Tomatoes at any given time has different maturity levels, therefore it is necessary to recognize the appropriate pattern to determine the level of maturity. One of the recognition patterns of tomatoes image is to use texture and color analysis. Texture analysis can be processed using the Gray Level Co-occurrence Matrix (GLCM) method. GLCM is chosen because it has a high degree of recognition based on the value of contrast, correlation, homogeneity, and energy. Furthermore, for color analysis one of the methods that can be used is Hue, Saturation, Value (HSV). By utilizing HSV, an object with a certain color can be detected and reduce the influence of the intensity of light from outside. The results of GLCM and HSV calculations can be classified to determined the maturity level of tomatoes by using K-Nearest Neighbour (K-NN) one of basic and simple classification method which utilizes the distance (k) as a comparison of the similarity level of the image. From the research we have done, using 100 data sets, consisting of 75 training data and 25 testing data that yields the highest accuracy rate of 100% with p value on GLCM is 9 and the membership value (k) in K-NN is 3. According to the experimental results, we can conclude that our proposed method can achieve the highest accuracy.

Background: Accurate classification of brain tumors in medical images is vital for effective diagnosis and treatment planning, which improves the patient’s survival rate. In this paper, we investigate the application of Convolutional Neural Networks (CNN) as a powerful tool for enhancing diagnostic accuracy using a Magnetic Resonance Imaging (MRI) dataset. Method: This study investigates the application of CNNs for brain tumor classification using a dataset of Magnetic Resonance Imaging (MRI) with a resolution of 200 × 200 × 1. The dataset is pre-processed and categorized into three types of tumors: Glioma, Meningioma, and Pituitary. The CNN models, including the Classic layer architecture and the ResNet50 architecture, are trained and evaluated using an 80:20 training-testing split. Results: The results reveal that both architectures accurately classify brain tumors. Classic layer architecture achieves an accuracy of 94.55%, while the ResNet50 architecture surpasses it with an accuracy of 99.88%. Compared to previous studies and 99.34%, our approach offers higher precision and reliability, demonstrating the effectiveness of ResNet50 in capturing complex features. Conclusions: The study concludes that CNNs, particularly the ResNet50 architecture, exhibit effectiveness in classifying brain tumors and hold significant potential in aiding medical professionals in accurate diagnosis and treatment planning. These advancements aim to further enhance the performance and practicality of CNN-based brain tumor classification systems, ultimately benefiting healthcare professionals and patients. For future research, exploring transfer learning techniques could be beneficial. By leveraging pre-trained models on large-scale datasets, researchers can utilize knowledge from other domains to improve brain tumor classification tasks, particularly in scenarios with limited annotated data.

A Combination of Inverted LSB, RSA, and Arnold Transformation to get Secure and Imperceptible Image Steganography

Cow is one of the animals that have many benefits for humans. There are various types of cows based on benefits such as dairy cows, beef cattle, worker cattle, and others. Cattle breeding should be tailored to the needs of the public. Less knowledge about different types of cattle can reduce the benefits of farmed cattle. Content Based Image Retrieval (CBIR) can be applied to help the problem of distinguishing or knowing the type of cow. The first step of the method proposed in this research is preprocessing by changing the background color, resizing and conversion of color space. Color feature extraction calculates the average and standard deviation of the color intensity of each color component. Next extract the texture feature using Gray Level Cooccurrence Matrix (GLCM) to look for contrast, energy, correlation, homogeneity and entropy at each angle 0 ^° , 45 ^° , 90 ^° and 135 ^° with a mean of 1 averaged. Six color features and five texture features are used as attributes to perform calculations with Euclidean Distance, so it can be known the similarity between images. Cattle types used include Limousin, Simental, Brangus, Peranakan Ongole (PO), and Frisien Holstein (FH). With 100 training images and 20 test images. To measure the accuracy of the proposed CBIR is used Confusion Matrix. Based on the measurement results obtained accuracy of 95% while the precision and recall obtained 100%.

A Good Performance OTP Encryption Image based on DCT-DWT Steganography