Ethical Considerations in AI and Information Technology Privacy and Bias

Md Alamgir Miah

Concerns about prejudice and privacy have become crucial ethical issues as information technology (IT) and artificial intelligence (AI) are increasingly integrated into society. Large volumes of demographic data are processed by AI systems, which frequently pose privacy problems and reinforce prejudices, especially those related to age and gender. This paper explores these ethical issues, concentrating on the effects of biased AI-driven decision-making on facial recognition, healthcare, and employment. This study uses a mixed-methods approach, combining quantitative data from 60 respondents with qualitative literature analysis. The results show a strong relationship between ethical concerns, privacy issues, and biased data gathering. Disenfranchised groups continue to be disadvantaged by AI models based on historically skewed datasets, which exacerbate discrimination and restrict justice in digital decision-making. Even though laws like the CCPA and GDPR offer some control, they are not enough to handle the growing ethical issues surrounding AI. Reducing discrimination and guaranteeing accountability requires using bias detection techniques, fairness-aware machine learning, and transparent AI governance. Giving ethical issues a top priority as AI develops will be essential to creating technology that upholds individual liberties and promotes inclusivity. To guarantee a fair and just technological environment for all users, future developments in AI must concentrate on creating equitable systems that protect privacy.

Technology-Assisted Parent Training Programs for Autism Management

Rayhan Khan

The developmental condition known as autism spectrum disorder (ASD) is defined by recurring behavioural patterns and challenges with social communication. Taking care of a kid with impairments presents parents with a lot of emotional and practical obstacles that might affect their family's arrangements. This article examines the integration and efficacy of technology-based parenting interventions for addressing ASD, focusing on how these programs are developed, which technologies are used, and how they affect parent-child relations and success rates. The phenomenology design, a qualitative research approach, was used to analyse the experiences of primary school students with disabilities in virtual education activities after the global pandemic 2020. The design allowed for a comprehensive understanding of students' perspectives and solutions. Face-to-face training techniques are effective but cannot reach all families due to transport, money, and time issues. Distance-based training and technology-assisted training solutions provide a solution by disseminating high-quality, evidence-based training to a broader audience. The results show that ADEPT and the PLAY Project are examples of potential supports involving the application of digital tools to provide parents with essential training content to create proper home conditions for further child development. Evaluating the success of these initiatives is crucial to assessing their impact and potentially modifying them. Scientific methods like randomised controlled trials or longitudinal studies provide insights into the efficacy of technology-supported training. At the same time, measurable quantities like parent-child interaction or behavioural changes prove its effectiveness.

Revolutionizing Drug Discovery AI-Driven Approaches to Personalized Medicine and Predictive Therapeutics

Afia Fairooz Tasnim

By discovering and creating novel drugs to treat a range of illnesses, the discipline of drug discovery and development plays a vital role in healthcare. Conventional approaches to drug development have been costly, time-consuming, and frequently produce drugs that don't work for every patient. Precision medicine, on the other hand, seeks to customize medical care to each patient's unique needs while accounting for lifestyle, environment, and genetics. Artificial Intelligence (AI) has revolutionized drug discovery and development in recent years when it has become a potent instrument. Machine learning and deep learning are two examples of AI technologies that could drastically speed up medication discovery, lower prices, and increase treatment efficacy. Researchers can find possible therapeutic targets, create new compounds, and forecast patient response to treatment with the use of artificial intelligence (AI), which analyzes vast datasets and find patterns. This research investigates how AI can be used to find and produce drugs for precision medicine. With adding that, it gives a summary of the conventional drug discovery procedure, emphasizing its drawbacks. Later this research describes how AI technologies are being applied to solve these obstacles, with particular attention to how they are being employed in clinical trials, target identification and validation, and computational drug design. The research also looks at how AI may help to provide personalized medicine, in which each patient receives a customized course of therapy. In summary, this study attempts to present a thorough analysis of the state of AI-driven drug development today and how it can revolutionize precision medicine. Understanding the developments and difficulties in this field helps us to better grasp how AI may transform healthcare in the future and enhance patient outcomes.

