Deep Neural Network Algorithm Research Articles

Fast-M Adversarial Training Algorithm for Deep Neural Networks

Fast-M Adversarial Training Algorithm for Deep Neural Networks

Predicting atrial fibrillation recurrence after catheter ablation using an artificial intelligence-enabled electrocardiogram algorithm

Abstract Introduction Predicting atrial fibrillation (AF) recurrence post catheter ablation may help to assess procedural eligibility and determine AF management. While several predictors of AF recurrence post ablation have been established and numerous clinical risk scores have been proposed, their performance remained underwhelming and clinical utility was limited. Hence, new predictors are needed. Artificial intelligence (AI) algorithms using deep neural networks (DNN) to analyze biometrical data might generate such predictors. DNN algorithms have been developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether these algorithms can function as a predictor of AF recurrence after AF ablation remains unknown. Purpose To evaluate the prediction of AF recurrence after ablation using an AI-enabled ECG algorithm trained to predict AF on an ECG in sinus rhythm. Methods This study retrospectively analyzed observational data from the DIGITOTAL study, that monitored AF recurrence with a PPG-based smartphone application in 96 subjects after AF ablation. Patients with a 12-lead ECG in SR available within a timeframe of 3 months before the ablation procedure were included in the analysis. Although all patients had a history of AF, an AF-risk score was calculated by the DNN described elsewhere.1 Results An ECG in sinus rhythm was available in 53 patients (14 women [26.4%]; mean [SD] age, 62.0 [9.7] years) out of the 96 patients followed-up in the DIGITOTAL study. Testing the DNN on the last ECG before the ablation procedure resulted in an area under the receiver operating curve (AUC) of 0.65 (95% CI, 0.49 - 0.80), and an area under the precision recall curve (AUPRC) of 0.56 (95% CI, 0.34 - 0.78). The optimal cutoff score resulted in a sensitivity of 60.0% (95% CI, 36.1% - 80.9%), specificity of 66.7% 66.7% (95% CI, 48.2% - 82.0%), accuracy of 0.64 (95% CI, 0.50 - 0.77), F1-score of 55.8% (95% CI, 38.5% - 74.4%), positive predictive value 52.2% (95% CI, 30.6% - 73.2%) and negative predictive value 73.3% (95% CI, 54.1% - 87.7%). Patients classified in the high-risk group versus low-risk group were more likely to exhibit AF recurrence up to one year after AF ablation (hazard ratio, 2.6; 95% CI, 1.1 - 6.5; P-value = 0.037). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a sinus rhythm ECG, was able to predict AF recurrence after ablation with an accuracy comparable to the existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Predicting post-operative atrial fibrillation after cardiac surgery using an artificial intelligence-enabled electrocardiogram algorithm

Abstract Introduction Postoperative atrial fibrillation (POAF) is common after cardiac surgery and is associated with adverse outcomes. Systematic monitoring of POAF is cumbersome, specifically beyond discharge. Therefore, risk stratification may aid to identify patients at high risk of POAF and guide monitoring strategies alongside preventive measures. However, the performance of bedside risk stratification models reliant on clinical risk factors remained underwhelming, necessitating the exploration of more sophisticated models that maintain clinical applicability. Hence, artificial intelligence algorithms (AI) have been suggested to reinforce or replace clinical risk scores. As such, a deep neural network (DNN) algorithm was developed to identify patients with AF based on a 12-lead electrocardiogram (ECG) in sinus rhythm. Whether this algorithm can identify patients at high risk of POAF remains unknown. Purpose To evaluate the usability of an AI-enabled ECG algorithm, that was trained to predict AF in non-surgical conditions, for the prediction of POAF. Methods This study retrospectively analyzed data from the SURGICAL-AF trial that monitored patients after cardiac surgery. The inclusion criteria for this subanalysis comprised: (1) patients without a history of AF prior to cardiac surgery; (2) availability of the raw data of a pre-operative 12-lead ECG in sinus rhythm; and (3) patients with POAF (during hospitalization or up to 91 days after discharge) or patients having completed PPG-based rhythm monitoring per protocol. The AF-risk score was calculated by the DNN described elsewere.1 Results In total, 127 patients (mean [SD] age, 63.4 [8.4] years; 30 women [23.6%];, median [interquartile range] CHA2DS2-VASc score, 2 [1-3]) complied with the inclusion criteria, out of the 450 patients randomized in the SURGICAL-AF trial. Testing the DNN on the last ECG before cardiac surgery resulted in an area under the receiver operating curve (AUC) of 0.66 (95% CI, 0.56 - 0.77) and an area under the precision-recall curve of 0.57 (95% CI, 0.42 -0.72). The optimal cut of score resulted in a sensitivity of 64.3% (95% CI, 48.0%-78.4%), specificity of 64.7% (95% CI, 53.6%-74.8%), accuracy of 0.65 (95% CI, 0.56 - 0.73), F1-score of 54.5% (95% CI, 42.9% - 66.8%), positive predictive value of 47.4% (95% CI, 34.0%-61.0%), and negative predictive value of 78.6% (95% CI, 67.1%-87.5%). POAF occurred within three months after cardiac surgery in 23 patients out of 57 patients classified in the high-risk group (40.4%) versus 15 patients (21.4%) out of 70 patients classified in the low-risk group (hazard ratio, 2.2; 95% CI, 1.2 – 4.3; P-value = 0.020). Conclusions The AI-enabled ECG algorithm, trained to predict AF on a pre-operative sinus rhythm ECG, was able to identify POAF with an accuracy comparable to existing clinical risk scores. Further studies are needed to determine whether the DNN score can be used as an independent predictor and improve existing risk scores.

