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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]

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]

The 2024 Southern Africa Telecommunication Networks and Applications Conference (SATNAC) emphasized the role of artificial intelligence (AI) in bridging the digital divide and promoting inclusive growth in Africa. The conference, hosted by Telkom, focused on harnessing AI to address local challenges in healthcare, education, and financial services. Telkom's chief digital officer, Sello Mmakau, highlighted the importance of talent and skills in adopting AI, as well as the need for an AI governance framework to ensure responsible and ethical use. The conference also discussed the ethical implications of AI, including data privacy and algorithmic bias. Additionally, the conference recognized the Best Student Papers Awards, showcasing innovative research from African universities. [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]

Keywords: Fuzhou; People's Republic of China; Asia; Cloud Computing; Deep Learning; Diagnostics and Screening; Genetics; Information Technology; Machine Learning; Testing EN Fuzhou People's Republic of China Asia Cloud Computing Deep Learning Diagnostics and Screening Genetics Information Technology Machine Learning Testing 1039 1039 1 05/02/23 20230505 NES 230505 2023 MAY 7 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- New research on Information Technology - Cloud Computing is the subject of a report. Keywords for this news article include: Fuzhou, People's Republic of China, Asia, Cloud Computing, Deep Learning, Diagnostics and Screening, Genetics, Information Technology, Machine Learning, Testing, Fuzhou University. [Extracted from the article]

Keywords: Hangzhou; People's Republic of China; Asia; CDC and FDA EN Hangzhou People's Republic of China Asia CDC and FDA 1180 1180 1 03/27/23 20230402 NES 230402 2023 APR 2 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Letter on the CDC & FDA -- Current study results on CDC and FDA have been published. Keywords for this news article include: Hangzhou, People's Republic of China, Asia, CDC and FDA, Zhejiang University. [Extracted from the article]

The article explores how deep learning models have evolved from relying on hand-crafted operator libraries to utilizing compiler-based approaches for optimization, especially with the growing diversity of hardware platforms. It highlights the Apache TVM project, which allows machine learning engineers to compile models for specific hardware targets, optimizing performance without altering model accuracy. The article points to an accompanying paper that proposes a unified transformation approach that optimizes model architectures through program transformations, avoiding expensive retraining and achieving significant performance gains without compromising accuracy.

Keywords: Talence; France; Europe; Alzheimer Disease; Brain Diseases and Conditions; Central Nervous System Diseases and Conditions; Dementia; Diagnostics and Screening; Frontotemporal Dementia; Frontotemporal Lobar Degeneration; Health and Medicine; Mental Health; Nervous System Diseases and Conditions; Neurodegenerative Diseases and Conditions; Nutritional and Metabolic Diseases and Conditions; Proteostasis Deficiencies; TDP-43 Proteinopathies; Tauopathies EN Talence France Europe Alzheimer Disease Brain Diseases and Conditions Central Nervous System Diseases and Conditions Dementia Diagnostics and Screening Frontotemporal Dementia Frontotemporal Lobar Degeneration Health and Medicine Mental Health Nervous System Diseases and Conditions Neurodegenerative Diseases and Conditions Nutritional and Metabolic Diseases and Conditions Proteostasis Deficiencies TDP-43 Proteinopathies Tauopathies 499 499 1 10/30/23 20231103 NES 231103 2023 OCT 30 (NewsRx) -- By a News Reporter-Staff News Editor at Mental Health Weekly Digest -- Current study results on Neurodegenerative Diseases and Conditions - Frontotemporal Dementia have been published. Talence, France, Europe, Alzheimer Disease, Brain Diseases and Conditions, Central Nervous System Diseases and Conditions, Dementia, Diagnostics and Screening, Frontotemporal Dementia, Frontotemporal Lobar Degeneration, Health and Medicine, Mental Health, Nervous System Diseases and Conditions, Neurodegenerative Diseases and Conditions, Nutritional and Metabolic Diseases and Conditions, Proteostasis Deficiencies, TDP-43 Proteinopathies, Tauopathies. [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]

