Disease Classification Model Research Articles

Artificial intelligence-enabled coconut tree disease detection and classification model for smart agriculture

Real-time and accurate plant disease recognition systems help in the development of disease mitigation strategies and ensure food security on a large scale compounded with small-scale economic crop protection. The current research article presents an Artificial Intelligence Enabled Coconut Tree Disease Detection and Classification (AIE-CTDDC) model for smart agriculture. The aim of the presented AIE-CTDDC technique is to classify the coconut tree diseases in a smart farming environment so as to enhance the crop productivity. Firstly, the AIE-CTDDC model applies median filtering-based noise removal technique. Then, the Bayesian fuzzy clustering-based segmentation method is employed for the detection of the affected leaf regions. Besides, the capsule network (CapsNet) method is exploited as a feature extractor. In this study, the Harris Hawks Optimization (HHO) with Gated Recurrent Unit (GRU) model is exploited for the detection of diseases in coconut trees. The experimental analysis was conducted upon AIE-CTDDC model and the outcomes confirmed the better performance of the proposed AIE-CTDDC model over recent state-of-the-art techniques.

Enhanced Heart Disease Prediction Based on Machine Learning and χ2 Statistical Optimal Feature Selection Model

Automatic heart disease prediction is a major global health concern. Effective cardiac treatment requires an accurate heart disease prognosis. Therefore, this paper proposes a new heart disease classification model based on the support vector machine (SVM) algorithm for improved heart disease detection. To increase prediction accuracy, the χ2 statistical optimum feature selection technique was used. The suggested model’s performance was then validated by comparing it to traditional models using several performance measures. The proposed model increased accuracy from 85.29% to 89.7%. Additionally, the componential load was reduced by half. This result indicates that our system outperformed other state-of-the-art methods in predicting heart disease.

A Novel Feature Selection with Hybrid Deep Learning Based Heart Disease Detection and Classification in the e-Healthcare Environment.

With the advancements in data mining, wearables, and cloud computing, online disease diagnosis services have been widely employed in the e-healthcare environment and improved the quality of the services. The e-healthcare services help to reduce the death rate by the earlier identification of the diseases. Simultaneously, heart disease (HD) is a deadly disorder, and patient survival depends on early diagnosis of HD. Early HD diagnosis and categorization play a key role in the analysis of clinical data. In the context of e-healthcare, we provide a novel feature selection with hybrid deep learning-based heart disease detection and classification (FSHDL-HDDC) model. The two primary preprocessing processes of the FSHDL-HDDC approach are data normalisation and the replacement of missing values. The FSHDL-HDDC method also necessitates the development of a feature selection method based on the elite opposition-based squirrel searchalgorithm (EO-SSA) in order to determine the optimal subset of features. Moreover, an attention-based convolutional neural network (ACNN) with long short-term memory (LSTM), called (ACNN-LSTM) model, is utilized for the detection of HD by using medical data. An extensive experimental study is performed to ensure the improved classification performance of the FSHDL-HDDC technique. A detailed comparison study reported the betterment of the FSHDL-HDDC method on existing techniques interms of different performance measures. The suggested system, the FSHDL-HDDC, has reached its maximum level of accuracy, which is 0.9772.

Empirical analysis of chronic disease dataset for multiclass classification using optimal feature selection based hybrid model with spark streaming

Recent advancement in the field of pervasive healthcare monitoring systems causes the generation of a huge amount of lifelog data in real-time. Chronic diseases are one of the most serious health challenges in developing and developed countries. According to WHO, this accounts for 73% of all deaths and 60% of the global burden of diseases. Chronic disease classification models are now harnessing the potential of lifelog data to explore better healthcare practices. This paper basically constructs an optimal feature selection-based hybrid model which is designed by integrating k-means clustering for handling unlabeled data, applying Synthetic Minority Oversampling Technique (SMOTE), selecting optimal features using PCA, and applying Random Forest (RF) classifier to classify chronic diseases. Since lifelog data analysis is crucial due to its sensitive nature; thus the conventional classification models show limited performance. Therefore, designing new classifiers for the classification of chronic diseases using lifelog data is the need of the age. The vital part of building a good model depends on pre-processing of the dataset, identifying important features, and then training a learning algorithm with suitable hyper parameters for better performance. The proposed approach improves the performance of existing methods using a series of steps such as (i) removing redundant or invalid instances, (ii) making the data labeled using clustering and partitioning the data into classes, (iii) making the classes balance to avoid biased result across majority class (iv) identifying the suitable subset of features by applying either some domain knowledge or selection algorithm, (v) hyper parameter tuning for models to get best results (vi) developing a new hybrid model i.e. optimal feature selection based unsupervised random forest classifier (OFS-URFC), which gives best results in binary classification as well as multiclass classification and (vii) performance of this newly designed classifier is also evaluated using Spark streaming environment for processing the data in real time fashion. For this purpose, two-time series datasets are used in the experiment to compute the accuracy, recall, precision, and f1-score etc. The experimental analysis proves the suitability of the proposed approach as compared to the conventional classifiers and our newly constructed model achieved highest accuracy and reduced training complexity among all.

