Recent Advances in Computer Science and Communications - Volume 17, Issue 5, 2024

Introduction: Any software is created to help automate manual processes most of the time. It is expected from the developed software that it should perform the tasks it is supposed to do. Methods: More formally, it should work in a deterministic manner. Further, it should be capable of knowing if any provided input is not in the required format. Correctness of the software is inherent virtue that it should possess. Any remaining bug during the development phase would hamper the application's correctness and impact the software's quality assurance. Software defect prediction is the research area that helps the developer to know bug-prone areas of the developed software. Results: Datasets are used using data mining, machine learning, and deep learning techniques to achieve study. A systematic literature survey is presented for the selected studies of software defect prediction. Conclusion: Using a grading mechanism, we calculated each study's grade based on its compliance with the research validation question. After every level, we have selected 54 studies to include in this study.

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

Background: Emotion is a strong feeling such as love, anger, fear, etc. Emotion can be recognized in two ways, i.e., External expression and Biomedical data-based. Nowadays, various research is occurring on emotion classification with biomedical data. Aim: One of the most current studies in the medical sector, gaming-based applications, education sector, and many other domains is EEG-based emotion identification. The existing research on emotion recognition was published using models like KNN, RF Ensemble, SVM, CNN, and LSTM on biomedical EEG data. In general, only a few works have been published on ensemble or concatenation models for emotion recognition on EEG data and achieved better results than individual ones or a few machine learning approaches. Various papers have observed that CNN works better than other approaches for extracting features from the dataset, and LSTM works better on the sequence data. Methods: Our research is based on emotion recognition using EEG data, a mixed-model deep learning methodology, and its comparison with a machine learning mixed-model methodology. In this study, we introduced a mixed model using CNN and LSTM that classifies emotions in valence and arousal on the DEAP dataset with 14 channels across 32 people. Result and Discussion: We then compared it to SVM, KNN, and RF Ensemble, and concatenated these models with it. First preprocessed the raw data, then checked emotion classification using SVM, KNN, RF Ensemble, CNN, and LSTM individually. After that with the mixed model of CNN-LSTM, and SVM-KNN-RF Ensemble results are compared. Proposed model results have better accuracy as 80.70% in valence than individual ones with CNN, LSTM, SVM, KNN, RF Ensemble and concatenated models of SVM, KNN and RF Ensemble. Conclusion: Overall, this paper concludes a powerful technique for processing a range of EEG data is the combination of CNNs and LSTMs. Ensemble approach results show better performance in the case of valence at 80.70% and 78.24% for arousal compared to previous research.

Introduction: Image caption generation has long been a fundamental challenge in the area of computer vision (CV) and natural language processing (NLP). In this research, we present an innovative approach that harnesses the power of Deep Convolutional Generative Adversarial Networks (DCGAN) and adversarial training to revolutionize the generation of natural and contextually relevant image captions. Method: Our method significantly improves the fluency, coherence, and contextual relevance of generated captions and showcases the effectiveness of RL reward-based fine-tuning. Through a comprehensive evaluation of COCO datasets, our model demonstrates superior performance over baseline and state-of-the-art methods. On the COCO dataset, our model outperforms current state-of-the-art (SOTA) models across all metrics, achieving BLEU-4 (0.327), METEOR (0.249), Rough (0.525) and CIDEr (1.155) scores. Result: The integration of DCGAN and adversarial training opens new possibilities in image captioning, with applications spanning from automated content generation to enhanced accessibility solutions. Conclusion: This research paves the way for more intelligent and context-aware image understanding systems, promising exciting future exploration and innovation prospects.

Objective: The purpose of this paper was to use Machine Learning (ML) techniques to extract facial features from images. Accurate face detection and recognition has long been a problem in computer vision. According to a recent study, Local Binary Pattern (LBP) is a superior facial descriptor for face recognition. A person's face may make their identity, feelings, and ideas more obvious. In the modern world, everyone wants to feel secure from unauthorized authentication. Face detection and recognition help increase security; however, the most difficult challenge is to accurately recognise faces without creating any false identities. Methods: The proposed method uses a Local Binary Pattern Histogram (LBPH) and Convolution Neural Network (CNN) to preprocess face images with equalized histograms. Results: LBPH in the proposed technique is used to extract and join the histogram values into a single vector. The technique has been found to result in a reduction in training loss and an increase in validation accuracy of over 96.5%. Prior algorithms have been reported with lower accuracy when compared to LBPH using CNN. Conclusion: This study demonstrates how studying characteristics produces more precise results, as the number of epochs increases. By comparing facial similarities, the vector has generated the best result.

Aim: To deal with the drawbacks of the traditional medical image fusion methods, such as the low preservation ability of the details, the loss of edge information, and the image distortion, as well as the huge need for the training data for deep learning, a new multi-modal medical image fusion method based on the VGG19 model and the non-subsampled contourlet transform (NSCT) is proposed, whose overall objective is to simultaneously make the full use of the advantages of the NSCT and the VGG19 model. Methodology: Firstly, the source images are decomposed into the high-pass and low-pass subbands by NSCT, respectively. Then, the weighted average fusion rule is implemented to produce the fused low-pass sub-band coefficients, while an extractor based on the pre-trained VGG19 model is constructed to obtain the fused high-pass subband coefficients. Result and Discussion: Finally, the fusion results are reconstructed by the inversion transform of the NSCT on the fused coefficients. To prove the effectiveness and the accuracy, experiments on three types of medical datasets are implemented. Conclusion: By comparing seven famous fusion methods, both of the subjective and objective evaluations demonstrate that the proposed method can effectively avoid the loss of detailed feature information, capture more medical information from the source images, and integrate them into the fused images.

Introduction: Cracks are one of the major problems in modern concrete buildings, especially in locations that are difficult to map manually, such as bridges and high-rise buildings. Accurate analysis of unmanned aerial vehicle (UAV) images has become the key to determining whether a building needs maintenance. Methods: Traditional image processing methods are easily interfered by high-frequency background. Neural network methods need fine datasets, which increase labor costs. Therefore, this paper proposes a segmentation algorithm based on UNet3+ network. After obtaining the UAV image, the rough location of the crack can be obtained by only rough labeling. And then, the sample balance can be carried out by clipping the target area. The UNet3+ network is used to train the processed datasets and extract the region of interest to ignore the non-target texture. Finally, the region of interest is further segmented by color clustering and edge detection methods. Results: The proposed method can detect the cracks accurately. In all test images, the relative errors are less than 13%. Especially in test images whose width is less than 0.2mm, the maximum absolute error is only 0.0237 mm, which is completely acceptable in actual production. The proposed method has higher practicability in the detection of concrete crack images taken by UAV. The results show that the proposed method outperforms the cutting-edge method published in the journal "Sensor", when the background is complex. Conclusion: The proposed method can segment and detect cracks effectively, which can remove the high-frequency interference region from the images.