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The use of information and communication technologies (ICTs) has become a major driving force in transforming education worldwide. Similarly, in the last ten years, the usage of ICTs and e-learning in Pakistan has increased. Whereas the latest educational policy of the Government of Pakistan has focused on using ICTs and e-learning in schools and universities. The national professional standards for teachers have also suggested teachers to integrate ICTs in their classrooms and develop e-learning platforms for teaching and learning processes. However, in this study the effectiveness of information and communication and e-learning technologies integration in teaching and learning has been assessed. The context selected for this research study is a public university of Sindh, Pakistan. The research gap, which has been founded through the extensive literature review, indicated that most of the students are not able to utilize ICTs effectively. The first objective of this study is to enable students to gain wider range of knowledge and access Internet for developing a global outlook. Moreover, the second objective of this study is to develop students' capabilities of processing information more effectively and efficiently for teaching and learning.

By nature, this study is quantitative survey-based research study. For this purpose, data has been collected from students and teachers of English, Computer Science and Business Administration departments of targeted university, survey questionnaires have been adopted as a data collection tool. Whereas the random sampling technique is used for the collection of data by using Morgan table of sample size. Additionally, data has been collected from 100 participants, 80 of them were students and 20 were teachers, and data has been analyzed by using SPSS 22.0 software.

The findings of this study have indicated the student's level of interest toward the integration of ICTs and e-learning in science and social science courses. Similarly, the findings specify that through ICT and e-learning materials, students can learn more effectively, which can also facilitate teachers for their teaching process in this modern era of technology. Whereas the issue that has been specified through the findings is that the teachers are not encouraged by the management of the university to implement the ICT in their teaching and learning due to their concerns on the limited resources and lack of competencies.

For the practical implication, the findings of this study will facilitate the teachers and learners for integrating the ICTs and e-learning in their course curriculums and interactive teaching practices as per modern era of technology. Also, this study will help the provincial leader and policymakers by addressing the teachers’ concern to encourage the integration of ICT tools and develop capabilities for interactive classrooms for effective teaching and learning.

This is the first study of its nature that has been conducted in the Pakistani public university in rural setting, and the finding of this study will facilitate and help the institutional and government stakeholders to where and how they can effectively integrate ICTs and e-learning in classrooms for interactive teaching and learning processes in the rural Sindh.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

The impact of cyberattacks all over the world has been increasing at a constant rate every year. Performing exploratory analysis helps organizations to identify, manage and safeguard the information that could be vulnerable to cyber-attacks. It encourages to the creation of a plan for security controls that can help to protect data and keep constant tabs on threats and monitor their organization’s networks for any breaches.

The purpose of this experimental study is to state the use of data science in analyzing data and to provide a more detailed view of the most common cybersecurity attacks, what are the most accessed logical ports, visible patterns, as well as the trends and occurrence of attacks. The data to be processed has been obtained by aggregating data provided by a company’s technology department, which includes network flow data produced by nine different types of attacks within every day user activities. This could be insightful for many companies to measure the damage caused by these breaches but also gives a foundation for future comparisons and serves as a basis for proactive measures within industry and organizations.

The most common cybersecurity attacks, most accessed logical ports and their visible patterns were found in the acquired data set. The strategies, which attackers have used with respect to time, type of attacks, specific ports, IP addresses and their relationships have been determined. The statistical hypothesis was also performed to check whether attackers were confined to perform random attacks or to any specific machines with some pattern.

Policies can be suggested such that if an attack is conducted on a specific machine, which can be prevented by identifying the machine, ports and duration of the attacks on which the attacker is targeting and to formulate such policies that the organization should follow to tackle these targeted attacks in the future.

Focusing on the Indian context, with the increase in the amount of data and its analysis in health-care knowledge management (KM), the privacy concerns rise which results in loss of trust of an individual in e-health-care systems. Privacy issues in health care, specific to India, are caused by prevalent complacency, culture, politics, budget limitations, large population and infrastructures. Because of these factors, data security requires a backseat that allows easy access to confidential information. Furthermore, the prevalent culture affects health-care disclosure in India. In many cultures, disclosing sensitive personal health-care data is considered ill mannered. This leads to discrepancies in the recorded health-care data and a decrease in the level of treatment meted out. The results and statistics of treatments given do not match the records because of inaccurate data reporting. With the significant rise in the analysis and use of technology in health-care KM systems, it is important to understand the perception of KM in terms of its use and awareness about data sharing in the KM system. The purpose of the paper is to measure the perception of privacy issues in the context of Indian healthcare management systems.

