ADKU/ResearchGPT_model_dataset huggingface dataset:
Effectiveness evaluation of sprint sports techniques and tactics based on deep learning
Authors: Jiankui Yan

The assessment of sprint-like sports techniques and tactics involves evaluating their underlying running principles, structures, functions, methods, and real-world applications using existing technology. Particularly in the context of the fast-evolving sprint domain, a deficiency in evaluation expertise can result in lagging behind. Using deep learning algorithms, useful features, and information can be automatically extracted from extensive training and competition data, providing a more accurate and objective basis for evaluating sprint sports techniques and tactics. We can reduce manual intervention and tedious workloads by applying deep learning algorithms in automated processing and analysis, enabling intelligent training and competition. Deep learning algorithms can uncover and analyze patterns and trends in data, assisting coaches and athletes in identifying potential problems and areas for improvement and providing a foundation for athletes to develop targeted training plans and competition strategies. Therefore, studying the role of Deep Learning (DL) in the technological process is of great significance. This paper aims to effectively combine DL algorithms with sprint skill evaluation, utilizing DL algorithms for mining and leveraging them to achieve optimal sprint skill performance through effective tactical evaluation. This study trains a new model based on the classic VGGNet-16 model and compares different CNN models. The experiment reveals that the CaffeNet model is more susceptible to objects that do not entirely contain the target, resulting in an enlarging search box and eventually losing track of the target. The experimental results demonstrate that maintaining a step frequency of approximately 95% is crucial for achieving the highest speed. The SDP algorithm was tested on the VOC2007 dataset and achieved good results, with a mean average precision value of 69.4%, significantly surpassing the benchmark Fast R-CNN algorithm. Additionally, we have implemented a traditional tracking algorithm, the CamShift algorithm, based on color features.
Image super-resolution reconstruction based on deep dictionary learning and A+
Authors: Yi Huang, Weixin Bian, Biao Jie, Zhiqiang Zhu, Wenhu Li

The method of image super-resolution reconstruction through the dictionary usually only uses a single-layer dictionary, which not only cannot extract the deep features of the image but also requires a large trained dictionary if the reconstruction effect is to be better. This paper proposes a new deep dictionary learning model. Firstly, after preprocessing the images of the training set, the dictionary is trained by the deep dictionary learning method, and the adjusted anchored neighborhood regression method is used for image super-resolution reconstruction. The proposed algorithm is compared with several classical algorithms on Set5 dataset and Set14 dataset. The visualization and quantification results show that the proposed method improves PSNR and SSIM, effectively reduces the dictionary size and saves reconstruction time compared with traditional super-resolution algorithms.
Efficient deep learning-based approach for malaria detection using red blood cell smears
Authors: Muhammad Mujahid, Furqan Rustam, Rahman Shafique, Elizabeth Caro Montero, Eduardo Silva Alvarado, Isabel de la Torre Diez, Imran Ashraf

Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting, and muscle discomfort; severe symptoms include coma, seizures, and kidney failure. The timely identification of malaria parasites is a challenging and chaotic endeavor for health staff. An expert technician examines the schematic blood smears of infected red blood cells through a microscope. The conventional methods for identifying malaria are not efficient. Machine learning approaches are effective for simple classification challenges but not for complex tasks. Furthermore, machine learning involves rigorous feature engineering to train the model and detect patterns in the features. On the other hand, deep learning works well with complex tasks and automatically extracts low and high-level features from the images to detect disease. In this paper, EfficientNet, a deep learning-based approach for detecting Malaria, is proposed that uses red blood cell images. Experiments are carried out and performance comparison is made with pre-trained deep learning models. In addition, k-fold cross-validation is also used to substantiate the results of the proposed approach. Experiments show that the proposed approach is 97.57% accurate in detecting Malaria from red blood cell images and can be beneficial practically for medical healthcare staff.
Optical electrocardiogram based heart disease prediction using hybrid deep learning
Authors: Avinash L. Golande, T. Pavankumar

