ADKU/ResearchGPT_model_dataset huggingface dataset:
An end-to-end intrusion detection system with IoT dataset using deep learning with unsupervised feature extraction
Authors: Yesi Novaria Kunang, Siti Nurmaini, Deris Stiawan, Bhakti Yudho Suprapto

The rapid growth of the Internet of things (IoT) platform has implications on security vulnerabilities that need to be resolved. This requires an intrusion detection system (IDS) to secure attacks on the platforms. In line with this, numerous machine and deep learning algorithms have been adopted to detect cyber-attacks. Real-time IoT devices transmit massive amounts of heterogeneous data, which affects the network. Traffic networks generate redundant and large amounts of data that must be reduced before processing. This study proposed a hybrid deep learning model for an IDS on the IoT platform. We used unsupervised approaches to extract data dimensions and features, then a neural network for classification. Several approaches were used to determine the effectiveness of the deep learning-based IoT IDS with two scenarios of feature extraction. The first case used autoencoder variants such as deep autoencoder (DAE), deep LSTM autoencoder (LSTM-DAE), and deep convolutional autoencoder. The second case used stacked models for feature extraction, including stacked autoencoder and deep belief network. The feature extraction output from the five models was fine-tuned to the fully connected layer using the BoT-IoT dataset. The results showed a good detection performance of almost 100% and a false positive rate (FPR) of nearly 0%. On the CSE-CIC-IDS2018 dataset, the proposed deep learning model was evaluated using a transfer learning approach with the highest detection rate of 99.17% and the lowest FPR of 0.18%. The model developed from the feature extraction process recognized attacks significantly better than the previous approach.
Application of machine learning and deep learning for cancer vaccine (rapid review)
Authors: Mohaddeseh Nasiri Hooshmand, Elham Maserat

Cancer is a common and dangerous disease based on the World Health Organization. Much research has been done on new and effective cancer treatments, including cancer vaccines and the prediction of neoantigens using machine learning. The purpose of this study is to review articles that use machine learning to design cancer vaccines. This study is a rapid review study using search strategies and related keywords in Google Scholar, PubMed, and science direct databases from 2010 to 2021 in 2021 and revised in August 2023. 1250 articles were searched and 13 articles were selected for this review. We investigated them and then due to the importance and popularity of using machine learning in cancer vaccines recently, we compared them based on their machine learning technique. it is shown that neural networks with Python are used to predict neoantigens in 4 articles and with MATLAB in 2 articles, one article was about using the Fontom, one article with PERL, and one article with R; Other studies were about data mining with flowsom algorithm, multiple linear regression, logistics, and oncopepVCA, and the rest of articles do not provide information about machine learning implementation tools. Providing neural networks with Python is useful in the prediction of neoantigens due to the precision and examination of complex data sets. They use to predict HLA and peptide binding affinity, vaccines outcome, personalized cancer vaccines based on new data, the immune response, processing RNA and DNA sequences, and immunological analysis.
Image Classification Algorithm Based on Proposal Region Clustering Learning-Unsupervised Deep Learning
Authors: Lei Li, Xiao-li Yin

Although deep learning has achieved certain results in image classification, images are susceptible to factors such as lighting conditions, shooting angles, complex backgrounds, rotation transformations or scale scaling, and image data sets in some areas are difficult to obtain. They make the deep learning framework unable to give full play to its generalization ability and nonlinear modeling ability in image classification. Therefore, this paper first proposes a proposal region clustering learning algorithm, which clusters the proposal regions in each image so that each cluster corresponds to the category of the image. Then, different clusters can be regarded as different multi-instance learning packets, and each packet uses the multi-instance learning method to learn the unsupervised image classification detector. It can effectively improve the generalization and modeling capabilities of deep learning models. In addition, this paper proposes an unsupervised deep learning method, which designs an unsupervised deep learning network structure and loss function according to the characteristics of the classified image, and combines densely connected blocks to extract features from the source image. It retains the multi-scale features of the middle layer of the classified image, and effectively solves the problem of insufficient image feature extraction information caused by the lack of image data. It also guarantees the accuracy of subsequent image classification. The experimental results show that the image classification method proposed in this paper not only solves the problem of insufficient image data sets and the interference of various complex factors, but also can accurately classify various image data sets. The accuracy of the image classification method proposed in this paper is 1.38–19% higher than other mainstream deep learning methods.
SkinMultiNet: Advancements in Skin Cancer Prediction Using Deep Learning with Web Interface
Authors: Md Nur Hosain Likhon, Sahab Uddin Rana, Sadeka Akter, Md. Shorup Ahmed, Khadiza Akter Tanha, Md. Mahbubur Rahman, Md Emran Hussain Nayeem

