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An intelligent heart disease prediction system based on swarm-artificial neural network

- Nandy, Sudarshan, Adhikari, Mainak, Balasubramanian, Venki, Menon, Varun, Li, Xingwang, Zakarya, Muhammad

  • Authors: Nandy, Sudarshan , Adhikari, Mainak , Balasubramanian, Venki , Menon, Varun , Li, Xingwang , Zakarya, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14723-14737
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  • Description: The accurate prediction of cardiovascular disease is an essential and challenging task to treat a patient efficiently before occurring a heart attack. In recent times, various intelligent healthcare frameworks have been designed with different machine learning and swarm optimization techniques for cardiovascular disease prediction. However, most of the existing strategies failed to achieve higher accuracy for cardiovascular disease prediction due to the lack of data-recognized techniques and proper prediction methodology. Motivated by the existing challenges, in this paper, we propose an intelligent healthcare framework for predicting cardiovascular heart disease based on Swarm-Artificial Neural Network (Swarm-ANN) strategy. Initially, the proposed Swarm-ANN strategy randomly generates predefined numbers of Neural Networks (NNs) for training and evaluating the framework based on their solution consistency. Additionally, the NN populations are trained by two stages of weight changes and their weight is adjusted by a newly designed heuristic formulation. Finally, the weight of the neurons is modified by sharing the global best weight with other neurons and predicts the accuracy of cardiovascular disease. The proposed Swarm-ANN strategy achieves 95.78% accuracy while predicting the cardiovascular disease of the patients from a benchmark dataset. The simulation results exhibit that the proposed Swarm-ANN strategy outperforms the standard learning techniques in terms of various performance matrices. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

An online scalarization multi-objective reinforcement learning algorithm : TOPSIS Q-learning

- Mirzanejad, Mohammad, Ebrahimi, Morteza, Vamplew, Peter, Veisi, Hadi

  • Authors: Mirzanejad, Mohammad , Ebrahimi, Morteza , Vamplew, Peter , Veisi, Hadi
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Knowledge Engineering Review Vol. 37, no. 4 (2022), p.
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  • Description: Conventional reinforcement learning focuses on problems with single objective. However, many problems have multiple objectives or criteria that may be independent, related, or contradictory. In such cases, multi-objective reinforcement learning is used to propose a compromise among the solutions to balance the objectives. TOPSIS is a multi-criteria decision method that selects the alternative with minimum distance from the positive ideal solution and the maximum distance from the negative ideal solution, so it can be used effectively in the decision-making process to select the next action. In this research a single-policy algorithm called TOPSIS Q-Learning is provided with focus on its performance in online mode. Unlike all single-policy methods, in the first version of the algorithm, there is no need for the user to specify the weights of the objectives. The user's preferences may not be completely definite, so all weight preferences are combined together as decision criteria and a solution is generated by considering all these preferences at once and user can model the uncertainty and weight changes of objectives around their specified preferences of objectives. If the user only wants to apply the algorithm for a specific set of weights the second version of the algorithm efficiently accomplishes that. ©

Scheduling and power control for connectivity enhancement in multi-hop I2V/V2V networks

- Nguyen, Bach, Ngo, Duy, Dao, Minh, Bao, Vo, Vu, Hai

  • Authors: Nguyen, Bach , Ngo, Duy , Dao, Minh , Bao, Vo , Vu, Hai
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 23, no. 8 (2022), p. 10322-10332
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  • Description: Infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V) communications are often combined to extend the connectivity and coverage in the Intelligent Transportation System (ITS) and its applications, e.g., augmented reality, real-time parking management and online shopping. Through multi-hop I2V and V2V communications, requesting vehicles are always connected to road side units (RSUs) even when they do not reside within the RSUs' coverage range. However, there may be not adequate network resource for several I2V and V2V links when multiple vehicles request services simultaneously. In this paper, we propose a joint frequency scheduling and power control scheme to enhance connectivity in multi-hop I2V/V2V networks. We associate I2V and V2V links with tuple-links, then formulate an NP-hard problem in which a frequency scheduler and a power controller are jointly designed for the tuple-links. The NP-hard problem is decomposed into two separate subproblems by employing the delayed column generation technique. Then, we employ a method for linear programming and a greedy algorithm to address these subproblems. Through numerical experiments with practical parameter settings, we demonstrate the proposed scheme outperforms several existing ones in terms of connectivity enhancement, measured by the service resumption number and average achieved throughput. Furthermore, the efficiency of our scheme is further enhanced when the number of available channels is high, and buffer size equipped to the requesting vehicles is large. © 2000-2011 IEEE.