Electrochemiluminescence in Graphitic Carbon Nitride Decorated with Silver Nanoparticles for Dopamine Determination Using Machine Learning.

Electrochemiluminescence (ECL) luminophores with wavelength-tunable multicolor emissions are essential for multicolor ECL imaging detection and multiplexed analysis. In this work, silver nanoparticle (Ag NP)-decorated graphitic carbon nitride (g-CN@Ag) nanocomposites were synthesized. The morphology, chemical composition, structure, and ECL property of g-CN@Ag were investigated. The prepared g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 can produce blue, blue-green, chartreuse, and yellow colored ECL emissions, respectively, by using K2S2O8 as the coreagent. The ECL emission wavelength of g-CN@Ag can be regulated from 460 to 565 nm by modulating the content of the immobilized Ag NPs. Then, a multicolor ECL detection array was fabricated by using g-CN, g-CN@Ag1, g-CN@Ag5, and g-CN@Ag10 as four ECL luminophores. Dopamine was detected based on its inhibition effect on the multicolor ECL emissions. The linear range is from 0.1 nM to 1 mM with the lowest detection limit of 44 pM. Then, machine learning-assisted multiparameter concentration prediction of dopamine was further carried out by combining the deep neural network (DNN) algorithm. This work provides a new avenue to regulate the ECL emission wavelength of g-CN by using the metal nanoparticle modification strategy and presents an effective machine learning-assisted multicolor ECL detection strategy for accurate multiparameter quantitative detection.

An Open-Source ML-Based Full-Stack Optimization Framework for Machine Learning Accelerators

Parameterizable machine learning (ML) accelerators are the product of recent breakthroughs in ML. To fully enable their design space exploration (DSE), we propose a physical-design-driven, learning-based prediction framework for hardware-accelerated deep neural network (DNN) and non-DNN ML algorithms. It adopts a unified approach that combines power, performance, and area (PPA) analysis with frontend performance simulation, thereby achieving a realistic estimation of both backend PPA and system metrics such as runtime and energy. In addition, our framework includes a fully automated DSE technique, which optimizes backend and system metrics through an automated search of architectural and backend parameters. Experimental studies show that our approach consistently predicts backend PPA and system metrics with an average 7% or less prediction error for the ASIC implementation of two deep learning accelerator platforms, VTA and VeriGOOD-ML, in both a commercial 12 nm process and a research-oriented 45 nm process.

Acoustic logging array signal denoising using U-net and a case study in TangGu oil field

Abstract This study developed a noise-reduction method for acoustic logging array signals using a deep neural network algorithm in the time-frequency domain. Initially, we derived analytical solutions for the received waveforms when the acoustic logging tool was positioned either at the centre or eccentrically within the borehole. To simulate the received waveforms across various formations, we developed a real-axis integration algorithm. Subsequently, we devised a noise-reduction algorithm workflow based on a convolutional neural network (CNN) and configured the structure and parameters of the U-net using TensorFlow. To address the scarcity of open datasets, we established both signal and noise datasets. The signal dataset was generated using theoretical simulation encompassing various model parameters, while the noise dataset was collected during tool testing and downhole operations. The trained model demonstrated substantial noise-reduction capabilities during validation. To validate the effectiveness of the algorithm, we applied noise reduction to actual data collected during downhole operations in the TangGu oilfield, yielding impressive results across different types of noisy data. Therefore, the U-net-based time-domain noise-reduction algorithm proposed in this paper holds the potential to significantly improve the quality of acoustic logging array signals.

