Global Journal of Universal Studies (ISSN: 3008-0509) is a Pakistan-based, globally recognized journal published by the GLOBAL ALLIANCE FOR SCIENTIFIC STUDIES. It welcomes submissions from various disciplines. Global Journal of Universal Studies operates under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. It follows a rigorous peer-review process and is freely accessible to readers. The journal aims to publish exceptional research papers, reviews, letters, and case reports in natural sciences, social sciences, applied sciences, formal sciences, arts, and related areas. Our goal is to improve the efficient dissemination of new research ideas and findings, enabling researchers to generate new knowledge, studies, and inventions that will serve as a valuable reference for the future. The Journal is affiliated with Global Alliance for Scientific Studies (SMC-Private) Limited Multan, Pakistan. Journal Description Journal Name Global Journal of Universal Studies ISSN Number 3008-0509 Editor in Chief Dr. Ali Haider| Department of Clinical Sciences, FV&AS, Muhammad Nawaz Shareef University of Agriculture, Multan, 25000, Pakistan Frequency a Half Year| June and Decemeber Research Scope Computer Science | Management | Veterinary | Mathematics | Agriculture | Engineering | Health
Federated and Edge Learning for Real-Time Data Analytics in Clinical Environments
The new machine learning paradigms that allow privacy-guaranteed real-time data analytics in clinical settings are federated and edge learning. As electronic health records, wearable devices, and Internet of Things continue to multiply the volume of healthcare data, the conventional centralized data management methods are under a challenge due to privacy, latency, and scaling. In federated learning, collaborative model training is possible without exchanging raw patient information, whereas edge learning has the data processed on devices in real-time to provide a faster response. Their integration enables efficient, secure and scalable healthcare systems to be used in applications like remote monitoring, in ICUs and in medical diagnostics. These technologies also have issues such as communication overhead and resource constraints, however, it does not mean that they lack promise of improved patient care.
Federated learning, Edge learning, Real-time healthcare analytics, Clinical environments, Data privacy, Medical imaging, Artificial intelligence in healthcare
Copyright (c) 2024 Global Journal of Universal Studies
Real-Time Data Analytics: Applications and Challenges in Smart Cities
Smart cities have adopted real-time data analytics making them a critical component in the cities dynamic monitoring, sound decision-making, and effective management of urban services. Using information gathered by the IoT devices, sensors, social media, and infrastructure systems, cities are able to streamline how they transport, power, provide healthcare, govern, monitor their environment, and ensure their safety. This review examines some of the main concepts, technologies, and structures that support real-time analytics, brings out major applications, challenges associated with the volume of data, privacy, scalability, and interoperability. The trends in the future are focused on AI integration, edge computing, standardization, and sustainability. Eventually, real-time analytics facilitates the creation of efficient, responsive and sustainable cities.
Copyright (c) 2025 Global Journal of Universal Studies
Catalysis and Mechanistic Insights in Contemporary Organic Chemistry
The current state of organic chemistry is based on the principles of catalysis and mechanistic knowledge, which allow performing efficient, selective, and sustainable transformations. In this review, recent progress in transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems is highlighted, with a focus on how transition metal catalysis, organocatalysis, photoredox catalysis, and bio-inspired systems are guided by mechanistic knowledge in reaction design, optimization, and substrate scope. Kinetic analysis, spectroscopy, and computational modeling techniques are important in order to gain critical information about intermediates, transition states, and reaction pathways; hence, to develop rational catalysts. The issues of catalyst stability, mechanistic complexity and scalability are addressed, as well as the new trends in AI-driven prediction and green chemistry. Combining catalysis with mechanistic information is still broadening the horizons of present-day organic synthesis.
Catalysis, Mechanistic Insight, Organic Synthesis, Transition Metal Catalysis, Organocatalysis, Photoredox Catalysis.
Big Data and Cloud Computing: A Review of Paradigms Transforming Computer Science
In this review, the author discusses the revolutionary conjunction between two emerging phenomena: Big Data and Cloud Computing, which, overturning the traditional paradigm in Computer Science, transform the outlook in the field. Big Data now allows us to process and analyses huge, varied volumes of data, and Cloud Computing can offer the scalable, on-demand infrastructure on which these can run. Combined them enable real-time analytics, AI applications, intelligent decisions, and smart city applications across industries including healthcare, finance, retail and real estate. The paper will explore some of the concepts on which it is based, how it has been integrated into technology, practical examples, advantages and some of the emerging trends like edge computing and server less architectures. It also deals with the major concerns relating to it implying the data privacy, security, vendor lock-in, and ethical issues. Finally, this review points out the synergy of the Big Data and the Cloud that is shaping a new age in innovation and power of computing.
