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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Venugopal, K.R. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India National Institute of Technology Tiruchirappalli India Bangalore University Bengaluru India

ISBN: (纸本)9789819757022

This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically acquired knowledge from Web 3.0. With advanced methods of term and category extraction, classification using both lightweight machine learning and strong deep learning methods, and clustering, the framework can exceed all similar frameworks in terms of performance. Initial terms are processed from the documents using the TF-IDF strategy. Static metadata stack is established from e-books, query logs, and glossaries. Renyi entropy is used to guide informative term selection for a decision tree classification;in this manner, the scale of the generated knowledge is managed by lightweight computing requirements. Dynamic knowledge from Web 3.0 is classified using GANs and is subjected to DBSCAN clustering. A multi-agent setup with parallelization enhances the efficiency of the clustering process. The knowledge graph is finally created using the Co-SIM rank and is evaluated for specificity using Hellinger's distance and volume of intersection index. These paradigms contribute to this framework significantly outperforming the baseline frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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Early Detection of lung Cancer Using Machine Learning Technique

Early Detection of lung Cancer Using Machine Learning Techni...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Rao, B.Devananda Arshad, Mahammad MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India Deccan College of Engineering & Technology Department of Information Technology Hyderabad India

ISBN: (纸本)9798350348217

The study and diagnosis of the lung diseases is the investigating point of interest for the medical experts existing from the a foretime to the current day. A diagnostic system is required to overcome this approach to reduce the time in diagnosis of lung cancers that is putting human life at risk. Few projects are proposed for this purpose and many other are under trails. To identify the cancer cells in the CT images a neural network model is proposed. It is also used to recognize issues in therapeutic imaging applications. A framework is originated for the identification of lung cancer that includes application of deep neural network system and AI. For staging of lung cancer classification of CNN is required. Deep learning methods are used on supervised learning to obtain better results. The proposed framework includes neural network that comprises of many steps like collection of images, pre-preparation, pixel enhancement, segmentation of images, and feature extraction. To outline the present research, it explains that to attain accurate results in detection and treatment of lung cancer cases at a low cost machine learning plays an unpredictable role. © 2023 IEEE.

关键词： Convolutional neural networks

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SVM-Guided Dynamic Routing for Post-Harvest Quality Preservation in Smart Warehouses with IoT 3

SVM-Guided Dynamic Routing for Post-Harvest Quality Preserva...

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3rd International Conference for Innovation in technology, INOCON 2024

作者： Umamaheswari, K. Antony Joseph Rajan, D. Ramasamy, Jayabharathi Seeni, Senthil Kumar Meenakshi, R. Thamizhamuthu, R. VR. Siddhartha Engineering College Department of Electronics and Instrumentation Engineering Andhra Pradesh Vijayawada India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

This research presents a new approach to smart warehouse quality preservation after harvest by combining the Internet of Things (IoT) with Support Vector Machine (SVM)-guided dynamic routing. Preserving the quality of harvested goods during storage and transportation is of utmost importance in today's agricultural supply chain. Critical characteristics like temperature and humidity are monitored using real-time data acquired from sensors via the IoT. It uses the SVM algorithm to optimize the items' route dynamically according to the changing environmental conditions. The adaptable and efficient flow of agricultural goods throughout the warehouse is guaranteed by this dynamic routing method, which is led by SVM decision-making. A number of aspects that impact quality preservation, the method lessen the possibility of degradation during transit and storage. The effectiveness of the suggested strategy is supported by simulation findings, which demonstrate better preservation of post-harvest quality in comparison to conventional approaches. Smart warehouses may benefit greatly from the combination of SVM and the IoT, which solves many problems in the agricultural supply chain and helps improve sustainable postharvest management. © 2024 IEEE.

关键词： Sensor networks

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Optimizing Crop Recommendation System Using Threshold and Decision-Level Fusion

Optimizing Crop Recommendation System Using Threshold and De...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Umamaheswari, R. Kannan, E. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509828

Nowadays crop recommendation systems face many obstacles in recommending suitable crops. A main challenge that has to be encounter is changes in environmental conditions and handling diverse dataset like soil and climate oriented dataset. Existing algorithm's like decision tree, random forest have limitations in adapting to local variability. To overcome this and to enhance the agricultural activities, this paper presents an innovative crop recommendation system based on threshold (CRST) values, through which crops are chosen by considering properties of soil, climatic parameters, and season factors such as Kharif, Rabi, and Zaid. Critical limit threshold values are set for most important features like fertility of soil, nutrition of the soil, pH, temperature, humidity, and rainfall to ensure optimal growth conditions. In order to recommend the suitable crop, a rule-based decision-fusion method is tied with confidence level analysis. To evaluate the effectiveness of the threshold method, ordinary least squares (OLS) regression is used. CRST model achieves an accuracy of 90% compared to existing algorithms. Integration of seasonal adaptation and threshold determination greatly enhances crop management decision-making and provides a strong answer to agricultural problems. © 2025 IEEE.

