Annals of Data Science最新文献

This paper introduces a new extension of the Gompertz function for estimating the survival rates. The actual survival rates from USA life tables 2015 is considered for assessment process under the ordinary least squares method. A real data application is presented under the maximum likelihood method. The new Gompertz function is compared with many other competitive ones such as the Gompertz, the exponentiated Gompertz, the Rayleigh Gompertz, Weibull Gompertz, the Burr type X Gompertz and Rayleigh generalized Gompertz models.

Underwater Acoustic (UWA) wireless communication systems are plagued by a slew of flaws that restrict their performance. This includes factors such as high attenuation in seawater, sediment type, acidity concentration, water temperature, and sound speed propagation. One of the available solutions is Orthogonal Frequency Division Multiplexing (OFDM). Unfortunately, the OFDM systems suffer from the Carrier Frequency Offset (CFO) phenomenon that causes Inter-Carrier-Interference. One of the means to overcome this problem is joint equalization and CFO compensation. In this paper, the conventional OFDM system is adapted for Multiple-Input-Multiple Output (MIMO)-OFDM communication utilizing Discrete Wavelet Transform (DWT) rather than Discrete Fourier Transform (DFT). The DWT-based OFDM system has certain benefits over the comparable DFT. The trade-off, on the other hand, is the necessity for an extra DFT/IDFT to complete the Frequency-Domain equalization procedure, which increases the total computational complexity. In addition, we present a Joint Low Regularized Linear Zero Forcing equalizer for MIMO-OFDM based on DWT that employs the banded-matrix approximation approach. The suggested approach avoids signal-to-noise ratio estimation. Simulation results show that the proposed scheme outperforms different schemes at the same UWA channel conditions spatially in the case of estimation errors.

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

In this article, we use Monte Carlo simulation study to calculate the generalized confidence interval of the difference between two recently proposed process capacity indices ((mathcal S^{prime }_{pk1}-{mathcal {S}}^{prime }_{pk2})) when the underlying process follows a normal process distribution. Method of moment estimate is used to estimate the parameters of the process distribution. The proposed generalized confidence interval can be effectively employed to determine which one of the two processes or manufacturer’s (or supplier’s) has a better process capability. Also Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average widths of the generalized confidence intervals of (({mathcal {S}}^{prime }_{pk1}-mathcal S^{prime }_{pk2})). The findings of the simulation demonstrated that as sample size rises, the mean squared errors decrease. To illustrate the generalized confidence intervals of the difference between two process capacity indices for improved supplier selection, three real data sets linked to the electronic industries are investigated.

Due to the digitization and Internet of Things revolutions, the present electronic world has a wealth of cybersecurity data. Efficiently resolving cyber anomalies and attacks is becoming a growing concern in today’s cyber security industry all over the world. Traditional security solutions are insufficient to address contemporary security issues due to the rapid proliferation of many sorts of cyber-attacks and threats. Utilizing artificial intelligence knowledge, especially machine learning technology, is essential to providing a dynamically enhanced, automated, and up-to-date security system through analyzing security data. In this paper, we provide an extensive view of machine learning algorithms, emphasizing how they can be employed for intelligent data analysis and automation in cybersecurity through their potential to extract valuable insights from cyber data. We also explore a number of potential real-world use cases where data-driven intelligence, automation, and decision-making enable next-generation cyber protection that is more proactive than traditional approaches. The future prospects of machine learning in cybersecurity are eventually emphasized based on our study, along with relevant research directions. Overall, our goal is to explore not only the current state of machine learning and relevant methodologies but also their applicability for future cybersecurity breakthroughs.

