ADKU/ResearchGPT_model_dataset huggingface dataset:
Identifying depression in the United States veterans using deep learning algorithms, NHANES 2005–2018
Authors: Zihan Qu, Yashan Wang, Dingjie Guo, Guangliang He, Chuanying Sui, Yuqing Duan, Xin Zhang, Linwei Lan, Hengyu Meng, Yajing Wang, Xin Liu

BackgroundDepression is a common mental health problem among veterans, with high mortality. Despite the numerous conducted investigations, the prediction and identification of risk factors for depression are still severely limited. This study used a deep learning algorithm to identify depression in veterans and its factors associated with clinical manifestations.MethodsOur data originated from the National Health and Nutrition Examination Survey (2005–2018). A dataset of 2,546 veterans was identified using deep learning and five traditional machine learning algorithms with 10-fold cross-validation. Model performance was assessed by examining the area under the subject operating characteristic curve (AUC), accuracy, recall, specificity, precision, and F1 score.ResultsDeep learning had the highest AUC (0.891, 95%CI 0.869–0.914) and specificity (0.906) in identifying depression in veterans. Further study on depression among veterans of different ages showed that the AUC values for deep learning were 0.929 (95%CI 0.904–0.955) in the middle-aged group and 0.924(95%CI 0.900-0.948) in the older age group. In addition to general health conditions, sleep difficulties, memory impairment, work incapacity, income, BMI, and chronic diseases, factors such as vitamins E and C, and palmitic acid were also identified as important influencing factors.ConclusionsCompared with traditional machine learning methods, deep learning algorithms achieved optimal performance, making it conducive for identifying depression and its risk factors among veterans.
DEL-Thyroid: deep ensemble learning framework for detection of thyroid cancer progression through genomic mutation
Authors: Asghar Ali Shah, Ali Daud, Amal Bukhari, Bader Alshemaimri, Muhammad Ahsan, Rehmana Younis

Genes, expressed as sequences of nucleotides, are susceptible to mutations, some of which can lead to cancer. Machine learning and deep learning methods have emerged as vital tools in identifying mutations associated with cancer. Thyroid cancer ranks as the 5th most prevalent cancer in the USA, with thousands diagnosed annually. This paper presents an ensemble learning model leveraging deep learning techniques such as Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Bi-directional LSTM (Bi-LSTM) to detect thyroid cancer mutations early. The model is trained on a dataset sourced from asia.ensembl.org and IntOGen.org, consisting of 633 samples with 969 mutations across 41 genes, collected from individuals of various demographics. Feature extraction encompasses techniques including Hahn moments, central moments, raw moments, and various matrix-based methods. Evaluation employs three testing methods: self-consistency test (SCT), independent set test (IST), and 10-fold cross-validation test (10-FCVT). The proposed ensemble learning model demonstrates promising performance, achieving 96% accuracy in the independent set test (IST). Statistical measures such as training accuracy, testing accuracy, recall, sensitivity, specificity, Mathew's Correlation Coefficient (MCC), loss, training accuracy, F1 Score, and Cohen's kappa are utilized for comprehensive evaluation.
Advancing horizons in remote sensing: a comprehensive survey of deep learning models and applications in image classification and beyond
Authors: Sidike Paheding, Ashraf Saleem, Mohammad Faridul Haque Siddiqui, Nathir Rawashdeh, Almabrok Essa, Abel A. Reyes

In recent years, deep learning has significantly reshaped numerous fields and applications, fundamentally altering how we tackle a variety of challenges. Areas such as natural language processing (NLP), computer vision, healthcare, network security, wide-area surveillance, and precision agriculture have leveraged the merits of the deep learning era. Particularly, deep learning has significantly improved the analysis of remote sensing images, with a continuous increase in the number of researchers and contributions to the field. The high impact of deep learning development is complemented by rapid advancements and the availability of data from a variety of sensors, including high-resolution RGB, thermal, LiDAR, and multi-/hyperspectral cameras, as well as emerging sensing platforms such as satellites and aerial vehicles that can be captured by multi-temporal, multi-sensor, and sensing devices with a wider view. This study aims to present an extensive survey that encapsulates widely used deep learning strategies for tackling image classification challenges in remote sensing. It encompasses an exploration of remote sensing imaging platforms, sensor varieties, practical applications, and prospective developments in the field.
Boosting in-transit entertainment: deep reinforcement learning for intelligent multimedia caching in bus networks
Authors: Dan Lan, Incheol Shin

