A comprehensive medical decision–support framework based on a heterogeneous ensemble classifier for diabetes prediction

Funding Information: Funding: This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337). Funding Information: This work was supported by National Research Foundation of Korea-Grant funded by the Korean Government (Ministry of Science and ICT)-NRF-2017R1A2B2012337).Peer reviewe

Multiomics analysis reveals that GLS and GLS2 differentially modulate the clinical outcomes of cancer

Acknowledgements: This work was supported by the Deanship of Scientific Research at King Saud University through Research Group Grant RGP-1438-044.Peer reviewe

Far-Field DOA Estimation of Uncorrelated RADAR Signals through Coprime Arrays in Low SNR Regime by Implementing Cuckoo Search Algorithm

For the purpose of attaining a high degree of freedom (DOF) for the direction of arrival (DOA) estimations in radar technology, coprime sensor arrays (CSAs) are evaluated in this paper. In addition, the global and local minima of extremely non-linear functions are investigated, aiming to improve DOF. The optimization features of the cuckoo search (CS) algorithm are utilized for DOA estimation of far-field sources in a low signal-to-noise ratio (SNR) environment. The analytical approach of the proposed CSAs, CS and global and local minima in terms of cumulative distribution function (CDF), fitness function and SNR for DOA accuracy are presented. The parameters like root mean square error (RMSE) for frequency distribution, RMSE variability analysis, estimation accuracy, RMSE for CDF, robustness against snapshots and noise and RMSE for Monte Carlo simulation runs are explored for proposed model performance estimation. In conclusion, the proposed DOA estimation in radar technology through CS and CSA achievements are contrasted with existing tools such as particle swarm optimization (PSO).This project has received funding from Universidad Carlos III de Madrid and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538

An effective approach for plant leaf diseases classification based on a novel DeepPlantNet deep learning model

IntroductionRecently, plant disease detection and diagnosis procedures have become a primary agricultural concern. Early detection of plant diseases enables farmers to take preventative action, stopping the disease's transmission to other plant sections. Plant diseases are a severe hazard to food safety, but because the essential infrastructure is missing in various places around the globe, quick disease diagnosis is still difficult. The plant may experience a variety of attacks, from minor damage to total devastation, depending on how severe the infections are. Thus, early detection of plant diseases is necessary to optimize output to prevent such destruction. The physical examination of plant diseases produced low accuracy, required a lot of time, and could not accurately anticipate the plant disease. Creating an automated method capable of accurately classifying to deal with these issues is vital. MethodThis research proposes an efficient, novel, and lightweight DeepPlantNet deep learning (DL)-based architecture for predicting and categorizing plant leaf diseases. The proposed DeepPlantNet model comprises 28 learned layers, i.e., 25 convolutional layers (ConV) and three fully connected (FC) layers. The framework employed Leaky RelU (LReLU), batch normalization (BN), fire modules, and a mix of 3×3 and 1×1 filters, making it a novel plant disease classification framework. The Proposed DeepPlantNet model can categorize plant disease images into many classifications.ResultsThe proposed approach categorizes the plant diseases into the following ten groups: Apple_Black_rot (ABR), Cherry_(including_sour)_Powdery_mildew (CPM), Grape_Leaf_blight_(Isariopsis_Leaf_Spot) (GLB), Peach_Bacterial_spot (PBS), Pepper_bell_Bacterial_spot (PBBS), Potato_Early_blight (PEB), Squash_Powdery_mildew (SPM), Strawberry_Leaf_scorch (SLS), bacterial tomato spot (TBS), and maize common rust (MCR). The proposed framework achieved an average accuracy of 98.49 and 99.85in the case of eight-class and three-class classification schemes, respectively.DiscussionThe experimental findings demonstrated the DeepPlantNet model's superiority to the alternatives. The proposed technique can reduce financial and agricultural output losses by quickly and effectively assisting professionals and farmers in identifying plant leaf diseases

Synthesis and in silico study of 2-furyl(4-{4-[(substituted)sulfonyl]benzyl}-1-piperazinyl)methanone derivatives as suitable therapeutic agents

Abstract In the study presented here, a new series of 2-furyl(4-{4-[(substituted)sulfonyl]benzyl}-1-piperazinyl)methanone derivatives was targeted. The synthesis was initiated by the treatment of different secondary amines (1a-h) with 4-bromomethylbenzenesulfonyl chloride (2) to obtain various 1-{[4-(bromomethyl)phenyl]sulfonyl}amines (3a-h). 2-Furyl(1-piperazinyl)methanone (2-furoyl-1-piperazine; 4) was then dissolved in acetonitrile, with the addition of K2CO3, and the mixture was refluxed for activation. This activated molecule was further treated with equi-molar amounts of 3a-h to form targeted 2-furyl(4-{4-[(substituted)sulfonyl]benzyl}-1-piperazinyl)methanone derivatives (5a-h) in the same reaction set up. The structure confirmation of all the synthesized compounds was carried out by EI-MS, IR and 1H-NMR spectral analysis. The compounds showed good enzyme inhibitory activity. Compound 5h showed excellent inhibitory effect against acetyl- and butyrylcholinesterase with respective IC50 values of 2.91±0.001 and 4.35±0.004 μM, compared to eserine, a reference standard with IC50 values of 0.04±0.0001 and 0.85±0.001 μM, respectively, against these enzymes. All synthesized molecules were active against almost all Gram-positive and Gram-negative bacterial strains tested. The cytotoxicity of the molecules was also checked to determine their utility as possible therapeutic agents

Modelling multiphase flow in vertical pipe using CFD method.

