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COVID-19 proved a pandemic that has affected the whole world on a large scale. Every walk of life got disturbed by this pandemic. Educational institutions not only in Pakistan but all over the globe remain closed, which causes a loss of study for the students of all Grades, notably Higher education (Postgraduate Level), which directly affected education, learners, and teachers in terms of learning, time, and economically. Virtual Teaching (VT) is proving an emerging method of teaching in the field of education all over the world. Developed countries have opted for this method of teaching much before. In Pakistan, universities under the directions of HEC started Virtual Teaching VT (Online Teaching) for the students, which was an attempt to cover the loss on an experimental basis. This study is conducted to know the impact of VT on ESL students' behavior. For this purpose among 100 students of KFUEIT, RYK University distributed a questionnaire to measure their behavior level. Students' participation was inspiriting, and their response found positive in this new field of Teaching.

Health care has become an essential social-economic concern for all stakeholders (e.g., patients, doctors, hospitals etc.), health needs, private care and the elderly class of society. The massive increase in the usage of health care Internet of things (IoT) applications has great technological evolvement in human life. There are various smart health care services like remote patient monitoring, diagnostic, disease-specific remote treatments and telemedicine. These applications are available in a split fashion and provide solutions for variant diseases, medical resources and remote service management. The main objective of this research is to provide a management platform where all these services work as a single unit to facilitate the users. The ontological model of integrated healthcare services is proposed by getting requirements from various existing healthcare services. There were 26 smart health care services and 26 smart health care services to classify the knowledge-based ontological model. The proposed ontological model is derived from different classes, relationships, and constraints to integrate health care services. This model is developed using Protégé based on each interrelated/correlated health care service having different values. Semantic querying SPARQL protocol and RDF query language (SPARQL) were used for knowledge acquisition. The Pellet Reasoner is used to check the validity and relations coherency of the proposed ontology model. Comparative to other smart health care services integration systems, the proposed ontological model provides more cohesiveness. [ABSTRACT FROM AUTHOR]

Cities are facing challenges of high rise in population number and consequently need to be equipped with latest smart services to provide luxuries of life to its residents. Smart integrated solutions are also a need to deal with the social and environmental challenges, caused by increasing urbanization. Currently, the development of smart services' integrated network, within a city, is facing the barriers including; less efficient collection and sharing of data, along with inadequate collaboration of software and hardware. Aiming to resolve these issues, this paper recommended a solution for a synchronous functionality in the smart services' integration process through modeling technique. Using this integration modeling solution, at first, the service participants, processes and tasks of smart services are identified and then standard illustrations are developed for the better understanding of the integrated service group environment. Business process modeling and notation (BPMN) language based models are developed and discussed for a devised case study, to test and experiment i.e., for remote healthcare from a smart home. The research is concluded with the integration process model application for the required data sharing among different service groups. The outcomes of the modeling are better understanding and attaining maximum automation that can be referenced and replicated. [ABSTRACT FROM AUTHOR]

Kubernetes, a container orchestrator for cloud-deployed applications, allows the application provider to scale automatically to match the fluctuating intensity of processing demand. Container cluster technology is used to encapsulate, isolate, and deploy applications, addressing the issue of low system reliability due to interlocking failures. Cloud-based platforms usually entail users define application resource supplies for eco container virtualization. There is a constant problem of over-service in data centers for cloud service providers. Higher operating costs and incompetent resource utilization can occur in a waste of resources. Kubernetes revolutionized the orchestration of the container in the cloud-native age. It can adaptively manage resources and schedule containers, which provide real-time status of the cluster at runtime without the user’s contribution. Kubernetes clusters face unpredictable traffic, and the cluster performs manual expansion configuration by the controller. Due to operational delays, the system will become unstable, and the service will be unavailable. This work proposed an RBACS that vigorously amended the distribution of containers operating in the entire Kubernetes cluster. RBACS allocation pattern is analyzed with the Kubernetes VPA. To estimate the overall cost of RBACS, we use several scientific benchmarks comparing the accomplishment of container to remote node migration and on-site relocation. The experiments ran on the simulations to show the method’s effectiveness yielded high precision in the real-time deployment of resources in eco containers. Compared to the default baseline, Kubernetes results in much fewer dropped requests with only slightly more supplied resources. [ABSTRACT FROM AUTHOR]

The conventional Close circuit television (CCTV) cameras-based surveillance and control systems require human resource supervision. Almost all the criminal activities take place using weapons mostly a handheld gun, revolver, pistol, swords etc. Therefore, automatic weapons detection is a vital requirement now a day. The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient-Net. Real time datasets, from local surveillance department's test sessions are used for model training and testing. Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism. This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension. The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution. Efficient-Net algorithm gives better results than existing algorithms. By using Efficient-Net algorithms the accuracy achieved 98.12% when epochs increase as compared to other algorithms. [ABSTRACT FROM AUTHOR]

