Ecpi University

In this paper, we develop a method based on a deep learning method in financial market prediction, which includes BRICS economies as the test cases. Financial markets are rife with volatility that is affected by a "bed of complexity," coddled by local and distal factors. To leverage these vast datasets both deep learning models such as Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks as well as hybrid architectures are used in this study. The paper evaluates the predictive accuracy of the models, and by so doing, identifies their strengths in predicting temporal dependencies and intricate market patterns. In particular, deep learning techniques are applied to case studies of individual countries in the BRICS to highlight the application of deep learning to disparate country specific problems, such as liquidity crises and market shocks. These findings show that classical statistical methods are outperformed by deep learning systems in a precise and reliable financial forecasting. This research highlights the ability of AI driven systems to change financial decision making processes, improving investor confidence and improving economic stability in BRICS nations. This study also readers the value of deep learning in financial market analysis, especially in economies in the developing countries. Application of techniques and architectures e.g. Convolutional Neural Networks (CNNs) that excel at identifying spatial patterns, and Long Short-Term Memory (LSTM) networks renowned for their prowess on sequential and time series data, for real world market prediction are explained. In addition, the research discusses hybrid architectures which extend knowledge, fusing strengths of both architectures to improve prediction accuracy and how deep learning develops to solve particular financial challenges. Through reading these notes readers get exposed to data preprocessing techniques such as normalization and feature selection which are important for boosting deep learning performance. The paper also includes an introduction to the evaluation of models using MSE and R-squared values for validating them in terms of reliable outputs. This research combines deep learning theory and practical case study to offer a useful educational resource for students, researchers, and practitioners who want to apply AI in financial forecasting in complex and dynamic global markets.

This paper analyzes how the Artificial Intelligence (AI) and Machine Learning (ML) are bridging the gap between economic growth in the BRICS countries. BRICS countries are emerging economies that are challenged by increasing income inequality, industrial transformation and the need for infrastructure development. Driven by AI, this study applies data analytics to macroeconomic datasets, tracking down patterns and functional takeaways regarding policy formulation and strategic decision making. The research employs techniques, including predictive modeling, clustering, and natural language processing (NLP), in areas such as trade optimization, resource allocation and labour market analysis. Case examples document successful introduction of AI systems to solve critical economic problems, from increasing healthcare access to raising productivity in agriculture. The findings illustrate the role of AI and ML in helping BRICS policymakers to an informed, data driven development. The research puts AI as core to the process of economic advancement, a solution to developmental gaps and a driver for growth. This research contributes both to its practical outcomes and by providing insights into how AI and ML can solve the complex economic problems of emerging markets. The paper introduces predictive modeling, which anticipates economic trends based on past data and clustering which groups similar economic behaviors to find patterns as tools that are important in economic analysis. Further, Natural Language Processing (NLP) is covered as a highly effective approach to understand policy documents, news, and unstructured data to improve the ability to make decisions. By helping students, researchers, and policymakers understand these AI powered techniques that optimize trade, resource management and labor, these scalable solutions to sustainable development are available. This study touts data driven innovation as a critical means to solve global challenges, well-equipped readers with the skills and knowledge to leverage AI for economic progress in a geography of the dynamic and connected.

We are at the 4th industrial revolution called industry 4.0. The world is turning from digitalization to automation. Technological advancement and adoption and utilization of changing pattern of technology is gaining the gear at all social, political, diplomatic, economic and commercial spheres including SMEs. Technological advancement is bringing speed, innovation and sophistication of products and services in SMEs in one hand and on the other hand, cyber hacking and various and changing pattern of cybercrimes are hampering the growth and development. The aim of the study is to analysis the impacts of cyberattacks on SME’s productivity, growth and development in USA and potential way to accelerate the cyber security in the area. The study is mainly based on secondary sources of data including journals, research publications, scholarly articles, thesis papers etc. The finding of the study revealed that Banking and finance, healthcare, small manufacturing firms, retailers and E-commerce, public transport and infrastructure with less than 500 employees are the most vulnerable sectors for the Cyber-attack in the USA. The most commonly practiced cyberattacks are Phishing attacks, Ransomware, Malware, DDoS attacks, MitM etc. The Study suggests a number of cyber security measures including employee awareness and training program, creation of security policy and practice, know how to distinguish between fake antivirus offer and real notification and protect OT-IT system to prevent the damage.

