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Environmental hazards are materials, states, or situations that threaten the natural environment or human health, such as pollution and natural catastrophes like hurricanes and earthquakes. In recent decades, natural hazards have become more dangerous due to developments affecting climate and land use/land cover (LULC), primarily driven by anthropic pressures such as urbanization, forest management methods, and agricultural activities. Advancements in Remote Sensing (RS) technology enable rapid, accurate terrain data collection, significantly aiding in mapping, monitoring, and assessing hazards. This research proposes a deep classification model combining hierarchical feature extraction and classification units to categorize LULC from remotely sensed images. Four filters of equal size (3 × 3) simultaneously extract features from the input image, which are then concatenated and classified into different LULC categories. Experiments on two datasets independently verify the model, demonstrating improved resilience compared to other state-of-the-art approaches. To ensure the generalizability and robustness of the model, 5-fold cross-validation is conducted, yielding consistently high AUC scores. Additionally, an independent T-test is performed to statistically validate the performance improvements over comparative models. This proposed model helps predict future impacts and manage risks through accurate and efficient LULC classification.

Since the last few decades, Artificial Neural Networks have been the center of attraction of a large number of researchers for solving diversified problem domains. Due to its distinguishing features such as generalization ability, robustness and strong ability to tackle nonlinear problems, it appears to be more popular in financial time series modeling and prediction. In this paper, a Pi-Sigma Neural Network is designed for foretelling the future currency exchange rates in different prediction horizon. The unrevealed parameters of the network are interpreted by a hybrid learning algorithm termed as Shuffled Differential Evolution (SDE). The main motivation of this study is to integrate the partitioning and random shuffling scheme of Shuffled Frog Leaping algorithm with evolutionary steps of a Differential Evolution technique to obtain an optimal solution with an accelerated convergence rate. The efficiency of the proposed predictor model is actualized by predicting the exchange rate price of a US dollar against Swiss France (CHF) and Japanese Yen (JPY) accumulated within the same period of time.

Drug discovery has customarily focused on a de novo design approach, which is extremely expensive and takes several years to evolve before reaching the market. Discovering novel therapeutic benefits for the current drugs could contribute to new treatment alternatives for individuals with complex medical demands that are safe, inexpensive, and timely. In this consequence, when pharmaceutically yield and oncology drug efficacy appear to have hit a stalemate, drug repurposing is a fascinating method for improving cancer treatment. This review gathered about how in silico drug repurposing offers the opportunity to quickly increase the anticancer drug arsenal and, more importantly, overcome some of the limits of existing cancer therapies against both old and new therapeutic targets in oncology. The ancient nononcology compounds' innovative potential targets and important signaling pathways in cancer therapy are also discussed. This review also includes many plant-derived chemical compounds that have shown potential anticancer properties in recent years. Here, we have also tried to bring the spotlight on the new mechanisms to support clinical research, which may become increasingly essential in the future; at the same time, the unsolved or failed clinical trial study should be reinvestigated further based on the techniques and information provided. These encouraging findings, combined together, will through new insight on repurposing more non-oncology drugs for the treatment of cancer.

In high-dimensional data analysis, Feature Selection (FS) is one of the most fundamental issues in machine learning and requires the attention of researchers. These datasets are characterized by huge space due to a high number of features, out of which only a few are significant for analysis. Thus, significant feature extraction is crucial. There are various techniques available for feature selection; among them, the filter techniques are significant in this community, as they can be used with any type of learning algorithm and drastically lower the running time of optimization algorithms and improve the performance of the model. Furthermore, the application of a filter approach depends on the characteristics of the dataset as well as on the machine learning model. Thus, to avoid these issues in this research, a combination of feature reduction (CFR) is considered designing a pipeline of filter approaches for high-dimensional microarray data classification. Considering four filter approaches, sixteen combinations of pipelines are generated. The feature subset is reduced in different levels, and ultimately, the significant feature set is evaluated. The pipelined filter techniques are Correlation-Based Feature Selection (CBFS), Chi-Square Test (CST), Information Gain (InG), and Relief Feature Selection (RFS), and the classification techniques are Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), and k-Nearest Neighbor (k-NN). The performance of CFR depends highly on the datasets as well as on the classifiers. Thereafter, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used for ranking all reduction combinations and evaluating the superior filter combination among all.

Worldwide, oral cancer is the sixth most common type of cancer. India is in 2nd position, with the highest number of oral cancer patients. To the population of oral cancer patients, India contributes to almost one-third of the total count. Among several types of oral cancer, the most common and dominant one is oral squamous cell carcinoma (OSCC). The major reason for oral cancer is tobacco consumption, excessive alcohol consumption, unhygienic mouth condition, betel quid eating, viral infection (namely human papillomavirus), etc. The early detection of oral cancer type OSCC, in its preliminary stage, gives more chances for better treatment and proper therapy. In this paper, author proposes a convolutional neural network model, for the automatic and early detection of OSCC, and for experimental purposes, histopathological oral cancer images are considered. The proposed model is compared and analyzed with state-of-the-art deep learning models like VGG16, VGG19, Alexnet, ResNet50, ResNet101, Mobile Net and Inception Net. The proposed model achieved a cross-validation accuracy of 97.82%, which indicates the suitability of the proposed approach for the automatic classification of oral cancer data.

