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SobelMNet: A Lightweight CNN based Approach for Median Filtering Detection in compressed images 27

SobelMNet: A Lightweight CNN based Approach for Median Filte...

引用

27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Kaushal, Anukriti Kumar, Rajeev Kumar, Sanjay Dept. of Computer Science & Engineering Delhi Technological University Delhi India

ISBN: (纸本)9791188428137

Digital image forgery is the process of manipulating an image to deceive or mislead observers with real or manipulated content. Median filtering is widely used to smooth images and obscure traces of tampering, making its detection critical for image forensics. However, identifying median filtering becomes more complex when additional operations, such as compression, resampling, or noise addition, are applied. To address this issue, we propose a lightweight convolutional neural network (CNN) model named SobelMNet, specifically designed for detecting median filtering in compressed images. The proposed model utilises a Sobel filter-based preprocessing step to enhance the residual differences between the original and manipulated images. These residuals, which capture subtle features indicative of median filtering, are analysed by CNN for classification. Further, the proposed model is evaluated on grayscale low-resolution images generated from the Dresden dataset for both binary and multiclass classification tasks. The model achieved a remarkable detection accuracy of 99.43% in median filter detection and outperformed state-of-the-art methods in various scenarios, including combinations of median filtering with Gaussian blur, resampling, and additive white Gaussian noise (AWGN) with an average accuracy of 98.32%. Finally, its lightweight architecture ensures computational efficiency, making it practical for real-world forensic applications. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Median filters

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Multisport Dynamics Explored Through Advanced Activity Recognition Techniques in Athletic Performance 8

Multisport Dynamics Explored Through Advanced Activity Recog...

引用

8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Salian, Supriya Preethi Salian, K. D'Souza, Ria Monterio, Rencita Fernandes, Rinette Veronica Almeida, Riya D. St. Joseph Engineering College Vamanjoor Dept. of Computer Science and Engineering Mangaluru India Dept. of Information Science and Engineering Udupi India

ISBN: (纸本)9798350350593

Recognition of human activities has drawn a lot of interest lately in the field of computer vision and machine learning. Group activity recognition is a significant subcategory in which several people participate in a common activity. The primary obstacle in these tasks is learning the relationships between individuals in a scene and understanding their evolution over time. The suggested study offers a new taxonomy to classify state-of-the-art (SOTA) group activity recognition approaches and subcategorizes the current literature. It also critically analyzes these techniques. To understand scenes involving multiple people, models must describe individual actions in context and infer collective activities. Accurately capturing relationships between actors and performing relational reasoning is crucial for comprehending group activities. However, modelling these relationships is challenging due to the limited availability of interaction information, relying only on individual action labels and collective activity labels. Inferring relationships from other aspects is thus essential. Group activity recognition has garnered increased research attention recently due to its importance in video understanding. The difficulties lie not only in recognizing individual actions but also in exploring scene information and collaborative relations among people. This research addresses these challenges by providing a comprehensive review of existing techniques, offering a structured approach to categorize them, and highlighting the importance of relational reasoning in understanding group activities. © 2024 IEEE.

关键词： Active learning

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Lung Cancer Disease Prediction using Hybrid Subtractive Clustering Method 2

Lung Cancer Disease Prediction using Hybrid Subtractive Clus...

引用

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

作者： Paul, Banibrata Mir, Mahmood Hussain Murugan, Stalin Kurian, Asha Alliance University Dept. of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798350360523

As of right now, lung cancer is the primary cause of cancer-related deaths worldwide for both men and women. One possible explanation for lung cancer's main cause is smoking. 86% to 96% of instances of lung cancer are thought to originate in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles), despite the fact that lung cancer can grow in any section of the lung. The primary objective of this work is to identify lung cancer using the ANFIS approach based on subtractive clustering method. Using Kaggle, we have made use of the Lung Cancer dataset. The network is trained using data from 1000 lung cancer patients, ranging in age from 14 to 73. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, and consistently achieves 100% accuracy for each clustering radii. © 2024 IEEE.

关键词： Diseases

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Latency Analysis of In-Vehicle Network for Advanced Driver Assistance System 100

Latency Analysis of In-Vehicle Network for Advanced Driver A...

