Developing analytical systems imposes several challenges related not only to the amount and heterogeneity of the involved data but also to the constant need to readapt and evolve to overcome new business challenges. Data is a determinant factor in the success of analytical and decision-making applications, being its nature, availability, and quality, crucial aspects for planning and structuring populating analytical systems. Today’s users are more demanding, requiring adaptable and flexible analytical applications, which impose serious challenges on ETL systems design and development for ensuring flexible and robust data populating services, operating 24/7, and managing and processing large volumes of data. Thus, we should design and implement ETL processes using innovative and up-to-date approaches, having real application evidence. In this paper, we present a service-oriented implementation for ETL design and development. We mapped and implemented some of the most conventional ETL processes in a service-oriented architecture, for demonstrating the application and benefits that this kind of approach will provide to ETL systems project development.

Background: Due to the highly coarse chromatin, multi-dimensionality of the histo image, irregularity of shape and size, texture, and appearance, nuclei extraction is challenging. To address these complexities, a deep learning algorithm called a stacked sparse autoencoder had been considered a research factor in this paper. Methods and Material: This paper focuses on detecting the epithelial regions and extracting high-level features to segment the patches based on the nuclei and classify the biomarkers concerning the nuclei patches. We used 6,53,400 microscopic image patches of 363 patients sourced from the BreakHis database, of which 4,90,050 prominent image patches containing only nuclei were utilized for Biomarker classification (Basically eliminating the non-nuclei patches from 363 Whole slide Images (WSI)). The non-nuclei patches were eliminated due to imbalanced class distribution. Results: The classifier finally classifies if the nuclei detected based on the features are benign or malignant, or normal with an accuracy of 99.73%, using which the early prediction is performed by extracting and classifying the biomarkers HER2 and ER. The overall classification rate of classifying HER-2 and ER is 97.52%. Conclusion: The HER2 +ve was classified with intensity above 23%, and Total nuclei in the range 150-1000 are termed ER positive. Based on these 40 patients with HER2 +ve and 25 patients with ER +ve were detected out of 363 patients. From the observation, it is concluded that 25-40 patients are risked of breast cancer in the next 5 years due to the cell proliferation rate of 7000.

The growing use of technology and social media has resulted in the emergence of digital influencers, a new profession capable of changing the mentalities and behaviours of those who follow them. This study arises to better understand the potential impact digital influencers might have on the Portuguese population’s purchase behaviour and patterns, and for this purpose, seven hypotheses were formulated. An online questionnaire was conducted to respond to these theoretical assumptions and collected data from 175 respondents. A total of 129 valid answers were considered. It was possible to conclude that purchase intention does not necessarily translate into a purchase action. It was also concluded that the relationship between social network use and the purchase of products/services recommended by influencers is only significant for Instagram. Furthermore, individuals’ Generation is not significantly linked with purchasing a product/service recommended by influencers. Furthermore, a small percentage of respondents have also identified themselves as impulsive shoppers and perceived Instagram as their favourite social network. With the results of this study, it is also possible to state that the influencer’s opinion was classified as the last factor considered in the purchase decision process. Additionally, there is a weak negative association between purchasing a product/service recommended by influencers with sponsorship disclosure and remunerated partnership, which decreases credibility and discourages purchasing.

A document by Mehul Mahrishi. Click on the document to view its contents.

Analysis of human emotions from multimodal data for making critical decisions is an emerging area of research. The evolution of deep learning algorithms has improved the potential for extracting value from multimodal data. However, these algorithms do not often explain how certain outputs from the data are produced. This study focuses on the risks of using black-box deep learning models for critical tasks, such as emotion recognition, and describes how human understandable interpretations of these models are extremely important. This study utilizes one of the largest multimodal datasets available - CMU-MOSEI. Many researchers have used the pre-extracted features provided by the CMU Multimodal SDK with black-box deep learning models making it difficult to interpret the contribution of individual features. This study describes the implications of individual features from various modalities (audio, video, text) in Context-Aware Multimodal Emotion Recognition. It describes the process of curating reduced feature models by using the GradientSHAP XAI method. These reduced models with highly contributing features achieve comparable and even better results compared to their corresponding all feature models as well as the baseline model GraphMFN proving that carefully selecting significant features can help improve the model robustness and performance and in turn make it trustworthy.

:30.0 The development of machine learning technologies are broadly changing how humans interact with their environments across all sectors. In industrial settings, this is referred to as the fourth industrial revolution, Industry 4.0, and encompasses several technologies that are pushing the boundaries of industrial automation. In this study, a general industrial process optimization (GIPO) methodology is formulated in the context of Industry 4.0 and tested on an industrial Injection Molding Machine (IMM). GIPO aims to encourage the practical inclusion of industrial artificial intelligence at all levels of the manufacturing process while enabling industrial equipment to adapt to a changing processing environment. Special attention is given to the generality of the methodology so that it can be extended to other applications. In the example case study presented here, GIPO combines nearest neighbors classification and nearest neighbors optimization methods to effectively optimize an Injection molding process. Practical implementation conducted on the IMM demonstrates a novel methodology to leverage data mining and machine learning methods in a real-world setting to improve the overall performance regarding production time, energy cost, and production quality.

Competitive Intelligence allows an organization to keep up with market trends and foresee business opportunities. This practice is mainly performed by analysts scanning for any piece of valuable information in a myriad of dispersed and unstructured sources. Here we present MapIntel, a system for acquiring intelligence from vast collections of text data by representing each document as a multidimensional vector that captures its own semantics. The system is designed to handle complex Natural Language queries and visual exploration of the corpus, potentially aiding overburdened analysts in finding meaningful insights to help decision-making. The system searching module uses a retriever and re-ranker engine that first finds the closest neighbors to the query embedding and then sifts the results through a cross-encoder model that identifies the most relevant documents. The browsing or visualization module also leverages the embeddings by projecting them onto two dimensions while preserving the multidimensional landscape, resulting in a map where semantically related documents form topical clusters which we capture using topic modeling. This map aims at promoting a fast overview of the corpus while allowing a more detailed exploration and interactive information encountering process. We evaluate the system and its components on the 20 newsgroups dataset, using the semantic document labels provided, and demonstrate the superiority of Transformer-based components. Finally, we present a prototype of the system in Python and show how some of its features can be used to acquire intelligence from a news article corpus we collected during a period of 8 months.