ADKU/ResearchGPT_model_dataset huggingface dataset:
New hybrid deep learning models for multi-target NILM disaggregation
Authors: Jamila Ouzine, Manal Marzouq, Saad Dosse Bennani, Khadija Lahrech, Hakim EL Fadili

Non-Intrusive Load Monitoring (NILM) technique or energy disaggregation is a technique used to detect the appliance’s states and estimate their individual energy consumption, given the aggregated data through the main smart meter. Indeed, energy efficiency is the main goal of the NILM techniques, which can be achieved by providing energy disaggregation feedback to the consumers. Unlike single models where training must be performed for each appliance, this work proposes multi-target disaggregation which is more appropriate due to the drastic reduction of resources when training is performed for all target appliances simultaneously. For this purpose, new hybrid models are proposed by combining well-known deep learning models: Convolutional Neural Network (CNN), Denoising Autoencoder (DAE), Recurrent Neural Network (RNN), and Long Short-Term Memory network (LSTM). An implementation and detailed comparative study is then suggested between the proposed hybrid deep learning models and conventional single models in terms of various performance metrics on the UK-Domestic Appliance-Level Electricity (UKDALE) benchmarking database. The experimental results show that the proposed hybrid models provide the best disaggregation performances for multi-target disaggregation compared to single models. Specifically, the CNN-LSTM and the DAE-LSTM are the best hybrid models with the highest overall F1-score of 78.90% and 72.94% respectively.
Advancing brain tumor classification accuracy through deep learning: harnessing radimagenet pre-trained convolutional neural networks, ensemble learning, and machine learning classifiers on MRI brain images
Authors: Nihal Remzan, Karim Tahiry, Abdelmajid Farchi

Brain tumors, a severe health concern across all age groups, present challenges for accurate grading in health monitoring and automated diagnosis. Choosing MRI scans for their superior quality and comprehensive anatomical insight, this study navigates the complexities of brain tumor classification. Deep learning (DL) algorithms revolutionize this field, empowering radiologists with enhanced diagnostic precision. Despite the pivotal role of extensive training data in DL success, medical image datasets often suffer from limitations in diversity and quantity. Transfer learning emerges as a solution, bridging gaps between similar yet distinct areas. The inadequacy of general pre-trained models on ImageNet for medical imaging characteristics prompts our recommendation for RadImageNet. This tailored solution aligns pre-trained models with the specific demands of medical imaging datasets, addressing the current mismatch. Our dataset includes 7023 MR images of Normal brain, Glioma, Meningioma, and Pituitary tumors. We employ two approaches: 'Feature Ensemble,' combining 2-top features, and 'Stacking Ensemble,' utilizing SVM, k-NN, Extra Trees, and MLP as base learners, and Logistic Regression (LR) as a meta-learner. In our initial approach with ResNet-50 and DenseNet121 features guided by MLP, we attained an impressive 97.71% accuracy and a remarkable Area Under the Curve (AUC) of 99.87%. Shifting to the Stacking Ensemble method, where DenseNet121 serves as the optimal feature extractor, we secured a commendable 97.40% accuracy, accompanied by an exceptional AUC of 99.83%. These outcomes highlight the efficacy of our ensemble learning strategies in enhancing both accuracy and the overall discriminative power of the model.
Image Analysis of the Automatic Welding Defects Detection Based on Deep Learning
Authors: Xiaopeng Wang, Baoxin Zhang, Jinhan Cui, Juntao Wu, Yan Li, Jinhang Li, Yunhua Tan, Xiaoming Chen, Wenliang Wu, Xinghua Yu

Automatic detection of welding flaws based on deep learning methods has aroused great interest in the non-destructive testing. However, few studies focus on the characteristics of welding flaws in the X-ray image. This study uses four deep learning models to train and test on a dataset containing 15,194 X-ray images. A hybrid prediction based on OR logic is proposed to avoid the miss detection as much as possible and reduce the miss detection rate to 0.61%, which is state of the art. Quantitative analysis of flaws’ characteristics, including the area, aspect ratio, mean, and variance, suggests the aspect ratios of miss detected flaws are smaller than 2, and the coefficient variances of miss detected flaws are smaller than 0.2. Tracking the critical pixels of X-ray images show that salt noises lead to false alarmed predictions. Error analysis indicates that when using the deep learning method for automatic welding flaws detection, the characteristics of flaws and the factors caused by inappropriate X-ray exposure techniques also should be noted.
Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data
Authors: Janjhyam Venkata Naga Ramesh, Pavithra Roy Patibandla, Manjula Shanbhog, Srinivas Ambala, Mohd Ashraf, Ajmeera Kiran

