ADKU/ResearchGPT_model_dataset huggingface dataset:
Image recognition algorithm based on hybrid deep learning
Authors: Tang Xiangdong

As we embrace the information age, our lives have experienced revolutionary transformations. With the continuous advancement of computer technology, data sharing and information exchange have become increasingly robust. Consequently, the widespread adoption of hybrid deep learning algorithms has been prioritized. The emergence of deep learning is intricately linked to the progress of artificial intelligence, with deep learning serving as a tangible manifestation of AI. Deep learning algorithms are an important part of the field of robotics research and development. In image recognition, deep learning algorithms are playing an irreplaceable role. Based on the technological breakthrough of convolutional neural network, deep learning is unique in image recognition. In addition, aspects such as speech recognition, body function monitoring, and sports data analysis all have deep learning, and they are advancing all the way with unstoppable development momentum. Of course, with the current development of computer technology, image data also shines on its basis. Not only has it achieved a substantial surpass in quantity, but its types and styles are coming out in a variety of colors. On the basis of this series of developments, we are faced with a problem: traditional recognition algorithms cannot meet our next development requirements. Therefore, a new type of algorithm to deal with this problem has emerged: the research of image recognition algorithms based on hybrid deep learning. In the research of this article, we will focus on comparing its various algorithms to find out their advantages and disadvantages. So as to promote the further development of image recognition.
Deep Ensemble learning and quantum machine learning approach for Alzheimer’s disease detection
Authors: Abebech Jenber Belay, Yelkal Mulualem Walle, Melaku Bitew Haile

Alzheimer disease (AD) is among the most chronic neurodegenerative diseases that threaten global public health. The prevalence of Alzheimer disease and consequently the increased risk of spread all over the world pose a vital threat to human safekeeping. Early diagnosis of AD is a suitable action for timely intervention and medication, which may increase the prognosis and quality of life for affected individuals. Quantum computing provides a more efficient model for different disease classification tasks than classical machine learning approaches. The full potential of quantum computing is not applied to Alzheimer’s disease classification tasks as expected. In this study, we proposed an ensemble deep learning model based on quantum machine learning classifiers to classify Alzheimer’s disease. The Alzheimer’s disease Neuroimaging Initiative I and Alzheimer’s disease Neuroimaging Initiative II datasets are merged for the AD disease classification. We combined important features extracted based on the customized version of VGG16 and ResNet50 models from the merged images then feed these features to the Quantum Machine Learning classifier to classify them as non-demented, mild demented, moderate demented, and very mild demented. We evaluate the performance of our model by using six metrics; accuracy, the area under the curve, F1-score, precision, and recall. The result validates that the proposed model outperforms several state-of-the-art methods for detecting Alzheimer’s disease by registering an accuracy of 99.89 and 98.37 F1-score.
A Review on Machine Learning and Deep Learning Based Systems for the Diagnosis of Brain Cancer
Authors: Prottoy Saha, Shanta Kumar Das, Rudra Das

Brain cancer is a disease of the brain caused by a brain tumor. A brain tumor is the development of cells in the brain that grow in an unregulated and unnatural manner. Patients may suffer irreversible brain damage or even death if these tumors are not detected and treated properly. As with all types of treatment, Positional information and tumor size are critical for conventional systems. Thus, establishing a meticulous and automated approach to providing information to medical practitioners is required. With machine learning, deep learning, and several imaging modalities, physicians may now more reliably detect tumor types in a shorter period. The paper aims to provide an overview of newly developed systems that use machine learning and deep learning approaches to analyze various medical imaging modalities in the case of diagnosing brain tumors. Datasets used by the authors, dataset partitioning strategies, and different performance evaluation matrices are also described in this paper. To better understand the policy of categorization, we propose a taxonomy here where we have categorized deep learning and machine learning based systems with respect to single classifier, multiple classifiers, single dataset and multiple dataset. Finally, we focus on the challenges of deep learning algorithms for brain tumor classification and possible future trends in this field.
Ensemble deep learning for Alzheimer’s disease characterization and estimation
Authors: M. Tanveer, T. Goel, R. Sharma, A. K. Malik, I. Beheshti, J. Del Ser, P. N. Suganthan, C. T. Lin

