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New Journal Launch: AESI
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Read More →Special Issue: AI in Healthcare
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Read More →Academic Journals
Journal of Advances in Medical Sciences and Artificial Intelligence
Journal of Information Technology Management and Business Horizons
Journal of Business Venturing, AI and Data Analytics
Advances in Engineering and Science Informatics
Advances in Machine Learning, IoT and Data Security
Progress on Multidisciplinary Scientific Research and Innovation
International Law Policy Review Organizational Management
Transactions on Banking, Finance, and Leadership Informatics
Most Viewed Articles
Strategic IT Project Management: Tackling Challenges and Implementing Best Practices
By Super Admin
IT project management is a critical discipline that involves the structured planning, execution, and oversight of information technology projects. The role of an IT project manager is vital, requiring a blend of project management expertise and technical IT skills to ensure that projects are aligned with a business’s strategic goals and values. The primary objective of IT project management is to provide effective leadership and direction, optimizing the use of human, financial, and temporal resources to achieve organizational objectives. Performance evaluation and adjustment are crucial aspects of IT project management. This involves continuous monitoring of project progress to ensure it remains aligned with business goals, and implementing corrective actions if deviations occur. Effective communication is another essential component, necessitating clear and efficient channels among all stakeholders from team members to senior management to keep everyone informed and engaged throughout the project lifecycle. This transparency not only fosters collaboration but also helps resolve issues promptly, enhancing project success. Risk management also plays a significant role in IT project management. By proactively identifying potential risks and planning appropriate mitigation strategies, IT project managers can minimize adverse impacts, avoid potential losses, and ensure smoother project execution. Ultimately, the goal is to achieve high customer satisfaction by delivering products and services that meet or exceed customer expectations, thereby bolstering the company’s reputation and market position. This article aims to elucidate IT project management, highlighting best practices, common challenges, and practical solutions. As the Project Management Institute (PMI) forecasts a 33% global growth in project management, leading to 22 million new jobs by 2027, understanding these principles is increasingly crucial for businesses.
Read More →Revolutionizing Drug Discovery AI-Driven Approaches to Personalized Medicine and Predictive Therapeutics
By Super Admin
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.
Read More →Ethical Considerations in AI and Information Technology Privacy and Bias
By Super Admin
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
Read More →Technology-Assisted Parent Training Programs for Autism Management
By Super Admin
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.
Read More →AI-Driven Solutions for Mental Health: Addressing the Global Mental Health Crisis
By Super Admin
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.
Read More →Legal and Ethical Frameworks for Regulating Artificial Intelligence in Business
By Super Admin
Although AI is a helpful tool for the evolving business environment, its uncontrolled and fast integration into business processes can bring legal and ethical challenges. This research paper delves into the legal and ethical considerations essential for effectively regulating AI in business environments. As AI systems increasingly influence decision-making processes and operational efficiencies, the need for comprehensive and forward-thinking regulation becomes imperative. Thus, in the aspects of ethics, algorithms are utilized to protect rights or prohibit discrimination and ensure the openness of AI’s decisions. This paper examines key legal issues such as data privacy, intellectual property rights, and liability. For example, while the General Data Protection Regulation (GDPR) provides a framework for data protection, it remains unclear how these provisions apply to AI's sophisticated data handling and automated decision-making processes.. Ethical considerations further complicate the regulatory landscape. AI systems can inadvertently perpetuate biases present in their training data, leading to discriminatory practices and fairness concerns. This paper explores the ethical implications of such biases and the need for transparency in AI algorithms to ensure that they operate in a manner consistent with societal values. To address these challenges, the paper proposes a regulatory approach. It advocates for the modernization of existing legal frameworks to better encompass AI-related issues, and the establishment of ethical standards to guide AI deployment. Indeed, this article will explore some legal and ethical aspects of using Artificial Intelligence in the business world. Ethical AI guidelines and policies will be discussed, annual checkers will be conducted, and ethical AI standards will be promoted. In addition, identifying potential opportunities and threats likely to emerge when AI is even more entrenched in organizational activities will be relevant. After reviewing this article, you should know how to harness AI's power while avoiding legal issues.
