ADKU/ResearchGPT_model_dataset huggingface dataset:
Deep learning based multiclass classification for citrus anomaly detection in agriculture
Authors: Ebru Ergün

In regions where citrus crops are threatened by diseases caused by fungi, bacteria, pests and viruses, growers are actively seeking automated technologies that can accurately detect citrus anomalies to minimize economic losses. Recent advances in deep learning techniques have shown potential in automating and improving the accuracy of citrus anomaly categorization. This research explores the use of deep learning methods, specifically DenseNet, to construct robust models capable of accurately distinguishing between different types of citrus anomalies. The dataset consists of high-resolution images of different orange leaves of the species Citrus sinensis osbeck, collected from orange groves in the states of Tamaulipas and San Luis Potosi in northeastern Mexico was used in study. Experimental results demonstrated the effectiveness of the proposed deep learning models in simultaneously identifying 12 different classes of citrus anomalies. Evaluation metrics, including accuracy, recall, precision and the confusion matrix, underscore the discriminative power of the models. Among the convolutional neural network architectures used, DenseNet achieved the highest classification accuracy at 99.50%. The study concluded by highlighting the potential for scalable and effective citrus anomaly classification and management using deep learning-based systems.
Ensemble of deep learning and machine learning approach for classification of handwritten Hindi numerals
Authors: Danveer Rajpal, Akhil Ranjan Garg

Given the vast range of factors, including shape, size, skew, and orientation of handwritten numerals, their machine-based recognition is a difficult challenge for researchers in the pattern recognition field. Due to the abundance of curves and resembling shapes of the symbols, the recognition of Devnagari numerals can leverage the difficulty level of the recognition. The suggested low-classification-cost method for obtaining fine features from given numeral images used benchmark deep learning models, VGG-16Net, VGG-19Net, ResNet-50, and Inception-v3, to address these issues. Principal component analysis, a powerful dimensionality reduction method, was used to efficiently reduce the number of dimensions in the information that pre-trained deep convolutional neural network models provided. The method for improving recognition accuracy by fusing features was provided in the scheme. A machine learning algorithm: support vector machine was employed for the recognition task due to its capacity to distinguish between patterns belonging to distinct classes. The system was able to obtain a recognition accuracy of 99.72% and was effective in demonstrating the importance of ensemble machine learning and deep learning approaches.
Strengthening KMS Security with Advanced Cryptography, Machine Learning, Deep Learning, and IoT Technologies
Authors: Justin Onyarin Ogala, Shahnawaz Ahmad, Iman Shakeel, Javed Ahmad, Shabana Mehfuz

This paper presents an innovative approach to strengthening Key Management Systems (KMS) against the escalating landscape of cyber threats by integrating advanced cryptographic technologies, machine learning, deep learning, and the Internet of Things (IoT). As digital reliance and cyber-attacks surge, strengthening KMS security becomes paramount. Our research provides a comprehensive overview of the state-of-the-art in cloud data security, identifying key vulnerabilities in existing KMS. The paper also outlines a distinctive framework based on the combined application of advanced cryptography, machine learning, deep learning, and IoT, which represents a novel approach in the quest for robust KMS security. Our experimental results substantiate the efficacy of this unique blend of technologies, providing solid empirical evidence that such a fusion can successfully strengthen KMS against potential threats. As technologies and threat landscapes continue to evolve, our framework can serve as a benchmark for future research and practical implementations. It highlights the potential of integrated technological solutions to counter complex cybersecurity issues. Moreover, the approach we've developed can be adapted and expanded to cater to the specific needs of different sectors, such as finance, healthcare, and e-commerce, which are particularly vulnerable to cyber threats. The novelty of our work lies in the amalgamation of the four technologies and the creation of an empirically backed, robust framework, marking a significant stride in KMS security.
Skin cancer detection using ensemble of machine learning and deep learning techniques
Authors: Jitendra V. Tembhurne, Nachiketa Hebbar, Hemprasad Y. Patil, Tausif Diwan

