Papers by Md Alamgir Miah
This study discusses how AI can be combined with multi-source geospatial big data. It improves pr... more This study discusses how AI can be combined with multi-source geospatial big data. It improves predictions and advisory activities to combat climate change at regional and global levels. The issue of climate change is a burning world problem that requires technological breakthroughs. The phenomenon of access to geospatial big data by satellites, sensors, and drones. Climate monitoring systems are giving an unprecedented chance to know environmental dynamics. Scalable models of artificial intelligence learn meaningful dissimilarities amid rich and large-scale data sources. This paper uses
Journal of Business and Management Studies, 2023
The Agile Project Management methodology has evolved in the face of fast-moving and changing info... more The Agile Project Management methodology has evolved in the face of fast-moving and changing information technology, focusing on adaptability, iterative development, and collaboration of teams. The road to maximizing the benefits, with agile methodologies increasingly supported by organizations through MIS on data-driven decisions, real-time communication, and smooth processing of projects. It reviews how MIS integrates into agile project management to drive better factors of organizational success like adaptability, resource management, and decision-making efficiency. The case studies and current literature on the various ways MIS supports agile projects were investigated, enabling IT teams to act quickly on changing project requirements to continuously improve the project outcomes. Also, the review discusses issues of MIS implementation in agile settings with regard to data security and integration problems. The aim of this research paper is to provide insight into how MIS tools can be utilized effectively by IT managers and professionals in achieving agile success that would lead to optimized project performance and maximization of satisfaction for stakeholders.
Fuel Cells Bulletin, 2023
The study presents a strategic model that combines big data analytics features alongside cloud co... more The study presents a strategic model that combines big data analytics features alongside cloud computing abilities for improving IT project success rates. IT project management underwent profound changes because of big data and cloud computing to deliver superior decision processes, amplified efficiency, and increased scalability features. The management of intricate IT projects needs advanced technological solutions because they increase operational efficiency and enhance both project management decisions and their execution speed. Research scholars developed integrated framework elements of big data with cloud computing systems for IT project management to optimize resources while reducing project risks so it improves decision quality. Project managers utilize these technologies to analyze massive dataset fields quickly enhancing both risk evaluation abilities and resource allocation and project performance metrics. This research examines both published research about Big Data along with qualitative assessments of information technology projects where Cloud Computing and Big Data succeed. The research examines different IT projects implementing Big Data and Cloud Computing to determine their effects on project outcomes as well as cost efficiency alongside project decision protocols. Project management technology evaluation requires assessing important performance indicators known as KPIs. The proposed framework enables project managers to obtain analytic data for optimizing processes and strengthening their strategic decision capabilities. Research should develop methods for how artificial intelligence and …
Journal of Computer Science and Technology Studies, 2024
This paper discusses how AI-enabled analytics are used to detect emerging relative skill shortage... more This paper discusses how AI-enabled analytics are used to detect emerging relative skill shortages, track labor market patterns. It improves the competitiveness of the economy in the United States. The intense use of Artificial Intelligence in workforce analytics has revolutionized how governments and industries forecast the labor market needs the study throws light on the role of real-time data variables and prediction modelling in making workforce development meet changing industry demands. The research project has adopted quantitative research design. A systematic questionnaire was sent to a sampling of 300 participants comprising HR analysts, labor economists and policymakers in different industries of the U.S. Variables that were measured included the AI Integration Level, Labor Market Responsiveness, Real-Time Data Utilization, Predictive Accuracy and the dependent variable, Economic Competitiveness. The findings showed that there were significant correlations among AI integration (r = 0.71, p < 0.01), predictive accuracy (r = 0.68, p < 0.01), and economic competitiveness, which are significant. Regression outcome showed that AIL and PA were the most powerful determinants of EC (R² = 0.61). AI-based analytics in the establishment would promote not only labor market predictions but also boost the strategic position of the U.S. in the global economy. The government and business in scaling the use of AI. It is ensuring the training programs reflect the areas of skill shortage and fostering the development of data infrastructure.
Journal of Computer Science and Technology Studies, 2023
Given that the size and complexity of digital ecosystems are constantly growing, cybersecurity is... more Given that the size and complexity of digital ecosystems are constantly growing, cybersecurity is constantly under attack by increasingly complex threats ranging from ransomware attacks to cyber-attacks based on artificial intelligence. Thus, this paper focuses on these new generation cyber threats and assesses the old and advanced security technologies that address them. This study addresses the future of advanced threats and emerging solutions deep diving into the application of AI, blockchain and the use of Zero Trust architectural concepts in mitigating cybersecurity risks. Moreover, a number of strategic measures-such as dynamic threat identification, multiple-tier protection systems, and sound data security programs-are discussed to enhance the organizational defense against possible weaknesses. Results show that it is crucial for companies to adopt an amalgamated model that incorporates innovative technologies together with active cybersecurity measures. In turn, this paper suggests directions for further research and policy implications for enhancing a current and relevant cybersecurity framework.
