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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Venketesh M. Caleb Gunalan Manoj Hariharan R Rahul S Surender R Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu India B.Tech Computer Science and Engineering Kalasalingam Academy of Research and Education Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Remote disk imaging is an emerging cybersecurity risk that compromises sensitive information integrity. Malicious actors exploit legitimate tools like dd and Clonezilla to clone and exfiltrate data remotely. This paper introduces a novel anti-remote disk imaging system that integrates process monitoring, dynamic firewall configurations, and full-disk encryption to mitigate unauthorized disk access. Unlike existing singular approaches, our unified solution offers real-time detection, prevention, and administrative alerts. Comparative analyses reveal that our tool outperforms existing techniques with a 92% effectiveness rate and an average response time of 3 seconds while maintaining low resource usage. These results highlight its practicality for safeguarding critical systems in resource-constrained environments. Remote disk imaging poses challenges such as real-time detection, resource constraints, and distinguishing between legitimate and malicious activities. Existing techniques, like full-disk encryption and firewall-based blocking, face limitations in adaptability and performance. This paper introduces a comprehensive system that addresses these challenges by integrating process monitoring, dynamic firewall configurations, and encryption to prevent unauthorized disk imaging.

关键词： Process monitoring Firewalls (computing) Prevention and mitigation Imaging Machine learning Real-time systems Software Encryption Time factors Information integrity

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Customer Analytics using Surveillance Video

arXiv

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arXiv 2025年

作者： Ijjina, Earnest Paul Joshi, Aniruddha Srinivas Kanahasabai, Goutham Priyanka, P. Keerthi Department of Computer Science and Engineering National Institute of Technology Warangal506004 India B.Tech Department of Computer Science and Engineering National Institute of Technology Warangal506004 India

The analysis of sales information, is a vital step in designing an effective marketing strategy. This work proposes a novel approach to analyse the shopping behaviour of customers to identify their purchase patterns. An extended version of the Multi-Cluster Overlapping k-Means Extension (MCOKE) algorithm with weighted k-Means algorithm is utilized to map customers to the garments of interest. The age & gender traits of the customer;the time spent and the expressions exhibited while selecting garments for purchase, are utilized to associate a customer or a group of customers to a garments they are interested in. Such study on the customer base of a retail business, may help in inferring the products of interest of their consumers, and enable them in developing effective business strategies, thus ensuring customer satisfaction, loyalty, increased sales and profits. Copyright © 2025, The Authors. All rights reserved.

关键词： Sales

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Dynamic Website Authentication Using Time-based One-Time Password (TOTP) for Enhanced Security

Dynamic Website Authentication Using Time-Based One-Time Pas...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： M Caleb Gunalan bavani K Mathew Hayden Manoj Hariharan R Rahul S B.Tech Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In today's digital landscape, the proliferation of fake websites poses significant threats to user security, trust, and data integrity. This paper presents a novel solution to address this issue through the implementation of a dynamic website authentication system using Time-based One-Time Password (TOTP) verification. The system allows websites to register securely with a central authentication server, generating unique TOTP codes that users can verify to determine the authenticity of the website. Website owners are issued a secret key upon registration, which is used to generate TOTP codes that refresh every 30 seconds. This project emphasizes user-friendly verification mechanisms and secure key management to protect users from phishing attacks. The system architecture, implementation, and potential applications are discussed, along with future directions for integrating multi-factor authentication and expanding security features.

关键词： Codes Multi-factor authentication Phishing Prevention and mitigation Data integrity Systems architecture Passwords QR codes Registers Servers

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Text recognition by air drawing 4

Text recognition by air drawing

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4th International Conference on Computational Intelligence and Communication technologies, CCICT 2021

作者： Patel, Jay Mehta, Umang Panchal, Kevin Tailor, Dev Zanzmera, Devam Pandit Deendayal Energy University B.Tech in Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665423922

In this paper, the text drawn by the user in the air is captured by the computer's camera, followed by the identification of that text. So, the video camera will be turned on at the time of capturing the written text. Now the particular object is defined based on its colour to detect a movement done by the user. The colour is captured by the lower and upper bound of HSV (Hue Saturation Value), which finally leads to object detection at every instant. Lastly, the text will be recognized by the trained model. The model is trained by CNN (Convolution Neural Network) with an accuracy of 98.64% (training) and 98.24% (testing). For completion of the project, OpenCV, python programming language, and its libraries are used. This project requires only a camera and a defined object. © 2021 IEEE.

