American Jewish University

Buildings and additional structures utilize a significant helping of the energy in the sphere. HVAC systems are mostly responsible for building energy use in middle class households in Pakistan. This study proposes a simulation-based optimization approach for efficiently measuring HVAC systems choices in the early design stages to get the optimal configuration in middle class households in Pakistan. This strategy is applied establishing HVAC systems in middle class households in Pakistan. The ventilated Dunkel cycle performs better than the other two designs for high COP systems in all three climate zones; nevertheless, ventilation is the superior choice in middle class households in Pakistan. Systematic simulations can help with the difficult process of optimizing HVAC systems designs in middle class households in Pakistan. This invention will assist HVAC systems design are selecting the optimal systems configuration and design parameters during the early design phase by accounting for various middle-class households in Pakistan.

ABSTRACT This research analyze the multifaceted influences on environmental sustainability in five ASEAN countries. We employ the Environmental Kuznets Curve (EKC) framework to analyze the relationships between ecological footprint (dependent variable) and tourism arrivals, geopolitical risk, renewable energy consumption, Gross Domestic Product (GDP), and GDP squared. The EKC theory asserts that an inverted U‐shaped correlation between GDP and environmental deterioration (this paper ecological footprint is the proxy). The study expect tourism arrivals, geopolitical risk, and renewable energy consumption is expected to have a reductive influence on environmental footprint, signifying their potential to contribute to environmental sustainability. The empirical outcomes confirm the of an EKC in ASEAN countries. Initially, rising GDP increases the ecological footprint, reflecting the environmental pressures driven by development. However, this trend reverses at a certain point, suggesting that further economic development or GDP squared can lead to a decline in the ecological footprint as countries prioritize environmental protection and adopt cleaner technologies. Additionally, tourists' arrivals negatively impact the ecological footprint, potentially due to increased awareness and implementation of sustainable tourism practices. Geopolitical risk also demonstrates a negative association with the environmental footprint. Finally, renewable energy consumption negatively influences ecological footprint, highlighting its role in diminishing environmental degradation. Overall, the findings emphasize the importance of promoting sustainable tourism activities, fostering renewable energy adoption, and implementing targeted environmental policies alongside green economic growth strategies.

This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain, and the potential influence of quantum computing on economic modeling. A decentralized finance system examines the application of Reinforcement Learning in financial market prediction, highlighting its potential for continuous learning from dynamic market conditions. The study discusses the development of hybrid prediction models, stock market machine learning systems, and AI-driven investment portfolio management. The potential of quantum computing enhances portfolio analysis, fraud detection, optimization, and asset valuation for complex market predictions, as well as the impact of blockchain technologies on transparency, security, and efficiency. Machine learning techniques can significantly automate data collection and purification. Financial decision-making and the application of time-series analysis techniques can be readily learned through deep reinforcement learning for stock price prediction. Deep Neural Networks and Strategic Asset Allocation can be managed by evaluating performance and portfolio using real-time market insights from AI models. Although there are numerous ethical, sentimental, regulatory, and data quality issues in market prediction, the future job market is heavily dependent on these criteria, particularly through effective risk management and fraud detection.

Long-term economic viability and social development can be influenced by public health outcomes. Data analytic and machine learning methods are used to identify factors influencing public health due to the increasing availability of health-related data. This paper explores the key factors determining life expectancy using an open-source Life Expectancy dataset‚ to create an avenue for data driven public healthcare decisions. Exploratory data analysis is used to show the correlation between adult mortality‚ infant mortality‚ gross domestic product (GDP) per capita and schooling and their effect on life expectancy. Random Forest‚ Support Vector Regression (SVR)‚ and XGBoost are three different machine learning models that show that the proposed ensemble models are efficient in predicting life expectancy. The 3 models are XGBoost with R-squared of 0.998‚ Random Forest with R-squared of 0.997 and Support Vector Regression with R-squared of 0.980. The 3 most important features in prediction of life expectancy are under-five deaths‚ adult deaths and infant deaths. The study shows that predictive analytics can be a useful tool to assist policy makers to improve public health and sustainable economic growth.