Today's wireless technologies are largely based on inflexible designs, which make them inefficient and prone to a variety of wireless attacks. To address this key issue, wireless receivers will need to (i) infer on-the-fly the physical layer parameters currently used by transmitters; and if needed, (ii) change their hardware and software structures to demodulate the incoming waveform. In this paper, we introduce PolymoRF, a deep learning-based polymorphic receiver able to reconfigure itself in real time based on the inferred waveform parameters. Our key technical innovations are (i) a novel embedded deep learning architecture, called RFNet, which enables the solution of key waveform inference problems, and (ii) a generalized hardware/software architecture that integrates RFNet with radio components and signal processing. We prototype PolymoRF on a custom software-defined radio platform and show through extensive over-the-air experiments that PolymoRF achieves throughput within 87% of a perfect-knowledge Oracle system, thus demonstrating for the first time that polymorphic receivers are feasible. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to develop surrogate models, using deep learning (DL), that can facilitate the application of EM analysis software. In the current status quo, electrical systems can be found in an ever-increasing range of products that are part of everyone's daily live. With the advances in technology, industries such as the automotive, communications and medical devices have been disrupted with new electrical and electronic systems. The innovation and development of such systems with increasing complexity over time has been supported by the increased use of electromagnetic (EM) analysis software. Such software enables engineers to virtually design, analyze and optimize EM systems without the need for building physical prototypes, thus helping to shorten the development cycles and consequently cut costs. Design/methodology/approach: The industry standard for simulating EM problems is using either the finite difference method or the finite element method (FEM). Optimization of the design process using such methods requires significant computational resources and time. With the emergence of artificial intelligence, along with specialized tools for automatic differentiation, the use of DL has become computationally much more efficient and cheaper. These advances in machine learning have ushered in a new era in EM simulations where engineers can compute results much faster while maintaining a certain level of accuracy. Findings: This paper proposed two different models that can compute the magnetic field distribution in EM systems. The first model is based on a recurrent neural network, which is trained through a data-driven supervised learning method. The second model is an extension to the first with the incorporation of additional physics-based information to the authors' model. Such a DL model, which is constrained by the laws of physics, is known as a physics-informed neural network. The solutions when compared with the ground truth, computed using FEM, show promising accuracy for the authors' DL models while reducing the computation time and resources required, as compared to previous implementations in the literature. Originality/value: The paper proposes a neural network architecture and is trained with two different learning methodologies, namely, supervised and physics-based. The working of the network along with the different learning methodologies is validated over several EM problems with varying levels of complexity. Furthermore, a comparative study is performed regarding performance accuracy and computational cost to establish the efficacy of different architectures and learning methodologies. [ABSTRACT FROM AUTHOR]

A recent study conducted by researchers at Sharda University in Greater Noida, India, explores the use of machine learning and non-machine learning approaches in the categorization of brain tumors. The researchers compiled and reviewed 169 research papers on brain tumor detection from 2013 to 2023, evaluating the efficacy of different methodologies based on criteria such as accuracy, sensitivity, specificity, and computing efficiency. The study provides a comprehensive summary of the current state of brain tumor categorization and highlights the application and development of machine learning methods in this field. The research has significant implications for brain tumor classification research. [Extracted from the article]

