Exploring the relationship between testosterone and diabetes within the UK Biobank data
- Authors: Oatley, Giles
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Australasian Computer Science Week, ACSW 2023, Melbourne Australia, 31 January-3 February 2023, ACSW '23: Proceedings of the 2023 Australasian Computer Science Week p. 244-247
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- Description: The UK Biobank (UKB) cohort data aims to improve the prevention, diagnosis, and treatment of a wide range of serious diseases, including diabetes. Presented is a population-based retrospective cohort study to explore the relationship between steroid hormones and the prevalence of diabetes. In particular, free testosterone is calculated from available serum biochemical markers in the UKB data, prevalent diabetes is calculated across a range of UKB data fields and ICD10 codes are generalized to their top-level classifications. It is then possible to explore relationships between testosterone levels, diabetes presence, and associated morbidities. © 2023 ACM.
Federated learning based trajectory optimization for UAV enabled MEC
- Authors: Nehra, Anushka , Consul, Prakhar , Budhiraja, Ishan , Kaur, Gagandeep , Nasser, Nidal , Imran, Muhammad
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Communications, ICC 2023 Vol. 2023-May, p. 1640-1645
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- Description: We present a moving mobile edge computing architecture in which unmanned aerial vehicles (UAV) serve as an equipment, providing computational power and allowing task offloading from mobile devices (MD). By improving user association, resource allocation, and UAV trajectory, we optimizing the energy consumption of all MDs. Towards that purpose, we provide a Trajectory optimization technique for making real-time choices while considering all the situation of the environment, followed by a DRL-based Trajectory control approach (RLCT). The RLCT approach may be adapted to any UAV takeoff point and can find the solution faster. The FL is introduced to address the Optimization problem in a Semi-distributed DRL technique to deal with UAV trajectory constraints. The proposed FRL approach enables devices to rapidly train the models locally while communicating with a local server to construct a network globally. The simulation results in the result section shows that the proposed technique RLCT and FRL in the paper outperforms the existing methods' while the FRL performs best among all. © 2023 IEEE.
Fine-grained image classification based on knowledge distillation
- Authors: Liu, Ying , Feng, Hao , Zhang, Weidong , Fang, Jie , Xiao, Peng , Zhang, Dengsheng
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023, Harbin, China, 29-31 July 2023, 2023 19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
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- Description: Despite the outstanding performance of deep learning-based fine-grained image classification methods, the commonly used models still suffer from high cost of computation and memory Therefore, this paper proposes a mobile-based CNN network that focuses on discriminative features of fine-grained images by embedding a hybrid-domain attention module to achieve higher accuracy in recognition. Specifically, under the premise of reducing network parameters, this paper presents a classification method that combines transfer learning and knowledge distillation to enhance the model's generalization performance and resistance to overfitting. Different knowledge transfer strategies are validated through the experiments in the knowledge distillation process. Mobile models such as SqueezeNet, MobileNetV2, and CBAM MobileNetV2 all demonstrate enhanced performance the knowledge distillation optimization. The proposed method in this paper can be used to develop a lightweight mobile-based CNN model with comparable performance to complex models making it more advantageous in real-life scenarios with limited storage resources and low hardware computation levels. Additionally, the model compression process utilizes only the intermediate features of the original dataset, meeting the confidentiality requirements of the original data in the field of public security. © 2023 IEEE.
