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Effect of Feeding Saturated Fat on Milk Production and Composition in Crossbred Dairy Cows
Sarwar, Muhammad,Sohaib, Amer,Khan, Muhammad Ajmal,Nisa, Mahr-un Asian Australasian Association of Animal Productio 2003 Animal Bioscience Vol.16 No.2
To see the effect of Beragfat T-300, a by pass fat, on the production and composition of milk, four primiparous crossbred cows in their early lactation were used in a $4{\times}4$ Latin Square Design. Each period was of 30 days including 15 days of adjustment period. The diets were formulated to contain 0, 2.5, 3.5 and 4.5% of Bergafat and were isonitrogenous and isoenergetic. The intake of DM, OM, CP, NDF, ADF, Cellulose and ADL were not affected, however, the EE intake was increased by the supplementation of Bergafat in the diet of cows. The digestibilities of NDF and EE remained unaffected, whereas the digestibilites of DM, OM and CP were reduced. Milk yield remained unaltered, while 4%FCM yield increased as a result of adding Bergafat in the daily ration. Bergafat upto 4.5% of the diet DM can be added in the diet of crossbred cows without any adverse effect on the DM intake and digestibilities of DM and NDF. Furthermore, Bergafat does not cause any butter fat depression in the milk of cows.
SARWAR, Danish,SARWAR, Bilal,RAZ, Muhammad Asif,KHAN, Hadi Hassan,MUHAMMAD, Noor,AZHAR, Usman,ZAMAN, Nadeem uz,KASI, Mumraiz Khan Korea Distribution Science Association 2020 The Journal of Asian Finance, Economics and Busine Vol.7 No.12
This empirical research is aimed at testing the relationship of the big five personality traits namely openness to experience, extraversion, consciousness, agreeableness, neuroticism, and risk aversion with the investment intention of individual investors belonging to Balochistan, Pakistan. The primary data is collected through a self-administered questionnaire (a structured form that consists of a series of closed-ended and open-ended questions) from a sample of 397 active individual investors belonging to different districts of the province. The data is empirically analyzed by applying the Partial Least Square (PLS) path modeling technique by using the estimation package available in Smart-PLS. The findings of this study suggest that all the variables are statistically significant with investors' investment intention with risk aversion as the strongest predictor. Moreover, openness to experience, extraversion, consciousness, agreeableness, and risk are significantly and positively related to an investor's investment intention, whereas neuroticism is negatively related to an investor's investment intention. The results extended by this study can be used by financial planners and investment bankers to channelize the available financial resources in diversified portfolios. The results will help financial planners to make available diverse investment alternatives for investors in Balochistan, thus catering to their unique needs. Academia must offer courses on contemporary finance paradigm based on behavioral finance to enable future business graduates to make wise financial decisions.
IoT Enabled Intelligent System for Radiation Monitoring and Warning Approach using Machine Learning
Muhammad Saifullah,Imran Sarwar Bajwa,Muhammad Ibrahim,Mutyyba Asgher International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.5
Internet of things has revolutionaries every field of life due to the use of artificial intelligence within Machine Learning. It is successfully being used for the study of Radiation monitoring, prediction of Ultraviolet and Electromagnetic rays. However, there is no particular system available that can monitor and detect waves. Therefore, the present study designed in which IOT enables intelligence system based on machine learning was developed for the prediction of the radiation and their effects of human beings. Moreover, a sensor based system was installed in order to detect harmful radiation present in the environment and this system has the ability to alert the humans within the range of danger zone with a buzz, so that humans can move to a safer place. Along with this automatic sensor system; a self-created dataset was also created in which sensor values were recorded. Furthermore, in order to study the outcomes of the effect of these rays researchers used Support Vector Machine, Gaussian Naïve Bayes, Decision Trees, Extra Trees, Bagging Classifier, Random Forests, Logistic Regression and Adaptive Boosting Classifier were used. To sum up the whole discussion it is stated the results give high accuracy and prove that the proposed system is reliable and accurate for the detection and monitoring of waves. Furthermore, for the prediction of outcome, Adaptive Boosting Classifier has shown the best accuracy of 81.77% as compared with other classifiers.
An Ensemble Approach to Detect Fake News Spreaders on Twitter
Sarwar, Muhammad Nabeel,UlAmin, Riaz,Jabeen, Sidra International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.5
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%.
Sarwar, Ghulam,Ibrahim, Muhammad,Tahir, Mukkram Ali,Iftikhar, Yasir,Haider, Muhammad Sajjad,Noor-Us-Sabah, Noor-Us-Sabah,Han, Kyung-Hwa,Ha, Sang-Keun,Zhang, Yong-Seon Korean Society of Soil Science and Fertilizer 2011 한국토양비료학회지 Vol.44 No.3
Salt-affected soils are present in Pakistan in significant quantity. This experiment was conducted to assess the effectiveness of compost for reclamation and compare its efficiency with gypsum. For this purpose, various combinations of compost and gypsum were used to evaluate their efficacy for reclamation. A saline-sodic field having $pH_s$ 8.90, $EC_e$ $5.94dS\;m^{-1}$ and SAR $34.5(mmol\;L^{-1})^{1/2}$, SP (saturation percentage) 42.29% and texture Sandy clay loam, gypsum requirement (GR) $8.75Mg\;ha^{-1}$ was selected for this study. The experiment comprised of seven treatments (control, gypsum alone, compost alone and different combinations of compost and gypsum based on soil gypsum requirements). Inorganic and organic amendments (gypsum and compost) were applied to a saline sodic soil. Rice and wheat crops were grown. Soil samples were collected from each treatment after the harvest of both crops and analyzed for chemical properties (electrical conductivity, soil reaction and sodium adsorption ratio) and fertility status (organic matter, available phosphorus and potassium contents) of soil. Results of this study revealed that compost and gypsum improved chemical properties (electrical conductivity, soil reaction and sodium adsorption ratio) of saline sodic soil to the desired levels. Similarly, all parameters of soil fertility like organic matter, available phosphorus and potassium contents were built up with the application of compost and gypsum.
HYBRID FIXED POINT RESULTS VIA E.A AND TANGENTIAL PROPERTIES IN METRIC SPACES
Muhammad Shoaib,Muhammad Sarwar,Cemil Tunc 호남수학회 2018 호남수학학술지 Vol.40 No.4
In this manuscript, hybrid xed point results for fourmaps using (E:A) and tangential properties in the setting of metricspace are studied. Application to system of functional equations isalso studied.