Anum Ali

This study employs advanced AI-driven statistical analyses to compare organic and processed foods across multiple dimensions, including nutritional content, health outcomes, consumer behavior, and environmental impact. AI models reveal significant nutritional differences, with organic foods exhibiting higher levels of vitamins and antioxidants and lower pesticide residues, while processed foods contain more additives. Predictive machine learning models associate organic food consumption with reduced risks of chronic diseases, contrasting with the elevated health risks linked to processed food intake. Consumer sentiment analysis and clustering algorithms highlight distinct preference patterns favoring organic products for health and environmental reasons. Environmental assessments demonstrate lower carbon footprints and water usage in organic food production. Robustness checks, including cross-validation, sensitivity analyses, and confounder controls, ensure model reliability and fairness. Explainability techniques such as SHAP and LIME enhance transparency and bias detection. This comprehensive AI statistical framework advances understanding of the complex differences between organic and processed foods, supporting informed consumer and policy decisions.

The rapid evolution of artificial intelligence has introduced sophisticated cyber security threats, particularly in the form of AI-driven social engineering. Social workers, who handle highly sensitive and confidential client data, are increasingly targeted by these advanced attacks despite often lacking extensive technical training. This paper explores the intersection of human behavior, survey data analytics, and cyber security to propose a comprehensive framework designed specifically for the social work sector. By leveraging hypothetical survey data to identify psychological vulnerabilities and utilizing AI-simulated phishing campaigns for targeted education, this study outlines a method to transition cyber security awareness from passive compliance to active behavioral change. Ultimately, this research demonstrates that mitigating modern cyber threats in non-technical domains requires a multidisciplinary approach that prioritizes human-centric training alongside technological safeguards.

The rapid evolution of digital health technologies (DHTs) presents unprecedented opportunities to mitigate maternal mortality and morbidity, particularly in resource-constrained environments. However, current interventions often operate in technological silos, failing to integrate crucial components such as clinical risk prediction, nutritional monitoring for safe food consumption, and culturally grounded patient interaction. This paper proposes a comprehensive, multi-modal framework that synergizes predictive analytics, explainable artificial intelligence (XAI), and continuous health monitoring to support pregnancy care. By integrating predictive models capable of analyzing temporal health data with visualization dashboards designed for clinical trust, the proposed system aims to provide actionable insights for maternal health and gestational nutrition. Ultimately, this approach bridges the gap between complex algorithmic outputs and the practical, socio-cultural realities of expectant mothers, paving the way for more holistic and effective digital healthcare interventions.

The intersection of artificial intelligence and physical manufacturing presents novel opportunities for textile art creation. This paper introduces a comprehensive framework that integrates AI-driven asset calculation with advanced machine application to automate and enhance the production of intricate textile designs. By employing generative algorithms and structured support vector machines, we calculate and classify high-fidelity digital textile assets. These assets are subsequently realized using high-speed parallel kinematic machines (PKMs), leveraging their superior structural rigidity and dynamic capacities. Ultimately, this approach bridges the gap between digital generative art and physical textile machining, offering a scalable solution for modern industrial design and custom fabrication.

Global immigration drives significant cross-border financial movements, notably through remittances, which impact macroeconomic stability and banking system operations. However, the lack of transparent tracking mechanisms often leads to money flow disturbances, characterized by inefficiencies, systemic friction, and diminished institutional trust. This paper proposes a novel framework utilizing efficient detection coding languages to identify, monitor, and mitigate these disturbances in banking systems. By integrating algorithmic detection with economic theories of immigration, this research provides a scalable approach to restoring trust and operational efficiency in transnational financial networks.

Climate change has increased the frequency and severity of environmental hazards, including floods, droughts, heatwaves, storms, and abnormal weather conditions. Conventional environmental monitoring and communication systems may experience network congestion and delayed transmission during critical events, reducing the effectiveness of early-warning mechanisms. This paper proposes a Climate Change Notification through Priority-Routing IoT Devices (CCN-PRIoT) framework for real-time environmental monitoring, intelligent event classification, and rapid emergency notification. The proposed system integrates distributed Internet of Things (IoT) sensors for collecting environmental parameters, including temperature, humidity, rainfall, atmospheric pressure, wind speed, water level, and air quality. The collected data are processed at edge or cloud nodes to detect abnormal environmental patterns and estimate climate-related risk levels. Based on event severity, urgency, location, and potential impact, a priority score is assigned to each notification. A priority-routing mechanism then selects communication paths that minimize delay and maximize reliability for high-priority alerts. Critical notifications are transmitted before routine monitoring traffic, allowing emergency information to reach disaster management authorities and affected communities more efficiently. The proposed framework supports multi-hop IoT networks, wireless sensor networks, edge computing, and cloud-based analytics. A performance evaluation model is developed using latency, packet delivery ratio, energy consumption, throughput, and priority-based notification success as key performance indicators. The framework can contribute to smart-city climate resilience and disaster risk reduction by improving the speed, reliability, and intelligence of environmental early-warning systems.

