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An Edge-Aware Guided Filtering Technique for Multiplicative Noise Reduction in Satellite Images 1

8th International Conference on Information and Communication technology for Competitive Strategies, ICTCS 2023

作者： Subhahan, D. Abdus Vinoth Kumar, C.N.S. Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (数字)9789819702107

ISBN: (纸本)9789819702091

In data acquisition, satellite images are easily affected by speckle noise, which degrades image quality. An edge-aware guided filtering technique for removing speckle noise has been proposed in this paper. In this technique, initially, the satellite images are applied on the joint bilateral filter. The non-additive noise was then transformed into additive noise using the log transform on the filtered image. The filtered image is split into low-frequency and high-frequency sub-bands using a 2D-DWT. The soft threshold eliminates additive noise from horizontal and the vertical sub-band images, in contrast to IGF, which only eliminates additive noise from low-frequency sub-band images. The simulation has been performed on real-time SAR image. The simulation outcomes show that the edge-aware guided filtering technique removes speckle noise more effectively than existing methods while conserving edges and minimizing computational complexity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Additive noise

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Smart System for Vechicle Number Plate Recognition Using Convolutional Neural Network(CNN) 1

Smart System for Vechicle Number Plate Recognition Using Con...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Nidhya, R. Kalpana, R. Sudhakar, R. Maranco, M. Smilarubavathy, G. Madanapalle Institute of Technology & Science Department of Computer Science & Engineering Andhra Pradesh India Srm Institute of Science and Technology Department of Networking and Communications Tamilandu Chennai India St. Joseph's Institute of Technology Department of Computer Science & Engineering Tamilandu Chennai India

ISBN: (纸本)9798350328042

In the era of smart cities and advanced transportation systems, the need for efficient and accurate vehicle identification has become paramount. This paper introduces a sophisticated system for vehicle number plate recognition leveraging the capabilities of Convolutional Neural Networks (CNNs). The proposed system aims to enhance security, streamline traffic management, and contribute to the evolution of intelligent transportation infrastructure. Its is also used to check for suspicious vehicles or vehicles that were involved in crime, in that case it is useful for the polices to browse the details of the accused vehicles. Integration of the trained CNN model into a comprehensive system capable of real-time video processing demonstrates the system's applicability in diverse environments. Deployment considerations span various platforms, from centralized servers to edge devices, ensuring adaptability to different deployment scenarios. The existing systems are based on different methodologies but still, it is a challenging task as some of the factors like high speed of the vehicle, non-uniform vehicle number plate, the language of vehicle number and different lighting conditions can affect a lot in the overall recognition rate. Proposed system, driven by CNN technology, stands as a robust solution for accurate and efficient automated identification. © 2023 IEEE.

关键词： Convolutional neural networks

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"Survey and analysis of blockchain technologies with respect to: properties, algorithms, architecture, models, evolution and framework"

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Multimedia Tools and Applications 2025年第14期84卷 13571-13615页

作者： Tiwari, Nikita Ranjan, Prakash Biswal, Pradeep Kumar Barde, Chetan Sinha, Neha Department of Computer Science and Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India Department of Electronics and Communication Engineering Indian Institute of Information Technology Bhagalpur Bhagalpur India

The growing realm of blockchain technology has captivated researchers and practitioners alike with its promise of decentralized, secure, and transparent transactions. This paper presents a comprehensive survey and analysis of various blockchain technologies, focusing on their fundamental properties, architectural designs, operational models, evolutionary trends, and overarching frameworks. Through an extensive review of existing literature and empirical studies, this research synthesizes the multifaceted landscape of blockchain, elucidating key concepts such as consensus mechanisms, smart contracts, scalability, interoperability, and privacy protocols. By examining the evolution of blockchain from its inception to its current state and beyond, this paper delineates the trajectory of technological advancements and identifies emerging paradigms. Furthermore, it critically evaluates the strengths and limitations of diverse blockchain architectures and models, offering insights into their applicability across different domains and use cases. Finally, this study culminates in the proposal of a comprehensive framework that encapsulates the essential elements necessary for understanding, implementing, and harnessing the transformative potential of blockchain technologies. Through this holistic analysis, this research contributes to a deeper understanding of blockchain ecosystems and provides valuable guidance for future research directions and practical implementations. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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AI-Based Security Protocols for IoT Applications: A Critical Review

