The paper explores the development of decision-making based on data in business intelligence strategies. It grows more and more significant in today's data-driven atmosphere. Business intelligence begins to become more prominent. Business link to existing databases with the help of business intelligence solutions. It can search through immense databases like a sea, searching for nuggets of crucial information.Business intelligence greatly improves modern database systems and boosts operational effectiveness. Artificial intelligence technologies are essential in providing business owners with pertinent information that enables them to outperform competitors. This includes present consumer trend data, which helps firms better understand their target market. It is crucial to decision-making, together with operational effectiveness requirements and market trends.The analytics is vital for assisting organizations in preserving their competitive advantage since it can forecast future market developments. Business intelligence incorporates large databases; it also promotes a culture where data is the primary tool for decision-making. In short, business analytics and intelligence tools assist firms in examining the world of data-driven decision-making. They allow innovation by exposing previously unexplored possibilities. They offer a variety of benefits and alter the way companies make decisions, but there are also obstacles that must be surmounted for deployment to be successful.

This research discusses the use of machine learning and deep learning techniques for cancer detection and screening. The use of machine learning and deep learning algorithms enables survey specialists, including healthcare professionals, to increase the accuracy of risk assessments for diseases such as cancer, and hence identify the best time and techniques for screening. The use of these technologies enhances the accuracy of diagnosis while simultaneously aiding in the formulation of treatment regimens based on patients’ profiles. The Conventional approaches in cancer prevention and early detection sometimes act poorly as they sometimes do not cover many aspects of changes and variations of risks in people. Skin cancer is a prevailing issue, and the worldwide effects have amplified the importance of early and appropriate diagnosing. In previous years, the field of medical research has expanded through the use of machine learning and deep learning approaches, particularly in the diagnosis and classification of skin cancers. The importance of using machine learning and deep learning techniques in the early detection and prevention of cancer. The study emphasizes the importance of machine learning and deep learning approaches in the future struggle against cancer, which will necessitate the incorporation of early detection into normal operations. Due to the machine learning approach, multifaceted connections and relationships are found in high-dimensional data and hence sharper and individualized prophylactic measures. However, issues like quality of gathered data, interpretability of models, and of course, ethical issues are still there to hold back the progress of these technologies. In this regard, the present review seeks to provide detailed information regarding the current landscape of skin cancer research by showcasing the power of ML and DL models to the scholars, clinicians, and other healthcare professionals. This paper underscores the need for care and detection of cancer at an early stage as well as preventing the cancer illness. In particular, highlighting achievements along with unexplored opportunities for further development for this kind of research, this venture aims to introduce new advances in the field of skin cancer identification.

This paper investigates the key methods and technologies used to realize cybersecurity value to gain an appreciation of how analytical tools support threat identification and management choices. The field of cybersecurity is becoming a problem area that requires new approaches. Cybersecurity specialists analyze patterns, anticipate threats, and improve organizational security through technology tools like artificial intelligence, machine learning, and big data. It involves the constant evaluation of the IT infrastructure to check for vulnerabilities and address them when they are discovered. Big data analytics implemented improves the existing cybersecurity regulation because it provides a holistic view of the network’s health, which the administrator can use in managing vulnerabilities. Data analytics enhances compliance and risk management since it can be used to monitor security controls and policies continuously as well as facilitate policy compliance and compliance with regulatory standards. The ways in which technology and analytics are incorporated into cybersecurity are crucial for turning this data into such a tool for improving organizational security. Data analysis helps to avoid risks, monitor abnormal activities, and protect against cybercrimes in real time. Analytics helps in insider threat mitigation by analyzing the behavior of users and detecting a potentially dangerous activity so that organizations can take preventive measures against insider threats. The organizations that are able to implement these capabilities to enhance their defense against cyber risks and thus ensure the protection of this information as well as maintaining confidence among their stakeholders. The convergence of technologies and factorization of data analytics is changing the face of the security technology universe through converting massive volumes of raw data into intelligence. When big data refined patterns are retrieved from numerous datasets, any organization can accurately predict threats. The advanced technologies like AI, machine learning, and big data technologies are explored for an early identification of cyber threats and for automated remediation of incidents. The study is focused on the continuity of the organization as well as on such prerequisites as data, people, processes, and technologies within the context of cybersecurity.

This paper aims to discuss the evolution of machine learning based approaches for identifying higher risk individuals. It is relevant preventive measures with the reference to the individual characteristics of patients. It is big data that include genetic, environmental, and lifestyle data, these models improve the accuracy of risk prediction and identify protective strategies on the individual level. A paradigm shift in the cancer prevention strategies has occurred owing to the incorporation of Artificial Intelligence and machine learning in the modern world. The role of machine learning in early recognition of high-risk personalities of contracting cancer is examined to support malt measures. It overviews the available literature regarding the ethical dilemma, direction and obstacle on the ordeal of integrating AI guided targeted treatment in clinical setting. The results set the stage for exploring the possibilities of big data and AI for cancer prevention, which could eventually result in equitable benefits to the quality of individual patients’ care and expenses for the entire healthcare system. Cancer is among the most prevalent diseases resulting in morbidity and mortality of patients globally. The recent advancements in diagnostics, evaluation of the cancer prognosis and primary treatment of the patients, data-personalized therapy has not been systematically addressed. Artificial Intelligence deployed to forecast and orchestrate many cancers, has being seen as a talented instrument for enhancing healthcare predicates and patients. The AI applications in the field of Oncology are risk evaluation, diagnosis, prognosis and decision making in treatments with precise and accurate knowledge. Artificial intelligence, a broad category of which a segment involves machine learning which involves using past data to develop a solution to solve a problem has been able to predict most forms of cancer such as breast, brain, lung, liver, and prostate among others. It is observing that both AI and ML exhibit a more favorable performance compared to clinicians in terms of cancer prediction. These technologies also have the competency to enhance the diagnostic and prognostic possibilities for numerous diseases including but not exclusive to cancer, as well as enhance the quality of life of the affected patient. Thus, further enhancement of the existing Artificial Intelligence and Machine learning schemes is essential. In conclusion, it is evident that Artificial Intelligence and Machine learning strategies are interwoven, the overall health of the society. The incidence of the burden of cancer can be significantly enhanced with the provision of an experts’ proactive approach.

