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Multiple Safety Equipment's Detection at Active Construction sites Using Effective Deep Learning Techniques
The safety of human labour is the most important thing in this era no matter where the labour force works. Governments and various NGOs focus on ensuring the delivery of the top safety to the labor class of the country. One such example is the working of the labour force at huge construction sites. For them a lot of work includes a huge amount of risks hence following full safety is the need of the hour for the workers working at construction sites. In order to deal with proper monitoring of the safety being followed at Construction sites. In order to make use of the latest technologies in this field also some of the good object detection models can be used for detecting the safety equipment of the workers which include things like Hard Hats, Masks, Vest, Boots. A lot of research is going on in improving the detection speed and accuracy of objects using state-of-the-art techniques in Computer Vision and this could lead to providing better results. Based on the available research and compute resources future work can be done to improve the results in this specific domain also. 2022 IEEE. -
Multiplier-free Realization of High throughout Transpose Form FIR Filter
This paper presents a multiplier-free realization of the block finite impulse response (FIR) filter in transpose form configuration using binary constant shifts method (BCSM). The proposed architecture is synthesized using Xilinx Vivado and Cadence RTL Encounter compiler for the area and power analysis and is compared with the existing works in the literature. The comparison highlights the advantages of the proposed architecture in terms of power, hardware complexity and throughput for realizing reconfigurable high throughput block FIR filters. 2020 IEEE. -
Nano ZnO@PEG catalyzed one-pot green synthesis of pyrano[2,3-d] pyrimidines in ethanol via one-pot multicomponent approach
A facile one-pot multicomponent protocol for the synthesis of bio-active Pyrano[2,3-d]pyrimidine derivatives by a one- step condensation reaction of substituted aldehyde, malononitrile/methyl cyanoacetate, barbituric acid has been demonstrated using nano ZnO@PEG as a catalyst at room temperature. The present approach offers several advantages, such as shorter reaction time, higher yields, and environmental friendliness. Easy isolation of products, absence of column chromatographic purification, use of commercially available low-cost starting materials and reusability of the catalyst make the methodology viable in organic synthesis. 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Second International Symposium ''Functional Nanomaterials in Industrial Applications: Academy - Industry Meet''. -
Narrowband and Wideband Directional Beamformer with Reduced Side Lobe Level
In this paper, the synthesis of narrow and wideband beamformers with reduced side lobe level and wide beam steering capability is presented. A closed form expression with slope equalization technique is derived for array factor of the beamformer to meet the desired beam-pattern specifications of Half Power Beam-Width (HPBW)and Side Lobe Level (SLL). The proposed beamformer design is adaptable to any bandwidth and null placement in the desired direction. The slope equalization method improves the SLL of the beamformer. Compared to Kaiser, Chebyshev, DPSS and Taylor beamformers, the proposed narrowband and wideband beamformers exhibit lower and tapered side lobes, hence improved First Null to Last Null (FNLN)ratio. The proposed wideband beamformer exhibits superior performance in the wideband frequency range of 1-3GHz. 2019 IEEE. -
Natural Disaster Prediction by Using Image Based Deep Learning and Machine Learning
In recent years, diseases and disaster have become more unpredictable. The advent of technology has not only making our lives easier but also technology-dependent. Nevertheless, the natural disasters cause great adversity by disrupting considerable human lives. Also, the disasters obstruct and affect many industries and services either directly or indirectly. Hence, it is necessary to study and observe data patterns and warning signs that lead to a natural disaster, its potential risk and its ability to resolve management strategies, which can be implemented immediately to minimize the socio-economic loss. This article reviews the state-of-the-art research works and findings through a technological perspective on data analysis, natural disaster prediction, and the utilization of technology for deploying management strategy. Also, this paper focuses on investigating the today's Industry 4.0 that utilizes cognitive computing. The primary aim of this article is to review the research ideas that leverage big data and data mining to observe and track patterns, which can impelment predictive analysis to anticipate the forthcoming disasters. Furthermore, this research work analyzed the posed predictive models by specifically using ANN (Artificial Neural Networks), sentiment model, and smart disaster prediction application (SDPA) to predict the flash flood. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Natural Language Processing on Diverse Data Layers through Microservice Architecture
