Asian Journal of Research in Computer Science
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Enhancing Governance and Public Sector Efficiency through Blockchain Technology: A Simulation Study
No full textSince its inception by Satoshi Nakamoto in 2008, blockchain technology has become a pivotal advancement, primarily due to its decentralized, transparent, and immutable properties. This paper examines the potential of blockchain technology to improve governance and enhance efficiency in the public sector through a simulation-based approach. Focusing on blockchain-enabled voting systems and public records management, the study investigates how security, transparency, and efficiency can be significantly improved. The paper explores current blockchain applications within governance, the challenges facing traditional systems, and the transformative power of blockchain. Through simulation, performance indicators such as security, transparency, and operational efficiency are analyzed, culminating in recommendations for integrating blockchain into public sector frameworks
Financial Risk Management in Digital-Only Banks: Addressing Fraud and Cybersecurity Threats in a Cashless Economy
No full textThis study examines financial risk management in digital-only banking by analyzing fraud and cybersecurity threats using quantitative methodologies. Leveraging Verizon’s Data Breach Investigations Report (2024), the European Central Bank Cyber Resilience Oversight Report (2023), and the Financial Action Task Force Money Laundering & Fraud Prevention Report (2024), this research employs descriptive statistics, logistic regression, and Difference-in-Differences (DiD) analysis to assess fraud risks, cybersecurity framework effectiveness, and regulatory impact. Results indicate that phishing (35%) and ransomware (20%) account for the highest financial losses, averaging 7.1M per incident, respectively. Logistic regression confirms that Basel III compliance reduces fraud risks (-70.759 coefficient), while AI-driven fraud monitoring shows inefficiencies (21.918 coefficient). Regulatory enforcement leads to a 1.90% greater fraud reduction in strictly regulated banks. Recommendations include enhanced AI fraud detection, stricter compliance enforcement, multi-layered security measures, and targeted fraud awareness programs to strengthen digital banking resilience
Integrating Post-Quantum Cryptography and Advanced Encryption Standards to Safeguard Sensitive Financial Records from Emerging Cyber Threats
No full textThis study examines the integration of Post-Quantum Cryptography (PQC) and Advanced Encryption Standard (AES) to safeguard financial records against quantum-enabled cyber threats. A quantitative approach was employed using data from the NIST Post-Quantum Cryptography Project Dataset, Google Homomorphic Encryption Benchmark Dataset, Hyperledger Fabric Blockchain Performance Dataset, and World Bank Financial Stability Indicators Dataset. Multi-Criteria Decision Analysis (MCDA) with the Analytic Hierarchy Process (AHP) assessed cryptographic agility, while Multiple Linear Regression (MLR) analyzed encryption efficiency. Results indicate that CRYSTALS-Kyber achieves the highest agility score (8.35), making it the most adaptable PQC algorithm for financial institutions. Blockchain-based key exchange mechanisms integrating PQC reduced transaction finality time by 25%, enhancing security and efficiency. A post-quantum cyber breach could result in a 3.2% GDP loss and $150 billion in cybercrime costs. Financial institutions must prioritize PQC adoption, enforce regulatory standardization, deploy blockchain-based PQC key exchange, and invest in cryptographic agility to mitigate quantum security risks
Emerging Trends in E-commerce: A Review of Consumer Behavior, Marketplaces and Digital Platforms
No full textRapid technological developments intersect with changes in consumer habits within the ever-changing digital environment. This paper discusses future trends in e-commerce, where. A literature review strategy was used in an attempt to identify gaps that companies can leverage to keep up with the rapid changes within the digital environment. It addressed the integration of digital and traditional channels as a means to improve customer experiences. Secondly, the importance of artificial intelligence and personalization in enhancing operational efficiency and customer experience through personalized suggestions and big data management. Thirdly, it addressed the opportunity and challenges faced by emerging markets, including issues related to digital infrastructure as well as differences in culture. The results showed that the integration of digital and traditional channels is effective in improving user experience and that artificial intelligence helps in improving service delivery through recommendations and big data management
Addressing Bias and Data Privacy Concerns in AI-Driven Credit Scoring Systems Through Cybersecurity Risk Assessment
