• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• Journal of applied mathematics & informatics
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• 제37권5_6호
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• pp.507-519
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• 2019

The ground for this paper is to examine the generalized Stokes' first and second issues for an incompressible couple pressure liquid under isothermal conditions. Exact solutions for each problem are acquired by using the Laplace transform (LT) with respect to the time variable t and the sine Fourier transform (FT) with respect to the y-variable. Further, a comparison is given of the obtained results and the results of Devakar and Lyengar [1] and by using the four inverse Laplace transform algorithms (Stehfest's, Tzou's, Talbot, Fourier series) in the space time domain utilizing a numerical methodology. Moreover, velocity profiles are plotted and considered for various occasions and distinctive estimations of couple stress parameters. At the end, the outcomes are exhibited by graphs and in tabular forms.

• Asian-Australasian Journal of Animal Sciences
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• 제11권2호
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• pp.152-154
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• 1998

The data 1241 birth, weights of Teddy goats maintained at Livestock Production Research Institute, Bahadurnagar, Okara, Pakistan, during 1975-1990 were used for this study. The mean birth weight during study period ranged from $1.55{\pm}0.06$ to $1.78{\pm}0.09kg$ with and over all mean of $1.66{\pm}0.031kg$. It was found that year of birth, type of birth, sex of kid and age of dam had significant (p < 0.01) effects on birth weight while the effect of season was found to be non-significant. It was inferred that single born kids were significantly heavier than twins and twins were heavier than triplets. The mean values of birth weight for male and female kids were $1.70{\pm}0.032$ and $1.61{\pm}0.03kg$ respectively. The birth weight was found to be the highest in 8 year age group that was $1.82{\pm}0.016kg$ and the lowest in one year group $(1.55{\pm}0.02kg)$.

• ETRI Journal
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• 제43권3호
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• pp.414-426
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• 2021

In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I-UWSANs and cause high end-to-end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z-Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well-known algorithms like energy-aware scalable reliable and void-hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.

• Asian-Australasian Journal of Animal Sciences
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• 제12권3호
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• pp.336-340
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• 1999

Age at maturity was studied in 661 Nili Ravi buffaloes maintained at six dairy farms in Pakistan. The mean age at maturity in the overall data from the six farms was $976.49{\pm}9.2$ days. Significantly lower mean age at maturity $(957.93{\pm}10.68\;days)$ was observed at Military Dairy Farm, Khyber Okara, Military Daiiy Farm, Punjnad and Livestock Research Station, National Agricultural Research Centre, Islamabad (Group I) compared to $(1015.26{\pm}17.39\;days)$ other three Military Dairy Farms, Peshawar, Nowshera and Rawalpindi (Group II). The advantages associated to early age at maturity were as following. Male and female calves were heavier $(38.35{\pm}0.17\;and\;31.84{\pm}15kg,\;respectively)$ in Group I as compared to $(29.27{\pm}0.26\;and\;26.27{\pm}0.26kg)$ in Group II. Milk yield per lactation was significantly higher in Group I $(1912{\pm}12\;lit.)$ as compared to $(1833.36{\pm}16.56\;lit.)$ in Group II. Lactation length was significantly longer $(284.41{\pm}1.23\;days)$ in Group I as compared to $(277.77{\pm}2.02\;days)$ in Group II. Dry period and service period were significantly shorter $(241.59{\pm}4.18\;and\;217.05{\pm}4.95\;days,\;respectively)$ in Group I as compared to $(306.39{\pm}78\;and\;280.95{\pm}9.32\;days)$ in Group II. The mean age at first calving and sex ratio were low ($1282.75{\pm}10.14$ days and 100 ♀ ♀:130.7 ♂ ♂) in Group I as compared to ($1308.7{\pm}16.44$ days and 100 ♀ ♀:152.15 ♂ ♂) in Group II but the differences were non significant.

• Asian-Australasian Journal of Animal Sciences
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• 제8권5호
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• pp.499-503
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• 1995

The performance records of 410 Holstein Friesian crossbred cows belonging to seven genetic groups (Fl, 3/4, 1/4, 5/8, 3/8, triple cross and miscellaneous cross) maintained at Livestock Production Research Institute, Bahadurnagar, Okara were analyzed for various parameters of lifetime traits. For the analysis 2 data sets were made. Data set I included all the cows disposed off from the herd which have completed at least one lactation while for data set II performance traits for only first five lactations were considered. The data was analyzed by Mixed Model Least squares and Maximum Likelihood computer programme PC-I version. The least squares means ${\times}$ standard errors for data set I (periods are in days and milk yield is in litres) were $994.5{\pm}15.5$, $1,877.0{\pm}70.9$, $1,651.9{\pm}19.3$, $2,533.7{\pm}36.5$, $3,530.0{\pm}40.5$, $15,785.2{\pm}320.0$, $8.46{\pm}0.19$, $5.66{\pm}0.16$ and $3.79{\pm}0.08$, respectively for age at first calving (APC), Ist lactation milk yield (FLMY), productive life (PL), herd life (HL), total life (TL), lifetime milk yield (LTMY), milk yield per day of productive life (MY/PL), milk yield per day of herd life (MY/HL) and milk yield per day of total life (MY/TL). For data set II these values were $1,004.2{\pm}21.2$, $2,220.5{\pm}113.1$, $1,429.1{\pm}40.8$, $2,302.1{\pm}73.3$, $3,307.2{\pm}77.3$, $13,189.7{\pm}667.4$, $9.10{\pm}0.34$, $5.66{\pm}0.25$ and $4.02{\pm}0.18$ in the same order. For data set I the effect of year of first calving was significant for AFC, FLMY, PL, HL, LTMY and MY/PL. The season of Ist calving was significant only for MY/PL. The effect of genetic group was significant for AFC, FLMY, MY/PL and MY/TL while the effect of parity was significant for all the traits. For data set II the effect of year of Ist calving was significant only for AFC, FLMY and PL while the season of Ist calving was significant for FLMY and PL while the effect of genetic groups was significant for MY/HL only. The lifetime production performance is in general close to the various estimates reported in the literature.