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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic Engineering

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computer Systems science & Engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana Murthy Sree Jagadeesh Malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large number of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(MPSO) intrusion detection system(IDS) kalman filter

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Exploratory Data Analysis using Autoviz for Machine Learning Classification Problem

Exploratory Data Analysis using Autoviz for Machine Learning...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Guru Prasad, M.S. Praveen Gujjar, J. Kotiyal, Arnav Prasanna Kumar, H.R. Devadas, Raghavendra M Jahan, Afsar Department of Computer Science and Engg. Dehradun India Faculty of Management Studies Bengaluru India Visvesvaraya Technological University PESITM Dept. of Information Science & Engg. Shivamogga India Gitam School of Technology Department of Computer Science and Engineering Gitam University Bengaluru India Department of Computer Application Dehradun India

ISBN: (纸本)9798350376470

As the field of machine learning continues to advance, the importance of effective exploratory data analysis (EDA) cannot be overstated, especially in the context of classification problems. This research paper introduces an innovative and automated approach to exploratory data analysis using Autoviz, a powerful Python library designed to streamline the visualization process for machine learning datasets. The study aims to demonstrate the efficacy of Autoviz in meaningful insights, facilitating feature understanding, and supporting data preprocessing for classification tasks. Furthermore, the paper discusses how Autoviz assists in feature selection by highlighting the relevance and importance of each feature about the target variable. This feature-centric analysis contributes to optimizing machine learning models by guiding the selection of relevant input features, thereby improving model accuracy and interpretability. Autoviz is a Python library designed to automate the exploratory data analysis (EDA) process for machine learning tasks. It is specifically tailored to generate a variety of visualizations that aid in understanding the structure and characteristics of a dataset. The results indicate that Autoviz significantly reduces the time and effort required for exploratory data analysis while maintaining the quality and depth of insights obtained. The differences between Autoviz and Pycaret is discussed in this paper. It has been observed that AutoViz automates the process of exploring and analyzing data, while PyCaret is designed for the comparison, construction, and tuning of machine learning models. © 2024 IEEE.

关键词： Adversarial machine learning

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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2023 International Conference on computer science, Information technology and Engineering, ICCoSITE 2023

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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Elevating Cybersecurity in Defence: A Collaborative and Technologically Driven Approach 6

Elevating Cybersecurity in Defence: A Collaborative and Tech...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Ansar, Syed Anas Aggarwal, Shruti Arya, Swati Sayeed, Mohd Asim Soni, Nupur Pathak, Prabhash Chandra School of Computer Application Babu Banarasi das University Lucknow India Thapar Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350304480

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices. © 2023 IEEE.

关键词： artificial intelligence blockchain cybersecurity machine learning national security quantum computing zero-trust security

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Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework 4

Oddity Based Botnet Recognition Utilizing AI Model in the In...

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4th International Conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： Rani, Rupa Chakraborty, Sudeshna Singh, Pooja Varshney, Neha Abes Institute of Technology Computer Science & Engineering U.P Ghaziabad India Banasthali Vidyapith Department of Computer Science & Engineering Rajasthan India School of Computer Application and Technology Galgotias University UP India Abes Institute of Technology Department of Computer Science & Engineering U.P Ghaziabad India

ISBN: (纸本)9798350393248

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with © 2023 IEEE.

关键词： Cybersecurity

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FViT: Finetuned Vision Transformer for Improved Brain Tumor Classification

FViT: Finetuned Vision Transformer for Improved Brain Tumor ...

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2025 International Conference on Ambient Intelligence in Health Care, ICAIHC 2025

作者： Chandra, Lalini Chandra, Tej Bahadur Rathy, Anirud Rai, Anushka National Institute of Technology Department of Computer Application Chhattisgarh Raipur India Bennett University School of Computer Science Engineering and Technology UP Greater Noida India

ISBN: (纸本)9798331530259

Timely detection and treatment of brain tumors (BT) remain a critical challenge due to their rapid growth and complex morphologies. Recent deep learning (DL) approaches and large vision models (LVMs) have promising potential in early and accurate diagnosis of BTs. However, the optimal tuning of hyperparameters is crucial for increasing the model's efficacy and robustness. Large vision models have significant potential for improving the accuracy of diagnosing BT using MRI images. Additionally, we evaluated and compared the performance of recent DL models ResNet50, VGG16, and Long Short-Term Memory (LSTM) using BT MRI dataset. Through rigorous experimentation, our results highlight the superior diagnostic capabilities of the FViT model with an accuracy (ACC) of 99.68% and an F1-score (FS) of 99.68% for BT classification (using training-validation set). This result is also verified with high ACC of 99.31% and FS of 99.31%using testing set. The study demonstrates how well FViT model performs in classifying BT accurately, showcasing its potential in clinical settings. These findings emphasize the critical role of fine-tuning LVM models in advancing BT diagnosis through brain MRI images. © 2025 IEEE.

