Sentiment Analysis for COVID-19 Vaccine Popularity

( Muhammad Saeed ),( Naeem Ahmed ),( Abid Mehmood ),( Muhammad Aftab ),( Rashid Amin ),( Shahid Kamal ) 한국인터넷정보학회 2023 KSII Transactions on Internet and Information Syst Vol.17 No.5

Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.

Derivers of green buying behavior for organic skincare products through an interplay of green brand evaluation and green advertisement

Mansoor Mahnaz,Saeed Abid,Kartawinata Budi Rustandi,Khan Muhammad Kamran Naqi 한국마케팅과학회 2022 Journal of Global Fashion Marketing Vol.13 No.4

Considering the climate issues, there is a need to investigate the various motivators and triggering factors influencing consumers’ green buying behaviors. The current study examines the influence of green brand knowledge and credibility on the consumers’ green brand evaluation, leading to their green buying behavior. Moreover, the moderating role of green advertising on consumers’ green brand evaluation has been assessed. Surveying 587 organic skincare product consumers via time-lagged research design, data were analyzed using measurement and structural models employing SmartPLS software. Results showed the positive and significant direct and indirect influence of consumers’ green brand knowledge and credibility on their green buying behavior via green brand evaluation as a mediator. Besides, this study is incremental in presenting the significant moderating role of green advertisement to augment consumers’ positive evaluation of green brands that further influence their green buying behaviors while purchasing skincare products.

Evaluation of different woods against fungus-growing termite Odontotermes obesus (Rambur) (Blattodea: Termitidae: Macrotermitinae) for use in bait stations

Naeem Iqbal,Abid Mahmood Alvi,Muhammad Shoaib,Abdul Rashied,Qamar Saeed,Muhammad Amjad Bashir 한국응용곤충학회 2018 Journal of Asia-Pacific Entomology Vol.21 No.2

Fungus-growing termites are important pests in buildings and agriculture in Pakistan and are difficult to control with existing bait systems. Development of bait systems requires the knowledge of foraging behavior of termite species. Behavior of foraging workers depends upon the quality and quantity of the food placed in the bait stations. In the current study, we tested 16 different woods (of varying density) for their susceptibility to an important fungus-growing termite, Odontotermes obesus (Rambur). The aim was to find a highly susceptible wood for use in bait stations. The woods were evaluated in no-choice and choice feeding experiments in the field by mass loss and visual ratings to the termites. Statistically significant differences were recorded (P < .001). Woods having low density were preferred to high density woods. Highest mass losses (%) were recorded from Ficus religiosa (86.49–87.8%), Bombax malabaricum (86.53–88.43%) and Populus euramericana (75.62–76.31%) in both no-choice and choice tests under “very heavy attack (almost collapsed) to completely consumed” visual rating category. The woods having least mass losses were Albizia lebbeck heartwood (7.03–9.91%), Syzygium cumini (14.25–19.89%) and Dalbergia sissoo heartwood (14.35–24.88%) and had “slightly to superficial attack” with minimum rating values. Ficus religiosa, B. malabaricum and P. euramericana appear suitable woods for use in bait stations for fungus-growing termites.

Autism Spectrum Disorder Detection in Children using the Efficacy of Machine Learning Approaches

Tariq Rafiq,Zafar Iqbal,Tahreem Saeed,Yawar Abbas Abid,Muneeb Tariq,Urooj Majeed,Akasha International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.4

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

Pollination biology of Callistemon viminalis (Sol. Ex Gaertn.) G. Don (Myrtaceae), Punjab, Pakistan

Abdul Latif,Naeem Iqbal,Muhammad Ejaz,Saeed Ahmad Malik,Allah Bakhsh Gulshan,Abid Mahmood Alvi,Khaliq Dad 한국응용곤충학회 2016 Journal of Asia-Pacific Entomology Vol.19 No.2

A research was conducted to find out the floral traits and pollinator's community of bottle brush (Callistemon viminalis: Myrtaceae). Total numbers of pollen grains, pollen grain viability, stigma receptivity, nectar volume and nectar concentration, numbers of ovules and pollen/ovule ratio were recorded. The pollinators' abundance, visitation rate and frequency were also observed. In field experiment, capsule weight, seed setting and seed numbers in open and caged flowers were also evaluated. The results revealed a total of 128,139 pollen grains/ flower. Among total pollen grains, viable pollen grains were 84.3% and non-viable were 15.69%. There were 275 ovules/flower and pollen ovule ratio was 472.50. Stigma receptivity was decreased with the age of the flowers and stigma remained receptive for about 6 days. The nectar volume and nectar concentration were 13 μL and 29%, respectively. The flowers were visited by nine Hymenopteran, four Lepidopteran, one Dipteran and one bird species. Among all pollinators, bees represented the most abundant species (1290) and showed the highest visitation rate (3.6–13.8 numbers of flowers/min) and visitation frequency (0.22–1.92 individuals/ branch/5 min). Open-pollinated flowers showed statistically higher capsule weight (0.0824 ± 0.001), seed setting (0.052 ± 0.0001) and seed numbers (242.4 ± 2.87) as compared to flowers in cages.

Investigation of thermal, antibacterial, antioxidant and antibiofilm properties of PVC/ABS/ZnO nanocomposites for biomedical applications

Muhammad Shabbir Shakir,Muhammad Kaleem Khosa,Khalid Mahmood Zia,Muhammad Saeed,Tanveer Hussain Bokhari,Muhammad Abid Zia 한국화학공학회 2021 Korean Journal of Chemical Engineering Vol.38 No.11

The Present study deals with synthesis of PVC/ABS/ZnO nanocomposites with Zinc oxide nanoparticles of particle size less than 50 nm by sonication and solution casting techniques. After characterization, such nanocomposite materials were subjected to thermal study, antibiofilm, antibacterial and antioxidant screening. Nanocomposites films showed higher thermal stability than pure polymer matrix loaded with different ZnO-Nps concentration with homogeneous distribution. Antibacterial studies were carried out against selected gram-positive bacteria: Staphylococcus aureus and gram-negative bacteria: Pseudomonas aeruginosa. Selective antibiofilm activity was studied against Staphylococcus aureus and Pseudomonas aeruginosa, which showed a higher to lower activity as a model pathogenic strains (~93 and ~89 at 160 g/ml concentration, respectively), while free radical scavenging capacity was assessed by DPPH, ABTH·+ and FRAP methods. PVC/ABS/ZnO nanocomposite showed larger zones of inhibition and higher antibiofilm and antioxidant activity than PVC/ABS polymer matrix. PVC/ABS/ZnO nanocomposite showed enhanced thermal stability and biological properties that qualify them for different biomedical and industrial applications.