Concept Of Deep Learning Research Articles

A methods guideline for deep learning for tabular data in agriculture with a case study to forecast cereal yield

Machine learning (ML) and its branch, deep learning (DL), is rapidly evolving and gaining popularity as it outperforms other, more traditional methods in different areas of agriculture. However, ML and DL techniques must be correctly applied to a problem to produce an acceptable solution. This article provides guidelines for using DL techniques with a case study using different models/methods to forecast yields in cereals; some of the concepts presented here are also applicable to ML more broadly. The objective is to provide clarity for new users around the use of DL techniques to solve agronomic problems. DL concepts are introduced; best practices for data pre-processing steps and metrics are recommended. Cross-validation is clarified, and its importance is highlighted. It is shown that DL performance can vary with architecture and that the optimal choice is task-dependent. Emphasis on practical aspects for applying DL models for agricultural datasets is provided, such as dataset size (26 representative samples in each field sufficed) and cross-validation (indispensable on small datasets). Lastly, a standard guideline for DL applied to tabular data is recommended.

CAPTCHA RECOGNITION USING DCNN

Completely Automated Public Turing Test to Tell Computers and Humans Apart (CAPTCHA) is an important human-machine distinction technology for websites to prevent the automatic malicious program attack. CAPTCHA recognition studies can find security breaches in CAPTCHA technology By using the concept of deep learning and computer vision, the very purpose of the CAPTCHAs can be defeated. This test can be passed automatically with the help of Convolutional Neural networks(CNN). A CNN is an algorithm of deep learning which takes an image as input and then assigns some value to various features in the image which further helps to differentiate one feature from the other. Its main purpose is to transform the images into a form which is much easier to process, without losing features which are essential for getting an optimized prediction. The proposed system for this project is to expand this CAPTCHA recognition system for larger and more noisy CAPTCHA containing all the symbols possible, a method based on the Deep Convolutional Neural Network (DCNN) model to identify CAPTCHA and avoid the traditional image processing technology such as location and segmentation. The adaptive learning rate is introduced to accelerate the convergence rate of the model, and the problem of over fitting and local optimal solution has been solved. The multi task joint training model is used to improve the accuracy and generalization ability of model recognition. The experimental results show that the model has a good recognition effect on CAPTCHA with background noise and character adhesion distortion. The future scope of this project lies in technologies where more noisier images can be processed such as license plates, handwriting recognition etc.

后疫情时代背景下未来学校深度学习的构想

自全球新冠肺炎疫情爆发以来，正常课堂授课方式转变为大规模网络授课，学校也迫切要求转变教育体系，完成教育系统的恢复和重新设定。本研究从疫情影响下揭示的现行学校教育系统存在的问题展开论述，并对迈克尔·富兰团队的教育体系过渡理论进行阐述，描述了后疫情时代下深度学习对学生能力培养的重要性，分析未来学校教育系统与深度学习之间存在辩证的促进关系，以深度学习理念展开对未来学校教育系统的改革与重构，保障深度学习的落实，为学生、教师以及未来学校的进一步发展提供可能。 Since the outbreak of the global COVID-19 epidemic, the normal classroom teaching mode has changed to large-scale online teaching, and schools are also urgently demanding to change the education system and complete the restoration and resetting of the education system. This study discusses the problems existing in the current school education system revealed under the influence of epidemic situation, and expounds the transition theory of education system of Michael Fullan's team. It describes the importance of deep learning in the post-epidemic era to the cultivation of students' ability, analyzes the dialectical promoting relationship between the future school education system and deep learning, reforms and reconstructs the future school education system with the concept of deep learning, guarantees the implementation of deep learning, and provides the possibility for the further development of students, teachers and future schools.

An Empirical Studying: Blended Teaching Design Based on Deep Learning

In this paper, the studying targeted to make a practical application demonstration approach studying based on deep learning elements for the blended teaching and learning within the areas of education. The studying creates a DBTA model of the blended teaching method based on the concept of deep learning, which enhances the effective consistency of online and offline teaching activities, and improves the professional competence of students. A questionnaire is designed to verify the feasibility and science of the DBTA model, which obtained valid questionnaires 197 from the education practitioners of high education in G province of mainland China. Through research analysis, the studying gained several practical views, illustrated the applicability of DBTA approach to developing critical thinking awareness to get high-order thinking and enhance Lifelong-learning ability of students.

Construction of a Mixing Mode of Online and Offline College English Deep Learning in the Context of the New Liberal Arts

Based on the interpretation of the goal of cultivating college foreign language talents in the context of the new liberal arts, it is proposed that college English teaching guided by the concept of deep learning is one of the feasible ways to achieve this goal. From the three objective dimensions of cognition, interpersonal relationship, and self, the construction of an online and offline mixing mode of college English deep learning integrating mutual evaluation, self-evaluation, and teacher evaluation is divided into three stages: before class, during class, and after class. With the aim of providing an operable action guidance framework for deep teaching and learning, it proposes alternative paths for achieving college English teaching objectives in the context of the new liberal arts.

Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts

Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts

Deep Learning and MachineLearning to Diagnose Melanoma

The most dangerous disorders include melanoma. Yet, a precise diagnosis of skin cancer is difficult. Recent research has shown that a variety of activities can be performed better using deep learning and machine learning techniques. For skin conditions, these algorithms are highly useful. In this article, we examine various deep learning and machine learning techniques and how they could be applied to the detection of melanoma. This paper provides a number of publicly downloadable datasets, information on common melanoma, instructions for getting dermatology pictures, and more. Once machine learning and deep learning concepts have been introduced, our attention shifts to analysing common machine learning and deep learning architectures as well as popular frameworks for putting machine and deep learning algorithms into practice. Metrics for performance evaluation are then offered. In this section, we will cover the research on machine learning and deep learning and how they can be applied to the detection of melanoma skin illnesses. We also go over potential research avenues and the difficulties in the field. The main objective of this work is to discuss modern machine learning and deep learning techniques for melanoma diagnosis.

Anas platyrhynchos optimizer with deep transfer learning-based gastric cancer classification on endoscopic images

<abstract><p>Gastric Cancer (GC) has been identified as the world's fifth most general tumor. So, it is important to diagnose the GC at initial stages itself to save the lives. Histopathological analysis remains the gold standard for accurate diagnosis of the disease. Though Computer-Aided Diagnostic approaches are prevalently applied in recent years for the diagnosis of diseases, it is challenging to apply in this case, due to the lack of accessible gastric histopathological image databases. With a rapid progression in the Computer Vision (CV) technologies, particularly, the emergence of medicinal image classifiers, it has become feasible to examine all the types of electron micrographs in a rapid and an effective manner. Therefore, the current research article presents an Anas Platyrhynchos Optimizer with Deep Learning-based Gastric Cancer Classification (APODL-GCC) method for the classification of GC using the endoscopic images. The aim of the proposed APODL-GCC method is to identify the presence of GC with the help of CV and Deep Learning concepts. Primarily, the APODL-GCC technique employs a contrast enhancement technique. Next, the feature extraction process is performed using a neural architectural search network model to generate a collection of feature vectors. For hyperparameter optimization, the Anas Platyrhynchos Optimizer (APO) algorithm is used which enhances the classification performance. Finally, the GC classification process is performed using the Deep Belief Network method. The proposed APODL-GCC technique was simulated using medical images and the experimental results established that the APODL-GCC technique accomplishes enhanced performance over other models.</p></abstract>

Teaching Medical Professionalism with a Scenario-based Approach Using Role-Playing and Reflection: A Step towards Promoting Integration of Theory and Practice.

Professionalism, as one of the core competencies of physicians, is essential for providing the patients with higher quality care. It is an abstract concept and its education and assessment need objective and operational methods. The present study aimed at teaching the concepts of professionalism based on a scenario-based approach using role-playing and reflection. This is a pre-experimental study (one-group pretest-posttest design) with a mixed method approach. The study was conducted on 18 medical students (by voluntary sampling method) who had enrolled in the Medical Ethics Course at Kashan University of Medical Sciences in 2020. Twelve scenarios were designed about the most prevalent issues of medical professionalism. In each session, one group of students played out their scenarios and then, the participants and instructors discussed their role-playing. Participants' knowledge about professionalism was assessed at the beginning and end of each session, and they completed a satisfaction questionnaire and a reflection form. T-tests (one-sample and paired T-test) were applied for statistical analysis. Quantitative and qualitative data were analyzed using SPSS software (version 26), and P<0.05 was considered statistically significant. Conventional content analysis was used to analyze the qualitative data. The mean scores of the participants' knowledge in post-test were significantly higher than those in the pre-test (P=0.042, t=-2.074). The mean scores of the participants' role-playing quality (p<0.001) and satisfaction (p=0.001) were significantly higher than their corresponding test values. Qualitative analysis of the participants' reflections revealed their satisfaction with the study intervention. The scenarios, role-playing, and reflection could provide an opportunity for operationalizing the concepts of professionalism and deep learning of students. Medical instructors need to improve their knowledge and skills of using active methods in teaching professionalism to medical students.

