Papers by Srikanth Suryadevara
International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2021
Long-term wireless monitoring of photoplethysmographic (PPG) signals is crucial for continuous he... more Long-term wireless monitoring of photoplethysmographic (PPG) signals is crucial for continuous health monitoring without tethering patients to wired devices. This paper explores advancements aimed at enhancing resource-efficiency and reliability in such monitoring systems. We review techniques including signal processing algorithms, energy-efficient sensor designs, adaptive data transmission protocols, and robust signal quality assessment methods. These innovations aim to extend battery life, ensure data integrity over extended periods, and enhance the overall reliability of PPG monitoring in various healthcare applications.
International Journal of Advanced Engineering Technologies and Innovations, 2023
The rapid advancement of Artificial Intelligence (AI) has revolutionized numerous sectors, offeri... more The rapid advancement of Artificial Intelligence (AI) has revolutionized numerous sectors, offering unprecedented opportunities and capabilities. However, the integration of AI technologies has raised significant concerns regarding data privacy and protection. This paper evaluates the impact of data protection regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), on AI development and deployment. By analyzing the regulatory requirements and their implications, we identify the challenges and opportunities these regulations present for AI practitioners. The study highlights the balance between innovation and compliance, providing insights into how organizations can navigate the complex landscape of data protection while leveraging AI technologies. Furthermore, we explore case studies that illustrate the practical application of compliance strategies in AI systems. Our findings suggest that robust data protection frameworks not only ensure legal compliance but also foster trust and transparency, ultimately enhancing the sustainable development and deployment of AI.
International Journal of Advanced Engineering Technologies and Innovations, 2023
In the rapidly evolving landscape of Artificial Intelligence (AI), ensuring robust data protectio... more In the rapidly evolving landscape of Artificial Intelligence (AI), ensuring robust data protection has become paramount. This review explores recent advances, trends, and challenges at the intersection of data protection and AI technologies. Regulatory frameworks such as the General Data Protection Regulation (GDPR) and technological innovations like federated learning are examined for their impact on privacy preservation and regulatory compliance. Ethical considerations, including fairness and transparency in AI algorithms, are also discussed. The synthesis of empirical findings and theoretical insights provides a comprehensive overview of current developments and future directions in AI-driven data governance.
REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA, 2024
Artificial Intelligence (AI) technologies are transforming industries worldwide, yet their integr... more Artificial Intelligence (AI) technologies are transforming industries worldwide, yet their integration raises significant challenges related to data protection. This comprehensive review explores the intersection of AI and data protection, focusing on regulatory frameworks, ethical considerations, and technological innovations. Key topics include the impact of regulations like the GDPR and CCPA, ethical implications of AI-driven decision-making, and emerging technologies such as federated learning and differential privacy. By synthesizing current literature and empirical findings, this review aims to provide insights for policymakers, practitioners, and researchers navigating the complexities of AI-driven data governance.
International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2024
Artificial Intelligence (AI) presents unprecedented opportunities for innovation across various d... more Artificial Intelligence (AI) presents unprecedented opportunities for innovation across various domains, yet it also introduces significant challenges in data protection. This paper explores the evolving landscape of data protection within AI frameworks, focusing on emerging challenges and innovative solutions. Key issues include privacy concerns, ethical implications, regulatory frameworks, and technological advancements. This study surveys current strategies and proposes future directions to harmonize AI development with robust data protection principles.
International Journal of Advanced Engineering Technologies and Innovations, 2022
Predicting hospital readmission is crucial for improving patient care and optimizing healthcare r... more Predicting hospital readmission is crucial for improving patient care and optimizing healthcare resource allocation. Traditional methods often overlook the imbalanced costs associated with different types of prediction errors. This study proposes a cost-sensitive deep learning approach tailored for hospital readmission prediction. By integrating cost-sensitive learning techniques into deep neural networks, the model prioritizes minimizing costly prediction errors, such as false negatives. Experimental results on a large-scale healthcare dataset demonstrate the effectiveness of the proposed approach in achieving higher predictive accuracy while mitigating readmission risks. The findings highlight the potential of cost-sensitive deep learning to enhance healthcare outcomes and resource utilization.
REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA, 2023
Healthcare security practices are increasingly adopting data-driven and artificial
intelligence ... more Healthcare security practices are increasingly adopting data-driven and artificial
intelligence (AI) approaches to enhance their effectiveness and responsiveness in safeguarding
sensitive medical information. This systematic review explores the current landscape of research
and practices in utilizing AI for modeling and analyzing healthcare security. By synthesizing
findings from peer-reviewed literature and industry reports, this review identifies key trends,
methodologies, and challenges in AI-driven healthcare security. The review highlights
advancements in anomaly detection, predictive analytics, and risk assessment models tailored to
healthcare contexts. Furthermore, it discusses the implications of AI adoption on data privacy,
regulatory compliance, and organizational resilience within healthcare settings. The synthesis of
this systematic review provides insights into future research directions and practical implications
for improving healthcare cybersecurity frameworks.
REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA, 2022
Brain-computer interface (BCI) technology holds promise for revolutionizing interaction between h... more Brain-computer interface (BCI) technology holds promise for revolutionizing interaction between humans and machines, enabling direct communication pathways based on neural signals. However, optimizing BCI applications requires overcoming challenges such as signal noise, latency, and system integration complexities. This paper proposes leveraging Internet of Things (IoT) technologies to enhance BCI applications through real-time data processing, adaptive feedback mechanisms, and seamless connectivity. By integrating IoT with BCI systems, this approach aims to improve usability, reliability, and performance, thereby advancing the capabilities and accessibility of brain-controlled devices in various domains including healthcare, gaming, and assistive technology.
REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA, 2021
Artificial Neural Networks (ANNs) have emerged as powerful computational models inspired by biolo... more Artificial Neural Networks (ANNs) have emerged as powerful computational models inspired by biological neural networks. This paper provides a comprehensive overview of ANNs, covering their evolutionary development, diverse architectures ranging from feedforward to recurrent and convolutional networks, and their wide-ranging applications across various domains.
International Journal of Advanced Engineering Technologies and Innovations, 2021
Energy-proportional computing aims to achieve optimal energy efficiency in data centers by ensuri... more Energy-proportional computing aims to achieve optimal energy efficiency in data centers by ensuring that energy consumption is directly proportional to the workload. This paper explores the fundamental concepts and practical implementations of energy-proportional computing in data centers. We analyze the key design principles, including dynamic power management, adaptive resource allocation, and energy-aware scheduling. Furthermore, we investigate various techniques and technologies, such as virtualization, consolidation, and efficient cooling systems, that contribute to achieving energy proportionality. Case studies and experimental results are presented to demonstrate the effectiveness of these techniques in realworld data center environments. The findings highlight significant energy savings and performance improvements, emphasizing the importance of adopting energy-proportional strategies in modern data centers.
REVISTA DE INTELIGENCIA ARTIFICIAL EN MEDICINA
Artificial Neural Networks (ANNs) have revolutionized neuroscientific research by simulating the ... more Artificial Neural Networks (ANNs) have revolutionized neuroscientific research by simulating the complex dynamics of biological neural networks. This paper explores the fundamental principles of ANNs, focusing on their application in neuroscientific studies. We delve into the architecture, training methods, and computational models of ANNs, illustrating their ability to model cognitive processes and neurological phenomena. Case studies highlight how ANNs are utilized in analyzing brain imaging data, predicting neuronal activity, and elucidating brain connectivity patterns. Additionally, we discuss challenges and future directions in integrating ANNs with experimental neuroscience, paving the way for enhanced understanding of brain function and dysfunction.
International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2022
In the realm of pervasive computing, where interconnected devices seamlessly integrate into every... more In the realm of pervasive computing, where interconnected devices seamlessly integrate into everyday environments, the role of middleware becomes pivotal in managing and leveraging contextual information. This study proposes a reconfigurable context-sensitive middleware framework designed to dynamically adapt to changing environmental contexts and user interactions. By incorporating context awareness into its design, the middleware enhances the interoperability and intelligence of pervasive computing systems, facilitating efficient resource management and personalized services delivery. Key functionalities include context acquisition, interpretation, and adaptation, enabling the middleware to optimize device interactions based on situational awareness. Through a comprehensive evaluation, including performance metrics and usability assessments, the framework demonstrates its efficacy in enhancing system flexibility, responsiveness, and user experience in diverse pervasive computing scenarios.
International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2020
Artificial intelligence (AI) is increasingly integrated into healthcare systems, promising
adva... more Artificial intelligence (AI) is increasingly integrated into healthcare systems, promising
advancements in diagnostics, treatment optimization, and patient care. However, patient
apprehensions regarding AI adoption persist, influenced by concerns over privacy, data security,
ethical implications, and the potential for AI to replace human interaction in healthcare delivery.
