Strategic Initiatives in Hastening Transformation to Ultra Adaptive and Smart Cities

The study on SMART CITY evolution is an ongoing quest for sustainable innovation. A dedicated team of international subject matter experts from the disciplines of computer science, informational technology, management, and education undertook this study. The findings and formulated actionable initiatives are customizable and deployable as Knowledge and Strategic Actionable initiatives, specific to making a remarkable difference to smart cities anywhere in the world. The aim is to contribute to innovation, with beneficial ramifications to society. The possible contribution to economic growth, city development, entrepreneurship, may be through laying a foundation for critical thinking, knowledge, and SMART initiatives to flourish. Recommended Citation de Souza, L., Aravind, A., & Prabhu, S. (2020, October 1-2). Strategic initiatives in hastening transformation to ultra-adaptive and smart cities [Poster presentation]. Walden University Research Conference 2020 (online). https://scholarworks.waldenu.edu/researchconference/2020/posters/9

Growth of relational model: Interdependence and complementary to big data

A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system

Automated fundus image quality assessment and segmentation of optic disc using convolutional neural networks

An automated fundus image analysis is used as a tool for the diagnosis of common retinal diseases. A good quality fundus image results in better diagnosis and hence discarding the degraded fundus images at the time of screening itself provides an opportunity to retake the adequate fundus photographs, which save both time and resources. In this paper, we propose a novel fundus image quality assessment (IQA) model using the convolutional neural network (CNN) based on the quality of optic disc (OD) visibility. We localize the OD by transfer learning with Inception v-3 model. Precise segmentation of OD is done using the GrabCut algorithm. Contour operations are applied to the segmented OD to approximate it to the nearest circle for finding its center and diameter. For training the model, we are using the publicly available fundus databases and a private hospital database. We have attained excellent classification accuracy for fundus IQA on DRIVE, CHASE-DB, and HRF databases. For the OD segmentation, we have experimented our method on DRINS-DB, DRISHTI-GS, and RIM-ONE v.3 databases and compared the results with existing state-of-the-art methods. Our proposed method outperforms existing methods for OD segmentation on Jaccard index and F-score metrics

Supervised Learning Models for the Preliminary Detection of COVID-19 in Patients Using Demographic and Epidemiological Parameters

The World Health Organization labelled the new COVID-19 breakout a public health crisis of worldwide concern on 30 January 2020, and it was named the new global pandemic in March 2020. It has had catastrophic consequences on the world economy and well-being of people and has put a tremendous strain on already-scarce healthcare systems globally, particularly in underdeveloped countries. Over 11 billion vaccine doses have already been administered worldwide, and the benefits of these vaccinations will take some time to appear. Today, the only practical approach to diagnosing COVID-19 is through the RT-PCR and RAT tests, which have sometimes been known to give unreliable results. Timely diagnosis and implementation of precautionary measures will likely improve the survival outcome and decrease the fatality rates. In this study, we propose an innovative way to predict COVID-19 with the help of alternative non-clinical methods such as supervised machine learning models to identify the patients at risk based on their characteristic parameters and underlying comorbidities. Medical records of patients from Mexico admitted between 23 January 2020 and 26 March 2022, were chosen for this purpose. Among several supervised machine learning approaches tested, the XGBoost model achieved the best results with an accuracy of 92%. It is an easy, non-invasive, inexpensive, instant and accurate way of forecasting those at risk of contracting the virus. However, it is pretty early to deduce that this method can be used as an alternative in the clinical diagnosis of coronavirus cases

Predicting cervical cancer biopsy results using demographic and epidemiological parameters: a custom stacked ensemble machine learning approach

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine learning algorithms on multiple levels to produce reliable predictions. In the beginning, a deep exploratory analysis is conducted using univariate and multivariate statistics. Later, the one-way ANOVA, mutual information and Pearson’s correlation techniques are utilized for feature selection. Since the data was imbalanced, the Borderline-SMOTE technique was used to balance the data. The final stacked machine learning model obtained an accuracy, precision, recall, F1-score, area under curve (AUC) and average precision of 98%, 97%, 99%, 98%, 100% and 100%, respectively. To make the model explainable and interpretable to clinicians, explainable artificial intelligence algorithms such as Shapley additive values (SHAP), local interpretable model agnostic explanation (LIME), random forest and ELI5 have been effectively utilized. The optimistic results indicate the potential of automated frameworks to assist doctors and medical professionals in diagnosing and screening potential cervical cancer patients