No full textThe increasing reliance on artificial intelligence (AI) in credit scoring has raised concerns about algorithmic bias and data privacy, necessitating robust cybersecurity risk assessment frameworks. This study investigates the role of cybersecurity risk assessment in mitigating these risks, utilizing multiple datasets, including the Home Mortgage Disclosure Act (HMDA) dataset, the Equifax Data Breach Report, the Financial Cybersecurity Incidents Database, and the MITRE ATT&CK Financial Sector Threat Intelligence Dataset. We employ statistical fairness metrics, Bayesian Probability Modeling, Markov Chain Analysis, and Monte Carlo Simulations to evaluate the extent of bias, privacy risks, and cybersecurity vulnerabilities. Findings reveal significant disparities in loan approvals, with Black applicants receiving approval rates 28% lower than White applicants (χ² = 59.83, p < 0.001), highlighting systemic bias in AI-driven credit scoring. Data privacy remains a pressing issue, as financial sector breaches affect an average of 5,069,760 individuals per incident. Insider threats pose the greatest risk, with a probability of 0.81 of leading to financial fraud. These findings underscore the urgency of integrating fairness-aware machine learning, enhancing regulatory compliance with AI governance policies, and deploying AI-driven cybersecurity tools to fortify financial AI applications against emerging threats. This research contributes to the broader discourse on ethical AI by providing a structured cybersecurity risk assessment approach to mitigate algorithmic bias and strengthen data privacy protections. Implementing these recommendations will enhance fairness, security, and transparency in AI-driven financial decision-making, ensuring compliance with evolving regulatory frameworks and fostering trust in automated credit scoring systems
Deep Learning-based Weather Prediction: A Focused Case Study on Mosul City, Iraq
No full textBackground: Despite technological advancements, accurate weather forecasting remains a complex and challenging task. This looks at climate forecasts in Mosul City, Iraq, using a deep getting-to-know-you model that uses Long Short-Term Memory (LSTM) networks. More than a century\u27s worth of historical weather records, including temperature, humidity, and precipitation, was used to teach and validate the model. The results demonstrate the effectiveness of LSTM in enhancing the dependability of climate forecasting, with an accuracy charge of above 88%. This takes a look at offers a strong basis for similar studies and operational forecasting structures while demonstrating the innovative capability of deep mastering in meteorological packages.
Aims: The study presents an intensive teaching model using a long short-term memory (LSTM) network to predict weather conditions in Mosul City, Iraq, with an accuracy rate of more than 88%. The research examines deep learning ability in meteorological and weather applications and suggests future research on LSTM variants and network architecture.
Study Design: The study takes a look at outlines and the procedure of constructing a Long Short-Term Memory (LSTM) model using Pandas. The dataset is loaded, preprocessed, and normalized using MinMaxScaler. Sequence creation is carried out through the use of Keras\u27s Sequential API. The version is compiled using the Adam optimizer and MSE loss function for regression duties. The version is trained on the dataset, making predictions for the next day\u27s climate in Mosul.
Place and Duration of Study: Departments: Administrative Institute at Northern Technical University, Institution: Northern Technical University, Location: Mosul, Iraq, Duration: April 23 – May 2, 2024.
Methodology: The method for predicting destiny climate entails loading a weather dataset, preprocessing it, creating sequences, building an LSTM version with MSE and MAE metrics, compiling the model with the usage of the Adam optimizer, and using mean squared mistakes for regression tasks. The LSTM version is educated on the dataset through the use of the match () technique, and the model predicts the next day\u27s climate using inverse transformation and information manipulation. The predictions are displayed and stored in a brand-new CSV file for efficient time series analysis and preservation of ancient climate information.
Results: A weather forecasting version becomes advanced through the use of Long Short-Term Memory (LSTM) neural networks and weather facts for Mosul. The model produced correct predictions for destiny climate parameters like humidity, temperature extremes, rainfall, and UV index. The model finished with an 88% average accuracy throughout all variables, with the lowest accuracy (60%) occurring on April 29 because of combined errors in humidity and MAX temperature. The version has proven reliable performance for temperature and humidity but calls for refinement for rainfall prediction, mainly throughout high-variability periods. The 88% common accuracy offers actionable insights for agricultural and disaster control planning.