Cancer remains the leading cause of death worldwide, significantly impacting individuals and healthcare systems alike. In recent decades, skin cancer has surged in prevalence compared to other major cancer types. Various factors such as texture, color, morphological characteristics, and structure are employed in categorizing different forms of skin cancer. However, traditional methods of identification often prove time-consuming and costly. Skin cancer classification predominantly relies on machine learning, with the primary method being convolutional neural networks (CNNs). Our ‘SkinMultiNet’ framework, presented in this study and based on transfer learning principles, integrates the InceptionV3 and Xception CNN models for predicting skin cancer using image data. While other machine learning models such as ResNet50, NasNet, and MobileNet were explored, the 'SkinMultiNet' framework demonstrated the most promising outcomes. Utilizing a publicly available dataset comprising 6086 skin images, we trained, tested, and evaluated our models extensively. Proposed system employed a train generator to feed image data into our deep learning CNN models, followed by implementing a learning rate reducer on the datasets within the model. Through rigorous testing and validation procedures, our models successfully processed a substantial volume of skin image data. In contrast to conventional approaches, our proposed architecture offers the potential for more reliable diagnoses, achieving an optimal accuracy rate of 94% in skin cancer prediction. This advancement holds promise for early detection and improved patient outcomes following therapy.
Deep learning-based prediction of post-pancreaticoduodenectomy pancreatic fistula
Authors: Woohyung Lee, Hyo Jung Park, Hack-Jin Lee, Ki Byung Song, Dae Wook Hwang, Jae Hoon Lee, Kyongmook Lim, Yousun Ko, Hyoung Jung Kim, Kyung Won Kim, Song Cheol Kim

Postoperative pancreatic fistula is a life-threatening complication with an unmet need for accurate prediction. This study was aimed to develop preoperative artificial intelligence-based prediction models. Patients who underwent pancreaticoduodenectomy were enrolled and stratified into model development and validation sets by surgery between 2016 and 2017 or in 2018, respectively. Machine learning models based on clinical and body composition data, and deep learning models based on computed tomographic data, were developed, combined by ensemble voting, and final models were selected comparison with earlier model. Among the 1333 participants (training, n = 881; test, n = 452), postoperative pancreatic fistula occurred in 421 (47.8%) and 134 (31.8%) and clinically relevant postoperative pancreatic fistula occurred in 59 (6.7%) and 27 (6.0%) participants in the training and test datasets, respectively. In the test dataset, the area under the receiver operating curve [AUC (95% confidence interval)] of the selected preoperative model for predicting all and clinically relevant postoperative pancreatic fistula was 0.75 (0.71–0.80) and 0.68 (0.58–0.78). The ensemble model showed better predictive performance than the individual ML and DL models.
Debunking multi-lingual social media posts using deep learning
Authors: Bina Kotiyal, Heman Pathak, Nipur Singh

Fake news on social media has become a growing concern due to its potential impact on shaping public opinion. The proposed Debunking Multi-Lingual Social Media Posts using Deep Learning (DSMPD) approach offers a promising solution to detect fake news. The DSMPD approach involves creating a dataset of English and Hindi social media posts using web scraping and Natural Language Processing (NLP) techniques. This dataset is then used to train, test, and validate a deep learning-based model that extracts various features, including Embedding from Language Models (ELMo), word and n-gram counts, Term Frequency-Inverse Document Frequency (TF-IDF), sentiments, polarity, and Named Entity Recognition (NER). Based on these features, the model classifies news items into five categories: real, could be real, could be fabricated, fabricated, or dangerously fabricated. To evaluate the performance of the classifiers, the researchers used two datasets comprising over 45,000 articles. Machine learning (ML) algorithms and Deep learning (DL) model are compared to choose the best option for classification and prediction.
Designing observables for measurements with deep learning
Authors: Owen Long, Benjamin Nachman

Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables are designed using physics intuition and heuristics. We propose to design targeted observables with machine learning. Unfolded, differential cross sections in a neural network output contain the most information about parameters of interest and can be well-measured by construction. The networks are trained using a custom loss function that rewards outputs that are sensitive to the parameter(s) of interest while simultaneously penalizing outputs that are different between particle-level and detector-level (to minimize detector distortions). We demonstrate this idea in simulation using two physics models for inclusive measurements in deep inelastic scattering. We find that the new approach is more sensitive than classical observables at distinguishing the two models and also has a reduced unfolding uncertainty due to the reduced detector distortions.
Autonomous multi-drone racing method based on deep reinforcement learning
Authors: Yu Kang, Jian Di, Ming Li, Yunbo Zhao, Yuhui Wang