Incorporating a Machine Learning Model into a Web-Based Administrative Decision Support Tool for Predicting Workplace Absenteeism

Productivity losses caused by absenteeism at work cost U.S. employers billions of dollars each year. In addition, employers typically spend a considerable amount of time managing employees who perform poorly. By using predictive analytics and machine learning algorithms, organizations can make better decisions, thereby increasing organizational productivity, reducing costs, and im-proving efficiency. Thus, in this paper we propose hybrid optimization methods in order to find the most parsimonious model for absenteeism classification. We utilized data from a Brazilian courier company. In order to categorize absenteeism classes, we preprocessed the data, selected the attributes via multiple methods, balanced the dataset using the synthetic minority over-sampling method, and then employed four methods of machine learning classification: Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), Artificial Neural Network (ANN), and Random Forest (RF). We selected the best model based on several validation scores, and compared its performance against the existing model. Furthermore, project managers may lack experience in machine learning, or may not have the time to spend developing machine learning algorithms. Thus, we propose a web-based interactive tool supported by cognitive analytics management (CAM) theory. The web-based decision tool enables managers to make more informed decisions, and can be used without any prior knowledge of machine learning. Understanding absenteeism patterns can assist managers in revising policies or creating new arrangements to reduce absences in the workplace, financial losses, and the probability of economic insolvency

Use of Machine Learning for Early Detection of Knee Osteoarthritis and Quantifying Effectiveness of Treatment Using Force Platform

Knee osteoarthritis is one of the most prevalent chronic diseases. It leads to pain, stiffness, decreased participation in activities of daily living and problems with balance recognition. Force platforms have been one of the tools used to analyse balance in patients. However, identification in early stages and assessing the severity of osteoarthritis using parameters derived from a force plate are yet unexplored to the best of our knowledge. Combining artificial intelligence with medical knowledge can provide a faster and more accurate diagnosis. The aim of our study is to present a novel algorithm to classify the occurrence and severity of knee osteoarthritis based on the parameters derived from a force plate. Forty-four sway movements graphs were measured. The different machine learning algorithms, such as K-Nearest Neighbours, Logistic Regression, Gaussian Naive Bayes, Support Vector Machine, Decision Tree Classifier and Random Forest Classifier, were implemented on the dataset. The proposed method achieves 91% accuracy in detecting sway variation and would help the rehabilitation specialist to objectively identify the patient’s condition in the initial stage and educate the patient about disease progression

Monitoring water hyacinth in Kuttanad, India using Sentinel-1 SAR data

Water Hyacinth is an aquatic macrophyte and highly invasive species, indigenous to Amazonia, Brazil and tropical South America. It was first introduced to India in 1896 and has now become and environmental and social nuisance throughout the country in community ponds, freshwater lakes, irrigation channels, rivers and most other surface waterbodies. Considering the adverse impact the infesting weed has, a constant monitoring is needed to aid policy makers involved in remedial measures. Due to the synoptic coverage provided by satellite imaging and other remote sensing practices, it is convenient to find a solution using this type of data. This paper looks at the use of Synthetic Aperture Radar (SAR) Sentinel-1 to detect water hyacinth at an early stage of its life-cycle. While SAR has been used prominently to monitor wetlands, the technique is yet to be fully exploited for monitoring water hyacinth and we seek to fill this knowledge gap. We compare different change detection methodologies based on dual po-larimetric data. We also demonstrate how Sentinel-1 can be used to monitor this type of aquatic weeds in our study areas, which is Vembanad Lake in Kuttanad, Kerala

Monitoring Aquatic Weeds In Indian Wetlands Using Multitemporal Remote Sensing Data With Machine Learning Techniques

The main objective of this paper to show the potential of mul-titemporal Sentinel-1 (S-1) and Sentinel-2 (S-2) for detection of water hyacinth in Indian wetlands. Water hyacinth (Pontederia crassipes, also called Eichhornia crassipes) is one of the most destructive invasive weed species in many lakes and river systems worldwide, causing significant adverse economic and ecological impacts. We use the expectation maximization (EM) as a benchmark machine learning algorithm and compare its results with three supervised machine learning classifiers, Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbour (kNN), using both synthetic aperture radar (SAR) and optical data to distinguish between clean and infested waters