Identification of signature of gene expression in biliary atresia using weighted gene co-expression network analysis.

Biliary atresia (BA) is the most common cause of obstructive jaundice during the neonatal period. This study aimed to identify gene expression signature in BA. The datasets were obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis identified a critical module associated with BA, whereas Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed the functions of the essential modules. The high-connectivity genes in the most relevant module constructed protein–protein interaction networks via the string website and Cytoscape software. Hub genes screened by lasso regression consisted of a disease classification model using the randomforest method. Receiver operating characteristic curves were used to assess models’ sensitivity and specificity and the model was verified using the internal and external validation sets. Ten gene modules were constructed by WGCNA, of which the brown module had a strong positive correlation with BA, comprising 443 genes. Functional enrichment analysis revealed that module genes were mainly involved in biological processes, such as extracellular matrix organization, cell adhesion, inflammatory response, and the Notch pathway (P < .001), whereas these genes were involved in the metabolic pathways and cell adhesion molecules (P < .001). Thirty-nine high-connectivity genes in the brown module constructed protein-protein interaction networks. keratin 7 (KRT7) and C-X-C motif chemokine ligand 8 (CXCL8) were used to construct a diagnostic model that had an accuracy of 93.6% and the area under the receiver operating curves for the model was 0.93. The study provided insight into the signature of gene expression and possible pathogenesis of BA; furthermore, it identified that the combination of KRT7 and CXCL8 could be a potential diagnostic model for BA.

Soybean rust detection and disease severity classification by remote sensing

AbstractThe detection and monitoring of soybean rust (SBR) through remote sensing is promising because of the importance of the crop and the aspects of the disease. We evaluated the effects of different levels of SBR severity on soybean [Glycine max (L.) Merr.] leaflets reflectance aiming for the construction of a disease classification model. Leaflet reflectance was evaluated on two cultivars (susceptible and partially resistant) at four disease severity levels: healthy, low, moderate, and high. Leaflets were collected in the field and taken to the laboratory for spectral evaluation through the spectrophotometer UV 2700 coupled with Integrating Sphere Attachment ISR‐603, in the range of 270–1000 nm. The feasibility of using a collection of vegetation indices (VIs) and data dimensionality reduction through multiple factor analysis (MFA) was evaluated, and a classification model was constructed. Ten algorithms were assessed based on precision, sensibility, and accuracy parameters, using 80% of the dataset as training data and 20% as testing dataset. The visible range and red edge region contributed more significantly to the disease prediction and classification model. The MFA performed satisfactorily in the dimensionality reduction and unveiled the effect of specific wavelengths on the classification of each class. Most of the VIs studied had high correlation performance across the severity classes. Classification accuracy and precision were &gt;70% for all models. Linear support vector machine with the collection of VIs achieved the best results. This study provides a practical path for developing a detection model to be integrated into SBR management programs.

A Smart Solution for Tomato Leaf Disease Classification by Modified Recurrent Neural Network with Severity Computation

The artificial intelligence-assisted deep learning approach plays a significant task in identifying diseases through a large set of plant leaf images. Thus, the major aim of the designed model is to design and develop the tomato leaf disease classification model with intelligent approaches. Initially, the images are gathered from standard datasets and real time datasets. Then, the pre-processing is conducted for cleaning, shadow removal, and enhancing the images using the image enhancement approach. Then, the spot segmentation from the leaf is performed by the Optimized K-Means Clustering (OKMC) using Standard Deviation-based Grasshopper Horse Herd Optimization (SD-GHHO). Further, the deep feature extraction is extracted from the segmented spot using Convolutional Neural Network (CNN), VGG16, and Residual Networks (ResNet). The gathered features are further concatenated. Then, the optimal features are selected using a new meta-heuristic algorithm called SD-GHHO. Finally, the disease classification is performed with the help of the Modified Recurrent Neural Network (MRNN), where the weight of RNN is optimized using the same SD-GHHO technique. Once after classifying the spots, the disease severity computation is performed. The proposed method has enhanced classification efficiency in high accuracy, specificity, and sensitivity values.