关键词： Temperature Rain Decision making Crops Humidity Soil Decision trees Recommender systems Random forests Faces

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A Hybrid Blockchain Framework for Secure, Scalable, and Energy-Efficient Digital Identity Management with Token-based Selective Disclosure 3

A Hybrid Blockchain Framework for Secure, Scalable, and Ener...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Velmurugan, M. Shinzeer, C.K. Maya, G. Ramya, R. Kanimozhi, R. Lathif, Syed Ismail Abdul Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science And Technology Department Of Computer Science & Engineering Chennai600 062 India Lovely Professional University Department of School of Computer Applications Punjab Phagwara144411 India Srm Institute of Science and Technology Faculty of Management Kattankulathur603 203 India K.Ramakrishnan College of Engineering Department of Electronics and Communication Engineering Samayapuram621112 India Muthayammal Engineering College Computer Science and Engineering Rasipuram637408 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India

ISBN: (纸本)9798331509675

Data breaches, identity theft, and the lack of user control within traditional digital identity management systems, it begs a more secure and decentralized alternative. In this study we propose a blockchain based digital identity management framework that implements privacy, security and transparency by employing distributed ledger technology. To provide tamper proof identity storage and automated verification, the proposed model uses cryptographic hashing and smart contracts. The use of a novel token-based access mechanism enables users to safely share their identity data with third parties, while keeping sensitive information out of sight. Security metrics are enhanced by 30% for data integrity with 45% reduction in unauthorized access attempts all through simulations. The framework also achieves improved efficiency reducing identity verification time by 40 percent compared to traditional systems. It offers a promising approach to address important vulnerabilities in centralized identity management in line with the development of marketable and user-friendly digital identity solutions. © 2025 IEEE.

关键词： Data integrity

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Permissioned Blockchain Security Pqfabric from Quantum and Classical Attacks 1

Permissioned Blockchain Security Pqfabric from Quantum and C...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Tamilarasi, C. Bhavadrarani, K. Kittappa, Thiagarajan Subashini, N. Anitha, V. Vimalraj, E. B.S.Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India V.S.B. Engineering College Department of Computer Science and Engineering Karur India Rajalakshmi Institute of Technology Department of Mathematics Chennai600124 India Sathyabama Institute of Science and Technology Department of Mathematics Chennai600119 India Panimalar Engineering College Department of CSE Poonamallee Chennai600123 India School of Computing Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798331518578

Old style cryptographic techniques are genuinely compromised by the development of quantum computing, which requires the formation of safety ideal models that are impervious to quantum mistakes. A new permissioned blockchain security system called PqFabric is intended to oppose both traditional and quantum attacks. PqFabric ensures the validness, mystery, and respectability of exchanges in a quantum-empowered future by including post-quantum cryptographic techniques like hash-based and grid based marks. As opposed to ordinary blockchains, PqFabric utilizes a half breed agreement process that gets information trades and members characters by melding Byzantine Fault Tolerance (BFT) with quantum-safe encryption. The system gives protection against calculation based attacks by Shor and Grover, tending to huge imperfections in existing blockchain structures. Besides, when quantum-safe guidelines advance, its secluded plan empowers smooth enhancements. With this procedure, PqFabric is situated as a ground breaking answer for organizations and areas wishing to safeguard their blockchain networks from new cryptographic risks. © 2025 IEEE.

关键词： Quantum computers

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A private Ethereum blockchain implementation for secure data handling in Internet of Medical Things

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Journal of Reliable Intelligent Environments 2022年第4期8卷 379-396页

作者： Mohan, Dheeraj Alwin, Lakshmi Neeraja, P. Lawrence, K. Deepak Pathari, Vinod Department of Computer Science and Engineering National Institute of Technology Calicut Kerala Kozhikode India Department of Mechanical Engineering National Institute of Technology Calicut Kerala Kozhikode India

The Internet of Medical Things (IoMT) is set to create a revolutionary shift in healthcare by transforming traditional methods of disease diagnosis to a data-centered, technology-driven analysis. However, data security and privacy issues are significant hurdles that need to be addressed. Blockchain technology has radically transformed data storage mechanisms using decentralization and cryptographic principles, making it a better choice for sensitive medical data than cloud storage services. A majority of the existing research focus on implementing blockchain in IoT devices which have much higher processing capabilities than IoMT devices such as pacemakers. Most of the existing blockchain frameworks are unsuitable for miniature IoMT devices due to high computational and storage requirements. This work aims to overcome this challenge by proposing a private blockchain framework in which different stakeholders of a medical system such as patients, doctors, IoMT devices, etc. act as nodes, creating a decentralized network in which physiological data output by IoMT devices can be stored securely and tamper-free forever. The proposed design is implemented in a Raspberry Pi network, using Proof of Authority (PoA) consensus mechanism which has minimal computational requirements. Data confidentiality is ensured using a double-encryption mechanism by means of Elliptic Curve Integrated Encryption Scheme. It is observed to perform well in comparison with existing approaches with a transaction speed of a minimum of 25 transactions per second and is easily scalable to accommodate different personnel of the healthcare sector. Further development on this design could potentially result in a major innovation in the IoMT industry in the form of a new array of real-time health monitoring devices with high security and data privacy. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Blockchain