To measure the perception of the use of the KM system, a set of 20 questions was circulated with a sample size of 337 which includes health-care researchers, doctors, practitioners and patients. The questions focused upon the use, share the sensitive health data in the KM platform. All the demographic information such as age, sex, religion, occupation is recorded. The privacy of the individual is maintained while circulating the questionnaire. The usage of health KM system and its privacy is measured through means and t-test.

The results of the t-test were found positive. This research study finds that the privacy factor is important among the Indians to share the information with the KM repository. It is also found that medical practitioners or data custodians are not much serious about sensitive data is being stored for analysis. From the statistical perception of usage of KM and its privacy, new architecture and privacy guidelines were suggested which can be considered in future research.

From the literature review, the questionnaire has developed which can help policymakers and hospital administrators collect information about KM processes in health-care organizations, and this can result in higher performance of health organizations. The privacy factor can also be included in typical health KM architecture ensure that while knowledge acquisition process, privacy of individual or organization can be maintained.

KM enhances the value of corporations and business industries through knowledge production, distribution and provides reliable access to the knowledge resources. KM in health care can comprise a confluence of formal methodologies and techniques to facilitate the creation, identification, acquisition, development, preservation, dissemination and finally the utilization of the various facets of a health-care enterprise’s knowledge assets. According to IBM Global executive report in the year 2012, the entire health-care system has changed from diseases-centric to patient-centric. India is emerging in terms of revenue and employment in the health-care field. The advances of information and communication technology help the health-care sector streamline for data structure and access and health analytics.

In India, the KM is frequently used in health-care industries majorly by health-care practitioners and professionals. As health-care data and knowledge are considered to be sensitive, the privacy of an individual while using the data cannot be compromised. The proposed empirical work will provide a solution in determining the main barriers of implementing privacy policies that need to be solved first and to ensure effective implementation of KM in the health care of India.

Cloud computing is becoming increasingly popular as it facilitates convenient, ubiquitous, on-demand network access to a shared pool of configurable computing resources and applications that can be quickly retrieved and released. Despite its numerous merits, it faces setbacks in data security and privacy. Data encryption is one of the most popular solutions for data security in the cloud. Various encryption algorithms have been implemented to address security concerns. These algorithms have been reviewed along with the Jumbling Salting algorithm and its applications. The framework for using Jumbling Salting to encrypt text files in the cloud environment (CloudJS) has been thoroughly studied and improvised. The purpose of this paper is to implement the CloudJS algorithm, to discuss its performance and compare the obtained results with existing cloud encryption schemes.

The paper uses six research questions to analyze the performance of CloudJS algorithm in the cloud environment. The research questions are about measuring encryption time and throughput, decryption time and throughput, the ratio of cipher to the plain text of CloudJS algorithm with respect to other Cloud algorithms like AES and DES. For this purpose, the algorithm has been implemented using dockers-containers in the Linux environment.

It was found that CloudJS performs well in terms of encryption time, decryption time and throughput. It is marginally better than AES and undoubtedly better than DES in these parameters. The performance of the algorithm is not affected by a number of CPU cores, RAM size and Line size of text files. It performs decently well in all scenarios and all resultant values fall in the desired range.

CloudJS can be tested with cloud simulation platforms (CloudSim) and cloud service providers (AWS, Google Cloud). It can also be tested with other file types. In the future, CloudJS algorithm can also be implemented in images and other files.

To the best of the knowledge, this is the first attempt to implement and analysis of a custom encryption algorithm (CloudJS) in the cloud environment using dockers-containers.

This paper aims to design a secure and seamless system that ensures quick sharing of health-care data to improve the privacy of sensitive health-care data, the efficiency of health-care infrastructure, effective treatment given to patients and encourage the development of new health-care technologies by researchers. These objectives are achieved through the proposed system, a “privacy-aware data tagging system using role-based access control for health-care data.”

