This thesis presents a comprehensive predictive maintenance system and application interface that integrates deep learning and blockchain technologies in order to enhance maintenance strategies in industrial systems. Traditional predictive maintenance systems have significant issues regarding data security and decentralization. This study aims to address these limitations by leveraging blockchain technology, with a specific focus on improving the reliability and verifiability of predictive maintenance processes. In this study, an LSTM-CNN hybrid model was developed to evaluate complex patterns in both time and features, thereby enabling high-accuracy fault prediction. The proposed model is designed to perform binary classification for fault prediction in industrial equipment. During the implementation phase of the study, an open-source dataset was used to train and test the developed model. The Randomized Search method was used in the hyperparameter optimization process to increase the prediction success of the proposed model. The hybrid model was trained with 5-fold cross-validation, and class weighting and threshold value optimization methods were applied to eliminate the class imbalance problem. In the threshold optimization phase, F1-score-based methods are applied to maximize recall at three predefined minimum precision levels (0.05, 0.2, and 0.85), while identifying the most balanced trade-off between precision and recall. In the proposed system, sensor data are stored in a database (SQLite3), and cryptographic proofs generated using zero-knowledge techniques are transmitted to the Ethereum network. The Poseidon hash function is used to ensure data integrity, and the Groth16 protocol is used for Zk-Snark proof generation. This approach enables secure verification of data validity without publicly disclosing sensor data and simultaneously addresses scalability concerns. The system architecture is designed to include manager, operator, and engineer nodes, and all smart contracts are implemented using Solidity. In addition, a graphical user interface is developed using the Tkinter library in Python. The experimental results demonstrate that the proposed LSTM–CNN hybrid model produces successful outcomes in terms of fault prediction performance. According to scenario where the decision threshold is optimized based on the F1-score, the model achieves an accuracy of 0.987, an AUC value of 0.979, and an F1-score of 0.794. In future studies, the proposed system is planned to be implemented on the Ethereum mainnet instead of a test network, with a comprehensive evaluation of on-chain operational costs. However, instead of Zk-Snark proofs, which have a centralized structure, the use of Zk-Stark proofs, which are transparent and do not violate the principle of decentralization, is planned.
Shiji Mol F R, J. Ayyana Prabu, R. Santhana Krishnan, S. Jeeva Paulin · 6 authors
Ensuring vaccine integrity requires rigorous cold-chain management, as even minor temperature deviations can compromise safety, cause financial losses, and endanger public health. Conventional monitoring systems, dependent on centralized databases and manual oversight, remain prone to delays, inaccuracies, and tampering. To address these limitations, we propose an integrated framework that combines deep learning and blockchain for secure, predictive, and real-time cold-chain monitoring. A distributed network of IoT sensors captures temperature, humidity, vibration, and GPS data at one-minute intervals. Long Short-Term Memory (LSTM) networks forecast short-term temperature trends, Autoencoders (AE) detect anomalies, and one-dimensional Convolutional Neural Networks (1D CNN) classify shipment states as Safe, At Risk, or Spoiled. A decision engine fuses outputs from these models into a unified risk score, enabling timely and data-driven interventions. Critical events and metadata are immutably recorded on the Ethereum blockchain, while raw sensor data is stored off-chain using IPFS to ensure auditability and efficiency. The system also supports automated alerts, real-time monitoring through Grafana dashboards, and adaptive model retraining for continuous improvement. Evaluation covers forecasting accuracy, anomaly detection precision, classification performance, and blockchain efficiency. By uniting predictive analytics with tamper-proof logging, the proposed framework reduces vaccine spoilage, strengthens supply chain resilience, and offers a scalable solution for broader temperature-sensitive logistics applications.
