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Design of Computationally Efficient IFIR based Filter Structure for Digital Channelizer'
A low computational complexity digital channelizer is essential for a wide band system. FRM is a widely used method to generate a sharp transition width sub-bands or channels in a digital channelizer. The aim of this work is to design a uniform and non-uniform sharp transition width FIR filter bank with low computational complexity compared to FRM based digital channelizer. The design parameters of the proposed structure are evaluated in an efficient way. The proposed structure is designed based on IFIR filter and complex exponential modulation technique (CEMT). The performance of the proposed structure is demonstrated with the help of an example. Results show that the number of multipliers of the proposed structure is less compared to other existing methods. 2022 IEEE. -
A Review on Synchronization and Localization of Devices in WSN
Wireless sensor networks are communication networks that deal with sensor devices that are wirelessly interconnected in order to collect and forward data between different environments. Network scaling of small sensor devices with all its limitations has a foolproof scope for future applications. The advantage of minimal infrastructural cost and applicability within challenging environments make it an attractive choice. Statistics have been shown to prove the demand for research for synchronization and localization as a research problem. WSNs are capable of dynamically building virtual infrastructure and getting synchronized with the rhythm of communication setup. Limitations in the amount of energy that can be utilized make it a necessity for the networks to be more optimal in terms of energy consumption. These challenges necessitate the need to study and analyze the recent advancements implemented in approaching synchronization and localization problems. This paper reviews recent research proposals and methodologies to identify related attributes and their relation to the system. A detailed comparative study is conducted to identify relevant patterns that influence the performance of the networks in terms of energy consumption. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Internet of Things Enhancing Sustainability of Business
When one assumes that the current era is the era for digital revolution then the Internet of Things (IoT) is supposed to be one of the most significant among all. It is the IoT which is assisting the bussinesses. Current IoT applications, on the other hand, are still in their early stages, and the true capacity of viable business opportunities has yet to be realised. However, IoT adoption may need considerable integration and experienced personnel. It also frequently generates new requirements in terms of security and interoperability, or the ability for different computer hardware systems as well as software applications to "speak"to one another. 2022 IEEE. -
Analysis of MRI Images to Discover Brain Tumor Detection Using CNN and VGG-16
Brain tumor is a malignant illness where irregular cells, excess cells and uncontrollable cells are grown inside the brain. Now-a-days Image processing plays a main role in discovery of breast cancer, lung cancer and brain tumor in initial stage. In Image processing even the smallest part of tumor is sensed and can be cured in early stage for giving the suitable treatment. Bio-medical Image processing is a rising arena it consists of many types of imaging approaches like CT scans, X-Ray and MRI. Medical image processing may be the challenging and complex field which is rising nowadays. CNN is known as convolutional neural network it used for image recognition and that is exactly intended for progression pixel data. The performance of model is measured using two different datasets which is merged as one. In this paper two models are used CNN and VGG-16 and finding the best model using their accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE. -
Wireless Sensor Networks in Precision Monitoring of Crops
The sensor-based breadboard is rapidly covering almost every application from human health monitoring to prediction of diseases in accordance with the weather change. This paper presents a sensor based precision crop monitoring system for agriculture application and estimates the energy consumption of the sensor nodes. This high accuracy energy efficient system drastically reduces the damages to the crops and investment made to it. The main focus of the proposed research work is to reduce the energy consumption and minimize the traffic between the nodes of the sensor during the transmission of sensor information. The qualitative metrics has been carried to evaluate the performance of the proposed system which outperform the existing scenario. 2022 IEEE. -
Talent acquisition-artificial intelligence to manage recruitment
The research aims to examine the awareness of Artificial Intelligence among the HR managers and Talent Acquisition managers in the process of Talent Acquisition, Investigating the factors influencing the adoption and usage of Assisted Intelligence, and evaluating the impact of Artificial Intelligence on Talent Management. Multi-Stage sampling method was adopted to collect the responses from the 384 customers across the HR and TA managers working across the IT companies situated in Bangalore, Mysore, Pune, and Chennai & Hyderabad. SAS was applied to perform the Simple Percentage Analysis, Correlation Analysis, Multiple Linear Regression Analysis to validate the hypothesis. The demographic & construct variables considered were Adoption, Actual usage, Perceived usefulness, Perceived Ease of Use, & Talent Management. Awareness of the Artificial Intelligence technology and its adoption in managing Talent Acquisition has the positive and high correlation and followed by its actual usage. Candidate experience is the most influencing variable from the first factor, Competency and Easy to use is the most influencing variable from the second factor, Effectiveness in the adoption and actual usage of Artificial Intelligence in Talent Acquisition. Talent Management is the highest predictor of using the technology and its adoption is the most influencing predictor in the effective implementation of the technology among the Information Technology Companies. The Authors, published by EDP Sciences. -