Identification of fake news : a semantic driven technique for transfer domain
- Authors: Ferdush, Jannatul , Kamruzzaman, Joarder , Karmakar, Gour , Gondal, Iqbal , Das, Raj
- Date: 2023
- Type: Text , Conference paper
- Relation: 29th International Conference on Neural Information Processing, ICONIP 2022, Virtual, online, 22-26 November 2022, Communications in Computer and Information Science Vol. 1793 CCIS, p. 564-575
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- Description: Fake news spreads quickly on online social media and adversely impacts political, social, religious, and economic stability. This necessitates an efficient fake news detector which is now feasible due to advances in natural language processing and artificial intelligence. However, existing fake news detection (FND) systems are built on tokenization, embedding, and structure-based feature extraction, and fail drastically in real life because of the difference in vocabulary and its distribution across various domains. This article evaluates the effectiveness of various categories of traditional features in cross-domain FND and proposes a new method. Our proposed method shows significant improvement over recent methods in the literature for cross-domain fake news detection in terms of widely used performance metrics. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Improved model predictive torque control with reduced active prediction vectors for voltage source inverter driven induction motor drives
- Authors: Nahin, Nahin , Biswas, Shuvra , Hosain, Md Kamal , Bin Islam, Md Anas , Islam, Md Rabiul , Shah, Rakibuzzaman
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023, Tianjin, China, 27-29 October 2023, 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
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- Description: High-performance control, precise torque regulation, and minimal stator current total harmonic distortion (THD) of induction motor drives (IMDs) have always been considered an industrial concern. The conventional finite control set model predictive control (FCS-MPC) strategy suffers from high computational complication, increased torque ripple, and stator current THD, which is employed to drive the voltage source inverter (VSI) based IMD. This paper proposes an optimized method of selecting prediction vectors to minimize the computational cost of the traditional FCS-MPC for a two-level VSI-based IMD. By minimizing the number of prediction vectors from six to three utilizing the proposed strategy, the cost function is assessed for only four vectors. The proposed improved model predictive control (MPC) is based on finite control set predictive torque control (FCS-PTC). The proposed improved MPC strategy also prioritizes the selection of the zero vector by avoiding the phase arm that carries the high current. © 2023 IEEE.
Improved switching scheme to reduce the junction temperature and power loss of CHB inverters
- Authors: Afrin, Sadia , Biswas, Suvra , Bin Islam, Md Sabbir , Islam, Md Rabiul , Shah, Rakibuzzaman
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023, Tianjin, China, 27-29 October 2023, 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
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- Description: Excessive heating and power loss due to high-frequency switching are always alarming issues in the case of multilevel inverter (MLI) based applications such as solar photovoltaic (PV) systems and industrial drives. Both the heating and power loss of power semiconductor switches significantly rely on the switching pulse width modulation (PWM) scheme employed. This paper proposes an improved switching technique for a solar PV-fed grid-tied 5-level cascaded H-bridge (CHB) inverter, which also reduces the power semiconductor losses of the inverter in relation to several existing switching schemes. The proposed switching scheme employs a modified discontinuous standard mode signal to develop the modulating signal of the proposed method. Level-shifted triangular carrier signals are considered with the proposed switching signal to produce the gate pulses for the 5-level CHB inverter. The performance of the proposed switching scheme is validated through MATLAB/Simulink and PLECS computer simulation environments. © 2023 IEEE.
Improved voltage balancing discontinuous PWM scheme for solar PV Fed grid-tied NPC inverters
- Authors: Hossain, Shahriar , Biswas, Shuvra , Islam, Md Rabiul , Shah, Rakibuzzaman
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023, Tianjin, China, 27-29 October 2023, 2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023
- Full Text: false
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- Description: The total harmonic distortion (THD) of inverter output voltage, power loss due to high-frequency switching, and dc-link capacitor voltage balancing are considered as the major research concerns for neutral point clamped (NPC) inverter-based solar photovoltaic (PV) systems, which are largely affected by the pulse width modulation (PWM) scheme. This work proposes an improved voltage balancing discontinuous PWM (DPWM) scheme for reducing the inverter output voltage THD and power loss with balanced DC-link capacitor voltages compared to existing discontinuous PWM schemes. © 2023 IEEE.
