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2nd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2023

作者： Raja Sree, T. Mary Shamala, L. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350348002

A secure fair electronic voting system is required for the smooth working of any democratic process. This paper implements a voting-based system application that runs with the support of blockchain to create a decentralized voting system. The proposed system aims to eliminate the aspect of trust from an election to make it more secure and transparent. © 2023 IEEE.

关键词： Blockchain

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A Secure Data Sharing Framework with Blockchain in IoMT using Bald Eagle Search Optimization

A Secure Data Sharing Framework with Blockchain in IoMT usin...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Jose, F.L. Mecline Stewartkirubakaran, S. Paulraj, Getzi Jeba Leelipushpam Jebadurai, Immanuel Johnraja Jebadurai, Jebaveerasingh Karunya Institute of Technology and Sciences Department of Computer Science and Engineering India

ISBN: (纸本)9781665474511

In health information systems, present interoperability problems are solved by blockchain technology (BT). Moreover, blockchain technology offers the high-tech standard that assures a secure sharing of computerized healthcare information among healthcare providers, medical care organizations, individuals, and medical experts. In healthcare management, Internet of medical things (IoMT) devices can share sensory information from patients to be investigated and prepared. However, in IoMT devices the secrecy of patients' health information transmission is still a major problem within a range of material regions. In order to overcome such challenges, an effective BES_SecDataSharing method is proposed for securely sharing the data with blockchain in IoMT. The entities involved in the proposed BES_SecDataSharing method are data owner, data user, cloud user, attribute authority, and blockchain. Six stages are involved in this proposed method are initialization phase, registration phase, key issuance phase, data preservation phase, authentication phase, and data sharing phase. The proposed BES_SecDataSharing methods has attained a minimum memory usage of 85 MB, validation time of 28.767 Sec, and normalized variance of 1.594. In addition, the method has obtained a maximum conditional privacy of 0.787. © 2023 IEEE.

关键词： Blockchain

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Precision Monitoring of Health-Care Using Big Data and Java from Social Networking and Wearable Devices 3

Precision Monitoring of Health-Care Using Big Data and Java ...

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3rd International Conference on Intelligent Communication and Computational Techniques, ICCT 2023

作者： Goel, Rishabh Satish, C.J. Vellore Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9781665453578

Wearable sensors and social networking sites help gather participant information for healthcare monitoring. Wearable sensors generate a lot of healthcare data for continuous patient monitoring. Social networking sites' user-generated healthcare data is huge and unstructured. Existing healthcare monitoring systems have trouble gathering and evaluating sensor and social network data, and traditional machine learning methods are insufficient to anticipate abnormalities in big healthcare data. A novel cloud-based healthcare monitoring architecture is presented to properly save and evaluate healthcare data and increase classification results. A Wireless Sensor Network and Big Data Analytics based Intelligent Health Monitoring System (IHMS) is suggested in this paper. The suggested large data analytics engine uses data mining, ontologies, and Bidirectional Long Short-Term Memory (Bi-LSTM). Data processing approaches preprocess information and minimize dimensionality. Proposed ontologies give semantic information about diabetes and blood pressure entities, aspects, and relationships (BP). Bi-LSTM categorizes information properly to forecast medication side effects and patient abnormalities. The suggested approach defines patients' health using diabetes, BP, mental health, and medicine reviews, and this model uses Java and Protégé Web Ontology Language. The findings demonstrate that the suggested system accurately manages healthcare information and predicts pharmacological side effects. © 2023 IEEE.

