Advances in Deep Learning Techniques
type: channel
description: Welcome to the forefront of innovation! Delve into the boundless realm of 'Advances in Deep Learning Techniques', where groundbreaking discoveries and transformative breakthroughs shape the future of AI. Explore cutting-edge research, insightful analyses, and real-world applications driving the evolution of deep learning. Join us on a journey of discovery, innovation, and limitless possibilities in the realm of artificial intelligence.
publisher: The Science Brigade Publishers
language: en
publication name: Advances in Deep Learning Techniques
issn: 2457-0982
copyright: License Terms Ownership and Licensing: Authors of this research paper submitted to the journal owned and operated by The Science Brigade Group retain the copyright of their work while granting the journal certain rights. Authors maintain ownership of the copyright and have granted the journal a right of first publication. Simultaneously, authors agreed to license their research papers under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. License Permissions: Under the CC BY-NC-SA 4.0 License, others are permitted to share and adapt the work, as long as proper attribution is given to the authors and acknowledgement is made of the initial publication in the Journal. This license allows for the broad dissemination and utilization of research papers. Additional Distribution Arrangements: Authors are free to enter into separate contractual arrangements for the non-exclusive distribution of the journal's published version of the work. This may include posting the work to institutional repositories, publishing it in journals or books, or other forms of dissemination. In such cases, authors are requested to acknowledge the initial publication of the work in this Journal. Online Posting: Authors are encouraged to share their work online, including in institutional repositories, disciplinary repositories, or on their personal websites. This permission applies both prior to and during the submission process to the Journal. Online sharing enhances the visibility and accessibility of the research papers. Responsibility and Liability: Authors are responsible for ensuring that their research papers do not infringe upon the copyright, privacy, or other rights of any third party. The Science Brigade Publishers disclaim any liability or responsibility for any copyright infringement or violation of third-party rights in the research papers.
items: Artificial Intelligence for Scalable Cloud Systems: Innovations in Resource Optimization
items: Deep Learning for Network Traffic Analysis: Detecting Security Breaches in Real-Time
items: Distributed Systems for Financial Reconciliation and Clearing: Enhancing Real-Time Performance and Resilience
items: Machine Learning-Driven Anomaly Detection and Proactive Insights for Cloud Telemetry and Monitoring
items: Automating Security Incident Mitigation Using AI/ML-Driven SOAR Architectures
items: AI/ML Algorithms for Phishing Detection and Automated Response Systems in Cloud-Based Email Security
items: AI-Powered Data Loss Prevention (DLP) for Detecting and Mitigating Cloud-Based Sensitive Data Leaks
items: Deep Learning Approaches for Healthcare Cost Forecasting: Reducing Uncertainty in Financial Planning for Hospitals and Providers
items: Leveraging Natural Language Processing for Business Process Mining from Unstructured Data Sources
items: Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response
items: AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation
items: Explainable AI: Interpreting Deep Learning Models for Decision Support
items: Artificial Intelligence and Cloud Services for Enhancing Patient Care: Techniques, Applications, and Real-World Case Studies
items: Comparative Analysis of Advanced Time Series Forecasting Techniques
items: Reimagining Digital Identity Management: A Critical Review of Blockchain-Based Identity and Access Management (IAM) Systems - Architectures, Security Mechanisms, and Industry-Specific Applications
items: AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality
items: Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis
items: Optimizing Cloud Resource Allocation: A Comparative Analysis of AI-Driven Techniques
items: Assessing Microservice Security Implications in AWS Cloud for to implement Secure and Robust Applications
items: Pushing Boundaries with Deep Generative Models: Innovations and Applications of VAEs and GANs
items: Data-Driven Dental Public Health: Improving Community Oral Health through Analytics
items: AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare
items: Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance
items: Meta-learning Algorithms for Few-shot Learning: Analyzing meta-learning algorithms designed to enable deep learning models to quickly adapt to new tasks with limited training data
items: Artificial Intelligence on the Global Stage: Transforming Diplomacy and International Relations
items: Quantum-Inspired Optimization Techniques for IoT Networks: Focusing on Resource Allocation and Network Efficiency Enhancement for Improved IoT Functionality
items: Harnessing Quantum Computing for Drug Discovery and Molecular Modelling in Precision Medicine: Exploring Its Applications and Implications for Precision Medicine Advancement
items: Standardization and Regulation of V2X Cybersecurity: Analyzing the Current Landscape, Identifying Gaps, and Proposing Frameworks for Harmonization
items: Variational Autoencoders - Theory and Applications: Exploring Variational Autoencoder Models and Their Applications in Generative Modeling, Representation Learning, and Beyond
items: Transformer Networks - Architectures and Applications: Investigating Transformer Network Architectures and Their Diverse Applications in Natural Language Processing and Beyond

Artificial Intelligence for Scalable Cloud Systems: Innovations in Resource Optimization
type: item
description: Resource optimization for intelligent and scalable solution for exponential growth of cloud computing demands which ensures performance efficiency, cost-effectiveness, and reliability. The objective of this paper is to explore the integration of artificial intelligence (AI) techniques which includes machine learning, deep reinforcement learning, and predictive analytics in cloud infrastructure management for dynamic resource provisioning, workload prediction, and anomaly detection.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Computing
  topic: keywords
subject: Resource Optimization
  topic: keywords
subject: Dynamic Provisioning
  topic: keywords
creator: Vinay Kumar Deeti
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-06-03
publication date: 2023-06-03
starting page: 146
ending page: 159

Deep Learning for Network Traffic Analysis: Detecting Security Breaches in Real-Time
type: item
description: The exponential growth of network traffic presents significant challenges in real-time security breach detection. Traditional intrusion detection systems (IDS) struggle to efficiently analyze vast data streams, leading to delays and undetected anomalies. This paper explores the application of deep learning models for network traffic analysis, leveraging their ability to autonomously detect anomalous patterns and potential security threats. We investigate various architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models, evaluating their effectiveness in identifying malicious activities. Feature engineering techniques, dataset preprocessing, and model optimization strategies are discussed to enhance real-time detection capabilities. Furthermore, we analyze computational overhead, detection accuracy, and false positive rates, highlighting trade-offs in deploying deep learning-based IDS in large-scale networks. Case studies demonstrate the superiority of AI-driven approaches over conventional methods, underscoring their potential for proactive cybersecurity defense.
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: network security
  topic: keywords
subject: AI
  topic: keywords
subject: real-time detection
  topic: keywords
creator: Hassan Rehan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-02-24
publication date: 2022-02-24
starting page: 154
ending page: 193

