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Copy-Move Forgery Detection in Digital Forensic Images Using CNN 7

Copy-Move Forgery Detection in Digital Forensic Images Using...

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7th International Conference on computer science and engineering, UBMK 2022

作者： Kaya, Mustafa Sani, Khalid Jibril Karakus, Serkan Firat University Dept. of Digital Forensics Engineering Elazig Turkey Federal University of Kashere Dept. of Computer Science Gombe Nigeria

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

Digital images are commonly used for sharing visual information and can be manipulated easily. The detection forgery in digital images has become a hot domain of research in digital image forensics due to the prevalent use of image editing tools for manipulating an image to conceal or distort information in the image. One of the most common image forgeries performed is the copy-move forgery. This type of forgery involves copying a segment of the image, which is then pasted to a different segment of the same image. The need for detecting whether an image is authentic becomes essential. The existing methods implemented for detecting image forgeries were based on traditional feature extraction algorithms such as block-based and key point-based algorithms. These traditional techniques employed produce a low-performance result. Deep learning techniques have proven to provide better performance in image processing tasks. In this research, a convolutional neural network based on a pre-trained ResNet50 network was proposed to detect copy-move forgeries in digital images. The proposed model uses the CoMoFoD image dataset in experimenting. The metric evaluation results achieved in the proposed model show that deep learning methods performance is more effective in digital image copy-move forgery detection. © 2022 IEEE.

关键词： Digital forensics

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Text Categorization using Supervised Machine Learning Techniques 6

Text Categorization using Supervised Machine Learning Techni...

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6th International Conference of Women in data science at Prince Sultan University, WiDS-PSU 2023

作者： Dawar, Ishaan Kumar, Narendra Negi, Sakshi Pathan, Sayeedakhanum Layek, Shirshendu DIT University School of Computing Uttarakhand Dehradun India VNR Vignana Jyothi Institute of Engineering & Technology Department of Computer Science Engineering Hyderabad India Dept. of Mining Engineering Dhanbad India

ISBN: (纸本)9781665477239

Text categorization is a task for text mining that involves pattern classification and is essential for the effective management of textual information systems (TIS). Each document is automatically assigned one or more categories from a set of predetermined categories as part of TIS. There is increased interest in creating tools to aid people in more effectively finding, filtering, and managing existing digital resources as the amount of information is rising. Those developed tools are involved in the administrative sectors and social benefits. This paper provides a general overview of text categorization models using various supervised machine learning techniques, including Logistic Regression (LR), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and AdaBoost, and compares their performance on various metrics, including accuracy and precision. The comparative analysis shows that the most effective algorithm for text classification is NVM with the highest accuracy of 96.86%, furthermore, AdaBoost is the worst in this case study with its minimum accuracy of 74.49%. We have also shown a comparison of other supervised machine-learning algorithms in this paper. © 2023 IEEE.

关键词： Classification (of information)

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Detecting Diseases in Potato Leaves using Deep Learning and Machine Learning Approaches: A Review 4

Detecting Diseases in Potato Leaves using Deep Learning and ...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Bommala, Harikrishna Babu, N Junnu Srikanth, Pothuganti Mallidi, S. Kumar Reddy Sai, Thota Siva Ratna Mounika, Rudrapati Kg Reddy College of Engineering and Technology Dept. of Cse Telangana Hyderabad India Dept. of Computer Science and Technology Andhra Pradesh Madanapalle517325 India Dept. of Cse Maisammaguda Telangana 500100 India Sri Vasavi Engineering College Dept. of Cse Andhra Pradesh Tadepalligudem534101 India Dept. of Cse Telangana India