Depth-based sampling and steering constraints for memoryless local planners

- Nguyen, Binh, Nguyen, Linh, Choudhury, Tanveer, Keogh, Kathleen, Murshed, Manzur

  • Authors: Nguyen, Binh , Nguyen, Linh , Choudhury, Tanveer , Keogh, Kathleen , Murshed, Manzur
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Intelligent and Robotic Systems: Theory and Applications Vol. 109, no. 3 (2023), p.
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  • Description: By utilizing only depth information, the paper introduces a novel two-stage planning approach that enhances computational efficiency and planning performances for memoryless local planners. First, a depth-based sampling technique is proposed to identify and eliminate a specific type of in-collision trajectories among sampled candidates. Specifically, all trajectories that have obscured endpoints are found through querying the depth values and will then be excluded from the sampled set, which can significantly reduce the computational workload required in collision checking. Subsequently, we apply a tailored local planning algorithm that employs a direction cost function and a depth-based steering mechanism to prevent the robot from being trapped in local minima. Our planning algorithm is theoretically proven to be complete in convex obstacle scenarios. To validate the effectiveness of our DEpth-based both Sampling and Steering (DESS) approaches, we conducted experiments in simulated environments where a quadrotor flew through cluttered regions with multiple various-sized obstacles. The experimental results show that DESS significantly reduces computation time in local planning compared to the uniform sampling method, resulting in the planned trajectory with a lower minimized cost. More importantly, our success rates for navigation to different destinations in testing scenarios are improved considerably compared to the fixed-yawing approach. © 2023, The Author(s).

Corrosion and coating defect assessment of Coal Handling and Preparation Plants (CHPP) using an ensemble of deep convolutional neural networks and decision-level data fusion

- Yu, Yang, Hoshyar, Azadeh, Samali, Bijan, Zhang, Guang, Rashidi, Maria, Mohammadi, Masoud

  • Authors: Yu, Yang , Hoshyar, Azadeh , Samali, Bijan , Zhang, Guang , Rashidi, Maria , Mohammadi, Masoud
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 25 (2023), p. 18697-18718
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  • Description: In view of the problems of ineffective feature extraction and low detection accuracy in existing detection system, this study presents a novel machine vision-based approach composed of an ensemble of deep convolutional neural networks (CNNs) and improved Dempster-Shafer (D-S) theory-based data fusion to evaluate corrosion and coating defect of coal handling and preparation plants. To start with, the structural surface image is sent to each transferred CNN for initial defect identification. Then, an improved D-S fusion algorithm is proposed to combine the identification results from different CNNs, which are vectors consisting of statistical indicators of all the potential damage severity categories. The decision-level fusion of different CNNs can effectively improve image classification. To validate the performance of the proposed method, a dataset made of 3593 surface images with different defect severities captured from mining infrastructure in field is established together with data augmentation. The validation result demonstrates that the proposed method is able to effectively improve the recognition accuracy of defect severity and reduce the wrong recognition rate. Finally, the robustness of the proposed approach is also appraised by polluting the images with different types and intensities of noise, with satisfactory results. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Discrete-to-deep reinforcement learning methods

- Kurniawan, Budi, Vamplew, Peter, Papasimeon, Michael, Dazeley, Richard, Foale, Cameron

