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Wrapper Based Linear Discriminant Analysis(LDA)for Intrusion Detection in IIoT

computer Systems Science & engineering 2023年第5期45卷 1625-1640页

作者： B.Yasotha T.Sasikala M.Krishnamurthy Department of Computer Science and Engineering MNM Jain Engineering CollegeChennai600097India Department of School of Computing Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering KCG College of TechnologyChennai600097India

The internet has become a part of every human ***,various devices that are connected through the internet are ***,the Industrial Internet of things(IIoT)is an evolutionary technology interconnecting various industries in digital platforms to facilitate their ***,IIoT is being used in various industrial fields such as logistics,manufacturing,metals and mining,gas and oil,transportation,aviation,and energy *** is mandatory that various industrial fields require highly reliable security and preventive measures against *** detection is defined as the detection in the network of security threats targeting privacy information and sensitive *** Detection Systems(IDS)have taken an important role in providing security in the field of computer *** of intrusion is completely based on the detection functions of the *** an IIoT network expands,it generates a huge volume of data that needs an IDS to detect intrusions and prevent network *** research works have been done for preventing network *** day,the challenges and risks associated with intrusion prevention are increasing while their solutions are not properly *** this regard,this paper proposes a training process and a wrapper-based feature selection With Direct Linear Discriminant Analysis LDA(WDLDA).The implemented WDLDA results in a rate of detection accuracy(DRA)of 97%and a false positive rate(FPR)of 11%using the Network Security Laboratory-Knowledge Discovery in Databases(NSL-KDD)dataset.

关键词： Intrusion detection IIoT wrapper support vector machine(SVM) LDA random forest(RF) feature selection

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Contextual Flow of Information in Tourism using BLE Proximity Detection to Enhance the Tourism Experience 5

Contextual Flow of Information in Tourism using BLE Proximit...

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5th International Conference on Emerging Smart Computing and Informatics, ESCI 2023

作者： Gala, Ayush Borgaonkar, Chinmay Kulkarni, Vaishnavi Wakode, Madhuri Kale, Geetanjali Pune Institute of Computer Technology Department of Computer Engineering Pune411043 India

ISBN: (纸本)9781665475242

The IoT represents a great opportunity for tourism and hospitality to increase customer satisfaction while simulta-neously reducing operational costs. Smart tourism involves using smart technology and practices to boost resource management and the sustainability of tourism, while growing the overall competitiveness. Travel and tourism companies will depend on IoT technology to create an array of benefits both in internal and external environments. Through our study, we explore BLE beacon based proximity detection and information delivery, and its various use cases to overcome the complications of the travel and tourism industry. As the key technology, Bluetooth Low Energy (BLE) beacons are deployed at the core of our model along with a front-end application that interacts with the beacons to personalize and satisfy the tourists' demands. By combining smartphone capabilities with beacon technology, messages can be sent to tourists at the point they are most relevant, based on their proximity to the beacon. This would be especially effective on walking tours around a city. When tourists walk past a site of historical or ideological importance, an ancient ruin for example, they can be sent prompts or messages which describe what they are seeing and what it means in terms of the destination's history or culture. Our implementation emulates a tourist scenario enabled by BLE beacons and cloud resources using our experimental model. Moreover, we examine the practical implications about the role and use of IoT in tourism which should enable the industry to keep up with global tourism trends and put them on an equal footing with other participants in the online tourism and travel market. © 2023 IEEE.

关键词： Internet of things

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Quantitative Assessment of XAI Methods for COVID-19 Detection: A Comparative Approach

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SN computer Science 2025年第2期6卷 1-16页

作者： Rajpoot, Reenu Jain, Sweta Semwal, Vijay Bhaskar Singh, Deepankar Department of Computer Science and Engineering Maulana Azad National Institute of Technology MP Bhopal 462003 India

With the growing use of deep learning models in medical imaging, it is critical to interpret these models to ensure precise diagnoses. A recent study using the SARS-COV2 dataset evaluated the efficiency of several Explainable Artificial Intelligence (XAI) techniques—LIME, GradCAM, SHAP, and GradCAM++—on deep learning models for detecting anomalies in COVID-19 CT scans. The study assessed five models based on accuracy, precision, recall, and F1-score. ResNet50 outperformed other models, achieving the highest accuracy of 95.26%, along with the best precision (96.24%), recall (94.14%), and F1-score (96.17%). The study further compared the effectiveness of different XAI techniques applied to ResNet50. Among the techniques, GradCAM++ provided the best interpretability, achieving a confidence score of 99.41% and the lowest prediction entropy of 5.21, outperforming GradCAM, LIME, and SHAP. GradCAM achieved a confidence score of 98.82% with a prediction entropy of 9.25, while LIME and SHAP showed lower confidence scores of 95.23% and 78.91% respectively, with higher prediction entropy values. The attribution maps revealed that the models focused on different regions of the CT scans. The quantitative analysis highlighted significant differences in model performance and XAI technique efficiency. These findings can guide the selection of suitable XAI techniques and deep learning models for similar applications in the future. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Black Box Explainable artificial intelligence GradCAM GradCAM++ LIME SHAP

