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2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Rana, Kapil Rathi, Vishwas Goyal, Puneet Indian Institute of Technology Ropar Computer Science and Engineering Punjab India Thapar Institute of Engineering and Technology Computer Science and Engineering Punjab India

ISBN: (纸本)9798350302585

Recognizing information about the camera model of digital images has been identified as a crucial task in the field of image forensics. Numerous methods have been proposed for camera model identification (CMI), with recent emphasis on convolutional neural networks (CNNs) based methods for their better efficacy. However, current approaches generally consider original images without consideration to privacy preserving aspects. This paper introduces a novel CNN-based approach for CMI even in privacy preserving settings, where unlike conventional approaches, the training and evaluation is performed on encrypted images. For encryption, we consider position scrambling at pixel-level and we also present block-level position scrambling for better efficacy. Experimental results demonstrate the feasibility of our approach in terms of accuracy and privacy preservation. Furthermore, the experiments highlight the effectiveness of our CNN-based approach, having around 10% better accuracy in comparison to traditional statistical models for CMI of encrypted images. © 2023 IEEE.

关键词： Convolution

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Transformer-Based Classification Outcome Prediction for Multimodal Stroke Treatment 2

Transformer-Based Classification Outcome Prediction for Mult...

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2nd IEEE International Conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Ma, Danqing Wang, Meng Xiang, Ao Qi, Zongqing Yang, Qin Computer Science Stevens Institute of Technology HobokenNJ United States University of Sofia Department of Computer Science Palo AltoCA United States School of Computer Science & Engineering University of Electronic Science and Technology of China Sichuan China School of Integrated Circult Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Stevens Institute of Technology Computer Science HobokenNJ United States

ISBN: (纸本)9798350373646

This study proposes a multi-modal fusion framework Multitrans based on the Transformer architecture and self-attention mechanism. This architecture combines the study of non-contrast computed tomography (NCCT) images and discharge diagnosis reports of patients undergoing stroke treatment, using a variety of methods based on Transformer architecture approach to predicting functional outcomes of stroke treatment. The results show that the performance of single-modal text classification is significantly better than single-modal image classification, but the effect of multi-modal combination is better than any single modality. Although the Transformer model only performs worse on imaging data, when combined with clinical meta-diagnostic information, both can learn better complementary information and make good contributions to accurately predicting stroke treatment effects. © 2024 IEEE.

关键词： computerized tomography

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Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

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computer Systems science & Engineering 2023年第7期46卷 181-193页

作者： V.S.Vaisakhi D.Surendran Department of Electronics and Communication Engineering Nehru Institute of Engineering and TechnologyCoimbatore641105India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641105India

In signal processing and communication systems,digital filters are widely *** some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter *** strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the *** technology advances,more complicated systems with several filters become *** of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input ***,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was *** fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the *** primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty ***,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image *** approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR *** the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of *** were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

关键词： Fault tolerance filters efficiency redundancy image denoising error correction codes double modular redundancy

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Complexity Results and Algorithms for Preferential Argumentative Reasoning in ASPIC+ 21

Complexity Results and Algorithms for Preferential Argumenta...

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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Lehtonen, Tuomo Odekerken, Daphne Wallner, Johannes P. Järvisalo, Matti Department of Computer Science Aalto University Finland Department of Information and Computing Sciences Utrecht University Netherlands National Police Lab AI Netherlands Police Netherlands Institute of Software Technology Graz University of Technology Austria Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781956792058

We provide complexity results and algorithms for reasoning in the central structured argumentation formalism of ASPIC+. Considering ASPIC+ accommodated with preferences under the last-link principle, the results are made possible by rephrasing several argumentation semantics—admissible, complete, stable, preferred and grounded—in terms of defeasible elements of an ASPIC+ theory for both democratic and elitist last-link lifting. Via the rephrasing, we establish that acceptance is polynomial-time computable under grounded semantics, and complete for either NP, coNP, or ΠP2 depending on the reasoning mode and semantics. We also detail answer set programming encodings for deciding acceptance for the NP/coNP-complete reasoning tasks and empirically show that it scales significantly better than first translating ASPIC+ reasoning tasks to abstract argumentation. Finally, we show that, in contrast to the last-link principle, it is NP-hard to compute the grounded extension under the weakest-link principle. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Knowledge representation

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Cloud-based Application for managing placement related information

