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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Raj, Vivek Kumar, Biresh Thakur, Kanika Mitra, Dipra Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Dept. of Computer Science Engineering Jharkhand Ranchi India

ISBN: (纸本)9798350385199

As a way to provide ubiquitous connectivity, satellite constellations are becoming more and more popular due to the growing need for worldwide internet access. In order to provide dependable and seamless worldwide internet coverage, this study investigates how satellite constellations might be optimized by comparing and contrasting communication protocols and orbital topologies. This ambitious aim involves a number of problems and complications, which are explored in the research along with the trade-offs and design considerations that must be made in order for the implementation to be effective. Two main elements form the framework of the comparative analysis. First, by considering factors like latency, data throughput, and coverage efficiency, it evaluates a range of communication protocols, including traditional geostationary satellites, Low Earth Orbit (LEO) constellations, and Medium Earth Orbit (MEO) satellites. Second, to ascertain the effect of various orbital configurations on worldwide internet coverage, the study considers satellite dispersion, orbital altitudes, and coverage patterns. By means of this study, the research endeavors to provide a valuable contribution to the current discussion on satellite constellations for worldwide internet coverage, therefore facilitating well-informed decisions about the design and implementation of these systems. © 2024 IEEE.

关键词： Geostationary satellites

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AI-Based Indian Currency Detection for Visually Challenged Users

AI-Based Indian Currency Detection for Visually Challenged U...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Shopika, S. Senthil Velan, A.S. Dharun Prakash, S.R. Pavithra, S. Lakshmi, R Poornima Ponnusamy, R. Chennai Institute of Technology Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331510022

For denomination recognition, this Indian Currency Detection System will use a CNN trained on an extremely large and diverse set of Indian banknotes from 10 to 2000. It possesses an amicable interface through which images can be captured using the camera of the user's smartphone, providing real-time audio feedback to denominate the banknote without requiring any visual assistance. Made with the features of capturing an image, real-time recognition, and user-friendly design, these are made to add a great experience for smooth navigation by a blind person. The novel and innovative features make this Indian Currency Detection System unique. Using CNN, which has been trained on the comprehensive set of Indian banknotes from 10 to 2000, the software lets a user capture banknote image directly by the smartphone camera. It allows knowing the denomination through real-time audio feedback without any visual assistance. Such characteristics include image capture, real-time identification, and designing user-friendly user interfaces that make easy navigation of the surroundings possible for the visually impaired. It does more than detect currency. It significantly contributes to the discussion regarding the development and availability of more assistive technologies. It is usable and aims to bring about greater accessibility and inclusivity in promoting financial independence among visually impaired people. Contributing to the ability of cash transactions among the visually impaired to gain confidence in freedom and security, this project aligns with a broader discussion related to assistive technologies. © 2024 IEEE.

关键词： Assistive technology

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Smart Mushroom Cultivation: Enhancing Growth Conditions through Sensor-Based Monitoring

Smart Mushroom Cultivation: Enhancing Growth Conditions thro...

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Rana, Ishaan Gupta, Ankit Mittal, Rakshit Chaudhary, Harshita Tyagi, Shobha Chawla, Pronika Mriirs Dept. of Computer Science and Engineering Faridabad India

ISBN: (纸本)9798350344387

Smart mushroom cultivation using remote sensing involves the integration of advanced technology to optimize the growth and productivity of mushrooms. Remote sensing techniques using arduino, sensors and cloud services are employed to monitor and manage various farming and growth related parameters in mushroom farms. Using the remote sensing of mushroom fields provides a macroscopic view of environmental conditions, helping farmers identify suitable locations for mushroom cultivation based on factors like temperature, humidity, and vegetation cover. Ground-based sensors are strategically placed within the cultivation area to measure crucial variables like temperature and humidity levels. These sensors transmit data to a central control system, allowing farmers to maintain optimal conditions for mushroom growth. Automated climate control systems can adjust parameters in response to real-time data, ensuring consistent and ideal growing conditions. This enables timely intervention, reducing crop loss and the need for chemical treatments. Furthermore, remote sensing can improve resource management. Precise water and nutrient monitoring based on sensor data can lead to more efficient resource utilization, reducing waste and operating costs. In summary, smart mushroom cultivation using remote sensing technology optimizes environmental monitoring, and resource usage. By harnessing the power of remote sensing, mushroom farmers can increase yields, reduce environmental impact, and enhance the overall sustainability and profitability of their operations. © 2023 IEEE.