Machine learning driven digital twin for industrial control black box system : a novel framework and case study
- Authors: Siddiqui, Mustafa , Kahandawa, Gayan , Hewawasam, Hasitha , Rehman Siddiqi, Muftooh
- Date: 2023
- Type: Text , Conference paper
- Relation: 28th International Conference on Automation and Computing, ICAC 2023, Birmingham, UK, 30 August-1 September 2023, ICAC 2023 The 28th International Conference on Automation and Computing Digitalisation for Smart Manufacturing and Systems
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- Description: Industrial control systems are excessively used in advanced manufacturing environments. The lack of information and data regarding the internal workings of certain systems makes virtual modelling for their Digital Twin challenging. As a result, these systems are often classified as 'black box' systems. There is minimal research found on DT models for industrial control black box systems. Therefore, a novel algorithm to model the Digital Twin of the industrial control black box system in the cyber domain has been presented in this paper. Machine Learning techniques were used to develop a high-fidelity Digital Twin model of a black box system. Real-time sensor data were recorded and used to validate the proposed novel algorithm. This paper presents the proposed algorithm's effectiveness in developing a robust Digital Twin model of industrial control back box system. © 2023 IEEE.
Missing health data pattern matching technique for continuous remote patient monitoring
- Authors: Arora, Teena , Balasubramanian, Venki , Stranieri, Andrew
- Date: 2023
- Type: Text , Conference paper
- Relation: 20th International Conference on Smart Living and Public Health, ICOST 2023, Wonju, Korea, 7-8 July 2023, Digital Health Transformation, Smart Ageing, and Managing Disability, 20th International Conference, ICOST 2023, Wonju, South Korea, July 7–8, 2023, Proceedings Vol. 14237 LNCS, p. 130-143
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- Description: Remote patient monitoring (RPM) has been gaining popularity recently. However, health data acquisition is a significant challenge associated with patient monitoring. In continuous RPM, health data acquisition may miss health data during transmission. Missing data compromises the quality and reliability of patient risk assessment. Several studies suggested techniques for analyzing missing data; however, many are unsuitable for RPM. These techniques neglect the variability of missing data and provide biased results with imputation. Therefore, a holistic approach must consider the correlation and variability of the various vitals and avoid biased imputation. This paper proposes a coherent computation pattern-matching technique to identify and predict missing data patterns. The performance of the proposed approach is evaluated using data collected from a field trial. Results show that the technique can effectively identify and predict missing patterns. © 2023, The Author(s).
Modeling and analysis of finite-scale clustered backscatter communication networks
- Authors: Wang, Qiu , Zhou, Yong , Dai, Hong-Ning , Zhang, Guopeng , Imran, Muhammad , Nasser, Nidal
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 IEEE International Conference on Communications, ICC 2023, Rome, 28 May-1 June 2023, ICC 2023 - IEEE International Conference on Communications Vol. 2023-May, p. 1456-1461
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- Description: Backscatter communication (BackCom) is an intriguing technology that enables devices to transmit information by reflecting environmental radio frequency signals while consuming ultra-low energy. Applying BackCom in the Internet of things (IoT) networks can effectively address the power-unsustainability issue of energy-constraint devices. Considering many practical IoT applications, networks are finite-scale and devices are needed to be deployed at hotspot regions organized in clusters to cooperate for specific tasks. This paper considers finite-scale clustered backscatter communication networks (F-CBackCom Nets). To ensure communications, this paper establishes a theoretic model to analyze the communication connectivity of F-CBackCom Nets. Different from prior studies analyzing the connectivity with a focus on the transmission pair located at the center of the network, this paper analyzes the connectivity of a transmission pair located in an arbitrary location, because the performance of transmission pairs potentially varies with their network location. Extensive simulations validate the accuracy of our analytical model. Our results show that the connectivity of a transmission pair can be affected by its network location. Our analytical model and results can offer beneficial implications for constructing F-CBackCom Nets. © 2023 IEEE.
