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Development and evaluation of the bootstrap resampling technique based statistical prediction model for Covid-19 real time data : A data driven approach
The objective of the article is to develop earlyR package based novel coronavirus disease (COVID-19) forecasting model. The reported COVID-19 serial interval data is applied for obtaining maximum likelihood value of the reproduction number (R0) using maximum likelihood approach and projections package is applied for getting trajectories of epidemic curve. The minimum, median, mean and maximum projected value of R0 with 95% confidence interval (CI) is obtained by using bootstrap resampling strategy and the predicted cumulative probable count of new cases is also presented with different quantile. To validate the results with real scenario, the past COVID-19 data is considered. The % error rate ranges from -7.91% to 21.27% for the developed model for the five Indian States. 2022 Taru Publications. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Mechanical and Wear Behavior of Aluminium Metal Matrix Composites Reinforced Ceramics Materials for Light Structures
Aluminium Alloy based Metal Matrix Composites (AAMMCs) has widely used in defense, aircraft and automobile applications because of their enhanced engineering properties with light weight metals. Nano sized silicon nitride (80 ?m) is used as a reinforcement in this study, whereas aluminium alloy 8011 is selected as the matrix material. Using the stir casting method, metal matrix composites made of aluminium alloy 8011 with varying weight percentages of Si3N4(0, 4, 8, 12, and 16) are created. The stir casted AL 8011/Si3N4composites further heated under T6 condition. The AL 8011/Si3N4 T6 composites are further subjected to Energy Dispersive X ray Analysis (EDAX) and Scanning Electron Microscope (SEM) to identify by the presence of elements and study the microstructure characterization, respectively. The density, microhardness and wear test are conducted by employing Archimedes principle, Vickers hardness tested and pin on disc equipment, respectively. The wear test is done at different sliding distances like (500, 1000, 1500 and 2000 m), applied load like (10, 20, 30 and 40 N) and kept sliding at a speed of 1 m/s. The increasing weight percentage of silicon nitride expands the increasing of density and Vickers hardness up to 12 wt % of silicon nitride and decreasing by 16 wt % addition. The wear resistances of AL 8011/12 wt % Si3N4T6 composite exhibits higher wear resistance than other Al8011 based composites. 2024, Informatics Publishing Limited. All rights reserved. -
Static analysis tool for identification of permission misuse by android applications
Android is one of the most important and widely used mobile operating systems in the world. The Android operating system utilizes the permission-based model, which permits Android applications to get user data, framework data, gadget data and other assets of Smartphone. These permissions are affirmations declared by the developer of an application. The permissions granted varies from one application to another, depending on its functionality. During installation, permissions to access the resources of the smartphone are requested by apps. Once the client grants the permission, the apps are allowed to access the granted resources as per its requirement. Android OS is susceptible to different security issues owing to the loopholes in security. This paper mainly focuses on identifying how the permissions granted to a specific application is misused by another application using SharedUserID. The paper also proposes a security tool that identifies a list of applications which are misusing the permissions in a user's Android smartphone. The viability of the tool is tested by using a Proof-of-Concept (PoC) implementation of the security tool. Research India Publications. -
Android security issues and solutions
