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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.

The study examines the prospects and challenges of machine learning (ML) applications in academic forecasting. Predicting academic activities through machine learning algorithms presents an enhanced means to accurately forecast academic events, including the academic performances and the learning style of students. The use of machine learning algorithms such as K-nearest neighbor (KNN), random forest, bagging, artificial neural network (ANN), and Bayesian neural network (BNN) has potentials that are currently being applied in the education sector to predict future events. Many gaps in the traditional forecasting techniques have greatly been bridged by the use of artificial intelligence-based machine learning algorithms thereby aiding timely decision-making by education stakeholders. ML algorithms are deployed by educational institutions to predict students’ learning behaviours and academic achievements, thereby giving them the opportunity to detect at-risk students early and then develop strategies to help them overcome their weaknesses. However, despite the benefits associated with the ML approach, there exist some limitations that could affect its correctness or deployment in forecasting academic events, e.g., proneness to errors, data acquisition, and time-consuming issues. Nonetheless, we suggest that machine learning remains one of the promising forecasting technologies with the power to enhance effective academic forecasting that would assist the education industry in planning and making better decisions to enrich the quality of education.

The antibiotic activity of lichenic compounds atranorin, physodic, and stictic acid isolated from Menegazzia terebrata were analyzed. The antibiotic bioactivity was assessed against eight bacterial inoculums i.e., 3 g positive and 5 g negative by analyzing the minimal inhibitory concentrations (MICs) using the broth tube dilution method. All lichenic compounds showed antibiotic activities. The maximum bacterial growth inhibition activity was observed by atranorin (MIC, 0.030–0.5 mg/mL) and the minimum in physodic acid (MIC, 1 mg/mL). Stictic acid (MIC, 0.25–0.5 mg/mL) exhibited moderate antibiotic activity. Among the tested bacterial inoculums, all lichenic compounds reported antibacterial activity against a streptomycin-resistant strain of Pseudomonas aeruginosa. As a result, the study referred to the antibiotic potential of lichenic compounds, which could be developed further for pharmaceutical purposes.

Patients must take significant doses of drugs to acquire the therapeutic effects required for disease therapy due to the absence of selectivity and accessibility of medicinal molecules. Drugs contain a range of drug carriers that are available to transport therapeutic chemicals to the targeted issues in the body. Mesoporous materials are choice for overcoming the aforementioned issues and producing effects in a predictable and long-term way. Because of its chemical characteristics, thermal stability, & biocompatibility, mesophoric nanoparticles are commonly utilized as release reagents. The innovative silica mesophore technology allows for efficient drug loading and administration after the target site has been reached. The additives used to manufacture MSNs can affect the property of mesoporous materials, including pore width, porosity, drug load, and surface characteristics. The need for an active surface provides for surface treatment as well as the coupling of therapeutic substances. They are widely employed in the bio-medical industry for diagnosis, target medication administration, bio-sensing, cellular absorption, and so on. The purpose of this study is, to sum up the existing level of information about mesoporous nanomaterials and their applications in diverse healthcare sectors.

The severe acute respiratory syndrome coronavirus-2 (SARS CoV-2) is β-coronavirus that is responsible for the pandemic coronavirus disease 2019 (COVID-19) all over the world. The rapid spread of th...

Garden Peas are edible seeds that have an essential nutrient required for human diet. Peas contain phytochemicals that shows antioxidant activities. A computational approach was adopted to analyse the characteristic properties and structure of the Peas antioxidant proteins. Superoxidase dismutase (SOD), catalase, phospholipid hydroperoxide g lutathione p eroxidase-like p rotein (PHGPX) and ascorbate peroxidase (APX) are the antioxidant proteins which have been used in this study. Physiochemical properties such as isoelectric point, molecular weight, aliphatic index etc. were analysed by using the Expasy's Prot Param server. Secondary structure of these proteins was analysed to study the functional characteristic of the protein. The crystal structure of the ascorbate peroxidase was available, but the crystal structures of other proteins were not available in any of the protein structure database. Hence the threedimensional structures of these proteins were generated using Swiss Modeller and Geno3D followed by validation through SAVES server. This analysis will provide additional help for the analysis of crystal structure for further experimentation.

