataset. The models implementation has been justified by the above results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Modified Non-local Means Model for Speckle Noise Reduction in Ultrasound Images
In the modern health care field, various medical imaging modalities play a vital role in diagnosis. Among the modalities, Medical Ultrasound Imaging is the most popular and economic modality. But its vulnerability to multiplicative speckle noise is challenging, which obscure accurate diagnosis. To reduce the influence of the speckle noise, various noise filtering models have been proposed. But while filtering the noise, these filters exhibit limitations like high computational complexity and loss of detailed structures and edges of organs. In this article, a novel Non-local means (NLM)-based model is proposed for the speckle reduction of Ultrasound images. The design parameters of the NLM filter are obtained by applying the Grey Wolf Optimization (GWO) to the input image. The optimized parameters and the noisy image are passed to the NLM filter to get the denoised image. The efficiency of this proposed method is evaluated with standard performance metrics. A comparative analysis with existing methods highlights the merit of the proposal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Leaf Disease Identification in Rice Plants Using CNN Model
Rice is a staple food crop for more than 10 countries. High consumption of rice demands better yield of crop. Fungal, bacterial and viral are different classes of diseases damaging rice crops which results in low and bad yield as per quality and quantity of the crop. Some of the most common diseases affecting plants are fungal blast, fungal brown spot, fungal sheath blight, bacterial blight and viral tungro. The deep learning CNN model with ResNet50V2 architecture was used in this paper to identify disease on the paddy leaves. Mobile application proposed in this paper will help farmers to detect disease on the leaves during their regular visit. Images were captured using this application. The captured images were tested using the trained deep learning model embedded with mobile application. This model predicts and displays input images along with the probabilities compared to each disease. The mobile application also provides necessary remedies for the identified disease with the help of hyperlink available in mobile application. The achieved probability that the model can truly classify the input image in this project was 97.67%, and the obtained validation accuracy was 98.86%. A solution with which farmers can identify diseases in rice leaves and take necessary actions for better crop yield has been demonstrated in this paper. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Forecasting Stock Market Indexes Through Machine Learning Using Technical Analysis Indicators and DWT
In recent years, the stock market prices have become more volatile due to refinement in technology and a rise in trading volume. As these seemingly unpredictable price trends continue, the stock market investors and consumers refer to the security indices to assess these financial markets. To maximise their return on investment, the investors could employ appropriate methods to forecast the stock market trends, taking into account the nonlinearity and nonstationarity of the stock market data. This research aims to assess the predictive capability of supervised machine learning models for the stock market regression analysis. The dataset utilised in this research includes the daily prices and additional technical indicator data of S&P 500 Index of US stock exchange and Nifty50 Index of Indian stock exchange from January 2008 to June 2016; both the indexes are weighted measurements of the top companies listed on respective stock exchanges. The model proposed in this research combines the discrete wavelet transform and support vector regression (SVR) with various kernels such as Linear, Poly and Radial basis function kernel (RBF) of the support vector machine. The results show that using the RBF kernel on Nifty 50 index data, the proposed model achieves the lowest MSE and RMSE error during testing are 0.0019 and 0.0431, respectively, and on S&P 500 index data, it achieves 0.0027 and 0.0523, respectively. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Support Vector Machine Performance Improvements by Using Sine Cosine Algorithm
The optimization of parameters has a crucial influence on the solution efficacy and the accuracy of the support vector machine (SVM) in the machine learning domain. Some of the typical approaches for determining the parameters of the SVM consider the grid search approach (GS) and some of the representative swarm intelligence metaheuristics. On the other side, most of those SVM implementations take into the consideration only the margin, while ignoring the radius. In this paper, a novel radiusmargin SVM approach is implemented that incorporates the enhanced sine cosine algorithm (eSCA). The proposed eSCA-SVM method takes into the account both maximizing the margin and minimizing the radius. The eSCA has been used to optimize the penalty and RBF parameter in SVM. The proposed eSCA-SVM method has been evaluated against four binary UCI datasets and compared to seven other algorithms. The experimental results suggest that the proposed eSCA-SVM approach has superior performances in terms of the average classification accuracy than other methods included in the comparative analysis. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Political Optimizer-Based Optimal Integration of Soft Open Points and Renewable Sources for Improving Resilience in Radial Distribution System
