Sophisticated Deep Learning Research Articles

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification.

Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.

BubbleID: A deep learning framework for bubble interface dynamics analysis

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interface shape, and velocity over its lifetime and capturing dynamic events such as bubble departure. BubbleID is trained and tested on boiling images across diverse heater surfaces and operational settings. This paper also offers a comparative analysis of bubble interface dynamics prior to and post-critical heat flux conditions.

Integrating Multimodal Deep Learning for Enhanced News Sentiment Analysis and Market Movement Forecasting

This paper presents a novel multimodal deep learning framework for analyzing news sentiments and forecasting market movements by leveraging natural language processing, deep learning, and auxiliary data sources. Traditional methods often rely solely on textual news data, limiting their predictive power due to the complexity and ambiguity of language. Our approach incorporates additional modalities such as stock prices, social media sentiment, and economic indicators to capture a more comprehensive view of market dynamics. We employ a hybrid deep learning architecture that combines convolutional neural networks (CNNs) for text feature extraction, long short-term memory (LSTM) networks for capturing sequential dependencies, and attention mechanisms to selectively focus on the most relevant features. To address data scarcity, we introduce advanced data augmentation techniques, generating synthetic news headlines based on historical stock price movements and sentiment patterns. The proposed system is evaluated on a comprehensive dataset spanning multiple years, including news headlines, stock prices, social media data, and economic indicators. Our method achieves an accuracy of 77.51%, significantly outperforming traditional methods and demonstrating improved robustness and predictive power. This study highlights the potential of integrating diverse data sources and sophisticated deep learning techniques to enhance news sentiment analysis and market movement forecasting.

Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits

This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical models to sophisticated deep learning frameworks and evaluate their effectiveness in identifying and addressing zero-day threats. The integration of ML with other cybersecurity mechanisms to develop adaptive, robust defense systems is also explored, alongside challenges such as data scarcity, false positives, and the constant arms race against cyber attackers. Special attention is given to innovative strategies that enhance real-time response and prediction capabilities. This review aims to synthesize current trends and anticipate future developments in machine learning technologies to better equip researchers, cybersecurity professionals, and policymakers in their ongoing battle against zero-day exploits.

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks.

Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model's architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model's accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.

Unfolding Explainable AI for Brain Tumor Segmentation

Brain tumor segmentation (BTS) has been studied from handcrafted engineered features to conventional machine learning (ML) methods, followed by the cutting-edge deep learning approaches. Each recent approach has attempted to overcome the challenges of previous methods and brought conveniences in efficacy, throughput, computation, explainability, investigation, and interpretability. Recently, deep learning (DL) algorithms show excellent performance regarding diverse fields, including image process, computer vision, health analytics, autonomous vehicles, and natural language processes; however, ultimately impediment in making the artificial intelligence explainable and interpretable to clinicians while dealing with critical health informatics and radiomics. Besides the sophisticated deep learning models for brain tumor segmentation, notorious notions like explainability, investigation, trust, and interpretability of DL raised significant concerns for clinicians in their domains. Among many DL methods, the neuro-symbolic learning (NSL) concept has gained more attention as it can contribute to explainable and interpretable AI. In the current study, we survey the prominent approaches, from handcrafted engineering conventional ML to deep learning algorithms, highlight the challenges in DL algorithms, and propose NSL architectures for BTS. Compared to existing surveys, our study not only outlines handcrafted to DL methods for BTS but also proposed explainable and interpretable pipelines appropriate for clinical practices. Our study can better facilitate novice learners in explainable AI and propose efficient, robust, interpretable DL models to facilitate the diagnosis, prognosis, and treatment of BTS.

