This research proposes a novel approach for strep throat detection utilizing the Multi-Task Cascaded Convolutional Neural Network (MTCNN) algorithm and a smartphone. By incorporating advanced facial detection techniqu...
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Object detection and pattern detection are fundamental problems in computer vision, and have real-world uses such as in autonomous vehicles, healthcare, and security. Over the recent years, improvements made in the de...
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Optimization of industrial activities is significantly helped by Advanced Process Control (APC), which increases efficiency, decreases costs, and improves product quality. Artificial Neural Networks (ANNs) and the Int...
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diabetic retinopathy (dr) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, andresulting in vision loss. The critical need for early detection and effective...
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ISBN:
(数字)9798350352689
ISBN:
(纸本)9798350352696
diabetic retinopathy (dr) emerges as a serious complication associated with diabetes mellitus, causing damage to retinal blood vessels, andresulting in vision loss. The critical need for early detection and effective management to curb the progression towards vision impairment and potential blindness underscores the significance of this issue. To address these challenges, have propose a novel deep learning system utilizing a densely connected Convolutional Network (denseNet-201) for the efficient detection of dr. This model is specifically trained to classify fundus images into six distinct categories: no dr, mild, moderate, and severe, proliferative dr, and hard exudates. The datasets utilized in this study include the publicly available diabetic retinopathy detection 2015 and Aptos 2019 blindness detection datasets from Kaggle. The proposed system comprises multiple stages, including data Collection, Preprocessing, Augmentation, and Modeling. during the preprocessing phase, normalization techniques are to eliminate biases ordistortions present in the fundus images. Furthermore, the augmentation of data is employed to enhance the diversity and quantity of the training dataset. In the modeling stage, have designed and implemented the deep learning model, with the robust and high-performing denseNet-201 architecture selected as the model’s backbone. The model exhibits a significant accuracy of 87.7% in its classification task. Additionally, have introduced a deep Neural Network (dNN) model, achieving an impressive accuracy rate of 95.8%. These results underscore the effectiveness of our proposed system in accurately and efficiently detecting dr. The primary objective of this research is to develop a reliable and automated system fordiabetic retinopathy detection, with the overarching goal of improving the quality of life for individuals affected by diabetes.
Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greaterresearch to offer appropriate assistance. Interaction and communication with others are a big challenge. Si...
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ISBN:
(数字)9798331532420
ISBN:
(纸本)9798331532437
Persons who are deaf and who cannot speak face a variety of challenges, which emphasises the need for greaterresearch to offer appropriate assistance. Interaction and communication with others are a big challenge. Since sign language is the major means of communication for these people, there is a significant knowledge gap among hearing individuals when it comes to understand and interpret sign language. As a result, there has been a notable expansion in the field of sign language study to close this gap. This work highlights the importance of sign language detection and offers a thorough analysis of relevant studies carried out in this domain using deep learning technique. It approaches the issue from multiple perspective, including translation, sign language recognition, and access to relevant datasets. Understanding these facets is intended to create a meaningful impact on sign language literature and its practical implementation. VGG16 deep learning model for sign language recognition offers the best solution for solving communication challenges faced by deaf and muted individuals. The results show that the VGG16 based technique achieves accuracy of around 95.8%. The VGG16 model uses a Convolutional Neural Network (CNN) architecture, which applies many layers of convolutional filters to identify patterns and attributes in the input images. recognizing sign language using VGG16
Continuously monitoring of drilling fluid properties and simulating of down hole hydraulics is critical and beneficial for safe and efficient drilling. However, the drilling fluids properties are generally taken once ...