This study presents SmartMix Web3, a framework combining ensemble machine learning and blockchain technology to optimize low-carbon concrete design. It addresses two key challenges: (1) the limitations of conventional models in predicting concrete performance, and (2) ensuring data reliability and overcoming collaboration issues in AI-driven sustainable construction. Validated with 61 real-world experiments in Cameroon and 752 mix designs, the framework shows major improvements in predictive accuracy and decentralized trust. To address the first research question, a stacked ensemble model comprising Extreme Gradient Boosting (XGBoost)–Random Forest and a Convolutional Neural Network (CNN) was developed, achieving a 22% reduction in Root Mean Square Error (RMSE) for compressive strength prediction and embodied carbon estimation compared to traditional methods. The 29% reduction in Mean Absolute Error (MAE) results confirms the superiority of Extreme Learning Machine (EML) in low-carbon concrete performance prediction. For the second research question, SmartMix Web3 employs blockchain to ensure tamper-proof traceability and promote collaboration. Deployed on Ethereum, it automates verification of tokenized Environmental Product Declarations via smart contracts, reducing disputes and preserving data integrity. Federated learning supports decentralized training across nine batching plants, with Secure Hash Algorithm (SHA)-256 checks ensuring privacy. Field implementation in Cameroon yielded annual cost savings of FCFA 24.3 million and a 99.87 kgCO2/m3 reduction per mix design. By uniting EML precision with blockchain transparency, SmartMix Web3 offers practical and scalable benefits for sustainable construction in developing economies.
The orchestration of agents to optimize a collective objective without centralized control is challenging yet crucial for applications such as controlling autonomous fleets, and surveillance and reconnaissance using sensor networks. Decentralized controller design has been inspired by self-organization found in nature, with a prominent source of inspiration being flocking; however, decentralized controllers struggle to maintain flock cohesion. The graph neural network (GNN) architecture has emerged as an indispensable machine learning tool for developing decentralized controllers capable of maintaining flock cohesion, but they fail to exploit the symmetries present in flocking dynamics, hindering their generalizability. We enforce rotation equivariance and translation invariance symmetries in decentralized flocking GNN controllers and achieve comparable flocking control with 70% less training data and 75% fewer trainable weights than existing GNN controllers without these symmetries enforced. We also show that our symmetry-aware controller generalizes better than existing GNN controllers. Code and animations are available at http://github.com/Utah-Math-Data-Science/Equivariant-Decentralized-Controllers.
Bitcoin is the world's first decentralized cryptocurrency, using blockchain technology to secure and verify transactions. A hybrid model based on stochastic configuration network (SCN) with conformal prediction is proposed in this study. Initially, an SCN model is built, and predictions are generated by the model. Subsequently, the predicted values from SCN are fed into the conformal prediction model, resulting in the generation of confidence intervals that validate the reliability of these values. Finally, the dataset of historical Bitcoin prices sourced from Wikipedia have been utilized. The results indicate that the SCN-conformal prediction combination enhances prediction reliability.
Moiz Qureshi, Hasnain Iftikhar, Paulo Canas Rodrigues, Mohd Ziaur Rehman · 5 authors
Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments.
Blockchain has attracted widespread attention due to its unique features such as decentralization, traceability, and tamper resistance. With the rapid development of blockchain technology, an increasing number of industries are gradually applying blockchain technology to various fields such as the Internet of Things, healthcare, finance, agriculture, and government affairs. However, there are certain differences in the underlying architecture, data structures, consensus algorithms, and other aspects of blockchain technology across different sectors, which restrict transactions to occur within a single blockchain. Achieving interoperability between different blockchains is challenging, hindering data exchange and collaborative business to some extent, inevitably leading to the problem of "data silo". Against this backdrop, this study aims to explore a cross-chain solution based on relay technology to address the current challenges of interoperability between blockchain systems. By employing relay-based cross-chain technology, a blockchain cross-chain collaboration platform is established to simulate the construction of a real cross-chain network. By deploying business contracts, data and resources between heterogeneous blockchains can seamlessly communicate, resolving the challenge of cross-chain interoperability. The research findings demonstrate that the blockchain cross-chain solution based on relay technology can effectively enhance interoperability between different blockchain systems, enabling cross-chain asset circulation and information transmission, highlighting the practical applicability and scalability of this study.
