Decisions in Economics and Finance, Journal Year: 2024, Volume and Issue: unknown
Published: Nov. 25, 2024
Language: Английский
Advances in finance, accounting, and economics book series, Journal Year: 2025, Volume and Issue: unknown, P. 437 - 460
Published: Jan. 22, 2025
The integration of quantum machine learning (QML) into finance presents significant opportunities and challenges. QML offers immense computational power for analyzing large datasets complex financial models, promising advancements in risk management, asset allocation, predictive modeling, algorithmic trading. However, its adoption faces hurdles such as technological limitations, data security issues, the need specialized expertise. This study explores these challenges, strategic opportunities, ethical regulatory considerations finance, emphasizing importance transparent accountable decision-making. insights provided offer a comprehensive view QML's potential impact on future finance.
Language: Английский
Citations
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International Journal of Scientific Research in Computer Science Engineering and Information Technology, Journal Year: 2025, Volume and Issue: 11(1), P. 1305 - 1312
Published: Jan. 31, 2025
This article examines how quantum computing revolutionizes sophisticated risk management systems in several industries, focusing on cybersecurity and financial services. It looks at technology transforms conventional assessment methods, fraud prevention, security architecture by utilizing bits the concepts of superposition entanglement. The creation quantum-resistant cryptographic algorithms, application quantum-enhanced probabilistic analysis for management, obstacles to broad adoption are all examined this article. illustrates substantial potential enhancing frameworks while stressing factors that must be considered successful examining current market trends, technological advancements, industry applications. convergence with other cutting-edge technologies like blockchain artificial intelligence improved capabilities is also examined, as broader ramifications industries than finance, such healthcare, manufacturing, telecommunications.
Language: Английский
Citations
0
Computational Economics, Journal Year: 2025, Volume and Issue: unknown
Published: March 6, 2025
Language: Английский
Citations
0
Published: Feb. 21, 2025
Language: Английский
Citations
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International Journal of Applied Research in Social Sciences, Journal Year: 2024, Volume and Issue: 6(7), P. 1310 - 1320
Published: July 7, 2024
Effective strategic negotiation and contract management are essential for the success of high-stakes projects, which involve significant investments, complex stakeholder dynamics, substantial risks. This paper explores best practices in these areas, emphasizing integration management, digital tools use, collaboration's importance, commitment to continuous improvement. Integrating processes ensures coherence adaptability while leveraging technology enhances efficiency transparency. Fostering a collaborative environment harnesses diverse expertise, leading innovative solutions. Continuous improvement through feedback loops training helps organizations learn from past projects stay ahead future challenges. The findings highlight critical role strategies mitigating risks, managing changes, achieving project objectives. Practitioners encouraged adopt enhance outcomes build sustainable projects. Keywords: Strategic Negotiation, Contract Management, High-Stakes Projects, Collaboration
Language: Английский
Citations
3
International Medical Science Research Journal, Journal Year: 2024, Volume and Issue: 4(8), P. 806 - 833
Published: Aug. 23, 2024
Artificial intelligence (AI) is transforming personalized medicine by enhancing drug efficacy and reducing adverse effects, promising a new era of precision healthcare. This paper explores the role AI in revolutionizing therapies tailoring treatments to individual patient profiles, thereby optimizing therapeutic outcomes minimizing risks. leverages vast amounts medical data, including genetic information, electronic health records (EHRs), real-time monitoring create comprehensive profiles. Machine learning algorithms analyze these profiles identify patterns correlations that might not be apparent human practitioners. enables development treatment plans consider patient's unique makeup, lifestyle, existing conditions. One critical applications pharmacogenomics, which studies how genes affect person’s response drugs. can variations influence metabolism, efficacy, toxicity, allowing healthcare providers predict medications dosages will most effective for patients. reduces trial-and-error approach traditionally used prescribing medications, incidence reactions (ADRs). also plays significant repurposing development. By analyzing data outcomes, uses potential side effects before clinical trials, accelerating process costs. Moreover, AI-driven predictive analytics continuously monitor responses treatment, adjusting maintain optimal levels. particularly beneficial managing chronic conditions such as diabetes, hypertension, cancer, where maintaining correct dosage crucial disease management. Despite its promise, integration faces challenges, privacy concerns, need robust regulatory frameworks, ensuring equitable access innovations. Addressing challenges requires collaborative efforts from providers, researchers, policymakers, technology developers. In conclusion, at forefront medicine, Continued advancements technologies supportive policies realizing full ultimately leading more safer solutions. Keywords: AI, Drug Efficacy, Personalized Medicine, Enhancing, Reducing Adverse Effect.
Language: Английский
Citations
2
International Journal of Applied Research in Social Sciences, Journal Year: 2024, Volume and Issue: 6(8), P. 1824 - 1847
Published: Aug. 21, 2024
The integration of artificial intelligence (AI) in healthcare has revolutionized chronic disease management, particularly rural areas where access to medical services is often limited. AI-enhanced remote monitoring systems leverage advanced machine learning algorithms, wearable devices, and telehealth platforms provide continuous, real-time health for patients with conditions such as diabetes, hypertension, cardiovascular diseases. AI algorithms analyze data from which track vital signs like heart rate, blood pressure, glucose levels, physical activity. This continuous stream allows early detection potential issues, enabling timely interventions reducing the need frequent hospital visits. For instance, can predict a hypertensive crisis by identifying abnormal patterns pressure readings, prompting immediate attention. Telehealth facilitate communication between providers, overcoming geographical barriers. Patients receive personalized care plans, medication adjustments, lifestyle recommendations through virtual consultations. AI-driven chatbots assistants enhance patient engagement providing instant responses health-related queries, thus improving adherence treatment regimens. Moreover, enhances analytics integrating electronic records (EHR) data, creating comprehensive profiles. These profiles assist providers making informed decisions personalizing plans. Predictive identify high-risk forecast progression, proactive management strategies. implementation also addresses resource constraints settings. Automated collection analysis reduce workload professionals, allowing them focus on critical tasks. Furthermore, AI's scalability ensures that even underserved communities benefit technologies. Despite these advancements, challenges privacy, cybersecurity, reliable internet connectivity must be addressed. Ensuring security confidentiality paramount gaining trust widespread adoption technologies healthcare. In conclusion, presents transformative approach areas, outcomes, enhancing accessibility, optimizing utilization. Continued innovation investment are essential bridging gap urban populations. Keywords: AI-Enhanced, Remote Monitoring, Chronic Disease, Management, Rural Areas.
