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Rice (Oryza sativa) is India’s major crop. India has the most land dedicated to rice agriculture, which includes both brown and white rice. Rice cultivation creates jobs and contributes significantly to the stability of the gross domestic product (GDP). Recognizing infection or disease using plant images is a hot study topic in agriculture and the modern computer era. This study paper provides an overview of numerous methodologies and analyses key characteristics of various classifiers and strategies used to detect rice illnesses. Papers from the last decade are thoroughly examined, covering studies on several rice plant diseases, and a survey based on essential aspects is presented. The survey aims to differentiate between approaches based on the classifier utilized. The survey provides information on the many strategies used to identify rice plant disease. Furthermore, model for detecting rice disease using enhanced convolutional neural network (CNN) is proposed. Deep neural networks have had a lot of success with picture categorization challenges. We show how deep neural networks may be utilized for plant disease recognition in the context of image classification in this research. Finally, this paper compares the existing approaches based on their accuracy.

Purpose This paper aims to introduce recently an unregulated unsupervised algorithm focused on voice activity detection by data clustering maximum margin, i.e. support vector machine. The algorithm for clustering K-mean used to solve speech behaviour detection issues was later applied, the application, therefore, did not permit the identification of voice detection. This is critical in demands for speech recognition. Design/methodology/approach Here, the authors find a voice activity detection detector based on a report provided by a K-mean algorithm that permits sliding window detection of voice and noise. However, first, it needs an initial detection pause. The machine initialized by the algorithm will work on health-care infrastructure and provides a platform for health-care professionals to detect the clear voice of patients. Findings Timely usage discussion on many histories of NOISEX-92 var reveals the average non-speech and the average signal-to-noise ratios hit concentrations which are higher than modern voice activity detection. Originality/value Research work is original.

The recommended tool for assessing knee injury is magnetic resonance imaging (MRI). However, knee MRI interpretation takes time and is vulnerable to clinical errors and inconsistency. A deep learning automated technique for reading knee MRI might help physicians identify high-risk patients and make diagnosis easier. In this study, we have proposed a deep learning-based model to detect ACL and meniscus tears and other knee abnormalities. At its core, this model is based on the ResNet50 transfer learning technique. In this paper, we have focused to present a ResNet50-based model for detecting different knee problems using MRIs. The best models for every option achieved the objectives that were probably similar. The models were developed using 18, 3, and 1 slice. These models’ outcomes were rather startling. The AUC findings obtained with 1 slice per MRI exam were equivalent to those obtained with 18 and 3 slices and, in some cases, were significantly better. The dataset used in this model is from Stanford University. We trained this model in three different settings of MRI slices (18, 3, and 1). The best results that our models were able to achieve were when trained using 3 slices of each MRI sample. The area under the receiver operating characteristic curve, or AUC curve, values that our best models were able to achieve for detecting ACL, meniscus, and other knee abnormalities are 0.87, 0.82, and 0.90, respectively. The results of our models are comparable to some state-of-the-art models. These models are very fast and efficient to train and hence will be helpful to doctors for making an effective and fast diagnosis based on knee MRIs.

Voice Activity Detection (VAD) is a method of detecting speech and non-speech in noisy environments. Vaious methods for this purpose have also been proposed. In general, the research has been divided into supervised and unsupervised speech recognition and produced various algorithms to depict the occurring of speech signal. Research aims to examine window overlapping and detection of speech and non-speech segments. A speech signal seems to be a slowly non stationary signal, and its characteristics are short time constant when examined over a short span of time (between 10 and 30 ms). As a result, frames windowing is used to enable us to use a speech signal and interpret its characteristics. However, a widespread study is required in the selection of techniques from predefined VAD and problems and opportunities to increase research in the emerging region. The advantage of the new unsupervised K-means approach over the supervised method is that it will not have to pre-train classifiers and pre-know any previous knowledge about audio streams.

Voice Activity Detection (VAD) is a technique to classify speech signal into two parts as speech signal and background noises, and widely used in emerging speech recognition technologies such as mobile communication, high-quality multimedia transmission, forensic science, and voice recognition applications. As this technique is integral part of speech communication system, selection of precise VAD is the most challenging part in terms of complexity, feature extractions, threshold selection, and percentage of correctness. The researchers have generally classified VAD into supervised and unsupervised system and introduced various characteristics-based algorithm to reflect the occurrence of speech signal. However, a pervasive study is desired for the selection of appropriate techniques from predefined VAD along with the challenges and solutions to set the future research directions in the emerging area of voice recognition. Therefore, an extensive study is presented in this manuscript especially to set a tradeoff between obstacles and performance of earlier developed VAD. The authors believe that this review will be helpful to researchers working in the challenging speech processing and recognition domain.