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Hy-BTree: An efficient Tree based topology for FPGA based NoC implementation 7

Hy-BTree: An efficient Tree based topology for FPGA based No...

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7th IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2021

作者： Prabhu Prasad, B M Parane, Khyamling Talawar, Basavaraj IIIT Dharwad Dept. of Computer Science and Engineering India NITK Surathkal Dept. of Computer Science and Engineering India

ISBN: (纸本)9781665428491

Due to their hierarchical structure, Binary Tree (BTree) topology can be employed in Network-on-Chip (NoC) applications. Because of its lower bisection bandwidth, the performance degradation is observed in communication intensive applications. The Fat tree topology has been proposed to overcome the disadvantages of the BTree topology. But, the complexity of the Fat Tree topology's router becomes more complicated as we move towards the root node of the tree and occupying a huge amount of hardware resources compared to the BTree variant. Instead of going for Fat Tree topology, the number of hops taken by a packet in the BTree topology can be reduced by introducing new links in the network with an increase in the bisection bandwidth. In this work, we propose a variant of BTree topology called Hy-BTree by introducing additional links at the intermediate levels of the network to reduce the number of hops taken for the communication. The proposed design is implemented on the FPGA and compared with the other topologies from state-of-the-art the FPGA based NoC architectures. A reduction in average latency and an improvement in throughput have been observed in Hy-BTree with respect to the BTree network with negligible overhead. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Twitter Sentiment Analysis and Prediction using NLP

Twitter Sentiment Analysis and Prediction using NLP

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2023 International Conference on Data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sindhu, M. Sabareeswari, T.C. Tamilselvi, A. Chennai Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamilnadu Kundrathur India Chennai Institute of Technology Dept. of Electronics and Communication Engineering Kanchipuram Tamilnadu Kundrathur India

ISBN: (纸本)9798350348910

Social media has emerged as a pivotal platform for individuals to convey their thoughts and emotions, making it imperative for businesses, governments, and organizations to leverage artificial intelligence, such as sentiment analysis. This tool allows them to gauge public reactions on social media, particularly in the context of significant events like lockdown announcements. Employing relevant hashtags on Twitter, we aim to conduct sentiment analysis on how Indians are responding to these announcements and construct a predictive analytics model to discern people's behaviour. The developed model incorporates the nuances of modern internet slang to capture and interpret user sentiments accurately. This technology not only facilitates an in-dept. understanding of public sentiment but also offers a visually intuitive representation for easy comprehension. The applications of this solution are broad, benefiting organizations, businesses, and governments in various *** methodology involves the collection of tweets using Twitter's provided services, followed by the storage of this data in our database. Subsequent processing focuses on extracting meaningful and pertinent elements from the text to facilitate a comprehensive analysis. © 2023 IEEE.

关键词： Sentiment analysis

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Effect of Hyper-Parameter Tuning on the Performance of Augmented Random Search 1

Effect of Hyper-Parameter Tuning on the Performance of Augme...

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1st International Conference on Informatics, ICI 2022

作者： Rana, Misha Kumar, Nimesh Sharma, Himanshi Pandey, Kavita Jaypee Institute of Information Technology Dept. of Computer Science & Engineering and Information Technology Noida India

ISBN: (纸本)9781665437219

Recently, researchers working in the field of Artificial Intelligence have been keen on finding new algorithms that provide optimal results. One such reinforcement learning algorithm is the Augmented Random Search. This algorithm is imperative as it provides better results over other reinforcement learning algorithms. Finding the optimal values of hyper-parameters helps the algorithm to work up to its full potential. In this work, we have made an effort to find out the best hyper-parameter settings for the learning rate and direction, for this specific task. Our results show how a change in these values can impact the final maximum rewards obtained during the training process. We report novel findings of the impact of hyper-parameters on the performance of ARS algorithm. Finally, an effort has been made to find out by what percentage the ARS performs better than other Reinforcement Learning algorithm, TRPO. © 2022 IEEE.

关键词： Reinforcement learning

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VOID - An Assistant (Chatbot) to Education Queries System

VOID - An Assistant (Chatbot) to Education Queries System

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2022 International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2022

作者： Chakraborty, Debaissh Mishra, Anjana Kumar, Vikash Singh, Saloni Hani, Harshita C.V. Raman Global University Dept. of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9781665461092

Chatbots are the future of technology and it has become a basic requirement of various industries. Chatbots are designed to interact with human-like humans with the help of AI-based technology. Every industry cannot afford man-power to interact with clients and customers 24×7 so that chatbot has come into play which does the same work with no extra cost. It is a new and emerging technology that is in high demand across a wide range of businesses. It is used to simulate a human like conversation and a machine to relieve the workload. It can be easily deployed in any field where products must be developed and evaluated before being released. During the admission time in college, reception is packed, and people have to wait for their turn, and this entire process takes too much time. Although every college has their own official website, but everyone cannot find the appropriate answer of their query. The objective of this paper is to reduce the workload by developing an interactive environment between a human and a machine that gives a reply to the query of the user on behalf of a human for the education system. © 2022 IEEE.