By 2050, the world’s population will have increased by 34%, to more than 9 billion people, needing a 70% increase in food production. Prepare more dishes with fewer ingredients. Therefore, the critical goal of manufacturers is to increase production while being ecologically benign. Supply chain systems that do not enable direct farmer-to-consumer connection and rising input costs influence data collection, security, and sharing. Constraints on data security, manipulation, and single-point failure are unfulfilled due to a lack of centralized IoT agricultural infrastructure. To address these issues, the article proposes a blockchain-based IoT model. This study also shows one-of-a-kind energy savings. The decentralization of data storage improves the supply chain’s transparency and quality through blockchain technology, thus farmers can engage more efficiently. Blockchain technology improves supply chain traceability and security. This article provides a transparent, decentralized blockchain tracking solution and proposes an intelligent model protocol for several Internet of Things (IoT) devices that monitor crop development and the agricultural environment. A new approach has resolved the bulk of the supply chain difficulties. Smart contracts were utilized to organize all transactions in decentralized supply networks. The use of blockchain technology improves transaction quality, and customers may verify the legitimacy of an item’s authenticity and legality by using the system. A total of 100 IoT nodes were distributed randomly to each 500 m2 cluster farm. The Internet of Things nodes were used to assess soil moisture, temperature, and crop disease. Network stability period and network life of the proposed method show 90.4% accuracy. The food supply chain will be more efficient and trustworthy with an intelligent model. The immutability of ledger technology and smart contract support further increases supply chain security, privacy, transparency, and trust among all stakeholders in the multi-party system. By 2050, the world’s population will need a 70% increase in food production. The food supply chain will be more efficient and trustworthy with an intelligent model. This article provides a transparent, decentralized, and intelligent model protocol for several Internet of Things (IoT) devices.

Several research that assesses, or assess computer systems has been undertaken in previous decades. Choosing an appropriate DBMS system in a computer application, though, was never completely arbitrary, based on the professional study. Developing a viable answer for such a challenge depending on business goals and needs from judgment necessitates a thorough study on information access as well as comprehensive professional evaluation. The research presents a DSS to help non-kinetics discover their proper DBMS solutions and otherwise information retention types faster. This suggested DSS is unique in that it uses MoSCoW to evaluate criterion weighting or deliberate, as well as assessment frameworks for quantifying overall levels of quasi criterion and ISO/IEC qualitative features to show the link between criterion based upon industry specialists’ expertise. Companies that produce programs have difficulty integrating new innovations, such as Internet computing or information management platforms, into their operations. Because computer engineers and top programmers are usually specialists in their field, users need to consult or train other professionals. Therefore, computer development is an appropriate area for implementing judgment support technologies that can proactively help this prediction to choose the best technologies for their products. We offer a decision support system (DSS) to assist in the selection of the best appropriate information architecture. According to both example reports and specialists, this technique improves visibility into the choice method gives a deeper prioritized choice range than if customers have conducted their study individually, or saves the duration and expense of the judicial procedure.

Breast cancer is a lethal illness that has a high mortality rate. In treatment, the accuracy of diagnosis is crucial. Machine learning and deep learning may be beneficial to doctors. The proposed backbone network is critical for the present performance of CNN-based detectors. Integrating dilated convolution, ResNet, and Alexnet increases detection performance. The composite dilated backbone network (CDBN) is an innovative method for integrating many identical backbones into a single robust backbone. Hence, CDBN uses the lead backbone feature maps to identify objects. It feeds high-level output features from previous backbones into the next backbone in a stepwise way. We show that most contemporary detectors can easily include CDBN to improve performance achieved mAP improvements ranging from 1.5 to 3.0 percent on the breast cancer histopathological image classification (BreakHis) dataset. Experiments have also shown that instance segmentation may be improved. In the BreakHis dataset, CDBN enhances the baseline detector cascade mask R-CNN (mAP = 53.3). The proposed CDBN detector does not need pretraining. It creates high-level traits by combining low-level elements. This network is made up of several identical backbones that are linked together. The composite dilated backbone considers the linked backbones CDBN.

Aphids and beetles are known to be the most destructive house hold pests. The insecticides used against these pests are known to be toxic and harmful for plants, animals and human beings. Weeds are...