The core focus of this review is to show how immediate and valid feedback, qualitative assessment influence enhances students learning in a higher education environment. With the rising trend of online education especially in this COVID-19 pandemic, the role of assessment and feedback also changes. Earlier the assessment part is not considered the main focus in learning and teaching in HEIs, but now with the increase in online education, it is observed that the paradigm is shifted toward assessing those activities of students that enhance their learning outcomes. A lot of research work has been done on developing assessment strategies and techniques that can support learning and teaching effectively. Yet, there is limited research that looks at how methods applied in learning analytics can be used and possibly constitutes the assessment process. The objective of this work is to provide an exploratory and comparative study of how assessment and feedback practices can enhance students learning outcomes using AI. The key contribution of this study attempts to capture an outline of the most used artificial intelligence and machine learning algorithms for student success. The results showed that I-FCN performed better than other techniques (ANN, XG Boost, SVM, Random Forest, and Decision Trees) in all measured performance metrics. Also, the result of the comparative analysis study will help the educators, instructors, and administrators on how they could take the advantage of a data-driven approach, design less pressurized, more valid, reliable, constructive assessment findings, and connect the power of assessment and feedback to enhance the learning outcomes.

EDM and LA are two fields that study how to use facts to get more academic learning and enhance the students’ entire performance. Both areas are concerned with a broad range of issues such as curriculum strategies, coaching, mental well-being of students, learning motivation, and academic achievement. The COVID-19 pandemic highly disrupted the higher education sector and shifted the old, chalk-talk teaching-learning model to an online learning format. This meant that the structure and nature of teaching, learning, assessment, and feedback methodologies also changes. With the empowerment in technology, timely and effective feedback is provided by the teachers to achieve greater learning. Through these studies, it is noted that negative feedback discourages the effort and achievement of learners, so it should be carefully crafted and delivered. In this work, a new methodology is planned based on an improved FCN (fully connected network). The key impartial of the proposed method is to regulate the assessment of the quality of students in Higher Education HE. The proposed methodology is composed of different phases: The first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method. The second phase is data orientation, in which the information is oriented in a specific file format. After that, data are cleaned, and preprocessing methods are applied. In the fourth phase, a machine learning-based model is developed to predict student’s academic performance. The fully connected neural network is enhanced with LA to better assess student quality in higher education. The proposed work is evaluated with the OULAD database, which was gathered from the students of Open University. The proposed methodology has attained an accuracy of 84%, more significant than the conventional ANN model accuracy rate. The proposed methodology’s Recall, F1-score, and precision rates are 0.88, 0.91, and 0.93, respectively.

Regression testing is about running the entire test ensemble again to ensure that amendments do not negatively affect the system. A popular approach in regression testing is test case prioritization which reorders test cases in such a way that those with higher priorities are run earlier than those with lower priorities based on some criterion. Numerous researchers have worked on different aspects of prioritization approaches. This paper presents the result of the study conducted on different prioritization approaches showing mostly stressed areas by researchers and the areas where there is a future scope. For it, studies related to test case prioritization in regression testing from the year 2004 to 2018 are analyzed by dividing this time period into three slots of five years each. 36 studies were selected from 948 studies to answer the research questions framed for this study. The trends followed in TCP along with the approaches evolved are thus documented to find the current trends and future scope for the researchers to work upon.

Cloud computing plays a pivotal role in sharing resources and information. It is challenging to secure cloud services from different intruders. Intrusion detection system (IDS) plays a vital role in detecting intruder attacks, and it is also used to monitor the traffic in the network. The paper is aimed to control the attacks using the machine learning (ML) technique integrated with the artificial bee colony (ABC) named Group-ABC (G-ABC). The IDS detector has been implemented and further simulation results have been determined using the G-ABC. The evaluation has been carried out using the measures such as precision, recall, accuracy, and F-measure. Different attacks such as user to root (U2R), probe, root to local (R2L), backdoors, worms, and denial-of-service (DoS) attacks have been detected. The simulation analysis is performed using two datasets, namely, the NSL-KDD dataset and UNSW-NB15 dataset, and comparative analysis is performed against the existing work to prove the effectiveness of the proposed IDS. The objective of the work is to determine the intruder attacker system using the deep learning technique.

The information has gained a big boom during cyber world. Hiding text into an image or adding a payload to the image is known as steganography. Steganography started in BC when the rulers send hidden text from one place to another. Steganography combines data compression, cryptography technologies to fulfill the need for privacy on the internet. Steganography (by definition) is the hiding of one file within another. In this paper, we have used the concept of image steganalysis which helps us to detect the image that is hidden within an image. This paper attempts to use image steganalysis with image decoder using LSB and MSB technique and analysis of various steganography techniques used in today's world.

Generative models, span from GANs, VAEs, and Diffusion Models, are revolutionizing the-healthcare domain using fake data, enhancing medical images, and enhancing drug purposes. GANs optimize image reconstruction by increasing the PSNR by approximately 23%, whereas diffusion models produced a test accuracy of over 95% in molecular simulations. VAEs are particularly suitable for predictive modelling for decision support in the field of pharmacogenomics to improve patient outcome prognosis by 15%. This chapter provides a discussion of the architectural development and deployment of these models in data-driven and privacy-preserving healthcare. Quantitative and qualitative comparisons with other methods are given, e.g., GANs are stated to decrease drug discovery time by 70%; problems and limitations, including mode collapse and model interpretability, are mentioned. The future directions discussed here are the hybrid model designs and the possibility of the large-scale implementation of generative models as critical elements of present-day healthcare.