Mining Public Opinion about Hybrid Working With RoBERTa

As the businesses recover from the COVID-19 epidemic, a new working paradigm is emerging: the hybrid work arrangement. A hybrid work method is a working approach that enables workers to work from several places, such as at home, on the move, or in the workplace. People are expressing their opinions on different social media outlets about the new work model. Organizations and businesses value public views. Because public perspectives will allow decision-makers to adapt promptly to rapidly transforming cultural, commercial, and social environments. Opinion mining is traditionally used to summarize the quantity of positive and negative responses in a given text using sentiment analysis techniques. Opinionated material from social media sites is used to identify people's enthusiasm or displeasure with a certain issue under debate. This study analyzes the public sentiments (positive, negative, and neutral) on a hybrid work model using Twitter API and the Robustly Optimized BERT Pre-training Approach (RoBERTa).   Out of 1 thousand tweets containing the term “hybrid work”, 37 (4.2%), 305 (33.3%), and 658 (62.5%) tweets were classified as negative, neutral, and positive, respectively.  We also compared the public sentiments about hybrid work with those of remote work. The RoBERTa classified 8(1.6%), 436 (85.9 %), and 62 (12.5%) tweets as negative, neutral, and positive, respectively.  The results showed that The majority of individuals showed favorable sentiment toward the hybrid work arrangement. The findings also demonstrate that “hybrid work” has an affinity with “remote work”, “ai”, “digital transformation” and “future of work”

The Impact of Artificial Intelligence Integration on Minimizing Patient Wait Time in Hospitals

Reduced patient wait-times benefit not just patients' health but also the overall efficiency of the healthcare system, which is particularly crucial given the aging population and rising demand for medical services in recent decades. Reducing the time that outpatients have to wait is one of the most crucial actions that must be taken to improve the patient experience. Artificial intelligence and machine learning may be applied in health care and medicine to enhance insights, reduce waste and wait time, and increase speed, service efficiency, accuracy, and efficiency. The purpose of this research is to determine whether or not the deployment of AI in hospital management system help reduce the amount of time that patients have to wait for their appointments. The Random Forest Regression, Pairwise multiple regression, and the pairwise Pearson correlation have been performed. This research also included additional features such as the number of the office personnel, the number of doctors, the quantity of equipment, and the health expenses in order to eliminate any potential omitted variable biases. According to the findings of the Random Forest Regression, the integration of AI and ML seems to be required to cut down on the amount of time that patients have to wait. The size of the office personnel, the number of doctors, and the number of pieces of equipment are found to be significant factors in lowering the amount of time spent waiting. It was determined that the aspect of the cost was the least significant in terms of reducing the amount of time spent waiting. According to the findings of our study, the healthcare care center needs to expand the integration of AI in order to cut down on the waiting time for patients and to improve the overall experience they provide for them. The findings also suggest that wait times depend on many factors. Thus, focusing on a few factors does not significantly reduce wait time

Leveraging Predictive Modeling, Machine Learning Personalization, NLP Customer Support, and AI Chatbots to Increase Customer Loyalty

AI, ML, and NLP are profoundly altering the way organizations work. With the increasing influx of data and the development of AI systems to understand it in order to solve business challenges, the excitement surrounding AI has grown. Massive datasets, computer capacity, improved algorithms, accessible algorithm libraries, and frameworks have compelled today's organizations to use AI to enhance their operations and profits. These technologies aid every kind of industry, from agriculture to finance. More specifically, AI and ML, and NLP are assisting organizations in areas such as customer service, predictive modeling, customer personalization, picture identification, sentiment analysis, offline and online document processing. The purpose of this study was twofold. We first review the several applications of AI in business and then empirically test whether these applications increase customer loyalty using the datasets of 910 firms around the world.  The datasets include the integration scores of four different AI features, namely, AI-powered customer service, predictive modeling, ML-powered personalization, and natural language processing integration. The target is the customer loyalty measure as binary. All the features are measured on a 5-pint Likert scale. We applied six different supervised machine learning algorithms, namely, Logistic regression, KNN, SVM, Decision Tree, Random Forest, and Ada boost Classifiers. the performance of each algorithm was evaluated using confusion matrices and ROC curves. The Ada boost and logistic classifiers performed better with test accuracies of 0.639 and 0.631, respectively. The decision tree and KNN had the performance with accuracies of 0.532 and 0.570, respectively.  The findings of this study highlight that by incorporating AI, ML, and NLP, businesses may analyze data to uncover what's useful, gaining valuable insights that can be used to automate processes and drive business strategies. As a result, firms that wish to remain competitive and increase customer loyalty should adopt them

