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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Aarthirani, M. Rejitha, D. Arulmozhiselvan, L. Meenakshi College of Engineering Department of Computer science and engineering Chennai India

ISBN: (纸本)9798350370249

Cardio Vascular Disease poses a substantial worldwide health problem, highlighting the urgent requirement for the development of precise and more efficient diagnostic techniques. Existing studies have provided useful insights in this area, but there is still a need to improve the models for prediction and fill the gaps in the current detection methods. Traditional models often overlook the subtle nuances and interdependencies between different health determinants. The Contextual Feature Learning model introduces a sophisticated advancement in Machine Learning, tailored to elevate the accuracy of Cardio Vascular Disease predictions. This study aims to introduce a prediction model for Cardio Vascular Disease using the deep Contextual Feature Learning techniques. The proposed Cardio Vascular Disease model achieves the accuracy, precision, recall, F1 score and AUC of 100%, 95.7%, 97.6%, 96.3% and 96.9%. The findings exhibit that Contextual Feature Learning model effectively captures and use complex feature interferences, prompting highly accurate predictions. © 2024 IEEE.

关键词： Cardio Vascular Disease Contextual Feature Learning Deep Learning Heart Disease Dataset Machine Learning

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Implications of Virtual Reality on Environmental Sustainability in Restaurants based on AI 10

Implications of Virtual Reality on Environmental Sustainabil...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Adithya Narayanan, C. Bharath, E. Vijayakumar, R. Rajalakshmi Engineering College Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350353068

This talks about the various ways that augmented reality (AR) affects environmental sustainability in the restaurant business. When combined with artificial intelligence (AI), augmented reality has the power to completely change the restaurant industry by improving eco-friendly operations and changing many facets of their operations. This article examines how AR and AI might help restaurants become more environmentally sustainable. It focuses on important topics including menu design, waste management, customer interaction, and operational efficiency. Easy access to menus and simplicity are two important elements that make restaurant food ordering simpler. The eating experience for customers is entirely revolutionized by the HOLOGRAM menu. Current programs offer an application that eateries can utilize to include their menus into tablets with CONTROL & HOLOGRAM functionality, making it simpler for customers to swipe, swipe, and tap through menus. The objective of this project is to give the restaurants a tablet menu that suggests items using a novel recommendation algorithm. We store the database on a cloud-based server, which is safe & affordable. © 2024 IEEE.

关键词： Augmented reality

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Campus Surveillance with Violence Insight and Suspect Profiling: A Survey

Campus Surveillance with Violence Insight and Suspect Profil...

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2024 International Symposium on Computing and Intelligent Systems, SCI 2024

作者： Deepak, N.R. Paital, Santosh Kumar Kulsum, Umme Afreen, Shaima Verma, Shrey Department of Computer Science and Engineering HKBK College of Engineering Bengaluru India

Campus safety is a paramount concern in educational institutions worldwide, especially in combating the prevalence of campus violence. This comprehensive review utilizes advanced image processing and computer vision technologies to proactively tackle security challenges. The appraised surveillance system integrates sophisticated methods, employing Convolutional Neural Networks (CNNs), bidirectional LSTM models, and the Convolutional 3D (C3D) network architecture. Leveraging transfer learning from ImageNet and strategic data augmentation, the system aims to refine its capabilities for robust performance. Through the integration of advanced techniques in image analysis and data processing, the system seeks to enhance security measures while respecting individual privacy. By leveraging an integrated surveillance framework, the emphasis is on delivering timely alerts to security personnel and providing comprehensive behavioural profiles. This research significantly contributes to the progression of security systems within educational contexts, emphasizing the significance of employing state-of-the-art computational methodologies. By prioritizing the creation of safer educational environments on a global scale, this approach stands as a pivotal stride in fortifying campus security measures. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： computer vision

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DNA Computing with Water Strider Based Vector Quantization for Data Storage Systems

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computers, Materials & Continua 2023年第3期74卷 6429-6444页

作者： A.Arokiaraj Jovith S.Rama Sree Gudikandhula Narasimha Rao K.Vijaya Kumar Woong Cho Gyanendra Prasad Joshi Sung Won Kim Department of Networking and Communications College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science Engineering Aditya Engineering CollegeSurampalemAndhra Pradesh533437India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Information and Communication Engineering Yeungnam UniversityGyeongsan-siGyeongbuk-do38541Korea

