scholarly journals Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Sachi Nandan Mohanty ◽  
Paulo Alonso Gaona García ◽  
Vicente García-Díaz
Keyword(s):  
Artificial Intelligence ◽  
Feature Selection ◽  
Financial Crisis ◽  
Internet Of Things ◽  
Simulation Analysis ◽  
Classification Performance ◽  
Financial Data ◽  
Superior Performance ◽  
Learning Machine ◽  
Financial Technology

AbstractFinancial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

scholarly journals An Optimal Feature Selection with Wavelet Kernel Extreme Learning Machine for Big Data Analysis of Product Reviews

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
R. Sathya , Et. al.
Keyword(s):  
Feature Selection ◽  
Big Data ◽  
Extreme Learning Machine ◽  
Selection Process ◽  
Classification Performance ◽  
Product Reviews ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

In recent times, generation of big data takes place in an exponential way from diverse textual data sources like review sites, media, blogs, etc. Sentiment analysis (SA) finds it useful to classify the opinions of the big data to different kinds ofsentiments. Therefore, SA on big data helps a business to take beneficial commercial understandings from text based content. Though several SA approaches have been presented, yet, there is a need to improve the performance of SA to interpret the customer’s feedback and increase the product quality.This paper introduces a novel social spider optimization based feature selection based wavelet kernel extreme learning machine (SSO-WKELM) model. The proposed model initially undergoes pre-processing to remove the unwanted word removal. Then, Term Frequency-Inverse Document Frequency (TF-IDF) is utilized as a feature extraction technique to extract the set of feature vectors. Besides, a social spider optimization (SSO) algorithm is utilized for feature selection process and thereby achieves improved classification performance. Subsequently, WKELM is employed as a classifier to classify the incidence of positive or negative user reviews. For experimental validation, a Product review dataset derived from Amazon along with synthetic data is used. The experimental results stated the superior classification performance of the SSO-WKELM model.   

scholarly journals Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

2019 ◽  
Vol 8 (7) ◽  
pp. 986 ◽  
Author(s):  
Owais ◽  
Arsalan ◽  
Choi ◽  
Mahmood ◽  
Park
Keyword(s):  
Artificial Intelligence ◽  
Short Term Memory ◽  
Three Dimensional ◽  
Area Under The Curve ◽  
Gastrointestinal Diseases ◽  
Classification Performance ◽  
Superior Performance ◽  
Temporal Features ◽  
Endoscopic Videos

Various techniques using artificial intelligence (AI) have resulted in a significant contribution to field of medical image and video-based diagnoses, such as radiology, pathology, and endoscopy, including the classification of gastrointestinal (GI) diseases. Most previous studies on the classification of GI diseases use only spatial features, which demonstrate low performance in the classification of multiple GI diseases. Although there are a few previous studies using temporal features based on a three-dimensional convolutional neural network, only a specific part of the GI tract was involved with the limited number of classes. To overcome these problems, we propose a comprehensive AI-based framework for the classification of multiple GI diseases by using endoscopic videos, which can simultaneously extract both spatial and temporal features to achieve better classification performance. Two different residual networks and a long short-term memory model are integrated in a cascaded mode to extract spatial and temporal features, respectively. Experiments were conducted on a combined dataset consisting of one of the largest endoscopic videos with 52,471 frames. The results demonstrate the effectiveness of the proposed classification framework for multi-GI diseases. The experimental results of the proposed model (97.057% area under the curve) demonstrate superior performance over the state-of-the-art methods and indicate its potential for clinical applications.

scholarly journals Correction to: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

2021 ◽  
Author(s):  
Irina V. Pustokhina ◽  
Denis A. Pustokhin ◽  
Sachi Nandan Mohanty ◽  
Paulo Alonso Gaona García ◽  
Vicente García-Díaz
Keyword(s):  
Artificial Intelligence ◽  
Financial Crisis ◽  
Internet Of Things

XHAC

2022 ◽  
pp. 146-164
Author(s):  
Duygu Bagci Das ◽  
Derya Birant
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Internet Of Things ◽  
Window Size ◽  
Classification Performance ◽  
Black Box ◽  
Data Exploration ◽  
Learning Models ◽  
Explainable Artificial Intelligence ◽  
Machine Learning Models

Explainable artificial intelligence (XAI) is a concept that has emerged and become popular in recent years. Even interpretation in machine learning models has been drawing attention. Human activity classification (HAC) systems still lack interpretable approaches. In this study, an approach, called eXplainable HAC (XHAC), was proposed in which the data exploration, model structure explanation, and prediction explanation of the ML classifiers for HAR were examined to improve the explainability of the HAR models' components such as sensor types and their locations. For this purpose, various internet of things (IoT) sensors were considered individually, including accelerometer, gyroscope, and magnetometer. The location of these sensors (i.e., ankle, arm, and chest) was also taken into account. The important features were explored. In addition, the effect of the window size on the classification performance was investigated. According to the obtained results, the proposed approach makes the HAC processes more explainable compared to the black-box ML techniques.

scholarly journals Analisis Bibliometrik untuk Memetakan Tren Penelitian Covid-19 dalam Topik Ilmu Komputer

Techno Com ◽  
2021 ◽  
Vol 20 (3) ◽  
pp. 440-454
Author(s):  
Tri Wahyu Widyaningsih ◽  
Meta Amalya Dewi ◽  
- Andrianingsih
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Internet Of Things ◽  
Google Scholar ◽  
Link Strength ◽  
Learning Machine ◽  
Internet Of Thing