Algorithm trading and its application in stock broking services
Purpose: Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct. The aim of the study is to examine the level of awareness among the brokers when integrated with technology for the purpose of executing the trades. Design/Methodology: A self-administered and structured 350 questionnaires were designed and circulated to collect the preliminary information from the stock brokers operating in NSE and BSE within the geographical limits of Bangalore district using the Systematic Sampling method to obtain a sample size of 235. Awareness, Automated trading, Elimination of human error, portfolio management, tracking order, order placement were the critical variables observed to validate the hypothesis using Simple Percentage Analysis & Chi-Square Analysis using Statistical Analysis Software (SAS). Findings: It was found that there is robust association between the level of awareness of the mentioned technology in its application by the stock brokers of NSE and BSE operating in Bangalore. Portfolio management and automated trading are the highly associated application of Algorithmic trading among the stock brokerage services. Originality: Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. It can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second. The Authors, published by EDP Sciences. -
Monitoring and Controlling Data Through the Internet of Things (IOT) System: A Framework to Measure the Public Health
Associating and sharing information by means of the web between actual things, or 'things,' coordinated with sensors, programming, and different advances are known as the Internet of Things (IoT). In order to improve technology through IoT, there have been a number of important studies and investigations. This study exhibits how the Internet of Things might be utilized to screen wellbeing. In this research work, with the help of IoT based human wellbeing checking framework the information circulatory strain, beat rate, internal heat level, pulse, and other crucial signs are providing to the internet. The use of IoT for the human health monitoring system in later on future, need a very accurate assessment of risk and this is required to provide a long term information to the device. 2022 IEEE. -
Ethical Tenets of Stock Price Prediction Using Machine Learning Techniques: A Sustainable Approach
The visible decline of ethics primarily gets reflected in financial markets, as it portrays human actions and sentiments in numerical terms than any sector. Accuracy in Stock market prediction remains inefficient due to many known and unknown variables. Academia and industry recently relied on ML at large to track the market and monetise the movements. The norms of fairness, accuracy, dependability, transparency in financing are left unattended in ML prediction models with assumptions far from reality. This study focuses on the ethical dimension of Machine Learning models and generates a sustainable framework for investors. Specifically, the Sustainable Development goals (SDG) can enhance the prediction models in ML with improved efficiency. Along with SDG, this research broadens the variables' horizon of prediction in ML of computer science domain with concepts of Socially responsible Investing (SRI), Environmental Social and Corporate Governance (ESG), and Carbon footprints. With One hundred fifteen articles reviewed, the proposed framework ensures sustainability in investments at the grassroots level. The Electrochemical Society -
Information extraction and text mining of Ancient Vattezhuthu characters in historical documents using image zoning
The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient characters conversion still poses a big challenge. Because image recognition technology has reached near-perfection when it comes to scanning English and other language text. But optical character recognition (OCR) software capable of digitizing printed Tamil text with high levels of accuracy is still elusive. Only a few people are familiar with the ancient characters and make attempts to convert them into written documents manually. The proposed system overcomes such a situation by converting all the ancient historical documents from inscriptions and palm manuscripts into Tamil digital text format. It converts the digital text format using Tamil unicode. Our algorithm comprises different stages: i) image preprocessing, ii) feature extraction, iii) character recognition and iv) digital text conversion. The first phase conversion accuracy of the Brahmi script rate of our algorithm is 91.57% using the neural network and image zoning method. The second phase of the Vattezhuthu character set is to be implemented. Conversion accuracy of Vattezhuthu is 89.75%. 2016 IEEE. -
Effective Tensor Based PCA Machine Learning Techniques for Glaucoma Detection and ASPP EffUnet Classification
Main problem in current research area focused on generating automatic AI technique to detect bio medical images by slimming the dataset. Reducing the original dataset with actual unwanted noises can accelerate new data which helps to detect diseases with high accuracy. Highest level of accuracy can be achieved only by ensuring accuracy at each level of processing steps. Dataset slimming or reduction is NP hard problems due its resembling variants. In this research work we ensure high accuracy in two phases. In phase one feature selection using Normalized Tensor Tubal PCA (NTT-PCA) method is used. This method is based on tensor with single value decomposition (SVD) for accurate dimensionality reduction problems. The dimensionality reduced output from phase one is further processed for accurate classification in phase two. The classification of affected images is detected using ASPP EffUnet. The atrous spatial pyramid pooling (ASPP) with efficient convolutional block in Unet is combined to provide ASPP EffUnet CNN architecture for accurate classification. This two phase model is designed and implemented on benchmark datasets of glaucoma detection. It is processed efficiently by exploiting fundus image in the dataset. We propose novel AI techniques for segmenting the eye discs using EffUnet and perform classification using ASPP-EffUnet techniques. Highest accuracy is achieved by NTT-PCA dimensionality reduction process and ASPP-EffUnet based classification which detects the boundaries of eye cup and optical discs very curiously. Our resulting algorithm NTT-PCA with ASPP-EffUnet for dimensionality reduction and classification process which is optimized for reducing computational complexity with existing detection algorithms like PCA-LA-SVM,PCA-ResNet ASPP Unet. We choose benchmark datasets ORIGA for our experimental analysis. The crucial areas in clinical setup are examined and implemented successfully. The prediction and classification accuracy of proposed technique is achieved nearly 100%. 2021, Springer Nature Switzerland AG. -