The exponential growth of data necessitates an effective data storage scheme,which helps to effectively manage the large quantity of *** accomplish this,Deoxyribonucleic Acid(DNA)digital data storage process can be employed,which encodes and decodes binary data to and from synthesized strands of *** quantization(VQ)is a commonly employed scheme for image compression and the optimal codebook generation is an effective process to reach maximum compression *** article introduces a newDNAComputingwithWater StriderAlgorithm based Vector Quantization(DNAC-WSAVQ)technique for Data Storage *** proposed DNAC-WSAVQ technique enables encoding data using DNA computing and then compresses it for effective data ***,the DNAC-WSAVQ model initially performsDNA encoding on the input images to generate a binary encoded *** addition,aWater Strider algorithm with Linde-Buzo-Gray(WSA-LBG)model is applied for the compression process and thereby storage area can be considerably *** order to generate optimal codebook for LBG,the WSA is applied to *** performance validation of the DNAC-WSAVQ model is carried out and the results are inspected under several *** comparative study highlighted the improved outcomes of the DNAC-WSAVQ model over the existing methods.

关键词： DNA computing data storage image compression vector quantization ws algorithm space saving

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A Comparative Analysis on Various Machine Learning Methods for GAN Based Video Anomaly Detection 7th

A Comparative Analysis on Various Machine Learning Methods f...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Chiranjeevi, Rahul Shyam, C. Hirthik Kaushiik, K. Kumar, P. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9783031699818

In recent years, surveillance has undergone tremendous change and developed into an essential instrument for maintaining security and keeping an eye on sensitive areas. This essay investigates the idea of what defines surveillance. It explores the crucial topic of anomaly detection, which is a vital component of contemporary surveillance systems. The expanding importance of the use of deep learning methods is highlighted in this paper’s discussion of current developments in surveillance technology. Through the processing of massive volumes of data, deep learning has transformed surveillance by allowing more precise and effective anomaly detection. Examining several kinds of deep learning techniques, their special qualities and uses are highlighted. This study concludes with an in-depth analysis of the monitoring, highlighting the role of deep learning in improving anomaly detection. It is an invaluable tool for researchers, professionals and decision makers interested in the development of surveillance technology and its use in different contexts. © IFIP International Federation for Information Processing 2024.

关键词： Contrastive Learning

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Transforming Human-Machine Interaction: Generative AI Virtual Asst

Transforming Human-Machine Interaction: Generative AI Virtua...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Babu, Gotru Jeevan Safrinfathima, S. Reddy, K. Deekshith Sen, Sanjay Kumar Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350359688

By creating a sophisticated virtual assistant (VA), this study investigates how generative AI might revolutionise human-machine interaction (HMI). Our suggested solution overcomes conventional constraints by utilising cutting-edge generative models, which allow the VA to produce contextually relevant responses and adjust to a variety of user inputs. The project's goals include increasing user engagement, broadening the Virtual Assistant's scope of work, and maximising the effectiveness of human-machine interactions overall. The paper explores the technical aspects of putting generative AI into practice, emphasising its real-time understanding of user intent, context, and sentiment. We tackle issues including potential biases, ethical concerns, and the requirement for ongoing learning to guarantee the responsible and efficient use of generative AI in virtual assistant applications. By means of this study, we hope to add to the current conversation on the direction of HMI by illuminating the revolutionary potential of Generative AI in producing more perceptive, flexible, and human-like virtual assistants. © 2024 IEEE.

关键词： Chatbots

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An Empirical Assessment of Botnets and Detection Methods 15

An Empirical Assessment of Botnets and Detection Methods

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Shivanna, D. Pooja, M.R. Department of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

ISBN: (纸本)9798331300579

a botnet is a group of devices which are controlled by the Attacker and used for fraud, scam and *** attacker is also known as *** Bots are used to conduct many activities varies from stealing user credentials, sending spam, stealing data, ransom ware, fraudulently clicking on ads or distributed denial-of-service (DDoS) *** traffic made up by bots is increasing day by day, now nearly a quarter of the whole *** them becomes an uphill task as the time *** developers are adopting new technologies at a rapid rate in an effort to avoid bot *** some bot problems are industry-specific, researchers found out that bot problems run across all *** top five industries which are targeted with bad bot traffic are the financial, education, IT & services industries, marketplaces, and *** assessment paper briefs the different botnet detection methods that exist at *** main aim of this paper is to give the insight knowledge of various bots and their implications on the present cyber world and also shedding some light on the current methods using to detect them... © Grenze Scientific Society, 2024.