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Intelligent Secure Authentication of Smart Devices Using Eye Tracking: Design and Implementation 18

Intelligent Secure Authentication of Smart Devices Using Eye...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Upreti, Rohit Garg, Divisha Singh, Harpreet Rana, Prashant Singh Thapar Institute of Engineering & Technology Computer Science Engineering Department Punjab Patiala India

ISBN: (纸本)9798350391725

Pattern-based, password-based, and PIN-based authentication techniques are typically the most common among smartphone users. However, according to prior studies these authentication techniques are susceptible to keylogger, smear, and shoulder surfing assaults. These attacks take advantage of data from the user's handwriting or finger movements. Some research studies restrict eye-tracking authentication for smartphone users because of the regularly controlled environmental circumstances. Currently, no smartphone models support eye-tracking-based authentication. This paper introduces a novel eye-tracking user identification technique using a machine-learning model with inception modules and an ensemble structure for smartphones that eliminates the need for unique hardware or calibration procedures. The goal is to have a favorable influence on the development of an eye-tracking-based method for smart device user verification. The eye movement trajectories as the input to build a character for authentication. These are recorded by the front camera of a smartphone and show the user's eye movement direction. The proposed application would assist users in situations where they can't touch their screens for various reasons, such as dirty or stained hands. The proposed method reduces the preprocessing steps typically used in conventional methods. A prototype of the security-based application would be constructed to test the model on a smartphone. © 2024 IEEE.

关键词： Eye movements

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Blockchain Breakthrough: A Review of its Role in Modern technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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Secure probe-based latency measurement using NTP and PTP for optimized gateway placement in LoRaWAN

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International Journal of Information technology (Singapore) 2025年 1-10页

作者： Bhatnagar, Kumar Vaibhav Kushwah, Rashmi Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology Uttar Pradesh Noida 201304 India

The paper presents a novel framework for optimizing LoRaWAN gateway placement to enhance network performance and reliability. By integrating Network Time Protocol (NTP) for global synchronization and Precision Time Protocol (PTP) for local precision, the approach ensures accurate and secure latency measurements. A robust hash algorithm is employed to safeguard the integrity of latency data. The Performance Efficiency Index (PEI) is introduced as a comprehensive metric to evaluate key performance indicators such as latency, throughput, energy efficiency, and packet delivery ratio (PDR). The model incorporates factors like geographical distribution, signal strength, and gateway density to achieve optimal placement. Simulation results demonstrate the framework’s effectiveness in improving network latency, coverage, energy efficiency, and security, providing actionable insights for LoRaWAN-based IoT network design. © Bharati Vidyapeeth's institute of computer Applications and Management 2025.

关键词： Gateway placement LoRaWAN Network performance NTP PTP Timestamp

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DSPWE: distributed sentiment polarized word embedding for voluminous textual data

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Journal of Ambient Intelligence and Humanized Computing 2023年第7期14卷 9419-9433页

作者： Dhanani, Jenish Mehta, Rupa Rana, Dipti P. Department of Computer Science and Engineering S. V. National Institute of Technology Surat India

Sentiment analysis provides an insight of the public’s emotions towards governments, organizations, product developers etc. Word embedding is a group of feature learning techniques transferring the raw textual data into real-valued vectors, which can be used to analyze the sentiment of text. Word2vec is a prominent word embedding approach designed to learn the semantic specific low-dimensional word vectors using contextual information. It has demonstrated poor performance for voluminous text data due to in-memory processing of the entire vocabulary and associated word embeddings. Additionally, it does not effectively preserve the sentiment information, which results in the closeness of opposite sentiment words in embedding space causing performance loss. In fulfillment of the aforesaid challenges, this paper presents a novel Distributed Sentiment Polarized Word2Vec (DSPW2V) approach to compute the sentiment polarized word embedding in a distributed computing framework (i.e. MapReduce and Spark) over the cluster of commodity hardware. The word embedding and sentiment polarity are computed simultaneously to generate the sentiment polarized word embedding. The proposed approach is compared with existing non-distributed approach SPW2V (Sentiment Polarized Word2Vec), and existing distributed approach of word2vec from Spark’s MLLib library. Experiment results demonstrate the outperforming reduction in execution time with comparable accuracy and F1-Score, which reveal the superiority of the proposed approach. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： MapReduce

