An Intelligent Healthcare system for detecting diabetes using machine learning algorithms

The human disease prediction is specifically a struggling piece of work for an accurate and on time treatment. Around the world, diabetes is a hazardous disease. It affects the various essential organs of the human body, for example, nerves, retinas, and eventually heart. By using models of machine learning algorithms, we can recommend and predict diabetes on various healthcare datasets more accurately with the assistance of an intelligent healthcare recommendation system. Not long ago, for the prediction of diabetes, numerous models and methods of machine learning have been introduced. But despite that, enormous multi-featured healthcare datasets cannot be handled by those systems appropriately. By using Machine Learning, an intelligent healthcare recommendation system is introduced for the prediction of diabetes. Ultimately, the model of machine learning is trained to predict this disease along with K-Fold Cross validation testing.  The evaluation of this intelligent and smart recommendation system is depending on datasets of diabetes and its execution is differentiated from the latest development of previous literatures. Our system accomplished 99.0% of efficiency with the shortest time of 12 Milliseconds, which is highly analyzed by the previous existing models of machine learning. Consequently, this recommendation system is superior for the prediction of diabetes than the previous ones. This system enhances the performance of automatic diagnosis of this disease. Code is available at (https://github.com/RaoHassanKaleem/Diebetes-Detection-using-Machine-Learning-Algorithms). &nbsp

The role of random forest and Markov chain models in understanding metropolitan urban growth trajectory

IntroductionThis study delves into the spatiotemporal dynamics of land use and land cover (LULC) in a Metropolitan area over three decades (1991–2021) and extends its scope to forecast future scenarios from 2031 to 2051. The intent is to aid sustainable land management and urban planning by enabling precise predictions of urban growth, leveraging the integration of remote sensing, GIS data, and observations from Landsat satellites 5, 7, and 8.MethodsThe research employed a machine learning-based approach, specifically utilizing the random forest (RF) algorithm, for LULC classification. Advanced modeling techniques, including CA–Markov chains and the Land Change Modeler (LCM), were harnessed to project future LULC alterations, which facilitated the development of transition probability matrices among different LULC classes.ResultsThe investigation uncovered significant shifts in LULC, influenced largely by socio-economic factors. Notably, vegetation cover decreased substantially from 49.21% to 25.81%, while forest cover saw an increase from 31.89% to 40.05%. Urban areas expanded significantly, from 7.55% to 25.59% of the total area, translating into an increase from 76.31 km2 in 1991 to 258.61 km2 in 2021. Forest area also expanded from 322.25 km2 to 409.21 km2. Projections indicate a further decline in vegetation cover and an increase in built-up areas to 371.44 km2 by 2051, with a decrease in forest cover compared to its 2021 levels. The predictive accuracy of the model was confirmed with an overall accuracy exceeding 90% and a kappa coefficient around 0.88.DiscussionThe findings underscore the model’s reliability and provide a significant theoretical framework that integrates socio-economic development with environmental conservation. The results emphasize the need for a balanced approach towards urban growth in the Islamabad metropolitan area, underlining the essential equilibrium between development and conservation for future urban planning and management. This study underscores the importance of using advanced predictive models in guiding sustainable urban development strategies

Studies on two polyherbal formulations (ZPTO and ZTO) for comparison of their antidyslipidemic, antihypertensive and endothelial modulating activities