A novel and simple meta-heuristic optimization technique viz., political optimizer (PO) is proposed in this paper to identify the size and optimal location of solar photovoltaic (SPV) system. The main objective is to minimize the distribution loss and is solved using proposed PO. The computational efficiency of PO is compared with the literature, and its superiority is highlighted in terms of global solution at initial stage. The physical requirements of SPV system via soft open points (SOPs) among multiple laterals are solved considering radiality constraints in second stage. The proposed concept of interoperable-photovoltaic (I-PV) system has been applied on standard IEEE 69-bus system and has shown the effectiveness in performance enhancement of the system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Framework for Enhancing Classification in BrainComputer Interface
Over the past twenty years, the various merits of braincomputer interface (BCI) have garnered much recognition in the industry and scientific institutes. An increase in the quality of life is the key benefit of BCI utilization. The majority of the published works are associated with the examination and assessment of classification algorithms due to the ever-increasing interest in electroencephalography-based (EEG) BCIs. Yet, another objective is to offer guidelines that aid the reader in picking the best-suited classification algorithm for a given BCI experiment. For a given BCI system, selecting the best-suited classifier essentially requires an understanding of the features to be utilized, their properties, and their practical uses. As a feature extraction method, the common spatial pattern (CSP) will project multichannel EEG signals into a subspace to highlight the variations between the classes and minimize the similarities. This work has evaluated the efficacy of various classification algorithms like Naive Bayes, k-nearest neighbor classifier, classification and regression tree (CART), and AdaBoost for the BCI framework. Furthermore, the work has offered the proposal for channel selection with recursive feature elimination. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture
Revolutions in information technology have been helping agriculturists to increase the productivity of the cultivation. Many techniques exist for farming, but precision agriculture (PAg) is one technique that has gained popularity and has become a valuable tool for agriculture. Nowadays, farmers find it difficult to get expert advice regarding crops on time. As a solution, image processing techniques (IPTs) embedded PAg applications are developed to support farmers for the benefit of agriculture. In recent years, IPT has contributed a lot to provide a significant solution in PAg. This systematic review provides an understanding on preprocessing and feature extraction in PAg applications along with limitations. Preprocessing and feature extraction are the major steps of any application using IPTs. This study gives an overall view of the different preprocessing, feature extraction, and classification methods proposed by the researchers for PAg. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
XGBoost Design byMulti-verse Optimiser: An Application forNetwork Intrusion Detection
This article presents the results of an experimental study, which aims to assess the efficiency of the performance of a novel multi-verse optimiser algorithm for the optimisation of parameters of a network intrusion detection system event classifier. The article gives an overview of computer network intrusion detection, outlines common issues faced by software solutions tackling this problem, and proposes using a machine learning algorithm to help solve some of these common issues. An XGBoost classification model with a multi-verse optimisation algorithm for adaptive search and optimisation is used to solve the network intrusion detection system event classifier hyper-parameter optimisation problem. Results of this experimental study are presented and discussed, the improvements compared to previous solutions is shown, and a possible direction of future work in this domain is given in the conclusion. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deducing Water Quality Index (WQI) by Comparative Supervised Machine Learning Regression Techniques for India Region
Water quality is of paramount importance for the wellbeing of the society at large. It plays avery important role in maintaining the health of the living being. Several attributes like biological oxygen demand (BOD), power of hydrogen (pH), dissolved oxygen (DO) content, nitrate content (NC) and so on help to identify the appropriateness of the water to be used for different purposes. In this research study, the focus is to deduce the Water Quality Index (WQI) by means of artificial intelligence (AI)-based machine learning (ML) models. Six parameters, namely BOD, DO, pH, NC, total coliform (CO) and electrical conductivity (EC) are used to measure, analyze and predict WQI using nine supervised regression machine learning techniques. Bayesian Ridge regression (BRR) and automatic relevance determination regression (ARD regression) yielded a low mean squared error (MSE) value when compared to other regression techniques. ARD regression model parameters as independent a priori so that non-zero coefficients do not exploit vectors that are not just sparse, but they are dependent. In the estimation process, BRR contains regularization parameters; regularization parameters are not set hard but are adjusted to the relevant data. Due to these reasons, ARD regression and BRR models performed better. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Geospatial crime analysis and forecasting with machine learning techniques