Distributed Systems for Financial Reconciliation and Clearing: Enhancing Real-Time Performance and Resilience
type: item
description: Accounting and clearing have long plagued the banking industry. These duties ensure accurate, transparent, and timely financial transactions. Batch processing and centralized architecture hinder real-time transaction handling, scalability, and robustness within traditional approaches. With distributed systems, the financial industry may improve its working abilities and solve key difficulties.
subject: Articles
  topic: section
subject: distributed systems
  topic: keywords
subject: financial reconciliation
  topic: keywords
creator: Koushik Sundar
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-10-11
publication date: 2023-10-11
starting page: 75
ending page: 114

Machine Learning-Driven Anomaly Detection and Proactive Insights for Cloud Telemetry and Monitoring
type: item
description: Machine learning-driven anomaly detection has emerged as a transformative approach for enhancing cloud telemetry and monitoring systems. Cloud environments are characterized by massive amounts of dynamic, real-time telemetry data generated by a plethora of services, applications, and infrastructure components. As cloud computing continues to evolve, the need to proactively identify anomalies, predict resource utilization trends, and automate incident resolution becomes increasingly critical. Traditional monitoring systems often rely on rule-based approaches or simplistic threshold settings, which are limited in their ability to detect novel or complex patterns that deviate from expected behavior. Machine learning (ML) offers a more sophisticated and scalable solution to this challenge, enabling the automation of anomaly detection and providing proactive insights for effective cloud management. This research paper explores the application of ML algorithms in the context of cloud telemetry, focusing on their role in anomaly detection, trend prediction, and incident resolution. Machine learning provides significant advantages over traditional approaches by leveraging data-driven models that continuously adapt to changing cloud environments. By analyzing large datasets from cloud platforms, ML algorithms can detect outliers, unusual patterns, and performance degradations with high accuracy. These capabilities empower organizations to detect potential issues before they impact users, reducing downtime and improving system reliability. Anomaly detection in cloud telemetry involves identifying deviations from normal operational behavior, which can indicate a range of issues such as performance bottlenecks, security breaches, or system failures. Machine learning models, such as supervised learning, unsupervised learning, and reinforcement learning, are employed to recognize these anomalies through training on historical telemetry data. Supervised learning techniques, includ…
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: anomaly detection
  topic: keywords
subject: cloud telemetry
  topic: keywords
creator: Muthuraman Saminathan
creator: Sayantan Bhattacharyya
creator: Aarthi Anbalagan
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-07-12
publication date: 2021-07-12
starting page: 23
ending page: 70

Automating Security Incident Mitigation Using AI/ML-Driven SOAR Architectures
type: item
description: The integration of artificial intelligence (AI) and machine learning (ML) within Security Orchestration, Automation, and Response (SOAR) platforms represents a transformative evolution in the cybersecurity domain. This paper explores the automation of security incident mitigation through the application of AI/ML-driven SOAR architectures, emphasizing advanced methodologies for incident prioritization, classification, and response automation. By leveraging sophisticated deep learning models, these platforms enable the dynamic creation of adaptive playbooks and facilitate autonomous threat mitigation processes. Such capabilities significantly enhance the efficiency and scalability of modern security operations centers (SOCs), addressing challenges posed by increasing attack vectors, rising incident volumes, and the shortage of skilled cybersecurity professionals. The research delves into the integration of AI/ML technologies within SOAR platforms, providing a systematic analysis of their role in enhancing key functionalities such as event correlation, root cause analysis, and decision-making for incident response. Notable SOAR platforms, including Palo Alto Cortex XSOAR and IBM Resilient, serve as focal points for this study. These platforms exemplify the deployment of advanced ML models and natural language processing (NLP) for context-aware threat detection and automated remediation. Furthermore, the adaptability of these systems to evolving threats is highlighted, underscoring their capacity for continuous learning through reinforcement learning mechanisms and real-time data ingestion. The paper investigates the critical components of AI/ML-enabled SOAR platforms, including data preprocessing pipelines, feature engineering techniques, and model deployment strategies tailored to cybersecurity requirements. Special attention is given to the development of autonomous playbooks, which employ predictive analytics to dynamically recommend or execute response actions base…
subject: Articles
  topic: section
subject: AI-driven SOAR
  topic: keywords
subject: ML for cybersecurity
  topic: keywords
subject: automated incident response
  topic: keywords
creator: Abdul Samad Mohammed
creator: Vincent Kanka
creator: Aarthi Anbalagan
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-08-08
publication date: 2022-08-08
starting page: 22
ending page: 65

AI/ML Algorithms for Phishing Detection and Automated Response Systems in Cloud-Based Email Security
type: item
description: The increasing reliance on cloud-based email services has significantly amplified the threat posed by phishing attacks, necessitating robust and adaptive mechanisms for detection and response. This paper explores the application of artificial intelligence (AI) and machine learning (ML) algorithms for phishing detection and the development of automated response systems within cloud-based email security frameworks. By leveraging deep learning models, particularly those trained on email metadata and natural language processing (NLP) for textual analysis, the proposed methodologies aim to detect and mitigate phishing attempts with high accuracy. These models analyze various indicators, including sender reputation, domain spoofing patterns, content anomalies, and contextual signals, to identify malicious activities in real-time. The integration of these AI/ML-powered systems into Security Orchestration, Automation, and Response (SOAR) platforms enables seamless workflows for automated quarantine, alert generation, and remediation. A case study of Microsoft Defender for Office 365 demonstrates the practical application of such systems, highlighting the use of deep neural networks, transformer architectures, and ensemble techniques for phishing detection. The architecture incorporates automated incident response mechanisms, such as removing malicious emails, blocking suspicious senders, and notifying administrators or end-users of potential threats, ensuring rapid containment and mitigation of risks. Furthermore, the paper discusses challenges associated with model training, such as the handling of imbalanced datasets, adversarial email crafting, and the computational overhead involved in processing large-scale email traffic. Advanced techniques, including data augmentation, active learning, and adversarial training, are employed to address these challenges and enhance model robustness. The study also evaluates the role of federated learning in preserving data privacy whil…
subject: Articles
  topic: section
subject: phishing detection
  topic: keywords
subject: machine learning
  topic: keywords
subject: cloud-based email security
  topic: keywords
creator: Akhil Reddy Bairi
creator: Vincent Kanka
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-02-13
publication date: 2023-02-13
starting page: 99
ending page: 145

