Deep Learning Techniques Research Articles

An Expert Model Using Deep Learning for Image-based Pest Identification with the TSLM Approach for Enhancing Precision Farming

The rise of digital agriculture has sparked considerable interest in its potential to revolutionize farming practices by integrating of advanced technologies. Unmanned aerial vehicles (UAVs), commonly known as drones, have emerged as a crucial tool in precision agriculture, offering diverse applications for real-world scenarios. This research focuses on harnessing the power of drones in the context of digital agriculture support, particularly in addressing the challenges of crop protection and pest management. The objective is to develop an innovative approach for crop-disease diagnosis using an upgraded Transfer-Driven Self-Adaptive Learning Model (TSLM) that leverages multispectral remote sensing data of drone-captured images to improve pest classification and streamline pesticide application. The study explores nine potent deep neural network models' capabilities for identifying plant diseases using various approaches within the digital agriculture framework. Transfer learning and advanced feature extraction techniques are employed to tailor these deep neural networks to the specific crop protection context. Using of pre-trained deep learning models for feature extraction and fine-tuning enhances the effectiveness of the proposed model. Evaluating the model's performance, precision, sensitivity, specificity, and F1-score metrics are assessed, leading to the discovery that deep feature extraction and Self-Adaptive Learning Model (SLM) classification outperform traditional transfer learning methods. The novelty of this work lies in its application of communication protocols to coordinate a fleet of drones for crop protection within the digital agriculture framework. By combining deep learning techniques with transfer-driven self-adaptive learning, the proposed approach significantly enhances the accuracy and efficiency of pest classification in precision agriculture. The study's findings offer valuable insights into optimizing drone-based technologies to combat plant disease epidemics, thereby contributing to the advancement of digital agriculture and its role in supporting sustainable farming practices.

A systematic review of deep learning techniques for plant diseases

Agriculture is one of the most crucial sectors, meeting the fundamental food needs of humanity. Plant diseases increase food economic and food security concerns for countries and disrupt their agricultural planning. Traditional methods for detecting plant diseases require a lot of labor and time. Consequently, many researchers and institutions strive to address these issues using advanced technological methods. Deep learning-based plant disease detection offers considerable progress and hope compared to classical methods. When trained with large and high-quality datasets, these technologies robustly detect diseases on plant leaves in early stages. This study systematically reviews the application of deep learning techniques in plant disease detection by analyzing 160 research articles from 2020 to 2024. The studies are examined in three different areas: classification, detection, and segmentation of diseases on plant leaves, while also thoroughly reviewing publicly available datasets. This systematic review offers a comprehensive assessment of the current literature, detailing the most popular deep learning architectures, the most frequently studied plant diseases, datasets, encountered challenges, and various perspectives. It provides new insights for researchers working in the agricultural sector. Moreover, it addresses the major challenges in the field of disease detection in agriculture. Thus, this study offers valuable information and a suitable solution based on deep learning applications for agricultural sustainability.

Image colorization: A survey and dataset

Image colorization estimates RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality. Over the last decade, deep learning techniques for image colorization have significantly progressed, necessitating a systematic survey and benchmarking of these techniques. This article presents a comprehensive survey of recent state-of-the-art deep learning-based image colorization techniques, describing their fundamental block architectures, inputs, optimizers, loss functions, training protocols, training data, etc. It categorizes the existing colorization techniques into seven classes and discusses important factors governing their performance, such as benchmark datasets and evaluation metrics. We highlight the limitations of existing datasets and introduce a new dataset specific to colorization. We perform an extensive experimental evaluation of existing image colorization methods using both existing datasets and our proposed one. Finally, we discuss the limitations of existing methods and recommend possible solutions and future research directions for this rapidly evolving topic of deep image colorization. The dataset and codes for evaluation are publicly available at https://github.com/saeed-anwar/ColorSurvey.

Network Traffic Prediction Performance Using LSTM

As networks expand to support various applications involving text, audio, video, and images, data traffic increases correspondingly. Traffic classification, which identifies the origin of observed traffic, has multiple applications, including dynamic bandwidth allocation, traffic analysis, quality of service, and network security. Traditional network traffic classification methods like deep packet inspection rely on manually creating and maintaining communication profiles for various applications. However, these methods face challenges such as dynamic port changes and encrypted traffic. Machine Learning (ML) classifiers offer effective solutions to these issues, providing accurate network traffic classification. Due to these advancements, deep learning models are now utilized for network traffic classification and prediction. Long Short-Term Memory (LSTM) has emerged as a highly effective deep learning technique for addressing time series prediction challenges. This study aims to analyze the performance of forecasting network traffic using LSTM, with different activation functions and optimizers with Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Coefficient of Determination (R-Squared) parameters as the model evaluation index. This demonstrates how these parameters impact network traffic forecasting performance.