Computer simulation of diesel fueled engine processes using matlab and experimental investigations on research engine
The depletion of conventional fuel source at a fast rate and increasing environmental pollution have motivated extensive research in combustion modeling and energy efficient engine design. In the present work, a computer simulation incorporating progressive combustion model using thermodynamic equations has been carried out using MATLAB to evaluate the performance of a diesel engine. Simulations at constant speed and variable load have been carried out for the experimental engine available in the laboratory. For simulation, speed and Air/Fuel ratios, which are measured during the experiment, have been used as input apart from other geometrical details. A state-of-the-art experimental facility has been developed in-house. The facility comprises of a hundred horsepower water cooled eddy current dynamometer with appropriate electronic controllers. A normal load test has been carried out and the required parameters were measured. A six gas analyzer was used for the measurement of NOx, HC, CO2, O2, CO and SOx. and a smoke meter was used for smoke opacity. The predicted Pressure-Volume (PV) diagram was compared with measurements and found to match closely. It is concluded that the developed simulation software could be used to get quick results for parametric studies. Copyright 2017 ASME. -
Deformation Diagnostic Methods for Transformer Winding through System Identification
Transformers play a critical role in the power system. Dynamics of the power system changes if the transformers are out of service for scheduled and unscheduled maintenance work under contingency situations. Faults, overloading, and mechanical abnormalities causes the incipient and critical damages to the transformer. The isolation of transformers leads to the voltage profile change, load curtailments, high compensation, economic loss, and many more problems. It is very important to know the problems occurred in the transformer parts to repair and restore it into the system to attain better stability, reliability, and economics. The transformer health monitoring system consisting of prediction, identification, and diagnostics in online as well as offline mode that will provide sufficient content to the managerial utility to take actions against the problem anticipated or occurred. The heuristic survey inks, the probability of damage in the transformer winding is more compared to the other parts. A novel method using system identification is proposed for the diagnosis of transformer winding. The location and extent of mechanical deformations can be ascertained along with specifically detecting radial and axial deformations in the transformer windings. A system identification approach in frequency and time domain were employed in the diagnostic algorithms for the sweep frequency response dataset. For both transfer function and state space model, a reference table called deformation information tableau has been synthesized for lumped parameter transformer model by varying series and shunt circuit elements systematically. The details of deformation are extracted from the tableau for actual frequency response data for a specified frequency range and winding type. The crosscorrelation of two-dimensional frequency response arrays, one being a signature array and other being deformation array, is used to represent relativity as a singleton. A toolbox is developed for the generation of heuristic deformation information tableau and to diagnose using the diagnostics algorithm developed. The proposed algorithms were verified and simulated for continuous disk type winding. 2019 IEEE. -
Internet of things based metaheuristic reliability centered maintenance of distribution transformers
The transformer is a vital component of the power system. Continuous stress on the transformer due to overload, transient and faults will lead to physical damages. The isolation of the transformer causes significant revenue loss and inconvenience to the consumers at the distribution level. This invites the need to achieve a reliable power supply to the consumers and to perform maintenance activity appropriately. Optimized and predictive maintenance strategies are evolved to improve power availability for consumers. The model considers dispersive generation at the customer end, namely solar photovoltaics standalone system, diesel generation, and vehicle to load capabilities. Incipient or critical status of transformers' functional parameters are observed through the transformer terminal unit and sent to the internet of things platform. The remote processing unit acquires the information from all the distribution transformer and generates the optimized and reliability-centered maintenance schedule. In the proposed work, new reliability indices concerning the consumer dispersive generation are defined. The maximization of the reliability problem is solved using the coconut tree optimization technique. The highest reliability of power supply to the consumer and maintenance schedule are obtained. Economic facet of the estimated maintenance schedule exhibit benefit for both utility and consumer as it encapsulate time of use tariff. The heuristic dataset is used to synthesize the trained model by the machine learning algorithm and future maintenance schedule is predicted. The comparative study is made for the outcome of time-based optimized and predicted maintenance schedules against reliability. 2020 Institute of Physics Publishing. All rights reserved. -
Lightweight Sybil attack detection framework for wireless sensor network with cluster topology