AI-Powered Data Loss Prevention (DLP) for Detecting and Mitigating Cloud-Based Sensitive Data Leaks
type: item
description: In the digital era, the adoption of cloud-based platforms has significantly transformed data storage and processing, but it has also amplified concerns over the security of sensitive information. Data Loss Prevention (DLP) systems are essential for safeguarding sensitive data from unauthorized access and potential exfiltration. This research focuses on the application of Artificial Intelligence (AI)-powered DLP solutions for detecting and mitigating cloud-based sensitive data leaks. Leveraging advanced deep learning Natural Language Processing (NLP) models, these systems enable the real-time identification of sensitive data patterns such as personally identifiable information (PII), financial data, and intellectual property embedded in unstructured and structured datasets. Concurrently, machine learning algorithms analyze data access behaviors to detect anomalies and identify unauthorized data movements, thus enabling proactive measures to mitigate potential data breaches. The implementation of AI in DLP systems introduces several innovations. Deep learning models trained on domain-specific datasets excel in recognizing complex data structures and contextual information, improving classification accuracy. Additionally, unsupervised and semi-supervised machine learning techniques enhance behavioral analytics by identifying deviations from established baselines of user activity. The integration of these technologies into DLP frameworks is exemplified by case studies involving AWS Macie and Google Cloud DLP, two leading cloud-based solutions. These case studies highlight the effectiveness of AI-powered tools in ensuring compliance with data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Despite the significant advantages, the deployment of AI-powered DLP systems in cloud environments presents challenges. These include the computational overhead associated with training and deploying deep lear…
subject: Articles
  topic: section
subject: data loss prevention
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: cloud security
  topic: keywords
creator: Manish Tomar
creator: Akhil Reddy Bairi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-03-06
publication date: 2022-03-06
starting page: 110
ending page: 153

Deep Learning Approaches for Healthcare Cost Forecasting: Reducing Uncertainty in Financial Planning for Hospitals and Providers
type: item
description: This research paper explores the application of deep learning techniques for forecasting healthcare costs, with a focus on improving the accuracy and reliability of financial planning for hospitals and healthcare providers. The study aims to address the growing challenge of cost uncertainty in healthcare, driven by factors such as varying patient demographics, fluctuating resource utilization, and the increasing complexity of medical treatments. Traditional statistical models and cost-estimation methods often fall short in capturing the intricate patterns within healthcare data, leading to inaccurate financial projections. In contrast, deep learning models, which are capable of identifying non-linear relationships and learning from large, multidimensional datasets, offer a more robust approach to predicting healthcare costs. The core objective of this research is to investigate how deep learning methods—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and autoencoders—can be utilized to forecast costs by analyzing patient data, treatment pathways, and operational expenditures. By leveraging a combination of structured and unstructured data, such as electronic health records (EHRs), medical imaging, diagnostic codes, and hospital resource management systems, these models can discern underlying trends and predictive indicators that traditional models may overlook. The ability of deep learning algorithms to handle vast amounts of heterogeneous data enables the generation of more accurate cost forecasts, which is essential for effective budgeting, resource allocation, and long-term financial planning in healthcare institutions. In this study, the methodology involves training several deep learning architectures using historical patient data and operational cost records from multiple healthcare systems. These models are evaluated in terms of their predictive accuracy, generalization capabilities, and …
subject: Articles
  topic: section
subject: deep learning
  topic: keywords
subject: healthcare cost forecasting
  topic: keywords
creator: Srinivasan Ramalingam
creator: Praveen Sivathapandi
creator: Jawaharbabu Jeyaraman
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-01-17
publication date: 2022-01-17
starting page: 69
ending page: 110

Leveraging Natural Language Processing for Business Process Mining from Unstructured Data Sources
type: item
description: This research explores the integration of Natural Language Processing (NLP) within Business Process Mining (BPM) to enhance the analysis of unstructured data sources, such as emails, documents, and customer interactions. Traditionally, BPM has relied heavily on structured data, limiting its applicability in contexts where valuable process-related information is embedded in unstructured formats. By leveraging NLP techniques, this paper investigates how textual data can be transformed into actionable insights for process analysis, specifically focusing on customer service and administrative processes. The study outlines key methodologies, including entity recognition, sentiment analysis, and topic modeling, to extract relevant process-related information from unstructured data streams. Additionally, the paper addresses the challenges in automating these processes, such as the complexities of natural language understanding, context recognition, and data integration. It emphasizes the potential for enhancing process discovery, conformance checking, and performance analysis by incorporating unstructured data. The research contributes to expanding the scope of BPM by introducing a new paradigm for extracting meaningful process insights from diverse data types. This methodology is expected to significantly improve decision-making and operational efficiency in business environments, particularly in sectors that rely on large volumes of text-based data for process execution.
subject: Articles
  topic: section
subject: Natural Language Processing
  topic: keywords
subject: Business Process Mining
  topic: keywords
creator: Amish Doshi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-11-24
publication date: 2022-11-24
starting page: 11
ending page: 21

Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response
type: item
description: In the contemporary landscape of digital transformation, organizations increasingly adopt Agile methodologies to enhance their responsiveness to market demands and improve operational efficiencies. However, this rapid evolution presents significant cybersecurity challenges, as traditional security measures often fall short in accommodating the dynamic nature of Agile environments. This research paper delves into the critical role of Artificial Intelligence (AI) in mitigating cybersecurity risks during Agile-driven digital transformation, with a particular emphasis on real-time vulnerability scanning and automated incident response mechanisms. By leveraging advanced AI algorithms, organizations can enhance their security postures and proactively address vulnerabilities, thereby fostering a resilient digital infrastructure. The paper begins by establishing the foundational concepts of Agile digital transformation, elucidating how its iterative processes and continuous integration/continuous deployment (CI/CD) pipelines contribute to heightened risk exposure. It further examines the multifaceted nature of cybersecurity threats that emerge within Agile frameworks, including but not limited to vulnerabilities introduced by rapid software development cycles, inadequate security training, and the complexity of multi-cloud environments. A comprehensive literature review synthesizes existing studies on AI's applicability in cybersecurity, highlighting its potential to revolutionize traditional security paradigms through enhanced detection, response, and remediation capabilities. One of the central themes of this paper is the implementation of real-time vulnerability scanning facilitated by AI technologies. Unlike conventional scanning techniques, which may operate on a periodic basis, AI-driven vulnerability assessments can continuously monitor systems and applications for emerging threats. Machine learning algorithms, such as anomaly detection and supervised learning, empow…
subject: Articles
  topic: section
subject: Cybersecurity
  topic: keywords
subject: Agile transformation
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: vulnerability scanning
  topic: keywords
creator: Seema Kumari
creator: Sahil Dhir
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-12-12
publication date: 2023-12-12
starting page: 50
ending page: 74

AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation
type: item
description: In the rapidly evolving landscape of digital product management, the adoption of Agile methodologies has significantly transformed the ways in which products are developed, iterated, and brought to market. However, the increasing complexity of product development cycles and the demand for enhanced market responsiveness necessitate more advanced tools and methodologies to optimize these processes. This paper explores the integration of Artificial Intelligence (AI) into Agile development frameworks, with a focus on leveraging data-driven insights for iterative improvement and market adaptation in digital product management. The intersection of AI and Agile methodologies introduces a paradigm shift, where machine learning algorithms, predictive analytics, and natural language processing (NLP) augment traditional Agile practices by automating repetitive tasks, optimizing decision-making processes, and providing real-time feedback loops that drive faster and more informed iterations. AI-enhanced Agile development introduces capabilities such as predictive sprint planning, intelligent backlog prioritization, and automated user feedback analysis. Through the application of machine learning algorithms, historical project data, and real-time market information, AI can predict potential bottlenecks, forecast resource allocation needs, and recommend the most efficient paths to delivery. This dynamic forecasting ability allows product teams to anticipate challenges before they arise, ensuring that development cycles remain adaptive and aligned with both short-term deliverables and long-term product goals. Additionally, AI-driven backlog prioritization employs advanced data analytics to evaluate user behavior, market trends, and competitor actions, enabling teams to dynamically reorder tasks based on real-time strategic importance, ultimately fostering a more agile and responsive product development lifecycle. The paper also examines how AI contributes to enhancing market adapta…
subject: Articles
  topic: section
subject: AI-enhanced Agile development
  topic: keywords
subject: data-driven insights
  topic: keywords
subject: digital product management
  topic: keywords
subject: iterative improvement
  topic: keywords
creator: Seema Kumari
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-03-14
publication date: 2022-03-14
starting page: 49
ending page: 68

Explainable AI: Interpreting Deep Learning Models for Decision Support
type: item
description: As artificial intelligence (AI) systems, particularly those based on deep learning models, increasingly influence decision-making processes across various sectors, the imperative for explainable AI (XAI) becomes more pronounced. This paper addresses the critical need for interpretability in AI-driven decision support systems, focusing on methodologies and techniques that enhance the transparency of deep learning models. The discussion encompasses key approaches such as SHAP (SHapley Additive exPlanations) values, LIME (Local Interpretable Model-agnostic Explanations), and model-specific interpretability tools, all of which contribute to elucidating the decision-making processes of complex AI systems. SHAP values provide a unified measure of feature importance by attributing contributions to individual predictions, derived from cooperative game theory. This approach offers insights into the model’s decision-making process by evaluating the marginal contributions of each feature. Similarly, LIME focuses on local interpretability by approximating the behavior of a black-box model with an interpretable surrogate model in the vicinity of a given prediction. This method enables users to understand model behavior on a case-by-case basis, which is crucial for validating the model’s predictions and understanding its limitations. Model-specific interpretability tools, such as activation maximization and saliency maps, offer additional layers of transparency. Activation maximization techniques involve identifying input patterns that maximize activations of particular neurons or layers within the network, providing insights into what features drive the model’s predictions. Saliency maps visualize gradients of the output with respect to the input features, highlighting areas of the input that significantly influence the model’s output. These techniques, while valuable, are often limited by their reliance on the model’s architecture and may not always generalize across different …
subject: Articles
  topic: section
subject: Explainable AI
  topic: keywords
subject: XAI
  topic: keywords
subject: SHAP values
  topic: keywords
subject: LIME
  topic: keywords
subject: interpretability
  topic: keywords
subject: deep learning
  topic: keywords
subject: decision support systems
  topic: keywords
subject: model transparency
  topic: keywords
creator: Tanzeem Ahmad
creator: Pranadeep Katari
creator: Ashok Kumar Pamidi Venkata
creator: Chetan Sasidhar Ravi
creator: Mahammad Shaik
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-02-15
publication date: 2024-02-15
starting page: 80
ending page: 108

Artificial Intelligence and Cloud Services for Enhancing Patient Care: Techniques, Applications, and Real-World Case Studies
type: item
description: In the evolving landscape of healthcare, the integration of Artificial Intelligence (AI) and cloud services has emerged as a transformative force in enhancing patient care. This paper delves into the synergistic effects of AI and cloud technologies, exploring their techniques, applications, and real-world case studies that collectively illustrate their potential to revolutionize patient outcomes and experiences. AI's capability to analyze vast amounts of medical data through machine learning and natural language processing enables the development of predictive models that aid in early diagnosis, personalized treatment, and efficient management of chronic conditions. Meanwhile, cloud services provide scalable infrastructure that supports the deployment and management of AI tools, ensuring accessibility and interoperability across disparate healthcare systems. The first section of this paper elaborates on the fundamental techniques employed in AI-driven healthcare solutions, including supervised and unsupervised learning algorithms, deep learning networks, and reinforcement learning methodologies. It provides a comprehensive overview of how these techniques contribute to advanced diagnostic systems, therapeutic recommendations, and patient monitoring. The discussion extends to the role of cloud computing in facilitating these AI applications, emphasizing the benefits of data storage, computational power, and real-time analytics provided by cloud platforms. Subsequent sections address the practical applications of AI and cloud services within the healthcare domain. Notable applications include AI-powered imaging systems that enhance diagnostic accuracy, virtual health assistants that offer personalized patient engagement, and cloud-based electronic health record (EHR) systems that improve data management and accessibility. The paper also examines the impact of these technologies on healthcare workflows, including their contribution to reducing administrative burdens, s…
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Cloud Services
  topic: keywords
subject: Patient Care
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Electronic Health Records
  topic: keywords
subject: Healthcare Applications
  topic: keywords
subject: Data Privacy
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Navajeevan Pushadapu
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-02-02
publication date: 2021-02-02
starting page: 111
ending page: 158

Comparative Analysis of Advanced Time Series Forecasting Techniques
type: item
description: This paper presents a rigorous comparative analysis of advanced time series forecasting techniques, specifically focusing on the application of ARIMA, Prophet, and deep learning models to the prediction of key financial indicators, including inflation rates and exchange rates. The study seeks to address the growing demand for accurate and reliable forecasting methods in financial markets, where precise predictions are crucial for informed decision-making. By systematically evaluating the performance of these models, this research contributes to the ongoing discourse on the efficacy of traditional versus modern forecasting approaches in the context of financial time series data. The analysis begins with an exploration of the theoretical foundations and mathematical formulations underlying each model. ARIMA, a widely used statistical method, is known for its capacity to model linear relationships in time series data by capturing autoregressive, differencing, and moving average components. Prophet, developed by Facebook, is a relatively recent addition to the forecasting landscape, designed to handle seasonality, holidays, and trends with a focus on ease of use and interpretability. The deep learning models, on the other hand, represent a paradigm shift, employing neural networks to capture complex, nonlinear relationships in time series data. These models, including Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs), have demonstrated significant potential in various domains but require extensive computational resources and expertise. In this study, we implement these models on a comprehensive dataset comprising historical inflation rates, exchange rates, and other critical financial metrics. The dataset is pre-processed to ensure consistency and to account for missing values, outliers, and non-stationarities, which are common challenges in financial time series analysis. Each model is trained and tested using a rolling window approach, allowing fo…
subject: Articles
  topic: section
subject: time series forecasting
  topic: keywords
subject: ARIMA
  topic: keywords
subject: Prophet
  topic: keywords
subject: deep learning models
  topic: keywords
subject: financial indicators
  topic: keywords
subject: inflation rates
  topic: keywords
subject: exchange rates
  topic: keywords
subject: computational efficiency
  topic: keywords
subject: seasonality
  topic: keywords
subject: non-linear relationships
  topic: keywords
creator: Tingting Deng
creator: Shuochen Bi
creator: Jue Xiao
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-02-09
publication date: 2023-02-09
starting page: 52
ending page: 98