With the rapid growth in Natural Language Processing (NLP), all types of industries find a need for analyzing a massive amount of data. Sentiment analysis is becoming a more exciting area for the businessmen and researchers in Text mining NLP. This process includes the calculation of various sentiments with the help of text mining. Supplementary to this, the world is connected through Information Technology and, businesses are moving toward the next step of the development to make their system more intelligent. Microservices have fulfilled the need for development platforms which help the developers to use various development tools (Languages and applications) efficiently. With the consideration of data analysis for business growth, data security becomes a major concern in front of developers. This paper gives a solution to keep the data secured by providing required access to data scientists without disturbing the base system software. This paper has discussed data storage and exchange policies of microservices through common JavaScript Object Notation (JSON) response which performs the sentiment analysis of customer's data fetched from various microservices through secured APIs. 2020 IEEE. -
Nature's Lament: A Comparative Psychoanalytical Reading of Childhood Trauma in Select War Narratives
Sustainable Development has become an inevitable need of the hour. This paper problematizes the trauma of children as represented in the narratives, Beasts of No Nation by Uzodinma Iweala and A Long Way Gone by Ishmael Beah. The incomprehensibility of trauma, it's varied representation in fiction, dissociation of child psyche, and its detrimental effect on children is substantiated using psychoanalytic theory of trauma proposed by Cathy Caruth and contemporary trauma theorists. The paper argues the atrocities children are forced to be involved into, causes profound trauma in themselves leading to, encumbering of sustainable developmental goals. A comparative study of interpretive textual analysis is employed to study the havoc the society endears as a result of war, that wrecks the child, hindering the overall sustainable development. As it voices out the voiceless trauma of children the paper also aims in divulging the decisive influence of the select literary narratives in sensitizing the society in achieving societal as well as environmental sustainability. The Electrochemical Society -
Negative Domination inNetworks
We introduce s-domination in signed graphs which is based on the number of negative edges between the dominating set and its complement. The s-domination in both the positive and negative homogeneous signed graph will be studied for each value of s. As a special case, the properties of s-domination in sum signed graphs will be analyzed. The maximum value of s for a graph for which the s-domination exists is identified. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Network Security Tools and Applications in Research Perspective
The modern world technology is civilized, globalized and modernized. The technological development of social networks and e-commerce applications produce larger data. This data communication is major task, because device to device communication need network terminal. This data transmission is not safe because of different types of tools and software available to destroy the existing network. In the field of network security during data transfer from one particular node to other node some security vulnerability is happened this is the one of the critical issue in this sector. The reason for this network security is different types of data attacks are happen in day to day life. It is easy to establish a new network but protecting the entire network is a big issue. This network security is generally two parameter first one is communication and second one is data automation. The network security field is directly or indirectly linked with the concept of data encryption. The development in this network security has taken us to a level that from signature again we came back to thumb print. For example maintain the data secure we use the lock system which is a finger print type. This technology helps us to protect the physical data theft, but logical data theft is still problem for data transmission. This article will brief about the network security it also presents the various network security types. Those types are wired and wireless network security. Apart from the network security the following topics is also discussed in this article. Those are network security protocols and simulation tools in network security. The research problems in network security are privacy and vulnerability of data. 2019 IEEE. -
Networks Simulation: Research Based Implementation using Tools and Approaches
The advancements in computer networks and communication technology keep network-related research in high demand. Protocols are designed to improve the environment and it is mandatory to test their effectiveness before deploying them. Deploying an untested protocol in a full-fledged real environment is not desirable as there exists uncertainty about its success. Simulation software is one of the essential tools in network research areas. It gives a platform for testing and observing newly developed protocol's behavior with less cost and risk. Different kinds of network simulators are available., some are exclusive for wired or wireless., and some are for both. There are many simulators available hence selecting the most appropriate simulation tool among them is a difficult task. This paper focuses on giving a detailed review of popular simulation tools. 2022 IEEE. -