Skin cancer is one of the most common forms of cancer, which makes it pertinent to be able to diagnose it accurately. In particular, melanoma is a form of skin cancer that is fatal and accounts for 6 of every 7-skin cancer related death. Moreover, in hospitals where dermatologists have to diagnose multiple cases of skin cancer, there are high possibilities of false negatives in diagnosis. To avoid such incidents, there here has been exhaustive research conducted by the research community all over the world to build highly accurate automated tools for skin cancer detection. In this paper, we introduce a novel approach of combining machine learning and deep learning techniques to solve the problem of skin cancer detection. The deep learning model uses state-of-the-art neural networks to extract features from images whereas the machine learning model processes image features which are obtained after performing the techniques such as Contourlet Transform and Local Binary Pattern Histogram. Meaningful feature extraction is crucial for any image classification roblem. As a result, by combining the manual and automated features, our designed model achieves a higher accuracy of 93% with an individual recall score of 99.7% and 86% for the benign and malignant forms of cancer, respectively. We benchmarked the model on publicly available Kaggle dataset containing processed images from ISIC Archive dataset. The proposed ensemble outperforms both expert dermatologists as well as other state-of-the-art deep learning and machine learning methods. Thus, this novel method can be of high assistance to dermatologists to help prevent any misdiagnosis.
Deep transfer learning for tool condition monitoring under different processing conditions
Authors: Yongqing Wang, Mengmeng Niu, Kuo Liu, Haibo Liu, Bo Qin, Yiming Cui

Deep learning methods have developed rapidly in the field of tool condition monitoring, but due to the complexity and diversity of working conditions, it is difficult to ensure the high accuracy, strong generalization performance, and wide applicability of monitoring models. Therefore, this article proposes a deep transfer learning method with center loss for tool condition monitoring under different processing conditions. Firstly, Deep Extreme Learning Machine (DELM) is used to extract sample features and tool condition monitoring, and deep coral is integrated into the last feature extraction layer of DELM for domain adaptation. Secondly, the center loss is introduced into the transfer learning model to improve the intra-class compactness by minimizing the center loss, thereby obtaining a broader decision boundary. A tool wear experiment was conducted on a milling machine. Research shows that the proposed method can achieve the tool condition monitoring under different processing conditions. The introduction of central loss is beneficial for promoting the separation of samples from different categories in the target domain and effectively improves the model’s applicability. Compared with other domain adaptation methods, this method has better accuracy and generalization ability.
Breast Mammograms Diagnosis Using Deep Learning: State of Art Tutorial Review
Authors: Osama Bin Naeem, Yasir Saleem, M. Usman Ghani Khan, Amjad Rehman Khan, Tanzila Saba, Saeed Ali Bahaj, Noor Ayesha

Usually, screening (mostly mammography) is used by radiologists to manually detect breast cancer. The likelihood of identifying suspected cases as false positives or false negatives is significant, contingent on the experience of the radiologist and the kind of imaging screening device/method utilized. The confirmation of the type of tumour seen by the radiologist is sent for histological investigation (microscopic analysis) through a biopsy, where the tumor's grade and stage, which are used in the latter stages of treatment, are ascertained through biopsy. However, a secondary issue with the cancer detection process is that only 15 to 30% of instances that are referred for biopsy result in malignant findings. Since deep learning demonstrated remarkable performance in visual recognition challenges, it has been widely applied to a variety of tasks. Similar examples include deep learning applications in healthcare, which are gaining a lot of interest from the research community. Deep learning is used to identify, categories tumours, and breast cancer is a significant global health concern. The medical sciences could now make more accurate diagnoses and detections due to recent advancements in machine learning techniques. Hence due to systems potential accuracy, it could offer optimistic outcomes when used to read malignant images. In imaging domains, deep learning-based methods have achieved remarkable success in constituent segmentation (UNet), localization (DenseNet), and classification (VGG-19). This study examines, how deep learning methods are assisting in the highly accurate diagnosis of benign or malignant tumours based on screened images. In contrast to a mammogram, which is covered in detail, this paper briefly discusses imaging methods for cancer detection. Early detection and cost effectiveness are two main benefits of applying machine learning and deep learning techniques to mammograms.
A Survey on ensemble learning under the era of deep learning
Authors: Yongquan Yang, Haijun Lv, Ning Chen

Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning based on deep neural networks (ensemble deep learning) has shown significant performances in improving the generalization of learning system. However, since modern deep neural networks usually have millions to billions of parameters, the time and space overheads for training multiple base deep learners and testing with the ensemble deep learner are far greater than that of traditional ensemble learning. Though several algorithms of fast ensemble deep learning have been proposed to promote the deployment of ensemble deep learning in some applications, further advances still need to be made for many applications in specific fields, where the developing time and computing resources are usually restricted or the data to be processed is of large dimensionality. An urgent problem needs to be solved is how to take the significant advantages of ensemble deep learning while reduce the required expenses so that many more applications in specific fields can benefit from it. For the alleviation of this problem, it is essential to know about how ensemble learning has developed under the era of deep learning. Thus, in this article, we present discussions focusing on data analyses of published works, methodologies, recent advances and unattainability of traditional ensemble learning and ensemble deep learning. We hope this article will be helpful to realize the intrinsic problems and technical challenges faced by future developments of ensemble learning under the era of deep learning.
Realistic fault detection of li-ion battery via dynamical deep learning
Authors: Jingzhao Zhang, Yanan Wang, Benben Jiang, Haowei He, Shaobo Huang, Chen Wang, Yang Zhang, Xuebing Han, Dongxu Guo, Guannan He, Minggao Ouyang