Competition between antiferromagnetism and superconductivity, electron-hole doping asymmetry and "Fermi Surface" topology in cuprates

We investigate the asymmetry between electron and hole doping in a 2D Mott insulator, and the resulting competition between antiferromagnetism (AF) and d-wave superconductivity (SC), using variational Monte Carlo for projected wave functions. We find that key features of the T = 0 phase diagram, such as critical doping for SC-AF coexistence and the maximum value of the SC order parameter, are determined by a single parameter which characterises the topology of the "Fermi surface" at half filling defined by the bare tight-binding parameters. Our results give insight into why AF wins for electron doping, while SC is dominant on the hole doped side. We also suggest using band structure engineering to control the parameter for enhancing SC.Comment: 4 pages, 4 figure

The Determinants of AI Adoption in Healthcare: Evidence from Voting and Stacking Classifiers

Artificial intelligence (AI) has emerged as a disruptive force in the healthcare industry, driving new breakthroughs that promise to enhance treatment outcomes while simultaneously lowering costs. Artificial intelligence in healthcare has demonstrated promise to help doctors and patients at each step of the healthcare system, from an accurate diagnosis to urgent monitoring of patients and self-management of long-term illness. Despite physician and administrative interest, the use of these technologies in healthcare institutions remains limited. We hypothesized that risks such as black box issue, error rate, and legal risks. Similarly, technical combability in healthcare centers stemming from cloud adoption, the presence of IT skills in healthcare, and digitalized healthcare records significantly explain the AI adoption in healthcare. To test our hypotheses, we applied Ensemble Voting Classifier and Stacking Classifier algorithms. The ensemble voting classifier outperforms the stacking classifier in terms of accuracy. Our findings indicate that majority of healthcare institutions with limited technological compatibility and high perceived risks have no plans to use artificial intelligence at this time. The majority of healthcare institutions with moderate risk perceptions and moderate technical combability are indecisive about integrating artificial intelligence. Healthcare facilities with good technological combability and low (AI) perceived risks are either uncertain or eager to use artificial intelligence approaches. Both classifiers yielded almost identical results, demonstrating the validity of our empirical findings

Choosing Optimal Locations for Temporary Health Care Facilities During Health Crisis Using Binary Integer Programming

An Alternative Care Site (ACS) is a healthcare facility constructed in a non-traditional environment during a public-health crisis to provide extra capacity to provide medical care within a specific region. Many nations are proposing to develop ACS to improve capacity in response to expected resource shortages associated with COVID-19 treatment and testing. In many situations, these facilities are meant to be interim and are designed to satisfy an urgent need during a health crisis. When selecting where to establish new temporary facilities various variables need to be addressed, including the practicality of possible locations, current resource availability, projected use, and proximity between people and the new location. In this research, a facility site optimization model was designed using Binary Integer Programming to enable decision makers to select the optimal area, or locations, to construct a healthcare facility to satisfy predicted medical needs. The proposed model is concerned with the best positioning of ACS from a collection of candidate sites in order to reduce the travel distance between the healthcare facilities and the patients. Patients are presumed to be serviced by the facility that is geographically nearest to them. When the number of patients to be considered is excessive, it is possible to organize the patients into clusters. Then, instead of using the individual patient locations, the cluster centers can be used. This pre-processing works on the premise that the healthcare facility that is tasked with serving a particular cluster's customers will care for all of the patients who belong to that cluster. We showed 3 different case scenarios with varying parameters. Within the framework of Binary Integer Programming, the k-means method is employed, which seeks to split n patients into k unique and non-overlapping groups