Novel few-shot learning based fuzzy feature detection algorithms
- Authors: Luo, Yun , Lu, Liangfu , Cui, Xudong , Du, Yan , Bi, Yingying , Zhu, Limin , Liang, Christy
- Date: 2023
- Type: Text , Conference paper
- Relation: 10th IEEE International Conference on Data Science and Advanced Analytics, DSAA 2023, Thessaloniki, Greece, 9-12 October 2023, 2023 IEEE 10th International Conference on Data Science and Advanced Analytics, DSAA 2023 - Proceedings
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- Description: The Internet of Things (IoT) has significantly enhanced various aspects of our daily lives, including security, health, education, and energy efficiency, among others. Within the realm of IoT, image classification stands as a pivotal technique that has achieved notable success in domains such as facial recognition within security and scene recognition in transportation for traffic analysis. Nonetheless, the challenge emerges when tackling classification tasks with only limited labeled samples available for each category. Conventional machine learning techniques often struggle to attain satisfactory classification results under such circumstances. To address this issue, the concept of few-shot learning has emerged, aiming to achieve effective classification using only a small number of labeled samples. State-of-the-art few-shot learning models have introduced novel frameworks to tackle this problem. However, the inherent ambiguity and uncertainty within data often hinder the performance of classification methods. To overcome this limitation, this paper proposes the integration of fuzzy learning with few-shot learning in the context of feature extraction. The objective is to mitigate data fuzziness and enhance model performance. Leveraging a fuzzy extraction algorithm, we introduce fuzzy prototype networks and a fuzzy graph neural network with fuzzy reasoning. These frameworks are designed to analyze noisy and uncertain data, utilizing convolutional neural networks for feature extraction and applying fuzzy reasoning to capture ambiguity representations for features within each fuzzy set. The SoftMax function is then normalized to serve as a feature weight, effectively constraining the original feature vector. The effectiveness and efficiency of our proposed model are demonstrated through experimental evaluations conducted on various public datasets. The results showcase the model's capability in addressing the challenges posed by limited labeled data and data uncertainty, thus reaffirming its potential in enhancing the performance of image classification tasks within the IoT context. © 2023 IEEE.
Radiative heat transfer in solar particle receivers
- Authors: Chen, Jingling , Kumar, Apurv , Coventry, Joe , Lipiński, Wojciech
- Date: 2023
- Type: Text , Conference paper
- Relation: 10th International Symposium on Radiative Transfer, RAD 2023, Thessaloniki, Greece, 12-16 June 2023, Proceedings of the 10th International Symposium on Radiative Transfer Vol. 2023-June, p. 291-298
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- Description: Energy flow and conversion in high-temperature solar particle receivers are investigated by theoretical, numerical, and experimental approaches. Alumina–silica-based ceramic particle materials are synthesised, and optically and radiatively characterised. Advanced numerical models of particle–gas two-phase flows under direct high-flux solar irradiation are developed to understand the flow physics, predict receiver thermal characteristics, and enable receiver technology advancement. © 2023 Proceedings of the International Symposium on Radiative Transfer.
Real-time distributed trajectory planning for mobile robots
- Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Anh , Nguyen, Thang
- Date: 2023
- Type: Text , Conference paper
- Relation: 22nd IFAC World Congress Vol. 56, p. 2152-2157
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- Description: Efficiently planning trajectories for nonholonomic mobile robots in formation tracking is a fundamental yet challenging problem. Nonholonomic constraints, complexity in collision avoidance, and limited computing resources prevent the robots from being practically deployed in realistic applications. This paper addresses these difficulties by modeling each mobile platform as a nonholonomic motion and formulating trajectory planning as an optimization problem using model predictive control (MPC). That is, the optimization problem is subject to both nonholonomic motions and collision avoidance. To reduce computing costs in real time, the nonholonomic constraints are convexified by finding the closest nominal points to the nonholonomic motion, which are then incorporated into a convex optimization problem. Additionally, the predicted values from the previous MPC step are utilized to form linear avoidance conditions for the next step, preventing collisions among robots. The formulated optimization problem is solved by the alternating direction method of multiplier (ADMM) in a distributed manner, which makes the proposed trajectory planning method scalable. More importantly, the convergence of the proposed planning algorithm is theoretically proved while its effectiveness is validated in a synthetic environment. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
Scalar reward is not enough JAAMAS Track
- Authors: Vamplew, Peter , Smith, Benjamin , Källström, Johan , Ramos, Gabriel , Rădulescu, Roxana , Roijers, Diederik , Hayes, Conor , Heintz, Frederik , Mannion, Patrick , Libin, Pieter , Dazeley, Richard , Foale, Cameron
- Date: 2023
- Type: Text , Conference paper
- Relation: 22nd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2023, London, 29 May to 2 June 2023, Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS, Vol. 2023-May, p. 839-841
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- Description: Silver et al. [14] posit that scalar reward maximisation is sufficient to underpin all intelligence and provides a suitable basis for artificial general intelligence (AGI). This extended abstract summarises the counter-argument from our JAAMAS paper[19]. © 2023 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.
