Advanced Deep Learning Techniques Research Articles

Exposing Deep Fake Face Detection using LSTM and CNN

The rapid advancement of deep learning techniques, creating realistic multimedia content has become increasingly accessible, leading to the proliferation of DeepFake technology. DeepFake utilizes generative deep learning algorithms to produce or modify face features in a highly realistic manner, often making it challenging to differentiate between real and manipulated media. This technology, while beneficial in fields such as entertainment and education, also poses significant threats, including misinformation and identity theft. Consequently, detecting DeepFakes has become a critical area of research. In this paper, we propose a novel approach to DeepFake face detection by integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) networks. Our method leverages the strengths of CNNs in spatial feature extraction and LSTMs in temporal sequence modeling to enhance detection accuracy. The CNN component captures intricate facial features, while the LSTM analyzes the temporal dynamics of video frames. We evaluate our model on several benchmark datasets, including Celeb-DF (v2), DeepFake Detection Challenge Preview, and FaceForensics++. Experimental results demonstrate that our hybrid CNN-LSTM model achieves state-of-the-art performance, surpassing existing methods in both accuracy and robustness. This study highlights the potential of combining CNN and LSTM architectures for effective DeepFake detection, contributing to the ongoing efforts to safeguard against digital media manipulation

A comprehensive review of deep learning in EEG-based emotion recognition: classifications, trends, and practical implications

Emotion recognition utilizing EEG signals has emerged as a pivotal component of human–computer interaction. In recent years, with the relentless advancement of deep learning techniques, using deep learning for analyzing EEG signals has assumed a prominent role in emotion recognition. Applying deep learning in the context of EEG-based emotion recognition carries profound practical implications. Although many model approaches and some review articles have scrutinized this domain, they have yet to undergo a comprehensive and precise classification and summarization process. The existing classifications are somewhat coarse, with insufficient attention given to the potential applications within this domain. Therefore, this article systematically classifies recent developments in EEG-based emotion recognition, providing researchers with a lucid understanding of this field’s various trajectories and methodologies. Additionally, it elucidates why distinct directions necessitate distinct modeling approaches. In conclusion, this article synthesizes and dissects the practical significance of EEG signals in emotion recognition, emphasizing its promising avenues for future application.

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version 8 and Transfer Learning Approaches

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial resources, the natural environment, and global warming. Therefore, timely fire detection is essential for quick and effective response and not to endanger forest resources, animal life, and the human economy. This study introduces a forest fire detection approach utilizing transfer learning with the YOLOv8 (You Only Look Once version 8) pretraining model and the TranSDet model, which integrates an improved deep learning algorithm. Transfer Learning based on pre-trained YoloV8 enhances a fast and accurate object detection aggregate with the TranSDet structure to detect small fires. Furthermore, to train the model, we collected 5200 images and performed augmentation techniques for data, such as rotation, scaling, and changing due and saturation. Small fires can be detected from a distance by our suggested model both during the day and at night. Objects with similarities can lead to false predictions. However, the dataset augmentation technique reduces the feasibility. The experimental results prove that our proposed model can successfully achieve 98% accuracy to minimize catastrophic incidents. In recent years, the advancement of deep learning techniques has enhanced safety and secure environments. Lastly, we conducted a comparative analysis of our method’s performance based on widely used evaluation metrics to validate the achieved results.

Age-Invariant Cross-Age Face Verification using Transfer Learning

The integration of face verification technology has become indispensable in numerous safety and security software systems. Despite its promising results, the field of face verification encounters significant challenges due to age-related disparities. Human facial characteristics undergo substantial transformations over time, leading to diverse variations including changes in facial texture, morphology, facial hair, and eyeglass adoption. This study presents a pioneering methodology for cross-age face verification, utilizing advanced deep learning techniques to extract resilient and distinctive facial features that are less susceptible to age-related fluctuations. The feature extraction process combines handcrafted features like Local Binary Pattern/Histogram of Oriented Gradients with deep features from MobileNetV2 and VGG-16 networks. As the texture of the facial skin defines the age related characteristic the well-known texture feature extractors like LBP and HoG is preferred. These features are concatenated to achieve fusion, and subsequent layers fine-tune them. Experimental validation utilizing the Cross-Age Celebrity Dataset demonstrates remarkable efficacy, achieving an accuracy of 98.32%.

