A Review Study On Role Of AI In Healthcare

Artificial intelligence in healthcare is an overarching term used to describe the use of machine-learning algorithms and software, or artificial intelligence (AI), to mimic human cognition in the analysis, presentation, and comprehension of complex medical and health care data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data. This paper presents a review study on role of AI in healthcare in short.

Literature based study on Health Systems

Health system has been defined with a reductionist perspective. Some authors have developed arguments to expand the concept of health systems, indicating additional  dimensions that should be considered: Health systems should not be expressed in terms of their components only, but also of their interrelationships; Health systems should include not only the institutional or supply side of the health system but also the population; Health systems must be seen in terms of their goals, which include not only health improvement, but also equity, responsiveness to legitimate expectations, respect of dignity, and fair financing, among others; Health systems must also be defined in terms of their functions, including the direct provision of services, whether they are medical or public health services, but also "other enabling functions, such as stewardship, financing, and resource generation, including what is probably the most complex of all challenges, the health workforce." This paper is a review based study on existing literature.

Literature Study On Cloud Based Healthcare File Protection Algorithms

There is a huge development in Computers and Cloud computing technology, the trend in recent years is to outsource information storage on Cloud-based services. The cloud provides  large storage space. Cloud-based service providers such as Dropbox, Google Drive, are providing users with infinite and low-cost storage. In this project we aim at presenting a protection method through by encrypting and decrypting the files to provide enhanced level of protection. To encrypt the file that we upload in cloud, we make use of double encryption technique. The file is been encrypted twice one followed by the other using two algorithms. The order in which the algorithms are used is that, the file is first encrypted using AES algorithm, now this file will be in the encrypted format and this encrypted file is again encrypted using RSA algorithm. The corresponding keys are been generated during the execution of the algorithm. This is done in order to increase the security level. The various parameters that we have considered here are security level, speed, data confidentiality, data integrity and cipher text size. Our project is more efficient as it satisfies all the parameters whereas the conventional methods failed to do so. The Cloud we used is Dropbox to store the content of the file which is in the encrypted format using AES and RSA algorithms and corresponding key is generated which can be used to decrypt the file. While uploading the file the double encryption technique is been implemented

Literature Based Study On E-Learning Education In View Of COVID19

Learning Management System is a software based application which help us to administrate, document, track, report and evaluate the teaching learning process, training programs, virtual classes, and e-Learning programs. Functions of Learning Management System can be broadly divided into 4 major parts and then sub parts will be discussed under     the   main    category: Stakeholder Functionality-In this part the participant or stakeholder has their own space by which they are able to access the following services: View the status of course and completion of courses ; Facility to print or view the certificate- Content or Course Management; Manage, add or delete the content of the course or modules. Learning Management System has different categories depending upon their usage and accessibility

Review Study On Importance Of Rural Education In India

Quality and access to education is the major concern in rural schools as there are fewer committed teachers, lack of proper text books and learning material in the schools. Though Government schools exist, but when compared to private schools then quality is a major issue. Majority of people living in villages have understood the importance of education and know that it is the only way to get rid of poverty. But due to lack of money they are not able to send their children to private schools and hence depend upon government schools for education. Above that, in some of the government schools there is only one teacher for the entire school and if they don’t show up at work, then it is a holiday. If the quality along with number of teachers and, that too committed teachers can be improved in these schools, then aspiring rural children and India can fulfill their dreams of doing something great. Some government schools in rural India are overly packed with students, leading to a distorted teacher- student ratio. In one such remote village in Arunachal Pradesh there are more than 300 students in class X which makes nearly 100 students in each classroom. In such a situation it is impossible for teachers to pay full attention towards each and every student, even if they are willing to help

Literature Study On Some Computational Healthcare Systems For Sustainability

Medical data released and shared through the cloud are very popular in practice, and information and knowledge bases can be enriched and shared through the cloud. The revolution presented by the cloud and big data can have a huge impact on the healthcare industry, and a new healthcare system is evolving. This is why we need to design a more appropriate health care system to meet the challenges presented by this revolution. The diversity of data sources requires a uniform standard of heterogeneous data management. On the one hand, due to the diversification of medical equipment, the data formats and the amount of data generated by various devices may be quite different, which requires that the system support data access by various medical devices to ensure high scalability and satisfy actual medical needs. On the other hand, the system needs to convert the received data into a unified standard to improve the efficiency of data storage, query, retrieval, processing, and analysis. This paper presents Literature Study on Computational Healthcare Systems for sustainability