Multimedia content delivery in advanced networks faces exponential growth in data volumes, rendering existing solutions obsolete. This research investigates deep reinforcement learning (DRL) for autonomous optimization without extensive datasets. The work analyzes two prominent DRL algorithms, i.e., Dueling Deep Q-Network (DDQN) and Deep Q-Network (DQN) for multimedia delivery in simulated bus networks. DDQN utilizes a novel “dueling” architecture to estimate state value and action advantages, accelerating learning separately. DQN employs deep neural networks to approximate optimal policies. The environment simulates urban buses with passenger file requests and cache sizes modeled on actual data. Comparative analysis evaluates cumulative rewards and losses over 1500 training episodes to analyze learning efficiency, stability, and performance. Results demonstrate DDQN’s superior convergence and 32% higher cumulative rewards than DQN. However, DQN showed potential for gains over successive runs despite inconsistencies. It establishes DRL’s promise for automated decision-making while revealing enhancements to improve DQN. Further research should evaluate generalizability across problem domains, investigate hybrid models, and test physical systems. DDQN emerged as the most efficient algorithm, highlighting DRL’s potential to enable intelligent agents that optimize multimedia delivery.
Parameter-Free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients
Authors: Baturay Saglam, Furkan Burak Mutlu, Dogan Can Cicek, Suleyman Serdar Kozat

Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial underestimation bias. We first address the detrimental issues in the existing approaches that aim to overcome such underestimation error. Then, through extensive statistical analysis, we introduce a novel, parameter-free Deep Q-learning variant to reduce this underestimation bias in deterministic policy gradients. By sampling the weights of a linear combination of two approximate critics from a highly shrunk estimation bias interval, our Q-value update rule is not affected by the variance of the rewards received by the agents throughout learning. We test the performance of the introduced improvement on a set of MuJoCo and Box2D continuous control tasks and demonstrate that it outperforms the existing approaches and improves the baseline actor-critic algorithm in most of the environments tested.
Advanced deep learning and large language models for suicide ideation detection on social media
Authors: Mohammed Qorich, Rajae El Ouazzani

Recently, suicide ideations represent a worldwide health concern and pose many anticipation challenges. Actually, the prevalence of expressing self-destructive thoughts especially on forums and social media requires effective monitoring for suicide prevention, and early intervention. Meanwhile, deep learning techniques and Large Language Models (LLMs) have emerged as promising tools in diverse Natural Language Processing (NLP) tasks, including sentiment analysis and text classification. In this paper, we propose a deep learning model incorporating triple models of word embeddings, as well as various fine-tuned LLMs, to identify suicidal thoughts in Reddit posts. In effect, we implemented a Bidirectional Long Short-Term Memory (BiLSTM), and a Convolutional Neural Network (CNN) model to categorize posts associated with non-suicidal and suicidal thoughts. Besides, through the combination of Word2Vec, FastText and GloVe embeddings, our models learn intricate patterns and prevalent nuances in suicide-related language. Furthermore, we employed a merged version of CNN and BiLSTM models, entitled C-BiLSTM, and several LLMs, including pre-trained Bidirectional Encoder Representations from Transformers (BERT) models and a Generative Pre-training Transformer (GPT) model. The analysis of all our proposed models shows that our C-BiLSTM model with triple word embedding and our GPT model got the best performance compared to deep learning and LLMs baseline models, reaching accuracies of 94.5% and 97.69%, respectively. In fact, our best model’s capacity to extract meaningful interdependencies among words significantly promotes its classification performance. This analysis contributes to a deeper understanding of the psychological factors and linguistic markers indicative of suicidal thoughts, thereby informing future research and intervention strategies.
Comparative study of machine learning and deep learning techniques for fault diagnosis in suspension system
Authors: P. Arun Balaji, V. Sugumaran

