Computational Intelligence最新文献

Requirements engineering is one of the most crucial parts of the lifecycle of software engineering. Many programs fail annually due to deficiencies in requirements engineering. Requirements engineering documents are written in natural languages, which can lead to ambiguities. The presence of ambiguity in natural language causes misunderstandings. Accurate and timely identification of these requirements is vital for the development process. However, manual classification is time-consuming and necessitates automation. Today, with the rapid advancement of technology, machine learning and deep learning are being used to detect these ambiguities in requirement specification documents. The BERT word embedding technique and the Bi-LSTM algorithm were used in this research. We have used meta-heuristic algorithms to choose the best value of hyperparameters of our deep learning algorithm. The publicly available Fault-Prone SRS dataset was utilized to train the models. This dataset was also used to evaluate the performance of the proposed algorithm in terms of F1-score, accuracy, and other statistical metrics. The BERT-BiLSTM model outperformed other models in classifying and detecting ambiguities in requirement specification documents, achieving an F1-score and higher than 81% accuracy.

In recent years, much literature has determined the classification of music in order to classify lyrics that are inappropriate for young people. Many studies utilize different types of classifiers for analyzing the metadata and music lyrics that have failed to achieve the lyrics classification. In the music information retrieval (MIR) community, lyrics-based classification for music genre is still insufficiently examined. The earlier techniques have considered the lyrics only in the English language. Hence, it is important to replace the standard text classification approach with other advanced methods to classify the different language lyrics effectively. To solve these issues, an efficient deep learning-based lyrics text classification is developed in this work to classify the lyrics text. Initially, the required text data are collected from the benchmark dataset. Subsequently, the collected text data are fed into the preprocessing performance to enhance the text data quality. After that, the preprocessed text data are fed into the features extraction process, where the features are retrieved using GloVe embedding, term frequency inverse document frequency (TF-IDF), and bidirectional encoder representations from transformers (BERT). The extracted features are passed to the multilevel cross attention-based adaptive bidirectional long short-term memory (MCABi-LSTM) for recognizing the lyrics text. The text classification framework of the MCABi-LSTM is enhanced through fine-tuning the attributes using the modified ring toss game optimization (MRTGO). The classified outcome from the MCABi-LSTM is used for examining the musical patterns. Finally, the experimental validation is conducted on the developed lyric text classification model to prove its effectiveness.

Early detection of COVID-19 is crucial for effective clinical treatment, monitoring patients, and assessing the effectiveness of treatment. Furthermore, existing approaches struggle to analyze the severity of COVID-19 in a short period of time. Thus, the proposed study intends to use knowledge distillation-based multi-task learning to assess the severity of COVID-19. Pre-processing is first performed to remove noises and improve the quality of input images using Extended Median Filtering (Ex-MF) from the input COVID-CT dataset. These pre-processed images are used as input for the proposed knowledge distillation-based Multi-task Learning Framework. The proposed framework includes both teacher and student networks. In the teacher network, the Residual U-Net-based Position Attentional Convolutional Cascaded Capsule Network (RU_PAC³N) is used. To reduce feature dimensionality issues, a position attention mechanism assigns weight values to each feature to achieve the essential feature subset. Finally, a cascaded capsule network is used to classify input samples. The Residual U-Net is responsible for segmenting the affected regions from the input CT images. The teacher network is referred to as the pre-training network. In this study, the student network uses a unique Optimized Recurrent Twin Long Short Term Memory (OR-TLSTM) model to classify the severity of COVID-19 disease. Furthermore, the proposed network's efficiency is improved by lowering the loss function by optimizing the weight parameters using the Binary Kookaburra Optimization (BKO) approach. The proposed detection model is optimized using an adaptive chameleon optimization (ACO) technique. The proposed method is executed using the Python tool. The proposed method achieved a better performance rate and lower error rate with 98.6% accuracy in COVID-19 detection.

Age-related macular degeneration (AMD) is a progressive retinal disease that can lead to vision loss if not diagnosed early. Accurate and timely detection is essential for effective treatment. This study presents a comprehensive deep learning-based framework for the automated detection of AMD using optical coherence tomography (OCT) images. The proposed method integrates advanced preprocessing, segmentation, feature extraction, and classification techniques. An improved Wiener filter is applied to enhance image quality by reducing noise while preserving edge details. A U-Net architecture is then used for accurate segmentation of retinal regions. Subsequently, diverse features such as histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), scale-invariant feature transform (SIFT), VGG16, statistical features, and improved median ternary pattern (MTP) are extracted from segmented images. Then, an enhanced principal component analysis (PCA) with feature scaling is used for the optimal selection of features to improve classification robustness. Finally, a hybrid model combining deep convolutional neural networks (DCNNs) and AlexNet is proposed for AMD classification. Moreover, the proposed model is evaluated against traditional methods and achieves superior performance, with an accuracy of 0.958 and F-measure of 0.964. Experimental results confirm the model's effectiveness and reliability, highlighting its potential for clinical application in early AMD detection and diagnosis.

