Plant Disease Detection and Classification: A Systematic Literature Review.

A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.

Smart Wireless Sensor Technology for Healthcare Monitoring System Using Cognitive Radio Networks.

Programmable Object Interfaces are increasingly intriguing researchers because of their broader applications, especially in the medical field. In a Wireless Body Area Network (WBAN), for example, patients' health can be monitored using clinical nano sensors. Exchanging such sensitive data requires a high level of security and protection against attacks. To that end, the literature is rich with security schemes that include the advanced encryption standard, secure hashing algorithm, and digital signatures that aim to secure the data exchange. However, such schemes elevate the time complexity, rendering the data transmission slower. Cognitive radio technology with a medical body area network system involves communication links between WBAN gateways, server and nano sensors, which renders the entire system vulnerable to security attacks. In this paper, a novel DNA-based encryption technique is proposed to secure medical data sharing between sensing devices and central repositories. It has less computational time throughout authentication, encryption, and decryption. Our analysis of experimental attack scenarios shows that our technique is better than its counterparts.

Blockchain and Machine Learning Inspired Secure Smart Home Communication Network.

With the increasing growth rate of smart home devices and their interconnectivity via the Internet of Things (IoT), security threats to the communication network have become a concern. This paper proposes a learning engine for a smart home communication network that utilizes blockchain-based secure communication and a cloud-based data evaluation layer to segregate and rank data on the basis of three broad categories of Transactions (T), namely Smart T, Mod T, and Avoid T. The learning engine utilizes a neural network for the training and classification of the categories that helps the blockchain layer with improvisation in the decision-making process. The contributions of this paper include the application of a secure blockchain layer for user authentication and the generation of a ledger for the communication network; the utilization of the cloud-based data evaluation layer; the enhancement of an SI-based algorithm for training; and the utilization of a neural engine for the precise training and classification of categories. The proposed algorithm outperformed the Fused Real-Time Sequential Deep Extreme Learning Machine (RTS-DELM) system, the data fusion technique, and artificial intelligence Internet of Things technology in providing electronic information engineering and analyzing optimization schemes in terms of the computation complexity, false authentication rate, and qualitative parameters with a lower average computation complexity; in addition, it ensures a secure, efficient smart home communication network to enhance the lifestyle of human beings.

Mechanism and Origin of Remote Stereocontrol in the Organocatalytic Enantioselective Formal C(sp2)-H Alkylation Using Nitroalkanes as Alkylating Agents.

Experimental 13C kinetic isotope effects (KIEs) and density functional theory (DFT) calculations are used to evaluate the mechanism and origin of enantioselectivity in the formal C(sp2)-H alkylative desymmetrization of cyclopentene-1,3-diones using nitroalkanes as the alkylating agent. An unusual combination of an inverse (∼0.980) and a normal (∼1.033) KIE is observed on the bond-forming carbon atoms of the cyclopentene-1,3-dione and nitroalkane, respectively. These data provide strong support for a mechanism involving reversible carbon-carbon bond formation followed by rate- and enantioselectivity-determining nitro group elimination. The theoretical free-energy profile and the predicted KIEs indicate that this elimination event occurs via an E1cB pathway. The origin of remote stereocontrol is evaluated by distortion-interaction and SAPT0 analyses of the E1cB transition states leading to both enantiomers.

Enantioselective Total Synthesis of [3]-Ladderanol through Late-Stage Organocatalytic Desymmetrization.

Ladderane phospholipids, with their unusual ladder-like arrangement of concatenated cyclobutane rings, represent an architecturally unique class of natural products. However, despite their fascinating structure and other necessary impetus, only a few synthetic studies of these molecules have been reported so far. We have now devised a concise total synthesis of [3]-ladderanol, a component of natural ladderane phospholipids, using an organocatalytic enantioselective desymmetrizing formal C(sp2 )-H alkylation. Our synthetic strategy rests on the late-stage introduction of chirality, thus allowing facile access to both enantiomers of [3]-ladderanol as well as an analogue. This is the first time a desymmetrization strategy is applied to the synthesis of [3]-ladderanol. The scope of this desymmetrizing C(sp2 )-H alkylation of meso-cyclobutane-fused cyclohexenediones is also presented.

Total syntheses of several iridolactones and the putative structure of noriridoid scholarein A: an intramolecular Pauson-Khand reaction based one-stop synthetic solution.

A simple and general approach towards the total syntheses of several iridolactones such as (±)-boschnialactone, (±)-7-epi-boschnialactone, (±)-teucriumlactone, (±)-iridomyrmecin, (±)-isoboonein, (±)-7-epi-argyol, (±)-scabrol A, (±)-7-epi-scabrol A, and (±)-patriscabrol as well as the putative structure of scholarein A is delineated. The synthetic strategy features a diastereoselective intramolecular Pauson-Khand reaction (IPKR) to construct the iridoid framework followed by some strategic synthetic manipulations to access the targeted monoterpenes including those having diverse oxy-functionalization patterns and with 3-5 contiguous stereogenic centres in a highly stereocontrolled manner. Also, the present endeavour includes the first total synthesis of scabrol A.

A secure and privacy preserved data aggregation scheme in IoMT.

The Internet of Things (IoT) is a network of intelligent devices especially in healthcare-based systems. Internet of Medical Things (IoMT) uses wearable sensors to collect data and transmit to central repositories. The security and privacy of healthcare data is a challenging task. The aim of the study is to provide a secure data sharing mechanism. The existing studies provide secure data sharing schemes but still have limitations in terms of hiding the patient identify in the messages exchanged to upload the data on central repositories. This paper presents a Secure Aggregated Data Collection and Transmission (SADCT) that provides anonymity for the identities of patient's mobile device and the intermediate fog nodes. Our system involves an authenticated server for node registration and authentication by saving security credentials. The proposed scheme presents the novel data aggregation algorithm at the mobile device, and the data extraction algorithm at the fog node. The work is validated through extensive simulations in NS along with a security analysis. Results prove the supremacy of SADCT in terms of energy consumption, storage, communication, and computational costs.

Improving screening systems of autism using data sampling.

OBJECTIVES: Autism Spectrum Disorder (ASD) is a complex range of neurodegenerative conditions that impact individuals' social behaviour and communication skills. However, ASD data often contains far more controls than cases. This poses a serious challenge when creating classification models due to deriving models that favour controls during the classification of individuals. This problem is known as class imbalance, and it may reduce the performance in classification models derived by machine learning (ML) techniques due to individuals may remain undetected. METHODS: ML appears to help in the distressing disorder by improving outcome quality besides speeding up the access to early diagnosis and consequential treatment. A screening dataset that consists of over 1100 instances was used to perform extensive quantitative analysis using different data resampling techniques and according to specific evaluation metrics. We measure the effect of class imbalance on autism screening performance using different data resampling techniques with a ML classifier and with respect to sensitivity, specificity, and F1-measure. We would like to know which resampling methods work well in balancing autism screening data. RESULTS: The results reveal that data resampling, and especially oversampling, improve results derived by the considered ML classifier. More importantly, there was superiority in terms of sensitivity and specificity for models derived by Naive Bayes classifier when oversampling methods have been used for data pre-processing on the autism data considered. CONCLUSION: The results reported encourages further improvement of the design and implementation of ASD screening systems using intelligent technology.