Background and Aim Severe viral hemorrhagic fever (VHF) is caused by Marburg virus which is a member of the Filoviridae (filovirus) family. Many Marburg virus disease (MVD) outbreaks are reported in five decades. A major notable outbreak with substantial reported cases of infections and deaths was in 2022 in Uganda. The World Health Organisation (WHO) reported MVD outbreak in Ghana in July 2022 following the detection of two probable VHF patients there. Further, the virus was reported from two other African countries, the Equatorial Guinea (February 2023) and Tanzania (March 2023). There have been 35 deaths out of 40 reported cases in Equatorial Guinea, and six of the nine confirmed cases in Tanzania so far. Methods Data particularly on the several MVD outbreaks as reported from the African countries were searched on various databases including the Pubmed, Scopus, and Web‐of‐science. Also, the primary data and reports from health agencies like the WHO and the Centers for Disease Control and Prevention CDC) were evaluated and the efficacy reviewed. Results Chiroptera in general and bat species like Rousettus aegyptiacus and Hipposideros caffer in particular are natural reservoirs of the Marburg virus. MVD‐infected nonhuman primate African fruit‐bat and the MVD‐infected humans pose significant risk in human infections. Cross‐border viral transmission and its potential further international ramification concerns raise the risk of its rapid spread and a potential outbreak. Occurrence of MVD is becoming more frequent in Africa with higher case fatality rates. Effective prophylactic and therapeutic interventions to counter this deadly virus are suggested. Conclusion In the face of the lack of effective therapeutics and preventives against MVD, supportive care is the only available option which contributes to the growing concern and disease severity. In view of the preventive approaches involving effective surveillance and monitoring system following the “One Health” model is extremely beneficial to ensure a healthy world for all, this article aims at emphasizing several MVD outbreaks, epidemiology, zoonosis of the virus, current treatment strategies, risk assessments, and the mitigation strategies against MVD.

Due to massive growth of Web information, handling useful information has become a challenging issue in now-a-days.  In the past few decades, text summarization is considered as one of the solution to obtained relevant information from extensive collection of information. In this paper, a novel approach using modified shuffled frog leaping algorithm (MSFLA) to extract the important sentence from multiple documents is presented. The effectiveness of MSFLA algorithm for summarization model is evaluated by comparing the ROUGE score and statistical analysis of the model with respect to results of other summarization models. The models are demonstrated by the simulation results over DUC datasets. In the present work, it elucidates that MSFLA based model improves the results and find advisable solution for summary extraction

Globally, cancer stands as the second leading cause of mortality. Various strategies have been proposed to address this issue, with a strong emphasis on utilizing gene expression data to enhance cancer detection methods. However, challenges arise due to the high dimensionality, limited sample size relative to its dimensions, and the inherent redundancy and noise in many genes. Consequently, it is advisable to employ a subset of genes rather than the entire set for classifying gene expression data. This research introduces a model that incorporates Ranked-based Filter (RF) techniques for extracting significant features and employs Extreme Learning Machine (ELM) for data classification. The computational cost of using RF technique over high dimensional data is low. However extraction of significant genes using one or two stage of reduction is not effective. Thus, a 4-stage feature reduction strategy is applied. The reduced data is then utilized for classification using few variants of ELM model and activation function. Subsequently, a two-stage grading approach is implemented to determine the most suitable classifier for data classification. This analysis is conducted over four microarray gene expression data using four activation function with seven learning based classifiers, from which it is shown that II-ELM classifier outperforms in terms of performance matrix and ROC graph.

A smart system integrates appliances of sensing, acquisition, classification and managing with regard to interpreting and analyzing a situation to generate decisions depending on the available data in a predictive way . Remotely sensed images are an essential tool for evaluating and analyzing land cover dynamics, particularly for forest-cover change. The remote data gathered for this operation from different sensors are of high spatial resolution and thus suffer from high interclass and low intraclass vulnerability issues which retards classification accuracy. To address this problem, in this research analysis, a smart logistic fusion-based supervised multi-class classification (SLFSMC) model is proposed to obtain a thematic map of different land cover types and thereby performing smart actions. In the pre-processing stage of the proposed work, a pair of closing and opening morphological operations is employed to produce the fused image to exploit the contextual information of adjacent pixels. Thereafter quality assessment of the fused image is estimated on four fusion metrics. In the second phase, this fused image is taken as input to the proposed classifiers. Afterward, a multi-class classification model is designed based on the supervised learning concept to generate maps for analyzing and exporting decisions based on any critical climatic situation. In our paper, for estimating the performance of proposed SLFSMC among few conventional classification techniques such as the Naïve Bayes classifier, decision tree, Support vector machine, and K-nearest neighbors, a statistical tool called as Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is involved. We have implemented proposed SLFSMC system on some of the regions of Victoria, a state of Australia, after the deforestation caused due to different reasons.

In the current scenario, managing of a useful web of information has become a challenging issue due to a large amount of information related to many fields is online. The summarization of text is considered as one of the solutions to extract pertinent text from vast documents. Hence, a novel Cuckoo Search-based multi document summary extractor (CSMDSE) is presented to handle the multi-document summarization (MDS) problem. The proposed CSMDSE is assimilating with few other swarm-based summary extractors, such as Cat Swarm Optimization based Extractor (CSOE), Particle Swarm Optimization based Extractor (PSOE), Improved Particle Swarm Optimization based Extractor (IPSOE) and Ant Colony Optimization based Extractor (ACOE). Finally, a simulation of CSMDSE is compared with other techniques with respect to the traditional benchmark datasets for summarization problem. The experimental analysis clearly indicates CSMDSE has good performance than the other summary extractors discussed in this study.