Rizwan A. Qureshi

May 1, 2024 | lexology.com | Rizwan A. Qureshi

This incarceratory sentence is an important reminder that it is never too early to ensure that your AML controls, particularly around KYC and the filing of SARs, are consistent with U.S. law. Consider engaging counsel to conduct a privileged risk assessment of your controls to ensure you are engaging in self-help and are in a better position if and when the DOJ, SEC or other regulator comes knocking.

Mar 8, 2024 | connectwise.com | Rizwan A. Qureshi

Posted: | By: Enhance your security practice with best-in-class solutions. Explore our cybersecurity suite Having advanced and comprehensive cybersecurity systems in place is critical for managed service providers (MSPs) trying to protect their clients' most valuable digital assets. One major cyberattack is all it takes to completely destroy a business and tarnish its reputation with its customers moving forward.

Dec 27, 2023 | news.bloomberglaw.com | Daniel Ahn |Mark Bini |Rizwan A. Qureshi

Those who regularly exchange or sell cryptocurrency should pay careful attention to the implications of Harper v. Werfel, a case before the US Court of Appeals for the First Circuit that could decide whether there’s a constitutional right of privacy in cryptocurrency exchange records. Harper challenges the IRS’s use of a John Doe summons to obtain individuals’ financial information from a crypto exchange.

Sep 27, 2023 | mdpi.com | Tarique Hussain |Zulfiqar Memon |Rizwan A. Qureshi |Tanvir Alam

6.1. DiscussionOur study emphasizes the significance of normalization layers and activation functions in deep learning networks for stable optimization and enhanced generalization. We discuss the innovative EvoNorm layer, which outperforms the traditional Batch Normalization and Rectified Linear Unit (ReLU) combination. Two EvoNorm variants, EvoNorm-B and EvoNorm-S, are presented, with the latter incorporating the Swish activation function.

May 31, 2023 | frontiersin.org | SOFIA DIAS |Anna Kiss |Rizwan A. Qureshi

Jingmin Wang1 Chengyuan He2* Zhiwen Long2 1College of International Engineering, Xi’an University of Technology, Xi’an, China 2Recovery Plus Clinic, Chengdu, China Background: Malnutrition affects many worldwide, necessitating accurate and timely nutritional risk assessment. This study aims to develop and validate a machine learning model using facial feature recognition for predicting nutritional risk.