引用

100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Park, Hanyoung Cho, Minji Jang, Yongjae Choi, Ji-Woong Dept. of Electrical Engineering & Computer Science Daegu Korea Republic of

ISBN: (纸本)9798331517786

Considering the advancements in autonomous driving technologies, the necessity for an advanced driver assistance system (ADAS) to incorporate a multitude of sensors for enhanced precision has become paramount. Consequently, the increase of sensors correlates directly with the escalation of autonomous driving capabilities. However, this escalation causes a larger volume of data traffic and an increase in the latency within the in-vehicle network (IVN). As the IVN latency increases, the overall latency of ADAS from the sensor to the actuator increases. This increased latency can affect the safety of ADAS. Hence, latency analysis for various sensors is required. In this paper, we struct Ethernet-based zonal architecture considered for future automotive architecture and analyze the factor of end-to-end latency for the safety-related ADAS with multi-type sensors, including IVN latency. © 2024 IEEE.

关键词： Advanced driver assistance systems

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DevOps Practices Into Machine Learning 3

DevOps Practices Into Machine Learning

引用

3rd IEEE International Conference on Intelligent Systems, Smart and Green Technologies, ICISSGT 2024

作者： Jain, Samridhi Kumar, Puneet Chandigarh University Dept. of Computer Science & Engineering Mohali India

ISBN: (纸本)9798350304329

All industrial machine learning (ML) projects have their ultimate objective to quickly develop and deploy ML solutions. However, a lot of machine learning projects are failing, and never reach production. In order to address these concerns, this paper aims to investigate how manual ML processes can be automated and operationalised so that more ML concept can be put into production. The advantages of DevOps automation and machine learning capabilities are now being combined by industries. MLOps is the name of this combination. Our analysis found that while there are some similar aspects in defining MLOps, still there is a lack of standardisation in the term, which can occasionally lead to misunderstandings and uncertainty. We carried out the research, which includes a review of the literature and an evaluation of the MLOps tools, to fill this gap. These investigations have led us to define MLOps and identify unresolved issues in the field. In addition to this, we offer a workflow-based summary of all essential components. Finally, this paper gives guidance for applying DevOps practises to machine learning for researchers and practitioners. © 2024 IEEE.

关键词： DevOps Machine Learning MLOps MLOps tools

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An In-dept. Exploration of Machine Learning Algorithms and Performance Evaluation Approaches for Personalized Diabetes Prediction

An In-Depth Exploration of Machine Learning Algorithms and P...

引用

2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Kaur, Inderdeep Ali, Aleem Chandigarh University Dept. of Computer Science and Engineering Kharar India

ISBN: (纸本)9798350376470

Diabetes is now recognised as a major worldwide health problem, demanding effective early detection and prediction measures to limit its repercussions. Machine learning algorithms have shown to be excellent tools in healthcare for creating prediction models. This paper delves into an extensive investigation of machine learning (ML) algorithms and methodologies for assessing their performance in the context of personalized diabetes prediction. Different machine learning models are rigorously explored and evaluated for how well they can customise forecasts to the unique characteristics of individual patients. The study also critically assesses several performance evaluation techniques in an effort to improve the accuracy and reliability of diabetes prediction algorithms. Furthermore, interpretability problems and class imbalance are addressed as challenges and limitations to the application of machine learning in the prediction of diabetes. This study aims to motivate researchers, facilitating a deeper understanding of disease prediction algorithms and conduct related research. © 2024 IEEE.

关键词： Prediction models

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Data Analysis of Dark Web Marketplaces using Machine Learning 7

Data Analysis of Dark Web Marketplaces using Machine Learnin...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Pratham Rao, U.N. Guruprasad, Rachit Raam Shetty, Omkaar J Sarasvathi, V. Rapate, Gauri Sameer PES UNIVERSITY Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350368673

People refer to the dark web as the anonymous part of the internet that is very hard to access, it has been described as lying some distance beyond the easily accessible surface web and more obscure deep web. This means that the dark web is accessed by using specially designed software, and it hosts all manner of illicit activities that present huge challenges to law enforcement. This section, known as the dark web, may only be accessed with the use of special designed programs, such as Tor. In this sphere of the net, it is feasible for both the user and the owners of any site to claim anonymity. The dark web is still notorious for offering illegal activities such as drugs markets, selling firearms, and providing services in cybercrime, though it is on the gray area of applying it towards journalists, social activists, and whistleblowers. But it is pretty much important to note here that the deep web or parts of the internet and the dark web are very much different things. In order to shed some light on how to overcome these challenges, we have come up with a custom web crawler specifically designed to work inside the Tor network, extracting data from various dark web marketplaces. In this paper, we will be focusing on the profiling of vendors according to categories and geographical origins, with an emphasis on operational patterns of these cloaked entities. To sum it all, our customized web crawler provides great insights about profiling vendors in dark web markets and categories where they originated. © 2024 IEEE.

