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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Carmel Mary Belinda, M.J. Kannan, E. Alex David, S. Kannan, Sriram Ravi Kumar, S. Vathani, Anstey Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Fidelity Investments Data Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala RD Institute of Science and Technology Department of Civil Engineering India

ISBN: (纸本)9798331534950

In this research, a huge dataset of lung cancer images is processed using advanced image processing techniques gathered from different medical establishments. Images will be edited and color profiles retouched from original look into Strawberry corrected one. Key part of our research is to preprocess the images by using Gaussian preprocessing at initial front levels followed with Gabor filtering which improves clarity and focus in x-ray image. Findings also contribute to the field of biomedical science in studies related to lung cancer diagnosis, by proving that advanced image processing techniques are useful. These techniques are essential tools in many medical fields ensuring high quality imaging, which allows early detection and treatment planning. Lung cancer, characterized by rapid uncontrolled proliferation of cells in the lungs with a potential for distant spread either via blood or lymphatic system requires precise diagnostic techniques. After preprocessing, we apply segmentation and enhancement techniques on images to detect the salient features separating normal from affected lung tissues. Extensive experiments are conducted to validate our method compared with other methods using pixel level percentage and mask labels for anomaly detection. This work highlights the importance of a robust imaging approach for health care, especially in biomedical research where various diseases could be detected and treated. © 2024 IEEE.

关键词： Lung cancer

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Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised data-Driven Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 550-553页

作者： Zhenyong Zhang Yan Qin Jingpei Wang Hui Li Ruilong Deng the State Key Laboratory of Public Big Data and the College of Computer Science and Technology Guizhou UniversityGuiyang 550025China the School of Chemical and Biomedical Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Industrial Control Technology and the College of Control Science and Engineering Zhejiang UniversityHangzhou 310027China IEEE

Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is ***, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.

关键词： Dummy Power letter

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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

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IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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Boruta Feature Selection for Prediction of Coronary Artery Disease

Boruta Feature Selection for Prediction of Coronary Artery D...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Yuvalatha, S. Nithyapriya, S. Kuzhali, R. Tamizh Savitha, S. Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Sathyamangalam India Department of Artificial Intelligence and Data Science C K college of Engineering and Technology Chennai India Department of Computer Science nad Engineering PSR Engineering College Sivakasi India

ISBN: (纸本)9798350305708

Day by day cardio vascular disease death cases increasing. It mainly affects the human heart and blood vessels and it is difficult to diagnosis it. In this paper various machine learning algorithm is used to predict the cardio vascular disease. The data set of 14 attributes is used for prediction. The irrelevant features are handled using Boruta algorithm of 100 iterations. The proposed work uses Adaboosting with different hyper parameter and the multiple decision trees are built for the wrongly classified feature. At finally the Adaboosting algorithm achieve 93.74% accuracy in predicting the disease. © 2023 IEEE.

关键词： Adaboosting Boruta Algorithm Cardio vascular Hyper parameter

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Towards Safer Roads: A Comprehensive Review of Object Detection Techniques for Autonomous Vehicles

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SN computer science 2025年第5期6卷 1-20页

作者： Parisapogu, Samson Anosh Babu Narla, Nitya Juryala, Aarthi Ramavath, Siddhu Department of Computer Science and Engineering School of Computing Amrita Vishwa Vidyapeetham Amaravati Campus Andhra Pradesh Amaravati 522 503 India Department of Artificial Intelligence and Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India

Nowadays, people spend a lot of time in travel, and road safety has become an important area of research. As a result, self-driving cars have been gaining popularity in recent years because they have a lot of benefits in terms of reducing traffic congestion and improving road safety, thereby reducing accidents. They also have an environmental benefit as they reduce travel times and hence reduce the usage of fuel. Though a lot of research has already been done in this field, there are always new problems being created and new solutions being found. Apart from self-driving cars, automated navigation also has a huge number of applications. Therefore, it is important to stay up to date about the latest developments in this field. This research is a comprehensive survey that explores and evaluates the different object-detecting algorithms that have been used throughout the years, their advantages, disadvantages, and trade-offs. It also analyses the datasets and benchmarks that have been used for evaluating the models. It aims to identify the gaps in research in this growing field so that it can act as a roadmap for researchers to focus their study on. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning Object detection Road safety Self-driving YOLO