Big Data, Cloud Computing, Data Analytics, Scalability, Real-Time Processing, Distributed Systems.
Empowering Patients through Health Informatics: Trends, Challenges, and Opportunities
By granting access to health records, empowering patients through connection and strining to improve health outcomes, health informatics plays a vital role. This article examines the growth of patient empowerment in healthcare, showing how important technologies such as electronic health records (EHRs), mobile health (mHealth) apps, wearable devices and telemedicine platforms have become. They allow patients to handle their health, talk with doctors more openly and make the right choices. Even though the advantages exist, digital inequalities, worries about private data and compatibility issues still need attention for proper and equal healthcare. New trends, for example, precision medicine, blockchain and artificial intelligence, may lead to better personalized healthcare and more choices for patients. As a result, health informatics helps rebuild the patient-provider partnership, opening doors for more personal, efficient and patient-friendly healthcare systems.
Health informatics, patient empowerment, electronic health records (EHRs), mobile health (mHealth) apps, wearable devices, telemedicine.
Ethnobotanical Survey of Common Medicinal Plants Used by People for the Treatment of COVID-19, in Faisalabad, Pakistan
This herb-rich plant has long been used to treat a variety of ailments. Since the spread of COVID-19 and death cases are constantly increasing, which prompted the Health and Food Administration to recommend taking drugs that inhibit viral activity, in addition to the World Health Organization suggesting the need to rely on alternative medicine, especially those medicinal plants that have antiviral properties. The goal of this study is to collect ethnic data that members of the Pakistani Faisal community may utilize to cure certain ailments. Method: Traditional herbalists and adults provided ethnic data. The pharmacy at Faisalabad University School of Medicine received the form letters gathered in this investigation. This research now includes 70 different tree species from 39 different families. Of the 12 species, the Rosaceaefamily has been discovered to be the most popular, and the most part of plants is more effective is leaves. Conclusion: The herbal business, herbalists, and export authorities for this therapeutic plant benefit from this research. This dissertation contains critical information for future phytochemical research that may be required to keep this crop alive.
Ethnopharmacology, Folk Medicine, Medical Ethnobotany, Traditional Knowledge, Urban Phytotherapy, Medicinal Plants, Survey, Herbs, Pakistan.
AI for Smarter Product Development and Manufacturing
Artificial Intelligence (AI) is making product development and manufacturing more advanced by helping with smarter design, flexible production and better supply chains. This review focuses on important technologies such as machine learning, deep learning and generative design and how they bring about change in the manufacturing of products. AI is demonstrated in real situations to help businesses innovate, improve work quality and operate more efficiently, as it also manages problems like data quality, bias, creating clear systems and the effect on employees. The importance of ensuring trust and accountability in AI is better explained when using an approach that focuses on people, ethics, understandable AI and close human-AI cooperation. In the end, it proves that using AI responsibly leads to more flexible, open and equitable manufacturing systems. If we use AI responsibly, it could help industries produce creative and durable products which would benefit society and help the economy grow.
AI, product development, manufacturing, machine learning, deep learning, generative design.
AI in Healthcare and Beyond: Advanced Approaches for Disease Detection and Management
Advanced data analysis done by Artificial Intelligence is now transforming healthcare and supporting animal health. Using a range of data such as sensor measurements, videos and behavior observations, it gives machine learning and computer vision the ability to notice diseases ahead of time. Using data from the past and the present, predictive analytics can foresee outbreaks and so help develop better prevention plans. Although problems like data quality, access and following regulations are not fully solved, these AI-driven solutions are expected to considerably boost health results for members of all species. Here, we point out AI technologies play a wide role and can be used to manage diseases in ways that go beyond the standard healthcare system—including monitoring poultry diseases and supporting conservation efforts for endangered species.
AI, healthcare, data processing, machine learning, deep learning, Conversational AI, computer vision, Poultry diseases.
Navigating New Frontiers in Poultry Health and Productivity
The world depends on poultry farming as a foundation of food security but this sector battles with infections together with developing antimicrobial resistances while being affected by environmental problems. This paper assesses modern techniques for improving poultry wellness alongside techniques for sustainable production development. The poultry industry advances through new vaccine development together with probiotic implementation along with AI-based automatic farm monitoring systems. Genetic progress combined with optimized feeding methods accelerate the development of animals and their ability to resist illnesses. Keeping biosecurity standards and using sustainable practices in combination with reduced antibiotic use will sustain long-term productivity. Through innovation adoption the poultry sector can fulfill worldwide consumption needs by maintaining proper care of animals together with respect for ecological principles.
