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.

The quality of encryption is dependent on the complexity of the confusion-diffusion pattern and the quality of the keystream employed. To enhance complexity and randomness, this study proposes a combination of multiple interleaved patterns, including Zigzag, Hilbert, and Morton patterns to complicate the confusion-diffusion. The keystream is generated from the improved logistic map and the 6D hyperchaotic map, which complement each other due to their sensitivity to initial conditions and control parameters. This produces highly random and nonlinear keystreams, making them difficult to predict. The encryption process consists of four phases, alternating between diffusion and confusion. Furthermore, SHA-512 is used to enhance key space and sensitivity. Based on the test results, the proposed encryption technique can withstand various attacks, such as statistics, differential, brute force, and NIST randomness tests, as well as data loss and noise attacks. Most of the results are better than previous studies.

In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination ($R^2$), and mean average error (MAE), the results are 1.191621 × 10⁻⁵, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.

This research aims to develop a robust diabetes classification method by integrating the Synthetic Minority Over-sampling Technique (SMOTE)-Tomek technique for data balancing and using a machine learning ensemble led by eXtreme Gradient Boosting (XGB) as a meta-learner. We propose an ensemble model that combines deep learning techniques such as Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Units (BiGRU) with XGB classifier as the base learner. The data used included the Pima Indians Diabetes and Iraqi Society Diabetes datasets, which were processed by missing value handling, duplication, normalization, and the application of SMOTE-Tomek to resolve data imbalances. XGB, as a meta-learner, successfully improves the model's predictive ability by reducing bias and variance, resulting in more accurate and robust classification. The proposed ensemble model achieves perfect accuracy, precision, recall, specificity, and F1 score of 100% on all tested datasets. This method shows that combining ensemble learning techniques with a rigorous preprocessing approach can significantly improve diabetes classification performance.

Confusion, diffusion, and encryption keys affect the quality of image encryption. This research proposes combining bit- and pixel-level permutation and substitution methods based on three advanced chaotic logistic map methods. The three chaotic methods are the 2D Logistic-adjusted-Sine map (2D-LASM), the 2D Logistic-sine-coupling map (2D-LSCM), and the 2D Logistic ICMIC cascade map (2D-LICM). The encryption method’s design consists of six stages of encryption, involving permutation operations based on chaotic order, substitution based on modulus and bitXOR, and hash functions. Hash functions are employed to enhance key space and key sensitivity quality. Several testing tools are utilized to assess encryption performance, including histogram and chi-square analysis, information entropy, correlation of adjacent pixels, differential analysis, key sensitivity and key space analysis, data loss and noise attacks, NIST randomness tests, and TestU01. Compared to using a single 2D logistic map, the amalgamation of bit-level and pixel-level encryption and the utilization of three 2D cascade logistic maps has improved encryption security performance. This method successfully passes the NIST, TestU01, and chi-square tests. Furthermore, it outperforms the previous method regarding correlation, information entropy, NPCR, and UACI tests.