Cloud computing is an integral part of modern computational activities, offering on-demand resources via SaaS, PaaS, IaaS, and diverse deployment models. Cost management remains a key focus amid resource constraints. This paper explores cloud cost optimization, defining total cost as the sum of migration, downtime, operational overhead, execution, and communication costs with corresponding mathematical formulations. It classifies optimization techniques into static (advance resource allocation) and dynamic (pay-as-you-go) approaches, detailing methods like genetic algorithms and approximate dynamic programming. Practical cost-cutting strategies are proposed, including auto-scaling, optimized data transfer, high-availability architectures, and managed services. Priority factors for optimization include scheduling, demand trace, and reliability. Effective optimization enhances resource utilization, reduces costs, and boosts cloud system profitability and stability.

As decentralized blockchain technology continues to expand, safeguarding the transaction’s confidentiality and user’s anonymity has become increasingly crucial. While substantial research has been conducted on the design and development of privacy protocols, a comprehensive evaluation of these solutions remains lacking. To bridge this gap, we first categorized existing privacy solutions based on key components of blockchain technology and cryptography and examined the assessment criteria applied to these methods. The analysis reveals that many of these solutions have not undergone rigorous evaluation. To address this, we identified additional factors that influence or undermine blockchain privacy and created an assessment framework to measure the effectiveness of privacy solutions based on these factors. We also introduced a quantifiable metric to assess blockchain privacy performance. In this framework, privacy precision was determined by evaluating the efficacy, resilience, and potential risks associated with various privacy-enhancing features. Finally, we implemented the derived privacy index within a smart contract and demonstrated its practical application, providing a foundation for empirically evaluating blockchain privacy.

Security is a primary concern in modern applications where large volumes of data are exchanged, making authentication and protection from external attacks essential. Biometric systems offer enhanced robustness, with EEG signals providing unique, hard-to-replicate cognitive patterns. This work aims to design an EEG-based personalized authentication system with improved accuracy and reduced processing complexity through optimized channel selection. A novel spectral correlation-based channel selection method interfaced with selection logic for reduced processing is proposed to optimally cluster EEG channels. Signal fusion with minimum deviation constraints mitigates information loss from discarded channels in PCA/Wilcoxon approaches. The proposed method achieves a decision accuracy of 99.2% with lower computational overhead, outperforming PCA/Wilcoxon-based systems. The integration of optimized channel selection and signal fusion enables high-accuracy, real-time EEG-based authentication suitable for resource-constrained biomedical security applications.

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models collaboratively while preserving user data privacy. It enables decentralized learning across multiple clients without sharing raw data. However, challenges such as heterogeneous data distributions and varying client capabilities can hinder model accuracy, convergence speed, and communication efficiency. This paper investigates intelligent client sampling techniques that consider factors like network conditions and model quality to enhance training performance. It reviews and compares random, deterministic, and adaptive sampling methods, highlighting their trade-offs. Emphasis is placed on FL's application in healthcare informatics, where client selection remains underexplored despite its critical importance. Thus, the proposed Class Topper Optimization (CTO) approach is the foundation of the novel federated learning strategy presented in the suggested methodology. This paper utilizes medical data on multiple clients as inputs for the projected framework. In this suggested technique, the ANNs and CNNs operate as localized models for training homogenous customers. After that, the global model is updated frequently by receiving updates from locally trained models. The global model is implemented by employing LSTM networks. The precision level is around 92% and the accuracy rate is roughly 93%. Hence, in federated learning, the ideal client selection method is the optimization-based method.

If social media platforms are to serve as the primary sources of information, then this type of news article tends to weaken credibility and factual information significantly. The concept of this paper is to contribute to this issue by applying the current NLP technique built with meta-learning and the BERT model to identify synthetic content in the ISOT fake news dataset. The study explores the potential of Meta-learning with BERT in developing practical tools for analysis and improving fake news detection. It also highlights certain limitations related to the use of this technology, emphasizing the need for accountability. This, in turn, underscores the importance of responsible design and optimization to maximize the benefits of advanced artificial intelligence. An ensemble transformer-based model should be used in fake news analysis and detection, as it yielded the highest performance accuracy of 98.29% in this context.

The exponential increase in the number of cars has made parking a major problem. Parking availability leads to traffic jams and time wasting. This study connects two related fields: IoT-based smart parking systems and operating system process scheduling. Using computer simulations, the first section examines the effectiveness of the Round Robin (RR) and First Come First Serve (FCFS) scheduling algorithms by examining important performance indicators such CPU usage, throughput, turnaround time, and response time. The rising need for intelligent parking solutions in urban settings, where conventional methods cause traffic jams and inefficiencies, is covered in the work. An IoT-enabled smart parking system that uses Arduino, PIR sensors, and stepper motors to automate gate operations, minimize manual intervention, and enhance user experience is suggested as a solution to these problems. System designers may learn a lot from the simulation and hardware-based results, which show the advantages and disadvantages of both scheduling strategies. By combining sophisticated scheduling algorithms with IoT automation, this work advances computational and real-world applications while improving operational efficiency, performance, and decision-making.