An improved extrinsic monolingual plagiarism detection approach of the Bengali text

Plagiarism is an act of literature fraud, which is presenting others’ work or ideas without giving credit to the original work. All published and unpublished written documents are under the cover of this definition. Plagiarism, which increased significantly over the last few years, is a concerning issue for students, academicians, and professionals. Due to this, there are several plagiarism detection tools or software available to detect plagiarism in different languages. Unfortunately, negligible work has been done and no plagiarism detection software available in the Bengali language where Bengali is one of the most spoken languages in the world. In this paper, we have proposed a plagiarism detection tool for the Bengali language that mainly focuses on the educational and newspaper domain. We have collected 82 textbooks from the National Curriculum of Textbooks (NCTB), Bangladesh, scrapped all articles from 12 reputed newspapers and compiled our corpus with more than 10 million sentences. The proposed method on Bengali text corpus shows an accuracy rate of 97.31

LeafNet: A proficient convolutional neural network for detecting seven prominent mango leaf diseases

Fruit production plays a significant role in meeting nutritional needs and contributing to the lessening of the global food crisis. Plant diseases are quite a common phenomenon that hampers gross production and causes huge losses for farmers in tropical South Asian weather conditions. In context, early-stage detection of plant disease is essential for healthy production. This research develops LeafNet, a convolutional neural network (CNN)-based approach to detect seven of the most common diseases of mango using images of the leaves. This model is trained specially for the pattern of mango diseases in Bangladesh using a novel dataset of region-specific images and is classified for almost all highly available mango diseases. The performance of LeafNet is evaluated with an average accuracy, precision, recall, F-score, and specificity of 98.55%, 99.508%, 99.45%, 99.47%, and 99.878%, respectively, in a 5-fold cross-validation that is higher than the state-of-the-art models like AlexNet and VGG16. LeafNet can be helpful in the detection of early symptoms of diseases, ultimately leading to a higher production of mangoes and contributing to the national economy

SolNet: A Convolutional Neural Network for Detecting Dust on Solar Panels

Electricity production from photovoltaic (PV) systems has accelerated in the last few decades. Numerous environmental factors, particularly the buildup of dust on PV panels have resulted in a significant loss in PV energy output. To detect the dust and thus reduce power loss, several techniques are being researched, including thermal imaging, image processing, sensors, cameras with IoT, machine learning, and deep learning. In this study, a new dataset of images of dusty and clean panels is introduced and applied to the current state-of-the-art (SOTA) classification algorithms. Afterward, a new convolutional neural network (CNN) architecture, SolNet, is proposed that deals specifically with the detection of solar panel dust accumulation. The performance and results of the proposed SolNet and other SOTA algorithms are compared to validate its efficiency and outcomes where SolNet shows a higher accuracy level of 98.2%. Hence, both the dataset and SolNet can be used as benchmarks for future research endeavors. Furthermore, the classes of the dataset can also be expanded for multiclass classification. At the same time, the SolNet model can be fine-tuned by tweaking the hyperparameters for further improvements