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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Bokdia, Akshat Gunavathi, C. Vellore Institute of Technology Vellore School of Computer Science and Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Cancer gene expression data analysis using microarray technology is a critical domain in understanding the molecular intricacies of malignancy. With an emphasis on the incorporation of data augmentation techniques, this paper offers a thorough investigation into the categorization of binary class Lung cancer gene expression dataset. Effective dimensionality reduction is achieved by using univariate feature selection technique called SelectKBest method. Through the evaluation of performance indicators including accuracy and recall, the study offers detailed insights into the advantages and disadvantages of each augmentation strategy. The study's findings add to the current conversation in precision medicine and provide information for improving methods for analyzing gene expression data in relation to cancer classification. © 2024 IEEE.

关键词： Diseases

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Storage Optimization of Product Traceability System Based on Blockchain 14

Storage Optimization of Product Traceability System Based on...

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14th IEEE International Conference on Electronics Information and Emergency Communication, ICEIEC 2024

作者： Huang, Jiahuan Shen, Wei Yu, Jie Liu, Qiang Hu, Tao Wei, Jianghao Duan, Xiaoqiu School of Computer Science and Engineering Wuhan Institute of Technology Hubei Province Wuhan430205 China

ISBN: (纸本)9798350361889

To address the storage capacity limitations and high costs inherent in the existing single-chain storage model of blockchain-based traceability systems, a strategy combining on-chain and off-chain storage using IPFS as an alternative storage architecture is proposed. The goal is to enhance the storage model while ensuring data storage stability. Storing frequently accessed information off-chain helps alleviate the excessive pressure on the blockchain caused by high-frequency queries, while ensuring the security and immutability of on-chain data. Additionally, IPFS technology expands storage capacity and reduces storage costs through its distributed storage, thereby improving data storage and retrieval efficiency. Experimental validation shows that compared to traditional single-chain traceability models, this storage optimization approach can reduce data storage time by 8.05% and data retrieval time by 30.97%. © 2024 IEEE.

关键词： Blockchain

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Integrated VGG19 and Gated Recurrent Unit with Cosine Similarity Metric for Content based Image Retrieval 1

Integrated VGG19 and Gated Recurrent Unit with Cosine Simila...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Narayanaswamy, Rajath Arakere Gowda, Vidyalakshmi Krishne Gsss Institute of Engineering & Technology for Women Department of Computer Science & Engineering Mysuru India Sri Siddhartha Institute of Technology Ssahe University Tumakuru India Tumakuru India

ISBN: (纸本)9798350352931

Technological advancement has provided huge databases for different genres of image to obtain efficient visual information to satisfy users. The existing algorithms tried to extract the important feature vector from the images to analyze the similarities between the query image and dataset image but failed to reach their expected results. The noises generated during model training, overfitting and gradient vanishing problems are the problems faced by the existing models. To overcome the problems of the existing methods, the VGG19 and Gated Recurrent Unit (GRU) with cosine similarity are proposed for content-based image retrieval. The z-score normalization technique was used to standardize the intensity of the images which enhanced the training of the model. The VGG19 algorithm extracted spatial features and the GRU model extracted the temporal features from both database and query images. The cosine similarity metric was used for calculating the similarities of both the images and retrieved based on the feature vector similarities. The proposed VGG19 and GRU with cosine similarity metric has obtained a better average precision of 98.89% for Corel 1 K dataset and 84.90% for Corel 10 K dataset compared to the existing Hybrid Graph-based Gray Level Co-Occurrence Matrix (HGGLCM) model. © 2024 IEEE.

关键词： Content based retrieval

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An efficient hybrid multifactor mutual authentication and session key agreement scheme for patient monitoring system using IoMT

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Multimedia Tools and Applications 2024年第36期83卷 83805-83835页

作者： Rai, Shanvendra Paul, Rituparna Banerjee, Subhasish Meher, Preetisudha Department of Computer Science & amp Engineering National Institute of Technology Arunachal Pradesh Jote Itanagar Arunachal Pradesh Papumpare791113 India Department of Electronics & amp Communication Engineering National Institute of Technology Arunachal Pradesh Jote Itanagar Arunachal Pradesh Papumpare791113 India