Purpose: This paper aims to analyze the performance of artificial neural networks with filling methods in predicting the minimum fluidization velocity of different biomass types for bioenergy applications. Design/methodology/approach: An extensive literature review was performed to create an efficient database for training purposes. The database consisted of experimental values of the minimum fluidization velocity, physical properties of the biomass particles (density, size and sphericity) and characteristics of the fluidization (monocomponent experiments or binary mixture). The neural models developed were divided into eight different cases, in which the main difference between them was the filling method type (K-nearest neighbors [KNN] or linear interpolation) and the number of input neurons. The results of the neural models were compared to the classical correlations proposed by the literature and empirical equations derived from multiple regression analysis. Findings: The performance of a given filling method depended on the characteristics and size of the database. The KNN method was superior for lower available data for training and specific fluidization experiments, like monocomponent or binary mixture. The linear interpolation method was superior for a wider and larger database, including monocomponent and binary mixture. The performance of the neural model was comparable with the predictions of the most well-known correlations from the literature. Originality/value: Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression analysis were reported and discussed. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to present a deep-learning-based beamforming method for phased array weather radars, especially whose antenna arrays are equipped with large number of elements, for fast and accurate detection of weather observations. Design/methodology/approach: The beamforming weights are computed by a convolutional neural network (CNN), which is trained with input–output pairs obtained from the Wiener solution. Findings: To validate the robustness of the CNN-based beamformer, it is compared with the traditional beamforming methods, namely, Fourier (FR) beamforming and Capon beamforming. Moreover, the CNN is compared with a radial basis function neural network (RBFNN) which is a shallow type of neural network. It is shown that the CNN method has an excellent performance in radar signal simulations compared to the other methods. In addition to simulations, the robustness of the CNN beamformer is further validated by using real weather data collected by the phased array radar at Osaka University (PAR@OU) and compared to, besides the FR and RBFNN methods, the minimum mean square error beamforming method. It is shown that the CNN has the ability to rapidly and accurately detect the reflectivity of the PAR@OU with even less clutter level in comparison to the other methods. Originality/value: Motivated by the inherit advantages of the CNN, this paper proposes the development of a CNN-based approach to the beamforming of PAR using both simulated and real data. In this paper, the CNN is trained on the optimum weights of Wiener solution. In simulations, it is applied on a large 32 × 32 planar phased array antenna. Moreover, it is operated on real data collected by the PAR@OU. [ABSTRACT FROM AUTHOR]

This article discusses the use of clustering models or cluster analyses in mental health research and psychology to explore individual heterogeneity. It highlights the lack of guidance on model choice, analytical framework, and reporting requirements in this field. The paper provides an introduction to major algorithms, such as kernel methods and deep learning, and discusses methods for pre-clustering data processing, clustering evaluation, and validation. The article also presents a rapid review of publications in psychology and psychiatry journals, pointing out issues such as a lack of diversity in algorithm choice and reproducibility. Overall, this comprehensive paper offers researchers advanced tools and guidelines to use clustering methods efficiently, robustly, and transparently in mental health and other health application areas. [Extracted from the article]

As Information Retrieval (IR) researchers, we not only develop algorithmic solutions to hard problems, but we also insist on a proper, multifaceted evaluation of ideas. The IR literature on the fundamental topic of retrieval and ranking, for instance, has a rich history of studying the effectiveness of indexes, retrieval algorithms, and complex machine learning rankers and, at the same time, quantifying their computational costs, from creation and training to application and inference. This is evidenced, for example, by more than a decade of research on efficient training and inference of large decision forest models in Learning to Rank (LtR). As we move towards even more complex, deep learning models in a wide range of applications, questions on efficiency have once again become relevant with renewed urgency. Indeed, efficiency is no longer limited to time- and space-efficiency; instead it has found new, challenging dimensions that stretch to resource-, sample- and energy-efficiency with ramifications for researchers, users, and the environment. As a step towards bringing together experts from industry and academia and creating a forum for a critical discussion and the promotion of efficiency in the era of Neural Information Retrieval (NIR), we held the ReNeuIR workshop on July 15th, 2022 as a hybrid event---in person in Madrid, Spain along with online attendees---in conjunction with ACM SIGIR 2022. Recognizing the importance of this topic, approximately 80 participants answered our call and attended the workshop over three sessions. The event included a total of two keynotes and eight paper presentations, and concluded with a lively discussion where participants helped identify gaps in existing research and brainstormed future research directions. We had consensus in recognizing that efficiency is not simply latency, that a holistic, concrete definition of efficiency is needed to guide researchers and reviewers alike, and that more research is necessary in the development of efficiency-centered evaluation metrics and standard benchmark datasets, platforms, and tools. Date: 15 July, 2022. Website: https://ReNeuIR.org. [ABSTRACT FROM AUTHOR]

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]

