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检索条件"机构=Dept. of Computer Engineering and Application"

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Automated Landslide Detection using Ensemble Learning

Automated Landslide Detection using Ensemble Learning

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Jain, Tanishka Agrawal, Subhash GLA University Dept. of Computer Engineering & Application Mathura India

ISBN: (纸本)9798350378092

Landslides pose significant threats to human life, infrastructure, and the environment, making timely detection crucial for effective disaster management. This research paper introduces a novel approach to landslide detection utilizing Convolutional Neural Networks (CNNs) in conjunction with ensemble learning techniques. Leveraging the inherent capabilities of CNNs to extract discriminative features from remote sensing imagery, multiple CNN models are trained and aggregated into an ensemble. Through extensive experimentation on diverse satellite imagery datasets, the effectiveness of the ensemble model is evaluated against standalone CNNs and traditional methods. Results demonstrate that the ensemble approach outperforms individual CNN models and baseline methods regarding accuracy and robustness across varying environmental conditions, enhancing landslide detection capabilities and contributing to more effective disaster management strategies. Our Model achieves 95% Precision, 91% Recall, and 93% f1-score which makes it better than all comparative methods. After classification, we make predictions through GUI by uploading images, in which we are using the Nepal dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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A Graph Based Approach For Detecting Sybil Node In Online Social Networks 15

A Graph Based Approach For Detecting Sybil Node In Online So...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Chandra, Sucheta Das, Priyanka Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350370249

Sybil attacks pose a significant threat to the security and integrity of online social networks by allowing malicious actors to create multiple fake identities to manipulate network behavior. The abstract outlines a method for detecting Sybil attacks in online social networks using graph analysis. It involves constructing a graph representation of the network, calculating node centrality measures such as in-degree and out-degree, and setting a degree threshold to identify potentially malicious nodes. Additional parameters like clustering coefficient and community structure are also considered to improve detection accuracy. The proposed approach aims to enhance the security of online social networks by identifying and isolating Sybil identities, thus preserving network integrity and trustworthiness. The authors' firmly assert that employing graph-based theory for Sybil node detection in online social networks stands out as one of the most effective methods. This approach not only offers robustness but also scalability, allowing for efficient detection even in large-scale networks. With its ability to unveil intricate relationships, anomalies, and patterns within the network, graph-based theory emerges as a cornerstone in the fight against Sybil attacks. © 2024 IEEE.

关键词： Cyber attacks

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Code-Mixed Sentiment Corpus of Customer Reviews in Malayalam and English for Enhanced Local Market Analysis in India 6

Code-Mixed Sentiment Corpus of Customer Reviews in Malayalam...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Bhagya, Sree S Hrishiraj, G.L. NaderaBeevi, S. TKM College of Engineering Kollam APJ Abdul Kalam Technological University Dept. of Computer Application Kerala India TKM College of Engineering Dept. of Computer Application Kallam Kerala India

ISBN: (纸本)9798350306613

The popularity of online shopping has boosted the volume of customer reviews, which is crucial for business growth and decision-making as they influence consumers' purchasing decisions. Indians frequently engage in code-mixing, a social media practice where multilingual users publish comments that give information about consumer preferences, cultural relevance, and product or service improvement. Businesses with an Indian market emphasis should consider this factor to improve the consumer experience. Indian languages often face resource constraints, making it difficult to create precise language models and carry out operations like sentiment analysis, spam filtering, offensive text recognition, etc. Through this work, we draw the researcher's attention to the relevance of processing code-mixed customer reviews for e-commerce sites in linguistically diverse regions like India and how it helps to serve customers better, improve goods and services, and stay competitive in a globalised and multicultural market. This work aims to expand Malayalam-English (Manglish) code-mixed literature in the commercial domain by constructing a sentiment corpus featuring customer reviews in Manglish related to commercial products annotated by voluntary annotators. The performance of different machine learning (ML) and deep learning (DL) models for the sentiment identification task is then assessed using the new corpus, and a comprehensive analysis of the results is also presented. © 2024 IEEE.

关键词： code mixing customer reviews sentiment analysis

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Designing a Secure Audio / Text Based Captcha Using Neural Network 13

Designing a Secure Audio / Text Based Captcha Using Neural N...