The main objective of this study is to identify the impact of trust on the construction of corporate value in commerce and services microbusinesses. This work is based on identifying the challenges faced by SMEs (Small and Medium-sized Enterprises), which are conditioned by the type of business and the regulatory and incentive variables that exist in the territory, affecting their permanence and stability in the market and their financial and commercial development. A local study is carried out in Bogotá, Colombia, through a descriptive research project, using a quantitative analysis method (SPSS) to process data obtained from local microbusinesses. As a result, it was observed that trust has a discrete impact on the creation of corporate value, which is created from the use of ICT (Information and Communication Technology). This leads to the recognition that it is necessary to strengthen horizontal networks with suppliers, clients, and similar businesses, as well as vertical networks with entities and public associations, to generate lasting and strong links that increase the competitiveness of these business units in the face of exogenous risks shaped by the social, economic, and cultural characteristics of the territory, which are increasingly conditioned to the use of communication technology.

A practical, structurally simple, and scalable Gray-coded vectored hierarchical implementation is presented for $2^{2n}$ quadrature amplitude modulation with square constellation. The complexity of the vectored constellation mapping and the Gray codec is significantly reduced. The proposed modulator and demodulator algorithms are scalable, which make them favorable in adaptive and hierarchical modulations on software-defined radio platforms. The proposed algorithms are implemented on low-power Atmel SAM3X8E ARM Cortex-M3 microcontroller, as a test platform.

Therapeutic courts targeting recovery in parents and child permanency are known as family dependency treatment courts (FDTCs). FDTCs provide an example of a multi‐component intervention which addresses some social determinants of health. The goal of this manuscript is to examine how key ingredients of FDTCs are related to social determinants of health and the potential for this framework to be integrated into FDTCs. A specific FDTC is described and evidence‐based wrap services are used to illustrate opportunities to improve the health of women and children involved in FDTCs. Barriers and recommendations are discussed.

Two very expensive pieces of test equipment, the high resolution optical time domain reflectometer (OTDR) and the optical backscatter reflectometer (OBR) can quantitatively measure the reflectance of interconnections in short fiber optic cable assemblies. In this paper, the authors propose a simpler method to qualitatively measure interconnection reflectance in avionic and aerospace fiber optic cable assemblies with less sophisticated equipment and more quickly determine the status of the communication link.

A rootkit is cloaked software that infiltrates an operating system or a database with the intention to escape detection, resist removal, and perform a specific operation. Many rootkits are designed to invade the “root,” or kernel, of the program, and therefore operate without announcing their presence to the owner of the computer. Although some rootkits are written with noble intentions (e.g., to strengthen an anti-virus package), true rootkits have a malicious purpose. A rootkit infection can render a compromised computer system vulnerable to attacks and corruption. Rootkits are named for their origin in Linux systems, but the number of rootkits that attack Microsoft operating systems has recently proliferated. Not only are rootkits difficult to detect and assess, but at times the only effective way to remove them is to do a clean installation of the entire operating system. Recent discoveries of rootkits in other venues prove that the problem is spreading and is a major concern for administrators in information security. This paper presents a brief history of the development of rootkits and their possible effects.Prominent cases involving rootkits are described.The paper concludes with an overview of methods to prevent rootkits and to (hopefully) eradicate one that has infected an operating system.

Esse trabalho tem como finalidade mostrar as peças publicitárias produzidas pela mídia impressa que outrora eram apenas bidimensionais e que através das novas tecnologias desenvolvidas nessa área ampliaram o leque de opções a serviço dela, tornando-se assim propagandas interativas, criativas e lúdicas e que por sua vez estão em crescente formas e maneiras de utilização e publicação, aliando a futuros profissionais da área, possibilidades na área gráfica de trabalharem elementos convincentes, sedutores e persuasivos para a linguagem da publicidade e propaganda.

The aim of this paper is two-fold: (1) To give a unified treatment of the theory of order statistics when the parent distribution is not necessarily continuous. (2) To assess the effects of grouping on the distribution of order statistics and to indicate the convenience, under suitable conditions, of using order statistics for the estimation of parameters from grouped data with or without censoring.