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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Pradeep Kumar Mishra
Sharda University
Computer Science & IT Engineering

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Handwriting-based personality classification on Indian samples using long-short term memory Pradeep Kumar Mishra; Gouri Sankar Mishra; Ali Imam Abidi; Tarun Maini; Amit Kumar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2511-2520

Abstract

Traditional handwriting analysis methods have historically faced criticism for their lack of scientific basis, but more contemporary models based on layered artificial neural network (ANN) architecture have evidently been more successful. In the proposed model, a deep neural network (DNN) layered, long-short term memory (LSTM) model with contextual analysis has been proposed for handwriting-based personality classification. The model has been trained over a manually curated verbose dataset of ~6,000 Indian handwriting sample images, varying across genders, age groups, and regions. The classification is based on the five major personality traits. The proposed framework achieved an accuracy of 97.75%, which is over 10% better than the next best performing model on a comparably numerically bigger dataset; demonstrating the enhanced potential of context based neural networks on handwriting-based personality prediction when coupled with an appropriately varied and unbiased dataset.
A hybrid machine learning model for optimized mixed-crop recommendation Ahmed Mohammed Gimba; Pradeep Kumar Mishra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2314-2324

Abstract

Farmers today encounter more challenges when selecting appropriate variety of crops depending on their farm soil nutrients and climate. This research will assist farmers in choosing suitable mixed-crops depending on the individual farms soil and climate conditions in Andhra Pradesh, India. Using the dataset sourced from Indian Institute of Soil Science (IISS), Bhopal with 2,552 entries. Previous studies focused on only single-crop recommendations. This work proposes a novel hybrid mixed-crop recommendation system (CRS) that incorporates several machine learning (ML) techniques comprise of random forest-ExtraTrees (RF-ExtraTrees), decision tree-C4.5 (DT-C4.5), extreme gradient boosting-gradient boosting (XGBoost-GBoost), quadratic discriminant analysis-linear discriminant analysis (QDA-LDA), and support vector machine-stochastic gradient descent (SVM-SGD) were utilized to recommend mixed-crops. To enhance the reliability of the training process, 20% of the dataset was held in reserve for validation to analyze model performance. The result of the proposed work shows that all the hybrid ML models applied were viable, and RF ExtraTrees has achieved 95.91% best accuracy, 95.08% precision, and 95.91% recall, when contrasted to the other ML models.
Co-Authors Ahmed Mohammed Gimba Ali Imam Abidi Amit Kumar Gouri Sankar Mishra Tarun Maini