Reimagining Digital Identity Management: A Critical Review of Blockchain-Based Identity and Access Management (IAM) Systems - Architectures, Security Mechanisms, and Industry-Specific Applications
type: item
description: The ever-expanding digital landscape, characterized by the relentless proliferation of online services and applications, has firmly established digital identities as the cornerstone of secure interactions in our contemporary world. However, the efficacy of these interactions hinges on the robustness of the underlying Identity and Access Management (IAM) systems that safeguard them. Traditional, centralized IAM solutions, while serving as the bedrock of digital identity management for decades, are increasingly under fire for their susceptibility to data breaches and inherent privacy limitations. This paper presents a comprehensive exploration of blockchain-enabled IAM systems, investigating their potential to revolutionize the paradigm of digital identity management. We embark on a meticulous dissection of the architectural foundations of blockchain-based IAM, meticulously dissecting their distributed ledger structure, the intricacies of employed consensus mechanisms, and the cryptographic primitives that safeguard information integrity. Following this in-depth architectural exploration, a rigorous examination of the security features woven into these systems is presented. This analysis encompasses tamper-proof data storage mechanisms, the implementation of granular access control models that enable fine-tuned permission structures, and user-centric privacy preservation techniques that empower individuals with unprecedented control over their digital identities. To illuminate the practical value proposition of blockchain-based IAM, we delve into its application across diverse industry verticals. This includes exploring its transformative potential in e-governance by facilitating secure, transparent, and efficient citizen-government interactions. We investigate its role in the healthcare sector, enabling secure, auditable, and interoperable patient data management, fostering a more streamlined and patient-centric healthcare ecosystem. Furthermore, the paper examines i…
subject: Articles
  topic: section
subject: Blockchain Technology
  topic: keywords
subject: Decentralized Identity
  topic: keywords
subject: Self-Sovereign Identity (SSI)
  topic: keywords
subject: Distributed Ledger Technology (DLT)
  topic: keywords
subject: Cryptographic Primitives
  topic: keywords
subject: Access Control
  topic: keywords
subject: Privacy-Preserving Techniques
  topic: keywords
subject: E-governance
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Financial Services
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
creator: Ashok Kumar Reddy Sadhu
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-08-09
publication date: 2021-08-09
starting page: 1
ending page: 22

AI-Powered Automation in DevOps for Intelligent Release Management: Techniques for Reducing Deployment Failures and Improving Software Quality
type: item
description: The relentless pursuit of faster software delivery cycles necessitates a paradigm shift in DevOps practices. Traditional, manual release management processes struggle to keep pace with the ever-increasing complexity and velocity of modern software development. This research delves into the transformative potential of artificial intelligence (AI) within DevOps, specifically its application in intelligent release management. By leveraging AI-powered automation techniques, organizations can significantly reduce deployment failures and elevate software quality through continuous monitoring and analysis. The paper commences by establishing the context of the evolving DevOps landscape. The limitations of manual release management methods are highlighted, particularly their susceptibility to human error and inability to scale effectively in dynamic environments. Subsequently, the paper explores the fundamental concepts of AI, emphasizing its potential to revolutionize software delivery pipelines. Machine learning (ML) algorithms, a core tenet of AI, are introduced as the driving force behind intelligent automation. Their ability to learn from vast datasets and identify patterns paves the way for proactive release management strategies. The crux of the paper revolves around the specific AI-powered techniques that contribute to intelligent release management. A prominent technique is continuous monitoring. By deploying AI-powered monitoring tools, DevOps teams gain real-time insights into system behavior. These tools can ingest data from various sources, including infrastructure logs, application performance metrics, and user experience data. AI algorithms then analyze this data stream to detect anomalies and identify potential issues that could lead to deployment failures. Early detection of anomalies empowers proactive intervention, allowing teams to address concerns before they escalate into critical production disruptions. Another significant technique is anomaly detecti…
subject: Articles
  topic: section
subject: AI-powered DevOps
  topic: keywords
subject: Intelligent Release Management
  topic: keywords
subject: Deployment Failure Reduction
  topic: keywords
subject: Software Quality Improvement
  topic: keywords
subject: Continuous Monitoring
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Predictive Modeling
  topic: keywords
subject: Root Cause Analysis
  topic: keywords
subject: DevOps Pipeline Optimization
  topic: keywords
creator: Sumanth Tatineni
creator: Anirudh Mustyala
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-02-21
publication date: 2021-02-21
starting page: 74
ending page: 110

Evolutionary Landscape of Battery Technology and its Impact on Smart Traffic Management Systems for Electric Vehicles in Urban Environments: A Critical Analysis
type: item
description: The transportation sector faces a pivotal moment, driven by the urgent need for sustainable mobility solutions. Electric vehicles (EVs) have emerged as frontrunners in this revolution, promising cleaner air and reduced dependence on fossil fuels. However, their widespread adoption hinges critically on advancements in battery technology. This paper delves into the exciting realm of battery evolution, exploring its impact on optimizing urban mobility for EVs. The first part of the paper dissects the fascinating journey of lithium-ion (Li-ion) batteries, the current workhorse of EVs. We analyze the significant improvements witnessed in Li-ion technology, focusing on enhanced energy density, faster charging times, and improved thermal stability. These advancements directly translate to increased driving range, reduced charging anxiety, and ultimately, a more user-friendly EV experience. We further explore the potential of solid-state batteries, heralded as the next frontier in battery technology. By discussing groundbreaking innovations like China's CATL claiming a staggering 500 Wh/kg energy density, we illuminate the transformative power of solid-state technology. This exceptional energy density promises to revolutionize EVs, enabling longer driving ranges and potentially eliminating the need for frequent charging stops. Beyond propelling vehicles, battery longevity holds profound implications for the future of traffic management in cities. The second part of the paper delves into this crucial aspect. We highlight key breakthroughs and innovations that are extending battery lifespan and enhancing overall sustainability. This includes advancements in Battery Management Systems (BMS) that utilize machine learning algorithms to optimize charging cycles and minimize degradation. Additionally, advancements in Battery Recycling Technologies (BRT) are explored. Efficient BRT practices not only promote a circular economy for critical battery materials like lithium and cobalt …
subject: Articles
  topic: section
subject: Lithium-ion Battery Improvements
  topic: keywords
subject: Solid-State Batteries
  topic: keywords
subject: Battery Recycling Technologies
  topic: keywords
subject: Wireless Charging Innovations
  topic: keywords
subject: Adaptive Learning techniques for traffic management
  topic: keywords
subject: energy consumption
  topic: keywords
subject: charging station network
  topic: keywords
subject: Cost Reduction Strategies in Battery Manufacturing
  topic: keywords
subject: Future Battery Technologies and Their Potential
  topic: keywords
creator: Vamsi Vemoori
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-07-02
publication date: 2021-07-02
starting page: 23
ending page: 57

