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Revolutionizing Medical Data Exchange through Blockchain Technology: A
Secure and Decentralized Marketplace Solution
Research · November 2024
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Revolutionizing Medical Data Exchange through
Blockchain Technology: A Secure and
Decentralized Marketplace Solution
*
Ms. Judy Flavia B1 , Ms. Aarthi B2
Madhava Rao M3 , Shasank S4
Dept. of CSE with spzl in AIML
SRM IST - Ramapuram
Chennai, Tamilnadu, India
{judyflab, aarthib}@srmist.edu.in
Dept. of CSE with spzl in AIML
SRM IST - Ramapuram
Chennai, Tamilnadu, India
{mm2188, ss0067}@srmist.edu.in
Abstract—In the era of digital healthcare, the exchange of
medical data is crucial for enhancing patient care and advancing
medical research. However, traditional data exchange systems
often face challenges such as security breaches, data silos,
and lack of interoperability. This paper proposes a blockchainbased solution for revolutionizing medical data exchange by
creating a secure and decentralized marketplace. Leveraging
blockchain technology ensures data integrity, enhances privacy,
and facilitates seamless data sharing among stakeholders. Our
approach includes smart contracts for automating transactions
and ensuring compliance with regulatory standards. The proposed marketplace fosters trust, reduces costs, and promotes
innovation in healthcare by enabling efficient and secure data
exchange. This paper also discusses the implementation details,
potential challenges, and future directions for blockchain in
medical data management.
Index Terms—blockchain technology, medical data exchange,
secure data marketplace, decentralized healthcare, smart contracts, data privacy, interoperability, healthcare innovation, digital health, regulatory compliance
I. INTRODUCTION
In today’s digital healthcare landscape, efficient exchange
of medical data is crucial for enhancing patient outcomes
and driving medical research. Traditional methods face challenges such as security vulnerabilities, data silos, and interoperability issues among healthcare systems. Blockchain
technology presents a transformative solution by providing
a decentralized, secure, and transparent platform for managing medical data. Through cryptographic techniques and a
distributed ledger system, blockchain ensures data integrity
and privacy while facilitating seamless data exchange across
healthcare stakeholders without intermediaries. This paper
explores blockchain’s potential to revolutionize medical data
exchange through a secure, decentralized marketplace. Smart
contracts play a pivotal role in automating compliance and
transaction enforcement, thereby reducing costs, improving
data accessibility, fostering trust, and catalyzing healthcare
innovation. By examining theoretical foundations, practical
implementations, challenges, and future directions, this study
aims to demonstrate how blockchain can empower healthcare
organizations to securely exchange medical data, enhance
operational efficiencies, and spur collaborative research efforts.
II. LITERATURE SURVEY
A. Background
Decentralized data marketplaces are increasingly recognized as a solution to the shortcomings of centralized systems in handling sensitive medical data. Existing literature
highlights significant challenges faced by centralized data
markets, including security breaches, privacy concerns, and
high intermediary costs (Zyskind et al., 2015). Centralized
architectures, where data is managed by a single entity, are
vulnerable to cyber-attacks, as emphasized by previous studies.
Blockchain technology has emerged as a promising remedy,
offering a decentralized and immutable ledger that enhances
data security and user trust (Xu et al., 2019). Researchers
demonstrate blockchain’s potential to establish transparent and
secure environments for data exchange, reducing reliance on
third-party intermediaries and empowering users with greater
data control.
In addition to blockchain, the integration of Artificial Intelligence (AI) in data marketplaces has been explored to enhance
transaction efficiency and data quality (Chen et al., 2020).
AI-driven approaches facilitate advanced analytics, efficient
data matching, and dynamic pricing mechanisms, ensuring
that exchanged data meets quality and relevance requirements.
Recent studies suggest that combining AI with blockchain
technologies can mitigate traditional system limitations and
introduce innovative capabilities (Liu et al., 2021). This literature survey highlights the consensus on integrating AI and
blockchain to develop secure, efficient, and user-centric data
marketplaces, laying the groundwork for further exploration
and implementation in our research.
B. Literature Survey
The landscape of data marketplaces is evolving towards
decentralized models due to vulnerabilities inherent in centralized systems. Centralized data markets suffer from security
breaches, privacy issues, and operational inefficiencies, necessitating robust solutions like blockchain technology (Zyskind
et al., 2015). Blockchain offers a decentralized ledger that
enhances data security and transparency, as evidenced by
studies showing its effectiveness in mitigating risks associated
with centralized data management (Xu et al., 2019).