关键词： Python

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Plant Health Prediction Using RGb-D Method and VGG16 Model

Plant Health Prediction Using RGB-D Method and VGG16 Model

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Data engineering and Communication Systems (ICDECS), International Conference on

作者： Shivansh Tiwari Shyant Raj Chaudhary Prashant Pal Shashank Kumar Singh Saurabh bansod National Institute of Electronics and Information Technology Aurangabad B.Tech (Electronic System Engineering) National Institute of Electronics and Information Technology Aurangabad Scientist‘B’ National Institute of Electronics and Information Technology Aurangabad Scientist‘C’

Traditional plant disease detection methods rely on human expert visual inspection, which can be time-consuming and subjective. As a result, automated and objective methods for detecting plant health and disease are required. In this study, we offer a unique method for detecting plant health utilising RGb-D pictures and the VGG16 architecture. Plant health detection is a critical component of precision agriculture, which seeks to monitor and manage plants in an efficient and sustainable manner. In terms of precision and efficiency, our method exceeds previous solutions. On the validation set, our model achieves an accuracy of 98.9%, which is much greater than the accuracy of previous approaches. Moreover, in contrast to current approaches, our technique necessitates reduced computational resources and training/prediction time. Finally, utilising RGbD pictures and deep learning algorithms, we suggest a viable solution to the difficulty of plant health identification. Our method can give precise and timely information regarding plant health status, which can help to improve agricultural efficiency and sustainability.

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Precise Payload Delivery via Unmanned Aerial Vehicles: An Approach Using Object Detection Algorithms

arXiv

引用

arXiv 2023年

作者： Vadduri, Aditya benjwal, Anagh Pai, Abhishek Quadros, Elkan Kammar, Aniruddh Uday, Prajwal B.Tech Mechatronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India B.Tech Electronics & Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India B.Tech Mechanical Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

Recent years have seen tremendous advancements in the area of autonomous payload delivery via unmanned aerial vehicles, or drones. However, most of these works involve delivering the payload at a predetermined location using its GPS coordinates. by relying on GPS coordinates for navigation, the precision of payload delivery is restricted to the accuracy of the GPS network and the availability and strength of the GPS connection, which may be severely restricted by the weather condition at the time and place of operation. In this work we describe the development of a micro-class UAV and propose a novel navigation method that improves the accuracy of conventional navigation methods by incorporating a deep-learning-based computer vision approach to identify and precisely align the UAV with a target marked at the payload delivery position. This proposed method achieves a 500% increase in average horizontal precision over conventional GPS-based approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Monitoring state of mind using Natural Language Processing 7

Monitoring state of mind using Natural Language Processing

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7th IEEE International Conference on Advances in Computing, Communication and Control, ICAC3 2021

作者： Saketh, Vemula Sai Khatravath, Srinivas Guntupalli, Yashvanth Kumar PES University B.Tech Computer Science Bangalore India IIT Kharagpur M.Tech Electronics and Electrical Communication Kharagpur India

ISBN: (纸本)9781665426343

State of mind is essential in every person's life to keep their life stable, calm and peaceful. The key factors that contribute towards them are mood, emotion and thoughts. Monitoring them is key to a person's overall development and allows family and friends to support them to a greater extent. One aspect of getting a report on this is using the words being typed by the person on their computer or mobile, browser history, songs and movies played. This paper proposes a sentiment analysis system where information about mood and thoughts are conveyed by means of natural language processing. These logs captured during each time session are classified to gain insights about the person. This reduces the likelihood of any drastic decisions taken by the person and provides a better means of conveying the information and maintaining communication between them and their relations. © 2021 IEEE.

