There has been extensive use of machine learning (ML) based tools for mathematical symbol and phrase categorization and prediction. Aiming to thoroughly analyze the existing methods for categorizing brain tumors, this paper considers both machine-learning and non-machine-learning approaches. From 2013 to 2023, the writers compiled and reviewed research papers on brain tumor detection. Wiley, IEEE-Explore, Science-Direct, Scopus, ACM-Digital Library, and others provide the relevant data. A systematic literature review examines the efficacy of research methodologies over the last ten years or more by compiling relevant publications and studies from various sources. Accuracy, sensitivity, specificity, and computing efficiency are some of the criteria that researchers use to evaluate these methods. The availability of labeled data, the required degree of automation and accuracy in the classification process, and the unique dataset are generally the deciding factors in the method choice. This work integrates previous research findings to summarize the current state of brain tumor categorization. This paper summarizes the 169 research papers in brain tumor detection between 2013–2023 and explores the application and development of machine learning methods in brain tumor detection, which has significant research implications and value in the field of brain tumor classification research. All research findings of previous studies are arranged in this paper in the form of research questions and answers format.

This paper proposes the pulse width modulation (PWM) technique for the five-phase open-end winding induction motor (FPOEWIM) drive fed from five-phase dual inverter (FPDI) with a single dc source. The selected method gives the advantages of reduced switching losses specifically for induction motor drives working with higher power factors with the elimination of common-mode current (CMC). The method also reduces the computational burden over the processing unit. The proposed PWM technique is explained in detail with the implementation algorithm. The algorithm is used to select the proper switching states based on the reference vector location. Further, the proposed PWM technique is analyzed for the switching losses. The reduction in the switching losses is supported with the switching loss function (SLF) analysis. The SLF for the proposed PWM technique is plotted and compared with the conventional sinusoidal PWM (SPWM) technique. The presented method is simulated and the results are given. The validation of simulated results is done using the experimental results.

This paper presents a cascaded, highly efficient and reliable inverter configuration (HERIC) based multi-level inverter (MLI) to minimise the leakage current in the PV applications. The cascaded HERIC based nine-level MLI proposed in this paper requires less number of power semiconductor devices when compared with the conventional neutral point clamped (NPC), flying capacitor and cascaded H-bridge (CHB) MLI with same output voltage levels. Additionally, given MLI have lower switching and conduction losses. The pulse width modulation (PWM) technique given in this paper minimises voltage transitions in the terminal voltage. This minimises the magnitude of leakage current and maintains it below the permissible value of the leakage current as per the standards such as VDE 0126-1-1. Hence, the given MLI can be effectively utilised in the PV applications. The simulation and experimental results for the proposed cascaded HERIC based nine-level MLI with the given PWM technique is also presented in this paper.

Test suite prioritization and reduction are two approaches to managing large test suites. They play an important role in regression testing, where a large number of tests accumulate over time from previous versions of the system. Accumulation of tests is exacerbated in user-session-based testing of web applications, where field usage data is continually logged and converted into test cases. This paper presents a tool that allows testers to easily collect, prioritize, and reduce user-session-based test cases. Our tool provides four contributions: (1) guidance to users on how to configure their web server to log important usage information, (2) automated parsing of web logs into XML formatted test cases that can be used by test replay tools, (3) automated prioritization of test cases by length-based and combinatorial-based criteria, and (4) automated reduction of test cases by combinatorial coverage.