Simultaneously, integrating AI into data marketplaces aims
to optimize data transactions. AI-driven solutions provide
advanced capabilities in analytics, matching, and pricing,
ensuring high-quality data exchanges (Chen et al., 2020). The
synergy between AI and blockchain has been explored in
recent research, demonstrating how AI enhances blockchainbased marketplaces through intelligent contract execution and
automated compliance (Liu et al., 2021). These studies underscore the potential of AI and blockchain integration to
create secure, efficient, and user-centric data marketplaces,
forming a solid foundation for our project’s investigation and
implementation.
III. PROPOSED SYSTEM
The proposed system for a decentralized medical data
marketplace integrates blockchain technology and AI-driven
data analytics to revolutionize healthcare data management.
It ensures secure, transparent, and efficient data exchange by
leveraging blockchain’s immutable ledger and smart contracts
for data integrity and privacy. Advanced encryption and decentralized storage protect sensitive medical data, while AI
algorithms facilitate precise data matching and retrieval based
on user preferences. This system empowers patients with
control over data access, accelerates medical research and innovation, promotes interoperability across healthcare systems,
and enhances public health initiatives through comprehensive,
patient-centric data solutions.
A. Proposed System Architecture
The proposed system architecture of the decentralized medical data marketplace integrates blockchain technology and
artificial intelligence (AI) algorithms to ensure secure and
efficient data transactions. It comprises a decentralized network of nodes where data providers and consumers interact
through smart contracts. Blockchain ensures data immutability
and transparency, while AI algorithms facilitate accurate data
matching and retrieval based on user preferences.
B. Key Components
Key components of the system include:
Blockchain Network: Utilizes a distributed ledger to record
transactions and maintain data integrity.
Smart Contracts: Automated contracts that enforce transaction rules and conditions between parties.
Data Storage Layer: Decentralized storage ensures data
availability and reliability across the network.
Machine Learning Models: Employed for data analytics
and matching, enhancing the relevance and accuracy of data
retrieval.
C. Data Flow
Data flows through the system as follows:
Data Submission: Providers submit encrypted data to the
blockchain network.
Data Verification: Smart contracts validate data authenticity
and ownership.
Matching Algorithm: AI algorithms analyze user requests
and data attributes to suggest relevant matches.
Transaction Execution: Upon agreement, transactions are
executed securely using blockchain technology.
D. Blockchain Integration
Blockchain integration ensures:
Security: Data is encrypted and stored across multiple
nodes, reducing vulnerability to cyber threats.
Transparency: Immutable records provide auditable and
transparent transactions.
Trust: Smart contracts enforce trustless interactions, eliminating the need for intermediaries.
E. Machine Learning Algorithms
Machine learning algorithms enhance data matching by:
Pattern Recognition: Identifying patterns and correlations
within data sets.
Personalization: Tailoring recommendations based on user
preferences and historical data.
Prediction: Anticipating future trends and behaviors for
proactive decision-making.
F. Performance Evaluation
Performance evaluation metrics include:
Speed: Transaction processing and data retrieval times.
Accuracy: Precision and recall of data matches compared
to user queries.
Scalability: System capacity to handle increasing data volumes and user interactions.
The proposed decentralized medical data marketplace integrates blockchain for secure transactions and AI for efficient data matching. Utilizing smart contracts and distributed
storage, it ensures data integrity and facilitates personalized
data retrieval. This innovative system enhances healthcare
data management through advanced technologies, optimizing
reliability and accessibility.
Fig. 1. Proposed architecture for Medical Data Marketplace
IV. D ECENTRALIZED M EDICAL DATA M ARKETPLACE
The concept of a decentralized medical data marketplace
revolves around leveraging blockchain technology to address
current challenges in healthcare data management. By decentralizing data storage and transactions, the marketplace aims to
enhance security, privacy, and interoperability across healthcare systems. Utilizing blockchain’s inherent features like
decentralization, immutability, and smart contracts, the platform ensures secure data sharing among healthcare providers,
researchers, and patients. Key benefits include robust encryption methods, transparent access controls via smart contracts,
and potential integration with emerging technologies like AI
and IoT for advanced data analytics. This approach seeks to
improve trust, data integrity, and efficiency in medical data
exchange, thereby fostering innovation in patient care and
medical research.
