Deep learning is a subfield of artificial intelligence that allows the computer to adopt and learn some new rules. Deep learning algorithms can identify images, objects, observations, texts, and ...other structures. In recent years, scene text recognition has inspired many researchers from the computer vision community, and still, it needs improvement because of the poor performance of existing scene recognition algorithms. This research paper proposed a novel approach for scene text recognition that integrates bidirectional LSTM and deep convolution neural networks. In the proposed method, first, the contour of the image is identified and then it is fed into the CNN. CNN is used to generate the ordered sequence of the features from the contoured image. The sequence of features is now coded using the Bi-LSTM. Bi-LSTM is a handy tool for extracting the features from the sequence of words. Hence, this paper combines the two powerful mechanisms for extracting the features from the image, and contour-based input image makes the recognition process faster, which makes this technique better compared to existing methods. The results of the proposed methodology are evaluated on MSRATD 50 dataset, SVHN dataset, vehicle number plate dataset, SVT dataset, and random datasets, and the accuracy is 95.22%, 92.25%, 96.69%, 94.58%, and 98.12%, respectively. According to quantitative and qualitative analysis, this approach is more promising in terms of accuracy and precision rate.
In computer vision and medical image processing, object recognition is the primary concern today. Humans require only a few milliseconds for object recognition and visual stimulation. This led to the ...development of a computer-specific pattern recognition method in this study for identifying objects in medical images such as brain tumors. Initially, an adaptive median filter is used to remove the noise from MRI images. Thereafter, the contrast image enhancement technique is used to improve the quality of the image. To evaluate the wireframe model, the cellular logic array processing (CLAP)-based algorithm is then applied to images. The basic patterns of three-dimensional (3D) images are then identified from the input image by scanning the whole image. The frequency of these patterns is also used for object classification. A deep neural network is then utilized for the classification of brain tumor. In the proposed model, the syntactic pattern recognition technique is used to find the feature vector and 3D AlexNet is used for brain tumor classification. To evaluate the performance of the proposed work, three benchmark brain tumor datasets are used, i.e., Figshare, Brain MRI Kaggle, and Medical MRI datasets and BraTS 2019 dataset. The comparative analyses reveal that the proposed brain tumor classification model achieves significantly better performance than the existing models.
Hand-drawn diagrams have been a standard visual communication tool in many disciplines, including architectural design, engineering, and education. The inherent diversity and absence of standardized ...formats of hand-drawn diagrams make it difficult to recognize them. As a result, there is an increasing need for efficient strategies and approaches for correctly identifying and interpreting hand-drawn diagrams. This research study comprehensively reviews hand-drawn diagram recognition (HDDR) techniques, emphasizing their importance and usefulness in numerous sectors. For the past ten years, articles from the Scopus database on HDDR have been extracted and reviewed. The study explores the approaches, steps, and benchmark datasets available to recognize hand-drawn diagrams. An attempt is made to get insights into the most recent state-of-the-art methodologies, their limits, and potential future advancement directions. This paper also suggests probable solutions to overcome the limitations and develop new techniques for efficiently and robustly recognizing hand-drawn diagrams.
Handling the uncertainties in the utility grid and uncertainties in renewable energy sources is a main challenge to accomplish the modern grid code requirements. This article proposes a new control ...strategy based on interval type‐2 fuzzy sets for controlling the grid‐connected dispersed generation (DG) system tied to the distorted electric grid. The proposed control technique can easily model and handle the uncertainties in the system parameters and renewable energy sources. The existence of three‐dimensional membership functions of type‐2 fuzzy sets offers an additional degree of freedom to counter the uncertainties in the system. The proposed strategy is effective in improving the dynamic response during system uncertainties such as distortions in grid voltage, variations in grid frequency, variations in renewable energy sources and due to presence of non‐linear and unbalanced loads. Also, it improves the quality of the current being injected to the grid during uncertainties. The proposed control strategy is applied to control the dc side capacitor voltage as well as to control the current loop of the grid connected inverter. The proposed system is simulated in MATLAB Simulink environment and the performance is compared with traditional controllers to show the effectiveness of the proposed control strategy during system abnormalities.
Use of technology on campuses of higher education has changed how students are engaged in the process of learning. It also has brought lot of asynchrony to the definition of class room teaching, ...active learning, student-staff interactions, and dealing with various academic challenges. The paper presents a study conducted to measure student engagement in a generic and online learning management system-based teaching learning environment. The paper presents threefold results. The first one is on identification of student engagement styles. The styles identified can further be used to design, develop, and implement most student engaging policies on campus which are beneficial to all the stakeholders. Second, the central point of the study is the student and her/his engagement with the learning process. The paper presents a student engagement report card to individual students for their analysis. Informing and involving students to know about their engagement report card would be beneficial. The third is feedback on a trail left by students' logs on the learning management system that can help the teachers to plan the teaching methodology. The methodology used was based on the data collected by the students of the institute/university. A student engagement questionnaire was used to measure student engagement in both generic and online learning environments. A cluster analysis was conducted on the data collected to identify the student engagement styles. A subcategory analysis was reported as a student engagement report card. The student-logged data on the institute learning management system was used to present the third analysis.
