Here, we have introduced a novel probability distribution called the WeibullGeneralized inverted exponential{Log-logistic} distribution (WGIELLD) adopting the Transformed-Transformer method. The density curves of the new distribution show that it can model data sets that are approximately normal, left and right skewed; while the hazard function graphs indicate that the distribution can model components with various shapes of failure rate. We have derived various statistical features of this distribution, such as the reliability function (rf), hazard function (hf), mode, quantile function (qf), median, Renyi entropy (Re), moment generating function (mgf), mean absolute deviations (MAD) from the mean and median, and order statistics. We have used maximum likelihood estimation (MLE) approach to estimate the parameters of this distribution and also run a simulation study to verify the suitability of the approach. Finally, two sets of data are employed to demonstrate the versatility of the distribution compared to the baseline distribution and other related distributions.

Forecasts are never as accurate as organizations need or desire them to be. Yet, lots of forecasting consultants and software vendors are willing to take organizations money in exchange for unfulfilled promises (SAS, 2019). Hence, for users and/or organizations to manage their expectations, we emphasized that it is unrealistic to expect any computation software to produce perfect outputs or forecasts since nothing works perfectly in this world. Therefore, in this paper, we compared the advantages and disadvantages (pros and cons) of Time-Series (TS) forecast computation via the Microsoft Excel (FORECAST.ETS) built-in function and the three different SAS products, prescribed as a roadmap (Rahardja, 2020). Those three SAS products are the SAS Forecast Studio 14.1 drop-down menu, the SAS/ETS 9.4 drop-down menu, and the SAS/ETS 9.4 manual coding procedures or PROCs. Finally, we summarize our conclusion and recommendations for users and/or organizations.

In this paper, we investigate the use of predictive analytics and machine learning to improve patients’ adherence to prescribed medications. By analyzing past patient data, we are able to create a model that may foresee adherence behaviors based on characteristics like age, gender, health status, and income. In order to analyze and identify major drivers of non-adherence and enable personalized therapies, our method incorporates cutting-edge machine learning techniques. The methodology takes into account each individual’s situation while making suggestions, leading to a more proactive and specific strategy for improving drug adherence. Predictive analytics allows healthcare providers to foresee adherence issues, intervene at the right time, and boost patient outcomes. This research contributes to the developing landscape of healthcare by utilizing the potential of machine learning for precision medicine in drug management, addressing a vital part of patient care and public health.

In this paper, through a transformation in the random variable with log-normal distribution, a new distribution was introduced called unit log-normal. Some properties of this new distribution are investigated. It is shown that the unit log-normal distribution belongs to the exponential family and that the maximum likelihood estimators for the two parameters that index the distribution have closed-forms. The accuracy of the maximum likelihood estimators for the two parameters is performed through a simulation study. Two applications to real data are considered, showing the potentiality of the new distribution.

The paper collects the daily data from January 20th, 2020 to September 30th, 2020 and refers to the BW index model of Baker and Wurgler (2006) to construct the investor sentiment index of Shanghai stock market at the initial stage of COVID-19. Differ from the previous research, after excluding the influence of macroeconomics, the paper found that the investor sentiment shows a significantly negative relation with the SSE A-share Index.

In this study, we present the joint design for process mean, standard deviation, and tolerance. In the first phase, Taguchi’s quality loss and process adjustment cost are used. In the second phase, the quality investment and sampling inspection are addressed with quality protection under the specified capability value and consumer’s risk.

With evolving technology, Hindi web content is growing considerably and catching popularity as the larger audience feels more connected and heard using their native language. A volcanic growth in online movie reviews (MRs) in Hindi has been observed lately; manually analyzing them is impossible. Hence, the research problem of automatic organization and classification of Hindi reviews is apparent as this can help viewers decide whether a movie is worth watching or not. This work focuses on developing a deep learning-based system for bi-polar sentiment classification of MRs for resource deficient language – Hindi. To this end, a primary Hindi movie review (MR) corpus is made and manually annotated with binary polarity class labels - positive or negative. The corpus is preprocessed using the preprocessing steps, and Random Word Embeddings (WEs) are utilized for feature extraction. This paper proposes an ensemble CNN_BiGRU, which is an integration of 1D CNN with BiGRU for the bipolar classification of Hindi MRs. To prove our ensemble’s efficacy; other widely used mainstream deep learning models (DLMs) such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) based models – Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), are also applied and compared with proposed ensemble using average classification accuracy. Empirical results show the effectiveness of the proposed ensemble in achieving a reasonably good average accuracy of 89.366%. The results indicate that proposed CNN_BiGRU compares favorably to the state-of-the-art DLMs applied and hence gives an effective solution for sentence-level bi-polar classification of MRs in a resource deficient scenario.

Graphical User Authentication (GUA) is an image-based password system that leverages the human tendency to remember visuals more effectively. This methodology enables users to effortlessly generate and recall passwords. The three important parameters for any graphical based password system are Usability, Randomness and Security. Usability deals with the system adaptability, memorability, flexibility, User satisfaction etc. On the other side randomness is about Hotspot Coverage, Click Point Clustering, Spatial Patterns, J-statistics etc. This paper is an attempt to initially develop an image-based authentication system that is usable, venerable in terms of randomness. In addition, it is necessary to build a strong security authentication process where the attackers are unable to access the system. The past research study claims the diversity between the usability and security aspect. The focus of this research study is to analyze the impact of various security attacks on the proposed system and the possible solution to avoid them.

The Covid-19 pandemic has made telemedicine one of the most relevant topics in recent years, so data mining about telemedicine obtained from Twitter offers a unique opportunity. The motivation of this study is to identify the emotional groupings of Twitter data users’ opinions for telemedicine using opinion mining techniques such as Sentiment Analysis combined with high-dimensional data classification and prediction methods. Data was collected from Twitter in August and September of 2021 related to telemedicine and official World Health Organization and World Bank documents for the years 2018 and 2021, respectively. Sentiment Analysis showed that 56.2% (n = 5351 tweets) of the sample had predominantly positive emotions toward telemedicine. Telemedicine, telehealth, and ivermectin are the most frequent words in the word cloud. Twitter data users’ opinions consist of nine mood classes (SIL index = 0.80) and the differences between these classes are statistically significant in terms of positive (p < 0.05), negative (p < 0.05), and neutral (p < 0.05) emotions. Neural Network (AUC = 0.842, F1 = 0.492) and Random Forest (AUC = 0.841, F1 = 0.494) are the best predictors of Twitter users’ mood classes compared with other machine learning techniques. The pythagorean tree generated by Random Forest showed that retweet is the best predictor of Twitter users’ opinions and emotional classes towards telemedicine. Future studies will create big social media datasets for a deep understanding of the emotional classes of individuals towards telemedicine technologies.

Bernoulli selection processes and power contractions constitute two classes of probabilistic concepts of theoretical and practical importance. The paper’s theoretical contribution consists of the development of a characterization of power contractions through the use of Bernoulli selection processes. Moreover, the interpretations in risk management, stochastic discounting modelling, and investment evaluation of Bernoulli selection processes for power contractions constitute the practical contribution. In consequence, the presence of proactivity in these disciplines makes quite clear that the results of the paper facilitate the implementations of strategic thinking, decision making, and management operations.