Optimizing Cloud Resource Allocation: A Comparative Analysis of AI-Driven Techniques
type: item
description: Efficient resource allocation is a critical aspect of cloud computing, impacting performance, cost-effectiveness, and overall user satisfaction. With the growing complexity and scale of cloud environments, traditional manual or rule-based approaches to resource allocation are becoming inadequate. This research paper presents a comparative analysis of AI-driven techniques for optimizing cloud resource allocation, aiming to enhance efficiency and responsiveness while minimizing costs. Artificial intelligence (AI) has emerged as a powerful tool for automating decision-making processes in various domains, including resource management. Machine learning algorithms, in particular, have shown promise in learning patterns from historical data and making predictions or recommendations for resource allocation in dynamic cloud environments. Additionally, optimization techniques such as genetic algorithms and reinforcement learning offer alternative approaches to finding optimal resource allocation strategies. The paper begins by providing an overview of the challenges and complexities associated with cloud resource allocation. These include dynamic workload patterns, varying resource demands, and the need to balance competing objectives such as performance, cost, and energy efficiency. Traditional approaches often struggle to adapt to these dynamic conditions, leading to underutilization, overprovisioning, or performance bottlenecks. Next, we review existing literature and industry practices related to AI-driven techniques for cloud resource allocation. This includes a survey of machine learning models commonly applied to resource allocation tasks, such as regression, classification, clustering, and time series forecasting. We also explore optimization algorithms and metaheuristic techniques used to search for optimal resource allocation configurations. To empirically evaluate the effectiveness of AI-driven techniques for cloud resource allocation, we conducted a comparative a…
subject: Articles
  topic: section
subject: cloud computing
  topic: keywords
subject: resource allocation
  topic: keywords
subject: artificial intelligence
  topic: keywords
subject: machine learning
  topic: keywords
subject: optimization algorithms
  topic: keywords
subject: comparative analysis
  topic: keywords
subject: efficiency
  topic: keywords
subject: cost-effectiveness
  topic: keywords
creator: Vishal Shahane
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-09-20
publication date: 2023-09-20
starting page: 23
ending page: 49

Assessing Microservice Security Implications in AWS Cloud for to implement Secure and Robust Applications
type: item
description: As organizations increasingly embrace microservices architecture hosted on the Amazon Web Services (AWS) Cloud, the paramount importance of securing these distributed components becomes evident. This research paper introduces a specialized Security Implementation Assessment Approach tailored for AWS Cloud, aiming to ensure the security and scalability of microservices. The paper delves into the nuances of microservices security challenges, explores AWS native security services, and proposes a systematic framework for assessing and implementing security measures. Real-world case studies exemplify successful security implementations, while discussions on challenges and solutions provide practical insights. The paper concludes by emphasizing the symbiotic relationship between security and scalability in microservices on AWS, setting the stage for future research and advancements in this evolving domain.
subject: Articles
  topic: section
subject: Microservice
  topic: keywords
subject: Cloud Migration
  topic: keywords
subject: Containerization Distributed Systems
  topic: keywords
subject: Microservice Security
  topic: keywords
creator: Amarjeet Singh
creator: Alok Aggarwal
rights: Copyright (c) 2023 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-04-27
publication date: 2023-04-27
starting page: 31
ending page: 51

Pushing Boundaries with Deep Generative Models: Innovations and Applications of VAEs and GANs
type: item
description: This paper delves into the cutting-edge realm of deep generative models, specifically focusing on variational autoencoders (VAEs) and generative adversarial networks (GANs). We explore the innovations and applications that have pushed the boundaries of these models, enabling them to generate realistic data across various domains. Beginning with an overview of VAEs and GANs, we delve into recent advancements such as conditional generation, style transfer, and multimodal synthesis. We discuss how these models have been utilized in diverse fields including image generation, text-to-image synthesis, and drug discovery. Furthermore, we examine challenges and future directions in the field, emphasizing the importance of ethical considerations and interpretability. Through this comprehensive analysis, we illustrate the immense potential of VAEs and GANs in driving innovation and fostering novel applications across disciplines.
subject: Articles
  topic: section
subject: deep generative models
  topic: keywords
subject: variational autoencoders
  topic: keywords
subject: VAEs
  topic: keywords
subject: generative adversarial networks
  topic: keywords
subject: GANs
  topic: keywords
subject: conditional generation
  topic: keywords
subject: style transfer
  topic: keywords
subject: multimodal synthesis
  topic: keywords
subject: applications
  topic: keywords
subject: innovations
  topic: keywords
creator: Prabu Ravichandran
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-06-08
publication date: 2022-06-08
starting page: 37
ending page: 48

Data-Driven Dental Public Health: Improving Community Oral Health through Analytics
type: item
description: Data-driven approaches are revolutionizing public health interventions, and dental public health is no exception. This paper explores the pivotal role of data analytics in enhancing community oral health outcomes. By harnessing population-level data, disparities in oral health can be identified, interventions can be targeted effectively, and resource allocation can be optimized. Through the integration of advanced analytics techniques, including machine learning and predictive modeling, dental public health practitioners can gain deeper insights into the factors influencing oral health outcomes within communities. This abstract provides an overview of the key components of data-driven dental public health, including data collection methods, analysis techniques, intervention strategies, and challenges faced. By leveraging the power of data analytics, dental public health initiatives can achieve greater precision, efficiency, and effectiveness in promoting oral health equity and improving overall community well-being.
subject: Articles
  topic: section
subject: Data-driven
  topic: keywords
subject: Dental public health
  topic: keywords
subject: Analytics
  topic: keywords
subject: Population-level data
  topic: keywords
subject: Disparities
  topic: keywords
subject: Interventions
  topic: keywords
subject: Resource allocation
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Predictive modeling
  topic: keywords
subject: Oral health equity
  topic: keywords
creator: Aneesh Reddy Pappireddy
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-05-10
publication date: 2024-05-10
starting page: 67
ending page: 79

