Complexity reduction of influence nets using arc removal

© 2015 - IOS Press and the authors. The model building of Influence Nets, a special instance of Bayesian belief networks, is a time-consuming and labor-intensive task. No formal process exists that decision makers/system analyst, who are typically not familiar with the underlying theory and assumptions of belief networks, can use to build concise and easy-to-interpret models. In many cases, the developed model is extremely dense, that is, it has a very high link-to-node ratio. The complexity of a network makes the already intractable task of belief updating more difficult. The problem is further intensified in dynamic domains where the structure of the built model is repeated for multiple time-slices. It is, therefore, desirable to do a post-processing of the developed models and to remove arcs having a negligible influence on the variable(s) of interests. The paper applies sensitivity of arc analysis to identify arcs that can be removed from an Influence Net without having a significant impact on its inferencing capability. A metric is suggested to gauge changes in the joint distribution of variables before and after the arc removal process. The results are benchmarked against the KL divergence metric. An empirical study based on several real Influence Nets is conducted to test the performance of the sensitivity of arc analysis in reducing the model complexity of an Influence Net without causing a significant change in its joint probability distribution

Implications of Altered Glutathione Metabolism in Aspirin-Induced Oxidative Stress and Mitochondrial Dysfunction in HepG2 Cells

We have previously reported that acetylsalicylic acid (aspirin, ASA) induces cell cycle arrest, oxidative stress and mitochondrial dysfunction in HepG2 cells. In the present study, we have further elucidated that altered glutathione (GSH)-redox metabolism in HepG2 cells play a critical role in ASA-induced cytotoxicity. Using selected doses and time point for ASA toxicity, we have demonstrated that when GSH synthesis is inhibited in HepG2 cells by buthionine sulfoximine (BSO), prior to ASA treatment, cytotoxicity of the drug is augmented. On the other hand, when GSH-depleted cells were treated with N-acetyl cysteine (NAC), cytotoxicity/apoptosis caused by ASA was attenuated with a significant recovery in oxidative stress, GSH homeostasis, DNA fragmentation and some of the mitochondrial functions. NAC treatment, however, had no significant effects on the drug-induced inhibition of mitochondrial aconitase activity and ATP synthesis in GSH-depleted cells. Our results have confirmed that aspirin increases apoptosis by increased reactive oxygen species production, loss of mitochondrial membrane potential and inhibition of mitochondrial respiratory functions. These effects were further amplified when GSH-depleted cells were treated with ASA. We have also shown that some of the effects of aspirin might be associated with reduced GSH homeostasis, as treatment of cells with NAC attenuated the effects of BSO and aspirin. Our results strongly suggest that GSH dependent redox homeostasis in HepG2 cells is critical in preserving mitochondrial functions and preventing oxidative stress associated complications caused by aspirin treatment

Covariate shift detection-based nonstationary adaptation in motor-imagery-based brain–computer interface

Nonstationary learning refers to the process that can learn patterns from data, adapt to shifts, and improve performance of the system with its experience while operating in the nonstationary environments (NSEs). Covariate shift (CS) presents a major challenge during data processing within NSEs wherein the input-data distribution shifts during transitioning from training to testing phase. CS is one of the fundamental issues in electroencephalogram (EEG)-based brain-computer interface (BCI) systems and can be often observed during multiple trials of EEG data recorded over different sessions. Thus, conventional learning algorithms struggle to accommodate these CSs in streaming EEG data resulting in low performance (in terms of classification accuracy) of motor imagery (MI)-related BCI systems. This chapter aims to introduce a novel framework for nonstationary adaptation in MI-related BCI system based on CS detection applied to the temporal and spatial filtered features extracted from raw EEG signals. The chapter collectively provides an efficient method for accounting nonstationarity in EEG data during learning in NSEs

Suspicious activity reporting using dynamic bayesian networks

AbstractSuspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer’s monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results

An artificial intelligence and NLP based Islamic FinTech model combining Zakat and Qardh-Al-Hasan for countering the adverse impact of COVID 19 on SMEs and individuals