Literature Based Study On Sustainability Of Global Business In View Of Lock Down Situation

Global Business sustainability has come a long way. From the dawn of the modern environmental movement and the establishment of environmental regulations in the 1970s, it has become a strategic concern driven by market forces. Today, more than 90 percent of CEOs state that sustainability is important to their company’s success, and companies develop sustainability strategies, market sustainable products and services, create positions such as chief sustainability officer, and publish sustainability reports for consumers, investors, activists, and the public at large. In view of lock down situation, this trend will not abate anytime soon. Surveys show that 88 percent of business school students think that learning about social and environmental issues in business is a priority, and 67 percent want to incorporate environmental sustainability into their future jobs. To meet this demand, the percentage of business schools that require students to take a course dedicated to business

Literature Study on Data Protection for Cloud Storage

Many data security and privacy incidents are observed in today Cloud services. On the one hand, Cloud service providers deal with    a large number of external attacks. In 2018, a total of 1.5 million Sing Health patients’ non-medical personal data were stolen from the health system in Singapore. On the other hand, Cloud service providers cannot be entirely trusted either. Personal data may be exploited in a malicious way such as in the Face book and Cambridge Analytical data scandal which affected 87 million users in 2018. Thus, it becomes increasingly important for end users to efficiently protect their data (texts, images, or videos) independently from Cloud service providers. In this paper, we aim at presenting a novel data protection scheme by combining fragmentation, encryption, and dispersion with high performance and enhanced level of protection as Literature study

Geospatial Artificial Intelligence (GeoAI): Applications in Health Care

GeoAI is a new emerging research area that refers to set of technologies that integrate AI technology with a diversity of GIS (Geographic Information System) techniques. The present study observed that GeoAI goes beyond current GIS expectations and into the domain of possibility in the not-too-distant future. This emerging interdisciplinary science will lead us to sustainable decisions and explore the most suitable solutions to the existing problems. GeoAI has the potential to transform current geography and geomatics programs by incorporating a GeoAI dimension into modern GIS curricula. In this review, we have studied the application GeoAI in various healthcare fields. GeoAI has the potential to revolutionize healthcare, public health, infectious disease control, disaster aid, and the achievements of Sustainable Development Goals (SDG). in healthcare, GeoAI can help with disease diagnosis, treatment planning, and resource allocation. In public health, it can aid in disease surveillance, emergency response planning, and identifying health disparities. In infectious disease control, GeoAI can help predict and track disease outbreaks and support vaccination campaigns. In disaster aid, GeoAI can provide real time data on environmental hazards and their impact on public health. In achieving Sustainable Development Goals, it can support in land use planning, urban development, and resource allocation to promote health and environmental sustainability. Overall GeoAI has the potential to transform multiple sectors and improve the well-being of populations worldwide

