ADKU/ResearchGPT_model_dataset huggingface dataset:
An interpretable deep learning framework for genome-informed precision oncology
Authors: Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu

Cancers result from aberrations in cellular signalling systems, typically resulting from driver somatic genome alterations (SGAs) in individual tumours. Precision oncology requires understanding the cellular state and selecting medications that induce vulnerability in cancer cells under such conditions. To this end, we developed a computational framework consisting of two components: (1) a representation-learning component, which learns a representation of the cellular signalling systems when perturbed by SGAs and uses a biologically motivated and interpretable deep learning model, and (2) a drug-response prediction component, which predicts drug responses by leveraging the information of the cellular state of the cancer cells derived by the first component. Our cell-state-oriented framework notably improves the accuracy of predictions of drug responses compared to models using SGAs directly in cell lines. Moreover, our model performs well with real patient data. Importantly, our framework enables the prediction of responses to chemotherapy agents based on SGAs, thus expanding genome-informed precision oncology beyond molecularly targeted drugs.
VResNet: A Deep Learning Architecture for Image Inpainting of Irregular Damaged Images
Authors: Sariva Sharma, Rajneesh Rani

In computer vision, image inpainting is a famous problem to automatically reconstruct the damaged part of the image according to the undamaged portion of an image. Inpainting irregular damaged areas in the image is still challenging. Deep learning-based techniques have given us a fantastic performance over the last few years. In this paper, we propose VResNet, a deep-learning approach for image inpainting, inspired by U-Net architecture and the residual framework. Since deeper neural networks are extra hard to train, the superficial convolution block in U-Net architecture is replaced by the residual learning block in the proposed approach to simplify the training of deeper neural networks. To develop an effective and adaptable model, an extensive series of experiments was conducted using the Paris-Street-View dataset. Our proposed method achieved notable results, including a PSNR of 20.65, an SSIM of 0.65, an L1 Loss of 6.90, and a total loss (L\(_{\hbox {Total}}\)) of 0.30 on the Paris-Street-View dataset. These outcomes clearly demonstrate the superior performance of our model when compared to other techniques. The paper presents both qualitative and quantitative comparisons to provide a comprehensive assessment of our approach.
Deep Learning-based Pilot Adaptation and Channel Estimation in OFDM Systems
Authors: Mohammadamin Shahmohammadi, Mohammadali Sebghati, Hassan Zareian

Clear communication over wireless channels demands overcoming their disruptive effects. Doubly-selective fading channels, with rapidly changing parameters, pose a particular challenge for accurate channel estimation. Traditional models often falter, lacking solutions or becoming overly complex. This is where deep learning takes center stage. In OFDM systems, pilots assist in estimating the channel response, but they also come at the cost of reduced data throughput. Adaptive adjustment of pilot patterns based on the channel state offers a promising solution. This paper introduces a deep learning-based framework that leverages adaptive pilots for fast-varying channels. We employ two deep neural networks. First, the pilot adaptation network dynamically selects the pilot pattern, reacting to the channel coherence bandwidth. Second, the channel estimation network extracts features from the channel frequency response using a 1D convolutional neural network. It then harnesses the power of long short-term memory layers to learn the channel behavior and estimate the response across all pilots and data subcarriers. Training and testing datasets are generated using WINNER II. The entire communication link, equipped with our proposed method, undergoes rigorous simulations, evaluated by both bit error rate and pilot overhead. The simulation results illustrate that the proposed scheme outperforms the previous methods, because it yields the same or better errors with less pilot overhead. This translates to a substantial data rate boost, paving the way for faster wireless communication.
Intelligent upper-limb exoskeleton integrated with soft bioelectronics and deep learning for intention-driven augmentation
Authors: Jinwoo Lee, Kangkyu Kwon, Ira Soltis, Jared Matthews, Yoon Jae Lee, Hojoong Kim, Lissette Romero, Nathan Zavanelli, Youngjin Kwon, Shinjae Kwon, Jimin Lee, Yewon Na, Sung Hoon Lee, Ki Jun Yu, Minoru Shinohara, Frank L. Hammond, Woon-Hong Yeo

