According to the most recent estimates from global cancer statistics for 2020, liver cancer is the ninth most common cancer in women. Segmenting the liver is difficult, and segmenting the tumor from the liver adds some difficulty. After a sample of liver tissue is taken, imaging tests, such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US), are used to segment the liver and liver tumor. Due to overlapping intensity and variability in the position and shape of soft tissues, segmentation of the liver and tumor from computed abdominal tomography images based on shade gray or shapes is undesirable. This study proposed a more efficient method for segmenting liver and tumors from CT image volumes using a hybrid ResUNet model, combining the ResNet and UNet models to address this gap. The two overlapping models were primarily used in this study to segment the liver and for region of interest (ROI) assessment. Segmentation of the liver is done to examine the liver with an abdominal CT image volume. The proposed model is based on CT volume slices of patients with liver tumors and evaluated on the public 3D dataset IRCADB01. Based on the experimental analysis, the true value accuracy for liver segmentation was found to be approximately 99.55%, 97.85%, and 98.16%. The authentication rate of the dice coefficient also increased, indicating that the experiment went well and that the model is ready to use for the detection of liver tumors.

During the recent years, the issue of preserving the integrity of digital text has become a focus of interest in the transmission of online content on the Internet. Watermarking has a useful tool in the protection of digital text content as it solves the problem of tampering, duplicating, unauthorized access and security breaches. The rapid development currently observable in information transfer and access is the consequences of the widespread usage of the Internet. When it comes to the different types of digital data, text constitutes the most complex and challenging type to which the method of text watermarking can be applied. Text watermarking constitutes a highly complex task, most of all since only limited research has been done in this field. In order to ensure the successful evaluation, analysis and implementation, a comprehensive research needs to be performed. This article studies the theory, methods and applications of text watermarking, which includes the discussion on the definition, embedding and extracting processes, requirements, approaches, and language applications of the established text watermarking methods. The article reviews in detail the new classification of text watermarking, which is through embedding process and its related issues of attacks and language applicability. Open research challenges and future directions are also investigated, with focus on its information integrity, information availability, originality preservation, information confidentiality, protection of sensitive information, document transformation, cryptography application, and language flexibility.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women’s health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer‐aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early‐stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet‐50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet‐50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

Virtual classrooms based on the metaverse or virtual reality are useful and effective for imparting basic chemistry concepts. Interactive and immersive environments can effectively teach fundamental chemistry concepts, such as chemical bonding and formulas, thereby making these otherwise abstract and intangible ideas more accessible and understandable. With the outbreak of Covid-19, e-learning platforms have also been developed for chemistry education. However, these platforms are unable to make learning chemistry interactive and enjoyable. Therefore, there is a need to motivate students to learn basic chemistry concepts in an immersive and interactive environment. In this paper, we propose an immersive virtual reality-based Virtual Chemistry Classroom for Chemical Bonding (VC3B) to facilitate the learning of chemical bonding and formulas through a game-based learning approach. It includes two different games for learning chemical bonding and formulas. In the first game, molecule construction, students reconstruct the structure of molecules by rearranging the atoms in order to learn about chemical bonding. In the second game, chemical formula, students compose the chemical formula of a given compound to help them memorize chemical formulas. The study, conducted on 90 middle school students, employed a randomized controlled study design, dividing participants into three groups. Each group learned about chemical bonding and formulas through three different mediums. After conducting the experiment, the students were given a questionnaire to evaluate the usability of VC3B. The results of the study were positive, with participants finding the VC3B to be more interactive than traditional book and online lecture methods. Participants were also motivated to learn and enhance their knowledge of chemistry.

In the context of 6G technology, the Internet of Everything aims to create a vast network that connects both humans and devices across multiple dimensions. The integration of smart healthcare, agriculture, transportation, and homes is incredibly appealing, as it allows people to effortlessly control their environment through touch or voice commands. Consequently, with the increase in Internet connectivity, the security risk also rises. However, the future is centered on a six-fold increase in connectivity, necessitating the development of stronger security measures to handle the rapidly expanding concept of IoT-enabled metaverse connections. Various types of attacks, often orchestrated using botnets, pose a threat to the performance of IoT-enabled networks. Detecting anomalies within these networks is crucial for safeguarding applications from potentially disastrous consequences. The voting classifier is a machine learning (ML) model known for its effectiveness as it capitalizes on the strengths of individual ML models and has the potential to improve overall predictive performance. In this research, we proposed a novel classification technique based on the DRX approach that combines the advantages of the Decision tree, Random forest, and XGBoost algorithms. This ensemble voting classifier significantly enhances the accuracy and precision of network intrusion detection systems. Our experiments were conducted using the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets. The findings of our study show that the DRX-based technique works better than the others. It achieved a higher accuracy of 99.88% on the NSL-KDD dataset, 99.93% on the UNSW-NB15 dataset, and 99.98% on the CIC-IDS2017 dataset, outperforming the other methods. Additionally, there is a notable reduction in the false positive rates to 0.003, 0.001, and 0.00012 for the NSL-KDD, UNSW-NB15, and CIC-IDS2017 datasets.