Optimizing OR Efficiency through Surgical Case Forecasting with ARIMA Averaging

Operating rooms, often known as ORs, are among the most critical parts in hospitals, and their performance has a considerable bearing on how well the hospital functions as a whole. Uncertainty contributes significantly to the difficulty of an operating room. Credible forecasts are essential for operating room efficiency because they can provide signals for the monitoring of surgical overflows in periods of peak and trough demand for surgery; and minimize the related costs in equipment and workforce redundancy, and improve overall health care services. Optimizing the efficiency of the operating room has significant consequences for cost reductions, patient happiness, and the morale of the surgical department. Forecast averaging, also known as prediction combining, is a system for merging several predictions into a single prediction, which is often a better way than deciding which one forecast was best out of the available individual predictions. We applied the ARIMA Forecast Averaging method to demonstrate the surgical volume case predictions. We also showed that in forecasting surgical volume cases, the ARIMA models with lower AR and MA terms performed well in terms of different model selection criteria such as AIC. BC, and HQ. Medical care service problems are caused not just by a mismatch between resource demand and supply, but also by poor management. Operating rooms requires a significant investment of both time and money. Ineffective usage of operating rooms results in lost efforts and time, increased expenses, and a lower number of patients treated compared to what was originally anticipated. This cluster of problems leads to losses as well as a reduction in the level of satisfaction experienced by patients. We argued that the cost of usage of the operating room (OR) may be significantly decreased by increasing the predictive accuracy of the surgical case volume

Virtual Employee Monitoring: A Review on Tools, Opportunities, Challenges, and Decision Factors

There has been a significant reduction in human-to-human contact since the beginning of the COVID-19 epidemic. Many workplaces have taken the initiative to allow staff to work from home. However, monitoring workers and determining whether or not they are executing the tasks assigned to them has proven to be a significant difficulty for all firms and organizations that facilitate Work From Home. Individual workers' hours of work, presence, and active, idle, and break periods are being automatically tracked. To increase accountability, the program may take computer screen captures at random or at predetermined intervals and check the remote team's online activity and analytics in real-time to see how they are spending their time and where they might improve. This research discusses the 4 popular mentoring tools, namely, Virtual time tracking (VTT), Random screen capture (RSC), Tracking of Websites and Apps, and Face Identification/biometrics. We also examined the opportunities these tools offer and the challenges they pose. Finally, we briefly outlined various decision factors before implanting remote employee monitoring. We argue that a firm must first assess the local legal framework before employing staff monitoring and should assess if their industry is conducive to monitoring. Finally, Employee monitoring will be effective only if the necessary information technology infrastructure is in place

Multi-class Brain Tumor Detection using Convolutional Neural Network

Brain tumour detection is one of the most critical and arduous function in the domain of healthcare. Brain tumour, if not detected at an early stage, can be fatal. At the present time, detection and classification of brain tumour is done by the method of Biopsy which is very time-consuming and complex. . By looking at the brain MRI or CT scan, it is possible for the experts to identify whether tumour is present or not and the region of the tumour, but it is difficult to identify the small dissimilarities in the structure of tumour and classify it into types. Hence this manual process gets stuck here for verification of type of tumour. For the sole purpose overcoming the above-mentioned gigantic hurdles we have pursued this research of multi-class brain tumour detection using deep learning. Our project will help doctors in quick decision-making regarding detection of the tumour and its type as well, and due to the early detection of the disease the treatment can be initiated at the right time, resulting in speedy recovery of the patient. We propose a deep learning model employing Convolutional Neural Network architecture which we have implemented using Keras and Tensorflow because it yields to a better performance than the traditional ones. In our research work, CNN gained an accuracy of 94.95%. Further, we have integrated our model with a web-app which we have built using Streamlit. Hence, users can provide their MRI scans via our web-app and get their medical results in a quick and efficient manner