The diagnosis and categorization of cardiac disease using the low-cost tool electrocardiogram (ECG) becomes an intriguing study topic when contemplating intelligent healthcare applications. An ECG-based cardiac disease prediction system must be automated, accurate, and lightweight. The deep learning methods recently achieved automation and accuracy across multiple domains. However, applying deep learning for automatic ECG-based heart disease classification is a challenging research problem. Because using solely deep learning approaches failed to detect all of the important beats from the input ECG signal, a hybrid strategy is necessary to improve detection efficiency. The main objective of the proposed model is to enhance the ECG-based heart disease classification efficiency using a hybrid feature engineering approach. The proposed model consists of pre-processing, hybrid feature engineering, and classification. Pre-processing an ECG aims to eliminate powerline and baseline interference without disrupting the heartbeat. To efficiently classify data, we design a hybrid approach using a conventional ECG beats extraction algorithm and Convolutional Neural Network (CNN)-based features. For heart disease prediction, the hybrid feature vector is fed successively into the deep learning classifier Long Term Short Memory (LSTM). The results of the simulations show that the proposed model reduces both the number of diagnostic errors and the amount of time spent on each one when compared to the existing methods.
Deep learning-based automated assessment of canine hip dysplasia
Authors: Cátia Loureiro, Lio Gonçalves, Pedro Leite, Pedro Franco-Gonçalo, Ana Inês Pereira, Bruno Colaço, Sofia Alves-Pimenta, Fintan McEvoy, Mário Ginja, Vítor Filipe

Radiographic canine hip dysplasia (CHD) diagnosis is crucial for breeding selection and disease management, delaying progression and alleviating the associated pain. Radiography is the primary imaging modality for CHD diagnosis, and visual assessment of radiographic features is sometimes used for accurate diagnosis. Specifically, alterations in femoral neck shape are crucial radiographic signs, with existing literature suggesting that dysplastic hips have a greater femoral neck thickness (FNT). In this study we aimed to develop a three-stage deep learning-based system that can automatically identify and quantify a femoral neck thickness index (FNTi) as a key metric to improve CHD diagnosis. Our system trained a keypoint detection model and a segmentation model to determine landmark and boundary coordinates of the femur and acetabulum, respectively. We then executed a series of mathematical operations to calculate the FNTi. The keypoint detection model achieved a mean absolute error (MAE) of 0.013 during training, while the femur segmentation results achieved a dice score (DS) of 0.978. Our three-stage deep learning-based system achieved an intraclass correlation coefficient of 0.86 (95% confidence interval) and showed no significant differences in paired t-test compared to a specialist (p > 0.05). As far as we know, this is the initial study to thoroughly measure FNTi by applying computer vision and deep learning-based approaches, which can provide reliable support in CHD diagnosis.
An Explainable Deep Learning Approach for Oral Cancer Detection
Authors: P. Ashok Babu, Anjani Kumar Rai, Janjhyam Venkata Naga Ramesh, A. Nithyasri, S. Sangeetha, Pravin R. Kshirsagar, A. Rajendran, A. Rajaram, S. Dilipkumar

With a high death rate, oral cancer is a major worldwide health problem, particularly in low- and middle-income nations. Timely detection and diagnosis are crucial for effective prevention and treatment. To address this challenge, there is a growing need for automated detection systems to aid healthcare professionals. Regular dental examinations play a vital role in early detection. Transfer learning, which leverages knowledge from related domains, can enhance performance in target categories. This study presents a unique approach to the early detection and diagnosis of oral cancer that makes use of the exceptional sensory capabilities of the mouth. Deep neural networks, particularly those based on automated systems, are employed to identify intricate patterns associated with the disease. By combining various transfer learning approaches and conducting comparative analyses, an optimal learning rate is achieved. The categorization analysis of the reference results is presented in detail. Our preliminary findings demonstrate that deep learning effectively addresses this challenging problem, with the Inception-V3 algorithm exhibiting superior accuracy compared to other algorithms.
Semantic speech analysis using machine learning and deep learning techniques: a comprehensive review
Authors: Suryakant Tyagi, Sándor Szénási

Human cognitive functions such as perception, attention, learning, memory, reasoning, and problem-solving are all significantly influenced by emotion. Emotion has a particularly potent impact on attention, modifying its selectivity in particular and influencing behavior and action motivation. Artificial Emotional Intelligence (AEI) technologies enable computers to understand a user's emotional state and respond appropriately. These systems enable a realistic dialogue between people and machines. The current generation of adaptive user interference technologies is built on techniques from data analytics and machine learning (ML), namely deep learning (DL) artificial neural networks (ANN) from multimodal data, such as videos of facial expressions, stance, and gesture, voice, and bio-physiological data (such as eye movement, ECG, respiration, EEG, FMRT, EMG, eye tracking). In this study, we reviewed existing literature based on ML and data analytics techniques being used to detect emotions in speech. The efficacy of data analytics and ML techniques in this unique area of multimodal data processing and extracting emotions from speech. This study analyzes how emotional chatbots, facial expressions, images, and social media texts can be effective in detecting emotions. PRISMA methodology is used to review the existing survey. Support Vector Machines (SVM), Naïve Bayes (NB), Random Forests (RF), Recurrent Neural Networks (RNN), Logistic Regression (LR), etc., are commonly used ML techniques for emotion extraction purposes. This study provides a new taxonomy about the application of ML in SER. The result shows that Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) are found to be the most useful methodology for this purpose.
Deep Learning Based Video Compression Techniques with Future Research Issues
Authors: Helen K. Joy, Manjunath R. Kounte, Arunkumar Chandrasekhar, Manoranjan Paul

