| 1. |
- Ahmed, Tauheed, et al.
(författare)
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FIMBISAE : A Multimodal Biometric Secured Data Access Framework for Internet of Medical Things Ecosystem
- 2023
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 10:7, s. 6259-6270
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Tidskriftsartikel (refereegranskat)abstract
- Information from the Internet of Medical Things (IoMT) domain demands building safeguards against illegitimate access and identification. Existing user identification schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. Significant advancement has been achieved in the domain of biometrics and health informatics. This can take a step ahead with the usage of multimodal biometrics for the identification of healthcare system users. With this aim, the proposed work explores the fingerprint and iris modality to develop a multimodal biometric data identification and access control system for the healthcare ecosystem. In the proposed approach, minutiae-based fingerprint features and a combination of local and global iris features are considered for identification. Further, an index space based on the dimension of the feature vector is created, which gives a 1-D embedding of the high-dimensional feature set. Next, to minimize the impact of false rejection, the approach considers the possible deviation in each element of the feature vector and then stores the data in possible locations using the predefined threshold. Besides, to reduce the false acceptance rate, linking of the modalities has been done for every individual data. The modality linking thus helps in carrying out an efficient search of the queried data, thereby minimizing the false acceptance and rejection rate. Experiments on a chimeric iris and fingerprint bimodal database resulted in an average of 95% reduction in the search space at a hit rate of 98%. The results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.
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| 2. |
- Akbarzadeh, Saeed, et al.
(författare)
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A Simple Fabrication, Low Noise, Capacitive Tactile Sensor for Use in Inexpensive and Smart Healthcare Systems
- 2022
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 22:9, s. 9069-9077
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Tidskriftsartikel (refereegranskat)abstract
- Tactile sensors are among the most important devices used in industrial and biomedical fields. Sensors' profiles are significantly affected by their structures and material used. This article presents a robust, low-cost, low noise, accurate and simple fabrication capacitive tactile sensor as a single taxel fabricated on foam. This highly scalable design provides excellent noise immunity, accuracy, and due to a unique printable elastic conductor, it is flexible and stretchable with more than 200% strain. Furthermore, the taxel is based on the capacitive Wheatstone bridge. As a result, noise immunity and stability in case of temperature fluctuation is accomplished. Additionally, the sensor's innovative, simple fabrication, made of Polyurethane foam and printable elastic conductor, allows the system to adapt and achieve relevant results necessary for the purpose of the sensor's application. Therefore, the proposed sensor has potential applications in industrial and biomedical contexts, such as sleep monitoring, etc.
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| 3. |
- Awais, Muhammad, et al.
(författare)
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Healthcare Professional in the Loop (HPIL) : Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging
- 2020
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 20:20
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Tidskriftsartikel (refereegranskat)abstract
- Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche–Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
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| 4. |
- Bhatt, Dulari, et al.
(författare)
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CNN Variants for Computer Vision : History, Architecture, Application, Challenges and Future Scope
- 2021
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Ingår i: Electronics. - : MDPI. - 2079-9292. ; 10:20
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Forskningsöversikt (refereegranskat)abstract
- Computer vision is becoming an increasingly trendy word in the area of image processing. With the emergence of computer vision applications, there is a significant demand to recognize objects automatically. Deep CNN (convolution neural network) has benefited the computer vision community by producing excellent results in video processing, object recognition, picture classification and segmentation, natural language processing, speech recognition, and many other fields. Furthermore, the introduction of large amounts of data and readily available hardware has opened new avenues for CNN study. Several inspirational concepts for the progress of CNN have been investigated, including alternative activation functions, regularization, parameter optimization, and architectural advances. Furthermore, achieving innovations in architecture results in a tremendous enhancement in the capacity of the deep CNN. Significant emphasis has been given to leveraging channel and spatial information, with a depth of architecture and information processing via multi-path. This survey paper focuses mainly on the primary taxonomy and newly released deep CNN architectures, and it divides numerous recent developments in CNN architectures into eight groups. Spatial exploitation, multi-path, depth, breadth, dimension, channel boosting, feature-map exploitation, and attention-based CNN are the eight categories. The main contribution of this manuscript is in comparing various architectural evolutions in CNN by its architectural change, strengths, and weaknesses. Besides, it also includes an explanation of the CNN's components, the strengths and weaknesses of various CNN variants, research gap or open challenges, CNN applications, and the future research direction.
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| 5. |
- Borah, Jintu, et al.
