ADKU/ResearchGPT_model_dataset huggingface dataset:
Biological gender identification in Turkish news text using deep learning models
Authors: Pınar Tüfekci, Melike Bektaş Kösesoy

Identifying the biological gender of authors based on the content of their written work is a crucial task in Natural Language Processing (NLP). Accurate biological gender identification finds numerous applications in fields such as linguistics, sociology, and marketing. However, achieving high accuracy in identifying the biological gender of the author is heavily dependent on the quality of the collected data and its proper splitting. Therefore, determining the best-performing model necessitates experimental evaluation. This study aimed to develop and evaluate four learning algorithms for biological gender identification in news texts. To this end, a comprehensive dataset, IAG-TNKU, was created from a Turkish newspaper, comprising 43,292 news articles. Four models utilizing popular machine learning algorithms, including Naive Bayes and Random Forest, and two deep learning algorithms, Long Short Term Memory and Convolutional Neural Networks, were developed and evaluated rigorously. The results indicated that the Long Short Term Memory (LSTM) algorithm outperformed the other three models, exhibiting an exceptional accuracy of 88.51%. This model's outstanding performance underpins the importance of utilizing innovative deep learning algorithms for biological gender identification tasks in NLP. The present study contributes to extant literature by developing a new dataset for biological gender identification in news texts and evaluating four machine learning algorithms. Our findings highlight the significance of utilizing innovative techniques for biological gender identification tasks. The dataset and deep learning algorithm can be applied in many areas such as sociolinguistics, marketing research, and journalism, where the identification of biological gender in written content plays a pivotal role.
Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review
Authors: Sancho Salcedo-Sanz, Jorge Pérez-Aracil, Guido Ascenso, Javier Del Ser, David Casillas-Pérez, Christopher Kadow, Dušan Fister, David Barriopedro, Ricardo García-Herrera, Matteo Giuliani, Andrea Castelletti

Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn.
Deep-learning based sleep apnea detection using sleep sound, SpO2, and pulse rate
Authors: Chutinan Singtothong, Thitirat Siriborvornratanakul

Sleep apnea, a common sleep disorder where breathing is repeatedly interrupted during sleep, poses significant health risks. Traditional diagnostic methods like overnight polysomnography (PSG) are complex and costly, limiting widespread screening. This study suggests a deep learning model to identify sleep apnea using sleep sounds, oxygen saturation (SpO2), and pulse rate. Mel-spectrogram, computed from PSG data of 24 patients, serves as input. The study shows the effectiveness of deep learning, with the combined model achieving 96% accuracy in inferring apnea severity, outperforming individual models using SpO2 and pulse rate (79%) and sleep sound (83%).
Deep learning automatically assesses 2-µm laser-induced skin damage OCT images
Authors: Changke Wang, Qiong Ma, Yu Wei, Qi Liu, Yuqing Wang, Chenliang Xu, Caihui Li, Qingyu Cai, Haiyang Sun, Xiaoan Tang, Hongxiang Kang

The present study proposed a noninvasive, automated, in vivo assessment method based on optical coherence tomography (OCT) and deep learning techniques to qualitatively and quantitatively analyze the biological effects of 2-µm laser-induced skin damage at different irradiation doses. Different doses of 2-µm laser irradiation established a mouse skin damage model, after which the skin-damaged tissues were imaged non-invasively in vivo using OCT. The acquired images were preprocessed to construct the dataset required for deep learning. The deep learning models used were U-Net, DeepLabV3+, PSP-Net, and HR-Net, and the trained models were used to segment the damage images and further quantify the damage volume of mouse skin under different irradiation doses. The comparison of the qualitative and quantitative results of the four network models showed that HR-Net had the best performance, the highest agreement between the segmentation results and real values, and the smallest error in the quantitative assessment of the damage volume. Based on HR-Net to segment the damage image and quantify the damage volume, the irradiation doses 5.41, 9.55, 13.05, 20.85, 32.71, 52.92, 76.71, and 97.24 J/cm² corresponded to a damage volume of 4.58, 12.56, 16.74, 20.88, 24.52, 30.75, 34.13, and 37.32 mm³. The damage volume increased in a radiation dose-dependent manner.
Cytopathic Effect Detection and Clonal Selection using Deep Learning
Authors: Yu Yuan, Tony Wang, Jordan Sims, Kim Le, Cenk Undey, Erdal Oruklu