Alzheimer’s disease, which is characterized by a continual deterioration of cognitive abilities in older people, is the most common form of dementia. Neuroimaging data, for example, from magnetic resonance imaging and positron emission tomography, enable identification of the structural and functional changes caused by Alzheimer’s disease in the brain. Diagnosing Alzheimer’s disease is critical in medical settings, as it supports early intervention and treatment planning and contributes to expanding our knowledge of the dynamics of Alzheimer’s disease in the brain. Lately, ensemble deep learning has become popular for enhancing the performance and reliability of Alzheimer’s disease diagnosis. These models combine several deep neural networks to increase a prediction’s robustness. Here we revisit key developments of ensemble deep learning, connecting its design—the type of ensemble, its heterogeneity and data modalities—with its application to AD diagnosis using neuroimaging and genetic data. Trends and challenges are discussed thoroughly to assess where our knowledge in this area stands.
Prediction of crop yield in India using machine learning and hybrid deep learning models
Authors: Krithikha Sanju Saravanan, Velammal Bhagavathiappan

Crop yield prediction is one of the burgeoning research areas in the agriculture domain. The crop yield forecasting models are developed to enhance productivity with improved decision-making strategies. The highly efficient crop yield forecasting model assists farmers in determining when, what and how much to plant on their cultivable land. The main objective of the proposed research work is to build a high efficacious crop yield prediction model based on the data available for the period of 21 years from 1997 to 2017 using machine learning and hybrid deep learning approaches. Two prediction models have been proposed in this research work to predict the crop yield accurately. The first model is a machine learning-based model which uses the CatBoost regression model and its hyperparameters are tuned which improves the performance of the yield prediction using the Optuna framework. The second model is the hybrid deep learning model which uses spatio-temporal attention-based convolutional neural network (STACNN) for extracting the features and the bidirectional long short-term memory (BiLSTM) model for predicting the crop yield effectively. The proposed models are evaluated using the error metrics and compared with the latest contemporary models. From the evaluation results, it is shown that the proposed models significantly outperform all other existing models and CatBoost regression model slightly performs better than the STACNN-BiLSTM model, with the R-squared value of 0.99.
Deep learning CT reconstruction improves liver metastases detection
Authors: Achraf Kanan, Bruno Pereira, Constance Hordonneau, Lucie Cassagnes, Eléonore Pouget, Léon Appolinaire Tianhoun, Benoît Chauveau, Benoît Magnin

ObjectivesDetection of liver metastases is crucial for guiding oncological management. Computed tomography through iterative reconstructions is widely used in this indication but has certain limitations. Deep learning image reconstructions (DLIR) use deep neural networks to achieve a significant noise reduction compared to iterative reconstructions. While reports have demonstrated improvements in image quality, their impact on liver metastases detection remains unclear. Our main objective was to determine whether DLIR affects the number of detected liver metastasis. Our secondary objective was to compare metastases conspicuity between the two reconstruction methods.MethodsCT images of 121 patients with liver metastases were reconstructed using a 50% adaptive statistical iterative reconstruction (50%-ASiR-V), and three levels of DLIR (DLIR-low, DLIR-medium, and DLIR-high). For each reconstruction, two double-blinded radiologists counted up to a maximum of ten metastases. Visibility and contour definitions were also assessed. Comparisons between methods for continuous parameters were performed using mixed models.ResultsA higher number of metastases was detected by one reader with DLIR-high: 7 (2–10) (median (Q₁–Q₃); total 733) versus 5 (2–10), respectively for DLIR-medium, DLIR-low, and ASiR-V (p < 0.001). Ten patents were detected with more metastases with DLIR-high simultaneously by both readers and a third reader for confirmation. Metastases visibility and contour definition were better with DLIR than ASiR-V.ConclusionDLIR-high enhanced the detection and visibility of liver metastases compared to ASiR-V, and also increased the number of liver metastases detected.Critical relevance statementDeep learning-based reconstruction at high strength allowed an increase in liver metastases detection compared to hybrid iterative reconstruction and can be used in clinical oncology imaging to help overcome the limitations of CT.Key Points

Detection of liver metastases is crucial but limited with standard CT reconstructions.


More liver metastases were detected with deep-learning CT reconstruction compared to iterative reconstruction.


Deep learning reconstructions are suitable for hepatic metastases staging and follow-up.

