ISSN: 3029-2786 (Online)

   Journal Abbreviation: Comput. Artif. Intell.

   Publication Frequency: Bi-annual

   Publishing Model: Open Access


About the Journal

Computing and Artificial Intelligence (CAI) is a peer-reviewed, open access journal of computer science and Artificial Intelligence. The journal welcomes submissions from worldwide researchers, and practitioners in the field of Artificial Intelligence, which can be original research articles, review articles, editorials, case reports, commentaries, etc.

Latest Articles

  • Open Access


    Article ID: 545

    Plant leaf disease classification using FractalNet

    by Hmidi Alaeddine, Malek Jihene

    Computing and Artificial Intelligence, Vol.2, No.2, 2024; 15 Views, 9 PDF Downloads

    In this work, an effort is made to apply the FractalNet model in the field of plant disease classification. The proposed model was trained and tested using a “PlantVillage” plant disease image dataset using a central processing unit (CPU) environment for 300 epochs. It produced an average classification accuracy of 99.9632% on the test dataset. The experimental results demonstrate the efficiency of the proposed model and show that the model achieved the highest values compared to other deep learning models in the PlantVillage datasets.

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  • Open Access


    Article ID: 1427

    The computational analysis of COVID-19-induced socio-economic, environmental, and climatic disruptions on the Indian food production system

    by Adya Aiswarya Dash, Sonu Sharma

    Computing and Artificial Intelligence, Vol.2, No.2, 2024; 4 Views, 0 PDF Downloads

    COVID-19 dominantly affected all the sectors of the Indian economy, surprisingly the impact is much lower with respect to the agricultural production (−2.7%) in India. The increase in yield of the crops can be attributed to the variables such as environmental, climatic, and socio-demographic factors. The study illustrates the relationship of the increase in crop yield in India during the first wave of COVID-19 along with the increase in the infection count and the land under cultivation attributed to supporting factors during the year 2020. The relation is explained by the method of ordinary least square (OLS) and geographically weighted regression (GWR). The distribution of the increase in crop yield across India is analyzed against COVID-19 infections along with other dominant factors. Useful intuitions against crop yield can be generated by studying the spatial relationships between them. The geographically weighted regression method depicted an increase in R 2 value as compared to the global ordinary least regression method. The Akaike information criterion in the geographically weighted regression method is also lower as compared to the ordinary least square therefore explaining GWR as a better model as compared to OLS. The combination of the various variables affecting agricultural yield in India is found to vary geographically as well as with the type of crops.

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  • Open Access


    Article ID: 1409

    Predicting manipulated regions in deepfake videos using convolutional vision transformers

    by Mohan Bhandari, Sushant Shrestha, Utsab Karki, Santosh Adhikari, Rajan Gaihre

    Computing and Artificial Intelligence, Vol.2, No.2, 2024; 0 Views, 0 PDF Downloads

    Deepfake technology, which uses artificial intelligence to create and manipulate realistic synthetic media, poses a serious threat to the trustworthiness and integrity of digital content. Deepfakes can be used to generate, swap, or modify faces in videos, altering the appearance, identity, or expression of individuals. This study presents an approach for deepfake detection, based on a convolutional vision transformer (CViT), a hybrid model that combines convolutional neural networks (CNNs) and vision transformers (ViTs). The proposed study uses a 20-layer CNN to extract learnable features from face images, and a ViT to classify them into real or fake categories. The study also employs MTCNN, a multi-task cascaded network, to detect and align faces in videos, improving the accuracy and efficiency of the face extraction process. The method is assessed using the FaceForensics++ dataset, which comprises 15,800 images sourced from 1600 videos. With an 80:10:10 split ratio, the experimental results show that the proposed method achieves an accuracy of 92.5% and an AUC of 0.91. We use Gradient-Weighted Class Activation Mapping (Grad-CAM) visualization that highlights distinctive image regions used for making a decision. The proposed method demonstrates a high capability of detecting and distinguishing between genuine and manipulated videos, contributing to the enhancement of media authenticity and security.

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