MLNAS: Meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks.

Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system's model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.

Classification of Lung Diseases Using an Attention-Based Modified DenseNet Model.

Lung diseases represent a significant global health threat, impacting both well-being and mortality rates. Diagnostic procedures such as Computed Tomography (CT) scans and X-ray imaging play a pivotal role in identifying these conditions. X-rays, due to their easy accessibility and affordability, serve as a convenient and cost-effective option for diagnosing lung diseases. Our proposed method utilized the Contrast-Limited Adaptive Histogram Equalization (CLAHE) enhancement technique on X-ray images to highlight the key feature maps related to lung diseases using DenseNet201. We have augmented the existing Densenet201 model with a hybrid pooling and channel attention mechanism. The experimental results demonstrate the superiority of our model over well-known pre-trained models, such as VGG16, VGG19, InceptionV3, Xception, ResNet50, ResNet152, ResNet50V2, ResNet152V2, MobileNetV2, DenseNet121, DenseNet169, and DenseNet201. Our model achieves impressive accuracy, precision, recall, and F1-scores of 95.34%, 97%, 96%, and 96%, respectively. We also provide visual insights into our model's decision-making process using Gradient-weighted Class Activation Mapping (Grad-CAM) to identify normal, pneumothorax, and atelectasis cases. The experimental results of our model in terms of heatmap may help radiologists improve their diagnostic abilities and labelling processes.

Prospecting the Potential of Plant Growth-Promoting Microorganisms for Mitigating Drought Stress in Crop Plants.

Drought is a global phenomenon affecting plant growth and productivity, the severity of which has impacts around the whole world. A number of approaches, such as agronomic, conventional breeding, and genetic engineering, are followed to increase drought resilience; however, they are often time consuming and non-sustainable. Plant growth-promoting microorganisms are used worldwide to mitigate drought stress in crop plants. These microorganisms exhibit multifarious traits, which not only help in improving plant and soil health, but also demonstrate capabilities in ameliorating drought stress. The present review highlights various adaptive strategies shown by these microbes in improving drought resilience, such as modulation of various growth hormones and osmoprotectant levels, modification of root morphology, exopolysaccharide production, and prevention of oxidative damage. Gene expression patterns providing an adaptive edge for further amelioration of drought stress have also been studied in detail. Furthermore, the practical applications of these microorganisms in soil are highlighted, emphasizing their potential to increase crop productivity without compromising long-term soil health. This review provides a comprehensive coverage of plant growth-promoting microorganisms-mediated drought mitigation strategies, insights into gene expression patterns, and practical applications, while also guiding future research directions.

Mycorrhizae set the stage for plants to produce a higher production of biomolecules and stress-related metabolites: a sustainable alternative of agrochemicals to enhance the quality and yield of beetroot (Beta vulgaris L.).

Population explosions, environmental deprivation, and industrial expansion led to an imbalanced agricultural system. Non-judicial uses of agrochemicals have decreased agrodiversity, degraded agroecosystems, and increased the cost of farming. In this scenario, a sustainable agriculture system could play a crucial role; however, it needs rigorous study to understand the biological interfaces within agroecosystems. Among the various biological components with respect to agriculture, mycorrhizae could be a potential candidate. Most agricultural crops are symbiotic with arbuscular mycorrhizal fungi (AMF). In this study, beetroot has been chose to study the effect of different AMFs on various parameters such as morphological traits, biochemical attributes, and gene expression analysis (ALDH7B4 and ALDH3I1). The AMF Gm-Funneliformis mosseae (Glomus mosseae), Acaulospora laevis, and GG-Gigaspora gigantean were taken as treatments to study the effect on the above-mentioned parameters in beetroot. We observed that among all the possible combinations of mycorrhizae, Gm+Al+GG performed best, and the Al-alone treatment was found to be a poor performer with respect to all the studied parameters. This study concluded that the more the combinations of mycorrhizae, the better the results will be. However, the phenomenon depends on the receptivity, infectivity, and past nutrient profile of the soil.

A Comprehensive Survey of Deep Learning Techniques in Protein Function Prediction.

