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A Scoping review of Deep Reinforcement Learning methods in Visual Navigation
Reinforcement Learning (RL) is a subset of Machine Learning that trains an agent to make a series of decisions and take action by interacting directly with the environment. In this approach, the agent learns to attain the goal by the response from its action as rewards or punishment. Recent advances in reinforcement learning combined with deep learning methods have led to breakthrough research in solving many complex problems in the field of Artificial Intelligence. This paper presents recent literature on autonomous visual navigation of robots using Deep Reinforcement Learning (DRL) algorithms and methods. It also describes the algorithms evaluated, the environment used for implementation, and the policy applied to maximize the rewards earned by the agent. The paper concludes with a discussion of the new models created by various authors, their merits over the existing methods, and a briefing on further research. 2023 IEEE. -
Comparative Study of Graph Theory for Network System
The historical background of how graph theory emerged into world and gradually gained importance in different fields of study is very well stated in many books and articles. Some of the most important applications of graph theory can be seen in the field network theory. Its significance can be seen in some of the complex network systems in the field of biological system, ecological system, social systems as well as technological systems. In this paper, the basic concepts of graph theory in terms of network theory have been provided. The various network models like star network model, ring network model, and mesh network model have been presented along with their graphical representation. We have tried to establish the link between the models with the existing concepts in graph theory. Also, many application-based examples that links graph theory with network theory have been looked upon. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Alzheimer's Disease Detection using Machine Learning: A Review
Alzheimer's is a progressive brain disorder which is an untreatable, and inoperable and mostly affect the elderly people. There is a new case of Alzheimer's disease being discovered globally in every four seconds. The outcome is fatal, as it results in death. Timely identification of Alzheimer's disease can be beneficial for us to get necessary care and possibly even avert brain tissue damage by the time. Effective automated techniques are required for detecting Alzheimer's disease at very early stage. Researchers use a variety of novel approaches to classify Alzheimer's disease. machine learning, an AI branch use probabilistic technique that allow system to acquire knowledge from huge amount of data. In this paper we represent a analysis report of the work which is done by researcher in this field. Research has achieved quite promising prediction accuracies however they were evaluated the the non-existent datasets from various imaging modalities which makes it difficult to make the fair comparison with the other methods comparison among them. In this paper, we conducted a study on the effectiveness of using human brain MRI scans to detect Alzheimer's disease and ended with a future discussion of Alzheimer's research trends. 2021 IEEE. -
3D Modelling and Rendering Using Autodesk 3ds Max
This is outlined how to create a 3D custom kitchen design, including how to set up the sources, details, work with managing various modifiers like edit poly, slice, mesh select, turbo smooth, lattice, bend, shell modifier, so to provide the kitchen an authentic appearance. The method materials are fitted to the model output, together with illuminating the environment leveraging Arnold lights that are intended to be utilized with this renderer only. It has features that are optimised for rendering with Arnold. Procedures and methods regarding rendering are indeed specified. The final rendering was made out of several drawings. Our report's intention is to develop a kitchen design that enriches models with materials and ample shapes from standard extended primitive along with the mostly utilization of pro-boolean. Finally, a material editor was used to improve the model. target illumination, too. 2023 IEEE. -
A Study of Simulated Working of A* and RRT* for Cargo Ship in ASVs
With the increased amount of algorithms for the path planning and collision avoidance of ASVs. The need for an unbiased protective path planning directs the need for decision in stochastic areas in the vast ocean for cargo ship. Autonomous surface vehicles should take appropriate decision on the path according to the dynamic environment and the obstacle that is before them. In some cases, environment, time, and size should be considered to acquire the fastest path and methods that could be suited for collision avoidance. This paper investigates the need for a well-known path planning method that has handled the situation based on the dynamic properties of the vehicle in the ocean. The simulated result shows a slight variation in their proposed path in terms of time and collision in terms of size. Therefore, using a realistic approach of the A* algorithm and the RRT*, we can handle the scenario of dynamic environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Design and simulation of CPW fed circular spike antenna for wireless applications
The aim of the paper is to design, and simulate circular spike Coplanar Waveguide (CPW) fed antenna for wireless applications. The size of the antenna is very small occupying a space of 36mm 36mm including the substrate board. The antenna is designed using FR-4 substrate of thickness 1.6mm with dielectric permittivity of 4.4. The Coplanar Waveguide (CPW) fed system is used, so we can avoid double side printed board. This proposed antenna covers the bandwidth frequency range from 2.85GHz to 3.31GHZ and 5.09GHz to 5.65GHz for various wireless applications. The antenna design and performance are analyzed by using High Frequency Structure Simulator (HFSS) electromagnetic software for wireless applications according to frequency bands. The results of proposed antenna simulation on return loss, VSWR, gain and directivity are calculated. 2015 IEEE. -