An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creation of deep learning systems that analyze clinical image data to detect disease-specific features with maximum sensitivity. Moreover, these systems hold promise of early accurate diagnosis and treatment of patients with common progressive diseases. DenseNet, ResNet, and VGG-16 are among a few of the deep learning Convolutional Neural Network (CNN) algorithms that have been introduced and are being investigated for potential application within ophthalmology. In this study, the authors sought to create and evaluate a novel ensembled deep learning CNN model that analyzes a dataset of shuffled retinal color fundus images (RCFIs) from eyes with various ocular disease features (cataract, glaucoma, diabetic retinopathy). Our aim was to determine (1) the relative performance of our finalized model in classifying RCFIs according to disease and (2) the diagnostic potential of the finalized model to serve as a screening test for specific diseases (cataract, glaucoma, diabetic retinopathy) upon presentation of RCFIs with diverse disease manifestations. We found adding convolutional layers to an existing VGG-16 model, which was named as a proposed model in this article that, resulted in significantly increased performance with 98% accuracy (p<0.05), including good diagnostic potential for binary disease detection in cataract, glaucoma, diabetic retinopathy. The proposed model was found to be suitable and accurate for a decision support system in Ophthalmology Clinical Framework.

A Novel Rice Plant Leaf Diseases Detection Using Deep Spectral Generative Adversarial Neural Network

The farming industry widely requires automatic detection and analysis of rice diseases to avoid wasting financial and other resources, reduce yield loss, improve processing efficiency, and obtain healthy crop yields. The proposed Deep Spectral Generative Adversarial Neural Network (DSGAN2) method is used for detecting rice plant leaf disease. Initially, fed into the input of healthy and non-healthy leaves from the collected dataset. Then, apply an Improved Threshold Neural Network (ITNN) method to enhance the image quality. Next, it uses a Segmentation using a Segment Multiscale Neural Slicing (SMNS) algorithm to identify the support-intensive color saturation based on the enhanced image. After that, the Spectral Scaled Absolute Feature Selection (S2AFS) method is applied to select optimal features and the closest weight from segmented rice plant leaves. Social Spider Optimization will select the feature using the Closest Weight (S2O-FCW) algorithm to analyze the feature weight values. Finally, the proposed Soft-Max Logistic Activation Function with Deep Spectral Generative Adversarial Neural Network (DSGAN2) algorithm detects rice plant disease based on selected features. With an accuracy of 97%, the model helps farmers identify and identify Rice Plant diseases. The proposed system Deep Spectral Generative Adversarial Neural Network (DSGAN2) produces a decreasing false rate compared to the existing system of ACPSOSVM-Dual Channels Convolutional Neural Network (APS-DCCNN) is 55.2%, Alex Net is 50.4%, and Convolutional Neural Network (CNN) is 49.5%.

An intelligent risk assessment on prediction of COVID-19 pandemic using DNN and TSA: An empirical case study in Thailand

The WHO has declared that the COVID-19 pandemic is a severe health crisis. Currently, variants of concern are delta and omicron, including sub-lineages of the omicron which are XBB and BQ.1 variants. Decision planning with situation awareness is important during the COVID-19 pandemic, especially demand planning for medical supplies following pandemic probability or severity via pandemic risk assessment. Therefore, this research proposes an intelligent risk assessment on the prediction of the COVID-19 pandemic using deep learning with deep neural network (DNN) and tunicate swarm algorithm (TSA). The results show the model can accurately predict the distance and elapsed time of the next COVID-19 case based upon the previous case and evaluate the associated risks. The contribution of this research, as prediction model is based upon a DNN, it has the ability to learn and by implementing the TSA, it can improve theoretically the performance of the DNN for more precise prediction and faster convergence to the optimal solution. The prediction results are practically expanded to analyze risk assessment using probability and the data envelopment analysis (DEA). The benefit of this research is that the proposed methodology demonstrates the prediction results using risks assessment based upon intelligent risk assessment charts. The Government or those involved can use the proposed methodology to achieve a better decision-making and management to control the COVID-19 pandemic in terms of supplying the medical supplies into pandemic areas.

