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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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An effective compressed image authentication scheme based on N-variant AMBTC

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Multimedia Tools and Applications 2024年第2期83卷 3801-3829页

作者： Su, Guo-Dong Chang, Chin-Chen Lin, Chia-Chen School of Big Data and Artificial Intelligence Fujian Polytechnic Normal University Fuzhou350300 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Road Seatwen Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chin-Yi University of Technology Taichung41170 Taiwan

Image authentication is one technique that provides integrity protection for digital images, making them sensitive to any slight modification. Recently, the detectability of absolute moment block truncation coding (AMBTC) compressed images has been attracting more attention due to its integrity authentication concerns. Thus, this paper proposes an effective N-variant AMBTC based image authentication scheme for AMBTC compressed images. First, a novel AMBTC compression code is derived using a variant AMBTC technique that can provide a better quality of compressed images. Then, each AMBTC compression code is further eliminated into a version requiring fewer bits using the proposed N-variant AMBTC technique, and those vacated N-bits are used to conceal authentication codes, which is generated by hashing the AMBTC compression code and location information. Experimental results confirm that the proposed scheme has superior stability and accuracy in tampering detection while providing better visual quality of the watermarked image. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Authentication

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Task Offloading Based on Vehicular Edge Computing for Autonomous Platooning

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computer Systems science & Engineering 2023年第7期46卷 659-670页

作者： Sanghyuck Nam Suhwan Kwak Jaehwan Lee Sangoh Park School of Computer Science and Engineering Chung-Ang UniversityDongjak-guSeoul06974Korea Department of Computer Science and Engineering Kongju National UniversityCheonan31080Korea

Autonomous platooning technology is regarded as one of the promising technologies for the future and the research is conducted *** autonomous platooning task generally requires highly complex computations so it is difficult to process only with the vehicle’s processing *** solve this problem,there are many studies on task offloading technique which transfers complex tasks to their neighboring vehicles or computation ***,the existing task offloading techniques which mainly use learning-based algorithms are difficult to respond to the real-time changing road environment due to their *** are also challenging to process computation tasks within 100 ms which is the time limit for driving *** this paper,we propose a novel offloading scheme that can support autonomous platooning tasks being processed within the limit and ensure driving *** proposed scheme can handle computation tasks by considering the communication bandwidth,delay,and amount of *** also conduct simulations in the highway environment to evaluate the existing scheme and the proposed *** result shows that our proposed scheme improves the utilization of nearby computing nodes,and the offloading tasks can be processed within the time for driving safety.

关键词： Task offloading vehicular edge computing vehicular ad-hoc network dedicated short-range communication autonomous platooning

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Performance Challenges and Solutions in Big data Platform Hadoop

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Recent Advances in computer science and Communications 2023年第9期16卷 27-41页

作者： Singh, Balraj Verma, Harsh K. Madaan, Vishu Department of Computer Science and Engineering Dr. B.R. Ambedkar NIT Jalandhar India School of Computer Science and Engineering Lovely Professional University Phagwara India

Background: The present era demands continuous support to bring improvements in executing complex analytics on large-scale data and to work beyond traditional systems. Objective: The need for processing diverse data types and solutions for different domains of the industry is rising. Such needs increase the requirement for sophisticated techniques and methods to enhance the existing platforms and mechanisms further. It provides an opportunity for the research community to investigate further into the existing systems, find potential issues, and propose new ways to improve the current systems. Hadoop is a popular choice to manage and process Big data. It is an open-source platform and a front-runner in the batch processing of large-scale jobs. The economy associated with the cluster in scaling is low as compared to other platforms. However, this popularity by no means guarantees high performance in all scenarios. With the continuous evolution in data development and industrial requirements, it is imperative to investigate and look into new methods and techniques to bring advancements to the existing system. Method: A systematic review is represented in this paper to have an insight into the current progress in this field. Research publications from various sources are taken and analyzed. The performance of a cluster largely depends upon the different job processing mechanisms and policies associated with it. Conclusion: While extensive studies and solutions are proposed, the performance bottlenecks in terms of load balancing, resource utilization, content management, and efficient processing prevail. Not many of the solutions are there on scheduling about the trade-off between different parameters, the process of content splitting and merging is not explored to a large extent and the skew mitigation solutions are more focused on Reduce side of the MapReduce while the Map side is not utilized much for load balancing. © 2023 Bentham science Publishers.

关键词： Batch data processing

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Sentiment Analysis System for Image and Text Based Social Media data 1

Sentiment Analysis System for Image and Text Based Social Me...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Baoyue, Hu Ashraf, Humaira Jhanjhi, Nz Verma, Sahil Taylor's University School of Computer Science and Engineering Malaysia Uttaranchal University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350373042

The paper provides an explicit outlook on the development of a comprehensive sentiment analysis systems for online social media by targeting user-generated text and images on platforms such as Twitter, Facebook, and Instagram. The system is developed to provide a complete sentiment analysis of users' emotions and expressions on social media. The project is composed of three interconnected sections, a Text-based sentiment analysis model, an Image-based sentiment analysis model and a web application prototype that integrates these models to provide real time results. The text-based model includes, data preprocessing, analysis using Word Cloud, analysis using the techniques of Word2Vec and implementation of multiple machine learning and deep learning models. Similarly, image-based model includes data preprocessing, data augmentation, deep learning methods for sentiment assessment. Final module is focused on representing application with real time demonstration, consisting with the practical implementation of these models. The prototype displays a real-time sentiment analysis and visualizes data. The paper is focused on a wide range of the field where the importance of the sentiment analysis is immense. For instance, companies can analyze how products are being analyzed in the market, politicians can determine how people are thinking of their speeches, teachers can know the interest of the lecture for the students. Therefore, the research signifies the importance of the Sentiment Analysis to extract the meaning or insight from the overwhelming unstructured, dynamic and always evolving nature of the social media content. © 2024 IEEE.

