Explore the vast range of titles available.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Accurate reservoir characterization is necessary to effectively monitor, manage, and increase production. A seismic inversion methodology using a genetic algorithm (GA) and particle swarm optimization (PSO) technique is proposed in this study to characterize the reservoir both qualitatively and quantitatively. It is usually difficult and expensive to map deeper reservoirs in exploratory operations when using conventional approaches for reservoir characterization hence inversion based on advanced technique (GA and PSO) is proposed in this study. The main goal is to use GA and PSO to significantly lower the fitness (error) function between real seismic data and modeled synthetic data, which will allow us to estimate subsurface properties and accurately characterize the reservoir. Both techniques estimate subsurface properties in a comparable manner. Consequently, a qualitative and quantitative comparison is conducted between these two algorithms. Using two synthetic data and one real data from the Blackfoot field in Canada, the study examined subsurface acoustic impedance and porosity in the inter-well zone. Porosity and acoustic impedance are layer features, but seismic data is an interface property, hence these characteristics provide more useful and applicable reservoir information. The inverted results aid in the understanding of seismic data by providing incredibly high-resolution images of the subsurface. Both the GA and the PSO algorithms deliver outstanding results for both simulated and real data. The inverted section accurately delineated a high porosity zone ( > 20 % ) that supported the high seismic amplitude anomaly by having a low acoustic impedance (6000–8500 m/s ∗ g/cc). This unusual zone is categorized as a reservoir (sand channel) and is located in the 1040–1065 ms time range. In this inversion process, after 400 iterations, the fitness error falls from 1 to 0.88 using GA optimization, compared to 1 to 0.25 using PSO. The convergence time for GA is 670,680 s, but the convergence time for PSO optimization is 356,400 s, showing that the former requires 88 % more time than the latter.

The Internet of Things (IoT) has recently attracted substantial interest because of its diverse applications. In the agriculture sector, automated methods for detecting plant diseases offer numerous advantages over traditional methods. In the current study, a new model is developed to categorize plant diseases within an IoT network. The IoT network is simulated for monitoring crop diseases. Routing is performed with Henry Gas Chicken Swarm Optimization (HGCSO), which is designed by integrating Henry Gas Solubility Optimization (HGSO) and Chicken Swarm Optimization (CSO). The fitness parameters of the model include delay, energy, distance, and link lifetime (LLT). At the Base Station (BS), plant disease categorization is performed by collecting plant leaf images. Preprocessing is done on the input images using median filtering. Various features, such as Histogram of Oriented Gradient (HoG), statistical features, Spider Local Image Features (SLIF), and Local Ternary Patterns (LTP) are extracted. Plant disease categorization is carried out using a Deep Residual Network (DRN), which is trained using the developed Caviar Henry Gas Chicken Swarm Optimization (CHGCSO) that combines the CAViaR model with HGCSO. Comparative results show an accuracy of 94.3%, a maximum sensitivity of 93.3%, a maximum specificity of 92%, and an F1-score of 93%, indicating that the CHGCSO-based DRN outperforms existing methods. Graphic Abstract

The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Women and brains have always been an epicentre of intrigue and controversy delineating that women must use brains in dimensions that have been predestined for them by misogynists. An intelligent woman is often marginalized as unfeminine and hoydenish capable of threatening the heteropatriarchy thereby rendering it impotent. Several pioneering works on gender identity and equality began to be written in the eighteen and nineteenth century drawing attention of the intelligentsia as well as the common folk equally, towards this burning issue. Feminist reforms were initiated as a result of the untiring endeavour of writers and critics throughout the world. The first wave of feminism was a signal for the society to revoke the existing patriarchal norms and it was strengthened further by the second and third wave of feminism with formidable writers, activists and revolutionists who fought a long drawn battle to equip women with their share of rights. Women's continued and persistent struggle against patriarchy the world over has led to society's much needed changed perspectives towards women and their intellect. Women have proved the concocted saying \"women and brains do not go together\" false with their sheer grit and persistent determination. Reverberating similar deliverance, this paper investigates Alice Munro's biography of the renowned first ever female mathematics professor Sophia Kovalevsky in her short story 'Too Much Happiness'' with the archetypal lens of Carl Jung. Sophia, the protagonist in the story is a woman with an extraordinary intellect, a mathematician and a novelist with a rare fascinating power to conquer the world. In times when most women are compulsorily confined to the kitchen, she dares it all to make it to the University of Stockholm in Sweden and challenge the myth that a woman has less of an intellect than man. She is aware of the animus in her which is the so called male domain of a women's psyche and represents the logical thinking faculty in a woman. This paper aims at tracing the renowned Swiss psychologist Carl Jung's archetype of the animus in Alice Munro's portrayal of Sophia, to discern her psyche and to analyse and interpret how her animus affects her life and career as an intellectual in the old school patriarchal world.

In India, the river Ganga is believed as a goddess, and people worship it. Despite all the respect for the river, the river's condition is worsening, and we Indians are unable to maintain the purity of the river. The Ganga is a river of faith, devotion, and worship. Indians accept its water as \"holy,\" which is known for its \"curative\" properties. The river is not limited to these beliefs but is also a significant water source, working as the life-supporting system for Indians since ancient times. The Ganga river and its tributaries come from cold, Himalayan-glacier-fed springs, which are pure and unpolluted. But when the river flows downgradient, it meets the highly populated cities before merging into the Bay of Bengal. From its origin to its fall, its water changes from crystal clear to trash-and sewage-infested sludge. Thousands of years passed since the river Ganga, and its tributaries provide substantial, divine, and cultural nourishment to millions of people living in the basin. Nowadays, with the increasing urbanization, the Ganges basin sustains more than 40 percent of the population. Due to the significant contribution of the growing population and rapid industrialization along its banks, river Ganga has reached an alarming pollution level.

