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5th International Conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Singh, Mimansha Yadav, Vrinda Centre for Advanced Studies Department of Computer Science and Engineering Uttar Pradesh Lucknow226031 India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Every day, the amount of textual data created increases exponentially, both in terms of complexity and volume. Massive amounts of information are generated by social media, news articles, emails, text messages and other resources, making it difficult to read lengthy language materials. Our main objective in the paper is to obtain a short understandable and fluent abstractive summary of any given text. The Abstractive Text Summarizer automatically gives the summary of the text by generating new phrase, rephrasing or using the new words which are not present in the original text. In this paper, a machine learning architecture i.e. Stacked LSTM based on attention mechanism using Sequence-to-Sequence model is proposed, to generate the summary using abstractive approach for Amazon reviews of fine foods dataset. Our approach allows the model to accept content and provide a concise summary that may clearly describe the gist of the original text. The experiments on Amazon reviews of fine foods dataset show that our model obtained BLEU Score as 0.91 for a test set. © 2022 IEEE.

关键词： Long short-term memory

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Drone/UAVs Surveillance feeds Video Object Recognition Using Transfer Learning 1

Drone/UAVs Surveillance feeds Video Object Recognition Using...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Kumar, Sushil Kumar, C.R.S. Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

The objective of this research is to demonstrate the use of a convolutional neural network (CNN) for object detection (OD) on drone videos (CNN). The goal of the study is to determine how well the YOLO V3 (Y-V3) and feature extractor recognise objects in drone-shot video frames the *** to the study, application of several target identification algorithms to 'regular' images captured by 'normal' cameras has different efficiency impacts on the quantity, precision, and target performance consumption, as well as when applying the technique to image data collected by drones. Unmanned aerial vehicles (UAVs), a very active domain in this field, are crucial to the implementation of any robot's complete autonomy due to its enhanced capability of OD. We have conducted numerous experiments to address our functional issues to investigate the efficacy of the most sophisticated target detection algorithm in the image data acquired by UAV. In this study, the algorithm's performance metrics are tallied after being tested on 1099 photos. © 2022 IEEE.

关键词： Object detection

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Fortaleza: The Emergence of a Network Hub

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arXiv 2023年第1期3卷 272-279页

作者： Bragion, Eric Akter, Habiba Kumar, Mohit Xu, Minxian Abdelmoniem, Ahmed M. Gill, Sukhpal Singh School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Information Technology National Institute of Technology Jalandhar India Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Digitalisation, accelerated by the pandemic, has brought the opportunity for companies to expand their businesses beyond their geographic location and has considerably affected networks around the world. Cloud services have a better acceptance nowadays, and it is foreseen that this industry will grow exponentially in the following years. With more distributed networks that need to support customers in different locations, the model of one-single server in big financial centres has become outdated and companies tend to look for alternatives that will meet their needs, and this seems to be the case with Fortaleza, in Brazil. With several submarine cables connections available, the city has stood out as a possible hub to different regions, and this is what this paper explores. Making use of real traffic data through looking glasses, we established a latency classification that ranges from exceptionally low to high and analysed 800 latencies from Roubaix, Fortaleza and Sao Paulo to Miami, Mexico City, Frankfurt, Paris, Milan, Prague, Sao Paulo, Santiago, Buenos Aires and Luanda. We found that non-developed countries have a big dependence on the United States to route Internet traffic. Despite this, Fortaleza proves to be an alternative for serving different regions with relatively low latencies. © 2023, CC0.

关键词： Cloud data centre Latency Network hubs Fortaleza Roubaix Networking

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Forensic Techniques to Detect Hidden Data in Alternate Data Streams in NTFS 4

Forensic Techniques to Detect Hidden Data in Alternate Data ...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Hermon, Rahul Singh, Upasna Singh, Bhupendra Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

Alternate Data Streams (ADS) have been a feature of the New Technology File System (NTFS) since its introduction in 1993. Alternate Data Streams (ADS) were introduced to address compatibility within the existing Operating Systems. Lately Hackers/Cyber Criminals have used Alternate Data Streams (ADS) as a means for launching Cyber- Attacks. Alternate Data Streams (ADS) allow data hiding, same being difficult to detect without adequate knowledge. In this paper we shall bring out the various Forensic techniques in which hidden data in Alternate Data Streams (ADS) can be detected. Finally, we compared the Forensic techniques to detect data hidden in Alternate Data Streams (ADS) in both Windows 10 and 11 Operating System. © 2022 IEEE.