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A Centralized Blockchain Architecture with Optimum Sharing 8

A Centralized Blockchain Architecture with Optimum Sharing

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8th International Conference on Communication and Electronics Systems, ICCES 2023

作者： Ramasamy, Viswanathan Padmapriya, S. Kavitha, G. Lekha, D. Kl Univrsity Department of Computer Science and Engineering Andhra Pradesh Gundoore India Velammal Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Department of Computer Science and Engineering Tamil Nadu Namakkal India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350396638

The monolithic network architectures are used in Bitcoin and other big cryptos. Different hierarchical ways to protect the decentralized blockchain have been discussed to improve its usability. However, no debate in the literature on how an appropriate shard size can be calculated to optimize the output and how deceptive or unreliable nodes can affect the optimum shard size range. This study includes an exchange and authentication scheme for hierarchical blockchain architecture in order to fix these issues. The hierarchy splits the network nodes into many disjoint fragments, with most transfers unregulated between these shards. Taking into account the performance and precision of the transaction validation in the case of malicious or defective nodes, the optimum shard size is calculated. It ensures that most of the good shards are available with the right scale, according to the highest acceptable threshold of defective nodes. It also offers a method to distinguish damaged shards by overlapping a tiny proportion of purchases across all shields and discarding all transactions. The theoretical efficiency analysis and the influence of network sharing on performance were based on experimental findings conducted on Amazon EC-2 nodes. © 2023 IEEE.

关键词： Authentication

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A Technical Bas System Through Cetralised System for Grid Systems in Trnsmission of data

A Technical Bas System Through Cetralised System for Grid Sy...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kurmi, Raviraj Singh Gupta, Ashish Krishnamoorthy, Ramkumar Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India

ISBN: (纸本)9798350357769

Industrial communication protocols are used by SCADA (supervising control and data acquisition) networks and Industrial Control Systems (ICS) to enhance their functionality. Modbus is an example of an application protocol that facilitates communication between millions of automation units. Sadly, Modbus lacks essential security features, which makes it vulnerable to errors in both design and implementation. These flaws leave systems open to eavesdropping, replay, and man-in-the-middle assaults, among other types of attacks. Taking advantage of these vulnerabilities may have serious repercussions for businesses as well as the general public, especially if important infrastructure assets like power stations, water distribution systems, and train transportation networks are attacked. The Modbus association has published security requirements in an effort to fix the security flaws in the protocol. Through the integration of TLS (Transport Layer Security) with the conventional Modbus protocol, these standards seek to improve security. Modbus packets are encapsulated by TLS in order to safeguard message integrity and offer authentication. Digital certificates based on X.509v3 are used by the security features to authenticate the client and server. This paper proposes a new secure version of the role-based access control architecture (RBAC) in order to solve security vulnerabilities with the Modbus protocol. This approach uses a message authorized via unit ID, a unique identifier for approving the Modbus frame, and an authorization procedure via roles, introduced as an arbitrary extensions within the X.509v3 certificate, to approve only the client & the Modbus frame. To summarise, the incorporation of TLS into Modbus, in conjunction with the suggested RBAC framework, effectively tackles security issues and fortifies the protocol's general resilience against any intrusions, guaranteeing the safe and effective functioning of industrial systems. © 2023 IEEE.

关键词： Industrial Control Systems Modbus protocol architecture role-based access control (RBAC) SCADA security requirements and centralized architecture security vulnerabilities TLS X.509v3 certificate

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Leveraging Blockchain for Development of Time Limit-Based Multi-authorized Secure Digital Signature Framework 5th

Leveraging Blockchain for Development of Time Limit-Based Mu...