The conventional surveillance and control system of Closed-Circuit Television (CCTV) cameras require human resource supervision. Almost all the criminal activities take place using weapons mostly handheld gun, revolver, or pistol. Automatic gun detection is a vital requirement now-a-days. The use of real-time object detection system for the improvement of surveillance is a promising application of Convolutional Neural Networks (CNN). We are concerned about the real-time detection of weapons for the surveillance cameras, so we focused on the implementation and comparison of faster approaches such as Region (R-CNN) and Region Fully Convolutional Networks (R-FCN) with feature extractor Visual Geometry Group (VGG) and ResNet respectively. Training and testing are done on database that consists of local environment images. These images are taken with different type and high- resolution cameras that minimize the idealism. Some metrics also defined to reduce the false positives which are specific to the solution of problem. This research also contributes to the constitution of a hybrid CNN model of both faster-based R-CNN and R-FCN. Both hybrid and existing models experimented to reduce false positive in weapon detection. Result represented in graph with calculation during and after training with confusion matrix and hybrid model results better than other models. [ABSTRACT FROM AUTHOR]

Coronavirus disease (COVID-19), also known as Severe acute respiratory syndrome (SARS-COV2) and it has imposed deep concern on public health globally. Based on its fast-spreading breakout among the people exposed to the wet animal market in Wuhan city of China, the city was indicated as its origin. The symptoms, reactions, and the rate of recovery shown in the coronavirus cases worldwide have been varied . The number of patients is still rising exponentially, and some countries are now battling the third wave. Since the most effective treatment of this disease has not been discovered so far, early detection of potential COVID-19 patients can help isolate them socially to decrease the spread and flatten the curve. In this study, we explore state-of-the-art research on coronavirus disease to determine the impact of this illness among various age groups. Moreover, we analyze the performance of the Decision tree (DT), K-nearest neighbors (KNN), Naïve bayes (NB), Support vector machine (SVM), and Logistic regression (LR) to determine COVID-19 in the patients based on their symptoms. A dataset obtained from a public repository was collected and pre-processed, before applying the selected Machine learning (ML) algorithms on them. The results demonstrate that all the ML algorithms incorporated perform well in determining COVID-19 in potential patients. NB and DT classifiers show the best performance with an accuracy of 93.70%, whereas other algorithms, such as SVM, KNN, and LR, demonstrate an accuracy of 93.60%, 93.50%, and 92.80% respectively. Hence, we determine that ML models have a significant role in detecting COVID-19 in patients based on their symptoms. [ABSTRACT FROM AUTHOR]