Poultry Health, Productivity, Biosecurity, Sustainability, Precision Farming, Antimicrobial Resistance.
The Role of Bioinformatics in Genetic Improvement and Tree Breeding for Sustainable Forestry
Biological approach helps enhance tree breeding and genetic improvement through innovative analytical methods which sustain forest practices and increase their resistance rates. The application of genomic sequencing methods along with genetic editing techniques and genomic selection under bioinformatics control allows development of improved tree species with enhanced resistant features and growth dynamics and environmental tolerance. The new technologies play an essential role in solving difficulties which stem from climate change and biodiversity loss and ecosystem degradation. Bioinformatics allows forest managers to preserve tree genetic resources while creating sustainable climate-friendly forest practice methods. The review presents crucial uses of bioinformatics for forestry and outlines its developmental issues together with future possibility horizons while demonstrating worldwide exemplary cases. Bioinformatics technology will continue advancing tree breeding operations through precise breeding methods combined with improvements in forest resistance and global sustainable forest management and climate change reduction initiatives.
Bioinformatics, Tree breeding, Genetic improvement, Forest sustainability, Genomic selection, Climate resilience, Disease resistance
Cognitive Computing in Healthcare: Past Lessons, Present Realities, and Future Horizons
The healthcare sector experiences transformation through cognitive computing which provides improved diagnosis methods and designs treatment plans as well as cares for patients and operates medical facilities. Modern medical decision-making has experienced a revolution through both early expert systems and current AI-driven diagnostics together with robotic surgery and precise medical treatments. The vast potential requires solution of privacy issues with datasets together with algorithmic discrimination prevention and adaptation of relevant regulations and resolution of ethical problems. AI healthcare development will focus on three main areas which involve predictive knowledge analysis and real-time illness prevention along with sophisticated drug innovation processes to deliver patient-specific proactive medical services in the future. The introduction of AI must meet both ethical requirements and legal responsibilities in order to gain public acceptance because patient rights and AI clarity and cybersecurity protection matter most. A complete adoption of cognitive computing requires global collaboration between healthcare professionals and strong regulatory frameworks as well as AI literacy for medical staff and cognitive computing implementation methods. A responsible implementation of AI healthcare will generate an enhanced system that provides more precise and efficient patient-focused medical treatment to enhance global health results.
Cognitive computing, artificial intelligence, healthcare innovation, healthcare technology, medical research, hospital management.
The Convergence of AI and Medicine: Progress, Constraints, and Future Possibilities
Healthcare experiences a transformative shift because of Artificial Intelligence (AI) medical integration which improves diagnosis processes and enables better treatment strategies as well as patient care administration. Medical decision-making benefits from AI-driven advancements including robotic surgery together with virtual health assistance and predictive analytics which enhance efficiency and accuracy. The adoption of AI encounters important hurdles because it triggers privacy dangers to patient data and builds barriers to regulation while being complicated to integrate and subject to ethical doubts about AI systems. The exceptional power of AI computers needs human specialists to handle decisions based on ethics while delivering empathy and grasping the nature of situations. AI technology will find its best application point in medicine through combining forces with healthcare professionals rather than offering their position. Achieving responsible AI implementation demands solutions for bias-related problems and security measures and accountability systems and doctor-patient relationship maintenance. Medical professionals can reach patient-centered and safer healthcare while achieving more efficiency by implementing ethical AI practices and using AI as a supportive tool to provide equitable benefits to every individual.
AI in medicine, artificial intelligence, healthcare innovation, responsible AI implementation, medical decision-making.
Integrating Health Informatics into Modern Healthcare Systems: A Comprehensive Review
The multidisciplinary field of health informatics uses IT, data analytics with healthcare expertise to optimize health data management for better care services. Health informatics applications within contemporary healthcare organizations drive streamlined clinical operations while delivering high-quality decisions that boost patient recovery results. A review examines the developmental history and elementary elements and connective approaches of health informatics as well as the accomplishments and difficulties with new developing technologies in this field. Operations and patient care through improvements have become attainable using four critical health informatics components which consist of Electronic Health Records (EHRs), Health Information Exchange (HIE), Clinical Decision Support Systems (CDSS) and telehealth. Successful integration of healthcare systems withstands various obstacles such as technology difficulties as well as staff member opposition and security issues and limited financial means. The achievement of operational excellence demands proper interoperability standards and complete training programs as well as strict adherence to privacy guidelines. Modern healthcare depends on emerging technologies which include Artificial Intelligence (AI), Machine Learning, the Internet of Medical Things (IoMT), block chain, and genomics to transform patient-specific care and predictive healthcare and enhance patient engagement. Real-life healthcare implementations at Kaiser Permanente alongside the NHS along with the enhanced use of telehealth systems since the COVID-19 pandemic show that informatics successfully improves healthcare service quality while enhancing patient access to medical care and system efficiency.
