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  Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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Journal of Integrated Science and Technology
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2321-4635
Published by Integrated Science Publishing Homepage  [3 journals]
  • Framework, semantic and standard approaches in multi-clouds to achieve
           interoperability: A survey

    • Authors: Zameer Ahmed Adhoni; Dayanand Lal N
      Abstract: Cloud computing being the latest powerful way of storing data and hiring services on a server with no burden of hardware procurement. The facility to access data from the comfort of office without having the server physically has raised huge interest in the industry. Number of new cloud service providers has emerged with their own agreements and business models. Over a period of time clients have showed a proclivity towards switching cloud service providers for various reasons ranging from cost, efficiency, nature of business, operability, services, uptime and so on. There are clients who wish to draw more benefit from having multi cloud operations again due to various business reasons. This leads to need of amalgamation of clouds, interfaces between clouds, Application Programming Interfaces (API), collaboration of services and so on. This paper presents the existing different approaches so far which are popular along with the need for further research with respect to semantics, stndard and framework of cloud for interoperability. There are however semantic, standard and Framework endeavors are deficient. The objective of the research is to feature the difficulties changes needed in the semantic of cloud for operating on multi clouds distinguishing semantic, standard and Framework activity that would be expected for relocating and coordinating services in the multi-cloud environment sooner rather than later.
      PubDate: Wed, 01 Jun 2022 00:00:00 +053
  • Association of gender, age, and comorbidities with COVID-19 infection in

    • Authors: Sunita Kumari Yadav; Priya Bhardwaj, Praveen Gupta, Daman Saluja, Sunita Jetly, Jyoti Taneja
      Abstract: Due to a lack of data on various parameters with COVID-19 in the Indian population, this study was carried out to understand the relation among gender, age and comorbidities in Indian population. The data was collected using a questionnaire-based survey form that included questions on demographic characteristics, infection and any pre-underlying conditions (n=1146). The data showed that the male patients had suffered more from COVID-19 (58.6%). Also, the patients suffering from comorbidity are more likely to suffer from a severe form of COVID-19 and obesity/overweight was identified as the most prevalent (n=69) comorbid condition, followed by diabetes (n=35), thyroid (n=19) and hypertension (n=11). In severe COVID-19 cases, 85% of patients had a comorbid condition. In another study of COVID-19 hospitalized-cases, about 97% of patients were found to have an underlying medical condition. Among these, diabetes (55.9%) was identified as the most prevalent comorbidity. Males and older people are at a higher risk of developing COVID-19 infection in Indian population. The comorbid conditions also predisposed individuals to COVID-19 and aggravated the infection.
      PubDate: Mon, 25 Apr 2022 21:13:57 +053
  • An effective feature descriptor method to classify plant leaf diseases
           using eXtreme Gradient Boost

    • Authors: A Usha Ruby; Chaithanya B N, Swasthika Jain T J, Smita Darandale, Sudarshana Kerenalli, Renuka Patil
      Abstract: Identifying plant leaf diseases will be highly difficult due to the difficulties in gathering lesion characteristics from a quickly changing atmosphere, imbalanced illumination reflection of the incoming light source, and numerous other factors. A practical strategy for classifying plant leaf diseases is provided in this research. Using HSV, HU moments, and color histograms, we first created a leaf feature improvement framework that can enhance leaf characteristics in a complicated environment. Then, to increase feature classification capacity, a competent extreme boost method is modelled. Batch normalization is used to avoid network overfitting while also improving the model's resilience. The plant leaf disease feature improvement approach is favorable to boosting the efficiency of the XGBoost classification, as demonstrated in studies from various perspectives. For plant leaf disease photos obtained in the natural environment, our technique displays significant resilience, serving as a benchmark for the intelligent categorization of additional plant leaf diseases.
      PubDate: Sat, 05 Mar 2022 00:00:00 +053
  • Design of efficient S-box for Advanced Encryption Standard

    • Authors: Sarita Devanand Sanap; Vijayshree More
      Abstract: In digital era, data security is a necessary requirement. To establish secure communication modern encryption techniques plays a vital role. By employing an efficient S-box constraints of area, power and speed are achievable. In this paper method for efficient S-box is presented which provides promising solution in terms of required constraints. Comparison of proposed method with other existing method is also done by implementing it on field programmable gate array .It shows that proposed method uses only 6.14% slices resulting 13% improvement in comparison with other methods. Reduction in LUTs are done by 12.42 % in proposed method. Thus optimization is achieved in terms of number of slices and number of LUTs. Delay and memory usage is also reduced significantly.
      PubDate: Sat, 05 Mar 2022 00:00:00 +053
  • Review of computational approaches to model transcranial direct current
           stimulations tDCS and its effectiveness

    • Authors: Utkarsh Vinodchandra Pancholi; Vijay Dave
      Abstract: Neurological and psychological disorders are being treated by health professionals using medical technologies including drug therapy, electrical stimulation, and psychotherapy in some cases. Because of side effects caused by required drugs and social stigma for psychotherapy, these techniques have some limitations for their applicability in Mild cognitive impairment (MCI), Alzheimer’s disease (AD), Huntington disease (HD), dementia, major depressive disorder (MDD) and related neurological abnormalities.  Transcranial direct current stimulation is a non-invasive brain stimulation (NIBS) technique that uses small currents to alter characteristics of a healthy and diseased neuron. Even though sophisticated tDCS devices are being used for treatment, treatment protocol and its efficacy is still a debatable question. Researchers have found ways to model tDCS computationally to know the outcome of treatment. This review provides details of computational approaches used to model tDCS. We have reviewed clinical and computational practices carried out by researchers to model treatment modality for tDCS. 
      PubDate: Fri, 25 Feb 2022 12:49:30 +053
  • The COVID-19 havoc and clues from Sex disaggregated data in the Indian

