Clustering is one of the critical parts of machine learning algorithms. K-Means clustering is the standard technique that various data analysts use for clustering the data among the various clusters. Even though the K means clustering algorithm can work effectively, there is a need to tune the value of K according to the dataset under consideration. The process of tuning for the value of k requires the execution of the K-means algorithm with different values of k. The values of k with the best cluster quality based on specific metrics are selected. The elbow method and silhouette coefficient is the most popular approach for selecting the number of clusters. However, both approaches are time-consuming, as they have to execute K-means for each value of k to find a good score. This approach is iterative. This paper proposed a divide and conquer approach that performs the same task in less time. From the proposed approach, the task has been completely 2.5 times faster in comparison to the iterative method at the cost of some memory required to store the results.

Cloud computing provides outsourcing of computing services at a lower cost, making it a popular choice for many businesses. In recent years, cloud data storage has gained significant success, thanks to its advantages in maintenance, performance, support, cost, and reliability compared to traditional storage methods. However, despite the benefits of disaster recovery, scalability, and resource backup, some organizations still prefer traditional data storage over cloud storage due to concerns about data correctness and security. Data integrity is a critical issue in cloud computing, as data owners need to rely on third-party cloud storage providers to handle their data. To address this, researchers have been developing new algorithms for data integrity strategies in cloud storage to enhance security and ensure the accuracy of outsourced data. This article aims to highlight the security issues and possible attacks on cloud storage, as well as discussing the phases, characteristics, and classification of data integrity strategies. A comparative analysis of these strategies in the context of cloud storage is also presented. Furthermore, the overhead parameters of auditing system models in cloud computing are examined, considering the desired design goals. By understanding and addressing these factors, organizations can make informed decisions about their cloud storage solutions, taking into account both security and performance considerations.

This paper provides an overview of blockchain technology’s security and privacy features, as well as an overview of IoT-based cache memory and single-bit six transistor static random-access memory cell sense amplifier architecture. Each chip’s memory is used for recorded as blocks, which are encrypted and used as a blockchain for other memory devices. The architectures comprise of the circuit of write driver, six transistor static random access memory cells, and sense amplifiers such as current differential sense amplifier, charge transfer differential sense amplifier, and voltage latch sense amplifier. Furthermore, different parameters such as the number of transistors, sensing delay, and power consumption have been analyzed for varying resistance values (i.e., R=42.3Ω and R=42.3KΩ). Apart from that, power reduction techniques such as dual sleep, forced stack, sleep transistor, and sleep stack are used to optimize power consumption. These power reduction techniques are applied over different blocks of architecture, such as six transistors static random access memory cell and sense amplifier to optimize power consumption of the architecture. The conclusion arises that a single-bit six transistor static random access memory cell with power reduction dual sleep technique voltage latch sense amplifier with power reduction dual sleep technique in architecture consumes 11.65μW of power and has 33 transistors which are lowest from other architectures.

Online cloud data storage, a pillar of the IT industry, offers data owners a plethora of attractive developments in highly sought-after online scalable storage services for them to willing new inventive and investment business profit. Today, most cloud data security research focuses on improving the accuracy of outsourced data audits rather than paying attention to internal and external enemies who could hack a cloud server. Even though the data owner enviously envies the data auditing job of stored data to a trusted Third Party Auditor (TPA) to save communication as well as the computational overhead of data which outsourcing to a reputable Cloud Service Provider (CSP), TPA is unreliable in a realistic context. A TPA or a CSP may occasionally be a malicious attacker or be under the sway of other hostile attackers. As a result, either the message of the audit result might be faked or TPA may produce a phony audit message without carrying out any data verification work, in which case CSP would be in for a fight. Instead of establishing a secure communication route between all parties, a progressive technique is therefore required to thwart enemies’ attempts to collaborate with data owners who have outsourced their data. In this paper, we propose a model for an effective data integrity and audit message verification system based on the ZSS (Zhang, Safavi, and Susilo) signature, which protects data privacy in cloud storage and uses faked data recovery to pay back original data using a modular approach. We also consider our proposed model for a existing Blockchain-based Medical system that helps a patient party to check the intactness of their medical record in shared cloud storage via a third-party auditor. Finally, the experimental results of the performance of the proposed prototype of the system model have been measured and illustrated better efficiency of the proposed model with the comparison of the existing auditing model.

Online cloud data storage is a rapidly growing pillar of the IT industry that offers data owners an array of attractive developments in highly sought-after online scalable storage services. Cloud users can easily access these services and have the flexibility to manage their process data effectively without worrying about the deployment or maintenance of personal storage devices. As a result, the number of cloud users has increased to purchase these convenient and cost-effective services, while Cloud Service Providers (CSP) are also rising to meet this demand for appealing cloud solutions.However, there is one major security issue related to outsourced data on shared cloud storage: its privacy and accuracy cannot be guaranteed as it may be vulnerable to unauthorized access by malicious insiders or hackers from outside sources. To address these issues, we suggest proposing a partial signature-based data auditing system so that both privacy and accuracy can be fortified while reducing the computational cost associated with auditing processes significantly. This system would involve using cryptographic techniques such as homomorphic encryption and hash functions, which would enable secure sharing between multiple parties while ensuring integrity checks on stored files at regular intervals for any potential tampering attempts made by external attackers or malicious insiders who may try to gain unauthorized access into confidential user information stored within cloud sites. Another benefit of the plan is that it supports dynamic operation on outsourced data. This research work may achieve the desired security qualities, according to the security analysis, and it is effective for real-world applications, as demonstrated by simulation outcomes of dynamic operations on various numbers of data blocks and sub-blocks.