  • Authors: Kurniawan, Budi , Vamplew, Peter , Papasimeon, Michael , Dazeley, Richard , Foale, Cameron
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 34, no. 3 (2022), p. 1713-1733
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  • Description: Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. In complex problems, a neural RL approach is often able to learn a better solution than tabular RL, but generally takes longer. This paper proposes two methods, Discrete-to-Deep Supervised Policy Learning (D2D-SPL) and Discrete-to-Deep Supervised Q-value Learning (D2D-SQL), whose objective is to acquire the generalisability of a neural network at a cost nearer to that of a tabular method. Both methods combine RL and supervised learning (SL) and are based on the idea that a fast-learning tabular method can generate off-policy data to accelerate learning in neural RL. D2D-SPL uses the data to train a classifier which is then used as a controller for the RL problem. D2D-SQL uses the data to initialise a neural network which is then allowed to continue learning using another RL method. We demonstrate the viability of our algorithms with Cartpole, Lunar Lander and an aircraft manoeuvring problem, three continuous-space environments with low-dimensional state variables. Both methods learn at least 38% faster than baseline methods and yield policies that outperform them. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Robust piecewise linear L 1-regression via nonsmooth DC optimization

- Bagirov, Adil, Taheri, Sona, Karmitsa, Napsu, Sultanova, Nargiz, Asadi, Soodabeh

  • Authors: Bagirov, Adil , Taheri, Sona , Karmitsa, Napsu , Sultanova, Nargiz , Asadi, Soodabeh
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Optimization Methods and Software Vol. 37, no. 4 (2022), p. 1289-1309
  • Relation: http://purl.org/au-research/grants/arc/DP190100580
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  • Description: Piecewise linear (Formula presented.) -regression problem is formulated as an unconstrained difference of convex (DC) optimization problem and an algorithm for solving this problem is developed. Auxiliary problems are introduced to design an adaptive approach to generate a suitable piecewise linear regression model and starting points for solving the underlying DC optimization problems. The performance of the proposed algorithm as both approximation and prediction tool is evaluated using synthetic and real-world data sets containing outliers. It is also compared with mainstream machine learning regression algorithms using various performance measures. Results demonstrate that the new algorithm is robust to outliers and in general, provides better predictions than the other alternative regression algorithms for most data sets used in the numerical experiments. © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Graph lifelong learning : a survey

- Febrinanto, Falih, Xia, Feng, Moore, Kristen, Thapa, Chandra, Aggarwal, Charu

  • Authors: Febrinanto, Falih , Xia, Feng , Moore, Kristen , Thapa, Chandra , Aggarwal, Charu
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Computational Intelligence Magazine Vol. 18, no. 1 (2023), p. 32-51
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  • Description: Graph learning is a popular approach for perfor ming machine learning on graph-structured data. It has revolutionized the machine learning ability to model graph data to address downstream tasks. Its application is wide due to the availability of graph data ranging from all types of networks to information systems. Most graph learning methods assume that the graph is static and its complete structure is known during training. This limits their applicability since they cannot be applied to problems where the underlying graph grows over time and/or new tasks emerge incrementally. Such applications require a lifelong learning approach that can learn the graph continuously and accommodate new information whilst retaining previously learned knowledge. Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure. As a result, graph lifelong learning is gaining attention from the research community. This survey paper provides a comprehensive overview of recent advancements in graph lifelong learning, including the categorization of existing methods, and the discussions of potential applications and open research problems. © 2005-2012 IEEE.

Classifying and completing word analogies by machine learning

- Lim, Suryani, Prade, Henri, Richard, Gilles

  • Authors: Lim, Suryani , Prade, Henri , Richard, Gilles
  • Date: 2021
  • Type: Text , Journal article
  • Relation: International Journal of Approximate Reasoning Vol. 132, no. (2021), p. 1-25
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  • Description: Analogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted a:b::c:d. They are the basis of analogical reasoning which is often considered as an essential ingredient of human intelligence. For this reason, recognizing analogies in natural language has long been a research focus within the Natural Language Processing (NLP) community. With the emergence of word embedding models, a lot of progress has been made in NLP, essentially assuming that a word analogy like man:king::woman:queen is an instance of a parallelogram within the underlying vector space. In this paper, we depart from this assumption to adopt a machine learning approach, i.e., learning a substitute of the parallelogram model. To achieve our goal, we first review the formal modeling of analogical proportions, highlighting the properties which are useful from a machine learning perspective. For instance, the postulates supposed to govern such proportions entail that when a:b::c:d holds, then seven permutations of a,b,c,d still constitute valid analogies. From a machine learning perspective, this provides guidelines to build training sets of positive and negative examples. Taking into account these properties for augmenting the set of positive and negative examples, we first implement word analogy classifiers using various machine learning techniques, then we approximate by regression an analogy completion function, i.e., a way to compute the missing word when we have the three other ones. Using a GloVe embedding, classifiers show very high accuracy when recognizing analogies, improving state of the art on word analogy classification. Also, the regression processes usually lead to much more successful analogy completion than the ones derived from the parallelogram assumption. © 2021

