Aim: In this investigation, the Taguchi coupled with Grey relational analysis (TGRA) Multi optimization approach was used in order to optimise the machining parameters as well as the minimum quantity lubrication (MQL) parameter. This technique was utilised to find the optimal values for both parameters. Milling with MQL and coated carbide inserts is used to grind the face of the Al-6061. Background: MQL is a kind of machining that sees widespread use and offers advantages to the economy, the environment, and human health. Objective: Milling the face of Al-6061 was accomplished with the use of the MQL method and TiN-coated carbide inserts over the course of this experiment. Method: The following are some of the input parameters that may be chosen: cut depth, cutting speed, feeds per revolution, and coolant flow rate (CFR). In order to determine these characteristics at three different levels, the Taguchi L9 orthogonal array was used. "the higher, the better" is applied to MRR when assessing signal-to-noise ratio, while "the smaller, the better" is used to Ra when calculating signal-to-noise ratio. The analysis of variance is used so that the relevance and contribution of each input parameter may be determined. Results: According to the TGRA based ANOVA approach, the depth of cut contributes substantially (43.39%) to the material removal rate, the feed per revolution adds significantly (34.33%), and the cutting velocity contributes considerably (18.04%) to maximise MRR and Ra. Conclusion: The optimal input parameter settings are CV3, FPR1, DOC3, and CFR2; this is done so that MRR can be optimized for the highest value and Ra can be optimized for the least value simultaneously.

In this study, a cylindrical ferromagnetic work-part was finished using the magnetorheological finishing method. The strength of the magnetic flux controls the density and forces in processes which are assisted by magnetic fields. The mechanism was studied parametrically in this research work using response surface methodology. Optimal process parameters were determined using response surface methodology to accurately execute the finishing procedure. Each parameter's percentage consumption to the process's finishing output was also estimated. The finishing of an industrial extrusion punch was carried out using the optimum parameters obtained from the parametric analysis. The RSM optimisation of process parameters is validated using the batch gradient descent (BGD) algorithm which is best fit and innovation algorithm for these type of optimization solutions. The mathematical model that BGD provides confirms the RSM mathematical model.

Background: There is a rapid expansion of data in today's digital age. Due in large part to social media sites, the Internet produces enormous quantity of unstructured data every second. Numerous users have many opinions and reviews to impart on everything from items and services to common pastimes. Opinions, feelings, attitudes, impressions, etc. concerning subjects, products, and services are collected and analyzed through a method called sentiment analysis. Web-based networking mediums that rely on textual communication can be overwhelming. Understanding human psychology requires the real-time processing of data using techniques like sentiment analysis. Aim: This study provides a thorough examination of the differences between methods of sentiment analysis, as well as its obstacles and emerging trends. The paper exemplifies the analysis's practical uses, examines its challenges, and outlines common methods of conducting it. Objective: The objective of current overview is to better understand the market, gauge public opinion, and make strategic decisions, enterprises, governments, and scholars can all benefit from conducting sentiment analysis. Methods: In this research, we review and categorise the most widely applied methods of deep learning and machine learning for analysing sentiment. From the paper, we learn that which sentiment analysis technique is best depends on the data at hand. When confronted with large amounts of data and a lengthy procedure, traditional machine learning-based algorithms flop. The ability to train deep learning models to learn more features using larger datasets is why they currently beat machine learning methodologies. Considerations include textual and temporal context, as well as data volume. Results: Regardless the fact that the English language has traditionally been the focus of research in this field, other spoken languages has recently attracted a growing amount of interest. The lack of resources for these languages continues to present numerous obstacles. Consequently, it can be an intriguing line of future effort to tackle other natural languages outside English by generating beneficial resources like building databases, and addressing the problems with Natural language processing that have been stated in the context of sentiment examination. Conclusion: The difficulties of sentiment analysis are examined as well, with the goal of illuminating potential solutions.

: Remote sensing technology is a powerful tool for a wide range of applications, from medical diagnoses to environmental monitoring. Quality inspection, inventory management, environmental monitoring, supply chain analysis, and predictive maintenance are just few of the many industrial uses for remote sensing image classification using deep learning. It's a tool for lowering production costs without sacrificing quality or long-term viability. Remote Sensing Image Classification with Deep Learning aids production and sustainability by offering data-driven decision-making and useful insights. In this paper, we review the application of deep learning techniques in the field of remote sensing data analysis. This paper aims to investigate several techniques for visualising model decisions and attributing them to specific aspects within the dataset. The proposed techniques include deep convolutional neural networks (CNNs) with saliency stream and RGB stream fusion techniques. In addition, we also discuss the use of extreme learning machine (ELM) classifiers with fused features as input for result. Finally, we discuss the performance of the proposed techniques on UC Merced Land-Use dataset, Aerial Image dataset (AID), and NWPU-RESISC45 datasets. The results of the experiments demonstrate that the proposed techniques outperform other existing techniques. Additionally, the fused features from different streams improve the performance of the model significantly. This paper focused on the information of various related research works and their models including datasets. The main purpose is to make the already existed bridge between social life and the computer system even more robust.

The rapid development of wireless communications and mobile computation has given rise to the novel Internet of Things (IoT) systems, which is causing considerable research attention and an industrial development. However, the lack of synchronization between the timers of IoT devices compromises the network's security. The purpose of this patent application is to present a technique for synchronizing the timepieces of IoT gadgets and establishing a secure channel for the transmission of data from source to destination. This patent is proposed to develop a Synchronization Selection Method (SSM) for IoT system. For the purpose of synchronizing the timers of the sensor nodes, the time lay approach was implemented. Prior to synchronizing the sensor nodes' timings, the cluster leaders initiate the process. Utilizing a finite number of nodes, the proposed method was executed in MATLAB. The proposed patent results in enhanced network security and decreased network packet loss.

The previous decades have seen a sharp increase in the population and a fast urbanization, which has highlighted the need for more intelligent and long-lasting solutions to urgent environmental and social issues. In order to fulfill the notion of smart cities, this study investigates the revolutionary potential of integrating Internet of Things (IoT) and blockchain technology into numerous aspects of urban life. Smart city programs seek to maximize resource use, boost operational efficiency, and promote sustainability in several areas like healthcare, education, transportation, energy, and building management through the exploitation of sensor-driven data and powerful analytics. The report describes a number of important goals, processes, and elements that are essential to putting blockchain-enabled Internet of Things applications into practice in smart cities. The article provides a thorough review, highlighting the importance of decentralized techniques in reducing single points of failure and improving the integrity, security, and privacy of data. Personalized learning pathways and transparent data management can revolutionize education. Other specific applications include improving patient care through real-time monitoring and secure medical record storage; optimizing transportation systems for efficiency and safety; and transforming energy grids for reliability and integration of renewable sources. The report also explores how blockchain and IoT technologies are combining to provide smart grid services, intelligent transportation systems, building automation, healthcare management, and innovative educational opportunities. Blockchain and Internet of Things integration promises to transform urban government, enhance service delivery, and promote sustainable development in smart cities by enabling safe data interchange, automating operations through smart contracts, and guaranteeing data integrity.In summary, the significance of decentralized blockchain technologies for guaranteeing the robustness, confidentiality, and effectiveness of Applications of smart cities facilitated by IoT. This research adds to a thorough knowledge of the potential of blockchain and IoT technologies in influencing the future of smart cities by addressing security and privacy problems and encouraging decentralized design principles.