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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]
  • Niosomal formulation of Quercetin and Resveratrol and in-vitro release
           studies

    • Authors: Nivetha Murugesan; Chandraprabha Damodaran, Selvakumar Krishnamoorthy
      Abstract: Dietary polyphenols from plant origins play a major role in the human diet. They supply efficient antioxidants that reduce or prevent ROS production depending on the concentration. However, these polyphenols are less bio-available in the body due to various parameters, including low intrinsic activity, poor absorption, high metabolism, inactivity of metabolic products, and/or rapid elimination. Quercetin and resveratrol are dietary polyphenols that are often found in the human diet. However, they lag in bioavailability, which makes them less preferred nutraceuticals. This particular study is aimed at increasing the bioavailability of quercetin and resveratrol through the nano vector system, niosomes. In this study, niosomes entrapped with Quercetin and Resveratrol were produced in different concentrations of Span 60 and cholesterol using the thin film hydration method. The best suitable composition, which provides maximum entrapment, was taken for further study. The niosomal formulation of quercetin and resveratrol was evaluated using various methods like solubility and shape. The entrapment efficiency was determined to be 61.55%. The niosomes were then characterized using a zeta sizer and a potential. The average particle size of niosomes was 194 diameter values in nanometers, and their zeta potential was -20 mV, which indicated their good stability. The results of the in vitro drug release research, which was conducted using phosphate buffer saline pH 7.4, were that 92.6% in 24 hours was significantly increased compared to quercetin and resveratrol release, 71.30%. The ex vivo drug release was 94.5% after 24 hours, which was higher when compared to quercetin and resveratrol release of 75.74%. The results of this study indicate that the niosomes significantly enhanced the bioavailability of quercetin and resveratrol.
      PubDate: Wed, 10 Aug 2022 00:00:00 +000
       
  • Review of fundamentals of Artificial Intelligence and application with
           medical data in healthcare

    • Authors: Renuka R. Patil; A. Usha Ruby, Chaithanya B N, Swasthika Jain T J, Geetha K
      Abstract: Artificial Intelligence (AI) can identify substantial interactions by considering the datasets in the emerging era of technologies. It is widely applied in various applications, and healthcare is among them. AI plays a vital role in clinical applications in predicting the disease type, treating the disease, managing chronic situations, and diagnosing the same. AI in the healthcare environment has simplified the lives of doctors, patients, and administrators at hospitals by operating various tasks with lesser computation time and accurate results. The unique challenges such as availability, accessibility, and affordability of AI have contributed to healthcare applications' success. Another factor that enhances the functionality of AI is based on the sourced medical data, which is analyzed, and a model is built to predict the disease by the application of Machine Learning (ML). The other reason for the successful application of AI in the healthcare environment is Computational Intelligence (CI), which is an analysis, design, theory, and development of linguistically and biologically motivated computational techniques. The functionality of CI is identified on the three pillars such as Fuzzy Systems, Neural Networks, and Evolutionary Computation. This research work mainly discusses the various applications of AI in the current healthcare environment. The discussion also includes the different branches of AI with their applications and working principles.Artificial Intelligence and Medical Data
      PubDate: Fri, 22 Jul 2022 00:00:00 +053
       
  • Review of reliability indices of the water distribution system

    • Authors: Suja S. Nair; T R Neelakantan
      Abstract: Over the last few years, there has been a growing emphasis on reliability in water dissemination networks. The water supply network's dependability is crucial in today's water delivery system. The capacity of a water distribution network to fulfil requirements with significant pressure under normal and abnormal situations is referred to as system reliability. The development of a system for analyzing and enhancing the reliability of water delivery systems is underway. Enhanced options are offered to increase network dependability, and then an optimization study is used to choose the best upgrade option based on a predetermined goal function. Reliability does not rely on certain criteria. In today's world, computer-aided programs impact the simulation model and the water supply network study. This analysis shows the factors that can be utilized to determine dependability.
      PubDate: Thu, 21 Jul 2022 00:00:00 +053
       
  • Automated hybrid Deep Neural Network model for fake news identification
           and classification in social networks

    • Authors: Roshan R. Karwa; Sunil R. Gupta
      Abstract: The rapid growth of social media has far-reaching impacts on civilization, traditions, and economics, including both beneficial and unfavourable implications. Since social networking sites have become more frequently utilized for transmitting data, they have also become a gateway for the distribution of fake news for diverse financial and legislative goals. Artificial Intelligence (AI) and Natural Language Processing (NLP) approaches have a lot of ability for academics who wish to design models that can recognize fake news automatically. On the other hand, identifying fake news is a difficult issue because it demands systems that describe the news and then contrast it to the actual news to categorize it as fake. Thus, to overcome this, this paper introduces Hybrid Deep Neural Network Model, in which C-DSSM and Deep CNN models have been utilized. It identifies and classifies fake news using the LIAR dataset. According to experimental results, the proposed model obtained an accuracy of 92.60%, a recall of 92.40%, a precision of 92.50%, and an F1 score of 92.50%. Furthermore, the proposed model is compared to earlier studies for fake news identification using the LIAR dataset, and the proposed model's performance is remarkable. As a result, the proposed hybrid model gives better results in detecting and classifying fake news on social networks.
      PubDate: Thu, 07 Jul 2022 00:00:00 +053
       