Health Informatics, Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), Security, Emerging Technologies
Deep Learning for Multi-Modal Cancer Imaging: Integrating Radiomics, Genomics, and Clinical Data for Comprehensive Diagnosis and Prognosis
At both the cellular and molecular levels, cancer is a heterogeneous disease. While traditional diagnostic and prognostic methods are often deficient in the ability to capture this complexity, limiting the potential to inform personalized treatment strategies. Deep learning (DL) results on multi-modal data sources have the potential to improve cancer diagnosis and prognosis by combining several types of data sources in the past few years: radiomics, genomics and clinical data. The first, radiomics, extracts quantitative features from medical images to reveal tumor characteristics invisible to the human eye; the second, genomic data, provides information about what genetic mutations are driving cancer progression. These modalities are complemented by clinical data with patient demographics and treatment history contributing context specific information to the data. The application of deep learning techniques on multi-modal cancer imaging is reviewed as well and their ability to combine radiomics, genomics and clinical data to improve diagnostic and prognostic accuracy is emphasized. We discuss the challenges and future directions for clinical implementation of this approach and its potential to transform cancer care, improve patient outcomes and empower precision medicine.
Cancer Genomics, Tumor Heterogeneity, Artificial Intelligence, Machine Learning, Precision Medicine, Genetic Mutations, Oncogenes, Tumor Suppressor Genes, Next-Generation Sequencing (NGS), Radiomics.
AI in Product Management: Efficiency, Quality, and Innovation
Artificial Intelligence (AI) is transforming the way products are managed, manufactured, and quality controlled by providing data-driven choices, enabling intelligent automation, and preliminary information. This is a review of the way AI helps in increasing efficiency, improving the quality of products and also in promoting innovation throughout the product lifecycle. AI is useful in product management in supporting market forecasting, optimization by design, and personalization. In the factory industry, it initiates intelligent factories, proactive maintenance, and cost-efficiency. AI can be used in quality management to ensure real-time monitoring, detection of defects, and compliance. Although there are advantages, namely the cost savings, agility and sustainability, there still are challenges associated with the development like data dependency, integration, workforce adaptation, and ethical concerns. The future trends point to generative AI, digital twins and sustainability.
Artificial Intelligence, Product Management, Manufacturing, Quality Management, Efficiency, Innovation, Predictive Analytics, Smart Factories, Sustainability.
Understanding AI-Driven Cardiovascular Risk Prediction in Underserved Populations: Addressing Social Determinants of Health through Data Analytics
Cardiovascular disease (CVD) is a leading cause of morbidity and mortality internationally, and underserved populations bear an out sized burden of risk. In these communities, access to adequate healthcare, burdened by social determinants of health (SDOH) like socioeconomic status, education, and environmental factors, can pose a barrier to many individuals. The crucial role of Artificial Intelligence (AI) and Machine Learning (ML) to address problems of health disparity by leveraging large scale data to predict cardiovascular risks and impact appropriate interventions is discussed. The integration of diverse sources of information, such as electronic health records (EHR) social data, and behavioral data, allows AI models to identify at risk individuals even in settings with limited resources. In this paper, we investigate the use of AI driven cardiovascular risk prediction models in underserved populations and how they can consider the effect of SDOH. The paper also addresses ethical considerations, data challenges, and opportunities for use of these prediction tools in public health initiatives to address disparities in health. The potential of combining AI and data analytics is to enhance cardiovascular health outcomes, bridging gaps in equitable access to preventive care, especially for vulnerable communities.
Cardiovascular Disease, Artificial Intelligence, Social Determinants of Health, Underserved Populations, Risk Prediction, Data Analytics, Health Disparities, Machine Learning, Public Health, Health Equity.
AI-Driven Approaches to Overcoming Tumor Heterogeneity in Breast Cancer: Modelling Resistance and Therapy Outcomes
Breast cancer is a highly heterogeneous disease consisting of distinct molecular subtypes and differential response to treatment. The major challenge associated with predicting treatment responses and overcoming resistance mechanisms in tumor heterogeneity (genetic, phenotypic, and molecular diversity of a single tumor) remains yet to be overcome. Now artificial intelligence (AI) and machine learning (ML) promise to help us address these challenges by modelling complex interactions within the tumor. Using large-scale genomic, transcriptomic, imaging and clinical data, AI can incorporate cancer heterogeneity by characterizing and identifying key drivers for cancer heterogeneity and predicting resistance mechanisms. They help to generate more accurate prediction of chemotherapy, targeted therapy, and immunotherapy treatment responses and it can pave the way for more personalized treatment strategy. But AI is also capable of monitoring tumor evolution over time, and uncovering real-time insights about how effective treatment is and if relapse is starting, early. While promising, practitioners have been slow to adopt AI models for clinical work, due to data quality, model interpretability and compatibility in the current healthcare workflow. In translating AI based tools for practice, these challenges will need to be addressed. In this paper, we discuss how AI may be used to target tumor heterogeneity, model resistance mechanisms and optimize treatment responses in breast cancer as well as presenting obstacles to clinical implementation of such methods.
