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Continuous Tweet Stream Analysis using Apache Spark Streaming API & Kafka 15

Continuous Tweet Stream Analysis using Apache Spark Streamin...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Nisha, A.G. Shivaprasad, N. Nischitha, C.B. Shashikala, B.M. Sujithra, M. Chaba, Yogesh Electronics & Communications Engineering JSS Science & Technology University Mysuru India Dept.of Electronics & Communications Engineering Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Dept.of Computer Science St.Joseph's First Grade College Mysore India Dept.of Computer Applications Sri Jayachamarajendra College of Engineering JSS Science & Technology University Mysuru India Department of Computing-Data Science Coimbatore Institute of Technology Coimbatore India Department of Computer Science and Engineering Guru Jambeshwar University of Science and Technology Haryana Hisar India

ISBN: (纸本)9798331300579

Social media has become a trend nowadays people are spending more time in social media platform like Facebook, Twitter, Instagram to share their opinions, creating new interests, ideas and generate *** provide a major platform for collecting information which helps us to analyze them for our *** such microblogging social network is twitter which allows information collaboration and quick information sharing that provides relief from rising *** has several millions of people tweeting and expressing their perspectives on a due to its distinctively brief and straightforward expressing *** enormous volume of raw data from tweets can be utilised for analysis and processing based on industrial or business *** this technical paper, the application of tweet sentiment analysis by extracting a vast number of tweets using continuous tweet stream provided by Spark and Kafka has been *** system fetches live tweets from Twitter based on the search criteria, process the tweets, by using text mining techniques and classify them based on different labels. © Grenze Scientific Society, 2024.

关键词： Tweets

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Exploring Explainable AI in Large Language Models: Enhancing Transparency and Trust 11

Exploring Explainable AI in Large Language Models: Enhancing...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Ijas, H.A. Jo, Ashly Ann Raj, Ebin Deni FACTS-H Lab Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9798350379136

Large Language Models (LLMs) are at the forefront of technological evolution, significantly enhancing digital interactions and automating complex processes across various sectors. While these models facilitate advancements in data analytics, content generation, and strategic decision-making, their opaque nature poses challenges regarding user trust and model comprehension. Addressing the critical need for transparency, this paper focuses on enhancing LLM explainability. We propose a framework to demystify the internal mechanisms of these models, facilitating a deeper understanding that aligns with stringent ethical standards. Our approach integrates advanced explanatory tools that elucidate model decisions and foster accountability and fairness in Artificial Intelligence applications. Through rigorous analysis and the development of novel interpretative methodologies, we aim to bridge the gap between LLM capabilities and ethical AI practices, ensuring that these powerful tools are leveraged responsibly and transparently in critical infrastructures. © 2024 IEEE.

关键词： Accountability AI Applications Ethical AI Explainable AI Fairness Large Language Models (LLMs) Model Interpretability Transparency

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Detection of Coal Quarry and Coal Dump Regions Using the Presence of Mine Water Bodies from Landsat 8 OLI/TIRS Images 1

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7th International Conference on computer Vision and Image Processing, CVIP 2022

作者： Mukherjee, Jit Mukherjee, Jayanta Chakravarty, Debashish Dept. Computer Science and Engineering Birla Institute of Technology Ranchi Mesra India Dept. Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Dept. Mining Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9783031314179

ISBN: (纸本)9783031314162

Surface mining has major environmental, social, and economical adversities, which makes it an active area of research in remote sensing. Surface coal mining has additional adversities of coal seam fires. Thus, the detection, classification, and monitoring of such regions have various research challenges. The surface coal mining land classes cover smaller areas compared to mid-resolution satellite images making them challenging to detect. Coal quarry and coal dump regions are such kinds of smaller land classes. They can be detected as a single land class as discussed in the literature. However, these land classes are observed to be difficult to detect separately as they follow near similar spectral characteristics. Hence, this paper proposes a novel technique to separate these regions using the presence of water bodies. Coal dump regions do not have water bodies, whereas some coal quarry regions may have water bodies. Such quarry regions are detected at first and further, they are used to train an unsupervised single class support vector machine (SVM ). This model is used to detect the coal dump regions by detecting the outliers. The proposed technique provides average precision and recall for coal quarry, and coal dump regions as [84.88%, 61.44%], and [70.91%, 52.79%], respectively over the seasons. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Quarries

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Gesture Recognition for ISL-Question Signs in Videos using DCDW-LSTM Attention 5

Gesture Recognition for ISL-Question Signs in Videos using D...

