Miracle-dz/UniFAR-FTUs huggingface dataset:
Dense U-Nets for Enhancement of Undersampled MRI Using Cross-Contrast Feature Transfer

MRI suffers from an inherent trade-off between signal-to-noise ratio, acquisition time, and spatial resolution [1]–[3]. K-space undersampling allows for a reduction in acquisition time at the cost of decreased signal-to-noise ratio and/or spatial resolution [7]. Initially developed to aid in tissue segmentation [18], [19], U-Nets have also been utilized effectively to improve the image quality of a diverse array of MRI contrasts [20]–[22]. The performance of U-Nets can be enhanced by providing the network with a fully sampled complementary contrast as a prior to allow for cross-contrast feature transfer [29]. We implement aim to verify that cross-contrast feature transfer improves the quality of images output by a dense U-Net (DU-Net). We assess whether the choice of complementary T1 or T2 weighted MRI contrasts for undersampling affects the quality of output images. We also aim to improve the sensitivity of image quality metrics used to compare networks by restricting their calculation to areas bounded around regions of clinical diagnostic interest. The quality of DU-Net outputs does not change significantly when the contrast used as a prior is exchanged; image quality metrics are all within one standard deviation of each other. There is also no quantifiable difference between models trained with a cross-contrast prior and those that are not. There are, however, qualitative improvements, particularly in the regions around tumors. Bounding error calculation regions leads to an increase in the significance of the measured difference between DU-Net outputs in most cases.
Early Prediction of Stock Market Movements Using News Analytics and Deep Learning

Stocks are an event based on data that depend on various factors, such as a company's introduction of new products, the company's economy, and trends concerning competitors. The time series data is highly volatile, and hard to predict the nature and behaviour of the data due to its complex nature. There are many uncertainties inherent in attempting to predict stock price fluctuations. Stock prices do not follow a consistent seasonal cycle, but rather are influenced by a variety of external factors that contribute to an irregular pattern of movement over time. Predicting stock performance precisely is challenging due to the role of exogenous variables in driving market dynamics in non-linear ways. The stocks definitely have temporary and permanent rise or fall values that justifies the pulses and steps. Various research studies employ comparable metrics to predict the trajectory of the primary stock market index. These studies either use only textual data or only market data for prediction or even if they use the inter-dependency between them, the results have only been marginally better. Our proposed system uses time series stock market data and textual news data to study their correlation. We use appropriate deep-learning models and techniques to predict the possible outcome over the next ten days. A Long-Short Term Memory (LSTM) model predicts whether the stock market will increase or decrease over the next ten days. This model predicts a value between -1 and 1: -1 indicating the decrease and one indicating the increase.
Automated Diagnosis of Lung and Colorectal Pathologies Using a Shallow Capsule Network Model

One of the most hazardous diseases is cancer caused by many biochemical abnormalities. Among all the cancers, lung and colon cancer are the most common and tragic diseases. Effective detection of thoracic and colorectal pathology is vital for timely diagnosis and treatment. In this study, we propose a shallow capsule network model for detecting malignancies in lung and colorectal imaging. Our proposed model is trained and tested on three different datasets: LC25000, IQ-OTHNCCD Lung Cancer Dataset and Chest CT-Scan Dataset with varying train and test data ratios. Despite its shallow architecture, the proposed model achieves high accuracy, with test accuracy metrics of 99.32%, 89.05%, and 99.09% on the respective datasets. We have also shown that the proposed capsule network outperforms traditional deep learning models using less training data. Our findings show that the suggested shallow capsule network model is effective in identifying lung and colorectal disease.
Design and Stability Analysis of Non-smooth Integral Sliding Surface for Coupled-Tank System with Matching/Non-matching Disturbance

This paper examine the challenges posed by conventional sliding mode design in the presence of matching/nonmatching disturbances. Previously designed approaches have proven inadequate in offering an efficient and straightforward solution to non-matching disturbances. To overcome this, a novel sliding mode control strategy centered on a non-smooth integral sliding manifold is introduced. The newly proposed sliding manifold possesses two crucial characteristics. Firstly, it functions transform to a reduced-order disturbance observer by autonomously estimating the mismatched disturbances, eliminating the need for an additional observer design. Secondly, the system consistently initiates its operation from the sliding surface, eliminating any reaching phase dynamics. As a result, the system trajectory remains resilient to disturbances right from the outset. To validate the effectiveness of the proposed method, it is simulated for a coupled tank system.
Design of Microstrip Patch Antenna Using Artificial Magnetic Conductor for 5G Base Station