Heterogeneity, Artificial Intelligence, Machine Learning, Treatment Resistance, Personalized Treatment, Genomic Data, Breast cancer.
AI-Enhanced Liquid Biopsy: Advancements in Early Detection and Monitoring of Cancer through Blood-based Markers
The inclusion of AI technology into liquid biopsy brings us important new ways to find and track cancer. The non-invasive blood test called liquid biopsy shows great potential for early cancer detection and helps doctors track disease changes and create custom treatments. Machine learning algorithms in AI technology helps liquid biopsy find better biomarkers and detect cancer earlier while also monitoring patient health in real time. Advanced technologies find cancer-related genetic faults along with tumor markers and detect remaining disease cells accurately. Using advanced AI to solve quality and accuracy problems helps liquid biopsy systems transform how doctors treat cancer by identifying diseases sooner and giving better treatment plans for patients. These advancements help identify cancer-related genetic mutations, tumor markers, and minimal residual disease that may otherwise be missed. By addressing the challenges of data quality, algorithmic accuracy, and clinical integration, AI-enhanced liquid biopsy can transform cancer care, offering earlier detection, more precise monitoring, and tailored treatments for individual patients. This report explores latest advancements and explains how AI helps transform liquid biopsy use in medical oncology.
AI, Liquid Biopsy, Cancer Detection, Machine Learning, Blood-based Biomarkers, Early Diagnosis, Cancer Monitoring, Personalized Medicine.
Leveraging AI and Machine Learning to Predict and Prevent Sudden Cardiac Arrest in High-Risk Populations
Cardiac arrest claims a lot of lives, mainly among people with heart diseases, diabetes, or abnormal heartbeats. Using AI and ML helps us find people at risk of heart failure and helps them avoid experiencing this condition. The research review looks at what AI and ML methods currently do to forecast SCA and checks how well they work. The models work by using lots of collected health information - from medical records, user monitors, and genetic studies - to forecast SCA danger right when needed. AI technology with machine learning algorithms can study large patient data to spot unique patterns that show which people are more likely to develop SCA. These models show two abilities: they estimate chance of events happening and suggest what steps to take to reduce risks. The study looks at AI implementation issues, including keeping patient data confidential, connecting with how doctors work today, and making sure doctors understand how AI systems make decisions. The research stresses that professionals should make transparent how their AI decisions work. Robust artificial intelligence and machine learning tools can help healthcare improve patient care by spotting those at highest risk of dangerous heart failure sooner and lowering deaths linked to cardiac emergencies.
Artificial Intelligence, Machine Learning, Sudden Cardiac Arrest, Prediction Models, High-Risk Populations, Healthcare
Investigation of Heat Transfer in Pipes Using Dimensionless Numbers
This study investigates heat transfer in pipes, focusing on the calculation of three key dimensionless numbers to analyze thermal behavior. Although the experiment was conducted only once, measures were taken to ensure accuracy, and potential sources of error were minimized. Repeating the experiment multiple times and averaging the results would have further improved reliability by reducing the impact of anomalies. Despite this limitation, the primary objective of understanding heat transfer mechanisms through dimensionless numbers was successfully achieved, providing valuable insights into thermal processes.