The rising popularity of intelligent mobile devices and the computational cost of deep learning-based models call for efficient and accurate on-device inference schemes. We propose a novel model compression scheme that allows inference to be carried out using bit-level sparsity, which can be efficiently implemented using in-memory computing macros. In this paper, we introduce a method called BitS-Net to leverage the benefits of bit-sparsity (where the number of zeros are more than number of ones in binary representation of weight/activation values) when applied to compute-in-memory (CIM) with resistive RAM (RRAM) to develop energy efficient DNN accelerators operating in the inference mode. We demonstrate that BitS-Net improves the energy efficiency by up to 5x for ResNet models on the ImageNet dataset. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification. [ABSTRACT FROM AUTHOR]

This paper reports on the 28th Italian Symposium on Advanced Database Systems (SEBD 2020), held online as a virtual conference from the 21st to the 24th of June 2020. The topics that were addressed in this edition of the conference were organized in the sessions: ontologies and data integration, anomaly detection and dependencies, text analysis and search, deep learning, noSQL data, trajectories and diffusion, health and medicine, context and ranking, social and knowledge graphs, multimedia content analysis, security issues, and data mining. [ABSTRACT FROM AUTHOR]

A study conducted by researchers at the University of Zululand explores the use of Internet of Things (IoT) technologies combined with deep learning (DL) techniques for pandemic detection. The study reviews 19 papers published between 2019 and 2024 that examine the contributions and unresolved issues of IoT-DL models in pandemic detection. The research provides a comprehensive overview of current trends and open issues in this field, making it a valuable resource for DL researchers and practitioners. The study emphasizes the potential of IoT and DL technologies in pandemic preparedness and control. [Extracted from the article]

A comprehensive review conducted by researchers at the University of Sfax in Tunisia explores the use of deep learning techniques for the early detection and decision-making in Alzheimer's disease (AD). The review focuses on the use of convolutional neural networks (CNN) and vision transformers (ViT) for the classification of AD using brain imaging data. The paper provides a detailed comparison of CNN and ViT, highlighting their strengths and limitations, and discusses the latest advancements in architecture, training methods, and performance evaluation metrics. The review also addresses ethical considerations and challenges associated with the use of deep learning models for AD classification. The findings aim to provide valuable insights for future research and clinical applications in the field of AD classification using deep learning techniques. [Extracted from the article]

Purpose: Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information. Design/methodology/approach: This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA. Findings: The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively. Originality/value: This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction. [ABSTRACT FROM AUTHOR]

DRAM devices require periodic refresh operations to preserve data integrity. Slowing down the refresh rate can reduce the energy consumption; however, it may cause a loss of data stored in the DRAM cell. This paper proposes a new memory architecture of soft approximation for deep learning applications, which reduces the refresh energy consumption while maintaining accuracy and high performance. Utilizing the error-tolerant property of deep learning applications, the proposed memory architecture avoids the accuracy drop caused by data loss by flexibly controlling the refresh operation for different bits, depending on their criticality. For data storage, the approximate DRAM architecture reorganizes the data so that these data are sorted according to their bit significance. Critical bits are stored in more frequently refreshed devices while non-critical bits are stored in less frequently refreshed devices. In addition, for further reduction of the DRAM energy consumption, this paper combines hard approximation, which reduces the number of accesses to DRAM, with soft approximation. Simulation results show that the refresh energy consumption is reduced by 69.71%, and the total energy consumption is reduced by 26.0 % for the hybrid memory with a negligible drop in both training and testing phases on state-of-the-art deep networks. [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]

This paper presents a novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It is employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one-versus-multiple class application. A novel validation method is also suggested for evaluating the structural risk, both in a quantitative and a qualitative manner. The effect of the DTGNN on the performance of the classifier is statistically validated through the repeated random subsampling using different sets of CTS, from different medical applications. The validation involves four medical databases, comprised of 108 recordings of the electroencephalogram signal, 90 recordings of the electromyogram signal, 130 recordings of the heart sound signal, and 50 recordings of the respiratory sound signal. Results of the statistical validations show that the DTGNN significantly improves the performance of the classification and also exhibits an optimal structural risk. [ABSTRACT FROM AUTHOR]