People use social media to engage, connect, and exchange ideas, for professional interests, and for sharing images, videos, and other contents. According to the investigation, social media allows researchers to examine individual behavior features and geographic and temporal interactions. According to studies, criminology has become a prominent subject of study globally, using data gathered from online social media sites such as Facebook, News feed articles, Twitter, and other sources. It is possible to obtain useful information for the analysis of criminal activity by using spatiotemporal linkages in user-generated content. The study refers to the application of text-based data science by gathering data from several news sources and visualizing it. This research is motivated by the abovementioned work from various social media crimes and government crime statistics. This chapter looks at 68 various crime keywords to help you figure out what kind of crime you are dealing with concerning geographical and temporal data. For categorizing crime into subgroups of categories with geographical and time aspects using news feeds, the Naive Bayes classification algorithm is used. For retrieving keywords from news feeds, the Mallet package is used. The hotspots in crime hotspots are identified using the K-means method. The KDE approach is utilized to address crime density and this methodology has solved the difficulties that the current KDE algorithm has. The study results demonstrated equivalence between the suggested crimes forecasting model as well as the ARIMA model. 2022 Elsevier Inc. All rights reserved. -
Revisiting the efficacy of policies in the Indian primary healthcare sector: Interventions and approaches during the COVID-19 pandemic
The COVID-19 pandemic has wracked even the most modern healthcare systems worldwide and has influenced Indias healthcare sector and vastly affected the governments and corporate stakeholders healthcare reform plans; hence, this chapter is intended to unfold the paradigm shift in Indias primary healthcare industry due to the pandemic in the last one and half years. This chapter described Indias experience with the coronavirus during the first and second waves and tried examining the public health difficulties in the COVID era. It provides a timeline of significant events of the pandemics growth in India and worldwide and how India responded to the situations through their economic and healthcare policies. We also go through some of the pandemics impacts and Indias recovery approach and strategies for its revival. All possibly available secondary data like Scopus, Web of Science, Medline/PubMed, and Google Scholar search engines, newspapers, government websites, etc., were excavated to meet this purpose; secondary sources were used to analyze the data. This chapter also examined the effect of COVID-19 on healthcare workers in India. This chapter critically examined the primary healthcares role during this pandemic and the governments policies and processes to deal with COVID-19 and any other unforeseen situations which the country may encounter in the future. 2022 Elsevier Inc. All rights reserved. -
Impact of Innovative Technology on Quality Education: The Resourceful Intelligence for Smart Society Development
Quality education is the systematic learning and execution road map that build confidence among the learners and develop employability skills. Innovative techniques are the central facilitators of providing quality education for the younger generation. The economists, scientists, management experts, and research initiators are putting their efforts to develop a certain sustainable system in quality education through innovative technology. This is about digital equity, customized education, activity-based classroom, where the young mind is to be in synchronizing with the technology to explore new possibilities of learning and accomplishment. The research initiative reveals the system of implementing the innovative technology for quality education that has a direct impact on smart society development. The principal outcomes of the research initiative include the innovative ideas that transform the traditional education system into a dynamic education framework. The framework includes the integration of tools and techniques for standard mode of operations that reflects the productive and realistic education system. The researchers gracefully interconnected the concepts, methods, and applications of a quality education system that will open up new vistas for future research initiatives in the area of digital education, industry-institution collaboration, developing smart society, and economy of a nation at large. There is significant level of impact of innovative technology on quality education that leads to independent employability skills, creative, and innovative projects for facilitating future generation. All the influencing factors of resourceful intelligence together have great impact on smart society development that leads to provide modern facilities for the residents of smart society and create favorable environment for the future generations. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Nanomaterials-Based Chemical Sensing
