International Journal of Innovations in Science & Technology
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Meme Detection of Journalists from Social Media by Using Data Mining Techniques
Get PDFWith regard to today\u27s social media networks, memes have become central character where millions of memes are shared per second on different social media networks. The detection of memes is a very concentrated and demanding subject in the current era. Today\u27s social media (What\u27s App, Twitter, and Facebook) is widespread around the world. People in all countries use these networks and spend their plentiful time on daily basis. As social media has an enormous amount of data overall in the world. Meme detection from media networks can be done by using their authenticated APIs. For this analysis we used some opinion mining techniques and sentiment analysis like statistical descriptive and content analysis. In our society, it is the better way to analyze about any journalist because social media can provide very huge amounts of data about any journalist however the authenticity is compromised, what is true or false, no one bother to check. Anyone can make approximate correct perceptions by using sentiment analysis and text mining techniques. It will provide highly wanted and hidden characteristics and perceptions for searchers and demanding people about journalists. Finally use for sentiment analysis by using Python
Assessment of climate change projections in the Chenab River Basin, Western Himalaya
Get PDFGeneral circulation models (GCMs) are vital to project potential changes in futureclimate under different emissions scenarios. Raw GCM output is not applicable atregional scale due to biases relative to observational data and coarse spatial scale forfuture climate predictions. Here, statistical downscaling method was employed to generatedaily maximum temperature (Tmax), minimum temperature (Tmin) and precipitation ofcoarse spatial resolution of GCM (0.5 degree) which fall within the boundary of CRB. In thisstudy, the fifth generation ECMWF atmospheric reanalysis (ERA5) data was used as observeddata to downscale and bias-correct GFDL-ESM2M data under RCP4.5 and RCP8.5 emissionscenarios for the near future (2020-2050), mid-century (2051-2080) and end of century (2081-2100) in the Chenab River Basin (CRB). The refined output from the GCM was furtheranalyzed to depict climate changes in the CRB. It was found that a consistent increase inmaximum temperature (Tmax) and minimum temperature (Tmin) was recorded under RCP4.5and RCP8.5 in the future scenarios. In the CRB, the magnitude of increase in predicted Tminwas higher than Tmax. However, precipitation showed an increasing trend in near future whiledecreasing trend in the mid-century and end of century under RCP4.5
A Review on Impacts, Resistance Pattern and Spoilage of Vegetables Associated Microbes
Get PDFVegetable spoilage produces various microbes of different origins like parasites, fungi, viruses, and bacteria. This causes infections and diseases in vegetables, and later on, when humans eat these vegetables; diseases induce in humans. So, to prevent human diseases, the symptoms of various infections in vegetables must be known. Moreover, the conditions supporting the infections in vegetables must be understood. So that spoiled vegetable consumption can be prevented. Sometimes spoiled vegetables are regarded as disease free and suitable for consumption. These misconceptions sometimes lead to lethal human diseases, which in history led to major outbreaks. The antimicrobial resistance is faced by microbes which deteriorate the situation and make the cure of diseases
Personality Construct Among Patients With Substance Use Disorder: An Explanatory Study In Pakistan
Get PDFObjective: Many researches defined the critical predictors and significant risk factors associated with various substance use behaviors, revealing the personality traits as important determinants. Thus, the present study aimed to explore the personality constructs of individuals with a history of substance use disorder in Pakistan.
Method: The Qualitative Study was conducted through purposive sampling by selecting the professionals (psychiatrists=9, clinical psychologists=4, patients with SUD = 5) with having a minimum of 5 years to a maximum of 29 years of experience working with substance use disorder patients and participants with the history of at least three years of diagnosis. A total of 18 semi-structured interviews were conducted with nine (N=13) mental health professionals and five (N=05) patients with substance use disorder. The interviews were audio-recorded and transcribed by independent researchers. The transcripts were analyzed using a systematic approach that incorporates inductive thematic analysis.
