Intelligent and sustainable waste classification model based on multi-objective beluga whale optimization and deep learning.

Resource recycling is considered necessary for sustainable development, especially in smart cities where increased urbanization and the variety of waste generated require the development of automated waste management models. The development of smart technology offers a possible alternative to traditional waste management techniques that are proving insufficient to reduce the harmful effects of trash on the environment. This paper proposes an intelligent waste classification model to enhance the classification of waste materials, focusing on the critical aspect of waste classification. The proposed model leverages the InceptionV3 deep learning architecture, augmented by multi-objective beluga whale optimization (MBWO) for hyperparameter optimization. In MBWO, sensitivity and specificity evaluation criteria are integrated linearly as the objective function to find the optimal values of the dropout period, learning rate, and batch size. A benchmark dataset, namely TrashNet is adopted to verify the proposed model's performance. By strategically integrating MBWO, the model achieves a considerable increase in accuracy and efficiency in identifying waste materials, contributing to more effective waste management strategies while encouraging sustainable waste management practices. The proposed intelligent waste classification model outperformed the state-of-the-art models with an accuracy of 97.75%, specificity of 99.55%, F1-score of 97.58%, and sensitivity of 98.88%.

Circulating miRNA's biomarkers for early detection of hepatocellular carcinoma in Egyptian patients based on machine learning algorithms.

Liver cancer, which ranks sixth globally and third in cancer-related deaths, is caused by chronic liver disorders and a variety of risk factors. Despite therapeutic improvements, the prognosis for Hepatocellular Carcinoma (HCC) remains poor, with a 5-year survival rate for advanced cases of less than 12%. Although there is a noticeable decrease in the frequency of cases, liver cancer remains a significant worldwide health concern, with estimates surpassing one million cases by 2025. The prevalence of HCC has increased in Egypt, and it includes several neoplasms with distinctive messenger RNA (mRNA) and microRNA (miRNA) expression profiles. In HCC patients, certain miRNAs, such as miRNA-483-5P and miRNA-21, are upregulated, whereas miRNA-155 is elevated in HCV-infected people, encouraging hepatocyte proliferation. Short noncoding RNAs called miRNAs in circulation have the potential as HCC diagnostic and prognostic markers. This paper proposed a model for examining circulating miRNAs as diagnostic and predictive markers for HCC in Egyptian patients and their clinical and pathological characteristics. The proposed HCC detection model consists of three main phases: data preprocessing phase, feature selection based on the proposed Binary African Vulture Optimization Algorithm (BAVO) phase, and finally, classification as well as cross-validation phase. The first phase namely the data preprocessing phase tackle the main problems associated with the adopted datasets. In the feature selection based on the proposed BAVO algorithm phase, a new binary version of the BAVO swarm-based algorithm is introduced to select the relevant markers for HCC. Finally, in the last phase, namely the classification and cross-validation phase, the support vector machine and k-folds cross-validation method are utilized. The proposed model is evaluated on three studies on Egyptians who had HCC. A comparison between the proposed model and traditional statistical studies is reported to demonstrate the superiority of using the machine learning model for evaluating circulating miRNAs as diagnostic markers of HCC. The specificity and sensitivity for differentiation of HCC cases in comparison with the statistical-based method for the first study were 98% against 88% and 99% versus 92%, respectively. The second study revealed the sensitivity and specificity were 97.78% against 90% and 98.89% versus 92.5%, respectively. The third study reported 83.2% against 88.8% and 95.80% versus 92.4%, respectively. Additionally, the results show that circulating miRNA-483-5p, 21, and 155 may be potential new prognostic and early diagnostic biomarkers for HCC.

A novel melanoma prediction model for imbalanced data using optimized SqueezeNet by bald eagle search optimization.

Skin lesion classification plays a crucial role in diagnosing various gene and related local medical cases in the field of dermoscopy. In this paper, a new model for the classification of skin lesions as either normal or melanoma is presented. The proposed melanoma prediction model was evaluated on a large publicly available dataset called ISIC 2020. The main challenge of this dataset is severe class imbalance. This paper proposes an approach to overcome this problem using a random over-sampling method followed by data augmentation. Moreover, a new hybrid version of a convolutional neural network architecture and bald eagle search (BES) optimization is proposed. The BES algorithm is used to find the optimal values of the hyperparameters of a SqueezeNet architecture. The proposed melanoma skin cancer prediction model obtained an overall accuracy of 98.37%, specificity of 96.47%, sensitivity of 100%, f-score of 98.40%, and area under the curve of 99%. The experimental results showed the robustness and efficiency of the proposed model compared with VGG19, GoogleNet, and ResNet50. Additionally, the results showed that the proposed model was very competitive compared with the state of the art.