Background Cardiovascular disorders (CVDs) are the leading cause of disease burden worldwide. Apart from available synthetic drugs used in CVDs, there are many herbal formulations including POL-10 (containing 10 herbs), which have been shown to be effective in animal studies but POL-10 was found to cause tachycardia in rodents as its side effect. This study was designed to modify the composition of POL-10 for better efficacy and/or safety profile in CVDs. Methods To assess the antidyslipidemic, antihypertensive and endothelial modulatory properties of two herbal formulations, (ZPTO and ZTO) containing Z: Zingiber officinalis, P: Piper nigrum, T: Terminalia belerica and O: Orchis mascula, different animal models including, tyloxapol and high fat diet-induced dyslipidemia and spontaneously hypertensive rats (SHR) were used. Effect on endothelial function was studied using isolated tissue bath set up coupled with PowerLab data acquisition system. The antioxidant activity was carried out using DPPH radical-scavenging assay. Results Based on preliminary screening of the ingredients of POL-10 in tyloxapol-induced hyperlipidemic rats, ZPTO and ZTO containing four active ingredients namely; Z, P, T and O were identified for further studies and comparison. In tyloxapol-induced hyperlipidemic rats, both ZPTO and ZTO caused significant reduction in serum triglyceride (TG) and total cholesterol (TC). In high fat diet-fed rats, ZPTO decreased TC, low-density lipoproteins cholesterol (LDL-C) and atherogenic index (AI). ZTO also showed similar effects to those of ZPTO with additional merits being more effective in reducing AI, body weight and more importantly raising high-density lipoproteins. In SHR, both formulations markedly reduced systolic blood pressure, AI and TG levels, ZTO being more potent in reversing endothelial dysfunction while was devoid of cardiac stimulatory effect. In addition, ZTO also reduced LDL-C and improved glucose levels in SHR. In DPPH radical-scavenging activity test, ZTO was also more potent than ZPTO. Conclusion The modified formulation, ZTO was not only found more effective in correcting cardiovascular abnormalities than ZPTO or POL-10 but also it was free from tachycardiac side-effect, which might be observed because of the presence of Piper nigrum in ZPTO

How website quality affects online impulse buying:moderating effects of sales promotion and credit card use

Purpose The purpose of this paper is to investigate the impact of website quality on online impulse buying behavior (OIBB) in China, and assess the moderating roles of sales promotion and credit card use. Design/methodology/approach An online and personal survey from 1,161 online shoppers belonging to three big cities of China – Beijing, Shanghai, and Nanjing – was conducted. A random sampling technique was utilized for data collection. Data were analyzed using validity and reliability tests, confirmatory factor analysis, and structural equation modeling. Findings Three major findings discovered are: first, the website quality positively affects the OIBB; second, the sales promotion significantly influences OIBB and acts as a strong moderator on the relationship between website quality and online impulse buying; and third, the online impulse purchases are positively influenced by use of credit card, and the use of credit card enhances the relationship between website quality and online impulse buying. Research limitations/implications First, the website quality positively affects the OIBB; second, the sales promotion significantly influences OIBB and acts as a strong moderator in the relationship between website quality and online impulse buying; and third, online impulse purchases are positively influenced by credit card use. Moreover, credit card use enhances the relationship between website quality and online impulse buying. Originality/value This research is the first to investigate the relationship between website quality and OIBB in China, with sales promotion and credit card use as moderators

State-of-the-Art CNN Optimizer for Brain Tumor Segmentation in Magnetic Resonance Images

Brain tumors have become a leading cause of death around the globe. The main reason for this epidemic is the difficulty conducting a timely diagnosis of the tumor. Fortunately, magnetic resonance images (MRI) are utilized to diagnose tumors in most cases. The performance of a Convolutional Neural Network (CNN) depends on many factors (i.e., weight initialization, optimization, batches and epochs, learning rate, activation function, loss function, and network topology), data quality, and specific combinations of these model attributes. When we deal with a segmentation or classification problem, utilizing a single optimizer is considered weak testing or validity unless the decision of the selection of an optimizer is backed up by a strong argument. Therefore, optimizer selection processes are considered important to validate the usage of a single optimizer in order to attain these decision problems. In this paper, we provides a comprehensive comparative analysis of popular optimizers of CNN to benchmark the segmentation for improvement. In detail, we perform a comparative analysis of 10 different state-of-the-art gradient descent-based optimizers, namely Adaptive Gradient (Adagrad), Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Cyclic Learning Rate (CLR), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (RMS Prop), Nesterov Adaptive Momentum (Nadam), and Nesterov accelerated gradient (NAG) for CNN. The experiments were performed on the BraTS2015 data set. The Adam optimizer had the best accuracy of 99.2% in enhancing the CNN ability in classification and segmentation

