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Utilization of MFCC in Conjuction with Elaborated LSTM Architechtures for the Amplification of Lexical Descrimination in Acoustic Milieus Characterized by Pronounced Nocturnal Disturbance

Utilization of MFCC in Conjuction with Elaborated LSTM Archi...

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Disruptive Technologies (ICDT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Ramesh Singh Rawat Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India

In this pioneering research endeavor, we delved into the intricate realm of speech recognition technology, aiming to surmount the formidable challenges posed by acoustically complex environments, notably those marred by pronounced nocturnal disturbance. Through the synergistic amalgamation of Mel-frequency cepstral coefficients (MFCC) and meticulously crafted Long Short-Term Memory (LSTM) architectures, we charted a transformative path toward the amplification of lexical discrimination. Our multifaceted approach not only enhanced the discernment of subtle linguistic nuances but also showcased unparalleled efficacy in the face of disruptive nocturnal noise. The meticulously calibrated fusion of advanced signal processing techniques and neural network architectures culminated in a novel paradigm, revolutionizing the landscape of speech recognition technology. By pushing the boundaries of computational linguistics, this study not only advances scientific understanding but also paves the way for real-world applications in domains reliant on precise and resilient speech recognition systems.

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Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation

Blockchain Based Secure and Decentralized Smart Licensing of...

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Information and Communication Technology for Sustainable Development (ICICT4SD), International Conference on

作者： Momenul Haque Subrata Kumer Paul Kamrul Islam Mursheda Nusrat Della Rakhi Rani Paul Sultan Fahim Dept. of Computer Science and Engineering Bangladesh Army University of Engineering & Technology Bangladesh Dept. of Computer Science and Engineering University of Rajshahi Rajshahi Bangladesh Dept. of Computer Science and Engineering Rabindra Maitree University Kusbtia Bangladesh Dept. of Information and Communication Engineering Bangladesh Army University of Engineering & Technology Bangladesh

The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city.

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Machine Learning Approaches to Predict Post Covid-19 Severity: A Detail Case Study

Machine Learning Approaches to Predict Post Covid-19 Severit...

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Big Data Analytics in Bioinformatics (DABCon), International Conference on

作者： Richa Mathur Ritam Dutta Payal Bansal Rajeev Kumar Dept. of Computer Scinece & Engineering Poornima University Jaipur Rajasthan India Dept. of Computer Science & Engineering (IoT) Poornima Institute of Engineering & Technology Jaipur Rajasthan India Dept. of Electronics & Comm. Engineering Poornima Institute of Engineering & Technology Jaipur Rajasthan India Dept. of Computer Science & Engineering Moradabad Institute of Technology Moradabad Uttar Pradesh India

ISBN: (数字)9798350378047

ISBN: (纸本)9798350378054

The entire world has witnessed a horrific pandemic in last three years, whose long term and short-term impact to human body has been observed in large scale in these post covid-19 years. Machine learning (ML) techniques have been successfully implemented in medical field since last one decade. The emergence of post covid-19 pandemic symptoms are compelling researchers to re-search for minute detection and predict patient's probable infection rate. Long lasting chest pain, breathing issues, liver and kidney dialysis issues have been apparently visible as post covid-19 symptoms. In this paper, the journals regarding the prediction of covid-19 severity based on symptoms have been discussed. Several researchers have established deep learning-based models for the prediction. They have used the test data from the hospitals for their research process. The KNN models, ANN models, and SVM models have been discussed. The limitations of the past research process have been evaluated to be the biased test data set and missing values. The methodology that was used for the research process has been described. Mixed method of data collection was used for the research processes. The secondary data was collected from reliable sources for this research process. Considerable amount of test data has been retrieved from Kaggle database regarding post covid-19 patient's symptoms. The data analysis is performed on the Weka tool using various machine learning models.

关键词： COVID-19 Support vector machines Pandemics Pain Liver Machine learning Predictive models Nearest neighbor methods Reliability Kidney

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Optimizing Atmospheric Water Generation Through Advanced Weather Prediction Using Solar Energy Data 9

Optimizing Atmospheric Water Generation Through Advanced Wea...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Dar, Eman Rana, Khadija Hussain, Muhammad Zunnurain Zulkifl Hasan, Muhammad Khalid, Aqsa Javaid, Arslan Altaf, Jawad Sattar, Basit Chuhan, Saad Hussain Atif Yaqub, Muhammad Qureshi, Ali Moiz Mubarak, Zaima Information Technology University Computer Engineering Department Lahore Pakistan Bahria University Lahore Campus Dept. of Computer Science Lahore Pakistan University of Central Punjab Faculty of Information Technology Lahore Pakistan National College of Business Adm. & Economics Department of Computer Science Lahore Pakistan National College of Ireland Nci Department of Computer Science Ireland Umt Department of Ai Lahore Pakistan