Secure-and privacy-preserving policies for distributed cooperative control of multiple vehicle systems
- Authors: Nguyen, Binh , Nghiem, Truong , Nguyen, Linh , Nguyen, Tuy , Nguyen, Thang
- Date: 2023
- Type: Text , Conference paper
- Relation: Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023, Orlando USA, 2-4 May 2023, Proceedings of SPIE - The International Society for Optical Engineering Vol. 12540
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- Description: Conventional distributed formation control of multiple vehicle systems (MVSs) has two drawbacks: inflexible formation changes and explicit-value exchanges of vehicles' information such as position and velocity among all vehicles. Since formation changes are needed, each vehicle is required to update its relative position which is quite difficult in large spatial applications. Firstly, the explicit-value exchanges possibly result in two critical issues. When each vehicle policy needs to keep its information confidential from another unexpected listener, the explicit-value exchanges are invalid for the privacy policies. Additionally, the explicit-value storing or exchanging signals or parameters are much more vulnerable and dangerous to security threats. This work proposes an approach to overcome the above challenges by taking advantage of model predictive control-consensus algorithms to achieve desired formations. We will also allow the computation to be effectively distributed among the vehicle agents according to their computational capabilities. Secondly, we use the highly secure encryption scheme that empowers all computations carried out in encrypted forms, including system parameters and signals. Our results are verified by the formation control of multiple vehicles working in large-scale environments where a ground station does not touch all vehicles due to limited communication ranges and security problems. Compared to cutting-edge studies, the formation of vehicles is still able to be changed securely by the ground station without updating new formations to all vehicles. Besides, the data privacy of each vehicle is preserved by encrypting all physical signals. © 2023 SPIE. All rights reserved.
SMGKM : an efficient incremental algorithm for clustering document collections
- Authors: Bagirov, Adil , Seifollahi, Sattar , Piccardi, Massimo , Zare Borzeshi, Ehsan , Kruger, Bernie
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018, Hanoi, Vietnam, 18-24 March 2018, Computational Linguistics and Intelligent Text Processing Vol. 13397 LNCS, p. 314-328
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- Description: Given a large unlabeled document collection, the aim of this paper is to develop an accurate and efficient algorithm for solving the clustering problem over this collection. Document collections typically contain tens or hundreds of thousands of documents, with thousands or tens of thousands of features (i.e., distinct words). Most existing clustering algorithms struggle to find accurate solutions on such large data sets. The proposed algorithm overcomes this difficulty by an incremental approach, incrementing the number of clusters progressively from an initial value of one to a set value. At each iteration, the new candidate cluster is initialized using a partitioning approach which is guaranteed to minimize the objective function. Experiments have been carried out over six, diverse datasets and with different evaluation criteria, showing that the proposed algorithm has outperformed comparable state-of-the-art clustering algorithms in all cases. © 2023, Springer Nature Switzerland AG.