Human engagement providing evaluative and informative advice for interactive reinforcement learning

- Bignold, Adam, Cruz, Francisco, Dazeley, Richard, Vamplew, Peter, Foale, Cameron

  • Authors: Bignold, Adam , Cruz, Francisco , Dazeley, Richard , Vamplew, Peter , Foale, Cameron
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 25 (2023), p. 18215-18230
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  • Description: Interactive reinforcement learning proposes the use of externally sourced information in order to speed up the learning process. When interacting with a learner agent, humans may provide either evaluative or informative advice. Prior research has focused on the effect of human-sourced advice by including real-time feedback on the interactive reinforcement learning process, specifically aiming to improve the learning speed of the agent, while minimising the time demands on the human. This work focuses on answering which of two approaches, evaluative or informative, is the preferred instructional approach for humans. Moreover, this work presents an experimental setup for a human trial designed to compare the methods people use to deliver advice in terms of human engagement. The results obtained show that users giving informative advice to the learner agents provide more accurate advice, are willing to assist the learner agent for a longer time, and provide more advice per episode. Additionally, self-evaluation from participants using the informative approach has indicated that the agent’s ability to follow the advice is higher, and therefore, they feel their own advice to be of higher accuracy when compared to people providing evaluative advice. © 2022, The Author(s).

Automated methods for diagnosis of Parkinson’s disease and predicting severity level

- Ayaz, Zainab, Naz, Saeeda, Khan, Naila, Razzak, Imran, Imran, Muhammad

  • Authors: Ayaz, Zainab , Naz, Saeeda , Khan, Naila , Razzak, Imran , Imran, Muhammad
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 20 (2023), p. 14499-14534
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  • Description: The recent advancements in information technology and bioinformatics have led to exceptional contributions in medical sciences. Extensive developments have been recorded for digital devices, thermometers, digital equipments and health monitoring systems for the automated disease diagnosis of different diseases. These automated systems assist doctors with accurate and efficient disease diagnosis. Parkinson’s disease is a neurodegenerative disorder that affects the nervous system. Over the years, numerous efforts have been reported for the efficient automatic detection of Parkinson’s disease. Different datasets including voice data samples, radiology images, and handwriting samples and gait specimens have been used for analysis and detection. Techniques such as machine learning and deep learning have been used broadly and reported promising results. This review paper aims to provide a comprehensive survey of the use of artificial intelligence for Parkinson’s disease diagnosis. The available datasets and their various properties are discussed in detail. Further, a thorough overview is provided for the existing algorithms, methods and approaches utilizing different datasets. Several key peculiarities and challenges are also provided based on the comprehensive literature review to diagnose a healthy or unhealthy person. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

AI apology : interactive multi-objective reinforcement learning for human-aligned AI

- Harland, Hadassah, Dazeley, Richard, Nakisa, Bahareh, Cruz, Francisco, Vamplew, Peter

  • Authors: Harland, Hadassah , Dazeley, Richard , Nakisa, Bahareh , Cruz, Francisco , Vamplew, Peter
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 23 (2023), p. 16917-16930
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  • Description: For an Artificially Intelligent (AI) system to maintain alignment between human desires and its behaviour, it is important that the AI account for human preferences. This paper proposes and empirically evaluates the first approach to aligning agent behaviour to human preference via an apologetic framework. In practice, an apology may consist of an acknowledgement, an explanation and an intention for the improvement of future behaviour. We propose that such an apology, provided in response to recognition of undesirable behaviour, is one way in which an AI agent may both be transparent and trustworthy to a human user. Furthermore, that behavioural adaptation as part of apology is a viable approach to correct against undesirable behaviours. The Act-Assess-Apologise framework potentially could address both the practical and social needs of a human user, to recognise and make reparations against prior undesirable behaviour and adjust for the future. Applied to a dual-auxiliary impact minimisation problem, the apologetic agent had a near perfect determination and apology provision accuracy in several non-trivial configurations. The agent subsequently demonstrated behaviour alignment with success that included up to complete avoidance of the impacts described by these objectives in some scenarios. © 2023, The Author(s).

