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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Poongodi, A. Kopperundevi, N. Poorani, S. Tiwari, Mohit Rao, B. Srinivasa Abilash, N. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Computer Technology-UG Kongu Engineering College Perundurai India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India Gokaraju Rangaraju Institute of Engineering & Technology Computer Science and Engineering Hyderabad Bachupally500090 India Annai Vailankanni College of Engineering Mechanical Engineering Kanyakumari District Pottalkulam India

ISBN: (纸本)9798350322842

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density Detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection. © 2023 IEEE.

关键词： Deep learning

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Moiré modulation of bulk electronic structures in CuxTiSe2-based mixed two- and three-dimensional heterostructures

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Physical Review B 2024年第8期110卷 085148页

作者： Renzhe Li Yiwei Li Shihao Zhang Shenghao Dai Lixuan Xu Hao Zhong Qiang Wan Chunlong Wu Cao Peng Keming Zhao Shangkun Mo Dingkun Qin Shuming Yu Xingzhe Wang Yimin Xiong Nan Xu Institute for Advanced Studies School of Physics and Electronics Department of Physics School of Physics and Optoelectronics Engineering Hefei National Laboratory Hefei 230028 China Wuhan Institute of Quantum Technology Wuhan 430206 China

Moiré superlattices provide a striking tool to controllably engineer electronic structures and realize various exotic quantum phenomena in two-dimensional (2D) systems. Surprisingly, moiré potential has recently been reported to extend its influence into three-dimensional (3D) graphitic thin films. Here, using angle-resolved photoemission spectroscopy, we report that 3D electronic states of transition-metal dichalcogenides (TMDs) can be modulated by interfacial moiré potential. The bulk electronic states of CuxTiSe2 unambiguously exhibit moiré clones due to the lattice mismatch with the noble-gas monolayer atop. We demonstrate that the interfacial moiré effect strongly couples with charge ordering with 3D wave vectors, which plays an important role in the formation of the mixed-dimensional moiré effect. We further exclude the final-state diffraction mechanism for the observed replicas. The moiré period can be further in situ tuned by different noble-gas atoms and annealing temperatures. Our results broaden the horizon of the mixed-dimensional moiré family from graphitic systems to TMD-based heterostructures and shed light on constructing functional devices based on versatile 2D-3D mixed-dimensional heterostructures.

关键词： Electronic structure Twistronics Superlattices Transition metal dichalcogenides Van der Waals systems Angle-resolved photoemission spectroscopy

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Artificial Driving and Spectral Graph Theory for Automated Plant Leaf Disease Classification

Artificial Driving and Spectral Graph Theory for Automated P...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Bajpai, Chhavi Gaur, Manish Suganthi, J Roselin Vinayagam, S. Kaliappan, S. Manoj Senthil, K. Centre for Advanced Studies Department of Computer Science & Engineering Lucknow India Institute of Engineering and Technology Department of Computer Science & Engineering Lucknow India K. Ramakrishnan College of Engineering Department of Ece Trichirapalli India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Bioscience Chennai India Lovely Professional University Department of Research and Development Jalandhar India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350375442

Having a certified plant pathologist visually inspect the diseased leaves is now the gold standard for disease diagnosis in plants. This is because symptoms are present in the vast majority of plant illnesses. Because manual disease diagnosis is slow and results are highly dependent on the skill of the pathologist, this is an ideal domain for computer-aided diagnostic *** one of the suggested methods are preprocessing, step two is feature extraction, and step three is training the model. Pictures are acquired, then processed, enhanced, resized, segmented, and finally converted from RGB to HSI as part of the preprocessing phase. The topic of this study is the extraction of numerous features for the purpose of leaf disease categorization. Hexagons of colors are retrieved for classification purposes. The model was trained using Spectral Graph Theory. On average, this state-of-the-art technique achieves a higher accuracy rate of 92.48% than CNN and SVM. © 2024 IEEE.

关键词： Graph theory

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Automated Heart Disease Diagnosis and Monitoring System Using Similarity-Navigated Graph Neural Networks with Leopard Seal Optimization in IoT Healthcare Applications 4

Automated Heart Disease Diagnosis and Monitoring System Usin...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Raja, V. Sripathi Rawat, Ruchira Pandit, Karuna Dhumpati, Raghu Vekariya, Vipul Vijitha, S. B V Raju Institute of Technology Department of Biomedical Engineering Telangana Narsapur502313 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Department of Information Science and Engineering Nitte Karkala Karnataka Udupi District India Bahrain Polytechnic Isa Town Department of Computer Science and Engineering Isa Town3339 Bahrain Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Vels Institute of Science Technology and Advanced Studies Department of computer science and Engineering Pallavaram Tamil Nadu Chennai600043 India

