AI-Driven Pattern Recognition in Medicinal Plants: A Comprehensive Review and Comparative Analysis

The pharmaceutical industry increasingly values medicinal plants due to their perceived safety and costeffectiveness compared to modern drugs.Throughout the extensive history of medicinal plant usage,various plant parts,including flowers,leaves,and roots,have been acknowledged for their healing properties and employed in plant identification.Leaf images,however,stand out as the preferred and easily accessible source of information.Manual plant identification by plant taxonomists is intricate,time-consuming,and prone to errors,relying heavily on human perception.Artificial intelligence(AI)techniques offer a solution by automating plant recognition processes.This study thoroughly examines cutting-edge AI approaches for leaf image-based plant identification,drawing insights from literature across renowned repositories.This paper critically summarizes relevant literature based on AI algorithms,extracted features,and results achieved.Additionally,it analyzes extensively used datasets in automated plant classification research.It also offers deep insights into implemented techniques and methods employed for medicinal plant recognition.Moreover,this rigorous review study discusses opportunities and challenges in employing these AI-based approaches.Furthermore,in-depth statistical findings and lessons learned from this survey are highlighted with novel research areas with the aim of offering insights to the readers and motivating new research directions.This review is expected to serve as a foundational resource for future researchers in the field of AI-based identification of medicinal plants.

Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,etc.However, the research on RomanUrdu is not up to the mark.Hence, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

Image captioning has gained increasing attention in recent years.Visual characteristics found in input images play a crucial role in generating high-quality captions.Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption generation.However,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features.Consequently,this leads to enhanced captioning network performance.In light of this,we present an image captioning framework that efficiently exploits the extracted representations of the image.Our framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language model.The VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features matrix.Subsequently,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative description.Integrating the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s performance.Using the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve performance.The implementation code can be found here:https://github.com/althobhani/VFDICM(accessed on 30 July 2024).

基于多阶段分形组合的点云补全算法

点云是一种常见的三维物体表示形式,然而由于传感器设计和精度等原因,得到的点云通常存在几何形状缺失和稀疏性缺失。为了解决该问题,本文提出一种基于多阶段分形组合的点云补全算法。第一阶段先对输入点云进行多次采样并且分别提取特征,再利用金字塔模型生成多尺度几何形状丢失的点云,最后将生成点云与输入点云进行拼接。第二阶段利用KNN聚类和PointNet堆叠网络提取局部特征,并且将拼接的点云下采样作为粗略预测,最后将粗略预测与局部输入折叠网络生成精细化的高质量点云。本文算法基于局部到整体多阶段进行补全,损失函数可针对不同阶段进行权重调整,有效地优化了补全过程,并且在ShapeNet数据集上获得不错的补全效果。

Text Sentiment Analysis Using Frequency-Based Vigorous Features

Sentiment Analysis, an un-abating research area in text mining, requires a computational method for extracting useful information from text. In recent days, social media has become a really rich source to get information about the behavioral state of people(opinion) through reviews and comments. Numerous techniques have been aimed to analyze the sentiment of the text, however, they were unable to come up to the complexity of the sentiments. The complexity requires novel approach for deep analysis of sentiments for more accurate prediction. This research presents a three-step Sentiment Analysis and Prediction(SAP) solution of Text Trend through K-Nearest Neighbor(KNN). At first, sentences are transformed into tokens and stop words are removed. Secondly, polarity of the sentence, paragraph and text is calculated through contributing weighted words, intensity clauses and sentiment shifters. The resulting features extracted in this step played significant role to improve the results. Finally, the trend of the input text has been predicted using KNN classifier based on extracted features. The training and testing of the model has been performed on publically available datasets of twitter and movie reviews. Experiments results illustrated the satisfactory improvement as compared to existing solutions. In addition, GUI(Hello World) based text analysis framework has been designed to perform the text analytics.

