Recognition of Urdu Handwritten Alphabet Using Convolutional Neural Network (CNN)

Handwritten character recognition systems are used in every field of life nowadays,including shopping malls,banks,educational institutes,etc.Urdu is the national language of Pakistan,and it is the fourth spoken language in the world.However,it is still challenging to recognize Urdu handwritten characters owing to their cursive nature.Our paper presents a Convolutional Neural Networks(CNN)model to recognize Urdu handwritten alphabet recognition(UHAR)offline and online characters.Our research contributes an Urdu handwritten dataset(aka UHDS)to empower future works in this field.For offline systems,optical readers are used for extracting the alphabets,while diagonal-based extraction methods are implemented in online systems.Moreover,our research tackled the issue concerning the lack of comprehensive and standard Urdu alphabet datasets to empower research activities in the area of Urdu text recognition.To this end,we collected 1000 handwritten samples for each alphabet and a total of 38000 samples from 12 to 25 age groups to train our CNN model using online and offline mediums.Subsequently,we carried out detailed experiments for character recognition,as detailed in the results.The proposed CNN model outperformed as compared to previously published approaches.

Innovative Fungal Disease Diagnosis System Using Convolutional Neural Network

Fungal disease affects more than a billion people worldwide,resulting in different types of fungus diseases facing life-threatening infections.The outer layer of your body is called the integumentary system.Your skin,hair,nails,and glands are all part of it.These organs and tissues serve as your first line of defence against bacteria while protecting you from harm and the sun.The It serves as a barrier between the outside world and the regulated environment inside our bodies and a regulating effect.Heat,light,damage,and illness are all protected by it.Fungi-caused infections are found in almost every part of the natural world.When an invasive fungus takes over a body region and overwhelms the immune system,it causes fungal infections in people.Another primary goal of this study was to create a Convolutional Neural Network(CNN)-based technique for detecting and classifying various types of fungal diseases.There are numerous fungal illnesses,but only two have been identified and classified using the proposed Innovative Fungal Disease Diagnosis(IFDD)system of Candidiasis and Tinea Infections.This paper aims to detect infected skin issues and provide treatment recommendations based on proposed system findings.To identify and categorize fungal infections,deep machine learning techniques are utilized.A CNN architecture was created,and it produced a promising outcome to improve the proposed system accuracy.The collected findings demonstrated that CNN might be used to identify and classify numerous species of fungal spores early and estimate all conceivable fungus hazards.Our CNN-Based can detect fungal diseases through medical images;earmarked IFDD system has a predictive performance of 99.6%accuracy.

EC330, a small-molecule compound, is a potential novel inhibitor of LIF signaling

Dear Editor,LIF,a multi-functional cytokine,is frequently overexpressed in many human cancers,including breast,colorectal,and pancreatic cancers(Liu et al.,2013;Li et al.,2014;Yu et al.,2014;Pascual-Garcia et al.,2019;Shi et al.,2019;Wang et al.,2019).LIF overexpression is frequently associated with poor prognosis in human cancers(Liu et al.,2013;Li et al.,2014;Yu et al.,2014).LIF functions through binding to LIF receptor complex composed of LIF receptor(LIF-R)and glycoprotein gp130(Taga and Kishimoto,1997;Heinrich et al.,2003;Watanabe et al.,2006).LIF overexpression induces activation of several oncogenic signaling pathways in a cell/tissue type-specific manner,including STAT3,PI3K/AKT,and mTOR,which in turn promotes proliferation,metastasis,and therapeutic resistance of cancer cells(Liu et al.,2013;Li et al.,2014;Yu et al.,2014;Shi et al.,2019).Recent studies have suggested that LIF is a potential important target for cancer therapy,especially for cancers with LIF overexpression.LIF neutralization antibodies(LIF neu Abs)have been reported to block LIF signaling and largely abolish the promoting effect of LIF on cancer progression(Li et al.,2014;Yue et al.,2016;Shi et al.,2019).