An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.

Geochemistry of subsurface Late Quaternary ironstones in Rajshahi and Bogra Districts, Bangladesh: implications for genetic and depositional conditions

The present study deals with the geochemistry of Late Quaternary ironstones in the subsurface in Rajshahi and Bogra districts, Bangladesh with the lithological study of the boreholes sediments. Major lithofacies of the studied boreholes are clay, silty clay, sandy clay, fine to coarse grained sand, gravels and sands with(fragmentary) ironstones. The ironstones contain major oxides, Fe_2 O_3*(*total Fe)(avg. 66.6 wt%), SiO_2(avg. 15.3 wt%), Al_2 O_3(avg. 4.0 wt%), MnO(avg. 7.7 wt%), and CaO(avg. 3.4 wt%). These geochemical data imply that the higher percentage of Fe_2 O_3* along with Al_2 O_3 and MnO indicate the ironstone as goethite and siderite, which is also validated by XRD data. A comparatively higher percentage of SiO_2 indicates the presence of relative amounts of clastic quartz and manganese-rich silicate or clay in these rocks. These ironstones also have significant amounts of MnO(avg. 7.7 wt%) suggesting their depositional environments under oxygenated condition. Chemical data of these ironstones suggest that the source rock suffered deep chemical weathering and iron was mostly carried in association with the clay fraction and organic matter. Iron concretion was mostly formed by bacterial build up in swamps and marshes, and was subsequently embedded in clayey mud.Within the coastal environments, the water table fluctuates and goethite and siderite with mud and quartz became dry and compacted to form ironstone.