Explainable AI and Deep Neural Networks for Continuous PCI DSS Compliance Monitoring
Publication Date : 18/12/2024
DOI: 10.5281/zenodo.14514153
Author(s) :
Sandeep Belidhe, Phani Monogya Katikireddi, Sandeep Kumar Dasa.
Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)
Abstract :
Ensuring constant PCI DSS compliance is
essential but not easy when dealing with PCI DSS-sensitive
payment card information. This paper looks into XAI and
DNNs to examine their possibilities of implementing and
improving PCI DSS compliance check automation. XAI
makes a model explain itself, making it easy for compliance
officers to address non-compliance when identified by the
model. For their part, DNNs can sift through large amounts of
security data to look for anomalies, determine the
effectiveness of the access control measures, verify the
implementation of encryption for data, and monitor the
effectiveness of controls of vulnerabilities. Applying these
high-level AI methodologies can allow organizations to gain
better, even real-time, control over compliance, thus
significantly reducing the probabilities of security
infringements and enhancing data protection measures in
general. Not only does it build up compliance capabilities, but
it also provides scalable and preventive solutions in reaction
to the emerging threats in the cyber security domain
Author :
Sandeep Belidhe, Phani Monogya Katikireddi, Sandeep Kumar Dasa
No. of Downloads :
0
pharmacogenetics and pharmacogenomics of opioids
Publication Date : 16/12/2024
Author(s) :
Samrudhi yashwant pawar.
Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)
Abstract :
Pain can be reduced in a variety of ways, including pharmacotherapy and surgery. Opioids are a class of drugs that efficiently treat moderate to severe acute and ongoing discomfort, which can accentuate several characteristics of their daily lives. Regarding acute discomfort, the expected physiological response to disagreeable stimuli, which is typically linked to serious illness or trauma (State Medical Boards Federation of the United States, 1998. The term "chronic pain" refers to suffering that lasts longer than the expected duration of recuperation or everyday suffering for the expected duration of recuperation or everyday suffering for extending beyond three months (Davis et al., 2017). The utilization of opioids is commonly used as the standard of care for both acute and chronic pain related to palliative care.
No. of Downloads :
0
REVIEW OF APPLICATIONS OF PHASE CHANGE MATERIAL FOR THERMAL ENERGY STORAGE
Publication Date : 12/12/2024
Author(s) :
Dr. M. Rajagopal.
Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)
Abstract :
Thermal energy storage (TES) systems are crucial for enhancing energy efficiency and enabling the integration of renewable energy sources. Phase Change Materials (PCM) is one of the most suitable materials for storing renewable energy. Phase change materials (PCMs) have emerged as an effective medium for TES due to their high energy density and ability to maintain nearly constant temperatures during phase transitions. Latent heat storage using phase change materials has applications in many areas, including building energy thermal management, waste heat recovery, temperature control, smart data, battery thermal control, microelectronic temperature control, photovoltaic thermal applications, space and vehicles, thermal energy storage applications, and greenhouse temperature control. In this review, topics include an overview of phase change materials (PCMs), use of PCMs in energy storage, use of PCMs in heating and cooling of buildings, use of PCMs in vehicles, and battery thermal management.
No. of Downloads :
0
The Influence of Communication and Digital Marketing on Consumer Behavior: Advancements in the Era of Artificial Intelligence
Publication Date : 11/12/2024
DOI: 10.5281/zenodo.14390376
Author(s) :
madhusudan pathak.
Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)
Abstract :
The advent of digital marketing, coupled with the incorporation of artificial intelligence (AI) in communication strategies, has significantly transformed consumer behavior in the 21st century. This research paper examines the influence of AI-powered digital marketing on consumer decision-making, engagement, and brand loyalty. By analyzing existing literature and empirical studies, the paper uncovers key positive shifts in consumer behavior due to improved communication methods and personalized marketing initiatives. The study emphasizes how businesses can harness AI and digital marketing to build deeper connections with their target audiences, enhancing customer relationships and driving better market outcomes.
No. of Downloads :
0
OPTIMISING PHASE CHANGE MATERIALS USING ARTIFICIAL INTELLIGENCE FOR THERMAL ENERGY STORAGE
Publication Date : 10/12/2024
Author(s) :
Dr. M. Rajagopal.
Volume/Issue :
Volume 10
,
Issue 12
(12 - 2024)
Abstract :
Artificial intelligence (AI) is increasingly being integrated into thermal management systems that use phase change materials (PCMs) to enhance energy efficiency and temperature control. AI can analyze large datasets from thermal management systems, identifying patterns and correlations that traditional methods might miss. Machine learning algorithms can predict how PCMs will behave under different conditions, optimizing their performance for applications like building energy management, thermal energy storage, and electronics cooling. AI models can simulate the thermal behavior of PCMs in real time. This allows for dynamic adjustments to thermal systems, ensuring optimal temperatures are maintained and preventing overheating or excessive cooling. By utilizing AI-driven algorithms, researchers can optimize the formulation of PCMs, enhancing their thermal properties such as melting and solidification temperatures. This can lead to improved energy efficiency in various applications. AI can be used to monitor the health of thermal management systems utilizing PCMs. By analyzing operational data, AI can predict failures or inefficiencies, allowing for timely maintenance and reducing downtime. AI can assist in energy demand forecasting, helping to manage the use of PCMs in systems like solar thermal energy storage. Predictive analytics can optimize charging and discharging cycles based on expected energy consumption patterns. AI can enhance control strategies for systems using PCMs, enabling more responsive and adaptive management based on real-time conditions and forecasts. This ensures maximum efficiency and performance of thermal management systems. AI can work alongside Internet of Things (IoT) technologies to gather real-time data from various sensors in thermal management systems. This integration allows for more sophisticated predictive analytics and decision-making.
No. of Downloads :
0