Nanotechnology Perceptions, 2021
This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the po... more This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the potential to change the outlook in personalized medicine. Chronic diseases have become widespread and offer great challenges to the world's healthcare system. It is calling for potential approaches to diagnosis and treatment. It is a revolutionary concept that predictive analytical model underpinned by AI that helps healthcare risk forecast and introduce timely intercessions. The patient data analyzed using AI-based models, which can thereby generate specific information used to make much more accurate diagnosis and treatment plans. This research is an exemplar of big data. It aims at using algorithms in machine learning to process EHRs, genotype and phenotype data and lifestyle data among individuals in the world. The data are used for the educational process of building the predictive models, which assists in the early diagnosis of chronic diseases. It includes diabetes, cardiovascular diseases, cancer and others. The methods are used to supervised learning ranging from decision tree algorithms and support vector machines to neural networks where required. The real-time data analysis is included in the study for the purpose of real-time monitoring and risk analysis for proper healthcare preventative methodologies. The insights derived show that AI-advanced analytics of the chronic disease patients' data hold massive opportunities to redefine chronic illness treatment. AI for early detection of cases does not only lead to better care of patients but also helps lower health care expenditures since fewer intensive treatments are required. The proactive approach in connection with the individualization of treatment with the help of intelligent analytics is aimed at providing patient-tailored care.
Journal of Posthumanism, 2025
Modern-day AI infrastructure development requires more urgent need for reliable and efficient ene... more Modern-day AI infrastructure development requires more urgent need for reliable and efficient energy resources. Renewable energy obtains increasing attention, but coal-based energy generates substantial power in the worldwide energy consumption alongside emerging markets. The need for innovation becomes essential to optimize coal utilization because coal production contains efficiency problems alongside environmental challenges. The analysis draws data from multiple high-demand coal plant regions through their production logs with IoT sensors and their connected SCADA systems. Predictive models with machine learning algorithms, evaluate operational trends and breakdown patterns and environmental compliance performance. The implementation of BDA in AIsupported energy infrastructures is studied through case-based research that proves how better decisions, and reduced costs accompany balanced power distribution. Analysis of big data has proven to enhance coal-based energy operations its compatibility with AI-driven systems, which delivers better process efficiency and sustained energy production capabilities. The coal energy, artificial intelligence and big data analytics form a practical method to achieve smarter and more responsible energy operations in a data-centered environment. The study recommends political and energy sector investments in data infrastructure along with qualified personnel to bring out the complete advantages of these benefits.
Journal of Posthumanism, 2025
Business analytics has undergone significant transformation because of artificial intelligence an... more Business analytics has undergone significant transformation because of artificial intelligence and machine learning evolution, which now fulfills a critical function in economic expansion and organizational decision-making operations. Organization within the United States economy utilizes Management Information Systems to gain AI-powered insights, which enhance productivity and optimize resources and market prediction accuracy. The research evaluates how AI analytics drive economic growth because they enhance predictions while reducing possible hazards and generate strategic decisions through data-based approaches. The research uses both quantitative data methods together with qualitative case studies to investigate the subject. A combination of secondary market data, business reports, and economic statistics undergoes ML algorithm analysis, which reveals economic patterns and correlations because of their impact on performance. The research obtains practical information and usage barriers from both business managers and policymakers by conducting structured interviews regarding their implementation experiences with AI-based economic decision systems. The paper examines three significant AI methods, including predictive analytics with natural language processing and deep learning, which are applied to business intelligence. Voluntary business analytics, which run on artificial intelligence systems, boost decision-making by generating instant analytic information and simplifying complicated economic analysis tasks. Organizations that integrate AI and MIS systems to build data-based strategies boost operational performance while gaining competitive markets and ensuring durable economic expansion. Organizations should resolve the integration challenges together with data privacy concerns and ethical issues. The research findings demonstrate why organizations need to adopt AIbased analytics systems for developing business resilience and economic innovation in the United States.