Comfort and stability are the prime reasons to own an automobile (car). Suspension system of an automobile plays a major role in providing comfort, stability and control. Over a period of time, internal components in the suspension system exhibit faults due to fatigue and wear. Hence, it is essential to perform fault diagnosis such that the performance of the suspension components is restored. However, high instrumentation cost, skilled labor requirement and expertise in the particular field of study are certain drawbacks of traditional fault diagnosis techniques. Such challenges have made industrialists and the research communities look for advanced fault diagnosis techniques. Advancements in machine learning and deep learning techniques can be used to fulfill the need of a high degree intelligent fault diagnosis system. In the current study, the performance of machine learning (ML) classifiers are compared with the performance of deep learning (DL) models and the best performing model among them is adopted to detect faults in the automobile suspension system. A total of eight test conditions, namely strut external damage, strut mount failure, ball joint worn out, control arm bush worn out, control arm ball joint worn out, strut worn out, low wheel pressure and good condition, were considered in the study. The vibration measurements were acquired for three load conditions. Among all the techniques considered for classification, the pre-trained VGG16 model outperformed other DL and ML models with an overall classification accuracy of 98.10%.
A Deep Learning Framework for Monitoring Audience Engagement in Online Video Events
Authors: Alexandros Vrochidis, Nikolaos Dimitriou, Stelios Krinidis, Savvas Panagiotidis, Stathis Parcharidis, Dimitrios Tzovaras

This paper introduces a deep learning methodology for analyzing audience engagement in online video events. The proposed deep learning framework consists of six layers and starts with keyframe extraction from the video stream and the participants’ face detection. Subsequently, the head pose and emotion per participant are estimated using the HopeNet and JAA-Net deep architectures. Complementary to video analysis, the audio signal is also processed using a neural network that follows the DenseNet-121 architecture. Its purpose is to detect events related to audience engagement, including speech, pauses, and applause. With the combined analysis of video and audio streams, the interest and attention of each participant are inferred more accurately. An experimental evaluation is performed on a newly generated dataset consisting of recordings from online video events, where the proposed framework achieves promising results. Concretely, the F1 scores were 79.21% for interest estimation according to pose, 65.38% for emotion estimation, and 80% for sound event detection. The proposed framework has applications in online educational events, where it can help tutors assess audience engagement and comprehension while hinting at points in their lectures that may require further clarification. It is effective for video streaming platforms that want to provide video recommendations to online users according to audience engagement.
Automated detection and recognition system for chewable food items using advanced deep learning models
Authors: Yogesh Kumar, Apeksha Koul, Kamini, Marcin Woźniak, Jana Shafi, Muhammad Fazal Ijaz

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.
A deep learning model for brain segmentation across pediatric and adult populations
Authors: Jaime Simarro, Maria Ines Meyer, Simon Van Eyndhoven, Thanh Vân Phan, Thibo Billiet, Diana M. Sima, Els Ortibus

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across various life stages. However, existing solutions are specifically designed for certain age ranges, limiting their applicability in monitoring brain development from infancy to late adulthood. This retrospective study aims to develop and validate a brain segmentation model across pediatric and adult populations. First, we trained a deep learning model to segment tissues and brain structures using T1-weighted MR images from 390 patients (age range: 2–81 years) across four different datasets. Subsequently, the model was validated on a cohort of 280 patients from six distinct test datasets (age range: 4–90 years). In the initial experiment, the proposed deep learning-based pipeline, icobrain-dl, demonstrated segmentation accuracy comparable to both pediatric and adult-specific models across diverse age groups. Subsequently, we evaluated intra- and inter-scanner variability in measurements of various tissues and structures in both pediatric and adult populations computed by icobrain-dl. Results demonstrated significantly higher reproducibility compared to similar brain quantification tools, including childmetrix, FastSurfer, and the medical device icobrain v5.9 (p-value< 0.01). Finally, we explored the potential clinical applications of icobrain-dl measurements in diagnosing pediatric patients with Cerebral Visual Impairment and adult patients with Alzheimer’s Disease.
An experimental evaluation of deep reinforcement learning algorithms for HVAC control
Authors: Antonio Manjavacas, Alejandro Campoy-Nieves, Javier Jiménez-Raboso, Miguel Molina-Solana, Juan Gómez-Romero

Heating, ventilation, and air conditioning (HVAC) systems are a major driver of energy consumption in commercial and residential buildings. Recent studies have shown that Deep Reinforcement Learning (DRL) algorithms can outperform traditional reactive controllers. However, DRL-based solutions are generally designed for ad hoc setups and lack standardization for comparison. To fill this gap, this paper provides a critical and reproducible evaluation, in terms of comfort and energy consumption, of several state-of-the-art DRL algorithms for HVAC control. The study examines the controllers’ robustness, adaptability, and trade-off between optimization goals by using the Sinergym framework. The results obtained confirm the potential of DRL algorithms, such as SAC and TD3, in complex scenarios and reveal several challenges related to generalization and incremental learning.
An Outlook for Deep Learning in Ecosystem Science
Authors: George L. W. Perry, Rupert Seidl, André M. Bellvé, Werner Rammer