关键词： Bot (Internet)

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UAV Detection and Classification using Deep Learning Techniques with YOLOv8 4

UAV Detection and Classification using Deep Learning Techniq...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Priyanka, R. Pooja, M.R. Vidyavardhaka College of Engineering Department of Computer Science & Engineering Mysuru India

ISBN: (数字)9798331528348

ISBN: (纸本)9798350352931

Accurately detecting and tracking drones in real-time poses main challenges due to factors such as varying scales, perspectives, occlusions, and environmental conditions. The proposed implementation helps in the identification of UAV objects in images and video footage using Deep Learning [DL] techniques, specifically the YOLOv8 architecture, by gathering a diverse dataset of images and video clips from the Roboflow 100 object detection benchmark dataset. Then the process is followed by pre-processing the data using resizing, normalization, and augmentation, which will be next initialized to the YOLOv8 architecture with pre-trained weights to estimate the performance using accuracy, precision, recall, and f1-score. The system finally involves integrating the classification system with a user-friendly interface for visualization of detection and classification in real time, where it results in which type of the drone is detected. The system achieved accuracy above 0.9 for all the classes of drones with different lights background and backgrounds in both images and video. © 2024 IEEE.

关键词： Drones

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A Deep Learning Model-Based Approach for Brain Tumor Detection in Low-Brightness and Low-Contrast MRI 4th

A Deep Learning Model-Based Approach for Brain Tumor Detecti...

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4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities, and Applications, ICMISC 2023

作者： Komal Kour, S. Ramachandrula, Aashrith Sagar Patnaik, Prerna Adilakshmi, T. Department of Computer Science and Engineering Vasavi College of Engineering Hyderabad India

ISBN: (纸本)9789819994410

Magnetic resonance imaging (MRI) has become a valuable diagnostic assessment means for the detection, segmentation, and characterization of brain tumors. However, low brightness and low contrast in MRI images pose a significant challenge for accurate tumor detection, especially in the early stages. Several approaches have been proposed to address this challenge, including image enhancement and filtering techniques. However, these methods often result in loss of image details, making it difficult to discern the tumor regions from the non-tumor ones. To overcome these limitations, deep learning-based approaches have gathered attention in recent years for their capability to automatically learn features from the input images and achieve high accuracy in various medical imaging tasks. The aim of our research is to present a deep learning-based methodology for detecting brain tumors in low-brightness and low-contrast MRI images. We employ a neural network with convolutions’ (CNN) architecture, which has been proven to be effective in acquiring complex image features. Previous studies have used deep learning techniques for brain tumor segmentation and detection (Ramin Ranjbarzadeh et al. in Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images [1]). However, these studies did not specifically address the problem of low brightness and low contrast in MRI images. In contrast, our proposed method is designed to capture the subtle differences between tumor regions and non-tumor regions in such MRI images. Our CNN model has been trained and validated on a larger dataset of MRI images, including both normal and tumor-containing images. Our results demonstrate that our proposed method achieves high accuracy and specificity in detecting brain tumors, even in low-brightness and low-contrast MRI images. Additionally, our method has the potential to aid healthcare professionals in precisely and promptly pronouncing tumors

关键词： Tumors

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Machine Learning Powered Genre Prediction for Next Level Book Recommendations 5

Machine Learning Powered Genre Prediction for Next Level Boo...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Julian, Anitha Pavithra, B. Saveetha Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

The proposed work delves into how recommender systems, like those on YouTube and Amazon, shape our online experiences, particularly in book recommendations. It addresses the challenge of the 'cold start problem' with the help of a Popularity-based recommender initially, adapting to personalized suggestions through Collaborative Filtering as users engage. This dual approach aims for a comprehensive system that overcomes challenges in new user on boarding and adapts to evolving preferences. The importance of continuous evaluation is emphasized for ongoing effectiveness. In a separate study, the research explores the application of Natural Language Processing (NLP) in predicting book genres. It uses algorithms to analyze textual data, employing techniques like Exploratory Data Analysis (EDA) to uncover patterns. The modeling phase involves text classification algorithms, contributing to a deeper understanding of genre prediction through the application of NLP techniques. © 2024 IEEE.

关键词： collaborative filtering continuous evaluation Exploratory Data Analysis (EDA) genre prediction Naive Bayes Natural Language Processing (NLP) Popularity-based recommendation Recommendation system text classification algorithms

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