In addition to the points mentioned, decentralized medical
data marketplaces also foster innovation by enabling seamless interoperability across disparate healthcare systems and
applications. They facilitate real-time data access and sharing,
promoting collaborative research and rapid insights generation.
Moreover, the use of decentralized storage solutions ensures
data redundancy and availability, reducing the risk of data
loss or downtime. These marketplaces empower patients with
greater control over their health data, allowing them to securely share information while maintaining autonomy over its
usage and access.
Furthermore, decentralized medical data marketplaces support scalability by accommodating diverse data types and
formats, facilitating comprehensive analytics and machine
learning applications. They incentivize data sharing through
tokenization and incentivization mechanisms, encouraging participation and data contribution from various stakeholders.
Advanced consensus mechanisms within blockchain frameworks ensure reliable transaction validation and consensus
among network participants, enhancing the overall reliability
and trustworthiness of the marketplace ecosystem.
These marketplaces also promote interoperability by supporting standardized data formats and communication protocols, enabling seamless data exchange across different systems
and organizations. By decentralizing data storage, they reduce
the risk of single points of failure and mitigate the impact
of cyberattacks. Additionally, they offer real-time data access
and updates, which is crucial for timely medical decisionmaking and research advancements. The use of decentralized
identifiers (DIDs) ensures that participants can control their
identities and data access permissions, enhancing user autonomy and trust in the system.
A. Client-Server Transactions in a Decentralized Medical
Data Marketplace
In a decentralized medical data marketplace, client-server
transactions are fundamental for securely exchanging medical
information among stakeholders like healthcare providers, researchers, and patients. These transactions leverage blockchain
technology to ensure data integrity, privacy, and transparency.
Security is enhanced through cryptographic techniques and
smart contracts, guaranteeing strict adherence to data access
protocols. Data integrity is maintained via blockchain’s immutable ledger, preventing unauthorized alterations. Robust
privacy measures safeguard sensitive patient information from
unauthorized access, ensuring compliance with regulatory
standards.
Figure 2, which depicts the client-server communication,
illustrates how requests and responses are handled securely
within the marketplace. In this system, cryptocurrency is used
to facilitate payments, providing a secure and efficient method
for transaction settlements. This integration of cryptocurrency
ensures that all financial transactions are transparent and
tamper-proof, further enhancing the overall security and trustworthiness of the data exchange process. Additionally, the
decentralized nature of the marketplace mitigates single points
of failure, enhancing the system’s resilience to cyberattacks.
By decentralizing control, the marketplace reduces the risk
of data breaches and unauthorized access, fostering a secure
environment for data sharing. Moreover, the use of smart
contracts automates compliance with regulatory requirements,
reducing administrative overhead and minimizing the risk of
human error. This innovative approach not only secures transactions but also streamlines operations, making the exchange
of medical data more efficient and reliable.
Equations (1) and (2) illustrate how relevance scores are
computed in sequential data matching algorithms. Here, Wi
represents user-defined weights and Dj denotes data characteristics. Scoreij measures the similarity between user preferences and data attributes, normalized by their magnitudes.
The aggregate score Matchi in Equation (2) combines these
scores across relevant data items j, weighted by their relevance Relevanceij . These algorithms are crucial in predictive
analytics and personalized recommendations, optimizing data
retrieval and decision-making based on specific user criteria
in the decentralized medical data marketplace.
V. DATA PRIVACY ALGORITHMS
A. Encryption Algorithm
Utilizes AES encryption with a designated key to encrypt
sensitive data, safeguarding it from unauthorized access. This
ensures data confidentiality and integrity within the decentralized medical data marketplace, adhering to stringent security
standards.
Algorithm 1 Encryption Algorithm
Function encryptData(data) is
encryptedData = AES.encrypt(data, encryptionKey) return encryptedData
end
B. Access Control Algorithm
Fig. 2. Client and server communication
B. Sequential Data Matching Algorithm
Sequential data matching algorithms are crucial in various
domains, including healthcare, finance, and telecommunications, adept at identifying patterns and relationships within
sequential or time-series data. These algorithms analyze data
streams to detect similarities, anomalies, or predictive patterns.