The Internet of Medical Things (IoMT) provides such flexibility in our society where anyone can get medical treatment at any time, from anywhere. IoMT is a type of network where different resource-constraint physiological sensors are deployed in and/or on the human body that connects with the internet through the Gateway node, for monitoring purposes. However, due to the open nature of communication in the IoMT;the security, and privacy of patients' sensed data is very challenging, and that needs to be addressed, because any modification or alteration to it may lead to putting the life of a patient in danger. In this context, Chunka et al. proposed an authentication and key agreement (AKA) scheme for IoMT and claimed that the scheme has many security features and is easy to deploy. Unfortunately, it came to notice during this research that the scheme is vulnerable to multiple attacks, including replay, insider, smart card loss, eavesdropping, and server spoofing attacks, additionally failing to establish the session key agreement. So to overcome this issue, an efficient and improved multi-factor lightweight mutual AKA scheme is proposed through this article by incorporating a PUF-enabled sensor node and smart card for the users. To prove the superiority of the proposed schemes and to demonstrate the security features, the scheme is verified by formal security proof using the ROR model and informal proof using the AVISPA tool kit. In the end, a comprehensive analysis covering security, performance, and a comparative evaluation with existing similar approaches along with the Chunka et al. scheme demonstrates that the suggested approach not only achieves a higher level of protection against commonly recognized threats but also maintains an economically efficient mechanism concerning sensor nodes. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Sensor nodes

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Classification of imbalanced datasets utilizing the synthetic minority oversampling method in conjunction with several machine learning techniques

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Iran Journal of computer science 2024年第1期8卷 51-68页

作者： Datta, Shrayasi Ghosh, Chinmoy Choudhury, J. Pal Department of Information Technology Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Jalpaiguri Government Engineering College West Bengal Jalpaiguri 735102 India Department of Computer Science & amp Engineering Narula Institute of Technology West Bengal Agarpara 700109 India

Pharmacological datasets like Yeast and Escherichia coli (E. coli) have a massive impact on the healthcare industry for the production of human drugs. Yeast is well recognized as a significant constituent in the production of pharmaceuticals for human use, owing to its cellular structure that bears resemblance to that of human cells. E. coli is widely regarded as the preferred expression host within the biotechnology industry for the efficient and cost-effective production of proteins on a large scale. This preference is particularly evident in the context of non-glycosylated proteins, which are of significant interest for their therapeutic applications. This study aims to determine the efficacy of machine learning (ML) methods in classifying pharmacological datasets like Yeast and E. coli. As Yeast and E. coli both are imbalanced datasets by nature, to handle the imbalanced classification problem, the synthetic minority oversampling technique (SMOTE) is used in this study. The application of the combined feature subset selection (CFSS) approach has been also employed to find out the overall classification performance, but classification with CFFS has not been very promising. According to the findings, classification on SMOTE-oversampled data gives better results and from several performance and evaluation metrics, random forest achieves the best overall prediction performance for both Yeast and E. coli datasets with an accuracy of 87% and 97%, precision of 87% and 97%, recall of 87% and 98%, and f1 score of 85% and 98%, respectively. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Classification E. coli Feature selection SMOTE Yeast

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A Real-Time crisis informatics system by fusing Geo-Spatial Twitter streams and User-Posted images

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Materials Today: Proceedings 2022年 62卷 4738-4744页

作者： Rajarathinam, Ragul Bhuvaneswari, A. Jothi, R. School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu India

Social media such as Twitter is these days one of the fundamental news hotspots for a great many individuals all over the planet because of their minimal expense, simple access, and quick spread. In this paper a real-time disaster mining from twitter disaster event mining using apache spark for natural disasters is proposed by fusing text, images and geo-spatial data. The way of transmitting messages or news reports is not good for developing societies where even the emergency information even can't be transmitted to locals who may get affected due to the disaster. Locating disasters from different locations of the world and mapping location collected from the Twitter sources from different areas at risk of natural disaster and gather them to Geo-parsed real-time tweet data streams. In addition, the Geo-statistical analysis to generate real-time disaster mapping is plotted in a base map. Then report is generated for two case studies tweet disaster mapping to find post-event impact assessment is carried out and detection accuracy is computed. The dataset applied Streaming K-means algorithm and CNN is validated to be a very effective technique to extract event clusters of the identical spatial entities in diversified contexts such as text, photos, geo-tags are observed with an accuracy of 91% which is reasonably acceptable. © 2022

关键词： Mapping

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The Threat of Quantum Computing to the Cryptographic Security of Blockchain 2

The Threat of Quantum Computing to the Cryptographic Securit...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Modi, Pranit Asgola, Abhishek Mishra, Hiya Charanya, R. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore Vellore India

ISBN: (纸本)9798350328202

Blockchain technology, born as the backbone of cryptocurrencies like Bitcoin, has rapidly expanded into a versatile platform spanning various industries. This review paper delves into the critical aspects of security and anonymity within blockchain applications. It simplifies complex technical concepts while exploring the foundation of blockchain security, including cryptography, decentralization, consensus mechanisms, and the challenges posed by smart contracts. Additionally, it examines the pseudonymous nature of blockchain addresses, privacy-enhancing cryptocurrencies, and the delicate balance between transparency and anonymity. As we navigate these intricacies, we address challenges such as scalability, regulatory concerns, and potential threats from quantum computing. This paper aims to provide a comprehensive yet easily digestible understanding of the security and anonymity dimensions in blockchain applications. © 2024 IEEE.

