Journal of Science & Technology (JST)
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Biosynthesis of Zinc oxide nanoparticles using Royal poinciana leaf extract and evaluating its biomedical potential against biological agents
Get PDFThe field of nanotechnology is concerned with numerous creations and it has various application of materials and it had a nanoscale spatial dimensioning. It possesses higher surface area to volume ratio and have analytical properties as well. Zinc is considered to be the essential element for living organisms. Several chemical and physical methods are used to prepare zinc oxide nanoparticles. By using precipitation method, the Zinc oxide Nanoparticles had been synthesized from zinc nitrate. ZnO NPs were considered to be inexpensive and relatively less toxic and it exhibits excellent biomedical applications, such as larvicidal activity, anticancer, antibacterial, anti-fungal, & anti-diabetic, properties. This study will help to understand the potential biomedical applications and its activity against biological agents. Finally, the UV- spectroscopy analysis was performed to know its concentration of the synthesized nanoparticle. The synthesized Zinc oxide nanoparticles have high biodegradable and biocompatible factor against therapeutic disease and microorganism
Interception amelioration of Chipless RFID Tags Using Deep Learning Techniques
Get PDFIn this paper, we have proposed data-driven methods to enhance the interrogation and reliability of Chip-less Radio Frequency Identification systems. Six binary combinations of an 8-bit RFID tag are fabricated on a Rogers RT / Duroid ® 5880 substrate with a permittivity of 2.2 and a loss tangent of 0.0009 to create the model dataset. The tag's frequency response encompasses eight identifiable frequency resonances in the 3–10 GHz frequency band. By analyzing the spectral signature of the backscattered chip-less RFID tags at possible reach and orientation, two distinct datasets corresponding to ideal (within the anechoic chamber) and natural environments are prepared. The data sets are trained and evaluated using the SVM, KNN, DT and DNN approaches. A validation accuracy of 97.5% is obtained for the DNN model in actual environmental conditions in the presence of clutter and noises. The DNN model attains an accuracy of 99.5% on the ideal dataset and 97.5% on the actual dataset. The model extracted tag information up to 70cm from the interrogator, which is about a 20% increase in reading range compared to conventional interrogation methods
SECURE AND EFFICIENT BIOMETRIC BASED SAFE ACCESSMECHANISM FOR CLOUD SERVICES DEVELOPMENT
Get PDFUser authentication with unlink capability is one of the corner gravestone services for numerous security and separateness services which are needed to protect dispatches in wireless detector nets (WSNs). This document describes SESAME (guard European network for operations in a Multivendor Environment), a security framework for public assigned networks evolved by Bull, ICL and Siemens Nixdorf. The generalities behind the infrastructure, what parcels it has and what features it provides are carried. Particular emphasis has been given away to inflexibility, administration and directness. A figure of the system of the SESAME factors is also carried, displaying its effectiveness with regard to performance and authority and its defense rates. A particularized Real- Or- Random (ROR) design predicated regular protection anatomy, irregular(non-mathematical) shield assay and alike routine safeguard verification utilizing the astronomically- accepted Automated proof of Internet Security Protocols and Applications (AVISPA) device expose that the offered approach can oppose several given attempts against (unresistant/alive) adversary. hence, the suggested scheme not only specifics its shield defects but similarly improves its version. It's further capable for functional operations of WSNs device
Installation and Performance Evaluation of On-grid 640 kWp Capacity Rooftop SolarPower Plant at the University Campus – a case study
Get PDFDr. Y S Parmar University of Horticulture & Forestry, Nauni is the 1st university in Asia that is growing rapidly. Electricity consumption is around 2.23 million units annually contributing GHGs responsible for climate change. To reduce electricity consumption, a power developer has installed an on-grid 640 kWp capacity rooftop solar power plant at their own cost on RESCO mode at the university campus. The university has provided rooftop space free of cost for 25 years while the university will purchase electricity @ 1.90/unit. The university campus has 280-290 sunny days annually with solar radiation ranging from 3.56 to 7.53 kWh/m2 /d. Solar modules of 20 kW to 80 kW capacities were installed at 13 selected buildings. The university buildings have inclined rooftops which reduces the structural cost. About 823,340 units of solar electricity have been generated until May 2023, thereby saving Rs. 4 million annually and a reduction in CO2 of 658,672 kg. The overall performance ratio of rooftop solar power plants comes out to be 77.95% capacity utilization factor of 20.98% and a specific yield of 5.03 kWh/kWp. The RTSVP meets the UN sustainable goals 7 and 13. The system will generate solar power on a sustainable basis for 25 years
PRIVACY PRESERVING LOCATION DATA PUBLISHING: A MACHINE LEARNING APPROACH
Get PDFPublishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users‟ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework
ADVANCED NEURAL NETWORK ARCHITECTURE FOR DETECTING FRAUD IN INTERNET LOAN APPLICATIONS
Get PDFThe background of the modernized loan approval system lies in the inefficiencies and limitations of traditional loan approval processes. The history of modernizing loan approval systems using machine learning techniques can be traced back to the early 2000s when financial institutions started exploring data-driven approaches to assess credit risks. With the growth of the internet and digitalization, lenders began collecting vast amounts of data on borrowers, including transaction history, social media activities, and online behavior. This data became valuable for predicting creditworthiness and revolutionized the way loans were approved. Traditional loan approval systems typically involved manual paperwork, face-to-face interviews, and subjective judgment. Loan officers would assess applicants based on credit scores, income statements, and collateral. The process was time-intensive and often led to delays in loan approvals. Moreover, these methods were not always accurate in predicting repayment capabilities, leading to higher default rates. In addition, existing