Purpose: This paper seeks to determine how AI can be used in the furtherment and management of distributed generation of renewable power, and how it can address challenges arising from the use of distributed generation of renewable energy sources like solar and wind. Thus, this research addresses a gap in the knowledge base related to AI technologies to plan and optimize the performance of decentralized energy systems, based on the assessment of opportunities, risks, and applications of the technology. Methodology: Convenience sampling was used and to incorporate mixed methods, the Research Onion of Saunders was applied to attain methodological credibility. Self-administered questionnaires were distributed to 210 professionals drawn from renewable energy and AI industries. The data was gathered by an online survey instrument and closed was done by employing basic descriptive statistics, chi-square tests, one-way ANOVA, sentiment analysis, clustering method, and multiple linear regression. Fortunately, the analysis also incorporated liberal graphical displays so that there was a better feel of the data distribution and association patterns. Results: The study reveals a generally positive perception of AI’s benefits in managing decentralized energy grids among respondents, who average 13.41 years of experience in renewable energy and 14.59 years in AI applications, though this experience varies significantly (SD = 8.84 and 7.91, respectively). Statistical analyses showed no significant correlation between respondents' familiarity with decentralized grids and their perceptions of AI's role. The Chi-Square Test for Independence (Chi-Square = 21.8, p = 0.15) and ANOVA (F = 1.17, p = 0.324) indicate no significant association between grid familiarity and AI perceptions, nor differences in AI experience across varying levels of grid familiarity. Sentiment analysis also confirmed these patterns when AI was only rated neutrally to slightly positively; the average score was 1. 0 illustrating people’s relative agreement about the potential of AI, even if they may not personally have a lot of experience with it. Primary data analysis by clustering showed no significant dissimilarities of response patterns indicative of the homogeneity of the sample. Furthermore, the result obtained from multiple linear regression (R² = 0. 01, p = 0. 329) indicates that both experience in the renewable and energy and experience in the use of AI have no significant impact towards the perception of the role of AI in distributed grids. Conclusion: AI has a high potential and applicability for decentralized energy grids but to achieve successful implementation certain technical, operational and policy issues need to be addresses. Hence, the study points out the required future research and stakeholder cooperation in order to explore all the possibilities of AI application in this field with the specific suggestions of the directions of future studies and interdisciplinary cooperation. Practical Implications: The study focuses on the shift in the use of artificial intelligence, in the operation of decentralized re-new able energy grids and offers guidance for grid operators, policymakers as well as technology developers. By drawing awareness of the advantage and drawbacks of AI implementation, the experts will be able to solve the issues related to the improvement of grid reliability and the stable integration of renewable sources into the global energy infrastructure. Originality/Value: Following this introduction, this paper aims to present a quantitative and qualitative analysis of decentralized renewable energy grids with consideration of AI with the following organization of the work. By applying Research Onion framework of Saunders, this research enhances the methodological robustness and can be helpful for major stake holders, including academics, business and or policymakers in the domain of AI as well as renewable energy.

This paper focuses on the use of data science approaches like machine learning, Big Data Analytics and Artificial Intelligence is complement cybersecurity initiatives. The nature of the threats is constantly evolving. The technical solutions to prevent any unauthorized access to the systems are not adequate. The use of data science within cybersecurity establishes new ways of averting and countering cyber threats. Using big data and sophisticated methods such as data mining, an organization can find out what is going on out there, future uncertainties that may threaten its existence, and carry out pre-determined actions on its behalf. This kind of approach to fighting cyber threats is amply illustrated by the concepts presented in this paper together with their case and practical applications. This study conveys the need for interdisciplinary work and brings out directions for further research in this ever-evolving area. cybersecurity is quickly becoming a potent tool for data science, enabling organizations to counter new kinds of cyber threats. Security data science this is the use of machine learning, anomaly detection, and predictive analytics approaches that have brought in change in threat detection, fraud, vulnerability, and adaptive security. With the help of huge amounts of data from different sources, data science can offer useful information about threats and help to develop reactions on definite threats. The framework suggested in this study is that there should be protection layers that are continuously defended through the analysis of real-time data as well as threat intelligence. It is more like giving the organizations the ability to go on the offense when it comes to foreseen and unseen cyber threats. The challenges that are yet to be fully solved include issues on data quality and data privacy, issues on model stability and false positives and negatives. Cyber threats are evolving and hence, the integration of data science with cybersecurity is inevitable to shape up a safer world. The right of cybersecurity is therefore to combine a talented workforce and modern method of data analysis to improve the security of servers and personal identity information.