Results: The themes that emerged after analyses were combined under the main three headings: Social, Cultural and Psychological/individual factors. The main results indicated that authoritative parenting style, family lifestyles, pleasure-seeking, enhanced energy, dependent personality traits, emotional instability and conflicting interpersonal relationship lead to substance use which can be addressed through early intervention
Trauma, Helplessness, and Quality of Life among Arthritis Patients Moderated by Perceived Social Support
Get PDFThe study explores trauma and helplessness to explore quality of life among arthritis patients moderated by perceived social support. It was a cross-sectional study conducted at different hospital communities in district Gujrat. The purposive sampling technique was used to select 385 participants. The instruments used were demographic form, Post-Traumatic stress disorder scale, arthritis helplessness index, world Health \u27sQuality of Life scale, and multidimensional Social support scale. For the Analysis of the data, multiple regression and structure equation modeling hasused. The result has confirmed [R²=.676 F (1, 383) = 106.7, p<.01] that trauma, helplessness, and social support was the predictor of Quality of life with 45.7% variance. The .E.M.S.E.M. model has significantly established the relationship among variables. The CMIN/DF was 2.10, a value less than three indicate the best-fitted model..The value of G.F.I., A.G.F.I., and CFI, are 0.957, 0.932, and 0.945, respectivelyThis shows that the Model is best fitted if this value is greater than 0.90. The regression estimates of trauma predicting social support were 0.083(P=.043), and helplessness was 0.229 (P=0.000). It established the fact that a one-unit increase in trauma will lead to an increase in social support by 0.083 and helplessness by 0.229. The regression estimates of Quality of life predicting helplessness -0.003 (P= 0.765), which indicates a non-significant inverse relationship . The regression estimates of Quality of life predicting social support and trauma were 0.052 (P=0.000) and -0.01(P=0.780), respectively. It established the fact that Quality of life determines social support by 0.052, whereas trauma was inversely non-significant
Soil Classification & Prediction of Crop Status with Supervised Learning Algorithm: Random Forest
Get PDFCrop Management System (CMS) was developed in an Ionic framework with a Real-Time Firebase database for loop backing and decision support. The main two features were; Soil classification where the soil was classified based on temperature, humidity, and soil properties such as soil moisture, soil nutrients, and soil PH level using Random Forest Algorithm. By Bootstrap method using Random Forest, samples from the dataset were selected & then classification trees was generated. The other feature was crop precision where the condition of the crop was and examined using temperature, humidity, soil moisture, soil PH levels, and soil nutrients (N, P, K). IoT device was used to fetch data from the field and then compare with already stored ideal values, suitable for optimal yield, in CMS database then process using the application to suggest the crop for cultivation and to optimize the usage of water and fertilizers. Currently, we classify the soil using Random Forest Algorithm & suggest the suitable crop for the classified type of soil & also measure the soil moisture and soil nutrients of agricultural field Acre based on the reading results we are suggesting the crop to is cultivated and pre-requisite which would be needed in future. The proposed method gives an accuracy of 96.5% as compared to existing methods of Artificial Neural Networks and Support Vector Machines
A Deep Learning Framework for Multi Drug Side Effects Prediction with Drug Chemical Substructure
Get PDFNowadays, side effects and adverse reactions of drugs are considered the major concern regarding public health. In the process of drug development, it is also considered the main cause of drug failure. Due to the major side effects, drugs are withdrawan from the market immediately. Therefore, in the drug discovery process, the prediction of side effects is a basic need to control the drug development cost and time as well as launching of an effective drug in the market in terms of patient health recovery. In this study, we have proposed a deep learning model named “DLMSE” for the prediction of multiple side effects of drugs with the chemical structure of drugs. As it is a common experience that a single drug can cause multiple side effects, that’s why we have proposed a deep learning model that can predict multiple side effects for a single drug. We have considered three side effects (Dizziness, Allergy, Headache) in this study. We have collected the drug side effects information from the SIDER database. We have achieved an accuracy of ‘0.9494’ with our multi-label classification based proposed model. The proposed model can be used in different stages of the drug development process.
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Non-invasive EEG based Feature Extraction framework for Major Depressive Disorder analysis
Get PDFDepression and several other behavioral health disorders are serious public health concerns worldwide. Persistent behavioral health issues have a wide range of consequences that affect people personally, culturally and socially. Major depressive disorder (MDD) is a psychiatric ailment that affects people of all ages worldwide. It has grown into a major global health issue as well as an economic burden. Clinicians are using several medications to limit the growth of this disease at an early stage in young people. The goal of this research is to improve the depression diagnosis by altering Electroencephalogram (EEG) signals and extracting the Differential Entropy (DE) and Power Spectral Density (PSD), using machine learning and deep learning techniques. This study analyzed the EEG signals of 30 healthy people and 34 people with Major Depressive Disorder (MDD). K-nearest neighbors (KNN) had the highest accuracy among machine learning algorithms of 99.7%, while Support vector machine (SVM) had acquired 95.7% accuracy. The developed Deep Learning approach, convolution neural network (CNN), achieved 99.6% accuracy. With these promising results, this study establishes the viability of an Electroencephalogram based diagnosis of MDD.
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A Smart Contract Approach in Pakistan Using Blockchain for Land Management
Get PDFManagement of land records includes actions such as registration and transfer of property ownership. For many nations, land ownership and management are important sources of income. Corrupted spans from small-scale payments to large-scale cause an abuse for government. In the literature, a number of concerns have been raised about Land Record Management. There are several problems with Land Record Management in developing nations, such as tampering with land records and no methods of retrieving a full property ownership record, operating multiple linked Land Record Management Systems independently, etc. Traditional land record management solutions do not solve these challenges. We propose a Blockchain-based Land Record Management system for Pakistan to solve these concerns. It has been decided to use the suggested system, and the specifics of its implementation are described in this thesis.
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First Report of Genus Parmeliella Müll. (Peltigerales; Lecanoromycetes; Ascomycota) from Pakistan
Get PDFWhen studying lichens in Pakistan, we came across a crustose species with small to moderate squamulose on a thin blackish hypothallus with a dry, rough, gray-brown to the black upper surface. The standard chemical tests integrated with conventional to modern taxonomic tools were used to name the specimen. Consequently, with minor differences in the morphology, and no difference in nucleotides, the lichen species was baptized Parmeliella thriptophylla (Ach.) Müll. Arg. The descriptive taxonomy and n-ITS-based phylogeny of this species with its habitus are presented in this study. No previous record of this species, genus, or family was found in Pakistan.
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