Graphical Abstract
Predicting Renal Toxicity of Compounds with Deep Learning and Machine Learning Methods
Authors: Bitopan Mazumdar, Pankaj Kumar Deva Sarma, Hridoy Jyoti Mahanta

Renal toxicity prediction plays a vital role in drug discovery and clinical practice, as it helps to identify potentially harmful compounds and mitigate adverse effects on the renal system. Compound with inherent renal-toxic potential is one of the major concerns for drug development as it leads to failure in drug discovery. Predicting nephrotoxic probabilities of a compound at an early stage can be effective for reducing the drug failure rate. Hence, it is crucial to develop a mechanism to analyze the renal toxicity of a drug-candidate optimally and quickly. To mitigate the risks associated with renal toxicity, predictive models leveraging machine learning and deep learning techniques have gained significant attention. In this study, 287 human renal-toxic drugs and 278 non-renal-toxic drugs were collected to develop a deep learning model and 27 machine learning models using 8 kinds of fingerprints and Rdkit descriptors. The deep neural network model shows better generalization scores on five-fold cross-validation and Extra-tree model shows better performance score on test data. Structural alerts, specific chemical substructures associated with renal toxicity, offer a valuable tool for early toxicity assessment. Therefore, the substructures of renal toxic compounds were studied by applying association rule mining technique based on frequent itemset patterns. A method has been proposed for generating structural alerts and 10 structural alerts have been generated using the method.
A deep ensemble learning method for cherry classification
Authors: Kiyas Kayaalp

In many agricultural products, information technologies are utilized in classification processes at the desired quality. It is undesirable to mix different types of cherries, especially in export-type cherries. In this study on cherries, one of the important export products of Turkey, the classification of cherry species was carried out with ensemble learning methods. In this study, a new dataset consisting of 3570 images of seven different cherry species grown in Isparta region was created. The generated new dataset was trained with six different deep learning models with pre-learning on the original and incremental dataset. As a result of the training with incremental data, the best result was obtained from the DenseNet169 model with an accuracy of 99.57%. The two deep learning models with the best results were transferred to ensemble learning and a 100% accuracy rate was obtained with the Maximum Voting model.
The application of deep learning technology in integrated circuit design
Authors: Lihua Dai, Ben Wang, Xuemin Cheng, Qin Wang, Xinsen Ni

This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.
Alzheimer Disease Detection Using MRI: Deep Learning Review
Authors: Pallavi Saikia, Sanjib Kumar Kalita

Deep learning for Alzheimer disease detection using MRI is an emerging area of research in medical image processing. With the advent of new technologies based on methods of Deep Learning, medical diagnosis of certain diseases has become possible. Alzheimer’s is a disease which till date has no cure but the progression of the disease can be slowed down or a person who might develop Alzheimer in future can be treated if early prediction of it is possible. Early prediction of the disease benefits medical professionals a lot for the correct diagnosis. Medical professionals label Alzheimer patients based on the progression of the disease as AD (Alzheimer’s), CN (cognitive impairment) and MCI (mild cognitive impairment). In literature many Deep Learning models are used for the early detection of Alzheimer’s disease. Though there are many image modalities, MRI images being non-invasive are considered best for these types of medical experiments. In the present study, we have studied the evolution of Alzheimer’s disease over time, research gaps, challenges towards building advanced models, possible recommendations to overcome those challenges and determining the best performance model. We have focussed on an exhaustive and comprehensive survey of very deep learning-based research papers on Alzheimer’s disease detection. The present work will benefit researchers by providing a clear direction for future scope in Alzheimer disease detection and analysis.
Medical Image Analysis Through Deep Learning Techniques: A Comprehensive Survey
Authors: K. Balasamy, V. Seethalakshmi, S. Suganyadevi

Deep learning has been the subject of a significant amount of research interest in the development of novel algorithms for deep learning algorithms and medical image processing have proven very effective in a number of medical imaging tasks to help illness identification and diagnosis. The shortage of large-sized datasets that are also adequately annotated is a key barrier that is preventing the continued advancement of deep learning models used in medical image analysis, despite the effectiveness of these models. Over the course of the previous 5 years, a great number of research have concentrated on finding solutions to this problem. In this work, we present a complete overview of the use of deep learning techniques in a variety of medical image analysis tasks by reviewing and summarizing the current research that have been conducted in this area. In particular, we place an emphasis on the most recent developments and contributions of state-of-the-art semi-supervised and unsupervised deep learning in medical image analysis. These advancements and contributions are shortened based on various application scenarios, which include image registration, segmentation, classification and detection. In addition to this, we explore the significant technological obstacles that lie ahead and provide some potential answers for the ongoing study.
Deep-SEA: a deep learning based patient specific multi-modality post-cancer survival estimation architecture
Authors: Ibtihaj Ahmad, Saleem Riaz