关键词： Fans Fuzzy logic Humidity Schedules Temperature

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World Coin Currency Detection: A Comparative Study of Machine Learning and Deep Learning

World Coin Currency Detection: A Comparative Study of Machin...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Dumka, Ankur Pandey, Arnav Mishra, Amit Kumar Bhaskarrao Bahadure, Nilesh Tripathi, Neha Pandey, Neeraj Kumar Women Institute of Technology Dept. of Computer Science & Engineering Dehradun India St. Joseph's Academy Dehradun India Graphic Era Hill University Dept. of Computer Science & Engineering Dehradun India Symbiosis Institute of Technology Nagpur Campus Pune India Dept. of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798350394412

In the economy where trade and finance are closely linked it is essential to accurately identify and authenticate various currencies, especially in industries such, as banking, tourism and retail. Conventional methods, for identifying coins typically require inspections or simple automated processes, which might take up a lot of time and are susceptible, to mistakes. The advancements, in machine learning (ML) and deep learning (DL) technologies, over the years have revolutionized currency detection providing efficient solutions. This article discusses the evaluation of machine learning and deep learning approaches to identify types of coins. It explores how algorithms, like MobileNetV2, Support Vector Machines (SVM), Random Forest, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) perform in distinguishing and classifying coin denominations across currencies. The results of experiments conducted on data sets show how these methods compare in terms of accuracy, effectiveness, and reliability. Furthermore, we examine the advantages and constraints of each approach while suggesting directions for advancing research, in the realm of identifying cryptocurrency. © 2024 IEEE.

关键词： Convolutional neural networks

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Airline Sentiments Unplugged: Leveraging Deep Learning for Customer Insights 5

Airline Sentiments Unplugged: Leveraging Deep Learning for C...

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5th International Conference on Sustainable Technologies for Industry 5.0, STI 2023

作者： Arpita, Habiba Dewan Al Ryan, Abdullah Hossen, Md Shakil Rahman, Md. Sadekur Daffodil International University Dept of Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350394290

Over the past twenty years, the competitive airline industry has expanded at an exponential rate. As more people travel through various airlines, they encounter more amenities and issues, and based on those encounters, they offer their personal reviews. This study significantly advances the area of sentiment analysis in the airline industry by giving businesses the means of extracting insightful information from customer reviews and use that information to inform data-driven service improvement choices. The review of airline programs effectiveness heavily relies on feedback from consumers. An extensively gathered dataset of 67,993 reviews from the Google Play Store and the App Store based on the ten most well-known airlines, was divided into three categories: 'Positive,' 'Negative,' and 'Neutral'. Word embedding technique was integrated with deep learning models, such as CNN, LSTM, and BiLSTM to improve sentiment analysis accuracy. Both the LSTM and BiLSTM models achieved remarkable accuracy rates at 90% and 91% respectively, which is intriguing. However, BiLSTM was found to outperform other deep learning models regarding precision at 92%, recall at 91%, and F1-score at 91% among other outcomes. © 2023 IEEE.

关键词： Classification (of information)

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MetaSPICE: Metaprogramming SPICE Framework for the Design Space Exploration of PUF Circuits 12

MetaSPICE: Metaprogramming SPICE Framework for the Design Sp...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Xynos, Athanasios Tenentes, Vasileios University of Ioannina Dept. of Computer Science & Engineering Greece

ISBN: (纸本)9798350321074

Silicon Physical Unclonable Functions (PUFs) generate challenge-response pairs that are unique to each die, allowing for secure identification, authentication and cryptographic keys generation. However, designing and evaluating PUFs is a complex task that can require a vast amount of SPICE netlists simulation. In this paper, we present MetaSPICE, which is a novel metaprogramming SPICE framework for the design space exploration of PUFs. Metaprogramming refers to the process of writing programs that manipulate other programs, data structures or even themselves. The designer in MetaSPICE, instead of defining multiple SPICE netlists, has to define a single SPICE template that is used to generate multiple netlists. The MetaSPICE is complementary to classical SPICE simulators, and enables the design exploration impact on PUFs' performance. The framework consists of a template engine for generating SPICE models from SPICE templates, and software for evaluating the performance of PUF circuits in terms of uniqueness, uniformity and reliability from SPICE simulation results. We demonstrate the effectiveness of MetaSPICE by evaluating the performance and presenting improvements on a recently proposed PUF design. © 2023 IEEE.

关键词： SPICE

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Advancements in Left Ventricular Myocardium Analysis: A Comprehensive Review of Myocardium Segmentation, Fibrosis Detection and Wall Motion Estimation Using Cardiac Magnetic Resonance Imaging

Advancements in Left Ventricular Myocardium Analysis: A Comp...