Covid-19 berdampak pada seluruh penduduk di dunia, pandemi ini tidak hanya mempengaruhi sektor kesehatan, namun juga ekonomi, pendidikan, transportasi, industri, dan pemerintahan. Covid-19 hadir sebagai topik menarik bagi para peneliti, hal tersebut nampak pada data yang diperoleh dari Scopus, Crossref, IEEEXplore, dan Google Scholar yang di dalamnya memuat penelitian di bidang ilmu komputer yang membahas covid-19, dengan berbagai tujuan penelitian untuk memperoleh inovasi maupun solusi dari permasalahan yang timbul akibat pandemi. Penelitian ini dilakukan untuk memperoleh topik apa saja yang paling diminati oleh para peneliti terkait dengan covid 19, dan menganalisis serta membandingkan relasi antara topik Artificial Intelligence, Data Mining, Deep Learning, Machine Learning, dan Internet of Thing dari sumber google scholar, scopus, IEEEXplore, dan crossref dengan menggunakan analisis bibliometrik. Metode occurrence dan link strength digunakan untuk memvisualisasikan jejaring berdasarkan kata kunci dari ke lima topik bidang ilmu komputer serta hubungan antara lima topik tersebut dengan topik riset lainnya. Hasil analisis bibliometric menunjukkan peringkat dari ke empat penyedia sumber data artikel di lihat dari persentase setiap topik penelitian adalah sebagai berikut : Scopus, Crossref, IEEEXplore, dan Google Scholar. Analisis link strength dan occurence  terhadap kelima topik penelitian menunjukkan peringkat yang dapat dilihat dari banyaknya link strength dan occurrence di setiap penyedia sumber artikel, dengan hasil peringkat sebagai berikut : Deep Learning, Artificial Intelligence, Internet of Things, Machine Learning, dan Data Mining. Kata kunci: Bibliometrik, Covid-19, Occurrence,  Link Strength, Ilmu Komputer

Utilising internet of things and artificial intelligence to enable twin precision treatment for reversal of type 2 Diabetes

2020 ◽  
Author(s):  
Paramesh Shamanna ◽  
Suresh Damodharan ◽  
Banshi Saboo ◽  
Rajeev Chawla ◽  
Jahangir Mohammed ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Type 2 Diabetes ◽  
Internet Of Things

Innovative Marketing Strategies for Select E-Commerce Companies in India - A Review.

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Amit Kumar Bhanja ◽  
P.C Tripathy
Keyword(s):  
Artificial Intelligence ◽  
Internet Of Things ◽  
Business Environment ◽  
Marketing Strategies ◽  
Point Of View ◽  
Digital Marketing ◽  
Customer Base ◽  
Innovative Methods ◽  
Online Shoppers ◽  
Drastic Increase

Innovation is the key to opportunities and growth in today’s competitive and dynamic business environment. It not only nurtures but also provides companies with unique dimensions for constant reinvention of the existing way of performance which enables and facilitates them to reach out to their prospective customers more effectively. It has been estimated by Morgan Stanley that India would have 480 million shoppers buying products online by the year 2026, a drastic increase from 60 million online shoppers in the year 2016. E-commerce companies are aggressively implementing innovative methods of marketing their product offerings using tools like digital marketing, internet of things (IoT)and artificial intelligence to name a few. This paper focuses on outlining the innovative ways of marketing that the E-Commerce sector implements in orders to increase their customer base and aims at determining the future scope of this area. A conceptual comparative study of Amazon and Flipkart helps to determine which marketing strategies are more appealing and beneficial for both the customers and companies point of view.

International Financial Centres after the Global Financial Crisis and Brexit

2018 ◽  
Keyword(s):  
New York ◽  
Financial Crisis ◽  
Global Economy ◽  
Global Financial Crisis ◽  
New Technology ◽  
Interdisciplinary Approach ◽  
Important Change ◽  
Social Scientists ◽  
The Global Financial Crisis ◽  
Financial Technology

This book gathers leading economic historians, geographers, and social scientists to focus on the developments in key international financial centres following the 2008 Global Financial Crisis and to consider the likely effects of Brexit on these centres. Eleven centres in eight countries are taken into consideration: New York, London, Frankfurt, Paris, Zurich/Geneva, Hong Kong/Shanghai/Beijing, Tokyo, and Singapore. The book addresses three main issues. The first is the hierarchy of international financial centres, in particular whether Asian financial centres have taken advantage of the crisis in the West. The second is the medium-term effects of the crisis, with respect to the volume of business activity (including employment), and the level of regulation, with concerns regarding the risks of regulatory overkill. And the third is the rise of new technology, known as fintech, possibly the most important change in the decade following the crisis, with questions as to whether it will render financial centres, as we know them, unnecessary for the functioning of the global economy, and which cities are likely to emerge as hubs of new financial technology. Finally, the book discusses the likely effects of Brexit on international financial centres, in particular London, Paris, and Frankfurt. The book takes a decidedly interdisciplinary approach, with a general introduction providing a global overview from a historical perspective, and a general conclusion providing a global overview from a geographical perspective. Its focus on the implications for global financial centres is unique among books about the aftermath of the Global Financial Crisis.

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