Tuberculosis is one of the leading causes of millions of deaths across the world, mainly due to growth of drug-resistant strains. Anti-tubercular peptides may facilitate an alternate way to combat antibiotic tolerance. This study describes a novel approach for enhancing the prediction of anti-tubercular peptides by feature extraction from sequence of the peptides, selection of optimal features from the extracted features, and selection of suitable learning algorithm. Firstly, we extract different sequence features by using iFeature web server. Then, the optimal features are obtained by using a novel divergence measure-based intuitionistic fuzzy rough sets-assisted feature selection technique. Furthermore, an attempt has been made to develop models using different machine learning techniques for enhancing the prediction of anti-tubercular (or anti-mycobacterial peptides) with other antibacterial peptides (ABP) as well non-antibacterial peptides (non-ABP). Moreover, the best prediction result is obtained by vote-based classifier. Using 80:20 percentage split, the proposed method performs well, with sensitivity of 92.0%, 96.4%, specificity of 83.3%, 88.4%, overall accuracy of 87.80%, 92.90%, Mathews correlation coefficient of 0.757, 0.857, AUC of 0.922, 0.914, and g-means of 87.5%, 92.3% for anti-tubercular and ABP (primary dataset), anti-tubercular and non-ABP (secondary dataset), respectively. Finally, we have evaluated the performances of different machine learning algorithms by using the reduced training sets as produced by our proposed feature selection technique as well as already existing intuitionistic fuzzy rough set based and ensemble feature selection technique. Moreover, the performance of our proposed approach is evaluated on few benchmark and AMP datasets. From the experimental results, it can be observed that our proposed method is outperforming the previous methods.

Traditional methods of fraud detection rely on rule-based systems or supervised machine learning models that require labelled data and domain knowledge. However, these methods have limitations such as high false positive rates, low scalability, and vulnerability to adversarial attacks. In this paper, a novel approach for bank fraud detection using hyper ensemble machine learning (HEML), which combines multiple unsupervised and semi-supervised models with different features and hyperparameters to achieve high accuracy and robustness, including—logistic regression (LR), decision tree (DT), support vector machine (SVM), neural network (NN), one-class SVM (OCSVM), and isolation forest (IF) are studied.The approach is evaluated on a real-world dataset of bank transactions from a large European bank and compared with several baseline methods.The accuracies of base learners and ensemble learners on the test data of LR, DT, SVM, NN, OCSVM and IF are as follows in order 0.95,0.91,0.96, 0.97, 0.93, 0.92. The results show that HEML outperforms the baselines in terms of precision, recall, F1-score, and AUC-ROC, while reducing the computational cost and human intervention. Additionally, the effectiveness of HEML in detecting new types of frauds that were not seen in the training data is demonstrated. Thus, HEML is a promising technique for bank fraud detection that can adapt to dynamic and complex fraud scenarios. By utilizing multiple models and features, HEML can provide accurate and robust fraud detection while reducing false positives and minimizing human intervention. By employing multiple models and features, HEML has the potential to improve the financial security and stability for both banks and their customers. Graphical Abstract

L-Asparaginase (L-asparagine aminohydrolase, E.C. 3.5.1.1) has been proven to be competent in treating Acute Lymphoblastic Leukaemia (ALL), which is widely observed in paediatric and adult groups. Currently, clinical L-Asparaginase formulations are derived from bacterial sources such as Escherichia coli and Erwinia chrysanthemi . These formulations when administered to ALL patients lead to several immunological and hypersensitive reactions. Hence, additional purification steps are required to remove toxicity induced by the amalgamation of other enzymes like glutaminase and urease. Production of L-Asparaginase that is free of glutaminase and urease is a major area of research. In this paper, we report the screening and isolation of fungal species collected from the soil and mosses in the Schirmacher Hills, Dronning Maud Land, Antarctica, that produce L-Asparaginase free of glutaminase and urease. A total of 55 isolates were obtained from 33 environmental samples that were tested by conventional plate techniques using Phenol red and Bromothymol blue as indicators. Among the isolated fungi, 30 isolates showed L-Asparaginase free of glutaminase and urease. The L-Asparaginase producing strain Trichosporon asahii IBBLA1, which showed the highest zone index, was then optimized with a Taguchi design. Optimum enzyme activity of 20.57 U mL −1 was obtained at a temperature of 30 °C and pH of 7.0 after 60 hours. Our work suggests that isolation of fungi from extreme environments such as Antarctica may lead to an important advancement in therapeutic applications with fewer side effects.

Kamala Markandaya voices the untold sufferings of the rural farmers, middle-class city dwellers, impact of industrialization and the position of women in society. She is interested in both 'what' and 'how' of the narratives. She uses different techniques to make her story powerful and instils message in readers mind. Her narrative techniques abound in modes of expression and narrations. A Handful of Rice (1966) is known for her experimental techniques to uncover the individual rebellion and acceptance of social expectations. This study analyses narratological approach to the novel to identify the narrative levels. Narratology is the study of'how'narratives create meaning, and focuses upon 'what,' pertaining to the basic mechanism and procedures of it. The study traces Gerard Genette's concept of narrative levels, narratives and narrators in Kamala Markandaya's A Handful of Rice. The narrative of the novel covers period of ten years accompanying different narrators, narratives and their levels. This study elucidates the correlation of narratological interpretation in emphasizing themes such as poverty, hunger, struggle of middle class city dwellers and impact of industrialization on them through excerpts of the novel.