A Heterogeneous Systems Public Key Encryption with Equality Test in Smart City

Smart cities have been identified as areas which are urbanized and utilize diverse types of electronic data collection sensors that are used to oversee resources and assets efficiently. Smart meters are a unit of smart cities and they collect information about users and their consumption patterns. Consequently, the Internet of Things (IoT) being at a steady evolution has prompted multiple users into having their data collected from smart meters, stored on cloud servers. This is a way of saving costs and time involved in accessing the data. In spite of that, the cloud-assisted IoT faces privacy and security issues. This is as a result of the cloud servers possessing an untrusted nature. Due to this, it is essential for the data accumulated from the smart meters be encrypted hitherto outsourcing it to the cloud server. However, having encrypted data in the cloud server leads to a complication when it comes to accessing the data. For users who are on a different public key system, it becomes illogical for the users to first download the entire data on the cloud in order to access the required data. Therefore to resolve this issue, a heterogeneous systems public key encryption with equality test (HS-PKE-ET) scheme was proposed. The HS-PKE-ET scheme integrates certificateless public cryptography with equality test (CLC-ET) with the public key encryption with equality test (PKI-ET). This scheme allows an authorized cloud server to determine if two encryptions encrypted within heterogeneous systems possess equivalent messages. Basing on the random oracle model, the proposed scheme’s security is stated under the bilinear Diffie-Hellman assumption together with the computational Diffie-Hellman assumption. Ultimately the size of storage, computation complexities and properties with other related works are focused on and illustrations indicate our proposed scheme reflects a good performance

Enhancing the K-Means Algorithm through a Genetic Algorithm Based on Survey and Social Media Tourism Objectives for Tourism Path Recommendations

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

Lung Cancer classification in histopathology images using Multi-Resolution Efficient Nets

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