A report from Soran University discusses the use of machine learning and deep learning techniques in the detection of breast cancer tumors. The paper emphasizes the importance of accurate diagnosis and the limitations of traditional methods, while exploring the potential of medical imaging technology. The research also examines how these techniques can enhance processes such as feature extraction, picture segmentation, and classification in breast tumor detection systems. The study provides valuable insights into the application of emerging technologies in the field of oncology. [Extracted from the article]

This article reports on what makes a datasets useful for machine learning including size and the use of competition to generate them. The model dataset discussed in the article is Imagenet, a large dataset that was created using crowdsourcing to compile its more than three million images.

An introduction to an article about network traffic classification is presented.

Explicit model predictive control (EMPC) maps offline the control laws as a set of regions as a function of bounded uncertain parameters using multi-parametric programming. Then, in online mode, it seeks the best solution within these areas. Unfortunately, the offline solution can be computationally demanding because the number of regions can grow exponentially. Thus, this paper presents the application of a deep neural network (DNN) to learn the EMPC's regions for a photovoltaic-based charging station. The main uncertain parameters in this study are the forecast error of photovoltaic power production and the battery's state of charge. Additionally, the connection or disconnection of an electric vehicle is considered a disruption. The final controller creates the regions at the start of each prediction time or when a disruption occurs, only using the previously created DNN. The obtained solution is validated using data from an e-vehicle charging station installed at the University of Trieste, Italy. • Uncertainties like EV consumption affect the performance of optimization techniques. • EMPC creates offline critical regions that are a function of uncertain parameters. • The dimensionality of the problem can be untractable and time-consuming with EMPC. • DNN can be trained to create critical regions in a reduced computation time. [ABSTRACT FROM AUTHOR]

FEATURES PHOTOGRAPHS BY DJENEBA ADUAYOM AFTERNOON IN LATE NOVEMBER of last year, Timnit Gebru was sitting on the couch in her San Francisco Bay Area home, crying. Gebru was the coleader of a group at the company that studies the social and ethical ramifications of artificial intelligence, and Kacholia had ordered Gebru to retract her latest research paper - or else remove her name from its list of authors, along with those of several other members of her team. Marian Croak, the executive who had shown interest in Gebru's work, was given the task of consolidating the various teams working on what the company called responsible AI, including Mitchell and Gebru's. Still others surmised that Gebru was the casualty of a different kind of turf battle: that other internal groups working on responsible AI - ones with closer relationships to Google's product teams - felt that Gebru and her coauthors were encroaching where they didn't belong. [Extracted from the article]

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]

Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has motivated hardware accelerators to enhance compute efficiency, but where data movement and accessing are proving to be key bottlenecks. In-Memory Computing (IMC) is an approach with the potential to overcome this, whereby computations are performed in-place within dense 2-D memory. However, IMC fundamentally trades efficiency and throughput gains for dynamic-range limitations, raising distinct challenges for training, where compute precision requirements are seen to be substantially higher than for inferencing. This paper explores training on IMC hardware by leveraging two recent developments: (1) a training algorithm enabling aggressive quantization through a radix-4 number representation; (2) IMC leveraging compute based on precision capacitors, whereby analog noise effects can be made well below quantization effects. Energy modeling calibrated to a measured silicon prototype implemented in 16 nm CMOS shows that energy savings of over $400\times $ can be achieved with full quantizer adaptability, where all training MVMs can be mapped to IMC, and $3\times $ can be achieved for two-level quantizer adaptability, where two of the three training MVMs can be mapped to IMC. [ABSTRACT FROM AUTHOR]

In the contemporary data-driven era, the fields of machine learning, deep learning, big data, statistics, and data science are essential for forecasting outcomes and getting insights from data. This paper looks at how machine learning approaches can be used to anticipate solar power generation, assess heat exchanger heat transfer efficiency, and predict the thermo-physical properties of nanofluids. The review specifically focuses on the potential use of machine learning in solar thermal applications, perovskites, and photovoltaic power forecasting. Predictions of nanofluid characteristics and device performance may be more accurately made with the development of machine learning algorithms. The use of machine learning in the creation of new perovskites and the assessment of their effectiveness and stability is also included in the review. Additionally, the paper explores developments in artificial intelligence, particularly deep learning, in this area and offers insights into techniques for forecasting solar power, including PV production, cloud motion, and weather classification. [ABSTRACT FROM AUTHOR]