关键词： Query processing

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Protein Structure Prediction Using CNN-Based Feature Extraction

Protein Structure Prediction Using CNN-Based Feature Extract...

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International Conference on Computational Innovations and engineering Sustainability (ICCIES)

作者： Sourav Mondal Saranya Nayudu Nagireddy Anjali M.Mahendar Reddy Ahmad Raza Dept. of Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Vadlamudi India

ISBN: (数字)9798331536695

ISBN: (纸本)9798331536701

Predicting protein structures is essential for understanding biological functions and advancing drug discovery. In this project, we developed a stacking-based model that integrates various machine learning techniques, including models like Support Vector Machines (SVM), Neural Networks (ANN), and Random Forests (RF) Feature extraction techniques were employed to identify key patterns in protein sequence data, enhancing the quality of inputs for the predictive models. By stacking these algorithms, we combined their strengths, resulting in improved prediction accuracy for protein structures. Our results show that the stacking-based approach significantly outperforms individual models, offering a more robust solution for challenging protein sequences.

关键词： Support vector machines Radio frequency Technological innovation Biological system modeling Stacking Predictive models Feature extraction Data models Sustainable development Random forests

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Design and Development of Human Following Autonomous Airport Baggage Transportation System

Design and Development of Human Following Autonomous Airport...

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2022 IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2022

作者： Shashank, M. Shvejan Saikrishna, P. Acharya, G. Prasad Reddy, Srikar Lavanya, P. Sreenidhi Institute of Science and Technology Dept. of Computer Science and Engineering Hyderabad India Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9781665495295

This paper aims to the design and development of a miniaturized model of a baggage carrying robot that can autonomously navigate from its parking place to the passenger at the airport and follow the passenger by avoiding the obstacles in its path. In airports, trolleys are used by the passengers to carry the baggage during check-in or exit. It becomes very difficult when the passenger has to carry a large amount of luggage as well as need to take care of their children or elderly people. Under these circumstances, the proposed autonomous robot, here named Smart Luggage Robot will be of great help. The robot is equipped with a GPS system that navigates the robot from its parking location to the passenger who has booked it using a mobile app. After loading the baggage, the user/passenger enable the app and the robot follows the passenger up to the check-in or exit point. The robot finally generates the bill based on the distance and weight of the baggage. This paper discusses the hardware and software components used to build the robot, and also presents an obstacle avoidance algorithm for the robot to follow the passenger. The built prototype has been successfully tested at the airport. © 2022 IEEE.

关键词： Robots

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CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveiling Secure Communication in Resource-Constrained Environments Using Cellular Automata and Henon Map

CAPLC: Cellular Automata-Powered Lightweight Cipher-Unveilin...

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Next Generation Communication & Information Processing (INCIP), International Conference on

作者： Biswarup Yogi Satyabrata Roy Umashankar Rawat Shweta Redkar Utsav Upadhyay Dept. of Computational Sciences Brainware University Kolkata West Bengal India Dept. of Computer Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Data Science and Engineering Manipal University Jaipur Rajasthan India Dept. of Computer Science & Engineering Sir Padampat Singhania University Udaipur Rajasthan India

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

In today's interconnected world, ensuring secure communication in resource-constrained contexts is still challenging. This study offers CAPLC, a novel technique for lightweight encryption that uses Cellular Automata (CA) and Henon maps. Its goal is to improve security in such situations. The method uses 1D Cellular Automata for initial data transformation and incorporates the Henon map to improve encryption strength. We used conventional metrics to evaluate CAPLC's effectiveness on image encryption, and the findings were promising: NPCR of 99.6154%, UACI of 33.88235, and PSNR of 9.1924. These numbers show the method's capacity to dramatically change image while maintaining quality and anonymity. The implications of CAPLC go beyond image encryption, providing a scalable solution for secure communication in circumstances with limited computational resources. Future studies ought to explore CAPLC's real-time application and performance across various kinds of datasets, with a focus on the potential of merging CA with Henon maps to create efficient encryption methods for improving secure communications technology.

关键词： Measurement Merging Automata Medical services Information processing Parallel processing Real-time systems Encryption Resilience Principal component analysis

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Sparse Long Short-Term Memory Approach for Energy-Efficient Adaptive Cluster Fuzzy-based Controller in Wireless Sensor Network 5

Sparse Long Short-Term Memory Approach for Energy-Efficient ...