Artificial Intelligence Hybrid AI-Econometric Models for Forecasting Volatile US Equities: A Comparative Study of Apple and Microsoft

Financial forecasting in the US stock market has traditionally relied on econometric models such as ARIMA, SARIMA, and GARCH, which offer interpretability and robust performance in stable environments. However, the increasing complexity and volatility of modern markets— 25 Oct 2025 (Published Online) driven by nonlinear dynamics and high-frequency trading—have exposed the limitations of these AI-augmented forecasting, Prophet traditional econometric models and AI-augmented methods, with a special focus on the Prophet model, ARIMA-GARCH hybrid, stock model, in forecasting stock prices and volatility for major US firms, specifically Apple (AAPL) price prediction, volatility clustering, and Microsoft (MSFT). The study seeks to determine whether hybrid AI-econometric US equities. frameworks provide superior accuracy and risk quantification compared to standalone models. Historical daily price data (January–June 2024) from Yahoo Finance underwent preprocessing: log-return transformation, stationarity enforcement (ADF/PP tests), outlier winsorization, and volatility clustering validation. Models were trained on 80% of the data (105 observations) and tested on 20% (26 observations). Performance was measured via RMSE, MAE, AIC/BIC, and uncertainty interval accuracy. Prophet outperformed traditional models, reducing Apple’s RMSE by 6% (7.02 vs. 7.46) and MAE by 8.9% (4.70 vs. 5.16) compared to AI-augmented ARIMA. For Microsoft, Prophet achieved 11% lower RMSE (9.46 vs. 10.64) and 14.4% better MAE (5.89 vs. 6.88). AI-augmented GARCH improved volatility forecasts by 19% for Apple, capturing asymmetric responses missed by classical GARCH. Hybrid models (e.g., Prophet-GARCH) demonstrated superior trend reversal detection but increased operational complexity. Integrating AI with econometric models significantly enhances forecasting accuracy and risk quantification, particularly through Prophet’s uncertainty intervals and adaptability to structural breaks. While computational demands and small-sample biases remain challenges, these hybrids offer actionable insights for portfolio optimization and crisis preparedness in volatile markets.

Forecasting Stock Prices: A Machine Learning-Based Approach for Predictive Analytics Through a Case Study

Stock price prediction has always been a challenging task, requiring careful observation of trends and dynamics of the market because of the volatile and complex nature of financial markets. Various factors affect market behavior all the time. Even some unquantifiable factors like 25 Oct 2025 (Published Online) emotions of the masses, social and political dynamics, etc., also play a great role. So perfect Machine Learning, Deep Learning, behaviors into consideration is crucial for better prediction of the ups and downs of prices. SMA, EMA, RSI, MACD, Bollinger Various machine learning and deep learning models have been proposed to tackle the challenges Bands, RFE, Random Forest by capturing and interpreting complex patterns and relationships in historical price data. Regressor, Multivariate Analysis, Technical features are important for understanding market trends and thus improving the LSTM. accuracy of stock price predictions. In this paper, we calculate key technical indicators such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, and others. We then focus on selecting the most relevant indicators by employing feature selection methods from these to enhance the extraction of meaningful features reflecting underlying market behavior and increase the probability of more precise prediction. Here, Recursive Feature Elimination (RFE) and Random Forest Regressor-based importance ranking methods have been applied for the feature selection task. To get a better forecast of market price, it is important to capture long- term dependencies and patterns over time. Long Short-Term Memory (LSTM) networks are well- suited for modeling and predicting sequential data like stock prices. By leveraging an LSTM model and taking the selected features, we do a multivariate analysis to forecast stock price based on historical data, identifying the trends fairly accurately with some lags here and there.