This research aims at determining the suitability of the trending PM tools; Microsoft Project, Ji... more This research aims at determining the suitability of the trending PM tools; Microsoft Project, Jira, Trello, and Asana based on the functionality, security, usability and cost needs of different organisations. The study reveals that while Microsoft Project and Jira offer better-developed features, their complexity and cost make them more suitable for large companies. On the other hand, Trello and Asana have simple and easy to use interface, which is suitable for small and medium sized business and also they are less costly, but they lack the depth of features and security features which may be required by some large companies or companies in some highly regulated industries. In addition, the study reveals an increasing interest in flexible PM tools that support different methodologies and have security measures to meet the data protection challenges of cloud projects. The results of this study imply that PM tool selection criteria should be matched to particular organisational capability, weighing utility, security, and cost. The study also suggests new tendencies concerning further developments such as the demand for PM solutions that are suitable for Agile and Waterfall methodologies concurrently, as well as the need for more elastic pricing strategies.
The infusion of the genomics with machine learning provides hope as a mechanism of creating bette... more The infusion of the genomics with machine learning provides hope as a mechanism of creating better solutions to cancer treatment. Cancer is still one of the leading diseases that cause death around the globe. Chemotherapy is one of the major treatment methods in cancer therapy which is non-specific and often causes side effects. The concept of treatment plans based on cell changes, may bring a new light to cancer therapy. This approach helps to distinguish those specific genetic changes and other biomarkers that contribute to cancer development, and that means to make proper diagnoses and perform targeted therapy. The role of amachine learning framework for processing molecular data of cancer patients, such as gene expression, mutationand other related biomolecules, is demonstrated. The study uses supervised learning methods, including support vector machines and random forests, for screening the genetic biomarkers reflecting the treatment outcomes. The identification of marker gene features associated with the cancer subtypes, convoluted neural networks and more broadly deep learning. These models are created using data from public databases and patient populations for describing treatment prognosis and patient survival. The paper delineates how different types of OMICS studies to improve the reliability of the modeling approach. The results indicate that the incorporation of genomic information and machine learning algorithms offers far superior prediction of treatment outcomes and optimal cancer therapies. Machine learning algorithms to the large genomic datasets presents an important strategy for the discovery of new biomarkers and optimization of precision oncology. This research indicates that, with machine learning cancer treatments gradually become more precise and ultimately progress as improved methods of treatment for patients. There are issues that still need to be addressed among them information heterogeneity, model interpretability and clinical translation to bring the full potential of genomic data in cancer management.
Journal of Posthumanism, 2025
The clinical use of artificial intelligence for precision medicine delivers transformative result... more The clinical use of artificial intelligence for precision medicine delivers transformative results in drug discovery through its combination of big data analytics and machine learning with genomic research. The traditional methods used in the drug discovery process are expensive to implement and take lengthy durations while failing to deliver customized therapy for patients. The advent of artificial intelligence now conducts fast genomic database analysis to find new drug aims and develop targeted medicine for individual patients. The application of ML algorithms enables researchers to make disease progression forecasts as well as identify drug effectiveness and potential adverse effects leading to improved outcomes for genomic drug discovery patients. The research discusses how deep learning and reinforcement learning models are applied to work with big genomic and biomedical information datasets. Data storage through cloud computing platforms together with high-performance computing systems allows precision medicine to scale and become more efficient. AI computational biology combined with clinical information enhances pharma research by optimally structuring drug reuse analysis and promoting new drug development processes. The use of artificial intelligence leads genomics-based drug discovery to a new direction through high efficiency and cost reduction and individual patient therapy delivery. Precision medicine based on AI and bioinformatics advancements push forward personal healthcare into the future despite ongoing obstacles such as data security problems and interpretation challenges and regulatory limitations.
Journal of Management World
This paper focuses on how AI and ML have changed decisions in retailing, healthcare, financing, a... more This paper focuses on how AI and ML have changed decisions in retailing, healthcare, financing, and manufacturing careers. They demonstrate how AI is used in supply chain management to support the decision-making process by making forecasts, processing data, and optimizing operations, leading to higher efficiency, decreased costs, and increased customer satisfaction. Thus, the research incorporates quantitative and qualitative approaches, such as surveys and interviews with key stakeholders, and employs statistical and content analysis methods. Significant outcomes include a 20% enhancement of forecasting precision reduction while the operational cost decreases by 21 percent. Nonetheless, the research also discovers an essential issue that employs complex challenges that embrace high-cost implementation, resistance from the workforce, allowance of data privacy, and bias besides algorithms. Some are ethical concerns, and the importance of their regulation is noted. While adopting the decision theory and systems thinking perspectives, this research paper highlights the necessity of effectively and adequately implementing AI into an organization permanently to achieve more benefits. The following are realizable out-ofthe-box solutions that the study suggests, including audiences for employees, data protection for compliance, and conscientization of fairness in AI algorithms. Future directions include situations where these applications are to be broadened to weigh on ethical issues and to encourage optimal technological fairness that will, in turn, ensure sustainable business improvement and innovation.