AI-Driven Fraud Detection Systems: A Comparative Study across Banking, Insurance, and Healthcare
type: item
description: Artificial intelligence (AI)-driven fraud detection systems have emerged as crucial tools in safeguarding the integrity of financial, insurance, and healthcare sectors. This paper presents a comprehensive comparative study across these domains, evaluating the efficacy, scalability, and adaptability of AI-based fraud detection mechanisms. Through an analysis of existing literature and empirical data, we examine the diverse approaches employed in detecting fraudulent activities, considering the unique challenges and regulatory frameworks within each sector. Our findings highlight the advancements in machine learning algorithms, anomaly detection techniques, and data analytics driving the evolution of fraud detection systems. We discuss key factors influencing the performance of AI-driven solutions, including data quality, model interpretability, and computational resources. Moreover, this study explores the implications of AI adoption on fraud prevention strategies, organizational risk management, and customer trust. By synthesizing insights from banking, insurance, and healthcare contexts, this research aims to provide valuable guidance for stakeholders seeking to enhance their fraud detection capabilities in an increasingly digitalized landscape.
subject: Articles
  topic: section
subject: AI
  topic: keywords
subject: Fraud Detection
  topic: keywords
subject: Banking
  topic: keywords
subject: Insurance
  topic: keywords
subject: Healthcare
  topic: keywords
subject: Comparative Study
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Data Analytics
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Pankaj Zanke
rights: Copyright (c) 2023 Pankaj Zanke https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2023-08-17
publication date: 2023-08-17
starting page: 1
ending page: 22

Artificial Intelligence Applications in Predictive Underwriting for Commercial Lines Insurance
type: item
description: Artificial intelligence (AI) has emerged as a transformative force in the insurance industry, revolutionizing traditional underwriting processes. This paper investigates the application of AI techniques, including neural networks and decision trees, in predictive underwriting for commercial lines insurance. By leveraging vast amounts of data, AI models enhance risk selection and pricing strategies, leading to more accurate assessments of potential liabilities and improved profitability for insurers. This research explores the theoretical foundations of AI in underwriting, examines real-world case studies, and evaluates the benefits and challenges associated with its implementation. Through a comprehensive analysis, this paper aims to provide insights into the opportunities presented by AI in commercial lines insurance underwriting and its implications for the industry's future.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Predictive Underwriting
  topic: keywords
subject: Commercial Lines Insurance
  topic: keywords
subject: Neural Networks
  topic: keywords
subject: Decision Trees
  topic: keywords
subject: Risk Selection
  topic: keywords
subject: Pricing Strategies
  topic: keywords
subject: Data Analysis
  topic: keywords
subject: InsurTech
  topic: keywords
subject: Industry Transformation
  topic: keywords
creator: Pankaj Zanke
creator: Dipti Sontakke
rights: Copyright (c) 2021 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2021-05-25
publication date: 2021-05-25
starting page: 23
ending page: 38

Meta-learning Algorithms for Few-shot Learning: Analyzing meta-learning algorithms designed to enable deep learning models to quickly adapt to new tasks with limited training data
type: item
description: Meta-learning algorithms have gained significant attention in the field of deep learning for their ability to enable models to quickly adapt to new tasks with limited training data, a scenario known as few-shot learning. This paper provides an analysis of various meta-learning algorithms, focusing on their effectiveness in addressing the challenges of few-shot learning. We discuss the key concepts of meta-learning, including meta-training, meta-testing, and the use of task distributions, and review prominent algorithms such as MAML, Reptile, and ProtoNets. Additionally, we examine the applications of meta-learning in computer vision, natural language processing, and robotics, highlighting its potential for enhancing the adaptability of deep learning models in real-world scenarios. Through this analysis, we aim to provide insights into the current state of meta-learning research and its implications for future developments in few-shot learning.
subject: Articles
  topic: section
subject: Meta-learning
  topic: keywords
subject: Few-shot learning
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Meta-training
  topic: keywords
subject: Meta-testing
  topic: keywords
subject: MAML
  topic: keywords
subject: Reptile
  topic: keywords
creator: Dr. Sofia Kovacs
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-28
publication date: 2024-03-28
starting page: 58
ending page: 66

Artificial Intelligence on the Global Stage: Transforming Diplomacy and International Relations
type: item
description: The emergence of Artificial Intelligence (AI) as a transformative force in diplomacy and international relations has marked the beginning of a new era in global geopolitics. This article explores how AI is redefining traditional paradigms of diplomacy, presenting both significant opportunities and challenges. Through detailed analysis, the implications of AI for diplomacy are discussed, including strengthening intercultural communication, data-driven decision making, and ethical and security challenges. Furthermore, frameworks for international AI governance are proposed, underscoring the importance of transnational cooperation to address emerging dilemmas.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Transforming Diplomacy
  topic: keywords
subject: International Relations
  topic: keywords
creator: Damián Tuset Varela
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-15
publication date: 2024-03-15
starting page: 53
ending page: 57

Quantum-Inspired Optimization Techniques for IoT Networks: Focusing on Resource Allocation and Network Efficiency Enhancement for Improved IoT Functionality
type: item
description: Internet of Things (IoT) networks are characterized by a vast number of interconnected devices that require efficient resource allocation and network management. Traditional optimization techniques may not fully address the complex nature of IoT networks. This paper presents a comprehensive review of quantum-inspired optimization techniques for enhancing resource allocation and network efficiency in IoT environments. We examine how quantum-inspired algorithms such as Quantum Annealing, Quantum Genetic Algorithms, and Quantum Particle Swarm Optimization can be applied to address challenges in resource allocation, network routing, and energy efficiency. By leveraging principles from quantum computing, these techniques offer novel approaches to solving optimization problems in IoT networks. We also discuss the potential benefits and challenges of integrating quantum-inspired optimization techniques into IoT systems, including considerations for scalability, security, and implementation complexity. Overall, this paper provides insights into the promising future of quantum-inspired optimization for enhancing IoT network performance and efficiency.
subject: Articles
  topic: section
subject: Quantum-Inspired Optimization
  topic: keywords
subject: IoT Networks
  topic: keywords
subject: Resource Allocation
  topic: keywords
subject: Network Efficiency
  topic: keywords
subject: Quantum Annealing
  topic: keywords
subject: Genetic Algorithms
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Scalability
  topic: keywords
subject: Security
  topic: keywords
subject: Implementation
  topic: keywords
creator: Mohan Raparthi
creator: Bhavani Prasad Kasaraneni
creator: Krishna Kanth Kondapaka
creator: Swaroop Reddy Gayam
creator: Sudharshan Putha
creator: Siva Sarana Kuna
creator: Venkata Siva Prakash Nimmagadda
creator: Mohit Kumar Sahu
creator: Sandeep Pushyamitra Pattyam
creator: Praveen Thuniki
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-07-07
publication date: 2022-07-07
starting page: 1
ending page: 9

