Enhancements In Sorting Algorithms: A Review

One of the important issues in designing algorithms is to arrange a list of items in particular order. Aalthough there is a large number of sorting algorithms, sorting problem has concerned a great compact of research, because efficient sorting is important to optimize the use of other algorithms. In many applications, sorting plays an important role as to easily handling of the data by arranging it in ascending or descending order.[2] In this paper, we are presenting enhancements in various sorting algorithms such as bubble sort, insertion sort, selection sort, and merge sort. Aa sorting algorithm consists of comparison, swap, and the use of assignment operations. Bubble sort, selection sort and insertion sort are algorithms, which are easy to understand but have the worst time complexity of O (n2). The new algorithms are discussed, analyzed, tested, and executed for reference. Eenhanced selection sort is based on sorting the items by making it slightly faster and stable sorting algorithm. Mmodified bubble sort is an modification on both bubble sort and selection sort algorithms with O (n log n) complexity instead of O (n2) for bubble sort and selection sort algorithms. [3] [1]

Unravelling the potential of susceptibility genes in plant disease management: Present status and future prospects

The increasing global population requires an equivalent increase in food production to meet the global food demand. Crop production is challenged by various biotic and abiotic stresses, which decrease crop yield and production. Thus, proper disease management for crops ensures global food security. Various chemical, physical, and biological disease control methods have been devised and used for plant protection. However, due to the low efficiency of these methods, modern research has shifted to genetic engineering approaches. The recent advances in molecular techniques have revealed the molecular mechanisms controlling the plant’s innate immune system and plant-pathogen interactions. Earlier studies revealed that the pathogens utilize the susceptibility (S) genes in hosts for their sustainability and disease development. The resistance achieved by suppressing the S genes expression provides resistance against pathogens. Exploiting S genes for imparting/enhancing disease resistance would offer a more durable and effective alternative to conventional disease control methods. Therefore, the present review highlights the potential of this novel tool for inducing disease resistance in plants

Early Diagnosis of Lung Tumors for Extending Patients’ Life Using Deep Neural Networks

Funding Information: Funding Statement: This work was funded by the Researchers Supporting Project Number (RSP2023R 509) King Saud University, Riyadh, Saudi Arabia. This work was supported in part by the Higher Education Sprout Project from the Ministry of Education (MOE) and National Science and Technology Council, Taiwan, (109-2628-E-224-001-MY3), and in part by Isuzu Optics Corporation. Dr. Shih-Yu Chen is the corresponding author. Publisher Copyright: © 2023 Tech Science Press. All rights reserved.Peer reviewedPublisher PD

A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases

IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models

A Novel Hybrid Severity Prediction Model for Blast Paddy Disease Using Machine Learning

Hypothesis: Due to the increase in the losses in paddy yield as a result of various paddy diseases, researchers are working tirelessly for a technological solution to assist farmers in making decisions about disease severity and potential danger to the crop. Early prediction of infection severity would facilitate resources for the treatment of the infection and prevent contamination to the whole field. Methodology: In this study, a hybrid prediction model was developed to predict various levels of severity of blast disease based on diseased plant images. The proposed model is a four-fold severity prediction model. The level of severity is defined based on the percentage of leaf area affected by the disease. The image dataset is derived from both primary and secondary resources. Tools: The features are first extracted with the help of the Convolutional Neural Network (CNN) approach. Then the identification and classification of the severity level of blast disease are conducted using a Support Vector Machine (SVM). Conclusion: Mendeley, Kaggle, GitHub, and UCI are the secondary resources used for dataset generation. The number of images in the dataset is 1908. The proposed hybrid model achieves 97% accuracy

Adaptation of IoT with Blockchain in Food Supply Chain Management: An Analysis-Based Review in Development, Benefits and Potential Applications

In today’s scenario, blockchain technology is an emerging area and promising technology in the field of the food supply chain industry (FSCI). A literature survey comprising an analytical review of blockchain technology with the Internet of things (IoT) for food supply chain management (FSCM) is presented to better understand the associated research benefits, issues, and challenges. At present, with the concept of farm-to-fork gaining increasing popularity, food safety and quality certification are of critical concern. Blockchain technology provides the traceability of food supply from the source, i.e., the seeding factories, to the customer’s table. The main idea of this paper is to identify blockchain technology with the Internet of things (IoT) devices to investigate the food conditions and various issues faced by transporters while supplying fresh food. Blockchain provides applications such as smart contracts to monitor, observe, and manage all transactions and communications among stakeholders. IoT technology provides approaches for verifying all transactions; these transactions are recorded and then stored in a centralized database system. Thus, IoT enables a safe and cost-effective FSCM system for stakeholders. In this paper, we contribute to the awareness of blockchain applications that are relevant to the food supply chain (FSC), and we present an analysis of the literature on relevant blockchain applications which has been conducted concerning various parameters. The observations in the present survey are also relevant to the application of blockchain technology with IoT in other areas

A constructive non-local means algorithm for low-dose computed tomography denoising with morphological residual processing.

Low-dose computed tomography (LDCT) has attracted significant attention in the domain of medical imaging due to the inherent risks of normal-dose computed tomography (NDCT) based X-ray radiations to patients. However, reducing radiation dose in CT imaging produces noise and artifacts that degrade image quality and subsequently hinders medical disease diagnostic performance. In order to address these problems, this research article presents a competent low-dose computed tomography image denoising algorithm based on a constructive non-local means algorithm with morphological residual processing to achieve the task of removing noise from the LDCT images. We propose an innovative constructive non-local image filtering algorithm by means of applications in low-dose computed tomography technology. The nonlocal mean filter that was recently proposed was modified to construct our denoising algorithm. It constructs the discrete property of neighboring filtering to enable rapid vectorized and parallel implantation in contemporary shared memory computer platforms while simultaneously decreases computing complexity. Subsequently, the proposed method performs faster computation compared to a non-vectorized and serial implementation in terms of speed and scales linearly with image dimension. In addition, the morphological residual processing is employed for the purpose of edge-preserving image processing. It combines linear lowpass filtering with a nonlinear technique that enables the extraction of meaningful regions where edges could be preserved while removing residual artifacts from the images. Experimental results demonstrate that the proposed algorithm preserves more textural and structural features while reducing noise, enhances edges and significantly improves image quality more effectively. The proposed research article obtains better results both qualitatively and quantitively when compared to other comparative algorithms on publicly accessible datasets

A novel deep learning model for detection of severity level of the disease in citrus fruits

Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for quality productions of fruits. In current work, citrus fruits dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then Selective search, which combines the capabilities of both an extensive search and graph based segmentation, is applied. The proposed DNN (deep neural network) model is trained to detect targeted area of the disease with its severity level using citrus fruits that have been labeled by taking help of a domain expert with four severity level(high, medium ,low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement multi- classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. Model produces 96% accuracy in detecting 1 healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work 1 shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four 1 levels of severity

Intensity profile of reference image (L035) against the denoised images of m-BM3D [19], IDSR [20], DRCNN [34], PSL [46], RCN-FTVL [29] and the proposed method, respectively.

Intensity profile of reference image (L035) against the denoised images of m-BM3D [19], IDSR [20], DRCNN [34], PSL [46], RCN-FTVL [29] and the proposed method, respectively.</p