The age and stroke-associated decline in musculoskeletal strength degrades the ability to perform daily human tasks using the upper extremities. Here, we introduce an intelligent upper-limb exoskeleton system that utilizes deep learning to predict human intention for strength augmentation. The embedded soft wearable sensors provide sensory feedback by collecting real-time muscle activities, which are simultaneously computed to determine the user’s intended movement. Cloud-based deep learning predicts four upper-limb joint motions with an average accuracy of 96.2% at a 500–550 ms response rate, suggesting that the exoskeleton operates just by human intention. In addition, an array of soft pneumatics assists the intended movements by providing 897 newtons of force while generating a displacement of 87 mm at maximum. The intent-driven exoskeleton can reduce human muscle activities by 3.7 times on average compared to the unassisted exoskeleton.
Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities
Authors: Heng Zhang, Jianfeng Yan, Zhike Lu, Yangfan Zhou, Qingfeng Zhang, Tingting Cui, Yini Li, Hui Chen, Lijia Ma

Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the “sweet spot” in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.
Deep learning model for intravascular ultrasound image segmentation with temporal consistency
Authors: Hyeonmin Kim, June-Goo Lee, Gyu-Jun Jeong, Geunyoung Lee, Hyunseok Min, Hyungjoo Cho, Daegyu Min, Seung-Whan Lee, Jun Hwan Cho, Sungsoo Cho, Soo-Jin Kang

This study was conducted to develop and validate a deep learning model for delineating intravascular ultrasound (IVUS) images of coronary arteries.Using a total of 1240 40-MHz IVUS pullbacks with 191,407 frames, the model for lumen and external elastic membrane (EEM) segmentation was developed. Both frame- and vessel-level performances and clinical impact of the model on 3-year cardiovascular events were evaluated in the independent data sets. In the test set, the Dice similarity coefficients (DSC) were 0.966 ± 0.025 and 0.982 ± 0.017 for the lumen and EEM, respectively. Even at sites of extensive attenuation, the frame-level performance was excellent (DSCs > 0.96 for the lumen and EEM). The model (vs. the expert) showed a better temporal consistency for contouring the EEM. The agreement between the model- vs. the expert-derived cross-sectional and volumetric measurements was excellent in the independent retrospective cohort (all, intra-class coefficients > 0.94). The model-derived percent atheroma volume > 52.5% (area under curve 0.70, sensitivity 71% and specificity 67%) and plaque burden at the minimal lumen area site (area under curve 0.72, sensitivity 72% and specificity 66%) best predicted 3-year cardiac death and nonculprit-related target vessel revascularization, respectively. In the stented segment, the DSCs > 0.96 for contouring lumen and EEM were achieved. Applied to the 60-MHz IVUS images, the DSCs were > 0.97. In the external cohort with 45-MHz IVUS, the DSCs were > 0.96. The deep learning model accurately delineated vascular geometry, which may be cost-saving and support clinical decision-making.
Deep Learning for Perfusion Cerebral Blood Flow (CBF) and Volume (CBV) Predictions and Diagnostics
Authors: Salmonn Talebi, Siyu Gai, Aaron Sossin, Vivian Zhu, Elizabeth Tong, Mohammad R. K. Mofrad

Dynamic susceptibility contrast magnetic resonance perfusion (DSC-MRP) is a non-invasive imaging technique for hemodynamic measurements. Various perfusion parameters, such as cerebral blood volume (CBV) and cerebral blood flow (CBF), can be derived from DSC-MRP, hence this non-invasive imaging protocol is widely used clinically for the diagnosis and assessment of intracranial pathologies. Currently, most institutions use commercially available software to compute the perfusion parametric maps. However, these conventional methods often have limitations, such as being time-consuming and sensitive to user input, which can lead to inconsistent results; this highlights the need for a more robust and efficient approach like deep learning. Using the relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF) perfusion maps generated by FDA-approved software, we trained a multistage deep learning model. The model, featuring a combination of a 1D convolutional neural network (CNN) and a 2D U-Net encoder-decoder network, processes each 4D MRP dataset by integrating temporal and spatial features of the brain for voxel-wise perfusion parameters prediction. An auxiliary model, with similar architecture, but trained with truncated datasets that had fewer time-points, was designed to explore the contribution of temporal features. Both qualitatively and quantitatively evaluated, deep learning-generated rCBV and rCBF maps showcased effective integration of temporal and spatial data, producing comprehensive predictions for the entire brain volume. Our deep learning model provides a robust and efficient approach for calculating perfusion parameters, demonstrating comparable performance to FDA-approved commercial software, and potentially mitigating the challenges inherent to traditional techniques.
Deep representation learning and reinforcement learning for workpiece setup optimization in CNC milling
Authors: Vladimir Samsonov, Enslin Chrismarie, Hans-Georg Köpken, Schirin Bär, Daniel Lütticke, Tobias Meisen