关键词： Diagnostic radiography

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Exploring Consumer Behavior in E-Commerce with AI Personalization and Market Trends Analysis Models 3

Exploring Consumer Behavior in E-Commerce with AI Personaliz...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kumar, Gaurav Devayani, G. Saraswathi, C. Melba Kani, R. Dongre, Sandeep Mallick, M. S. Mohamed School of Computer Application Lovely Professional University Phagwara India M.Kumarasamy College of Engineering Department of Computer Science and Business Systems Karur India School of Management Studies Bannari Amman Institute of Technology Erode India Mar Ephraem college of Engineering and Technology Kanyakumari India Nagpur India Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798331508456

Online retailers must monitor and respond to changing consumer trends to stay competitive. AI-driven customization's revolutionary impact on market trends is the focus of this study on online buying and customer behavior. As AI advances, e-commerce platforms will be able to customize user experiences. Preprocessing, feature extraction, and model training comprise the AI-driven personalization process. Customer data from online marketplaces is collected first. It manages missing values, balance the data, encode categorical variables, scale the features, and minimize the dimensions as part of our thorough data preprocessing to ensure fair and applicable results. To trained the algorithms with CNN and Convolutional LSTM Based on key criteria, the AI model beat LSTM and CNN. It provides personalized insights and recommendations with an average accuracy of 92.03%. Personalized experiences and product suggestions enabled by AI-powered personalization have transformed e-commerce. It's important to use smart algorithms to engage customers and examine large databases. This proposed approach to shows that AI can transform online shopping and improve customer experiences. © 2025 IEEE.

关键词： Marketplaces

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Employing Sequence-to-Sequence Stacked LSTM Autoencoder Architecture to Forecast Indian Weather 2

Employing Sequence-to-Sequence Stacked LSTM Autoencoder Arch...

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

作者： Maharatha, Arpita Das, Ratnakar Mishra, Jibitesh Nayak, Soumya Ranjan Aluvala, Srinivas Dept. of Computer Science & Application Odisha University of Technology and Research Bhubaneswar751029 India School of Computer Engineering KIIT Deemed to Be University Odisha Bhubaneswar751024 India Department of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

One of the buzz words in current research depends for a weather forecast is the atmospheric temperature, and this value may change at any time. Predicting air temperature exactly is necessary since it is particularly important for various communities and events. In today's world of massive amounts of data and unforeseen data fluctuations, long-short term memory (LSTM), a sort of neural network method, is becoming more and more popular. The present work employed a sequence to sequence LSTM autoencoder to forecast Indian weather patterns. The prediction model was trained using historical data obtained from the Khordha district administration. The proposed method primarily emphasizes two phases of the procedure. The present study involves an examination of a proposed model, wherein a comparison is made between four distinct variants of LSTM techniques: vanilla LSTM, stacked LSTM, bidirectional LSTM, and convLSTM. The objective of this analysis is to predict temperature patterns. Additionally, this paper utilizes an LSTM autoencoder model to forecast the temperature for the subsequent two-year period in the Khordha district of Odisha, India. The experimental investigation demonstrates that the utilization of Stacking encoder-decoders with bidirectional LSTM cells leads to a notable enhancement in the accuracy of the model, resulting in maximum efficacy. A notably lower Mean Absolute Error (MAE) result suggests that the 9-layer stacked autoencoder model has superior performance in predicting both the minimum and maximum temperature. © 2024 Elsevier B.V.. All rights reserved.

关键词： Weather forecasting

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Detection and Alerting of Driver Drowsiness using a Machine Learning Approach

Detection and Alerting of Driver Drowsiness using a Machine ...

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2023 International Conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Zade, Amol Gaikwad, Amit Dhunde, Ranjit Thakare, Shailesh Vaidya, Rajeshri G H Raisoni University Department of Computer Science Dhoda Borgaon India SVKM's Nmims School of Technology Management Department of Computer Science Navi Mumbai India Vidhyabharati Mahavidyalaya Department of Computer Application Amravati India Sipna COET India Department of Computer Science Amravati India

ISBN: (纸本)9798350315455

Driver fatigue is a significant causative factor to accidents worldwide. To signify this issue, we have created a prototype system for detecting tiredness in drivers. This system monitors the driver's level of drowsiness by tracking their eye movements and issuing an alert if prolonged eye closure is detected. Our focus is on improving driver safety while maintaining a non-intrusive approach. In this project, we utilize the Haarcascade library in OpenCV to detect facial features and accurately detect eye blinks. Through research in the field of computer engineering, we aim to prevent accidents caused by fatigued or sleepy drivers. The project discusses various strategies, their outcomes, and the implemented fixes. By providing insights into the system's functionality and potential adjustments, the article aims to enhance the system's overall effectiveness. Furthermore, the project offers observations and recommendations for future optimization in this field, with the goal of maximizing utility at a lower cost and promoting safer roads. The article provides a practical understanding of the system's operation and serves as a valuable resource for advancing research and development efforts in driver drowsiness detection. © 2023 IEEE.

关键词： blink pattern Driver drowsiness detection eye detection OpenCV SVM

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