Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient’s chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

Speech enhancement (SE) aims to improve the intelligibility and perceptual quality of speech contaminated by noise signals through spectral or temporal changes. Deep learning models achieve speech enhancement and estimate the magnitude spectrum. This paper proposes a novel and computationally efficient deep learning model to enhance noisy speech. The model pre-processes the noisy speech magnitude by redistributing energy from high-energy voiced segments to low-energy unvoiced segments using an adaptive power law transformation while maintaining the total energy of the speech signals constant. A U-shaped fuzzy long short-term memory (UFLSTM) estimates the magnitude of a time-frequency (T-F) mask by using the pre-processed data. Residual connections to the similar-shaped layers are added to avoid gradient decay. Attention process is adopted by modifying the forget gate of UFLSTM. To make a causal speech enhancement system, the processing does not include any future audio frames. We compare the proposed speech enhancement to other deep learning models in different noisy environments with signal-to-noise ratios of 0 dB, 5 dB, and 10 dB. The experiments show that the proposed SE system outscores the competing deep learning models and considerably improves speech intelligibility and quality. In terms of STOI and PESQ, the LibriSpeech database improves results by (0.211) 21.1% and (0.95) 36.39%, respectively, over noisy speech in seen noisy conditions, and by (0.199) 19.9% and (0.94) 35.69% over noisy speech in unseen noisy conditions. Further, the cross-corpus analysis shows that proposed SE system performs better when trained with the DNS dataset as compared to the LibriSpeech, VoiceBank, and TIMIT datasets.

Emotion recognition from facial expression is an exciting field of research with applications like safety, security, personal information and marketing. Researchers want to develop techniques that can interpret, and extract facial expressions so that computers can make better emotional predictions. In recent years, different types of architectures have been used in machine learning to improve facial expression performance. In this paper, machine learning techniques are used to study facial emotion recognition. We present various machine learning techniques to identify the best methodology for the test at hand. Support Vector Machine (SVM), Convolution Neural Network (CNN) and Artificial Neural Network (ANN) along with face detection and preprocessing techniques for the expressions in Japanese Female Facial Expression (JAFFE) dataset and the Extended Cohn-Kanade (CK+) dataset are exploited to achieve best accuracy of 98.47% on CK+ dataset using CNN, and 89.18% accuracy for JAFFE dataset using ANN.

Economic Dispatch (ED) is a fundamental optimization problem in power system operation, often characterized by nonlinearity, non-convexity, and practical constraints such as valve-point effects (VPE), ramp-rate limits (RRL), and transmission losses (TL). This paper presents a systematic evaluation of the Hunger Games Search (HGS) algorithm for constrained economic dispatch (ED) problems under diverse operating conditions. A comprehensive and systematically controlled evaluation framework is developed, considering benchmark systems ranging from small-scale (6 units) to large-scale (up to 160 generating units). All experiments are conducted over 30 independent runs, and performance is assessed using statistical indicators including best, mean, worst, and standard deviation of fuel cost. Constraint feasibility is enforced using the hybrid strategy, with power balance error maintained below 1e-6. To ensure fair comparison, computational effort is standardized using the number of function evaluations (NFE). In addition to conventional benchmarking, a detailed parameter tuning and sensitivity analysis is performed to examine the influence Wilcoxon signed-rank test and Friedman test, are employed to rigorously validate the significance of performance differences across algorithms. The results indicate that HGS achieves generally stable performance with variability in complex cases, across a wide range of ED formulations, particularly in large-scale and highly constrained scenarios. The combined use of sensitivity analysis and statistical validation provides deeper insight into the behavior of HGS beyond outcome-based comparison. These findings demonstrate that HGS is a competitive and effective metaheuristic, with performance dependent on problem complexity and constraint severity for solving complex ED problems, while also providing a reproducible and statistically grounded framework for performance evaluation.