Computer Numerical Control (CNC) milling is a commonly used manufacturing process with a high level of automation. Nevertheless, setting up a new CNC milling process involves multiple development steps relying heavily on human expertise. In this work, we focus on positioning and orientation of the workpiece (WP) in the working space of a CNC milling machine and propose a deep learning approach to speed up this process significantly. The selection of the WP’s setup depends on the chosen milling technological process, the geometry of the WP, and the capabilities of the considered CNC machining. It directly impacts the milling quality, machine wear, and overall energy consumption. Our approach relies on representation learning of the milling technological process with the subsequent use of reinforcement learning (RL) for the WP positioning and orientation. Solutions proposed by the RL agent are used as a warm start for simple hill-climbing heuristics, which boosts overall performance while keeping the overall number of search iterations low. The novelty of the developed approach is the ability to conduct the WP setup optimization covering both WP positioning and orientation while ensuring the axis collision avoidance, minimization of the axis traveled distances and improving the dynamic characteristics of the milling process with no input from human experts. Experiments show the potential of the proposed learning-based approach to generate almost comparably good WP setups order of magnitude faster than common metaheuristics, such as genetic algorithms (GA) and Particle Swarm Optimisation (PSA).
A novel deep facenet framework for real-time face detection based on deep learning model
Authors: B Lakshmanan, A Vaishnavi, R Ananthapriya, A K Aananthalakshmi

Real-time face detection has many challenges, such as non-frontal faces, tiny faces, occlusions, and multifarious backgrounds. Real-time face detection can be done by using Convolutional Neural Network (CNN) models, which result in elevated performance but have a huge computation time. It needs to be implemented on high-end computational devices to produce more accurate face detection results for high resolution images. We proposed a light architecture based on CNN for deep learning-based feature extraction and detection of human faces. The challenges faced during real-time face detection, such as occlusions, different scales, different backgrounds, varying positions, lighting, and poses, are resolved, and faces are detected accurately using the proposed framework. The amount of computation required for real-time face detection is reduced. This light architecture consists of two modules: the backbone module is used to contract the input size of the image and extract the features; the detection module transforms the feature map between prediction layers and detects faces at various scales. In our architecture, we use mini-inception blocks that minimize the computation cost and are implemented using available low-end system configurations without the need for external hardware. The proposed model uses anchor boxes to predict bounding boxes using dimensional clusters. The model is trained and tested using images from the WIDER Face dataset, which has images of various challenging conditions. Finally, images with multiple faces detected are displayed as output. The proposed work shows an increased accuracy rate with reduced computation cost over state-of-the-art performance on the benchmark dataset.
C-net: a deep learning-based Jujube grading approach
Authors: Atif Mahmood, Amod Kumar Tiwari, Sanjay Kumar Singh

Jujube grading is a crucial process in the jujube-associated industry to ascertain the quality, ripeness, value, and security of the product. Traditionally, jujube grading has been done manually, which may be expensive, time-consuming, and prone to human mistakes. With the expansion of innovation, Machine Learning (ML)/Deep Learning (DL) turned out as a potent technique for automating the fruits grading process. Within this work, we deployed and analyzed the Concatenated-Convolutional Neural Network (C-Net) based on the residual network concept and seven cutting-edge CNNs for sorting the Indian jujube into six classes. To train and evaluate the models, we collected and assembled the dataset of jujube images. The performance analysis of the model relies upon two varying hyperparameters, batch size, and epochs as well as some performance metrics like F1-score, precision, and recall. The finding indicates that the proposed C-Net model was able to classify jujube images with high precision of 98.61% which surpasses other models but lags slightly behind the EfficientNet-B0 model. Our C-Net model has several advantages over most of the cutting-edge CNN models for jujube grading including increased accuracy, efficiency, cost-effectiveness, better decision-making, scalability, and real-time grading. The use of a C-Net model for jujube grading has the capability to revolutionize the jujube grading task and improve the fruit’s overall quality.
Hierarchical Goal-Guided Learning for the Evasive Maneuver of Fixed-Wing UAVs based on Deep Reinforcement Learning
Authors: Yinlong Yuan, Jian Yang, Zhu Liang Yu, Yun Cheng, Pengpeng Jiao, Liang Hua