Manipulating Spin Polarization of Defected Co_(3)O_(4)for Highly Effi cient Electrocatalysis

Electrocatalytic water splitting is limited by kinetics-sluggish oxygen evolution,in which the activity of catalysts depends on their electronic structure.However,the infl uence of electron spin polarization on catalytic activity is ambiguous.Herein,we successfully regulate the spin polarization of Co_(3)O_(4)catalysts by tuning the concentration of cobalt defects from 0.8 to 14.5%.X-ray absorption spectroscopy spectra and density functional theory calculations confi rm that the spin polarization of Co_(3)O_(4)is positively correlated with the concentration of cobalt defects.Importantly,the enhanced spin polarization can increase hydroxyl group absorption to signifi cantly decrease the Gibbs free energy change value of the OER rate-determining step and regulate the spin polarization of oxygen species through a spin electron-exchange process to easily produce triplet-state O_(2),which can obviously increase electrocatalytic OER activity.In specifi c,Co_(3)O_(4)-50 with 14.5%cobalt defects exhibits the highest spin polarization and shows the best normalized OER activity.This work provides an important strategy to increase the water splitting activity of electrocatalysts via the rational regulation of electron spin polarization.

Dehydration and volume depletion:How to handle the misconceptions

Dehydration and volume depletion describe two distinct body fluid deficit disorders with differing pathophysiology,clinical manifestations and treatment approaches.However,the two are often confused or equated with each other.Here,we address a number of commonly encountered misconceptions about body-fluid deficit disorders,analyse their origins and propose approaches to overcome them.

Renal Replacement Therapy in Qatar—Past, Present and Future

The economic development of Qatar alongside the resultant lifestyle changes in the last few decades has contributed to increasing rates of obesity, diabetes mellitus and hypertension with consequent increased incidence and prevalence of chronic kidney disease and end-stage-renal-disease (ESRD). This article describes renal replacement therapy (RRT) services in Qatar and their evolution in response to challenges posed by the growth of ESRD with reference to regional and international data. It covers the history of RRT, highlighting significant advances in chronological order, as well as providing an overview of the current status of RRT in the multicultural and socioeconomically diverse society that inhabits Qatar. Finally, it casts a glance into the future, predicting how RRT services will further evolve to address the current limitations.

Channel aware receiver for next generation wireless communication system

We consider an iterative phase synchronization scheme based on maximum a posteriori probability algorithm.In classical approaches,the phase noise estimation model considers one sample per symbol at the channel and receiver.However,information theoretic studies suggested use of more than one sample per symbol at the channel and receiver for achieving higher performance.In this article,a soft-information aided iterative receiver is derived,which uses off-the-shelf blocks for detection and demodulation by keeping the complexity of the receiver acceptable.We consider here two samples per symbols at the channel and receiver in a pragmatic paradigm.It is shown that phase noise estimation can be significantly improved at the expense of modest processing overhead.Simulation results are presented for low-density parity check coded quadrature amplitude modulations.Our results show a significant performance improvement for strong phase noise values compared to classical receiver approaches.

Restructuring nephrology services to combat COVID-19 pandemic:Report from a Middle Eastern country

Coronavirus disease 2019 has spread across the world and has been classified as a pandemic.It has overwhelmed the healthcare systems.Specifically,it has overstretched the intensive care units and renal replacement therapy services in many countries.In this paper,we discuss the reconfiguration of nephrology services in the State of Qatar during the current pandemic.We highlight the key strategies that have been implemented to ensure that renal replacement therapy capacity is not constrained in either the intensive care or ambulatory setting.Some innovative approaches for the safe delivery of ambulatory care to dialysis and kidney transplant patients are also discussed.