A Survey of Quantum-safe Digital Signatures and their building blocks

Sikkerhet og personvern på internett som vi kjenner i dag, er truet av den nye teknologien for å bygge større og kommersielle kvantecomputere. Selv om kvantecomputere som er bygget er relativt små, og ikke veldig effektive for å forstyrre Public Key Infrastructure, er det likevel viktig å gjenkjenne den forestående trusselen. Dessuten har de berømte kvantealgoritmene på grunn av Shor, Grover og Brassard-Høyer-Tapp, rystet de klassiske grunnlagene for kryptografi med offentlig nøkkel. Disse årsakene har utløst NIST til å stå i spissen for PQC Standardiseringsprosjektet, for å muliggjøre kvalitetsutvikling og raskere distribusjon av kvantesikre kryptosystemer. Dette er for tiden i tredje runde med 7 finalister og 8 alternative kandidater - både nøkkelutvekslingsmekanismer og ordninger for digital signatur. For å forstå fremtiden for offentlig nøkkelkryptografi i post-quantumverdenen, ville den naturlige tilnærmingen være å utforske forskjellige matematiske problemer som ennå ikke kan løses av kvantecomputere. Bygget på denne forutsetningen presenterer denne avhandlingen en teoretisk studie av kryptografiske harde problemer som angivelig er kvantebestandige, og NIST runde 3 PQC signaturskjemaer konstruert av dem. For å forstå de praktiske implikasjonene av disse ordningene, er det i tillegg gjort en helhetlig evaluering av sikkerhetssikring og ytelse som en del av dette arbeidet. Open source-verktøy, som Open Quantum Safe (OQS)-prosjektet og PQClean, har blitt brukt i forbindelse med Dockercontainere for å evaluere disse ordningene kvantitativt. For å adressere den første delen av de ovennevnte forskningsspørsmålene, har kategoriene av kvantebestandige kryptografiske systemer blitt studert, nemlig (i) Latticebasert (ii) Multivariate kvadratiske (MQ) polynomer basert (iii) Hash-basert (iv) Kodebasert og (v) Isogeny-basert. I den andre delen har de seks signaturskjemaene blitt studert, som er (a) Hash / Symmetric-primitive based - Picnic og Sphincs+ (b) MQ polynomials based - GeMSS og Rainbow (c) Latticebasert - Crystals-Dilithium og Falcon. Opprinnelig inspirert av den enkle konstruksjonen av Picnic, har de 3 alternative kandidatene (i.e. Picnic, Sphincs+ og GeMSS) i NIST Round III blitt studert mer detaljert sammenlignet med finalistene (i.e. Dilithium, Falcon og Rainbow) som en del av dette arbeidet. Senere ble denne avgjørelsen også styrt av utviklingen i NIST PQC-standardiseringen, og nysgjerrigheten rundt hvorfor Picnic og de to andre ordningene ikke gjorde det som finalister. I tillegg til de kryptografiske konstruksjonene, har robustheten til ordningene gjennom deres sikkerhetsbevis og deres sårbarhet i forhold til forskjellige angrep også blitt diskutert. Videre ble det under den kvantitative analysen av ordningene innsett at GeMSS for øyeblikket ikke er i Liboqs-biblioteket til OQS. Derfor ble integrering av GeMSS i Liboqs, for å tillate evaluering av alle seks ordningene på en felles plattform og i kryptografiske protokoller som TLS, et av målene med dette arbeidet. På grunn av et nylig alvorlig nøkkelgjenopprettingsangrep [Tao et. al., IACR Cryptol. ePrint Arch 1424 (2020)] på det underliggende problemet med GeMSS, har denne oppgaven blitt ansett som irrelevant for nå. Den delvise implementeringen som er gjort til det punktet å vite om angrepet, har blitt presentert. Likevel er det gjort en omfattende sammenligning av alle de seks ordningene under hensyntagen til programvaren og maskinvarensimplementeringskompleksitet i disse ordningene. Observasjonen fra den kvantitative analysen av skjemaene har vært at Dilithium i programvareimplementeringer er den klart beste algoritmen når det gjelder ytelse og størrelse på komponentene (nøkler og signaturer). Imidlertid ser Rainbow ut til å ha veldig god ytelse i høyhastighets maskinvareimplementeringer. I denne forbindelse, til tross for at de er konstruert av enklere kryptografiske primitiver, har Picnic og Sphincs+ gjennomsnittlig ytelse sammenlignet med andre ordninger i begge plattformene. Når det gjelder den kvalitative analysen, er hver ordning sårbar for forskjellige angrep, og styrken og sikkerheten til en ordning bestemmes av dens evne til å motvirke kraftige angrep. Selv om symmetrisk baserte ordninger generelt antas å være kvantebestandige, og bare trenger å doble parametrene for å være sikre, er de fremdeles sårbare for angrep med flere mål. MQ-polynombaserte ordninger er derimot utsatt for angrep som utnytter deres matematiske konstruksjoner. Mens de latticebaserte ordningene er robuste når det gjelder konstruksjon, kan implementeringene være kompliserte og utsatt for generiske feil og sidekanalangrep som alle andre ordninger