The summer season in India is marked by a severe shortage of water, which poses significant challenges for daily usage and agricultural practices. With unpredictable weather patterns and irregular rainfall, it is crucial to monitor and maintain water bodies such as domestic ponds and lakes in urban areas to ensure they provide clean and safe water for regular use, free from industrial pollutants. In this research paper, we propose an innovative ensemble deep learning approach (e-DLA) that leverages deep learning models to predict the turbidity of Dooskal Lake, located in Telangana, India, using remote sensing data. The proposed approach utilizes various deep learning models, including bagging, boosting, and stacking, to analyze the complex relationships between remote sensing data and turbidity levels in the lake. The study aims to provide accurate and efficient predictions of turbidity levels, which can aid in the management and conservation of water resources in the region. Hyperparameter tuning is employed, and dynamic climatic features are extracted and integrated with the ensemble learning global protective intelligent algorithm to reveal the complex relationship between in situ and measured values of turbidity during the measuring timeline. The proposed approach provides accurate predictions of turbidity levels, enabling the implementation of effective control measures to maintain water quality standards. Experimental results demonstrate that the proposed approach significantly reduces prediction errors compared to existing deep learning models. Overall, this research highlights the potential of machine learning techniques in monitoring and maintaining water resources, particularly in urban areas, to support sustainable water management and usage, and addresses an urgent and pressing issue in India and around the world.
Analyzing tourism reviews using Deep Learning and AI to predict sentiments
Authors: Piergiorgio Marigliano

In this study, we investigate the application of Artificial Intelligence (AI), specifically through Deep Learning and neural networks, in analyzing and predicting sentiments expressed in tourism reviews. Our dataset comprised various hotel reviews, with the objective to predict whether each textual review indicates positive or negative feedback. The primary challenge was to use solely the textual data of the reviews for this prediction. Through meticulous data processing and analysis, we developed neural network-based models that highlight the efficacy of Deep Learning in the accurate interpretation of reviews. Our findings reveal a significant correlation between the content of reviews and their overall ratings, thereby providing new insights into the application of AI in automating and enhancing understanding of customer needs and perceptions in the tourism sector. The principal contribution of this study is the practical demonstration of how AI techniques can be effectively employed to analyze large volumes of textual data, opening new avenues for marketing strategies and service optimization in the hospitality industry. Each review represents a client’s assessment of a hotel. For each textual review, we aim to predict whether it corresponds to a positive review (the customer is satisfied) or a negative review (the customer is dissatisfied). The overall ratings of the reviews can range from 2.5/10 to 10/10. To simplify the issue, we’ll categorize them as follows: negative reviews have overall ratings of less than 5; positive reviews have ratings of 5 or higher. The challenge lies in predicting this information using only the raw textual data of the review.
ConIQA: A deep learning method for perceptual image quality assessment with limited data
Authors: M. Hossein Eybposh, Changjia Cai, Aram Moossavi, Jose Rodriguez-Romaguera, Nicolas C. Pégard

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall’s tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.
Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial
Authors: Peter J. Illingworth, Christos Venetis, David K. Gardner, Scott M. Nelson, Jørgen Berntsen, Mark G. Larman, Franca Agresta, Saran Ahitan, Aisling Ahlström, Fleur Cattrall, Simon Cooke, Kristy Demmers, Anette Gabrielsen, Johnny Hindkjær, Rebecca L. Kelley, Charlotte Knight, Lisa Lee, Robert Lahoud, Manveen Mangat, Hannah Park, Anthony Price, Geoffrey Trew, Bettina Troest, Anna Vincent, Susanne Wennerström, Lyndsey Zujovic, Thorir Hardarson

To assess the value of deep learning in selecting the optimal embryo for in vitro fertilization, a multicenter, randomized, double-blind, noninferiority parallel-group trial was conducted across 14 in vitro fertilization clinics in Australia and Europe. Women under 42 years of age with at least two early-stage blastocysts on day 5 were randomized to either the control arm, using standard morphological assessment, or the study arm, employing a deep learning algorithm, intelligent Data Analysis Score (iDAScore), for embryo selection. The primary endpoint was a clinical pregnancy rate with a noninferiority margin of 5%. The trial included 1,066 patients (533 in the iDAScore group and 533 in the morphology group). The iDAScore group exhibited a clinical pregnancy rate of 46.5% (248 of 533 patients), compared to 48.2% (257 of 533 patients) in the morphology arm (risk difference −1.7%; 95% confidence interval −7.7, 4.3; P = 0.62). This study was not able to demonstrate noninferiority of deep learning for clinical pregnancy rate when compared to standard morphology and a predefined prioritization scheme. Australian New Zealand Clinical Trials Registry (ANZCTR) registration: 379161.
Cyber Digital Twin with Deep Learning Model for Enterprise Products Management
Authors: Ziqian Wang