Nanotechnology is an achievement in the modern period because of its adaptable properties as per its size alterations. Nanomaterials with their size ranging from 1 to 100nm hold incredible novel properties and functionalities because of their molecular arrangements in nano-scale. Nanotechnologies add to pretty much every field of science, including material science, materials chemistry, physics, biology, software and computational engineering and so on. Lately, nanotechnology has been applied to different fields with promising outcomes, particularly in the field of detecting and remediation of toxicity levels, imperilling the ecological solidness just as it does to human wellbeing. One of the principal research interests using nanomaterials is detecting poisonous heavy metal ions. Carbon-based nanomaterials, which are remarkable in view of their toxic-free nature, high surface area and biocompatibility, are valuable for ecological treatments. Heavy metal pollution of water resources is a major issue that poses danger to health and wellbeing. Carbon-based nanomaterials have incredible potential for the detection as well as treatment of heavy metals from water sources in light of their large surface area, nano-scale and accessibility towards various functionalities as they are simpler to be chemically altered and hence reused. Apart from the conventional gas sensors based on SnO2, Fe2O3, In2O3 etc., gas sensors based on nanocarbons materials like carbon nanotubes (CNTs), nanosheets of graphene, carbon nano-fibres etc. exhibit high efficacy when it comes to gas-sensing strategy. Likewise, nanocarbon with hybrids of noble metals or semiconducting oxides can lead to a better performance considering gas-sensing applications. Here in this review, we describe the progress of carbon-based nanomaterials in toxicity detection and remediation. In addition to that, recent trends in nanomaterials-based sensing revealed the advancement of gas sensors based on nanocarbons. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fabrication of disposable sensor strips for point-of-care testing of environmental pollutants
Biosensors are potentially used in detection of trace amount of environmental pollutants. Nanostructured materials are being widely explored for its application in the field of biosensors for monitoring environmental pollutants. Advances in biosensor technology with the use of micro-/nanomaterials can detect and analyze living and chemical matter with high specificity, which is relatively fast, sensitive, accurate, and inexpensive for the determination of chemical and biological contaminants. Recent finding shows that carbon nanotubes (CNTs) and nanomaterial-based biosensors utilizing the electrochemical and optical properties are being used for the analysis of contaminants at an incredible sensitivity and accuracy. In this chapter, the application of CNT-based biosensors and the fabrication of paper-based sensors in monitoring hazardous environmental pollutants are discussed. 2022 Elsevier Inc. All rights reserved. -
Analysis of the spread of infectious diseases with the effects of consciousness programs by media using three fractional operators
In this chapter, the mathematical model spread of infectious diseases exemplifying the effects of awareness programs by media is studied with the help of newly proposed fractional operators. The solution for the system of equations exemplifying the model is obtained with the help of the q-homotopy analysis transform technique (q-HATT). The projected method is an elegant amalgamation of the q-homotopy analysis scheme and the Laplace transform. Three fractional operators are employed in this study to show their essence in generalizing the models associated with power-law distribution: kernel singular, nonlocal, and nonsingular. The fixed-point theorem employed to present the existence and uniqueness for the hired arbitrary-order model and converges for the solution is derived with Banach space. The projected scheme springs the series solution rapidly convergent, and it can guarantee the convergence associated with the homotopy parameter. Moreover, for diverse fractional-order, the physical nature has been captured in plots. 2022 Elsevier Inc. All rights reserved. -
A novel secured ledger platform for real-time transactions
The present disclosure relates to a new centralized ledger technology with a centralized validation process. It offers a single platform for all categories of real-time transactions and validations, unlike existing conventional blockchain technology. It offers three levels of hashing placed at the generator, server, and validator end for data security from data tampering and two levels of encryption for communication lines between generator-server and server-validator for packet security. This system ensures trustworthiness, authenticity, and CIA (confidentiality, integrity, and availability) to its end users while being real-time in execution. The proposed system does not follow a chain-based file architecture. Due to this, no concept of chain break arises, and the problems that arise as a result of chain break in the blockchain are avoided. 2022 Elsevier Inc. All rights reserved. -
