ADKU/ResearchGPT_model_dataset huggingface dataset:
Abstraction, mimesis and the evolution of deep learning
Authors: Jon Eklöf, Thomas Hamelryck, Cadell Last, Alexander Grima, Ulrika Lundh Snis

Deep learning developers typically rely on deep learning software frameworks (DLSFs)—simply described as pre-packaged libraries of programming components that provide high-level access to deep learning functionality. New DLSFs progressively encapsulate mathematical, statistical and computational complexity. Such higher levels of abstraction subsequently make it easier for deep learning methodology to spread through mimesis (i.e., imitation of models perceived as successful). In this study, we quantify this increase in abstraction and discuss its implications. Analyzing publicly available code from Github, we found that the introduction of DLSFs correlates both with significant increases in the number of deep learning projects and substantial reductions in the number of lines of code used. We subsequently discuss and argue the importance of abstraction in deep learning with respect to ephemeralization, technological advancement, democratization, adopting timely levels of abstraction, the emergence of mimetic deadlocks, issues related to the use of black box methods including privacy and fairness, and the concentration of technological power. Finally, we also discuss abstraction as a symptom of an ongoing technological metatransition.
COVID-19 classification based on a deep learning and machine learning fusion technique using chest CT images
Authors: Gerges M. Salama, Asmaa Mohamed, Mahmoud Khaled Abd-Ellah

Coronavirus disease (COVID-19), impacted by SARS-CoV-2, is one of the greatest challenges of the twenty-first century. COVID-19 broke out in the world over the last 2 years and has caused many injuries and killed persons. Computer-aided diagnosis has become a necessary tool to prevent the spreading of this virus. Detecting COVID-19 at an early stage is essential to reduce the mortality risk of patients. Researchers seek to find rapid solutions based on techniques of Machine Learning and Deep Learning. In this paper, we introduced a hybrid model for COVID-19 detection based on machine learning and deep learning models. We used 10 different deep CNN network models to extract features from CT images. We extract features from different layers in each network and find the optimum layer that gives the best-extracted features for each CNN network. Then, for classifying these features, we used five different classifiers based on machine learning. The dataset consists of 2481 CT images divided into COVID-19 and non-COVID-19 categories. Three folds are extracted with a different size between testing and training. Through experiments, we define the best layer for all used CNN networks, the best network, and the best-used classifier. The measured performance shows the superiority of the proposed system over the literature with a highest accuracy of 99.39%. Our models are tested with the three folds that gained maximum average accuracy. The result is 98.69%.
A framework for training larger networks for deep Reinforcement learning
Authors: Kei Ota, Devesh K. Jha, Asako Kanezaki

The success of deep learning in computer vision and natural language processing communities can be attributed to the training of very deep neural networks with millions or billions of parameters, which can then be trained with massive amounts of data. However, a similar trend has largely eluded the training of deep reinforcement learning (RL) algorithms where larger networks do not lead to performance improvement. Previous work has shown that this is mostly due to instability during the training of deep RL agents when using larger networks. In this paper, we make an attempt to understand and address the training of larger networks for deep RL. We first show that naively increasing network capacity does not improve performance. Then, we propose a novel method that consists of (1) wider networks with DenseNet connection, (2) decoupling representation learning from the training of RL, and (3) a distributed training method to mitigate overfitting problems. Using this three-fold technique, we show that we can train very large networks that result in significant performance gains. We present several ablation studies to demonstrate the efficacy of the proposed method and some intuitive understanding of the reasons for performance gain. We show that our proposed method outperforms other baseline algorithms on several challenging locomotion tasks.
Deep Learning-Based Watermarking Techniques Challenges: A Review of Current and Future Trends
Authors: Saoussen Ben Jabra, Mohamed Ben Farah

The digital revolution places great emphasis on digital media watermarking due to the increased vulnerability of multimedia content to unauthorized alterations. Recently, in the digital boom in the technology of hiding data, research has been tending to perform watermarking with numerous architectures of deep learning, which has explored a variety of problems since its inception. Several watermarking approaches based on deep learning have been proposed, and they have proven their efficiency compared to traditional methods. This paper summarizes recent developments in conventional and deep learning image and video watermarking techniques. It shows that although there are many conventional techniques focused on video watermarking, there are yet to be any deep learning models focusing on this area; however, for image watermarking, different deep learning-based techniques where efficiency in invisibility and robustness depends on the used network architecture are observed. This study has been concluded by discussing possible research directions in deep learning-based video watermarking.
Advancing plant biology through deep learning-powered natural language processing
Authors: Shuang Peng, Loïc Rajjou

