In this era of digital and modern education, the existence of psychological stress on students cannot be denied. The surplus aggregation of the stress may lead to different problems like a decline in student grade (performance), an increase of violence in behavior, and even more extreme cases. The advent of Information Communication and Technology (ICT) and its tools opened the doors to innovations that facilitate interactions among things and humans. In this utilization, the paper proposes a novel, IoT-aware student-centric stress monitoring and real-time alert generating framework to predict student stress index in a particular context. In elaboration, we respectively used extended VGG16, Bidirectional Long Short Term Memory network (Bi -LSTM), and Multinomial Naïve Bayes techniques to generate the scores of emotions from student facial expressions, speech pitch, and content of student speech at the cloud layer. Specifically, the model aims to classify the stress events as normal or abnormal on basis of the overall emotion of the students’ physiological data readings. The activation of the abnormal event in case of higher values for negative emotions like stress, fear, sadness, disgust, etc.; a stern alert is sent to the student, coordinators, and caretakers. This proposed framework will ultimately be a great tool that will support the education institutions, students, their parents, and guardians to get a real-time alert on students’ overall emotions. The prior knowledge of stress accumulated on the mind of the student will help in overcoming major problems of student dropout, decrease student academic performance, and tackle the stress situation that may lead to the student attempting suicide.

Ministry of HFW, Government of India ordered the NIMNS - National Institute of Mental Health and Neuro Sciences, Bengaluru, in alliance with 15 institutions from across India and made a survey on mental health issues. This commission covered 12 states, one among that is Punjab from Northern region. As per the report, 15% of the adults in India need treatment for mental disorder. Machine Learning is one of the most substantial proportions of Artificial Intelligence. Machine Learning is widely used in many fields like online fraud detection, speech recognition, and social media. It plays a vital role in healthcare sector. This boosts the interest on the detection of the mental illness using machine learning algorithm. The big challenge is to predict the state of mind. Psychologists impose assessment and therapy to their patients by one to one physical interactions. There are multiple causes to put the person into critical situation like depression, pressure etc. Hence, this research paper proposes an ideal solution to identify the sickness in the person by checking with the recorded dataset. The most preferred Supervised Machine Learning algorithm, Decision Tree Classifier is used for this purpose. The initial goal of the Decision Tress is to create training ideal which is used to forecast the target variable class. The parameters considered here are anxiety disorder, depression disorder and the stress. Random Forest algorithm is applied to predict the illness in the people. The result obtained is to have accurate prediction level compared to the existing model.

Managing clones proactively is one of the hot research areas under Software Cloning domain. Researchers of the Clone Research Community argue that it is better to avoid clones rather than to refactor them. Integrated Clone Management Systems are the ways of implementing preventive (proactive) clone management approaches and are basically implemented as an add-on to the Integrated Development Environments. There are different techniques that are implemented as an add-on to IDEs, thus, literature has a corpus of plug-in tools that need to be organized and analyzed to have an efficient plug-in in the future. To identify the related literature, the well-known methodology proposed by Kitchenham [Guidelines for Performing Systematic Literature Reviews in Software Engineering. Keele University and University of Durham, EBSE Technical Report; 2007.; Procedures for performing systematic reviews. School of Computer Science and Mathematics, Keele University, Keele, Technical Report; 2004] for conducting systematic literature review is adopted in this paper along with the snowballing technique. Utilizing these techniques, near about 33 IDE based real-time code clone detection tools are identified. This paper presents a comprehensive and systematic literature review of all the clone management techniques found in the literature that are integrated with the software development environments as a plug-in tool. An in-depth investigation and comparative analysis of all the plug-ins is carried out in this survey paper so that the future work can be identified for the further research under this domain.

COVID-19, over time, has spread around multiple countries and has affected a large number of humans. It has influenced diverse people’s lives, consisting of social, behavioral, physical, mental, and economic aspects. In this study, we aim to analyze one such social impact: the behavioral aspects of agriculture stakeholders during the pandemic period in the Indian region. For this purpose, we have gathered agriculture-related tweets from Twitter in three phases: (a) initial phase, (b) mid-phase, and (c) later phase, where these phases are related to the period of complete lockdown implemented in India in the year 2020. Afterward, we applied machine-learning-based qualitative-content-based methods to analyze the sentiments, emotions, and views of these people. The outcomes depicted the presence of highly negative emotions in the initial phase of the lockdown, which signifies fear of insecurity among the agriculture stakeholders. However, a decline in unhappiness was noted during the later phase of the lockdown. Furthermore, these outcomes will help policymakers to obtain insights into the behavioral responses of agricultural stakeholders. They can initiate primitive and preventive actions accordingly, to tackle such issues in the future.