Purpose:In biotechnology, microscopic cell imaging is often used to identify and analyze cell morphology and cell state for a variety of applications. For example, microscopy can be used to detect the presence of cytopathic effects (CPE) in cell culture samples to determine virus contamination. Another application of microscopy is to verify clonality during cell line development. Conventionally, inspection of these microscopy images is performed manually by human analysts. This is both tedious and time consuming. In this paper, we propose using supervised deep learning algorithms to automate the cell detection processes mentioned above.Methods:The proposed algorithms utilize image processing techniques and convolutional neural networks (CNN) to detect the presence of CPE and to verify the clonality in cell line development.Results:We train and test the algorithms on image data which have been collected and labeled by domain experts. Our experiments have shown promising results in terms of both accuracy and speed.Conclusion:Deep learning algorithms achieve high accuracy (more than 95%) on both CPE detection and clonal selection applications, resulting in a highly efficient and cost-effective automation process.
A secured deep learning based smart home automation system
Authors: Chitukula Sanjay, Konda Jahnavi, Shyam Karanth

With the expansion of modern technologies and the Internet of Things (IoT), the concept of smart homes has gained tremendous popularity with a view to making people’s lives easier by ensuring a secured environment. Several home automation systems have been developed to report suspicious activities by capturing the movements of residents. However, these systems are associated with challenges such as weak security, lack of interoperability and integration with IoT devices, timely reporting of suspicious movements, etc. Therefore, the given paper proposes a novel smart home automation framework for controlling home appliances by integrating with sensors, IoT devices, and microcontrollers, which would in turn monitor the movements and send notifications about suspicious movements on the resident’s smartphone. The proposed framework makes use of convolutional neural networks (CNNs) for motion detection and classification based on pre-processing of images. The images related to the movements of residents are captured by a spy camera installed in the system. It helps in identification of outsiders based on differentiation of motion patterns. The performance of the framework is compared with existing deep learning models used in recent studies based on evaluation metrics such as accuracy (%), precision (%), recall (%), and f-1 measure (%). The results show that the proposed framework attains the highest accuracy (98.67%), thereby surpassing the existing deep learning models used in smart home automation systems.
Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a review
Authors: Mohammed Abo-Zahhad, Ahmed H. Abd El-Malek, Mohammed S. Sayed, Susan Njeri Gitau

Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients’ bodies after surgery, which can lead to severe consequences such as infections and death. The repercussions highlight the critical need to address this issue. Machine learning (ML) and deep learning (DL) have displayed considerable potential for enhancing the prevention of RSIs through heightened precision and decreased reliance on human involvement. ML techniques are finding an expanding number of applications in medicine, ranging from automated imaging analysis to diagnosis. DL has enabled substantial advances in the prediction capabilities of computers by combining the availability of massive volumes of data with extremely effective learning algorithms. This paper reviews and evaluates recently published articles on the application of ML and DL in RSIs prevention and diagnosis, stressing the need for a multi-layered approach that leverages each method’s strengths to mitigate RSI risks. It highlights the key findings, advantages, and limitations of the different techniques used. Extensive datasets for training ML and DL models could enhance RSI detection systems. This paper also discusses the various datasets used by researchers for training the models. In addition, future directions for improving these technologies for RSI diagnosis and prevention are considered. By merging ML and DL with current procedures, it is conceivable to substantially minimize RSIs, enhance patient safety, and elevate surgical care standards.
FPGA implementation of deep learning architecture for kidney cancer detection from histopathological images
Authors: Shyam Lal, Amit Kumar Chanchal, Jyoti Kini, Gopal Krishna Upadhyay