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Recent Advances in computer science and Communications 2023年第5期16卷 2-18页

作者： Chadha, Harshita Gupta, Shruti Khanna, Ashish Kumar, Neeraj Department of Computer Science and Engineering Maharaja Agrasen Institute of Technology New Delhi India Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Punjab Chandigarh India

The Internet Of Things (IoT) is a network of heterogeneous nodes that exchange data and critical information amongst themselves with minimum human intervention. The utility of this technology is large, thus it is used today in most modern applications. With billions of nodes connected across multiple applications, the area for potential security attacks is ever increasing. In addition to this, the nature of the data being transmitted also becomes more sensitive to the latest applications and this calls for effective security provisions. Due to its unique nature, traditional security provisions are not as successfully applicable in IoT networks. This leaves these networks vulnerable to malicious intruders. In such a scenario, Artificial Intelligence (AI) comes out as a powerful solution. This article serves to provide an overview of previously proposed AI-based solutions that can be applied to IoT networks to secure them. An industry-wide overview of security provisions is provided by categorizing IoT applications into three broad sectors, namely, healthcare, smart grid, and smart city. The survey strives to give a clear industry-oriented vision of the available AI solutions and address the requirement of an application-ready security survey in the field. © 2023 Bentham science Publishers.

关键词： Internet of things

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BERT: A Paradigm Shift in Natural Language Processing 8th

BERT: A Paradigm Shift in Natural Language Processing

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Shinde, Rohan Helen Victoria, A. Department of Networking and Communication School of Computing SRM Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9789819786657

The field of natural language processing has seen a significant evolution. Due to the emergence of deep learning techniques, it has become easier to perform various tasks in the field. One of the most prominent innovations in this area is the use of bidirectional characterizations from the transformer model, i.e., Bidirectional Encoder Representations from Transformers (BERT) model. The BERT model uses a transformer structure as a framework to process text. It can capture meaning and context both from the right and the left sides of a sequence, and it has been shown that it can perform exceptionally well in various NLP applications, such as language comprehension and sentiment analysis. The paper explores the BERT model’s architecture, pre-training steps, and the model’s performance in various tasks. It also covers how it has revolutionized the field by overcoming previous benchmarks. The paper also explores the various limitations and opportunities of the BERT framework. It concludes by showing how it represents a significant shift in the field of NLP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Decentralizing Voting: Block Chain based E-Voting System Using Ethereum Smart Contracts

Decentralizing Voting: Block Chain based E-Voting System Usi...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Mahalakshmi, M. Bhatnagar, Vedant Pandita, Kumar Adarsh SRM Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur603203 India

ISBN: (纸本)9798350375237

Developing a robust electronic voting system that upholds the principles of loveliness and sequestration essential in traditional voting styles, while employing the translucency and rigidity of electronic systems, has posed a patient challenge. This ongoing exploration trials to explore the operation of block chain technology as a means to produce allotted electronic voting systems. The study introduces an ingenious electronic voting model grounded on block chain, leveled at mollifying some of the failings observed in being systems. colorful block chain fabrics are assessed for their felicity in constructing such a system. Through a case study fastening on the electoral process, the paper illustrates the eventuality of allotted tally technologies in enhancing the screen and obscurity of choices. Throughout the design evolution, we remain aware of two intermediary questions Can block chain technology be effectively employed to expedite the deployment of meaningful, high- performing operations that extend distinct vantages over traditional styles? And can block chain grease the deployment of operations while maintaining respectable interpretation within being constraints? We cast to manipulate these inquiries through our exploration bid, which involves expansive testing, dissection, and iterative refinement of the proffered block chain grounded electronic voting system. By using creations in block chain technology and integrating them with established principles of advancing veracity, our end is to establish a complete result that not only meets the rigid demands of secure electronic voting but also sets a new metric for translucency, loveliness, and effectiveness in electoral *** collaboration with stakeholders, policymakers, and experts in the field, we strive to ensure that our result isn't only technically sound but also ultra practical and scalable, paving the expressway for wide relinquishment and trust in electronic voting systems. © 2024 IEEE.