Multimodal data can more vividly and strangely represent consumers' feelings and sentiments than single-modal content. Users are moving beyond traditional text-based content on social media and are now more commonly incorporating images and text to convey their experiences and articulate their opinions. Traditional text-based techniques have given way to multimodal sentiment analysis, which poses a more complex challenge. This work attempts to address the challenge of sentiment analysis in image-text posts by presenting a refined recognition method that efficiently leverages textual and visual content information. Initially, a single dataset can be created by combining the MVSA-Single and MVSA-Multiple datasets. It is gathered to improve data quality by pre-processing the data with a Gaussian bilateral filter (GBF) for picture features and lemmatization, stemming, and stop word removal for text characteristics. The normalized term-inverse document frequency (NorTID) model is used to extract text features. The Convolutional VGG-16 (ConV-16) model extracts image characteristics. The Dense Stacked Long Short-Term Memory Network (DStaL) model is used to independently analyze the collected multimodal information. The obtained features are fused together, and the sentiments are effectively classified using the Multilevel Fusion Classifier (MFuse) model. The study illustrates the enhanced efficacy of the suggested strategy by comparing the results to those of standard approaches using several performance measures. For the MVSA-Single and MVSA-Multiple datasets, the findings show a 97.3% accuracy, 94.8% precision, 94.8% recall, 97.3% specificity, 92.1% Matthews Correlation Coefficient (MCC), 36.3% Root Mean Squared Error (RMSE), and 0.07% Mean Absolute Error (MAE).

For solving the problem of dynamic task topology change in multi-manipulator cooperative system, a distributed control scheme based on topology transformation is proposed. In this scheme, a topological transformation mechanism is introduced so that the multi-manipulator cooperative system dynamically adapts to the multi-stage tasks with topological changes. To facilitate the solution of the problem, the distributed scheme is converted into an optimization problem. In addition, a gradient neural network solver with an activation function is designed, which greatly improves the convergence speed of the solver. Theoretical analysis is given, which confirms the convergence of the solver. Furthermore, the effectiveness of the proposed scheme is demonstrated.

With the rapid expansion of intelligent medical equipment and their interconnectedness through the Internet of Things (IoT), addressing safety issues in the communicating system has become increasingly critical. A learning mechanism is proposed for an intelligent healthcare-based communication system that uses blockchain for secure network communication and incorporates a data evaluation layer based on cloud which actively segregates and ranks transactions into three main categories: Good, Moderate, and Malware. Fog servers are utilized to route the communicating nodes via Rician and Rayleigh channels. The learning mechanism employs a deep neural network to instruct and classify categories, thereby improving the blockchain layer's decision-making process. This paper introduces several significant contributions, such as the development of a secure blockchain framework for user authentication and a protected digital ledger for communication. Additionally, it incorporates a cloud-driven data analysis layer combined with a neural network to improve training accuracy and category classification. The developed algorithm surpassed the existing works in terms of quality of service (QoS) parameters with low latency, bit error rate (BER), higher signal to inference plus noise ratio (SINR), packet delivery ratio (PDR), true detection rate (TDR), false detection rate (FDR), and throughput. Also, a thorough comparison of consensus mechanisms like practical Byzantine fault tolerance (pBFT), proof of work (PoW), Raft, and Paxos is done to ensure which consensus helps optimize the proposed system in terms of security and fault tolerance with low latency and energy-efficient operations. It also establishes a secure and efficient communication network for smart healthcare, aimed at enhancing the overall quality of life for individuals.

Current diagnosis of autism spectrum disorder (ASD) and developmental language/reading disorder (DLD/DD) relies predominantly on subjective behavioral assessments, this underscoring the urgent need for objective biomarkers to enable early intervention. This study proposes a Multi-Tier Transformer (MTT) model for early identification of ASD and DLD/DD using resting-state EEG baseline power values. To address severe class imbalance, we augmented the dataset using the SMOTE method. The MTT architecture integrates a Feature-Embedding layer, a Feature-Attention mechanism that dynamically weights multi-spectral inputs, and a dual-attention encoding block comprising both self-attention and cross-attention to enhance contextual representation learning from limited samples. Transfer learning was further employed to improve robustness by pre-training on augmented data and fine-tuning on original samples. Evaluated on clinical infant EEG data, the proposed MTT achieved an accuracy of 0.91 (95% CI: 0.89–0.93), recall of 0.89, and AUC of 0.97, significantly outperforming the state-of-the-art FT-transformer (p = 0.00091). The results indicate that MTT provides a robust and interpretable deep learning tool for auxiliary diagnosis of neurodevelopmental disorders in infancy.