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Multi-Modal Feature Fusion with Fuzziness based Semi-Supervised Learning for Alzheimer's Disease Diagnosis

Multi-Modal Feature Fusion with Fuzziness based Semi-Supervi...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Bushra, Umme Habiba Chowdhury Priya, Fahmida Patwary, M. Jamshed Alam International Islamic University Chittagong Department of Computer Science and Engineering Chattogram Bangladesh Chittagong University of Engineering and Technology Institute of Information and Communication Technology Chattogram Bangladesh

ISBN: (纸本)9798331529765

Alzheimer's disease is a prominent kind of dementia that is caused by a slow decline of cognitive abilities, including memory loss, impaired speech and diminished reactivity. The condition primarily affects brain regions responsible for cognition, memory and language and typically becomes apparent after the age of 60, with a greater risk among older individuals. To meet the requirement for early identification, researchers have explored various machine-learning techniques. To enhance the accuracy of diagnosing Alzheimer's disease, researchers have employed supervised and unsupervised learning approaches derived from machine learning. While supervised learning relies on labelled data, obtaining sufficient labelled samples can be time-consuming and requires specialized assistance. The focus is on multiclass classification, harnessing MRI and PET data. SGLDM and SIFT feature extraction algorithms are utilized to enable the fusion of features from diverse perspectives, resulting in a robust classification model. This research introduces a novel strategy for AD diagnosis, combining multimodal feature fusion with a fuzziness-based-semi-supervised learning method. The method leverages both labeled and mislabeled data to enhance the model's reliability. The proposed method demonstrated notable enhancements in the classifier's performance compared to existing approaches on the dataset. © 2024 IEEE.

关键词： Self-supervised learning

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Cluster-Factors of Mobile Sensor Network technology for Security Enhanced PEGASIS

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第18期17卷 143-155页

作者： Sivakumar, T.K. Dhyani, Saurabh Manikanthan, S.V. Kaliappan, Vishnu Kumar Baskaran, K. Kishore, Kakarla Hari Department of Computing Technologies SRM Institute of Science and Technology Tamil Nadu Kattankulathur Chennai India Department of USCS Uttaranchal University Uttarakhand Dehradun India Melange Academic Research Associates Puducherry India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Coimbatore India Department of Biomedical Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile wireless sensor networks (MWSNs) have been a hot topic of research, and numerous routing methods have been developed to increase energy efficiency and extend longevity. Nodes close to the sink often use more energy to transmit data from their neighbors to the sink, which causes them to run out of energy faster. These places are also referred to as rendezvous points, and choosing the best one is a hard task. The likelihood of choosing an ideal node as the rendezvous point will be extremely low because hierarchical algorithms only use their local information to select these places. The warm spot problem is addressed from four angles in this work using the Enhanced Power-Efficient Gathering in Sensor Information Systems (EPEGASIS) technique. In order to limit the amount of energy used during transmission, the ideal communication distance is first calculated. To balance the energy consumption among the nodes, mobile sink technology is employed once a threshold value is set to safeguard the dying nodes. The node can then modify its communication range based on how far away the sink node is from it. Thorough testing has been done to demonstrate that our suggested EPEGASIS works better in terms of longevity, drive usage, and web latency. © 2023 by the authors of this article. Published under CC-BY.

关键词： Energy efficiency

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HSCR: Hierarchical structured cluster routing protocol for load balanced wireless sensor networks

HSCR: Hierarchical structured cluster routing protocol for l...

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作者： Gangwar, Saumitra Prasad, Ikkurthi Bhanu Yogita Yadav, Satyendra Singh Pal, Vipin Kumar Patra, Sarat Department of Computer Science and Engineering National Institute of Technology Meghalaya Meghalaya India Department of Electronics and Communication Engineering National Institute of Technology Meghalaya Meghalaya India Indian Institute of Information Technology Vadodara Gujarat Vadodara India

The robustness of wireless sensor networks (WSNs) has made it a preferred choice for many applications. WSNs should perform the intended sensing task with a limited amount of energy. Clustering methodology is used to reduce sensor nodes' energy consumption and increase the network lifetime by organizing nodes into independent groups. In this article, a hierarchical structured cluster routing (HSCR) protocol based on the principle of an m-way balanced tree has been proposed for multi-hop intra-cluster communication and offload the cluster head. It enhances the network performance by providing an underlying architecture that reduces the intra-cluster communication distance and the amount of data sent to the cluster head. In the proposed HSCR protocol, an m-way tree-based hierarchical structure has been proposed in which balanced structure is formed within each cluster so that data aggregation is performed at each intermediate parent node. The proposed approach can be implemented over any communication protocol used in WSNs. Simulation results prove that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT-RPL, in terms of network stability, energy consumption, and network lifetime. Simulation results affirm that the proposed algorithms outperform traditional clustering algorithms, LEACH and CT-RPL, in terms of network stability, energy consumption, average intra-cluster communication distance and network lifetime by 105.32%, 49.23%, 14.98%, 109.31%, and 35.75%, 15.32%, 17%, 24.31%, respectively. © 2022 John Wiley & Sons, Ltd.

关键词： Routing protocols

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