Android operating system uses the permission-based model which allows Android applications to access user information, system information, device information and external resources of Smartphone. The developer needs to declare the permissions for the Android application. The user needs to accept these permissions for successful installation of an Android application. These permissions are declarations. At the time of installation, if the permissions are allowed by the user, the app can access resources and information anytime. It need not re-request for permissions again. Android OS is susceptible to various security attacks due to its weakness in security. This paper tells about the misuse of app permissions using Shared User ID, how two-factor authentications fail due to inappropriate and improper usage of app permissions using spyware, data theft in Android applications, security breaches or attacks in Android and analysis of Android, iOS and Windows operating system regarding its security. 2017 IEEE. -
Artificial Intelligence Based Enhanced Virtual Mouse Hand Gesture Tracking Using Yolo Algorithm
Virtual mouse technology has revolutionized human computer interaction, allowing users to interact with digital environments without physical peripherals. The concept traces back to the late 1970s, and over the years, it has evolved with significant advancements in computer vision, motion tracking, and gesture recognition technologies. In recent times, machine learning techniques, particularly YOLOv8, have been integrated into virtual mouse technology, enabling accurate and swift detection of virtual objects and surfaces. This advancement enhances seamless interaction, intuitive hand gestures, and personalized virtual reality experiences tailored to individual user preferences. The proposed model, EHT (Enhanced Hand Tracking), leverages the power of YOLOv8 to address the limitations of existing models, such as Mediapipe. EHT achieves higher accuracy in hand tracking, real-Time hand gesture recognition, and improved multi-user interactions. It adapts to users' unique gestures over time, delivering a more natural and immersive computing experience with accuracy rates exceeding those of Mediapipe. For instance, across multiple sample datasets, EHT consistently outperformed Mediapipe in hand tracking accuracy. In Sample Dataset 1, EHT demonstrated an accuracy of 98.3% compared to Mediapipe's 95.65%. Similarly, in Sample Dataset 2, EHT achieved an accuracy of 99.35%, surpassing Mediapipe's 94.63%. Even in Sample Dataset 3, EHT maintained its superiority with an accuracy of 98.54 %, whereas Mediapipe achieved 98.26%. The successful implementation of EHT requires a custom dataset and optimization techniques to ensure efficiency on virtual reality hardware. EHT model is anticipated redefining how users interact with digital environments, unlocking new possibilities for intuitive and immersive computing experiences. 2023 IEEE. -
Professional chat application based on natural language processing
There has been an emerging trend of a vast number of chat applications which are present in the recent years to help people to connect with each other across different mediums, like Hike, WhatsApp, Telegram, etc. The proposed network-based android chat application used for chatting purpose with remote clients or users connected to the internet, and it will not let the user send inappropriate messages. This paper proposes the mechanism of creating professional chat application that will not permit the user to send inappropriate or improper messages to the participants by incorporating base level implementation of natural language processing (NLP). Before sending the messages to the user, the typed message evaluated to find any inappropriate terms in the message that may include vulgar words, etc., using natural language processing. The user can build an own dictionary which contains vulgar or irrelevant terms. After pre-processing steps of removal of punctuations, numbers, conversion of text to lower case and NLP concepts of removing stop words, stemming, tokenization, named entity recognition and parts of speech tagging, it gives keywords from the user typed message. These derived keywords compared with the terms in the dictionary to analyze the sentiment of the message. If the context of the message is negative, then the user not permitted to send the message. 2018 IEEE. -
Employee development and training as a tool for improving employee performance in an organization /
Patent Number: 202241025596, Applicant: Dr. Rekha N Patil.
