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DSF-GAM: a location inference model in social network Twitter

International Journal of computers and Applications 2025年第2期47卷 141-161页

作者： Paul, Amit Changder, Narayan Dutta, Animesh Department of Computer Science and Engineering National Institute of Technology West Bengal Durgapur India TCG CREST Research and Education in Science and Technology West Bengal Kolkata India

Analyzing the social interactions and texts on Twitter can provide valuable insights into users' behavior, opinions, and even their geographical locations. Location inference of users within a social network finds applications in various domains, such as targeted advertising and public health monitoring. Inference techniques through social interactions, such as co-mentions, remain a favored method for constructing user social networks. Co-mentions, however, introduce a substantial number of noisy relationships that impact the overall network structure and prediction of the location of a user. Furthermore, previous approaches have overlooked the examination of cohesive groups or community structures within an individual's ego network for location prediction. This paper introduces a predictive model called DSF-GAM (DS stand for document similarity, F stands for frequencies and GAM stands for Generalized Additive Model). which redesigns social interactions based on mentions to infer user location on Twitter. DSF-GAM introduces a novel and innovative social graph, termed a user relation graph. DSF-GAM employs a generalized additive model (GAM) for location inference. The fundamental contribution of DSF-GAM lies in the representation of data using a new graph structure with enriched community features and edges. The experimental results on well-known datasets demonstrate that DSF-GAM outperforms other state-of-the-art methods based on both content and network approaches and mixed results in hybrid approaches. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Tweets

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Reinforcement Learning based Adaptive Healthcare Decision Support Systems using Time Series Forecasting 5

Reinforcement Learning based Adaptive Healthcare Decision Su...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Shirley, C.P. Berin Jeba Jingle, I. Abisha, M.B. Venkadesan, R. Absin, S.J. Karunya Institute of Technology and Sciences Department of Computer Science Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350389609

ISBN: (纸本)9798350389609

Healthcare decision-making is a complex and crucial process that must take into account changing patient situations and medical trends. It is frequently difficult for conventional decision support systems to adjust to these shifting circumstances. In this work, we present a novel method for creating adaptive healthcare decision support systems by combining time series forecasting and reinforcement learning (RL). The main goal is to create a system that can use reinforcement learning (RL) to develop optimal policies for making decisions by utilizing time series data of patients from the past. Our system uses RL in conjunction with time series forecasting methods like LSTM (Long Short-Term Memory) networks and ARIMA (Auto Regressive Integrated Moving Average) to estimate future patient outcomes and healthcare resource requirements. There are several different components in the system architecture. The time series forecasting models are trained using historical healthcare data, which includes patient demographics, medical histories, and treatment outcomes. Forecasts for important healthcare parameters like patient recovery rates, disease progression, and resource consumption are produced by these models. According to the anticipated results, RL algorithms are used to learn decision policies. While interacting with a simulated environment that represents patient care scenarios, the RL agent aims to maximize long-term rewards, which may include reduced expenses, better patient outcomes, or more efficient use of available resources. Using actual healthcare datasets, the adaptability and efficacy of suggested approach will be assessed. The efficacy of RL-based decision policies, accuracy of projected results, and the system's adaptability to shifting healthcare dynamics are all examples of performance measures. © 2024 IEEE.

关键词： Decision support systems Adaptive systems Accuracy Time series analysis Systems architecture Medical services Reinforcement learning Predictive models Forecasting Long short term memory

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RAPID-AV: Realtime Adaptive and Intelligent Detection For AV's Against DDoS Attacks 18

RAPID-AV: Realtime Adaptive and Intelligent Detection For AV...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Kotha, Aditya Venkanna, U. International Institute of Information Technology Department of Computer Science and Engineering Naya Raipur India National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350391725

Autonomous vehicles (AVs) increasingly rely on vehicle-to-everything (V2X) networks for communication. However, due to the devices' heterogeneity, they are more susceptible to attacks like distributed denial of service (DDoS), resulting in packet loss and transmission delays. Existing solutions to these issues often create bottlenecks and lack post-attack recovery mechanisms. To solve these problems, this paper proposes RAPID-AV, an ML-based approach that uses programmable P4 switches for real-time detection and mitigation of DDoS attacks in AV networks. The proposed system uses a threefold strategy: collect traffic statistics from AV, use P4-based programmable packet processing for anomaly detection, and use majority voting to mitigate attacks. In our experiments, we found that the decision tree algorithm achieves an accuracy of 94.33% in distinguishing traffic from DDoS traffic. In addition, RAPID-AV demonstrated a nearly 10% reduction in CPU load during attacks and maintained consistent communication between AV and RSU. This approach can help improve AV network security while addressing the limitations of existing solutions. © 2024 IEEE.

关键词： Network security

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Enhancing COVID-19 Detection with YOLOv5 Object Detection Algorithm in Chest X-ray Images 3

Enhancing COVID-19 Detection with YOLOv5 Object Detection Al...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Duraisamy, Premkumar Arunkumar, D. Niranjani, V. Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350381931

In the ongoing fight against COVID-19, accurate diagnostic tools are crucial. This study focuses on the detection of COVID-19, primarily by analyzing Chest X-ray images to identify and understand the disease. We introduce a new approach that utilizes the YOLOv5 (You Only Look Once) object detection algorithm. YOLOv5 is known for its ability to quickly identify objects, and we have fine-tuned it to perform effectively with X-ray images. The result is a model that excels at identifying intricate COVID-19-related patterns in X-ray images, achieving a high accuracy score of 98%. By carefully adjusting and training the model with specific data, our method achieves even higher accuracy. This modification makes YOLOv5 a powerful tool for automating the detection of COVID-19, offering hope for early detection and timely action in the ongoing global fight against the pandemic. © 2024 IEEE.

