Predicting Financial Distress Within Indian Enterprises: A Comparative Study on the Neuro-Fuzzy Models and the Traditional Models of Bankruptcy Prediction

The financial distresses is of major importance in the financial management system particularly in the case of this competitive environs. There are several traditional methods existing for predicting the financial distress within the country. Major factors influencing the financial distress is the stock market, credit risk and so on. Hence there is a need of models which could make dynamic predictions with the use of dynamic variables. There are several machine learning and artificial intelligence-based bankruptcy prediction models available. The neural network concepts and the computational intelligence-based methods are highly acceptable in the prediction arena. This research presents a comprehensive review of the existing prediction approaches and suggests future research directions and ideas. Some of the existing methods are support vector machines, artificial neural network, multi-layer perceptron, and the linear models such as principal component analysis. Neuro-fuzzy approaches, Deep belief neural networks, Convolution neural networks are also discussed

Experimental data of designing an optimal system for storage, collection and transfer of household waste in the GIS environment: A case study of Tehran, district 22, Iran

This study was conducted to correctly manage the system of storage, collection and transfer of wastes in district 22, Tehran. After reviewing existing methods, an optimal system was designed in the GIS environment and appropriate solutions were suggested. Analytical Hierarchy Process (AHP) method was used. After extracting result criteria, these criteria were provided to 15 experts and managers by means of a Delphi questionnaire. Screening of the criteria was done using the criterion importance graph; a necessary condition to apply criteria and sub-criteria, is having at least half the numerical value of each vertical and horizontal vector. The results of the study showed that the most important criterion associated with the selection of waste transfer station is ''distance from residential houses'' with a final weight of 0.341. ''Suitable traffic conditions'' and ''lack of noise pollution'' are the next important criteria with weights of 0.259 and 0.118, respectively. Finally, ''non-destruction of recreational facilities'' was chosen as the least important (weight of 0.03). Transfer in this district is also 100% mechanized. At the district level, there are 10 garbage trucks, of which 7 collect during night and 3 during day. Given per capita of the district, it takes about 10 min to collect each ton of waste. In general, in order to investigate and plan specific methods in the study district, using Geographic Information System, the location of reservoirs in residential and commercial districts has been determined and suggested with a coefficient of 0.75. Keywords: Household waste, Storage, Collection, Transfer, GI

Antimicrobial resistant strains of Salmonella typhi: The role of illicit antibiotics sales, misuse, and self-medication practices in Pakistan

Typhoid fever, caused by the bacterium Salmonella typhi, is an often-fatal illness prevalent in Africa and South Asia. The illness has seen an alarming rise in multi-drug-resistant (MDR) and extensive drug-resistant (XDR) strains, particularly in Pakistan. The MDR strain links to the H58 haplotype, and its XDR variant exhibits fluoroquinolone resistance due to an IncY plasmid. The increasing prevalence of these resistant strains is concerning, given the global antimicrobial resistance (AMR) issue. Causes include misuse of antibiotics in self-limiting infections and an unregulated drug market. Pakistan's Sindh province first reported the XDR typhoid strain, highlighting the urgent need to investigate the relationship between AMR development and external factors. This narrative review intends to scrutinize the state of AMR in Pakistan, considering illicit drug sales, healthcare worker education gaps, and self-medication behaviors

Cell state prediction through distributed estimation of transmit power

Determining the state of each cell, for instance, cell outages, in a densely deployed cellular network is a difficult problem. Several prior studies have used minimization of drive test (MDT) reports to detect cell outages. In this paper, we propose a two step process. First, using the MDT reports, we estimate the serving base station’s transmit power for each user. Second, we learn summary statistics of estimated transmit power for various networks states and use these to classify the network state on test data. Our approach is able to achieve an accuracy of 96% on an NS-3 simulation dataset. Decision tree, random forest and SVM classifiers were able to achieve a classification accuracy of 72.3%, 76.52% and 77.48%, respectively .peerReviewe

Frequency of COVID‐19 vaccine side effects and its associated factors among the vaccinated population of Pakistan: A cross‐sectional study

Abstract Background Coronavirus disease 2019 (COVID‐19) vaccine side effects have an important role in the hesitancy of the general population toward vaccine administration. Therefore, this study was conducted to document the COVID‐19 vaccine side effects in our population. Materials and Methods An online survey‐based, cross‐sectional study was carried out from September 1, 2021, to October 1, 2021, to document the side effects of the COVID‐19 vaccine among the general public. The questionnaire included participants’ sociodemographic data, type of vaccine, comorbidities, previous COVID‐19 infection, and assessment of side effects reported by them. Results The majority of the participants were <20 years of age (62.2%), females (74.9%), belonged to the educational sector (58.1%), residents of Sindh (65.7%), and were previously unaffected by COVID‐19 infection (73.3%). Sinovac (38.7%) followed by Sinopharm (30.4%) and Moderna (18.4%) were administered more frequently. Commonly reported side effects were injection site pain (82%), myalgia (55%), headache (46%), fatigue/malaise (45%), and fever (41%). Vaccine side effects were more likely to be reported with the first dose as compared to the second dose. On regression analysis, factors associated with occurrence of side effects included younger age (odds ratio [OR]: 6.000 [2.065–17.431], p < 0.001), female gender (OR: 2.373 [1.146–4.914], p = 0.020), marital status (OR: 0.217 [0.085–0.556], p < 0.001), graduate level of education (OR: 0.353 [0.153–0.816], p = 0.015), and occupation being either retired, freelancers, or social workers (OR: 0.310 [0.106–0.909]), p = 0.033). Previous infection with COVID‐19 (p = 0.458) and comorbidities were found unrelated (p = 0.707) to the occurrence of side effects. Conclusion The overall prevalence of local side effects was quite higher than the systemic ones. Further large‐scale studies on vaccine safety are required to strengthen public confidence in the vaccination drive