Antibiotic resistance is a major emerging global public health threat. Farmers in the Khartoum state are believed to misuse antibiotics in animal farming leading to daily exposure to resistant bacteria and antibiotic residues. Hence, farmers are at potential risk exposure to bacteria, zoonotic infection and toxicity. We hypothesized that farmers' misuse of antibiotics could be due to their ignorance of the importance of optimal use of antibiotics, the potential health hazards and the economical waste associated with antibiotic misuse practices. In the present study, we investigated knowledge and practices among farmers regarding antibiotic use and resistance. For this purpose, a cross-sectional study was conducted in Khartoum state where data were collected from 81 farmers using structured interviews. Data were analysed both quantitatively and qualitatively. Fifty-two per cent of farmers were uneducated or had studied for < 6 years. The majority reported antibiotic use for treatment and prevention while only 5% stated use for growth promotion. Antibiotic group treatment for both sick and healthy animals was commonly practiced among most farmers. The most commonly used group of antibiotics was the quinolones, which was reported by one-third. Only 30% of the farmers had heard of antibiotic resistance and provided their definition. Almost half were not aware of the commonly transferred zoonotic infections between humans and animals. The farmers consume 1-2 meals/day from their own farm products. A significant association between low education, poor knowledge of farmers on antibiotic use, antibiotic resistance and zoonotic infections was found. This association may play a vital role in the present practiced misuse of antibiotics. Our findings on farmers' practices could be used as baseline information in defining the gaps related to antibiotic use and resistance in animal farming in Sudan. It can thus serve as a foundation for future interventions.

Although there are many attempts to build an optimal model for feature selection in Big Data applications, the complex nature of processing such kind of data makes it still a big challenge. Accordingly, the data mining process may be obstructed due to the high dimensionality and complexity of huge data sets. For the most informative features and classification accuracy optimization, the feature selection process constitutes a mandatory pre-processing phase to reduce dataset dimensionality. The exhaustive search for the relevant features is time-consuming. In this paper, a new binary variant of the wrapper feature selection grey wolf optimization and particle swarm optimization is proposed. The K-nearest neighbor classifier with Euclidean separation matrices is used to find the optimal solutions. A tent chaotic map helps in avoiding the algorithm from locked to the local optima problem. The sigmoid function employed for converting the search space from a continuous vector to a binary one to be suitable to the problem of feature selection. Crossvalidation K-fold is used to overcome the overfitting issue. A variety of comparisons have been made with well-known and common algorithms, such as the particle swarm optimization algorithm, and the grey wolf optimization algorithm. Twenty datasets are used for the experiments, and statistical analyses are conducted to approve the performance and the effectiveness and of the proposed model with measures like selected features ratio, classification accuracy, and computation time. The cumulative features picked through the twenty datasets were 196 out of 773, as opposed to 393 and 336 in the GWO and the PSO, respectively. The overall accuracy is 90% relative to other algorithms ' 81.6 and 86.8. The total processing time for all datasets equals 184.3 seconds, wherein GWO and PSO equal 272 and 245.6, respectively. INDEX TERMS Particle swarm optimization (PSO), grey wolf optimization (GWO), data mining, big data analytics, feature selection.

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Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

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