DEEP LEARNING METHODS FOR CARDIOVASCULAR RISK ASSESSMENT WITH RETINAL IMAGES

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide, necessitating accurate risk assessment for early intervention. Deep learning methodologies offer promising avenues for such assessments, leveraging retinal images as a non-invasive proxy for cardiovascular health. This paper surveys recent advancements in deep learning techniques for cardiovascular risk assessment utilizing retinal images. It explores convolutional neural networks (CNNs), transfer learning, and ensemble methods, highlighting their efficacy in feature extraction and risk prediction. Key challenges such as data scarcity and interpretability are addressed, alongside potential solutions. By integrating machine learning with medical imaging, this research contributes to enhanced early detection and management of CVDs, fostering improved patient outcomes. Key Words- Deep learning, cardiovascular risk assessment, retinal images, convolutional neural networks, transfer learning, ensemble methods, medical imaging.

Field Validation of Deep-Learning-Based Ground Penetrating Radar Image Analysis for Advancing Subsurface Distress Detection

This research introduces an innovative method for detecting subsurface cracks within pavements by leveraging ground penetrating radar (GPR) technology in conjunction with advanced deep learning techniques. Its primary aim is to significantly improve the accuracy and efficiency of pavement assessment, particularly for operational and maintenance purposes. The proposed model, GPR-YOLOR (You Only Learn One Representation), extends the YOLOR framework and incorporates a region of interest within the top pavement layer to detect subsurface cracks. While the model can be trained with annotated data, the main challenge lies in validating results in the field because of the inability to visually inspect subsurface conditions and the high cost associated with direct coring. To overcome this challenge, we propose an alternative approach that utilizes the co-occurrence of surface cracks as pseudo labels, allowing for easy verification. To ensure that surface cracks correspond to subsurface cracks, the focus is exclusively on transverse cracks that develop in a bottom-up manner, such as fatigue and reflective cracks. Through this methodology, our GPR-YOLOR model achieves an F1 score of 0.72, with a precision of 0.76 and a recall of 0.68. The results from field validation underscore the effectiveness of the GPR-YOLOR model in accurately identifying subsurface cracks, highlighting its practical significance in conducting field condition assessments.

A bizarre synthesized cascaded optimized predictor (BizSCOP) model for enhancing security in cloud systems

Due to growing network data dissemination in cloud, the elasticity, pay as you go options, globally accessible facilities, and security of networks have become increasingly important in today's world. Cloud service providers, including AWS, Azure, GCP, and others, facilitate worldwide expansion within minutes by offering decentralized communication network functions, hence providing security to cloud is still remains a challenging task. This paper aims to introduce and evaluate the Biz-SCOP model, a novel intrusion detection system developed for cloud security. The research addresses the pressing need for effective intrusion detection in cloud environments by combining hybrid optimization techniques and advanced deep learning methodologies. The study employs prominent intrusion datasets, including CSE-CIC-IDS 2018, CIC-IDS 2017, and a cloud intrusion dataset, to assess the proposed model's performance. The study's design involves implementing the Biz-SCOP model using Matlab 2019 software on a Windows 10 OS platform, utilizing 8 GB RAM and an Intel core i3 processor. The hybrid optimization approach, termed HyPSM, is employed for feature selection, enhancing the model's efficiency. Additionally, an intelligent deep learning model, C2AE, is introduced to discern friendly and hostile communication, contributing to accurate intrusion detection. Key findings indicate that the Biz-SCOP model outperforms existing intrusion detection systems, achieving notable accuracy (99.8%), precision (99.7%), F1-score (99.8%), and GEO (99.9%). The model excels in identifying various attack types, as demonstrated by robust ROC analysis. Interpretations and conclusions emphasize the significance of hybrid optimization and advanced deep learning techniques in enhancing intrusion detection system performance. The proposed model exhibits lower computational load, reduced false positives, ease of implementation, and improved accuracy, positioning it as a promising solution for cloud security.