A Survey on Different Algorithms Used in Deep Learning Process

Deep Learning is a significant tool that communicates with the computer to perform task as a natural human being. Deep learning algorithms exhibits a vital role in defining the features and can assist the large number of processes for the data which are structured or unstructured. In recent years, this concept is receiving quite a lot attention for its exceeding achievements in human-level performance. In deep learning, systematic and rapid development of diverse lifestyle, including marketing, policing, finance, manufacturing and education modeling which creates pioneering technologies such as autonomous systems. Deep learning being a subset of machine learning is tackling problems with advanced features. Deep Learning is basically a neural network with processing units consisting of multiple layers with which it has efficaciously applicable to a wide set of problems in the areas such as image processing, image recognition and natural language processing. This paper gives a brief review on the different approaches, algorithms used in deep learning techniques from the beginning to the present scenario. The main aim of this study is to reveal the advantages features of the handsome approaches utilized in the deep learning process. Deep learning methods are contributing a diversity of assistances not only to online learners but also to institutions that advance in current eLearning platforms. This survey will afford an overview of the key ideas on the concept, approaches, advantages and impact of deep learning in the area of research.

Detecting Danger: AI-Enabled Road Crack Detection for Autonomous Vehicles

The present article proposes the deep learning concept termed ―Faster-Region Convolutional Neural Network‖ (Faster-RCNN) technique to detect cracks on road for autonomous cars. Feature extraction, preprocessing, and classification techniques have been used in this study. Several types of image datasets, such as camera images, faster-RCNN laser images, and real-time images, have been considered. With the help of GPU (graphics processing unit), the input image is processed. Thus, the density of the road is measured and information regarding the classification of road cracks is acquired. This model aims to determine road crack precisely as compared to the existing techniques.

A Voyage on Neural Machine Translation for Indic Languages

With the invention of deep learning concepts, Machine Translation (MT) migrated towards Neural Machine Translation (NMT) architectures, eventually from Statistical Machine Translation (SMT), which ruled MT for a few decades. Slowly, NMT paved its path into Indian MT research and witnessed many works for various language pairs in this regard. Numerous NMT architectures are floating across the international and national research pool; many claims to be state-of-the-art architectures. Though NMT for Indic languages (ILNMT) is giving better results for majority speaking language pairs, the translation quality is low due to a lack of significant resources. Automated machine translation models are unavailable for some less spoken Indic languages like Kashmiri and Dogri. Hence, there is increasing demand in the research to address the challenges of developing applicable MT models even when minuscule training data is available. Based on the corpus availability, the languages are categorized into High Resource Languages (HRLs), Low Resource Languages (LRLs), and Zero Resource Languages (ZRLs). Many Indic languages are classified into HRLs, LRLs, and ZRLs based on corpus availability. The vision behind this literature survey paper is to make this paper a collective source for all information regarding the predominant ILNMT architectures, the toolkits available for building NMT models, and various pre-trained language models needed by researchers who contribute to the ILNMT research community. In this survey paper, ILNMT architectures for different Indic languages are covered, e.g., Hindi, Tamil (HRLs), Kannada, Marathi (LRLs), Sinhala, and Nepali (ZRLs). There are a few language-specific survey papers on ILNMT, and this is one of the first kinds of survey papers where all the information is gathered under one canopy.

"SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"

"SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"

"SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"

"SCNN Based Classification Technique for the Face Spoof Detection Using Deep Learning Concept"

Convolutional Neural Network (CNN) Method for Classification of Images by Age

Image classification is one of the studies that is currently being developed. The details of the characteristics that must be captured make researchers compete to find the most suitable method for classifying. The Convolutional Neural Network (CNN) algorithm is one of the most superior algorithms in the field of object classification and identification today. With the help of several packages contained in Google Colab for classification, this algorithm is easier to use. In this study, the target case is the age of a person who will be classified using photos or images taken from the internet which are then stored in the form of Google Drive. The research data used is divided into 2 parts, namely for training data as many as 23.440 images, and 10,046 for testing data. Then to facilitate the extraction of features from the features to be identified, the researchers carried out the preprocessing stage, namely grayscale images, and data augmentation. The purpose of this study is to implement the concept of Deep Learning with Convolutional Neural Networks (CNN) in image classification and to determine the level of accuracy of the CNN model in classifying images. After the algorithm is run and the model has been formed, an accuracy of 78.5% is obtained. It can be concluded that the Convolutional Neural Network (CNN) method is good at classifying images

Deep learning - cancer genetics and application of deep learning to cancer oncology

Arguably the human body has been one of the most sophisticated systems we encounter but until now we are still far from understanding its complexity. We have been trying to replicate human intelligence by way of artificial intelligence but with limited success. We have discovered the molecular structure in terms of genetics, performed gene editing to change an organism’s DNA and much more, but their translatability into the field of oncology has remained limited. Conventional machine learning methods achieved some degree of success in solving problems that we do not have an explicit algorithm. However, they are basically shallow learning methods, not rich enough to discover and extract intricate features that represent patterns in the real environment. Deep learning has exceeded human performance in pattern recognition as well as strategic games and are powerful for dealing with many complex problems. High-throughput sequencing and microarray techniques have generated vast amounts of data and allowed the comprehensive study of gene expression in tumor cells. The application of deep learning with molecular data enables applications in oncology with information not available from clinical diagnosis. This paper provides fundamental concepts of deep learning, an essential knowledge of cancer genetics, and a review of applications of deep learning to cancer oncology. Importantly, it provides an insightful knowledge of deep learning and an extensive discussion on its challenges. The ultimate purpose is to germinate ideas and facilitate collaborations between cancer biologists and deep learning researchers to address challenging oncological problems using advanced deep learning technologies.