Enhancing Arabic Sign Language Recognition using Deep Learning

The present time, Sign language is very important for people who suffer from hearing loss or who cannot speak. Normal humans tend to disregard the significance of signal language, which is a mere supply of communique to mute and deaf societies. So, this study proposes a developed model for sign Language Recognition for Arabic using the Deep learning Convolutional Neural Network (CNN) algorithm. Then set the algorithm by developing programming on Open-CV, using Python language. The dataset contains 54049 snapshots of Arabic signal language alphabets. The 32 folders were created, and each one of them included 1500 images incorporating hand gestures in at-variance environments. The data set was divided into a training section with a percentage of 70 %, a section for testing with a percentage of 20 %, and a section for validation with a percentage of 10 %. The results show that the suggested model achieved an accuracy rate of 94.8%, and it has proven its effectiveness and success, especially after being tried and tested by several users and obtaining their comments and feedback.

Advances in Speech Emotion Recognition and Analysis: A Review of Applied Machine Learning Methodologies

Abstract: The most important marketing tactics in the current environment is emotion detection based on individual’s interests, allowing extensive customization to cater to the varied needs of customers. For instance, contact centers may start playing music while a caller is on the line if they are agitated. A smart car that slows down when the driver is angry or scared is another illustration.The recognition of speech emotion has become a vital task in the field of computer science department . A very important aspect of human emotional state information is speech emotion that is the source of communication between human and computer interactions . The recognition of speech emotion is currently a very hot topic in the field of machine learning and deep learning and many systems have been build and are currently working on the recognition of these speech emotions using deep learning and neural networks algorithms. In this paper, various types of models and features like Mel-frequency Cepstral Coefficients (MFCC), Modulation spectral features (MSFs), Low-Level Descriptors(LLDs) for speech emotion recognition are categorized. Ultimately, the algorithms like recurrent neural network(RNN), support vector machine(SVM) used in different models are tabulated presenting their advantages and disadvantages along with their features and efficiency.

Investigation into designing VLSI of a flexible architecture for a deep neural network accelerator

Deep learning, a branch of AI, makes use of specialised neural networks. Computational acceleration of high-performance deep neural network algorithms continues to necessitate efficient architecture, high memory bandwidth, parallel processing, and resources, despite decades of study on such algorithms. Excessive space requirements are a problem for DNN implementations caused by resource-intensive components like activation functions (AF) and multiply-and-accumulate (MAC) units. In addition, edge-AI applications necessitate a densely packed, power-hungry, high-throughput DNN accelerator. If we want to build a DNN accelerator that uses little power and occupies little space, we need to optimise the MAC architecture, the AF, and the network complexity so that data flows efficiently. In addition, providing functional configurability while working with restricted chip surface is a difficulty for DNN hardware designs based on ASICs. This dissertation explores the efficient and low-power VLSI architecture of DNN accelerators, addressing the hardware implementation of DNN and targeting applications with limited resources. In order to assess MAC and non-linear AF operations, we investigate and enhance the CORDIC architecture. The poor throughput is a major downside of CORDIC-based architectures, even though they are area and power efficient. Consequently, we suggest a pipelined design for CORDIC-based MAC and AF that focuses on performance. Due to the increased hardware resource consumption that comes with pipeline stages, this study investigates the mutual exclusivity of CORDIC stages and investigates in depth the accuracy variation related to the number of stages needed to achieve high throughput.

Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.