Employee development and training as a tool for improving employee performance in an organization Abstract: A company's long-term success depends on how well its employees are trained and how well they are taught new things. Workers can use these programmes to improve their skills, but businesses can use them to improve employee productivity and the company's culture at the same time. The 2020 Work Institute found that cutting down on employee turnover has a big impact on a company's bottom line. -
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Photocatalytic activity of bismuth silicate heterostructures synthesized via surfactant mediated sol-gel method
A surfactant mediated sol-gel method is employed to synthesize bismuth silicate heterostructures with tunable morphologies and properties. The synthesized nanoparticle samples were characterized by XRD, FTIR Spectroscopy, SEM-EDAX and UV-DRS. The synthesized bismuth silicates exhibit excellent photodegradation against malachite green and rhodamine B dyes in the aqueous medium. Bismuth silicates (10% SiO2-Bi2O3) show superior photocatalytic property and outstanding reusability compared to pure bismuth oxide. The kinetics of the photodegradation of the dyes shows that the reaction follows first-order kinetics with the regression coefficient of 0.99. Thus, enabling Bismuth silicates heterostructures practical application as a photocatalyst for clean water. 2019 Elsevier Ltd -
Synthesis of bismuth silicate nanostructures with tunable morphology and enhanced photocatalytic activity
Bismuth oxide due to its narrow bandgap has attracted significant attention as a photocatalyst. A facile and efficient method to synthesize bismuth silicate with tunable morphology and property is achieved in this study. Bismuth oxide and bismuth silicate have been synthesized by surfactant-assisted modified sol-gel method. The fabricated bismuth oxide nanoparticle samples are characterized by various analytical tools such as X-Ray diffractometer, Infra-Red spectroscopy, Scanning Electron microscopy and UV-Diffuse Reflectance spectroscopy. The synthesized nanoparticles exhibit excellent photocatalytic activity for the degradation of Rhodamine B dye in aqueous medium. Bismuth silicate exerts more satisfactory catalytic property and outstanding reusability compared to pure bismuth oxide. The superior stability and enhanced activity enables the application of bismuth silicate as a photocatalyst for environmental remediation. 2019, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved. -
Influence of Surfactant on the Phase Transformation of Bi 2 O 3 and its Photocatalytic Activity
Bismuth oxide with its unique narrow bandgap has gained significant attention in the field of photocatalysis. A new and efficient method to synthesise bismuth oxide with tuneable properties is proposed herein. A surfactant assisted modified sol-gel method is used to synthesise bismuth oxide with excellent photocatalytic activity for the degradation of Rhodamine B dye. Three different surfactants, namely polyethylene glycol-400, sodium lauryl sulfate, and cetyltrimethylammonium bromide (CTAB) have been used. The fabricated bismuth oxide nanoparticles were characterised by X-ray diffraction, IR, scanning electron microscopy, and UV-diffuse reflectance spectroscopy analysis. Evolution of both the ? and ? crystalline phases of bismuth oxide was observed. The bandgap of the synthesised bismuth oxides ranges from 2.03 to 2.37 eV. The CTAB assisted synthesised bismuth oxide with a bandgap of 2.19 eV showed the highest photocatalytic activity of 93.6 % under visible light for the degradation of Rhodamine B. This bismuth oxide based catalyst opens a new avenue for efficient photocatalysis for environmental remediation. 2019 CSIRO. -
An Intrusion Detection Model Based on Hybridization of S-ROA in Deep Learning Model for MANET
A kind of wireless network called a mobile ad hoc network (MANET) can transfer data without the aid of any infrastructure. Due to its short battery life, limited bandwidth, reliance on intermediaries or other nodes, distributed architecture, and self-organisation, the MANET node is vulnerable to many security-related attacks. The Internet of Things (IoT), a more modern networking pattern that can be seen as a superset of the paradigms discussed above, has recently come into existence. It is extremely difficult to secure these networks due to their scattered design and the few resources they have. A key function of intrusion detection systems (IDS) is the identification of hostile actions that impair network performance. It is extremely important that an IDS be able to adapt to such difficulties. As a result, the research creates a deep learning-based feature extraction to increase the machine learning technique's classification accuracy. The suggested model uses outstanding network-constructed feature extraction (RNBFE), which pulls structures from a deep residual network's many convolutional layers. Additionally, RNBFE's numerous parameters cause a lot of configuration issues because they require manual parameter adjustment. Therefore, the integration of the Rider Optimization Algorithm (ROA) and the Spotted Hyena Optimizer (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA), is used to adjust the RNBFEs settings. Attack classification is performed on the resulting feature vectors using fuzzy neural classifiers (FNC). The experimental analysis uses two datasets that are publicly accessible. The Author(s), under exclusive licence to Shiraz University 2024. -