Machine Learning Driven Options Pricing Model for Changing Market Conditions

This paper presents the design and implementation of an Adaptive Options Pricing Model utilizing deep learning techniques to enhance the accuracy of options pricing in volatile financial markets. Traditional models such as the Black-Scholes and Binomial frameworks have served as foundational tools in options pricing; however, they are constrained by assumptions of constant volatility and market efficiency, which often fail to reflect real-world dynamics. To address these limitations, the proposed model incorporates historical pricing data alongside real-time market sentiment derived from news articles and social media. By leveraging advanced deep learning architectures, particularly Long Short-Term Memory (LSTM) networks, the model dynamically adapts to changing market conditions, capturing complex, nonlinear relationships in the data. The performance of the model is evaluated against traditional pricing methods, demonstrating significant improvements in pricing accuracy and responsiveness to market fluctuations. This research contributes valuable insights for traders and risk managers seeking to optimize their strategies in the derivatives market.

Zero shot plant disease classification with semantic attributes

In the rapidly evolving field of plant disease detection, the number and complexity of crop diseases are increasing, made worse by factors like climate change. Addressing these challenges requires robust and efficient methodologies capable of early and accurate disease identification. This paper explores the integration of advanced deep learning techniques, including pre-trained models, zero-shot learning, and semantic attributes to enhance the effectiveness of plant disease detection systems. High level features extracted from the images by these pretrained models capture crucial patterns, while domain-specific semantic attributes, such as leaf texture and color variations, enhance the understanding. Incorporating zero-shot learning enables adaptation to new and unseen diseases using semantic descriptions. Experimental validation across diverse plant species and disease types underscores the approach’s reliability in real-world agricultural scenarios. Our approach has demonstrated superior performance with plant village dataset, showing a significant improvement in accuracy and generalization. These results underscore the potential of our method to revolutionize plant disease detection and management in agricultural practices.

Using Deep Learning Techniques for Anomaly Detection of Wood Surface

Using Deep Learning Techniques for Anomaly Detection of Wood Surface

Exploring Techniques for Abnormal Event Detection in Video Surveillance

This study explores and evaluates the effectiveness of various abnormal event detection techniques in video surveillance, addressing challenges such as intrusions, accidents, and suspicious activities. Through a systematic review of related papers, the study reveals the prevalence of traditional methods like background subtraction and motion detection despite their limitations in complex scenarios. It highlights the increasing use of deep learning techniques, particularly CNNs and RNNs, which show promise but require substantial labeled data. The findings underscore the importance of selecting proper detection techniques based on specific surveillance scenarios and emphasize the need for extensive labeled datasets for deep learning methods. The originality of this study lies in its comprehensive review and comparison of various abnormal event detection techniques, providing valuable insights and practical implications for advancing video surveillance systems.

INVESTIGATION OF DEEP LEARNING MODELS BASED ON SINGLE-LAYER SimpleRNN, LSTM AND GRU NETWORKS FOR RECOGNIZING SOUNDS OF UAV DISTANCES

In recent years, the potential risks posed by easily moving objects have highlighted the need for intelligent surveillance systems in protected areas, primarily to ensure the safety of human lives. Among the most common of these objects are unmanned aerial vehicles (UAVs). Recent advances in deep learning techniques for recognizing audio signals have made these techniques effective in identifying moving or aerial objects, especially those powered by engines. And the growing deployment of UAVs has made their rapid recognition in various suspicious or unauthorized circumstances critical. Detecting suspicious drone flights, especially in restricted areas, remains a significant research challenge. It is vital to perform the task of determining their distance in order to quickly detect drones approaching people in such protected areas. Therefore, this paper aims to study the research question of recognizing UAV audio data from different distances. That is, recognizing drone audio at different distances was experimentally studied using Simple RNN, LSTM and GRU based deep learning models. The main objective of this study is based on finding one of the capable types of recurrent network for the task of recognizing UAV audio data at different distances. During the experimental study, the recognition abilities of Single-layer Simple RNN, LSTM and GRU recurrent network types were studied from two basic directions: with recognition accuracy curves and classification reports. As a result, LSTM and GRU based models showed high recognition ability for these types of audio signals. It was noted that UAVs can reliably predict distances greater than 10 meters based on the proposed deep learning architecture.