Rapid advances in hardware and software, accompanied by public- and private-sector investment, have led to a new generation of data-driven computational tools. Recently, there has been a particular focus on deep learning—a class of machine learning algorithms that uses deep neural networks to identify patterns in large and heterogeneous datasets. These developments have been accompanied by both hype and scepticism by ecologists and others. This review describes the context in which deep learning methods have emerged, the deep learning methods most relevant to ecosystem ecologists, and some of the problem domains they have been applied to. Deep learning methods have high predictive performance in a range of ecological contexts, leveraging the large data resources now available. Furthermore, deep learning tools offer ecosystem ecologists new ways to learn about ecosystem dynamics. In particular, recent advances in interpretable machine learning and in developing hybrid approaches combining deep learning and mechanistic models provide a bridge between pure prediction and causal explanation. We conclude by looking at the opportunities that deep learning tools offer ecosystem ecologists and assess the challenges in interpretability that deep learning applications pose.
Deep learning models for webcam eye tracking in online experiments
Authors: Shreshth Saxena, Lauren K. Fink, Elke B. Lange

Eye tracking is prevalent in scientific and commercial applications. Recent computer vision and deep learning methods enable eye tracking with off-the-shelf webcams and reduce dependence on expensive, restrictive hardware. However, such deep learning methods have not yet been applied and evaluated for remote, online psychological experiments. In this study, we tackle critical challenges faced in remote eye tracking setups and systematically evaluate appearance-based deep learning methods of gaze tracking and blink detection. From their own homes and laptops, 65 participants performed a battery of eye tracking tasks including (i) fixation, (ii) zone classification, (iii) free viewing, (iv) smooth pursuit, and (v) blink detection. Webcam recordings of the participants performing these tasks were processed offline through appearance-based models of gaze and blink detection. The task battery required different eye movements that characterized gaze and blink prediction accuracy over a comprehensive list of measures. We find the best gaze accuracy to be 2.4° and precision of 0.47°, which outperforms previous online eye tracking studies and reduces the gap between laboratory-based and online eye tracking performance. We release the experiment template, recorded data, and analysis code with the motivation to escalate affordable, accessible, and scalable eye tracking that has the potential to accelerate research in the fields of psychological science, cognitive neuroscience, user experience design, and human–computer interfaces.
Assessment of indoor risk through deep learning -based object recognition in disaster situations
Authors: Irshad Khan, Ziyi Guo, Kihwan Lim, Jaeseon Kim, Young-Woo Kwon

Disasters can devastate individuals and their properties, highlighting the importance of risk assessment to promote safety. Recently, deep learning techniques have shown the potential in identifying hazardous situations during disasters. Recognizing potentially dangerous objects in indoor environments can be essential for assisting individuals in responding appropriately to emergencies. In this article, we present an indoor-risk analysis framework for disasters based on deep learning. Our framework utilizes modern deep learning techniques to calculate an indoor risk rating based on dangerous objects’ sizes, enabling comprehensive risk assessment of indoor environments during disasters. To that end, we use (Mask R-CNN) to identify hazardous indoor objects in disaster situations with 94% accuracy. By incorporating object size information, our framework offers a more nuanced and detailed risk assessment than previous approaches. Our proposed system provides a valuable tool for promoting ongoing safety improvement and enhancing indoor safety during natural disasters.
A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy
Authors: Vaibhav Sharma, Artur Yakimovich

High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefacts could be addressed using modern-day machine learning and deep learning algorithms, this is widely impeded by the lack of suitable datasets. To address this, here we present a purpose-created open dataset of high-content microscopy sample preparation artefact. It consists of high-content microscopy of laboratory dust titrated on fixed cell culture specimens imaged with fluorescence filters covering the complete spectral range. To ensure this dataset is suitable for supervised machine learning tasks like image classification or segmentation we propose rule-based annotation strategies on categorical and pixel levels. We demonstrate the applicability of our dataset for deep learning by training a convolutional-neural-network-based classifier.