Support vector machine based disease classification model employing hasten eagle Cuculidae search optimization

SummaryThe enormous growth in the field of medicine utilizes machine learning for the well‐being and robustness of human beings. The emerging rate of the disease due to urbanization or various other global facts increases the mortality rate of the individual every year. The detection and classification of disease at the proper time helps an individual to be alive and healthy. Various methods of machine learning are accomplished to classify the diseases, but there is a necessity for the enhancement in the classification methods. In this research, the disease classification is performed using the proposed hasten eagle Cuculidae search optimization algorithm based support vector machine classifier (HECSO‐SVM) with the hybrid kernel function. The feature extraction with the statistical and correlation features presents the effective feature vector for classification and the curse of dimensionality is handled using the proposed HECSO algorithm. Initially, the data will be collected and preprocessing is performed and the features are extracted and selected using the optimization techniques. The proposed method effectively reduces the computation time and increases the accuracy of classification. The analysis is made based on the four different datasets such as Parkinson, heart, lung, and hepatitis that obtains an accuracy rate of 94.582%, 94.100%, 93.985%, 94.582%.

Modelling of deep learning enabled lung disease detection and classification on chest X-ray images

ABSTRACT The survival rate of people who use medical imaging modalities is improved by earlier detection of lung illness. The most recent developments in deep learning (DL) make it possible to correctly recognize and categorize lung disorders in various medical images. To categorize diseases in a publicly available chest X-ray picture dataset, a variety of machine learning (ML), deep learning (DL), and transfer learning algorithms have been explored in the current study. This research offers an effective deep learning (DL) enabled lung disease detection and classification (DL-LDDC) model with this as its goal. Using chest X-ray pictures, the proposed DLLDDC approach seeks to identify and categorize lung disease. Pre-processing, feature extraction, and classification are just a few of the processes that make up the proposed DL-LDDC approach. Pre-processing for the DL-LDDC technique is primarily divided into three phases, including contrast enhancement, thresholding-based segmentation, and region of interest (RoI) extraction using pretrained U-Net. An extensive range of simulations are performed on the benchmark chest X-ray image dataset to ensure the improved classification performance of the DL-LDDC approach. A thorough comparison of the results showed that the DL-LDDC technique outperformed the most recent state-of-the-art procedures in terms of numerous evaluation parameters.

Assessing the impact of MRI based diagnostics on pre-treatment disease classification and prognostic model performance in men diagnosed with new prostate cancer from an unscreened population

IntroductionPre-treatment risk and prognostic groups are the cornerstone for deciding management in non-metastatic prostate cancer. All however, were developed in the pre-MRI era. Here we compared categorisation of cancers using either only clinical parameters or with MRI enhanced information in men referred for suspected prostate cancer from an unscreened population.Patient and methodsData from men referred from primary care to our diagnostic service and with both clinical (digital rectal examination [DRE] and systematic biopsies) and MRI enhanced attributes (MRI stage and combined systematic/targeted biopsies) were used for this study. Clinical vs MRI data were contrasted for clinico-pathological and risk group re-distribution using the European Association of Urology (EAU), American Urological Association (AUA) and UK National Institute for Health Care Excellence (NICE) Cambridge Prognostic Group (CPG) models. Differences were retrofitted to a population cohort with long-term prostate cancer mortality (PCM) outcomes to simulate impact on model performance. We further contrasted individualised overall survival (OS) predictions using the Predict Prostate algorithm.ResultsData from 370 men were included (median age 66y). Pre-biopsy MRI stage reassignments occurred in 7.8% (versus DRE). Image-guided biopsies increased Grade Group 2 and ≥ Grade Group 3 assignments in 2.7% and 2.9% respectively. The main change in risk groups was more high-risk cancers (6.2% increase in the EAU and AUA system, 4.3% increase in CPG4 and 1.9% CPG5). When extrapolated to a historical population-based cohort (n = 10,139) the redistribution resulted in generally lower concordance indices for PCM. The 5-tier NICE-CPG system outperformed the 4-tier AUA and 3-tier EAU models (C Index 0.70 versus 0.65 and 0.64). Using an individualised prognostic model, changes in predicted OS were small (median difference 1% and 2% at 10- and 15-years’ respectively). Similarly, estimated treatment survival benefit changes were minimal (1% at both 10- and 15-years’ time frame).ConclusionMRI guided diagnostics does change pre-treatment risk groups assignments but the overall prognostic impact appears modest in men referred from unscreened populations. Particularly, when using more granular tiers or individualised prognostic models. Existing risk and prognostic models can continue to be used to counsel men about treatment option until long term survival outcomes are available.