Air Pollution Prediction using Attention Module With CNN -OptBiLSTM

<p>Predicting air pollution using environmental data assessment parameters becomes increasingly significant amid growing fears about climate change and the sustainability of urban areas. The use of sophisticated deep learning (DL) methods to model the intricate relation among these variables represents a promising area of research. However, current approaches have not effectively taken advantage of the temporal features derived from spatiotemporal correlations among air quality prediction systems, leading to poor long-term predictions. This work presents an AM (attention module) with CNN (convolutional neural network)-OptBiLSTM (optimal bidirectional long- and short-term memory) for AQIP (air quality index prediction). Here, the optimal process is carried out by the WSA (white shark algorithm). The analysis is demonstrated on the dataset and achieved better MSE and MAE values of 0.72 and 0.532 respectively. The developed model has the potential for application to other air pollutants. This proposed AM-CNN-OptBiLSTM has the capacity to substantially improve information services related to air quality prediction for the public. In addition, it provides support for regional pollution control and early warning systems.</p>

Voltage Control in Distributed Photovoltaic Power Distribution Networks Using Multi-Agent Deep Reinforcement Learning

Abstract This study introduces a unique method employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), a sophisticated deep reinforcement learning algorithm, for efficient voltage control in photovoltaic (PV) power distribution networks. The algorithm’s design emphasizes minimizing the need for communication and effectively managing delays, which are pivotal in ensuring consistent and reliable control in environments with fluctuating renewable energy sources. In simulations using the IEEE-33 power distribution system, the research demonstrates the algorithm’s ability to ensure stable and efficient network functioning, even under varying environmental and load conditions. This highlights its potential as a robust solution for modern and renewable energy-integrated power systems.

Time-Series Embeddings from Language Models: A Tool for Wind Direction Nowcasting

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data from multiple stations in the complex terrain of an international airport, TELMo incorporates the horizontal u (east–west) and v (north–south) wind components to significantly reduce forecasting errors. On a day with high wind direction variability, TELMo achieved mean absolute error values of 5.66 for 2-min, 10.59 for 10-min, and 14.79 for 20-min forecasts, processed within a swift 9-ms/step timeframe. Standard degree-based analysis, in comparison, yielded lower performance, emphasizing the effectiveness of the u and v components. In contrast, a Vanilla neural network, representing a shallow-learning approach, underperformed in all analyses, highlighting the superiority of deep learning methodologies in wind direction nowcasting. TELMo is an efficient model, capable of accurately forecasting wind direction for air traffic operations, with an error less than 20° in 97.49% of the predictions, aligning with recommended international thresholds. This model design enables its applicability across various geographical locations, making it a versatile tool in global aviation meteorology.

Investigating Financial Risk Behavior Prediction Using Deep Learning and Big Data

This paper introduces a sophisticated deep learning model designed to predict high-risk behaviors in financial traders by analyzing vast amounts of transaction data. The model begins with an unsupervised pre-training phase, learning distributed representations that capture complex data relationships autonomously. It then utilizes a deep neural network, enhanced through supervised learning, to classify and predict traders' risk levels effectively. We specifically focus on financial spread trading related to Contracts For Difference (CFD), identifying potential misuse of insider information and assessing the risks it poses to market makers. By distinguishing between high-risk (A-book) and lower-risk (B-book) clients, the model supports strategic hedging decisions, crucial for market stability. Our extensive evaluations confirm the model's robustness and accuracy, highlighting its significant potential for practical implementation in dynamic and speculative financial markets where past trading performance may not predict future outcomes. This advancement not only refines risk management strategies but also contributes broadly to the domain of financial technology.

