Machine Learning

The rise of e-commerce in Latin America has been driven by the digital presence of the younger generations and the adaptation of retail businesses to online sales channels. The COVID-19 pandemic has further accelerated this shift, forcing businesses to enhance their online commerce strategies. Peru has witnessed a notable 131\% increase in online shoppers from 2019 to 2021. However, the absence of a unique global code for product identification negatively affects the Zero Moment of Truth (ZMOT) in customer decision-making.

Predicting future events always comes with uncertainty, but traditional non-probabilistic methods cannot distinguish certain from uncertain predictions. In survival analysis, probabilistic methods applied to state-of-the-art solutions in the healthcare and biomedical field are still novel and their implications have not been fully evaluated. In this paper, we study the benefits of modeling uncertainty in deep neural networks for survival analysis with a focus on prediction and calibration performance.

Register allocation is an important phase in compiler optimization. Often, its resolution involves graph coloring, which is an NP-complete problem. Because of their significance, numerous heuristics have been suggested for their resolution. Heuristic development is a complex process that requires specialized domain expertise. Recently, several machine learning based approaches have been proposed to solve compiler optimization problems.

Manual palpation of organs played a vital role in detecting abnormalities in open surgeries. However, surgeons
have lost this ability with the development of minimally invasive surgeries. This challenge led to the development of artificial sensors for palpating the patient's organs and tissue. The majority of research done is related to improving the measurement of tissue compliance by the development of versatile force sensors for surgical

The dataset contains 2100 different observations each having 1099 absorption data points for different types of cells. The reflection absorption data were obtained from terahertz metamaterials on top of which the cells are placed. The 2100 observations made were for varying size specimen size and for four different types of cells

The dataset utilized in this research originates from two primary sources: the Central Bureau of Statistics of Indonesia, which provides data on Harvested Area and Production, and the Meteorology, Climatology, and Geophysics Agency of Indonesia, responsible for data on Rainfall, Humidity, and Temperature. This dataset encompasses six years of observations, collected annually from 2018 to 2023. It is important to note that the data for December 2023 are predictive estimates from these agencies.

We have obtained data from May 2022 to October 2023 for our suggested framework modelling. This set of data incorporates seasonality-related speech, which we convert into text, Facebook, and Twitter posts. On the whole, 4646 data elements have been acquired, comprising 3716 representing affected individuals and the remainder of 930 representing unaffected individuals, which generated a proportional 4:1 ratio.

Distribution and power transformers are essential components of any electricity network, hence electrical and mechanical safety of the transformer unit is among the highest concerns of electricity providers. Over the course of their operation, transformers face with a wide range of internal and external disturbances which may lead to a partial or full malfunction of the equipment. The service life and condition requirements for distribution and power transformers are now changed and utilities altered their maintenance policy from time-based to condition-based approach.

The enhanced dataset is a sophisticated collection of simulated data points, meticulously designed to emulate real-world data as collected from wearable Internet of Things (IoT) devices. This dataset is tailored for applications in safety monitoring, particularly for women, and is ideal for developing machine learning models for distress or danger detection.

Acute myocardial infarction (AMI) is the main cause of death in developed and developing countries. AMI is a serious medical problem that necessitates hospitalization and sometimes results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Many studies have been conducted on the prognosis of AMI with hemogram parameters. However, no study has investigated potential hemogram parameters for the diagnosis of AMI using an interpretable artificial intelligence-based clinical approach.