Fahad F. Alruwaili, Manal Abdullah Alohali, Nouf Aljaffan, Asma A. Alhashmi · 6 authors
The fast development of smart home devices and the Internet of Things (IoTs) presents unprecedented accessibility into our day-to-day lives; however, it has also increased major problems regarding security and privacy. A smart home network is a vital element of modern home automation systems, enabling the interconnectivity and control of different smart devices. These networks allow homeowners to remotely control lighting, security, temperature, and entertainment systems via voice commands or smartphones. These offer energy efficiency, convenience, and improved security by permitting residents to monitor and modify their living surroundings. Safeguarding the flexibility of smart home networks against cyberattacks and unauthorized access is important to comprehending the maximum ability of smart living while retaining data integrity and privacy of connected devices. This research develops the Blockchain with Red-Tailed Hawk Algorithm-Enabled Deep Learning (BC-RTHADL) model, aimed to strengthen the safety of smart home systems. BC-RTHADL integrates the safety features of blockchain with a strong malicious action recognition procedure. The blockchain module certifies immutability, transparency, and decentralization, donating to a safe smart home atmosphere. The malicious action detection influences the Red-Tailed Hawk Algorithm for feature selection and an ensemble of Extreme Learning Machine (ELM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) techniques for precise recognition. The Equilibrium Optimizer algorithm enhances parameters for improved effectiveness. Complete tests show the greater performance of BC-RTHADL across numerous metrics, reaffirming its promising potential in safeguarding smart home networks.
With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.
Ning Lin, Shaocong Wang, Yi Li, Bo Wang · 22 authors
The human brain is a complex spiking neural network (SNN), capable of learning multimodal signals in a zero-shot manner by generalizing existing knowledge. Remarkably, it maintains minimal power consumption through event-based signal propagation. However, replicating the human brain in neuromorphic hardware presents both hardware and software challenges. Hardware limitations, such as the slowdown of Moore's law and Von Neumann bottleneck, hinder the efficiency of digital computers. Additionally, SNNs are characterized by their software training complexities. To this end, we propose a hardware-software co-design on a 40 nm 256 Kb in-memory computing macro that physically integrates a fixed and random liquid state machine (LSM) SNN encoder with trainable artificial neural network (ANN) projections. We showcase the zero-shot LSM-based learning of multimodal events on the N-MNIST and N-TIDIGITS datasets, including visual and audio data association, as well as neural and visual data alignment for brain-machine interfaces. Our co-design achieves classification accuracy comparable to fully optimized software models, resulting in a 152.83 and 393.07-fold reduction in training costs compared to SOTA contrastive language-image pre-training (CLIP) and Prototypical networks, and a 23.34 and 160-fold improvement in energy efficiency compared to cutting-edge digital hardware, respectively. These proof-of-principle prototypes demonstrate zero-shot multimodal events learning capability for emerging efficient and compact neuromorphic hardware.
Kasliono Kasliono, Niken Candraningrum, Kartika Sari
The price of cryptocurrencies such as Ethereum often experiences high fluctuations and is difficult to predict. This study aims to predict Ethereum prices using the Extreme Learning Machine (ELM) algorithm which is a fast and efficient machine learning method. Ethereum price data is collected from CoinMarketCap by scraping the data using CoinmarketCap Scraper from the cryptocmd library using Python. An ELM model is built by changing the number of hidden nodes to determine the optimal prediction model of Ethereum prices based on the smallest average MAPE. Model performance was evaluated using the mean absolute percentage error (MAPE) on the test data set. The results show that the ELM model built can predict Ethereum prices with an accuracy of 96.96%. The MAPE obtained is 3.035334%, with 9 hidden nodes in the ELM network architecture model that was built. This shows that the model can explain about 96.96% of the variation in Ethereum price data. Therefore, the ELM model can be used as an aid in making investment decisions
T. Gobinath, Sanjay Kumar Sonkar, Vinod N. Alone, C. Thiripurasundari
As a distributed and decentralized ledger that ensures secure and transparent transactions, blockchain technology has attracted considerable interest. In the context of wireless sensor networks (WSNs), where nodes with limited resources conduct transactions, ensuring efficient and trustworthy validation becomes a challenge. Using random forests, this paper proposes a novel method for enhancing blockchain transaction validation in WSNs. The proposed method enhances the accuracy and efficiency of transaction validation in WSNs by leveraging the ensemble-learning capabilities of random forests. The random forests model is trained with transaction content, originating node information, and network metrics extracted from WSN transactions. Experimental results indicate that the proposed method improves transaction validation precision and decreases validation time in comparison to conventional methods. In addition, the random forests model is resistant to multiple types of attacks, assuring the security and integrity of WSN transactions. The results demonstrate that random forests are a promising technique for improving blockchain transaction validation in wireless sensor networks.
