Histopathological images are very effective for investigating the status of various biological structures and diagnosing diseases like cancer. In addition, digital histopathology increases diagnosis precision and provides better image quality and more detail for the pathologist with multiple viewing options and team annotations. As a result of the benefits above, faster treatment is available, increasing therapy success rates and patient recovery and survival chances. However, the present manual examination of these images is tedious and time‐consuming for pathologists. Therefore, reliable automated techniques are needed to effectively classify normal and malignant cancer images. This paper applied a deep learning approach, namely, EfficientNet and its variants from B0 to B7. We used different image resolutions for each model, from 224 × 224 pixels to 600 × 600 pixels. We also applied transfer learning and parameter tuning techniques to improve the results and overcome the overfitting problem. We collected the dataset from the Lung and Colon Cancer Histopathological Image LC25000 image dataset. The dataset acquisition consists of 25,000 histopathology images of five classes (lung adenocarcinoma, lung squamous cell carcinoma, benign lung tissue, colon adenocarcinoma, and colon benign tissue). Then, we performed preprocessing on the dataset to remove the noisy images and bring them into a standard format. The model’s performance was evaluated in terms of classification accuracy and loss. We have achieved good accuracy results for all variants; however, the results of EfficientNetB2 stand excellent, with an accuracy of 97% for 260 × 260 pixels resolution images.

The fast progression of the Internet of Vehicles (IoV) has resulted to a large number of vehicles connecting to networks. This leads to a massive growth in the data collected from vehicles via IoV. Fortunately, cloud computing provides a vast range of services such as operating systems, hardware, software and resources. Therefore, the massive amount of data cumulated through IoV can be outsourced to the cloud. However, considering the untrusted nature of the cloud, the cumulated data must be encrypted before it is outsourced to the cloud server. Unfortunately, this ensues to difficulty while searching the data. To address this challenge, an efficient certificateless public key cryptography with equality test (CL-PKC-ET) is presented in this paper. In this scheme, the authorized cloud server has the permission to execute the equality test on encrypted data and retrieve the result without knowing any relevant information about the ciphertext. Our CL-PKC-ET scheme is demonstrated under the Bilinear Diffie-Hellman assumption in the random oracle model. Ultimately, we compare the CL-PKE-ET with a state-of-art scheme and the performance evaluation indicates that our scheme accomplishes 96.40%, 32.08% and 43.98% reduction in computation costs during the encryption, decryption and test stages, respectively. Therefore, we assert that our scheme is ideal for deployment in both cloud and IoV environments.

Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively.

The rise of e-commerce and the adoption of cloud servers have revolutionized retail by providing greater convenience and selection to online shoppers. However, personalizing the customer experience also poses important challenges regarding privacy, security, and trust. To tackle this issue, the public key encryption with equality test (PKEET) is employed. Customer experiences are securely transmitted to the cloud server in an encrypted manner. Consequently, the cloud server is authorized to carry out an equality test on the encrypted data and retrieval without revealing private details. Edward Snowden’s disclosures underscored the risk of capable adversaries infiltrating user devices and surreptitiously accessing private information via covert backdoors. To tackle this, cryptographic reverse firewalls (CRFs) were proposed. However, applying CRF techniques to PKEET for securing personalization and contextualization in e-commerce has yet to be realized. Thus, this work introduces a novel equality test public key encryption with cryptographic reverse firewalls (ET-PKE-CRF) approach. The performance evaluation demonstrates that the ET-PKE-CRF scheme substantially enhances efficiency in terms of communication and computation, outperforming current advanced solutions.

The emergence of the COVID-Omicron XBB variant has intensified the need for wireless body area networks (WBANs) in telemedicine, underscoring their critical role in remote patient monitoring and demanding robust security solutions to protect health data and patient privacy. To address this need, we introduce the equality test on identity-based encryption with cryptographic reverse firewalls (ET-IBE-CRFs). This protocol allows the medical server in a telemedicine system to execute the equality test on the encrypted data and retrieve the result without knowing any relevant information about the ciphertext. By incorporating cryptographic reverse firewalls (CRFs), the ET-IBE-CRF protocol effectively counters offline message recovery attacks (OMRAs) and algorithm substitution attacks (ASAs) without requiring secure communication channels. Our evaluation indicates that ET-IBE-CRF not only meets the strict requirements for confidentiality and privacy in telemedicine applications but also maintains high efficiency. This makes it well-suited for high-performance telemedicine systems.