Techniques such as dynamic time warping (DTW), hidden
Markov models (HMMs), and sequence alignment algorithms
are commonly employed to compare sequences and compute
similarity scores based on temporal or sequential patterns.
Their applications range from fraud detection in financial
transactions to monitoring patient health trends, underscoring
their versatility in data-driven decision-making.
In the context of the decentralized medical data marketplace,
blockchain ensures secure data transactions and AI optimizes
data matching efficiency. Blockchain’s distributed ledger guarantees data integrity and provenance, while AI algorithms
enhance the accuracy and relevance of data matches. The
matching process involves analyzing data characteristics and
user preferences, as demonstrated by the following equations:
Scoreij =
Matchi =
X
j
Wi · Dj
∥Wi ∥∥Dj ∥
Scoreij · Relevanceij
(1)
(2)
Controls data access based on user roles, specifically permitting doctors to access patient data via smart contracts. This
algorithm ensures that only authorized personnel can retrieve
sensitive medical information, maintaining patient privacy and
compliance with healthcare regulations.
Algorithm 2 Access Control Algorithm
Function grantAccess(user, data) is
if user.role == ”Doctor” then
smartContract.grantAccess(user, data)
end
end
C. Data Masking Algorithm
Masks sensitive data to preserve privacy while allowing
for analysis or testing. By obscuring identifiable information
such as names or addresses, this algorithm protects individual
privacy in data-driven operations and research endeavors.
Algorithm 3 Data Masking Algorithm
Function maskData(data) is
maskedData = DataMaskingAlgorithm.mask(data) return
maskedData
end
D. Anonymization Algorithm
•
Ensures anonymity by removing personally identifiable details from datasets. It enhances privacy protection in medical
data analytics and research, supporting ethical data handling
practices and regulatory compliance.
Algorithm 4 Anonymization Algorithm
Function anonymizeData(data) is
anonymizedData
=
AnonymizationAlgorithm.anonymize(data) return anonymizedData
end
C=
•
•
•
Encrypts data based on user roles, ensuring that only authorized users can decrypt and access specific data sets. This rolespecific encryption enhances security in environments where
data access varies by user role, maintaining confidentiality and
integrity across transactions.
Algorithm 6 Role-Based Encryption Algorithm
Function roleBasedEncrypt(data, role) is
encryptedData
=
RoleBasedEncryptionAlgorithm.encrypt(data, role) return encryptedData
end
In the context of a decentralized medical data marketplace,
it’s essential to use standardized units to ensure clarity and
consistency across data exchanges. Primarily, SI (MKS) units
are recommended for all measurements related to medical
products and transactions. Here are some key units and their
applications:
• Dosage (D) of medical products is measured in milligrams (mg) or grams (g). For example, the dosage for
a medication might be represented as:
D = 250 mg
•
T
0.01 BTC
=
= 4 × 10−5 BTC/mg
D
250 mg
(3)
The volume (V ) of liquid medications or solutions is
measured in milliliters (mL) or liters (L). For instance:
V = 500 mL
(4)
(7)
In cases where a medical product is out of stock, the
purchase quantity (Q) required to replenish the inventory
is measured in units such as bottles, packages, or boxes.
For example, if a stock level alert (A) indicates the need
for replenishment:
(8)
The cost (Ctotal ) to restock an out-of-stock item can be
calculated by multiplying the price per unit dosage by the
required quantity:
Ctotal = P × Q = (4 × 10−5 BTC/mg) × (50 bottles) (9)
H. Equations
Number equations consecutively. To make your equations
more compact, you may use the solidus ( / ), the exp
function, or appropriate exponents. Italicize Roman symbols
for quantities and variables, but not Greek symbols. Use a
long dash rather than a hyphen for a minus sign. Punctuate
equations with commas or periods when they are part of a
sentence, as in:
Scoreij =
G. Units
(6)
The price (P ) of a medical product per unit dosage
can be expressed in cryptocurrency per milligram (e.g.,
BTC/mg). For example:
Q = 50 bottles
•
(5)
Transaction amounts (T ) in the marketplace are often measured in cryptocurrency units (e.g., Bitcoin or
Ethereum). For example:
P =
Algorithm 5 Data Obfuscation Algorithm
Function obfuscateData(data) is
obfuscatedData
=
DataObfuscationAlgorithm.obfuscate(data) return obfuscatedData
end
F. Role-Based Encryption Algorithm
250 mg
D
=
= 0.5 mg/mL
V
500 mL
T = 0.01 BTC
E. Data Obfuscation Algorithm
Alters data representation without changing its meaning or
usability, protecting it against unauthorized access or breaches.