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

作者： Ramanan, K. Ramesh, S.P. Kingsly, C. Sahaya Rajkumar, G. Vinoth Ponkumar, D. David Neels Vargheese, M. Npr College of Engineering and Technology Dept. of Ece Dindigul India Amet University Department of Computer Science and Engineering Tamil Nadu Chennai India Mangalam College of Engineering Department of Computer Science and Engineering Kottayam Ettumanoor India Jp College of Engineering Department of Ece Tamil Nadu Tenkasi India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Dept. of Ece Chennai India Psn College of Engineering and Technology Department of Cse Tamil Nadu Tirunelveli India

ISBN: (纸本)9781665474672

Wireless Sensor Network (WSN) gadgets began with limited-scope WSNs and have expanded to larger-scope and Internet of Things -based WSNs. Clustering increases WSN activity. Before picking Cluster Heads (CHs), Nodes are clustered. Nodes in a clustered WSN, transmit CH environmental variables. Current sink-selection approaches presume unlimited data transfer. Multiple CHs using the same instance, might create complications. Insufficient drop-off space causes package loss. Sinks should have airy, less-restrictive regulators. SIS reduces sink nodes. Looser regulators choose the CH. R-recommendations are used to standardize. Reductions standardize regulators. These methods reduce energy, residual energy, First Node Dead, Half Nodes Dead, Last Node Dead, packet loss, and latency. Good throughput, latency, reliability, packet loss, durability, power consumption, and end-to-end delay are accomplished. The objective of this work is to examine the accessible clustering systems for further developing sensor network execution due to different plan limits and streamlining philosophies. © 2023 IEEE.

关键词： Packet loss

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Efficient Stochastic Error Injection for Optimizing Large Language Models in Arabic Spelling Correction

Efficient Stochastic Error Injection for Optimizing Large La...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Moath R. Khaleel Gheith A. Abandah Computer Engineering Department The University of Jordan Amman Jordan Dept. of Computer Science and Engineering American University of Sharjah UAE

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Spelling mistakes in written communication among Arabic speakers have become more common, especially with the rise of social media. Correcting these errors requires effective natural language processing (NLP) tools. This paper introduces a ByT5-based approach to address two types of spelling mistakes: directed and general. ByT5, a token-free, pre-trained transformer model, processes UTF-8 text as raw bytes, which minimizes preprocessing and enhances robustness. We perform experiments with varying error injection rates to identify the optimal rate for correction. Our results are evaluated on two test sets: Test200 and TSMTS. The findings demonstrate ByT5's strong performance in spelling error correction, reducing the character error rate (CER) from 5% to 1.37% on the Test200 set and from 5% to 1.77% on the TSMTS set. These test sets include real Arabic sentences containing actual spelling mistakes. Overall, our results show that this approach offers a promising solution for spelling error correction and contributes to generating effective synthetic datasets for training large language models.

关键词： Training Social networking (online) Large language models Stochastic processes Transformers Market research Natural language processing Robustness Error correction Synthetic data

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EMS-BERT: A Pre-Trained Language Representation Model for the Emergency Medical Services (EMS) Domain 23

EMS-BERT: A Pre-Trained Language Representation Model for th...

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Proceedings of the 8th ACM/IEEE International Conference on Connected Health: Applications, Systems and engineering Technologies

作者： M Arif Rahman Sarah Masud Preum Ronald D. Williams Homa Alemzadeh John Stankovic Dept. of Computer Science University of Virginia Charlottesville VA USA Dept. of Computer Science Dartmouth College Hanover NH USA Dept. of Electrical and Computer Engineering University of Virginia Charlottesville VA USA

ISBN: (纸本)9798400701023

Emergency Medical Services (EMS) is an important domain of healthcare. First responders save millions of lives per year. Machine learning and sensing technologies are actively being developed to support first responders in their EMS activities. However, there are significant challenges to overcome in developing these new solutions. One of the main challenges is the limitations of existing methods for EMS text mining, and developing a highly accurate language model for the EMS domain. Several important Bidirectional Encoder Representations from Transformer (BERT) models for medical domains, i.e., BioBERT and ClinicalBERT, have significantly influenced biomedical text mining tasks. But extracting information from the EMS domain is a separate challenge due to the uniqueness of the EMS domain, and the significant scarcity of a high-quality EMS corpus. In this research, we propose EMS-BERT - a BERT model specifically developed for EMS text-mining tasks. For data augmentation on our small, classified EMS corpus which consists of nearly 2.4M words, we use a simultaneous pre-training method for transfer-learning relevant information from medical, bio-medical, and clinical domains; and train a high-performance BERT model. Our thorough evaluation shows at least 2% to as much as 11% improvement of F-1 scores for EMS-BERT on different classification tasks, i.e., entity recognition, relation extraction, and inferring missing information when compared both with existing state-of-the-art clinical entity recognition tools, and with various medical BERT models.

关键词： emergency medical services (EMS) data processing and analysis

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