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2024 IEEE International Conference on Emerging Technologies in computer science for Interdisciplinary Applications, ICETCS 2024

作者： Sunanda, H.G. Kavitha, K.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru560004 India Visvesvaraya Technological University Belagavi590018 India Dayananda Sagar College of Engineering Dept. of Computer Science and Engineering Bengaluru560078 India

ISBN: (纸本)9798350372502

Cardiac Magnetic Resonance Imaging (CMRI) has established itself as a cornerstone in cardiovascular diagnostics, enabling precise assessment of left ventricular (LV) myocardium. This review meticulously examines the multifaceted aspects of LV myocardium analysis, focusing on three pivotal components: myocardium segmentation, fibrosis detection, and wall motion estimation. By exploring cutting-edge techniques and methodologies in these domains, this article sheds light on the profound impact CMR has had on the accurate diagnosis, prognosis and structuring treatment plans for diseases of the heart. In the realm of cardiac diagnostics, Left Ventricular (LV) myocardium analysis utilizing CMRI has undergone a transformation advancement. This review encapsulates the comprehensive exploration of LV myocardium analysis, Myocardium segmentation techniques, ranging from traditional methods to contemporary machine learning-driven approaches, are scrutinized for their accuracy and efficiency. The detection and characterization of myocardial fibrosis, crucial in diagnosing various cardiac conditions, are dissected through cutting-edge CMR techniques such as Late Gadolinium Enhancement (LGE) imaging and quantitative mapping methods. Additionally, advanced methods of wall motion estimation, including feature tracking and strain imaging, highlighting their applications in assessing myocardial function. Real-world clinical scenarios underscore the transformation impact of these techniques on diagnosing heart diseases, guiding treatment decisions, and improving patient outcomes. The current landscape of LV myocardium analysis, emphasizing the pivotal role of CMR in revolutionizing cardiac healthcare. © 2024 IEEE.

关键词： Motion estimation

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Diabetes Prediction using Extreme Learning Machine: Application of Health Systems 5

Diabetes Prediction using Extreme Learning Machine: Applicat...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Reddy, S. Nava Bharath Reddy, K.V. Narasimha Rao, S.N. Tirumala Kumar, K.S.M.V. Dept. of Computer Science and Engineering Andhra Pradesh India

ISBN: (纸本)9781665474672

Recently, the health sector is widely adopting artificial intelligence models such as machine learning (ML), deep learning for data analysis, disease prediction, and disease classification. However, the conventional models failed to analyze the data. Therefore, this work is focused on analysis of diabetes prediction using extreme learning machine (DP-ELM) model. Initially, Pima Indian diabetes is considered, which is pre-processed for missing data symbols identification. Then, the statistical features from pre-processed dataset are extracted using principal component analysis (PCA). Then, ELM model is trained with the PCA features and forms the trained feature dataset. Then, a random test combination is applied for ELM testing, which classifies the positive and negative status of diabetes. The simulations proved that;the proposed DP-ELM outperformed in terms of accuracy as compared to existing methods. © 2023 IEEE.

关键词： Forecasting

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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A comparative study of different scheduling approaches for splittable latency sensitive tasks in Fog-Cloud environment 2

A comparative study of different scheduling approaches for s...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Sandeep, Kone Sai Koundinya, Challa Aditya Prabhas, Adapa Venkata Swain, Chinmaya Kumar Mishra, Sambit Kumar SRM University Dept. of Computer Science Engineering India

ISBN: (纸本)9798350344882

IoT has revolutionized the way we live and the work we do by connecting different devices through the Internet. In the present scenario, the number of IoT devices are increasing rapidly due to the increase in technology and the increase in the comforts of life. Nowadays we can see that many of them are using IoT devices regularly, it's estimated that by the end of 2030, there will be 30 billion users who will be using IoT applications. These devices send data to the cloud for processing. Due to the distance of the cloud from the IoT devices, the application requests get delayed service responses. So to handle the latency sensitive applications we require the micro cloud service like fog servers deployed near to the data generation points. The fog layer lies between the IoT devices and Cloud which acts as an intermediate layer. This helps in reducing latency of the tasks and provide better performance. As the number of IoT applications keeps on increasing, the resources available with the fog nodes may not handle the upcoming demands. So to overcome these demands, we are using splittable methods to allocate the tasks to Fog/ Cloud nodes more compactly. If a task can be splitted before the deadline into different modules, then we split the given task and allocate those tasks to different fog nodes/ servers and then collecting back the data from the fog nodes/ servers and merging them into a single unit. With the help of this method, we can increase the performance of the system. © 2023 IEEE.

关键词： Fog computing

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