PHARMACEUTICAL WORLD OF PERMEATION ENHANCERS

The drugs with poor solubility results in delayed absorption which consequently affects the bioavailability. There are many drugs which are having good therapeutic value but not used commercially because of this reason. The permeation enhancers are therefore being utilized to counter this problem. There are many such synthetic and natural materials which have the ability to enhance the drug permeation rate. The essential oils, alcohols, terpenes, azoles and many other chemical derivatives have the capability to be used for permeation enhancer. The present review work suggested the role of permeation enhancer in the pharmaceutical world

HFRAS : design of a high-density feature representation model for effective augmentation of satellite images

Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms : Principles and Perspectives

Funding Information: This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Science and ICT, under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program.Peer reviewe

Deep learning approach for discovery of in silico drugs for combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19. [Abstract copyright: Copyright © 2021 Nishant Jha et al.

SDSWSN—A Secure Approach for a Hop-Based Localization Algorithm Using a Digital Signature in the Wireless Sensor Network

Localization and security are among the most dominant tasks of wireless sensor networks (WSN). For applications containing sensitive information on the location parameters of the event, secure localization is mandatory and must not be compromised at any cost. The main task, as if any node is malicious, is to authenticate nodes that are involved in the localization process. In this paper, we propose a secure hop-based algorithm that provides a better localization accuracy. In addition, to maintain the security of the localization process, the digital signature approach is used. Moreover, the impact of malicious nodes on the proposed scheme has also been observed. The proposed approach is also contrasted with the basic DV-Hop and improved DV-Hop based on error correction. From the simulation outcomes, we infer that this secure digital-signature-based localization strategy is quite robust against any node compromise attacks, thereby boosting its precision. Comparisons between the proposed algorithm and the state of the art were made on the grounds of different parameters such as the node quantity, ratio of anchor nodes, and range value towards the localization error

COMBINATION EFFECT OF LULICONAZOLE AND CLOBETASOL FOR TREATMENT OF SKIN AILMENTS

Objective: The new combination for Luliconazole and Clobetasol Propionate was approved for the treatment of a variety of skin disease. The main objective of this research is development and validation of novel, simple, fast and responsive derivative spectroscopic process for simultaneous estimation of newly approved combination Luliconazole (LLZ) and Clobetasol Propionate (CLP). Methods: Here in this first derivative spectroscopic method, the absorbance of LLZ and CLP was taken at 312 nm (ZCP of CLP) and 249 nm (ZCP of LLZ), respectively. Establishment of linearity was in a concentration varies from 10-50 µg/ml for Luliconazole and 5-25µg/ml for Clobetasole Propionate. Results: From the method developed above the R2 value observed for LLZ and CLP is 0.9961. Statistical validation of accuracy and reproducibility was done for planned procedure with the help of recovery studies. The mean % recovery of Luliconazole and Clobetasol Propionate was found to be 99.45 % and 99.43%, respectively. For LLZ the Limit of detection is 0.9988 µg/ml and limit of quantification is 0.0009µg/ml and for CLP the Limit of detection is0.0164µg/ml and limit of quantification 0.0027µg/ml. Conclusion: From research work the method development was done and shows fast, precise, exact and easy accessible laboratory procedure for routine evaluation of combination drugs

Blockchain-Based Traceability and Visibility for Agricultural Products: A Decentralized Way of Ensuring Food Safety in India

The globalization of the food supply chain industry has significantly emerged today. Due to this, farm-to-fork food safety and quality certification have become very important. Increasing threats to food security and contamination have led to the enormous need for a revolutionary traceability system, an important mechanism for quality control that ensures sufficient food supply chain product safety. In this work, we proposed a blockchain-based solution that removes the need for a secure centralized structure, intermediaries, and exchanges of information, optimizes performance, and complies with a strong level of safety and integrity. Our approach completely relies on the use of smart contracts to monitor and manage all communications and transactions within the supply chain network among all of the stakeholders. Our approach verifies all of the transactions, which are recorded and stored in a centralized interplanetary file system database. It allows a secure and cost-effective supply chain system for the stakeholders. Thus, our proposed model gives a transparent, accurate, and traceable supply chain system. The proposed solution shows a throughput of 161 transactions per second with a convergence time of 4.82 s, and was found effective in the traceability of the agricultural products

Integrating IoT and Blockchain for Ensuring Road Safety: An Unconventional Approach

The Internet of things (IoT), the Internet of vehicles, and blockchain technology have become very popular these days because of their versatility. Road traffic, which is increasing day by day, is causing more and more deaths worldwide. The world needs a product that would reduce the number of road accidents. This paper suggests combining IoT and blockchain technology to mitigate road hazards. The new intelligent transportation system technologies and the subsequent emergence of 5G technologies will be a blessing, delivering the necessary speed to ensure both safety and quality of service (QoS). Hashgraph technology, a distributed ledger technology is used to create communication networks between the different vehicles and other relevant parameters. Scheduling the requests according to the priorities for ensuring better QoS quotient can be effectively done using hashgraph. We demonstrated how the hashgraph outstrips other equivalents platforms. The proposed model was simulated using OMNeT++ with proper design and network description files. A hardware implementation of the proposed model was also done. Messages were transferred between the vehicles and prioritized using a hashgraph. This paper proposes an effective model in reducing the accidents in terms of parameters like speed, security, stability, and fairness

A Novel Approach for Securing Nodes Using Two-Ray Model and Shadow Effects in Flying Ad-Hoc Network

In the last decades, flying ad-hoc networks (FANET) have provided unique features in the field of unmanned aerial vehicles (UAVs). This work intends to propose an efficient algorithm for secure load balancing in FANET. It is performed with the combination of the firefly algorithm and radio propagation model. To provide the optimal path and to improve the data communication of different nodes, two-ray and shadow fading models are used, which secured the multiple UAVs in some high-level applications. The performance analysis of the proposed efficient optimization technique is compared in terms of packet loss, throughput, end-to-end delay, and routing overhead. Simulation results showed that the secure firefly algorithm and radio propagation models demonstrated the least packet loss, maximum throughput, least delay, and least overhead compared with other existing techniques and models

Using machine learning ensemble method for detection of energy theft in smart meters

Abstract Electricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively