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Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network
In today's digital age, manual attendance tracking is plagued by inefficiency and the potential for inaccuracies, often leading to proxy attendance. The main aim of this research work is to manage and monitor the student's attendance by using face recognition technology. This proposed model is mainly categorized four major modules. First module is database creation. Second module is face detection. Then third module is face recognition and final module is automatic attendance updating process. Student images are compiled to create a comprehensive database, ensuring inclusivity across the class roster. The system utilizes the face recognition library, which relies on deep learning based algorithms for face detection and recognition during testing. This face recognition part Convolutional Neural Network algorithm is used. The system matches detected faces with the known database and marks attendance, ensuring a streamlined and accurate attendance tracking process. This innovative approach has the potential to revolutionize attendance management in educational settings, offering a contactless and efficient solution while mitigating proxy attendance concerns. The proposed model is to compare the accuracy level of face recognition. 2023 IEEE. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Deep learning based federated learning scheme for decentralized blockchain
Blockchain has the characteristics of immutability and decentralization, and its combination with federated learning has become a hot topic in the field of artificial intelligence. At present, decentralized, federated learning has the problem of performance degradation caused by non-independent and identical training data distribution. To solve this problem, a calculation method for model similarity is proposed, and then a decentralized, federated learning strategy based on the similarity of the model is designed and tested using five federated learning tasks: CNN model training fashion-mnist dataset, alexnet model training cifar10 dataset, TextRnn model training thusnews dataset, Resnet18 model training SVHN dataset and LSTM model training sentiment140 dataset. The experimental results show that the designed strategy performs decentralized, federated learning under the nonindependent and identically distributed data of five tasks, and the accuracy rates are increased by 2.51, 5.16, 17.58, 2.46 and 5.23 percentage points, respectively. 2024 The Author(s). -
Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques
Sentiment analysis plays a vital role in real time environment for knowing the history of a product or any other specific entity. Due to large number of users in the www, chances are there that many fake users may upload the fake reviews to damage the business for the sake of money. Identifying the fake reviews or percentage of fake content in the review is yet a challenging task. In this paper, an attempt has been made to find the percentage of fake in the review data. Two methodologies are combined to address this issue. Concept of spelling checking, topic modelling and deep learning for context extraction is extensively used to build the effective model. Proposed technique is exhaustively checked for efficiency with many trails of experiments. Also, the training and testing samples were shuffled for experimentation. The results of the models show its goodness. The details of the results can be found at experiments section. 2024 The Author(s) -
Deep learning based modeling of groundwater storage change
The understanding of water resource changes and a proper projection of their future availability are necessary elements of sustainable water planning. Monitoring GWS change and future water resource availability are crucial, especially under changing climatic conditions. Traditional methods for in situ groundwater well measurement are a significant challenge due to data unavailability. The present investigation utilized the Long Short Term Memory (LSTM) networks to monitor and forecast Terrestrial Water Storage Change (TWSC) and Ground Water Storage Change (GWSC) based on Gravity Recovery and Climate Experiment (GRACE) datasets from 20032025 for five basins of Saudi Arabia. An attempt has been made to assess the effects of rainfall, water used, and net budget modeling of groundwater. Analysis of GRACE-derived TWSC and GWSC estimates indicates that all five basins show depletion of water from 20032020 with a rate ranging from -5.88 1.2 mm/year to -14.12 1.2 mm/year and -3.5 1.5 to -10.7 1.5, respectively. Forecasting based on the developed LSTM model indicates that the investigated basins are likely to experience serious water depletion at rates ranging from -7.78 1.2 to -15.6 1.2 for TWSC and -4.97 1.5 to -12.21 1.5 for GWSC from 20202025. An interesting observation was a minor increase in rainfall during the study period for three basins. 2022 Tech Science Press. All rights reserved. -
Deep Learning Based Multi Constraint Hybrid Optimization Algorithm for Transshipment-Based Inventory Routing with Dynamic Demands
The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniqueswere unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP). The Author(s) 2024. -
Deep Learning Character Recognition of Handwritten Devanagari Script: A Complete Survey