Arash Heidari, Nima Jafari Navimipour, Mehmet Ünal
The Internet of Drones (IoD) is built on the Internet of Things (IoT) by replacing “Things” with “Drones” while retaining incomparable features. Because of its vital applications, IoD technologies have attracted much attention in recent years. Nevertheless, gaining the necessary degree of public acceptability of IoD without demonstrating safety and security for human life is exceedingly difficult. In addition, intrusion detection systems (IDSs) in IoD confront several obstacles because of the dynamic network architecture, particularly in balancing detection accuracy and efficiency. To increase the performance of the IoD network, we proposed a blockchain-based radial basis function neural networks (RBFNNs) model in this article. The proposed method can improve data integrity and storage for smart decision-making across different IoDs. We discussed the usage of blockchain to create decentralized predictive analytics and a model for effectively applying and sharing deep learning (DL) methods in a decentralized fashion. We also assessed the model using a variety of data sets to demonstrate the viability and efficacy of implementing the blockchain-based DL technique in IoD contexts. The findings showed that the suggested model is an excellent option for developing classifiers while adhering to the constraints placed by network intrusion detection. Furthermore, the proposed model can outperform the cutting-edge methods in terms of specificity, F1, recall, precision, and accuracy.
Abstract Intelligent and networked vehicles help build an efficient vehicular network's infrastructure. The widespread use of electronic software exposes these networks to cyber‐attacks. Intrusion detection systems (IDS) are useful for preventing vehicle network assaults. IDS have been customized using machine and deep learning networks for greater real‐time performance. Current learning‐based intrusion detection systems demand substantial processing capabilities to train and update intricate training models in vehicular devices, resulting in decreased efficiency and ability to defend against assaults. This study presents Blockchain‐based Multi‐Layer Federated Extreme Learning Machines (MLFEM) enabled IDS (BEF‐IDS) for safe data transfers. The proposed IDS leverages federated learning to generate Multi‐Layered Extreme Learning Machines, which are offloaded to dispersed vehicular edge devices such as Road‐Side Units (RSU) and connected vehicles. This federated strategy decreases resource use without sacrificing security. Blockchain technology records and shares training models, assuring network security. Using real‐time data sets, the suggested algorithm's performance under different attack scenarios were extensively tested. The suggested method obtained 98% accuracy and Recall, 97.9% Precision, and 97.9% F1 Score performance, which suggests it's incredibly secure and costs very little to transmit.
The engaging ambitions of regeneration & growth of manufacturing in several nations, as well as the speed, flexibility, or expense benefits that might arise from the architecture of the industrial Internet of Things (IIoT), are attracting considerable interest. While blockchain or machine learning techniques, particularly deep learning, might offer the latest viable use cases for IIoT, they operate in a rather antagonistic manner. Underneath the assumption of information regulatory standards such as information protections, blockchain helps the crucial information collecting for machine learning. However, it may be susceptible to a data breach as a result of big information insights using machine learning. To enable machine learning/blockchain relevant & applicable for a variety of industrialized applications, it is of the utmost essential to have a thorough grasp of their evolution within the framework of IIoT. In this paper, we present a summary & analytics of the opportunity of blockchain as well as machine learning in the IIoT, focusing on the agreement method, preservation, or transmission. This study gives a better knowledge of the protection & confidentiality issues of a blockchain’s vital aspects from the viewpoint of machine learning, and that is beneficial for the creation of viable blockchain alternatives for IIoT.