Purpose: With the rapid development of the financial market, stock index futures have been the one of important financial instruments. Predicting stock index futures accurately can bring considerable benefits for investors. However, traditional models do not perform well in stock index futures forecasting. This study put forward a novel hybrid model to improve the predictive accuracy of stock index futures. Design/methodology/approach: This study put forward a multivariate deep learning framework based on extreme gradient boosting (XGBoost) for stock index futures price forecasting. First, the original sequences were decomposed into several sub-sequences by variational mode decomposition (VMD), and these sub-sequences were reconstructed by sample entropy (SE). Second, the gradient boosting decision tree (GBDT) was used to rank the feature importance of influential factors, and the top influential factors were chosen for further prediction. Next, reconstructed sequence and the multiple factors screened were input into the bidirectional gate recurring unit (BiGRU) for modeling. Finally, XGBoost was used to integrate the modeling results. Findings: For the sake of examining the robustness of the proposed model, CSI 500 stock index futures, NASDAQ 100 index futures, FTSE 100 index futures and CAC 40 index futures are selected as sample data. The empirical consequences demonstrate that the proposed model can serve as an effective tool for stock index futures prediction. In other words, the proposed model can improve the accuracy of stock index futures. Originality/value: In this paper, an innovative hybrid model is proposed to enhance the predictive accuracy of stock index futures. Meanwhile, this method can be applied in other financial products prediction to achieve better forecasting results. [ABSTRACT FROM AUTHOR]

Purpose: The bank is termed as an imperative part of the marketing economy. The failure or success of an institution relies on the ability of industries to compute the credit risk. The loan eligibility prediction model utilizes analysis method that adapts past and current information of credit user to make prediction. However, precise loan prediction with risk and assessment analysis is a major challenge in loan eligibility prediction. Design/methodology/approach: This aim of the research technique is to present a new method, namely Social Border Collie Optimization (SBCO)-based deep neuro fuzzy network for loan eligibility prediction. In this method, box cox transformation is employed on input loan data to create the data apt for further processing. The transformed data utilize the wrapper-based feature selection to choose suitable features to boost the performance of loan eligibility calculation. Once the features are chosen, the naive Bayes (NB) is adapted for feature fusion. In NB training, the classifier builds probability index table with the help of input data features and groups values. Here, the testing of NB classifier is done using posterior probability ratio considering conditional probability of normalization constant with class evidence. Finally, the loan eligibility prediction is achieved by deep neuro fuzzy network, which is trained with designed SBCO. Here, the SBCO is devised by combining the social ski driver (SSD) algorithm and Border Collie Optimization (BCO) to produce the most precise result. Findings: The analysis is achieved by accuracy, sensitivity and specificity parameter by. The designed method performs with the highest accuracy of 95%, sensitivity and specificity of 95.4 and 97.3%, when compared to the existing methods, such as fuzzy neural network (Fuzzy NN), multiple partial least squares regression model (Multi_PLS), instance-based entropy fuzzy support vector machine (IEFSVM), deep recurrent neural network (Deep RNN), whale social optimization algorithm-based deep RNN (WSOA-based Deep RNN). Originality/value: This paper devises SBCO-based deep neuro fuzzy network for predicting loan eligibility. Here, the deep neuro fuzzy network is trained with proposed SBCO, which is devised by combining the SSD and BCO to produce most precise result for loan eligibility prediction. [ABSTRACT FROM AUTHOR]

