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Recognizing and understanding human activities in real-time videos is a challenging task due to the complex nature of video data and the need for efficient and accurate analysis. This research pioneers a breakthrough in video activity recognition by introducing a robust framework leveraging the power of a stacked Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Unit (GRU) architecture, harmonized within a fusion-based deep model. The stacked Bi-LSTM-GRU model capitalizes on its dual recurrent architecture, capturing nuanced temporal dependencies within video sequences. The fusion-based deep architecture synergizes spatial and temporal features, enabling the model to discern intricate patterns in human activities. To further enhance the discriminative power of the model, we introduce a fusion module in the proposed deep architecture. The fusion module integrates multi-modal features extracted from different levels of the network hierarchy, allowing for a more comprehensive representation of video activities. We demonstrate the efficacy of our approach through rigorous experimentation on UCF50, UCF101, and HMDB51 datasets. In experiments on the UCF50 dataset, our model achieves an accuracy of 97.01% and 95.86% on training and validation sets respectively, showcasing its proficiency in discerning activities across a diverse range of scenarios. The evaluation extends to the UCF101 dataset, where the proposed approach achieves a competitive accuracy of 97.62% and 96.93% on training and validation sets, surpassing previous benchmarks by a margin of approx 1%. Further-more, on the challenging HMDB51 dataset, the model demonstrates a robust accuracy of 89.71%and 88.88% on training and validation sets, solidifying its efficacy in intricate action recognition tasks. [ABSTRACT FROM AUTHOR]

Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost.

Encoder-decoder-based video captioning gives a holistic description as per the training data. These captions are missing the object's motionspecific features. Object motion knowledge in video allows object-oriented video captions. Similarity action knowledge-based models allow action-based features. Furthermore, the traditional encoder-decoder method uses frame-level scene features. Advanced existing methods extract spatial and temporal features for extracting context vectors. The unavailability of methods to extract action knowledge with the object's motion features limits the models to produce action-object-oriented captions. Further presence of multiple objects' motion gives disoriented captions in state-of-the-art methods. We propose a method that is a partial grid-based method for action-object-oriented features. This facilitates comprehension of an object's motion and its interactions with other objects, as well as movement within the scene. The proposed method takes these features and constructs a graph neural network, which is then used with graph-based filters. Object activity and interaction based re-annotated 75 videos from MSVD datasets which were used for training, validation, and evaluation. The proposed model demonstrates object-action-based video captioning with object-action and object-background interaction. The BLEU and METEORbased evaluation results demonstrate the workability of graph neural network-based methods and the superiority of the process. [ABSTRACT FROM AUTHOR]

Many real-world optimization problems usually require a large number of conflicting objectives to be optimized simultaneously to obtain solution. It has been observed that these kinds of many-objective optimization problems (MaOPs) often pose several performance challenges to the traditional multi-objective optimization algorithms. To address the performance issue caused by the different types of MaOPs, recently, a variety of many-objective particle swarm optimization (MaOPSO) has been proposed. However, external archive maintenance and selection of leaders for designing the MaOPSO to real-world MaOPs are still challenging issues. This work presents a MaOPSO based on entropy-driven global best selection strategy (called EMPSO) to solve the many-objective software package restructuring (MaOSPR) problem. EMPSO makes use of the entropy and quality indicator for the selection of global best particle. To evaluate the performance of the proposed approach, we applied it over the five MaOSPR problems. We compared it with eight variants of MaOPSO, which are based on eight different global best selection strategies. The results indicate that the proposed EMPSO is competitive with respect to the existing global best selection strategies based on variants of MaOPSO approaches. [ABSTRACT FROM AUTHOR]

Poor design choices at the early stages of software development and unprincipled maintenance practices usually deteriorate software modularity and subsequently increase system complexity. In object-oriented software, improper distribution of classes among packages is a key factor, responsible for modularity degradation. Many optimization techniques to improve the software modularity have been proposed in the literature. The focus of these optimization techniques is to produce modularization solutions by optimizing different design quality criteria. Such modularization solutions are good from the different aspect of quality; however, they require huge modifications in the existing modular structure to realize the suggested solution. Thus these techniques are costly and time consuming if applied at early stages of software maintenance. This paper proposes a search-based optimization technique to improve the modularity of the software system with minimum possible variation between the existing and produced modularization solution. To this contribution, a penalized fitness function, namely, penalized modularization quality, is designed in terms of modularization quality and the Move or Join Effectiveness Measure metric. Furthermore, this fitness function is used in both single-objective genetic algorithm (SGA) and multi-objective genetic algorithm (MGA) to generate the modularization. The effectiveness of the proposed remodularization approach is evaluated over five open-source and three random generated software systems. The experimentation results show that the proposed approach is able to generate modularization solutions with improved quality along with lesser perturbation compared to their non-penalty counterpart and at the same time it performs better with the MGA compared to the SGA. The proposed approach can be very useful, especially when total remodularization is not feasible/desirable due to lack of time or high cost. [ABSTRACT FROM AUTHOR]

Recently, many computational intelligence algorithms have been proposed to address software remodularization problem. Unfortunately, it has been observed that the performance of optimizers degrades with the optimization problem containing more than three objectives. In this paper, we propose a many‐objective discrete harmony search (MaDHS) to address the software remodularization problem having more than three objectives. The basic idea of MaDHS is that it uses the quality indicator Iϵ + and external archive to rank and store the nondominated solutions. Along with MaDHS, five remodularization objectives, ie, low coupling, high cohesion, low modification degree, quality of class distribution, and low package instability have also been adapted to improve the package structure of existing object‐oriented software systems. To improve the accuracy of modularization solution, the coupling and cohesion objectives are formulated in terms of various dimensions of direct coupling relationships. To test the supremacy of the proposed approach, it is evaluated over eight real‐world object‐oriented software systems. Simulation results show that the proposed approach outperforms the other existing approaches in terms of couplings, cohesion, modularization quality, modularization merit factor, rate per refactoring of achieved improvement, and external developers view. [ABSTRACT FROM AUTHOR]