The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure––function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.
Machine learning and deep learning techniques for detecting malicious android applications: An empirical analysis
Authors: Parnika Bhat, Sunny Behal, Kamlesh Dutta

The open system architecture of android makes it vulnerable to a variety of cyberattacks. Cybercriminals use android applications to intrude into the system and steal confidential data. This situation poses a threat to user privacy and integrity of the system. This paper proposes a static analysis approach to detect malicious and benign Android applications using various machine learning and deep learning algorithms. The proposed work has been validated using a bench marked dataset comprising 11,449 benign and malicious Android applications. The proposed approach applies a wrapper-based feature selection method to filter irrelevant features. The results clearly show that the deep learning algorithms of DBN and MLP outperformed machine learning algorithms in detecting malicious Android applications.
Deep reinforcement learning-based scheduling in distributed systems: a critical review
Authors: Zahra Jalali Khalil Abadi, Najme Mansouri, Mohammad Masoud Javidi

Many fields of research use parallelized and distributed computing environments, including astronomy, earth science, and bioinformatics. Due to an increase in client requests, service providers face various challenges, such as task scheduling, security, resource management, and virtual machine migration. NP-hard scheduling problems require a long time to implement an optimal or suboptimal solution due to their large solution space. With recent advances in artificial intelligence, deep reinforcement learning (DRL) can be used to solve scheduling problems. The DRL approach combines the strength of deep learning and neural networks with reinforcement learning’s feedback-based learning. This paper provides a comprehensive overview of DRL-based scheduling algorithms in distributed systems by categorizing algorithms and applications. As a result, several articles are assessed based on their main objectives, quality of service and scheduling parameters, as well as evaluation environments (i.e., simulation tools, real-world environment). The literature review indicates that algorithms based on RL, such as Q-learning, are effective for learning scaling and scheduling policies in a cloud environment. Additionally, the challenges and directions for further research on deep reinforcement learning to address scheduling problems were summarized (e.g., edge intelligence, ideal dynamic task scheduling framework, human–machine interaction, resource-hungry artificial intelligence (AI) and sustainability).
Deep-WET: a deep learning-based approach for predicting DNA-binding proteins using word embedding techniques with weighted features
Authors: S. M. Hasan Mahmud, Kah Ong Michael Goh, Md. Faruk Hosen, Dip Nandi, Watshara Shoombuatong

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/. The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
DDoS attack traffic classification in SDN using deep learning
Authors: Nisha Ahuja, Debajyoti Mukhopadhyay, Gaurav Singal

Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an automation network. To meet the needs of the current scenario, this architecture redesign becomes mandatory. Besides, machine learning (ML) and deep learning (DL) techniques provide a significant solution in network attack detection, traffic classification, etc. The DDoS attack is still wreaking havoc. Previous work for DDoS attack detection in SDN has not yielded significant results, so the author has used the most recent deep learning technique to detect the attacks. In this paper, we aim to classify the network traffic into normal and malicious classes based on features in the available dataset by using various deep learning techniques. TCP, UDP, and ICMP traffic are considered normal; however, malicious traffic includes TCP Syn Attack, UDP Flood, and ICMP Flood, all of which are DDoS attack traffic. The major contribution of this paper is the identification of novel features for DDoS attack detection. Novel features are logged into the CSV file to create the dataset, and machine learning algorithms are trained on the created SDN dataset. Various work which has already been done for DDoS attack detection either used a non-SDN dataset or the research data is not made public. A novel hybrid machine learning model is utilized to perform the classification. The dataset used by the ML/DL algorithms is a collection of public datasets on DDoS attacks as well as an experimental DDoS dataset generated by us and publicly available on the Mendeley Data repository. A Python application performs the classification of traffic into one of the classes. From the various classifiers used, the accuracy score of 99.75% is achieved with Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP). To measure the effectiveness of the SDN-DDoS dataset, the other publicly available datasets are also evaluated against the same deep learning algorithms, and traffic classification accuracy is found to be significantly higher with the SDN-DDoS dataset. The attack detection time of 216.39 s also serve as experimental evidence.
A Deep Learning-Based Object Representation Algorithm for Smart Retail Management
Authors: Bin Liu