ISBN: (纸本)9798350394474

This research focuses on enhancing the efficiency of Atmospheric Water Generation (AWG) systems through the development of an accurate weather prediction model. Leveraging solar energy data, the project aims to forecast key weather parameters such as solar radiation, temperature, and humidity. The objective is to optimize the performance of the AWG device across diverse weather conditions. By integrating advanced weather prediction, the study endeavors to enable proactive adjustments in the AWG operation, ensuring optimal water production. This research contributes to the sustainable utilization of solar energy for atmospheric water generation, addressing water scarcity challenges with a forward-thinking and adaptive approach. © 2024 IEEE.

关键词： Solar energy

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Automated Classification of Chest CT Scans for Accurate Diagnosis and Risk Stratification: A Deep Learning Approach

Automated Classification of Chest CT Scans for Accurate Diag...

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computer, Electronics & Electrical engineering & their Applications (IC2E3), International Conference on

作者： Deepak Upadhyay Mridul Gupta Devansh Yadav Abhay Upadhyay Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350388534

ISBN: (纸本)9798350388541

The performance assessment metrics of the topic of classification models, such as recall, accuracy, and F1-score were considered in this work. We examined how many types of events our model categorizes well and view the more detailed metrics for every class. A special account of the precision-recall trade-off even shows that there is an undeniable advantage, and there may be a negative side. The outcomes also show how good the model is. For example, a perfect accuracy score in the 2nd Class demonstrates that the algorithm can differentiate occurrences of such a category. The weighted average metrics consider the fundamental dissimilarity in a group better. The model has an average 0.91 F1-score, indicating that it makes a balanced decision between accuracy and recall. While I can be proud of these results, I understand that the presented weaknesses realized that additional study of potential feature engineering and other model architectures remain. This experiment will be valuable for future research and usage in real-life.

关键词： Measurement Deep learning Accuracy Computed tomography Classification algorithms

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Improvisation of QoS in SDN-Frame Work for UAV Networks Using Dijkstra Shortest Path Routing Algorithm 1

Improvisation of QoS in SDN-Frame Work for UAV Networks Usin...

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Mathapati, Mahantesh Nandihal, Priya Mishra, Priti Kotagi, Veeranna Dept.of Cse Bengaluru India Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Engineering Bangalore India Information Science and Engineering Atria Institute of Technology Bengaluru India Nitte Meenakshi Institute of Technology Dept. of Ise Bengaluru India

ISBN: (纸本)9798350316469

This work presents a unique SDN-based Dijkstra shortest path routing algorithm for QoS improvisation in UAV Networks. It discusses the various problems pertaining to the networking of unmanned aerial vehicles in disaster rescue operations and unique networking algorithmic solutions to the problems. Involving the SDN technology in networks provides faster service delivery than the traditional technology, as it isolates the control plane and data plane. This SDN technology has the centralised governing management service on the distributed network that affords improved security. But, SDN with Wireless Sensor Network (WSN) does not provide enhanced Quality of Services (QoS) in sophisticated cloud computing (CC) applications. Across the research community, end-users seek to attain a QoS while accessing the cloud-based services depending on their knowledge in using today's technology. Therefore, this research study is aimed to prioritize the cloud application data without any delay from the normal data and also to provide the fastest communication using the SDN framework applying shortest path routing. The traditional networking infrastructure's speed, scalability, and agility are increased by the key functionality of SDN, which is used to intellectual the network information from the applications. Moreover, massive size data are managed and customized by using OpenContrail, which is a more responsive and scalable network. The QoS provided by the SDN with cloud-based services using shortest path, stimulates this research to apply the integrated technology in UAV. UAV can transfer the data through cloud service using SDN, in order to maintain a communication with remote experts. The proposed Shortest Path (SP) direction-finding algorithm provides appropriate deployment & connectivity to the modules of group of UAVs, also decreases time delay within the network. The simulation results reveal that the proposed technique is suitable and more effective to set up an emergency

关键词： Cloud Computing OpenStack Quality of Service Shortest Path Algorithm Software-Defined Networking UAV

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Implementation of Pruned and Quantized Semantic Segmentation Neural Network Using Cambridge-Driving Labeled Video Database (CamVid) Dataset