This algorithm enhances data security by making it challenging for malicious actors to decipher or misuse sensitive
information.
The concentration (C) of a solution, which is the amount
of solute per unit volume of solution, can be measured
in mg/mL or g/L. An example would be:
Wi · Dj
∥Wi ∥∥Dj ∥
(10)
Be sure that the symbols in your equation have been defined
before or immediately following the equation. Use “(10)”, not
“Eq. (10)” or “equation (10)”, except at the beginning of a
sentence: “Equation (10) is . . .”
In the context of a decentralized medical data marketplace,
the following equations are used to compute relevance scores
and manage product inventory:
Matchi =
X
Scoreij · Relevanceij
(11)
j
Here, the aggregate score Matchi combines the similarity
scores across all relevant data items j, weighted by their
relevance Relevanceij .
For inventory management, if a product is out of stock,
the required purchase quantity (Q) to replenish inventory is
calculated as follows:
Q = Required Stock Level − Current Stock Level
reliability, security, and ease of use in managing and accessing
medical data.
(12)
The cost to restock the out-of-stock product is calculated
by multiplying the price per unit by the required quantity:
Ctotal = P × Q
(13)
where P is the price per unit and Q is the quantity needed.
These equations ensure efficient data matching and inventory management, optimizing the operations of the decentralized medical data marketplace.
VI. WORK DONE AND RESULT ANALYSIS
A. Work Done
The project focused on developing and implementing a
decentralized medical data marketplace, integrating blockchain
technology and AI-driven data matching algorithms to ensure secure and efficient data transactions. Key components
included:
1. System Architecture Design: A comprehensive architecture was designed to facilitate secure data transactions between
clients and servers using blockchain and AI algorithms. 2.
Implementation of Data Privacy Algorithms: Various algorithms, including encryption, access control, data masking,
anonymization, data obfuscation, and role-based encryption,
were implemented to enhance data security and privacy. 3. Sequential Data Matching Algorithm: Developed and integrated
a sequential data matching algorithm to efficiently match data
items with user requests, ensuring high relevance and accuracy.
4. Smart Contract Development: Created and deployed smart
contracts to automate access control and payment processes,
ensuring adherence to data access protocols and secure financial transactions. 5. Testing and Validation: Conducted
extensive testing and validation to ensure system robustness,
security, and performance under various scenarios.
B. Result Analysis
The implementation of the decentralized medical data marketplace yielded significant improvements in data security,
privacy, and matching accuracy. Key findings include:
1. Enhanced Data Security: The use of blockchain and
cryptographic techniques significantly improved data integrity
and protection against unauthorized access and tampering.
2. Efficient Data Matching: The AI-driven sequential data
matching algorithm demonstrated high accuracy in matching
user requests with relevant data items, resulting in better user
satisfaction and data utilization. 3. Compliance with Regulatory Standards: The implemented data privacy algorithms
ensured compliance with healthcare regulations, protecting
sensitive patient information and maintaining confidentiality. 4. Performance Metrics: The system exhibited robust
performance metrics, with low latency in data transactions
and efficient processing of user requests. 5. User Feedback:
Positive feedback from stakeholders highlighted the system’s
Fig. 3. Transaction architecture for Medical Data Marketplace
The proposed architecture, as depicted in Figure 3, illustrates the transaction of data between clients and servers,
detailing the request and response mechanisms and the use
of cryptocurrency for payments, thereby highlighting the innovative approach and technological integration in the project.
VII. CONCLUSION
The proposed decentralized medical data marketplace
demonstrates a significant advancement in the secure and
efficient exchange of medical data. By leveraging blockchain
technology, the system ensures data integrity, privacy, and
transparency, addressing critical concerns in medical data management. The integration of AI algorithms for data matching
enhances the accuracy and relevance of data transactions, providing stakeholders with precise and timely information. The
inclusion of robust security measures, such as cryptographic
techniques and smart contracts, guarantees strict adherence
to data access protocols and regulatory compliance. The innovative architecture, which incorporates cryptocurrency for
payments, further exemplifies the project’s forward-thinking
approach to addressing the complexities of medical data exchange. Overall, this project paves the way for a more secure,
transparent, and efficient medical data marketplace, benefiting
healthcare providers, researchers, and patients alike.
