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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Shanthi, S. Nirmaladevi, K. Mathiarasi, N. Tharani, As. Krithika Monisha, A. Kuppuchamy, R. Krishnamoorthy, N. Kongu Engineering College Department of Computer Science & Engineering Perundurai India PSNA College of Engineering and Technology Department of Computer Applications Tamilnadu Dindigul India Vellore Institute of Technology Department of Software Systems and Engineering Vellore Campus India

ISBN: (纸本)9798350348217

A pneumothorax is the accumulation of air in the pleural space between the chest and lungs. Air that is in the lungs during breathing can leak out of the lungs and become trapped between the chest and lungs in the thoracic cavity, causing chest pain, difficulty breathing, and ultimately the complete collapse of the taut lungs. pneumothorax. Therefore, early diagnosis and treatment should be started. Neural networks have proven effective in image classification tasks, and many researchers believe that training neural network models can detect radiologist-diagnosed chest abnormalities such as pneumonia and pneumothorax. is proving. Moreover, we utilize various techniques, including Stochastic Weight Averaging (SWA), aggressive data augmentation, and learning rate schedule. We use the chest x-ray dataset provided by the 2019 SIIM-ACR In this study, a ResNet-34 neural network is trained on a data set of chest x-rays containing over 10,675 images to create a model that predicts the presence of pneumothorax from chest *** suggested system receives a Dice Similarity Coefficient (DSC) of 0.8410 © 2023 IEEE.

关键词： Image classification

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A Proposed Architecture of Mobile Service Provider Based Interactive Systems 8

A Proposed Architecture of Mobile Service Provider Based Int...

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Magoo, Chandni Singh, Manjeet J.C. Bose University of Science and Technology Department of Computer Science and Engineering Faridabad India Manav Rachna University Department of Computer Science and Technology Faridabad India Department of Computer Science and Applications J.C. Bose University of Science and Technology India

ISBN: (纸本)9798350334012

Chatbots are basically designed to imitate any human interaction to automate interaction and support. Numerous benefits of chatbots has led to its increased usage by different organizations to provide essential assistance to its various customers. The current interactive systems are mainly developed from two main concepts namely NLP and Machine Learning. Despite of this, there are certain challenges and limitations of current chatbots. In this paper we will review some of the current chatbots with their limitations and highlight some of the errors identified through survey analysis of different chatbots of some companies like Airtel, Vodafone and Jio etc. A survey is done to analyze the chatbots on various metrics. © 2023 IEEE.

关键词： Machine learning

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Traffic Sign Detection Using SSD Mobilenet & Faster RCNN 2

Traffic Sign Detection Using SSD Mobilenet & Faster RCNN

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2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Asif, Shaik Mohammad Anil, T. Tangudu, Satwik Keertan, G.H. Sai Don, S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (纸本)9798350347982

This paper presents a deep learning-based approach to traffic sign detection. The proposed approach utilizes state- of-the-art object detection models, such as SSD and RCNN, to detect traffic signs in real time. Tensorflow is used as a platform for training deep learning models and the models are being implemented on Google Colab and Kaggle cloud due to the GPU availability on the respective platforms. The models are evaluated on a traffic sign dataset from Kaggle, and it achieves high detection accuracy. Moreover, the proposed approach is robust to different lighting and weather conditions and is capable of detecting traffic signs from various distances and angles. The findings of this study show that by effectively detecting and recognizing traffic signs, deep learning-based techniques can potentially increase the safety and effectiveness of transportation networks. © 2023 IEEE.

关键词： Object detection

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Potential Impacts of Online-Based Learning 2.0 and Certification on Employability 1

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3rd International Conference on Universal Threats in Expert applications and Solutions, UNI-TEAS 2024

作者： Saini, Dilip Kumar Jang Bahadur Singh, Sumeshwar Negi, Neerja Bhandari, Sonam Kumar, Raj Joshi, Kapil Department of Computer Science and Engineering Himalayan School of Science and Technology Swami Rama Himalayan University Dehradun India Department of Computer Science and Engineering Graphic Era Hill University Uttarakhand Dehradun India School of Computer Applications Manav Rachna International Institute of Research and Studies Faridabad India Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of School of Computer Science and Engineering Dev Bhoomi Uttarakhand University Dehradun Dehradun India

ISBN: (数字)9789819738106

ISBN: (纸本)9789819738090

Rapid advancement in technology innovation has seen an expansion in online-based learning programs as a major aspect of numerous organizations’ course offerings around the globe. Online learning in colleges has gotten more interactive, adaptable, collaborative, and participative. It has gained a fundamental part to play later on as a classroom teaching tool and a self-study platform for skill improvement. The reason for this examination is to consider the likely effects of online-based learning and accreditation regarding employability. The term employability implies a set of abilities, understanding, and individual qualities that make a graduate bound to pick up work and prevail in their chosen occupation. This paper has especially investigated the issues of online certification real availability, their impacts on employability, and their overall points of interest and weaknesses. Recommendations for further work and refinements of the pedagogical implementation that builds on these findings are presented in the final stages of this research paper. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： E-learning