Read More →Latest Articles
Impact of COVID-19 on Consumer Behavior in Fresh Food Ecommerce: A Data-Driven Study
Super Admin
The COVID-19 epidemic has had a significant impact on customer behavior, especially in the ecommerce industry for fresh foods. To understand how the crisis altered buying habits, hoarding tendencies, and demographic reactions, this study examines a dataset of 5,000 transactions from a Bangladeshi e-commerce platform that spans pre-pandemic and pandemic periods. According to the research, there was a noticeable shift toward digital transactions and a sharp increase in online fresh food purchases throughout the pandemic, with sales volumes rising by more than 200% in some areas. Fears of shortages and supply chain disruptions caused hoarding behavior, which was previously uncommon, to rapidly increase, particularly among customers between the ages of 18 and 65. The most hoarded foods were fruits, vegetables, dairy, eggs, and meat, indicating a desire for wholesome, perishable items. Despite the fact that the majority of customers did not engage in panic buying, gender analysis revealed that hoarding was common across all categories. Due to logistical challenges and shifting priorities for purchases, the survey also shows a notable rise in delivery delays and greater order values among hoarders. Consumers prioritized safety, convenience, and food security as a result of these behavioral changes brought on by lockdown procedures, public health concerns, and the closing of traditional food shops. The research emphasizes how important data-driven analysis is to comprehending changing customer demands and the necessity of flexible supply chain adjustments. There are still questions over the long-term viability of these patterns, even though some behavioral changes might continue after the pandemic. The study highlights important topics for further investigation, such as the factors that contribute to long-lasting behavioral change, the robustness of e-commerce supply chains, and the consequences for food retail innovation in the wake of the epidemic.
Read More →The Spatial and Temporal Distribution of The Key Phenological Periods of Fuji Apples
Super Admin
This study looks at how the main phenological stages of Fuji apples (Malus domestica) are spread out over time and space in China, with a focus on how environmental elements like temperature, sunshine hours, and humidity affect them. The goal of the study is to find trends in the timing of important growth stages in different apple-growing areas of China, such as bud break, flowering, fruit set, and ripening. Data from many places over a number of years demonstrate that these stages change a lot based on the climate in each place. In warmer places like the North China Plain, phenological occurrences happen earlier. In cooler places like the Loess Plateau, they happen later. The report also talks about how climate change is affecting us, especially the rising temperatures and more extreme weather events that happen more often. These changes have produced alterations in the timing of phenological events. For example, warmer spring temperatures have prompted buds to break and flowers to bloom sooner, which may not be in line with when pollinators are available. Also, warmer temperatures in the fall can speed up the ripening of fruit, which could make it less tasty and less likely to sell. The correlation study shows that temperature and sunlight have moderate effects on each other, both of which are important for apple growth. The effect of humidity is less clear. We employed machine learning methods like Random Forest and Support Vector Machines to guess when phenological stages will happen based on data about the environment. These models were good at predicting future growth milestones, which made it easier to manage crops when the weather changed. In general, the study shows how important it is to know how the environment affects Fuji apple phenology, especially when the climate changes. The results give us useful information that can help us improve farming methods, make better predictions about yields, and make sure that production is of excellent quality. These results can help farmers deal with climate change, lower their risks, and keep growing Fuji apples in China.
Read More →Student-Centered Learning in University Sports Education: A Comprehensive Review
Super Admin
This review looks at how sport studies at universities can use student-centered learning (SCL) approaches and how they can help students improve and achieve change. To learn more, five main studies were examined using a structured literature review approach to study how methods such as the Sport Education Model, Tactical Games Approach, Cooperative Learning, ProblemBased Learning, and Flipped Classrooms work. It was found that SCL improves student participation in learning, mental growth, and physical skills, and helps them develop key transferable skills such as working on their own, with others, and analyzing problems. Additionally, video analysis, virtual reality, and live feedback tools help make learning more adaptable and welcoming to all students. Although SCL encourages students to learn for a long time, many barriers, such as organizational, systemic, and educational challenges, prevent its successful deployment. With this alignment, SCL becomes more important for education today. The review states that SCL isn’t only a way of teaching but also a necessary strategy to link university sports programs with updated demands in professional jobs. Synthesis demonstrates that including SCL in education increases student achievements and develops graduates who are flexible and ready to keep learning after graduation.
Read More →Forecasting Financial Crashes with Advanced Time-Series Methods: A Predictive Framework
Super Admin
The research involves examining how financial markets, particularly the NASDAQ and S&P 500 indices, react when under stress, as well as applying advanced time series techniques in an attempt to predict crashes. Accurate prediction of crashes is important due to the tremendous impact financial market collapses, including the 2008 and COVID-19 epidemics, have on the worldwide economy. To model non-linear market dynamics, the study combines dynamic GARCH extensions and wavelet-based time series decomposition with ARIMA and GARCH models to forecast market volatility. The sample period ranged from January 2021 to August 2024, with total observations of 787 and 921 for the S&P500 and NASDAQ, respectively. The selection of the ARIMA and GARCH models was confirmed by the ADF and PP tests to determine whether the time series is stationary. The GARCH model with the GARCH effect of 0.912741 has most certainly accommodated the volatility clustering phenomenon, due to which an episode of high (low) volatility was followed by another episode of the same kind and successive spikes in the volatility, especially in the case of NASDAQ. The volatility persistence of the S&P 500 was lower (0.6785330 GARCH effect). For a relatively small level autoregressive table, the forecasts demonstrate that the variance of S&P 500 substantially increases in high volatility periods for most by up to 0.006. The NASDAQ was somewhat more persistent, as indicated by a variance of 0.00024. These findings illustrate how efficiently the proposed forecasting model is able to predict market crashes and offer valuable information for investors and policymakers.
