ADKU/ResearchGPT_model_dataset huggingface dataset:
Leveraging self-paced learning and deep sparse embedding for image clustering
Authors: Yanming Liu, Jinglei Liu

Deep clustering outperforms traditional methods by incorporating feature learning. However, some existing deep clustering methods overlook the suitability of the learned features for clustering, leading to insufficient feedback received by the clustering model and hampering the accuracy improvement. To tackle these issues, we propose a joint self-paced learning and deep sparse embedding for image clustering. Our method consists of two stages: pretraining and finetuning. In the pretraining stage, the autoencoder learns basic features and constructs the feature space. In the finetuning stage, method performs two tasks: feature learning and cluster assignment. Specifically, we finetune the encoder with both original and augmented data to preserve the local structure in the feature space. Self-paced learning guarantees that the most confident features are used for each iteration and mitigates the influence of boundary samples. Furthermore, sparse embedding ensures that the model encodes only key features in feature learning tasks, thereby avoiding incorrect calculations resulting from redundant features. Finally, we jointly optimize these two tasks to complete the feature learning for clustering. Extensive experiments on various datasets demonstrate that our approach outperforms existing solutions.
Automated abnormalities detection in mammography using deep learning
Authors: Ghada M. El-Banby, Nourhan S. Salem, Eman A. Tafweek, Essam N. Abd El-Azziz

Breast cancer is the second most prevalent cause of cancer death and the most common malignancy among women, posing a life-threatening risk. Treatment for breast cancer can be highly effective, with a survival chance of 90% or higher, especially when the disease is detected early. This paper introduces a groundbreaking deep U-Net framework for mammography breast cancer images to perform automatic detection of abnormalities. The objective is to provide segmented images that show areas of tumors more accurately than other deep learning techniques. The proposed framework consists of three steps. The first step is image preprocessing using the Li algorithm to minimize the cross-entropy between the foreground and the background, contrast enhancement using contrast-limited adaptive histogram equalization (CLAHE), normalization, and median filtering. The second step involves data augmentation to mitigate overfitting and underfitting, and the final step is implementing a convolutional encoder-decoder network-based U-Net architecture, characterized by high precision in medical image analysis. The framework has been tested on two comprehensive public datasets, namely INbreast and CBIS-DDSM. Several metrics have been adopted for quantitative performance assessment, including the Dice score, sensitivity, Hausdorff distance, Jaccard coefficient, precision, and F1 score. Quantitative results on the INbreast dataset show an average Dice score of 85.61% and a sensitivity of 81.26%. On the CBIS-DDSM dataset, the average Dice score is 87.98%, and the sensitivity reaches 90.58%. The experimental results ensure earlier and more accurate abnormality detection. Furthermore, the success of the proposed deep learning framework in mammography shows promise for broader applications in medical imaging, potentially revolutionizing various radiological practices.
Deep Learning Based Entropy Controlled Optimization for the Detection of Covid-19
Authors: Jiong Chen, Abdullah Alshammari, Mohammed Alonazi, Aisha M. Alqahtani, Sara A. Althubiti, Romi Fadillah Rahmat

Emerging technological advancements open the door for employing deep learning-based methods in practically all spheres of human endeavor. Because of their accuracy, deep learning algorithms can be used in healthcare to categorize and identify different illnesses. The recent coronavirus (COVID-19) outbreak has significantly strained the global medical system. By using medical imaging and PCR testing, COVID-19 can be diagnosed. Since COVID-19 is highly transmissible, it is generally considered secure to analyze it with a chest X-ray. To distinguish COVID-19 infections from additional infections that are not COVID-19 infections, a deep learning-based entropy-controlled whale optimization (EWOA) with Transfer Learning is suggested in this paper. The created system comprises three stages: a preliminary processing phase to remove noise effects and resize the image, then a deep learning architecture using a pre-trained model to extract features from the pre-processed image. After extracting the features, optimization is carried out. EWOA is utilized to combine and optimize the optimum features. A softmax layer is used to reach the final categorization. Various activation functions, thresholds, and optimizers are used to assess the systems. Numerous metrics for performance are utilized to measure the performance of the offered methodologies for assessment. Through an accuracy of 97.95%, the suggested technique accurately categorizes four classes, including COVID-19, viral pneumonia, chest infection, and routine. Compared to current methodologies found in the literature, the proposed technique exhibits advantages regarding accuracy.
Data set terminology of deep learning in medicine: a historical review and recommendation
Authors: Shannon L. Walston, Hiroshi Seki, Hirotaka Takita, Yasuhito Mitsuyama, Shingo Sato, Akifumi Hagiwara, Rintaro Ito, Shouhei Hanaoka, Yukio Miki, Daiju Ueda

Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word ‘validation’ in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the ‘training set’, the data for tuning of parameters referred to as the ‘validation (or tuning) set’, and the data for the evaluation of models as the ‘test set’. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.
A Study on Machine Learning and Deep Learning Techniques for Identifying Malicious Web Content
Authors: Sarita Mohanty, Asha Ambhakar

