Explore the vast range of titles available.

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

This research addresses the lack of publicly available datasets for Bangladeshi medicinal plants by presenting a comprehensive dataset comprising 5000 images of ten species collected under controlled conditions. To improve performance, several preprocessing techniques were employed, such as image selection, background removal, unsharp masking, contrast-limited adaptive histogram equalization, and morphological gradient. Then, we applied five state-of-the-art deep learning models to achieve benchmark performance on the dataset: VGG16, ResNet50, DenseNet201, InceptionV3, and Xception. Among these models, DenseNet201 demonstrated the highest accuracy of 85.28%. In addition to benchmarking the deep learning models, three novel neural network architectures were developed: dense-residual–dense (DRD), dense-residual–ConvLSTM-dense (DRCD), and inception-residual–ConvLSTM-dense (IRCD). The DRCD model achieved the highest accuracy of 97%, surpassing the benchmark performances of individual models. This highlights the effectiveness of the proposed architectures in capturing complex patterns and dependencies within the data. To further enhance classification accuracy, an ensemble approach was adopted, employing both hard ensemble and soft ensemble techniques. The hard ensemble achieved an accuracy of 98%, while the soft ensemble achieved the highest accuracy of 99%. These results demonstrate the effectiveness of ensembling techniques in boosting overall classification performance. The outcomes of this study have significant implications for the accurate identification and classification of Bangladeshi medicinal plants. This research provides valuable resources for traditional medicine, drug discovery, and biodiversity conservation efforts. The developed models and ensemble techniques can aid researchers, botanists, and practitioners in accurately identifying medicinal plant species, thereby facilitating the utilization of their therapeutic potential and contributing to the preservation of biodiversity.

This study aims to investigate the effects of psychological contract breach, self-efficacy, mental health, and abusive supervision on employee life satisfaction. Additionally, it examines how work engagement and job satisfaction mediate these effects. Analyzing data from 380 corporate employees in Bangladesh, a survey methodology was employed to test the claimed associations using structural equation modeling (SEM). Self-efficacy and mental health boost work, life, and job satisfaction. Unsurprisingly, abusive supervision and psychological contract breaches do not affect work engagement. Work engagement and job satisfaction affect psychological contract breach, self-efficacy, mental health, abusive supervision, and life satisfaction. By examining how psychological contract breach, self-efficacy, mental health, and abusive supervision affect employee life satisfaction, this study advances understanding level. The study investigates these factors in a developing country's corporate sector. Employees' work engagement, job satisfaction, and life satisfaction can be improved by improving self-efficacy, mental health, and psychological contract breaches. These elements should be included in HR policy and staff development programs to create a healthier and more productive workplace.

Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. To enhance cybersecurity and address these challenges, this paper proposes a hybrid deep learning approach that combines Bidirectional Gated Recurrent Units (Bi-GRUs) and Convolutional Neural Networks (CNNs), referred to as CNN-Bi-GRU, for the accurate identification and classification of smishing attacks. The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications.

In recent years suicide has become a major public health problem in Pakistan. To identify major risk factors associated with suicides in Karachi, Pakistan. A matched case-control psychological autopsy study. Interviews were conducted for 100 consecutive suicides, which were matched for age, gender and area of residence with 100 living controls. Both univariate analysis and conditional logistic regression model results indicate that predictors of suicides in Pakistan are psychiatric disorders (especially depression), marital status (being married), unemployment, and negative and stressful life events. Only a few individuals were receiving treatment at the time of suicide. None of the victims had been in contact with a health professional in the month before suicide. Suicide in Pakistan is strongly associated with depression, which is under-recognised and under-treated. The absence of an effective primary healthcare system in which mental health could be integrated poses unique challenges for suicide prevention in Pakistan.

Understanding how anatomical shapes evolve in response to developmental covariates and quantifying their spatially varying uncertainties is critical in healthcare research. Existing approaches typically rely on global time-warping formulations that ignore spatially heterogeneous dynamics. We introduce PRISM, a novel framework that bridges implicit neural representations with uncertainty-aware statistical shape analysis. PRISM models the conditional distribution of shapes given covariates, providing spatially continuous estimates of both the population mean and covariate-dependent uncertainty at arbitrary locations. A key theoretical contribution is a closed-form Fisher Information metric that enables efficient, analytically tractable local temporal uncertainty quantification via automatic differentiation. Experiments on three synthetic datasets and one clinical dataset demonstrate PRISM's strong performance across diverse tasks within a unified framework, while providing interpretable and clinically meaningful uncertainty estimates.

The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provide insight into individual as well as population level variation. We develop \\(\\texttt{LucidAtlas}\\), an approach that can represent spatially varying information, and can capture the influence of covariates as well as population uncertainty. As a versatile atlas representation, \\(\\texttt{LucidAtlas}\\) offers robust capabilities for covariate interpretation, individualized prediction, population trend analysis, and uncertainty estimation, with the flexibility to incorporate prior knowledge. Additionally, we discuss the trustworthiness and potential risks of neural additive models for analyzing dependent covariates and then introduce a marginalization approach to explain the dependence of an individual predictor on the models' response (the atlas). To validate our method, we demonstrate its generalizability on two medical datasets. Our findings underscore the critical role of by-construction interpretable models in advancing scientific discovery. Our code will be publicly available upon acceptance.

Deep implicit functions (DIFs) have emerged as a powerful paradigm for many computer vision tasks such as 3D shape reconstruction, generation, registration, completion, editing, and understanding. However, given a set of 3D shapes with associated covariates there is at present no shape representation method which allows to precisely represent the shapes while capturing the individual dependencies on each covariate. Such a method would be of high utility to researchers to discover knowledge hidden in a population of shapes. For scientific shape discovery, we propose a 3D Neural Additive Model for Interpretable Shape Representation (\\(\\texttt{NAISR}\\)) which describes individual shapes by deforming a shape atlas in accordance to the effect of disentangled covariates. Our approach captures shape population trends and allows for patient-specific predictions through shape transfer. \\(\\texttt{NAISR}\\) is the first approach to combine the benefits of deep implicit shape representations with an atlas deforming according to specified covariates. We evaluate \\(\\texttt{NAISR}\\) with respect to shape reconstruction, shape disentanglement, shape evolution, and shape transfer on three datasets: 1) \\(\\textit{Starman}\\), a simulated 2D shape dataset; 2) the ADNI hippocampus 3D shape dataset; and 3) a pediatric airway 3D shape dataset. Our experiments demonstrate that \\(\\textit{Starman}\\) achieves excellent shape reconstruction performance while retaining interpretability. Our code is available at \\(\\href{https://github.com/uncbiag/NAISR}{https://github.com/uncbiag/NAISR}\\).