Nanotechnology Perceptions, 2019
Abstract:
Cardiovascular diseases are the most common diseases around the world and result in hig... more Abstract:
Cardiovascular diseases are the most common diseases around the world and result in high morbidity and mortality rates. It proves the need to develop new approaches to the disease’s early diagnosis and prevention. Portable health monitoring is characterized by state-of-the-art sensors. It produces a continuous flow of physiological data in real-time. It presents a great opportunity for preventative Cardiovascular disease management. Using deep learning algorithms that is used to accurately and effectively. It creates preventive early diagnosing models for diseases. The authors discuss the integration of wearable health technology and deep learning towards the improvement of individual-oriented technologies in cardiology. The collected data are cleaned for noise and further normalized to provide uniform input in deep learning models. To detect anomalies that are likely to lead to cardiovascular risks, state-of-the-art algorithms. The models are trained and validated using a dataset of wearable device data and clinically diagnosed Cardiovascular disease cases. Wearable health devices combined with deep learning algorithms provide an innovative platform for cardiovascular disease screening and prevention. The incorporation of the proposed system proved its efficiency and accuracy in the identification of potential probable Cardiovascular disease risk, making it possible for early, real-time, noninvasive, and personalized healthcare services. This approach improves early detection but also provides handy information to the patients. This type of research includes the acquisition of larger databases and better models, and incorporating those into more offices and healthcare practices.
Journal of Posthumanism, 2025
This research examines how MIS frameworks strengthen energy infrastructure resilience through con... more This research examines how MIS frameworks strengthen energy infrastructure resilience through consolidated use of predictive models alongside data analytics and crisis management resources. There are multi factors, such as escalating natural disasters and elevated cyber threats with aging infrastructure systems, constantly push. The U.S. energy system toward declining resilience levels. The strategic decision-making and performance enhancement now depends heavily on Management Information Systems. This study uses qualitative research methods and relies on secondary data from energy reports alongside energy grid failure analysis and MIS implementation studies. This analysis reviews various MIS systems, such as SCADA and ERP, to identify how they could improve monitoring operations and evaluation procedures and quick response functionality. MIS development based on specific system needs leads to greater energy system surveillance capabilities and better resource management with improved recovery protocols. The research demonstrates how energy infrastructure protection improves when intelligent MIS combines real-time analysis along with predictive artificial intelligence technologies towards reaching national security goals for U.S. energy systems.
Conference Presentations by Md Alamgir Miah
IEEE, 2024
This paper investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML)... more This paper investigates the integration of Artificial Intelligence (AI) and Machine Learning (ML) in IT project management systems, focusing on the potential for optimizing task allocation, enhancing project timeline predictions, and improving resource management. By integrating these technologies with Management Information Systems (MIS), this study aims to create a cohesive, data-driven framework that addresses common challenges in IT project management. The research leverages a dataset comprising project requests, titles, components, assignments, and completion timelines to develop predictive models and automated task allocation frameworks, assessing their impact on efficiency. The findings demonstrate that AI-driven methodologies markedly enhance project results by equilibrating workloads, accurately forecasting completion timelines, and optimizing resource allocation. The integration of AI with MIS offers a flexible framework that enhances efficient, real-time decision-making, aiding managers in managing intricate project demands and interdependencies. This study underscores AI's potential as a transformative tool in modern project management, demonstrating its ability to streamline tasks, deliver actionable insights, and enhance project efficiency.
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Papers by Md Alamgir Miah
Cardiovascular diseases are the most common diseases around the world and result in high morbidity and mortality rates. It proves the need to develop new approaches to the disease’s early diagnosis and prevention. Portable health monitoring is characterized by state-of-the-art sensors. It produces a continuous flow of physiological data in real-time. It presents a great opportunity for preventative Cardiovascular disease management. Using deep learning algorithms that is used to accurately and effectively. It creates preventive early diagnosing models for diseases. The authors discuss the integration of wearable health technology and deep learning towards the improvement of individual-oriented technologies in cardiology. The collected data are cleaned for noise and further normalized to provide uniform input in deep learning models. To detect anomalies that are likely to lead to cardiovascular risks, state-of-the-art algorithms. The models are trained and validated using a dataset of wearable device data and clinically diagnosed Cardiovascular disease cases. Wearable health devices combined with deep learning algorithms provide an innovative platform for cardiovascular disease screening and prevention. The incorporation of the proposed system proved its efficiency and accuracy in the identification of potential probable Cardiovascular disease risk, making it possible for early, real-time, noninvasive, and personalized healthcare services. This approach improves early detection but also provides handy information to the patients. This type of research includes the acquisition of larger databases and better models, and incorporating those into more offices and healthcare practices.
Conference Presentations by Md Alamgir Miah