Kidney cancer is the most common type of cancer, and designing an automated system to accurately classify the cancer grade is of paramount importance for a better prognosis of the disease from histopathological kidney cancer images. Application of deep learning neural networks (DLNNs) for histopathological image classification is thriving and implementation of these networks on edge devices has been gaining the ground correspondingly due to high computational power and low latency requirements. This paper designs an automated system that classifies histopathological kidney cancer images. For experimentation, we have collected Kidney histopathological images of Non-cancerous, cancerous, and their respective grade of Renal Cell Carcinoma (RCC) from Kasturba Medical College (KMC), Mangalore, Karnataka, India. We have implemented and analyzed performances of deep learning architectures on a Field Programmable Gate Array (FPGA) board. Results yield that the Inception-V3 network provides better accuracy for kidney cancer detection as compared to other deep learning models on Kidney histopathological images. Further, the DenseNet-169 network provides better accuracy for kidney cancer grading as compared to other existing deep learning architecture on the FPGA board.
Sapotech Reveal CAST—Deep Learning Based Surface Quality Analysis of Hot Steel Slabs in Continuous Casting
Authors: Saku Kaukonen, Eemil Kiviahde, Hannu Suopajärvi

In this contribution, the Sapotech Reveal CAST machine vision-based surface inspection solution is presented in the context of assessing the surface quality of hot steel slabs during the process of continuous casting.Further, a Deep Learning (DL) based approach for defect detection and classification is presented. Deep Learning enables the assessment of surface quality and the detection of defects, which is extremely challenging to be performed using classical machine vision methods. In Reveal CAST, the tools and methods for training, testing, and applying Deep Learning are integrated into a single product.Finally, a cloud enabled Reveal CAST case for Deep Learning is presented with example results from a hybrid cloud-edge reference production system.
Classification of Plant Leaf Disease Using Deep Learning
Authors: K. Indira, H. Mallika

Agriculture is critical in human existence. Practically, 60% of the population is occupied with some sort of farming, either straight forwardly or in a roundabout way. The important factor which determines the quality of crops grown in fields is early detection, classification and severity of plant leaf diseases. Traditional plant disease detection was based on manual design of features and classifiers. The traditional methods faced many challenges in real complex environment such as low contrast, noise in the image being captured. In recent years, deep learning model widely used in image classification task is convolutional neural network, and hence, plant disease detection based on deep learning has important academic research value. In this paper, a CNN model with different convolution layers, AlexNet and MobileNet, was designed to classify the plant leaf diseases of pepper bell, tomato, potato, rice, apple and sorghum with a total of 26 classes. The dataset for pepper bell, tomato, potato, rice and apple is taken from kaggle, whereas sorghum dataset is generated by taking pictures from the field. Along with identification of disease, severity detection is performed on two selected diseases of tomato crop using the best model. Using an available dataset of 24,156 images of diseased and healthy plant leaves, the performance of the model was evaluated. Simulation results revealed an accuracy of 84.24% with CNN model consisting of five layers and an accuracy of 91.19% with pretrained AlexNet model and an accuracy of 97.33% with MobileNet with a learning rate of 0.001. Severity detection was performed on tomato early blight and tomato bacterial spot with levels 1–5 using MobileNet. Simulation result revealed an accuracy of 87.08% and 88.75% for tomato early blight and tomato bacterial spot, respectively.
Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning
Authors: Sung-Jae Lee, Hyun Jun Oh, Young-Don Son, Jong-Hoon Kim, Ik-Jae Kwon, Bongju Kim, Jong-Ho Lee, Hang-Keun Kim

BackgroundOral potentially malignant disorders (OPMDs) are associated with an increased risk of cancer of the oral cavity including the tongue. The early detection of oral cavity cancers and OPMDs is critical for reducing cancer-specific morbidity and mortality. Recently, there have been studies to apply the rapidly advancing technology of deep learning for diagnosing oral cavity cancer and OPMDs. However, several challenging issues such as class imbalance must be resolved to effectively train a deep learning model for medical imaging classification tasks. The aim of this study is to evaluate a new technique of artificial intelligence to improve the classification performance in an imbalanced tongue lesion dataset.MethodsA total of 1,810 tongue images were used for the classification. The class-imbalanced dataset consisted of 372 instances of cancer, 141 instances of OPMDs, and 1,297 instances of noncancerous lesions. The EfficientNet model was used as the feature extraction model for classification. Mosaic data augmentation, soft labeling, and curriculum learning (CL) were employed to improve the classification performance of the convolutional neural network.ResultsUtilizing a mosaic-augmented dataset in conjunction with CL, the final model achieved an accuracy rate of 0.9444, surpassing conventional oversampling and weight balancing methods. The relative precision improvement rate for the minority class OPMD was 21.2%, while the relative \({F}_{1}\) score improvement rate of OPMD was 4.9%.ConclusionsThe present study demonstrates that the integration of mosaic-based soft labeling and curriculum learning improves the classification performance of tongue lesions compared to previous methods, establishing a foundation for future research on effectively learning from imbalanced data.
A deep learning-based car accident detection approach in video-based traffic surveillance
Authors: Xinyu Wu, Tingting Li

