Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has 350 images. Three fully connected (FC) layers were utilized for feature extraction, namely fc6, fc7, and fc8. The classifiers employed were support vector machine (SVM), k-nearest neighbors (KNN), and Naive Bayes. The study demonstrated that the most effective feature extraction layer was fc6, achieving an accuracy of 90.7% with SVM. SVM outperformed KNN and Naive Bayes, exhibiting an accuracy of 90.7%, sensitivity of 83.5%, specificity of 93.7%, and F1-score of 83.5%. This research successfully addressed the challenges in classifying cassava species by leveraging deep learning and machine learning methods, specifically with SVM and the fc6 layer of AlexNet. The proposed approach holds promise for enhancing plant classification techniques, benefiting researchers, farmers, and environmentalists in plant species identification, ecosystem monitoring, and agricultural management.
Twisting and coiling actuators (TCAs) are lightweight artificial muscles that can produce large linear contractions while lifting heavy loads with low power. A TCA consists of two or more strings connected to a rotational motor and a load. When activated, the strings twist and coil, causing contraction. This study presents a data-driven framework for modeling TCA behavior using experimental data. Polynomial regression, Support Vector Regression (SVR), and symbolic regression were applied using the motor angle and payload weight as inputs. The models were evaluated under various loads and implemented in a neck-rehabilitation prototype. SVR showed the highest accuracy (RMSE: 6.17 mm upward, 4.98 mm downward); however, it lacks a closed-form e
... Show MoreDeveloping and researching antenna designs are analogous to excavating in an undiscovered mine. This paper proposes a multi-band antenna with a new hexagonal ring shape, theoretically designed, developed, and analyzed using a CST before being manufactured. The antenna has undergone six changes to provide the best performance. The results of the surface current distribution and the electric field distribution on the surface of the hexagonal patch were theoretically analyzed and studied. The sequential approach taken to determine the most effective design is logical, and prevents deviation from the work direction. After comparing the six theoretical results, the fifth model proved to be the best for making a prototype. Measured results rep
... Show More
Abstract
The main goal of this study is to showcase the aesthetic values contained in Nahj Al-Balagha by exploring its wide horizons and enriching the subject of rhetoric with new forms and images that did not exist in other books, as they characterized no other book but Nahj Al-Balagha. Imam Ali mastered the use of rhetorical forms in his speech to the fullest extent that such forms raced to his thought one after the other to book their place amongst the topics of religious and earthly life. One of those rhetoric forms is synecdoche, in its various parts. Due to this form great importance and high status, it was used by the Imam at many
... Show MoreAbstract:
Witness the current business environment changes rapidly reflected on the performance of the facility wishing to stay , which is no longer style reaction enough to handle installations with their environment , and quickly began to lose its luster with the emergence of a message and the vision of contemporary business environment from a set of parts interacting with each other and the concept of behavioral includes all dimensions of performance, it is imperative to adopt a system installations influence variables and positive interaction through the development of strategic plans and the use of implementation and follow-up strategies to ensure the effectiveness of the method for meas
... Show MoreCurrent research aims to find out:
- Effect of using the active learning in the achievement of third grade intermediate students in mathematics.
- Effect of using of active learning in the tendency towards the study of mathematics for students of third grade intermediate.
In order to achieve the goals of the research, the researcher formulated the following two hypotheses null:
- There is no difference statistically significant at the level of significance (0.05) between two average of degrees to achievement
ABSTRACT This study presents an efficient approach for the separation and preconcentration of norepinephrine (NOR) from pharmaceutical formulations, environmental water, and human urine samples using a dispersive micro – solid phase extraction (DμSPE) technique employing magnetic nanoadsorbents. Two adsorbents, Fe3O4@TTAB and Fe3 O4@SiO2@TTAB, were prepared by functionalising iron oxide and silicacoated iron oxide nanoparticles with the cationic surfactant tetradecyltrimethylammonium bromide (TTAB). NOR was first converted into a sensitive diazonium dye via reaction with diazotised sulphamethazine and then extracted using mixed ademicelle – hemimicelle magnetic solid-phase extraction, followed by spectrophotometric quantification. Key
... Show MoreIn aspect-based sentiment analysis ABSA, implicit aspects extraction is a fine-grained task aim for extracting the hidden aspect in the in-context meaning of the online reviews. Previous methods have shown that handcrafted rules interpolated in neural network architecture are a promising method for this task. In this work, we reduced the needs for the crafted rules that wastefully must be articulated for the new training domains or text data, instead proposing a new architecture relied on the multi-label neural learning. The key idea is to attain the semantic regularities of the explicit and implicit aspects using vectors of word embeddings and interpolate that as a front layer in the Bidirectional Long Short-Term Memory Bi-LSTM. First, we
... Show More