ISBN: (纸本)9798331540364

One of the leading causes of death worldwide continues to be heart disease;therefore, early detection and precise diagnosis are essential to better patient outcomes. The conventional methods of diagnosing chemotherapy-induced nail changes involve a great deal of laboratory testing, which is more than exhaustive. In order to tackle these obstacles, this research introduces a novel methodology for the automated diagnosis and continuous tracking of heart diseases in the IoT -based health care system, integrated with deep learning algorithms. The proposed system includes several state-of-the art techniques to improve diagnostic reliability and speed. First of all, min-max normalization is used as a pre-processing technique that aims at pre-scaling the data of patients to a consistent level and, as a result, enhancing the performance of the model. The Mud Ring Algorithm is then used for feature selection, whereby the best features are chosen together with the reduction in dimensionality to decrease computational intensity, thus improving diagnostic accuracy. The core diagnostic engine is based on Similarity Navigated Graph Neural Networks (SNGNN) and has been enhanced with Leopard Seal Optimization (LSO), a new meta-method motivated by the hunting patterns of leopard seals. This combination enables the system to relate various features and give the correct prognosis for heart diseases. These techniques, applied within the IoT framework, enable real-time monitoring and diagnosis and therefore have the benefit of being a scalable solution for modern health care systems. The results show that the proposed method for providing diagnosis of heart disease is accurate, timely, and reliable and thus has the potential to revolutionize patient management in healthcare based on the IoT. © 2024 IEEE.

关键词： Chemotherapy

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A Decision Support System for Stock Selection and Asset Allocation Based on Fundamental Data Analysis

arXiv

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arXiv 2024年

作者： Abrishami, Ali Habibi, Jafar Jarrahi, AmirAli Amiri, Dariush Fazli, Mohammadamin Department of Computer Engineering Sharif University of Technology Tehran Iran Tehran Institute for Advanced Studies University of Khatam Iran

Financial markets are integral to a country's economic success, yet their complex nature raises challenging issues for predicting their behaviors. There is a growing demand for an integrated system that explores the vast and diverse data in financial reports with powerful machine-learning models to analyze financial markets and suggest appropriate investment strategies. This research provides an end-to-end decision support system (DSS) that pervasively covers the stages of gathering, cleaning, and modeling the stock's financial and fundamental data alongside the country's macroeconomic conditions. Analyzing and modeling the fundamental data of securities is a noteworthy method that, despite its greater power, has been used by fewer researchers due to its more complex and challenging issues. By precisely analyzing securities' fundamental data, the proposed system assists investors in predicting stock future prices and allocating assets in major financial markets: stock, bond, and commodity. The most notable contributions and innovations of this research are: (1) Developing a robust predictive model for mid- to long-term stock returns, tailored for investors rather than traders, (2) The proposed DSS considers a diverse set of features relating to the economic conditions of the company, including fundamental data, stock trading characteristics, and macro-economic attributes to enhance predictive accuracy, (3) Evaluating the DSS performance on the Tehran Stock Exchange that has specific characteristics of small to medium-sized economies with high inflation rates and showing the superiority to novel researches, and (4) Empowering the DSS to generate different asset allocation strategies in various economic situations by simulating expert investor decision-making. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

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IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking 5

IoT and AI for Real-Time Customer Behavior Analysis in Digit...

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5th IEEE International Conference on Recent Trends in Computer Science and technology, ICRTCST 2024

作者： Madhumita, G. Das, Tapas Das, Seshanwita Khatri, Eti Ravisankar, P. Hemachandu, P. Vels Institute of Science Technology and Advanced Studies Department of MBA Tamil Nadu Chennai India Christ University Department of School of Business Management UP Ghaziabad India Christ University Department of School of Commerce Finance & Accountancy UP Ghaziabad India Nitte Meenakshi Institute of Technology Department of Management Studies Karnataka Bengaluru India Saveetha College of Liberal Arts and Sciences Department of Commerce Thandalam Chennai India Sasi Institute of Technology and Engineering EEE Department Andhrapradesh Tadepallidusem534101 India

ISBN: (纸本)9798350351378

Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. © 2024 IEEE.

关键词： Internet of things

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Predictive Modeling for Asset Bubble Detection in Financial Markets

Predictive Modeling for Asset Bubble Detection in Financial ...

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Smart Systems for Electrical, Electronics, Communication and Computer engineering (ICSSEECC), International Conference on

作者： Suganiya Murugan Pradeep Kumar Sivakumar Arpit Goyal Pranay Goenka Department of Computing Technologies SRM Institute of Science and Technology Chennai India Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

In response to the volatile nature of financial markets, our project, titled ‘A Comprehensive Approach to Stock Market Crash Prediction using Time Series Forecasting, Machine Learning, and Deep Learning Techniques,’ endeavors to create a robust prediction model for anticipating stock market crashes. Leveraging a diverse dataset gathered from various open-source links, with a primary source being YahooFinance, we employ an extensive feature set including time stamps, economic indicators such as unemployment, consumer price index (CPI), price-to-earning ratio (P/E), and market yields. Our innovative approach involves labeling major crashes on the S&P 500 chart and correlating them with diverse economic data. By filtering and marking significant crash phases, we create a binary classifier. Forecasting S&P 500 values for 24 months using ARIMA and SARIMAX models allows for better evaluation and economic analysis. The project integrates various machine learning algorithms, with random forests and decision trees emerging as top performers. Deep learning techniques using Keras, such as Convolutional Neural Networks (CNNs) and Dense Neural Networks (DNNs), achieve notable accuracy. Additionally, real-time stock-related tweets obtained through the Twitter API and market sentiments from the Alpha Vantage API enhance the predictive capabilities. The model employs ARIMA and SARIMAX for forecasting, alongside a CNN architecture for crash classification. The UI/UX showcases data visualizations, sentiment analysis, and predictive models. The technology stack includes REACT, NODE JS, TENSORFLOW, A WS, and more. This work presents a holistic strategy to equip investors with valuable insights and aid in making informed decisions during potential market downturns.