Accurate evaluation of Li-ion battery (LiB) safety conditions can reduce unexpected cell failures, facilitate battery deployment, and promote low-carbon economies. Despite the recent progress in artificial intelligence, anomaly detection methods are not customized for or validated in realistic battery settings due to the complex failure mechanisms and the lack of real-world testing frameworks with large-scale datasets. Here, we develop a realistic deep-learning framework for electric vehicle (EV) LiB anomaly detection. It features a dynamical autoencoder tailored for dynamical systems and configured by social and financial factors. We test our detection algorithm on released datasets comprising over 690,000 LiB charging snippets from 347 EVs. Our model overcomes the limitations of state-of-the-art fault detection models, including deep learning ones. Moreover, it reduces the expected direct EV battery fault and inspection costs. Our work highlights the potential of deep learning in improving LiB safety and the significance of social and financial information in designing deep learning models.
Optimization of Fed-Batch Baker’s Yeast Fermentation Using Deep Reinforcement Learning
Authors: Wan Ying Chai, Min Keng Tan, Kenneth Tze Kin Teo, Heng Jin Tham

Fermentation is widely used in chemical industries to produce valuable products. It consumes less energy and has a lesser environmental impact compared to conventional chemical processes. However, the inherent nonlinearity of the fermentation process and limited comprehension of its metabolic mechanisms present challenges for control and optimization, particularly in minimizing the formation of by-products. Reinforcement learning is a machine learning method where an agent learns through exploration and experience. By receiving feedback in the form of rewards, it computes an optimal policy which produces maximum cumulative reward. The integration of deep learning with reinforcement learning has further improved the efficiency of classical reinforcement learning, particularly in continuous control. This paper focuses on optimizing substrate feeding rate in a simulated fed-batch baker’s yeast fermentation using deep reinforcement learning. Artificial neural network (ANN) was applied as the function approximator to estimate the state-action function for determining the substrate feeding rate within a large state space, which includes substrate concentration, yeast concentration, and the change in ethanol concentration. The deep reinforcement learning algorithm was formulated based on the optimization objective of maximizing yeast production while minimizing ethanol formation. The performance of the feeding strategy proposed using deep reinforcement learning was compared to a commonly used pre-determined exponential feeding profile. The results show that the proposed feeding strategy outperformed the exponential feeding strategy with the yeast yield increased by 25.66% with negligible ethanol production. In addition, the proposed algorithm exhibits effective handling of various initial conditions compared to the exponential feeding approach.
An integrated deep-learning model for smart waste classification
Authors: Shivendu Mishra, Ritika Yaduvanshi, Prince Rajpoot, Sharad Verma, Amit Kumar Pandey, Digvijay Pandey

Efficient waste management is essential for human well-being and environmental health, as neglecting proper disposal practices can lead to financial losses and the depletion of natural resources. Given the rapid urbanization and population growth, developing an automated, innovative waste classification model becomes imperative. To address this need, our paper introduces a novel and robust solution — a smart waste classification model that leverages a hybrid deep learning model (Optimized DenseNet-121 + SVM) to categorize waste items using the TrashNet datasets. Our proposed approach uses the advanced deep learning model DenseNet-121, optimized for superior performance, to extract meaningful features from an expanded TrashNet dataset. These features are subsequently fed into a support vector machine (SVM) for precise classification. Employing data augmentation techniques further enhances classification accuracy while mitigating the risk of overfitting, especially when working with limited TrashNet data. The results of our experimental evaluation of this hybrid deep learning model are highly promising, with an impressive accuracy rate of 99.84%. This accuracy surpasses similar existing models, affirming the efficacy and potential of our approach to revolutionizing waste classification for a sustainable and cleaner future.
Prediction of fiber Rayleigh scattering responses based on deep learning
Authors: Yongxin Liang, Jianhui Sun, Jialei Zhang, Yuyao Wang, Anchi Wan, Shibo Zhang, Zhenyu Ye, Shengtao Lin, Zinan Wang