Conclusion: A weather forecasting model that uses Long Short-Term Memory (LSTM) neural networks and Mosul metropolis weather records completed 88% common accuracy for destiny weather parameters like humidity, temperature extremes, rainfall, and UV index. The version offers actionable insights for agricultural and catastrophe control planning; however, it calls for refinement for rainfall prediction. The findings should improve weather forecasts, useful resource groups in choice-making, and observe industries like catastrophe alleviation, transportation, and agriculture
Smarter Marketing with AI: How Cloud Technology is Changing Business
No full textThe integration of Artificial Intelligence (AI) and cloud computing has revolutionized enterprise systems, particularly in predictive marketing. AI-powered enterprise solutions enable businesses to analyze vast amounts of data in real-time, enhancing decision-making, customer engagement, and operational efficiency. Predictive analytics allows companies to anticipate consumer behavior, refine marketing strategies, and optimize customer interactions. Cloud computing further supports AI-driven predictive marketing by providing scalable and cost-effective solutions that enhance data processing capabilities and business intelligence. AI-integrated enterprise resource planning (ERP) and customer relationship management (CRM) systems facilitate automated decision-making, improving supply chain management and personalized marketing campaigns. Despite its advantages, AI adoption in enterprise systems and predictive marketing presents challenges such as data privacy concerns, cybersecurity risks, and ethical considerations. The complexity of AI integration requires substantial investment in infrastructure and regulatory compliance to mitigate biases and ensure transparency in AI-driven decisions. Explainable AI (XAI) is increasingly necessary to build trust and accountability in enterprise applications. Future advancements in AI, including blockchain, augmented reality (AR), and quantum computing, will enhance predictive analytics and business intelligence, further transforming marketing automation and decision-making processes. The convergence of AI and blockchain is particularly promising in securing digital transactions and improving data transparency in enterprise operations. As AI continues to reshape enterprise systems and predictive marketing, businesses must adopt responsible AI practices, strengthen cybersecurity measures, and comply with global regulations to maximize its benefits. Companies that leverage AI-driven insights will gain a competitive edge by improving customer engagement, optimizing marketing strategies, and driving sustainable growth in the evolving digital economy
Real-time Weather Estimation and Forecasting Using Hybrid Machine Learning Approaches
No full textIn real-time forecasting, estimation is the most crucial measurement for atmospheric observations by time and area. Present-day weather conditions observations are most important for many things, like agriculture, and different types of calamities that are unexpected in normal life. Machine learning techniques are used for weather forecasting. Among machine learning regression techniques, a key role is played in this problem. It monitors continuously from time to time. Temperature predictions also include weather forecasting by using regression techniques. Multiple regression techniques were used in our research. We measure the MSE rate for every technique. Multiple variety of metrics were tested. We consider short MSE to indicate for best forecasting outcome
A Survey of Deep Learning-based Pan-sharpening Techniques for Remote Sensing Images
No full textRemote sensing images are crucial for applications such as land-use mapping, environmental monitoring, and disaster management. Pan-sharpening enhances the spatial resolution of multispectral images by fusing them with high-resolution panchromatic images. Despite this, low spatial resolution can occur due to sensor limitations. To address this, image fusion methods, particularly pan-sharpening, have been developed to merge high-resolution and low-resolution images effectively. Recently, deep learning-based pan-sharpening techniques have gained prominence for achieving high-quality results. This survey offers a comprehensive overview of advancements in these techniques, reviewing and comparing various deep learning architectures, including autoencoder methods, generative adversarial networks (GANs), conditional GANs, convolutional neural networks (CNNs), and deep residual networks. We discuss the challenges, future directions, and advantages of deep learning in pan-sharpening while providing an in-depth analysis of state-of-the-art methods, their architectures, experimental results, evaluation metrics, and a comparative analysis of the surveyed techniques
Brute Force Attack Detection in Network Traffic Using Convolutional Neural Networks
No full textIntroduction: This study presents a deep learning-based approach for detecting brute force attacks in network traffic using a Convolutional Neural Network (CNN) model.
Methodology: Flow-based data from the NF-UQ-NIDS dataset was preprocessed and balanced using the Downsampling and Synthetic Minority Over-sampling Technique (SMOTE) techniques. The CNN architecture was designed to extract temporal and spatial features from the input data, enabling accurate binary classification between brute force attacks and benign traffic.
Results: The model was evaluated using standard performance metrics, including accuracy, precision, recall, F1-score, and AUROC that revealed exceptional results from the CNN-SMOTE configuration achieving an accuracy of 99.82% and a recall of 99.95%. Comparative analysis against benchmark models from previous studies confirmed the superiority of the proposed approach, particularly in handling class imbalance.
Conclusion: The results demonstrate that deep learning models, especially when trained with the appropriate data balancing technique, can significantly enhance intrusion detection systems. Recommendations for further improvement include exploring hybrid models and integrating explainable AI components