关键词： Marketplaces

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Spatial Data Discovery Using Small Language Model 5

Spatial Data Discovery Using Small Language Model

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Thakur, Sujit Kumar Tyagi, Neha Amity University Dept. of Computer Science & Engineering Noida India

ISBN: (纸本)9798350383522

The ubiquitous use of data in a plethora of use cases and different organizational dependence on it for various insight has made data a valuable resource across business and scientific domains. Data has attained much more significance than oil itself, unlike the British mathematician Clive Humby's quote in 2006 'data is the new oil'. Every business and processes generate data and with the advent of AI and machine learning, all types of data have valuable use case attached. This also ensued a series of initiatives to clean, augment and provide data as data-products. This has given rise to businesses and solutions using inhouse data or data purchased from a dedicated data provider. Data consumers often face the challenge of inefficient data product discovery. More frequently than not they require active support from data product experts to point them to right dataset. The semantic meaning of the attributes and keywords of a data product is also heavily domain oriented. Data providers find it easy to update and augment metadata of a data set with details more feasible solution than modifying the dataset itself with attribute rename or adding composite attribute. The expected next step is that the metadata be self-explanatory enough to help in data cataloguing, but often we find consuming data scientist looking for help from data experts to make sense of the data in the context of the consumer's use case. This research seeks to analyse if the role of a data expert can be reduced and a finetuned small language model can help a data consuming data scientist in discovering the right data product, based on the metadata of the data product. Overall, this research contributes to the advancement of data discovery techniques and highlights the potential of leveraging language models to streamline and democratize access to valuable data resources, thereby empowering organizations to make more informed and data-driven decisions. © 2024 IEEE.

关键词： data discovery finetuning llama2 small llm spatial data

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Optimizing Cloud Security in Dynamic Environments Through Adaptive Workload Management Algorithm

Optimizing Cloud Security in Dynamic Environments Through Ad...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Yadav, Monika Sharma, Garima The Northcap University Dept. of Computer Science and Engineering Haryana India

ISBN: (纸本)9798350375237

With the growing interest of researchers in the area of cloud computing, security emerges as a major area of concern. As the complexity of cloud environments grows and the dependency on cloud services deepens across various sectors, ensuring robust security measures is crucial. This study proposes an innovative approach to enhance cloud security through efficient workload scheduling strategies. Leveraging insights from existing studies on green cloud computing, clonal selection algorithms, and energy-efficient resource allocation, the research introduces a novel security-aware workload organizing algorithm. The proposed algorithm aims to optimize both energy consumption and security in cloud data centers. Through comprehensive simulations on the Cloud Sim platform, the results demonstrate the algorithm's efficiency in reducing energy consumption while fortifying security measures. Moreover, the present study contributes to the evolving field of cloud security, offering a promising avenue for shielding data in dynamic cloud environments. © 2024 IEEE.

关键词： Energy utilization

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Integrating Tradition and Technology: A Survey of AI Applications in the Design of Temple Architecture

Integrating Tradition and Technology: A Survey of AI Applica...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Meghana, J. Bellad, Harshavardana R. Pardhi, Diya Magdum, Ishwari Nagaraj, Chaitra Dept. Computer Science Engineering PES University Bengaluru India

ISBN: (纸本)9798350353488

This article will delve into the complicated inter-building of artificial intelligence and traditional skill of temple architecture of South India. It meticulously analyzes the historical roots of temple design, explaining in detail the subtle means and methods that have always been used in temple construction. Apart from detailed preparation and skilled craftsmanship, it is the cultural heritage that is demonstrated with these monumental creations. In the sequel, the paper sheds light on how AI technologies are transforming temple architecture through systems analysis and optimization, generative design and other methods. Living examples of Artificial Intelligence work do not only confirm the successful AI implementations but also illustrate the smooth link between tradition and novelty in the modern temple designs. © 2024 IEEE.

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