关键词： COVID-19

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A Quantum Approach for Text based Steganography 15

A Quantum Approach for Text based Steganography

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

作者： Naik, Darsana A. Pallavi, N. Keerthi, P. Rai, Abhinav Kumar Jha, Adarsh Kumar Bindal, Avaneesh Chauhan, Shubhi Singh M S Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India Impact Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331300579

In or daily lives, maintaining the security of sensitive information has proven to be very difficult. communications sent frequently through a communication channel like the Internet may catch the attention of crackers and hackers, leading to possible attempts to decrypt and reveal the odd communications. With the development of digital communication, the Internet, and cloud-based storage, there is an increasing need for novel and effective methods for protecting the privacy and security of digital data. Steganogra-phy and cryptography are often used together as a solid privacy protection strategy. However, numerous studies on steganography have been established by various academics. Here, a text steganography method has been developed using quantum circuits and XOR-based symmetric cryptography. In this case, the quantum technique also includes boosting security. using a quantum gate truth table to map the logic allows for the embedding of cov-ert and hidden messages. Since text messages are generally regarded as being safe and not requiring further investigation, this technique offers an excel-lent data protection system. Steganography is an art form that can be used to transmit data securely through any form of open communication. © Grenze Scientific Society, 2024.

关键词： Quantum electronics

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Deep Learning-Based Robust Hybrid Approaches for Brain Tumor Classification in Magnetic Resonance Images

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Journal of The Institution of Engineers (India): Series B 2023年 1-15页

作者： Krishna, S. Rama Ravela, Chittibabu Mantena, Srihari Varma Sirajuddin, Mohammad Deol, G. Joel Sunny Hyderabad India Computer Science and Engineering Koneru Lakshmaiah Education Foundation K L University Vaddeswaram India Department of Computer Science and Engineering SRKR Engineering College Bhimavaram India Department of Information Technology Kallam Haranadhareddy Institute of Technology Dasarapalem India Department of Computer Science and Engineering Kallam Haranadhareddy Institute of Technology Andhra Pradesh Dasarapalem India

Classification of brain images is a very challenging problem among the most helpful and commonly employed procedures in the medical system. Deep learning, a subset of artificial intelligence, has pioneered new techniques for automatic medical image investigation. The goal of this study was to create two deep learning-based hybrid models to segment and classify brain MRI data. The first hybrid model is a combination of Fully Convolutional Networks and Residual Networks for segmentation and classification. The second is a hybrid of SegNet for segmentation and MobileNet for classification. The analysis is based on a labelled dataset containing images of glioma, meningioma, pituitary, and no tumor. The hybrid models proposed above are built using Python. The suggested models exhibit a high accuracy of 93.9% and 91.3% for a brain MRI dataset, respectively. Furthermore, the proposed models were compared and evaluated against other existing hybrid models in terms of dice score, Positive predictive value, False predictive value, precision, recall, specificity, and F1 score. As a result, the proposed deep-learning technique can assist doctors as well as radiologists in detecting brain tumors early. © 2023, The Institution of Engineers (India).

关键词： Magnetic resonance imaging

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Implementing Machine Learning Techniques for Hair Health Prediction in Follicle Futures 2

Implementing Machine Learning Techniques for Hair Health Pre...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Duraisamy, Premkumar Vidhya, V. Preetha, T. Niranjani, V. Kpr Institute Of Engineering And Technology Department Of Computer Science And Engineering Coimbatore India Sri Eshwar College Of Engineering Department Of Computer Science And Engineering Coimbatore India

ISBN: (纸本)9798350381665

This study explores the effectiveness of various machine learning algorithms in forecasting hair health using a comprehensive dataset incorporating individual traits and lifestyle elements. Logistic regression, random forest, and AdaBoost were assessed, with random forest notably achieving a remarkable 94.6% accuracy. These results emphasize machine learning's potential for precise, non-invasive personalized hair health predictions. Key factors such as genetics, diet, and stress were pinpointed, informing targeted care approaches. The research underscores machine learning's promise in accurately anticipating hair loss and suggests future avenues for expanding datasets, integrating diverse data sources, and developing user-friendly tools for improved hair care management. © 2024 IEEE.

关键词： Support vector machines

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Implementation of YOLOv9 in Agricultural AI for Enhanced Weed Detection

Implementation of YOLOv9 in Agricultural AI for Enhanced Wee...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, V. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Deep Fake Analyser: A Review Based on Detecting the Deepfakes 1

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Srivastava, Santosh Kumar Srivastava, Durgesh Kantha, Praveen Mahto, Manoj Kumar Kumar, Sheo Singh, Hare Ram Department of Computer Science and Engineering School of Computing Science and Engineering and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Chitkara University Baddi Himachal Pradesh Baddi India Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Department of Computer Science and Engineering GNIOT Greater Noida India

ISBN: (数字)9789819725083

ISBN: (纸本)9789819725076

With the speedy growth in the technology and automation sectors, different techniques have been developed which can easily manipulate multimedia content such as videos and images with the ultimate level of realism. It becomes very hard for a person to find out which of the media content is real and which is fake. With the help of deep learning and GANs (Generative Adversarial Networks), deep fakes can be produced. A Deep fake is a fake video or image where a person in an existing video or image is replaced by someone else by using an algorithm. If we look at the positives, we can see how deep fake technology can be used in advertisements, filmmaking, and video games. But on the contrary, it also results in the spread of fake news and the generation of pornographic content. This seriously impacts society and presents enormous threats. This research paper aims to present a paper based on a deep fake analyzer that will be able to identify and recognize fake media with the help of deep neural networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Generative adversarial networks

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