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Identification of Phony Profiles on Social Platforms using Random Forest and Decision Tree Algorithms 15

Identification of Phony Profiles on Social Platforms using R...

引用

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

作者： Rao, M.S.P.Durga Nandini, U.Naga Nandhini, T. Sree, R.Navya Lahari, V. Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331300579

Given the pervasiveness of social media in today's world, the problem of identifying fraudulent accounts on sites like Social Media has taken on great *** is the main technology used in this project, "Social Media Fake Account Detection using Machine Learning," to address this *** system employs 2 effective machine learning algorithms, the Classifier of Random Forests and its Decision Tree Classifier, to do *** random forest classification system performs exceptionally well, achieving an accuracy of 100% on the training database and a startling accuracy rate of 93% on the test *** Decision Tree Classifier simultaneously shows its efficiency by achieving ninety-two testing and ninety-two accuracy when trained.576 records make up the dataset used for this project, and each record has 12 unique *** presence or absence of a profile picture, the ratio of numbers of individuals in usernames and passwords the splitting of the full name into phrase tokens, the equality of login credentials and full names, the total length of user bios, which stands for the existence of other people's URLs, the private position of user accounts, the number of posts, the number of followers, the amount of takes into account followed, the and the last classification of a profile as "phony" or "Real" are just a few of the important aspects of online social networking user profiles covered by these *** project seeks to provide an efficient and dependable approach for recognizing Fake user profiles using Python and these advanced machine learning *** contributes to keeping the software's validity and privacy of users intact. © Grenze Scientific Society, 2024.

关键词： Anonymity

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Revolutionizing Brain Tumour Prediction: A Pioneering GAN-based Framework for Synthetic Data Generation 7

Revolutionizing Brain Tumour Prediction: A Pioneering GAN-ba...

引用

7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Kumpatla, Gautam Veresi, Hemanth Bala Subrahmanyam, P. Abhishek, S. Anjali, T. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350341485

Medical analysis is a key component of contemporary healthcare since it helps doctors diagnose patients accurately to plan and track their treatments. Accurate identification of brain tumors is essential for doctors to treat patients swiftly and efficiently. This research investigates a novel approach to overcome the constraints imposed by scarce and sensitive medical data, generating synthetic images of brain tumors using Generative Adversarial Networks (GANs). The proposed method has the potential to enhance activities involving medical processing, which will aid in making diagnoses and formulating treatment plans. The preliminary findings demonstrate its potential relevance to a wider variety of medical processing tasks, demonstrating that this augmentation method yields significant benefits. © 2023 IEEE.

关键词： Generative adversarial networks

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An ECC-Based Lightweight CPABE Scheme with Attribute Revocation 3rd

An ECC-Based Lightweight CPABE Scheme with Attribute Revoca...

引用

3rd International Conference on Machine Learning, Internet of Things and Big Data, ICMIB 2023

作者： Chandel, Avinash Debnath, Sourabh Kumar, Jitendra Mohapatra, Ramesh Kumar Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela India

ISBN: (纸本)9789819939312

Data storage on cloud servers has become a common practice across businesses. Data security and accessibility in cloud environments are jeopardized when stored on unreliable cloud servers, making it necessary to convert user information into encrypted text that is difficult to understand and analyze, even if it is compromised. The novel encryption method known as Ciphertext Policy Attribute Based Encryption (CPABE) can be used to provide fine-grained access control and privacy in a cloud environment. To avoid the costly bilinear pairings, several researchers suggested CPABE approaches based on the elliptic curve cryptosystem (ECC). But there is a key-escrow concern with these systems. Hence, in this paper, we propose a multi-authority lightweight CPABE methodology. In addition, time and geographical attributes are also taken into consideration to enhance fine-grained access control. Our proposed solution also solves the issue related to dynamic attribute revocation by relegating repeated and tedious tasks to proxy servers. The detailed performance analysis demonstrates that the suggested method outperforms current schemes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Access control

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Multilingual Hate Speech Detection Using Transformer-Based Deep Learning Approaches 11

Multilingual Hate Speech Detection Using Transformer-Based D...

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11th IEEE International Conference on Social Networks Analysis, Management and Security, SNAMS 2024

作者： Chakravorty, Debamalya Das, Arijit Saha, Diganta Computer Science and Engineering Jadavpur University Kolkata India Computer Science and Engineering National Institute of Technology Jamshedpur India

ISBN: (纸本)9798331518349

Hate speech is those forms of expression that demean an individual or a collection of individuals based on attributes like ethnicity, gender, religion, sexual preference, etc. In this work, multilingual hate speech detection is performed with deep learning and transformer-based models which are trained using gold-standard datasets. The presented approach is language-independent and has been employed to detect hateful content in English, Italian, German, and Bengali datasets. The results of the proposed methodology have been promising when compared to the baseline approach. The F1 score obtained is 0.86 in the Bengali dataset, 0.91 in the English dataset, 0.74 in the Italian dataset, and 0.9 in the German dataset. So, the presented approach shows an improvement over the baseline method. © 2024 IEEE.

关键词： Contrastive Learning

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Real-Time Gesture Detection for Deaf and Dumb Communication in Video Calls using WebRTC 15

Real-Time Gesture Detection for Deaf and Dumb Communication ...

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

作者： Kunchur, Pavan Kulkarni, Krishnaprasad Balgi, Kishor Maldar, Chinmay V. Alate, Dhirj Pujar, Sagar Korlahalli, Pavan Department of Computer Science and Engineering KLS Gogte Institute of Technology Belagavi India

ISBN: (纸本)9798331300579

In this article we propose to detect the hand and face gestures in a peer to peer video call. First the frames are captured from webcam using openCV, then hand and facial landmarks in video frames are detected using Media Pipe Holistic model. Then the extracted key point values from frames are processed and fed to the trained LSTM model which classifies the gestures into specific texts. The unique feature added is detects accurate text based on highest probability value. Then this is streamed in a peer to peer video call using WebRtc which uses signalling to establish connection among peers using http and SDP protocol. © Grenze Scientific Society, 2024.