Fixed-wing unmanned aerial vehicles (UAVs) will play a vital role in forthcoming military conflicts. Effectively avoiding threats and improving the survivability of fixed-wing UAV in dynamic hostile environments are the keys to the success of combat missions. Hence, endowing fixed-wing UAVs with the ability to autonomously generate evasive maneuver is the primary problem that should be solved. With considering the threat of air-to-air missile attacks, this paper designs a novel hierarchical goal-guided learning (HGGL) method, which combines with traditional off-policy deep reinforcement learning (DRL) algorithms and endows the agent with the ability to evade a series of air-to-air missiles. The pivotal idea of the proposed algorithm is to use the hierarchical features of the goal, it improves the availability of training data to eliminate the limitation of the convergence rate of traditional DRL algorithms owing to sparse rewards. We demonstrate the performance of our algorithm in several simulation experiments. All experiments are applied on the XSimStudio platform. The results demonstrate that the proposed algorithm improves the convergence speed and outperforms the state-of-the-art traditional algorithms.
Study on automatic lithology identification based on convolutional neural network and deep transfer learning
Authors: Shiliang Li, Yuelong Dong, Zhanrong Zhang, Chengyuan Lin, Huaji Liu, Yafei Wang, Youyan Bian, Feng Xiong, Guohua Zhang

Automatic and fast rock classification identification is an important part of geotechnical intelligent survey system. Image based supervised deep learning analysis, especially for convolutional neural networks (CNN), has potential in optimizing lithologic classification and interpretation using borehole core images. However, the accuracy and efficiency of lithology identification models are low at present. In this work, a systematic and enormous rock data framework based on the geological rock classification system is firstly established to provide rock learning datasets. The dataset is composed of approximately 150,000 images of rock samples, which covers igneous rocks, sedimentary rocks, and metamorphic rocks. Secondly, based on CNN-deep transfer learning algorithm, an end-to-end, image-to-label rock lithology identification is established. Finally, the generalization of the proposed model and the field drilling core verification test show that the constructed intelligent rock recognition model has an ability to identify rocks quickly and accurately, and the recognition accuracy of 12 kinds of common engineering rocks is more than 95%. The proposed rock intelligent classification model provides a convenient and fast tool for field geologists and scientific researchers.
Optimized COVID-19 detection using sparse deep learning models from multimodal imaging data
Authors: MohammadMahdi Moradi, Alireza Hassanzadeh, Arman Haghanifar, Seok Bum Ko

This paper explores the global spread of the COVID-19 virus since 2019, impacting 219 countries worldwide. Despite the absence of a definitive cure, the utilization of artificial intelligence (AI) methods for disease diagnosis has demonstrated commendable effectiveness in promptly diagnosing patients and curbing infection transmission. The study introduces a deep learning-based model tailored for COVID-19 detection, leveraging three prevalent medical imaging modalities: computed tomography (CT), chest X-ray (CXR), and Ultrasound. Various deep Transfer Learning Convolutional Neural Network-based (CNN) models have undergone assessment for each imaging modality. For each imaging modality, this study has selected the two most accurate models based on evaluation metrics such as accuracy and loss. Additionally, efforts have been made to prune unnecessary weights from these models to obtain more efficient and sparse models. By fusing these pruned models, enhanced performance has been achieved. The models have undergone rigorous training and testing using publicly available real-world medical datasets, focusing on classifying these datasets into three distinct categories: Normal, COVID-19 Pneumonia, and non-COVID-19 Pneumonia. The primary objective is to develop an optimized and swift model through strategies like Transfer Learning, Ensemble Learning, and reducing network complexity, making it easier for storage and transfer. The results of the trained network on test data exhibit promising outcomes. The accuracy of these models on the CT scan, X-ray, and ultrasound datasets stands at 99.4%, 98.9%, and 99.3%, respectively. Moreover, these models’ sizes have been substantially reduced and optimized by 51.93%, 38.00%, and 69.07%, respectively. This study proposes a Computer-aided-coronavirus-detection system based on three standard medical imaging techniques. The intention is to assist radiologists in accurately and swiftly diagnosing the disease, especially during the screening process, by providing high accuracy and speed in the identification of COVID-19 cases.