Admission Prediction Using Time Series Analysis

Efficient resource allocation and strategic planning in educational institutions heavily rely on accurate predictions of student admissions. This paper presents a detailed investigation into the application of time series analysis techniques for admission prediction. We explore the utilization of historical admission data, spanning multiple academic cycles, to train and evaluate several time series models.The study encompasses a comparative analysis of traditional statistical models such as autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) alongside more advanced deep learning techniques like long short-term memory (LSTM) networks. By evaluating the performance metrics, including accuracy, robustness, and computational efficiency, we aim to identify the most suitable model for admission prediction tasks. Moreover, the research delves into the impact of various external factors such as changes in the academic calendar, socio-economic indicators, and demographic shifts on admission patterns. Understanding these factors is crucial for enhancing the predictive capabilities of the models and enabling institutions to adapt their strategies accordingly. The experimental results and comparative analysis provide valuable insights for educational institutions, enabling them to make data-driven decisions regarding enrollment management strategies and resource allocation. Ultimately, this research contributes to the advancement of admission prediction methodologies, facilitating more efficient and informed decision-making processes in the educational domain.

Enhancing Emergency Vehicle Detection: A Deep Learning Approach with Multimodal Fusion

Emergency vehicle detection plays a critical role in ensuring timely responses and reducing accidents in modern urban environments. However, traditional methods that rely solely on visual cues face challenges, particularly in adverse conditions. The objective of this research is to enhance emergency vehicle detection by leveraging the synergies between acoustic and visual information. By incorporating advanced deep learning techniques for both acoustic and visual data, our aim is to significantly improve the accuracy and response times. To achieve this goal, we developed an attention-based temporal spectrum network (ATSN) with an attention mechanism specifically designed for ambulance siren sound detection. In parallel, we enhanced visual detection tasks by implementing a Multi-Level Spatial Fusion YOLO (MLSF-YOLO) architecture. To combine the acoustic and visual information effectively, we employed a stacking ensemble learning technique, creating a robust framework for emergency vehicle detection. This approach capitalizes on the strengths of both modalities, allowing for a comprehensive analysis that surpasses existing methods. Through our research, we achieved remarkable results, including a misdetection rate of only 3.81% and an accuracy of 96.19% when applied to visual data containing emergency vehicles. These findings represent significant progress in real-world applications, demonstrating the effectiveness of our approach in improving emergency vehicle detection systems.

STABLE DIFFUSION TEXT TO IMAGE USING AI

The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adversarial networks (GANs) and natural language processing (NLP). It aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework. The key goal is to generate highly realistic and coherent images from text prompts, leveraging recent deep learning Stable Diffusion. The Stable Diffusion training framework plays a central role in this project. It’s a sophisticated training methodology for GANs, designed to stabilize the training process. GANs have exhibited great potential in generating images but often suffer from issues like mode collapse and training instability. Keywords: Stable Diffusion, Text-to-Image Generation, Image Synthesis, Natural Language Processing(NLP), Generative Adversarial Networks (GANs)

Surface Defect Detection of Aluminum Profiles Based on Multiscale and Self-Attention Mechanisms.

To address the various challenges in aluminum surface defect detection, such as multiscale intricacies, sensitivity to lighting variations, occlusion, and noise, this study proposes the AluDef-ClassNet model. Firstly, a Gaussian difference pyramid is utilized to capture multiscale image features. Secondly, a self-attention mechanism is introduced to enhance feature representation. Additionally, an improved residual network structure incorporating dilated convolutions is adopted to increase the receptive field, thereby enhancing the network's ability to learn from extensive information. A small-scale dataset of high-quality aluminum surface defect images is acquired using a CCD camera. To better tackle the challenges in surface defect detection, advanced deep learning techniques and data augmentation strategies are employed. To address the difficulty of data labeling, a transfer learning approach based on fine-tuning is utilized, leveraging prior knowledge to enhance the efficiency and accuracy of model training. In dataset testing, our model achieved a classification accuracy of 97.6%, demonstrating significant advantages over other classification models.