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

- Ali, Farman, El-Sappagh, Shaker, Islam, S., Kwak, Daehan, Ali, Amjad, Imran, Muhammad, Kwak, Kyung-Sup

  • Authors: Ali, Farman , El-Sappagh, Shaker , Islam, S. , Kwak, Daehan , Ali, Amjad , Imran, Muhammad , Kwak, Kyung-Sup
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Information Fusion Vol. 63, no. (2020), p. 208-222
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  • Description: The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods. © 2020

A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting

- Dai, Yong, Khandelwal, Manoj, Qiu, Yingui, Zhou, Jian, Monjezi, Monjezi, Yang, Peixi

  • Authors: Dai, Yong , Khandelwal, Manoj , Qiu, Yingui , Zhou, Jian , Monjezi, Monjezi , Yang, Peixi
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 34, no. 8 (2022), p. 6273-6288
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  • Description: Backbreak is a rock fracture problem that exceeds the limits of the last row of holes in an explosion operation. Excessive backbreak increases operational costs and also poses a threat to mine safety. In this regard, a new hybrid intelligence approach based on random forest (RF) and particle swarm optimization (PSO) is proposed for predicting backbreak with high accuracy to reduce the unsolicited phenomenon induced by backbreak in open-pit blasting. A data set of 234 samples with six input parameters including special drilling (SD), spacing (S), burden (B), hole length (L), stemming (T) and powder factor (PF) and one output parameter backbreak (BB) is set up in this study. Seven input combinations (one with six parameters, six with five parameters) are built to generate the optimal prediction model. The PSO algorithm is integrated with the RF algorithm to find the optimal hyper-parameters of each model and the fitness function, which is the mean absolute error (MAE) of ten cross-validations. The performance capacities of the optimal models are assessed using MAE, root-mean-square error (RMSE), Pearson correlation coefficient (R2) and mean absolute percentage error (MAPE). Findings demonstrated that the PSO–RF model combining L–S–B–T–PF with MAE of 0.0132 and 0.0568, RMSE of 0.0811 and 0.1686, R2 of 0.9990 and 0.9961 and MAPE of 0.0027 and 0.0116 in training and testing phases, respectively, has optimal prediction performance. The optimal PSO–RF models were compared with the classical artificial neural network, RF, genetic programming, support vector machine and convolutional neural network models and show that the PSO–RF model has superiority in predicting backbreak. The Gini index of each input variable has also been calculated in the RF model, which was 31.2 (L), 23.1 (S), 27.4 (B), 36.6 (T), 23.4 (PF) and 16.9 (SD), respectively. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Deep graph learning for anomalous citation detection

- Liu, Jiaying, Xia, Feng, Feng, Xu, Ren, Jing, Liu, Huand

  • Authors: Liu, Jiaying , Xia, Feng , Feng, Xu , Ren, Jing , Liu, Huand
  • Date: 2022
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Neural Networks and Learning Systems Vol. 33, no. 6 (2022), p. 2543-2557
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  • Description: Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to scholarly data, that is, anomaly detection in a citation network. Citation is considered as one of the most crucial metrics to evaluate the impact of scientific research, which may be gamed in multiple ways. Therefore, anomaly detection in citation networks is of significant importance to identify manipulation and inflation of citations. To address this open issue, we propose a novel deep graph learning model, namely graph learning for anomaly detection (GLAD), to identify anomalies in citation networks. GLAD incorporates text semantic mining to network representation learning by adding both node attributes and link attributes via graph neural networks (GNNs). It exploits not only the relevance of citation contents, but also hidden relationships between papers. Within the GLAD framework, we propose an algorithm called Citation PUrpose (CPU) to discover the purpose of citation based on citation context. The performance of GLAD is validated through a simulated anomalous citation dataset. Experimental results demonstrate the effectiveness of GLAD on the anomalous citation detection task. © 2012 IEEE.