Recognition of handwritten characters is a concept in which the single characters are classified, it is a facility of an electronic device to scan and decipher the handwritten input from a variety of sources, including written texts, images, and other digital touch-screen devices. This concept is being used in distinctive sectors such as the processing of bank checks, form data entry, and parcel posting and nowadays it is becoming a very important issue in the pattern recognition domain and a very challenging task to resolve it. Since deep learning is a crucial strategy in solving detection and pattern recognition problems, several algorithms are available to classify the characters with better prediction rates on different datasets, and ultimately, whichever algorithm gives the optimized results will be considered the best solution for the character recognition problem. As a result, various solutions proposed by the existing researchers are discussed using deep learning algorithms in this survey article. 2023 IEEE. -
Deep Learning Decision Support Model for Police Investigation
A police investigation is an exciting task with many complicated processes that may or may not succeed. However, it is the sole duty of a police officer to understand the crime scene, reconstruct the event and predict the criminal with accuracy. There are various methods for interrogations, predictions, and confirmation after identifying a person as a criminal or upon concluding their actions as a criminal act. However, we can see massive growth in crime rates every day. This massive growth rate makes conventional prediction or analysis very strenuous. In such times we can use or take the help of deep learning and machine learning methods for crime analysis and suspect prediction by identifying the data points in a set. This prediction methodology is known as intelligence analysis which simulates the dataset to draw a connection or pattern collectively from millions of data points to identify the instigator and linkman. This chapter will summarize the uses of deep learning and artificial intelligence in a decision support model for police investigation. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment
Recently, big data becomes evitable due to massive increase in the generation of data in real time application. Presently, object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation, augmented reality, surveillance, etc. This paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN (AIA-IFRCNN) model in big data environment. The AIA-IFRCNN model annotates the images by Discriminative Correlation Filter (DCF) with Channel and Spatial Reliability tracker (CSR), named DCF-CSRT model. The AIA-IFRCNN technique employs Faster RCNN for object detection and tracking, which comprises region proposal network (RPN) and Fast R-CNN. In addition, inception v2 model is applied as a shared convolution neural network (CNN) to generate the feature map. Lastly, softmax layer is applied to perform classification task. The effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%. 2022 Tech Science Press. All rights reserved. -
Deep Learning Enabled Parent Involvement and Its Influence on Student Academic Achievement Analysis
Studying the substantial effect that Deep Learning Enabled Parent Involvement (DLEPI) has on kid academic success. Using a made-up data set and a neural network model, we find that parents' level of involvement, as measured by the Parental Involvement Score (PIS), is positively correlated with their children's academic performance. DLEPI, driven by cutting-edge deep learning algorithms, equips parents with unique insights and suggestions regardless of where they live, therefore promoting educational equality and diversity. This study underlines the potential of technology to reduce performance inequalities and highlights its central role in increasing parental participation. Critical elements for future study include ethical issues, real-world validation, effect evaluations over time, and chances for personalization. This research lays the groundwork for reinventing education in a future where DLEPI improves student outcomes and offers a more inclusive and personalized educational environment. 2024 IEEE. -
Deep Learning for Arrhythmia Classification: A Comparative Study on Different Deep Learning Models
Arrhythmias, or irregular heart rhythms, are a major global health concern. Since arrhythmias can cause fatal conditions like cardiac failure and strokes, they must be rapidly identified and treated. Traditional arrhythmia diagnostic techniques include manual electrocardiogram (ECG) image interpretation, which is time consuming and frequently required for expertise. This research automates and improves the identification of heart problems, with a focus on arrhythmias, by utilizing the capabilities of deep learning, an advanced machine learning technique that performs well at recognizing patterns in data. Specifically, we implement and compare Custom CNN, VGG19, and Inception V3 deep learning models, which classify ECG images into six categories, including normal heart rhythms and various types of arrhythmias. The VGG19 model excelled, achieving a training accuracy of 95.7% and a testing accuracy of 93.8%, showing the effectiveness of deep learning in the comprehensive diagnosis of heart diseases. 2023 IEEE. -
Deep Learning for Early Detection of Tomato Leaf Diseases: A ResNet-18 Approach for Sustainable Agriculture