This paper discusses a method for utilizing frequency selective surface to increase the performance of microstrip patch antennas. This method increases the antenna's gain by using artificial magnetic conductor (AMC) as the ground plane. Square cells are used in the construction of the AMC structures. Between 2 and 6 GHz, the return loss is simulated and quantified. With AMC and FR-4 as the substrate, the suggested antenna is intended to have an impedance bandwidth of 2.7 GHz to 3.2 GHz. Computer simulation technology is used to do numerical analysis and provide the best possible design (3D simulation software).
Multi-Modal Deep Feature Integration for Alzheimer's Disease Staging

Alzheimer's disease (AD) is one of the leading causes of dementia and 7th leading cause of death in the United States. The provisional diagnosis of AD relies on comprehensive examinations, including medical history, neurological and psychiatric examinations, cognitive assessments, and neuroimaging studies. Integrating diverse sets of clinical data, including electronic health records (EHRs), medical imaging, and genomic data, enables a holistic view of AD staging analysis. In this study, we propose an end-to-end deep learning architecture to jointly learn from magnetic resonance imaging (MRI), positron emission tomography (PET), EHRs, and genomics data to classify patients into AD, mild cognitive disorders, and controls. We conduct extensive experiments to explore different feature-level and intermediate-level fusion methods. Our findings suggest intermediate multiplicative fusion achieves the best stage prediction performance on the external validation dataset. Compared with unimodal baselines, we can observe that integrative approaches that leverage all four modalities demonstrate superior performance to baselines reliant solely on one or two modalities. In an age-wise comparison, we observe a unique pattern that all fusion methods exhibited superior performance in the earlier age brackets (50–70 years), with performance diminishing as the age group advanced (70–90 years). The proposed integration framework has the potential to augment our understanding of disease diagnosis and progression by leveraging complementary information from multimodal patient data.
Improving Student Grade Prediction with Machine Learning: Addressing Imbalanced Classifications to Gain Accurate Performance Insights

Machine learning is a branch of artificial intelli-gence(AI) that builds models and algorithms to enable computers to learn from data, and make predictions, and decisions. Machine Learning(ML) has gained increasing recognition in various do-mains, including education. In this article, we explore the concept of ML in education and its potential to transform teaching and learning. We discuss the process of ML, the importance of data analytic, and how machine learning can be applied to predict student performance. Additionally, we delve into the issue of imbalanced classification and its relevance to student grade prediction. To tackle this challenge, we explore various strategies, including data-level, algorithm-level, and hybrid approaches. We also discuss the emergence of data-driven education and how ML can aid in predicting student grades. Finally, we emphasize the need for a balanced prediction model and present a comprehensive research framework that involves data extraction, analytic, and visualization to enhance educational practices.
Object Detection Algorithms with Deep Learning: An Overview

Deep learning is a key branch of machine learning that leads the frontier of scientific research and has a significant impact on the field of computer vision. With the development of deep learning, object detection, as a research hotspot in computer vision, has attracted a great number of researchers to conduct in-depth research. This paper introduces single-stage, two-stage and Transformer-based object detection algorithms and its backbone network structures, and provides a detailed comparison of the advantages and shortcomings of these algorithms. Finally, the practical applications of these algorithms in various fields and their performance evaluation on critical datasets are presented.
Transfer-DDG: Prediction of protein-protein binding affinity changes with mutations based on large pre-trained model transfer learning

The majority of proteins in organisms function through protein-protein interactions (PPIs). Moreover, specific amino acid mutations on these proteins may alter their functionality, which in turn may impact their interactions with other proteins. The investigation of the effect of mutations on protein interactions can be possible by using the numerical index of change in protein-protein binding affinity (ΔΔG). The conventional lab experiments to determine ΔΔG values are both inefficient and costly. In recent years, machine learning and deep learning techniques have been developed for rapid prediction of ΔΔG values. However, many of these approaches fail to extract the deep information embedded in protein amino acid sequences, resulting in unstable prediction results, and they do not generalize well to different types of protein ΔΔG prediction tasks. In this paper, we introduce a deep learning framework for predicting ΔΔG based on protein amino acid sequences, named TransferDDG. The framework utilizes large pre-trained models to learn features of individual amino acid levels, as well as a BiLSTM module to learn features of amino acid sequence levels. This enables the extraction of deep semantic information in protein sequences across multiple dimensions and channels. The model has achieved good results on both SKP1102s and SKP1400m datasets containing single amino acid mutations, surpassing other baseline models.
Indoor Sign Recognition System for Visually Impaired People