The development of communication and networking technology has made it possible for wireless sensor networks to play a significant role in many fields. Wireless sensor networks are vulnerable to a variety of security threats because of their remote hostile features. The Sybil attack, which generates several identities to gain access to wireless sensor networks, is one such devastating but simple to spread exploit. This Paper proposes a novel identity and trust-based system to ensure protection against Sybil attacks. Analysis of the RSSI and location parameter increases the accuracy. It recognises the attackers and broadcasts information about them to all adjacent sensor nodes. Additionally, it offers other crucial security features. 2025 Author(s). -
Mental Health Stigma: Strategies for Destigmatization in Healthcare Settings
Mental illness is one of the most common disabilities in the world. The term "mental illness stigma"describes harmful practices and misconceptions that lead to a detrimental effect on the mental health, motivation, and self-worth of those who suffer from mental illnesses. Health care services are important for treating and reducing the negative stigma of mental health, as they are areas where patients seek relief and support. The study aims to investigate the causes and how to reduce them. Explores ways to disrupt the health care environment, specifically the RESHAPE program, which focuses on the concept of "critical". This review paper looks at 8-10 papers on mental health and stigma and how stigma will be reduced. The results show that a large number of doctors and students are stigmatized, negatively affecting the lives of people affected by mental illness. RESHAPE, KAP, and IBH therapies are also effective ways to minimize mental health stigma. This intervention aims to educate public health workers, promote social cohesion, and integrate treatment into primary health care, improving treatment into primary health care, improving treatment quality and patient outcomes. The study draws attention to the importance of stigma reduction efforts in the long term in health education and practice emphasis. 2024 IEEE. -
A Study on the Factors Affecting Infants' Health-Related Issues and Child Mortality using Machine Learning
Child mortality and infant health-related issues remain significant challenges worldwide. Understanding the factors that influence these outcomes is crucial for implementing effective interventions and improving child health outcomes. In this study, we employ machine learning techniques to identify and analyze the key factors affecting infants' health-related issues and child mortality. Further, we identify several significant factors that influence infants' health-related issues and child mortality. These factors include maternal health indicators, access to healthcare services, socioeconomic status, environmental factors, and demographic characteristics. The machine learning models provide insights into the relative importance of these factors, enabling policymakers and healthcare professionals to prioritize interventions and allocate resources effectively. Additionally, we investigate the potential interaction effects among these factors and their impact on child health outcomes. This analysis helps in understanding the complex relationships and causal pathways involved in infants' health-related issues and child mortality. The findings of this study contribute to the existing knowledge by leveraging machine learning techniques to identify and analyze the factors affecting infants' health-related issues and child mortality. The insights gained from this research can inform evidence-based policies and interventions aimed at reducing child mortality rates and improving infant health outcomes globally. By addressing the underlying factors identified through this study, we can work towards achieving better health outcomes for infants and reducing the burden of child mortality worldwide. 2023 IEEE. -
Effectiveness of Telemedicine in Disaster Relief Response Management
Due to climate change many parts of the worlds are prone to natural disasters. Thus, disaster management is the need of the hour. Effectiveness of Telemedicine in Disaster Relief response management shows the demand for telemedicine in the current time to tackle disasters. This paper investigates the history and evolution of telemedicine, their types, demand, challenges and its prospects. The proposed model, CrisisResponsive E-Health Recovery, places an approach on a concise way to manage disaster in the least time without giving up accuracy. The suggested model has the best response time as compared to the other existing model. Wide implementation of this model will result in better recovery rates in disasters. 2024 IEEE. -
Enhancing IoT Security Through Multilayer Unsupervised Learning and Hybrid Models
This research addresses the challenge of limited unsupervised learning in current IoT security research, which heavily relies on labelled datasets, hindering the detection of unknown threats. To overcome this constraint, the study proposes a sophisticated methodology integrating K-means clustering, autoencoders, and a hybrid model (combining both). The aim is to enhance detection capabilities without being reliant on prior labelled data. Emphasizing the need to go beyond traditional models, the research underscores the significance of incorporating a diverse range of smart home IoT devices to gain comprehensive insights. Tests conducted on the N-BaIoT dataset, which incorporates authentic traffic data from nine commercial IoT devices afflicted with Mirai and BASH-LITE infections, demonstrate the effectiveness of the suggested models. K-means clustering demonstrates excellence in precision, recall, and F1-scores, particularly in Doorbell and Thermostat categories. The Hybrid model consistently achieves high precision and recall metrics across various device categories by leveraging the strengths of both Kmeans and autoencoder techniques. Notably, the Autoencoder model stands out for its exceptional ability to achieve a perfect 100% detection rate for anomalies across all devices. This study highlights the robust performance of the proposed unsupervised learning models, emphasizing their strengths and potential areas for refinement in enhancing IoT network security. 2024 IEEE.