Blockchain: Opportunities in the healthcare sector and its uses in COVID-19
As the world grapples with the COVID-19 pandemic and major populations are getting vaccinated, increasing realization processes healthcare industry needs to be augmented. It includes managing supply chains, healthcare records, and patient care. With a scarcity of time and resources, adaptation of blockchain technology will help mitigate the pressures on existing infrastructure. A blockchain distributed ledger helps to exchange health information securely without complex intermediation of trust with secure access. The organizations and persons in the blockchain network can verify and authorize the data, thus protecting patient identity, privacy, medical information system, and reducing transaction costs. The chapter examines managing and protecting electronic medical records and personal health records data using blockchain. It also analyses issues in healthcare, blockchain implementation, and its uses in the COVID-19 pandemic. The authors put forward the idea that even after the world is free of the Coronavirus, there will be pressures on the healthcare industry and Blockchain technology is the way to alleviate this pressure. 2022 Elsevier Inc. All rights reserved. -
GutBrain Axis: Role in Hunger and Satiety
The human gastrointestinal tract consists of nearly 100 trillion microorganisms, referred as gut microbiota or gut microbiome. The microbial colonization in the human gut begins at the time of birth and its colonization increases with age which is influenced by factors like age, diet, and antibiotic treatment. Gut microbiota is believed to play a major role in human health as well as various physiological activities like metabolism, nutrition, physiology, etc. Imbalance of the normal gut microbiota has been linked with gastrointestinal conditions such as inflammatory bowel disease (IBD), irritable bowel syndrome (IBS) as well as wider systemic manifestations of disease such as obesity, type 2 diabetes, and atopy. Gutbrain axis, a two-way (bi-directional) connection and communication between the gut and the brain has potentially huge influence over our health which integrates neural, hormonal, and immunological signaling between the gut and the brain. There is growing evidence on the influence of gastrointestinal (GI) microbiota that modulates appetite, feeding, and metabolism as well as regulates the mechanisms of digestion. Gut hormones like Ghrelin, Cholecystokinin (CCK), Pancreatic Polypeptide (PP), Peptide YY (PYY), Glucagon-Like Peptide 1 (GLP-1), Oxyntomodulin (OXM), Glucagon, Gastric Inhibitory Polypeptide (GIP), and Amylin have been identified in the gastrointestinal system which have a fundamental role in coordinating digestive process within the gastrointestinal system, thus regulating feeding behavior and energy balance. Studies have indicated that the modulation in gut microbiota regulates the production of ghrelin and PYY in overweight and obese patients and helps in promoting weight loss and improves glucose regulation. Considering the importance of the role of gut microbiota on hunger and satiety, this chapter was written where we have discussed the gutbrain axis and its role in hunger and satiety. Further, mechanism of appetite regulation by gut microbiota and their role in obesity control have also been discussed. Finally, future perspectives on application of gut microbiota as potential probiotic solutions for obesity and related metabolic disorders will be discussed. Springer Nature Singapore Pte Ltd. 2022. -
BERT-Based Secure and Smart Management System for Processing Software Development Requirements from Security Perspective
Software requirements management is the first and essential stage for software development practices, from all perspectives, including the security of software systems. Work here focuses on enabling software requirements managers with all the information to help build streamlined software requirements. The focus is on ensuring security which is addressed in the requirements management phase rather than leaving it late in the software development phases. The approach is proposed to combine useful knowledge sources like customer conversation, industry best practices, and knowledge hidden within the software development processes. The financial domain and agile models of development are considered as the focus area for the study. Bidirectional encoder representation from transformers (BERT) is used in the proposed architecture to utilize its language understanding capabilities. Knowledge graph capabilities are explored to bind together the knowledge around industry sources for security practices and vulnerabilities. These information sources are being used to ensure that the requirements management team is updated with critical information. The architecture proposed is validated in light of the financial domain that is scoped for this proposal. Transfer learning is also explored to manage and reduce the need for expensive learning expected by these machine learning and deep learning models. This work will pave the way to integrate software requirements management practices with the data science practices leveraging the information available in the software development ecosystem for better requirements management. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.