ISBN: (纸本)9798350300888

As a mostly agricultural country, India is deeply concerned about its crop production rate. When crop yields drop, food prices rise, and people who can't afford even potatoes go hungry. Physical examination is a component of conventional approaches for spotting diseases in plants. This approach is exceedingly costly, laborious, and doesn't yield good outcomes. A unique approach for diagnosing potato leaf ailments should be developed to detect sick leaves at the start of their developmental period, which will assist to boost the output and consequently reduce the financial losses suffered by agriculturalists. Fortunately, new Deep Learning (DL) and Machine Learning (ML) models have flourished as a method of reducing plant disease and increasing crop yields, alleviating some of the burdens on farmers. Deep Neural Networks (DNN), a kind of artificial intelligence technology, may be used to identify disease-infected plants so that they can be treated early on, in the bud stage, before the sickness has spread too far. Based on a comprehensive literature assessment, the authors of this research concluded that Convolution Neural Networks (CNN) are superior to other methods for detecting leaf diseases and it is determined that CNN provides the highest achievable accuracy of 91% to 100%. © 2023 IEEE.

关键词： Diagnosis

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Vehicle Detection and Speed Estimation Using Deep Learning

Vehicle Detection and Speed Estimation Using Deep Learning

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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Vishnu Kumar Mishra Megha Mishra Rajesh Tiwari T Ram Kumar Tarun Dhar Diwan Raj Kumar Patra Dept. of Computer Science and Engineering Malla Reddy Engineering college for Women Hyderabad India Department of Computer Science and Engineering Shri Shankaracharya Technical Campus CSVTU Bhilai India Department of Computer Science and Engineering CMR Engineering College Hyderabad India Dept. of Computer Science and CoE Atal Bihari Vajpayee University Bilaspur Chhattisgarh India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

The use of deep learning algorithms for vehicle detection and speed estimate in traffic surveillance systems is investigated in this research study. Convolutional Neural Networks (CNNs) are the main tool used in this study to solve the problems related to precise and timely monitoring of vehicle movement. By improving the effectiveness and dependability of current systems, the suggested approach hopes to promote traffic management for smart transportation.

关键词： Deep learning YOLO Statistical analysis Vehicle detection Surveillance Estimation Traffic control Cameras Trajectory Automobiles

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Deep Learning: A New Way of Looking into Fashion 4

Deep Learning: A New Way of Looking into Fashion

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4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Thomas, Josh Hanley Roopashree, S. Joy, Jacob Charles Abdullah Kollarath, Haseeb Siby, Arun Dept. CSE-AI and Ml Bengaluru India Rv Institute of Technology and Management Dept. Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665474368

Currently, Fashion-Fit using AI focuses on finding the perfect fit for users using GAN's (General Adversarial Net¬works) and various methods proposed in the Artificial Intelligence Industry. However, due to changes in fashion trends and various preferences of the "type of fit"users want for their clothing the prior methodology is not meant for the perfect fit. These days a lot of people wear tighter or even get an oversized fit based on the user's preference. In this paper we use Deep Learning techniques like CNNs and GANs to implement neural body fit by looking at various human body poses from large datasets and concatenating this data to make an accurate "desired"fit based on different human poses. This will set a new benchmark for virtual try-on. In the model being proposed, we are building Convolutional Neural Networks (CNNs) for body segmentation to make it easier to map a human pose. We will then map the human body using CNNs but not necessarily to find the "perfect fit". The CNNs will be used to make measurements that are similar to sizes available within the fashion industry allowing users to see how they would fit within those garments virtually. We will then use 2D renders, using uploaded pictures of the user from 4-5 different angles in order to allow the user to see how he/she would look in the outfit of their choice. These methods can give an accurate prediction helping every fashion e-commerce business. Personalized size and fit recommendations are critical for a fashion e-commerce business. Predicting the optimal fit increases consumer happiness while lowering expenses associated with size-related returns. © 2022 IEEE.

关键词： Convolutional neural networks

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Batch Active Learning for Text Classification and Sentiment Analysis 22

Batch Active Learning for Text Classification and Sentiment ...

