Dementia is a neuropsychiatric brain disorder that usually occurs when one or more brain cells stop working partially or at all. Diagnosis of this disorder in the early phases of the disease is a vital task to rescue patients’ lives from bad consequences and provide them with better healthcare. Machine learning methods have been proven to be accurate in predicting dementia in the early phases of the disease. The prediction of dementia depends heavily on the type of collected data which usually are gathered from Normalized Whole Brain Volume (nWBV) and Atlas Scaling Factor (ASF) which are normally measured and corrected from Magnetic Resonance Imaging (MRIs). Other biological features such as age and gender can also help in the diagnosis of dementia. Although many studies use machine learning for predicting dementia, we could not reach a conclusion on the stability of these methods for which one is more accurate under different experimental conditions. Therefore, this paper investigates the conclusion stability regarding the performance of machine learning algorithms for dementia prediction. To accomplish this, a large number of experiments were run using 7 machine learning algorithms and two feature reduction algorithms namely, Information Gain (IG) and Principal Component Analysis (PCA). To examine the stability of these algorithms, thresholds of feature selection were changed for the IG from 20% to 100% and the PCA dimension from 2 to 8. This has resulted in 7×9 + 7×7= 112 experiments. In each experiment, various classification evaluation data were recorded. The obtained results show that among seven algorithms the support vector machine and Naïve Bayes are the most stable algorithms while changing the selection threshold. Also, it was found that using IG would seem more efficient than using PCA for predicting Dementia. These promising results open the door to a new era of early prognosis of Alzheimer’s Disease and Related Dementias (ADRD).

After the outbreak of COVID-19, Taiwan has implemented rigorous border control and taken specific measures such as virus detection, contact tracing, and quarantine since 2020. Its epidemic prevention performance has been quite outstanding. Even in May 2021, when the epidemic situation worsens, the people in Taiwan fully cooperate with the government’s control measures so as to successfully alleviate and control the epidemic in less than three months. Among them, the detection policy has played a pivotal role. We analyze and discuss the false positive and false negative problems from rapid antigen and PCR detection in the screening policy as well as the timing of using these two instruments. This paper provides theoretical verification of the appropriateness of screening policy in Taiwan, offering a few feasible suggestions for related policies in other countries or regions at different stages of this and other potential epidemics.

With increasing concerns and challenges to climate change in recent years, green operations management (GOM) has gained significant attention from society for achieving sustainable growth. GOM is a set of practices that can be applied in production processes to produce goods with improved productivity and significantly reduced threats of carbon emission to the environment and Mother Nature. GOM mainly includes green manufacturing, green design, green logistics, and green purchases. In the paper, fuzzy best-worst method (FBWM) is used to determine the best and worst criteria affected by GOM practices. Thus, the paper attempts to explicitly analyze and highlight the significance of GOM in preserving the environment and manage the triple bottom line for achieving overall sustainable business operations.

Financial system stability is not only supported by the banking sector, but also the role of insurance companies that operate efficiently. The study aims to analyze the efficiency performance of general insurance companies using two stages of data envelopment analysis during the 2017 – 2018 period. The first stage of efficiency measurement using a non-parametric data envelopment analysis (DEA) approach shows the efficiency level of general insurance companies experiencing a positive trend. The performance of general insurance companies in 2018 was more efficient than in 2017 based on the value of technical efficiency (CRS) and the value of pure technical efficiency (VRS). This means that in general there has been an increase in the efficiency of general insurance companies in Indonesia from 2017 to 2018. Testing the efficiency determinants in the second stage using the Tobit regression model found that the cost ratio is the only factor that significantly influences the efficiency level of general insurance companies in Indonesia. Meanwhile, company ownership and investment adequacy ratio have no significant effect on the efficiency level of general insurance companies in Indonesia. The results of the study provide recommendations to the management of general insurance companies that efficiency performance has not reached the maximum, and to improve it, it is necessary to control costs without disturbing routine operations and development activities.

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

The current study analyzed the determinants of the Peruvian coca illegal plantations in the period 2003-2019. Hence, the DEVIDA database variables were gathered at first. Then, a machine learning-based technique is employed to select the most relevant variables for the study. That technique, Lasso, selected as accurate variables eradication of coca plantations and pasta base. Both OLS and VAR are employed to analyze the relevance of the selected variables. OLS finds that eradication was negatively related to the dependent variable. Nonetheless, pb confiscation had a positive relationship with illegal coca crops. Furthermore, VAR encounters that only pb confiscation affected the dependent variable. Supplementary tests are carried to ensure the accuracy of the results. In consequence, it is concluded that eradication policies by themselves were not enough to discourage the coca plantations. Farmers should get instruction about alternative crops and financial help. Furthermore, it has been claimed that pb confiscation generates scarcity of the drug, which elevates its price. Thus, coca farmers are more motivated to plant coca because of the higher prices. Therefore, as long as the international demand, which is disposed to pay high prices, the coca illegal crops and its illicit products will exist.

Higher education institutions, like any business institution, should satisfy their clients (students) for them to survive in the higher education service business market. As a service business, higher education institutions also need to follow marketing principles in their attempt to attract potential students. We investigated the effect of marketing mix dimension on internal and external marketing in universities. The research used primary data from a questionnaire survey of 526 students in West Java Province, Indonesia, and then drew conclusions by a structural equation model (SEM) analysis. The research findings revealed that place, product, price and promotion have a positive effect on external marketing. Whereas physical evidence, people and processes have positive, significant effects on internal marketing. There was also positive, significant correlation between external marketing and internal marketing. The research findings were hopefully beneficial for higher education management, to be made as guidance in implementing their marketing strategy. Higher education leaders may apply the external marketing policy to attract potential student interest and the internal marketing policy to improve the quality of their service and internal marketing. The study delivered a broader picture of the application of marketing mix model on universities. In addition, the discussion presented the implication of the offered theory and practice, the research limitation, and the direction of future researchers.

In the current paper, the authors are intended to manufacture the aluminum based metal matrix composite (MMC) employing the stir casting process. Further, the fabricated composite sample is investigated for machining characteristics during the die sink electrical discharge machining process (EDM). EDM is most commonly employed to satisfy the special needs of industry such as developing deep holes and complex contours from high strength materials such as composites, alloys, smart materials, and functionally graded materials. In the current study A356 and 4%, tungsten carbide (WC) powder are considered as matrix and strengthening materials respectively to fabricate the MMCs. During the machining activity, the input factors like discharge current (Ip), Voltage (Vg), Pulse On-Time (Ton), and flushing pressure (P) are optimized for achieving optimum surface roughness (SR), Tool Wear Rate (TWR) and Material Removal Rate (MRR). To estimate the ideal set of process factors grey regression analysis (GRA) is used. From the results, it was observed that the GRA is found to perform better than the RSM.

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.