Implementation of Pruned and Quantized Semantic Segmentation...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Deepak Upadhyay Sonal Malhotra Mridul Gupta Somesh Mishra Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era deemed to be University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

In This research investigates the streamlined implementation of Semantic Segmentation Neural Networks through advanced techniques: pruning and quantization. Leveraging the CamVid dataset, our study achieved remarkable reductions in computational complexity and memory usage. Pruning removed redundant connections, effectively reducing learnable parameters, while quantization significantly minimized memory footprint. Despite these optimizations, the networks maintained high semantic segmentation accuracy. The implications of our findings are pivotal, particularly in resource-constrained applications like autonomous driving and image analysis. By enhancing efficiency without compromising accuracy, this research facilitates the seamless integration of deep learning models into real-time systems. Our study not only advances the field of computer vision but also underscores the practical feasibility of deploying sophisticated neural networks in practical, resource-efficient contexts.

关键词： computer vision Quantization (signal) Image analysis Semantic segmentation Computational modeling Artificial neural networks Real-time systems

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Predictive Modeling of Fire Hazard: Integrating Environmental Factors with Machine Learning

Predictive Modeling of Fire Hazard: Integrating Environmenta...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Tanjim Taharat Aurpa Samiha Maisha Jeba Khaza Minhaz Bin Monib Maherun Nessa Ela Md Shoaib Ahmed Md Musfique Anwar Dept. of Data Science and Engineering Bangabandhu Sheikh Mujibur Rahman Digital University Gazipur Bangladesh Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Boise State University Boise Idaho USA Dept. of Computer Science and Engineering Jahangirnagar University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

In today’s fast-paced world, where technology is advancing rapidly, the tragic loss of lives due to fire accidents is not only heart-wrenching but also poses a significant risk to public safety. Early prediction of such risks can play a crucial role in educating people about fire hazards, enabling them to be better prepared in case they find themselves in such unfortunate circumstances. Our study employed various machine learning (ML) techniques and evaluated their effectiveness by analyzing different factors. These factors are derived from the diverse living conditions in Bangladesh and were gathered through surveys. To refine and identify the critical features for classification, we utilized feature selection methods like ANOVA-f and the chi-square test. Our approaches have achieved a success rate of up to 97.98%.

关键词： Surveys Accuracy Feature extraction Hazards Encoding Vectors Public security Bayes methods Analysis of variance Random forests

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Support Chatbot based on ChatGPT for Customer Relationship Management in Educational Institutions 6

Support Chatbot based on ChatGPT for Customer Relationship M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Mohamed, Islam Samih Abdelsalam, Mohamed Moawad, Ibrahim F. Dept. Business Information Systems Faculty of Commerce and Business Administration Helwan University Cairo Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt

ISBN: (纸本)9798350373875

Chatbot technology is considered one of the latest artificial intelligence tools used to achieve interaction between the organization and the customer. It relies on natural language processing, as it helps the organization know the desires and needs of the customer, in addition to collecting customer data and displaying the services provided by the organization. This research proposes an architectural model for supporting chatbots based on the ChatGPT model. It receives customer interaction inputs, understands questions, and responds quickly and appropriately. In addition, this model was designed and implemented using the Voiceflow platform. This model was named EDUBot to build communication elements and components to customize the structure of the virtual conversation and manage the dialogue. This model can be applied to the Educational City institution as a case study to improve customer service and help the institution manage customer relationships more efficiently. © 2024 IEEE.

关键词： Sales

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Orchestration-Aware Optimization of ROS2 Communication Protocols

Orchestration-Aware Optimization of ROS2 Communication Proto...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Mirco De Marchi Nicola Bombieri Dept. of Engineering for Innovation Medicine University of Verona Italy Dept. of Computer Science University of Verona Italy

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

The robot operating system (ROS) standard has been extended with different communication mechanisms to address real-time and scalability requirements. On the other hand, containerization and orchestration platforms like Docker and Kubernetes are increasingly being adopted to strengthen platform-independent development and automatic software deployment. In this paper, we quantitatively analyze the impact of topology, containerization, and edge-cloud distribution of ROS nodes on the efficiency of the ROS2 communication protocols. We then present a framework that automatically binds the most efficient ROS protocol for each node-to-node communication by considering the architectural characteristics of both software and edge-cloud computing platforms. The framework is available at https://***/PARCO-LAB/ros4k.

关键词： Protocols Service robots Scalability Operating systems Software Real-time systems Topology

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