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3rd International Conference on Control, Robotics and Intelligent System, CCRIS 2022

作者： Sahan, Marko Smidl, Vaclav Marik, Radek Dept. of Computer Science Fee Ctu Prague Czech Republic Dept. of Telecommunication Engineering Fee Ctu Prague Czech Republic

ISBN: (纸本)9781450396851

Supervised learning of classifiers for text classification and sentiment analysis relies on the availability of labels that may be either difficult or expensive to obtain. A standard procedure is to add labels to the training dataset sequentially by querying an annotator until the model reaches a satisfactory performance. Active learning is a process that optimizes unlabeled data records selection for which the knowledge of the label would bring the highest discriminability of the dataset. Batch active learning is a generalization of a single instance active learning by selecting a batch of documents for labeling. This task is much more demanding because plenty of different factors come into consideration (i. e. batch size, batch evaluation, etc.). In this paper, we provide a large scale study by decomposing the existing algorithms into building blocks and systematically comparing meaningful combinations of these blocks with a subsequent evaluation on different text datasets. While each block is known (warm start weights initialization, Dropout MC, entropy sampling, etc.), many of their combinations like Bayesian strategies with agglomerative clustering are first proposed in our paper with excellent performance. Particularly, our extension of the warm start method to batch active learning is among the top performing strategies on all datasets. We studied the effect of this proposal comparing the outcomes of varying distinct factors of an active learning algorithm. Some of these factors include initialization of the algorithm, uncertainty representation, acquisition function, and batch selection strategy. Further, various combinations of these are tested on selected NLP problems with documents encoded using RoBERTa embeddings. datasets cover context integrity (Gibberish Wackerow), fake news detection (Kaggle Fake News Detection), categorization of short texts by emotional context (Twitter Sentiment140), and sentiment classification (Amazon Reviews). Ultimately, we sh

关键词： Entropy

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Twitter Sentiment Analysis and Prediction using NLP

Twitter Sentiment Analysis and Prediction using NLP

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Sindhu, M. Sabareeswari, T.C. Tamilselvi, A. Chennai Institute of Technology Department of Computer Science and Engineering Kanchipuram Tamilnadu Kundrathur India Chennai Institute of Technology Dept. of Electronics and Communication Engineering Kanchipuram Tamilnadu Kundrathur India

ISBN: (纸本)9798350348910

Social media has emerged as a pivotal platform for individuals to convey their thoughts and emotions, making it imperative for businesses, governments, and organizations to leverage artificial intelligence, such as sentiment analysis. This tool allows them to gauge public reactions on social media, particularly in the context of significant events like lockdown announcements. Employing relevant hashtags on Twitter, we aim to conduct sentiment analysis on how Indians are responding to these announcements and construct a predictive analytics model to discern people's behaviour. The developed model incorporates the nuances of modern internet slang to capture and interpret user sentiments accurately. This technology not only facilitates an in-dept. understanding of public sentiment but also offers a visually intuitive representation for easy comprehension. The applications of this solution are broad, benefiting organizations, businesses, and governments in various *** methodology involves the collection of tweets using Twitter's provided services, followed by the storage of this data in our database. Subsequent processing focuses on extracting meaningful and pertinent elements from the text to facilitate a comprehensive analysis. © 2023 IEEE.

关键词： Sentiment analysis

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Evaluating Path Selection Strategies with Blockchain-based Routing in Multi-Domain SDNs

Evaluating Path Selection Strategies with Blockchain-based R...

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2022 International Balkan Conference on Communications and Networking, BalkanCom 2022

作者： Guler, Evrim Karakus, Murat Uludag, Suleyman Bartin University Dept. of Computer Engineering Bartin Turkey Bayburt University Dept. of Computer Technologies Bayburt Turkey U. of Michigan - Flint Dept. of Computer Science FlintMI United States