Theoretical Determination and Experimental Verification of Transport Index of Rectangular Shaped Radioactive Containers

Ionizing radiations are widely used to sustain and enhance our quality of life in the areas such as medical diagnosis, therapy, scientific research and industry etc. Ionizing radiations are available from radioactive sources which are made of radioactive materials. The radioactive materials are produced in either nuclear power or research reactors or nuclear accelerators or extracted from the naturally found radioactive ores. These radioactive sources and radioactive materials need to be transported from their places of production to the places of applications and finally to waste repositories. The radioactive materials are transported in well designed packages having various shapes and sizes. In the field of radioactive transport, it is a mandatory to find the Transport Index (TI) to be mentioned on each package for transportation. This research is focused on the determination of the maximum γ-ray radiation dose at one meter from the surface of cubic and rectangular shaped package or containers. A computer code “Solid Angle for Transport Index” (SAFTI) has been developed using MATLAB to determine the location of maximum value of the radiation dose rate from the surface of a rectangular or square container. This maximum dose rate is used to determine the transport index. Some of the results of the code have been compared with the experimental results. The results of this research are useful not only to determine TI for individual packages but also to find the TI of the vehicles carrying the transport packages.

Taurolock Effectively Reduces Tunneled Dialysis Catheter-Related Infection and Catheter Exchange Rates among Hemodialysis Patients

Aims: Catheter-related infection, which is one of the major side effects of the use of dialysis catheters, leads to increases in hospitalization, morbidity and mortality. Antibiotic lock is an option for reducing the incidence of these infections, but there are concerns regarding antibiotic resistance. A prior study demonstrated that Taurolock (a taurolidine lock) may reduce the rate of catheter-related infection. Methods and Material: This investigation was a prospective before-and-after study. During period one, patients continued to use a heparin lock (5000 units/ml) for 6 months. During period two, they were shifted to Taurolock (a solution of 1.35% taurolidine, 4% citrate, and 500 units/ml of heparin) for 6 months. The primary outcome was the incidence of tunneled catheter-related infection and/or catheter exchange, and the secondary outcomes were the effects of Taurolock on catheter flow rate, dialysis adequacy, and catheter malfunction rate. Results: A total of 49 patients were included in this study. During period one, the primary outcome occurred 17 times, with an incidence rate of 86.8 per 100 patient-years;during period two, the primary outcome occurred 7 times, with an incidence rate of 32.6 per 100 patient-years (incidence rate ratio: 2.65, 95% confidence interval (CI): 1.05 - 7.6, P = 0.023). There was no significant difference between the two periods with respect to mean catheter blood flow rate (P = 0.29). During period one, thrombolytic therapy (TPA) lock was indicated on 19 occasions, with an incidence rate of 97 per 100 patient-years;during period two, TPA lock was indicated on 53 occasions, with an incidence rate of 247.5 per 100 patient-years (incidence rate ratio: 0.3, 95% CI: 0.21 - 0.67, P = 0.0003). Conclusions: We demonstrated that Taurolock usage is safe and effective for the prevention of dialysis catheter-related infection and/or catheter exchange.

林烟草NsylCBL10互作蛋白激酶NsylCIPK的鉴定与分析

植物中的类钙调神经素B亚基蛋白CBL (calcineurin B-like protein)家族成员与其互作蛋白激酶CIPK (CBL-interacting kinase protein)家族成员形成了复杂精细的CBL-CIPK信号调控网络,在解码生物和非生物胁迫激发的钙信号及信号的进一步传导中发挥重要作用。CBL家族成员CBL10参与植物抵抗高盐胁迫、应对病菌侵染、参与分生组织和花器官发育、调节离子平衡等多个过程,对其下游互作CIPK家族成员的筛选与鉴定对于明确上述调节机制至关重要。为了鉴定林烟草(Nicotiana sylvestris) NsylCBL10下游互作NsylCIPK家族成员,本研究首先以拟南芥(Arabidopsis thaliana) AtCIPK家族成员和水稻(Oryza sativa) OsCIPK家族成员基因的CDS序列为检索序列,利用NCBI和中国烟草基因组数据库对林烟草中可能存在的NsylCIPK家族成员进行了预测;然后通过RT-PCR方法对预测到的NsylCIPK家族成员进行了克隆并利用生物学分析软件对其基因结构和蛋白保守结构域进行了分析;最后通过酵母双杂交实验鉴定了与Nsyl CBL10互作的NsylCIPK家族成员。结果表明,在林烟草中可能存在28个NsylCIPK家族成员,实际克隆获得了20个NsylCIPK家族成员,在酵母双杂交体系中与NsylCBL10互作的只有1个NsylCIPK家族成员Nsyl CIPK14a。本研究为解析林烟草及其他物种中CBL-CIPK信号通路的调节功能提供了试验数据。

