Intelligent Data-Driven Precision Marketing

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production

Motivated by the recent supply chain management practice of the Chinese PC industry, we examine the impact of demand forecasting and demand substitution estimation on inventory management and pricing with short selling seasons for a PC supply chain consisting of one retailer and two manufacturers. Based on PC products’ demand characteristics, product life cycle (PLC) is incorporated into the traditional multiplication demand model and a multi-period dynamic inventory and pricing integrated decision model is developed. This model assumes that retailers always place an initial order at pre-season, and the order quantity would be split into multiple batches following the retailers’ dynamic inventory replenishment requirements under the constraint that the sum of the split orders should be almost the same as the initial order. Meanwhile, the retailer would dynamically determine the selling price according to effective demand and demand forecasting per cycle, and the manufacturers decide the wholesale price and rebate depending upon the retailer’s total order quantities. Finally, empirical analysis is given, and analysis results show that the model enjoys good feasibility and effectiveness, that can overcome the negative impact of high demand fluctuations on profits and service levels. Keywords: Dynamic Inventory Decision, Dynamic Pricing, Demand Forecasting, Product Life Cycle, Demand Substitution

The 2020 Big Data and Artificial Intelligence Conference was successfully held on September 17-18, 2020 online due to COVID-19 restrictions. The conference is devoted to current challenges in Big Data analytics & AI and comprises three tracks: business, technology and science.There were 3 major sections of the scientific track of the conference:-Cluster analysis-Applied systems for data analysis-Natural Language ProcessingThis volume of IOP Conference Series: Journal of Physics: Conference Series (JPCS) is a compilation of the accepted papers in Big Data and AI Conference 2020 and represents contributions that were presented in the conference.On behalf of the organization committee, I would like to thank all of the conference sponsors, partners and volunteers who made conference possible.Looking forward to meeting you during Big Data and AI Conference 2021.On behalf of organizing and program committees of the Big Data and AI Conference 2020Igor Balk, Big Data and AI Conference 2020 co-chair.

Process Dynamic Optimization Using ROMeo

The rapid growth of digital platforms has fundamentally reshaped network and viral marketing, profoundly transforming how information spreads across social networks and influences consumer behavior. This comprehensive review synthesizes theoretical, computational, and ethical perspectives into an integrated narrative, providing novel insights into the mechanisms driving information diffusion within contemporary interactive marketing. By integrating foundational concepts from social network theory, advanced graph models, and behavioral dynamics, the paper demonstrates how the interplay between network structures, influencer behaviors, and AI-driven algorithms significantly redefines traditional marketing paradigms. A distinctive theoretical contribution of this study lies in its innovative combination of Big Data analytics with AI-based predictive modeling, explicitly revealing how real-time algorithmic personalization not only enhances marketing effectiveness but also creates new ethical tensions surrounding misinformation, algorithmic bias, and consumer vulnerability. Addressing recent calls for greater theoretical originality and narrative coherence in interactive marketing research, this review explicitly highlights how these insights resolve critical theoretical puzzles and clarify contemporary ethical dilemmas. Additionally, the paper identifies emerging trends—including Web3 marketing, decentralized platforms, and neuroscience-driven targeting—offering clear future research directions. Through its integrative, narrative-driven framework, this study significantly advances interactive marketing theory, providing essential guidance for scholars and practitioners navigating the evolving complexities of digital influence.

The new technology helps collect and store a considerable amount of information about consumers’ preferences and decisions in real-time. Thus, the unprecedented volume, speed and variety of primary data, Big Data, are available from all sources and individual consumers. The term “Big Data” is now widespread and accepted globally. The term Big Data has become increasingly known and used in many industries. Big Data is a new source of product development ideas, customer service, shelf location, distribution, dynamic pricing, etc. Big Data will have an impact on almost every area of marketing. Firms that do not develop the resources and capabilities to use Big Data capabilities effectively will be challenged to develop a sustainable competitive advantage to survive the Big Data revolution. Consumer analysis is at the heart of a Big Data revolution. Marketing specialists are beginning to recognize the importance of Big Data as the new capital and that access to Big Data offers a company new ways to differentiate its products. In this article, exploratory research has been conducted to highlight areas where Big Data allows organizations to fundamentally know about their business and translate it into better decision-making and improved performance in marketing and consumer behavior. Moreover, a global analysis (based on public data) was performed using large volumes of data in marketing and the main reasons they were stored and used.KeywordsBig DataMarketingBig Data’s growth