关键词： Network security

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AI-Enabled Predictive Maintenance for Industrial Equipment in the Era of IoT 9

AI-Enabled Predictive Maintenance for Industrial Equipment i...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Subbiah, Muthu Raman Devi, Kabirdoss Dhanamalar, M. Shareef, Ashraf Mohammed Ai-Shaikhli, Taha Raad Nidamanuri, Sudheer University of Technology And Applied Sciences Mie Section Department of Engineering Muscat Oman School of Management Studies Vels Institute-of Science Technology And Advanced Studies Chennai Tamil Nadu Pallavaram600117 India Kristu Jayanti College Department of Computer Science Karnataka Bengaluru India College of Pharmacy National University of Science And Technology DhiQar Iraq AL-Nisour University College Department of Medical Laboratories Technology Baghdad Iraq Madanapalle Institute of Technology And Science Computer Science And Technology Andhra Pradesh Madanapalle517325 India

ISBN: (纸本)9798350365092

Modern digital technology breakthroughs include machine learning. It's used in medical, image processing, manufacturing, aviation, autonomics, and more. Our main goal is to find an automated system that can accurately rate production machines at any given moment. The critical energy meter characteristics needed to create power reports in automation system to solve production difficulties at defined intervals were also not captured. We apply machine learning to estimate manufacturing machine accuracy in this study. We employed supervised machine learning of binary decision tree using CART method and power report, obtained data using RS232 to RS485 convertor through Modbus communication protocol, to overcome these challenges. This study presents issue formulation, energy meter data analysis and retrieval, and ML approaches used to estimate production machine accuracy. After retrieving the parameters, we provide power reports for each machine and a graphical warning of deteriorating performance. © 2024 IEEE.

关键词： Internet of things

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Detection of Multiclass Objects in Satellite Images Using an Improved Algorithmic Approach 4

Detection of Multiclass Objects in Satellite Images Using an...

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4th IEEE Bombay Section Signature Conference, IBSSC 2022

作者： Singh, Abhimanyu Nene, Manisha J Defense Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665492911

Object Detection (OD) in natural images has made tremendous strides during the last ten years. However, the outcomes are infrequently adequate when the natural image OD approach is used straight to Satellite Images (SI). This results from the intrinsic differences in object scale and orientation introduced by the omniscient viewpoint of the SI. Detecting objects is a challenging task especially when small object areas and complicated backgrounds appear in satellite images under analysis. Occlusion and intense item overlap have a further negative effect on the detection performance. The self-attention mechanisms are proposed to search for minute details in an image. However such searches mechanism come with complexity or high computational cost due to uncertainty induced in visual resolutions. The study in this research paper addresses the problems experienced in the accuracy and precision and the efficacy of the proposed model is demonstrated with the result in this paper. © 2022 IEEE.

关键词： Object detection

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COEVOL: Constructing Better Responses for Instruction Finetuning through Multi-Agent Cooperation

COEVOL: Constructing Better Responses for Instruction Finetu...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Renhao Tan, Minghuan Wong, Derek F. Yang, Min NLP2CT Lab Department of Computer and Information Science University of Macau China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9798891761643

In recent years, instruction fine-tuning (IFT) on large language models (LLMs) has garnered considerable attention to enhance model performance on unseen tasks. Attempts have been made on automatic construction and effective selection for IFT data. However, we posit that previous methods have not fully harnessed the potential of LLMs for enhancing data quality. The responses within IFT data could be further enhanced by leveraging the capabilities of LLMs themselves. In this paper, we propose COEVOL, an LLM-based multi-agent cooperation framework for the improvement of responses for instructions. To effectively refine the responses, we develop an iterative framework following a debate-advise-edit-judge paradigm. A two-stage multi-agent debate strategy is further devised to ensure the diversity and reliability of editing suggestions within the framework. Empirically, models equipped with COEVOL outperform competitive baselines evaluated by MT-Bench and AlpacaEval, demonstrating its effectiveness in enhancing instruction-following capabilities for LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Graph Clustering Through Users’ Properties and Social Influence 16th

Graph Clustering Through Users’ Properties and Social Infl...