    • Authors: Divya Bajaj; Varunendra Singh Rawat, Kanika Malik, Neetu Kukreja Wadhwa
      Abstract: The coronavirus infectious disease (COVID-19) has created a turmoil across the globe, with India emerging as one of the worst-hit countries. The Global scenario indicates a gender bias with a higher COVID-19 Case fatality rate (CFR) in males as opposed to females. However, countries like India, Nepal, Vietnam and Slovenia have reported a reverse trend in mortality. Real-time disaggregated data empowers countries to design more effective, sustainable, and people-centered approaches to treat and prevent COVID-19. Our study aimed to procure sex-disaggregated data in the Indian population by using a google form based online health survey. We have analyzed parameters like age, gender, occupation, sex and severity of infection based on CT score, steroid dependence, need for hospitalization, etc. The responses were evaluated by descriptive statistics by excluding arbitrary correlation. We found that the males were at a significantly greater risk of severe disease and mortality (~ twice) than females. We also found that the males as compared to females, presented almost eighteen times the odds of requiring intensive care unit (ICU) admission; reflecting severity of the infection. A sex-informed approach to COVID-19 research would reveal novel responses of the host immune system to SARS-CoV-2, which can then be utilized in formulation of policies for equitable health outcomes.
      PubDate: Fri, 25 Feb 2022 00:00:00 +053
  • Association of ABO blood group and antibody class with susceptibility and
           severity of COVID-19 infection in Indian Population

    • Authors: Jyoti Taneja; Priya Bhardwaj, Sunita K Yadav, Daman Saluja
      Abstract: Since the COVID-19 eruption in December 2019, the investigation has been focused on its treatment and preventing the disease spread. Currently, there is no biomarker available that can predict the predisposition and severity of COVID-19 infection. In the present study, we have used the cross-sectional survey study data to decipher the association between the ABO blood group and susceptibility, severity and breakthrough COVID-19 infections. Further, we have also investigated the association between antibody class and the risk of contracting COVID-19 infection. Our results indicated that individuals with blood group B had higher susceptibility to acquire COVID-19 infection. In contrast, blood group A was found to be associated with a low risk of acquiring severe COVID-19. In addition, we did not find any correlation between ABO blood groups and breakthrough COVID-19 infections. Further, we examined the association of antibodies; anti-A (blood groups B and O) and anti-B (blood groups A and O) with COVID-19 infection. The analysis of antibody classes showed that anti-A antibody associated with a high predisposition to acquire COVID-19 infection. The present study indicates that blood group B and anti-A antibodies are associated with proneness to COVID -19 infection and severity.
      PubDate: Fri, 25 Feb 2022 00:00:00 +053
  • Synthesis, spectral studies and biological activity of novel
           2-(substituted phenyl)-6-phenylimidazo[2,1-b]1,3,4-oxadiazole

    • Authors: Mandeep Kaur; Satvir Singh, Harpreet Kaur, Navni Sharma
      Abstract: Diverse series of 2-(substitutedphenyl)-6-phenylimidazo[2,1-b]1,3,4-oxadiazole were synthesized. Five of the synthesized compounds were evaluated for their anticancer activity on MCF-7 cancer cell lines. The recently synthesized compounds were illustrated by IR, 1HNMR. The anticancer activity of the compounds was carried out at Anti-Cancer Drug Screening Facility (ACDSF), Advanced Centre for Treatment, Research & Education in Cancer (ACTREC),Tata Memorial Centre, Kharghar, Navi Mumbai. The anticancer activity would be evaluated by In vitro testing using SRB assay protocols.All the screened compounds showed good to moderate activity against MCF-7 cancer cell line. Compound 5b, 6c, 7a were found to be active with GI50 <10 µg/ml.All the synthesized compounds were screened against Gram Positive and Gram Negative bacteria Streptococcus aureus, Bacillus subtilisand E.coli respectively.
      PubDate: Fri, 25 Feb 2022 00:00:00 +053
  • Context aware human activity prediction in videos using Hand-Centric
           features and Dynamic programming based prediction algorithm

    • Authors: S.N. Kakarwal; Ashwini Subhash Gavali
      Abstract: Activity prediction in videos deals with predicting human activity before it is fully observed. This work presents a context-aware activity prediction approach that can predict long-duration complex human activities from partially observed video. Here, we consider human poses and interacting objects as a context for activity prediction. The major challenges of context-aware activity predictions are to consider different interacting objects and to differentiate visually similar activity classes, such as cutting a tomato and cutting an apple. This article explores the use of hand-centric features for predicting human activity, consisting of various human-object interactions. A Dynamic Programming Based Activity Prediction Algorithm (DPAPA) is proposed for finding the future activity label based on observed actions. The proposed DPAPA algorithm do not employ Markovian dependencies or Hierarchical representation of activities, and hence is well suited for predicting human activities which are often Non-Markovian and Non-hierarchical. We evaluate results on MPPI Cooking activity dataset which consist of complex and long-duration activities.
      PubDate: Thu, 24 Feb 2022 00:00:00 +053
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