Cancer survival estimation is essential for post-cancer patient care, cancer management policy building, and the development of tailored treatment plans. Existing survival estimation methods use censored data; therefore, standard machine learning methods can not be used directly. Some censoring-based semi-machine learning methods have recently been proposed; however, these methods pose challenges. They are less patient-specific and non-linear. Furthermore, they rely on single-modality features. These drawbacks result in lower survival estimation performance. To address these issues, this work proposes a framework named Deep-SEA. Compared to the state-of-the-art, Deep-SEA uses multi-modality features, i.e., clinical, radiology, and histology features. These features are analyzed with statistical methods, and only significant features are selected. Then, the baseline hazard of the Cox model is estimated using Breslow’s estimator, which is optimized using stochastic gradient descent. Finally, the risk function, i.e., the parameters of our model, are estimated via an ANN with time as additional input. ANN makes it non-linear while training on the patient-specific features makes it more patient-specific than the state-of-the-art. We train and evaluate Deep-SEA on five datasets, including head, neck, and colorectal-liver cancer. We have achieved a Concordance-index (C-index) score of 0.7181, the highest compared to the state-of-the-art. Results and ablation studies on Deep-SEA suggest that the proposed method improves cancer survival estimation and can be applied to other estimations, such as cancer recurrence estimation.
A survey on deep learning and machine learning techniques over histopathology image based Osteosarcoma Detection
Authors: K. V. Deepak, R. Bharanidharan

Osteosarcoma is a common type of cancer that occurs in the cells and spreads to the bones. Osteosarcoma can develop due to genetic mutations, but most cases are not inherited. It often starts at the ends of long bones in the arms and legs during periods of rapid growth. Osteosarcoma can be diagnosed by Histopathological examination using microscopic images. In recent trends, pathological images are increasingly being examined by computer-intelligent approaches, also known as computational pathology. This field utilizes machine learning and deep learning algorithms to analyze digital pathology slides. Histopathology image based osteosarcoma detection is not the subject of any review papers. So, this study examines historical and current literature to present a concise review of Histopathology image based Osteosarcoma detection. This review discussed the types, clinical diagnosis, and modern and future treatment methods of Osteosarcoma. Also, a review of machine learning (ML) and deep learning (DL) approaches for histopathology image osteosarcoma detection is analyzed. Sophisticated methods and the future scope of osteosarcoma detection have also been discussed. Comparative analysis and results of reviewed papers were also analyzed in this review study. This article aims to generate fresh concepts for creating more potent treatment alternatives.
Learning sparse and smooth functions by deep Sigmoid nets
Authors: Xia Liu

To pursue the outperformance of deep nets in learning, we construct a deep net with three hidden layers and prove that, implementing the empirical risk minimization (ERM) on this deep net, the estimator can theoretically realize the optimal learning rates without the classical saturation problem. In other words, deepening the networks with only three hidden layers can overcome the saturation and not degrade the optimal learning rates. The obtained results underlie the success of deep nets and provide a theoretical guidance for deep learning.
Parametric RSigELU: a new trainable activation function for deep learning
Authors: Serhat Kiliçarslan, Mete Celik

Activation functions are used to extract meaningful relationships from real-world problems with the help of deep learning models. Thus, the development of activation functions which affect deep learning models’ performances is of great interest to researchers. In the literature, mostly, nonlinear activation functions are preferred since linear activation functions limit the learning performances of the deep learning models. Non-linear activation functions can be classified as fixed-parameter and trainable activation functions based on whether the activation function parameter is fixed (i.e., user-given) or modified during the training process of deep learning models. The parameters of the fixed-parameter activation functions should be specified before the deep learning model training process. However, it takes too much time to determine appropriate function parameter values and can cause the slow convergence of the deep learning model. In contrast, trainable activation functions whose parameters are updated in each iteration of deep learning models training process achieve faster and better convergence by obtaining the most suitable parameter values for the datasets and deep learning architectures. This study proposes parametric RSigELU (P+RSigELU) trainable activation functions, such as P+RSigELU Single (P+RSigELUS) and P+RSigELU Double (P+RSigELUD), to improve the performance of fixed-parameter activation function of RSigELU. The performances of the proposed trainable activation functions were evaluated on the benchmark datasets of MNIST, CIFAR-10, and CIFAR-100 datasets. Results show that the proposed activation functions outperforms PReLU, PELU, ALISA, P+FELU, PSigmoid, and GELU activation functions found in the literature. To access the codes of the activation function; https://github.com/serhatklc/P-RsigELU-Activation-Function.