In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no exception that convolutional neural networks (CNNs) have achieved the latest accomplishment in many aspects of human life and the farming sector. Semantic image segmentation is considered the main problem in computer vision. Despite tremendous progress in applications, approximately all semantic image segmentation algorithms fail to achieve sufficient hash results because of the absence of details sensitivity, problems in assessing the global similarity of image pixels, or both. Methods of post-processing improvement, as a wonderfully critical means of improving the underlying flaws mentioned above from algorithms, depend almost on Conditional Random Fields (CRFs). Therefore, plant disease prediction plays important role in the premature notification of the disease to alleviate its effects on disease forecast investigation purposes in the smart farming arena. Hence, this work proposes an efficient IoT-based plant disease recognition system using semantic segmentation methods such as FCN-8 s, CED-Net, SegNet, DeepLabv3, and U-Net with the CRF method to allocate disease parts in leaf crops. Evaluation of this network and comparison with other networks of the state art. The experimental results and their comparisons proclaim over F1-score, sensitivity, and intersection over union (IoU). The proposed system with SegNet and CRFs gives high results compared with other methods. The superiority and effectiveness of the mentioned improvement method, as well as its range of implementation, are confirmed through experiments.

The photovoltaic (PV) system industry is continuously developing around the world due to the high energy demand, even though the primary current energy source is fossil fuels, which are a limited source and other sources are very expensive. Solar cell defects are a major reason for PV system efficiency degradation, which causes disturbance or interruption of the generated electric current. In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K-means, MobileNetV2 and linear discriminant algorithms to cluster solar cell images and develop a detection model for each constructed cluster. It can extract the distinct features between defective and nondefective solar cell clusters and overcome the confusion between different cell shapes. The proposed system was assessed using a benchmark dataset of electroluminescence images. The results demonstrate that it achieved the highest accuracy at a substantial rate compared to recent studies.

Text plagiarism has greatly spread in the recent years, it becomes a common problem in several fields such as research manuscripts, textbooks, patents, academic circles, etc. There are many sentence similarity features were used to detect plagiarism, but each of them is not discriminative to differentiate the similarity cases. This causes the discovery of lexical, syntactic and semantic text plagiarism types to be a challenging problem. Therefore, a new plagiarism detection system is proposed to extract the most effective sentence similarity features and construct hyperplane equation of the selected features to distinguish the similarity cases with the highest accuracy. It consists of three phases; the first phase is used to preprocess the documents. The second phase is depended on two paths, the first path is based on traditional paragraph level comparison, and the second path is based on the computed hyperplane equation using Support Vector Machine (SVM) and Chi-square techniques. The third phase is used to extract the best plagiarized segment. The proposed system is evaluated on several benchmark datasets. The experimental results showed that the proposed system obtained a significant superiority in the performance compared to the systems with a higher ranking in the recent years. The proposed system achieved the best values 89.12% and 92.91% of the Plagdet scores, 89.34% and 92.95% of the F-measure scores on the complete test corpus of PAN 2013 and PAN 2014 datasets, respectively.

The phenomenon of scientific burglary has seen a significant increase recently due to the technological development in software. Therefore, many types of research have been developed to address this phenomenon. However, detecting lexical, syntactic, and semantic text plagiarism remains to be a challenge. Thus, in this study, we have computed and recorded all the features that reflect different types of text similarities in a new database. The created database is proposed for intelligent learning to solve text plagiarism detection problems. Using the created database, a reliable plagiarism detection system is also proposed, which depends on intelligent deep learning. Different approaches to deep learning, such as convolution and recurrent neural network architectures, were considered during the construction of this system. A comparative study was implemented to evaluate the proposed intelligent system on the two benchmark datasets: PAN 2013 and PAN 2014 of the PAN Workshop series. The experimental results showed that the proposed system based on long short-term memory (LSTM) achieved the first rank compared to up-to-date ranking systems.