Generalizable data driven full waveform inversion for complex structures and severe topographies

Traditionally, simplification has been used in scientific modeling practices. However, recent advancements in deep learning techniques have provided a means to represent complex models. As a result, deep neural networks should be able to approximate the complex models, with a high degree of generalization. To achieve generalization, it is necessary to have a diverse range of examples in the training of the neural network, for example in data-driven FWI, training data should cover the expected subsurface models. To meet this requirement, we porposed a method to creat geologically meaningful velocity models with complex structures and severe topography. However, it is important to note that generalization comes with its own set of challenges.Because of significant variation in topography of the generated velocity models, we need to include this information as an additional input data in training of the network. Therefore, we have transformed the seismic data to a fixed datum to incorporate geometric information. Additionally, we have enhanced the network's performance by introducing a term in the network loss function. Multiple metrics have been employed to evaluate the performance of the network. The results indicate that by providing the necessary information to the network and employing computational techniques to refine the model's accuracy, deep neural networks are capable of accurately estimating velocity models in complex environments characterized by extreme topography.

Enhancing breast cancer segmentation and classification: An Ensemble Deep Convolutional Neural Network and U-net approach on ultrasound images

Breast cancer is a condition where the irregular growth of breast cells occurs uncontrollably, leading to the formation of tumors. It poses a significant threat to women’s lives globally, emphasizing the need for enhanced methods of detecting and categorizing the disease. In this work, we propose an Ensemble Deep Convolutional Neural Network (EDCNN) model that exhibits superior accuracy compared to several transfer learning models and the Vision Transformer model. Our EDCNN model integrates the strengths of the MobileNet and Xception models to improve its performance in breast cancer detection and classification. We employ various preprocessing techniques, including image resizing, data normalization, and data augmentation, to prepare the data for analysis. By following these measures, the formatting is optimized, and the model’s capacity to make generalizations is improved. We trained and evaluated our proposed EDCNN model using ultrasound images, a widely available modality for breast cancer imaging. The outcomes of our experiments illustrate that the EDCNN model attains an exceptional accuracy of 87.82% on Dataset 1 and 85.69% on Dataset 2, surpassing the performance of several well-known transfer learning models and the Vision Transformer model. Furthermore, an AUC value of 0.91 on Dataset 1 highlights the robustness and effectiveness of our proposed model. Moreover, we highlight the incorporation of the Grad-CAM Explainable Artificial Intelligence (XAI) technique to improve the interpretability and transparency of our proposed model. Additionally, we performed image segmentation using the U-Net segmentation technique on the input ultrasound images. This segmentation process allowed for the identification and isolation of specific regions of interest, facilitating a more comprehensive analysis of breast cancer characteristics. In conclusion, the study presents a creative approach to detecting and categorizing breast cancer, demonstrating the superior performance of the EDCNN model compared to well-established transfer learning models. Through advanced deep learning techniques and image segmentation, this study contributes to improving diagnosis and treatment outcomes in breast cancer.

Automated Medicinal Plant Identification and Use Case Generation: A Convolutional Neural Network-Xception Approach

Abstract: In the realm of botany, pharmacology, and herbal medicine, the identification of medicinal plants stands as a crucial yet labor-intensive endeavor. This project presents a pioneering approach to automate plant identification through the utilization of advanced deep learning techniques, specifically employing the Xception architecture within a Convolutional Neural Network (CNN). The methodology involves the acquisition of plant images, followed by preprocessing and dimension reduction to 299x299 pixels. Subsequently, the model undergoes rigorous training, achieving commendable accuracies of approximately 85% in training, 93% in validation, and 94% in testing phases. The trained model is then deployed through a user-friendly web interface, allowing seamless interaction for uploading images and receiving comprehensive plant classification results. Leveraging Flask for backend integration, this system bridges the gap between cutting-edge technology and botanical expertise, enabling researchers, botanists, pharmacologists, and herbalists to expedite plant identification processes with heightened accuracy and efficiency. Furthermore, this innovation not only streamlines identification tasks but also provides detailed insights into the diverse applications and attributes of medicinal plants, thereby fostering advancements in research, conservation, and utilization for the betterment of human health and well-being..