关键词： Image analysis

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Predicting Sea Level Rise in Coral Sea Using Machine Learning 6

Predicting Sea Level Rise in Coral Sea Using Machine Learnin...

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6th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2024

作者： Eka Rahmat, Lucky Yudhianto Kurniawan, Afdhal Diana Sanatha Dharadhipha, Gusti Agung Gede BINUS University Tangerang Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331533243

This research investigates the use of machine learning techniques, specifically Multiple Linear Regression (MLR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict sea level rise in the Coral Sea region. Climate change has led to a continuous rise in sea levels, posing significant risks, particularly in coastal areas. Traditional prediction methods often fail to capture the complex dynamics of sea level fluctuations. By leveraging historical sea level data from the Pacific Sea Level and Geodetic Monitoring Project, this study aims to improve prediction accuracy using MLR and ANFIS models. The dataset spans from 1995 to 2021 and includes variables such as sea level, water temperature, air temperature, and wind speed. Our findings indicate that while both models can predict sea level changes, the ANFIS model outperforms MLR in terms of accuracy, as evidenced by lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) scores. However, the predictive power of these models is limited by data quality and model parameter tuning. Further research should focus on improving data preprocessing and optimizing model parameters to enhance prediction reliability. This study contributes to the development of robust sea level prediction frameworks, supporting effective coastal management and climate adaptation strategies. © 2024 IEEE.

关键词： Global warming

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Occluded human pose estimation based on limb joint augmentation

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Neural Computing and Applications 2025年第3期37卷 1241-1253页

作者： Han, Gangtao Song, Chunxiao Wang, Song Wang, Hao Chen, Enqing Wang, Guanghui School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Computer Science Toronto Metropolitan University Toronto Canada

Human pose estimation aims at locating the specific joints of humans from the images or videos. While existing deep learning-based methods have achieved high positioning accuracy, they often struggle with generalization in occlusion scenarios. In this paper, we propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. Specifically, the occlusion blocks are at first employed to randomly cover the limb joints of the human bodies from the training images, imitating the scene where the objects or other people partially occlude the human body. Trained by the augmented samples, the pose estimation model is encouraged to accurately locate the occluded keypoints based on the visible ones. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints by evaluating the dependence between adjacent joints. Extensive experimental evaluations on two occluded datasets, OCHuman and CrowdPose, demonstrate significant performance improvements without additional computation cost during inference. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Joints (anatomy)

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The Politics of Fear and the Experience of Bangladeshi Religious Minority Communities Using Social Media Platforms

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-32页

作者： Rifat, Mohammad Rashidujjaman Das, Dipto Podder, Arpon Jannat, Mahiratul Soden, Robert Semaan, Bryan Ahmed, Syed Ishtiaque Department of Computer Science University of Toronto TorontoON Canada Khulna University of Engineering & Technology Khulna Bangladesh Department of Women and Gender Studies University of Dhaka Dhaka Bangladesh Computer Science and School of the Environment University of Toronto TorontoON Canada Department of Information Science University of Colorado Boulder BoulderCO United States

Despite significant research on online harm, polarization, public deliberation, and justice, CSCW still lacks a comprehensive understanding of the experiences of religious minorities, particularly in relation to fear, as prominently evident in our study. Gaining faith-sensitive insights into the expression, participation, and inter-religious interactions on social media can contribute to CSCW’s literature on online safety and interfaith communication. In pursuit of this goal, we conducted a six-month-long, interview-based study with the Hindu, Buddhist, and Indigenous communities in Bangladesh. Our study draws on an extensive body of research encompassing the spiral of silence, the cultural politics of fear, and communication accommodation to examine how social media use by religious minorities is influenced by fear, which is associated with social conformity, misinformation, stigma, stereotypes, and South Asian postcolonial memory. Moreover, we engage with scholarly perspectives from religious studies, justice, and South Asian violence and offer important critical insights and design lessons for the CSCW literature on public deliberation, justice, and interfaith communication. © 2024 Copyright held by the owner/author(s).

关键词： Social psychology

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Reversible data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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A Streamlined Method for Detecting Strawberry Leaf Diseases Utilizing Deep Learning Techniques 3

A Streamlined Method for Detecting Strawberry Leaf Diseases ...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Singh, Jashandeep Singh, Gursharan School of Computer Science and Engineering Lovely Professional University Phagwara India Department of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331527549

Strawberry leaf scorch presents major risks to fruit yields through its harmful effects on plant health so early detection methods are vital for growers. This research analyzes how different Convolutional Neural Networks classify strawberry leaf diseases while fixing problems found in earlier studies about speed and diagnosis precision. The proposed research evaluated five CNN models namely AlexNet, DenseNet, ResNet, MobileNet, and EfficientNet through implementation on a total of 6146 images that showcased both healthy and scorch-affected strawberry leaves. The proposed research optimized feature extraction techniques and added model comparison to find the most effective configuration. EfficientNet achieved higher accuracy and precision than all test models when detecting diseases in samples. The strong disease identification abilities of DenseNet and MobileNet show promise for developing better ways to manage agricultural health problems. This study expands precision agriculture methods while helping farmers sustain their operations through modern machine learning tools. © 2025 IEEE.

关键词： Fruits

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