Potato disease detection and control measure recommendation using CNN

Potato cultivation will continue to be a crucial agricultural endeavor, significantly enrich to global food supply. and economic prosperity. However, the ongoing vulnerability of potatoes to diseases, particularly named early Blight and late Blight, will persist as a substantial threat to crop yield and quality. Traditional disease identification methods, reliant on expert visual inspection, are expected to remain time-consuming, error-prone, and inadequate for timely intervention. This paper will outline to find Potato diseases and recommend effective control measures using convolutional Neural-Networks (CNN). The project will adopt an object-oriented methodology to ensure modularity and maintainability, leveraging TensorFlow for comprehensive dataset collection, data cleaning, and model training. TensorFlow Lite will be applied to optimize and quantify the developed model, enhancing the efficiency of the deployment process. The frontend will be crafted with React Native to facilitate user accessibility and seamless interaction. The resulting deep learning technique is anticipated to exhibit high accuracy in identifying early-blight and late-blight in potato plants, enabling users to receive prompt and reliable disease predictions and actionable control suggestions. This innovative approach aims to downgrade crop losses, improve productivity, and foster sustainable agricultural practices, thereby bolstering global food security in the future.

Evaluating deep neural networks in optimizing drug discovery and precision medicine: A review

Introduction/Background: Deep neural networks have shown great promise in advancing drug discovery and precision medicine. By leveraging large amounts of complex biomedical and chemical data, deep learning approaches can identify novel targets, predict drug-target and drug-drug interactions, generate new molecular structures, and assist in personalized treatment selection and development. However, fully utilizing deep learning techniques for optimization across the drug development pipeline remains an ongoing challenge. Materials and Methods: A comprehensive literature review was conducted using major bibliographic databases including PubMed, Web of Science, and Scopus. Search terms included combinations of "deep learning", "drug discovery", "precision medicine", "biomedical data", and "neural networks". Over 200 papers published between 2010-2023 related to deep learning applications in pharmacology and genomics were identified and reviewed. Results: Deep learning has been widely applied at various stages of the drug discovery process including target identification/prioritization, lead generation/optimization, and prediction of molecular properties. Convolutional neural networks are commonly used for the representation and classification of biological sequence and image data for tasks such as gene expression analysis and pathogen detection from microscopy images. Graph neural networks effectively model compound structures and interactome networks to predict molecular bindings and disease associations. Multi-modal neural networks integrate diverse data types for personalized treatment response prediction and biomarker discovery. Challenges remain around data and model interpretation, generalization to new targets/diseases, and integration across domains. Discussion: While deep learning has shown promise, rigorous benchmarking and validation on real-world clinical endpoints are still needed to establish usefulness in decision-making. Data and model transparency must be improved to enable scientific insights. Privacy and security risks accompanying "real world" biomedical big data will require ethical practices. Standardization and sharing of resources/protocols could accelerate progress by enabling comparison of techniques. Combining deep learning with other AI paradigms like causal inference may further improve utility in drug discovery and precision healthcare. Conclusion: Deep neural networks demonstrate potential for optimizing drug development and precision medicine applications. Continued advancement relies on addressing challenges around data, models, validation, and ethics. Multi-disciplinary collaborations integrating machine learning, molecular biology, medicine, and other domains are needed to fully realize benefits to patients.

Development of fig fruit ripeness classification using convolutional neural network

This study presents the design and evaluation of a deep convolutional neural network (CNN) model for accurately classifying fig ripeness stages. Traditionally, fruit ripeness classification has been conducted manually, which presents several drawbacks, including heavy reliance on human labor and inconsistencies in determining fruit ripeness. By leveraging advanced deep learning techniques, specifically CNNs, this research aims to automate the fig ripeness classification process. The CNN architecture was developed and trained using MATLAB software, targeting three ripeness categories: ripe, half-ripe, and unripe. The methodology involved pre-processing the fig images and configuring the CNN model with multiple convolutional, batch normalization, and max pooling layers specifically for fig classification tasks. The final CNN model achieved an impressive accuracy rate of 94.44%, significantly surpassing results from previously reported studies. The developed model is a promising tool for automating fig ripeness classification, contributing to advancements in precision agriculture and smart farming technologies.

