EFFECTIVE METHODS AND TOOLS FOR MINING APP STORE REVIEWS

Research on mining user reviews in mobile application (app) stores has noticeably advanced in the past few years. The main objective is to extract useful information that app developers can use to build more sustainable apps. In general, existing research on app store mining can be classified into three genres: classification of user feedback into different types of software maintenance requests (e.g., bug reports and feature requests), building practical tools that are readily available for developers to use, and proposing visions for enhanced mobile app stores that integrate multiple sources of user feedback to ensure app survivability. Despite these major advances, existing tools and techniques still suffer from several drawbacks. Specifically, the majority of techniques rely on the textual content of user reviews for classification. However, due to the inherently diverse and unstructured nature of user-generated online textual reviews, text-based review mining techniques often produce excessively complicated models that are prone to over-fitting. Furthermore, the majority of proposed techniques focus on extracting and classifying the functional requirements in mobile app reviews, providing a little or no support for extracting and synthesizing the non-functional requirements (NFRs) raised in user feedback (e.g., security, reliability, and usability). In terms of tool support, existing tools are still far from being adequate for practical applications. In general, there is a lack of off-the-shelf tools that can be used by researchers and practitioners to accurately mine user reviews. Motivated by these observations, in this dissertation, we explore several research directions aimed at addressing the current issues and shortcomings in app store review mining research. In particular, we introduce a novel semantically aware approach for mining and classifying functional requirements from app store reviews. This approach reduces the dimensionality of the data and enhances the predictive capabilities of the classifier. We then present a two-phase study aimed at automatically capturing the NFRs in user reviews. We also introduce MARC, a tool that enables developers to extract, classify, and summarize user reviews

A Conceptual Architecture for a Quantum-HPC Middleware

Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) coupled to classical computing nodes (HPC). With the increasing scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum-HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high-performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly.Comment: 12 pages, 3 figure

Catalytic Activity of Iron N-Heterocyclic Carbene Complexes

Recent research towards development of more efficient as well as cost effective catalyst as a substitute to traditional precious metal catalysts has witnessed significant growth and interest. Importance has been given to catalyst based on 3d-transition metals, especially iron because of the broad availability and environmental compatibility which allows its use in various environmentally friendly catalytic processes. N-Heterocyclic carbene (NHC) ligands have garnered significant attention because of their unique steric and electronic properties which provide substantial scope and potential in organometallic chemistry, catalysis and materials sciences. In the context of catalytic applications, iron-NHC complexes have gained increasing interest in the past two decades and could successfully be applied as catalysts in various homogeneous reactions including C–C couplings (including biaryl cross-coupling, alkyl-alkyl cross-coupling, alkyl-aryl cross-coupling), reductions and oxidations. In addition to this, iron-NHC complexes have shown the ability to facilitate a variety of reactions including C-heteroatom bond formation reactions, hydrogenation and transfer-hydrogenation reactions, polymerization reactions, etc. In this chapter, we will discuss briefly recent advancements in the catalytic activity of iron-NHC complexes including mono-NHC, bis-NHC (bidentate), tripodal NHC and tetrapodal NHC ligands. We have chosen iron-NHC complexes because of the plethora of publications available, increasing significance, being more readily available, non-toxic and economical

A Conceptual Architecture for a Quantum-HPC Middleware

Quantum computing promises potential for science and industry by solving certain computationally complex problems faster than classical computers. Quantum computing systems evolved from monolithic systems towards modular architectures comprising multiple quantum processing units (QPUs) coupled to classical computing nodes (HPC). With the increasing scale, middleware systems that facilitate the efficient coupling of quantum-classical computing are becoming critical. Through an in-depth analysis of quantum applications, integration patterns and systems, we identified a gap in understanding Quantum-HPC middleware systems. We present a conceptual middleware to facilitate reasoning about quantum-classical integration and serve as the basis for a future middleware system. An essential contribution of this paper lies in leveraging well-established high-performance computing abstractions for managing workloads, tasks, and resources to integrate quantum computing into HPC systems seamlessly

Study to determine serum vitamin D levels in patients with congestive heart failure