Distributed acoustic sensing (DAS) is a fiber sensing technology based on Rayleigh scattering, which transforms optical fiber into a series of sensing units. It has become an indispensable part in the field of seismic monitoring, vehicle tracking, and pipeline monitoring. Fiber Rayleigh scattering responses lay at the core of DAS. However, there are few in-depth studies on the purpose of acquiring fiber Rayleigh scattering responses. In this paper, we establish a deep learning framework based on the bidirectional gated recurrent unit, which is the first time to predict the fiber Rayleigh scattering responses, to the best of our knowledge. The deep learning framework is trained with a numerical simulation dataset only, but it can process experimental data successfully. Moreover, since the responses could have a wider effective bandwidth than the experimental probing pulses, a finer spatial resolution could be obtained after demodulation. This work indicates that the deep learning framework can capture the characteristics of the fiber Rayleigh scattering responses effectively, which paves the way for intelligent DAS.
Machine learning, deep learning and hernia surgery. Are we pushing the limits of abdominal core health? A qualitative systematic review
Authors: D. L. Lima, J. Kasakewitch, D. Q. Nguyen, R. Nogueira, L. T. Cavazzola, B. T. Heniford, F. Malcher

IntroductionThis systematic review aims to evaluate the use of machine learning and artificial intelligence in hernia surgery.MethodsThe PRISMA guidelines were followed throughout this systematic review. The ROBINS—I and Rob 2 tools were used to perform qualitative assessment of all studies included in this review. Recommendations were then summarized for the following pre-defined key items: protocol, research question, search strategy, study eligibility, data extraction, study design, risk of bias, publication bias, and statistical analysis.ResultsA total of 13 articles were ultimately included for this review, describing the use of machine learning and deep learning for hernia surgery. All studies were published from 2020 to 2023. Articles varied regarding the population studied, type of machine learning or Deep Learning Model (DLM) used, and hernia type. Of the thirteen included studies, all included either inguinal, ventral, or incisional hernias. Four studies evaluated recognition of surgical steps during inguinal hernia repair videos. Two studies predicted outcomes using image-based DMLs. Seven studies developed and validated deep learning algorithms to predict outcomes and identify factors associated with postoperative complications.ConclusionThe use of ML for abdominal wall reconstruction has been shown to be a promising tool for predicting outcomes and identifying factors that could lead to postoperative complications.
Big data and deep learning for RNA biology
Authors: Hyeonseo Hwang, Hyeonseong Jeon, Nagyeong Yeo, Daehyun Baek

The exponential growth of big data in RNA biology (RB) has led to the development of deep learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL studies in other fields, the successful implementation of DL in RB depends heavily on the effective utilization of large-scale datasets from public databases. In achieving this goal, data encoding methods, learning algorithms, and techniques that align well with biological domain knowledge have played pivotal roles. In this review, we provide guiding principles for applying these DL concepts to various problems in RB by demonstrating successful examples and associated methodologies. We also discuss the remaining challenges in developing DL models for RB and suggest strategies to overcome these challenges. Overall, this review aims to illuminate the compelling potential of DL for RB and ways to apply this powerful technology to investigate the intriguing biology of RNA more effectively.
Classification of hazelnut varieties based on bigtransfer deep learning model
Authors: Emrah Dönmez, Serhat Kılıçarslan, Aykut Diker

Hazelnut is an agricultural product that contributes greatly to the economy of the countries where it is grown. The human factor plays a major role in hazelnut classification. The typical approach involves manual inspection of each sample by experts, a process that is both labor-intensive and time-consuming, and often suffers from limited sensitivity. The deep learning techniques are extremely important in the classification and detection of agricultural products. Deep learning has great potential in the agricultural sector. This technology can improve product quality, increase productivity, and offer farmers the ability to classify and detect their produce more effectively. This is important for sustainability and efficiency in the agricultural industry. In this paper aims to the application of deep learning algorithms to streamline hazelnut classification, reducing the need for manual labor, time, and cost in the sorting process. The study utilized hazelnut images from three different varieties: Giresun, Ordu, and Van, comprising a dataset of 1165 images for Giresun, 1324 for Ordu, and 1138 for Van hazelnuts. This dataset is an open-access dataset. In the study, experiments were carried out on the determination of hazelnut varieties with BigTransfer (BiT)-M R50 × 1, BiT-M R101 × 3 and BiT-M R152 × 4 models. Deep learning models, including big transfer was employed for classification. The classification task involved 3627 nut images and resulted in a remarkable accuracy of 99.49% with the BiT-M R152 × 4 model. These innovative methods can also lead to patentable products and devices in various industries, thereby boosting the economic value of the country.