Cytokine profiles using whole-blood assays can discriminate between tuberculosis patients and healthy endemic controls in a BCG-vaccinated population

Whole-blood assays (WB) provide a simple tool for assessing immune cytokine profiles which may be useful laboratory predictors of early disease, aiding the evaluation of new tuberculosis (TB) vaccines and offering insights into disease pathogenesis. Although BCG does not provide protection against pulmonary disease in TB endemic areas, it does modulate immune responses to mycobacterial antigens. It is important, therefore, to evaluate any new tool in an endemic setting in both BCG vaccinees and patients with tuberculosis. We have assessed the optimal conditions in terms of dose and kinetics of those cytokines which are released early (TNF-α, IL6 and TGF-β, IL10) or (interferon [IFN]-γ and IL5) in WB cultures stimulated with mitogens and mycobacterial antigens. Responses were studied in parallel in untreated TB patients and endemic control groups. Optimal responses to LPS (predominantly monocyte-derived) occurred on days 1–2, whereas for PHA (predominantly T-cell-derived), they were on days 3–5. Secreted Mycobacterium tuberculosis culture filtrate proteins (CFP) provided a stronger stimulus for monocyte-derived cytokines compared to PPD, but both antigens were comparable for induction of T-cell cytokines. Using unpaired Student\u27s t-tests, pulmonary tuberculosis patients (P.TB; n=11), in response to CFP, showed higher monocyte-derived IL6 (p=0.023) and IL10 (p=0.042) compared to endemic controls (EC; n=13), and significantly suppressed T-cell-derived IFN-γ (p=0.028) and IL5 (p=0.012) secretion but increased IL10 (p=0.047) on day 5, indicating that CFP is a strong stimulus for IL10 secretion in pulmonary TB patients. Extrapulmonary TB patients (E.TB; n=6) showed no elevation of early monocyte-derived cytokines to either PPD or CFP, but showed a marked suppression of the T-cell-derived cytokines IFN-γ (PPD, p=0.015; CFP, p=0.05) and IL5 (PPD, p=0.05; CFP, p=0.015). Cytokine analysis in WB cultures is, therefore, able to discriminate between active tuberculosis infection and nondiseased healthy controls

Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies

Forest fires represent a critical global threat to both humans and ecosystems. This study examines the intensity and impacts of Chilgoza (Pinus gerardiana) Pine Forest fires by using advanced remote sensing techniques comprising Normalized Burn Ratio (NBR) and Difference Normalized Burn Ratio (dNBR) analyses based on Landsat 9 datasets. The study highlights the severe effect of these fires, resulting in noteworthy losses of livestock and private properties and widespread damage to 10,156.53 acres of the Chilgoza Pine Forest. A comprehensive variable correlation analysis is conducted to gain deeper insights into the influencing factors causing forest fires. Spearman's Rank Correlation Coefficient was used to assess the association between burnt and unburnt areas and various independent factors. The analysis reveals compelling evidence of significant correlations with forest fire prevalence. This study found moderate negative (-0.532, p < 0.05) and positive (0.513, p < 0.05) correlations with elevation and Land Surface Temperature (LST), respectively, and a weak positive correlation (0.252, p < 0.05) with a Wind Speed (V). To predict forest fire susceptibility and better understand the contributing factors, three machine learning models, Random Forest (RF), XGBoost, and logistic regression, are applied to assess variable importance scores. Among the considered factors, LST is the most critical variable, with consistently high variable importance scores (100 %, 96 %, and 59 %) across all three models. Wind Speed (V) also proved influential in all models, with variable importance scores of 78 %, 83 %, and 61 % for RF, XGBoost, and logistic regression, respectively. Moreover, elevation significantly influences the frequency of forest fires, as evidenced by variable importance scores ranging from 26 % to 100 %. Comparatively, the Random Forest model outperforms XGBoost and Logistic Regression in predicting forest fire vulnerability. During the training stage, the Random Forest (RF) model achieves an impressive classification accuracy of 99.1 %, followed by XGBoost with 94.5 % and Logistic Regression with 85.6 %. On evaluation with the validation dataset, the accuracies remain promising, with RF at 96.4 %, XGBoost at 91.1 %, and Logistic Regression at 84.6 %. Based on the Random Forest model, the identified high-risk sites offer valuable insights for proactive fire management and prevention strategies. This study provides a robust predictive model and a comprehensive understanding of forest fire severity and impacts. Future research should consider climate change scenarios and account for human activities to enhance fire behavior predictions and risk assessment models