Neural Network based Student Grade Prediction Model
Student final grade GPA is the collective efforts of their previous and ongoing efforts of each semester examination may predict accurately using the neural network which receives the input weight of each matrix element of variables to next neuron. The GPA prediction based on regular class performance and previous grades with background variables were found much significant. This research tries to explore the model comparison and evaluate student grade prediction using various neural network models. The single-layer half i.e., successful student model predicts 90 total accuracies than the single layer with five hidden layer neurons (88.5 percent). The multi-layer with two hidden layers (7,3) is 84 percent accuracy is less than one percent accuracy than multilayer with three hidden layers. Similarly, the multilayered with four hidden layered 25,12,7,3 model predicts the least accuracy (77 percent accuracy) for student grade. Similarly, the passed student prediction model has less accuracy than both students' 86 percent. 2022 IEEE. -
New developments in the study of parity signed graphs
A signed graph has all its edges signed with either negative or positive signs. Parity signed graphs are generated from the integer labeling of the vertices of a graph. An edge gets a positive sign if its end vertices are of the same parity and gets a negative sign if its end vertices are of the opposite parity. Signed graphs which admit parity labeling can be characterized. All parity signed graphs are balanced but not the converse. 2020 Author(s). -
New Paradigm of Marketing-Financial Integration Modelling for Business Performance: An IMC Model
When it comes to the provision of financial services, the integrated marketing communication (IMC) process is crucial in the creation and maintenance of client-provider bonds. This research presents a literature assessment on the theoretical basis for using marketing communication tools in the provision of financial services. This research is an attempt to bolster the little theoretical literature on the effectiveness of marketing communication techniques in the provision of financial services. Financial service providers use marketing communication as a channel for two-way exchanges with their clientele, with the ultimate goal of maximising the benefits their customers bring to the company. When it comes to providing financial services, an organisations success hinges on its ability to effectively manage its relationships with both current and potential consumers. As a result, it is important for practical reasons to be guided by well-defined marketing communications goals to identify the extent of usage and within the constraints of available resources. In this regard, businesses are free to establish specific communications objectives in accordance with their unique situations to direct the implementation of their IMC plan. This study aims to find out an impact of financial integration with IMC on business performance. This study is descriptive in nature. Primary data is collected with the help of questionnaire. The study finds that the financial integration in the IMC model has a statistically significant impact on business success. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Nexus Between Credit Conditions, Financial Literacy, and Loan Accessibility Among Indian MSMEs
We examine the interplay among commercial bank loan terms, financial literacy, and formal loan accessibility for micro, small and medium enterprises (MSMEs). Despite recent strides in integrating MSMEs into commercial bank portfolios via micro-lending initiatives, persistent challenges hinder their access to formal credit. Drawing from empirical data and existing literature, this study explores the nuanced impacts of loan terms and financial literacy on SMEs ability to secure formal loans. Addressing gaps in prior research, we concurrently analyse borrower characteristics and credit regulations influence on formal loan accessibility. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Nine Level Quadra Boost Inverter with Modified Level Shifted Pulse Width Modulation Technique
This research initiatives to introduce a switched capacitor based nine level boost inverter (SC-9LBI) powered by modified level shifted pulse width modulation (PWM) technique. The SC-9LBI equipped with single DC source along with three capacitors and eight controlled switches to develop nine level inverter output voltage. The suggested inverter configuration has the ability of boosting the inverter input voltage into 1:4 ratio. Also, this research involves modified level shifted PWM technique to enhance the quality of inverter output voltage. The effectiveness of the NLMLI is assessed through parameters such as harmonic distortion, peak voltage, and output voltage root mean square value (rms). Simulation studies have been conducted using MATLAB/Simulink to evaluate the proposed inverter's performance. 2024 IEEE. -