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ISBN:
(纸本)9781613998427
Continuously monitoring of drilling fluid properties and simulating of down hole hydraulics is critical and beneficial for safe and efficient drilling. However, the drilling fluids properties are generally taken once every regulated interval and the downhole wellbore conditions anddrilling fluids performance are judged accordingly and sometimes experientially for lack of updateddata. In this paper, we present a novel unit called On –line drilling Fluids Properties Logging System (OdFPLS) forreal-time measurement of drilling fluids property and software fordownhole hydraulics modeling by integrating of these real-time measureddata. The OdFPLS unit consists of four testing modules, they are density module, rheology module, Funnel Viscosity module, and Ion Content module. during working, the fluid sample is sucked in and flowed first to the density module, tested there and then flowed to other modules in above order and finally discharged into waste pits without any human intervention. The test data is stored in the database of well-site digital platform and shared to the remote Operational Centers (rOCs) fordrilling fluids performance diagnosis anddownhole hydraulics modeling The indoor and field pilot tests showed that the unit is capable of measuring fluid properties such as muddensity, funnel viscosity, rheological behavioral parameters, calcium and chlorine concentration and so on properly and accurately while drilling compared with manual measurement by lab operators. The drilling fluids performance diagnosis and hydraulics modeling provides not only rheological mode identification but also the real time equivalent circulating density ECd and equivalent static density ESd at any point in the wellbore during drilling. It also delivers surge and swab pressure calculation as well as ECddata while tripping drill pipe or setting casing. The timely availability of the information enables the mud engineer anddrilling engineer to aware the status and trends of
Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe we...
Crop prices in India are vulnerable to environmental factors such as rainfall and temperature which can cause supply variations and price shifts. Government policies are intended to keep prices stable during severe weather occurrences. An extensive crop price prediction approach utilizing diverse machine learning techniques and encompassing features such as temperature extremes, humidity, precipitation, and probabilities is introduced in this research work. Employing algorithms including linearregression, decision trees, random forests, K-Nearest Neighbors (KNN), XGBoost, and Bayesian regression, the research conducts comprehensive evaluation and comparative analysis to identify optimal techniques for crop price prediction, thereby yielding valuable insights for agricultural applications. Amid the intricacies of contemporary agricultural markets, where numerous factors influence prices, machine learning emerges as a potent tool capable of capturing intricate influences. By exploring a range of machine learning algorithms, complex relationships within data are unveiled, establishing a basis for evidence-based methodology selection. The research results heighten forecasting precision and empower stakeholders with actionable insights, facilitating informeddecisions, risk mitigation, and capitalization on opportunities. This signifies a substantial stride towards the convergence of machine learning and agriculture, fostering enhanced predictive accuracy in crop price forecasting.
Given its increasing impact and enormous global societal impact, Alzheimer’s disease (Ad) presents a serious issue in the healthcare industry. Effective therapies and management techniques for Addepend on early dete...
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ISBN:
(数字)9798350374957
ISBN:
(纸本)9798350374964
Given its increasing impact and enormous global societal impact, Alzheimer’s disease (Ad) presents a serious issue in the healthcare industry. Effective therapies and management techniques for Addepend on early detection and a precise diagnosis. On the other hand, traditional diagnostic approaches frequently depend on expensive imaging methods and arbitrary clinical judgments, which causes delays in diagnosis and less than ideal results. By utilizing MrI scans and handwriting samples, the suggested strategy embraces the ability of multi-modal data integration to offer a novel perspective to Ad prediction. A non-invasive and economical method of identifying minor motor and cognitive problems suggestive of early-stage $A d$ is handwriting analysis, whereas MrI offers important insights into the structural brain abnormalities linked to Ad. To rigorously pre-process and extract significant features from MrI and handwriting data, state-of-the-art deep learning techniques are used. A multimodal neural network architecture is then fed these features for combined analysis and prediction. Accuracy rates of $\mathbf{8 8 \%}$ for handwriting data and $\mathbf{9 0} \%$ for MrI images are attained by each independently trained model. A unified classifier is then used to aggregate the outputs of different models andrefine the categorization through the addition of denser layers A remarkable $\mathbf{9 7 \%}$ accuracy on the test set is achieved by this combined model, which considerably improves diagnostic accuracy. The technique provides a comprehensive approach to Ad prediction by combining structural and functional information from MrI with handwriting data, enabling early detection and individualized treatment plans. In order to create robust and trustworthy diagnostic tools, the results emphasize how crucial it is to integrate many data modalities and use deep learning approaches.
This paper presents a proof-of-concept study that examines the utilization of generative AI and mobile robotics for autonomous laboratory monitoring in the pharmaceutical r&d laboratory. The study investigates the...
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Cyberbullying, the malicious use of digital communication channels to intimidate, harass, or harm individuals, has emerged as a significant concern in the era of widespread internet usage and social media platforms. T...
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