Nanobiosensors for COVID-19
Coronavirus Disease (COVID-19) is an internationally recognized public health emergency. The disease, which has an incredibly high propagation rate, was discovered at the end of December 2019 in Wuhan, Hubei Province, China. The virus that causes COVID-19 is referred to as severe acute respiratory illness. Real-time reverse transcriptase (RT)-PCR assay is the primary diagnostic practice as a reference method for accurate diagnosis of this disease. There is a need for strong technology to detect and monitor public health. Early notification on signs and symptoms of the disorder is important and may be managed up to a few extents. To analyze the early signs and side effects of COVID-19 explicit techniques were applied. Sensors have been used as one of the methods for detection. These sensors are cost effective. These sensors will combine with a systematic device. It is utilized to detect the chemical compound and combined with a biological component. It is detected through physiochemical detector. Nanomaterials represent a robust tool against COVID-19 since they will be designed to act directly toward the infection, increase the effectiveness of standard antiviral drugs, or maybe to trigger the response of the patient. In this paper, we investigate how nanotechnology has been used in the improvement of nanosensor and the latest things of these nanosensors for different infections. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Identification of ambulance in traffic videos using image processing techniques
Traffic congestion is one of the commonly faced problems in the Urban areas. To eliminate these problems, there is a need for an Intelligent Transportation System (ITS) that proposes an efficient method to reduce the traffic problems and introduces the priority system for the Emergency vehicles. This paper proposes two frameworks that identify ambulance in traffic videos based on features such as color, siren and text. Frames are extracted from videos to employ methods like multilevel thresholding and region matching. Multilevel thresholding is used for segmenting the ambulance from the other occurring vehicles based on the white color. Region matching for text detection method is employed in the segmented vehicle. Color space thresholding is used for the detection of siren based on red or blue color feature. Optical character recognition (OCR) is employed to extract the text in the frame. Word comparison and Matching detects the ambulance text based on the outcome of OCR. The performance of Framework 1 and Framework 2 are evaluated based on Word accuracy and from the experimental results it is observed that Framework 2 is better from 75% word accuracy. 2018, Institute of Advanced Scientific Research, Inc. All Rights reserved. -
Click & Collect Retailing: A Study on Its Influence on the Purchase Intention of Customers
The retail sector, over the years, has evolved dramatically to provide better service to its customers. With the superior convenience of online shopping and tangible experience of in-store shopping, retail industries are looking forward to integrating both modes, thus embracing omni channel to provide better service to their customers. The prime objective of the research is to investigate the level of influence that using the Click & Collect online shopping mode can have on customer purchase intention and to ascertain the effects that online and offline shopping attributes have on this intention. The study emphasizes the usefulness of integrating both the shopping modes, thus embracing omni channel in the retail sector to provide a better shopping experience to the customers. The primary data were collected from 356 respondents. Secondary data were collected by reviewing articles, research papers, extant studies and newspaper articles. In the analysis, the buying behaviour through an e-commerce platform and customers purchase intentions are taken as the dependent variable. Product risk, online trust, website quality, offline experience and perceived usefulness are identified as the independent variables. The data thus collected were processed for regression tests using IBM SPSS 25 software to analyse the results. The Stimulus-Organism-Response model was deployed as the proposed model for the research. The results obtained from the research will allow retailers to understand the customer's buying behaviour towards the new Click & Collect system better by identifying the key variables that influence their purchase intention. The current study highlights the influence of the perceived usefulness of using the Click & Collect online shopping mode on the purchase intention of customers. 2021 Transnational Press London -
Evaluate and design the mini-hexagon-shaped monopole antenna controller to minimize losses in the unit