ISBN: (数字)9781665487641

ISBN: (纸本)9781665487641

Software-Defined Networking (SDN) has emerged as one of the most prolific inventions in the general area of computer communications over the past decade. Among many of the touted benefits, SDN architecture facilitated by OpenFlow has the potential to help network administrators to provide End-to-End (E2E) Quality of Service (QoS)-guaranteed paths for flows among networks while providing finer-granular flow management along with global network view. In our previous work, we have introduced QoSChain to amalgamate the advantages of blockchain and SDN for inter-network QoS provisioning to orchestrate an agile and software-driven traffic management framework in order to facilitate cross-network policy automation, assurance, E2E visibility, control and validation. In this study, we focus on the impact of the path selection strategies on the performance of the overall QoSChain performance. Specifically, we exploit three simple but effective path selection strategies, First Feasible Path Selection (FFPS), Random Feasible Path Selection (RFPS), and Minimum Hop Path Selection (MHPS), to evaluate their performance, impact, and overhead in terms of the flow setup time, message overhead, and network resource consumption metrics. Our preliminary simulations results show that the path selection plays an important role in the overall performance with hop count minimization delivering a superior performance offset by an initial higher setup times. © 2022 IEEE.

关键词： Quality of service

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Medical Image Classification Using Thermal Images and Diagnosis of Carcinogen

Medical Image Classification Using Thermal Images and Diagno...

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2024 IEEE International Conference on Innovation and Novelty in engineering and Technology, INNOVA 2024

作者： Govinda Rajulu, G. Sharmila, L. Venkatesan, D. Srinivas, K. Krishnaiah, Nallam Kumar, K. Laxman Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Tamil Nadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Dept. of Artificial Intelligence and Machine Learning St. Martin’s Engineering College Hyderabad India St. Martin’s Engineering College Hyderabad India Department of IT St. Martin's Engineering College Dhulapally Telangana Secunderabad India Deptartment of Computer Science and Design St. Martin’s Engineering College Hyderabad India

ISBN: (纸本)9798331505134

Lung and Colon (L&C) tumors are lethal sicknesses that can foster in a few organs all the while and, in specific circumstances, jeopardize human existence. Even though it is highly unlikely that these two types of cancer will develop simultaneously, delaying diagnosis significantly increases the likelihood of metastasis between the organs that are affected. To really treat specific sorts of malignant growth, histological determination is fundamental. In the past, doctors had to go through a long and difficult process to look at thermal images and figure out if a patient had cancer;however, this procedure may now be completed much more quickly thanks to the new technology options. Histological images of L&C tumors were classified using a hybrid Deep Learning (DL) model with an attention mechanism and a multipath network in this study. To zero in on the main attributes and negligence the less significant ones, a consideration component was utilized. In a multipath network, data travels over a number of channels before each channel is converted and the output from all of the branches is combined. The multipath network is similar to grouped convolution when simplified. The five thermal image categories of the LC25000 dataset were utilized. Among these classes were two for colon disease and three for cellular breakdown in the lungs. The proposed model was compared to a number of well-known DL models, such as ResNet-50, VGG-16, and AlexNet. The proposed approach showed the best exhibition concerning exactness (99.2%), particularity (99.12%), responsiveness (99.28%), accuracy (99.12%), and F1 score (99.2%), as indicated by the exploratory discoveries. © 2024 IEEE.

关键词： Lung cancer

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Image based and Point Cloud based Methods for 3D View Reconstruction in Real-time Environment

Image based and Point Cloud based Methods for 3D View Recons...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Arya Agrawal Teena Sharma Nishchal K. Verma Shantaram Vasikarla School of Computer Science and Engineering Vellore Institute of Technology Vellore India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Guwahati India Dept. of Electrical Engineering Indian Institute of Technology Kanpur India Dept. of Computer Science California State University Northridge CA

3D view reconstruction in a real-time unknown environment poses several challenges to vision-based applications. Herein, this paper outlines real-time experimental results and analysis of 3D view reconstruction methods. Since real-time experimentation requires inputs from different sensors, this paper outlines the results of methods from the following categories: image based methods and point cloud based methods. This paper explains the detailed steps involved in 3D view reconstruction followed by an explanation of the methods. It provides detailed discussions, advantages, limitations, and empirical results, along with presenting differences between algorithms. For analysis of experimental results, two benchmarked datasets i.e., KITTI 3D benchmark suite and ShapeNet are considered. Furthermore, the paper explores potential future scopes, including the utilization of generative models, unsupervised learning, advanced sensors, and many more.

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