Time series abnormalities might be signs of upcoming problems; thus, new computational anomaly detection techniques are needed for early warning systems and real-time system condition monitoring. Security and intrusion detection systems (IDS) are critical components of Internet of Things (IoT) devices. Current approaches are inadequate for handling complex data and unique intrusion detection systems (IDSs) in today’s network security platforms; deep learning techniques are needed. The Cybertwin-Enhanced self-attention mechanisms with long short-term memory anomaly detection (DL-Cyberwin-Enhanced SAM-LSTM-AD) model for business solutions that may achieve higher prediction accuracy for IoT devices is the main component of this suggested study. It is based on deep learning. To find the absolute error rate threshold of a new model, this model examines assaults against the Cybertwin-neural network. We looked at the CSE-CIC-IDS-2018 dataset to gauge the classifiers’ performance. Utilising the model's capacity to do time series analysis, this research combines the processed data into its suggested framework. These models show that the suggested model is feasible based on the high true positive rate (TPR) and low false positive rate (FPR) that were achieved. The model is evaluated using the test dataset using important metrics including F1-score, ROC-AUC, TPR, FPR, accuracy, and precision.
Deep transfer learning-based automated detection of blast disease in paddy crop
Authors: Amandeep Singh, Jaspreet Kaur, Kuldeep Singh, Maninder Lal Singh

A major proportion of the loss faced by the agricultural industry originates from the diseases of the crop during cultivation. Paddy crop is one of the dominant crops which provides food to a huge population. In this crop, the losses caused by such diseases vary from 30 to 90% of the yield. Therefore, the automated detection of different diseases in paddy crops seeks the attention of the research community. In this context, the present work proposes a deep transfer learning solution for the automated detection of blast disease of paddy, which is the major cause of its yield reduction. For this purpose, an image dataset of healthy and blast disease-infected leave images of paddy crop has been developed. These images are fed to five convolutional neural network-based deep transfer learning algorithms, viz., LeNet, AlexNet, VGG 16, Inception v1, and Xception models for binary classification. The performance analysis of given algorithms reveals that AlexNet provides better results for binary classification with an average accuracy of 98.7% followed by VGG 16 and LeNet architectures having accuracies of 98.2% and 97.8%. So, this deep transfer learning-based approach may assist in reducing the gap between experts and farmers by providing an automated expert advice platform for the timely detection of diseases in paddy crop.
Network intrusion detection and mitigation in SDN using deep learning models
Authors: Mamatha Maddu, Yamarthi Narasimha Rao

Software-Defined Networking (SDN) is a contemporary network strategy utilized instead of a traditional network structure. It provides significantly more administrative efficiency and ease than traditional networks. However, the centralized control used in SDN entails an elevated risk of single-point failure that is more susceptible to different kinds of network assaults like Distributed Denial of Service (DDoS), DoS, spoofing, and API exploitation which are very complex to identify and mitigate. Thus, a powerful intrusion detection system (IDS) based on deep learning is created in this study for the detection and mitigation of network intrusions. This system contains several stages and begins with the data augmentation method named Deep Convolutional Generative Adversarial Networks (DCGAN) to over the data imbalance problem. Then, the features are extracted from the input data using a CenterNet-based approach. After extracting effective characteristics, ResNet152V2 with Slime Mold Algorithm (SMA) based deep learning is implemented to categorize the assaults in InSDN and Edge IIoT datasets. Once the network intrusion is detected, the proposed defense module is activated to restore regular network connectivity quickly. Finally, several experiments are carried out to validate the algorithm's robustness, and the outcomes reveal that the proposed system can successfully detect and mitigate network intrusions.
Crop disease detection via ensembled-deep-learning paradigm and ABC Coyote pack optimization algorithm (ABC-CPOA)
Authors: M. Chithambarathanu, M. K. Jeyakumar