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13th International Conference on Cloud Computing, Data Science and engineering, Confluence 2023

作者： Pai, H. Aditya Anandkumar, M. Prasad, M.S. Guru Agarwal, Jyoti Christa, Sharon Dept. of Computer Science and Engineering Dehradun India Dept. of Computer Application Dehradun India

ISBN: (纸本)9781665462631

A CAPTCHA is used to reduce the misuse in the case of rising automated bots or software systems and also to avoid any corruption of the web services. The Captcha is used as solving a turing test problem by the users, before they get access to the software system. The paper discusses about the model that extracts the alphanumeric characters from either of audio or text-based captchas. Finally, the model's performance is found based on different parameters like accuracy level, specificity, precision etc. © 2023 IEEE.

关键词： Web services

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Using Ant Colony Optimised Algorithms for Shortest Path Exploration by Robots 2

Using Ant Colony Optimised Algorithms for Shortest Path Expl...

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Singh, Aashdeep Dhawan, Sakshi Verma, Jyoti Singh, Gurpreet Kaur, Amanpreet Dept. of Computer Science & Engineering Punjab Rajpura India Dept. of Computer Application Punjab Rajpura India Chitkara University Institute of Engineering & Technology Dept. of Computer Science & Engineering Punjab Rajpura India

ISBN: (纸本)9798350360523

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning. © 2024 IEEE.

关键词： Motion planning

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Real-Time Traffic Management System Using YOLOv8 and CNN: A Deep Learning Approach with IoT Integration 1

Real-Time Traffic Management System Using YOLOv8 and CNN: A ...

引用

1st International Conference on Advances in computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Saklani, Sumit Manchanda, Mahesh Sharma, Raman Singh, Devendra Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to be University Dept. of Computer Application Dehradun India

ISBN: (纸本)9798331518578

Traffic management also plays a crucial role in urban planning and development, with pressing challenges related to congestion, safety, and environmental impact. In this study, we proposed a real time traffic control system using YOLOv8 and CNNs combined with Internet of Things (IoT) technologies. As a high-performing object detection model, YOLOv8 is able to accurately and rapidly detect vehicles, pedestrians, and other traffic objects. When combined with CNN based data analytics, the proposed system can successfully monitor traffic flow, discover irregularities, and control the signals dynamically. Creating a cohesive framework for intelligent decision-making IoT integration ensures real-time data acquisition from sensors, cameras and edge devices. IoT framework for a smart urban environment the proposed architecture provides the versatility through scalability, low latency, and adjustment to several urban perspective scenarios. The combination leads to greatly enhanced traffic flow efficiency, accident prevention and environmental impact reduction as experimental results show. With data till October 2023, this study resolves a gap between the developing field of deep learning and the Internet of Things (IoT), that creates to be utilized to connect to advanced town environments. © 2025 IEEE.

关键词： Decision making

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Classification of Foods based on Ingredients

Classification of Foods based on Ingredients

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Guria, Sudipa Banerjee, Sudarshan Bhattacharjee, Suddhasattwa Paul, Swarup Halder, Sobhan Das, Priyanka Institute of Engineering and Management Dept. of Computer Application Kolkata India

ISBN: (纸本)9798350348217

Due to the rise of recipe sharing websites, it is now quite easy to find online cooking instructions that list ingredients and cooking techniques. The development of recipe suggestion systems has also been a focus of several recipe sharing websites. Little has been done to analyse the relationship between cuisines and ingredients, while the majority of contemporary food-related research works concentrate on recipe suggestion systems. The work presented here offers an insight into which cuisines are the most related and what are a cuisine's important elements because we know that each cuisine is unique and has a separate list of ingredients. While the cuisine of food has been regarded as the class label, the ingredients have been seen as the features. In order to categorise the cuisine of unlabeled data, this work is focused on selecting the most important ingredients using various feature selection strategies and classification approaches. This has been done to investigate the fundamental connection between ingredients and cuisine. © 2023 IEEE.

关键词： Classification (of information)

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Graph Based Keywords Extraction from Crime Data 16

Graph Based Keywords Extraction from Crime Data

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Das, Priyanka Munshi, Anuvab Naha, Annwesha Dey, Adrija Chakraborty, Sriparno Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata India

ISBN: (纸本)9798331505264

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crimerelated keywords from in-dept. police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied;following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime. © 2024 IEEE.

关键词： Crime

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