(författare)
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AiCareAir : Hybrid-Ensemble Internet-of-Things Sensing Unit Model for Air Pollutant Control
- 2024
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 24:13, s. 21558-21565
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Tidskriftsartikel (refereegranskat)abstract
- The detrimental effects on human health caused by air pollution show that being able to predict air quality is a task of utmost significance. The application of artificial intelligence (AI) and the Internet of Things (IoT) is seen as promising in this domain. The performances of state-of-the-art models in terms of prediction accuracy vary with different pollutants and are acceptable only for certain pollutants. This article uses machine learning (ML) and deep learning (DL) models to predict the concentrations of six major air pollutants. Data are collected over eight months with 1400 daily instances from sensors deployed in Kuala Lumpur, Malaysia. As an intelligibly robust system, in this article a hybrid-ensemble model is proposed using a combination of ML models, specifically random forest, K-nearest neighbor (KNN), extreme gradient boosting (XGBoost), and neural network (NN) models, namely, long short-term memory (LSTM), gated recurrent units (GRUs), and convolutional NNs (CNNs). Here, a hybrid-ensemble learning model is created using five various ML models as weak learners. In previous ensemble models, a homogeneous group of weak learners are used; however, this work uses a heterogeneous group of weak learners. The prediction accuracy is compared using R2 score, absolute, squared, and root-mean-squared errors (RMSEs).
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| 6. |
- Borah, Jintu, et al.
(författare)
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AiCareBreath : IoT Enabled Location Invariant Novel Unified Model for Predicting Air Pollutants to Avoid Related Respiratory Disease
- 2024
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Ingår i: IEEE Internet of Things Journal. - : IEEE. - 2327-4662. ; 11:8, s. 14625-14633
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Tidskriftsartikel (refereegranskat)abstract
- This article presents a location-invariant air pollution prediction model with good geographic generalizability. The model uses a Light GBR as part of a machine-learning framework to capture the spatial identification of air contaminants. Given the dynamic nature of air pollution, the model also uses a Random Forest to capture temporal dependencies in the data. Our model uses a transfer learning strategy to deal with location variability. The algorithm can learn concentration patterns because it has been trained on a vast dataset of air quality measurements from various locations. The trained model is then improved using information from a particular target site, customizing it to the features of the target area. Experiments are carried out on a comprehensive dataset containing air pollution measurements from various places to assess the efficacy of the proposed model. The recommended method performs better than standard models at forecasting air pollution levels, proving its dependability in various geographical settings. An interpretability analysis is also performed to learn about the variables affecting air pollution levels. We identify the geographical patterns associated with high pollutant concentrations by visualizing the learned representations within the model, giving important information for environmental planning and mitigation methods. The observations show that the model outperforms state-of-the-art forecasting based on RNNs and transformer-based models. The suggested methodology for forecasting air contaminants has the potential to improve air quality management and aid in decision-making across numerous regions. This helps safeguard the environment and public health by creating more precise and dependable air pollution forecast systems.
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| 7. |
- Cheng, Liu, et al.
(författare)
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EEG-CLNet : Collaborative Learning for Simultaneous Measurement of Sleep Stages and OSA Events Based on Single EEG Signal
- 2023
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE. - 0018-9456 .- 1557-9662. ; 72
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Tidskriftsartikel (refereegranskat)abstract
- Sleep-stage and apnea-hypopnea index (AHI) are the most important metrics in the diagnosis of sleep syndrome disease. In previous studies, these two tasks are usually implemented separately, which is both time- and resource-consuming. In this work, we propose a novel single electroencephalogram (EEG)-based collaborative learning network (EEG-CLNet) for simultaneous sleep staging and obstructive sleep apnea (OSA) event detection through multitask collaborative learning. The EEG-CLNet regards different tasks as a common unit to extract features from intragroups via both local parameter sharing and cross-task knowledge distillation (CTKD), rather than just sharing parameters or shortening the distance between different tasks. Our approach has been validated on two datasets with the same or better performance than other methods. The experimental results show that our method achieves a performance gain of 1%-5% compared with the baseline. Compared to previous works where two or even more models were required to perform sleep staging and OSA event detection, the EEG-CLNet could reduce the total number of model parameters and facilitate the model to mine the hidden relationships between different task semantic information. More importantly, it effectively alleviates the task bias problem in hard parameter sharing. As a consequence, this approach has notable potential to be a solution for a lightweight wearable sleep monitoring system in the future.
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| 8. |
- Ghayvat, Hemant, et al.
(författare)
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AI-enabled radiologist in the loop : novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia
- 2023
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Ingår i: Neural Computing & Applications. - : Springer. - 0941-0643 .- 1433-3058. ; 35, s. 14591-14609
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Tidskriftsartikel (refereegranskat)abstract
- A SARS-CoV-2 virus-specific reverse transcriptase-polymerase chain reaction (RT-PCR) test is usually used to diagnose COVID-19. However, this test requires up to 2 days for completion. Moreover, to avoid false-negative outcomes, serial testing may be essential. The availability of RT-PCR test kits is currently limited, highlighting the need for alternative approaches for the precise and rapid diagnosis of COVID-19. Patients suspected to be infected with SARS-CoV-2 can be assessed using chest CT scan images. However, CT images alone cannot be used for ruling out SARS-CoV-2 infection because individual patients may exhibit normal radiological results in the primary phases of the disease. A machine learning (ML)-based recognition and segmentation system was developed to spontaneously discover and compute infection areas in CT scans of COVID-19 patients. The computable assessment exhibited suitable performance for automatic infection region allocation. The ML models developed were suitable for the direct detection of COVID-19 (+). ML was confirmed to be a complementary diagnostic technique for diagnosing COVID-19(+) by forefront medical specialists. The complete manual delineation of COVID-19 often requires up to 225.5 min; however, the proposed RILML method decreases the delineation time to 7 min after four iterations of model updating.