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16th Annual International Conference on Combinatorial Optimization and Applications, COCOA 2023

作者： Guo, Jianxiong Zhu, Zhehao Gao, Yucen Gao, Xiaofeng Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9783031496134

Clustering is a basic technology in data mining, and similarity measurement plays a crucial role in it. The existing clustering algorithms, especially those for social networks, pay more attention to users’ properties while ignoring the global measurement across social relationships. In this paper, a new clustering algorithm is proposed, which not only considers the distance of users’ properties but also considers users’ social influence. Social influence can be further divided into mutual influence and self influence. With mutual influence, we can deal with users’ interests and measure their similarities by introducing areas and activities, thus better weighing the influence between them in an indirect way. Separately, we formulate a new propagation model, PR-Threshold++, by merging the PageRank algorithm and Linear Threshold model, to model the self influence. Based on that, we design a novel similarity by exploiting users’ distance, mutual influence, and self influence. Finally, we adjust K-medoids according to our similarity and use real-world datasets to evaluate their performance in intensive simulations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Data mining

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Air Quality Prediction and Monitoring using Machine Learning Algorithm based IoT sensor- A researcher's perspective 6

Air Quality Prediction and Monitoring using Machine Learning...

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6th IEEE International Conference on Communication and Electronics Systems, ICCES 2021

作者： Kalaivani, G. Mayilvahanan, P. Vels Institute of Science Technology and Advanced Studies Department of Computer Science

ISBN: (纸本)9781665435871

Air Pollution (AP) is one of the serious and major environmental problem worldwide. Many researchers have drawn attention and have focused about these problems keeping in mind human health. Air quality prediction information is one of the better ways through which people can be informed to be more vigilant about serious health issues and protect human health caused by air pollution. In many metropolitan cities air pollution is a major challenging environmental issue. To analyze the present traffic condition of the city, local authorities can be enabled by real time monitoring of pollution data which makes appropriate decisions. Hence an early system is required for monitoring and calculating the level of AP using Air Quality (AQ) which is essential for predicting exactly the pollutant concentrations. The prediction of AQ can be improved by deploying Internet of Things (IoT) based sensor which are considerably changing the prediction of AQ dynamically. The prediction of AP discussed and estimated using many existing techniques are very expensive and have very low accuracy. The technological advancement of Machine Learning (ML) algorithm can be very fast increasing and searching almost all fields and applications whereas AP prediction has not prohibited from those fields. This paper describes about various studies of ML algorithm relating to AP prediction and monitoring based on the IoT sensor data in the context of various cities. This paper also summaries real time and historical data based on the AQ prediction models tools and techniques and describes about recent research methodologies merits and demerits of AQ prediction, along with the challenges based on real time monitoring and prediction of AQ. © 2021 IEEE.

关键词： Internet of things

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Energy Efficent Protocols Leach - GWO vs PEGASIS Comparision 1

Energy Efficent Protocols Leach - GWO vs PEGASIS Comparision

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Chauhan, Sandeep Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665450461

The ad-hoc deployments of Wireless Sensor Network (WSN) have emerged as major technology which can enhance operational capability of critical surveillance operations at hostile as well as places where accessibility of human intervention is restricted however the main concern for their utilization in these areas is power consumption by WSNs model. To improve the power utilization many protocols are introduced such as Low Efficiency Adaptive Clustering Hierarchy (LEACH), Power efficient gathering in Sensor information System (PEGASIS), Threshold Sensitive energy Efficient Sensor (TEEN) etc. LEACH introduced the concept of clustering which was a part of Hierarchal protocols and works on Cluster Head concept. These protocols are generally classified in three parts flat, direct and hierarchal. Among all the other protocols LEACH and PEGASIS has shown improved results as compared to other. PEGASIS works on chain-based flow of information and works on leader head (LH), LH is chosen determining the closest distance of a Node from Sink station. PEGASIS gives a better efficiency in terms of transmission as compared to LEACH. This paper aims to compare two protocols PEGASIS and LEACH (Low Energy Adaptive Clustering Hierarchy) in a Dynamic WSN network with the help of algorithm. The result will help in building a better energy efficient protocol for WSN model. These protocols can be clubbed with other route optimizing techniques such as Ant Colony optimizer, Ant Bee Colony and Grey Wolf optimizer to improve their efficiency of the model therefore increasing the effectiveness of the model. Simulation is done in MATLAB. © 2022 IEEE.

关键词： Ant colony optimization

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