Enhancing Concrete Crack Image Detection Using MobileNetV2 and Transfer Learning

The safety of civil engineering structures such as bridges and buildings is paramount, necessitating the early detection and repair of cracks to prevent structural failures. Traditional crack detection methods, including manual inspections and basic image processing, are often hampered by slow detection speeds and limited accuracy. This study explores the integration of advanced deep learning techniques, specifically MobileNetV2, enhanced through transfer learning, to improve the efficiency and accuracy of crack detection. The "Concrete Crack Images for Classification" dataset was employed, featuring 40,000 images from real-world scenarios. Our approach leverages the lightweight architecture of MobileNetV2 and the efficiency of transfer learning to create a robust model that not only reduces computational demands but also achieves high accuracy, with a validation accuracy rate of 99.38%. This paper details the model training, the innovative use of MobileNetV2, and comparative analyses with other deep learning models, demonstrating significant improvements over traditional methods. The findings underscore the potential of MobileNetV2 and transfer learning in practical applications, offering a promising avenue for future research in automated crack detection across various engineering fields.

Addressing cyberbullying in Urdu tweets: a comprehensive dataset and detection system.

The prevalence of cyberbullying has reached an alarming rate, affecting approximately 54% of teenagers who experience various forms of cyberbullying, including offensive hate speech, threats, and racism. This research introduces a comprehensive dataset and system for cyberbullying detection in Urdu tweets, leveraging a spectrum of machine learning approaches including traditional models and advanced deep learning techniques. The objectives of this study are threefold. Firstly, a dataset consisting of 12,500 annotated tweets in Urdu is created, and it is made publicly available to the research community. Secondly, annotation guidelines for Urdu text with appropriate labels for cyberbullying detection are developed. Finally, a series of experiments is conducted to assess the performance of machine learning and deep learning techniques in detecting cyberbullying. The results indicate that fastText deep learning models outperform other models in cyberbullying detection. This study demonstrates its efficacy in effectively detecting and classifying cyberbullying incidents in Urdu tweets, contributing to the broader effort of creating a safer digital environment.

Wind Shear and Aircraft Aborted Landings: A Deep Learning Perspective for Prediction and Analysis

In civil aviation, severe weather conditions such as strong wind shear, crosswinds, and thunderstorms near airport runways often compel pilots to abort landings to ensure flight safety. While aborted landings due to wind shear are not common, they occur under specific environmental and situational circumstances. This research aims to accurately predict aircraft aborted landings using three advanced deep learning techniques: the conventional deep neural network (DNN), the deep and cross network (DCN), and the wide and deep network (WDN). These models are supplemented by various data augmentation methods, including the Synthetic Minority Over-Sampling Technique (SMOTE), KMeans-SMOTE, and Borderline-SMOTE, to correct the imbalance in pilot report data. Bayesian optimization was utilized to fine-tune the models for optimal predictive accuracy. The effectiveness of these models was assessed through metrics including sensitivity, precision, F1-score, and the Matthew Correlation Coefficient. The Shapley Additive Explanations (SHAP) algorithm was then applied to the most effective models to interpret their results and identify key factors, revealing that the intensity of wind shear, specific runways like 07R, and the vertical distance of wind shear from the runway (within 700 feet above runway level) were significant factors. The results of this research provide valuable insights to civil aviation experts, potentially revolutionizing safety protocols for managing aborted landings under adverse weather conditions, thereby improving overall airport efficiency and safety.