Investigations of gas-liquid-solid flows in large diameter vertical pipes are scarce and detailed three phase flow study is still required to understand the flow interactions. Further investigation using high fidelity modelling is thus necessary due to complex flow interactions of the phases. In this study, a Computational Fluid Dynamics (CFD) method is used to investigate multiphase gas-liquid-solid flow in vertical pipe. Firstly, an appropriate validated numerical simulation scheme for two phase gas-liquid flow using ANSYS Fluent has been used to simulate possible flow regime transitions in vertical pipe. The scheme could predict the various flow regimes spanning bubbly to annular flow without prior knowledge of the flow patterns. The scheme was further extended to investigate the impact of solid particles in the flow field. More importantly the impact of solid concentration on the flow regime development and sand deposition was investigated. The results showed that the particulate deposition is greatly influenced by the particle concentration. In addition, the regime transitions and development in gas-liquid flows are different than that of gas-liquid-solid flows

Convergent synthesis of new N -substituted 2-{[5-(1H -indol-3-ylmethyl)-1,3,4-oxadiazol-2-yl]sulfanyl}acetamides as suitable therapeutic agents

abstract A series of N-substituted 2-{[5-(1H-indol-3-ylmethyl)-1,3,4-oxadiazol-2-yl]sulfanyl}acetamides (8a-w) was synthesized in three steps. The first step involved the sequential conversion of 2-(1H-indol-3-yl)acetic acid (1) to ester (2) followed by hydrazide (3) formation and finally cyclization in the presence of CS2 and alcoholic KOH yielded 5-(1H-indole-3-yl-methyl)-1,3,4-oxadiazole-2-thiol (4). In the second step, aryl/aralkyl amines (5a-w) were reacted with 2-bromoacetyl bromide (6) in basic medium to yield 2-bromo-N-substituted acetamides (7a-w). In the third step, these electrophiles (7a-w) were reacted with 4 to afford the target compounds (8a-w). Structural elucidation of all the synthesized derivatives was done by 1H-NMR, IR and EI-MS spectral techniques. Moreover, they were screened for antibacterial and hemolytic activity. Enzyme inhibition activity was well supported by molecular docking results, for example, compound 8q exhibited better inhibitory potential against α-glucosidase, while 8g and 8b exhibited comparatively better inhibition against butyrylcholinesterase and lipoxygenase, respectively. Similarly, compounds 8b and 8c showed very good antibacterial activity against Salmonella typhi, which was very close to that of ciprofloxacin, a standard antibiotic used in this study. 8c and 8l also showed very good antibacterial activity against Staphylococcus aureus as well. Almost all compounds showed very slight hemolytic activity, where 8p exhibited the least. Therefore, the molecules synthesized may have utility as suitable therapeutic agents

Qur’anic Ethics for Environmental Responsibility: Implications for Business Practice

Despite the growing interest in examining the role of religious beliefs as a guide towards environmental conscious actions, there is still a lack of research informed by an analysis of divine messages. This deficiency includes the extent to which ethics for environmental responsibility are promoted within textual divine messages; types of environmental themes promoted within the text of divine messages; and implications of such religious environmental ethics for business practice. The present study attempts to fill this gap by conducting a thorough content analysis of environmental themes within the divine message of Muslims (the Qur’an) focusing on their related ethical aspects and business implications. The analysis has revealed 675 verses in 84 chapters throughout all 30 parts of the Qur’an, with environmental content relating to the core components of the natural world, i.e. human beings, water, air, land, plants, animals, and other natural resources. This environmental content and its related ethics are grounded on the belief that humans are vicegerents of God on the earth and their behaviours and actions are motivated by earthly and heavenly rewards. Implications of these findings for different sectors/businesses are also highlighted

An intelligent healthcare monitoring framework using wearable sensors and social networking data

Wearable sensors and social networking platforms play a key role in providing a new method to collect patient data for efficient healthcare monitoring. However, continuous patient monitoring using wearable sensors generates a large amount of healthcare data. In addition, the user-generated healthcare data on social networking sites come in large volumes and are unstructured. The existing healthcare monitoring systems are not efficient at extracting valuable information from sensors and social networking data, and they have difficulty analyzing it effectively. On top of that, the traditional machine learning approaches are not enough to process healthcare big data for abnormality prediction. Therefore, a novel healthcare monitoring framework based on the cloud environment and a big data analytics engine is proposed to precisely store and analyze healthcare data, and to improve the classification accuracy. The proposed big data analytics engine is based on data mining techniques, ontologies, and bidirectional long short-term memory (Bi-LSTM). Data mining techniques efficiently preprocess the healthcare data and reduce the dimensionality of the data. The proposed ontologies provide semantic knowledge about entities and aspects, and their relations in the domains of diabetes and blood pressure (BP). Bi-LSTM correctly classifies the healthcare data to predict drug side effects and abnormal conditions in patients. Also, the proposed system classifies the patients’ health condition using their healthcare data related to diabetes, BP, mental health, and drug reviews. This framework is developed employing the Protégé Web Ontology Language tool with Java. The results show that the proposed model precisely handles heterogeneous data and improves the accuracy of health condition classification and drug side effect predictions. © 2020 Elsevier B.V