Machine Learning Applications in U.S. Manufacturing: Predictive Maintenance and Supply Chain Optimization

Rakibul Hasan

Machine learning (ML) technologies are swiftly coming into the U.S. manufacturing industry to solve the old issues of equipment upkeep and supply chain management. There is a transformative research study about ML and its application to improve predictive maintenance 25 October, 2025 (Published Online) and plan inventory and logistics decisions. The study makes use of actual data and variable set Machine Learning (ML), Supply random forest) to forecast the failure of equipment and supply blockades. The methodology Chain, Industrial IoT, Predictive involves elaborate feature engineering as well as a breakdown of demand with model calibration Maintenance. to account for lead-time variability and heterogeneity of operations. It is also observed that, compared to conventional regression methods, XGBoost is better in predictive maintenance and has higher adaptability to nonlinear trends in demand prediction. Additionally, the paper examines model robustness, distribution regional impact, as well as anomaly identification in order to demonstrate how ML is to be utilized to reduce operational downtime and enhance inventory turnover. The most significant implementation issues are discussed, such as integrating previous generation equipment, data imbalance, and cybersecurity. This paper ends with a discussion of what can be expected in the future in terms of Edge AI and Federated Learning, and the importance of those technologies in securing and sustainable smart manufacturing systems. This study will provide practical results to manufacturers aiming to transform to smart and resilient models and data-driven manufacturing.

Blockchain-Based Banking Infrastructure for Securing Financial Transactions and Reducing Operational Costs in the U.S.

Blockchain technology is rapidly reshaping the financial landscape in the United States, offering improved security, transparency, and operational efficiency across banking, trade finance, and regulatory compliance. This study explores how U.S. financial institutions are integrating 25 October, 2025 (Published Online) blockchain to enhance performance, drawing on a range of case studies from both public and Blockchain Technology, Financial a 42% reduction in fraudulent transactions, a 58% decrease in trade finance settlement times, and Inclusion, Cybersecurity, Trade a 49% boost in compliance efficiency. In addition, blockchain is playing a critical role in Finance, Artificial Intelligence, protecting against cyber threats, with blockchain-secured institutions reporting a 47% drop in Regulatory Compliance cyberattacks and a 31% improvement in fraud detection through the use of AI-integrated blockchain systems. Mobile blockchain applications have also increased banking accessibility, particularly in underserved areas, supporting broader financial inclusion efforts. Furthermore, the convergence of blockchain with emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), and cloud computing has enabled real-time transaction monitoring, secure data sharing, and more robust trade verification processes. Despite ongoing challenges related to regulatory clarity and system integration, blockchain is emerging as a foundational technology in the U.S. financial system, with strong potential to drive innovation, strengthen cybersecurity, and create a more inclusive and efficient financial ecosystem.

Enhancing Digital Marketing Strategies in the Food Delivery Business through AI-Driven Ensemble Machine Learning Techniques

The digital marketing for food delivery business is the focus of this study, which investigates the use of ensemble machine learning (ML) approaches. The study's overarching goal is to pave the way for artificial intelligence (AI)-based recommendations by analyzing consumer 25 Oct 2025 (Published Online) data with the hope of discovering consumer preferences and predicting behavior. In order to Digital marketing, Food delivery trees, naïve Bayes, and k-nearest neighbor algorithms. Both the decision tree and nearest business, Machine learning, Artificial neighbor algorithms were able to obtain perfect predictions with zero error and 100% accuracy, intelligence, Accuracy. as seen in the accuracy matrix charts. On the other hand, the naïve Bayes method was able to accurately identify labels in all classes with a minimal error rate of 0.028 and a high accuracy of 97.175%. With a success rate of over 90%, the majority vote method allows models to be integrated using less than 50% of the randomized data, which minimizes customer dissatisfaction. When taken as a whole, these ML algorithms greatly improve the efficiency and efficacy of food delivery business digital marketing campaigns by cutting down on wasted time