Racing drones have attracted increasing attention due to their remarkable high speed and excellent maneuverability. However, autonomous multi-drone racing is quite difficult since it requires quick and agile flight in intricate surroundings and rich drone interaction. To address these issues, we propose a novel autonomous multi-drone racing method based on deep reinforcement learning. A new set of reward functions is proposed to make racing drones learn the racing skills of human experts. Unlike previous methods that required global information about tracks and track boundary constraints, the proposed method requires only limited localized track information within the range of its own onboard sensors. Further, the dynamic response characteristics of racing drones are incorporated into the training environment, so that the proposed method is more in line with the requirements of real drone racing scenarios. In addition, our method has a low computational cost and can meet the requirements of real-time racing. Finally, the effectiveness and superiority of the proposed method are verified by extensive comparison with the state-of-the-art methods in a series of simulations and real-world experiments.
Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer
Authors: Yogesh Kumar, Pertik Garg, Manu Raj Moudgil, Rupinder Singh, Marcin Woźniak, Jana Shafi, Muhammad Fazal Ijaz

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.
A survey on automatic generation of medical imaging reports based on deep learning
Authors: Ting Pang, Peigao Li, Lijie Zhao

Recent advances in deep learning have shown great potential for the automatic generation of medical imaging reports. Deep learning techniques, inspired by image captioning, have made significant progress in the field of diagnostic report generation. This paper provides a comprehensive overview of recent research efforts in deep learning-based medical imaging report generation and proposes future directions in this field. First, we summarize and analyze the data set, architecture, application, and evaluation of deep learning-based medical imaging report generation. Specially, we survey the deep learning architectures used in diagnostic report generation, including hierarchical RNN-based frameworks, attention-based frameworks, and reinforcement learning-based frameworks. In addition, we identify potential challenges and suggest future research directions to support clinical applications and decision-making using medical imaging report generation systems.
Rapid diagnosis of celiac disease based on plasma Raman spectroscopy combined with deep learning
Authors: Tian Shi, Jiahe Li, Na Li, Cheng Chen, Chen Chen, Chenjie Chang, Shenglong Xue, Weidong Liu, Ainur Maimaiti Reyim, Feng Gao, Xiaoyi Lv

Celiac Disease (CD) is a primary malabsorption syndrome resulting from the interplay of genetic, immune, and dietary factors. CD negatively impacts daily activities and may lead to conditions such as osteoporosis, malignancies in the small intestine, ulcerative jejunitis, and enteritis, ultimately causing severe malnutrition. Therefore, an effective and rapid differentiation between healthy individuals and those with celiac disease is crucial for early diagnosis and treatment. This study utilizes Raman spectroscopy combined with deep learning models to achieve a non-invasive, rapid, and accurate diagnostic method for celiac disease and healthy controls. A total of 59 plasma samples, comprising 29 celiac disease cases and 30 healthy controls, were collected for experimental purposes. Convolutional Neural Network (CNN), Multi-Scale Convolutional Neural Network (MCNN), Residual Network (ResNet), and Deep Residual Shrinkage Network (DRSN) classification models were employed. The accuracy rates for these models were found to be 86.67%, 90.76%, 86.67% and 95.00%, respectively. Comparative validation results revealed that the DRSN model exhibited the best performance, with an AUC value and accuracy of 97.60% and 95%, respectively. This confirms the superiority of Raman spectroscopy combined with deep learning in the diagnosis of celiac disease.
Prediction of intraoperative hypotension using deep learning models based on non-invasive monitoring devices
Authors: Heejoon Jeong, Donghee Kim, Dong Won Kim, Seungho Baek, Hyung-Chul Lee, Yusung Kim, Hyun Joo Ahn