The paper explores the application of Convolutional Neural Networks (CNNs), specifically ResNet-18, in revolutionizing the identification of diseases in tomato crops. Facing threats from pathogens like Phytophthora infestans, timely disease detection is crucial for mitigating economic losses and ensuring food security. Traditionally, manual inspection and labour-intensive tests posed limitations, prompting a shift to CNNs for more efficient solutions. The study uses a well-organized dataset, employing data preprocessing techniques and ResNet-18 architecture. The model achieves remarkable results, with a 91% F1 score, indicating its proficiency in distinguishing healthy and unhealthy tomato leaves. Metrics such as accuracy, sensitivity, specificity, and a high AUC score on the ROC curve underscore the model's exceptional performance. The significance of this work lies in its practical applications for early disease detection in agriculture. The ResNet-18 model, with its high precision and specificity, presents a powerful tool for crop management, contributing to sustainable agriculture and global food security. (2024), (Science and Information Organization). All Rights Reserved. -
Deep learning for intelligent transportation: A method to detect traffic violation
Smart transportation is being envisaged as an important parameter in building smart cities. Although conceptualized to have major advantages, lack of intelligent systems makes more vulnerable for disasters. The number of fatality due to road accident has increased up to 12% in 2022 as that of previous year says the WHO report. There are large number of new vehicles plying on roads which makes space constraint for the commuters. This makes a large number of traffic violations happening in urban areas. The smart cities insist and tries to adopt AI based methods for identifying traffic violations. Computer Vision are predominant solution in detecting traffic violation. This paper proposes a Deep learning method using famous YOLOV technique for object detection for effectively determining the traffic violation. The violations such as signal cross are concentrated in this research. The experimental results prove that the proposed technique has 95.1% of classification accuracy in detecting signal crosses. 2023 Author(s). -
Deep Learning for Stock Market Index Price Movement Forecasting Using Improved Technical Analysis
Equity market forecasting is difficult due to the high explosive nature of stock data and its impact on investor's stock investment and finance. The stock market serves as an indicator for forecasting the growth of the economy. Because of the nonlinear nature, it becomes a difficult job to predict the equity market. But the use of different methods of deep learning has become a vital source of prediction. These approaches employ time-series stock data for deep learning algorithm training and help to predict their future behavior. In this research, deep learning methods are evaluated on the India NIFTY 50 index, a benchmark Indian equity market, by performing a technical data augmentation approach. This paper presents a Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and the three variants of Gated Recurrent Unit (GRU) to analyze the model results. The proposed three GRU variants technique is evaluated on two sets of technical indicator datasets of the NIFTY 50 index (namely TA1 and TA2) and compared to the RNN and LSTM models. The experimental outcomes show that the GRU variant1 (GRU1) with TA1 provided the lowest value of Mean Square Error (MSE=0.023) and Root Mean Square Error (RMSE= 0.152) compared with existing methods. In conclusion, the NIFTY 50 index experiments with technical indicator datasetTA1 were more efficient by GRU. Hence, TA1 can be used to construct a robust predictive model in forecasting the stock index movements. 2021. All Rights Reserved. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Deep Learning for Sustainable Agriculture
The evolution of deep learning models, combined with with advances in the Internet of Things and sensor technology, has gained more importance for weather forecasting, plant disease detection, underground water detection, soil quality, crop condition monitoring, and many other issues in the field of agriculture. agriculture. Deep Learning for Sustainable Agriculture discusses topics such as the impactful role of deep learning during the analysis of sustainable agriculture data and how deep learning can help farmers make better decisions. It also considers the latest deep learning techniques for effective agriculture data management, as well as the standards established by international organizations in related fields. The book provides advanced students and professionals in agricultural science and engineering, geography, and geospatial technology science with an in-depth explanation of the relationship between agricultural inference and the decision-support amenities offered by an advanced mathematical evolutionary algorithm. 2022 Elsevier Inc. All rights reserved. -
Deep learning framework for stock price prediction using long short-term memory