Health-care data must be stored and shared in such a manner that the privacy of the patient is maintained. The method proposed, uses data tags to classify health-care data into various color codes which signify the sensitivity of data. It makes use of the ARX tool to anonymize raw health-care data and uses role-based access control as a means of ensuring only authenticated persons can access the data.

The system integrates the tagging and anonymizing of health-care data coupled with robust access control policies into one architecture. The paper discusses the proposed architecture, describes the algorithm used to tag health-care data, analyzes the metrics of the anonymized data against various attacks and devises a mathematical model for role-based access control.

The paper integrates three disparate topics – data tagging, anonymization and role-based access policies into one seamless architecture. Codifying health-care data into different tags based on International Classification of Diseases 10th Revision (ICD-10) codes and applying varying levels of anonymization for each data tag along with role-based access policies is unique to the system and also ensures the usability of data for research.

Pandemics always have an impact on everything; for wildlife it is somehow positive and for human health and safety it is negative. But yet there is not any critique on its impact on the agriculture sector and farmer community. The world is not aware of the hazards which will be caused by crop loss. On the globe the largest country by population is China, where natives in China consume rice as staple food. In past few years, it was among the highest rice producing and consuming countries. Rice is planted in February to May mostly in China, and rice crop production is strongly dependent on preplanting activates which farmers are unable to perform because of the coronavirus disease 2019 (COVID-19) spread and the need to follow safety measures. So, the purpose of this documentary analysis is to indicate the impact of coronavirus disease on the agricultural sector in China specifically for rice production.

In this article, a documentary analysis approach has been utilized. Moreover, due to the new and latest issue, not much research has been published in the context of the COVID-19 pandemic impact on the agriculture industry in China. Whereas, several online reports and expert reviews have been reviewed which have been published in online platforms and blogs websites. A total of 45 resources (journal articles, expert reviews) have been downloaded from reputed databases such as Scopus and Web of Science, out of which 31 of them were more relevant and related to the context of study.

The outcome based on this study has indicated that the rice cultivators and farmers are in uncertainty and not comfortable to work in their fields without proper safety measures due to the pandemic situation which will badly affect their lives and the economy of the country. Therefore, this study suggests that to overcome this issue, a proper safety framework is needed to be developed which can be implemented and which can facilitate the rice farming community.

Agriculture is important in so many ways including providing employment to the bulk of the population and providing the basic necessity of life and also contributing to the national economy. The outcome of this study will facilitate the agricultural scientist, economist and seasonal crop production stakeholders to indicate the most problematic domain and upcoming challenges faced by the rice farming community in China due to the COVID-19 pandemic.

This is the first documentary analysis which is addressing the current issues due to the COVID-19 pandemic in the agriculture industry and specifically in the rice cultivation and producing community safety concerns and issues in China which is the first and most affected nation due to the coronavirus.

Online learning is essential in today’s world. The COVID-19 has resulted in shutting down all the universities across the globe. Countries like India and Turkey (lower-income countries) are suffering a lot in giving the best classroom practice to their students through online mode. The entire way of teaching-learning has changed drastically, and it is a need of an hour. Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay. It is therefore important to understand from student’s perspectives about learning online. The paper systematically surveys the perception of learning online for Indian and Turkan students.

To achieve this goal, 594 samples of students (from India and Turkey country) have been taken into considerations, and through statistical measures, the results were analyzed. The set of four research questions comprising of effect of study on COVID-19 pandemic, perception of learning online in COVID-19 pandemic, perception of different genders in learning online and perception of Indians over Turkan students in learning online were analyzed through statistical measures such as mean, standard deviation and so on.

The descriptive statistics of various responses across various dimensions (gender, country) reveals that there is no effect in learning online as compared to classroom-based teaching. On the other hand, there is no significant difference in gender and country in learning online.

Online learning has become crucial in higher education as far as pandemic situation is concerned. Many higher education institutions across different countries are suffering various problems from student point of view. Middle-income countries who are with limited assets and less advancements in higher education need to adhere to certain guidelines in online learning. This empirical study will help to understand the perception of students in online learning across India and Turkey.