Protein function prediction is a major challenge in the field of bioinformatics which aims at predicting the functions performed by a known protein. Many protein data forms like protein sequences, protein structures, protein-protein interaction networks, and micro-array data representations are being used to predict functions. During the past few decades, abundant protein sequence data has been generated using high throughput techniques making them a suitable candidate for predicting protein functions using deep learning techniques. Many such advanced techniques have been proposed so far. It becomes necessary to comprehend all these works in a survey to provide a systematic view of all the techniques along with the chronology in which the techniques have advanced. This survey provides comprehensive details of the latest methodologies, their pros and cons as well as predictive accuracy, and a new direction in terms of interpretability of the predictive models needed to be ventured by protein function prediction systems.

Secure data authentication and access control protocol for industrial healthcare system.

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient's end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques.

Fuzzy-logic-based IoMT framework for COVID19 patient monitoring.

Smart healthcare is an integral part of a smart city, which provides real time and intelligent remote monitoring and tracking services to patients and elderly persons. In the era of an extraordinary public health crisis due to the spread of the novel coronavirus (2019-nCoV), which caused the deaths of millions and affected a multitude of people worldwide in different ways, the role of smart healthcare has become indispensable. Any modern method that allows for speedy and efficient monitoring of COVID19-affected patients could be highly beneficial to medical staff. Several smart-healthcare systems based on the Internet of Medical Things (IoMT) have attracted worldwide interest in their growing technical assistance in health services, notably in predicting, identifying and preventing, and their remote surveillance of most infectious diseases. In this paper, a real time health monitoring system for COVID19 patients based on edge computing and fuzzy logic technique is proposed. The proposed model makes use of the IoMT architecture to collect real time biological data (or health information) from the patients to monitor and analyze the health conditions of the infected patients and generates alert messages that are transmitted to the concerned parties such as relatives, medical staff and doctors to provide appropriate treatment in a timely fashion. The health data are collected through sensors attached to the patients and transmitted to the edge devices and cloud storage for further processing. The collected data are analyzed through fuzzy logic in edge devices to efficiently identify the risk status (such as low risk, moderate risk and high risk) of the COVID19 patients in real time. The proposed system is also associated with a mobile app that enables the continuous monitoring of the health status of the patients. Moreover, once alerted by the system about the high risk status of a patient, a doctor can fetch all the health records of the patient for a specified period, which can be utilized for a detailed clinical diagnosis.

COVID-19 and cyberbullying: deep ensemble model to identify cyberbullying from code-switched languages during the pandemic.

It has been declared by the World Health Organization (WHO) the novel coronavirus a global pandemic due to an exponential spread in COVID-19 in the past months reaching over 100 million cases and resulting in approximately 3 million deaths worldwide. Amid this pandemic, identification of cyberbullying has become a more evolving area of research over posts or comments in social media platforms. In multilingual societies like India, code-switched texts comprise the majority of the Internet. Identifying the online bullying of the code-switched user is bit challenging than monolingual cases. As a first step towards enabling the development of approaches for cyberbullying detection, we developed a new code-switched dataset, collected from Twitter utterances annotated with binary labels. To demonstrate the utility of the proposed dataset, we build different machine learning (Support Vector Machine & Logistic Regression) and deep learning (Multilayer Perceptron, Convolution Neural Network, BiLSTM, BERT) algorithms to detect cyberbullying of English-Hindi (En-Hi) code-switched text. Our proposed model integrates different hand-crafted features and is enriched by sequential and semantic patterns generated by different state-of-the-art deep neural network models. Initial experimental results of the proposed deep ensemble model on our code-switched data reveal that our approach yields state-of-the-art results, i.e., 0.93 in terms of macro-averaged F1 score. The dataset and codes of the present study will be made publicly available on the paper's companion repository [https://github.com/95sayanta/COVID-19-and-Cyberbullying].

Artificial Intelligence in Accelerating Drug Discovery and Development.

Drug discovery and development are critical processes that enable the treatment of wide variety of health-related problems. These are time-consuming, tedious, complicated, and costly processes. Numerous difficulties arise throughout the entire process of drug discovery, from design to testing. Corona Virus Disease 2019 (COVID-19) has recently posed a significant threat to global public health. SARS-Cov-2 and its variants are rapidly spreading in humans due to their high transmission rate. To effectively treat COVID-19, potential drugs and vaccines must be developed quickly. The advancement of artificial intelligence has shifted the focus of drug development away from traditional methods and toward bioinformatics tools. Computer-aided drug design techniques have demonstrated tremendous utility in dealing with massive amounts of biological data and developing efficient algorithms. Artificial intelligence enables more effective approaches to complex problems associated with drug discovery and development through the use of machine learning. Artificial intelligence-based technologies improve the pharmaceutical industry's ability to discover effective drugs. This review summarizes significant challenges encountered during the drug discovery and development processes, as well as the applications of artificial intelligence-based methods to overcome those obstacles in order to provide effective solutions to health problems. This may provide additional insight into the mechanism of action, resulting in the development of vaccines and potent substitutes for repurposed drugs that can be used to treat not only COVID-19 but also other ailments.