In this paper, we propose a label-less learning for emotion cognition (LLEC) to achieve the utilization of a large amount of unlabeled data. We first inspect the unlabeled data from two perspectives, i.e., the feature layer and the decision layer. By utilizing the similarity model and the entropy model, this paper presents a hybrid label-less learning that can automatically label data without human intervention. Then, we design an enhanced hybrid label-less learning to purify the automatic labeled data. To further improve the accuracy of emotion detection model and increase the utilization of unlabeled data, we apply enhanced hybrid label-less learning for multimodal unlabeled emotion data. Finally, we build a real-world test bed to evaluate the LLEC algorithm. The experimental results show that the LLEC algorithm can improve the accuracy of emotion detection significantly. [ABSTRACT FROM AUTHOR]

This paper addresses the biological plausibility of both backpropagation (BP) and contrastive Hebbian learning (CHL) used in the Boltzmann machines. The main claim of this paper is that CHL is a general learning algorithm that can be used to steer feedforward networks toward desirable outcomes, and steer them away from undesirable outcomes without any need for the specialized feedback circuit of BP or the symmetric connections used by the Boltzmann machines. After adding perturbations during the learning phase to all the neurons in the network, multiple feedforward outcomes are classified into Hebbian and anti-Hebbian sets based on the network predictions. The algorithm is applied to networks when optimizing a loss objective where BP excels and is also applied to networks with stochastic binary outputs where BP cannot be easily applied. The power of the proposed algorithm lies in its simplicity where both learning and gradient estimation through stochastic binary activations are combined into a single local Hebbian rule. We will also show that both Hebbian and anti-Hebbian correlations are evaluated from the readily available signals that are fundamentally different from CHL used in the Boltzmann machines. We will demonstrate that the new learning paradigm where Hebbian/anti-Hebbian correlations are based on correct/incorrect predictions is a powerful concept that separates this paper from other biologically inspired learning algorithms. [ABSTRACT FROM AUTHOR]

With the increasing popularity of social media and smart devices, the face as one of the key biometrics becomes vital for person identification. Among those face recognition algorithms, video-based face recognition methods could make use of both temporal and spatial information just as humans do to achieve better classification performance. However, they cannot identify individuals when certain key facial areas, such as eyes or nose, are disguised by heavy makeup or rubber/digital masks. To this end, we propose a novel deep spiking neural network architecture in this paper. It takes dynamic facial movements, the facial muscle changes induced by speaking or other activities, as the sole input. An event-driven continuous spike-timing-dependent plasticity learning rule with adaptive thresholding is applied to train the synaptic weights. The experiments on our proposed video-based disguise face database (MakeFace DB) demonstrate that the proposed learning method performs very well, i.e., it achieves from 95% to 100% correct classification rates under various realistic experimental scenarios. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new end-to-end deep neural network model for time-series classification (TSC) with emphasis on both the accuracy and the interpretation. The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS data. In addition, a feedforward fully connected network with the sparse group lasso (SGL) regularization is used to generate the final classification. The proposed architecture not only achieves satisfying classification accuracy, but also obtains good interpretability through the SGL regularization. All these networks are connected and jointly trained in an end-to-end framework, and it can be generally applied to TSC tasks across different domains without the efforts of feature engineering. Our experiments in various TS data sets show that the proposed model outperforms the traditional convolutional neural network model for the classification accuracy, and also demonstrate how the SGL contributes to a better model interpretation. [ABSTRACT FROM AUTHOR]

A preprint abstract from medrxiv.org discusses the need for more accurate prediction models to determine the mortality of patients diagnosed with Coronary Artery Disease (CAD), which is the third leading cause of mortality worldwide. The paper presents a modified framework that demonstrates better performance in predicting CAD patient mortality compared to traditional baseline machine learning models. The framework utilizes patients' medical history, time-related variables, and demographic information to make more accurate decisions for treatment and care, potentially increasing their life expectancy. However, it is important to note that this preprint has not yet undergone peer review. [Extracted from the article]

A study conducted by researchers at the Sepuluh Nopember Institute of Technology in Surabaya, Indonesia, explores the use of deep learning techniques for the segmentation of the Mandibular Canal (MC) in digital dental radiographs. The study reviews 30 primary research papers published between 2018 and 2023, categorizing them into two groups based on the type of digital dental radiograph used. The researchers identify challenges in previous studies, such as limited public datasets and complexities in deep learning models, and suggest collaborative efforts to create and share more datasets, improve radiograph quality, streamline annotation processes, and simplify deep learning models for practical implementation. This research aims to guide researchers, dentists, and oral surgeons in leveraging deep learning advancements for MC segmentation in oral and maxillofacial surgery. [Extracted from the article]

