The research presents a machine learning (ML) classifier designed to differentiate between schizophrenia patients and healthy controls by utilising features extracted from electroencephalogram (EEG) data, specifically focusing on event-related potentials (ERPs) and certain demographic variables. The dataset comprises data from 81 participants, encompassing 32 healthy controls and 49 schizophrenia patients, all sourced from an online dataset. After preprocessing the dataset, our ML model achieved an accuracy of 99.930%. This performance outperforms earlier research, including those that used deep learning methods. Additionally, an analysis was conducted to assess individual features' contribution to improving classification accuracy. This involved systematically excluding specific features from the original dataset one at a time, and another technique involved an iterative process of removing features based on their entropy scores incrementally. The impact of these removals on model performance was evaluated to identify the most informative features.

Since the inception of permissionless blockchains with Bitcoin in 2008, it became apparent that their most well-suited use case is related to making the financial system and its advantages available to everyone seamlessly without depending on any trusted intermediaries. Smart contracts across chains provide an ecosystem of decentralized finance (DeFi), where users can interact with lending pools, Automated Market Maker (AMM) exchanges, stablecoins, derivatives, etc. with a cumulative locked value which had exceeded 160B USD. While DeFi comes with high rewards, it also carries plenty of risks. Many financial crimes have occurred over the years making the early detection of malicious activity an issue of high priority. The proposed framework introduces an effective method for extracting a set of features from different chains, including the largest one, Ethereum and it is evaluated over an extensive dataset we gathered with the transactions of the most widely used DeFi protocols (23 in total, including Aave, Compound, Curve, Lido, and Yearn) based on a novel dataset in collaboration with Covalent. Different Machine Learning methods were employed, such as XGBoost and a Neural Network for identifying fraud accounts detection interacting with DeFi and we demonstrate that the introduction of novel DeFi-related features, significantly improves the evaluation results, where Accuracy, Precision, Recall, F1-score and F2-score where utilized.

What the marketing world witnessed in April 2020, was an unprecedented rise of eCommerce retail sales in North America by triple digits. And, Statista reports that in the same 2020, more than two billion people bought their products from eCommerce stores, with global e-retail sales of over $4.2 trillion. While this is big news for eCommerce stores, an unfortunate incident is that the amount of returns purchasers make may go over a trillion dollars a year if care is not taken. The main reason for this is that most eCommerce shops have not been able to create size charts, so we have sizing problems plaguing e-commerce stores in a lot of ways. The basis of sizing issues eCommerce stores face is that they are outlets and usually get their products from different manufacturers across the globe.

The adoption of machine learning models has grown exponentially across industries. This blog discussed how iOS Vision Framework and Google TensorFlow have revolutionized machine learning in healthcare applications.

Beating the Clock: Leveraging Machine Learning to Scope Ransomware Response. Tuesday, June 08 | 1:15p.m. – 1:35p.m. ET. Session Type: …

By Andrew Rathkopf Posted on August 20, 2020 Small- to medium-sized (SMBs) are the backbone of the American economy: 30.7 million SMBs operate across the country and account for 64 percent of new jobs.They represent 99.9 percent of all businesses in the U.S., but they face many hurdles. The U.S. Bureau of Labor Statistics estimates that 20 percent of SMBs fail in their first year and that approximately half do not survive for longer than five. Only 35 percent of such businesses make it past their first decade in operation.The reasons for these failures are varied, but many involve cash flow issues, as 29 percent report they ran out of money and 18 percent cite pricing or supply cost problems. The ongoing pandemic has added new obstacles, too, as social distancing and stay-at-home orders cut off key revenue streams. Seventy percent of SMBs are adding new digital capabilities or enhancing existing ones to continue their operations, but firms often require financing to implement these innovations.A multitude of SMB investors are looking to help, but lending to digitally focused SMBs comes with its own hurdles, including fraud risks and inefficient lending procedures driven by stiff regulatory measures.

Artificial Intelligence has become an integral part of business operations for many organizations. These business organizations might, on the other hand, using CRM software to help direct their marketing and sales efforts more efficiently and effectively. But can both these technologies be used simultaneously to unlock the company's greater selling potential? Machine learning is one of the four categories of Artificial Intelligence, the other three being Narrow Artificial Intelligence (Narrow AI), Natural Language Processing (NLP) and Perception. Artificial intelligence has become a hot topic these days due to its lack of boundaries.

Machine learning and artificial intelligence will become the most defining technologies in banking and beyond, which led some of the most powerful institutions to seek partnerships, investments, and in-house developments to take advantage of application potential of machine learning and AI. Let's look at a collection of examples of how leading institutions are utilizing machine learning to unlock value from the vast data pools they command and continuously accumulate. Aetna has launched a new security system for its consumer mobile and web apps that, in something of a twist, makes passwords optional. Instead of a password or fingerprint being the only barrier to entry, Aetna's new behavior-based security system monitors user devices and how and where a consumer uses that machine. Consumers can add biometric protection available on their devices. That risk engine takes in data from many attributes of the device (software configuration, operating system version, etc.), in addition to benign attributes of consumer behavior (for example, how a mobile device is held when texting and location of the device), and matches these attributes against a device signature and a model based on previous behavior. The risk engine binds a consumer to one or more of the devices they typically use.

Patrick Bower has a wide area of expertise, including S&OP, Demand Planning, Inventory, Network Optimization, and Production Scheduling. A recognized expert on demand planning and S&OP, and a self-professed "S&OP geek", Patrick was previously Practice Manager of Supply Chain Planning at the consulting firm Plan4Demand, where his client list included Diageo, Bayer, GlaxoSmithKline, Pfizer, Foster Farms, Cabot Industries and American Girl. Patrick's experience encompasses tenures with Cadbury, Kraft Foods, Unisys, and Snapple. Patrick also worked for the supply chain software company Numelix. He was the recipient of IBF's 2012 award for Excellence in Business Forecasting & Planning.

Like most business units at this time of year, biomedical and clinical teams will be reflecting on the last 12 months and trying, as best they can, to figure out what the new year will bring. Given the downward pressure on costs, increased intervention from state and federal regulators, and the explosion of new medical technologies, it's safe to say that the pace of change won't let up.