Ammar Falih Mahdi

Scopus Publications

The Role of 5G and Blockchain in Binance's Future Development
Alaa Salim Abdalrazzaq, Reeman Joma Abidali, Zainab Sami Abdel Aziz, Ammar Falih Mahdi, Bashar Mazin Basheer, Mykola Malenko, and Donya Y. Abdulhussain
IEEE
Background: The advent of 5G and blockchain technologies has signified rapid growth in the digital space. Their unique capabilities have already redefined communication and decentralized networks; however, their possible combined role is the subject of this research article.Objective: This article investigates the implications of 5G technology being used for integration with blockchain and how its disruptive nature would affect Binance's cryptocurrency trading platform.Methodology: A mixed-methods research strategy was followed that draws upon data analytics, in-depth case study investigations and expert consultations. The key point was finding out how unparalleled low latencies and high-throughput capacities from 5G would be a procedures-changer for blockchain operations.Results: The results indicate that the network capabilities of 5G can compensate for some blockchain major difficulties, including scalability and energy consumption. Quicker data transfer speeds up validation processes which leads to faster blockchain transaction times. This combination of advantages makes it possible to use microtransactions and facilitates scaling in terms of volume per transaction while retaining good levels of security and decentralization. Furthermore, with opportunities such as rapid cross-border payments and extensive smart contract capabilities available too — Binance certainly seems well-positioned to lead the charge in leveraging this convergence. But then again, the integration is accompanied by other challenges as well – mainly regulatory and technological challenges of innovation brought along by two cutting-edge technologies.Conclusions: The integration of 5th-generation mobile networks and blockchain has massive potential for cryptocurrency projects such as Binance. This partnership is poised to transform the existing blockchain landscape, ushering in a whole new era of fintech experience with an increased capability for speed and operational efficiency along safety systems.

Deep Learning Approaches for Predicting Climate Change Impacts: An Empirical Analysis
Mustafa Mohammmed Jassim, Haithem Kareem Abass, Azhar Raheem Mohammed Al-Ani, Ammar Falih Mahdi, Abdul Mohsen Jaber Almaaly, Yuliia Navrozova, and Nataliia Bodnar
IEEE
Background: Climate change stands as one of the most critical global challenges with enormous implications. Since its science is well understood, efforts now focus on modeling and predicting its effects to mitigate or adapt to these. Deep learning, with its remarkable aptitude for data representation and analysis, is a promising candidate to enhance weather attack predictions on a global scale.Objective: This empirical study will assess their central tendencies and relationships for understanding the effectiveness of deep learning models in anticipation of climate change impacts. The paper investigates whether recently proposed models can provide better predictions than traditional techniques.Methodology: Authors utilize a detailed dataset of past climate data and its consequences This dataset is used to train and test deep learning architectures, such as Convolutional Neural Networks (CNN) or Recurrent Neural Networks (RNN), but, for the first, comparing with traditional regression models.Results: The findings show that DL techniques are very effective in comparison to traditional methods when it comes to predicting the impacts of climate change. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have proven to be highly accurate at detecting complex relationships among climate factors and their impacts such as extreme weather events or sea level rise.Conclusion: The potential of deep learning approaches to improve our ability to model the consequences of climate change is substantial. Its forecasts have greater skill and the ability to inform policy and adaptation effectively. Given the continued acceleration of climate change, deploying advanced machine learning will be critical to maintaining a steady state.

Major depressive disorder diagnosis based on PSD imaging of electroencephalogram EEG and AI
Ammar Falih Mahdi and Aseel Khalid Ahmed
Institute of Advanced Engineering and Science
One of the most common causes of functional frailty is major depressive disorder (MDD). MDD is a chronic condition that requires long-term therapy and professional assistance. Additionally, MDD effective treatment requires early detection. Unfortunately, it has intricated clinical characteristics that make early diagnosis and treatment difficult for clinicians. Furthermore, there are currently no clinically effective diagnostic biomarkers that can confirm an MDD diagnosis. However, electroencephalogram (EEG) data from the brain have recently been used to make a quantitative diagnosis of MDD. In addition, As being among the most cutting-edge artificial intelligence (AI) technologies, deep learning (DL) has exhibited superior performance in a wide range of real-world applications, from computer vision to healthcare. However, an additional challenge could be the extraction of information from the ECG raw data. This paper presents a method for converting EEG data to power spectral density (PSD) images, and then they were classified as healthy or MDD using a deep neural network for feature extraction and a machine learning (ML) classifier. When employing the proposed approach, the images formed from the PSD show a considerably improved performance in classification results.

Advanced Smart Algorithm for Integrating RFID and IoT Security

Automated reasoning and inference for cloud-based

Ammar Falih Mahdi

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