International Journal of Formal Sciences: Current and Future Research Trends https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal <p style="text-align: justify;">The International Journal of Social Sciences: Current and Future Research Trends (IJSSCFRT) is an open access International Journal for scientists and researchers to publish their scientific papers in Social Sciences related fields. IJSSCFRT plays its role as a refereed international journal to publish research results conducted by researchers.</p> <p>This journal accepts scientific papers for publication after passing the journal's double peer review process within 4 weeks. For detailed information about the journal kindly check <a title="About the Journal" href="https://ijsscfrtjournal.isrra.org/index.php/Social_Science_Journal/about">About the Journal</a> page. </p> <p style="text-align: justify;">All IJSSCFRT published papers in Social Sciences will be available for scientific readers for free; no fees are required to download published papers in this international journal.</p> <p style="text-align: justify;"> </p> Mohammad Nassar for Researches (MNFR) en-US International Journal of Formal Sciences: Current and Future Research Trends 2790-7945 <p>Authors who submit papers with this journal agree to the <a title="Copyright_Notice" href="https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/Copyright_Notice" target="_blank" rel="noopener">following terms</a>.</p> Comparative Analysis of Deep Learning Models for Crop Diseases and Pest Classification https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/1019 <p>The deep learning models for crop diseases and pest classification research examined how deep learning might improve farming methods, particularly to accurately classify pests and diseases that affect crops. The importance of crop diseases and pests to world food security was highlighted in the introduction, along with the need for new approaches, such as deep learning models, to improve the accuracy and effectiveness of pest and disease control in farming. To evaluate the classification accuracy, the secondary datasets obtained from the Kaggle website were used to train and test various deep learning models, one of which was DenseNet. The researcher used a thorough assessment methodology to compare DenseNet's performance to that of other models, including AlexNet, EfficientNet, Visual Geometry Group, and Convolutional Neural Network. With an impressive accuracy score of 96.988% on the maize disease dataset and 96.9382% on the pest dataset, DenseNet proved to be the best model among the others. More accurate predictions were the result of DenseNet's capacity to effectively collect intricate characteristics and patterns within the visual data, which led to its improved performance. The researcher examined the implications of DenseNet's high accuracy in the discussion section, implying that its sophisticated design rendered it optimal for the categorization of agricultural diseases and pests. In addition, the researcher investigated the feasibility of incorporating DenseNet into practical agricultural systems, where its strong performance might greatly enhance methods of crop monitoring and disease control. The discussion came to a close with suggestions for future studies, such as looking at whether DenseNet can be used for other types of crops and if hybrid models or transfer learning may improve its performance.</p> Vincent Mbandu Ochango Geoffrey Mariga Wambugu Aaron Mogeni Oirere Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-01-26 2025-01-26 22 1 1 25 Effect of Water Salinity Concentration on Flathead Grey Mullet Performance https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/201 <p>Restricted Access.</p> Habtamu Getnet Demeke Kifle Tadesse Fetahi Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 The Road Transportation Infrastructure, Industrial Evolution and Economic Growth https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/216 <p>Restricted Access.</p> Tekleselase Michael Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 Modelling for The Risk Factors Early Introduction to Complementary Food for A Baby Under Ten Months Old https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/223 <p>Restricted Access.</p> Gabriel Solomon Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 Cottage Textile Dyeing and Weaving as an Empowerment Strategy for Rural Women in Nigeria https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/240 <p>Restricted Access.</p> Rahmawati Setiyani Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 Robust Detection of Glaucoma using Cup to Disc Ratio and Hybrid Features https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/178 <p>Restricted Access.</p> Sheriza Mohd Razali Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 Neural Fog Control in a Dual Feed Induction Generator with Wind Energy https://ijfscfrtjournal.isrra.org/index.php/Formal_Sciences_Journal/article/view/208 <p>Restricted Access.</p> Ansa Hayyat Copyright (c) 2024 International Journal of Formal Sciences: Current and Future Research Trends https://creativecommons.org/licenses/by-nc-nd/4.0 2025-02-12 2025-02-12 22 1 An Application of Maximum Likelihood Parameter Estimation for Financial Model Building in Excel 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