K. Kalyani, Velmurugan Subbiah Parvathy, Hikmat A. M. Abdeljaber, T. Satyanarayana Murthy · 7 authors
In recent times, financial globalization has drastically increased in different ways to improve the quality of services with advanced resources. The successful applications of bitcoin Blockchain (BC) techniques enable the stockholders to worry about the return and risk of financial products. The stockholders focused on the prediction of return rate and risk rate of financial products. Therefore, an automatic return rate bitcoin prediction model becomes essential for BC financial products. The newly designed machine learning (ML) and deep learning (DL) approaches pave the way for return rate predictive method. This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder (JSO-ELMAE) for return rate prediction of BC financial products. The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products. Besides, the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results. The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates. The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects. The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.
The return series of cryptocurrencies, which are emerging digital assets, exhibit nonstationarity, nonlinearity, and volatility clustering compared to other traditional financial markets, making them exceptionally difficult to forecast. Therefore, accurate cryptocurrency price forecasting is important for both market participants and regulators. It has been demonstrated that improved data forecasting accuracy can be achieved through decomposition, but few researchers have performed information extraction on the residual series generated by data decomposition. Based on the construction of a "decomposition-optimization-integration" hybrid model framework, in this paper, we propose a multi-scale hybrid forecasting model that combines the residual components after primary decomposition for secondary decomposition and integration. This model uses the variational modal decomposition (VMD) method to decompose the original return series into a finite number of components and residual terms; then, the residual terms are decomposed and the features are extracted using the completed ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. The components are predicted by an extreme learning machine optimized by the sparrow search algorithm, and the final predictions are summed to obtain the final results. Forecasts for the returns of Bitcoin and Ethereum, which are major cryptocurrency assets, are compared with other benchmark models constructed based on different ideas, and we find that the proposed quadratic decomposition VMD-Res.-CEEMDAN-SSA-ELM hybrid model demonstrates the optimal and most stable forecasting performance in both one-step and multi-step ahead prediction of the cryptocurrency return series.
The rapid expansion of data worldwide invites the need for more distributed solutions in order to apply machine learning on a much wider scale. The resultant distributed learning systems can have various degrees of centralization. In this work, we demonstrate our solution FLoBC for building a generic decentralized federated learning system using the blockchain technology, accommodating any machine learning model that is compatible with gradient descent optimization. We present our system design comprising the two decentralized actors: trainer and validator, alongside our methodology for ensuring reliable and efficient operation of said system. Finally, we utilize FLoBC as an experimental sandbox to compare and contrast the effects of trainer-to-validator ratio, reward-penalty policy, and model synchronization schemes on the overall system performance, ultimately showing by example that a decentralized federated learning system is indeed a feasible alternative to more centralized architectures.
Dayu Jia, Junchang Xin, Zhiqiong Wang, Guoren Wang
COVID-19 virus is raging across the planet. In countries where the epidemic is under control, the main mode of virus transmission is through the transport of imported refrigerated food from epidemic areas. Blockchain is a great way for the government to trace every piece of food. However, the high-performance requirements of the blockchain system for nodes limit its wide application. Several sharding-based blockchain systems have been proposed to solve this limitation. Which blocks should be saved by nodes in the sharding-based blockchain system is a new problem. To solve this problem, the optimized data storage method is proposed in this paper. Five features of block popularity are presented, including the objective feature of a block, the objective feature of the block associated with the node, the historical popularity, the hidden popularity and the storage requirements. Then the ELM classifier is used in the optimized model due to its high performance of training and classification. Finally, the experimental results on synthetic data demonstrate the accuracy and efficiency of the optimized data storage model.