Cognitive AmBC-NOMA IoV-MTS networks with IQI : reliability and security analysis

- Li, Xingwang, Zheng, Yike, Alshehri, Mohammad, Hai, Linpeng, Balasubramanian, Venki, Zeng, Ming, Nie, Gaofeng

  • Authors: Li, Xingwang , Zheng, Yike , Alshehri, Mohammad , Hai, Linpeng , Balasubramanian, Venki , Zeng, Ming , Nie, Gaofeng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Intelligent Transportation Systems Vol. 24, no. 2 (2023), p. 2596-2607
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  • Description: Internet-of-Vehicle (IoV) enabled Maritime Transportation Systems (MTS) communication is anticipated to support ultra-reliable and low latency, diverse quality-of-service (QoS) and large-scale connectivities. To meet such stringent demands, a cognitive ambient backscatter non-orthogonal multiple access (C-AmBC-NOMA) IoV-MTS network is proposed. We explore the reliable and secure performance of the proposed C-AmBC-NOMA IoV-MTS network with in-phase and quadrature phase imbalance (IQI) at radio-frequency (RF) front-ends and the existence of an eavesdropper. In particular, the analytical expressions on the outage probability (OP) and intercept probability (IP) are obtained after a series of calculations. For a deeper understanding, we discuss the asymptotic behavior of OPs in the high signal-to-noise ratio (SNR) region, the diversity orders of OPs, and IPs in the high main-to-eavesdropper ratio (MER) regime. The results of Monte-Carlo simulation and a series of corresponding theoretical analysis show that: i) As the SNR approaches infinity, the OPs tend to be fixed non-negative values, indicating that the diversity orders of the OPs have error floors; ii) When the MER approaches infinity, the IPs of legitimate users decrease continuously, while the IP of backscatter device (BD) increases; iii) Compared with the system performance under ideal condition, the system performance is less reliable under IQI condition, but the security performance is enhanced; iv) By carefully selecting the system parameters, a trade-off can be achieved between reliability and security. © 2000-2011 IEEE.

Graph learning : a survey

- Xia, Feng, Sun, Ke, Yu, Shuo, Aziz, Abdul, Wan, Liangtian, Pan, Shirui, Liu, Huan

  • Authors: Xia, Feng , Sun, Ke , Yu, Shuo , Aziz, Abdul , Wan, Liangtian , Pan, Shirui , Liu, Huan
  • Date: 2021
  • Type: Text , Journal article , Review
  • Relation: IEEE Transactions on Artificial Intelligence Vol. 2, no. 2 (2021), p. 109-127
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  • Description: Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field. Impact Statement—Real-world intelligent systems generally rely on machine learning algorithms handling data of various types. Despite their ubiquity, graph data have imposed unprecedented challenges to machine learning due to their inherent complexity. Unlike text, audio and images, graph data are embedded in an irregular domain, making some essential operations of existing machine learning algorithms inapplicable. Many graph learning models and algorithms have been developed to tackle these challenges. This article presents a systematic review of the state-of-the-art graph learning approaches as well as their potential applications. The article serves multiple purposes. First, it acts as a quick reference to graph learning for researchers and practitioners in different areas such as social computing, information retrieval, computer vision, bioinformatics, economics, and e-commence. Second, it presents insights into open areas of research in the field. Third, it aims to stimulate new research ideas and more interests in graph learning. © IEEE Transactions on Artificial Intelligence 2020.