The rapid proliferation of internet usage has led to an exponential increase in cyber threats, particularly malicious websites that can compromise user data and system integrity. Traditional methods of web security are increasingly becoming obsolete, necessitating more dynamic and adaptive approaches. This research paper presents a comprehensive comparative study of Machine Learning (ML) and Deep Learning (DL) techniques for the detection of malicious websites. Utilizing a dataset of over 420,000 web URLs, categorized into various features such as domain, subdomain, and domain suffix, the study aims to evaluate the effectiveness, precision, and computational efficiency of multiple algorithms. Two Convolutional Neural Network (CNN) models were developed and compared against traditional ML algorithms including Decision Trees, Random Forests, AdaBoost, K-Nearest Neighbors (KNN), Stochastic Gradient Descent (SGD), Extra Trees, and Gaussian Naive Bayes. The models were rigorously evaluated based on metrics such as accuracy, precision, recall, and F1-score. Preliminary results indicate that CNN models outperform traditional ML algorithms, achieving an accuracy rate of up to 98%, thereby highlighting the potential of DL in cybersecurity applications. Moreover, the study addresses the challenges posed by high cardinality and class imbalance in the dataset. Various data preprocessing techniques were employed to mitigate these issues, including feature engineering and oversampling of minority classes. The research contributes to the field by providing a detailed analysis of each algorithm’s strengths and weaknesses, thereby offering valuable insights into the adaptability and scalability of ML and DL techniques in malicious web detection.
Federal learning-based a dual-branch deep learning model for colon polyp segmentation
Authors: Xuguang Cao, Kefeng Fan, Huilin Ma

The incidence of colon cancer occupies the top three places in gastrointestinal tumors, and colon polyps are an important causative factor in the development of colon cancer. Early screening for colon polyps and colon polypectomy can reduce the chances of colon cancer. The current means of colon polyp examination is through colonoscopy, taking images of the gastrointestinal tract, and then manually marking them manually, which is time-consuming and labor-intensive for doctors. Therefore, relying on advanced deep learning technology to automatically identify colon polyps in the gastrointestinal tract of the patient and segmenting the polyps is an important direction of research nowadays. Due to the privacy of medical data and the non-interoperability of disease information, this paper proposes a dual-branch colon polyp segmentation network based on federated learning, which makes it possible to achieve a better training effect under the guarantee of data independence, and secondly, the dual-branch colon polyp segmentation network proposed in this paper adopts the two different structures of convolutional neural network (CNN) and Transformer to form a dual-branch structure, and through layer-by-layer fusion embedding, the advantages between different structures are realized. In this paper, we also propose the Aggregated Attention Module (AAM) to preserve the high-dimensional semantic information and to complement the missing information in the lower layers. Ultimately our approach achieves state of the art in Kvasir-SEG and CVC-ClinicDB datasets.
Deep learning in pulmonary nodule detection and segmentation: a systematic review
Authors: Chuan Gao, Linyu Wu, Wei Wu, Yichao Huang, Xinyue Wang, Zhichao Sun, Maosheng Xu, Chen Gao

ObjectivesThe accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature.MethodsThis study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information.ResultsAfter screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient.ConclusionsThis study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research.Clinical relevance statementDeep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility.Key Points

Deep learning shows potential in the detection and segmentation of pulmonary nodules.


There are methodological gaps and biases present in the existing literature.


Factors such as external validation and transparency affect the clinical application.
Towards optimized tensor code generation for deep learning on sunway many-core processor
Authors: Mingzhen Li, Changxi Liu, Jianjin Liao, Xuegui Zheng, Hailong Yang, Rujun Sun, Jun Xu, Lin Gan, Guangwen Yang, Zhongzhi Luan, Depei Qian

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM} that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM} achieve 1.79× improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway, across eight representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor.
Discovering cholinesterase inhibitors from Chinese herbal medicine with deep learning models
Authors: Fulu Pan, Yang Liu, Zhiqiang Luo, Guopeng Wang, Xueyan Li, Huining Liu, Shuang Yu, Dongying Qi, Xinyu Wang, Xiaoyu Chai, Qianqian Wang, Renfang Yin, Yanli Pan

Traditional Chinese medicine (TCM) holds distinctive advantages in the management of Alzheimer’s disease. Nonetheless, a considerable gap remains in our understanding of its pharmacologically active constituents. In this study, we harnessed the potential of deep learning models to swiftly and precisely predict drug-target interactions. We conducted a systematic screening of cholinesterase (ChE) inhibitors from an extensive array of TCM ingredients, followed by rigorous validation through in vitro experiments. We constructed both a drug-target interactions (DTI) model and a blood-brain barrier permeability (BBBP) model, with both models achieving an AUPRC score exceeding 0.9. Subsequently, we conducted a screening process that identified six compounds for in vitro ChE inhibitory assay. Notably, all six compounds exhibited a robust inhibitory effect on acetylcholinesterase (AChE), while four of the six compounds demonstrated potent inhibitory activity against butyrylcholinesterase (BChE). Our findings underscore the promise of leveraging deep learning to discover inhibitors from TCM.
Predict customer churn using combination deep learning networks model
Authors: Van-Hieu Vu