The intersection of criminology, social dynamics, and enterprise cybersecurity represents a critical frontier in modern organizational management. As the digital workplace expands, malicious insider activities have evolved beyond traditional data theft to encompass interpersonal cyberstalking and workplace harassment, disproportionately affecting vulnerable demographics such as single women. This paper proposes a novel, interdisciplinary framework that leverages artificial intelligence and statistical analysis to detect and mitigate these highly specific behavioral anomalies within enterprise networks. By integrating advanced feature selection techniques, neurosymbolic artificial intelligence, and interpretable machine learning models, the proposed system translates criminological indicators of harassment into measurable network traffic anomalies. Furthermore, we discuss the practical implications of deploying such systems in small and medium-sized enterprises, alongside the ethical complexities of monitoring employee behavior. Ultimately, this research bridges the gap between technical intrusion detection systems and human-centric criminological analysis, offering a foundational blueprint for protecting targeted individuals in hyper-connected organizational environments.

Coronal mass ejections (CMEs) pose a persistent and significant threat to orbital infrastructure, particularly by inducing ionospheric scintillations that disrupt Low Earth Orbit (LEO) satellite communications. This paper proposes a novel methodological framework to assess and predict localized LEO scintillations by applying statistical Artificial Intelligence (AI) to Geostationary Earth Orbit (GEO) satellite data. By utilizing the advanced vantage point of GEO satellites to capture upstream space weather precursors—such as magnetic field fluctuations and soft X-ray background enhancements—the proposed AI model hypothetically maps these signals to downstream LEO signal degradation. Through the integration of deep learning techniques on time-series telemetry, this work aims to transition space weather mitigation from reactive telemetry monitoring to proactive, predictive orbital management.

The integration of Artificial Intelligence (AI) and the Internet of Things (IoT) presents a transformative opportunity for the continuous monitoring and early detection of medical conditions. In this paper, we investigate the application of AI algorithms for cardiovascular disease detection through the statistical analysis of IoT data streams. Utilizing connected wearable devices for healthcare analytics introduces severe challenges, including erratic data quality, dynamic shifts in physiological baseline metrics, and stringent data privacy requirements. To address these multifaceted issues, we propose an adaptive, ensemble-based machine learning framework that continuously adjusts to physiological concept drift while operating within a secure, hardware-level trusted execution environment. By combining data trust evaluation methods with encrypted cloud processing, the proposed system ensures both the diagnostic integrity and the confidentiality of patient cardiovascular data. A hypothetical evaluation plan is outlined to demonstrate how the framework can maintain high accuracy and low latency when subjected to simulated, large-scale medical data streams.

The modern corporate environment is increasingly characterized by intense social dynamics, where workplace peer pressure acts as a significant catalyst for mental disorders such as depression, anxiety, and stress-related trauma. Early detection and intervention are crucial for occupational health, yet quantifying the psychological toll of peer-induced stress remains a complex challenge. This paper explores a comprehensive artificial intelligence (AI) statistical framework designed to analyze and detect mental disorders specifically triggered by workplace peer pressure. By integrating multimodal deep learning, emotion-based modeling, and synthetic data generation, we outline a robust pipeline capable of evaluating subtle behavioral and linguistic cues associated with occupational distress. We propose a methodology that extracts salient features from text and audio modalities, utilizes emotion regulation difficulties as an intermediate metric, and applies advanced statistical modeling to classify psychological states. Furthermore, this study extensively discusses the practical implications, ethical risks regarding employee privacy, and inherent limitations of deploying such computer-aided screening systems in corporate settings. Ultimately, this work provides a foundational roadmap for developing privacy-preserving, culturally aware AI tools that can mitigate the escalation of workplace-induced mental health crises.