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Spectral-Spatial Feature Extraction for Multi-Change Detection in Bi-Temporal Hyperspectral Images 6

Spectral-Spatial Feature Extraction for Multi-Change Detecti...

引用

6th International Conference on Electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Afjal, Masud Ibn Islam Mondal, Md. Nazrul Al Mamun, Md. Rajshahi University of Engineering and Technology Department of Computer Science and Engineering Rajshahi Bangladesh

ISBN: (纸本)9798350385779

Change detection (CD) is a critical task that involves assessing alterations between bi-temporal images. Hyperspectral images (HSIs), renowned for their extensive spectral information, excel in capturing subtle changes and distinguishing between various change classes. While recent research has predominantly focused on hyperspectral binary CD, it falls short in providing detailed information about fine change classes. Existing methods, which rely on image difference and band selection, encounter challenges in preserving spectral characteristics, neglecting nonlinear relationships among bands, and grappling with the high dimensionality of data, thus limiting their overall performance. This paper proposes a novel hybrid spectral-spatial feature extraction technique for detecting multiple changes in bi-temporal HSIs. Our approach excels at extracting significant spectral-spatial information while reducing the feature space. This is achieved by integrating a principal component analysis (PCA) based feature extraction module into a well-tuned hybrid 3D-2D convolutional neural network (CNN). The resulting subspace performs superior in multiclass CD compared to baseline and advanced approaches. Comprehensive experiments were undertaken on two publicly available HSI-CD datasets: China wetland agriculture land (CH) and USA irrigated farmland. These experiments meticulously validate the effectiveness and efficiency of the proposed methodology. The outcomes unequivocally showcase the superiority of the proposed approach, attaining the highest multi-class change detection performances, notably 98.07% and 97.89%, for the USA and CH datasets, respectively. © 2024 IEEE.

关键词： Feature extraction

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A Comprehensive Survey on Enhancing Digital Twin Security Systems with Explainable AI Techniques 3

A Comprehensive Survey on Enhancing Digital Twin Security Sy...

引用

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

作者： Lalithadevi, B. Krishnaveni, S. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798331527549

Digital Twin (DT) technology has emerged as a cornerstone in modern engineering, enabling real-time digital replication of physical assets for monitoring, diagnostics, and optimization. However, as DTs become increasingly integrated into critical infrastructure, their security has become a paramount concern. This paper presents a comprehensive survey on the integration of Explainable Artificial Intelligence (XAI) into Digital Twin security, highlighting the potential of XAI in enhancing the transparency, interpretability, and trustworthiness of AI - driven security mechanisms in DTs. We explore various XAI techniques, including model- agnostic approaches, interpretable neural networks, and rule-based systems, detailing their potential applications in bolstering the security of Digital Twins. Furthermore, we identify several key challenges in implementing XAI for DT security, such as the need for standardized metrics for interpretability, the trade-offs between model complexity and explainability, and the potential for adversarial attacks on explainable models. Through our analysis, we highlight the critical research gaps, including the security of Digital Twin (DT) systems and explores the integration of Explainable AI (XAI) to enhance transparency, interpretability, and trustworthiness. Outcomes include detailed XAI applications for DT security, identification of limitations, and a roadmap for future research. We discuss various XAI techniques, their applications, and the current challenges in implementing XAI for DT security. Our survey identifies key research gaps and offers a roadmap for future research in this emerging field. © 2025 IEEE.

关键词： Artificial intelligence

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Language Bridge - Bridging Voices Through NLP and Spoken Input Transcription 10

Language Bridge - Bridging Voices Through NLP and Spoken Inp...

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Anu Lisba Raj, M. Nixan, Nimble Sankar Krishna, K. Sunny, Sweety Sara Sreetha, E.S. Sahrdaya College of Engineering and Technology Department of Computer Science and Engineering Thrissur India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

This paper concentrates on enhancing customer care and booking services with the introduction of a system named Language Bridge. Utilizing advanced technologies, in-cluding speech recognition and natural language processing, the Language Bridge system aims to elevate customer satisfaction by effectively addressing language barriers. Through precise speech recognition capabilities, the system seamlessly converts spoken input into text, offering a user-centric experience that accommodates various dialects. The overarching goal is to redefine global standards for customer service, providing users with a seamless and inclusive platform that transcends linguistic limitations. Through the integration of cutting-edge technologies, Language Bridge endeavors to set a new benchmark in customer care and booking services, fostering improved communication and accessibility for diverse user bases. © 2024 IEEE.