Forecasting stock prices is always considered as complicated process due to the dynamic and noisy characteristics of stock data influenced by external factors. For predicting the stock market, several approaches have been put forward. Many academics have successfully forecasted stock prices using soft computing models. Recently, there has been growing interest in applying deep learning techniques in combination with technical indicators to forecast stock prices, attracting attention from both investors and researchers. This paper focuses on developing a reliable model for anticipating future stock prices in one day advance using Long Short-Term Memory (LSTM). Three steps make up the suggested model. The approach begins with ten technical indicators computed from previous data as feature vectors. The second phase involves data normalization to scale the feature vectors. Finally, in the third phase, the LSTM model analyzes the closing price for the next day using the normalized characteristics as input. Two stock markets, NASDAQ and NYSE are chosen to evaluate the efficacy of the developed model. To demonstrate how effective the new model is in making predictions, its performance is compared to earlier models. Comparing the suggested model to other models, the findings revealed that it had a high level of prediction accuracy. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder
The overlapped handwritten digit classification is a global challenge and a significant measure to assess the network recognition ability ratio. Most efficient models have been designed based on convolutional neural networks (CNN) for effective image classification and digit identification. Subsequently, multiple CNN models have inadequate accuracy because of high degree parameter dimensions that lead to abnormal digit detection error rates and computation complexity. We propose a Deep Digit Recognition Network (DDRNet) based on Deep ConvNets to minimize the number of parameters and features to keep the model light while maximizing the accuracy with an adaptive voting (AV) scheme for digit recognition. The individual digit is identified by CNN, and uncertain digits or strings are identified by Deep Convolutional Network (DCN) with AV scheme through Voting-Weight Conditional Random Field (VWCRF) strategy. These methods originated with the YOLO algorithm. The simulations show that our DDRNet approach achieves an accuracy of 99.4% without error fluctuations, in a stable state with less than 15 epochs contrast with state-of-art approaches. Additionally, specific convolution techniques (SqueezeNet, batch normalization) and image augmentation techniques (dropout, back-propagation, and an optimum learning rate) were examined to assess the system performance based on MNIST dataset (available at: http://yann.lecun.com/exdb/mnist/). 2022, King Fahd University of Petroleum & Minerals. -
Deep Learning-based Gender Recognition Using Fusion of Texture Features from Gait Silhouettes
The gait of a person is the manner in which he or she walks. The human gait can be considered as a useful behavioral type of biometric that could be utilized for identifying people. Gait can also be used to identify a persons gender and age group. Recent breakthroughs in image processing and artificial intelligence have made it feasible to extract data from photographs and videos for various classifying purposes. Gender can be regarded as soft biometric that could be useful in video captured using surveillance cameras, particularly in uncontrolled environments with erratic placements. Gender recognition in security, particularly in surveillance systems, is becoming increasingly popular. Popularly used deep learning algorithms for images, convolutional neural networks, have proven to be a good mechanism for gender recognition. Still, there are drawbacks to convolutional neural network approaches, like a very complex network model, comparatively larger training time and highly expensive in computational resources, meager convergence quickness, overfitting of the network, and accuracy that may need improvement. As a result, this paper proposes a texture-based deep learning-based gender recognition system. The gait energy image, that is created by adding silhouettes received from a portion of the video which portrays an entire gait cycle, can be the most often utilized feature in gait-based categorization. More texture features, such as histogram of oriented gradient (HOG) and entropy for gender identification, have been examined in the proposed work. The accuracy of gender classification using whole body image, upper body image, and lower body image is compared in this research. Combining texture features is more accurate than looking at each texture feature separately, according to studies. Furthermore, full body gait images are more precise than partial body gait images. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Deep Learning-Based Optimised CNN Model for Early Detection and Classification of Potato Leaf Disease
After rice and wheat, potatoes are the third-largest crop grown for human use worldwide. The different illnesses that can harm a potato plant and lower the quality and quantity of the yield cause potato growers to suffer significant financial losses every year. While determining the presence of illnesses in potato plants, consider the state of the leaves. Early blight and late blight are two prevalent illnesses. A certain fungus causes early blight, while a specific bacterium causes late blight. Farmers can avoid waste and financial loss if they can identify these diseases early and treat them successfully. Three different types of data were used in this study's identification technique: healthy leaves, early blight, and late blight. In this study, I created a convolutional neural network (CNN) architecture-based system that employs deep learning to categorise the two illnesses in potato plants based on leaf conditions. The results of this experiment demonstrate that CNN outperforms every task currently being performed in the potato processing facility, which needed 32 batch sizes and 50 epochs to obtain an accuracy of about 98%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.