The onset of the COVID-19 pandemic has increased volatility in financial markets, motivating researchers to investigate its impact. Some use the GARCH family of models to focus on long-memory persistence, while others use Markov chain models to better identify structural breaks and regimes. However, no study has addressed the occurrence of these two phenomena in a unified framework. Since both are important features of the data, to ignore one or the other could lead to poorly specified models. The outcome would be incorrect risk measurement, with implications for risk management, Value at risk, portfolio decisions, forecasting, and option pricing. This paper aims to fill this gap in the literature. We assemble an international dataset for 16 stock market indices in three continents over the period from August 1, 2019 to February 18, 2022, totalling 669 business days. Using R, we estimate 80 GARCH family models, 16 pure Markov-Switching models, and 900 combined GARCH/ Markov-Switching models using daily stock market log-returns. We allow for two volatility regimes (low and high). We also measure and incorporate News Impact Curves, which show how past shocks affect contemporaneous volatility. Our main finding, across estimated models, is that COVID-19 affected both long-memory persistence and volatility regimes in most markets. To describe the specific impact in each market, we report News Impact Curves. Lastly, the first wave of COVID-19 had a much greater impact on volatility than did subsequent waves linked to the emergence of new variants.

Retrieving legal texts is an important step for building a question answering system on law domain, which needs relevant articles to answer a query. Remarkable research has been done on legal information retrieval. However, retrieving relevant articles for a question is an extremely challenging task. In this paper, we describe a novel approach to retrieve relevant civil law article for a question from legal bar exams. We used three models Hiemstra, BM25 and PL2F implemented within Terrier. Our system retrieves top-ranked document from the collection according to the models specified and it outputs one single document per query. The best model has been selected on the basis of voting algorithm. Appropriate civil law articles are then retrieved using a mapping between document pair-id and the articles. The system achieved an accuracy of over 71.16% of correct civil law articles on training data and moderate scores on test data.

In recent years cryptocurrencies are emerging as a prime digital currency as an important asset and financial system is also emerging as an important aspect. To reduce the risk of investment and to predict price, trend, portfolio construction, and fraud detection some Artificial Intelligence techniques are required. The Paper discusses recent research in the field of AI techniques for cryptocurrency and Bitcoin which is the most popular cryptocurrency. AI and ML techniques such as SVM, ANN, LSTM, GRU, and much other related research work with cryptocurrency and Bitcoin have been reviewed and most relevant studies are discussed in the paper. Also highlighted some possible research opportunities and areas for better efficiency of the results. Recently in the past few years, artificial intelligence (AI) and cybersecurity have advanced expeditiously. Its implementation has been extensively useful in finance as well as has a crucial impact on markets, institutions, and legislation. It is making the world a better place. AI is responsible for the simulation of machines that are replicas of human beings and are intelligent enough. AI in finance is changing the way we communicate with money. It helps the financial industry streamline and optimize processes from credit judgments to quantitative analysis marketing and economic risk management. The main goal of this research has been investigating certain impacts of artificial intelligence in this contemporary world. It's centered on the appeal of artificial intelligence, confrontation, chances, and its influence on professions and careers. The research paper uses AI to enable banks to generate financial resources and to provide valuable customer services. The application of the growing Indian banking sector is part of everyday life made up of several banks like RBI, SBI, HDFC, etc. and these banks have digitally implemented using chat-bots that have brought benefits to the customers.

Accumulation of the amyloid-(beta ) (A(beta ) ) peptide in the brain gives rise to a cascade of key events in the pathogenesis of Alzheimer’s disease (AD). It is verified by different research trials that the sleep-wake cycle directly affects A(beta ) levels in the brain. The catalytic nature of amyloidosis and the protein aggregation can be understood with the help of enzyme kinetics. During this research, the chemical kinetics of the enzyme and substrate are used to explore the initiation of Alzheimer’s disease, and the associated physiological factors, such as the sleep wake cycles, related to this symptomatology. The model is based on the concentration of the A(beta ) fibrils, such that the resulting solution from the mathematical model may help to monitor the concentration gradients (deposition) during sleep deprivation. The model proposed here analyzes the existence of two phases in the production of amyloid fibrils in the sleep deprivation condition: a first phase in which the soluble form of amyloid A(beta ) is dominant and a second phase in which the fibrillar form predominates and suggests that such product is the result of a strong imbalance between the production of amyloid A(beta ) and its clearance. The time dependent model with delay, helps to explore the production of soluble A(beta ) amyloid form by a defective circadian cycle. The limitations of the time dependent model are facilitated by the artificial intelligence (AI) time series forecasting tools.