关键词： Smart contract

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Cardiovascular Disease Risk Assessment using Machine Learning 6

Cardiovascular Disease Risk Assessment using Machine Learnin...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Prakash, Nikkila Mahesh, Mohitth Gouthaman, P. Department of Networking and Communications SRM Institute of Science and Technology Chennai India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350398496

Cardiovascular diseases (CVD) are one of the highest causes of death in the world. The early detection of cardiac risk is a critical factor in proper diagnosis and treatment. This way, patients with critical needs get priority access to doctors and healthcare systems. In this study, a cardiac risk assessment system was developed using the Logistic Regression algorithm, a machine learning model that has a high accuracy and is easy to interpret. The dataset used in this study included information from various patients. A total of 13 features were used to train the Logistic Regression model, including age, gender, blood pressure, and cholesterol levels. The results demonstrated that the Logistic Regression algorithm achieved high accuracy in predicting CVD risk, with an accuracy of 86.89. The main challenge when it comes to CVD risk assessment is the complexity of algorithms which makes it difficult for healthcare practitioners to interpret the results. Some systems require the personnel to go through additional training to use the risk assessment system, which can be time consuming. Logistic Regression is straightforward and simple. It is easy to interpret, making it suitable for clinical settings. It also has a well-established framework, which makes it very practical and reliable. This study showcases the importance of machine learning in the field of healthcare and highlights the effectiveness of the Logistic Regression algorithm in predicting cardiac risk. The high accuracy achieved by the model enables the early identification of cardiovascular disease risk. This makes it a useful tool for the healthcare industry and public health initiatives. © 2023 IEEE.

关键词： Risk assessment

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Multi-Class Lung Cancer Classification with CT and Histopathology Images Using CNN Models 10

Multi-Class Lung Cancer Classification with CT and Histopath...

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

作者： Shivakumar, R. Sandeep Babu, S. Preethiya, T. Srm Institute of Science and Technology College of Engineering and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (纸本)9798350384369

Lung cancer is the second largest cause of fatalities and remains a significant global health challenge, necessitating advanced methods for accurate and timely diagnosis. This paper investigates the synergistic potential of combining three powerful deep learning architectures - NASNet, ResNet152V2, and Xception for the classification of lung cancer and its sub-types using medical imaging data. This paper aims to compare the benefits of each architecture to enhance overall predictive accuracy and provide insights into specific lung cancer classifications. The dataset employed in this study comprises a diverse set of lung CT-scans and Histopathology images, including cases of various lung cancer sub-types. This paper also contributes valuable insights into the comparative strengths of CT and histopathology images for lung cancer classification and the results showed that Xception model consistently demonstrates robust performance with high precision, recall, and F1-scores across various classes in both CT and histopathology images compared to the NASNet model and the ResNet152v2 model. © 2024 IEEE.

关键词： Lung cancer

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Techniques, promising directions, challenges, datasets, and representations of facial expression analysis

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Multimedia Tools and Applications 2025年 1-29页

作者： Maheswari, V. Uma Aluvalu, Rajanikanth Mudrakola, Swapna Chithaluru, Premkumar Kumar, Manoj Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad500075 India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International University Hyderabad Campus Hyderabad Pune509217 India Department of CSE-AIML Aurora University Uppal Hyderabad India School of Computer Science University of Wollongong in Dubai Dubai Knowledge Park Dubai United Arab Emirates Department of Information Technology Mahatma Gandhi Institute of Technology Hyderabad500075 India

Facial Expression Analysis and Recognition (FEAR) are evolutionary concepts in the field of computer vision and artificial intelligence to develop human–computer interaction. Over the last decade, technology is leading human lives through online in various fields like education, medical, entertainment, shopping, etc. FEAR is a challenging task for the researchers while deploying the system in real-time applications as human expressions are not general in all situations and with all content. In experimentation of expression analysis and recognition, a feature extraction, database, and similarity measure play a key role to justify whether the system achieves the required target or not. This paper represents the various benchmark approaches for feature extraction, expression classification, and facial expression datasets like JAFFE, RaFD, CK +, MMI, FER2013 etc., and their features, representations, and challenges given in the investigations according to the researchers requirement so far. Retrospectively, discusses the suggestions and recommendations of the forthcoming mechanisms to reduce the semantic gap to analyze the expressions in general. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Feature extraction

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of computer science 2025年第2期8卷 659-686页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

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