Harnessing Quantum Computing for Drug Discovery and Molecular Modelling in Precision Medicine: Exploring Its Applications and Implications for Precision Medicine Advancement
type: item
description: Quantum computing is poised to revolutionize various scientific fields, including drug discovery and molecular modeling in precision medicine. This paper explores the potential of quantum computing in advancing precision medicine through enhanced drug discovery and molecular modeling techniques. We discuss the principles of quantum computing relevant to these applications and highlight its advantages over classical computing. Additionally, we review current research and developments in utilizing quantum computing for drug discovery and molecular modeling, emphasizing its potential to accelerate the identification of novel drug candidates and improve understanding of molecular interactions. Furthermore, we examine the challenges and limitations of quantum computing in this context and propose future directions for research and application. Overall, this paper provides insights into the transformative impact of quantum computing on precision medicine and its potential to drive significant advancements in healthcare.
subject: Articles
  topic: section
subject: Quantum Computing
  topic: keywords
subject: Drug Discovery
  topic: keywords
subject: Molecular Modeling
  topic: keywords
subject: Precision Medicine
  topic: keywords
subject: Quantum Algorithms
  topic: keywords
subject: Quantum Chemistry
  topic: keywords
subject: Quantum Machine Learning
  topic: keywords
subject: Quantum Simulation
  topic: keywords
subject: Quantum Supremacy
  topic: keywords
subject: Quantum Advantage
  topic: keywords
creator: Mohan Raparthi
creator: Swaroop Reddy Gayam
creator: Bhavani Prasad Kasaraneni
creator: Krishna Kanth Kondapaka
creator: Sudharshan Putha
creator: Sandeep Pushyamitra Pattyam
creator: Praveen Thuniki
creator: Siva Sarana Kuna
creator: Venkata Siva Prakash Nimmagadda
creator: Mohit Kumar Sahu
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2022-02-02
publication date: 2022-02-02
starting page: 27
ending page: 36

Standardization and Regulation of V2X Cybersecurity: Analyzing the Current Landscape, Identifying Gaps, and Proposing Frameworks for Harmonization
type: item
description: In recent years, the automotive industry has witnessed a profound transformation propelled by the widespread adoption of Vehicle-to-Everything (V2X) communication technology. This innovation empowers vehicles to establish seamless communication between various elements of the transportation infrastructure, pedestrians, and other road users. While this interconnectedness promises enhanced safety, efficiency, and convenience on the roads, it also introduces a myriad of cybersecurity challenges. Recognizing the critical importance of safeguarding V2X systems against malicious threats and cyber-attacks, this research article delves into the imperative need for the establishment of robust standards and regulations. The primary objective of this study is to conduct a meticulous analysis of the prevailing global landscape of standards and regulations concerning V2X cybersecurity. To achieve this objective, the research meticulously identifies and scrutinizes existing standards, regulations, and best practices implemented across diverse jurisdictions and within various automotive stakeholders. Through a systematic evaluation of their efficacy and limitations, the study endeavors to pinpoint deficiencies and inadequacies in the current regulatory framework governing V2X cybersecurity. Furthermore, this article endeavors to transcend the mere identification of gaps by proposing comprehensive frameworks aimed at harmonizing cybersecurity measures. These frameworks are envisioned to facilitate coherence and consistency in cybersecurity protocols across different geographical regions and among diverse stakeholders within the automotive ecosystem. By promoting alignment and collaboration, the proposed frameworks aspire to fortify the overall security posture of V2X systems, thereby mitigating vulnerabilities and bolstering resilience against potential cyber threats. In essence, this research article serves as a clarion call for concerted action toward the establishment of a robus…
subject: Articles
  topic: section
subject: V2X
  topic: keywords
subject: Cybersecurity
  topic: keywords
creator: Babajide J Asaju
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-03-05
publication date: 2024-03-05
starting page: 33
ending page: 52

Variational Autoencoders - Theory and Applications: Exploring Variational Autoencoder Models and Their Applications in Generative Modeling, Representation Learning, and Beyond
type: item
description: Variational autoencoders (VAEs) have emerged as a powerful framework for generative modeling and representation learning in recent years. This paper provides a comprehensive overview of VAEs, starting with their theoretical foundations and then exploring their diverse applications. We begin by explaining the basic principles of VAEs, including the encoder and decoder networks, the reparameterization trick, and the variational lower bound. We then delve into various extensions and improvements to the basic VAE framework, such as conditional VAEs, hierarchical VAEs, and beta-VAEs, highlighting their respective advantages and use cases. Moving beyond theory, we survey the wide range of applications where VAEs have been successfully employed. This includes image generation, where VAEs have been used to create realistic images in domains such as fashion, art, and medical imaging. We also discuss the use of VAEs in representation learning, showing how they can be used to disentangle underlying factors of variation in data, leading to more interpretable and controllable representations. Additionally, we explore how VAEs have been applied in semi-supervised learning, anomaly detection, and data augmentation. Overall, this paper aims to provide a comprehensive understanding of VAEs, from their fundamental concepts to their practical applications, showcasing their versatility and potential for future research and development.
subject: Articles
  topic: section
subject: Variational Autoencoders
  topic: keywords
subject: Generative Modeling
  topic: keywords
subject: Representation Learning
  topic: keywords
subject: Encoder
  topic: keywords
subject: Decoder
  topic: keywords
subject: Disentanglement
  topic: keywords
subject: Semi-Supervised Learning
  topic: keywords
subject: Anomaly Detection
  topic: keywords
subject: Data Augmentation
  topic: keywords
creator: Dr. Miguel Sanchez
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-02-27
publication date: 2024-02-27
starting page: 18
ending page: 32

Transformer Networks - Architectures and Applications: Investigating Transformer Network Architectures and Their Diverse Applications in Natural Language Processing and Beyond
type: item
description: Transformer Networks, since their introduction in the seminal paper "Attention is All You Need," have revolutionized the field of natural language processing (NLP) and found wide-ranging applications beyond NLP. This paper provides a comprehensive overview of transformer network architectures and their diverse applications. We start by explaining the core components of transformer networks, including self-attention mechanisms and feed-forward neural networks. We then delve into various transformer-based architectures, such as BERT, GPT, and T5, highlighting their unique features and improvements over the original transformer model. Furthermore, we explore the applications of transformer networks in NLP tasks, such as machine translation, text summarization, and question answering. We also discuss their use in computer vision, speech recognition, and other domains. Additionally, we examine the challenges and limitations of transformer networks, including computational complexity and fine-tuning requirements. Overall, this paper aims to provide a comprehensive understanding of transformer networks, their architectures, and their wide-ranging applications, showcasing their significance in advancing the field of deep learning and artificial intelligence.
subject: Articles
  topic: section
subject: Transformer Networks
  topic: keywords
subject: Attention Mechanism
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Machine Translation
  topic: keywords
subject: Text Generation
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Speech Recognition
  topic: keywords
subject: Applications
  topic: keywords
subject: Challenges
  topic: keywords
creator: Prof. Kimiko Tanaka
rights: Copyright (c) 2024 https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2024-02-27
publication date: 2024-02-27
starting page: 1
ending page: 17