Pursose: The ongoing Corona virus (COVID 19) pandemic has already impacted almost everyone across the globe. The focus has now shifted from spread of the disease to the economic consequences it will bring to the society. The shortage of production will result into the shortage of supply and consequently will end as loss of jobs and employment for millions of people around the world. Two of the most important section of our society i.e., daily wage laborers and Small and Medium Enterprises (SMEs) will have to bear the major burnt of this crisis. The proposed integrated Artificial Intelligence and NLP based Islamic FinTech Model combining Zakat (Islamic tax) and Qardh-Al-Hasan (benevolent loan) can help the economy to minimize the adverse impact of COVID 19 on individuals and SMEs. Design/Methodology/Approach: The present study explores the possibility of Zakat and Qardh-Al-Hasan as a financing method to fight the adverse impact of Corona virus on poor individuls and SMEs. It provides the solution by proposing an Artificial Intelligence and NLP based Islamic FinTech Model combining Zakat and Qardh-Al-Hasan. Findings: The findings of the study reveals that Islamic finance has immense potential to fight any kind of situation/pandemic. Zakat and Qardh-Al-Hasan, if combined together can prove to be a deadly combination to fight the adverse effect of COVID 19. Practical Implications: To be used as an effective way to support individuals and SMEs in the period during and after the pandemic of COVID 19. Originality/value: There is no study combining Zakat and Qardh Al-Hasan to fight the adverse effect of poor individuals and SMEs. The study will contribute massively to the existing literature and will help the government and civil societies in fighting the economic impact of COVID 19 on individuals and SMEs.peer-reviewe

Achieving Accuracy in Early Stage Tumor Identification Systems based on Image Segmentation and 3D Structure Analysis

Cancer is a disease which can be removed if early stage tumor identification systems efficiently and accurately work at cancer hospitals. As the accuracy in detection of tumor means to detect exact size of the tumor. Because the best way to beat cancer is early stage tumor diagnosis and quality treatment. In this research article an accuracy module is proposed for computer aided tumor diagnosis system. The ultimate proposed CAD gets image of tumor infected lung and breast images from different state of the art early stage tumor detection methodologies as micrographic and mammographic based imaging systems. For accuracy in detection of early stage tumor, image enhancement and segmentation techniques are applied according to the imaging problems at input image. Also for accurate estimation of tumor the 3D image construction and 3D structure analysis are tried to realized. The realization of the proposed CAD proves that the accuracy module can assist well the computer aided tumor diagnosis systems with almost near to 100% accuracy in early stage tumor detection and size estimation for breast and lung cancer. Keywords: Computer Aided Tumor Detection, Accurate identificatio

Development of explainable AI-based predictive models for bubbling fluidised bed gasification process

© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).In this study, seven different types of regression-based predictive modelling techniques are used to predict the product gas composition (H2, CO, CO2, CH4) and gas yield (GY) during the gasification of biomass in a fluidised bed reactor. The performance of different regression-based models is compared with the gradient boosting model(GB) to show the relative merits and demerits of the technique. Additionally, S Hapley Additive ex Planations (SHAP)-based explainable artificial intelligence (XAI) method was utilised to explain individual predictions. This study demonstrates that the prediction performance of the GB algorithm was the best among other regression based models i.e. Linear Regression (LR), Multilayer perception (MLP), Ridge Regression (RR), Least-angle regression (LARS), Random Forest (RF) and Bagging (BAG). It was found that at learning rate (lr) 0.01 and number of boosting stages (est) 1000 yielded the best result with an average root mean squared error (RMSE) of0.0597 for all outputs. The outcome of this study indicates that XAI-based methodology can be used as a viable alternative modelling paradigm in predicting the performance of a fluidised bed gasifier for an informed decision-making process.Peer reviewe

Deep Learning based Prediction of EEG Motor Imagery of Stroke Patients' for Neuro-Rehabilitation Application

Due to the non-stationary nature of electroencephalography (EEG) signals, a Brain-computer Interfacing (BCI) system requires frequent calibration. This leads to intersession inconsistency which is one of the main reason that impedes the widespread adoption of non-invasive BCI for realworld applications, especially in rehabilitation and medicine. Domain adaptation and deep learning-based techniques have gained relevance in designing calibration-free BCIs to solve this issue. EEGNet is one such deep net architecture that has been successful in performing inter-subject classification, albeit on data from healthy participants. This is the first paper, which tests the performance of EEGNet on data obtained from 10 hemiparetic stroke patients while performing left and right motor imagery tasks. Results obtained on implementing EEGNet have been promising and it has comparably good performance as from expensive feature engineering-based approaches for both withinsubject and cross-subject classification. The less dependency on feature engineering techniques and the ability to extract generalized features for inter-subject classification makes EEGNet a promising deep-learning architecture for developing practically feasible solutions for BCI based neuro-rehabilitation applications