Machine Learning's Transformative Role in Human Activity Recognition Analysis
Human action recognition (HAR) is a burgeoning field of computer vision that seeks to automatically understand and classify the intricate movements performed by humans. From the graceful leaps of a ballerina to the decisive strides of a surgeon, HAR aims to decipher the language of motion, unlocking a plethora of potential applications. This abstract delves into the core of HAR, highlighting its key challenges and promising avenues for advancement. We begin by outlining the various modalities used for action recognition, such as RGB videos, depth sensors, and skeletal data, each offering unique perspectives on the human form. Next, we delve into the diverse set of algorithms employed for HAR, ranging from traditional machine learning techniques to the burgeoning realm of deep learning. We explore the strengths and limitations of each approach, emphasizing the crucial role of feature extraction and model selection in achieving accurate recognition. Challenges in Human Action Recognition (HAR), such as intra-class variations, inter-class similarities, and environmental factors. Ongoing efforts include robust feature development and contextual integration. The paper envisions HAR's future impact on healthcare, robotics, video surveillance, and augmented reality, presenting an invitation to explore the transformative world of human action recognition and its potential to enhance our interaction with technology. 2024 IEEE. -
Effect of Halloysite Nanotubes on Physico-Mechanical Properties of Silk/Basalt Fabric Reinforced Epoxy Composites
Natural fiber reinforced polymer composites have become more attractive due to their high specific strength, light weight and environmental concern. However, some limitations such as low modulus and poor moisture resistance were reported. This paper presents the role of halloysite nanotubes (HNTs) on physico-mechanical properties of bidirectional silk and basalt fiber reinforced epoxy (SF-BF/Ep) hybrid composites. Vacuum bagging and ultra-sonication method were used for the fabrication of hybrid composite slabs. The effect of HNT loadings (1.5, 3 and 4.5 wt. %) on physico-mechanical characteristics like density, hardness, flexural and impact properties of SF-BF/Ep composites were determined according to ASTM standards. Experimental results revealed that the incorporation of HNTs improves the mechanical properties. The impact strength of SF-BF/Ep is predominant at 3 wt. % HNT loading where the impact strength surges to 568.67 J/m, which may render HNT filled SF-BF/Ep desirable for various toughness-critical structural applications. The test results demonstrated that SF-BF/Ep-3HNT coded composites exhibited improved mechanical properties among the all composites. 2022 Trans Tech Publications Ltd, Switzerland. -
Effect of basalt fiber hybridization on mechanical properties of silk fiber reinforced epoxy composites
Poor mechanical properties and constraints on production presently limit the utilization of bio-based reinforcing agents to non-structural and structural automotive elements. The conjugation of natural fibre with volcanic rock fibre provides a way to improve the mechanical properties of composites over natural fibre alone. In this study, physico-mechanical properties of hybrid fibre (silk and basalt) reinforced epoxy composites were found by experimentation following acceptable ASTM standards. Hybrid composites were produced by combining silk/basalt fibres in the ratio of 50:0, 25:25 and 30:20, whereas overall weight fraction was maintained at 0.5. The experimental results showed that the performance of combined fibres were superior compared to that of silk fibre bolstered epoxy composites. Among the 2 varieties of hybrids, the silk/basalt (25:25 by weight ratio) combination offered the very best hardness, strength, modulus, and toughness to the epoxy matrix owing to the similar modulus and synergistic interaction between the two reinforcing fibres. The results also steered that the morphology and surface adhesion affected the strength of the hybrid composites. These observations give insight into the advantages of various fibre reinforcements to the mechanical performance of epoxy matrix which is considered to be brittle. The failure mechanisms and the adhesion between fibres and matrix were studied by analysing the photomicrographs of broken coupons. 2020 Elsevier Ltd. -
Mechanical and abrasive wear behaviour of waste silk fiber reinforced epoxy biocomposites using taguchi method
The aim of this research article is to study the static mechanical properties and abrasive wear behavior of epoxy biocomposites reinforced with different weight percentage of waste silk fibers. The effect of parameters such as velocity (A), load (B), fiber loading (C) and abrading distance (D) on abrasive wear has been considered using Taguchi's L25 orthogonal array. The objective is to examine parameters which significantly affect the abrasive wear of biocomposites. The addition of silk fiber has resulted in improved flexural properties of the epoxy matrix. The results of ANOVA indicated that the parameter which played a significant role was abrading distance followed by fiber loading, load and sliding velocity. 2019 Trans Tech Publications Ltd, Switzerland. -
Impedance and electrochemical studies of rGO/Li-ion/PANI intercalated polymer electrolyte films for energy storage application
