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Salmonella is a significant foodborne pathogen that has a significant impact on public health, and different strains of multidrug resistance (MDR) have been identified in this genus. This study used a combination of phenotypic and genotypic approaches to identify distinct Salmonella species collected from poultry broiler and layer farms, and antibiotic sensitivity testing was performed on these species. A total of 56 Salmonella isolates were serotyped, and phenotypic antibiotic resistance was determined for each strain. The enterobacterial repetitive intergenic consensus polymerase chain reaction (ERIC-PCR) method was also used to provide a genotypic description, from which a dendrogram was constructed and the most likely phylogenetic relationships were applied. Salmonella isolates were detected in 20 (17%) out of 117 samples collected from small-scale broiler flocks. Salmonella isolates were classified as MDR strains after showing tolerance to 4 antibiotics, but no resistance to cloxacillin, streptomycin, vancomycin, or netilmicin was observed. From a genotypic perspective, these strains lack dfrD, parC, and blasfo-1 resistant genes, while harboring blactx-M, blaDHA-L, qnrA, qnrB, qnrS, gyrA, ermA, ermB, ermC, ermTR, mefA, msrA, tet A, tet B, tet L, tet M resistance genes. The genotyping results obtained with ERIC-PCR allowed isolates to be classified based on the source of recovery. It was determined that Salmonella strains displayed MDR, and many genes associated with them. Additionally, the ERIC-PCR procedure aided in the generation of clusters with biological significance. Extensive research on Salmonella serotypes is warranted, along with the implementation of long-term surveillance programs to monitor MDR Salmonella serotypes in avian-derived foods., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9-100%), sensitivity (77.6-100%), precision (91.8-96.08%), specificity (91.6-100%) and F-Score (90-100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC'2022 benchmarks functions., (© 2024. The Author(s).)

In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Abdelaziz Darwiesh et al.)