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Dissimilar joining of austenitic stainless steels and ferritic steels is a challenging task and has a wide range of applications due to its excellent mechanical and thermal characteristics. They are joined mostly by using conventional modes. In the current investigation, the study and optimization of hot wire TIG welding parameters was carried out.

These parameters will govern the desired characteristics of the joint. Solutions were found out through multi-response optimization by using response surface methodology and single response optimization using particle swarm optimization.

Optimized input welding parameters that were achieved are electrode current 180 amps, wire feed rate 1870 mm/min and hot wire current 98 amps and the optimized UTS is 665.45 MPa. The results from PSO were compared with RSM and the optimized input welding parameters for the electrode current, hot wire current and wire feed rate exhibited maximum ultimate tensile strength which were also confirmed from response and contour plots.

Sensitivity analysis was also performed to understand the effect of each individual parameters on the response. Microstructure features were evaluated for the joints and was found that the characteristics are within the desired criteria.

The current and on-going coronavirus (COVID-19) has disrupted many human lives all over the world and seems very difficult to confront this global crisis as the infection is transmitted by physical contact. As no vaccine or medical treatment made available till date, the only solution is to detect the COVID-19 cases, block the transmission, isolate the infected and protect the susceptible population. In this scenario, the pervasive computing becomes essential, as it is environment-centric and data acquisition via smart devices provides better way for analysing diseases with various parameters.

For data collection, Infrared Thermometer, Hikvision’s Thermographic Camera and Acoustic device are deployed. Data-imputation is carried out by principal component analysis. A mathematical model susceptible, infected and recovered (SIR) is implemented for classifying COVID-19 cases. The recurrent neural network (RNN) with long-term short memory is enacted to predict the COVID-19 disease.

Machine learning models are very efficient in predicting diseases. In the proposed research work, besides contribution of smart devices, Artificial Intelligence detector is deployed to reduce false alarms. A mathematical model SIR is integrated with machine learning techniques for better classification. Implementation of RNN with Long Short Term Memory (LSTM) model furnishes better prediction holding the previous history.

The proposed research collected COVID −19 data using three types of sensors for temperature sensing and detecting the respiratory rate. After pre-processing, 300 instances are taken for experimental results considering the demographic features: Sex, Patient Age, Temperature, Finding and Clinical Trials. Classification is performed using SIR mode and finally predicted 188 confirmed cases using RNN with LSTM model.

The purpose of this paper is to develop an empirical relationship for predicting the strength of titanium to austenitic stainless steel fabricated by diffusion bonding (DB) process. Process parameters such as bonding pressure, bonding temperature and holding time play the main role in deciding the joint strength.

In this study, three-factors, five-level central composite rotatable design was used to conduct the minimum number of experiments involving all the combinations of parameters.

An empirical relationship was developed to predict the lap shear strength (LSS) of the joints incorporating DB process parameters. The developed empirical relationship was optimized using particle swarm optimization (PSO). The optimized value discovered through PSO was compared with the response surface methodology (RSM). The joints produced using bonding pressure of 14 MPa, bonding temperature of 900°C and holding time of 70 min exhibited a maximum LSS of 150.51 MPa in comparison with other joints. This was confirmed by constructing response graphs and contour plots.

Optimizing the DB parameters using RSM and PSO, PSO gives an accurate result when compared with RSM. Also, a sensitivity analysis is carried out to identify the most influencing parameter for the DB process.

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.

Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.

In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.

On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.

This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

Stock market forecasters are focusing to create a positive approach for predicting the stock price. The fundamental principle of an effective stock market prediction is not only to produce the maximum outcomes but also to reduce the unreliable stock price estimate. In the stock market, sentiment analysis enables people for making educated decisions regarding the investment in a business. Moreover, the stock analysis identifies the business of an organization or a company. In fact, the prediction of stock prices is more complex due to high volatile nature that varies a large range of investor sentiment, economic and political factors, changes in leadership and other factors. This prediction often becomes ineffective, while considering only the historical data or textural information. Attempts are made to make the prediction more precise with the news sentiment along with the stock price information.

This paper introduces a prediction framework via sentiment analysis. Thereby, the stock data and news sentiment data are also considered. From the stock data, technical indicator-based features like moving average convergence divergence (MACD), relative strength index (RSI) and moving average (MA) are extracted. At the same time, the news data are processed to determine the sentiments by certain processes like (1) pre-processing, where keyword extraction and sentiment categorization process takes place; (2) keyword extraction, where WordNet and sentiment categorization process is done; (3) feature extraction, where Proposed holoentropy based features is extracted. (4) Classification, deep neural network is used that returns the sentiment output. To make the system more accurate on predicting the sentiment, the training of NN is carried out by self-improved whale optimization algorithm (SIWOA). Finally, optimized deep belief network (DBN) is used to predict the stock that considers the features of stock data and sentiment results from news data. Here, the weights of DBN are tuned by the new SIWOA.

