Interval-valued fuzzy ku-ideals of ku-algebras

In this paper, we introduced the notion of interval-valued fuzzy KU-ideals of KU-algebras and some related properties are investigated. We proved that U is a KU-ideal if and only if the interval-valued fuzzy subset is an interval-valued fuzzy KU-ideal of a KU-algebra for e-

Integrated Use of Rational and Intuitive Decision Making Style: Modern Trends in Organizational Decision Making

The present study reviewed the literature based on a centuryof the theoretical and empirical work on decision making styles.Both in theory and research, the traditional trends limited thedecision makers to either rational or intuitive strategies in decisionmaking. Limited amount of literature emphasized on both rationalityand intuition in decision making until in the recent decades whensome researchers emphasized the use of mixed strategies in decisionmaking. Thus the present study illustrates the importance ofcombining the rational and intuitive style and using a mixed-stylein decisional scenarios. Thus the rational-intuitive and the intuitiverationalstyle double the benefits as both styles have some sharedand some other unique qualities which maximize the outcomes whenused in connection. Finally, the present study suggests a transitionfrom uni-style tradition to mixed style decision making

Intuitionistic fuzzy soft ordered ternary semigroups

Abstract: In this paper, we introduced the notion of intuitionistic fuzzy soft ideals over an ordered ternary semigroup and their basic properties are investigated

Utilizing Seismic and Well Logging Techniques for Locating Hydrocarbon of Kabirwala Area Punjab Platform Pakistan

Nandpur Gas field is situated at a distance of 64 km near District Multan. It lies in Middle Indus Basin Punjab Platform, Pakistan. The structure is dipping at a gentle angle toward the NW-SE. Tectonically the area fall in extensional regime and is dominated by Normal faults, favorable for accumulation of hydrocarbon. The interpretation of Seismic lines, time contour map and depth contour map confirms the Graben structure and stratigraphic traps (Pinchouts) in the study area. The high zone present in the South Eastern part of the contour maps is possible location of hydrocarbon entrapment, which is further confirmed by the presence of the well Nandpur -02.The reservoir quality of the Samana Suk Formation was much better in terms of clean Sandstone, porosity, water saturation and permeability. We use seismic lines, a base map and well data of Nandpur-02 area which was obtained from Landmark Resources, Pakistan. For subsurface mapping, structural interpretation, synthetic seismogram generation of investigated area we used four dip and five strike lines. These seismic lines and well data are in digital format. The reservoir discrimination and modeling is carried out with the help of geophysical parameter. Keywords: Seismic Interpretation, Time and depth contour map, Generating Synthetic Seismogram, 3D view, Reservoir Estimatio

Diagnostic Accuracy Of Barium Swallow For Dysphagia, Keeping Rigid Esophagoscopy As The Gold Standard

Objective: To determine the diagnostic accuracy of Barium Swallow in detection of patients presenting with dysphagia. Study Design: Cross-sectional validation study. Study Setting & Duration: Department of Otorhinolaryngology, Head & Neck Surgery, District Headquarter Hospital Rawalpindi from 01-09- 2022 to 01-03-2023. Materials and Methods: Approval of the study was obtained from the Hospital Ethical Committee. A total of 111 patients both male and female patients were selected. The patients suffering from dysphagia as per operational definitions and who have reported for work-up to the Department of ENT, District Headquarters Hospital, Rawalpindi, and fulfill the complete inclusion and exclusion criteria, were selected. Informed consent was obtained from all the patients. Patients were selected by consecutive non-probability sampling technique. The data was analyzed using SPSS 24. Results: A total of 111 patients were included in this study. The mean age of these patients was 50.79 ± 13.01 years, ranging from 28 to 70 years. The frequency distribution of females 70.27 % was found to be more than that of males 29.73 %. Majority of patients' barium swallow (74.77%) revealed pathologies, while only a small percentage of patients (25.23%) had normal barium swallow. Most of patients (87.39%) had pathologies found during rigid esophagoscopy, while just a small number (12.61%) had normal rigid esophagoscopy. Comparing both investigating tools, esophagoscopy discovered 87.39% of pathologies while Barium swallow detected 74.77%, indicating that esophagoscopy was a more accurate procedure. Patients had esophageal web 55 (25.2%) on barium swallow and 69 (62.2%) on the Rigid esophagoscopy. Barium Swallow had esophageal stricture 28 (52.2) and no Pathology was detected in 28 (25.2 %) patients. As well as Rigid esophagoscopy had esophageal growth13 (11.7), esophageal stricture 15 (13.5), and no Pathology was detected in 14 (12.6 %). Rigid esophagoscopy is more efficient in detecting esophageal pathology than Barium Swallow. In Barium swallows most patients had esophageal web 55 (25.2%) than the esophageal stricture 28 (52.2) and no pathology was detected 28 (25.2). In rigid esophagoscopy most patients had esophageal web 69 (62.2%) than the esophageal growth13 (11.7), esophageal stricture 15 (13.5) and no pathology detected 14 (12.6). Conclusion: A range of diseases are associated with dysphagia can be found in patients. Two often used diagnostic methods are barium swallow and rigid esophagoscopy. Both Barium swallow and Rigid esophagoscopy are successful in the diagnosis of esophageal cancer. The use of a Rigid esophagoscopy is still a gold standard diagnostic and therapeutic tool for upper aerodigestive tract pathologies

Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains

Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations

Machine learning is currently undergoing an explosion in capability, popularity, and sophistication. However, one of the major barriers to widespread acceptance of machine learning (ML) is trustworthiness: most ML models operate as black boxes, their inner workings opaque and mysterious, and it can be difficult to trust their conclusions without understanding how those conclusions are reached. Explainability is therefore a key aspect of improving trustworthiness: the ability to better understand, interpret, and anticipate the behaviour of ML models. To this end, we propose SMILE, a new method that builds on previous approaches by making use of statistical distance measures to improve explainability while remaining applicable to a wide range of input data domains

Keep your Distance: Determining Sampling and Distance Thresholds in Machine Learning Monitoring

Machine Learning~(ML) has provided promising results in recent years across different applications and domains. However, in many cases, qualities such as reliability or even safety need to be ensured. To this end, one important aspect is to determine whether or not ML components are deployed in situations that are appropriate for their application scope. For components whose environments are open and variable, for instance those found in autonomous vehicles, it is therefore important to monitor their operational situation to determine its distance from the ML components' trained scope. If that distance is deemed too great, the application may choose to consider the ML component outcome unreliable and switch to alternatives, e.g. using human operator input instead. SafeML is a model-agnostic approach for performing such monitoring, using distance measures based on statistical testing of the training and operational datasets. Limitations in setting SafeML up properly include the lack of a systematic approach for determining, for a given application, how many operational samples are needed to yield reliable distance information as well as to determine an appropriate distance threshold. In this work, we address these limitations by providing a practical approach and demonstrate its use in a well known traffic sign recognition problem, and on an example using the CARLA open-source automotive simulator