JOURNAL OF EDUCATION AND SCIENCE

In this paper, the energy levels of the ground state band (GSB), β-band and -bands for 170-174Os isotopes were calculated using the Interacting Boson Model(IBM-1), the Semi-Empirical Formula (SEF) by Al-Jubbori. All models were developed by IBM. In order to complete this study the above-mentioned models and equations were applied . The results of the Ground State Band, showed that IBM-1, Semi-Empirical Formal, and the available experimental data are all in agreement with certain variations this was shown by the results of the GSB. The Semi-Empirical Formal calculations have a higher level of compatibility with the experimental data when compared to the IBM-1 calculations. According to the findings of this research , the SEF equation are capable of describing the energy spectra of Os isotopes in comparison to IBM-1. The electric quadrupole transition probabilities B(E2) transitions which obtained through IBM-1 for the above isotopes were calculated and found to be in agreement with the available experimental data, On the other hand, Isotopes of Os exhibit behavior that is consistent with a gamma soft O(6) transition.

The solution of nonlinear Fredholm integro-differential equations plays a significant role in analyzing many nonlinear events that occur in chemistry, physics, mathematical biology, and a variety of other fields of science and engineering. A physical event can be represented by a differential equation, an integro-differential equation since many of these equations cannot be solved directly or it is difficult to solve. Numerical approaches that are useful combinations of numerical integration must frequently be used. This work presents a method for solving the type of nonlinear Fredholm integro-differential equation (NFIDE) of the second kind. The Leibnitz rule is used with the Haar wavelet collocation method in this paper to solve NFIDE numerically. Some techniques are used to transfer the equation into an algebraic system through an operational matrix. The convergence analysis had been proved through this work and the numerical experiments had been given to illustrate the effectiveness of the proposed method based on MATLAB programming.

This study was conducted in the fields of Nineveh Research Department (animal wealth) in the fall season to study the effect of different levels of potassium fertilization on the growth of maize plant Zea myas class 106, a completely randomized design was used with three replications. Each of the treatments included three levels of potassium sulfate fertilizer (K0, 0, K1, 20, K2, 30, K3 50) kg/D. The results of adding potassium fertilizer at its levels showed a significant effect on the studied traits (potassium K3 concentration showed a significant effect on the length of (the plant, leaf, ear, number of ears, the weight of ear leaves, number of ear rows, weight of (calcareous, and whole plant), which amounted to 195.5, 78.3, 10.5, 32.7, 2.50, 0.030, 16.8, 0.177, 0.30, compared with other concentrations of fertilizer and comparison treatment, which amounted to 176.6, 70.9, 9.5, 30.0, 1.75, 0.02, 14.7, 0.08, 0.29, gm/kg soil on The addition of potassium K3 level gave the highest value in terms of ash and water absorption of maize grains, which amounted to 0.420 and 61.23, respectively, compared with other concentrations and treatment, which amounted to 0.416 and 60.23 gm/kg soil, as well as an increase in the value of both dissolved and available potassium and phosphorous percentage. Which amounted to 22.0, 422.7, 26.67, gm/kg soil compared with other concentrations of fertilizer and control treatment, which amounted to 332.3, 14.07, and 23.91 gm/kg soil respectively compared to other concentrations of potassium fertilizer, and give significant effect to Both available-made ammonium, Ph. , electric conductivity but no significant nitrate content in the soil.

This research paper proposes a methodology for classifying aerial photographs and lands using deep learning with transfer learning. The study utilizes the Aerial Image Dataset (AID), which contains a diverse set of aerial images with 30 scene classes. The proposed methodology involves data preprocessing, dataset splitting, training images, model selection, model training, and evaluation using performance measures. Three neural network models (ResNet50, VGG19, and EfficientNetB3) are compared, and the best model is selected based on performance metrics such as precision, recall, F1-score, and the confusion matrix. The results show the effectiveness of the proposed methodology in accurately classifying aerial photographs. This indicates that EfficientNetB3 has a higher ability to classify aerial photographs and lands compared to ResNet50 and VGG19. ResNet50 achieved moderate performance with relatively lower precision, recall, and F1-score compared to EfficientNetB3. VGG19, on the other hand, demonstrated the lowest performance across all metrics, showing low precision, recall, and F1-score values. These results can contribute to various applications such as urban planning, real estate development, and land management.

Alzheimer\'s disease is a degenerative neurological disorder that primarily strikes older people. Alzheimer\'s disease is now a major health problem for anyone over the age of 65. The inability to remember what has been said or done before is the first symptom of the condition. Memory loss becomes severe, and daily functioning declines as the disease progresses. The memory-controlling region of the brain shows signs of impairment years before any symptoms occur. There are three possible disease stages: mild, moderate, and severe. The early stage, sometimes known as the middle stage, mild demented (MCI), is an intermediate state between Alzheimer’s patients and healthy individuals When someone is diagnosed with MCI, there is an opportunity to treat or stop the development of the disease into AD, which is the only solution to avoid AD. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for The Early detection of Alzheimer’s disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Based on MRI scans, The four phases of Alzheimer\'s disease are correctly categorized by the suggested technique with an accuracy of 95.17 % performance, 86.82 % precision, and 93.13 f1 score.