Dimensionless numbers, thermal behavior, accuracy, anomalies
Transformative AI Applications in Healthcare, Petroleum, Fraud Detection, Cybersecurity, and Conversational AI: Advancing Industries
AI is the most disruptive technology of the decade as it changes industries for the better by upgrading processes, increasing efficiencies, and introducing creativity in many industries. The liberties, in this case, are seen in health care where artificial intelligences is changing the ways diagnosis’s are done, treatment methods, drug discovery and many more. Some of the potential applications of AI in the petroleum industry include; Efficiency in the production of energy, issues to do with safety and even with those of maintenance with emphasis on prevention of future mishaps that may be costly or fatal. AI has also found its use in anti-fraud where it is applied to identify new financial crimes and encode machine learning models to adapt to their new schemes. Categorized under cyber security, Artificial Intelligence is strengthening the cybersecurity by identifying new threats, making accurate forecasts, and responding to threats proactively to protect sensitive data. A well-known example of conversational AI is the Chatgpt is one of the key applications of the AI industry. Chatgpt is disintermediating customer service, sales and internal communication by providing a highly personalized many turn contextually aware conversation. Making communications with customers faster, and more efficient and thus is a win-win for customers as well as the organization. It also enhances the issue of accessibility since it facilitates communication between different user groups. At the same time, when AI became more successful and extensively integrated into multiple company processes, it created new specific ethical concerns, such as data privacy, bias, and insufficient transparency. It is crucial to solve these problems to get the highest advantage from AI and do not cause distrust and misunderstanding. This current role of AI is set to grow in the future, across sectors, to deliver even higher levels of effectiveness, creativity and convenience, unlocking the era of a smarter connected world.
Healthcare, oil and gas, fraud, cybersecurity, Chatgpt, conversational AI for healthcare, AI ethical issues, availability, productivity enhancement, predictive analytics, ease-of-use.
Transformative AI Applications: Promising Value Faster Healthcare Development, Affording Petroleum Deception Suppression, Improving Cybersecurity, and Integrating Humanized Chatgpt for Conversational AI
In this field, the use of Artificial Intelligence (AI) is revolutionizing a number of industries bringing novel solutions that challenge conventional approaches to order. This review explores the significant impact of AI in five key sectors: healthcare, petroleum industry, credit card fraud, Cybersecurity, Chatgpt. AI is useful for improving diagnostic and clinical decisions and for making drug development and patient comparison faster. In the petroleum industry, by applying AI in Fraud management, companies are reducing their exposure to fraud, and increasing accountability, therefore reducing their frequency of finsancial loss and poor operational flow. In cybersecurity, AI is stepping up as the frontline technology to identify threats in real-time, analyze patterns, and respond proactively while organizations are fighting off more complex cyber threats. Furthermore, with advancements like Chatgpt, or AI tools it is clear that the dynamic of interaction between human and computer has also begun changing for the better generalizing human-computer-interaction for different contexts such as customer support, learning, and mental health. However, AI has its own issue such as data privacy, the ethical issues involved in adopting AI, issue of bias in data and risk of misuse of AI, all of which have to be taken into consideration. Thus, as the AI technologies develop individuals represent a great potential to positively progress and improve if the usage of AI technologies is reasonable and ethical. The focus of this review is on the value of integration AI in updating industries, potential vulnerabilities, and the role of future investigation, government control, and cross-discipline cooperation in enhancing positive outcomes of these technologies as well as the management of negative consequences.
AI, healthcare, petroleum fraud, cybersecurity threat, Chatgpt, conversational AI, machine learning, fraud, cybersecurity threats, natural language processing.
AI in Clinical Practice: Current Uses and the Path Forward
Machine learning is developing very quickly in the healthcare field in the field’s diagnostics, therapy and caring of patients and new drug development. Machine learning, deep learning, and so on have opened up new areas of diagnosis of diseases, understanding what particular treatment is required for a specific patient, and prognosis of disease. Radiology, Computer Aided Diagnosis and chronic disease applications are the current areas covered by AI to improve the health of patients and to hasten the speed of the health systems. Nevertheless, there are some of the issues that are associated with AI in clinical practice they include; Data privacy and protection, Issue of bias in machine learning algorithms, the black challenge. Some of the professional factors that are important when implementing artificial intelligence are: equity non –discrimination and patient engagement. Nonetheless, policies regarding AI in the healthcare context are still in their development process, and about responsibility or purse or assurance has been put forward, and it involves security. The future of AI in healthcare is consequently expected to continue evolution of precision medicine, enhance the speed of drug development, as well as enhance patient satisfaction through smart health including next generations of virtual assistants and telemedicine. For AI to be used in a way that optimally takes advantage of the potential that exists in significantly improving health care delivery as well as productivity, the technological, ethical, and regulatory factors that characterize the technology’s application need to be tamed cohesively to realize safe uses that are patient centered. Heated debates over the life of the next few decades are required to ensure great advancements in the future of AI in healthcare, but these advancements need to be managed carefully to safeguard the best interests of each patient/healthcare consumer and provider.