Time-minimum motion handling of open liquid-filled objects using sparse sequential quadratic programming
- Authors: Le, Hieu , Appuhamillage, Gayan , Nguyen, Linh
- Date: 2023
- Type: Text , Conference paper
- Relation: 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, Turkey, 11-13 October 2023, 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023
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- Description: The paper presents an efficient approach to minimize motion time of an industrial robot so that it can successfully manipulate an open and liquid-filled object in pick-and-place operations. It is first proposed a motion planning optimization problem, where the total motion duration is considered as a cost function. Moreover, the robot physical limits such as its joint positions, velocities and accelerations are used as the optimization constrains. On the other hand, to ensure an open and liquidfilled object always upright, orientation constraints of the robot end-effector are taken into account. More specifically, roll and pitch of the end-effector are proposed to be fixed during the transportation, which ensures there is no tipping over in the object. The formulated motion planning optimization problem is then efficiently solved by using the sparse sequential quadratic programming method. Our approach excels in optimizing the motion trajectory by leveraging its flexibility, accommodating various trajectory shapes that satisfy the kinematic conditions. The optimization leads to more efficient and effective motion execution, resulting in a substantial reduction in the overall motion profile duration. Extensive evaluation of the proposed approach on a KUKA robot model demonstrates its effectiveness. © 2023 IEEE.
Whose data are reliable : sensor declared data reliability
- Authors: Shafin, Sakib , Karmakar, Gour , Mareels, Iven , Balasubramanian, Venki , Kolluri, Ramachandra
- Date: 2023
- Type: Text , Conference paper
- Relation: 19th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2023, Montreal, Canada, 21-23 June 2023, International Conference on Wireless and Mobile Computing, Networking and Communications Vol. 2023-June, p. 249-254
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- Description: Sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor's historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications. © 2023 IEEE.
A cloud-based IoMT data sharing scheme with conditional anonymous source authentication
- Authors: Wang, Yan-Ping , Wang, Xiao-Fen , Dai, Hong-Ning , Zhang, Xiao-Song , Su, Yu , Imran, Muhammad , Nasser, Nidal
- Date: 2022
- Type: Text , Conference paper
- Relation: 2022 IEEE Global Communications Conference, GLOBECOM 2022, Virtual, online, 4-8 December 2022, 2022 IEEE Global Communications Conference, GLOBECOM 2022 - Proceedings p. 2915-2920
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- Description: As a rapidly growing subset of the Internet of Thing (IoT), the cloud-based Internet of Medical Thing (IoMT) has been widely applied in remote healthcare industries, which allows the physicians to monitor patients' body parameters remotely to offer continuous and timely healthcare. These healthcare parameters usually contain sensitive information, such as heart rates, glucose levels and etc., and the exposure of them may pose serious threats to the patients' health and lives. To guarantee security and privacy, many IoMT data sharing schemes have been proposed. However, most of these schemes either exhibit a one-to-one data sharing structure or fail to protect the patients' privacy. Since the data usually needs to be shared to different physicians, patients may want to be assisted without revealing their identities. To meet these requirements in healthcare systems, we propose a multi-receiver secure healthcare data sharing scheme, in which the patients are allowed to share their IoMT data to multiple physicians simultaneously for a multidisciplinary treatment, and the conditional anonymity is achieved where data source authentication is provided without revealing the patient's identity. When the patient health condition is abnormal, the hospital can correctly and quickly trace the patient's identity and inform him/her immediately. Our scheme is formally proved to achieve multiple security properties including confidentiality, unforgeability and anonymity. Simulation results demonstrate that the proposed scheme is efficient and practical. © 2022 IEEE.
A generative adversarial active learning method for effective outlier detection
- Authors: Bah, Mohamed , Zhang, Ji , Yu, Ting , Xia, Feng , Li, Zhao , Zhou, Shuigeng , Wang, Hongzhi
- Date: 2022
- Type: Text , Conference paper
- Relation: 34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022, Virtual, online, 31 October-2 November 2022, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2022-October, p. 131-139
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- Description: Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem. © 2022 IEEE.