Enhanced goal attainment method for solving multi-objective security-constrained optimal power flow considering dynamic thermal rating of lines

- Rahmani, Shima, Amjady, Nima

  • Authors: Rahmani, Shima , Amjady, Nima
  • Date: 2019
  • Type: Text , Journal article
  • Relation: Applied soft computing Vol. 77, no. (2019), p. 41-49
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  • Description: Security-constrained optimal power flow (SCOPF) is an important problem in power system operation. Dynamic thermal rating (DTR), as an effective method to increase transmission capacity of power systems, has been recently considered in some optimal power flow (OPF) and SCOPF models. Additionally, in today power systems, OPF problem involves various objectives leading to multi-objective OPF models. In this paper, a new multi-objective SCOPF model considering DTR of transmission lines is presented. In addition, a new multi-objective solution method is proposed to solve the multi-objective SCOPF problem. The proposed method is an enhanced version of goal attainment technique in which the search capability of this technique to cover borders of the Pareto frontier is enhanced. The proposed multi-objective DTR-included SCOPF model as well as the proposed multi-objective solution method are tested on the IEEE 118-bus test system and the obtained results are compared with the results of other alternatives. •A new multi-objective DTR-included SCOPF model is presented.•A new multi-objective solution method is proposed.•Proposed method can search the beyond-utopia-hyperplane parts of Pareto frontier.•Effectiveness of the proposed model and proposed method is extensively evaluated.

PastoralScape : an environment-driven model of vaccination decision making within pastoralist groups in East Africa

- Sottile, Matthew, Iles, Richard, McConnel, Craig, Amram, Ofer, Lofgren, Eric

  • Authors: Sottile, Matthew , Iles, Richard , McConnel, Craig , Amram, Ofer , Lofgren, Eric
  • Date: 2021
  • Type: Text , Journal article
  • Relation: JASSS Vol. 24, no. 4 (2021), p.
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  • Description: Economic and cultural resilience among pastoralists in East Africa is threatened by the interconnected forces of climate change and contagious diseases spread. A key factor in the resilience of livestock dependent communities is human decision making regarding vaccination against preventable diseases such as Rift Valley fever and Contagious Bovine Pleuropneumonia. The relationship between healthy and productive livestock and economic development of poor households and communities is mediated by human decision making. This paper describes a coupled human and natural systems agent-based model that focuses on One Health. Disease propagation and animal nutritional health are driven by historical GIS data that captures changes in foraging condition. The results of a series of experiments are presented that demonstrate the sensitivity of a transformed Random Field Ising Model of human decision making to changes in human memory and rationality parameters. Results presented communicate that convergence in the splitting of households between vaccinating or not is achieved for combinations of memory and rationality. The interaction of these cognition parameters with public information and social networks of opinions is detailed. This version of the PastoralScape model is intended to form the basis upon which richer economic and human factor models can be built. © 2021, University of Surrey. All rights reserved.

Explainable reinforcement learning for broad-XAI: a conceptual framework and survey

- Dazeley, Richard, Vamplew, Peter, Cruz, Francisco

  • Authors: Dazeley, Richard , Vamplew, Peter , Cruz, Francisco
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neural Computing and Applications Vol. 35, no. 23 (2023), p. 16893-16916
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  • Description: Broad-XAI moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machine learning algorithms into a coherent explanation of an agent’s behaviour that is aligned to the communication needs of the explainee. Reinforcement Learning (RL) methods, we propose, provide a potential backbone for the cognitive model required for the development of Broad-XAI. RL represents a suite of approaches that have had increasing success in solving a range of sequential decision-making problems. However, these algorithms operate as black-box problem solvers, where they obfuscate their decision-making policy through a complex array of values and functions. EXplainable RL (XRL) aims to develop techniques to extract concepts from the agent’s: perception of the environment; intrinsic/extrinsic motivations/beliefs; Q-values, goals and objectives. This paper aims to introduce the Causal XRL Framework (CXF), that unifies the current XRL research and uses RL as a backbone to the development of Broad-XAI. CXF is designed to incorporate many standard RL extensions and integrated with external ontologies and communication facilities so that the agent can answer questions that explain outcomes its decisions. This paper aims to: establish XRL as a distinct branch of XAI; introduce a conceptual framework for XRL; review existing approaches explaining agent behaviour; and identify opportunities for future research. Finally, this paper discusses how additional information can be extracted and ultimately integrated into models of communication, facilitating the development of Broad-XAI. © 2023, The Author(s).