Detection of animal intrusion using CNN and image processing

One of the greatest dangers to agricultural productivity is animal damage to agriculture. Crop raiding has become one of the most antagonistic human-wildlife conflicts as cultivated land has expanded into previous wildlife habitat. Farmers in India endures major risks from pests, natural disasters, and animal damage, all of which result in lesser yields. Traditional farming methods are unsuccessful and hiring guards to watch crops and keep animals at bay is not a practical solution. It is critical to protect crops from animal damage while also redirecting the animal without injuring it, as the safety of both animals and people is essential. To get over these obstacles and accomplish our goal, we employ the deep learning concept of convolutional neural networks, a subfield of computer vision, to identify animals as they enter our farm. The primary goal of this project is to constantly monitor the entire farm using a camera that records the surroundings at all hours of the day. We identify animal infiltration using a CNN algorithm and Xgboost and notify farmers when this occurs.

Image-based Seat Belt Fastness Detection using Deep Learning

The detection of seat belts is an essential aspect of vehicle safety. It is crucial in providing protection in the event of an accident. Seat belt detection devices are installed into many automobiles, although they may be easily manipulated or disregarded. As a result, the existing approaches and algorithms for seat belt detection are insufficient. Using various external methods and algorithms, it is required to determine if the seat belt is fastened or not. This paper proposes an approach to identify seat belt fastness using the concepts of image processing and deep learning. Our proposed approach can be deployed in any organizational setup to aid the concerned authorities in identifying whether or not the drivers of the vehicles passing through the entrance have buckled their seat belts up. If a seat belt is not detected in a vehicle, the number plate recognition module records the vehicle number. The concerned authorities might use this record to take further necessary actions. This way, the organization authorities can keep track of all the vehicles entering the premises and ensure that all drivers/shotgun seat passengers are wearing seat belts.

Retracted: Application of the Iot Network and the Deep Learning Concept in Music Teaching

Retracted: Application of the Iot Network and the Deep Learning Concept in Music Teaching

Shift invariant deep convolution neural learning for resource efficient healthcare data transmission in WSN

Wireless Sensor Network (WSN) comprises many small sensors nodes incorporated by IoT which plays vital role in several applications. IoT incorporates physical devices into a network structure that includes software, sensors to collect data from the surrounding environment, and sinks for data transmission. To increase network lifespan during data transmission, a resource-efficient routing system is needed. Different academics have been working on improving routing techniques recently to increase effectiveness and route discovery. The key issue affecting the performance of WSNs is an efficient resource usage of routing. In order to overcome this problem, a new technique called Hierarchical Point wise Mutual Informative Clustering-based Shift Invariant Deep Convolutive Neural Learning (HPMIC-SIDCNL) technique is introduced in WSN. The HPMIC-SIDCNL technique uses the Shift Invariant Convolutive Deep neural learning concept to learn the given input with help of several layers such as input, three hidden layers, and output layer. Initially, the IoT devices are used in sensor nodes for sensing and collecting patient data. In the input layer, sensor nodes are considered as input. Then input is transmitted into first hidden layer where resource of the sensor nodes such as energy and bandwidth are measured. Then estimated resources are transmitted into second hidden layer where the clustering process is carried out using the Hierarchical IBM Point wise Mutual Informative Clustering technique. The Hierarchical IBM clustering uses the Point wise Mutual Information for collection of sensor nodes depend on energy and bandwidth. In the third hidden layer, the cluster is selected on higher residual energy and minimum bandwidth for improving the data transmission and reduces the delay. The cluster head receives the patient data after it has been sent by the source node in that particular cluster. In order to perform data transmission from source to sink node at output layer, cluster then locates the closest cluster head. In this manner, cluster heads are used to execute resource-efficient healthcare data transmission from source to sink node, minimizing the latency. The obtained findings show that the suggested HPMIC-SIDCNL technique outperforms in terms of high delivery ratio with little packet loss and delay. The advantage of the proposed HPMIC-SIDCNL technique, it is simple to understand because it represents a solution to a given problem step by step and is not dependent on any programming language. Since everyone can understand it, even those without programming experience, it is simple.