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Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier’s performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

In the current era, a lot of research is being done in the domain of disease diagnosis using machine learning. In recent times, one of the deadliest respiratory diseases, COVID-19, which causes serious damage to the lungs has claimed a lot of lives globally. Machine learning-based systems can assist clinicians in the early diagnosis of the disease, which can reduce the deadly effects of the disease. For the successful deployment of these machine learning-based systems, hyperparameter-based optimization and feature selection are important issues. Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. Moreover, to enhance the efficacy of the predictions made by hyperparameter-optimized machine learning models, an ensemble model is proposed using a stacking classifier. Also, explainable AI was incorporated to define the feature importance by making use of Shapely adaptive explanations (SHAP) values. For the experimentation, the publicly accessible Mexico clinical dataset of COVID-19 was used. The results obtained show that the proposed model has superior prediction accuracy in comparison to its counterparts. Moreover, among all the hyperparameter-optimized algorithms, adaboost algorithm outperformed all the other hyperparameter-optimized algorithms. The various performance assessment metrics, including accuracy, precision, recall, AUC, and F1-score, were used to assess the results.

Previously, we developed and applied a glasshouse screen for potato tuber yield under heat stress and identified a candidate gene (HSc70) for heat tolerance by genetic analysis of a diploid potato population. Specific allelic variants were expressed at high levels on exposure to moderately elevated temperature due to variations in gene promoter sequence. In this study, we aimed to confirm the results from the glasshouse screen in field trials conducted over several seasons and locations including those in Kenya, Malawi and the UK. We extend our understanding of the HSc70 gene and demonstrate that expression level of HSc70 correlates with tolerance to heat stress in a wide range of wild potato relatives. The physiological basis of the protective effect of HSc70 was explored and we show that genotypes carrying the highly expressed HSc70 A2 allele are protected against photooxidative damage to PSII induced by abiotic stresses. Overall, we show the potential of HSc70 alleles for breeding resilient potato genotypes for multiple environments. Potato is a critical crop for food security; however, it is vulnerable to reduced tuber yield at even moderately elevated temperature. In this work, we show the potential of HSc70 alleles for breeding resilient potato genotypes for multiple environments.

Over 6.5 million people around the world have lost their lives due to the highly contagious COVID 19 virus. The virus increases the danger of fatal health effects by damaging the lungs severely. The only method to reduce mortality and contain the spread of this disease is by promptly detecting it. Recently, deep learning has become one of the most prominent approaches to CAD, helping surgeons make more informed decisions. But deep learning models are computation hungry and devices with TPUs and GPUs are needed to run these models. The current focus of machine learning research is on developing models that can be deployed on mobile and edge devices. To this end, this research aims to develop a concise convolutional neural network-based computer-aided diagnostic system for detecting the COVID 19 virus in X-ray images, which may be deployed on devices with limited processing resources, such as mobile phones and tablets. The proposed architecture aspires to use the image enhancement in first phase and data augmentation in the second phase for image pre-processing, additionally hyperparameters are also optimized to obtain the optimal parameter settings in the third phase that provide the best results. The experimental analysis has provided empirical evidence of the impact of image enhancement, data augmentation, and hyperparameter tuning on the proposed convolutional neural network model, which increased accuracy from 94 to 98%. Results from the evaluation show that the suggested method gives an accuracy of 98%, which is better than popular transfer learning models like Xception, Resnet50, and Inception.

PurposeThis study intends to investigate how an employee's proactive personality and a supervisor's idiosyncratic deals (i-deals) relate to their subordinates' affective commitment (AC) and occupational well-being (OWB), in light of the mediating role of subordinates' i-deals, using proactive motivation theory and the job demand–resource (JD-R) model as theoretical foundations.Design/methodology/approachThe study consisted of 342 employees working in the hospitality industry. To examine the proposed model, the researchers used the structural equation modelling approach and bootstrapping method in AMOS.FindingsThe results affirmed the influence of subordinates' proactiveness on AC and OWB, but no direct influence of supervisors' prior i-deals on subordinates' AC and OWB was established. When investigating the mediational role of subordinates' i-deals, a partial mediation effect was found between subordinates' proactive personality with AC and OWB, whereas full mediation was established between supervisors' i-deals and subordinates' AC and OWB.Practical implicationsThese findings shed light on how i-deals improve AC and OWB for both groups of supervisors and subordinates. In an era of increasing competition amongst organizations operating within the hospitality industry, i-deals serve as a human resource strategy to recruit, develop and retain talented individuals.Originality/valueThe novelty of this research lies in its specific investigation of the combined influence of proactive personality as an individual factor and supervisors' i-deals as an organizational factor on subordinates' i-deals within the context of the hospitality industry. Furthermore, it aims to analyse the potential impact of these factors on AC and OWB.

Genome-wide association studies (GWAS) have become a powerful tool for analyzing complex traits in crop plants. The current study evaluates the efficacy of various GWAS models and methods for elucidating population structure in potato. The presence of significant population structure can lead to detection of spurious marker-trait associations, as well as mask true ones. While appropriate statistical models are needed to detect true marker-trait associations, in most published potato GWAS, a ‘one model fits all traits’ approach has been adopted. We have examined various GWAS models on a large association panel comprising diverse tetraploid potato cultivars and breeding lines, genotyped with single nucleotide polymorphism (SNP) markers. Phenotypic data were generated for 20 quantitative traits assessed in different environments. Best Linear Unbiased Estimates (BLUEs) for these traits were obtained for use in assessing GWAS models. Goodness of fit of GWAS models, derived using different combinations of kinship and population structure for all traits, was evaluated using Quantile-Quantile (Q-Q) plots and genomic control inflation factors (λGC). Kinship was found to play a major role in correcting population confounding effects and results advocate a ‘trait-specific’ fit of different GWAS models. A survey of genome-wide linkage disequilibrium (LD), one of the critical factors affecting GWAS, is also presented and our findings are compared to other recent studies in potato. The genetic material used here, and the outputs of this study represent a novel resource for genetic analysis in potato.