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| 9. |
- Ghayvat, Hemant, et al.
(författare)
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AiCarePWP : Deep learning-based novel research for Freezing of Gait forecasting in Parkinson
- 2024
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Ingår i: Computer Methods and Programs in Biomedicine. - : Elsevier. - 0169-2607 .- 1872-7565. ; 254
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Tidskriftsartikel (refereegranskat)abstract
- Background and objectives: Episodes of Freezing of Gait (FoG) are among the most debilitating motor symptoms of Parkinson's Disease (PD), leading to falls and significantly impacting patients' quality of life. Accurate assessment of FoG by neurologists provides crucial insights into patients' conditions and disease symptoms. This proposed strategy involves utilizing a Weighted Fuzzy Logic Controller, Kalman Filter, and Kaiser-Meyer-Olkin test to detect the gait parameters while walking, resting, and standing phases. Parameters such as neuromodulation format, intensity, duration, frequency, and velocity are computed to pre-empt freezing episodes, thus aiding their prevention.Method: The AiCarePWP is a wearable electronics device designed to identify instances when a patient is on the brink of experiencing a freezing episode and subsequently deliver a brief electrical impulse to the patient's shank muscles to stimulate movement. The AiCarePWP wearable device aims to identify impending freezing episodes in PD patients and deliver brief electrical impulses to stimulate movement. The study validates this innovative approach using plantar insoles with a 3D accelerometer and electrical stimulator, analysing data from the inertial measuring unit and plantar-pressure foot data to detect and predict FoG.Results: Using a Convolutional Neural Network-based model, the study evaluated 47 gait features for their ability to differentiate resting, standing, and walking conditions. Variable selection was based on sensitivity, specificity, and overall accuracy, followed by Principal Component Analysis and Varimax rotation to extract and interpret factors. Factors with eigenvalues exceeding 1.0 were retained, and 37 features were retained.Conclusion: This study validates CNN's effectiveness in detecting FoG during various activities. It introduces a novel cueing method using electrical stimulation, which improves gait function and reduces FoG incidence in PD patients. Trustworthy wearable devices, based on Artificial Intelligence of Things (AIoT) and Artificial Intelligence of Medical Things (AIoMT), have been developed to support such interventions.
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| 10. |
- Ghayvat, Hemant, et al.
(författare)
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CP-BDHCA : Blockchain-Based Confidentiality-Privacy Preserving Big Data Scheme for Healthcare Clouds and Applications
- 2022
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Ingår i: IEEE journal of biomedical and health informatics. - : IEEE. - 2168-2194 .- 2168-2208. ; 26:5, s. 1937-1948
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Tidskriftsartikel (refereegranskat)abstract
- Healthcare big data (HBD) allows medical stakeholders to analyze, access, retrieve personal and electronic health records (EHR) of patients. Mostly, the records are stored on healthcare cloud and application (HCA) servers, and thus, are subjected to end-user latency, extensive computations, single-point failures, and security and privacy risks. A joint solution is required to address the issues of responsive analytics, coupled with high data ingestion in HBD and secure EHR access. Motivated from the research gaps, the paper proposes a scheme, that integrates blockchain (BC)-based confidentiality-privacy (CP) preserving scheme, CP-BDHCA, that operates in two phases. In the first phase, elliptic curve cryptographic (ECC)-based digital signature framework, HCA-ECC is proposed to establish a session key for secure communication among different healthcare entities. Then, in the second phase, a two-step authentication framework is proposed that integrates Rivest-Shamir-Adleman (RSA) and advanced encryption standard (AES), named as HCA-RSAE that safeguards the ecosystem against possible attack vectors. CP-BDAHCA is compared against existing HCA cloud applications in terms of parameters like response time, average delay, transaction and signing costs, signing and verifying of mined blocks, and resistance to DoS and DDoS attacks. We consider 10 BC nodes and create a real-world customized dataset to be used with SEER dataset. The dataset has 30,000 patient profiles, with 1000 clinical accounts. Based on the combined dataset the proposed scheme outperforms traditional schemes like AI4SAFE, TEE, Secret, and IIoTEED, with a lower response time. For example, the scheme has a very less response time of 300 ms in DDoS. The average signing cost of mined BC transactions is 3,34 seconds, and for 205 transactions, has a signing delay of 1405 ms, with improved accuracy of approximate to 12% than conventional state-of-the-art approaches.
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