Acne is a persistent skin disorder that typically affects children in the age range of 12 to 25. Both inflammatory and non-inflammatory skin diseases can coexist with various types of acne, such as papules, pustules, nodules, cysts, blackheads, and whiteheads. In recent times, the study of acne has been carried out conventionally, with a manual approach for determining the ROI. As a result, the patient's face will be physically counted and marked with the acne that was found in the ROI. This manual method could result in incorrect identification and diagnosis of acne. Moreover, it is still difficult to determine the type of acne related to another. The necessity for patients to visit a dermatologist is growing despite the difficulties in identifying acne manually. For a patient, waiting for the dermatologist to become available is challenging. Thus, an automated application for recognizing acne types is needed, as it may help these individuals. In order to address these problems, a dataset containing images of skin diseases is created. Lanczos resampling, which is frequently used to shift or enhance a digital signal's sampling rate by a fraction of the sampling interval, is employed in the preprocessing of the skin disease data. Subsequently, the pre-processed images are segmented using the Modified Link Net-B7 in order to eliminate noise and correctly categorize images of acne with the segmented skin images. After the model has been trained and validated, the Acne type prediction is forecast using the HR-Net algorithm. The performance metrics for this developed model are FPR, FOR, NPV, kappa, error, accuracy, precision, sensitivity, specificity, f1-score, kappa, training time, testing time, and execution time. Performance metrics values of 95.17%, 94.10%, 92.33%, 96.34%, 93.15%, 85.74%, 4.83%, 4%, 6%, 95%, 7.7%, 1492, 23 and 1515 have been reached for the proposed approach. Therefore, compared to the existing models, Acne type prediction using the different types of Acne disease images based on modified Link Net-B7 and HR-Net algorithm performs better.

Mismatch in speech data is one of the major reasons limiting the use of speaker recognition technology in real world applications. Extracting speaker specific features is a crucial issue in the presence of noise and distortions. Performance of speaker recognition system depends on the characteristics of extracted features. Devices used to acquire the speech as well as the surrounding conditions in which speech is collected, affects the extracted features and hence degrades the decision rates. In view of this, a feature level approach is used to analyze the effect of sensor and environment mismatch on speaker recognition performance. The goal here is to investigate the robustness of segmental features in speech data mismatch and degradation. A set of features derived from filter bank energies namely: Mel Frequency Cepstral Coefficients (MFCCs), Linear Frequency Cepstral Coefficients (LFCCs), Log Filter Bank Energies (LOGFBs) and Spectral Subband Centroids (SSCs) are used for evaluating the robustness in mismatch conditions. A novel feature extraction technique named as Normalized Dynamic Spectral Features (NDSF) is proposed to compensate the sensor and environment mismatch. A significant enhancement in recognition results is obtained with proposed feature extraction method.