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Article Citation - WoS: 3Citation - Scopus: 3Detection of spermatogonial stem/progenitor cells in prepubertal mouse testis with deep learning(Springer/plenum Publishers, 2023) Kahveci, Burak; Onen, Selin; Akal, Fuat; Korkusuz, PetekPurposeRapid and easy detection of spermatogonial stem/progenitor cells (SSPCs) is crucial for clinicians dealing with male infertility caused by prepubertal testicular damage. Deep learning (DL) methods may offer visual tools for tracking SSPCs on testicular strips of prepubertal animal models. The purpose of this study is to detect and count the seminiferous tubules and SSPCs in newborn mouse testis sections using a DL method.MethodsTesticular sections of the C57BL/6-type newborn mice were obtained and enumerated. Odd-numbered sections were stained with hematoxylin and eosin (H&E), and even-numbered sections were immune labeled (IL) with SSPC specific marker, SALL4. Seminiferous tubule and SSPC datasets were created using odd-numbered sections. SALL4-labeled sections were used as positive control. The YOLO object detection model based on DL was used to detect seminiferous tubules and stem cells.ResultsTest scores of the DL model in seminiferous tubules were obtained as 0.98 mAP, 0.93 precision, 0.96 recall, and 0.94 f1-score. The SSPC test scores were obtained as 0.88 mAP, 0.80 precision, 0.93 recall, and 0.82 f1-score.ConclusionSeminiferous tubules and SSPCs on prepubertal testicles were detected with a high sensitivity by preventing human-induced errors. Thus, the first step was taken for a system that automates the detection and counting process of these cells in the infertility clinic.Article Citation - WoS: 197Citation - Scopus: 296Co-Lstm: Convolutional Lstm Model for Sentiment Analysis in Social Big Data(Elsevier Sci Ltd, 2021) Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, SanjayAnalysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.Article Citation - WoS: 6Citation - Scopus: 6Ensemble Transfer Learning Using Maizeset: a Dataset for Weed and Maize Crop Recognition at Different Growth Stages(Elsevier Sci Ltd, 2024) Das, Zeynep Dilan; Alam, Muhammad Shahab; Khan, Muhammad UmerMaize holds significant importance as a staple food source globally. Increasing maize yield requires the effective removal of weeds from maize fields, as they pose a detrimental threat to the growth of maize plants. In recent years, there has been a drive towards Precision Agriculture (PA), involving the integration of farming methods with artificial intelligence and advanced automation techniques. In the realm of PA, deep learning techniques present a promising solution for addressing the complex challenge of classifying maize plants and weeds. In this work, a deep learning method based on transfer learning and ensemble techniques is developed. The proposed method is implementable on any number of existing CNN models irrespective of their architecture and complexity. The developed ensemble model is trained and tested on our custom-built dataset, namely MaizeSet, comprising 3330 images of maize plants and weeds under varying environmental conditions. The performance of the ensemble model is compared against individual pre-trained VGG16 and InceptionV3 models using two experiments: the identification of weeds and maize plants, and the identification of the various vegetative growth stages of maize plants. VGG16 attained an accuracy of 83% in Experiment 1 and 71% in Experiment 2, while InceptionV3 showcased improved performance, boasting an accuracy of 98% in Experiment 1 and 81% in Experiment 2. With the proposed ensemble approach, VGG16 when combined with InceptionV3, achieved an accuracy of 90% for Experiment 1 and 80% for Experiment 2. The findings demonstrate that integrating a suboptimal pre-defined classifier, specifically VGG16, with a more proficient model like InceptionV3, yields enhanced performance across various analytical metrics. This underscores the efficacy of ensemble techniques in the context of maize classification and analogous applications within the agricultural domain.