Crop disease is a significant issue that affects the growth and yield of crops, leading to financial loss for farmers. Identification and treatment of crop diseases have become challenging due to the increase in the variety of diseases and the lack of knowledge among farmers. To address this issue, this investigate uses an ensembled-deep-learning paradigm to propose a deep learning-based model for crop disease identification trained with an ABC-CPOA. Initially, collected raw images are pre-processed via Bilateral filter and gamma correction Feature Extraction: Then, from the pre-processed images, the features like texture feature (Local Quinary Pattern (LQP), Local Gradient Pattern (LGP), Enriched Local Binary Pattern (E-LBP), color features (Color Histogram, Color Moments), shape features (Contour-based features, Convex Hull). Optimal feature selection- Among the extracted features, the optimal features is designated by means of a self-improved meta-heuristic optimization model referred as ABC-CPOA. This ABC-CPOA model is an extended version of standard Coyote Optimization Algorithm (COA). Crop disease detection phase is modelled with a new ensembled-deep-learning paradigm. Ensembled-deep-learning paradigm comprises Attention-based Bi-LSTM, Recurrent Neural Networks (RNNs) and Optimized Deep Neural Network (O-DNN). The weight function of O-DNN is fine-tuned using the new ABC-CPOA. Precision, recall, sensitivity, and specificity, in addition to TPR, FPR, FNR, and TNR, F1-score, and accuracy are used to assess the suggested approach. The implementation was performed by the MATLAB tool (version: 2022B).
Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs
Authors: Hongru Shen, Meng Yang, Jilei Liu, Kexin Chen, Xiangchun Li

Accurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs. We include 4606 samples subjected to different types of cfDNA sequencing to develop EIMIT, and subsequently evaluate classification performance of linear projections of EMIT on six datasets and an additional inhouse testing set encopassing whole-genome, whole-genome bisulfite and 5-hydroxymethylcytosine sequencing. The linear projection of representations from EMIT achieved area under the receiver operating curve (AUROC) values ranged from 0.895 (0.835–0.955) to 0.996 (0.994–0.997) across these six datasets, outperforming its baseline by significant margins. Additionally, we showed that linear projection of EMIT representations can achieve an AUROC of 0.962 (0.914–1.0) in identification of lung cancer on an independent testing set subjected to whole-exome sequencing. The findings of this study indicate that a transformer-based deep learning model can learn cancer-discrimative representations from cfDNA end-motifs. The representations of this deep learning model can be exploited for discriminating patients with and without cancer.
An automated weed detection approach using deep learning and UAV imagery in smart agriculture system
Authors: Baozhong Liu

Weed detection plays a critical role in smart and precise agriculture systems by enabling targeted weed management and reducing environmental impact. Unmanned aerial vehicles (UAVs) and their associated imagery have emerged as powerful tools for weed detection. Traditional and deep learning methods have been explored for weed detection, with deep learning methods being favored due to their ability to handle complex patterns. However, accuracy rate and computation cost challenges persist in deep learning-based weed detection methods. To address this, we propose a method on the basis of the YOLOv5 algorithm to deal with high accuracy demand and low computation cost requirements. The approach involves model generation using a custom dataset and training, validation, and testing sets. Experimental results and performance evaluation validate the proposed method that indicates the research contributes to advancing weed detection in smart and precise agriculture systems, leveraging deep learning techniques for enhanced accuracy and efficiency.
Performance prediction in online academic course: a deep learning approach with time series imaging
Authors: Ahmed Ben Said, Abdel-Salam G. Abdel-Salam, Khalifa A. Hazaa

With the COVID-19 outbreak, schools and universities have massively adopted online learning to ensure the continuation of the learning process. However, in such setting, instructors lack efficient mechanisms to evaluate the learning gains and get insights about difficulties learners encounter. In this research work, we tackle the problem of predicting learner performance in online learning using a deep learning-based approach. Our proposed solution allows stakeholders involved in the online learning to anticipate the learner outcome ahead of the final assessment hence offering the opportunity for proactive measures to assist the learners. We propose a two-pathway deep learning model to classify learner performance using their interaction during the online sessions in the form of clickstreams. We also propose to transform these time series of clicks into images using the Gramian Angular Field. The learning model makes use of the available extra demographic and assessment information. We evaluate our approach on the Open University Learning Analytics Dataset. Comprehensive comparative study is conducted with evaluation against state-of-art approaches under different experimental settings. We also demonstrate the importance of including extra demographic and assessment data in the prediction process.