Car accident detection plays a crucial role in video-based traffic surveillance systems, contributing to prompt response and improved road safety. In the literature, various methods have been investigated for accident detection, among which deep learning approaches have shown superior accuracy compared to other methods. The popularity of deep learning stems from its ability to automatically learn complex features from data. However, the current research challenge in deep learning-based accident detection lies in achieving high accuracy rates while meeting real-time requirements. To address this challenge, this study introduces a deep learning approach using convolutional neural networks (CNNs) to enhance car accident detection, prioritizing accuracy and real-time performance. It includes a tailored dataset for evaluation, and the F1-scores reveal reasonably accurate detection for “damaged-rear-window” (62%) and “damaged-window” (63%), while “damaged-windscreen” exhibits exceptional performance at 83%. These results demonstrate the potential of CNNs in improving car accident detection, particularly for certain classes. Following extensive experiments and performance analysis, the proposed method demonstrates accurate results, significantly enhancing car accident detection in video-based traffic surveillance scenarios.
Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study
Authors: Joseph Chang, Bo-Ru Lin, Ti-Hao Wang, Chung-Ming Chen

BackgroundThe study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.MethodsIn this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists.ResultsThe CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong’s CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings.ConclusionThis study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.
Deep Learning-Based Empirical and Sub-Space Decomposition for Speech Enhancement
Authors: Khaoula Mraihi, Mohamed Anouar Ben Messaoud

This research presents a single-channel speech enhancement approach based on the combination of the adaptive empirical wavelet transform and the improved sub-space decomposition method followed by a deep learning network. The adaptive empirical wavelet transform is used to determine the boundaries of the segments, then we decompose the obtained spectrogram of the noisy speech into three sub-spaces to determine the low-rank matrix and the sparse matrix of the spectrogram under the perturbation of the residual matrix. The residual noise affecting the speech quality is avoided by the low-rank decomposition using the nonnegative factorization. Then, a cross-domain learning framework is developed to specify the correlations along the frequency and time axes and avoid the disadvantages of the time–frequency domain. Experimental results show that the proposed approach outperforms several competing speech enhancement methods and achieves the highest PESQ, Cov and STOI under different types of noise and at low SNR values in the two datasets. The proposed model is tested on a hardware-level manual design to accelerate the execution of the developed deep learning model on an FPGA.
Car crash detection using ensemble deep learning
Authors: Vani Suthamathi Saravanarajan, Rung-Ching Chen, Christine Dewi, Long-Sheng Chen, Lata Ganesan

With the recent advancements in Autonomous Vehicles (AVs), two important factors that play a vital role to avoid accidents and collisions are obstacles and track detection. AVs must implement an accident detection model to detect accident vehicles and avoid running into rollover vehicles. At present many trajectories-based and sensor-based multiple-vehicle accident prediction models exist. In Taiwan, the AV Tesla sedan's failure to detect overturned vehicles shows that an efficient deep learning model is still required to detect a single-car crash by taking appropriate actions like slowing down, tracking changes, and informing the concerned authorities. This paper proposes a novel car crash detection system for various car crashes using three deep learning models, namely VGG16(feature extractor using transfer learning), RPN (region proposal network), and CNN8L (region-based detector). The CNN8L is a novel lightweight sequential convolutional neural network for region-based classification and detection. The model is trained using a customized dataset, evaluated using different metrics and compared with various state-of-the-art models. The experimental results show that the VGG16 combined with the CNN8L model performed much better when compared to other models. The proposed system accurately recognizes car accidents with an Accident Detection Rate (ADR) of 86.25% and False Alarm Rate (FAR) of 33.00%.