Recently, cybersecurity threats have increased dramatically and the techniques used by the attackers continue to evolve and become ingenious during the attack. Moreover, the complexity and frequent occurrence of imbalanced class distributions in most datasets indicate the need for extra research efforts. The objective of this paper is to utilize various techniques for handling imbalanced datasets to build an effective intrusion detection system from the up-to-date CIDDS-001 dataset. The effectiveness of sampling methods on CIDDS-001 is carefully studied and experimentally evaluated through Deep Neural Networks (DNN), Random Forest, Voting, Variational Auto-Encoder (VAE), and Stacking machine learning classifiers. The proposed system was able to detect attacks with up to 99.99% accuracy when handling the imbalanced class distribution with less number of samples, making it more convenient in real-time data fusion problems that target data classification.

Speaker age and gender classification is one of the most challenging problems in speech signal processing. Recently with developing technologies, identifying speaker age and gender information has become a necessity for speaker verification and identification systems such as identifying suspects in criminal cases, improving human–machine interaction, and adapting music for awaiting people queue. Despite the intensive studies that have been conducted to extract descriptive and distinctive features, the classification accuracies are still not satisfactory. In this work, a model for generating bottleneck features from a deep neural network and a Gaussian Mixture Model–Universal Background Model (GMM–UBM) classifier are proposed for speaker age and gender classification problem. Deep neural network with a bottleneck layer is trained in an unsupervised manner for calculating the initial weights between layers. Then, it is trained and tuned in a supervised manner to generate transformed mel-frequency cepstral coefficients (T-MFCCs). The GMM–UBM is used to build a GMM model for each class, and the models are used to classify speaker age and gender. Age-annotated database of German telephone speech (aGender) is used to evaluate the proposed classification system. The newly generated T-MFCCs have shown potential to achieve significant classification improvements in speaker age and gender classification by using the GMM–UBM classifier. The proposed classification system achieved an overall accuracy of 57.63%. The highest accuracy is calculated as 72.97% for adult female speakers.

Background: Knee Osteoarthritis (KOA) is a prevalent and debilitating joint disorder that significantly impacts quality of life, particularly in aging populations. Accurate and consistent classification of KOA severity, typically using the Kellgren-Lawrence (KL) grading system, is crucial for effective diagnosis, treatment planning, and monitoring disease progression. However, traditional KL grading is known for its inherent subjectivity and inter-rater variability, which underscores the pressing need for objective, automated, and reliable classification methods. Methods: This study investigates the performance of an EfficientNetB5 deep learning model, enhanced with transfer learning from the ImageNet dataset, for the task of classifying KOA severity into five distinct KL grades (0–4). We utilized a publicly available Kaggle dataset comprising 9786 knee X-ray images. A key aspect of our methodology was a comprehensive data-centric preprocessing pipeline, which involved an initial phase of outlier removal to reduce noise, followed by systematic label correction using the Cleanlab framework to identify and rectify potential inconsistencies within the original dataset labels. Results: The final EfficientNetB5 model, trained on the preprocessed and Cleanlab-remediated data, achieved an overall accuracy of 82.07% on the test set. This performance represents a significant improvement over previously reported benchmarks for five-class KOA classification on this dataset, such as ResNet-101 which achieved 69% accuracy. The substantial enhancement in model performance is primarily attributed to Cleanlab’s robust ability to detect and correct mislabeled instances, thereby improving the overall quality and reliability of the training data and enabling the model to better learn and capture complex radiographic patterns associated with KOA. Class-wise performance analysis indicated strong differentiation between healthy (KL Grade 0) and severe (KL Grade 4) cases. However, the “Doubtful” (KL Grade 1) class presented ongoing challenges, exhibiting lower recall and precision compared to other grades. When evaluated against other architectures like MobileNetV3 and Xception for multi-class tasks, our EfficientNetB5 demonstrated highly competitive results. Conclusions: The integration of an EfficientNetB5 model with a rigorous data-centric preprocessing approach, particularly Cleanlab-based label correction and outlier removal, provides a robust and significantly more accurate method for five-class KOA severity classification. While limitations in handling inherently ambiguous cases (such as KL Grade 1) and the small sample size for severe KOA warrant further investigation, this study demonstrates a promising pathway to enhance diagnostic precision. The developed pipeline shows considerable potential for future clinical applications, aiding in more objective and reliable KOA assessment.

Various applications depend on speakers' age and gender to satisfy their customer needs. The age and gender information of a speaker are concealed in the speech signal, which is vary in time duration and is exposed to background noise. These effects make the speakers' age and gender prediction one of the most challenging problems in the field of speech processing. Recently, remarkable developments have been achieved in the neural network field. DNN is considered as one of the state-of-art classifiers. Due to the success of DNN in many speech applications, we propose combined posteriors of the softmax and last hidden layer for class model representation based on a trained DNN. DNN is used in this work to extract the softmax and last hidden layer posteriors from the concatenated frames for each utterance. The outputs of the combined posteriors for each frame in the training database are accumulated to form a new model for each class. Our experimental results showed that the proposed class model is capable of representing speakers age and gender for new utterances. The performance of the proposed work is evaluated by using GMM-UBM model on a publicly available Age-Annotated Database of a German Telephone Speech Database (aGender). The proposed work outperforms the baseline system by approximately 6% in terms of overall accuracy.