Language: Английский
Citations
1
International Journal of Applied Research in Social Sciences, Journal Year: 2024, Volume and Issue: 6(8), P. 1797 - 1823
Published: Aug. 21, 2024
Artificial intelligence (AI) and big data analytics have emerged as powerful tools in enhancing public health surveillance, particularly rural communities where traditional monitoring methods face significant challenges. These technologies offer the potential to transform how is collected, analyzed, utilized, enabling more effective timely responses threats. Rural often struggle with limited healthcare infrastructure, making it difficult monitor respond issues effectively. AI can bridge this gap by providing advanced capabilities for real-time collection analysis. algorithms process vast amounts of from various sources, including electronic records, mobile applications, social media, environmental sensors. This enables identification patterns trends that may indicate emerging threats, such outbreaks infectious diseases or increases chronic conditions. Big allows integration analysis diverse datasets, a comprehensive view areas. holistic approach officials identify high-risk populations, track spread diseases, evaluate effectiveness interventions. For instance, AI-powered predictive models forecast disease based on historical current trends, allowing proactive measures mitigate impact. Moreover, these enhance accuracy efficiency surveillance. automate processing tasks, reducing time resources required manual authorities quickly improving overall outcomes communities. Additionally, aid identifying correlations between factors issues, insights inform policies Despite benefits, implementing surveillance faces several Data privacy security concerns must be addressed ensure confidentiality information. Furthermore, areas lack technological infrastructure expertise needed fully leverage technologies. Overcoming challenges requires investment training professionals, development user-friendly applications tailored needs In conclusion, hold promise By leveraging technologies, improve detection, monitoring, response ultimately leading better populations. Keywords: AI, Analytics, Public Health, Surveillance, Communities.
Language: Английский
Citations
1
Engineering Science & Technology Journal, Journal Year: 2024, Volume and Issue: 5(8), P. 2670 - 2693
Published: Aug. 31, 2024
In Agile development, leveraging UX design and prototyping is crucial for delivering user-centered solutions that meet business goals user needs. This review explores the integration of within frameworks from a analyst’s perspective, highlighting how these practices enhance project outcomes ensure alignment with both expectations objectives. User Experience (UX) are essential in development creating intuitive effective interfaces. focuses on understanding needs, behaviors, pain points through research, personas, journey mapping. Prototyping involves early versions product to visualize test concepts. Integrating methodologies helps iterating designs quickly, gathering feedback, refining features based real-world use. From offer several benefits. Firstly, they facilitate clearer communication between stakeholders, designers, developers by providing tangible representations ideas requirements. reduces misunderstandings ensures all parties have shared vision outcomes. Secondly, continuous allows rapid validation assumptions, reducing risk costly changes later process. The also discusses analysts can leverage prioritize features, manage stakeholder expectations, drive decision-making. By incorporating feedback into iterative cycles, final not only meets needs but aligns delivers value. Additionally, use principles tools enhances agility process, enabling teams adapt changing requirements market conditions effectively. conclusion, integrating provides strategic advantage aligning experience For analysts, this approach products user-centered, functional, responsive evolving ultimately contributing success projects. Keywords: Design, Prototyping, Development, Business Analyst, Experience, Iterative Stakeholder Communication, Feature Prioritization, Feedback, Development Process.
Language: Английский
Citations
1
International Medical Science Research Journal, Journal Year: 2024, Volume and Issue: 4(8), P. 834 - 858
Published: Aug. 23, 2024
Antimicrobial resistance (AMR) poses a significant threat to global health, complicating the treatment of infectious diseases and leading increased morbidity mortality. Innovative drug delivery methods are emerging as critical strategies combat AMR by enhancing efficacy existing antibiotics facilitating development new therapeutic approaches. This paper explores role novel systems in addressing challenges. One primary approaches is targeted that improve precision antibiotic therapy. Nanotechnology has revolutionized this field, enabling creation nanoparticles nanocarriers can deliver drugs directly infection sites, reducing systemic side effects concentration at target. These advanced be engineered release controlled manner, overcoming bacterial mechanisms minimizing likelihood development. Another promising strategy involves use combination therapies delivered through innovative methods. By combining with adjuvants or resistance-modifying agents, these counteract restore effectiveness drugs. For instance, platforms co-deliver inhibitors efflux pumps biofilm formation enhance resistant infections. The integration smart systems, which respond environmental stimuli such pH changes specific enzymes, offers additional advantages. only presence bacteria, thereby overall exposure bacteria decreasing risk resistance. also include long-acting formulations implants provide sustained over extended periods. Such reduce frequency dosing, patient adherence, ensure consistent levels, crucial for managing chronic infections preventing In conclusion, pivotal fight against AMR. targeting, therapies, utilizing sustained-release offer solutions curb Continued research area essential advancing ensuring effective management face rising antimicrobial Keywords: Innovative, Drug Delivery Methods, Combating, Antimicrobial, Resistance.
Language: Английский
Citations
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