Advancements and Best Practices in Data Loss Prevention: A Comprehensive Review
DLP solutions are needed because there is more data and more reliance on it which people and businesses need. This paper will provide a further account of what DLP is, its significance, issues it encounters, and prospects in what follows from this article about DLP. Globalization and the rise in the kind and level of cyber threats, uptick in cloud use, and the enhancement of legal requirements for data protection have all moved DLP to higher prominence as a cybersecurity mechanism. In more detail, some challenges that are associated with DLP implementation and that refers to initiation and management, are described, such as data classification, security needs and organization productivity, insiders and their threats, regulation compliance, and others. The article also examines how data protection paradigms are revolutionized by AI, ML, cloud native DLP solutions, Zero Trust architecture, and XDR. Also, it underlines the need of privacy by design, and the controls concerning insider threats in the shift of the paradigm. As the type and functionality of DLP systems will further advance not in the distant future, utilization of such state of the art technologies shall assist organizations to secure data while the latter focus on continued operation. Therefore to meet this ever present need for such smart and dynamic DLP solutions to address data loss consideration in the multiple complex cloud environment there is need to rise new and complex cyber threats.
Integrating AI in Healthcare: Advancing Petroleum Fraud Detection and Enhancing Vaccine Development
Healthcare, the petroleum industry, and public health are just a few of the industries that artificial intelligence (AI) is revolutionizing with its cutting-edge solutions that promote efficiency and creativity. This article examines how AI is combining these many domains, with a special emphasis on healthcare, namely on vaccine research and delivery, and fraud detection in the petroleum sector. AI has completely changed vaccine development by speeding up the identification of strong candidates and streamlining clinical trials thanks to its rapid analysis of enormous volumes of data. Similar to this, AI has helped the petroleum industry by enhancing operational effectiveness and detecting fraud. The use of AI across industries demonstrates how it can promote cooperation and creativity across conventional industry boundaries. But integrating AI comes with a lot of work and ethical issues to work through, such protecting data privacy, dealing with algorithmic bias, and preserving openness in AI decision-making. These difficulties are crucial, particularly in industries like healthcare where judgments made using AI can significantly affect patient outcomes. The emphasis is on the necessity of strong legal frameworks and moral governance to manage these difficulties and guarantee the proper application of AI technologies. Artificial intelligence (AI) has the potential to revolutionize a wide range of industries, but it also comes with hazards and moral conundrums that need to be carefully managed. AI may be used to build a more just and sustainable future by tackling these issues through cooperation and moral behavior. This will advance both technical innovation and societal well-being.
AI-Powered Healthcare Reform: Lessons from Innovations in the Petroleum Industry and Fraud Detection Methods
By improving predictive analytics, personalized medicine, diagnostics, and operational efficiency, artificial intelligence (AI) has the potential to revolutionize the healthcare industry. This review examines how AI can be applied to key healthcare challenges like fraud detection, data management, and predictive maintenance by drawing on innovations in the financial and petroleum sectors. AI has a wide range of applications in the healthcare industry, from real-time patient monitoring and personalized treatment plans to advanced medical imaging and operational optimization. However, despite these advantages, there are a number of technical and ethical issues that need to be resolved, such as managing algorithmic bias, ensuring data quality and integration, and protecting patient privacy and data security. Additionally, ethical issues pertaining to informed consent, transparency, and equity need to be carefully considered in order to uphold justice and trust. Similar to how fraud detection techniques from the financial sector can improve the identification and prevention of fraudulent activities in healthcare billing and claims processing, lessons from the petroleum industry can be applied to improve the reliability and efficiency of medical equipment and healthcare operations. Examples of these lessons include predictive maintenance and operational optimization. The integration of artificial intelligence (AI) in healthcare offers substantial opportunities for advancement but also poses significant challenges that must be addressed. Healthcare organizations can fully realize the potential of AI to improve patient care, operational efficiency, and system integrity by addressing both technical and ethical issues and leveraging cross-industry insights. The path forward involves a commitment to innovation, ethical practices, and interdisciplinary collaboration to ensure that AI benefits are distributed fairly and effectively across the healthcare system.
Under the titles for Risk Assessment, Pricing, and Claims Management, write Modern Analytics
Modern analytics has become a revolutionary force in the insurance sector, redefining established methods and spurring innovation in all facets of the business in the ever-changing landscape. This abstract offers a thorough analysis of the function and implications of contemporary analytics in the insurance industry, with particular attention to how it is applied to risk assessment, pricing schemes, claims handling, and client involvement. With the help of cutting-edge algorithms, enormous data sets, and newly developed technology, insurers can now make better judgments, increase operational effectiveness, and provide individualized customer experiences. Insurance companies can improve client interactions, expedite workflows for claims management, establish dynamic pricing models, and improve risk assessment processes by utilizing AI-powered tools, predictive modelling, and machine learning. Modern analytics adoption in the insurance industry is not without its difficulties, though. To fully utilize modern analytics, insurers must traverse a number of important issues, including data protection and security, regulatory compliance, talent recruiting, and ethical considerations. The application of contemporary analytics in the insurance industry has a bright future ahead of it. Block chain, quantum computing, and augmented analytics are examples of emerging technologies that present insurers with new chances to spur innovation, reduce risk, and add value for stakeholders and clients. The path towards contemporary analytics in the insurance industry is demanding and fulfilling. In an industry that is changing quickly, insurers may achieve new heights of effectiveness, efficiency, and competitiveness by embracing innovation, teamwork, and customer-centricity. The future of modern analytics in insurance is bright, promising, and full of opportunity for those who dare to innovate and lead the path forward as we set out on this adventure together.