关键词： Data Analytics

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A Novel Contactless Middle Finger Knuckle Based Person Identification Using Ensemble Learning 38

A Novel Contactless Middle Finger Knuckle Based Person Ident...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Dey, Noboranjan Srinivas, M. Subramanyam, R.B.V. National Institute of Technology Department of Computer Science and Engineering Warangal India

ISBN: (纸本)9798350302196

In Modern times, automated security for identifying a person is one of the main concerns. There is a significant need for a trustworthy and secure identity verification solution. A reliable way to identify someone can be using a biometric identification system. The finger knuckle pattern offers excellent discriminatory features for biometric identification with indirect touch, including the advantages of long-range visibility. Existing models are failing to handle the depth information in finger knuckles that are highly relevant to understand the identification patterns. Therefore, we elaborate on the significance of utilizing the middle finger knuckle for biometric identification. We propose an ensemble approach that appropriately captures the rich features to identify a person based on their finger knuckle. The proposed model performance is evaluated on a standard dataset (HKPolyU 3D photometric stereo knuckle image dataset). Experimental results illustrate that the proposed model outperforms the existing results. Further, this approach would be advantageous in forensic investigations, security, and surveillance. © 2023 IEEE.

关键词： Biometrics

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Performance Comparison of Recurrent Neural Networks for Metamorphic Malware

Performance Comparison of Recurrent Neural Networks for Meta...

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2023 International Conference on Data science and Network Security, ICDSNS 2023

作者： Mittal, Shubh Chawla, Tisha Jajoo, Jay Bansal, Muskan Parashar, Harsha Ruby, D. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350301595

Metamorphic malware remains a significant challenge in the field of information security as it constantly changes its code and structure, utilizing sophisticated methods to evade detection by antivirus software. This necessitates advanced techniques such as machine learning and deep learning algorithms to effectively detect it. However, the major hurdles lie in the lack of a comprehensive dataset for training algorithms and the continuous evolution of metamorphic malware. To bridge this research gap, a deep learning-based method is proposed that employs bidirectional LSTM (BiLSTM) to detect metamorphic malware based on attack vectors. A comparative analysis is conducted of BiLSTM's performance against other recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent units (GRU), and a hybrid model of BiLSTM and GRU. The models were trained using a dataset comprising eight categories of metamorphic malware. The results demonstrate that BiLSTM surpasses other RNNs, achieving the highest accuracy of 93.25% compared to 93.20% for LSTM, 91.90% for GRU, and 86.41% for the hybrid model. The use of the outer activation function, Linear, and the inner activation function, SoftSign, in conjunction with the Adam optimizer, significantly contributed to this achievement. The proposed approach has the potential to greatly enhance current malware detection techniques and improve the overall security of computer systems. Furthermore, it can be extended to address other types of malware and cyber threats. By overcoming the challenges of limited training datasets and the ever-evolving nature of metamorphic malware, this research offers practical advantages, including enhanced threat detection capabilities, reduced false positives, and increased system security. © 2023 IEEE.

关键词： Long short-term memory

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Advanced Agro Management Using Machine Learning and IoT 2

Advanced Agro Management Using Machine Learning and IoT

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Parkavi, A. Shaha, Kaushiki Rajodiya, Purva Samruddha, S. Ramaiah Institute of Technology Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9798350314045

In this research study, we aim to create a revolutionary agricultural management portal that is capable of identifying diseases in leaves, flowers, and fruits deploying an Arduino Board, an Esp32-camera module to collect real-time visuals and machine learning algorithms. Through the integration of sensors and cameras into the agricultural environment, our system can collect information on crop development and health as well as recognize and diagnose illnesses. As a result, choices like irrigation strategies, fertilization, and pest control can be made knowing exactly what diseases are present and how they might impact crop health thanks to machine learning algorithms like *** an all-inclusive Full Stack web application, which will identify the ailment, prescribe items for treatment, and provide the option to purchase *** technology utilized in this research is projected to improve sustainable crop growing practices, raise flower, fruit, and leaf yields, and minimize crop losses, as well as provide a trained consultation alternative for one-on-one personal assistance. © 2023 IEEE.