Vision impairment can result in a loss of independence due to the inability to fully understand the surrounding environment. Indoor signage is a crucial factor in enabling visually impaired people to navigate unfamiliar environments. This paper proposes a system that automatically recognises a variety of indoor signs, including accessibility, elevator, exit, female toilet, male toilet, no smoking, restaurant, and Wi-Fi. Developing deep-learning algorithms that can accurately recognise similar signs or significant variations is challenging. To address this, the proposed method utilises a dataset of 1141 RGB images to train and test the system. Our approach employs a pre-trained AlexNet architecture with transfer learning and a customised sequential CNN architecture. Experimental results indicate an accuracy rate of 63% for the customised sequential CNN and 80% for AlexNet with transfer learning. The proposed system has the potential to enhance the independence and freedom of visually impaired individuals significantly.
An Efficient Video Summarization Technique Using Texture Feature and Spectral Clustering

In this paper, a novel approach for video summarization combining spectral clustering and texture feature extraction is presented. Local Optimal Oriented Pattern (LOOP) textural feature extraction technique is explored for each frame of the video in our method for extracting features. Then, a clustering algorithm based on spectral clustering technique is applied to obtain an informative and an efficient video summary by extracting interesting key-frames. We evaluate our an efficient video summarization technique on one standard benchmark SumMe dataset and result clearly illustrates the preeminence of the suggested model in comparison to some of state-of-the-art methods.
Deterministic Ray Tracing: A Promising Approach to THz Channel Modeling in 6G Deployment Scenarios

Terahertz (THz) communication is considered to be a key enabling technology for 6G due to its abundant available spectrum resources. One pre-requisite for implementing THz communication systems is to understand and model the THz radio channel in 6G deployment scenarios. However, new radio characteristics in THz bands (e.g., channel sparsity, near-field propagation, and large-scale antenna configuration) have brought new opportunities and challenges to channel modeling in terms of modeling complexity and accuracy. In this work, we aim to address these opportunities and challenges with the deterministic ray tracing (RT) approach. First, the propagation characteristics in THz bands are discussed. We elaborate on why deterministic RT can be a promising approach to model the propagation characteristics in THz bands for 6G. Second, an RT-based channel modeling approach is presented, which uses channel measurement data to calibrate simulation parameters. Third, the performance of the RT-based channel modeling approach is demonstrated through a comparison between simulations and channel measurements. The comparison results show that the RT-based channel modeling approach can well describe the propagation characteristics (i.e., the delay and spatial dispersion, channel sparsity, near-field propagation, and non-stationarity) with reduced simulation complexity in THz bands.
Fake Review Detection using Evidential Classifier

Online shopping is a major part of our lives and has proven to make our lives easier and more time-saving. Rating and reviewing those online products is how we spread the word and also get to know about a product’s quality. Ratings help the e-commerce sites rank the products in a category, and that’s how we purchase the best possible product. This affects a business, as the better the reviews, the more consumers there are and the higher the profit. On the negative side, we have human spammers and machine-generated automatic spamming of reviews and ratings, which can affect any business positively or negatively and would also create a false image of a product, leading to a bad customer experience. Therefore, if we are able to find a classifier that can tell us if a review is fake or real, it can help online shopping sites determine a product’s actual ratings and thus reduce unfair competition. Hence, in this paper, we propose the classification of reviews via the Dempster-Shafer (DS) evidential classifier. This classifier has proven to be very effective in classifying images and texts. This paper mainly focuses on how certain features, when fed to the Dempster-Shafer classifier, can help us detect fake reviews.
Metamorphic Testing for Traffic Sign Detection and Recognition

