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Oral cancer is a deadly form of cancerous tumor that is widely spread in low and middle-income countries. An early and affordable oral cancer diagnosis might be achieved by automating the detection of precancerous and malignant lesions in the mouth. There are many research attempts to develop a robust machine-learning model that can detect oral cancer from images. However, these are still lacking high precision in oral cancer detection. Therefore, this work aims to propose a new approach capable of detecting oral cancer in medical images with higher accuracy. In this work, a novel and robust oral cancer detection based on a convolutional neural network (CNN) and optimized deep belief network (DBN). The design parameters of CNN and DBN are optimized using a new optimization algorithm, which is developed as a hybrid of Particle Swarm Optimization (PSO) and Al-Biruni Earth Radius (BER) Optimization algorithms and is denoted by (PSOBER). Using a standard biomedical images dataset available on the Kaggle repository, the proposed approach shows promising results outperforming various competing approaches with an accuracy of 97.35%. In addition, a set of statistical tests, such as One-way analysis-of-variance (ANOVA) and Wilcoxon signed-rank tests, are conducted to prove the significance and stability of the proposed approach. The proposed methodology is solid and efficient, and specialists can adopt it. However, additional research on a larger scale dataset is required to confirm the findings and highlight other oral features that can be utilized for cancer detection.

During the pandemic of the coronavirus disease (COVID-19), statistics showed that the number of affected cases differed from one country to another and also from one city to another. Therefore, in this paper, we provide an enhanced model for predicting COVID-19 samples in different regions of Saudi Arabia (high-altitude and sea-level areas). The model is developed using several stages and was successfully trained and tested using two datasets that were collected from Taif city (high-altitude area) and Jeddah city (sea-level area) in Saudi Arabia. Binary particle swarm optimization (BPSO) is used in this study for making feature selections using three different machine learning models, i.e., the random forest model, gradient boosting model, and naive Bayes model. A number of predicting evaluation metrics including accuracy, training score, testing score, F-measure, recall, precision, and receiver operating characteristic (ROC) curve were calculated to verify the performance of the three machine learning models on these datasets. The experimental results demonstrated that the gradient boosting model gives better results than the random forest and naive Bayes models with an accuracy of 94.6% using the Taif city dataset. For the dataset of Jeddah city, the results demonstrated that the random forest model outperforms the gradient boosting and naive Bayes models with an accuracy of 95.5%. The dataset of Jeddah city achieved better results than the dataset of Taif city in Saudi Arabia using the enhanced model for the term of accuracy. [ABSTRACT FROM AUTHOR]

Nowadays, deepfake is wreaking havoc on society. Deepfake content is created with the help of artificial intelligence and machine learning to replace one person’s likeness with another person in pictures or recorded videos. Although visual media manipulations are not new, the introduction of deepfakes has marked a breakthrough in creating fake media and information. These manipulated pictures and videos will undoubtedly have an enormous societal impact. Deepfake uses the latest technology like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) to construct automated methods for creating fake content that is becoming increasingly difficult to detect with the human eye. Therefore, automated solutions employed by DL can be an efficient approach for detecting deepfake. Though the “black-box” nature of the DL system allows for robust predictions, they cannot be completely trustworthy. Explainability is the first step toward achieving transparency, but the existing incapacity of DL to explain its own decisions to human users limits the efficacy of these systems. Though Explainable Artificial Intelligence (XAI) can solve this problem by interpreting the predictions of these systems. This work proposes to provide a comprehensive study of deepfake detection using the DL method and analyze the result of the most effective algorithm with Local Interpretable Model-Agnostic Explanations (LIME) to assure its validity and reliability. This study identifies real and deepfake images using different Convolutional Neural Network (CNN) models to get the best accuracy. It also explains which part of the image caused the model to make a specific classification using the LIME algorithm. To apply the CNN model, the dataset is taken from Kaggle, which includes 70 k real images from the Flickr dataset collected by Nvidia and 70 k fake faces generated by StyleGAN of 256 px in size. For experimental results, Jupyter notebook, TensorFlow, NumPy, and Pandas were used as software, InceptionResnetV2, DenseNet201, InceptionV3, and ResNet152V2 were used as CNN models. All these models’ performances were good enough, such as InceptionV3 gained 99.68% accuracy, ResNet152V2 got an accuracy of 99.19%, and DenseNet201 performed with 99.81% accuracy. However, InceptionResNetV2 achieved the highest accuracy of 99.87%, which was verified later with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and dependability demonstrate its preference for detecting deepfake images effectively. [ABSTRACT FROM AUTHOR]

COVID'19 has caused the entire universe to be in existential health crisis by spreading globally in the year 2020. The lungs infection is detected in Computed Tomography (CT) images which provide the best way to increase the existing healthcare schemes in preventing the deadly virus. Nevertheless, separating the infected areas in CT images faces various issues such as lowintensity difference among normal and infectious tissue and high changes in the characteristics of the infection. To resolve these issues, a new inf-Net (Lung Infection Segmentation Deep Network) is designed for detecting the affected areas from the CT images automatically. For the worst segmentation results, the Edge-Attention Representation (EAR) is optimized using Adaptive Donkey and Smuggler Optimization (ADSO). The edges which are identified by the ADSO approach is utilized for calculating dissimilarities. An IFCM (Intuitionistic Fuzzy C-Means) clustering approach is applied for computing the similarity of the EA component among the generated edge maps and Ground-Truth (GT) edge maps. Also, a Semi-Supervised Segmentation (SSS) structure is designed using the Randomly Selected Propagation (RP) technique and Inf-Net, which needs only less number of images and unlabelled data. Semi-Supervised Multi-Class Segmentation (SSMCS) is designed using a Bi-LSTM (Bi-Directional Long-Short-Term-memory), acquires all the advantages of the disease segmentation done using Semi Inf-Net and enhances the execution of multi-class disease labelling. The newly designed SSMCS approach is compared with existing U-Net++, MCS, and Semi-Inf-Net. factors such as MAE (Mean Absolute Error), Structure measure, Specificity (Spec), Dice Similarity coefficient, Sensitivity (Sen), and Enhance-Alignment Measure are considered for evaluation purpose. [ABSTRACT FROM AUTHOR]

Technological advances integrating emotional maturity with established IoT systems are being examined with the emergence of the fourth industrial revolution. In this article, researchers propose an emotion-based music recommendation and classification framework (EMRCF) categorizing songs with high precision following individuals' interpersonal team with memory and emotional songs. In specific, when adding new tunes to an IoT app fortune, methods must be developed that immediately categorize the characters based on people's emotions. That's one of the essential questions for project management. The empathic framework is used to research to identify emotional information. Musical characteristics can be derived from discussions in a micro-enterprise with the task force. Correlation analysis and supporting neural network is used to perform dynamic designation. The innovative prediction accuracy proposed recognizes most of the emotional responses triggered by music audience members and effectively categorizes songs. Furthermore, a comparison study is made with proposed algorithms such as decision trees, deep cognitive system and neighbor-closest, and relevance vector machines. The EMRCF reaches the prediction accuracy of 96.12% and the precision rate of 96.69%, which is not achieved by existing approaches. [ABSTRACT FROM AUTHOR]