Read More →Transforming Healthcare Decisions in the U.S. Through Machine Learning
Super Admin
In the United States, early detection of diseases is critical to ensuring timely and effective treatment, as many conditions, if not diagnosed promptly, can become untreatable or even fatal. As a result, there is a growing reliance on advanced technologies to analyze complex medical data, reports, and images with both speed and precision. In many cases, subtle abnormalities in medical imaging may go unnoticed by the human eye, which is where machine learning (ML) has become indispensable. ML techniques are increasingly used in healthcare for data driven decision making, uncovering hidden patterns and anomalies that traditional methods might miss. Although developing such algorithms is complex, the greater challenge lies in optimizing them for higher accuracy while reducing processing time. Over the years, the integration of ML into biomedical research has significantly advanced the field, paving the way for innovations like precision medicine, which customizes treatments based on a patient’s genetic profile. Today, machine learning supports nearly every stage of healthcare delivery, from extracting critical information from electronic health records to diagnosing diseases through medical image analysis. Its role extends to patient management, resource optimization, and treatment development. Particularly, deep learning, powered by modern high-performance computing, has shown remarkable accuracy and reliability in these applications. It is now evident that in the U.S. healthcare system, computational biology and clinical decision making are deeply intertwined with machine learning, making it a core component of artificial intelligence in medicine. In this paper, the aim is to explore the current applications, challenges, and potential of machine learning in supporting healthcare decision making in the United States, with a focus on diagnosis, medical imaging, and personalized treatment strategies.
Read More →Enhancing Digital Marketing Strategies in the Food Delivery Business through AI-Driven Ensemble Machine Learning Techniques
Super Admin
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 data with the hope of discovering consumer preferences and predicting behavior. In order to improve the accuracy of predictions, the ensemble method combines the results of decision trees, naïve Bayes, and closest neighbor algorithms. Both the decision tree and nearest neighbor algorithms were able to obtain perfect predictions with zero error and 100% 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 and money.
Read More →Strategic Digital Transformation and Business Analytics for Optimizing U.S. Traditional Banking Operations
Super Admin
There is growing interest among leading academic institutions in the United States to explore the concept of digital transformation. Despite a shared focus, there is still no clear or universally accepted definition. Existing interpretations vary significantly, covering areas such as smart living, the future of work, automation, and industry convergence. These interpretations often lack consistency and are difficult to compare. Meanwhile, major consulting firms, technology companies, and analysts continue to promote their own models and frameworks. This study focuses on understanding the demand for digital transformation within the U.S. banking sector by analyzing how four major North American banks have approached the adoption of digital technologies. Using a combination of qualitative analysis, supported by quantitative techniques and visual data, the research examines five years of annual reports from these banks to identify key themes including drivers, perceived benefits, institutional readiness, and implementation efforts. From these findings, the study introduces a Digital Transformation Maturity Model that offers valuable guidance for financial institutions and technology providers seeking to navigate and assess their progress in digital adoption.
Read More →Smart Health Informatics Platform for Predictive Diagnosis and Resource Optimization in Rural U.S. Communities
Super Admin
This study presents a smart predictive healthcare framework tailored to support individuals in the United States living with chronic conditions, especially those receiving care at home. The framework incorporates a deep learning model that analyzes large volumes of patient data, including vital signs, physical activity, medication usage, and symptoms. These data are collected through ambient assisted living technologies. The model is part of an intelligent module that operates at the patient’s location to deliver accurate health status predictions and personalized care recommendations. The framework was tested using data from patients with chronic blood pressure conditions, collected every 15 minutes over one year. The proposed model achieved a prediction accuracy of approximately 97.6% % outperforming a standard baseline model by nearly 6%. Additionally, improvements in identifying critical health events were observed, with the F score increasing by 9% for hypertensive, 26% for hypotensive, and 10% for normotensive cases. These results demonstrate the model’s effectiveness in detecting early warning signs and enhancing the management of chronic diseases. The framework shows strong potential for improving healthcare access and reducing emergency risks in rural and underserved communities across the United States.
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