Cloud-based Application for managing placement related infor...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Aravind, S. Ashik, M.P. Chayadevi, M.L. Kennedy, Arun Joshua Bhatkal, Chinmay R. Patil, Annapurna P. Ramaiah Institute of Technology Computer Science and Engineering Karnataka Bengaluru India BNM Institute of Technology Computer Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9781665462631

Presently, all the information about the placements are being received through emails. This makes keeping track of all the placement-related information difficult, which might lead to overlooking some critical placement-related details. There is also a need for a structured presentation of data, based on the branch, the CTC offered, the company, etc. Better presentation makes it easier to identify relevant offers to each student and can immediately convey the required information. Additionally, there is no information available to the students about the previous companies that have visited the college and their CTC. A system that addresses the above issues is proposed. The cloud platform would maintain placement information, interview experiences, and experiences of seniors and alumni and would also provide better visualization and presentation of placement-related data, auto-filling of forms, and much more. Hence, the objective of the work is to develop a system for the placement activities that can be used by the college students, placement team, and the companies that visit the college for placements © 2023 IEEE.

关键词： Sentiment analysis

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Decomposition, Interaction, Reconstruction Meets Global Context Learning In Visual Tracking 48

Decomposition, Interaction, Reconstruction Meets Global Cont...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Tan, Huibin Hu, Kun Cao, Mingyu Wang, Mengzhu Xu, Liyang Yang, Wenjing Institute for Quantum Information State Key Laboratory of High Performance Computing China National University of Defense Technology College of Computer Science and Technology Changsha China

ISBN: (纸本)9781728163277

Tensor decomposition and reconstruction attention is a promising global context learning approach because it can remain efficient while avoiding feature compression. To exploit its potential even further in visual tracking, we redesign a 3D tensor modeling paradigm, namely tensor Decomposition, Interaction, Reconstruction attention (DIR), respectively corresponding to three function components, Tensor Decomposition Module (TDM), Tensor Interaction Module (TIM) and Context Reconstruction Module (CRM). Specifically, TDM decomposes a 3D tensor feature into rank-1 context fragments in different dimension views. The ingenuity here lies in the introduction of Circular Convolution for processing features at arbitrary scales and channel-sharing segments to enhance the interaction of the two branches in the Siamese network architecture. TIM obtains the tensor planes of each dimension by the Cross-Similarity operation of rank-1 tensors and fused cubic features, which brings more interactions between all feature dimensions. CRM reconstructs 3D context representations with the outputs of the above modules. In experiments, DIR is embedded into the tracker to verify its effectiveness. © 2023 IEEE.

关键词： context reconstruction Siamese tracking single object tracking tensor plane

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SECURENET: Advancing Cybersecurity Knowledge Graphs with Multi-Feature Perceptrons and Hierarchical Adversarial W2NER 5

SECURENET: Advancing Cybersecurity Knowledge Graphs with Mul...

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5th International Academic Exchange Conference on science and technology Innovation, IAECST 2023

作者： Su, Yiqi Ding, Yuxin Computer Science and Technology Harbin Institute of Technology Shenzhen Guangdong Nanshan China

ISBN: (纸本)9798350357738

To meet the demand for improving the construction and analysis efficiency of knowledge graphs in the field of cybersecurity, this paper introduces an innovative system - SECURENET. SECURENET successfully addresses the issue of a lack of annotated text datasets in the field of cybersecurity and effectively tackles the challenges of flat, overlapping, and discontinuous entity recognition in cybersecurity texts. The SECURENET system consists of three core modules: First, the Multifeature Security Averaged Perceptron (MSAP) module combines two types of multi-feature perceptron technologies to deeply analyze and understand complex features in cybersecurity texts, such as semantic and structural properties, and perform corresponding label annotations. Secondly, the entity extraction module uses the annotated dataset to train the Named Entity Recognition (NER) model. We employ the SecBERT pre-trained model combined with the W2NER framework, a unified NER neural network framework. Furthermore, SECURENET introduces an innovative Multi-Level Adversarial Training (MLAT) strategy to enhance the accuracy and robustness in the entity extraction process. Finally, the relationship extraction module defines a security ontology to extract relationships between entities, thereby forming the VulKG knowledge graph. In experiments, each module of the SECURENET system demonstrated excellent performance. © 2023 IEEE.

关键词： Knowledge graph

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Mining Customer Churns for Banking Industry using K-means and Multi-layer Perceptron

Mining Customer Churns for Banking Industry using K-means an...