Enhancing breast cancer detection accuracy through transfer learning: A case study using efficient net

Breast cancer continues to pose a significant global health challenge, emphasizing the need for advancements in early detection methods. This study explores the application of transfer learning techniques, specifically utilizing EfficientNet, to enhance the accuracy of breast cancer detection through medical imaging. Leveraging a dataset of mammography images from the Digital Database for Screening Mammography (DDSM), the research implements various data preprocessing methods, including median filtering, contrast enhancement, and artifact removal, to ensure the quality of input data. The EfficientNet model, trained with these preprocessed images, is evaluated against other transfer learning architectures, such as DenseNet and ResNeXt50, using metrics like accuracy, AUC, precision, and F1-score. The results demonstrate that EfficientNet outperforms other models, achieving an accuracy of 95.23%, with a sensitivity of 96.67% and specificity of 93.82%. These findings suggest that transfer learning, particularly with EfficientNet, can significantly improve the predictive accuracy of breast cancer detection, offering a reliable tool for early diagnosis and personalized treatment planning. The study also discusses the potential integration of these models into clinical workflows, addressing challenges such as data privacy, model generalizability, and clinical applicability. Future research will focus on expanding the dataset and exploring the use of other advanced deep learning techniques to further enhance detection accuracy and robustness.

MULTI-LEVEL CLASSIFICATION BASED ON DEEP LEARNING FOR ACCURATE RISK STRATIFICATION OF ARRHYTHMIAS

Arrhythmias, also known as irregular heartbeats, are important health problems that must be accurately identified to diagnose and treat cardiovascular disease. Within the scope of this study, a network for classifying arrhythmias, which are important in the diagnosis and treatment of cardiovascular diseases, was proposed by using one-dimensional convolutional neural network (1D CNN), one of the deep learning techniques. With the proposed 1D-CNN architecture, arrhythmia types and normal rhythm ECGs were subjected to a more detailed examination from general to specific according to urgency situations. In the classifications made, first of all, a binary classification was made and an evaluation was made as whether there was a life risk or not. In triple, quadruple and six-fold classification, the detection of arrhythmia status is detailed. More complex classifications have helped to define different types of arrhythmias in more detail. This study proposes a deep learning network for automatic identification and classification of arrhythmias and shows that different arrhythmia conditions can be diagnosed with a single network model by applying the proposed network structure to multi-class arrhythmia disorders.

Artificial Intelligence Detection of Cervical Spine Fractures Using Convolutional Neural Network Models.

To develop and evaluate a technique using convolutional neural networks (CNNs) for the computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. By leveraging deep learning techniques, the study might potentially lead to improved patient outcomes and clinical decision-making. This study obtained 500 lateral radiographic cervical spine x-ray images from standard open-source dataset repositories to develop a classification model using CNNs. All the images contained diagnostic information, including normal cervical radiographic images (n=250) and fracture images of the cervical spine fracture (n=250). The model would classify whether the patient had a cervical spine fracture or not. Seventy percent of the images were training data sets used for model training, and 30% were for testing. Konstanz Information Miner (KNIME)'s graphic user interface-based programming enabled class label annotation, data preprocessing, CNNs model training, and performance evaluation. The performance evaluation of a model for detecting cervical spine fractures presents compelling results across various metrics. This model exhibits high sensitivity (recall) values of 0.886 for fractures and 0.957 for normal cases, indicating its proficiency in identifying true positives. Precision values of 0.954 for fractures and 0.893 for normal cases highlight the model's ability to minimize false positives. With specificity values of 0.957 for fractures and 0.886 for normal cases, the model effectively identifies true negatives. The overall accuracy of 92.14% highlights its reliability in correctly classifying cases by the area under the receiver operating characteristic curve. We successfully used deep learning models for computer-assisted diagnosis of cervical spine fractures from radiographic x-ray images. This approach can assist the radiologist in screening, detecting, and diagnosing cervical spine fractures.

Integrated Iot Approaches for Crop Recommendation and Yield-Prediction Using Machine-Learning

In this study, we present an integrated approach utilizing IoT data and machine learning models to enhance precision agriculture. We collected an extensive IoT secondary dataset from an online data repository, including environmental parameters such as temperature, humidity, and soil nutrient levels, from various sensors deployed in agricultural fields. This dataset, consisting of over 1 million data points, provided comprehensive insights into the environmental conditions affecting crop yield. The data were preprocessed and used to develop predictive models for crop yield and recommendations. Our evaluation shows that the LightGBM, Decision Tree, and Random Forest classifiers achieved high accuracy scores of 98.90%, 98.48%, and 99.31%, respectively. The IoT data collection enabled real-time monitoring and accurate data input, significantly improving the models’ performance. These findings demonstrate the potential of combining IoT and machine learning to optimize resource use and improve crop management in smart farming. Future work will focus on expanding the dataset to include more diverse environmental factors and exploring the integration of advanced deep learning techniques for even more accurate predictions.