Main Aim: Hexagon-shaped mono-pole transmitters are developed, computed, and evaluated in a range of applications. Their whole performance is being compared. Methods: Various hexagon-shaped mono-pole transmitters are built and modeled using the HFSS. These transmitters are built with Defective Ground Structure (DGS) but include openings in the patch antenna for High-Frequency Spread Spectrum (HFSS), also on the surface, but also. That influence including its position including its slot upon this radiation pattern is examined. Evaluate the modeling, the controller was designed for the broadcast subsystems and respective reflectivity and VSWR have been found. Findings: The specifications of the antenna is return losses, VSWR, amplification and switching frequency, among other things are assessed as are usually uncertain and VSWR for the manufactured device. The transmissions are continuously monitored. Another most unclear wavelength is around 10 dB among a large bandwidth and that they are less than 10 dB over a specific frequency range. The value of VSWR is less than 2. Applications: These transmitters may be utilized for wirelessly and interior activities via UWB technology. 2021, SciTechnol, All Rights Reserved. -
Hybrid short term load forecasting using ARIMA-SVM
In order to perform a stable and reliable operation of the power system network, short term load forecasting is vital. High forecasting accuracy and speed are the two most important requirements of short-term load forecasting. It is important to analyze the load characteristics and to identify the main factors affecting the load. ARIMA method is most commonly used, as it predict the load purely based on the historical loads and no other assumptions are considered. Therefore there is a need for Outlier detection and correction method as the prediction is based on historical data, the historical data may contain some abnormal or missing values called outliers. Also the load demand is influenced by several other external factors such as temperature, day of the week etc., the Artificial Intelligence techniques will incorporate these external factors which improves the accuracy further. In this paper a hybrid model ARIMA-SVM is used to predict the hourly demand. ARIMA is used to predict the demand after correcting the outliers using Percentage Error (PE) method and its deviation is corrected using SVM. Main objective of this method is to reduce the Mean Absolute percentage Error (MAPE) by introducing a hybrid method employing with outlier detection technique. The historical load data of 2014-2015 from a utility system of southern region is taken for the study. It is observed that the MAPE error got reduced and its convergence speed increased. 2017 IEEE. -
A qualitative causal analysis on incremental behavioural complexities due to fomo (Fear of missing out) in indian youth
FOMO (Fear of Missing Out), a new threatening dampener of youth is prevalent across the world, and is shaping up as a wicked problem to Indian youth, especially in the category of Teens, Adolescents, including educated youth. The vulnerability is getting deeper and severe in terms of behavioural problems that turns as outcome. This qualitative paper contemplates on the human behavior with invasive nature of a newer and stronger, psychological stimulus to youth via, the digital connectivity, social media and mobile phones, called, or abbreviated as FOMO. Why FOMO has become a huge discomfort to almost all the organizations even, at times, separate teams are set to put things at control. This article qualitatively with the secondary statistics carried out across the world, and contemporary research outcome on the FOMO, tries to correlate, how the other countries are impacted, and tries to find a feasible practical moderating factors, that can wane down the impact or pull down the severity, the FOMO is causing in the life of youth. What are the strategies that can be adopted to bring down the level of damages, with suggestions for handling and managing the situation, rather than controlling, as most of the worlds work life balance is happening because of the FOM O. Also this study tries to validate the utilities of JOMO, and will it be possible in the Indian environment, since the youth are not in a position to bring d own the situation. Is NOMO too is growing up vividly across silently, is what the study concludes. 2019 ETA-Florence Renewable Energies. -
Impact of social media on consumer decisiveness in the food and grocery sector
Consumers are currently inclined to acknowledge online information but purchase food and grocery products offline. Also, the buyer's decision is coherent with the factors like income, age, social media influences, cost of products, etc. The chapter studies the Influence of Social Media on Consumer Decision-making in the food and Grocery Sector. As per the findings, the effectiveness of marketing tools and techniques has a homogeneous effect on all GenX, GenY, GenZ. Contrary to expectation, Gen X was most influenced by offers. Social media equally influenced all generations to make purchases, irrespective of their incomes. Post Covid there is a shift in consumption habits disregarding generations and income brackets of all the participants. 2023 by IGI Global. All rights reserved.