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning

Health monitoring and related technologies are a rapidly growing area of research. To date, the electrocardiogram (ECG) remains a popular measurement tool in the evaluation and diagnosis of heart disease. The number of solutions involving ECG signal monitoring systems is growing exponentially in the literature. In this article, underestimated Orthogonal Matching Pursuit (OMP) algorithms are used, demonstrating the significant effect of concise representation parameters on improving the performance of the classification process. Cardiovascular disease classification models based on classical Machine Learning classifiers were defined and investigated. The study was undertaken on the recently published PTB-XL database, whose ECG signals were previously subjected to detailed analysis. The classification was realized for class 2, class 5, and class 15 cardiac diseases. A new method of detecting R-waves and, based on them, determining the location of QRS complexes was presented. Novel aggregation methods of ECG signal fragments containing QRS segments, necessary for tests for classical classifiers, were developed. As a result, it was proved that ECG signal subjected to algorithms of R wave detection, QRS complexes extraction, and resampling performs very well in classification using Decision Trees. The reason can be found in structuring the signal due to the actions mentioned above. The implementation of classification issues achieved the highest Accuracy of 90.4% in recognition of 2 classes, as compared to less than 78% for 5 classes and 71% for 15 classes.

Plant Disease Detection and Classification Method Based on the Optimized Lightweight YOLOv5 Model

Traditional plant disease diagnosis methods are mostly based on expert diagnosis, which easily leads to the backwardness of crop disease control and field management. In this paper, to improve the speed and accuracy of disease classification, a plant disease detection and classification method based on the optimized lightweight YOLOv5 model is proposed. We propose an IASM mechanism to improve the accuracy and efficiency of the model, to achieve model weight reduction through Ghostnet and WBF structure, and to combine BiFPN and fast normalization fusion for weighted feature fusion to speed up the learning efficiency of each feature layer. To verify the effect of the optimized model, we conducted a performance comparison test and ablation test between the optimized model and other mainstream models. The results show that the operation time and accuracy of the optimized model are 11.8% and 3.98% higher than the original model, respectively, while F1 score reaches 92.65%, which highlight statistical metrics better than the current mainstream models. Moreover, the classification accuracy rate on the self-made dataset reaches 92.57%, indicating the effectiveness of the plant disease classification model proposed in this paper, and the transfer learning ability of the model can be used to expand the application scope in the future.

Retraction Note to: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

Retraction Note to: An optimal artificial neural network based big data application for heart disease diagnosis and classification model

Ensemble of weighted deep concatenated features for the skin disease classification model using modified long short term memory

Skin diseases are considered to be a common disease in human, which have many invisible dangers that may reduce the self-confidence and causes certain psychological depression and in-depth, these skin diseases may also results in skin cancer. These skin diseases needs to be diagnosed with the medical experts but it requires high-level instruments for diagnosing as they suffer from the visual resolution problem while analyzing the skin disease images. Hence, it is important to implement a computer-aided detection scheme for automatically diagnosing the skin disorder. Hence, this work plans to implement an effective skin disease classification method with the help of novel deep learning methodology. Initially, the dataset is gathered and pre-processed by the contrast enhancement technique through “histogram equalization”. After pre-processing, the segmentation of the images is done by the Fuzzy C Means segmentation (FCM). Further the segmented images assigned as a input for the deep feature extraction using Resnet50, VGG16, and Deeplabv3. The features are attained from the final layer of these three techniques and are concatenated. These concatenated features are provided to the feature transformation phase through the weighted feature extraction, which is performed by Hybrid Squirrel Butterfly Search Optimization (HSBSO). The transformed features are given to Modified Long Short Term Memory (MLSTM), where the architecture optimization is done by the same HSBSO for producing the final classified output. The analysis results confirms that the better effectiveness of the introduced method when observing the accuracy of the implemented skin disease classification than the conventional approaches.