Missing Data Imputation with Uncertainty-Driven Network

We study the problem of missing data imputation, which is a fundamental task in the area of data quality that aims to impute the missing data to achieve the completeness of datasets. Though the recent distribution-modeling-based techniques (e.g., distribution generation and distribution matching) can achieve state-of-the-art performance in terms of imputation accuracy, we notice that (1) they deploy a sophisticated deep learning model that tends to be overfitting for missing data imputation; (2) they directly rely on a global data distribution while overlooking the local information. Driven by the inherent variability in both missing data and missing mechanisms, in this paper, we explore the uncertain nature of this task and aim to address the limitations of existing works by proposing an u<u>N</u>certainty-driven netw<u>O</u>rk for <u>M</u>issing data <u>I</u>mputation, termed NOMI. NOMI has three key components, i.e., the retrieval module, the neural network gaussian process imputator (NNGPI) and the uncertainty-based calibration module. NOMI~ runs these components sequentially and in an iterative manner to achieve a better imputation performance. Specifically, in the retrieval module, NOMI~ retrieves local neighbors of the incomplete data samples based on the pre-defined similarity metric. Subsequently, we design NNGPI~ that merges the advantages of both the Gaussian Process and the universal approximation capacity of neural networks. NNGPI~ models the uncertainty by learning the posterior distribution over the data to impute missing values while alleviating the overfitting issue. Moreover, we further propose an uncertainty-based calibration module that utilizes the uncertainty of the imputator on its prediction to help the retrieval module obtain more reliable local information, thereby further enhancing the imputation performance. We also demonstrate that our NOMI~ can be reformulated as an instance of the well-known Expectation Maximization (EM) algorithm, highlighting the strong theoretical foundation of our proposed methods. Extensive experiments are conducted over 12 real-world datasets. The results demonstrate the excellent performance of NOMI in terms of both accuracy and efficiency.

Identification and Classification of Leaf Diseases using Deep Learning

For agriculture to be sustainable, it is essential to monitor a plant's health and look for diseases. It is quite challenging to manually monitor plant diseases. To improve the agriculture industry and the prosperity of our nation, plant disease must be effectively identified. Several diseases cause the plant's leaves to die. Farmers have a harder time seeing these diseases because it's difficult to care for those plants without knowing about them. The method described in this paper uses Tensor Flow to identify illnesses in plant leaf pictures. CNN training is used on the model proposed to automatically diagnose disease using the object detection API tensor flow. This data is acquired from multiple sources. In order to treat the sickness, the proposed effort will also identify the causes and manifestations of the illness. In the work proposed, to identify plant diseases, sophisticated deep machine learning models based on specific CNN topologies were developed using photos of healthy or diseased plants' leaves. In comparison to existing models, CNN's numerous layers offer a great rating and a 96 percent accuracy level.

Analyse the Enhancement of Sentiment Analysis in Arabic by doing a Comparative Study of Several Machine Learning Techniques

Abstract: Sentiment analysis is a crucial component of natural language processing that seeks to determine the emotional sentiment expressed in a given text. This study investigates sentiment analysis in the Arabic language through a comprehensive approach that integrates traditional machine learning methods with sophisticated deep learning models. We examine the efficacy of conventional algorithms such as Support Vector Machines (SVM) and Naive Bayes, as well as sophisticated neural network architectures such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and the Arabic variant of Bidirectional Encoder Representations from Transformers (BERT). The primary novelty of our approach is in the ensemble method, which combines many approaches to enhance the precision of sentiment categorization in Arabic text. To address the particular challenges presented by Arabic sentiment analysis, such as the intricate structure of the language and the diverse regional variations, we utilize a tailored preprocessing pipeline to effectively handle the nuances of Arabic text. Our comprehensive analysis of various datasets demonstrates that the ensemble technique outperforms individual model benchmarks and offers novel insights into the interplay between different machine learning paradigms in Arabic NLP. The results emphasize the ability of hybrid approaches to improve Arabic sentiment analysis, providing a solid basis for future research and practical applications in understanding the sentiments of Arabic consumers. This study is a significant addition to the expanding domain of Arabic Natural Language Processing (NLP). This resource offers a comprehensive and advanced methodology for utilizing machine learning and deep learning methods to comprehend and analyse the intricate aspects of sentiment in the Arabic language

The Application and Exploration of Data Visualization Technology in Artistic Expression and Exhibition