This study evaluated a deep neural network (DNN) algorithm for automated aortic diameter quantification and aortic dissection detection in chest computed tomography (CT). A total of 100 patients (median age: 67.0 [interquartile range 55.3/73.0] years; 60.0% male) with aortic aneurysm who underwent non-enhanced and contrast-enhanced electrocardiogram-gated chest CT were evaluated. All the DNN measurements were compared to manual assessment, overall and between the following subgroups: (1) ascending (AA) vs. descending aorta (DA); (2) non-obese vs. obese; (3) without vs. with aortic repair; (4) without vs. with aortic dissection. Furthermore, the presence of aortic dissection was determined (yes/no decision). The automated and manual diameters differed significantly (p < 0.05) but showed excellent correlation and agreement (r = 0.89; ICC = 0.94). The automated and manual values were similar in the AA group but significantly different in the DA group (p < 0.05), similar in obese but significantly different in non-obese patients (p < 0.05) and similar in patients without aortic repair or dissection but significantly different in cases with such pathological conditions (p < 0.05). However, in all the subgroups, the automated diameters showed strong correlation and agreement with the manual values (r > 0.84; ICC > 0.9). The accuracy, sensitivity and specificity of DNN-based aortic dissection detection were 92.1%, 88.1% and 95.7%, respectively. This DNN-based algorithm enabled accurate quantification of the largest aortic diameter and detection of aortic dissection in a heterogenous patient population with various aortic pathologies. This has the potential to enhance radiologists' efficiency in clinical practice.

Intelligent algorithm-based maximum power point tracker for an off-grid photovoltaic-powered direct-current irrigation system

Abstract This research aims to enhance the performance of photovoltaic (PV) systems on a 2-fold basis. Firstly, it introduces an advanced deep artificial neural network algorithm for accurate and fast maximum power point tracking, ensuring optimal extraction of electrical power from PV arrays. Secondly, it proposes the use of 96-V, 2.98-kW direct-current (DC) water pumps for farm irrigation, aiming to improve efficiency, reduce cost and complexity, and overcome challenges associated with connecting faraway farm irrigation systems to the utility grid. In this study, it has been demonstrated that the use of DC pumps greatly improves system performance and efficiency by eliminating the need for isolation transformers, power passive filters and inverters, therefore simplifying the architecture of the system. The efficacy of the proposed methodology is confirmed by MATLAB®/Simulink® simulation results, whereby the proposed algorithm attains a mean squared error of 6.5705 × 10–5 and a system efficiency approaching 99.8%, ensuring a steady voltage under varying load conditions.

Implementation of specifically designed deep neural networks for the prediction and optimization of tensile properties of aluminum-copper alloy

Alloys are engineered materials aimed at enhancing mechanical properties. Extensive research has focused on identifying the optimal metal composition for alloys with superior tensile strength. This study validates the stiffness and strength values of an aluminum-copper alloy through a comparison with a molecular dynamics simulation. Subsequently, 100 data points were obtained from the simulation, and a deep neural network (DNN) with three hidden layers was employed. The DNN was trained, tested, and its structure optimized using the Taguchi design of experiment. The proposed DNN structures successfully predicted the maximum values of the stiffness and strength, which were further verified using molecular dynamics simulation. Notably, the results demonstrated the complete reliability of the Taguchi-designed DNN algorithm in this application.

Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data

Sintering performance index can reflect the quality of sintered ore, and the level of sintered ore performance directly affects the stability of blast furnace production. Accurate prediction of sinter performance metrics is essential to optimise sinter quality, improve production efficiency and reduce energy consumption. Based on the sintering big data, this paper proposes the Optuna-DFNN model by combining the fuzzy neural network algorithm, the deep neural network algorithm and the Optuna framework to address the problems of difficulty in tuning the parameter, lack of self-learning ability, and poor generalisation of the model of the traditional method when faced with the input of multiple parameters. The paper predicts the four prediction indexes of yield rate, return fines ratio, drum index and RDI+3.15 respectively. Among them, the relative error of the prediction of the yield rate is within 1.3 %, the relative error of the prediction of the return fines ratio is within 2.8 %, the relative error of the prediction of the drum index is within 0.32 %, and the relative error of the prediction of the RDI+3.15 is within 1.4 %. The results indicate that the Optuna-DFNN model has better performance in predicting sintering performance metrics. Based on the Optuna-DFNN sintered ore performance prediction model, the quality of sintered ore can be accurately predicted, the sintering process parameters can be adjusted in a timely manner, the sintering process can be optimized, and the utilization coefficient of sintering can be improved. Provide a reference for the optimisation of production processes, with a positive impact on cost reduction, environmental protection and increased sustainability of production.

