Department of Electrical and Electronics Engineering

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Subject: search.filters.subject.Artificial intelligence (AI)

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    A Clustering and Image Processing Approach to Unsupervised Real-Time Road Segmentation for Autonomous Vehicles
    (IEEE, 2022) Chamola, Vinay
    Path planning is a crucial task in autonomous vehicles for which real-time road segmentation is very important. Most existing road segmentation techniques are supervised but, in many cases, their performance may be limited by the availability of and variety in a large training dataset. In contrast, we propose a research direction on unsupervised road segmentation that does not need any training or adaption and can be utilized widely. We use K-means clustering and image processing techniques to segment roads in RGB images. The scheme works well on the KITTI Road dataset (urban), giving a maximum, mean, and minimum IoU score of 93.75 %, 66.64% and 32.21% respectively. The minimum, mean and maximum time taken for segmentation were 1.084 s, 1.999 s and 3.794 s respectively on an Intel Core i5-8th Gen.(8GB RAM) CPU. A major reason for low values of minimum accuracy is that the scheme may segment the sidewalk also as a road. Although the mean IoU score is lower and the processing time higher relative to existing schemes, the results are very promising as our scheme is completely unsupervised and the processing time can be reduced by leveraging the capabilities of GPUs, parallel execution, hardware acceleration and the like.
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    Confluence of Blockchain and Artificial Intelligence Technologies for Secure and Scalable Healthcare Solutions: A Review
    (IEEE, 2022-12) Chamola, Vinay
    Blockchain (BC) and Artificial Intelligence (AI) technologies have independent applications in multiple industries, including banking, finance, health care, construction, transportation, hospitality, manufacturing, and insurance, to name a few. Moreover, these two technologies can be integrated seamlessly, thanks to their complementary and mutually-supportive features. AI algorithms can make the medical blockchain storage efficient by their processing algorithms, also playing the role of knowledgeable gatekeepers. Blockchain can support AI models by providing secure, sizeable, traceable, diverse, and immutable healthcare data for the training purpose. The integration of BC and AI has multiple use cases in the healthcare industry ranging from disease prediction to pandemic management. Previously, researchers have reviewed the applications of each of these technologies in health care independently. Although the integration of BC and AI has been fruitful, to the best of our knowledge, there has been no work in the past reviewing the confluence of these two technologies in the health care sector. We have classified the works based on two different classification schemes: application-based and AI-training paradigm-based classification. We have also provided a compilation of tools used in the integrated systems of BC and AI for healthcare. We identified that the integration of BC and AI technologies had been applied in quite different areas of healthcare ranging from biomedical research to pandemic management. It is also noted that the supervised learning algorithms and federated learning paradigm for secure decentralized AI model training are often used in the integration. Our findings reveal that majority of the reviewed works use blockchain as a secure database for AI models. Further, we also have pointed out the potential applications of these two technologies in health care.
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    Enabling Cost-Effective and Secure Minor Medical Teleconsultation Using Artificial Intelligence and Blockchain
    (IEEE, 2022-03) Chamola, Vinay
    While the onset of the COVID-19 pandemic has increased the popularity of home-based consultations, worries over privacy, high consultations costs, slow response times, and the burden on doctors due to the overwhelming number of COVID-19 cases have made current in-person and online models ineffective. In this study, we present an advanced, privacy-protected, artificial intelligence and blockchain-based consultation framework for minor medical conditions. Patients can post their medical queries anonymously on the blockchain network, which may be answered by any available medical professionals. The queries are sorted into their respective domains using naive Bayes and logistic regression. The consultations provided by medical specialists are evaluated based on their reputation, expertise, detail orientation, and the use of supporting documents, and rewards are given in accordance with the evaluation scheme. This fair and incentivized system provides cheaper and more accessible healthcare to patients, which is the need of the hour.