Embarking on the transformation of virtual art museums, the confluence of data visualization technology and deep learning emerges as a catalyst for redefining artistic expression. Driven by the desire to change traditional approaches, this work utilizes a sophisticated deep learning model, the self-attention-based cycle-consistent generative adversarial network (SA-CCGAN), for transformative purposes. The objective of proposed Modernizing Virtual Art: The Collaboration of Data Visualization Technology and Self-Attention-Based Cycle-Consistent Generative Adversarial Network for Artistic Expression (MVA: DVT & SA-CCGAN) is to elevate artistic expression and exhibition practices within the virtual space, marking a paradigm shift in traditional approaches. SA-CCGAN, renowned for its ability to generate realistic and coherent artistic representations, serves as the cornerstone of this transformative endeavor. The workflow intricately incorporates SA-CCGAN into the virtual art museum context, enhancing stylized outcomes and capturing global geometric features. This yields a computationally efficient representation of high-dimensional visual data, revolutionizing digital art presentation. The fusion of data visualization technology, creativity, and meticulous design not only signals innovation but also has the potential to redefine the virtual art landscape, fostering accessibility, engagement, and a deeper cultural exchange in digital art exploration.

A Comparison of Customer Churn Vector Embedding Models with Deep Learning

In the telecommunication industry, Deep learning has been utilized for churn prediction. Some companies have used sophisticated deep learning techniques to predict churn, which yielded good results. However, future studies are still required to evaluate several deep learning mechanisms as only SoftMax Loss has been used so far. By comparing customer churn vector embedding models with several methods, including SoftMax Loss, Large Margin Cosine Loss, Semi-Supervised Learning, and a combination of Large Margin Cosine Loss and Semi-Supervised Learning, we continue our previous research to apply deep learning in predicting customer churn in the telecommunications industry in this paper. The use of Large Margin Cosine Loss has been proven in face recognition which can increase the discrimination between vectors embedding in different classes. Understanding how mixing unlabeled and labeled input might alter developing algorithms and learning behavior that benefit from this combination are the goals of semi-supervised learning. This approach successfully encouraged feature discrimination in customer behavior as well as improved the overall accuracy of the model. Large Margin Cosine Loss in this study achieved 83.74% of the F1 Score compared to other methods. It was further demonstrated that the produced vectors for churn prediction are discriminative by examining the cluster's similarity and the t-SNE plot.

An Attention-Based Hybrid Optimized Residual Memory Network (AHRML) Method for Autism Spectrum Disorder (ASD) Detection

A neurological condition known as autism spectrum disorder (ASD) is marked by issues with speech, socializing, and monotonous activities. Even if genetics is the primary cause, prompt identification is vital, and using machine learning presents an intriguing opportunity to diagnose the condition more quickly and affordably. Even so, the specific problems of increased computational costs, extended execution times, and decreased efficacy concern the conventional approaches. In order to provide the highest level of disease prediction accuracy, the objective of the proposed study is to develop an automated tool for ASD detection that integrates a number of cutting-edge mining approaches. This study proposes a computer-aided and ultra-light framework called attention-based hybrid optimized residual memory network (AHRML) for accurate and efficient ASD detection. Here, a new hybridized Arithmetic Harris Hawks Optimizer is employed to minimize the dimensionality of features in order to streamline the disability identification process. Moreover, a sophisticated deep learning technique called attention-based residual term memory is developed to reliably and less frequently identify ASD from the provided data. The authors employed the ASD dataset to train and test the proposed model. The dataset includes demographic data (age and gender), behavioral characteristics (social skills and communication abilities), and ASD diagnosis data. In addition, a range of parameters were used to validate and test the proposed AHRML model’s performance using the popular ASD dataset.

Deep learning based characterization of human organoids using optical coherence tomography

Organoids, derived from human induced pluripotent stem cells (hiPSCs), are intricate three-dimensional in vitro structures that mimic many key aspects of the complex morphology and functions of in vivo organs such as the retina and heart. Traditional histological methods, while crucial, often fall short in analyzing these dynamic structures due to their inherently static and destructive nature. In this study, we leveraged the capabilities of optical coherence tomography (OCT) for rapid, non-invasive imaging of both retinal, cerebral, and cardiac organoids. Complementing this, we developed a sophisticated deep learning approach to automatically segment the organoid tissues and their internal structures, such as hollows and chambers. Utilizing this advanced imaging and analysis platform, we quantitatively assessed critical parameters, including size, area, volume, and cardiac beating, offering a comprehensive live characterization and classification of the organoids. These findings provide profound insights into the differentiation and developmental processes of organoids, positioning quantitative OCT imaging as a potentially transformative tool for future organoid research.