关键词： Customer satisfaction

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Performance Evaluation of Cotton Leaf Disease Detection Using Deep Learning Models

Performance Evaluation of Cotton Leaf Disease Detection Usin...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Singh, Gulbir Aggarwal, Ritu Bhatnagar, Vivek Kumar, Suneet Dhondiyal, Shiv Ashish Graphic Era Hill University Department Of Computer Science And Engineering Haldwani Campus Uttarakhand Nanital India Seth Jai Parkash Mukand Lal Institute Of Engineering And Technology Department Of Computer Science And Engineering Yamunanagar Haryana Radaur India Ambala Haryana Mullana India Department Of Computer Science And Engineering Ambala Haryana Mullana India Department Of Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350306446

Cotton plant diseases significantly impact developing nations' economies and pose a threat to the textile industry. Detecting these diseases is challenging due to lack of competence and fluctuating disease stages. Recent technology has enabled the use of computer-aided techniques to automatically identify and categorize disorders. This paper proposes various deep learning approaches for identifying and categorizing cotton leaf diseases, focusing on unhealthy and unhealthy areas. The study uses a dataset and various optimization techniques, with the Xception model showing the highest validation accuracy of 98.61% using the Adam optimizer. © 2024 IEEE.

关键词： Cotton

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Deep Feature Fusion Based Effective Brain Tumor Detection and Classification Approach Using MRI 2nd

Deep Feature Fusion Based Effective Brain Tumor Detection an...

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2nd International Conference on Big data, IoT and Machine Learning, BIM 2023

作者： Parvin, Farjana Al Mamun, Md. Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9789819989362

Brain tumors are a major global health issue, and their detection can be challenging. Typically, doctors visually inspect brain images to locate tumors, but this method is time-consuming and prone to errors. Recently, there has been progress in automated methods for early brain tumor diagnosis. However, challenges remain regarding limited precision and high false positive rate. Thus, an effective approach is needed for accurate tumor classification, utilizing the strong features of the tumor. This study presents a novel approach for brain tumor detection and classification using the fusion of deep features from MRI scans. Preprocessing, including cropping and resizing, eliminates irrelevant information from the brain MRI. After that, transfer learning models (DenseNet-121, InceptionV3, MobileNetV2) extract meaningful deep features, capturing complex patterns in MRI images for accurate tumor detection and classification. The extracted deep features are combined into a single feature vector and utilized as input for both a support vector machine (SVM) and a K-nearest neighbor (KNN) classifier for the final prediction. By combining the deep features into a unified feature vector, the model incorporates more information, resulting in improved classification performance. Two publicly available datasets are used to evaluate the effectiveness of the proposed approach, and the results demonstrate that the combined feature vector outperforms the individual feature vectors. Furthermore, the proposed approach demonstrated superior performance compared to existing methods, achieving the highest classification accuracy. This highlights its potential to support medical professionals in accurately detecting and classifying brain tumors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tumors

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A Robust Framework to Secure IoT Sensor data using Elliptic Curve Cryptography based CP-ABE and Secret Sharing Scheme 2

A Robust Framework to Secure IoT Sensor Data using Elliptic ...

引用

2nd International Conference on Information and Communication Technology, ICICT 2024

作者： Bushra, Anika Paul, Mahit Kumar Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi Bangladesh

ISBN: (纸本)9798331508227

The growth of the Internet of Things (IoT) sector has ushered in an unprecedented era of connectedness, enabling a profusion of applications ranging from smart homes to industrial automation. Researchers predict that by 2030, there will be about 50 billion IoT devices and gadgets globally, completely changing how we interact with our environment. However, because these interconnected systems are vulnerable to many cyber attacks, the rise of IoT devices has also raised worries about data security and privacy. This paper investigates the potential of cryptographic approaches to mitigate IoT data security issues. It proposes an all-encompassing framework to protect IoT data from various threats and vulnerabilities. The framework leverages the robustness of the Advanced Encryption Standard (AES) as the primary data encryption technology. Additionally, it integrates ciphertext-policy attribute-based encryption (CP-ABE) in conjunction with Elliptic Curve Cryptography to enable fine-grained access control, ensuring that only authorized entities can access specific IoT data. Furthermore, to fortify the encryption process, the framework implements a secret sharing method for the encryption key, distributing the key across multiple entities, reducing the risk of a single point of failure. The suggested framework seeks to improve the security posture of IoT systems by integrating these cryptographic algorithms and guaranteeing the confidentiality, integrity, and accessibility of data in IoT contexts. ©2024 IEEE.