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Word Segmentation and Sandhi Resolution on Ayurveda Classical Scriptures 6

Word Segmentation and Sandhi Resolution on Ayurveda Classica...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Amrita Varshini, E.R. Nair, Jayashree Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Computer Science and Applications Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350398496

Sanskrit is one of the oldest of the Indo-Aryan languages which has an abundant amount of texts in literature. One of the main sources of these Sanskrit texts is the textbooks written about the field of Ayurveda, the ancient Indian medical system. However, proper computational systems for processing this language are bare. A very challenging part of processing the Sanskrit language is the task of Sandhi resolution, which means the splitting of phonetically merged words. This study mainly focuses on this complex task of joint compound splitting and Sandhi resolution in Sanskrit specific to Ayurveda texts. This paper also surveys and compares all the existing research on Sandhi resolution in Sanskrit. In this work, an Ayurvedic dataset is compiled from some popular Ayurvedic texts, and a performance analysis of the state-of-the-art word segmentation model is carried out using this dataset. © 2023 IEEE.

关键词： Computational linguistics

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Novel Recoverable Audio Mosaic Technique Using Segmental and Hierarchical Permutations

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IEEE Internet of Things Journal 2024年第3期11卷 4857-4871页

作者： Tsai, Morris C.-H. Huang, Scott C.-H. Wu, Hsiao-Chun National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for AI Applications Chungli32003 Taiwan

Mosaic is a prevalent signal-processing approach to hide or protect critical information from users. The conventional mosaic schemes simply abolish or add artificial noise to the original signal content a sender wants to conceal. Thus, they are not recoverable simply by use of a key which can be represented as a very short sequence compared to the concealed signal content. In this work, we extend our previous effort in recoverable image mosaicing to design a novel audio mosaicing approach using hierarchical permutations. Besides, we establish the mathematical relationship between the popular signal-quality metric, namely, signal-to-noise ratio (SNR), and our previously proposed signal-destructuring metric, namely, Kullback-Leibler divergence of discrete cosine transform (DCT-KLD), so that the mosaicing or signal-destructuring effect in terms of DCT-KLD and the general signal-quality measure in terms of SNR can be translated into each other. As a result, one can easily judge if the mosaicked signal reaches the concealability which is equivalent to the maximum SNR to eliminate the intelligibility of an utterance. The new relationship between DCT-KLD and SNR we develop can thus be very useful to qualify an audio mosaic method without any need of human listening test. © 2014 IEEE.

关键词： Signal to noise ratio

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An Intellectual Diffused Configuration for High-Level Edge Network Elasticity 3

An Intellectual Diffused Configuration for High-Level Edge N...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Karthigeyan, A. Sathish Kumar, M. Ali Sabah, Hawraa Fouad, Laith Abbas Alwan, Adil Fouad, Laith Hindustan Institute of Technology & Science Department of Computer Applications Chennai India Hindustan Institute of Technology and Science Department of Computer Applications Chennai India National University of Science and Technology Collage of Engineering Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad Iraq

ISBN: (纸本)9798350399264

The Network Distribution System is a rapid evolution of the Internet of Everything (IOE). This system enabled the deployment of new solutions that reduce low-latency edge applications close to cloud servers and resilient edge applications to edge networks close to IoT devices. The network edge must make intelligent decisions that require fast response times and a wide range of edge application requirements. It also requires a heterogeneous dynamic edge network with constrained resources. Increasing the edge network capacity level increases the frequency and latency of many edge devices such as smart cars, robots, and smart IoT devices. With dynamic edge response time, applications automatically adapt to resilience requirements. This paper describes the role of edge networks. It also employs a lightweight, self-adapting, decentralised mechanism for deploying edge applications. It explains optimization methods for edge applications based on IoT technology. Finally, this paper increases the performance of smart waste management system by using the elasticity by reducing the latency of edge network. © 2023 IEEE.