Inventory management plays a very important role in the process of business operation, providing favorable backing for the smooth operation of production and sales. In this paper, LightGBM and PSO-LSTM models in big data technology are combined to improve inventory management and demand forecasting in supply chains. Then, the relationship between inventory, order, and forecast is elaborated, the two-level inventory cost components and the relationship between them are analyzed, the model constraints are formulated, and a mathematical model for two-level multi-cycle inventory control is constructed. Finally, the single demand forecasting model is compared with the improved model to explore the optimization effect of inventory management after the application of the LightGBM-PSO-LSTM model. The LightGBMPSO-LSTM model is the best fit and can be used for actual demand forecasting. After the optimization of inventory management, the inventory turnover ratio of Company H increased from 8.2 in 2022 to the maximum value of 9.2 in 2023, and the OTIF achievement rate of sales orders increased from 97.9% in 2022 to 99.3% in 2023. This paper provides a successful example of optimizing supply chain inventory management using big data analytics.

Nowadays a lot of massive data is stored and typically, the data itself contains a lot of non-trivial but useful information. Data mining techniques can be used to discover this information which can help the companies for decision-making. However, in real life applications, data is massive and is stored over distributed sites. One of my major research topics is to protect privacy over this kind of data. Previously, the important characteristics, issues and challenges related to management of the large amount of data has been explored. Various open source data analytics frameworks that deal with large amount of Data analytics workloads have been discussed. Comparative study between the given frameworks and suitability of the same has been proposed. Digital universe is flooded with huge amount of data generated by number of users worldwide. These data are of diverse in nature, come from various sources and in many forms. To keep with the desire to store and analyze ever larger volumes of complex data, relational databases vendors have delivered specialized analytical platforms that come in many shapes and sizes from software only to analytical services that run in third party hosted environments. In addition new technologies have emerged to address exploding volumes of complex data, including web traffic, social media content and machine generated data including sensor data, global positioning system data.Big data is defined as large amount of data which requires new technologies and architectures so that it becomes possible to extract value from it by capturing and analysis process. Due to such large size of data it becomes very difficult to perform effective analysis using the existing traditional techniques. Big data has become a prominent research field, especially when it comes to decision making and data analysis. However, Big data due to its various properties like volume, velocity, variety, variability, value and complexity put forward many challenges. Since Big data is a recent upcoming technology in the market which can bring huge benefits to the business organizations, it becomes necessary that various challenges and issues associated in bringing and adapting to this technology are brought into light. Another challenge is that data collection may not have enough accuracy which will lead to a non-consistent analysis that can critically affect the decision based on this analysis. Moreover, it is clearly apparent that organizations need to employ data-driven decision making to gain competitive advantage. Processing, integrating and interacting with more data should make it better data, providing both more panoramic and more granular views to aid strategic decision making. This is made possible via Big Data exploiting affordable and usable Computational and Storage Resources. Many offerings are based on the Map-Reduce and Hadoop paradigms and most focus solely on the analytical side. Nonetheless, in many respects it remains unclear what Big Data actually is; current offerings appear as isolated silos that are difficult to integrate and/or make it difficult to better utilize existing data and systems. Since data is growing at a huge speed making it difficult to handle such large amount of data (Exabyte). The main difficulty in handling such large amount of data is because that the volume is increasing rapidly in comparison to the computing resources. The Big data term which is being used now a days is kind of misnomer as it points out only the size of the data not putting too much of attention to its other existing properties.If data is to be used to make accurate decisions in time it becomes necessary that it should be available in accurate, complete and timely manner. This makes the data management and governance process bit complex adding the necessity to make Data open and make it available to government agencies in standardized manner with standardized APIs, metadata and formats thus leading to better decision making, business intelligence and productivity improvements.This paper presents a discussion and evaluation for the most prominent techniques used in the processes of data collection and analysis in order to identify the privacy defects in them that affects the accuracy of big data. Depending on the results of this analysis, recommendations were provided for improving data collection and analysis techniques that will help to avoid if not all then most of the problems facing the use of big data in decision making. Keywords: Big Data, Big Data Challenges, Big Data Accuracy, Big Data Collection, Big Data Analytics.