关键词： Deep learning Sentiment analysis Economic indicators Time series analysis Predictive models Computer crashes Real-time systems

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Comparative Analysis of Fractal and ArUco marker for Navigation and Landing of Drones

Comparative Analysis of Fractal and ArUco marker for Navigat...

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International Conference on Computing and Networking technology (ICCNT)

作者： Shubham Verma Anuj Kumar Sharma Tushar Sandhan Department of Mechatronics Centre For Advanced Studies AKTU Lucknow India Department of Electrical Engineering Institute of Technology Kanpur India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The detection and pose estimation of markers are increasingly crucial across various domains, including human-robot interaction, UAV navigation, robotic grasping, and underwater navigation and many more. This paper evaluates the robustness of two prominent fiducial markers ArUco and Fractal marker, under diverse environmental conditions. These conditions include static and dynamic platforms, varying light intensity, occlusion, reflection, distortion, and detection through glass medium and many other situations considering the area of both markers identical. Detection rates for all conditions were calculated for each marker by analyzing the proportion of detected frames out of approximately 1800 total frames per condition. The study reveals that Fractal markers outperform ArUco markers. They demonstrate faster detection, greater resilience to occlusion, better resolution handling, and consistent performance across multiple scenarios. These findings suggest that Fractal marker could significantly enhance the reliability and efficiency of marker based detection systems in complex real-world applications. The dataset will be made accessible to the general public, facilitating additional investigation and verification of the conclusions reported in this work.

关键词： Accuracy Pose estimation Glass Autonomous aerial vehicles Distortion Fractals Reflection Robots Resilience Drones

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Development of QOS Aware Spectrum Access Algorithm for Different Radio Access Techniques (RATS)

Development of QOS Aware Spectrum Access Algorithm for Diffe...

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Intelligent Technologies (CONIT), International Conference on

作者： M Monisha S Vinoth Kanna S Deepak Department of Electronic and Communication Engineering Vels Institute of Science Technology and Advanced Studies (VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

The paper introduces a novel approach to spectrum sensing within cognitive radio systems, proposing the utilization of sub-Nyquist sampling for wideband spectrum sensing to achieve substantial reductions in sample rates without compromising detection accuracy. Employing a subspace estimator, the method discerns occupied and unoccupied channels by analyzing the correlation matrix derived from limited noisy samples. Spectrum sensing, a critical task in cognitive radio, faces challenges due to varying conditions between major user signals and background noise. Leveraging statistical covariances of received signals and noise enables the differentiation between the two. Through comparative analysis utilizing the sample covariance matrix, the paper evaluates spectrum sensing techniques, offering insights into improving detection capabilities amidst dynamic and noisy environments. This research addresses a significant challenge in cognitive radio, providing valuable contributions to the field.

关键词： Wireless sensor networks Quality of service Robustness Sensors Radio access technologies Cognitive radio Noise measurement

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Internet of Things (IOT) based Smart Nebulizer System

Internet of Things (IOT) based Smart Nebulizer System

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： R. GOWTHAM R. CHANDRASEKARAN HARENDHARAN. B SAI AMRUTHA INAKOLLU Vels Institute of Science Technology & Advanced Studies Chennai Sri Venkateswara College of Engineering Chennai Parul University Vadodara

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The prevalence of asthma and other respiratory diseases is increasing due to continuous inhalation of contaminated air. To address this issue, an IoT-based smart nebulizer has been developed with advanced features to aid individuals suffering from chronic pulmonary conditions. Nebulizers are typically used to deliver medication as a mist to patients with respiratory diseases. The proposed smart nebulizer incorporates a high-efficiency jet motor capable of nebulizing 500 ml of air per cycle at a rate of 15 respiratory cycles per minute. It also includes a respiration rate sensor and IoT sensors to monitor patients' respiratory flow during nebulization. This technology is physician-friendly, enabling healthcare providers to remotely set and control the nebulizer's flow rate. The developed IoT-enabled smart nebulizer utilizes Wi-Fi for data transmission and reception, allowing real-time analysis and monitoring of patients' respiratory patterns. This innovation offers a more efficient and controlled way to manage respiratory treatments.

关键词： Technological innovation Hospitals Lung Motors Real-time systems Internet of Things Data communication Wireless fidelity Remote monitoring Asthma

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