• A new framework based on the PINNs is presented for bimaterial interface crack analysis. • An enriched PINNs is proposed to correctly capture the local crack-tip behavior. • The complex SIFs of interfacial cracks can be directly calculated with very high accuracy. This study explores the application of physics-informed neural networks (PINNs) to analyze interface crack problems within the context of elastic bimaterial fracture mechanics. Bimaterial interface cracks exhibit a distinct behavior compared to cracks in homogeneous materials, and this behavior often involves oscillatory phenomena that can pose challenges in numerical modeling. By employing neural networks for solution approximation, PINNs are meshless and are trained using batches of collocation points, which may be randomly or strategically sampled across the computational domain. To effectively capture the oscillatory singular behavior in the crack-tip regions, this paper introduces an enhanced PINNs formulation that enables the modeling of interface cracks without requiring any refinement near the crack-tip. The trainable parameters of the current PINNs are dynamically optimized throughout the training process to fulfill both the underlying differential equations and the associated initial/boundary conditions. One of the significant advantages of the present PINNs is that it uses enrichment functions to capture the behavior around the crack region, allowing for greater flexibility in handling irregular or complex crack paths. The method's accuracy and stability are validated across several benchmark examples. [ABSTRACT FROM AUTHOR]

With this method, multiple high-quality individual breast ultrasound images can be obtained from a single ultrasound report photo, improving the performance of the breast ultrasound image classification model. Keywords: Applied Sciences; Diagnostics and Screening; Health and Medicine; Science EN Applied Sciences Diagnostics and Screening Health and Medicine Science 5399 5399 1 06/12/23 20230617 NES 230617 2023 JUN 16 (NewsRx) -- By a News Reporter-Staff News Editor at Medical Imaging Week -- New research on applied sciences is the subject of a new report. In order to segment and denoise ultrasound report images of patients, this paper proposes a breast ultrasound report generation method, which mainly includes a segmentation model, a rotating classification model and a generative model. [Extracted from the article]

On this basis,it gives the back-ground and principle of backdoor attack,distinguishes the similar attack paradigms such as adversarial attack and data poisoning attack,then continues to elaborate on the attack principle and outstanding features of the classic methods of backdoor attack to date. According to the news editors, the research concluded: "Then,this paper surveys the state-of-the-art works of backdoor attack against various typical applications and popular deep learning paradigms,which further reveal the threat of backdoor attack towards deep learning models. [Extracted from the article]

Condition-based maintenance (CBM) policy can avoid premature or late maintenance and reduce system failures and maintenance costs. Most existing CBM studies cannot solve the dimensional disaster problem in multi-component complex systems. Only some studies consider the constraint of maintenance resources when searching for the optimal maintenance policy, which is hard to apply to practical maintenance. This paper studies the joint optimization of the CBM policy and spare components inventory for the multi-component system in large state and action spaces. We use Markov Decision Process to model it and propose an improved deep reinforcement learning algorithm based on the stochastic policy and actor-critic framework. In this algorithm, factorization decomposes the system action into the linear combination of each component's action. The experimental results show that the algorithm proposed in this paper has better time performance and lower system cost compared with other benchmark algorithms. The training time of the former is only 28.5% and 9.12% of that of PPO and DQN algorithms, and the corresponding system cost is decreased by 17.39% and 27.95%, respectively. At the same time, our algorithm has good scalability and is suitable for solving Markov decision-making problems in large-scale state and action space. • Considering minor and major repair, we model the joint optimization of CBM and spare ordering for large multi-component systems based on MDP. • An improved DRL algorithm is presented to deal with the MDP model in large-scale discrete state and action space. • We validate our DRL algorithm has good time performance and optimal decision-making series solution via comparisons with DQN and PPO algorithm. [ABSTRACT FROM AUTHOR]