A Deep Ranking Weighted Multihashing Recommender System for Item Recommendation.

Collaborative filtering (CF) techniques are used in recommender systems to provide users with specialised recommendations on social websites and in e-commerce. But they suffer from sparsity and cold start problems (CSP) and fail to interpret why they recommend a new item. A novel deep ranking weighted multihash recommender (DRWMR) system is designed to suppress sparsity and CSP. The proposed DRWMR system contains two stages: the neighbours' formation and recommendation phases. Initially, the data is fed to the deep convolutional neural network (CNN). The significant features are extracted from CNN. The CNN contains an additional layer; the hash code is generated by minimising pairwise ranking loss and classification loss. Therefore, a weight is assigned to different hash tables and hash bits for a recommendation. Then, the similarity between users is obtained based on the weighted hammering distance; the similarity between users helps to form the neighbourhood for the active user. Finally, the rating for unknown items can be obtained by taking the weighted average rating of the neighbourhood, and a list of the top n items can be produced. The effectiveness and accuracy of the proposed DRWMR system are tested on the MovieLens 100 K dataset and compared with the existing methods. Based on the evaluation results, the proposed DRWMR system gives precision (0.16), the root mean squared error (RMSE) of 0.73 and the recall (0.08), the mean absolute error (MAE) of 0.57, and the F - 1 measure (0.101).

Unraveling Microbial Volatile Elicitors Using a Transparent Methodology for Induction of Systemic Resistance and Regulation of Antioxidant Genes at Expression Levels in Chili against Bacterial Wilt Disease.

Microbial volatiles benefit the agricultural ecological system by promoting plant growth and systemic resistance against diseases without harming the environment. To explore the plant growth-promoting efficiency of VOCs produced by Pseudomonas fluorescens PDS1 and Bacillus subtilis KA9 in terms of chili plant growth and its biocontrol efficiency against Ralstonia solanacearum, experiments were conducted both in vitro and in vivo. A closure assembly was designed using a half-inverted plastic bottle to demonstrate plant-microbial interactions via volatile compounds. The most common volatile organic compounds were identified and reported; they promoted plant development and induced systemic resistance (ISR) against wilt pathogen R. solanacearum. The PDS1 and KA9 VOCs significantly increased defensive enzyme activity and overexpressed the antioxidant genes PAL, POD, SOD, WRKYa, PAL1, DEF-1, CAT-2, WRKY40, HSFC1, LOX2, and NPR1 related to plant defense. The overall gene expression was greater in root tissue as compared to leaf tissue in chili plant. Our findings shed light on the relationship among rhizobacteria, pathogen, and host plants, resulting in plant growth promotion, disease suppression, systemic resistance-inducing potential, and antioxidant response with related gene expression in the leaf and root tissue of chili.

Protein function prediction using functional inter-relationship.

With the growth of high throughput sequencing techniques, the generation of protein sequences has become fast and cheap, leading to a huge increase in the number of known proteins. However, it is challenging to identify the functions being performed by these newly discovered proteins. Machine learning techniques have improved traditional methods' efficiency by suggesting relevant functions but fails to perform well when the number of functions to be predicted becomes large. In this work, we propose a machine learning-based approach to predict huge set of protein functions that use the inter-relationships between functions to improve the model's predictability. These inter-relationships of functions is used to reduce the redundancy caused by highly correlated functions. The proposed model is trained on the reduced set of non-redundant functions hindering the ambiguity caused due to inter-related functions. Here, we use two statistical approaches 1) Pearson's correlation coefficient 2) Jaccard similarity coefficient, as a measure of correlation to remove redundant functions. To have a fair evaluation of the proposed model, we recreate our original function set by inverse transforming the reduced set using the two proposed approaches: Direct mapping and Ensemble approach. The model is tested using different feature sets and function sets of biological processes and molecular functions to get promising results on DeepGO and CAFA3 dataset. The proposed model is able to predict specific functions for the test data which were unpredictable by other compared methods. The experimental models, code and other relevant data are available at https://github.com/richadhanuka/PFP-using-Functional-interrelationship.