Una investigación sobre un sistema de recomendación musical basado en expresiones faciales mediante mecanismos de aprendizaje profundo

In this study, we propose a new music recommendation system (MRS) that combines facial expression recognition technology and deep learning algorithms to respond to the changing music industry environment and provide personalized music recommendations based on the user's emotional state. Our approach includes a thorough study of facial expression recognition, emotion-based music recommendation systems, and deep learning engines, as well as a detailed presentation of the MRS design, system architecture, and deep learning engines used. Through extensive experiments, we evaluate MRS's ability to accurately recognize facial expressions, filter music based on emotional states, and effectively recommend music to users. We analyze the results of follow-up experiments to identify the strengths and limitations of MRS compared to existing approaches, and conduct a comparative study with the latest music recommendation systems based on deep learning and emotion. This comparison highlights the originality and potential of the proposed MRS system to improve user experience and promote the development of artificial intelligence-based music recommendation systems. This study demonstrates the problem of accurately determining a user's emotional state from facial expressions, which requires the integration of facial expression recognition systems, deep learning, and music recommendation systems. Using advanced deep learning techniques and a comprehensive experimental setup, the proposed MRS provides a solution to this problem by facilitating accurate emotional state identification and personalized music recommendations. Overall, MRS represents a powerful and innovative response to the growing demand for accurate and reliable music recommendations and shows significant potential for future collaboration and development of AI-based music recommendation systems.

Integrating Artificial Intelligence and UAV-Acquired Multispectral Imagery for the Mapping of Invasive Plant Species in Complex Natural Environments

The proliferation of invasive plant species poses a significant ecological threat, necessitating effective mapping strategies for control and conservation efforts. Existing studies employing unmanned aerial vehicles (UAVs) and multispectral (MS) sensors in complex natural environments have predominantly relied on classical machine learning (ML) models for mapping plant species in natural environments. However, a critical gap exists in the literature regarding the use of deep learning (DL) techniques that integrate MS data and vegetation indices (VIs) with different feature extraction techniques to map invasive species in complex natural environments. This research addresses this gap by focusing on mapping the distribution of the Broad-leaved pepper (BLP) along the coastal strip in the Sunshine Coast region of Southern Queensland in Australia. The methodology employs a dual approach, utilising classical ML models including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) in conjunction with the U-Net DL model. This comparative analysis allows for an in-depth evaluation of the performance and effectiveness of both classical ML and advanced DL techniques in mapping the distribution of BLP along the coastal strip. Results indicate that the DL U-Net model outperforms classical ML models, achieving a precision of 83%, recall of 81%, and F1–score of 82% for BLP classification during training and validation. The DL U-Net model attains a precision of 86%, recall of 76%, and F1–score of 81% for BLP classification, along with an Intersection over Union (IoU) of 68% on the separate test dataset not used for training. These findings contribute valuable insights to environmental conservation efforts, emphasising the significance of integrating MS data with DL techniques for the accurate mapping of invasive plant species.

Hybrid RNN-LSTM Networks for Enhanced Intrusion Detection in Vehicle CAN Systems

Electric vehicles (EVs) use electric motors for propulsion, relying on electric energy stored in batteries or other energy storage devices. The standard communication protocol used in EVs is the Control Area Network (CAN), a communication protocol widely used in the automotive industry for networking and communication between various components within a vehicle. CAN protocol, designed without any care about protection, as automotive systems become more connected, the vulnerability to cyber threats, including intrusion attacks. The most common intrusion attacks on EVs are Denial of Service (DoS), Fuzzy, and Impersonation Attacks. These become a significant challenge due to the imperative need for robust Intrusion Detection Systems (IDS) in CAN networks. This paper explores the application of advanced deep learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, to enhance the effectiveness of intrusion detection in the EV domain. We will use a hybrid Deep-learning model to improve the analysis. First, we will apply the RNN model, and the output will come as input for the second model, LSTM. Our proposed hybrid model achieved an accuracy of 93%. The outcomes of this research contribute to advancing cybersecurity measures in vehicular networks, ensuring the integrity and safety of connected vehicles. The applicability of RNN and LSTM techniques in the context of CAN networks demonstrates their potential to evolve as integral components of next-generation intrusion detection systems, fostering a secure and resilient automotive ecosystem.