A deep neural network for predicting soil texture using airborne radiometric data

The ternary nature of soil texture, defined by its proportions of clay, silt, and sand, makes it challenging to predict through linear regression models from other soil attributes and auxiliary variables. The most promising results in this field have been recently achieved by Machine Learning methods which are able to derive non-linear, non-site-specific models to predict soil texture. In this paper we present a method of constructing a pair of Deep Neural Network (DNN) algorithms that can predict clay and sand soil contents from Airborne Gamma Ray Spectrometry data of K and Th ground abundances.We tested the algorithm's hyperparameters through various configurations to optimize the DNNs' performance, effectively avoiding underfitting and overfitting of the models. This led to the creation of a high-resolution 20 m × 20 m soil texture map from dense AGRS data, significantly refining the previous map's granularity. The application of the obtained DNN models to unseen sites can be supported by future training on additional textural classes.

IOT based Waste Segregation Using Deep Learning

In the rapidly urbanizing world, effective waste management is crucial to maintain environmental sustainability. This project presents an innovative solution utilizing Internet of Things (IoT) technology and deep learning Convolutional Neural Networks (CNN) algorithm for automated waste segregation. We propose a smart waste segregation system equipped with sensors to detect the level of waste in bins. The system utilizes a CNN model trained using Deep Learning to classify waste items into different categories, ensuring proper segregation. When a bin reaches its full capacity, the IoT sensors trigger an alert mechanism, sending real-time notifications to the concerned authorities or waste management personnel. This proactive approach not only optimizes waste collection but also promotes recycling by ensuring the segregated waste reaches the appropriate recycling facilities. The integration of IoT devices and deep learning algorithms enhances the efficiency of waste management processes, reducing environmental pollution and promoting a sustainable future. Key Words: IoT, waste management, deep learning, CNN algorithm, waste segregation, environmental sustainability, real-time monitoring, sensor technology, recycling, smart cities, automation.

Boosting Clear Cell Renal Carcinoma-Specific Drug Discovery Using a Deep Learning Algorithm and Single-Cell Analysis.

Clear cell renal carcinoma (ccRCC), the most common subtype of renal cell carcinoma, has the high heterogeneity of a highly complex tumor microenvironment. Existing clinical intervention strategies, such as target therapy and immunotherapy, have failed to achieve good therapeutic effects. In this article, single-cell transcriptome sequencing (scRNA-seq) data from six patients downloaded from the GEO database were adopted to describe the tumor microenvironment (TME) of ccRCC, including its T cells, tumor-associated macrophages (TAMs), endothelial cells (ECs), and cancer-associated fibroblasts (CAFs). Based on the differential typing of the TME, we identified tumor cell-specific regulatory programs that are mediated by three key transcription factors (TFs), whilst the TF EPAS1/HIF-2α was identified via drug virtual screening through our analysis of ccRCC's protein structure. Then, a combined deep graph neural network and machine learning algorithm were used to select anti-ccRCC compounds from bioactive compound libraries, including the FDA-approved drug library, natural product library, and human endogenous metabolite compound library. Finally, five compounds were obtained, including two FDA-approved drugs (flufenamic acid and fludarabine), one endogenous metabolite, one immunology/inflammation-related compound, and one inhibitor of DNA methyltransferase (N4-methylcytidine, a cytosine nucleoside analogue that, like zebularine, has the mechanism of inhibiting DNA methyltransferase). Based on the tumor microenvironment characteristics of ccRCC, five ccRCC-specific compounds were identified, which would give direction of the clinical treatment for ccRCC patients.

NOVEL METHOD VITAMIN DEFICIENCY DETECTION USING ALEXNET DNN ALGORITHM

Vitamin deficiencies can have significant impacts on overall health and well-being. Early detection plays a crucial role in preventing complications and improving outcomes. However, traditional methods for detecting deficiencies can be time-consuming and costly. This project aims to develop a novel method for detecting vitamin deficiencies using the AlexNet DNN algorithm, a powerful deep learning model for image classification. The purpose of this project is to explore the feasibility of using image analysis and deep learning techniques to detect vitamin deficiencies accurately and efficiently. The objectives include improving the accuracy of detection, reducing false positives and negatives, and developing a reliable and accessible tool for early detection. To achieve our objectives, we will gather a large dataset of images depicting various vitamin deficiencies. These images will be preprocessed to enhance features and reduce noise. The AlexNet DNN algorithm will be trained on this dataset, learning to recognize patterns and features associated with different deficiencies. The algorithm will undergo rigorous testing and evaluation to ensure its effectiveness. KEYWORDS— Vitamins, Deficiency, AlexNet, Deep Neural Network(DNN), Effectiveness