The advancements in the domain of video coding technologies are tremendously fluctuating in recent years. As the public got acquainted with the creation and availability of videos through internet boom and video acquisition devices including mobile phones, camera etc., the necessity of video compression become crucial. The resolution variance (4 K, 2 K etc.), framerate, display is some of the features that glorifies the importance of compression. Improving compression ratio with better efficiency and quality was the focus and it has many stumbling blocks to achieve it. The era of artificial intelligence, neural network, and especially deep learning provided light in the path of video processing area, particularly in compression. The paper mainly focuses on a precise, organized, meticulous review of the impact of deep learning on video compression. The content adaptivity quality of deep learning marks its importance in video compression to traditional signal processing. The development of intelligent and self-trained steps in video compression with deep learning is reviewed in detail. The relevant and noteworthy work that arose in each step of compression is inculcated in this paper. A detailed survey in the development of intra- prediction, inter-prediction, in-loop filtering, quantization, and entropy coding in hand with deep learning techniques are pointed along with envisages ideas in each field. The future scope of enhancement in various stages of compression and relevant research scope to explore with Deep Learning is emphasized.
Dermatological disease prediction and diagnosis system using deep learning
Authors: Neda Fatima, Syed Afzal Murtaza Rizvi, Major Syed Bilal Abbas Rizvi

The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. However, the cost of such a diagnosis is currently limited and prohibitively high and restricted to developed areas. The present paper develops a holistic, critical, and important skin disease prediction system that utilizes machine learning and deep learning algorithms to accurately identify up to 20 different skin diseases with a high F1 score and efficiency. Deep learning algorithms like Xception, Inception-v3, Resnet50, DenseNet121, and Inception-ResNet-v2 were employed to accurately classify diseases based on the images. The training and testing have been performed on an enlarged dataset, and classification was performed for 20 diseases. The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision.
Deep SqueezeNet learning model for diagnosis and prediction of maize leaf diseases
Authors: Prasannavenkatesan Theerthagiri, A. Usha Ruby, J. George Chellin Chandran, Tanvir Habib Sardar, Ahamed Shafeeq B. M.

The maize leaf diseases create severe yield reductions and critical problems. The maize leaf disease should be discovered early, perfectly identified, and precisely diagnosed to make greater yield. This work studies three main leaf diseases: common rust, blight, and grey leaf spot. This approach involves pre-processing, including sampling and labelling, while ensuring class balance and preventing overfitting via the SMOTE algorithm. The maize leaf dataset with augmentation was used to classify these diseases using several deep-learning pre-trained networks, including VGG16, Resnet34, Resnet50, and SqueezeNet. The model was evaluated using a maize leaf dataset that included various leaf classes, mini-batch sizes, and input sizes. Performance measures, recall, precision, accuracy, F1-score, and confusion matrix were computed for each network. The SqueezeNet learning model produces an accuracy of 97% in classifying four different classes of plant leaf datasets. Comparatively, the SqueezeNet learning model has improved accuracy by 2–5% and reduced the mean square error by 4–11% over VGG16, Resnet34, and Resnet50 deep learning models.
Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides
Authors: Nicolás Lefin, Lisandra Herrera-Belén, Jorge G. Farias, Jorge F. Beltrán

Viruses constitute a constant threat to global health and have caused millions of human and animal deaths throughout human history. Despite advances in the discovery of antiviral compounds that help fight these pathogens, finding a solution to this problem continues to be a task that consumes time and financial resources. Currently, artificial intelligence (AI) has revolutionized many areas of the biological sciences, making it possible to decipher patterns in amino acid sequences that encode different functions and activities. Within the field of AI, machine learning, and deep learning algorithms have been used to discover antimicrobial peptides. Due to their effectiveness and specificity, antimicrobial peptides (AMPs) hold excellent promise for treating various infections caused by pathogens. Antiviral peptides (AVPs) are a specific type of AMPs that have activity against certain viruses. Unlike the research focused on the development of tools and methods for the prediction of antimicrobial peptides, those related to the prediction of AVPs are still scarce. Given the significance of AVPs as potential pharmaceutical options for human and animal health and the ongoing AI revolution, we have reviewed and summarized the current machine learning and deep learning-based tools and methods available for predicting these types of peptides.
Deep learning-based fishing ground prediction with multiple environmental factors
Authors: Mingyang Xie, Bin Liu, Xinjun Chen