Renewable Energy Technologies: Present Patterns and Upcoming Paths in Ecological Power Production
In response to the increasing need for environmentally friendly and sustainable power sources, renewable energy technologies are developing quickly. This thorough analysis looks at the present and potential trends in a number of renewable energy areas, such as hydropower, geothermal, and biomass energy. Emerging as a flexible and sustainable energy source made from organic materials is biomass energy. Anaerobic digestion, pyrolysis, and gasification have all been improved by recent advances in biomass technology, increasing their effectiveness and enabling new uses. While biomass energy finds a variety of applications in power generation, transportation, heating, and industrial processes, sustainability concerns are being addressed by advanced approaches in feedstock management and lifetime emissions assessment. The internal heat of the Earth is captured by geothermal energy, which offers a steady and dependable power source. Geothermal heat pumps, better drilling methods, and upgraded geothermal systems (EGS) are examples of technological developments. Every one of these solutions helps to lower expenses and increase efficiency. Heat pumps, power plants, and direct use systems are the three types of geothermal systems; each has advantages and uses in particular. The global spread of geothermal energy is being facilitated by technological breakthroughs and regulations that promote it, addressing issues including resource exploration, environmental effect, and economic hurdles. Although new developments have redefined its place in contemporary energy systems, hydropower is still a key component of renewable energy sources. Technological advancements in turbines, such as hydrokinetic and fish-friendly models, are improving performance and lessening their negative environmental effects. Micro- and small-scale hydropower plants are expanding the versatility and accessibility of hydropower. These technologies are essential to developing a robust and diverse energy mix that supports sustainable development and climate goals as the global energy landscape moves toward cleaner and more sustainable solutions.
Host-parasite Interactions; From Co-evolutionary Changes to Genomic Insights
Host-parasite interaction belongs to the association between two biologically different organisms in which the host delivers food and shelter for the parasite and in return the parasite reproduces itself in the host body. There are many kinds of host-parasite relationships including parasitism, mutualism, and commensalism. The potent interaction between the host and parasite is regulated by the evolutionary changes over space and time. Host-parasite interconnection is the basic feature of the maintenance of ecology and biodiversity. The association between the host and parasite establishes an equilibrium in which the host unfolds protective strategies against parasites and the parasites develop mechanisms to elude the host’s defenses and spread infections. This dynamic interaction between the host and parasite mainly depends on the host's immune system, the severity of infection, the load of parasites in the body, and the ecological requirements. Moreover, environmental conditions, locomotion, and genetic factors of the host also influence host-parasite interactions. The pathogenicity of parasites is determined by the sensitivity and resistivity of the host's immune response. As parasites evolve, they employ diverse strategies to manipulate the host’s behavior and adapt to changing atmospheres. The education of the host-parasite association is essential for proper control and surveillance of parasitic diseases. For the prevention of parasitic infections, different therapeutic measures have been devised. Researchers are performing many types of procedures to sustain the ecosystem and fitness of individuals using host-parasite interactions. The purpose of this review was to enhance the importance of the host-parasite interaction in our environment.
Interactions between Gut Microbiota and Nanoparticles: Implications for Health and Disease
Many bacteria share a healthy human body, generating natural flora. According to many predictions, bacterial genetic material is much more important in the human body than a certain amount of DNA. Any modification in the composition of this flora could result in a wide range of disorders, considering that it plays a crucial role in human health and illness. In many different sectors, nanoparticles are frequently used: industry, food, and platforms for the transportation of pharmaceutical products in the medical domain. Different industries, fields, and occupations, such as pharmaceutical products, cosmetics, and medicines, widely use nanoparticles. Nanoparticles could interact with the human body in multiple ways at various stages because of their many potential applications. Due to the shape, type, and concentration of nanoparticles, their interaction can cause the microbiota to become unstable, regenerate themselves, or show no harmful effects. Additionally, the microbiota can be controlled or some disorders can be treated with nanoparticles. Nanoparticles may also be used as a vehicle to regulate the microbiota or to treat some of its diseases.