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2023 IEEE Conference on computer Applications, ICCA 2023

作者： Khine, Saw Thazin Myo, Win Win University of Information Technology Faculty of Computer Science Yangon Myanmar University of Information Technology Faculty of Computing Yangon Myanmar

ISBN: (纸本)9781665435994

Customer churns prediction is a major concern for banking industries all over the world, and market development is happening a more considerable task, and a more important challenge is taking place in business growth. The revenue of the banking sector totally depends on its important customers. By reducing the churn customers, commercial banks have many benefits on both gaining more profits and improving core competitiveness among the competitors. In this study, a mining customer churns using K-means and Multi-Layer Perceptron (MLP) is proposed for forecasting of customer churns in the banking industry. Churn-Modelling dataset from Kaggle site is used in this study. To develop the customer churns prediction model, the dataset is first cleaned, preprocessed and then K-means is used to find similar customer groups. To improve cluster quality, Silhouette method is applied before K-means. After that, Multi-Layer Perceptron classifier is applied to predict whether each customer from each group can leave or not from a bank. The proposed model gets good accuracy with low training time in all customers groups. The results show that the proposed mining customer churns prediction is able to find a certain number of customer churns identifying customer behavior and churn happens. To compare with the proposed model, the methods of K-means + SVM and the previous study on the same dataset are used. © 2023 IEEE.

关键词： Forecasting

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An Investigational Study of Detecting Acute Lymphoblastic Leukemia using computer Vision 2

An Investigational Study of Detecting Acute Lymphoblastic Le...

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2nd International Conference on Smart Technologies and Systems for Next Generation computing, ICSTSN 2023

作者： Ashok, M. Tharani, K. Venkatasriram, S. Ramasamy, Kumar Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350348002

Among all divesting cancers, Hematologists predict that the Leukemia is mostly occur on the children, teenagers, and young adults. Moreover 85% of cancer cases are detected younger than the age of 15. Due to a Genetic abnormality in the bone marrow, Acute lymphoblastic leukemia (ALL) is particularly susceptible to life-threatening infections. The laboratory method to detect the ALL is prolonged and slow process. Reviewing prior research on detection methods in ALL is the goal of this study. This review includes (a) the parameters necessary for the ALL detection (b) methods involved in the detection process (c) which algorithms gives the accurate prediction and (d) finally all the necessary context for the detection of the ALL-affected cells. The proposed system possesses the automated machine learning (Chabot) approach used to categorize the infected and healthy cells in the blood smears (microscopic images) in order to detect ALL. The Blood smears are converted into the CMYK color space and separated into clusters using K-means Algorithm. A cell from each cluster is picked and detected whether the cell is affected with ALL or not using the nuclei of the cell using supervised learning algorithm like SVM, XGBoost Classifier, Etc., The proposed system aids in enhancing the acute lymphoblastic leukemia detection system. © 2023 IEEE.

关键词： Cells

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A Synergistic Ensemble Approach for Enhanced Time Series Forecasting in Crime Analytics

A Synergistic Ensemble Approach for Enhanced Time Series For...

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2024 International Conference on Artificial Intelligence and Emerging technology, Global AI Summit 2024

作者： Rani, Reenu Mehta, Rohan Israr, Sabeeh Tamboli, Umar Rohith, M. Chavan, Sanvi School of Advanced Sciences Vellore Institute of Technology Vellore India School of Computer Science Engg. & Info. Systems Vellore Institute of Technology Vellore India School of Computer Science & Engg. Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350379716

This study proposes a novel ensemble approach of RF Regression, ANNs, and ARIMA for time series forecasting. To address the objectives of the present research, sensitive and restricted crime data obtained from the Mumbai police has been used. It covers a number of years and entails detailed records of various categories of offenses that have been committed in the area monthly. Several techniques of data preprocessing are applied to ensure that the data in the given set is fit to train the model and they include data cleaning, scaling, and handling of missing values. The hybrid ANN-ARIMA-RF approach captures the special advantages of each component technology: ANNs are employed for nonlinear patterns while ARIMA is employed for the linear patterns in residuals and RF is applied on both models outputs. The hybrid of ARIMA and ANN outperforms the individual ARIMA and ANN models when applied to the exclusive crime dataset to forecast future crime rates. This is because the developed model improves the prediction accuracy as shown by the lowest Mean Absolute Percentage Error. This study differs from other works due to the utilization of a unique dataset and the integration of several forecasting methods that improve upon the individual ones. © 2024 IEEE.

关键词： Prediction models

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