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A report from Diponegoro University discusses the use of artificial intelligence (AI) and deep learning in medical imaging to identify kidney stones. The research highlights that deep learning is more commonly used than traditional machine learning in this field. The aim of the study was to review existing literature on deep learning in medical imaging for detecting kidney stones. The research concludes that using the appropriate AI model with high accuracy can assist radiologists in accurately detecting kidney stones. This information may be useful for those interested in AI applications in medical diagnostics. [Extracted from the article]

A study conducted at the University of Maryland in College Park has developed a system for accurately indicating the freshness of salmon for consumers. The system consists of a colorimetric sensor made from sol-gel functionalized paper and a deep convolutional neural network (DCNN)-based freshness estimation system. The paper sensor is coated with sol-gel particles and indicators, allowing for the detection of volatile organic compounds. The system achieved high accuracy in freshness estimation and is suitable for use in food supply chains. The research was supported by the China Scholarship Council and has been peer-reviewed. For more information, contact Qin Wang at the University of Maryland. [Extracted from the article]

This article provides an overview of the scientific paper "Attention Is All You Need" written by Google researchers in 2017. The paper introduces a new AI architecture called transformers, which has had a significant impact on the field of artificial intelligence. The authors challenged the traditional ranking of contributors by listing themselves as equal contributors. The paper gained widespread recognition, and the authors have become well-known figures in the field. The article also discusses the development of transformer models, the challenges faced by the team, and the subsequent founding of new companies by some of the authors. It emphasizes the importance of a supportive and innovative environment for groundbreaking research. [Extracted from the article]

A report from the Thapar Institute of Engineering & Technology in Punjab, India discusses the use of machine learning and deep learning techniques for the detection of epileptic seizures. The paper compares different algorithms and models, such as support vector machine classifiers, artificial neural network classifiers, convolutional neural network classifiers, and long-short term memory networks. The effectiveness of different physiological signals, including electroencephalogram (EEG) and electrocardiogram (ECG), is also explored. The paper concludes that sentiment analysis could be another dimension of seizure detection. This research provides a summary of previous techniques and offers direction for further research in the field. [Extracted from the article]

To date, a large number of research papers have been written on malware classification, identification, classification into different families, and the distinction between malware and goodware. These works have been based on captured malware samples and have attempted to analyse malware and goodware using various techniques like the analysis of malware using malware visualization. These works usually convert malware samples capturing the malware structure into image structures which are then subject to image processing. In this paper, we propose an unconventional and novel approach to malware visualization based on its dynamical analysis, subsequent complex network conversion and fractal geometry, e.g. Julia sets visualization. Very interesting images being subsequently used to classify as malware and goodware. The classification is done by deep learning network. The results of the presented experiments of fractal conversion and subsequent classification are based on a database of 6,589,997 goodware, 827,853 potentially unwanted applications and 4,174,203 malware samples provided by ESET. 1 1 https://www.eset.com. This paper aims to show a new direction in visualizing malware using fractal geometry and possibilities in analysis and classification. [Display omitted] • Introduction of a new method for malware visualization based on fractal geometry. • Show interesting results when visualy comparing classified samples using fractal geometry. • Test novel malware image classification using deep learning. • To point out and define new research directions in visual malware analysis opened by our method. [ABSTRACT FROM AUTHOR]

Purpose: The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns. Design/methodology/approach: To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States. Findings: The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction. Originality/value: A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at Sichuan University of Science and Engineering in China explores the design of smart senior care (SSC) product app interfaces for the elderly. The researchers use deep learning (DL) technology to optimize the app interface design, taking into consideration the cognitive characteristics and habits of the elderly. The study finds that the proposed model based on the deep-Q-network (DQN) algorithm significantly improves the user experience and satisfaction of the elderly, enhancing their ability to utilize smart products and improve their quality of life. The research provides valuable insights for further research and practice in related fields. [Extracted from the article]

A report from Rajiv Gandhi University discusses the use of artificial intelligence (AI), specifically machine learning (ML) and deep learning (DL), in classifying schizophrenia through electroencephalogram (EEG) data. The review paper explores the challenges, methodologies, and findings in this field, highlighting the potential of AI in enhancing schizophrenia diagnosis. ML approaches like Support Vector Machines and Decision Trees are effective with smaller datasets, while DL techniques such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) are more adaptable to complex EEG patterns but require more data and computational power. The research emphasizes the importance of collaborative and ethically informed approaches in automated schizophrenia classification using AI. [Extracted from the article]

A recent study conducted by researchers from the University of Leicester in the United Kingdom explores the use of deep learning techniques in pediatric neuroimaging. The study highlights the potential of deep learning in various aspects of the field, including image processing, brain abnormalities detection, and brain development analysis. The researchers also discuss the challenges and limitations of implementing deep learning in pediatric neuroimaging, such as standardization issues and ethical concerns. They suggest future directions for research, including the integration of multi-modal data and the impact of maternal emotional well-being on brain development. Ultimately, the adoption of deep learning techniques in pediatric neuroimaging has the potential to improve patient outcomes and enhance our understanding of early brain development. [Extracted from the article]

A new report from researchers at Easwari Engineering College explores the use of artificial intelligence (AI) and thermal imaging in post-harvest fruit handling. The study highlights the potential of AI-based thermal imaging in detecting quality attributes and its applications in agriculture, food processing, and post-harvest fruit handling. The report discusses the challenges and advantages of AI-driven thermal imaging technology, as well as the use of machine learning and deep learning techniques. It also examines various applications of AI-based thermal imaging in post-harvest fruit quality assessment, such as bruise detection, maturity identification, and pest and disease detection. The research concludes by discussing future trends and the potential for increased automation and integration with emerging technologies in the post-harvest fruit handling sector. [Extracted from the article]