Code-Free Machine Learning Solutions for Microscopy Image Processing: Deep Learning.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

IoT-Cloud-based Smart Healthcare Monitoring System based on Seahorse Optimization-driven Mutated CNN

Healthcare is undergoing a transformation due to predictive analytics, which offers clinical decision support in real-time, facilitating effective, tailored therapy. By lowering unfavorable events and raising the standard of care, this approach supports patients at every step of diagnosis, prognosis and therapy. Predictive algorithms driven by artificial intelligence (AI), machine learning (ML) and Internet-of-Things (IoT) handle enormous amounts of data, with cardiovascular illnesses as the primary cause of death worldwide. IoT devices make sophisticated deep learning (DL) analytics and remote patient monitoring possible. This paper presents a novel 5-stage disease prediction model as an IoT-cloud-based smart healthcare management framework, constructed using a seahorse optimization-driven mutated convolutional neural network (SHO-MuCNN). In stage-1, data is gathered from a variety of sources through data collection. The stage-2 of data pre-processing involves the use of standardization to get the data acceptable for additional processing. Using a customized principal component analysis (CPCA), the set of characteristics is determined in stage-3. Next, the stage-4 diseases are predicted by the suggested SHO-MuCNN methodology. According to the prediction results, physicians are educating patients in stage-5 on exactly what measures can be taken to reduce the risk. The smart healthcare monitoring measures for accuracy (98.56%), precision (96.69%), F1-score (97.86%), Recall (94.59%)and AUC (95.02), were employed in the models' performance. In addition, this study utilizes the SPSS and t-test for statistical analysis. The experiment's findings indicate that the SHO-MuCNN strategy outperformed the remaining techniques in an intelligent healthcare monitoring system.

Contribution to historical manuscript dating: A hybrid approach employing hand-crafted features with vision transformers

Precisely dating historical manuscripts represents a paramount endeavor in the comprehension and the interpretation of their historical significance as well as in the preservation of our cultural heritage; however, despite the strides made in computer-based dating methodologies, the quest for heightened robustness persists. Recent advancements in vision transformers, renowned for their success across diverse image processing domains, have stimulated our inquiry into their potential applicability in the domain of historical manuscript dating. In our pursuit of achieving efficient refinement of the manuscript and ensuring pristine datasets for subsequent analysis, we initiate a meticulous dataset preprocessing step. This involves employing accurate methodologies for denoising through the Non-Local-Means algorithm, and binarization using the Canny-edge detector. Following these preprocessing steps, we delve into the intricate realm of feature detection using the Harris-corner detector. This detector is employed to extract keypoints from the manuscript, and we subsequently apply clustering to these keypoints using the k-means algorithm. Our dual objective here is to extract significant patches of specific dimensions and engage in a systematic augmentation of our dataset. Through this process, we aim to amplify the depth and diversity of our dataset, thereby empowering our models with an enriched corpus of historical knowledge. The final phase of our proposed system unfolds as we leverage the latent power of the sophisticated deep learning architecture known as vision transformers. The model is finetuned to our specific task and re-learned with the automatically extracted handcrafted features, emerges as a formidable classification framework. As an added layer of refinement, we deploy a majority vote mechanism on image patches, meticulously engineered to heighten system accuracy. Our rigorous testing regimen, carried out on the well-known MPS historical document dataset, has yielded results of remarkable caliber. Our system's prowess is vividly reflected in its performance metrics, boasting an impressive Mean Absolute Error (MAE) of 3.97 for document-level evaluations and a resounding 7.42 MAE for patch-level assessments. The significance of this work lies in its potential to revolutionize historical manuscript dating, not only by enhancing precision but also by providing a versatile framework adaptable to diverse historical contexts and document types. Beyond its immediate applications, this research paves the way for a deeper understanding of historical narratives, cultures, and the invaluable insights that lie within the annals of our past.