Forthcoming and Online First Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

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International Journal of Artificial Intelligence and Soft Computing (3 papers in press)

Regular Issues

  • The Ultimate Kernel Machine for Diagnosis of Breast cancer   Order a copy of this article
    by Pooja Shah, Trupti Shah 
    Abstract: In this paper, an extensive study of diagnosis of breast cancer is made using SupportVector Machine (SVM) technique.To build the cost-effective kernel machine for breast cancer diagnosis, the tools of Principal Component Analysis (PCA) and kfold Cross-Validation (CV) techniques are employed. The model is implemented on WDBC and WBC datasets to check the condition of the tumor for its malignancy. Classification accuracy and computation time are obtained and comparative experimental results are analysed under different conditions. For WBC dataset, 100% accuracy is obtained using Polynomial kernel in just 0.03 second.
    Keywords: Breast Cancer; Support Vector Machine (SVM); Principal Component Analysis (PCA); k-fold Cross Validation (CV).
    DOI: 10.1504/IJAISC.2021.10040808
     
  • An efficient hybrid approach for the prediction of Epilepsy using CNN with LSTM   Order a copy of this article
    by Anuj Singh, Arvind Kumar Tiwari, Arpita Srivastava 
    Abstract: Epileptic seizures are a severe neurological disorder with significant implications for public health.. Epileptic Seizure is one of the Neurological disorders which affect either child in the age group of 10-20 years old or adults in the age group of 65-70 years old. It affects brain cells. Electroencephalogram (EEG) is the best tool for the recording of brain electrical activity. Epileptic Seizures can be studied in four stages known as Pre-ictal, Ictal, Post-ictal, and Interictal. This paper, presents a literature review for the prediction of epilepsy using various machine learning based approached. This paper also presents the comparative analysis of various computational based techniques used to predict the epilepsy. This paper proposes a hybrid approach for the prediction of epilepsy using Convolutional Neural Network and Long Short Term Memory. Here, the proposed model achieved an accuracy of 98%, Precision of 98.21%, Recall of 92.02%, F1-Score of 95.01%, Specificity of 99.56%, MCC of 93.84% , TPR of 92.02%, FPR of 0.44% and AUC is 100%. IT is also observed that the proposed model performed better in comparison to other approaches.
    Keywords: Epileptic Seizure; Convolutional Neural Network; Long Short Term Memory; Deep learning; Support Vector Machine.

  • Machine Learning Classification Models for student Placement Prediction based on Skills   Order a copy of this article
    by LAXMI SHANKER MAURYA, Shadab Hussain, Sarita Singh 
    Abstract: Placement plays a vital role for engineering students in their career planning. Placement is also important for Engineering Institutions to maintain the ranking in University. In this paper, we have proposed a few supervised machine learning classification models which may be used to predict the placement of a student based on skills like Aptitude, Coding, Communication and Technical. We also compare the results of different proposed classification models. The classification algorithms Support Vector Machine, Gaussian Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Tree, Stochastic Gradient Descent and Logistic Regression were used.
    Keywords: Supervised Learning; Classification Model; Skill Level; Placement Decision.