关键词： Anonymity

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Iot Based Adaptive Street Lighting System for Rural Area 4

Iot Based Adaptive Street Lighting System for Rural Area

引用

4th IEEE International Conference on data engineering and Communication Systems, ICDECS 2024

作者： Selvalakshmi, C.B. Raja Alagurishi, N. Aravind, T. Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9798350393354

Adaptive street lighting system presents an innovative energy-efficient smart lighting technique that leverages advanced technologies, including Internet of Things (IoT) sensors and adaptive control algorithms. This introduces a cutting-edge energy-efficient smart street lighting technique designed to address the growing need for sustainability and energy conservation in rural environments. The proposed system utilizes a network of sensors to detect real-time environmental conditions, such as ambient light levels and pedestrian or vehicular presence. Based on this data, an adaptive control algorithm dynamically adjusts the brightness of streetlights, ensuring they provide adequate illumination while minimizing energy consumption. Depending upon number of vehicles passed during night, streetlights are operated at four different intensities that is from 20% to 50% with the step size of 10% in the absence of vehicles while operating at 100% intensity upon vehicle's arrival. This dimming operation not only enhances the life span of streetlights but also enables significant conservation of energy up to 84%. This system smartly takes an action based on the presence or absence of vehicles and sunlight via light dependent resistor (LDR), infrared (IR) sensors and Passive infrared (PIR) sensor. © 2024 IEEE.

关键词： Internet of things

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Unmasking the Abnormal Events in Videos Using Deep Learning

Unmasking the Abnormal Events in Videos Using Deep Learning

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2024 International Conference on Communication, Computing and Internet of Things, IC3IoT 2024

作者： Guhapriya, D. Kiruthika, K. Padmadevi, S. Velammal College Of Engineering And Technology Department Of Computer Science And Engineering Madurai India

ISBN: (纸本)9798350352689

Surveillance systems greatly benefit from video-based anomaly detection as it helps to improve security measures and situational awareness. This research paper presents a novel methodology for anomaly detection using deep learning techniques, specifically the Long-term Recurrent Convolutional Network (LRCN). The suggested methodology integrates long short-term memory (LSTM) layers to capture temporal relationships and convolutional neural networks (CNN) to extract spatial features within video sequences. This methodology is evaluated on a diverse dataset encompassing various human activities, both normal and anomalous. Performance metrics, including False Alarm Rate, Area Under the ROC Curve (AUC), and Accuracy, demonstrate the usefulness and superiority of the suggested approach over existing methods. The results highlight the potential of this approach to enhance security and surveillance applications. © 2024 IEEE.

关键词： Anomaly detection

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Enhanced QoS Optimization of Opportunistic Networks using Decision Tree

Enhanced QoS Optimization of Opportunistic Networks using De...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mathur, Mansi Sawanni, Ritu Kumar, Anuj Jain, Ayushi Pal, Atul Kumar Manwal, Manika Department Of Information Technology New Delhi India Graphic Era Hill University Department Of Computer Science And Engineering Uttarakhand Dehradun India Department Of Computer Science And Engineering Lucknow Kannauj India Graphic Era Deemed To Be University Department Of Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Opportunistic networks are a type of dynamic wireless network that lack a fixed infrastructure and rely on intermittent connections between nodes to facilitate communication. Quality of Service (QoS) is an essential factor for the efficient operation of opportunistic networks, as it ensures that data is delivered in a timely and reliable manner. This paper proposes an enhanced QoS optimization approach for opportunistic networks using a Decision Tree algorithm. The algorithm considers various network parameters such as node mobility, data transmission rate, and available storage capacity to predict the optimal routing path for data transmission. The Decision Tree algorithm uses a classification technique to build a model based on historical data to identify the most suitable routing path for each data transmission. This approach allows for dynamic adaptation to changing network conditions, improving the overall performance and QoS of the opportunistic network. By incorporating the Decision Tree algorithm into the routing decisions of the opportunistic network, we can achieve better QoS optimization than traditional routing protocols. Our experimental results show that the proposed approach outperforms existing routing protocols regarding data delivery ratio, end-to-end delay, and energy consumption, making it a promising solution for enhancing QoS in opportunistic networks. © 2024 IEEE.

关键词： Decision trees

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Leveraging LLM and RAG for Automated Answer Script Evaluation 8

Leveraging LLM and RAG for Automated Answer Script Evaluatio...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Darshan, Pranav Mandahas, Nihar Pratheek Rao, M.P. Rajesh, Raghuveer Narayanan Deepamala, N. R V College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331505462

In the realm of education, examinations play a very significant role. If writing an exam is considered tedious, the process of evaluating hundreds of answer scripts can be even more daunting. It usually takes weeks to evaluate all the answer scripts, and there is always a factor of bias in human corrections. Hence, to eliminate all these problems, this study uses a large language model to automate this task and eliminate human bias. The study leverages a fine-tuned LLM to assess answer scripts related to the subject of operating systems using a specific dataset. Along with the LLM, it incorporates Retrieval-Augmented Generation (RAG) to get the context of a given question from a prescribed textbook. The proposed platform also has the capability of analyzing handwriting from an actual answer script, and then this is passed onto the model as input along with context from RAG. Finally, the entire system is integrated into an interactive web platform, deployed using AWS SageMaker. By combining all the technology mentioned, this study has made a sincere attempt to solve the burden of correcting manuscripts. © 2024 IEEE.

关键词： Modeling languages

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