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Nowadays, there has been a rapidly increasing number of scientific submissions in multiple research domains. A large number of journals have various acceptance rates, impact factors, and rankings in different publishers. It becomes time-consuming for many researchers to select the most suitable journal to submit their work with the highest acceptance rate. A paper submission recommendation system is more critical for the research community and publishers as it gives scientists another support to complete their submission conveniently. This paper investigates the submission recommendation system for two main research topics: computer science and applied mathematics. Unlike the previous works (Wang et al., 2018; Son et al., 2020) that extract TF–IDF and statistical features as well as utilize numerous machine learning algorithms (logistics regression and multiple perceptrons) for building the recommendation engine, we present an efficient paper submission recommendation algorithm by using different bidirectional transformer encoders and the Mixture of Transformer Encoders technique. We compare the performance between our methodology and other approaches by one dataset from Wang et al. (2018) with 14012 papers in computer science and another dataset collected by us with 223,782 articles in 178 Springer applied mathematics journals in terms of top K accuracy (K = 1 , 3 , 5 , 10). The experimental results show that our proposed method extensively outperforms other state-of-the-art techniques with a significant margin in all top K accuracy for both two datasets. We publish all datasets collected and our implementation codes for further references. 1 1 https://github.com/BinhMisfit/PSRMTE. • Bidirectional transformer encoders can improve the performance of the paper submission recommendation system. • The Mixture of Transformer Encoders framework shows the efficiency in the paper submission recommendation problem. • Proposed techniques can surpass other recent techniques on two datasets related. [ABSTRACT FROM AUTHOR]

[Display omitted] • DL algorithms unveil hidden patterns in genomics and biomedicine to empower IHS. • DL optimization enables intricate analysis of molecular-level data in biomedicine. • Open-source DL frameworks democratize access to cutting-edge tools for researchers. • Integration of DL in IHS allows for real-time monitoring and prediction of disease progression for proactive interventions. • DL-powered decision support systems aid clinicians in treatment planning and predicting patient response. The advancements in genomics and biomedical technologies have generated vast amounts of biological and physiological data, which present opportunities for understanding human health. Deep learning (DL) and machine learning (ML) are frontiers and interdisciplinary fields of computer science that consider comprehensive computational models and provide integral roles for disease diagnosis and therapy investigation. DL-based algorithms can discover the intrinsic hierarchies in the training data to show great promise for extracting features and learning patterns from complex datasets and performing various analytical tasks. This review comprehensively discusses the wide-ranging DL approaches for intelligent healthcare systems (IHS) in genomics and biomedicine. This paper explores advanced concepts in deep learning (DL) and discusses the workflow of utilizing role-based algorithms in genomics and biomedicine to integrate intelligent healthcare systems (IHS). The aim is to overcome biomedical obstacles like patient disease classification, core biomedical processes, and empowering patient-disease integration. The paper also highlights how DL approaches are well-suited for addressing critical challenges in these domains, offering promising solutions for improved healthcare outcomes. We also provided a concise concept of DL architectures and model optimization in genomics and bioinformatics at the molecular level to deal with biomedicine classification, genomic sequence analysis, protein structure classification, and prediction. Finally, we discussed DL's current challenges and future perspectives in genomics and biomedicine for future directions. [ABSTRACT FROM AUTHOR]

In natural language processing (NLP), transformer based models have surpassed recurrent neural networks (RNN) as state of the art, being introduced specifically to address the limitations of RNNs originating from its sequential nature. As a similar sequence modeling problem, transformer methods can be readily adapted for deep learning time series prediction. This paper proposes a sparse transformer based approach for electricity load prediction. The layers of a transformer addresses the shortcomings of RNNs and CNNs by applying the attention mechanism on the entire time series, allowing any data point in the input to influence any location in the output of the layer. This allows transformers to incorporate information from the entire sequence in a single layer. Attention computations can also be parallelized. Thus, transformers can achieve faster speeds, or trade this speed for more layers and increased complexity. In experiments on public datasets, the sparse transformer attained comparable accuracy to an RNN-based SOTA method (Liu et al., 2022) while being up to 5× faster during inference. Moreover, the proposed model is general enough to forecast the load from individual households to city levels as shown in the extensive experiments conducted. [ABSTRACT FROM AUTHOR]

Modern businesses benefit significantly from advances in computer vision technology, one of the important sectors of artificially intelligent and computer science research. Advanced computer vision issues like image processing, object recognition, and biometric authentication can benefit from using deep learning methods. As smart devices and facilities advance rapidly, current networks such as 4 G and the forthcoming 5 G networks may not adapt to the rapidly increasing demand. Classification of images, object classification, and facial recognition software are some of the most difficult computer vision problems that can be solved using deep learning methods. As a new paradigm for 6Core network design and analysis, artificial intelligence (AI) has recently been used. Therefore, in this paper, the 6 G wireless network is used along with Deep Learning to solve the above challenges by introducing a new methodology named Optimizing Computer Vision with AI-enabled technology (OCV-AI). This research uses deep learning – efficiency algorithms (DL-EA) for computer vision to address the issues mentioned and improve the system's outcome. Therefore, deep learning 6 G proposed frameworks (Dl-6 G) are suggested in this paper to recognize pattern recognition and intelligent management systems and provide driven methodology planned to be provisioned automatically. For Advanced analytics wise, 6 G networks can summarize the significant areas for future research and potential solutions, including image enhancement, machine vision, and access control. [ABSTRACT FROM AUTHOR]