The present manuscript describes the synthesis of reduced graphene oxide (rGO) from coke by using modified Hummers method. The synthesized emeraldine poly aniline (PANI) polymer was used as a polymer host matrix. A series of polymer electrolyte films were prepared by varying concentration of rGO, PANI and Lithium carbonate. The synthesized PANI and rGO were soluble in common polar solvent. The structural, Nyquist and cyclic voltammetry studies of polymer electrolyte were investigated. The XRD and FTIR investigation confirms the formation of rGO and PANI in view of structural and chemical compositions respectively. The electrical property of polymer electrolyte was obtained by Nyquist plot which represents the perfect semicircular pattern. It confirms the charge transport mechanism with the decreased concentration of rGO in polymer electrolyte. The cyclic voltammetry performed at different scan rate on potential window ranged between-0.5 to 0.6 V represents the oxidation and reduction peaks. The overall results describe that the present electrolyte material can be a potential candidate for energy storage application.. 2019 Elsevier Ltd. -
Rendering View of Kitchen Design Using Autodesk 3Ds Max
The method of creating a 3D kitchen design model is clarified, including setting up the sources, working with editable poly, information in the inside of the kitchen design, and applying turbo-smooth and symmetry modifier. The way materials are introduced to the model which is defined in addition to lighting the environment and setting up the renderer. Rendering methods and procedures are also defined. Multiple images were drawn to create the final rendering. The goal of our research is to produce a kitchen design that uses materials to enhance models. Cylinder, sphere, box, plane, and splines were the shapes employed. Editable poly, editable spline, and UVW map are the modifiers. Finally, we enhanced the model using a material editor and target lighting. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multimodal Classification on PET/CT Image Fusion for Lung Cancer: A Comprehensive Survey
Medical image fusion has become essential for accurate diagnosis. For example, a lung cancer diagnosis is currently conducted with the help of multimodality image fusion to find anatomical and functional information about the tumor and metabolic measurements to identify the lung cancer stage and metastatic information of the disease. Generally, the success of multimodality imaging for lung cancer diagnosis is due to the combination of PET and CT imaging advantages while minimizing their respective limitations. However, medical image fusion involves the registration of two different modalities, which is time-consuming and technically challenging, and it is a cause of concern in a clinical setting. Therefore, the paper's main objective is to identify the most efficient medical image fusion techniques and the recent advances by conducting a collective survey. In addition, the study delves into the impact of deep learning techniques for image fusion and their effectiveness in automating the image fusion procedure with better image quality while preserving essential clinical information. The Electrochemical Society -
A Review of the Detection of Pulmonary Embolism from Computed Tomography Images Using Deep Learning Methods
Medical imaging has been evolving at a steady pace generating enormous amounts of health data, and the use of deep learning (DL) has helped a great deal in processing the detailed data. Deep learning-based methods are used in different medical imaging tasks to detect and diagnose diseases. For example, medical imaging is used to diagnose pulmonary embolism (PE), a commonly occurring cardiovascular disease with high mortality and prevalence and a low diagnosis rate. According to medical experts, PE has resulted in many deaths because of missed diagnoses for the medical condition. Another critical aspect of the disease is the possibility of permanent lung damage if left untreated. The use of deep learning methods in medical imaging is attributed to their ability to use learning-based methods to process enormous amounts of data. However, there are some unique challenges in the detection of PE. PE is not specific in its clinical presentation and is easily ignored, making it difficult to diagnose. Deep learning-based detection methods help a great deal in the disease detection in miniature sub-branches of the alveoli, and images with noisy artifacts easily compared to manual diagnosis. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Crowd Monitoring System Using Facial Recognition
The World Health Organization (WHO) suggests social isolation as a remedy to lessen the transmission of COVID-19 in public areas. Most countries and national health authorities have established the 2-m physical distance as a required safety measure in shopping malls, schools, and other covered locations. In this study, we use standard CCTV security cameras to create an automated system for people detecting crowds in indoor and outdoor settings. Popular computer vision algorithms and the CNN model are implemented to build up the system and a comparative study is performed with algorithms like Support Vector Machine and KNN algorithm. The created model is a general and precise people tracking and identifying the solution that may be used in a wide range of other study areas where the focus is on person detection, including autonomous cars, anomaly detection, crowd analysis, and manymore. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Advancements in e-Governance Initiatives: Digitalizing Healthcare in India
