Forthcoming and Online First Articles

International Journal of Artificial Intelligence and Soft Computing

International Journal of Artificial Intelligence and Soft Computing (IJAISC)

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International Journal of Artificial Intelligence and Soft Computing (5 papers in press)

Regular Issues

  • 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.

  • Statistical Growth Prediction Analysis of Rice Crop with Pixel-Based Mapping Technique   Order a copy of this article
    by Monica Mangla, Vaishali Mehta, Sachi Nandan Mohanty, Nonita Sharma, Anusha Preetham 
    Abstract: Agriculture has attracted eminent researchers during the past few decades owing to revolutionary advancements in the field of data analysis using machine learning and computer vision techniques. The continuous monitoring of plant growth is an important aspect in the field of agriculture and has associated challenges also. The current work aims to define the significance of the pixel-based clustering techniques for analyzing plant growth in terms of height calculation. In this study, pixel-based mapping has implemented its two applications viz. vertical and horizontal scaling for height calculation. Here, vertical mapping implements an image processing technique to monitor the height of a single plant whereas the horizontal mapping technique determines the average volume of the whole field using k-means. During the result analysis, it is observed that the proposed model provides an accuracy of 97.58% outperforming the state-of-the-art models.
    Keywords: Image Processing; Pixel based Mapping; Leaf Growth Analysis; Scaling; Machine learning; k-means clustering.

  • CovFakeBot: A Machine Learning based ChatBot using Ensemble Learning Technique for Covid-19 Fake News Detection   Order a copy of this article
    by Hunar Batra, Gunjan Kanwar Palawat, Kanika Gupta, Priadarshana ., Supragya ., Deepali Bajaj, Urmil Bharti 
    Abstract: The outbreak of the SARS-Cov-2 virus epidemic has been followed by a flood of misleading information on social media which is impacting millions of people every day. For this, we developed CovFakeBot, a conversational bot based on machine learning models to distinguish between fake and real news. The bot also provides the confidence score for the prediction that helps to ascertain trustworthiness of the news. Our system uses the Covid-19 tweet dataset and is trained over well-established state-of-the-art machine learning models. It is further optimized using ensemble learning methods for better accuracy. Results are evaluated using the accuracy and F1-score. We observed that ensemble learning using soft voting outperformed thus, we claim it as the best fit model. CovFakeBot is using WhatsApp Business API with Twilio to achieve conversational user interface. CovFakeBot will help the public to easily classify news as real or fake.
    Keywords: fake new detection; machine learning algorithms; ensemble learning; voting technique; Twilio sandbox; WhatsApp; chatbot; soft voting classifier; binary classification; natural language processing.

  • PigB: Intelligent Pig Breeds Classification Using Supervised Machine Learning Algorithms   Order a copy of this article
    by PRITAM GHOSH, Satyendra Nath Mandal 
    Abstract: Performance of different supervised machine learning algorithms varies when they are applied to different datasets. Moreover, using a generalized supervised algorithm may not be able to produce the optimal performance as the nature of data is different for different datasets. In this paper, we have used a pig breed dataset containing seven different statistical features such as entropy, standard deviation, mean, sum, max, min, variance, median, and mode and three colour component features such as hue, saturation, and value, extracted from individual pig images of five different pig breeds. Eight different well-established algorithms such as Logistic Regression, Multilayer Perceptron, Decision Trees, Gradient Boosted Decision Trees, Random Forest, Support Vector Machine, K-Nearest Neighbours and Na
    Keywords: Supervised Learning; Fine Grained Classification; Statistical Features; Performance Evaluation; Hyperparameter Optimization.