Ketamine Alleviates Fear Generalization Through GluN2B-BDNF Signaling in Mice

Fear memories are critical for survival.Nevertheless,over-generalization of these memories,depicted by a failure to distinguish threats from safe stimuli,is typical in stress-related disorders.Previous studies have supported a protective role of ketamine against stress-induced depressive behavior.However,the effect of ketamine on fear generalization remains unclear.In this study,we investigated the effects of ketamine on fear generalization in a fear-generalized mouse model.The mice were given a single sub-anesthetic dose of ketamine(30 mg/kg,i.p.)1 h before,1 week before,immediately after,or 22 h after fear conditioning.The behavioral measure of fear(indicated by freezing level)and synaptic protein expression in the basolateral amygdala(BLA)and inferior-limbic pre-frontal cortex(IL-PFC)of mice were examined.We found that only ketamine administered 22 h after fear conditioning significantly decreased the fear generalization,and the effect was dose-dependent and lasted for at least 2 weeks.The fear-generalized mice showed a lower level of brainderived neurotrophic factor(BDNF)and a higher level of GluN2B protein in the BLA and IL-PFC,and this was reversed by a single administration of ketamine.Moreover,the GluN2B antagonist ifenprodil decreased the fear generalization when infused into the IL-PFC,but had no effect when infused into the BLA.Infusion of ANA-12(an antagonist of the BDNF receptor TrkB)into the BLA or ILPFC blocked the effect of ketamine on fear generalization.These findings support the conclusion that a single dose of ketamine administered 22 h after fear conditioning alleviates the fear memory generalization in mice and the GluN2B-related BDNF signaling pathway plays an important role in the alleviation of fear generalization.

Molecular evolution,virology and spatial distribution of HCV genotypes in Pakistan:A meta-analysis

Background:Hepatitis C,caused by the Hepatitis C Virus(HCV),is the second most common form of viral hepatitis.The geographical distribution of HCV genotypes can be quite complex,making it challenging to ascertain the most prevalent genotype in a specific area.Methods:To address this,a review was conducted to determine the prevalence of HCV genotypes across various provinces and as a whole in Pakistan.The scientific literature regarding the prevalence,distribution,genotyping,and epidemiology of HCV was gathered from published articles spanning the years 1996-2020.Results:Genotype 1 accounted for 5.1%of the patients,with its predominant subtype being 1a at 4.38%.The frequencies of its other subtypes,1b and 1c,were observed to be 1.0%and 0.31%respectively.Genotype 2 had a frequency of 2.66%,with the most widely distributed subtype being 2a at 2.11%of the patients.Its other subtypes,2b and 2c,had frequencies of 0.17%and 0.36%respectively.The most prevalent genotype among all isolates was 3(65.35%),with the most frequent subtype being 3a(55.15%),followed by 3b(7.18%).The prevalence of genotypes 4,5,and 6 were scarce in Pakistan,with frequencies of 0.97%,0.08%,and 0.32%respectively.The prevalence of untypeable and mixed genotypes was 21.34%and 3.53%respectively.Estimating genotypes proves to be a productive method in assisting with the duration and selection of antiviral treatment.Different HCV genotypes can exhibit variations in their response to specific antiviral treatments.Different genotypes may have distinct natural histories,including variations in disease progression and severity.Some genotypes may lead to more rapid liver damage,while others progress more slowly.Conclusions:This information can guide screening and testing strategies,helping to identify individuals at higher risk of developing severe complications.Studying the distribution of HCV genotypes in a population can provide valuable insights into the transmission dynamics of the virus.