• Deep learning-based automatic bird vocalization recognition methods were reviewed. • Developments of Pre-processing, Feature extraction and Recognition are focused. • Existing issues and prospects of bird vocalization recognition are highlighted. • Deep learning-based bird vocalization recognition has made recent advances. • Further studies on reliable and general bird vocalization recognition are required. Birds are considered critical indicators of ecosystem condition. Automatic recording devices have emerged as a trending tool to assist field observations, contributing to biodiversity monitoring on large spatio-temporal scales. However, manually processing huge volumes of recordings is challenging. Consequently, there has been a growing interest in automatic bird vocalization recognition in recent years. Automatic bird vocalization recognition technology has advanced from classical pattern recognition to deep learning (DL), with significantly improved recognition performance. This paper reviews related works on DL-based automatic bird vocalization recognition technology in the last decade. In this review, we present the current state of research in the three key areas of pre-processing, feature extraction and recognition methods involved in automatic bird vocalization recognition. The related datasets, evaluation metrics and software are also summarized. Finally, existing challenges along with opportunities for future work are highlighted. We conclude that, while DL-based automatic bird vocalization recognition has made recent advances in specific species, more robust denoising approaches, larger public datasets, and stronger generalization capabilities of feature extraction and recognition are required to achieve reliable and general bird recognition in the wild. We expect that this review will serve as a firm foundation for new researchers working in the field of DL-based automatic bird vocalization recognition technologies, as well as become an insightful guide for computer science and ecology experts. [ABSTRACT FROM AUTHOR]

Objective: This work aims to provide a review of the existing literature in the field of automated machine learning (AutoML) to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise. We also identify the potential opportunities and barriers to using AutoML in healthcare, as well as existing applications of AutoML in healthcare.Methods: Published papers, accompanied with code, describing work in the field of AutoML from both a computer science perspective or a biomedical informatics perspective were reviewed. We also provide a short summary of a series of AutoML challenges hosted by ChaLearn.Results: A review of 101 papers in the field of AutoML revealed that these automated techniques can match or improve upon expert human performance in certain machine learning tasks, often in a shorter amount of time. The main limitation of AutoML at this point is the ability to get these systems to work efficiently on a large scale, i.e. beyond small- and medium-size retrospective datasets.Discussion: The utilization of machine learning techniques has the demonstrated potential to improve health outcomes, cut healthcare costs, and advance clinical research. However, most hospitals are not currently deploying machine learning solutions. One reason for this is that health care professionals often lack the machine learning expertise that is necessary to build a successful model, deploy it in production, and integrate it with the clinical workflow. In order to make machine learning techniques easier to apply and to reduce the demand for human experts, automated machine learning (AutoML) has emerged as a growing field that seeks to automatically select, compose, and parametrize machine learning models, so as to achieve optimal performance on a given task and/or dataset.Conclusion: While there have already been some use cases of AutoML in the healthcare field, more work needs to be done in order for there to be widespread adoption of AutoML in healthcare. [ABSTRACT FROM AUTHOR]

Deep learning techniques have been successfully used in recent years to learn useful image trans- formations and features, thus contributing significantly to the advancements in neural networks. However deep nets suffer from the drawback that they require large training times and mul- tifarious parameters that need to be hand tuned for optimal performance. In this paper we investigate the use Recurrent neural network architectures to learn useful transformations of an image(object), progressively over time. Learning these latent transformations enables the recur- rent architecture to correctly predict, to a high degree of accuracy, the original representation of an object from its transformed representations. [ABSTRACT FROM AUTHOR]

Nowadays, a variety of sequences could be recorded and used with the rapid development of intelligent devices and sensors’ integrated technology. Several analysis of the sequences are based on the sequence recognition or classification and most of them are implemented via traditional machine learning models or their variants, such as Dynamic Time Warping, Hidden Markov Model and Support Vector Machine. Some of them could achieve a relatively high classification accuracy but with a time-consuming training process. Some other models are just the opposite. In this paper, we proposed a novel framework to solve the recognition task for sequences with multi-channels with a higher accuracy in less training time. In our framework, we designed a novel deep Convolutional Neural Network using “Data-Bands” as inputs. We conducted contrast experiments between our framework and several baseline methods and the results demonstrate that our framework could outperform state-of-art models. [ABSTRACT FROM AUTHOR]

This paper describes a reinforcement learning architecture that is capable of incorporating deeply learned feature representation of a robot's unknown working environment. An autoencoder is used along with convolutional and pooling layers to deduce the reduced feature representation based on a set of images taken by the agent. This representation is used to discover and learn the best route to navigate to a goal. The features are fed to an actor layer that can learn from a value function calculated by a second output layer. The policy is ɛ-greedy and the effect is similar to actor-critic architecture where temporal difference error is back propagated from the critic to the actor. This compact architecture helps in reducing the overhead of setting up a desired fully fledged actor-critic architecture that typically needs extra processing time. Hence, the model is ideal for dealing with lots of data coming from visual sensor that needs speedy processing. The processing is accomplished off board due to the limitation of the used robot but latency was compensated by the speedy processing. Adaptability for the different data sizes, critical to big data processing, is realized by the ability to shrink or expand the whole architecture to fit different deeply learned feature dimensions. This added flexibility is crucial for setting up such model since the space dimensionality is not known prior to operating in the environment. Initial experimental results on real robot show that the agent accomplished good level of accuracy and efficacy in reaching the goal. [ABSTRACT FROM AUTHOR]

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal–spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min–max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic. • Composite Monte-Carlo (CMC) simulation is a forecasting method. • A case study of using CMC through deep learning network is developed. • Decision makers are benefited from a better fitted Monte Carlo outputs. • Novel Coronavirus Epidemic is studied. [ABSTRACT FROM AUTHOR]