Cloud computing security has emerged as one of the most significant trends in the information technology (IT) sector, and it is currently widely used in that sector, particularly with the growth of information in the cloud. The security of data stored in the cloud, and when it is transferred between users and servers, is very important. secure communication and data transfer can be achieved through the use of steganography and visual cryptography. It is one of the most important technologies for securing data and authenticating it so that only the sender and the receiver can be aware of the hidden information as well as transmitting it. In contrast, visual cryptography is a cryptographic technology that encrypts data. By having these two different branches, it is difficult for an attacker to compromise the data\'s confidentiality. The purpose of this paper is to review several steganography and visual cryptography that have been proposed to improve cloud security and make it more secure against cyberattacks and eavesdropping. The primary goal of this study is to investigate the capabilities of secured data, which are frequently employed by researchers. Additionally, each secured data domain\'s advantages and disadvantages are investigated. In order to strengthen the security mechanisms, this research concludes that visual cryptography approaches could be combined with steganography and cloud computing in the secured data realm.

The soft semi-totally continuous mappings that we have introduced in this study are stronger than the soft totally continuous mappings and provide fresh thoughts for soft continuous mappings between several soft topological spaces. By using evidence and guidelines to clarify and explain it, the relationships between these notions and several other concepts of soft mappings have been studied. Also, several of these functions\' characteristics have been looked into. Moreover, soft semi-totally open mappings have been shown and investigated. Additionally, we defined soft continuous mappings that depend on soft i-open sets and looked at how they are related to soft continuous mappings that depend on soft semi-open sets. In this paper we proved that each soft semi-totally continuous mapping is soft totally continuous , each soft strongly semi continuous mapping is soft strongly i-continuous, each soft totally semi continuous mapping is soft totally i-continuous, the composition of two soft semi totally open mappings is soft semi totally open mapping, and the composition of two soft semi totally continuous mappings is soft semi totally continuous mapping.

Imbalanced data is an important issues and challenges faced in data classification. This will lead to poor performance of binary classifiers, this is due to bias in classification in favour of the majority class and lack of understanding of the influence of the minority class, while the minority class is usually the most important in the classification process. In order to find a compromise between the information loss and balance the data set before applying the classification, the research proposed a hybrid algorithm based on the use of clustering methods to divide the majority class into subgroups in the first phase, and using a method to encode the majority class. The Algorithm used the code to group samples that are similar to each other and reduce the majority class count. At the same time, the Synthetic Minority Oversampling Technique (SMOTE) was used to increase the number of minority class samples in the next phase. The study examined the impact of the proposed algorithm on five classifiers based on the AUC and F-score post-classification performance parameters using benchmark datasets with different sizes and imbalance factors. The results showed that the proposed algorithm significantly improved the performance of the classifiers when applied to the resampled data.

The ability to recognize and distinguish between various textures in an image is made possible by feature extraction, which is a fundamental step in computer vision and image processing. Traditional methods of texture analysis fall short of capturing the perceptual characteristics that give texture its meaning and identity. Because Tamura texture attributes were developed through research into the spatial and frequency components of textures, they offer a more precise and discriminating representation of textures. Tamura features capture significant visual qualities that are crucial for comprehending and interpreting texture. Tamura descriptors enable to characterization and comparison of various textures, enabling tasks like texture classification, segmentation, and retrieval. SIFT processes Tamura descriptors to extract scale-invariant features, enhancing the texture representation\'s capacity for discrimination. The suggested method was evaluated on numerous benchmark datasets, and the findings revealed that it outperforms conventional texture analysis methods in terms of precision, recall, and other performance measures. The qualitative evaluation further verified the interpretability and perceptual significance of the retrieved texture elements, proving their appropriateness for texture analysis tasks. The evaluation\'s findings show how well the suggested technique extracts texture features and how it might boost the effectiveness of numerous computer vision and image processing applications.

Obesity is a complicated disorder involving an increasing amount of fat in the body. It raises the risk of a variety of illnesses, including heart disease, high blood pressure, certain cancers, and diabetes. Obesity has a complicated origin, comprising a series of interactions between genetics, and hormones, in addition to diet and physical activity. The regulation of whole-body energy is dependent on adipose tissue (fat tissues). It, on the other hand, stores energy in the form of fat and regulates fat mobilization and distribution in the body. It also works as an endocrine organ, producing a number of chemicals such as adipocytokines. Furthermore, fats are burned by beige and brown adipose tissue by dispersing energy in the form of heat in order to maintain a normal body temperature and have been proposed as a potential method of preventing obesity. In this review, we discuss the latest articles on different forms of adipose tissues and their role in metabolism control, with a focus on their endocrine function. The molecular function and clinical importance of various key adipocytokines are described as well