ADDLight: An Energy-Saving Adder Neural Network for Cucumber Disease Classification

It is an urgent task to improve the applicability of the cucumber disease classification model in greenhouse edge-intelligent devices. The energy consumption of disease diagnosis models designed based on deep learning methods is a key factor affecting its applicability. Based on this motivation, two methods of reducing the model’s calculation amount and changing the calculation method of feature extraction were used in this study to reduce the model’s calculation energy consumption, thereby prolonging the working time of greenhouse edge devices deployed with disease models. First, a cucumber disease dataset with complex backgrounds is constructed in this study. Second, the random data enhancement method is used to enhance data during model training. Third, the conventional feature extraction module, depthwise separable feature extraction module, and the squeeze-and-excitation module are the main modules for constructing the classification model. In addition, the strategies of channel expansion and = shortcut connection are used to further improve the model’s classification accuracy. Finally, the additive feature extraction method is used to reconstruct the proposed model. The experimental results show that the computational energy consumption of the adder cucumber disease classification model is reduced by 96.1% compared with the convolutional neural network of the same structure. In addition, the model size is only 0.479 MB, the calculation amount is 0.03 GFLOPs, and the classification accuracy of cucumber disease images with complex backgrounds is 89.1%. All results prove that our model has high applicability in cucumber greenhouse intelligent equipment.

VoiceLens: A multi-view multi-class disease classification model through daily-life speech data

Biomarkers in the human voice can offer insight into neurological disorders because voice signals are influenced by the underlying cognitive and neuromuscular functions. In the past decade, there is an increasing research attention on voice-based neural disorder detection using machine learning techniques. However, existing works only attempt to detect a single neurological disorder (e.g., Parkinson’s or Huntington’s). In this work, we present the first computational model, namely VoiceLens , that detects multiple neurological disorders at the same time. The proposed VoiceLens framework combines the effectiveness of the powerful Mel-Frequency-Cepstral-Coefficients (MFCC) within a two-phase multi-class classification module to build an accurate voice-based disease prediction model. The first phase captures the fine-grained details of these disorders and their sequential variation patterns within a stacked Long-Short-Term-Memory (LSTM) network to make the baseline disease detection, i.e., healthy v.s. pathology. In the second phase, the detected pathology samples are further analyzed by a deep multi-layer learned descriptor to identify the disease types. The VoiceLens method is developed and evaluated using a large-scale Saarbruecken-Voice-Database comprising of samples from 2000 individuals with multiple disease patterns, including Laryngeal Cancers, Dish-Syndrome, and Parkinson’s disease. Experimental results show the remarkable performance of VoiceLens by reporting Accuracy up to 97.5% in the disease detection, where the model also obtains 98.00% and 97.13% for F1-Score and Recall . Also, compared with existing machine learning models, the proposed VoiceLens system demonstrates around 15% (and 12%) average gain in the Accuracy (and F1-score ) in a multi-disease identification test including six (6) different pathology classes and one (1) healthy class.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification.

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N= 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

Abstract P5-07-03: Disease classification modeling of inflammatory breast cancer based on simultaneous profiling of coding and non-coding RNAs in tumor and blood samples by TGIRT-sequencing

