Yazar "Abdulrazzaq, Mohammed Majid" seçeneğine göre listele

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    A comparative study of classification algorithms for sentiment analysis of covid-19 vaccine opinions using machine learning
    (Institute of Electrical and Electronics Engineers Inc., 2024) Zainulabdeen, Dilber S.; Çevik, Mesut; Abdulrazzaq, Mohammed Majid
    Textual data analysis and classification into positive, negative, or neutral emotions in natural language processing are complex. This study uses machine learning techniques to classify coronavirus and vaccination attitudes. This study used Random Forest (RF), Gradient Boosting Classifier (GBC), Logistic Regression (LR), and Decision Tree. The RF and GBC algorithms had an impressive 89% accuracy rate, while the LR and DT algorithms had 87%. Word processing and data pre-processing were needed before categorization. The outcomes of this research can help policymakers overcome possible impediments to vaccination initiatives. contain the infection and reduce its public health effect. The examination of Kaggle data, which includes many pandemics and vaccine remarks, emphasizes the necessity for immediate herd immunity against COVID-19. This crucial goal is crucial to limiting the virus's spread and minimizing its negative effects on society. The task requires acknowledging and resolving public concerns and building faith in the immunization campaign. This study uses machine learning approaches and compares their importance. The upcoming study will produce an app that categorizes sickness and vaccination attitudes. To design effective plans for Covid-19 immunization, governments and organizations must understand the challenges. It is important to note that the research only included English tweets. This constraint may reduce the credibility and generalizability of sentiment results. To further understand public perceptions regarding COVID-19 vaccinations, deep learning algorithms might be used to analyze larger Twitter datasets.
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    Enhancing smart grid efficiency: a modified ANN-LSTM approach for energy storage and distribution optimization
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mohammed, Ramzi Qasim; Abdulrazzaq, Mohammed Majid; Mohammed, Ayoob Jasim; Mardikyan, Kevork; Çevik, Mesut
    The smart grid represents a paradigm shift in energy management, aiming to optimize energy storage and distribution while accommodating the growing demand for renewable energy sources. In this paper, we investigate the application of a modified Artificial Neural Network with Long Short-Term Memory (ANN-LSTM) in addressing the multifaceted challenges of the smart grid. Through rigorous experimentation and simulation, the ANN-LSTM is evaluated in four diverse scenarios, including normal operation, fluctuating renewable energy, peak demand, and grid instability. The results showcase the model's exceptional predictive accuracy, low Mean Squared Error (MSE), and rapid response times, outperforming other models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), Decision Tree (DT), and Fuzzy Logic. Our findings underscore the ANN-LSTM's potential to revolutionize energy storage and distribution in the smart grid, ushering in a new era of efficiency, sustainability, and resilience in energy management.
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    Exploring lightweight blockchain solutions for internet of things : review
    (Springer Science and Business Media Deutschland GmbH, 2024) Ismael, Omar Ayad; Abdulrazzaq, Mohammed Majid; Ramaha, Nehad T. A.; Mukhlif, Yasir Adil; Al Zakitat, Mustafa Ali Sahib
    The world is witnessing a major digital transformation and is moving towards more interaction, connectivity, ease, and intelligence through the Internet of Things (IoT). The IoT offers these advantages to the world by linking necessary devices with each other, making it easier to manage and deal with those devices. However, the IoT faces many challenges, such as authentication, privacy, security, and access management. The application of blockchain technology may provide a solution to these challenges. Nevertheless, applying blockchain technology may face limitations, such as the limited resources of the IoT devices used and the resource-intensive requirements of the blockchain. Therefore, to overcome these limitations, several studies have proposed using a lightweight blockchain; this blockchain is specifically designed for resource-limited IoT devices. In this paper, a comprehensive review has been made on the uses of lightweight blockchain in the IoT. Moreover, we identified some of the challenges facing the application of blockchain technologies in the IoT and the future directions.
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    Harnessing advanced techniques for image steganography: sequential and random encoding with deep learning detection
    (Springer Science and Business Media Deutschland GmbH, 2024) Al Zakitat, Mustafa Ali Sahib; Abdulrazzaq, Mohammed Majid; Ramaha, Nehad T. A.; Mukhlif, Yasir Adil; Ismael, Omar Ayad
    This study delves into the intricacies of steganography, a method employed for concealing information within a clandestine medium to enhance data security during transmission. Given that information is often represented in various forms, such as text, audio, video, or images, steganography offers a distinctive advantage over conventional cryptography by focusing on concealing the very existence of the message, rather than merely its content. This research introduces a novel steganographic technique that places equal emphasis on both message concealment and security enhancement. This study highlights two primary steganographic methods: sequential encoding and random encoding. By employing both encryption and image compression, these techniques fortify data security while preserving the visual integrity of cover images. Advanced deep learning models, namely Vgg-16 and Vgg-19, are proposed for the detection of image steganography, with their accuracy and loss rates rigorously evaluated. The significance of steganography extends across various sectors, including the military, government, and online domains, underscoring its pivotal role in contemporary data communication and security.
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    House price prediction using Artificial Neural Network with ADAGRAD optimizer
    (Springer Science and Business Media Deutschland GmbH, 2024) Abdulwahid, Ehab Saad; Ibrahim, Abdullahi Abdu; Abdulrazzaq, Mohammed Majid
    The real estate market is a dynamic and complex ecosystem influenced by myriad factors, making accurate price predictions a formidable challenge. Understanding the intricate relationships between variables such as location, property characteristics, economic indicators, and market trends is essential for making informed investment decisions. In this paper, we undertake a comprehensive exploration of machine learning and artificial neural networks (ANNs), establishing a framework to understand how these powerful computational tools can be harnessed to solve complex problems across various domains. The novel part of this study involves a comparative analysis of different optimization algorithms, including AdaGrad, Adam, SGD, and RMSprop, in the context of their application to ANN in real estate price prediction. AdaGrad’s unique approach to handling learning rates dynamically makes it particularly suited for data with varying scales, as demonstrated by our experimental results Comparative training and testing outcomes revealed that AdaGrad consistently outperformed other methods, showcasing lower mean squared error (MSE) and root mean squared error (RMSE), along with higher R2 values, indicating its superior predictive accuracy and efficiency. These findings underline the potential of AdaGrad to optimize real estate predictive models more effectively than traditional methods, marking a significant advancement in the application of machine learning techniques in real estate market analysis.