The majority of the accidents were happening perpetually due to driver drowsiness over the decades. Automation has been playing key role in many fields to provide conformity and improve the quality ...of life of the users. Though various drowsiness detection systems have been developed during last decade based on many factors, still the systems were demanding an improvement in terms of efficiency, accuracy, cost, speed, and availability, etc. In this paper, proposed an integrated approach depends on the Eye and mouth closure status (PERCLOS) along with the calculation of the new proposed vector FAR (Facial Aspect Ratio) similarly to EAR and MAR. This helps to find the status of the closed eyes or opened mouth like yawning, and any frame finds that has hand gestures like nodding or covering opened mouth with hand as innate nature of humans when trying to control the sleepiness. The system also integrated the methods and textural-based gradient patterns to find the driver's face in various directions identify the sunglasses on the driver's face and the scenarios like hands-on eyes or mouth while nodding or yawning were also recognized and addressed. The proposed work tested on datasets such as NTHU-DDD, YawDD, and a proposed dataset EMOCDS (Eye and Mouth Open Close Data Set) and proved better in terms of accuracy and provides results in general by considering various circumstances.
Offshore wind project planning requires resource assessment to account for wind uncertainties. Wind data can be gathered through meteorological measurements, satellite observations, and reanalysis ...datasets but must be validated and corrected to ensure accuracy. The present study aims to validate long-term reanalysis datasets (1979-present) to find the most reliable reanalysis datasets for wind resource assessment. Four reanalysis datasets, such as EMD-ERA, ERA5, CFSR2, and MERRA2 have been considered. Further, validated these datasets with the help of the short-term (8 months) wind data recorded by LIDAR at an offshore location in Gujarat, India. The reanalysis datasets have been observed to underestimate the wind speed recordings. Moreover, ERA5 is the most reliable among the four considered reanalysis datasets, with an utmost correlation coefficient of 0.9329 with reference to LIDAR data. According to the ERA5 dataset, a conceptual wind farm comprising 100 units of 6 MW wind turbines with an overall capacity factor of 39.27% can generate a maximum power of 2.064 TWh.
Chemotherapy is a widely used cancer treatment method globally. However, cancer cells can develop resistance towards single-drug-based chemotherapy if it is infused for extended periods, resulting in ...treatment failure in many cases. To address this issue, oncologists have progressed towards using multi-drug chemotherapy (MDC). This method considers different drug concentrations for cancer treatment, but choosing incorrect drug concentrations can adversely affect the patient’s body. Therefore, it is crucial to recognize the trade-off between drug concentrations and their adverse effects. To address this issue, a closed-loop multi-drug scheduling based on Fractional Order Internal-Model-Control Proportional Integral (IMC-FOPI) Control is proposed. The proposed scheme combines the benefits of fractional PI and internal model controllers. Additionally, the parameters of IMC-FOPI are optimally tuned using a random walk-based Moth-flame optimization. The performance of the proposed controller is compared with PI and Two degrees of freedom PI (2PI) controllers for drug concentration control at the tumor site. The results reveal that the proposed control scheme improves the settling time by 43% and 21% for VX, 54% and 48 % for VY, and 48% and 40% for VZ, respectively, compared to PI and 2PI. Therefore, it can be concluded that the proposed control scheme is more efficient in scheduling multi-drug than conventional controllers.
This article presents a new cascaded control strategy to control the power flow in a renewable-energy-based microgrid operating in grid-connected mode. The microgrid model is composed of an AC ...utility grid interfaced with a multi-functional grid interactive converter (MF-GIC) acting as a grid-forming converter, a photovoltaic (PV) power-generation system acting as grid-feeding distributed generation unit, and various sensitive/non-sensitive customer loads. The proposed control strategy consists of a fractional order PI (FO-PI) controller to smoothly regulate the power flow between the utility grid, distributed generation unit, and the customers. The proposed controller exploits the advantages of FO (Fractional Order) calculus in improving the steady-state and dynamic performance of the renewable-energy-based microgrid under various operating conditions and during system uncertainties. To tune the control parameters of the proposed controller, a recently developed evaporation-rate-based water-cycle algorithm (ERWCA) is utilized. The performance of the proposed control strategy is tested under various operating conditions to show its efficacy over the conventional controller. The result shows that the proposed controller is effective and robust in maintaining all the system parameters within limits under all operating conditions, including system uncertainties.
Efficiency of the installed wind farms is location-specific. Various research works used the concepts of Geographical Information Systems (GIS) and Multi-criteria-techniques (MCDM) to identify ...suitable locations. However, research works addressing the micro-level site selection are limited. This study proposes a two-stage GIS-MCDM based algorithm that can identify the plausible regions for installing wind farms at the microscopic level. The developed tool, built on the philosophy of fuzzy Analytical Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), differentiates the regions based on technological, economic, social, and environmental aspects. For demonstration purposes, India is chosen as a study region, implying national-level analysis as stage-1 and state-wise analysis as stage-2. Results suggest that the suitable area for wind farm development in India is approximately 1805131 km2, out of which about 650 km2 is considered as highly suitable, and the following best has 330321 km2. The most suitable locations are in the western and southern parts of India, mainly in Gujarat and Tamil Nadu states. These findings of stage-2 present the hierarchy of plausible regions within each state. The developed tool is the first of its kind, help the decision-maker to extend it for siting solar farms and other energy sources.
•Two-stage GIS-MCDM based algorithm that can identify plausible locations for wind farms at the micro-level is proposed.•Proposed algorithm is demonstrated by considering India as a case study.•Approximately 1805131 km2 area suitable for wind farm development in India.•A web-based tool is developed to examine the suitability of users for installing wind farms.
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