methods were often time-consuming, paper-based, and relied heavily on human judgment, making them prone to errors and biases. With the advent of technology and the availability of vast amounts of data, there was a need to develop a more efficient, accurate, and unbiased loan approval system. This need gave rise to the use of machine learning techniques to predict loan approvals based on various factors and data points. Therefore, this research work proposes a machine learning model to develop accurate predictive models that can assess a borrower's creditworthiness using diverse data sources. Further, the proposed model automates the loan approval process, which reduces the time taken for approval, enabling quicker disbursal of funds and it can analyze large datasets to make accurate predictions about a borrower's creditworthiness. This also reduces the operational costs associated with manual loan processing and it will reduce biases in loan approval decisions, promoting fairness and equal opportunities
PRESERVING PRIVACY IN THE ERA OF BIG DATA A ML BASED ANONYMIZATION FRAMEWORK
Get PDFPublishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users’ private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework
ELEVATING FILE SECURITY THROUGH ADVANCES MULTIPLE IMAGE STEGANOGRAPHY
Get PDFBackground and History: In an age of increasing digital communication and data transfer, ensuring the security and privacy of sensitive information is paramount. Steganography, the art of hiding information within other data, has been used for centuries. In the digital realm, it plays a critical role in secure communication and information concealment. Traditional steganography methods often involve embedding information within a single image. While effective, this approach may be susceptible to detection, as single-image steganography can leave detectable traces, especially under sophisticated analysis. The primary challenge is to develop a robust system for multiple image steganography that can securely hide sensitive files within a set of images. This involves designing algorithms that distribute the information effectively across the images while maintaining imperceptibility and ensuring reliable extraction. Therefore, the rise of cyber threats and privacy concerns, there's a growing need for advanced techniques to protect sensitive files from unauthorized access or interception. Multiple image steganography, an emerging field, offers the potential for heightened security by spreading information across multiple images, making it even more challenging for potential adversaries to detect or extract. The project, "Elevating file security through advances in multiple image steganography," seeks to enhance file security by leveraging advanced techniques in multiple image steganography. By distributing the information across a set of images, this research endeavors to develop a system capable of securely concealing sensitive files. The algorithms utilized in this approach are designed to ensure imperceptibility and robustness against detection efforts. This advancement holds great promise for significantly improving the security of file transmission and storage, safeguarding critical information from unauthorized access or interception
Evaluating the Pharmaceutical Sector Through Pharmacopoeial Standards: A Review
Get PDFFor the pharmaceutical tablet to be considered a standard drug approval, it must fulfillcertain requirements. Various standard factors, including identification, strength, quality,purity, and stability, are used by pharmaceutical companies to test tablets for accuracy. Forthis reason, pharmaceutical procedures must be controlled, regardless of the problems theymay resolve. Raw material inspection, process control, and final product targeting are allincluded in process control. For this reason, it is important to keep an eye on how wellprocess control is working. In this regard, the manufacturing process should be modifiedin accordance with the specifications as required, which may also include environmentaland equipment management. During the production process, the quality control unit shouldaccept or reject pharmaceutical items after properly examining them for identification,strength, quality, and purity. Highlights of this study include describing pharmaceuticalproduct quality control testing utilizing various equipment for the pharmaceutical sector inaccordance with pharmacopeias
Designing a Nitrosamines-Free Controlled Release Matrix Tablet of Paliperidone: A Quality by Design Approach
Get PDFPaliperidone, a psychiatric medication belonging to the atypical antipsychotic family, is the 9-hydroxy metabolite of risperidone.The racemates of paliperidone are (+)- and (-)-paliperidone. It functions centrally as a dopamine D2 antagonist withserotonergic 5-HT2A antagonistic activity. Invega ER tablets are made using ALZA OROS® osmotic drug release technology.Paliperidone is administered in a specific manner over a 24-hour period using this tri-layer longitudinally compressed tablet,which is based on an advanced osmotic administration mechanism. The goal of this study is to develop a generic paliperidonecontrolled-release single-layer matrix tablet. In order to help create a stable and robust formulation, several combinations ofPolyox and hypromellose were utilized in the core, followed by coating. The in-vitro disintegration characteristics of all strengthsare comparable. Both the challenge for the alcohol dosage dumping research and the nitrosamine risk assessment wereevaluated for the freeze formulation. With a pKa1 of 8.2 for the piperidine moiety and a pKa2 of 2.6 for the pyrimidine moiety,paliperidone is a basic chemical. Consequently, at healthy pH, a significant amount of the molecule is ionized. At a pH of 7.4, it iscomparatively insoluble in water (0.003 g/100 mL). The solubility dramatically rises at lower pH (3 g/100 mL at pH 5.3) andfalls at higher pH (0.001 g/100 mL at pH 12.9). Octanol/water's partition coefficient (log P) is 2.39. As a result, pH 2.75 bufferwas determined to be the discriminating medium. The in-vitro release profile was expressed using matrix composition and theHiguchi model. In vitro release experiments show that the formulation can resist alcoholic conditions ranging from 0% to 40%.It is stable, affordable, and simple to formulate. In order to minimize the chemical interaction between an active ingredient andother excipients, the production process consists of dry mixing, compression, and coating. Therefore, the formulation has a verylittle chance of producing nitrosamine impurities. When it comes to dosage dumping, the formulation is categorized as tough