ISOLATION AND STRUCTURAL CHARACTERIZATION OF NOVEL TERPENOIDS FROM DIFFERENT PLANT SPECIES: A REVIEW.

Terpenoids are the most essential compounds biosynthesized by plants as secondary metabolites. It is a diverse and vast class of naturally occurring organic compounds and are the derivatives of terpenes having simple and multi-cyclic rings in their structure. These are though found in small/trace amounts in plants, but they play an indispensable role in the survival of plants in the environment. These are used by the plants as a shield against internal and external stresses and for many of the basic functions in the development and growth and by humans as anti-bacterial agents, as anti-carcinogenic agents, as flavors, fragrances and drugs. This review paper will explain the extraction and isolation of terpenoids from different parts of plants i.e. leaves, stem, roots and other vegetal parts of plants. Further the review will focus on the structural characterization of terpenoids extracted from plants by different researchers and the methods they used for the separation and structural characterization of these compounds and their structures drawn with the help of NMR data. The extracted and characterized compounds were terpenoids, triterpenoids, nortriterpenoids, monoterpenes, sesquiterpenes and macro cyclic monoterpenes. These compounds were checked for their medicinal activity, cytotoxicity and their bioassay were also considered, where these compounds showed that they can be used as medicinal compounds as well as the bioassay confirmed their role as protective agents of the plants. Overall, eleven research articles have been considered in this review and the focus is on the novel terpenoids that have been extracted, isolated and characterized by different means. Apart from novel terpenoids the other known compounds have been studied and their activity has also been reported. Keywords: Terpenoids, Characterization, Extraction, NMR, Plant

The Effects of Heavy Metal Pollution on Soil Nitrogen Transformation and Rice Volatile Organic Compounds under Different Water Management Practices

One of the most concerning global environmental issues is the pollution of agricultural soils by heavy metals (HMs), especially cadmium, which not only affects human health through Cd-containing foods but also impacts the quality of rice. The soil’s nitrification and denitrification processes, coupled with the release of volatile organic compounds by plants, raise substantial concerns. In this review, we summarize the recent literature related to the deleterious effects of Cd on both soil processes related to the N cycle and rice quality, particularly aroma, in different water management practices. Under both continuous flooding (CF) and alternate wetting and drying (AWD) conditions, cadmium has been observed to reduce both the nitrification and denitrification processes. The adverse effects are more pronounced in alternate wetting and drying (AWD) as compared to continuous flooding (CF). Similarly, the alteration in rice aroma is more significant in AWD than in CF. The precise modulation of volatile organic compounds (VOCs) by Cd remains unclear based on the available literature. Nevertheless, HM accumulation is higher in AWD conditions compared to CF, leading to a detrimental impact on volatile organic compounds (VOCs). The literature concludes that AWD practices should be avoided in Cd-contaminated fields to decrease accumulation and maintain the quality of the rice. In the future, rhizospheric engineering and plant biotechnology can be used to decrease the transport of HMs from the soil to the plant’s edible parts