Background: Artificial Intelligence (AI)-based Deep Neural Networks (DNNs) can handle a wide range of applications in image analysis, ranging from automated segmentation to diagnostic and prediction. As such, they have revolutionized healthcare, including in the liver pathology field. Objective: The present study aims to provide a systematic review of applications and performances provided by DNN algorithms in liver pathology throughout the Pubmed and Embase databases up to December 2022, for tumoral, metabolic and inflammatory fields. Results: 42 articles were selected and fully reviewed. Each article was evaluated through the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool, highlighting their risks of bias. Conclusions: DNN-based models are well represented in the field of liver pathology, and their applications are diverse. Most studies, however, presented at least one domain with a high risk of bias according to the QUADAS-2 tool. Hence, DNN models in liver pathology present future opportunities and persistent limitations. To our knowledge, this review is the first one solely focused on DNN-based applications in liver pathology, and to evaluate their bias through the lens of the QUADAS2 tool.

/pdf/artificial-intelligence-based-opportunities-in-liver-2v91prww.pdf

Artificial Intelligence-Based Opportunities in Liver Pathology—A Systematic Review

Every day, hundreds of domain names, websites, and logos are being cloned by cyber criminals who want to gain our trust to steal our data. As a result, faking logos is becoming a big issue in the online world and needs to be addressed. As a result, fake logos on the internet have become a significant source of worry for businesses and consumers. The algorithm can detect differences in logo design, color, and positioning and assess the possibility of a fake logo. The system's accuracy was evaluated on a massive dataset of actual and false logos, and it obtained a high level of accuracy in recognizing fake logos. The fake logo identification technology has the potential to dramatically increase the credibility and dependability of online material, thereby protecting brand identity integrity. This research proposes a method for detecting fake logos using a Context-dependent similarity algorithm. Our approach involves extracting features from the logos and training a machine-learning classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy.

Online Fake Logo Detection System

Abstract With the increasing prevalence of online fraud and the use of fake logos to deceive consumers, there is a need for effective methods to detect and prevent the use of fake logos on the internet. In this paper, we propose a method for detecting fake logos using machine learning techniques. Our approach involves extracting features from the logos and training a classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy. Every day, hundreds of domain names, websites and logos are being cloned by cyber criminals who want to gain your trust so they can steal your data. It is becoming a big issue in the online world and needs to be addressed. This article will discuss the initial project background of our new Online Fake Logo Detection System 

/pdf/online-fake-logo-detection-system-80kc8qs4.pdf

The impact of green transformation in data centers on corporate carbon emission reduction: empirical evidence from China

Abstract Memory forensics is used to implement and investigate malware that is executed or stored in RAM. Whether it is static malware analysis or dynamic malware analysis,each time the malware investigator retrieves the result, it is displayed in plaintext, and the investigator begins examining each result in the plaintext and triaging the malicious request. It's a labor-intensive process, and occasionally an investigator will upload malicious files to his or her computer to be analyzed for malware. These malicious files could contain worms or have the potential to infect the investigator's computer; if that happens, the attacker will keep an eye on all future investigations and the evidence they produce. With the help of this research and algorithm, whenever a malicious DLL or request is made, the algorithm will be able to identify it and flag it. This will save the investigator a lot of time because the investigator can upload files to his or her computer without worrying about whether they will be flagged as malicious behavior. We experimented wih multiple malicious files and our algorithm shows 98% efficacy. 

Memory Malware Analysis: Detecting Malicious Signatures In Memory By VolatilityPlugin’s

Capping carbon emission from green data centers

Application of machine learning algorithms to classify steatohepatitis on liver biopsy

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Capping carbon emission from green data centers

Application of machine learning algorithms to classify steatohepatitis on liver biopsy