NLP-based Health Care- Hospital Recommendation Systems with Online Text Reviews by Patients Satisfaction
Recent times, these recommendations based on reviews play a vital role in the service industry. The hospital is assessing its quality of service using these surveys or studies posted in online forums. The ongoing pandemic also played a vital role in making the online review more popular. These statistical data and visualization are informative in representing the views of patient satisfaction towards health service. As the size of data is large and it is of varied size and format it is difficult to get consolidated results. The users share their emotions and feelings through this review. So, it is a challenge to assess the emotions of the patients. Sentiment analysis using machine learning makes our work easy in evaluating the scores visually. The reviews are analyzed using natural language processing (NLP), and the sentiment of the studies is analysed as positive, negative, and neutral using polarity ranking, which in turn is converted as the recommendation system based on patient reviews. This paper aims to propose a new method of recommending the hospital based on the sentiment of the previous user review. The thought of the user is collected from the various hospitals. The proposed (Healthcare Recommendation System) HRS system has nearly 0.5 mean absolute error, which states that the proposed HRS system is significantly effective. 2023 IEEE. -
Node Overlapping Detection for Draggable Node-Based Applications
Node-based interfaces are user interfaces that are based on the concept of nodes, which represent individual units of functionality, and edges, which represent the connections between nodes. In a node-based interface, nodes are connected by edges to form a graph, which represents the data flow and relationships between different parts of the system. The Node overlapping detection technique is only for react flow version 11 and higher. Users having previous versions are not able to use that functionality. To detect the overlapping, based on the output of this library, several user-defined functions can be used to resolve to overlap. It will see the single-pixel overlap. Using this library, users can avoid Node and edge overlapping by creating custom edges. It is a simple JavaScript function currently used for reactjs. In the future, if any other script develops a draggable node-based flowsheet-creating feature, the user can use this library accordingly. 2023 IEEE. -
Non invasive methods of blood glucose measurement: Survey, challenges, scope
Noninvasive body parameters monitoring and disease detection is one of the emerging research area now a days. In this paper a review on Non-invasive methods of blood glucose measurement has been made. A comparative study has been made which describes the methodology incorporated in the published literatures, research challenges and the used tools. This paper also describes about the factors which highly impacts the non-invasive measurement. Finally, a deep learning based noninvasive measurement method compatible with IOT is mentioned. This paper serves as a proper reference for future researchers working in non-invasive blood glucose measurement domain in selecting appropriate non-invasive method algorithm for glucose monitoring non-invasively. 2019 Bharati Vidyapeeth, New Delhi. Copy Right in Bulk will be transferred to IEEE by Bharati Vidyapeeth. -
Non-Alcoholic Fatty Liver Disease Prediction with Feature Optimized XGBoost Model
Non-alcoholic fatty liver disease (NAFLD) is an expanding health threat, posing significant risks for long-term complications. Early detection and intervention are crucial, but traditional diagnostic methods can be expensive and invasive.This study investigates the utilization of machine learning models for predicting liver diseases from various out-sourced datasets..We employed Decision Trees, Random Forests, and Support Vector Machines (SVMs) to predict NAFLD based on various clinical and demographic features. Model performance was evaluated by calculating accuracy, precision,deviation and accuracy-score.All these models achieved promising accuracy levels, ranging from 80% to 90%, showcasing their potential for NAFLD prediction. Among them, XG-Boost demonstrated the highest performance, with an accuracy of 90% and more.This study demonstrates the effectiveness of machine learning models in predicting NAFLD with high accuracy using readily available data. Further research with larger sized and more varied datasets will vindicate these models for real-world application in clinical settings. 2024 IEEE. -
Non-Antibacterial Carbon Nanoparticles and Its Fluorescence Properties
Highly fluorescent carbon nanoparticles are synthesized from corn starch via one-pot hydrothermal method. Upon treatment with the lime juice as the catalyst, carbon nanoparticles are functionalized with potassium, and an improvement in the luminescence behavior is also observed. The synthesized nanoparticles did not exhibit any antibacterial activity against gram-positive (Staphylococcus aureus, Bacillus subtilis) and gram-negative (Pseudomonas fluorescence, E.coli) bacteria. The excellent photoluminescence coupled with non-toxic behaviour of the carbon nanoparticles would be best suited for biomedical applications. The Electrochemical Society