A Survey of Checkpointing Algorithms in Mobile Ad Hoc Network

Checkpoint is defined as a fault tolerant technique that is a designated place in a program at which normal processing is interrupted specifically to preserve the status information necessary to allow resumption of processing at a later time. If there is a failure, computation may be restarted from the current checkpoint instead of repeating the computation from beginning. Checkpoint based rollback recovery is one of the widely used technique used in various areas like scientific computing, database, telecommunication and critical applications in distributed and mobile ad hoc network. The mobile ad hoc network architecture is one consisting of a set of self configure mobile hosts capable of communicating with each other without the assistance of base stations. The main problems of this environment are insufficient power and limited storage capacity, so the checkpointing is major challenge in mobile ad hoc network. This paper presents the review of the algorithms, which have been reported for checkpointing approaches in mobile ad hoc network

Early Prediction of ‘At-Risk’ Learners on Virtual Platforms using ODFs

This Learning analytics are one of the most important assistance tools used by educators for early identification of at-risk learners. Researchers have used many AI based tools for monitoring learning and improving learner’s performances by using any early intervention strategies to reduce dropout rates on online platforms that lacks face-to-face acknowledgement and feedback. Online platforms have Online Discussion Forums (ODFs) where a learner can post his queries and interact with other learners or the instructor. It becomes one of the useful indicators of tracking participation of a learner in the teaching learning process. Learners who actively participate in interaction on these online discussion platforms and contribute to the learning content required by other users are believed to give better performance as compared to those who do not participate in forum discussion. This paper focuses on the aspects of forum discussion like frequency of posts, sentimental analysis of forum post, number of threads initiated or replied to, and also how recent the post to predict the learners who could be at-risk of dropping out. The prediction model uses a data set from secondary resource. Various metrics like Confusion Matrix and Loss curve are employed to measure the accuracy of the model. Results indicate that data captured using forum posts can help in early identification of At-risk Learners

DeepSum: A Deep Learning Framework for Summarizing Animal Behavior

The burgeoning field of ethology necessitates efficient tools for analyzing extensive video recordings of animal behavior, as manually sifting through hours of footage is both time-consuming and susceptible to observer bias. Here we present an innovative deep learning framework tailored for summarizing animal behavior videos, aiming to distill lengthy recordings into concise, informative segments. Leveraging the latest advancements in hierarchical video summarization, our approach employs a combination of Convolutional Neural Networks (CNNs) and Transformer models to extract and understand complex spatial-temporal patterns inherent in animal movements and interactions. The model is designed to recognize and prioritize key behavioral events, ensuring the retention of critical moments in the summarized output. Additionally, an attention mechanism is incorporated to adaptively focus on salient features, enhancing the model’s capability to discern subtle yet significant behavioral nuances. We assess our framework on a range of datasets containing different species and behavioral situations, and find that it outperforms current state-of-the-art techniques in terms of accuracy, coherence, and informativeness of the generated summaries. In addition to providing a consistent, objective method of analyzing animal behavior, DeepSum dramatically reduces the amount of manual labor needed for behavioral analysis, opening the door for advancements in ethological research and wildlife conservation

Edge Detection via Edge-Strength Estimation Using Fuzzy Reasoning and Optimal Threshold Selection Using Particle Swarm Optimization

An edge is a set of connected pixels lying on the boundary between two regions in an image that differs in pixel intensity. Accordingly, several gradient-based edge detectors have been developed that are based on measuring local changes in gray value; a pixel is declared to be an edge pixel if the change is significant. However, the minimum value of intensity change that may be considered to be significant remains a question. Therefore, it makes sense to calculate the edge-strength at every pixel on the basis of the intensity gradient at that pixel point. This edge-strength gives a measure of the potentiality of a pixel to be an edge pixel. In this paper, we propose to use a set of fuzzy rules to estimate the edge-strength. This is followed by selecting a threshold; only pixels having edge-strength above the threshold are considered to be edge pixels. This threshold is selected such that the overall probability of error in identifying edge pixels, that is, the sum of the probability of misdetection and the probability of false alarm, is minimum. This minimization is achieved via particle swarm optimization (PSO). Experimental results demonstrate the effectiveness of our proposed edge detection method over some other standard gradient-based methods

Object Boundary Detection Using Active Contour Model via Multiswarm PSO with Fuzzy-Rule Based Adaptation of Inertia Factor

Active contour models, colloquially known as snakes, are quite popular for several applications such as object boundary detection, image segmentation, object tracking, and classification via energy minimization. While energy minimization may be accomplished using traditional optimization methods, approaches based on nature-inspired evolutionary algorithms have been developed in recent years. One such evolutionary algorithm that has been used extensively in active contours is the particle swarm optimization (PSO). However, conventional PSO converges slowly and gets trapped in local minimum easily which results in inaccurate detection of concavities in the object boundary. This is taken care of by using proposed multiswarm PSO in which a swarm is set for every control point in the snake and then all the swarms search for their best points simultaneously through information sharing among them. The performance of the multiswarm PSO-based search process is further enhanced by using dynamic adaptation of the inertia factor. In this paper, we propose using a set of fuzzy rules to adjust the inertia weight on the basis of the current normalized snake energy and the current value of inertia. Experimental results demonstrate the effectiveness of the proposed method compared to conventional approaches

Perfect Compression Technique in Combination with Training Algorithm and Wavelets

Abstract — Wavelets have emerged as powerful tools for signal coding especially bio-signal processing. Wavelet transform is used to represent the signal to some other time–frequency representation better suited for detecting and removing redundancies The Neural Networks are good alternative for solving many complex problems. In this paper multi-layer neural network has been employed to achieve image compression A novel algorithm for neural network with different techniques is proposed in this paper. Experimental results show that this algorithm outperforms than other coders such as SPIHT EZW STW exits in the literature in terms of simplicity and coding efficiency by successive partition the wavelet coefficients in the space frequency domain and send them using adaptive decimal to binary conversion. Spatial-orientation tree wavelet (STW) and Embedded Zero tree Wavelet (EZW) has been proposed as a method for effective and efficient embedded image coding. This method holds good for au important features like PSNR, MSE, BPP, CR, image size. SPHIT has been successfully used in many applications. (The techniques are compressed by using the performance parameter