The research paper analyses the impact of COVID-19 on different aspects of human life. The paper focuses on losses of lives which were reported during 2020 & 2021 when the pandemic was at its peak. It discusses the basic precautions adopted by states during this hard time. It highlights all the hardships which people had to face and are still facing. The paper covers the economic setbacks faced by states and educational obstacles which hindered the process of Education. There were organizations that adopted the online system in teaching. It also highlights the efforts made by scientists and doctors to fight against this menace. They were able to succeed by introducing different vaccines. The approach adopted to complete this paper is both qualitative and quantitative at the same time.
This research paper highlights the major causes of environmental pollution. Under this heading, we analyze different kinds of environmental pollution. It further focuses on all the ways through which environmental pollution can be decreased or completely eradicated from Pakistan.
Fake messages prove to have a great impact on society as well as the public. It does not only affect people’s perception but also fails to preserve the traditional news ecosystem based on the pillars of truth and reality. Considering this situation that affects the public worldwide, here we propose an application that can identify any false information that gets circulated through social media. Our system is proposed with a goal to identify the fake messages by making comparisons with the existing facts and data which Fake messages proves to have a great impact on society as well as the public. The text information given by the user as an input to the system can be easily distinguished either as fake or real with respective tags attached in the output. Our proposed model enables the ability to identify fake and misleading information and thus retain the trust of the public, leading to the protection of society from the negative impacts of fake news available in our datasets. To implement the model, various machine learning ensemble learners were used. The model is trained using an appropriate dataset in Python and performance evaluation was also done using various performance measures. Multi-perceptron neural network binary classifier has the highest accuracy of 96%.
This study is a modest attempt to determine the compatibility of Islamic leadership with conventional or Western leadership more specifically, transformational and transactional leadership styles. This research work is significant in terms of providing invaluable inputs and insights on the leadership style (s) being practiced by the present BARMM and its local government units which then become the foundation for a possible policy intervention. In this connection, the study findings showed that Islamic leadership has some similarities as well as differences with the conventional/Western leadership perspective especially the transformational leadership style. Furthermore, it was also found by the study that all the three leadership styles under study, namely, Islamic, transformational and transactional leadership styles are presently adopted in the BARMM, as a whole. The results of the study also disclosed that the present BARMM has demonstrated better performance under the transitional period. In fact, it was also revealed by the findings of the study that there is a significant relationship between the three leadership styles, and BARMM organizational performance. While this is a very much welcomed inputs to the BARMM, the study still recommends for the BARMM top leadership to continue upgrading, updating and enriching the leadership skills and knowledge of its officials at the central office and at the local government level, in both Islamic and conventional leadership. It was also suggested by the study that the top management of BARMM should conduct an Evaluative and Assessment Survey of its organizational performance through the assistance and guidance of an Organizational Development (OD) consultants or experts aside from encouraging other researchers to conduct similar research studies aimed at confirming or rejecting the major findings of the present study. In short, while the study results and inputs are favorable to BARMM, there is nothing to lose by not being complacent but rather to continue instituting and implementing the required administrative reforms that will enable the BARMM to keep abreast with the fast-paced changing environment and modern trends.
Social media is a strong tool for discussing crucial issues such as politics and other related matters but if not properly handled can cause problem in the society and also it may have a negative impact on both the society and its economy. The extensive use of social media has both potential positive and negative effects on culture, business, and politics around the world. Social media coverage of crisis events, for instance, may be used by authorities to manage disasters effectively or by malicious parties to spread rumors and false information for financial or political gain. Given the adverse effects of fake news on social media, it is crucial to identify false information, keep it under control, and stop it from spreading. This study uses textual information that passes through search engines to collect and analyze potential false or misleading content. By using a real-world dataset associated with politics and other world news to find the best Machine learning approach that can work for detecting unreliable news from the real news. In the same vine we tried to bridge the gap in the literature by deploying some powerful Algorithms which are not commonly used by most researchers. All our algorithms performed excellent with high accuracy. In order to get the accurate performance as well as the prediction of our models, a confusion matrix was used to statistically analyze the result and finally we arrived at a conclusion that, out of the several algorithms we used for the task, passive aggressive classifier come up with the highest accuracy of 99%, showing that our accuracy outperformed all the previous research in this area and can be used for the purpose of anomalies detection of news on social media ad any task of this kind.