Data protection and privacy, algorithmic fairness, informed consent and cybersecurity and an optimal liability framework are key issues that need to be taken into account for an ethically AI-based healthcare system. This chapter briefly apprises the readers about the ethical concerns with big data and AI in healthcare.

This paper explores aspects such as data privacy, algorithmic bias, and the potential risks of misinformation dissemination, highlighting the need for regulatory frameworks and ethical guidelines to regulate their applications. By synthesizing recent academic research and case studies, this paper comprehensively elaborates on the opportunities and challenges brought about by big data and artificial intelligence in communication research. Finally, a plan for future research is presented, emphasizing the need for interdisciplinary cooperation among computer science, media research, and ethics to reasonably tap the full potential of these technologies. It is suggested in the article that combining big data and artificial intelligence has made it much easier to guess what people will buy, which has huge implications for communication research and strategy development. However, ethical challenges like data privacy, algorithmic bias, and model transparency must be addressed to ensure sustainable and responsible application of these technologies. The research results highlight the significance of adopting a balanced approach, that is, maximizing innovation while adhering to ethical principles, ensuring that the integration of artificial intelligence and big data can actively promote the development of communication practices and social well - being.

Addressing the policy challenges and opportunities of “Big data”

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

Integrating Big Data into Demand-Driven Material Requirements Planning (DDMRP) has become a game- changer in supply chain management. This study explores Big Data’s significant impact in the context of DDMRP. Big Data, sourced from various channels such as online transactions, sensor data, social interactions, and more, can transform demand forecasting, inventory management, and overall supply chain optimization. By allowing real-time data analysis and predictive modeling, organizations can make informed and agile decisions that drive cost reduction, inventory optimization, and improved customer service. However, implementing Big Data in DDMRP has its challenges, including data security and the need for advanced analytics capabilities. This research delves into the application, benefits, challenges, and impact of Big Data implementation in DDMRP. The study provides valuable insights for organizations seeking to leverage their potential. The findings underscore the significance of Big Data in reshaping supply chain strategies and enhancing the responsiveness of modern businesses in a dynamic market environment. The uniqueness of this paper lies in examining how Big Data, sourced from various channels, can revolutionize demand forecasting, inventory management, and overall supply chain optimization. The research also highlights the challenges of Big Data adoption in DDMRP, including data security and the need for advanced analytics capabilities.

In the quest for sustainable urban development, smart cities are increasingly harnessing cloud computing and big data analytics to optimize energy usage and drive policy initiatives. This review explores how leveraging these technologies can transform energy management within urban environments. Cloud computing provides the scalable infrastructure necessary for processing and storing vast amounts of data generated by smart sensors and IoT devices in real-time. By utilizing cloud-based platforms, cities can ensure the efficient aggregation, analysis, and dissemination of energy data, facilitating informed decision-making and policy formulation. Big data analytics plays a crucial role in this framework by enabling the analysis of complex and voluminous datasets to uncover patterns and insights that traditional methods may miss. Through advanced analytics, cities can gain a deeper understanding of energy consumption trends, predict peak demand periods, and identify opportunities for efficiency improvements. This data-driven approach allows policymakers to design and implement targeted energy policies, optimize resource allocation, and reduce operational costs. Key benefits of integrating cloud computing and big data analytics in energy optimization include enhanced real-time monitoring capabilities, improved accuracy in demand forecasting, and more effective management of energy resources. The ability to analyze and visualize large datasets supports the development of dynamic policies that adapt to changing conditions and promote energy efficiency across various sectors. Despite these advantages, challenges such as data privacy concerns, the need for robust cybersecurity measures, and the requirement for skilled personnel to interpret complex data must be addressed. Additionally, ensuring equitable access to these technologies across diverse urban areas is essential for maximizing their impact. In conclusion, the strategic application of cloud computing and big data analytics offers significant potential for optimizing energy use in smart cities. By embracing these technologies, urban centers can drive more effective and responsive energy policies, contributing to broader sustainability goals and enhancing overall quality of life.