CREATIVE INDUSTRY REGULATION IN TIMES OF CRISIS: LESSONS FROM COVID-19 ENFORCEMENT AND HEALTHCARE CARTEL POLICIES ACROSS INDIA, AUSTRALIA, AND SINGAPORE Kshitij Kumar Rai, Shoaib Mohammad, Satish Kumar Mishra, Arti Sharma, Ramesh Prajapati, Rajiv Kumar Shodhkosh Journal of Visual and Performing Arts, 2025 Periods of global crisis significantly disrupt creative industries, particularly the visual and performing arts, where artistic production is closely linked to institutional support, regulatory frameworks, and cultural policy. This paper examines creative industry regulation in times of crisis by drawing comparative lessons from COVID-19 enforcement measures and healthcare cartel policies implemented in India, Australia, and Singapore. Through an interdisciplinary policy analysis, the study explores how emergency regulatory responses—originally designed for essential sectors such as healthcare—offer valuable insights for governing creative and design-led industries during periods of uncertainty. The research contextualizes these regulatory approaches within the creative economy, emphasizing their implications for artistic labour, cultural organizations, and design-driven enterprises. By comparing national responses, the study identifies key governance themes including regulatory flexibility, collaborative compliance, and crisis-driven innovation. The paper argues that adaptive regulatory models can support continuity in creative production while safeguarding artistic autonomy. The findings contribute to emerging scholarship on cultural governance by proposing transferable policy lessons that strengthen resilience in visual and performing arts ecosystems. This research offers strategic insights for policymakers, cultural institutions, and creative practitioners seeking sustainable regulatory frameworks for future crises.
An Investigation of Various Techniques to Improve Cyber Security Shoaib Mohammad, Ramendra Pratap Singh, Rajiv Kumar, Kshitij Kumar Rai, Arti Sharma, Saloni Rathore Natural Language Processing for Software Engineering, 2025 Cyber security threats, characterized by a series of assault stages, persistently aim to accomplish a pre-established goal. Due to the intricate nature of these attacks, the intruder is capable of bypassing the target's security defenses and gaining access to a majority of its systems. When accessing data kept in the cloud, there is a genuine risk of experiencing data breaches, compromised credentials, Denial of Service (DoS) assaults, hacked interfaces and Application Programming Interfaces (APIs), permanent data loss, and other significant cybersecurity concerns. Due to the constant innovation of cybercriminals, who continuously develop more advanced methods to avoid detection, it is challenging to both identify and prevent these malicious activities. As digital technology advances, gigabytes and terabytes of data are now generated every second. Businesses in a variety of industries are finding that using the internet to manage their resources and transactions is useful. Given the value of data and the need to safeguard its security and privacy, securing big data remains a major challenge for all solutions. Due to the exponential expansion of network data, intrusion detection is becoming increasingly important, and manual analysis would be either impossible or take the same amount of time as analyzing it. As a result, there is an urgent need for an automated system capable of extracting relevant information from enormous amounts of hitherto untapped data when it comes to network intrusion detection. Data mining can perform a variety of tasks, such as clustering, prediction, classification, and the extraction of association rules between data pieces. This paper discusses machine learning techniques for designing intrusion detection systems for big data networks. In this approach, the NSL KDD data set is used as input. First, the CFS-correlation feature selection approach is used to pick only relevant features from the NSL KDD data set. The NSL KDD data collection contains 41 features. The number of characteristics was reduced to 16 after applying the CFS algorithm. The 16 attributes are then used by machine learning techniques to classify and predict malware data in the NSL KDD data set.
Efficient Fog-to-Cloud Internet-of-Medical-Things System Mukesh Soni, Sarfraz Fayaz Khan, Dinesh Mavaluru, Shoaib Mohammad Image Based Computing for Food and Health Analytics Requirements Challenges Solutions and Practices Ibcfha, 2023
Fog-Cloud-IoT Enabled WSN Architecture P Anitha Christy Angelin., P.V Samuel Devakumar., Liwa H. Al-Farhani, Gandam Vijay Kumar, Shaurya Deep, Shoaib Mohammad Proceedings of 3rd International Conference on Intelligent Engineering and Management Iciem 2022, 2022 Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge.
Retracted: Fog-Cloud-IoT Enabled WSN Architecture (2022 3rd International Conference on Intelligent Engineering and Management (ICIEM) DOI: 10.1109/ICIEM54221.2022.10703470) P Anitha Christy Angelin., P.V Samuel Devakumar., Liwa H. Al-Farhani, Gandam Vijay Kumar, Shaurya Deep, Shoaib Mohammad Proceedings of 3rd International Conference on Intelligent Engineering and Management Iciem 2022, 2022 Remote Because of the exciting innovation it introduces, the WSN likely to be a subject of research in recent time. Because of the impromptu distant connections, adaptability, and simplicity of execution, this has all the makings of becoming the most maintainable innovation for ecological detecting, whether it's about limited or huge scope watching. In any case, the main drawbacks stem from the limited capacity, processing, and accessibility of organization centers for information. Virtual machines resources have been adopted to overcome such constraints, devices to expanded capacity, computation, and easy-to-understand availability. This was a natural progression from traditional WSN designs among pattern in recent theories evolved along advent of Internet-of-Things advancements. Against the growing popularity of WSN associated cloud checking frameworks, still the issues are detected because of the distributed comp ting's disadvantages, like dormancy with the capacity charges. In this paper, the advancements achieved to a fog-cloud-IoT associated with WSN architecture of putting a bit of computing at the network part are elaborated, a process, which follows the smart Fog associated with Cloud-computing concept of dissecting also following up on IoT data. A similar investigation was carried out to illustrate the improvements made possible by processing at the organization's edge.
Fuzzy Logic and Machine Learning-Enabled Recommendation System to Predict Suitable Academic Program for Students Tribhuwan Kumar, K. Sankaran, Mahyudin Ritonga, Shazia Asif, C. Sathiya Kumar, Shoaib Mohammad, Sudhakar Sengan, Evans Asenso Mathematical Problems in Engineering, 2022 In recent years, educational data mining has gained a considerable lot of interest as a consequence of the large number of pedagogical content that can be gathered from a range of sources. This is because there is a lot of instructional information that can be obtained. The data mining tools collaborate with academics to improve students’ learning strategies by analyzing, sifting through, and estimating components that are pertinent to students’ characteristics or patterns of behavior. This is accomplished through the following steps: EDM is utilized in the vast majority of instances to develop the classification model, which then assigns a certain class to each student based on the known properties of the training dataset. Before putting the classification model into use, it is possible to utilize a test dataset to verify that the model is accurate. This article provides a description of a recommendation system that determines the most beneficial academic program for students by utilizing fuzzy logic and machine learning. The compilation of a student dataset has begun. It includes a total of 21 features and 1000 individual cases. The initial step is to employ the CFS attribute selection method. This methodology selects 15 of the initial set of 21 characteristics. Following the completion of the data gathering, it is put through various machine learning methods such fuzzy SVM, random forest, and C4.5. This methodology that has been offered makes predictions about the academic program that is best suitable for students.