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5th IEEE PhD Colloquium on Emerging Domain Innovation and technology for Society, PhD EDITS 2023

作者： Dey, Arnab Biswas, Samit Indian Institute of Engineering Science and Technology Dept. of Computer Science and Technology Shibpur Howrah711103 India

ISBN: (纸本)9798350316278

Gesture recognition, specifically the identification of question signs, plays a pivotal role in advancing communication for the hearing-impaired population. However, this aspect of sign language classification has received limited attention, underscoring the need for comprehensive exploration. The recognition of question signs is instrumental in facilitating inclusive and accessible communication, which, in turn, promotes social integration and empowers individuals with hearing impairments. This study aims to bridge this gap by introducing an effective method for recognizing question signs in indian Sign Language (ISL) from video sequences, utilizing the proposed DCDW-LSTM Attention model. The approach is tested on a collected indian Question Word Video dataset (IQWVd) and INCLUDE50 benchmark dataset, and the results are encouraging. © 2023 IEEE.

关键词： Gesture recognition

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Recurrent Neural Network Based RACH Scheme Minimizing Collisions in 5G and beyond Networks

Recurrent Neural Network Based RACH Scheme Minimizing Collis...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Swain, Siba Narayan Subudhi, Ashit Indian Institute of Technology Dharwad Dept. of Computer Science and Engineering Karnataka India

ISBN: (纸本)9781665494274

Limited preambles in 5G New Radio (NR) can be a bottleneck on the performance of network access procedures. Due to the limited number of preambles, there is a non-zero probability that two mobile User Equipments (UEs) selecting same preamble signatures leading to collisions. Consequently, the base stations (gNBs) in 5G Radio Access Network (RAN) are unable to send a response to the UEs. Furthermore, with the increase in the number of cellular UEs and Machine Type Communication (MTC) devices, the probability of such preamble collisions further increases, thereby leading to reattempts by UEs. This in turn, results in increased latency and reduced channel utilization. In order to reduce contention during preamble access, we propose to use deep learning based models to design a Random Access Channel (RACH) procedure that predicts the incoming connection requests in advance and proactively allocates uplink resources to UEs. We have used Recurrent Neural Network (RNN) which is provided with the history of connection requests to predict UEs which are going to participate in contention based RACH procedure. Finally, we propose a RNN based RACH scheme where the gNB uses RNN model along with the standard RACH process to reduce preamble collisions. On doing extensive simulations, it is observed that there is a significant reduction in the number of collisions when the proposed scheme is employed in a dense user scenario thereby proving the efficacy of the proposed scheme in enabling massive access of users to 5G network. © 2023 IEEE.

关键词： Recurrent neural networks

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment 14

Image based and Point Cloud based Methods for 3D View Recons...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Agrawal, Arya Sharma, Teena Verma, Nishchal K. Vasikarla, Shantaram Vellore Institute of Technology School of Computer Science and Engineering Vellore India Indian Institute of Technology Mehta Family School of Data Science and Artificial Intelligence Guwahati India Indian Institute of Technology Dept. of Electrical Engineering Kanpur India California State University Dept. of Computer Science NorthridgeCA United States

ISBN: (纸本)9798350360134

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more. © 2024 IEEE.

关键词： Network coding

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11th International Symposium on Computing and Networking Workshops, CANDARW 2023

作者： Singh, Yashdeep Karmakar, Sushanta Indian Institute of Technology Guwahati Dept. of Computer Science & Engineering Guwahati India

ISBN: (纸本)9798350306941

In this work we investigate the problem of popular matching in a 3-uniform 3-partite hypergraph, where the first set contains a set of agents and the second and the third set contain different class of items respectively. Agents have a preference list for hyperedges, whereas the members of the other sets do not have preferences. Each agent votes in favor of a preferred matching. A matching M is called popular if there does not exist a matching M′ such that the number of agents that prefer M′ is more than the number of agents that prefer M. This paper provides a characterization of popular matching in a 3-uniform 3-partite hypergraph and shows that the problem of deciding whether a given 3-uniform 3-partite hypergraph has a popular matching is NP-hard. We also prove the NP-hardness of popular matching in a k-uniform k-partite hypergraph (k > 3). Assuming that a given 3-uniform 3-partite hypergraph admits at least one popular matching, we compare the cardinality of the maximum size popular matching (denoted as M∗) with a maximum matching (denoted as Mmax) in the 3-uniform 3-partite hypergraph and show that | Mast geq 23| Mmax|. © 2023 IEEE.