PurposeIntraoperative hypotension is associated with adverse outcomes. Predicting and proactively managing hypotension can reduce its incidence. Previously, hypotension prediction algorithms using artificial intelligence were developed for invasive arterial blood pressure monitors. This study tested whether routine non-invasive monitors could also predict intraoperative hypotension using deep learning algorithms.MethodsAn open-source database of non-cardiac surgery patients (https://vitadb.net/dataset) was used to develop the deep learning algorithm. The algorithm was validated using external data obtained from a tertiary Korean hospital. Intraoperative hypotension was defined as a systolic blood pressure less than 90 mmHg. The input data included five monitors: non-invasive blood pressure, electrocardiography, photoplethysmography, capnography, and bispectral index. The primary outcome was the performance of the deep learning model as assessed by the area under the receiver operating characteristic curve (AUROC).ResultsData from 4754 and 421 patients were used for algorithm development and external validation, respectively. The fully connected model of Multi-head Attention architecture and the Globally Attentive Locally Recurrent model with Focal Loss function were able to predict intraoperative hypotension 5 min before its occurrence. The AUROC of the algorithm was 0.917 (95% confidence interval [CI], 0.915–0.918) for the original data and 0.833 (95% CI, 0.830–0.836) for the external validation data. Attention map, which quantified the contributions of each monitor, showed that our algorithm utilized data from each monitor with weights ranging from 8 to 22% for determining hypotension.ConclusionsA deep learning model utilizing multi-channel non-invasive monitors could predict intraoperative hypotension with high accuracy. Future prospective studies are needed to determine whether this model can assist clinicians in preventing hypotension in patients undergoing surgery with non-invasive monitoring.
Deep learning in cancer genomics and histopathology
Authors: Michaela Unger, Jakob Nikolas Kather

Histopathology and genomic profiling are cornerstones of precision oncology and are routinely obtained for patients with cancer. Traditionally, histopathology slides are manually reviewed by highly trained pathologists. Genomic data, on the other hand, is evaluated by engineered computational pipelines. In both applications, the advent of modern artificial intelligence methods, specifically machine learning (ML) and deep learning (DL), have opened up a fundamentally new way of extracting actionable insights from raw data, which could augment and potentially replace some aspects of traditional evaluation workflows. In this review, we summarize current and emerging applications of DL in histopathology and genomics, including basic diagnostic as well as advanced prognostic tasks. Based on a growing body of evidence, we suggest that DL could be the groundwork for a new kind of workflow in oncology and cancer research. However, we also point out that DL models can have biases and other flaws that users in healthcare and research need to know about, and we propose ways to address them.
Phase unwrapping based on deep learning in light field fringe projection 3D measurement
Authors: Xinjun Zhu, Haichuan Zhao, Mengkai Yuan, Zhizhi Zhang, Hongyi Wang, Limei Song

Phase unwrapping is one of the key roles in fringe projection three-dimensional (3D) measurement technology. We propose a new method to achieve phase unwrapping in camera array light filed fringe projection 3D measurement based on deep learning. A multi-stream convolutional neural network (CNN) is proposed to learn the mapping relationship between camera array light filed wrapped phases and fringe orders of the expected central view, and is used to predict the fringe order to achieve the phase unwrapping. Experiments are performed on the light field fringe projection data generated by the simulated camera array fringe projection measurement system in Blender and by the experimental 3×3 camera array light field fringe projection system. The performance of the proposed network with light field wrapped phases using multiple directions as network input data is studied, and the advantages of phase unwrapping based on deep learning in light filed fringe projection are demonstrated.
Radiological age assessment based on clavicle ossification in CT: enhanced accuracy through deep learning
Authors: Philipp Wesp, Balthasar Maria Schachtner, Katharina Jeblick, Johanna Topalis, Marvin Weber, Florian Fischer, Randolph Penning, Jens Ricke, Michael Ingrisch, Bastian Oliver Sabel

BackgroundRadiological age assessment using reference studies is inherently limited in accuracy due to a finite number of assignable skeletal maturation stages. To overcome this limitation, we present a deep learning approach for continuous age assessment based on clavicle ossification in computed tomography (CT).MethodsThoracic CT scans were retrospectively collected from the picture archiving and communication system. Individuals aged 15.0 to 30.0 years examined in routine clinical practice were included. All scans were automatically cropped around the medial clavicular epiphyseal cartilages. A deep learning model was trained to predict a person’s chronological age based on these scans. Performance was evaluated using mean absolute error (MAE). Model performance was compared to an optimistic human reader performance estimate for an established reference study method.ResultsThe deep learning model was trained on 4,400 scans of 1,935 patients (training set: mean age = 24.2 years ± 4.0, 1132 female) and evaluated on 300 scans of 300 patients with a balanced age and sex distribution (test set: mean age = 22.5 years ± 4.4, 150 female). Model MAE was 1.65 years, and the highest absolute error was 6.40 years for females and 7.32 years for males. However, performance could be attributed to norm-variants or pathologic disorders. Human reader estimate MAE was 1.84 years and the highest absolute error was 3.40 years for females and 3.78 years for males.ConclusionsWe present a deep learning approach for continuous age predictions using CT volumes highlighting the medial clavicular epiphyseal cartilage with performance comparable to the human reader estimate.