Feature Matching-based Approaches to Improve the Robustness of Android Visual GUI Testing

In automated Visual GUI Testing (VGT) for Android devices, the available tools often suffer from low robustness to mobile fragmentation, leading to incorrect results when running the same tests on different devices. To soften these issues, we evaluate two feature matching-based approaches for widget detection in VGT scripts, which use, respectively, the complete full-screen snapshot of the application ( Fullscreen ) and the cropped images of its widgets ( Cropped ) as visual locators to match on emulated devices. Our analysis includes validating the portability of different feature-based visual locators over various apps and devices and evaluating their robustness in terms of cross-device portability and correctly executed interactions. We assessed our results through a comparison with two state-of-the-art tools, EyeAutomate and Sikuli. Despite a limited increase in the computational burden, our Fullscreen approach outperformed state-of-the-art tools in terms of correctly identified locators across a wide range of devices and led to a 30% increase in passing tests. Our work shows that VGT tools’ dependability can be improved by bridging the testing and computer vision communities. This connection enables the design of algorithms targeted to domain-specific needs and thus inherently more usable and robust.

Heart Disease Prediction Using Glowworm Swarm Optimization and Support Vector Machine Classifier

At present, health disorder is growing day by way of the day due to existence lifestyle, hereditary. Particularly, heart disease has ended up greater frequent these days. Heart disorder prognosis technique is very quintessential and integral trouble for the patient's health. Besides, it will help out to limit disorder to a larger distinctive level. The role of using strategy like machine learning and algorithm such as heart disease diagnosis using Data Mining(DM) techniques is very significant. In the previous system, the Fuzzy Extreme Learning Machine (FELM) was proposed to predict heart disease, ensuring an accurate and timely diagnosis. However, it only achieves 87.14 % of accuracy. To improve the classification accuracy, the proposed system designed an Improved Step Adjustment based Glowworm Swarm Optimization Algorithm with Weighted Feature based Support Vector Machine (ISAGSO-WFSVM) for Heart disease diagnosis. This proposed venture utilizes the dataset of heart disease for input. Using the Improved Step Adjustment based Glowworm Swarm Optimization Algorithm (ISAGSO) to enhance the true positive rate, optimal features are then selected. Finally, with the aid of the Weighted Feature based Support Vector Machine (WFSVM) classifier, classification is carried out relying selected features. In the proposed method, better performance obtained and that is validated through the experimental results in terms of precision, accuracy, recall and f-measures

Effect of process parameters on formability in two-point incremental forming-machining of planar and twisted AA5083 blades

The deformation machining process (DMP) involves machining and incremental forming of thin structures. It can be applied for manufacturing products such as curved-surface blades without using 5-axis computerised numerical control machines. This work presents the effect of tool diameter and forming temperature on spring-back and dimensional accuracy of a simple fabricated part. The results of the first phase of the study are utilised to design the fabrication process of a curved surface blade. A feature-based algorithm is used to design the tool path for the forming process. The dimensional accuracy of the final product is improved through warm forming, two-point incremental forming, and extension of the bending zone to the outside of the product edges. The results show that DMP can be used to fabricate complex curved-surface workpieces with acceptable dimensional accuracy.

Prosodic Feature-Based Discriminatively Trained Low Resource Speech Recognition System

Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.