关键词： Fruits

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End-to-End Network Slicing for 5G and Beyond Communications 18th

End-to-End Network Slicing for 5G and Beyond Communications

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18th International Conference on Data science and Artificial Intelligence, ICDSAI 2022

作者： Gupta, Rohit Kumar Kumar, Sudhir Kumar, Praveen Misra, Rajiv Department of Computer Science and Engineering Indian Institute of Technology Patna India

ISBN: (纸本)9783031161773

The 5G and beyond network slice provides the platform to connect several devices of different use cases to the network, and we can transfer the data from one UE to another UE in very less time. 5G comes with all the features of 4G and some additional features like virtualization and slicing to improve the data rate, channel availability, and less latency. These are the things that are crucial in the current scenario because IoT applications are increasing very rapidly, and hence we need a technique to provide the available network, high speed, less latency, etc. The MEC (Multi-access Edge Computing) plays a major role to improve computational power, because latency is the major issue if we want to transfer the data from one station to another station and also we used SDN and NFV for slicing and virtualization. In this chapter, the author depicts the role of SDN and NFV in the 5G network. With the help of these two, we can achieve virtualization on the side of the network, and hence we reduced the latency of the network. This chapter also shows the setup and configuration of the tools which is used in virtualization and creating the interface to connect any IoT applications. For the virtualization, we used Docker. All the setups are depicted in this chapter step by step. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Indoor Uncertain Semantic Trajectory Similarity Join

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Journal of computer science & Technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

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AutoDL: Enriching PowerBI AutoML Features with Deep Learning 14

AutoDL: Enriching PowerBI AutoML Features with Deep Learning

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Shankar, Sneha Doshi, Sujay Bhuvaneshwari, R. Praveen Joe, I.R. Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

PowerBI, a graphical analytics software developed by Microsoft, has incorporated various AI and ML tools to provide accurate predictions and insights for the data feeded into the software. PowerBI is equipped with the AutoML tool which automates the process of applying machine learning to obtain data driven insights. In recent years, the explosion of data has been phenomenal, and due to the rise in interconnectivity and the age of IoT it has led to the emergence of big data. Though AutoML, which uses machine learning, isn't equipped to handle big data. To tackle the presented challenge of volume and scalability, this study aims to provide a solution, AutoDL, with the usage of deep learning models and algorithms. In this study, we attempt to create a base automated deep learning pipeline providing better results on big data. The pipeline was tested on 2 big data sets and we received affirmative results. © 2023 IEEE.

关键词： Big data

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An Ensemble-based Deep Learning Model for Multi-class Flower Recognition

An Ensemble-based Deep Learning Model for Multi-class Flower...

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2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Rabbi, Md Fozle Dola, Sraboni Das Taha, Noorjahan Binte Kader Al Mahmud, Tamim Islam, Muhammad Nazrul Razzaque, Md. Abdur Al Faisal, Faiz Green University of Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh University of Dhaka Dept. of Computer Science and Engineering Dhaka1000 Bangladesh

ISBN: (纸本)9798350316001

Many species around the globe heavily depend on flowers, and even the use of flowers for medical purposes is enormous, which has been in practice since ancient times. In modern botany, agronomy, and species research, the identification and classification of flowers mainly depend on human intervention, which requires a lot of human resources, and the recognition accuracy rate is low. On the other hand, due to the vast number of flowers with different types, it is quite a challenge and increases the complexity of the accurate detection and classification process. Moreover, the traditional CNN model and other pre-trained models like- VGG16, VGG19, Xception, and MobileNet-V2 failed to ensure high validation accuracy. In this research, we have designed an ensemble-based deep learning model consisting of two pre-trained models DenseNet-201 and EfficientNet-B7, which we renamed 'FlowerConvNet'. This paper considers classifying five categories of flowers consisting of 4242 images, collected from the Kaggle flower recognition dataset. In this research study, the proposed deep learning model FlowerConvNet using transfer learning can obtain a competitive result with 95% validation accuracy. © 2023 IEEE.

关键词： Learning systems

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