Traffic sign detection and recognition (TSDR) is a crucial technology to realize vehicle autonomous driving and maintain road safety. Misdetection or misclassification of traffic signs can lead to serious traffic accidents. Therefore, it is essential to ensure the correctness of traffic sign detection and recognition systems. However, most existing methods use labeled data, but it isn't easy to obtain labeled data. The metamorphic testing can reduce the dependence on the labeled data, but the current metamorphic relations do not consider the domain characteristics of traffic signs, so it is difficult to effectively detect traffic sign detection and recognition systems faults. To solve the above problems, this paper introduces metamorphic testing to verify traffic sign detection and recognition systems and designs and implements a tool, MTSDR, which provides a series of metamorphic relations in the traffic domain. It can generate many follow-up test images based on these metamorphic relations and automatically performs tests for evaluation, focusing on practical failure situations. The experimental evaluation was carried out on four advanced traffic sign detection and recognition models on open datasets. The results show that MTSDR can generate natural images and find thousands of traffic sign detection and recognition faults. Compared with the previous methods, our method has a higher fault detection capability, and the highest fault detection rate reaches more than 40%. In addition, it finds that retraining the traffic sign detection and recognition models using the images that found the fault can improve the model's performance, reduce its fault, and reduce the fault rate by 11.77% on average.
Spectral and Graph Based Deep Learning Models for Schizophrenia Detection

Deep learning has intervened in the field of neuroscience by inspecting activities in the brain through Electroencephalogram (EEG) signals. This research investigates the application of two unconventional preprocessing approaches to EEG signals, aiming to enhance the classification of schizophrenic adolescents from healthy individuals. To analyze the EEG signals, the researchers used three different mathematical techniques called Fourier Transform (FFT), Short-Time Fourier Transform (STFT) and Laplace Transform. These techniques helped to extract important features from the signals, or to identify patterns in the brain activity that could be related to schizophrenia. After extracting the features, the researchers then used them as input for different deep learning models to detect schizophrenia. Deep learning models like VGG-16, Variational AutoEncoders, ChronoNet and GraphCNN were used. It was found that the STFT processed signals on VGG-16 gave the best results. Certain challenges like variation in data, the need for the large annotated datasets along with the interpretability of deep learning models were encountered. To address these issues, this study deployed preprocessing techniques and the domain knowledge has been integrated to enhance the model robustness and generalizability.
Elevated Mixing in the Periphery of Mesoscale Eddies in the South China Sea

AbstractDirect microstructure observations across three warm mesoscale eddies were conducted in the northern South China Sea during the field experiments in July 2007, December 2013, and January 2014, respectively, along with finestructure measurements. An important finding was that turbulent mixing in the mixed layer was considerably elevated in the periphery of each of these eddies, with a mixing level 5–7 times higher than that in the eddy center. To explore the mechanism behind the high mixing level, this study carried out analyses of the horizontal wavenumber spectrum of velocities and spectral fluxes of kinetic energy. Spectral slopes showed a power law of k−2 in the eddy periphery and of k−3 in the eddy center, consistent with the result that the kinetic energy of submesoscale motion in the eddy periphery was more greatly energized than that in the center. Spectral fluxes of kinetic energy also revealed a forward energy cascade toward smaller scales at the wavelength of kilometers in the eddy perip.
Movie Recommendation System Using TF-IDF Vectorizer and Bag of Words

Numerous advanced position systems, similar to information gathering, getting-to-know methods, Deep Learning and the IoT, have surfaced due to technological upgrades. The technologies are being used a long way and extensively to satisfy social demands. In addition, new structures were developed due to this. Recommendation systems have become significant in entertainment, schooling, or different agencies. This paper discusses the content-grounded recommender. The movie has numerous traits that set it piecemeal from different recommender structures, including range and oneness. Those capabilities are used to make a film prototype and decide similarity. We present a new device for calculating factor weights that improve film illustration. In this exploration paper, we've used more than one textbook to vector conversion methods and manipulated the multiple algorithms' results to get the last recommendation listing. In this paper, a huge variety of work is reviewed inside the field of a recommender machine for photographs wherein dataset supply, styles used, and delicacy are in comparison to deduce an elegant one and unborn compass for enhancement in this area are anatomized.
Numerical Methods for Model Predictive Control of Autonomous Surface Vehicles

Autonomous surface vehicles (ASVs) are seeing growing use in fields such as environmental surveys, weather data collection, military, and water transport. Given the high nonlinearity of ASVs, traditional linearized model predictive control (LMPC) struggles when the ASV veers from its design point. Nonlinear model predictive control (NMPC) is becoming a favored choice for these nonlinear systems, especially with the rise of powerful computing resources. Yet, the extensive computational demand, largely from intricate Hessian matrix evaluations and the Newton method, restricts its wider application. In this paper, we present an NMPC program harnessing a symbolic computation approach for the Hessian matrix. A central highlight of our work is the evaluation of three distinct numerical methods, each applying a different level of Hessian matrix simplification, for the task of two-dimensional ASV trajectory tracking.