  • Optimized energy-efficient multi-hop routing algorithm for better coverage
           in mobile wireless sensor networks

    • Authors: K. Phani Rama Krishna; Ramakrishna Thirumuru
      Abstract: The Mobile Wireless Sensor Network (MWSN) comprises transceivers that collect information and transfer it to the access point through other hubs. Both mobile networks and access points can be portable and work apace with stable devices in the network, depending on the application requirements. Numerous studies have been undertaken to develop sensor nodes considering energy and mobility, with LEACH-based routing algorithms providing the best results. However, the Low Energy Adaptive Clustering Hierarchy-based energy-efficient navigation system best suits small-scale systems. Whenever the connection is huge, long-distance transmission between cluster members and the Base Station (BS) consumes much energy. Thus to overcome it, this research grants an innovative scheduling algorithm that adapts to sensor node transmission to deliver dependable and energy-efficient navigation named Optimized Energy Efficient Routing Algorithm for Better Coverage in Wireless Sensor Networks. Firstly, our research paper presents a unique method called Low Energy Adaptive Tier Clustering Hierarchy (LEATCH), which achieves a reasonable balance of latency and energy usage while addressing various covering concerns in MWSN. Moreover, a novel Honey Bee Algorithm is utilized to find the potential and hyper-cluster heads. As a result, based on power, latency, coverage, stability period, and scalability, the optimized LEATCH protocol outperforms other energy-efficient clustering protocols.
      PubDate: Thu, 07 Jul 2022 00:00:00 +053
       
  • Effects of blending bioethanol with gasoline on spark–ignition
           engine – A review

    • Authors: Minal Deshmukh; Dhanashri S Pendse, Ashwini Pande
      Abstract: Due to strict government regulations and fossil fuel depletion, it was necessary to look for alternatives to traditional fuel sources. Energy demand is increasing day by day due to improved transportation and population growth. Biofuel is an alternative fuel derived from various types of biomasses. Biofuels are receiving scientific and public attention. This can be caused by factors such as the need to strengthen energy security, rising oil prices, and concerns about greenhouse gas (GHG) emissions from fossil fuels. Biofuels are especially attractive to developing countries because they can arouse economic development in rural areas and alleviate poverty by creating employment opportunities & higher incomes in the agriculture sector.  To evaluate the effects of diesel/ gasoline on engine operation much research had been carried out. The blending of bioethanol leads to improve physicochemical properties which are responsible to improve the SI engine. SI engine operation improves with the help of Physico-chemical properties of bioethanol. The present review illustrates some of the recent research findings on the production of bioethanol from different types of biomasses, their physicochemical properties, and their impact on the engine: combustion characteristics, engine performance, emissions, the effect of bioethanol gasoline blending & operating condition for NOx emission in SI engine.
      PubDate: Thu, 07 Jul 2022 00:00:00 +053
       
  • A novel attribute based access control model with application in IaaS
           cloud

    • Authors: Dilawar Singh; Shweta Sinha, Vikas Thada
      Abstract: Cloud computing is viewed as one of the most dominant ideal models in the Information Technology industry nowadays. It offers new savvy administrations on-request like Software as a Service, Infrastructure as a Service, and Platform as a Service. Nonetheless, with these administrations promising offices and advantages, there are yet various difficulties related to using cloud computing, for example, data security, maltreatment of cloud administrations, malicious insiders, and cyber-attacks. Among all security necessities of cloud computing, access control is one of the fundamental prerequisites to keep away from unapproved access to frameworks and safeguard association's resources. Albeit different access control models and policies have been grown for various conditions, these models may not satisfy the cloud's access control necessities. It used a portion of the PM's parts alongside a proof-of-idea execution to implement ABAC augmentation for OpenStack while keeping OpenStack's present RBAC design set up. This gives the advantages of upgrading access control flexibility with help of client attributes while limiting the upward of changing the current OpenStack access control structure. The use cases are presented to portray added advantages of the proposed model and show authorization results.
      PubDate: Thu, 07 Jul 2022 00:00:00 +053
       
  • Numerical study due to mixed convection nanofluid flow with the effect of
           velocity slip and thermal conductivity across curved stretching surface

    • Authors: Preeti Kaushik; Upendra Mishra
      Abstract: The current research investigates mixed convection across curved stretching surface. This analysis takes into account the effect of velocity slip as well as thermal conductivity. The boundary layer problem is expressed as a mathematical system of equations.  Equations in a non-dimensional form are derived by applying an appropriate similarity transformation. Matlab is employed to compute the numerical solutions of the highly nonlinear system of ordinary differential equations. For various values of relevant parameters, substantial variations in the velocity, temperature, and concentration profiles were found. Graphs and tables are used to illustrate the results. It has been shown that due to the rising value of curvature parameter the skin friction coefficient drops.
      PubDate: Wed, 22 Jun 2022 00:00:00 +053
       
  • 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
           India

    • 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
           population

    • 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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