关键词： Sentiment analysis

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State of Charge Estimation Using Deeplearning

State of Charge Estimation Using Deeplearning

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Innovations in Power and Advanced Computing technologies (i-PACT)

作者： Adwyck Gupta Siddharth S Harshit Sharan R Thirumalaivasan M Janaki B.Tech Electrical and Electronics Vellore Institute Of Technology Vellore India School of Electrical Engineering Vellore Institute Of Technology Vellore India

State of charge (SOC) estimation is an intricate task due to its dependencies on various battery parameters, external factors, and applications. Current methods implemented for state of charge estimation are prone to error accumulation, affected by fluctuations in external factors, or have high model complexity making them computationally intensive. This paper presents a deep-learning based approach using a feed-forward neural network with optimized hyperparameter selection for the real-time state of charge estimation of a Li-ion battery using an inexpensive hardware setup. Input to the model is circuit and ambient parameters which include current, voltage, average current, average voltage, and ambient temperature measured using appropriate sensors. The performance of the trained model is tested for various temperature settings showing highly accurate state of charge estimation results with lower RMSE than traditional methods. The trained model deployed in hardware setup shows accurate and reliable results for predicting the batteryâ€™s state of charge.

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Deep Learning for Glioblastoma Subtyping: Leveraging DenseNet-201 in brain Tumor Radiogenomic Classification

Deep Learning for Glioblastoma Subtyping: Leveraging DenseNe...

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Cognitive Robotics and Intelligent Systems (ICC - RObINS), International Conference on

作者： S. Alvin Jesuraj S. V. Evangelin Sonia C. P. Shirley B.Tech Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India Karunya Institute of Technology and Sciences Coimbatore Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

This research investigates the application of DenseNet-201, a deep convolutional neural network architecture, in the RSNA-MICCAI brain Tumor Radiogenomic Classification aimed at predicting the genetic subtype of glioblastoma using MRI imaging data. This study demonstrates the effectiveness of DenseNet-201 in accurately classifying glioblastoma cases based on MGMT promoter methylation status, a critical biomarker influencing treatment outcomes. Through comprehensive experimental evaluations, including training, validation, and testing phases, DenseNet-201 exhibits robust performance metrics such as high accuracy, precision, recall, F1-score, and AUC-ROC values. These results highlight the model's ability to effectively distinguish between MGMT promoter methylation-positive and negative glioblastoma cases, offering valuable support for clinical decision-making in treatment planning and prognosis assessment. Leveraging deep learning techniques and MRI imaging data, DenseNet-201 holds promise as a powerful tool for enhancing the understanding of glioblastoma genetics and guiding personalized therapeutic interventions, ultimately contributing to improved patient outcomes in brain cancer management.

关键词： Deep learning Training Magnetic resonance imaging Medical services Genetics Planning Prognostics and health management

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Lung Cancer Detecting using Radiomics Features and Machine Learning Algorithm

Lung Cancer Detecting using Radiomics Features and Machine L...

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technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： M.N. Rajesh Nikita Tanni Z. Tanveer baig Electronics and Communication Engineering Jain (Deemed-to be-University) Bengaluru India B.Tech in Computer Science and Engineering (Data Science) Christ (Deemed to be University) Bengaluru India Dept of IT Amity University Taskent Uzbekistan

Lung Cancer Incidence across the globe is the second leading cancer type tallying to about 2,206,771 during 2020 and is estimated to rise to about 3,503,378 by 2040 for both male and female sexes and for all ages accounting to 11.4% as per Globocan 2020 [1]. It is the leading death-causing cancer. Lung Cancer [2] in broad terms encompasses Trachea, bronchus as well as lungs. Purpose: The study is aimed to understand Radiomics based approach in the identification as well as classification of CT Images with Lung Cancer when Machine Learning (ML) algorithms are applied. Method: CT Image from LIDC-IDRI [4] Dataset has been chosen. CT Image Dataset was balanced and image features by PyRadiomics library were collected. Various ML features classification algorithms are utilized to create models and matrices adopted in judging their accuracies. The models, distinctive capacity is assessed by receiver operating characteristics (ROC) analysis. Result: The Accuracy scores and ROC-AUC values obtained for various Classification Model are as follows, for Ada boosting, the accuracy score was 0.9993 ROC-AUC was 0.9993 and followed by GbM, the accuracy score was 0.9993, was 0.9992. Conclusion: Extracting texture parameters on CT images as well as linking the Radiomics method with ML would categorize Lung Cancer commendably.

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