In Steganography, the total message will be invisible into a cover media such as text, audio, video, and image in which attackers don't have any idea about the original message that the media contain and which algorithm use to embed or extract it. In this paper, the proposed technique has focused on Bitmap image as it is uncompressed and convenient than any other image format to implement LSB Steganography method. For better security AES cryptography technique has also been used in the proposed method. Before applying the Steganography technique, AES cryptography will change the secret message into cipher text to ensure two layer security of the message. In the proposed technique, a new Steganography technique is being developed to hide large data in Bitmap image using filtering based algorithm, which uses MSB bits for filtering purpose. This method uses the concept of status checking for insertion and retrieval of message. This method is an improvement of Least Significant Bit (LSB) method for hiding information in images. It is being predicted that the proposed method will able to hide large data in a single image retaining the advantages and discarding the disadvantages of the traditional LSB method. Various sizes of data are stored inside the images and the PSNR are also calculated for each of the images tested. Based on the PSNR value, the Stego image has higher PSNR value as compared to other method. Hence the proposed Steganography technique is very efficient to hide the secret information inside an image.

The identification of suitable feature subsets from High-Dimensional Low-Sample-Size (HDLSS) data is of paramount importance because this dataset often contains numerous redundant and irrelevant features, leading to poor classification performance. However, the selection of an optimal feature subset from a vast feature space creates a significant computational challenge. In the domain of HDLSS data, conventional feature selection methods often face challenges in achieving a balance between reducing the number of features and preserving high classification accuracy. Addressing these issues, the study introduces an effective framework that employs a filter and wrapper-based strategy specifically designed to address the classification challenges inherent in HDLSS data. The framework adopts a multi-step approach where ensemble feature selection integrates five filter ranking approaches: Chi-square ( $\chi ^{2}$ ), Gini index (GI), F-score, Mutual Information (MI), and Symmetric uncertainty (SU) to identify the top-ranking features. In the subsequent stage, a wrapper-based search method is utilized, which employs the Differential Evaluation (DE) metaheuristic algorithm as the search strategy. The fitness of feature subsets during this search is assessed based on a weighted combination of the error rate of the Support Vector Machine (SVM) classifier and the ratio of feature cardinality. The datasets, after undergoing dimensionality reduction, are then utilized to construct classification models using SVM, K-Nearest Neighbors (KNN), and Logistic Regression (LR). The approach was evaluated on 13 HDLSS datasets to assess its efficacy in selecting appropriate feature subsets and improving Classification Accuracy (ACC) analog with Area Under the Curve (AUC). Results show that the proposed ensemble with wrapper-based approach produces a smaller number of features (ranging between 2 and 9 for all datasets), while maintaining a commendable average AUC and ACC (between 98% and 100%). The comparative analysis reveals that the proposed method surpasses both ensemble feature selection and non-feature selection approaches in terms of feature reduction and ACC. Additionally, when compared to various other state-of-the-art methods, this approach demonstrates commendable performance.

The paper investigates a partial exam assignment approach for solving the examination timetabling problem. Current approaches involve scheduling all of the exams into time slots and rooms (i.e., produce an initial solution) and then continuing by improving the initial solution in a predetermined number of iterations. We propose a modification of this process that schedules partially selected exams into time slots and rooms followed by improving the solution vector of partial exams. The process then continues with the next batch of exams until all exams are scheduled. The partial exam assignment approach utilises partial graph heuristic orderings with a modified great deluge algorithm (PGH-mGD). The PGH-mGD approach is tested on two benchmark datasets, a capacitated examination dataset from the 2nd international timetable competition (ITC2007) and an un-capacitated Toronto examination dataset. Experimental results show that PGH-mGD is able to produce quality solutions that are competitive with those of the previous approaches reported in the scientific literature.