关键词： Gesture recognition

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A Novel Hybrid CNN-Transformer Framework for Spatiotemporal Prediction of O3

A Novel Hybrid CNN-Transformer Framework for Spatiotemporal ...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kumar, Sudhir Kour, Vaneet Jain, Nimisha Misra, Rajiv Indian Institute of Technology Computer Science and Engg. Patna801106 India

ISBN: (纸本)9798350362480

Air pollution presents serious threats to public health and environmental sustainability, especially when it comes to concentrations of ozone (O3). Even though they are accurate, traditional monitoring methods have spatial limitations that cause gaps in the coverage of data. In order to tackle this issue, we provide a hybrid deep learning model that predicts O3 levels in different parts of India by integrating Transformer and Convolutional Neural Networks (CNN) using satellite and meteorological data. While the Transformer model uses meteorological data to identify temporal trends, the CNN component analyzes satellite pictures to extract spatial information. Multiple assessment criteria, including R2, MSE, RMSE, and MAE, show that we enhance prediction accuracy by merging both models. Our model performs better in both short- and long-term predictions than other deep learning techniques and RNN, such as CNN, GRU, and LSTM models. The results suggest that the hybrid CNN-Transformer model is a promising solution for accurate air pollution forecasting, offering critical insights for timely interventions and policy development. © 2024 IEEE.

关键词： Convolutional neural networks

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Prevention of Attacks in Vehicular Adhoc Networks

Prevention of Attacks in Vehicular Adhoc Networks

引用

2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Anjali, Thuvva Goyal, Rajeev G.n, Balaji Amity University Computer Science and Engineering Amity School of Engineering and Technology Gwalior India Vellore Institute of Technology Computer Science and Engineering School of Cse Vellore India

ISBN: (纸本)9798350348460

Over the past years, numerous methods have been developed to identify anomalies in traditional VANETs networks. A survey of VANET anomaly detection and mitigation methods is presented in this analysis. This survey looks into a variety of VANET research projects, attack types, anomaly detection methods, and mitigation strategies. VANETs provide wireless communication among vehicles as well as between vehicles and infrastructure. Its major objective is to improve road safety, comfort, and convenience. VANET varies from ad hoc networks due to its specific features. Several methods for identifying anomalies in conventional VANET networks have been developed over time. This work examines VANET anomaly detection and prevention technologies. Various VANET research studies, different types of attacks, anomaly detection, and VANET protection measures. The current investigation seeks to address various sorts of vehicle network assaults as well as existing security methods for coping with these attacks. This work will help academics understand the taxonomy of automotive network security vulnerabilities, which can subsequently be researched further to develop unique solutions. © 2024 IEEE.

关键词： Identity Based Signature Intelligent Transport System Intrusion Detection System Vehicle to Vehicle Vehicular Adhoc Network

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Design of Crop Classification Model Based on Soil Information 3

Design of Crop Classification Model Based on Soil Informatio...

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3rd International Conference on Sentiment Analysis and Deep Learning, ICSADL 2024

作者： Sharma, Krish L Patel, Yashaswi Jain, Ankur School of Computer Science and Engineering Vellore Institute of Technology Bhopal Bhopal India

ISBN: (纸本)9798350396157

The adoption of contemporary techniques to boost agricultural output is unavoidable given worries about the growth of the world's population and the scarcity of food. Artificial neural networks (ANN) in particular have attracted interest recently for the analysis of agricultural data so that farmers may maximise resource allocation, reduce waste, and improve overall efficiency by accurately identifying their crops, giving farmers useful information for making data-driven crop production decisions. ANNs are capable of learning intricate correlations between input characteristics and output labels. The paper proposes an ANN model that classifies crops based on a variety of soil properties. 22 different crops are included in the data collection. The study provides a comparative study of different ANN models with respect to activation function and loss function yielding 8 different models. The proposed model correctly detects crop types using specific soil parameters. This study aids in the creation of simple farming solutions that facilitate decision-making and raise overall agricultural productivity. © 2024 IEEE.

关键词： Neural networks

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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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Enhanced Clustering and Channel Allocation in Wireless Mesh Networks

Enhanced Clustering and Channel Allocation in Wireless Mes...

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Sushma Reddy, Ch. V. Harshini, V. Rayala, Abhyuday Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Mangaluru Surathkal India

ISBN: (纸本)9789819613472

Wireless mesh networks (WMNs) are crucial for establishing adaptable and scalable communication infrastructures among interconnected devices. Effective clustering and channel allocation are vital for enhancing WMN performance by addressing energy efficiency, latency, throughput, and interference challenges. Proper clustering facilitates the organization of network nodes into cohesive groups, enhancing communication efficiency and resource utilization. Additionally, channel allocation strategies ensure minimized collisions and improved overall network throughput, enhancing network stability and reliability. Existing approaches, such as clique-based channel assignment (CCCA) and two-hop neighbor clustering, present complexity, and interference level limitations. The significant contribution of this paper is to introduce a novel approach focused on clustering and channel assignment, referred to as enhanced clustering and channel allocation (ECCA), to optimize WMN performance—the clustering technique groups nodes based on maximal cliques in one-hop neighbors. Furthermore, channel assignment strategies are employed to minimize collisions and improve overall network throughput. The performance of ECCA is compared with state-of-the-art clique-based channel assignment (CCCA) in terms of the modularity, average number of nodes per cluster, average node degree, and coefficient of variance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Wireless mesh networks (WMN)

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