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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Mallikharjuna Rao, K. Kumar, Ashutosh Agrawal, Deepesh Reyya, Shikhar Data Science and Artificial Intelligence International Institute of Information Technology Naya Raipur Naya Raipur India Computer Science and Engineering International Institute of Information Technology Naya Raipur Naya Raipur India

ISBN: (纸本)9789819761050

This article deeply dives into a shift in document security by introducing innovative authentication measures. Conventional digital signature methods are less secure and can easily be forged. Our research explores the integration of multiple signatures authentication and time-constrained signature features, which will boost the security of digital signatures across documents;by introducing a time-constrained signing approach, we are addressing potential issues and establishing robust defense against various threats. Also, multiple files can be uploaded using a single CID in interplanetary file system (IPFS). Besides introducing multiple signers and time-limit features, our research utilizes IPFS for secure and collaborative document management. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication Protocol

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Resisting Visual Hacking: A Novel Graphical Password Authentication System 3

Resisting Visual Hacking: A Novel Graphical Password Authent...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Jebakumar Immanuel, D. Ramalingam, V. Tharun Kumar, P. Iyer, Akash Narayanan, M.S. Karpagam Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore641 105 India Sns College of Engineering Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350322842

Password-based authentication is a widely used method to ensure security and privacy in computer applications. However, the selection of weak passwords and insecure input methods by users is considered the 'weakest link' in the authentication process. Instead of employing arbitrary alphanumeric sequences, individuals tend to opt for passphrases that are either mnemonic or abbreviated in nature. With the proliferation of web and mobile applications, users can access these applications at any time and from anywhere with various devices. Although this convenience is highly desirable, it also increases the likelihood of passwords being exposed to Visual Hacking. The forms of attack may be executed through either direct visual observation or the utilization of external recording devices in order to obtain the user's login information. In response to this issue, our team has devised an authentication system that centers on graphical passwords and is made to combat the threat of Visual Hacking. The system incorporates a three-layered security mechanism, comprising color and pattern matching functionalities in conjunction with password verification. This makes it highly challenging for potential attackers to deduce or limit the password even amidst numerous camera-based hacking attempts. Additionally, we have created and put to the test a prototype of the system to assess its user-friendliness. Our experimental results indicate that our system can resist Visual Hacking more effectively than previous authentication systems while still maintaining a high degree of usability and security. © 2023 IEEE.

关键词： Pattern matching

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Spatial-Temporal Semantic Feature Interaction Network for Semantic Change Detection in Remote Sensing Images

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12090-12102页

作者： Zhang, Yuhang Zhang, Wuxia Ding, Songtao Wu, Siyuan Lu, Xiaoqiang Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing School of Computer Science and Technology Xi'an710121 China Xi'an University of Technology College of Computer Science and Engineering Shaanxi Xi'an710048 China Fuzhou University College of Physics and Information Engineering Fuzhou350002 China

Semantic Change Detection (SCD) in Remote Sensing Images (RSI) aims to identify changes in the type of Land Cover/Land Use (LCLU). The "from-to" information of the acquired image has more profound practical significance than Binary Change Detection (BCD). However, most deep learning-based SCD algorithms do not fully exploit the spatial-temporal information of multilevel features, leading to challenges in extracting LCLU features in complex scenes. To address these issues, we propose a Spatial-Temporal Semantic Feature Interaction Network (STS-FINet) to improve the performance of SCD in RSI. The proposed STS-FINet comprises a Multi-Scale Feature Extraction Encoder (MS-FEE), a Transformer-based Multilevel Feature Interaction module (TML-FI), and a Multilevel Feature Fusion Decoder (ML-FFD). The MS-FEE extracts deep semantic and differential information from the RSI. The TML-FI is designed to mine the spatial-temporal information by extracting long-range dependencies and spatial information from multilevel features to improve spatial perception. Moreover, Mixed Spatial Reasoning Convolution block (MixSrc) is presented to enrich the spatial information by extracting the multiscale features, thus improving the model's capability to interpret complex scenes. Finally, ML-FFD integrates the multilevel features, resulting in the generation of the semantic change map. The effectiveness of the proposed STS-FINet is verified on two high-resolution RSI datasets. Experimental results show that the proposed STS-FINet achieves better change detection performance than SOTA methods. © 2008-2012 IEEE.

关键词： Change detection

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