Abdul Quaiyum Ansari

Proposal for Applicability of Neutrosophic Set Theory in Medical AI

Soft computing is an enriching domain that helps to encode uncertainty and imprecision that exists in real world. Integration of soft computing techniques in the systems lends added advantage to the existing systems to allow solutions to otherwise unsolvable problems. Fuzzy architecture has been extensively researched and applied in medical domain. This paper suggests incorporating a new logic: Neutrosophic logic in medical domain and also discusses the possibility of extending the capabilities of the fuzzy systems by employing neutrosophic systems. General Terms Medical AI

Neutrosophic modeling and control

Quite recently, Neutrosophic Logic has been proposed by Florentine Smarandache which is based on non-standard analysis that was given by Abraham Robinson in 1960s. Neutrosophic Logic was developed to represent mathematical model of uncertainty, vagueness, ambiguity, imprecision, incompleteness, inconsistency, redundancy and contradiction in data [4],[5],[6],[7]. All the factors stated are very integral to human thinking, as it is very rare that we tend to conclude/judge in definite environments. This paper discusses how neutrosophic logic can be utilized for modeling and control for which block diagram of neutrosophic inference system is proposed, to illustrate this designing of relatively simple neutrosophic classifier has been attempted. Current problems and future directions for neutrosophic approaches are also addressed.

Neutrosophic classifier: An extension of fuzzy classifer

Fuzzy classification has become of great interest because of its ability to utilize simple linguistically interpretable rules and has overcome the limitations of symbolic or crisp rule based classifiers. This paper introduces an extension to fuzzy classifier: a neutrosophic classifier, which would utilize neutrosophic logic for its working. Neutrosophic logic is a generalized logic that is capable of effectively handling indeterminacy, stochasticity acquisition errors that fuzzy logic cannot handle. The proposed neutrosophic classifier employs neutrosophic logic for its working and is an extension of commonly used fuzzy classifier. It is compared with the commonly used fuzzy classifiers on the following parameters: nature of membership functions, number of rules and indeterminacy in the results generated. It is proved in the paper that extended fuzzy classifier: neutrosophic classifier; optimizes the said parameters in comparison to the fuzzy counterpart. Finally the paper is concluded with justifying that neutrosophic logic though in its nascent stage still holds the potential to be experimented for further exploration in different domains.

Generalized neural network and wavelet transform based approach for fault location estimation of a transmission line

Abstract To maintain the efficient and reliable operation of power systems, it is extremely important that the transmission line faults need to be detected and located in a reliable and accurate manner. A number of mathematical and intelligent techniques are available in the literature for estimating the fault location. However, the results are not satisfactory due to the wide variation in operating conditions such as system loading level, fault inception instance, fault resistance and dc offset and harmonics contents in the transient signal of the faulted transmission line. Keeping in view of aforesaid, a new approach based on generalized neural network (GNN) with wavelet transform is presented for fault location estimation. Wavelet transform is used to extract the features of faulty current signals in terms of standard deviation. Obtained features are used as an input to the GNN model for estimating the location of fault in a given transmission systems. Results obtained from GNN model are compared with ANN and well established mathematical models and found more accurate.

Automated diagnosis of coronary heart disease using neuro-fuzzy integrated system

Computational intelligence combines fuzzy systems, neural network and evolutionary computing. In this paper, Neuro-fuzzy integrated system for coronary heart disease is presented. In order to show the effectiveness of the proposed system, Simulation for automated diagnosis is performed by using the realistic causes of coronary heart disease. The results suggest that this kind of hybrid system is suitable for the identification of patients with high/low cardiac risk.

Prediction of quantitative intrathoracic fluid volume to diagnose pulmonary oedema using LabVIEW

Pulmonary oedema is a life-threatening disease that requires special attention in the area of research and clinical diagnosis. Computer-based techniques are rarely used to quantify the intrathoracic fluid volume (IFV) for diagnostic purposes. This paper discusses a software program developed to detect and diagnose pulmonary oedema using LabVIEW. The software runs on anthropometric dimensions and physiological parameters, mainly transthoracic electrical impedance (TEI). This technique is accurate and faster than existing manual techniques. The LabVIEW software was used to compute the parameters required to quantify IFV. An equation relating per cent control and IFV was obtained. The results of predicted TEI and measured TEI were compared with previously reported data to validate the developed program. It was found that the predicted values of TEI obtained from the computer-based technique were much closer to the measured values of TEI. Six new subjects were enrolled to measure and predict transthoracic impedance and hence to quantify IFV. A similar difference was also observed in the measured and predicted values of TEI for the new subjects.

Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions

As industrial systems become more widespread, they are quickly becoming network-enabled, and their behavior is becoming more complex and intelligent. The Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions is the best source for the most current, relevant, cutting-edge research in the field of industrial informatics. The book focuses on different methodologies of information technologies to enhance industrial fabrication, intelligence, and manufacturing processes. Industrial informatics uses the infrastructure of information technology for analysis, effectiveness, reliability, higher efficiency, security enhancement in the industrial environment, and this book collects the latest publications relevant to academics and practitioners alike.

Online signature verification using segment-level fuzzy modelling

This study presents a new online signature verification system based on fuzzy modelling of shape and dynamic features extracted from online signature data. Instead of extracting these features from a signature, it is segmented at the points of geometric extrema followed by the feature extraction and fuzzy modelling of each segment thus obtained. A minimum distance alignment between the two samples is made using dynamic time warping technique that provides a segment to segment correspondence. Fuzzy modelling of the extracted features is carried out in the next step. A user-dependent threshold is used to classify a test sample as either genuine or forged. The accuracy of the proposed system is evaluated using both skilled and random forgeries. For this, several experiments are carried out on two publicly available benchmark databases, SVC2004 and SUSIG. The experimental results obtained on these databases demonstrate the effectiveness of this system.

Extension to fuzzy logic representation: Moving towards neutrosophic logic - A new laboratory rat

Real world problems have been effectively modeled using fuzzy logic that gives suitable representation of real-world data/information and enables reasoning that is approximate in nature. It is quite uncommon that the inputs captured by the fuzzy models are 100% complete and determinate. Though, humans can take intelligent decisions in such situations but fuzzy models require complete information. Incompleteness and indeterminacy in the data can arise from inherent non-linearity, time-varying nature of the process to be controlled, large unpredictable environmental disturbances, degrading sensors or other difficulties in obtaining precise and reliable measurements. Neutrosophic logic is an extended and general framework for measuring the truth, indeterminacy and falsehood-ness of the information. It is effective in representing different attributes of information like inaccuracy, incompleteness and ambiguous, thus giving fair estimate about the reliability of information. This paper suggests extending the capabilities of fuzzy representation and reasoning system by introducing Neutrosophic representation of the data and Neutrosophic reasoning system.

Thorax Physiological Monitoring and Modeling for Diagnosis of Pulmonary Edema

In this paper, the authors prove that variations in thoracic volumes are greatly responsive to the act of breathing i.e., inspiration and expiration. These variations may be adopted for diagnosing various respiration related diseases and pulmonary edema. In this study, the authors present a method to estimate the thoracic volume non-invasively using anthropometric dimensions. The change in the geometry of thorax with the act of breathe is recorded by measuring the anthropometric parameters for nine healthy human subjects. The model based approach shows the extent of its sensitivity in terms of volumetric variations with the state of inspiration and expiration. Many deaths occur due to unavailability of health care and monitoring systems in rural areas and developing countries. The technique presented in this paper takes care of these situations and the volumetric estimation of thorax is independent of any instrumentation, expensive equipment, and clinical environment.