133 research outputs found
BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation
In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.publishedVersio
IoTNet: An Efficient and Accurate Convolutional Neural Network for IoT Devices
Two main approaches exist when deploying a Convolutional Neural Network (CNN) on resource-constrained IoT devices: either scale a large model down or use a small model designed specifically for resource-constrained environments. Small architectures typically trade accuracy for computational cost by performing convolutions as depth-wise convolutions rather than standard convolutions like in large networks. Large models focus primarily on state-of-the-art performance and often struggle to scale down sufficiently. We propose a new model, namely IoTNet, designed for resource-constrained environments which achieves state-of-the-art performance within the domain of small efficient models. IoTNet trades accuracy with computational cost differently from existing methods by factorizing standard 3 × 3 convolutions into pairs of 1 × 3 and 3 × 1 standard convolutions, rather than performing depth-wise convolutions. We benchmark IoTNet against state-of-the-art efficiency-focused models and scaled-down large architectures on data sets which best match the complexity of problems faced in resource-constrained environments. We compare model accuracy and the number of floating-point operations (FLOPs) performed as a measure of efficiency. We report state-of-the-art accuracy improvement over MobileNetV2 on CIFAR-10 of 13.43 with 39 fewer FLOPs, over ShuffleNet on Street View House Numbers (SVHN) of 6.49 with 31.8 fewer FLOPs and over MobileNet on German Traffic Sign Recognition Benchmark (GTSRB) of 5 with 0.38 fewer FLOPs
Polar Coded Merkle Tree: Mitigating Data Availability Attacks in Blockchain Systems Using Informed Polar Code Design
Data availability (DA) attack is a well-known problem in certain blockchains
where users accept an invalid block with unavailable portions. Previous works
have used LDPC and 2-D Reed Solomon (2DRS) codes with Merkle trees to mitigate
DA attacks. These codes perform well across various metrics such as DA
detection probability and communication cost. However, these codes are
difficult to apply to blockchains with large blocks due to large decoding
complexity and coding fraud proof size (2D-RS codes), and intractable code
guarantees for large code lengths (LDPC codes). In this paper, we focus on
large block size applications and address the above challenges by proposing the
novel Polar Coded Merkle Tree (PCMT): a Merkle tree encoded using the encoding
graph of polar codes. We provide a specialized polar code design algorithm
called Sampling Efficient Freezing and an algorithm to prune the polar encoding
graph. We demonstrate that the PCMT built using the above techniques results in
a better DA detection probability and communication cost compared to LDPC
codes, has a lower coding fraud proof size compared to LDPC and 2D-RS codes,
provides tractable code guarantees at large code lengths (similar to 2D-RS
codes), and has comparable decoding complexity to 2D-RS and LDPC codes.Comment: 36 pages, 10 figures, 2 tables, submitted to IEEE Journal on Selected
Areas in Information Theor
Using Artificial Intelligence and Cybersecurity in Medical and Healthcare Applications
Healthcare fields have made substantial use of cybersecurity systems to provide excellent patient safety in many healthcare situations. As dangers increase and hackers work tirelessly to elude law enforcement, cybersecurity has been a rapidly expanding field in the news over the past ten years. Although the initial motivations for conducting cyberattacks have generally remained the same over time, hackers have improved their methods. It is getting harder to identify and stop evolving threats using conventional cybersecurity tools. The development of AI methodologies offers hope for equipping cybersecurity professionals to fend against the ever-evolving threat posed by attackers. Therefore, an artificial intelligence- based Convolutional Neural Network (CNN) is introduced in this paper in which the cyberattacks are detected with more excellent performance. This paper presents unique conditions using the Ant Colony Optimization based Convolutional Neural Network (ACO-CNN) mechanism. This model has been built and supplied collaboratively with a dataset containing samples of web attacks for detecting cyberattacks in the healthcare sector. The results show that the created framework performs better than the modern techniques by detecting cyberattacks more accurately
Towards On-Device AI and Blockchain for 6G enabled Agricultural Supply-chain Management
6G envisions artificial intelligence (AI) powered solutions for enhancing the
quality-of-service (QoS) in the network and to ensure optimal utilization of
resources. In this work, we propose an architecture based on the combination of
unmanned aerial vehicles (UAVs), AI and blockchain for agricultural
supply-chain management with the purpose of ensuring traceability,
transparency, tracking inventories and contracts. We propose a solution to
facilitate on-device AI by generating a roadmap of models with various
resource-accuracy trade-offs. A fully convolutional neural network (FCN) model
is used for biomass estimation through images captured by the UAV. Instead of a
single compressed FCN model for deployment on UAV, we motivate the idea of
iterative pruning to provide multiple task-specific models with various
complexities and accuracy. To alleviate the impact of flight failure in a 6G
enabled dynamic UAV network, the proposed model selection strategy will assist
UAVs to update the model based on the runtime resource requirements.Comment: 8 pages, 5 figures, 1 table. Accepted to IEEE Internet of Things
Magazin
- …