Search

The Arab nation is seriously affected by computational propaganda. The detection of Arab computational propaganda has become a hot research topic in social networking platforms. Propaganda campaigns endeavor to influence people’s mindsets to improve a particular agenda. They automatically employ the anonymity of the Internet, the micro-profiling capability of social network platforms, and the ease of managing and creating coordinated networks to reach masses of social network users with persuasive messages, mainly aimed at topics each user is sensitive to, and ultimately affecting the outcomes on the targeted problem. Using computation techniques and methods, analysts and researchers can better understand the scope, scale, and impact of propaganda efforts in Arabic-speaking communities and develop strategies to counter them. In recent times, deep learning (DL) approaches targeted explicitly at analyzing, detecting, or countering propaganda within online platforms or Arabic-speaking communities. DL is a subset of machine learning (ML), which includes training artificial neural networks (ANNs) with multiple layers for learning data representation. This paper designs an improved fractal walrus optimization algorithm with DL-based Arab computation propaganda detection (IWOADL-ACPD) technique. The IWOADL-ACPD method mainly focuses on the recognition and classification of propaganda in the Arabic language. The IWOADL-ACPD method begins with a preprocessing step to standardize and clean raw Arabic text data. Consequently, BERT word embedding encodes meaningful data, capturing contextual nuances vital for accurately detecting propaganda. In addition, the stacked sparse autoencoder (SSAE) detection technique is employed to discern subtle patterns indicative of propaganda content. To improve the performance of the SSAE method, the IWOADL-ACPD method uses IWOA to fine-tune the hyperparameter effectively. The proposed IWOADL-ACPD method contributes to Arabic computation propaganda detection by providing an adaptive and comprehensive technique for the complexity of cultural, digital, and linguistic landscapes specific to the Arabic-speaking context. The robustness and efficacy of the IWOADL-ACPD technique are demonstrated through stimulation analysis on the Arabic dataset, which showcases its capability to perform better than other existing methods. The IWOADL-ACPD technique exhibited a superior accuracy value of 95.25% over existing methods. [ABSTRACT FROM AUTHOR]

Dermatologic oncology’s precision medicine revolutionizes skin cancer detection by integrating advanced technologies and personalized patient data. Dermatologic oncology concentrates on detecting skin cancer, utilizing modern techniques and technologies to detect and classify several kinds of cutaneous malignancies. Leveraging medical knowledge or advanced imaging approaches like dermoscopy and reflectance confocal microscopy; dermatologists effectively investigate skin cancer for subtle signs of malignancy. Furthermore, computer-aided diagnostic (CAD) systems, controlled by machine learning (ML) methods, are gradually deployed to boost diagnostic accuracy by investigating massive datasets of dermatoscopic images. It is a multi-disciplinary method that allows early recognition of skin lesions and enables precise prognostication and particular treatment approach, finally enhancing patient outcomes in dermatologic oncology. This paper presents the Fractals Snake Optimization with Deep Learning for Accurate Classification of Skin Cancer in Dermoscopy Images (SODL-ACSCDI) approach. The purpose of the SODL-ACSCDI approach is to identify and categorize the existence of skin cancer on Dermoscopic images. The SODL-ACSCDI technique applies a contrast enhancement process as the initial step. Next, the SODL-ACSCDI technique involves the SE-ResNet+FPN model for deriving intrinsic and complex feature patterns from dermoscopic images. Additionally, the SO technique can help boost the hyperparameter selection of the SE-ResNet+FPN approach. Furthermore, skin cancer classification uses the convolutional autoencoder (CAE) approach. The experimentation results of the SODL-ACSCDI technique could be examined using a dermoscopic image dataset. A wide-ranging result of the SODL-ACSCDI technique indicated a superior performance of 99.61% compared to recent models concerning various metrics. [ABSTRACT FROM AUTHOR]