In order to improve the quality of service delivery to the public, to encourage interactive communications between government and citizens or government and business, and to address development challenges in any given society, information and electronic governance is the sophisticated fusion of a wide range of information and communication technologies with non-technological measures and resources. Digital technology advancements over the past ten years have made it possible to quickly advance data gathering, analysis, display, and application for bettering health outcomes. Digital health is the study and practice of all facets of using digital technologies to improve ones health, from conception through implementation. Digital health strategies seek to improve the data that is already accessible and encourage its usage in decision-making. Digital patient records that are updated in real-time are known as electronic health records (EHRs). An electronic health record (EHR) is a detailed account of someones general health. Electronic health records (EHRs) make it easier to make better healthcare decisions, track a patients clinical development, and deliver evidence-based care. This concept paper is based on secondary data that was collected from a variety of national and international periodicals, official records, and public and private websites. This paper presents a review of advancements for scaling digital health within Indias overall preparedness for pandemics and the use of contact tracing applications in measuring response efforts to counter the impact of the pandemic. The paper provides information about the government of Indias EHR implementation and initiatives taken toward the establishment of a system of e-governance. The document also covers the advantages of keeping EHR for improved outreach and health care. Further, this paper discusses in depth the effectiveness of using contact tracing applications in enhancing digital health. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
ICT Policy Reforms for Innovation and Economic Development: A Comparative Study of India and China
The widespread adoption of Information and Communication Technologies (ICTs) has become essential for economic and social growth across the world. This paper aims to examine the impact of ICT policies and reforms on the level of economic development and adoption of ICTs in two countries, India and China. Previous studies have shown the positive impact of ICT adoption on economic growth, productivity, and innovation. However, the effectiveness of specific policy measures in promoting ICT adoption and economic development remains ambiguous to the users of ICT. This paper presents a comparative analysis of the ICT policies and reforms implemented in India and China from 2010 to 2021 and their impact on GDP per capita and internet usage. The study aims to identify and analyze the key ICT policies and reforms implemented in the two countries and examine their impact on economic development. The data for this study have been collected from the World Bank indicators database. The sample consists of the two fastest-growing economies in the world, India and China. The data analysis involves conducting descriptive statistics, correlation, and regression analysis to examine the relationship between ICT policies and reforms and their impact on GDP per capita, internet usage, and research and development expenditure. The findings of this study will contribute to the existing literature on the relationship between ICTs and economic development and provide insights into the policy measures that can promote ICT adoption and economic growth in different contexts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Effecient Approach to Detect Fraud Instagram Accounts Using Supervised ML Algorithms
Nowadays social media plays a vital role in different fields including business, economic communication and personal. Many person get profit from the different origins of availability of data from these social media, but cyber-crimes are increasing day by day. A person can generate many fake accounts and hence pretenders can easily be made. Instagram, as one of the popular types of online social media site, carries big information and messages through the posts. Most of the person use Instagram as a digital life marketing place because it is a one of the big social media site. The goal of the research paper is to recognize and stop fake IDs and pages. Because through the professional pages of Instagram, many fake cases and things are occurring present days. So the main thing is to recognize fake pages and fake accounts also. In this paper, we work on various IDs of Instagram. We want to observe an ID is real or not using Machine Learning techniques namely Logistic Regression, Naive Bayes, Support vector machine, Decision tree, Random Forest. 2022 IEEE. -
Web Platforms for Fintech Products
Internet marketing and digital marketing are not synonymous in the minds of the majority of the population, yet this may not be true. Given the rise in popularity of digital marketing as a marketing tactic, it is critical to comprehend the distinctions between the two methods. Even while it should be evident that they might be connected, there is very little difference between them. Internet marketing is merely a subclass of digital marketing, as well as the extent of digital marketing encompasses much more than internet marketing. This paper discussed digital marketing technologies, as well as the advantages and disadvantages of employing digital marketing and digital finance tools in general. In order to remain competitive, businesses must overcome obstacles and seize possibilities presented by digital marketing technologies. Lastly, it's critical to prioritise digital marketing and make use of digital finance techniques in order to maintain a good performance without wasting time or money. 2022 IEEE. -
Vehicular Propagation Velocity Forecasting Using Open CV
This work presents a predictive learning driven methodology for recognizing the vehicular velocity. The developed model uses machine vision models to trace and detect vehicular movement in timely manner. It further deploys a machine tested framework for estimation of its velocity on basis of the accumulated information. The technique depends upon a CNN model that is validated with a standardized instances of vehicular scans and corresponding velocity parameters. The proposed model generates good efficiency and robustness in determining velocities across test conditions which encompass various kinds of vehicles and lighting scenarios. An optimal vehicular frequency is noted with heavy-weight vehicles in place in comparison to other vehicles. A mean latency period of 1.25 seconds and an error rate of 0.05 is observed with less road traffic in place. The suggested approach can be of great help in transportation systems, traffic monitoring and enhancing road safety. 2023 IEEE.