This study underscores the vital role of object representation and detection in smart retail management systems for optimizing customer experiences and operational efficiency. The literature review reveals a preference for deep learning techniques, citing their superior accuracy compared to traditional methods. While acknowledging the challenges of achieving high accuracy and low computation costs simultaneously in deep learning-based object representation, the paper proposes a solution using the YOLOv7 framework. In order to navigate the ever-changing landscape of smart retail technologies, the study clarifies the potential scalability and flexibility of deep learning approaches. The method employs a custom dataset, and experimental results demonstrate the model’s efficacy, showcasing accurate results and enhanced performance in various experiments and analyses.
Shallow and deep learning classifiers in medical image analysis
Authors: Francesco Prinzi, Tiziana Currieri, Salvatore Gaglio, Salvatore Vitabile

An increasingly strong connection between artificial intelligence and medicine has enabled the development of predictive models capable of supporting physicians’ decision-making. Artificial intelligence encompasses much more than machine learning, which nevertheless is its most cited and used sub-branch in the last decade. Since most clinical problems can be modeled through machine learning classifiers, it is essential to discuss their main elements. This review aims to give primary educational insights on the most accessible and widely employed classifiers in radiology field, distinguishing between “shallow” learning (i.e., traditional machine learning) algorithms, including support vector machines, random forest and XGBoost, and “deep” learning architectures including convolutional neural networks and vision transformers. In addition, the paper outlines the key steps for classifiers training and highlights the differences between the most common algorithms and architectures. Although the choice of an algorithm depends on the task and dataset dealing with, general guidelines for classifier selection are proposed in relation to task analysis, dataset size, explainability requirements, and available computing resources. Considering the enormous interest in these innovative models and architectures, the problem of machine learning algorithms interpretability is finally discussed, providing a future perspective on trustworthy artificial intelligence.Relevance statement The growing synergy between artificial intelligence and medicine fosters predictive models aiding physicians. Machine learning classifiers, from shallow learning to deep learning, are offering crucial insights for the development of clinical decision support systems in healthcare. Explainability is a key feature of models that leads systems toward integration into clinical practice.
Key points
• Training a shallow classifier requires extracting disease-related features from region of interests (e.g., radiomics).• Deep classifiers implement automatic feature extraction and classification.• The classifier selection is based on data and computational resources availability, task, and explanation needs.Graphical Abstract
Survey of Research on Application of Deep Learning in Modulation Recognition
Authors: Yongjun Sun, Wanting Wu

Modulation recognition is an important research branch in the field of communication, which is widely used in civil and military fields. The classic methods depend on decision theory, signal feature and the choice of classifier, while the deep learning network can get the signal feature directly from the data, and its recognition accuracy is higher than the classic methods. This paper summarized the application of deep learning in modulation recognition. Firstly, the basic concept of deep learning and the common network structure in modulation recognition were introduced. Secondly, the common signal forms and signal preprocessing technologies of input deep learning network were given, and the characteristics and performance of different deep learning networks were summarized and analyzed. Finally, the challenges and future research directions in this field were discussed.
Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs
Authors: Eve Martin, Angus G. Cook, Shaun M. Frost, Angus W. Turner, Fred K. Chen, Ian L. McAllister, Janis M. Nolde, Markus P. Schlaich

Background/ObjectivesArtificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms.Subjects/MethodsPatients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants.ResultsOf the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy.ConclusionsThe results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.
Deep residual learning with Anscombe transformation for low-dose digital tomosynthesis
Authors: Youngjin Lee, Seungwan Lee, Chanrok Park

Deep learning-based convolutional neural networks (CNNs) have been proposed for enhancing the quality of digital tomosynthesis (DTS) images. However, the direct applications of the conventional CNNs for low-dose DTS imaging are limited to provide acceptable image quality due to the inaccurate recognition of complex texture patterns. In this study, a deep residual learning network combined with the Anscombe transformation was proposed for simplifying the complex texture and restoring the low-dose DTS image quality. The proposed network consisted of convolution layers, max-pooling layers, up-sampling layers, and skip connections. The network training was performed to learn the residual images between the ground-truth and low-dose projections, which were converted using the Anscombe transformation. As a result, the proposed network enhanced the quantitative accuracy and noise characteristic of DTS images by 1.01–1.27 and 1.14–1.71 times, respectively, in comparison to low-dose DTS images and other deep learning networks. The spatial resolution of the DTS image restored using the proposed network was 1.12 times higher than that obtained using a deep image learning network. In conclusion, the proposed network can restore the low-dose DTS image quality and provide an optimal model for low-dose DTS imaging.
Colon cancer diagnosis by means of explainable deep learning
Authors: Marcello Di Giammarco, Fabio Martinelli, Antonella Santone, Mario Cesarelli, Francesco Mercaldo

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images.