Improving the accuracy of fishing ground prediction for oceanic economic species has always been one of the most concerning issues in fisheries research. Recent studies have confirmed that deep learning has achieved superior results over traditional methods in the era of big data. However, the deep learning-based fishing ground prediction model with a single environment suffers from the problem that the area of the fishing ground is too large and not concentrated. In this study, we developed a deep learning-based fishing ground prediction model with multiple environmental factors using neon flying squid (Ommastrephes bartramii) in Northwest Pacific Ocean as an example. Based on the modified U-Net model, the approach involves the sea surface temperature, sea surface height, sea surface salinity, and chlorophyll a as inputs, and the center fishing ground as the output. The model is trained with data from July to November in 2002–2019, and tested with data of 2020. We considered and compared five temporal scales (3, 6, 10, 15, and 30 days) and seven multiple environmental factor combinations. By comparing different cases, we found that the optimal temporal scale is 30 days, and the optimal multiple environmental factor combination contained SST and Chl a. The inclusion of multiple factors in the model greatly improved the concentration of the center fishing ground. The selection of a suitable combination of multiple environmental factors is beneficial to the precise spatial distribution of fishing grounds. This study deepens the understanding of the mechanism of environmental field influence on fishing grounds from the perspective of artificial intelligence and fishery science.
DEEPBIN: Deep Learning Based Garbage Classification for Households Using Sustainable Natural Technologies
Authors: Yu Song, Xin He, Xiwang Tang, Bo Yin, Jie Du, Jiali Liu, Zhongbao Zhao, Shigang Geng

Today, things that are accessible worldwide are upgrading to innovative technology. In this research, an intelligent garbage system will be designed with State-of-the-art methods using deep learning technologies. Garbage is highly produced due to urbanization and the rising population in urban areas. It is essential to manage daily trash from homes and living environments. This research aims to provide an intelligent IoT-based garbage bin system, and classification is done using Deep learning techniques. This smart bin is capable of sensing more varieties of garbage from home. Though there are more technologies successfully implemented with IoT and machine learning, there is still a need for sustainable natural technologies to manage daily waste. The innovative IoT-based garbage system uses various sensors like humidity, temperature, gas, and liquid sensors to identify the garbage condition. Initially, the Smart Garbage Bin system is designed, and then the data are collected using a garbage annotation application. Next, the deep learning method is used for object detection and classification of garbage images. Arithmetic Optimization Algorithm (AOA) with Improved RefineDet (IRD) is used for object detection. Next, the EfficientNet-B0 model is used for the classification of garbage images. The garbage content is identified, and the content is prepared to train the deep learning model to perform efficient classification tasks. For result evaluation, smart bins are deployed in real-time, and accuracy is estimated. Furthermore, fine-tuning region-specific litter photos led to enhanced categorization.
Epileptic Seizures Detection Using iEEG Signals and Deep Learning Models
Authors: Nourane Abderrahim, Amira Echtioui, Rafik Khemakhem, Wassim Zouch, Mohamed Ghorbel, Ahmed Ben Hamida

Epilepsy is a common neurological disorder that affects millions of people worldwide, and many patients do not respond well to traditional anti-epileptic drugs. To improve the lives of these patients, there is a need to develop accurate methods for predicting epileptic seizures. Seizure prediction involves classifying preictal and interictal states, which is a challenging classification problem. Deep learning techniques, such as convolutional neural networks (CNNs), have shown great promise in analyzing and classifying EEG signals related to epilepsy. In this study, we proposed four deep learning models (S-CNN, Modif-CNN, CNN-SVM, and Comb-2CNN) to classify epilepsy states, which we evaluated on an iEEG dataset from the American Epilepsy Society database. Our models achieved high accuracy rates, with the S-CNN and Comb-2CNN models achieving 96.53%, CNN-SVM achieving 96.99%, and the Modif-CNN model achieving 97.96% in our experiments. These findings suggest that deep learning models could be an effective approach for classifying epilepsy states and could potentially improve seizure prediction methods, ultimately enhancing the quality of life for people with epilepsy.
Real-time deep learning-based model predictive control of a 3-DOF biped robot leg
Authors: Haitham El-Hussieny

Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.