ACKNOWLEDGEMENT
I would like to thank SRM Institute of Science and Technology - Ramapuram Campus for their invaluable support and
contributions to this project. Special thanks to my guides,
Ms. Judy Flavia B and Ms. Aarthi B, for their guidance and
encouragement.
REFERENCES
The following references were cited in the preparation of
this manuscript.
R EFERENCES
[1] D. Anguita, A. Ghio, L. Oneto, M. Ridella, ”A fast and effective heuristic
for the winner determination problem in combinatorial auctions,” Expert
Systems with Applications, vol. 39, no. 10, pp. 9291-9296, July 2012.
[2] F. A. Gers, J. Schmidhuber, F. Cummins, ”Learning to forget: Continual
prediction with LSTM,” Neural Computation, vol. 12, no. 10, pp. 24512471, October 2000.
[3] T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to
Algorithms, 3rd ed. Cambridge, MA: MIT Press, 2009.
[4] J. Li, ”Nonlinear system identification based on NARX neural network
with hybrid particle swarm optimization and gravitational search algorithm,” Neurocomputing, vol. 273, pp. 289-298, May 2018.
[5] A. Nagabushnam, P. N. Suganthan, ”Comprehensive learning particle
swarm optimizer for large scale optimization,” Soft Computing, vol. 22,
no. 5, pp. 1463-1476, March 2018.
[6] H. Yang, H. L. Zou, ”Global exponential stabilization of uncertain
nonlinear switched systems with asynchronous switching signals,” Automatica, vol. 93, pp. 96-104, November 2018.
[7] M. E. H. Pedersen, ”Nesting strategies for differential evolution,”
Applied Soft Computing, vol. 13, no. 2, pp. 1141-1152, February 2013.
[8] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan, ”A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary
Computation, vol. 6, no. 2, pp. 182-197, April 2002.
[9] G. J. Klir, B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River, NJ: Prentice Hall, 1995.
[10] D. E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning. Boston, MA: Addison-Wesley, 1989.
[11] S. Kirkpatrick, C. D. Gelatt, M. P. Vecchi, ”Optimization by simulated
annealing,” Science, vol. 220, no. 4598, pp. 671-680, May 1983.
[12] J. Kennedy, R. Eberhart, ”Particle swarm optimization,” in Proceedings
of IEEE International Conference on Neural Networks, Perth, WA, 1995,
pp. 1942-1948.
[13] I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools
and Techniques, 3rd ed. Burlington, MA: Morgan Kaufmann, 2011.
[14] A. L. Samuel, ”Some studies in machine learning using the game of
checkers,” IBM Journal of Research and Development, vol. 3, no. 3, pp.
210-229, July 1959.
[15] Y. LeCun, Y. Bengio, G. Hinton, ”Deep learning,” Nature, vol. 521, no.
7553, pp. 436-444, May 2015.
[16] R. Rojas, Neural Networks: A Systematic Introduction. Berlin: SpringerVerlag, 1996.
[17] Y. Bengio, A. Courville, P. Vincent, ”Representation learning: A review
and new perspectives,” IEEE Transactions on Pattern Analysis and
Machine Intelligence, vol. 35, no. 8, pp. 1798-1828, August 2013.
[18] C. M. Bishop, Pattern Recognition and Machine Learning. New York:
Springer, 2006.
[19] H. Wang, Y. Zhang, X. Zeng, ”Survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11-26,
October 2017.
[20] L. Breiman, ”Random forests,” Machine Learning, vol. 45, no. 1, pp.
5-32, October 2001.
[21] M. T. Hagan, H. B. Demuth, M. H. Beale, Neural Network Design.
Boston, MA: PWS Publishing Company, 1996.
[22] C. M. Bishop, ”Bayesian logistic regression,” Pattern Recognition, vol.
36, no. 2, pp. 259-271, February 2003.
[23] J. Schmidhuber, ”Deep learning in neural networks: An overview,”
Neural Networks, vol. 61, pp. 85-117, June 2015.
[24] L. Breiman, ”Bagging predictors,” Machine Learning, vol. 24, no. 2, pp.
123-140, August 1996.
[25] T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical
Learning: Data Mining, Inference, and Prediction, 2nd ed. New York:
Springer, 2009.
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