Customers churn is an important issue that is always concerned by banks, and is put at the forefront of the bank’s policies. The fact that banks can identify customers who are intending to leave the service can help banks promptly make policies to retain customers. In this paper, we propose a combined deep learning network models to predict customers leaving or staying at the bank. The proposed model consists of two levels, Level 0 consists of three basic models using three Deep Learning Neural Networks, and Level 1 is a logistic regression model. The proposed model has obtained evaluation results with accuracy metrics of 96.60%, precision metrics of 90.26%, recall metrics of 91.91% and F1 score of 91.07% on the dataset “Bank Customer Churn Prediction”.
Genome analysis through image processing with deep learning models
Authors: Yao-zhong Zhang, Seiya Imoto

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.
Prediction of mechanical properties for deep drawing steel by deep learning
Authors: Gang Xu, Jinshan He, Zhimin Lü, Min Li, Jinwu Xu

At present, iron and steel enterprises mainly use “after spot test ward” to control final product quality. However, it is impossible to realize on-line quality predetermining for all products by this traditional approach, hence claims and returns often occur, resulting in major economic losses of enterprises. In order to realize the on-line quality predetermining for steel products during manufacturing process, the prediction models of mechanical properties based on deep learning have been proposed in this work. First, the mechanical properties of deep drawing steels were predicted by using LSTM (long short team memory), GRU (gated recurrent unit) network, and GPR (Gaussian process regression) model, and prediction accuracy and learning efficiency for different models were also discussed. Then, on-line re-learning methods for transfer learning models and model parameters were proposed. The experimental results show that not only the prediction accuracy of optimized transfer learning models has been improved, but also predetermining time was shortened to meet real time requirements of on-line property predetermining. The industrial production data of interstitial-free (IF) steel was used to demonstrate that R2 value of GRU model in training stage reaches more than 0.99, and R2 value in testing stage is more than 0.96.
Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction
Authors: Zakary Georgis-Yap, Milos R. Popovic, Shehroz S. Khan

Epilepsy affects more than 50 million people worldwide, making it one of the world’s most prevalent neurological diseases. The main symptom of epilepsy is seizures, which occur abruptly and can cause serious injury or death. The ability to predict the occurrence of an epileptic seizure could alleviate many risks and stresses people with epilepsy face. We formulate the problem of detecting preictal (or pre-seizure) with reference to normal EEG as a precursor to incoming seizure. To this end, we developed several supervised deep learning approaches model to identify preictal EEG from normal EEG. We further develop novel unsupervised deep learning approaches to train the models on only normal EEG, and detecting pre-seizure EEG as an anomalous event. These deep learning models were trained and evaluated on two large EEG seizure datasets in a person-specific manner. We found that both supervised and unsupervised approaches are feasible; however, their performance varies depending on the patient, approach and architecture. This new line of research has the potential to develop therapeutic interventions and save human lives.
Leveraging deep learning-assisted attacks against image obfuscation via federated learning
Authors: Jimmy Tekli, Bechara Al Bouna, Gilbert Tekli, Raphaël Couturier, Antoine Charbel

Obfuscation techniques (e.g., blurring) are employed to protect sensitive information (SI) in images such as individuals’ faces. Recent works demonstrated that adversaries can perform deep learning-assisted (DL) attacks to re-identify obfuscated face images. Adversaries are modeled by their goals, knowledge (e.g., background knowledge), and capabilities (e.g., DL-assisted attacks). Nevertheless, enhancing the evaluation methodology of obfuscation techniques and improving the defense strategies against adversaries requires considering more "pessimistic” attacking scenario, i.e., stronger adversaries. According to a 2019 article published by the European Union Agency for Cybersecurity (ENISA), adversaries tend to perform more sophisticated and dangerous attacks when collaborating together. To address these concerns, our paper investigates a novel privacy challenge in the context of image obfuscation. Specifically, we examine whether adversaries, when collaborating together, can amplify their DL-assisted attacks and cause additional privacy breaches against a target dataset of obfuscated images. We empirically demonstrate that federated learning (FL) can be used as a collaborative attack/adversarial strategy to (i) leverage the attacking capabilities of an adversary, (ii) increase the privacy breaches, and (iii) remedy the lack of background knowledge and data shortage without the need to share/disclose the local training datasets in a centralized location. To the best of our knowledge, we are the first to consider collaborative and more specifically FL-based attacks in the context of face obfuscation.