关键词： Blockchain

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Covid prevention based on identification of incorrect position of face-mask 2

Covid prevention based on identification of incorrect positi...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sahoo, Madhusmita Priyadarshini Sridevi, M. Sridhar, Rajeswari Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India

Since the rise of the COVID-19 pandemic, safeguarding public health has taken paramount importance, drastically transforming lives in unprecedented ways. Mitigating the virus's spread necessitates various measures, including wearing face masks. However, there has been a concerning trend of laxity and incorrect use of masks in public spaces, heightening the fear of escalating cases globally. Numerous deep-learning models have been proposed for face mask detection. This paper presents s novel DenseMaskNet deep learning model which integrates real-time visualization with transfer learning techniques. This involves a two-step process. The first step is mask detection, which is built by fine-tuning the state-of-the-art pre-trained deep learning model, DenseNet201. The Face- Mask Recognition Dataset (FMRD), which is free from biases and contains masked, unmasked, incorrect masked faces, is utilized in the mask detection process. In the second step, Face detection and real-time visualization, prediction scores calculated for each image of the FMRD dataset are fed into a res10_300×300_ssditer_140000_caffemodel, a pre-trained single-shot MultiBox face detector model, which combines these scores to enable real-time face mask detection. The OpenCV library is utilized to capture real-time video from a camera and experiment with our model in practical scenarios. Comparative analysis against VGG16, InceptionV3, ResNet50, MobileNetV2, and Efficient-NetB7 reveals that DenseNet201 consistently outperforms all metrics. Notably, DenseMaskNet exhibits superior performance with precision, F1 score, recall, and accuracy of 98%, 98%, 99%, and 99%., respectively. These compelling results affirm the efficacy of DenseMaskNet for real-time face mask detection, suggesting its viability for widespread utilization in real-world settings. © 2024 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Enhanced Gradient Boosting Technique to Detect the Malware in API

Enhanced Gradient Boosting Technique to Detect the Malware i...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Balaji, M. Sathyalakshmi Hindustan Institute Of Technology And Sciences Department Of Computer Science And Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

Currently, one of the most crucial jobs in information security is malware detection. Since hackers are always coming up with new ways to break into computer networks and systems, it's critical to have a reliable system in place to detect and prevent any unwanted activity. Traditional antivirus software frequently misses new viruses or malware, leaving your computer open to intrusion and important data theft. It is necessary to create a new method to detect the malware. In this paper used the Enhanced Gradient Boosting method to detect the malware. It is employed to verify the outcomes of analysis and enhance the precision of detection. The implementation of the suggested method enables the identification of malicious software with a precision ranging from 98.1%, indicating a significantly high level of accuracy. © 2024 IEEE.

关键词： Machine learning

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MetaIClone: A Robust Iris Clone Detection Mechanism for the Metaverse

MetaIClone: A Robust Iris Clone Detection Mechanism for the ...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Verma, Palak Kaur, Harkeerat Indian Institute of Technology Jammu Department of Computer Science and Engineering Jammu and Kashmir181221 India

ISBN: (纸本)9798331533311

Recent advancements in artificial intelligence have paved the way for the metaverse to become an imminent reality. Within this virtual world, people can buy and sell virtual assets, meet up with friends, purchase goods, and attend event, all while using biometric traits for secure and seamless authentication. Iris recognition has become a highly prevalent method for human authentication due to its robustness and effectiveness. Despite its widespread use, these systems are vulnerable to various security threats, particularly when attackers use biometric replicas to impersonate legitimate users-commonly known as biometric cloning. To counteract such cloning attempts, an anti-cloning subsystem is incorporated with the sensor, enabling intelligent differentiation between genuine and clone iris traits. In this research, we present MetaIClone, a hybrid iris clone detection (ICD) system designed for secure identification within the metaverse. MetaIClone integrates the powerful features of two handcrafted feature extractors namely histogram of oriented gradient (HoG) and binary statistical image feature (BSIF), with the extracted feature vectors subsequently classified using two distinct support vector machines (SVMs). The final decision is achieved through score-level fusion with a fine-tuned MobileNet V2 model. We evaluated MetaIClone using benchmark datasets, including iris liveness detection Notre Dame 2017 (LivDet ND17) and Notre Dame cosmetic lenses dataset 2015 (NDCLD-15). Additionally, we introduced a digital clone generation technique using a generative adversarial network (GAN) model and tested the system on this novel dataset. MetaIClone excels in known attack scenarios, achieving an average classification error rate (ACER) of 0. 0 2%, and performs well in unseen attack scenarios with an ACER of 0. 0 5%, delivering competitive results in crossdatabase evaluations. Overall, MetaIClone outperforms other ICD models, offering superior accuracy with high computational

关键词： Support vector machines

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