Breast cancer is the most commonly diagnosed malignancy and a leading cause of cancer-related mortality among women in the United States (American Cancer Society, 2024). Although screening mammography has reduced mortality, its performance is limited by lower sensitivity in women with dense breasts, inter-reader variability, and frequent false-positive recalls (Skaane, 2019). At the same time, widespread adoption of electronic health records (EHRs) has created an opportunity to combine imaging with rich clinical information for more accurate, individualized risk assessment. This study proposes a deep learning framework for early breast cancer detection among U.S. women that integrates digital mammography with structured EHR variables. Mammography images are obtained from a curated public dataset such as the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM), which provides biopsy-verified benign and malignant cases (Lee et al., 2017). EHR-style data are organized following the structure of large U.S. clinical datasets, for example MIMIC-IV, and include demographics, breast density, reproductive and hormonal factors, comorbidities, family history, and prior screening history (Johnson et al., 2023). A convolutional neural network extracts high-level image features, which are fused with gradient-boosted clinical embeddings for malignancy prediction and compared with image-only and EHR-only baselines. In this practice framework, the multimodal model demonstrates superior discrimination and a better balance between missed cancers and false positives, particularly in women with dense breasts, highlighting the potential of integrating mammography and EHR data to support earlier detection and more informed, risk-adapted clinical decision-making.

With the high rate of Internet of Things (IoT) technologies, a new era of smart cities has been ushered in where the cities have been made more connected, efficient and innovative. However, this development poses great problems in ensuring security and stability of IoT networks. The proposed paper suggests a scalable machine-learning architecture that will enhance the security of IoT networks in smarter cities, and in particular, cyber-resilience in the modern AI age. The proposed architecture merges machine-learned models with digital governance approaches to provide real-time anomaly detection capabilities, intrusion prevention, and privacy-preserving solutions; and outlines the ways AI models can be used in IoT security and discusses the essential concerns, e.g., data privacy, compliance with regulations, and resource utilization. The suggested solution is tested with the help of simulations that prove its efficiency in resisting the process of cyber-attacks and in providing the means of scalability and flexibility in dynamic smart-city settings, which, in its turn, preconditions the establishment of the conceptual background of the formation of cyber-resilient smart cities in which the IoT networks will be able to operate safely and effectively even under the influence of the surfacing cybersecurity threats.

1969 through 2022, the Jewish-American Hall of Fame issued 2-inch medals in a variety of metals—bronze, silver-plated bronze, pure silver, gold-plated bronze, gold-plated silver, 10 kt and 14 kt gold; and in 2023, bronze-plated zinc, silver-colored zinc and gold-colored zinc. Most were struck, and some were cast. This article is a year-by-year guide of the metals used.

Enhancing decision-making accuracy and inter-pretability is a critical issue in the current financial information systems environment. This study proposes FiT-XGSHAP, a novel hybrid AI structure with a Financial Transformer for high-dimensional sequence learning, XGBoost for structured learning, and SHAP for post-hoc interpretability. The ‘Give Me Some Credit’ dataset, which comprises over 150,000 real credit records with a range of financial characteristics, including DebtRatio and Monthly Income, is used to train and test the algorithm. Predicting two-year severe delinquency-a binary classification challenge at the core of financial risk prediction-is the goal. FiT-XGSHAP is 96.74% accurate. precision = 96.00%, recall = 97.00%, F1-score = 96.00%, AUC = 0.98, particularly outstanding to the conventional single-model approaches. The experiments demonstrate the power and usefulness of hybrid architectures for financial predictive tasks, presenting high performance and interpretability. The study supports advancing intelligent support systems for financial decision-making by integrating the power of ensemble learning with the explainability of explainable AI.

The increasing capability of workforce management and the increase of corporate HRM reveal a need for intelligent decision-support tools in HRM. Structured MIS offers little in the way of prediction and more time pressure to be proactive. SOM/IRSPP-2018-0033Paper07 Machine Learning-Enhanced Management Information System for Predictive Human Resource Management. The solution uses machine learning algorithms to forecast employee turnover using a dataset of 311 employee records and 36 demographic, employment, and performance-related characteristics. To discover a sweet spot between interpretability and predictive strength, this research employed Logistic Regression and Random Forest. Logistic Regression was utilized as a baseline for evaluating significant aspects driving attrition, but Random Forest exhibited good prediction performance and provided a significance rating of each feature. Using a dashboard, the following suggested MIS architecture might allow an HR manager to monitor the results of the forecasts mentioned above to make informed strategic choices, such as planning for retention, performance assessment, workforce optimization, etc. The accuracy, precision, recall, F1-score, and ROC-AUC were utilized for the model assessment to ensure the system is stable and effective in forecasting the star attrition risk. Machine Learning will make MIS more powerful while boosting HRM by making HRM proactive & data-driven, which will eventually help firms make better choices & enhance employee retention.