Accurate and efficient brain tumor diagnosis remains a critical challenge in medical imaging. This study proposes a novel framework that integrates fuzzy logic-based segmentation with deep learning (DL) techniques to enhance brain tumor detection and classification in magnetic resonance imaging (MRI) scans. In the first stage, a fuzzy thresholding approach is applied to segment MRI images into healthy and abnormal regions, enabling the precise extraction of tumor areas. In the second stage, an optimized convolutional neural network (CNN) model classifies tumors into four categories: glioma, meningioma, pituitary tumor, and no tumor. The proposed method is evaluated across three large public datasets comprising more than 23,000 MRI images. Experimental results demonstrate that the model achieved an accuracy of 98% and a Dice similarity coefficient of 97.97%, confirming its high effectiveness in accurately extracting tumor regions and classifying them correctly. Furthermore, the proposed system outperforms conventional machine learning, deep learning, and transfer learning techniques. In addition to classification, the system accurately estimates tumor size, providing valuable clinical insights. These findings highlight the potential of combining fuzzy logic with DL to improve automated brain tumor diagnostics, enhance diagnostic reliability, and support clinical decision-making.

After analyzing and assessing a large number of previous studies of olive fruit classifications, the following conclusions are derived: first, most of these studies relied on their technology being applied to a database, which often contains few images and is not enough to confirm the efficacy of the suggested technology, in order to verify the accuracy of their technology. Moreover, a large number of these studies depend on the utilization of an unrelated image library. All of the images are prepared for testing since they each show a single fruit with a background that is completely different from the color of the fruit. As mentioned earlier, this problem has nothing to do with reality. When using it practically, one has to cope with a frame that contains hundreds of fruits. The fruits are placed on a conveyor with several channels to keep them steady. Moreover, it is noteworthy that most of this study included recommendations for functional technologies that remain amenable to development. Finally, it is important to emphasize that processing speed data is essential in this type of application and has not been collected in many of these experiments. The study presented in this presentation dealt with a new strategy for classifying the colors and diseases of olive fruits. The proposed strategy was based on dividing the separation process into more than one stage, for the purpose of scrutinizing the selection of the most appropriate feature extraction for each stage according to the type of separation process, and also checking the selection of the most appropriate classifier for each stage according to the type of separation process as well. Also checking the accuracy and efficiency of the separation process at each stage, by training, validating, and testing each separation process that took place at each stage. Also, consideration was given to choosing rapid processing techniques in order to complete the identification of the type of fruit in the shortest possible time. Also, this study relied on verifying the efficiency of the proposed technology by using a powerful database that contained about 15,000 images of olive fruits taken directly from the fruit conveyor. Efficiency was also confirmed by comparing our results with the results of related technologies. The results of the test used to compare the suggested technique’s accuracy to those of other methods for classifying eight different kinds of olive fruits showed that it was incredibly effective at classifying the fruits in the least amount of time. When the fruits were arranged on a white backdrop, the suggested method had an effectiveness of 99.26% for fruit classification. The most significant finding was that, in contrast to other methods, it was able to categorize fruits while they were being placed on a fruit conveyor with an efficiency of 97.25%. Additionally, the results show that fruit identification with the suggested method only takes a few seconds. All experiments in this paper were conducted using MATLAB (2016 b) on a computer running Windows 10 64-bit with an Intel 2.7 GHz processor and 8 GB of RAM. The high-performance correctness of the suggested strategy, after training it on about 9000 samples of healthy and damaged olive fruit images (real frames) and then testing it on about 6000 samples, indicates the high possibility of using it in computer vision applications.

This paper aims to assess the water quality of the Ismailia Canal, Egypt, in accordance with Article 49 of Law 92/2013. QUAL2K and Convolutional Neural Networks and Long Short-Term Memory (CNN-LSTM) are utilized to simulate the water quality parameters of dissolved oxygen (DO), pH, biological oxygen demand (BOD), chemical oxygen demand (COD), total phosphorus (TP), nitrate nitrogen (NO₃-N), and ammonium (NH₃-N) in winter and summer 2023. The parameters of the QUAL2K and CNN-LSTM models were calibrated and validated in both winter and summer through trial and error, until the simulated results agreed well with the observed data. Additionally, the model’s performance was measured using different statistical criteria such as mean absolute error (MAE), root mean square (RMS), and relative error (RE). The results showed that the simulated values were in good agreement with the observed values. The results show that all parameter concentrations follow and did not exceed the limit of Article 49 of Law 92/2013 in winter and summer, except for dissolved oxygen concentration (8.73–4.53 mg/L) in winter and summer, respectively, which exceeds the limit of 6 mg/L, and in June, biological oxygen demand exceeds the limit of 6 mg/L due to increased organic matter. It is imperative to compare QUAL2K and CNN-LSTM models because QUAL2K provides a physics-based simulation of water quality processes, whereas CNN-LSTM employs deep learning in modeling complex temporal patterns. The two models enhance prediction accuracy and credibility towards enabling enhanced decision-making for Ismailia Canal water management. This research can be part of a decision support system regarding maximizing the benefits of the Ismailia Canal.