关键词： Elasticity

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Transforming Agricultural Supply Chains: The Power of Blockchain Technology 3

Transforming Agricultural Supply Chains: The Power of Blockc...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kumar, Gotte Ranjith Lovely Professional University School of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Sr University School of Cs & Ai Department of Cse Telangana Warangal506371 India

ISBN: (纸本)9798331515911

Implementing blockchain technology in agricultural supply chains has the potential to revolutionize and increase transparency, efficiency and trust between all parties. A variation of the white paper discussed how blockchain can be a transformative tech in addressing major challenges especially on traceability, fraud prevention and sustainability on agriculture. Blockchain (for those hearing about this for the first time) can provide a system where every step of even complex supply chains is perfectly recorded, enabling transactions to take place in a secure way and offering transparency, immutability, accountability. Case studies and real-world examples showcased throughout the paper depict how blockchain could enhance operational efficiencies, boost food safety, authenticate products, and cut waste across agriculture. While very promising, the implementation of blockchain technology in agriculture is not without its own issues;this includes obligatory industry standards as well as highly costly technical implementations. The paper also highlights the potential for blockchain to contribute to sustainable agriculture by allowing better tracking of environmental footprints and ethical sourcing. In summary, we propose that the flexibility of blockchain technology renders it an ideal approach to revolutionizing supply chains in the agriculture sector, as we continue down a trajectory towards increased levels of visibility and productivity with improved environmental outcomes. This paper contributes towards a more comprehensive understanding of the role of blockchain in scenario-based selection as it addresses both opportunities and challenges due to adoption of block chain which gives technology and agriculture a new future(directions). © 2025 IEEE.

关键词： Sustainable agriculture

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Predictive Maintenance Model Using Hybrid Procedure of Improved Quantum Cat Swarm Optimisation for Asset Management in Industry 4.0

Predictive Maintenance Model Using Hybrid Procedure of Impro...

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International Conference on Data Analytics and Management, ICDAM 2023

作者： Kanchana, S. Rajan, Dhivya Mahaveerakannan, R. Sagar, K. V. Daya Subramanian, P. Rajakumar, B. Department of Computer Science Faculty of Science and Humanities SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Computer Applications CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Computer Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of AI & DS JNN Institute of Engineering Chennai Periyapalayam Highway Tamil Nadu Thiruvallur601102 India

ISBN: (纸本)9789819965465

Contemporary manufacturing system design centres on the Industry 4.0 paradigm. A versatile platform secondary the efficient optimisation of manufacturing-related activities, such as predictive keep, is necessary for the successful mixing of cutting-edge technologies like the (IoT), cyber-physical schemes, computing. Predictive maintenance research that already exists often either develops a model without taking maintenance decisions into account or seeks to optimise maintenance in the light of the degradation replicas of the already-known scheme. With the use of predictive maintenance, a company's owner can make decisions like fixing or replacing a failing part of the manufacturing line before it actually fails. Because of this, Industry 4.0 necessitates efficient asset management in order to maximise the distribution of duties and the accuracy of predictive maintenance models. To address the issue of predictive maintenance in fog computing, this paper proposes an enhanced version of the quantum cat swarm optimisation (QCSO) algorithm by combining the quantum bit (Q-bit) and the cat swarm procedure (CSO) and updating the cats based on the quantum rotation angle site. Finally, the (MR) worth is chosen based on the sum of times the procedure is run. FogWorkflowsim uses simulated real-time datasets to evaluate the forecasting performance of QCSO and other existing models. The outcomes prove that the projected method is superior to the currently used predictive maintenance resource allocation methods. In comparison to the approximately 91% to 95% accuracy achieved by previous models, the proposed model has an accuracy of 96%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Internet of things

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Analysis and Evaluation of Bio-Inspired Algorithmic Framework, Potential Application in Cloud/Multi-Cloud Environment 5

Analysis and Evaluation of Bio-Inspired Algorithmic Framewor...

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5th IEEE International Conference on Cybernetics, Cognition and Machine Learning applications, ICCCMLA 2023

作者： Kaur, Ramanpreet Anand, Divya Kaur, Upinder Verma, Sahil School of Computer Applications Lovely Professional University Phagwara India Baba Farid College Department of Computer Application Bathinda India School of Computer Science and Engineering Lovely Professional University Phagwara India Akal University Talwandi Sabo Department of Computer Science Bathinda India CGC Department of Computer Science and Engineering Mohali India

ISBN: (纸本)9798350338287

Cloud computing allows web-based services to use a variety of reasonably priced services or resources, removing the requirements of centralized knowledge access. There are many provocations available in cloud computing and multi-cloud, so bio-inspired algorithms frequently used to handle particular problems such as load imbalance, resource equipping, and performance optimization. Bio-inspired algorithms have a proclivity for spontaneously resolving a wide range of challenges by giving optimum solutions. The ability has an impact on how bio-inspired algorithms are used to tackle the crucial issues in cloud computing. This study presents a comprehensive review of bio-inspired methods for resolving and optimizing solutions in multi-cloud. ACO, GA, PSO, FPA, BA and COSMIC bio inspired techniques are considered based on cost, scalability, fault tolerance, security, energy consumption, and throughput. ACO is secure and having capability of fault tolerance, PSO is fast but less secure and costly than others. Thus, FPA has a better capability than others for various parameters. FPA is better in security, cost, fault tolerance and energy consumption © 2023 IEEE.

关键词： Cloud computing

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