Deep Learning for Stylometry and Authorship Attribution: a Review of Literature

The application of deep learning techniques to stylometry and authorship attribution has emerged as a promising frontier in computational linguistics, offering new possibilities for understanding literary style and authorship in both historical and contemporary contexts. This review paper synthesizes recent advances in the use of deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer architectures, for identifying and attributing authorship based on stylistic analysis. We examine the effectiveness of these models in comparison to traditional statistical methods, highlighting their ability to capture complex linguistic patterns and nuances that are often overlooked by conventional approaches. Furthermore, we explore how deep learning models handle challenges such as multilingual texts, limited data, and variations across genres and periods. This review also addresses the interpretability of neural networks in the context of stylometry and discusses the implications of these methods for fields ranging from literary studies to digital forensics. By providing a comprehensive overview of the current state of research, this paper identifies key trends, challenges, and future directions for the application of deep learning to stylometry and authorship attribution.

Enhanced Foreign Exchange Volatility Forecasting Using Ceemdan with Optuna-Optimized Ensemble Deep Learning Model

Foreign Exchange (FX) is the largest financial market in the world, with a daily trading volume that significantly exceeds that of stock and futures markets. The prediction of FX volatility is a critical financial concern that has garnered significant attention from researchers and practitioners due to its far-reaching implications in the financial markets. This paper presents a novel hybrid ensemble forecasting model integrating a decomposition strategy and three deep learning (DL) models: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Convolutional Neural Network (CNN). This combination addresses individual models' limitations and further improves the accuracy and stability of FX volatility forecasting. The proposed approach utilizes the CEEMDAN technique to decompose volatility into multiple distinct intrinsic mode functions (IMFs) and merges these IMFs with GARCH and EGARCH volatilities to form the input dataset for the DL models. In addition, we employed an attention mechanism to improve the effectiveness of the DL techniques. Furthermore, the hyperparameters for the DL models are optimized using the Optuna algorithm. Finally, a hybrid ensemble model for forecasting exchange rate volatility is developed by combining the predictions of three distinct DL models. The proposed approach is evaluated against various benchmark models using evaluation measures such as MSE, MAE, HMSE, HMAE, RMSE, Q-LIKE, and the model confidence set (MCS) approach. The results demonstrate that our proposed approach provides accurate and reliable forecasts of FX volatility under different forecasting regimes, making it a valuable tool for financial practitioners and researchers.

Ensemble Fusion Models Using Various Strategies and Machine Learning for EEG Classification.

Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%.

An Optimal Feature Selection Method for Human Activity Recognition Using Multimodal Sensory Data

Recently, the research community has taken great interest in human activity recognition (HAR) due to its wide range of applications in different fields of life, including medicine, security, and gaming. The use of sensory data for HAR systems is most common because the sensory data are collected from a person’s wearable device sensors, thus overcoming the privacy issues being faced in data collection through video cameras. Numerous systems have been proposed to recognize some common activities of daily living (ADLs) using different machine learning, image processing, and deep learning techniques. However, the existing techniques are computationally expensive, limited to recognizing short-term activities, or require large datasets for training purposes. Since an ADL is made up of a sequence of smaller actions, recognizing them directly from raw sensory data is challenging. In this paper, we present a computationally efficient two-level hierarchical framework for recognizing long-term (composite) activities, which does not require a very large dataset for training purposes. First, the short-term (atomic) activities are recognized from raw sensory data, and the probabilistic atomic score of each atomic activity is calculated relative to the composite activities. In the second step, the optimal features are selected based on atomic scores for each composite activity and passed to the two classification algorithms: random forest (RF) and support vector machine (SVM) due to their well-documented effectiveness for human activity recognition. The proposed method was evaluated on the publicly available CogAge dataset that contains 890 instances of 7 composite and 9700 instances of 61 atomic activities. The data were collected from eight sensors of three wearable devices: a smartphone, a smartwatch, and smart glasses. The proposed method achieved the accuracy of 96.61% and 94.1% by random forest and SVM classifiers, respectively, which shows a remarkable increase in the classification accuracy of existing HAR systems for this dataset.