The performance of the adopted scheme is computed over the existing models in terms of certain measures. The stock dataset includes two companies such as Reliance Communications and Relaxo Footwear. In addition, each company consists of three datasets (a) in daily option, set start day 1-1-2019 and end day 1-12-2020, (b) in monthly option, set start Jan 2000 and end Dec 2020 and (c) in yearly option, set year 2000. Moreover, the adopted NN + DBN + SIWOA model was computed over the traditional classifiers like LSTM, NN + RF, NN + MLP and NN + SVM; also, it was compared over the existing optimization algorithms like NN + DBN + MFO, NN + DBN + CSA, NN + DBN + WOA and NN + DBN + PSO, correspondingly. Further, the performance was calculated based on the learning percentage that ranges from 60, 70, 80 and 90 in terms of certain measures like MAE, MSE and RMSE for six datasets. On observing the graph, the MAE of the adopted NN + DBN + SIWOA model was 91.67, 80, 91.11 and 93.33% superior to the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively for dataset 1. The proposed NN + DBN + SIWOA method holds minimum MAE value of (∼0.21) at learning percentage 80 for dataset 1; whereas, the traditional models holds the value for NN + DBN + CSA (∼1.20), NN + DBN + MFO (∼1.21), NN + DBN + PSO (∼0.23) and NN + DBN + WOA (∼0.25), respectively. From the table, it was clear that the RMSRE of the proposed NN + DBN + SIWOA model was 3.14, 1.08, 1.38 and 15.28% better than the existing classifiers like LSTM, NN + RF, NN + MLP and NN + SVM, respectively, for dataset 6. In addition, he MSE of the adopted NN + DBN + SIWOA method attain lower values (∼54944.41) for dataset 2 than other existing schemes like NN + DBN + CSA(∼9.43), NN + DBN + MFO (∼56728.68), NN + DBN + PSO (∼2.95) and NN + DBN + WOA (∼56767.88), respectively.

This paper has introduced a prediction framework via sentiment analysis. Thereby, along with the stock data and news sentiment data were also considered. From the stock data, technical indicator based features like MACD, RSI and MA are extracted. Therefore, the proposed work was said to be much appropriate for stock market prediction.

This paper aims to respond to the following question: What does ongoing recovery from depression look like, and what role might spirituality have for individuals’ meanings of recovery if it has any meaning at all?

In this paper, the authors reconceptualize recovery from depression as ritual, as ongoing recovery, or recovery as a process, resonated with many of the 40 participants (all ages) from the study, and much of the sociological literature on recovery from depression (Fullagar and O’Brien, 2012; Garrett, 1997, 1998; Karp, 1994, 1996, 2016; O’Brien, 2012). To explore the interplay between participants’ accounts of recovery as ongoing, and the meanings of spirituality, the authors used a ritual analysis inspired by Collins (2004).

From the accounts presented in this paper, the authors suggest that participants are, if subconsciously, using objects with a special or spiritual significance to filter through their thoughts and memories as a way to create what Collins (2004) calls an emotional charge. The authors argue that these emotional charges assist people with lived experiences of depression in distancing from, and reconnecting to, certain social ties, including a particular family member, friend or social group, as part of their recovery. The authors are calling this process, ritual distancing.

Recovery from depression includes a process of reconnection to the self and others, and this process sometimes includes a self-defined spirituality (in objects and social connections).

Friction stir processing (FSP) is overviewed with the process variables, along with the thermal aspect of different metals.

With its inbuilt advantages, FSP is used to reduce the failure in the structural integrity of the body panels of automobiles, airplanes and lashing rails. FSP has excellent process ability and surface treatability with good corrosion resistance and high strength at elevated temperatures. Process parameters such as rotation speed of the tool, traverse speed, tool tilt angle, groove design, volume fraction and increase in number of tool passes should be considered for generating a processed and defect-free surface of the workpiece.

FSP process is used for modifying the surface by reinforcement of composites to improve the mechanical properties and results in the ultrafine grain refinement of microstructure. FSP uses the frictional heat and mechanical deformation for achieving the maximum performance using the low-cost tool; the production time is also very less.

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To propose a methodology based on genetic algorithm (GA) to solve the parallel machine scheduling problems with precedence constraints.

Workflow balancing helps to remove bottlenecks present in a shop floor yielding faster movements of components or jobs. Multiple machines are used in parallel for processing the jobs to meet the demand. In parallel machine scheduling with precedence constraints, there are m machines to which n jobs are assigned using suitable scheduling algorithms. Workflow of a machine is the sum of processing time of all jobs assigned. All the preceding jobs are allocated first to satisfy the constraints. GA is developed to solve parallel machine scheduling problems with precedence constraints based on the objective of workflow balancing. The GA was coded on IBM/PC compatible system in the C++ language for simulation to a standard manufacturing environment.

The relative percentage of imbalance (RPI) in workloads among the parallel machines is used to evaluate the performance of the GA developed. The proposed GA produces lesser RPI values against the RANDOM heuristic algorithm for a wider range of jobs and machines.

The performance of GA can be compared with the performance of other meta‐heuristic algorithms to find out the robustness of the results obtained by this research.

The proposed GA also gives better solution for a case study of assembly scheduling.

The allocation of assembly operations to the operators is modeled into a parallel machine scheduling problem with precedence constraints using the objective of minimizing the workflow among the operators.

To make more smart health-care system, the health-care data should be shared in the secure manner, and it improves health-care service quality. This paper aims to implement a modern decentralized blockchain, safe and easy-to-use health-care technology application in the cloud.

On observing the graph, the convergence analysis of proposed Levy Flight-integrated moth flame optimization method at 80th iteration was 4.59%, 2.80%, 3.316%, 8.92% and 2.55% higher than the traditional models MFO, artificial bee colony (ABC), particle swarm optimization (PSO), moth search algorithm (MSA) and glow worm swarm optimization (GWSO), respectively, for Hungarian data set. Particularly, in best case scenario, the adopted method attains low cost value (5.672671) when compared to all other traditional models such as MFO (5.727314), ABC (5.711577), PSO (5.706499), MSA (5.764517) and GWSO (5.723353).

The proposed method achieved effective performance in terms of key sensitivity, sanitization effectiveness, restoration effectiveness, etc.