Blind and visually impaired (BVI) users often have interface accessibility problems while using mobile applications. This study was conducted to reduce the cognitive effort required for interface navigation by identifying the accessibility issues according to the user's mental model. The study evaluated the accessibility of smartphone screens to solve organizational, presentation, and behavioral (OPB) problems of using mobile applications. Usability evaluation of an application was conducted and validated with a specific focus on BVI user experience. A total of 56 BVI participants were included in the evaluation. Overall, four tasks to assess organization, avoidance of redundant information, serialization of content, and style and text presentation were assigned to the selected participants for a newly developed smartphone application. Ethnography, task completion within a specific time, and system usability scale (SUS) techniques were used for analysis and post-task evaluation. The results showed that the organization of the application was 100% effective for both blind and visually impaired participants. The efficiency of visually impaired participants was better than blind participants. The highest level of satisfaction among the blind participants was with the presentation (with a score of 87.62), and the highest level of satisfaction among the visually impaired participants was with the organization and presentation (with a score of 89.21 in each category). Overall, this study indicated the application reduced the severity of OPB problems, offering higher usability with increased satisfaction level and enhanced effectiveness and efficiency for BVI users. Furthermore, the calculated results were provided a significant solution of OPB for BVI users. Hence, this study provides design and development guidelines based on a practical evaluation to overcome the smartphone application accessibility problems of BVI users. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the role of code-switching to students 'English language proficiency in intermediate level in Tehsil Sadiqabad. The study had two specific goals: to identifying the relationship of code exchanging and its role on the intermediate ESL students, and to assess the effect of the change in code, Urdu-English; on students' English language proficiency in Pakistan. Quantitative procedures were used. Data was collected from government college of Sadiqabad. The study involved 100 respondents. A questionnaire was used to gather information. To meet the study's objectives, the researcher chose a quantitative approach because it allows the research to produce calculated and generalizable conclusions. So, a questionnaire with 15 items on a 5-point Likert scale was created to collect data from 100 female intermediate ESL students, and the results were analyzed using SPSS version XX. The findings revealed that code switching aids in the comprehension of subject matter in a variety of English-language disciplines by clarifying concepts, explaining difficult terminology, and removing misunderstandings among students. Furthermore, the majority of instructors and students agreed that code switching has a great role on learning English as a second language. It is determined that when teachers utilize code switching in a systematic and cautious manner, learners are better able to grasp the subject matter in English-taught courses. However, code switching has a negative impact on English language skills, which cannot be preserved. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine the influence of listening comprehension methods on the listening comprehension of ESL students at the graduate level at KFUEIT (Khawaja Fareed University of Engineering and Information Technology, Rahimyar Khan). The population for this study was drawn from the KFUEIT BS English program. Two groups of twenty ESL learners each were formed. The groups were designated as control and experimental. The control group received standard instruction without using techniques, while the experimental group received instruction employing listening comprehension strategies. This research lasted ten weeks. A pretest was administered prior to the study's execution, and a post-test was administered following the study's conclusion to both groups. Post-test results revealed that the experimental group performed much better than the control group. This indicates that the adoption of reading comprehension methods has a beneficial effect on EL students. Research objectives were achieved as well in this research. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of Computer-Mediated Technology (CMT) on the writing proficiency skills of English learners at the secondary level. Due to the vast usage of computers, their role in the field of education cannot be neglected. This wide scope of computers has motivated researchers to use it in the field of education. A country like Pakistan is a developing country, and here, still traditional methods of teaching have been used, whereas developed countries are using Computers in the field of education. This research is conducted to find out the impact of computer-mediated technology in enhancing ESL learner's proficiency in writing skills in the Pakistani context. The study especially looked into to what extent CMT gives positive feedback from learners in improving their ESL writing skills proficiency level in Secondary public schools. This study also aimed to motivate teachers towards the use of CMT in the EFL classroom. This study is based on a quasi-experimental model. In this study, two groups of ESL students have been set. Both groups consist of 30 English learners. One Group is a Controlled Group, and the other is the Experimental Group. The controlled group is treated with traditional teaching methods, and the Experimental Group is treated with CMT technology. It is eight weeks of treatment. After successful treatment, it is observed that the experimental group has performed significantly better than that of the control group. ESL learners, treated with CMT, secured better scores as compared to the controlled group, which proved that the use of CMT in ESL classroom is helpful in the teaching-learning process. [ABSTRACT FROM AUTHOR]

Thisstudyisaimedtoinvestigate the impactof computer-mediatedtechnologyonthe readingproficiencyskills of ESL learners at the secondary level in Pakistan. In this research, a newly developed software Immersive reader has been used to treat ESL learnersto minimize and meet their reading deficiencies. Two groups of students were managed, i.e., the controlled group and experimental group.TheControl group wastaught through the traditional teaching method, and the experimental group was treated through CMI (Computer-Mediated-Instruction). A pre-test was conducted to assess ESL learners' present knowledge of reading skills from both the groups, and their obtained score was noted. Similarly, after treatment, a post-test of both the groups was conducted, and ESL learners obtained score was noted. It was noticed that the experimental group achieved a significantly good score as compared to the control group's ESL learners. Their reading skillsimproved amazingly. [ABSTRACT FROM AUTHOR]

Vocabulary is a difficulty for children, especially young learners. Young children's capacity to learn and recall words is limited. Vocabulary development is a challenging task for teachers due to the limitations of children's skills. The teacher can use practical, inspiring, and effective classroom teaching methods to help students to enhance their vocabulary knowledge. Using pictures to teach vocabulary can help teachers focus students' attention and encourage them to learn more efficiently. The study aims to investigate whether using pictures as a medium can help elementary school students learn new words. This study examined elementary ESL students who had divided into two groups: experimental and control. Images were delivered to the Experimental group, while the Control group received directions without pictures. The data for the study is collected using a pre-test and a post-test. Before administering the treatments, the researcher conducts a pre-test for both groups. After providing the students with the knowledge for two weeks, the researcher performs a post-test to see how their vocabulary knowledge had improved. The working hypothesis of this study is that teachers who utilize pictures get much better grades than those who do not. The difference in scores demonstrates the effectiveness of using images to teach vocabulary skills to students. As a result, English teachers can utilize pictures to teach vocabulary to their students. [ABSTRACT FROM AUTHOR]

In this paper it is discussed that how pronunciation errors occur among students due to silent letters in English language. What type of errors are made by the learners? The research participants were twenty students. These students were selected from elementary level of public schools. This is a descriptive study based on qualitative and quantitative mode of research. A reading test was designed to collect the data for the study. This reading test contains 30 underlined words based on silent letters. The data was collected through recording voice of students while reading. As the learners are not familiar with the rules of silent letters in English language, so they endeavor to read words as they are written. This paper reveals that the students made pre-systematic errors. They need to be familiarized with some basic silent letters' rules. They need to be made aware that English words are not necessarily pronounced as how they are written. [ABSTRACT FROM AUTHOR]