This review explores patient perspectives on AI in healthcare, highlighting key apprehensions and
factors influencing patient acceptance or reluctance. Understanding these concerns is crucial for
developing ethical guidelines, ensuring transparent AI deployment, and fostering patient-centered
AI implementations that prioritize trust, safety, and equitable access to healthcare innovations.
International Journal of Advanced Engineering Technologies and Innovations, 2022
Real-time task scheduling in WirelessHART networks presents significant challenges due to the str... more Real-time task scheduling in WirelessHART networks presents significant challenges due to the stringent timing requirements and dynamic nature of industrial environments. This paper investigates the optimization of real-time task scheduling to ensure efficient operation and reliable performance in WirelessHART networks. The study identifies key challenges such as resource constraints, varying network conditions, and the need for timely task execution. Several solutions are proposed, including dynamic scheduling algorithms and adaptive resource allocation strategies. Performance evaluation demonstrates the effectiveness of these approaches in meeting real-time requirements while optimizing resource utilization. The findings contribute to advancing the design and implementation of robust scheduling mechanisms for industrial IoT applications in WirelessHART networks.
International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 2024
In the era of digital transformation, Artificial Intelligence (AI) has emerged as a pivotal techn... more In the era of digital transformation, Artificial Intelligence (AI) has emerged as a pivotal technology driving innovation across various sectors. However, the rapid advancement of AI poses significant challenges to data protection and privacy. This paper explores the intersection of AI and data protection, examining the ethical, legal, and technical dimensions of maintaining privacy while leveraging AI's potential. Key issues such as data minimization, consent, transparency, and accountability are analyzed. The paper also highlights regulatory frameworks and best practices that can help balance innovation with privacy protection, ensuring that AI development aligns with ethical standards and legal requirements.
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Papers by Srikanth Suryadevara
intelligence (AI) approaches to enhance their effectiveness and responsiveness in safeguarding
sensitive medical information. This systematic review explores the current landscape of research
and practices in utilizing AI for modeling and analyzing healthcare security. By synthesizing
findings from peer-reviewed literature and industry reports, this review identifies key trends,
methodologies, and challenges in AI-driven healthcare security. The review highlights
advancements in anomaly detection, predictive analytics, and risk assessment models tailored to
healthcare contexts. Furthermore, it discusses the implications of AI adoption on data privacy,
regulatory compliance, and organizational resilience within healthcare settings. The synthesis of
this systematic review provides insights into future research directions and practical implications
for improving healthcare cybersecurity frameworks.
advancements in diagnostics, treatment optimization, and patient care. However, patient
apprehensions regarding AI adoption persist, influenced by concerns over privacy, data security,
ethical implications, and the potential for AI to replace human interaction in healthcare delivery.
This review explores patient perspectives on AI in healthcare, highlighting key apprehensions and
factors influencing patient acceptance or reluctance. Understanding these concerns is crucial for
developing ethical guidelines, ensuring transparent AI deployment, and fostering patient-centered
AI implementations that prioritize trust, safety, and equitable access to healthcare innovations.
intelligence (AI) approaches to enhance their effectiveness and responsiveness in safeguarding
sensitive medical information. This systematic review explores the current landscape of research
and practices in utilizing AI for modeling and analyzing healthcare security. By synthesizing
findings from peer-reviewed literature and industry reports, this review identifies key trends,
methodologies, and challenges in AI-driven healthcare security. The review highlights
advancements in anomaly detection, predictive analytics, and risk assessment models tailored to
healthcare contexts. Furthermore, it discusses the implications of AI adoption on data privacy,
regulatory compliance, and organizational resilience within healthcare settings. The synthesis of
this systematic review provides insights into future research directions and practical implications
for improving healthcare cybersecurity frameworks.
advancements in diagnostics, treatment optimization, and patient care. However, patient
apprehensions regarding AI adoption persist, influenced by concerns over privacy, data security,
ethical implications, and the potential for AI to replace human interaction in healthcare delivery.
This review explores patient perspectives on AI in healthcare, highlighting key apprehensions and
factors influencing patient acceptance or reluctance. Understanding these concerns is crucial for
developing ethical guidelines, ensuring transparent AI deployment, and fostering patient-centered
AI implementations that prioritize trust, safety, and equitable access to healthcare innovations.