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    A deep learning based misbehavior classification scheme for intrusion detection in cooperative intelligent transportation systems
    (Elsevier, 2022-07) Alladi, Tejasvi; Chamola, Vinay
    With the rise of the Internet of Vehicles (IoV) and the number of connected vehicles increasing on the roads, Cooperative Intelligent Transportation Systems (C-ITSs) have become an important area of research. As the number of Vehicle to Vehicle (V2V) and Vehicle to Interface (V2I) communication links increases, the amount of data received and processed in the network also increases. In addition, networking interfaces need to be made more secure for which existing cryptography-based security schemes may not be sufficient. Thus, there is a need to augment them with intelligent network intrusion detection techniques. Some machine learning-based intrusion detection and anomaly detection techniques for vehicular networks have been proposed in recent times. However, given the expected large network size, there is a necessity for extensive data processing for use in such anomaly detection methods. Deep learning solutions are lucrative options as they remove the necessity for feature selection. Therefore, with the amount of vehicular network traffic increasing at an unprecedented rate in the C-ITS scenario, the need for deep learning-based techniques is all the more heightened. This work presents three deep learning-based misbehavior classification schemes for intrusion detection in IoV networks using Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNNs). The proposed Deep Learning Classification Engines (DCLE) comprise of single or multi-step classification done by deep learning models that are deployed on the vehicular edge servers. Vehicular data received by the Road Side Units (RSUs) is pre-processed and forwarded to the edge server for classifications following the three classification schemes proposed in this paper. The proposed classifiers identify 18 different vehicular behavior types, the F1-scores ranging from 95.58% to 96.75%, much higher than the existing works. By running the classifiers on testbeds emulating edge servers, the prediction performance and prediction time comparison of the proposed scheme is compared with those of the existing studies.
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    Artificial intelligence-assisted blockchain-based framework for smart and secure EMR management
    (Springer, 2022) Chamola, Vinay
    Healthcare professionals, patients, and other stakeholders have been storing medical prescriptions and other relevant reports electronically. These reports contain the personal information of the patients, which is sensitive data. Therefore, there exists a need to store these records in a decentralized model (using IPFS and Ethereum decentralized application) to provide data and identity protection. Many patients recurrently visit doctors and undergo treatments while receiving different prescriptions and reports. In case of an emergency, the doctors and attendants may need and benefit from the patients’ medical history. However, they are unable to go through medical history and a wide range of previous reports and prescriptions due to time constraints. In this paper, we propose an AI-assisted blockchain-based framework in which the stored medical records (handwritten prescriptions, printed prescriptions, and printed reports) are stored and processed using various AI techniques like optical character recognition (OCR) to form a single patient medical history report. The report concisely presents only the crucial information for convenience and perusal and is stored securely over a decentralized blockchain network for later use.
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    Role of machine learning and deep learning in securing 5G-driven industrial IoT applications
    (Elsevier, 2021-12) Chamola, Vinay; Gupta, Shashank
    The Internet of Things (IoT) connects millions of computing devices and has set a stage for future technology where industrial use cases like smart cities and smart houses will operate with minimal human intervention. IoT’s cross-domain amalgamations with emergent technologies like 5G and blockchain affects human life. Hence, increase in reliance over IoT necessitates focus on its privacy and security concerns. Implementing security through encryption, authentication, access control and communication security is the need of the hour. These needs can be best catered with the use of machine learning (ML) and deep learning (DL) that can help in realizing secure intelligent systems. In this work, the authors present a comprehensive review for securing Industrial-IoT (I-IoT) devices to contribute to the development of security methods for I-IoT deployed over 5G and blockchain. The survey provides a general analysis of the state-of-the-art security implementation and further assesses the product life cycle of IoT devices. The authors present numerous virtues as well as faults in the machine learning and deep learning algorithms deployed over the fog architecture in context with the security solutions. The potential security algorithms can help overcome many challenges in the IoT security and pave way for implementation with emerging technologies like 5G, blockchain, edge computing, fog computing and their use cases for creating smart environments.
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    A Survey on Supply Chain Security: Application Areas, Security Threats, and Solution Architectures
    (IEEE, 2021-04) Chamola, Vinay
    The rapid improvement in the global connectivity standards has escalated the level of trade taking place among different parties. Advanced communication standards are allowing the trade of all types of commodities and services. Furthermore, the goods and services developed in a particular region are transcending boundaries to enter into foreign markets. Supply chains play an essential role in the trade of these goods. To be able to realize a connected world with no boundary restrictions in terms of goods and services, it is imperative to keep the associated supply chains transparent, secure, and trustworthy. Therefore, some fundamental changes in the current supply chain architecture are essential to achieve a secure trade environment. This article discusses the supply chain's security-critical application areas and presents a detailed survey of the security issues in the existing supply chain architecture. Various emerging technologies, such as blockchain, machine learning (ML), and physically unclonable functions (PUFs) as solutions to the vulnerabilities in the existing infrastructure of the supply chain have also been discussed. Recent studies reviewed in this work reveal a growing sentiment in the industry toward new and emerging technologies, such as Internet of Things (IoT), blockchain, and ML. While many organizations have already adopted IoT applications and artificial intelligence systems in their businesses, widespread adoption of blockchain remains distant. It has also been found that over the past decade, PUF-based authentication systems have gained much ground. However, a proper reference model for their implementation in complex supply chains is still missing.