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PSO optimised workflow scheduling and VM replacement algorithm using gaming concept in cloud data centre

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International Journal of Cloud Computing 2023年第6期12卷 586-604页

作者： Narayani, Raman Banu, Wahab Aisha Department of Computer Science and Engineering BSA Crescent Institute of Science and Technology Tamil Nadu Chennai India

The principal features of cloud computing are dynamic resource allocation and its pricing nature. This paper implies an algorithm that provides resources based on the demand to users in the cloud infrastructure as a service (IaaS) environment. This paper proposes an algorithm that optimises workflow scheduling and VM replacement algorithm using particle swarm optimisation with the gaming theory concept (GTPSO-WSP). It enhances system performance with metrics such as cost and makespan. The proposed algorithm in the cloud computing environment has two phases. In the first phase, the scheduler allocates the resources to the physical server based on a static scheduling algorithm. During the second phase, the proposed system applies the dynamic reconfiguration based on the GTPSO-WSP algorithm for reducing the cost and makespan of the workflow. In GTPSO-WSP, the multi-start method gives a solution to particle premature convergence. However, the experimental analysis in the WorkflowSim environment improves the makespan and monitory cost. The observed results indicate performance improvement of 4% in terms of makespan and 9% in terms of cost while comparing GTPSO-WSP with the traditional particle swarm optimisation (PSO) and Cuckoo search algorithm. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Resource allocation

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Forecasting Diabetes: Leveraging ML for Early Detection and Prevention 8

Forecasting Diabetes: Leveraging ML for Early Detection and ...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Shah, Dhwanit Vora, Akshat Joshi, Devam Swain, Debabrata Bhilare, Amol Pandit Deendayal Energy University Computer Science & Engineering Department Gandhinagar India Vishwakarma Institute of Technology Computer Engineering Department Pune India

ISBN: (纸本)9798350391770

Diabetes is one of the increasing chronic diseases that must be treated well as soon as it is detected. In today's world, diabetes is the most occurring disease. Diabetes is mainly of two types: Type 1 and Type 2 diabetes. The majority of the population is affected by Type 2 diabetes. Over the period, Type 2 Diabetes causes severe damage. Similarly, Type 1 can also cause serious damage, but its prevention is not known yet. So, modern technologies like machine learning would be helpful in predicting diabetes status. In the conventional healthcare system, the diagnosis of this disease primarily depends upon the test reports and the doctor's experience. It may not be accurate every time. Machine learning is a method of classifying different instances by utilizing the knowledge gained using historical data. In this work, the diabetes patient dataset available in the Kaggle repository is used. Different preprocessing methods are used for data cleansing efficiently. This cleaned data can be used for model training. After applying all classification algorithms, the three machine learning algorithms models, such as Logistic Regression (LR), Random Forest (RF), and KNN, are combined to form a hybrid classifier using stacking-based ensemble learning. The proposed system has shown the classification accuracy of 97.57%. © 2024 IEEE.

关键词： Random forests

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Empowering agricultural security with IoT and deep learning driven real-time alert systems

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International Journal of Information technology (Singapore) 2024年第7期16卷 4627-4635页

作者： Singh, Priya Krishnamurthi, Rajalakshmi Department of Computer Science Jaypee Institute of Information Technology

In the realm of smart agriculture, our primary objective is to enhance security in the agriculture field by combining IoT and computer vision technologies. By leveraging these advanced tools, we strive to protect field crops from unauthorized access, aligning with the core principles of smart agriculture that prioritize the safeguarding of agricultural assets. Various governmental initiatives have been innovated to tackle the issue of ensuring food security. However, the majority of farmers face limitation due to insufficient knowledge and financial means, to effectively manage issues such as field monitoring and object detection. Hence, we aim to create an energy-efficient real time system that intelligently detects objects and sends alerts to farmers specifically designed for agricultural purposes. By integrating ultrasonic sensors, Raspberry Pi edge, and real-time trained object detectors like Embed-YOLOv3 and EmbedT-YOLOv3, we achieve superior accuracy, faster processing, and enhanced speed. This system efficiently identifies and categorizes objects, instantly notifying farmers through a real time android application. Identification of objects followed by immediate action can preserve yields, significantly reducing crop damage and strengthening the country’s economy. This proactive approach can alleviate distress among numerous farmers. Achieving an exceptional precision rate of 97%, recall rate of 96%, and accuracy of 95.86%, our system harnesses low-power IoT devices coupled with deep learning to deliver robust crop protection in the agricultural field. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Deep learning Internet of things (IoT) Object detection Raspberry pi Ultrasonic sensors YOLOv3

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