Abstract Background: Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lags in disease-specific RNA biomarkers due in part to its paucity of large discrete tumors. A strategy to overcome this challenge is to identify blood-based RNA biomarkers that are minimally invasive and reflect the state of both the diseased breast tissue and the patient's immune response. Here, we identified IBC-specific RNA biomarkers by thermostable group II intron reverse transcriptase sequencing (TGIRT-seq), a recently developed comprehensive RNA-seq technology that enables simultaneous profiling of all RNA biotypes from small amounts of starting material. We used these biomarkers to develop novel disease classification models for IBC based on coding and non-coding RNAs from FFPE tumor slices, PBMCs, and plasma. Methods: We obtained biological samples including FFPE, PBMC, and plasma from a cohort of ten patients with IBC and compared them to samples from six patients with non-IBC and sixteen healthy donors using TGIRT-seq technology. Results: TGIRT-seq of FFPE tumor slices identified differentially expressed mRNAs and miRNAs found previously to distinguish IBC from non-IBC tumors, as well as numerous additional differentially expressed mRNAs and small non-coding RNAs characteristic of IBC. Surprisingly, TGIRT-seq revealed that the differentially expressed protein-coding gene transcripts fall into two categories: mature mRNAs with reads confined to exons, and pre-mRNAs-derived transcripts with reads distributed across exons and introns, to our knowledge, a distinction not made previously for any cancer type. Differentially expressed miRNAs included both mature miRNAs and other transcripts of miRNA loci. IBC PBMCs showed a characteristic inflammatory response not seen in PBMCs from non-IBC patients, as well as differentially expressed tRNAs, snoRNAs, and other sncRNAs, while plasma samples, although of variable quality, included coding and non-coding RNAs distinctive of IBC. Classification models using panels consisting of sets of 50 selected biomarkers profiled by TGIRT-seq achieved a high degree of accuracy under cross-validation, with models based on PBMCs and plasma RNAs correlating with those based on tumor RNAs, and models using both coding and non-coding RNA biomarkers outperforming those based on either alone. Conclusions: Our findings are the first to define a distinct IBC profile across three different tissue types and advance TGIRT-seq as a promising method for high-resolution RNA biomarker profiling of both primary tumors and liquid biopsies with potentially broad utility for diagnosing and defining treatment response in IBC and other cancers. COI: Thermostable group II intron reverse transcriptase (TGIRT) enzymes and methods for their use are the subject of patents and patent applications that have been licensed by the University of Texas to InGex, LLC. A.M.L., some former and present members of the Lambowitz laboratory, and the University of Texas are minority equity holders in InGex, and receive royalty payments from the sale of TGIRT enzymes and kits and from sublicensing of intellectual property to other companies. Citation Format: Dennis C. Wylie, Xiaoping Wang, Jun Yao, Hengyi Xu, Toshiaki Iwase, Savitri Krishnamurthy, Naoto T. Ueno, Alan M. Lambowitz. Disease classification modeling of inflammatory breast cancer based on simultaneous profiling of coding and non-coding RNAs in tumor and blood samples by TGIRT-sequencing [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P5-07-03.

Metaheuristics with Deep Learning-Enabled Parkinson's Disease Diagnosis and Classification Model.

Parkinson's disease (PD) affects the movement of people, including the differences in writing skill, speech, tremor, and stiffness in muscles. It is significant to detect the PD at the initial stages so that the person can live a peaceful life for a longer time period. The serious levels of PD are highly risky as the patients get progressive stiffness, which results in the inability of standing or walking. Earlier studies have focused on the detection of PD effectively using voice and speech exams and writing exams. In this aspect, this study presents an improved sailfish optimization algorithm with deep learning (ISFO-DL) model for PD diagnosis and classification. The presented ISFO-DL technique uses the ISFO algorithm and DL model to determine PD and thereby enhances the survival rate of the person. The presented ISFO is a metaheuristic algorithm, which is inspired by a group of hunting sailfish to determine the optimum solution to the problem. Primarily, the ISFO algorithm is applied to derive an optimal subset of features with a fitness function of maximum classification accuracy. At the same time, the rat swarm optimizer (RSO) with the bidirectional gated recurrent unit (BiGRU) is employed as a classifier to determine the existence of PD. The performance validation of the IFSO-DL model takes place using a benchmark Parkinson's dataset, and the results are inspected under several dimensions. The experimental results highlighted the enhanced classification performance of the ISFO-DL technique, and therefore, the proposed model can be employed for the earlier identification of PD.

Blockchain-based model for Image Encryption in IoT Communication Environment

Nowadays, the internet of things (IoT) has become increasingly common and finds application in various fields, particularly in the health care industry. However, the development and design of IoT data analysis methods face some problems such as lack of adequate training data, resource constraints, centralized framework, security, and privacy. In contrast, the increase in blockchain techniques provides a decentralized framework. It is promoted to remove centralized control and resolve the problem of AI as well as allow secured distribution of data and resources to the diverse nodes of IoT system. This paper devises a new IoT with Blockchain based Secure Image Encryption with Disease Classification model.