Background: It was to evaluate the association of serum levels of vitamin D in patients with congestive heart failure.Methods: The present study was conducted in the department of Medicine at Chattrapati Shivaji Subharti Hospital among 100 patients, aged 18 years and above diagnosed as congestive heart failure on the basis of clinical and echocardiographic evidence. Clinical manifestations looked for CHF were: Dyspnea, orthopnea, acute pulmonary edema, cerebral symptoms, cheyne-stokes respiration, cyanosis, sinus tachycardia, raised jugular venous pressure, congestive hepatomegaly and pedal edema. In the present study deficiency/ insufficiency of vitamin D was considered when the presence of levels of 25-hydroxyvitamin D was 30 respectively with statistically significant difference. The Mean±SD scores of CPK MB (IU/L) was found to be 33.1±20.8 and 18.6±13.3 among the subjects having vitamin D levels 30 respectively with statistically significant difference.Conclusions: The results of the present study suggest that low levels of vitamin D may adversely affect the cardiovascular system

Deep learning approach for discovery of in silico drugs for combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19. [Abstract copyright: Copyright © 2021 Nishant Jha et al.

WEARABLE SMART SYSTEM FOR EARLY DETECTION OF HEART ATTACK

In this invention developed will help in detection of all kinds of heart attacks along with silent heart attack when a person is sleeping. Various devices are there in the market for measuring the heart rates but none of them detects heart attack. The device developed by us will not only detect heart attack but when integrated with suitable machine learning algorithms will predict the future heart attack by analysis of heart rates and will alert the user to consult the doctor. The sensors used in our device will be able to detect the level of heartbeat and will alert the emergency contacts or the person himself to reach to the hospital if the heartbeat level rises to a certain limit. The device will also take care of other factors like high sweating and rise/fall in body temperature of the person. Our device will be using two circuits, namely a transmitter circuit which will be used for monitoring the heart rate and the other will be a receiver circuit which will be under supervision of the doctors.</p

AN IOT BASED SINGLE USE DEVICE FOR COLLECTING BLOOD SAMPLES

Disclosed herein an IoT based single use device for collecting blood samples comprises a syringe (101), display of collected sample (102), collector (103), Battery (104), sample collection device (105), and capacitive liquid sensor (106). The device is an IoT based device which collects a fixed volume of blood for testing and analysis. The device is easy to use no training program is required for its use. The device prevents the wastage of samples and will take only the volume required for performing tests. The system will be single use and the storage container will be made of eco-friendly material such as bamboo containers which would be easy to dispose of. This will limit the medical waste which is otherwise very difficult to dispose of. The device would be made by keeping in mind the sustainable development goals.</p

DISPOSABLE FACE MASKS AS BREATHALYSERS FOR REAL TIME MONITORING OF HEALTH OF LUNGS

Disclosed herein a method of disposable face masks as breathalyzers for real time monitoring of health of lungs comprising collection of breath samples from mouth piece, analyzing of environment through wireless sensors, application of machine learning algorithm for training and analyzing of data, analysis of foreign components, evaluation of set parameters so that results display on the mobile applications. A Breathalyzer is a device used for measurement for breath alcohol content (BrAC) from a person; which is also used for detection of viruses or diseases from a breath sample. During respiration, the alcohol in the blood vaporizes and carries out to lungs in the exhaled breath, which offers highly accurate results and are very easy to use for screening purposes.</p

CONTACTLESS MONITORING AND ANALYSIS SYSTEM OF BLOOD PRESSURE OF PATIENTS DURING COVID-19 USING BLOCKCHAIN AND MACHINE LEARNING

This invention is about developing a smartphone application based on integration of block chain with image processing and machine learning techniques, which can analyse the blood pressure of patients during COVID-19 and characterizing them into its various types such as low, high or neutral (normal). Various characteristics such as facial analysis, gesture recognition, body movements, etc is to be captured through a camera and then image processing techniques are applied and then this data is stored on a decentralized server developed using block chain. The block chain server/ application will help the doctors and patients to access the patient history, medical records and can upload or send the BP records remotely and securely on the block chain server so that the patient without coming to the hospital can collect the reports and thus data dispersal can be avoided.</p