The present study explores the relationship between career commitment and subjective career success among media journalism industry employees. Additionally, it investigates the mediating role of career resilience behavior while anchoring its theoretical foundation in the career self-determination theory. The media journalism industry, characterized by burnout, demanding schedules and the mismatch of personal and professional commitments, provides a fitting context to examine the dynamics of career commitment and subjective career success. Using a structural equation modeling approach, the hypotheses were tested on 303 participants from the Punjab and Chandigarh regions in India. The results of the structural equation modeling affirmed the career commitment’s substantial direct and indirect influence on subjective career success, mediated by the career resilience variable. The implications derived from this study are pertinent for the media journalism sector. It underscores the pivotal role of cultivating career-committed behavior among employees and advocating for strategic career development initiatives. This, in turn, fosters a resilient environment conducive to nurturing individual career accomplishments, transcending conventional benchmarks of organizational success. In conclusion, this research underscores the significance of transitioning from a traditional assessment of performance framework to a more subjective evaluation approach, ultimately contributing to cultivating a motivated and engaged workforce.

Purpose– This study examines the subjective dimension of career success in the dynamic global tourism industry, specifically the relationship between Career Commitment (CC) and Subjective Career Success (SCS). It uses a serial mediation framework with self-efficacy (SE) and career resilience (CR) as mediators and focuses on tourism professionals. Research methodology – We developed a theoretical serial mediation model to investigate this relationship. We conducted regression analysis using SPSS version 25 and AMOS (the Process Macro model 6) to test our proposed hypotheses. A total of 357 employees from various tourism-related organizations participated in this research. Findings – Employees who invested in their careers reported higher satisfaction with SCS in their working lives. Independently and consecutively, SE and CR influenced the association between CC and SCS. Research implications and Originality – The implications of this research extend to individuals and tourism organizations. For individuals, it provides a deeper understanding of how CC, SE and CR interact to manage the complexities of the tourism industry and promote professional success. For organizations, it highlights the importance of promoting CC through effective career development initiatives that can lead to a competent and motivated workforce, which ultimately increases employee engagement and retention.

Psittacula cyanocephala is an endemic parakeet from the Indian sub-continent that is widespread in the illegal bird trade. Previous studies on Psittacula parakeets have highlighted taxonomic ambiguities, warranting studies to resolve the issues. Since the mitochondrial genome provides useful information concerning the species evolution and phylogenetics, we sequenced the complete mitogenome of P . cyanocephala using NGS, validated 38.86% of the mitogenome using Sanger Sequencing and compared it with other available whole mitogenomes of Psittacula . The complete mitogenome of the species was 16814 bp in length with 54.08% AT composition. P . cyanocephala mito genome comprises of 13 protein-coding genes, 2 rRNAs and 22 tRNAs. P . cyanocephala mitogenome organization was consistent with other Psittacula mitogenomes. Comparative codon usage analysis indicated the role of natural selection on Psittacula mitogenomes. Strong purifying selection pressure was observed maximum on nad1 and nad4l genes. The mitochondrial control region of all Psittacula species displayed the ancestral avian CR gene order. Phylogenetic analyses revealed the Psittacula genus as paraphyletic nature, containing at least 4 groups of species within the same genus, suggesting its taxonomic reconsideration. Our results provide useful information for developing forensic tests to control the illegal trade of the species and scientific basis for phylogenetic revision of the genus Psittacula .

ObjectiveThe study examined the influence of the COVID-19 pandemic in India on variation in clinical features, management and in-hospital outcomes in patients undergoing percutaneous coronary intervention (PCI).DesignProspective registry-based observational study.SettingA tertiary care hospital in India participant in the American College of Cardiology CathPCI Registry.Participants7089 successive patients who underwent PCI from April 2018 to March 2023 were enrolled (men 5627, women 1462). Details of risk factors, clinical presentation, coronary angiography, coronary interventions, clinical management and in-hospital outcomes were recorded. Annual data were classified into specific COVID-19 periods according to Government of India guidelines as pre-COVID-19 (April 2018 to March 2019, n=1563; April 2019 to March 2020, n=1594), COVID-19 (April 2020 to March 2020, n=1206; April 2021 to March 2022, n=1223) and post-COVID-19 (April 2022 to March 2023, n=1503).ResultsCompared with the patients in pre-COVID-19 and post-COVID-19 periods, during the first COVID-19 year, patients had more hypertension, non-ST elevation myocardial infarction (NSTEMI), lower left ventricular ejection fraction (LVEF) and multivessel coronary artery disease (CAD). In the second COVID-19 year, patients had more STEMI, lower LVEF, multivessel CAD, primary PCI, multiple stents and more vasopressor and mechanical support. There were 99 (1.4%) in-hospital deaths which in the successive years were 1.2%, 1.4%, 0.8%, 2.4% and 1.3%, respectively (p=0.019). Compared with the baseline year, deaths were slightly lower in the first COVID-19-year (age-sex adjusted OR 0.68, 95% CI 0.31 to 1.47) but significantly more in the second COVID-19-year (OR 1.97, 95% CI 1.10 to 3.54). This variation attenuated following adjustment for clinical presentation, extent of CAD, in-hospital treatment and duration of hospitalisation.ConclusionsIn-hospital mortality among patients with CAD undergoing PCI was significantly higher in the second year of the COVID-19 pandemic in India and could be one of the reasons for excess deaths in the country. These patients had more severe CAD, lower LVEF, and more vasopressor and mechanical support and duration of hospitalisation.