关键词： b- matching matching in hypergraph popular matching

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RL-based Virtual Network Embedding using VNF Sharing for Network Slicing in 5G Networks 36

RL-based Virtual Network Embedding using VNF Sharing for Net...

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36th IEEE/IFIP Network Operations and Management Symposium, NOMS 2023

作者： Samar, Arihant Sivalingam, Krishna M. Indian Institute of Technology Madras Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665477161

This paper deals with network slicing in 5G networks, where a slice is defined as a set of virtual network function (VNF) instances that collaborate to create an end-toend (E2E) virtual network. A set of slices is implemented on a physical substrate network maintained by an infrastructure provider. The virtual network embedding (VNE) problem deals with the deployment of a network slice's virtual network request on the substrate. Typically, the resources allocated per slice's request are not shared with other slices due to privacy, security and performance considerations. However, there are situations in which VNF instances might be aggregated across many slices to further increase the utilization ratio of the substrate infrastructure. Given these shareable VNF nodes, deploying the network slices is effectively the embedding of the numerous virtual network where these slices are linked by the shared VNFs. This paper uses a reinforcement learning (RL) approach for the embedding problem. The approach incorporates sharing based virtual network functions in an existing RL scheme designed for virtual node embedding without much additional computation. The proposed scheme is implemented using a policy based RL method;the performance study shows an increase in the reward ratio by up to 20% compared to the non-sharing case, along with an increase in the acceptance percentage of slices. © 2023 IEEE.

关键词： 5G mobile communication systems

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An Incremental Algorithm for (2-ϵ)-Approximate Steiner Tree Requiring O(n) Update Time 11

An Incremental Algorithm for (2-ϵ)-Approximate Steiner Tree...

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11th International Symposium on Computing and Networking, CANDAR 2023

作者： Raikwar, Hemraj Karmakar, Sushanta Indian Institute of Technology Guwahati Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798350306705

Given an undirected connected weighted graph with non-negative edge weights and a subset of vertices called terminals, the goal of the Steiner tree problem is to find a tree that spans the terminals such that the weight of the tree is minimized. A dynamic graph is a graph where the edge set or the vertex set or both may change with time. In this paper, first, we give a lower bound on the worst case update time for maintaining a(2-\epsilon)-Approximate Steiner tree (\epsilon being a small fraction) based on MST heuristic in a general dynamic graph with edge insertions and deletions. Then we propose an incremental algorithm for the Steiner tree problem in general dynamic graphs with edge insertions. We consider a weighted undirected connected graph with positive real edge weights and each update inserts an edge with a specific weight in the graph. The goal is to maintain a Steiner tree of the terminals. The algorithm maintains a(2-\epsilon)-Approximate Steiner tree in O(n) update time where n is the number of vertices in the graph. © 2023 IEEE.

关键词： Undirected graphs

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Explainability to Business: Demystify Transformer Models with Attention-based Explanations 2

Explainability to Business: Demystify Transformer Models wit...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Thiruthuvaraj, Rajasekhar Jo, Ashly Ann Raj, Ebin Deni Indian Institute of Information Technology Dept. of Computer Science & Engineering Kottayam India

ISBN: (纸本)9781665456302

Recently, many companies are relying on Natural Language Processing (NLP) techniques to understand the text data generated daily. It has become very critical to deal with this data because finding the sentiments of text and summarizing them will help the company understand the pain points of the customers posting reviews on social media or understand the experience of the customer. These requirements have increasingly demanded many advanced algorithms to deal the text data. The introduction of Transformers led to businesses adopting NLP methods more and more to keep up with their needs. Models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformers (GPT), state-of-the-art results were achieved with billions of parameters learned. Although these advancements improved the accuracy and expanded the use of algorithms to a wide range of NLP tasks like language translation, text summarization, and language modeling. Businesses are more interested in the Explainability of the model compared to its accuracy. Explainable Artificial Intelligence (XAI) plays an important role to comprehend the complexities of the model as well as the influence of weights on predictions. In this paper, the complexities of the transformer model are unraveled by presenting a straightforward method for computing explainable predictions. The DistilBERT model is chosen as an example to implement the explainable system due to its lighter nature. Combining the strengths of a Posthoc expla-nation with those of a self-learning neural network, the method makes it simple to scale it to other algorithms to implement. With technologies like python, PyTorch, and Hugging Face, a detailed step-by-step algorithmic computation is demonstrated to explain the predictions from the attention-based explanations. © 2023 IEEE.

关键词： Forecasting

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