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Analysing of diverse Stress pattern in ECG using Machine Learning Techniques 2

Analysing of diverse Stress pattern in ECG using Machine Lea...

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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Anbarasi, S. Dhanalakshmi, B. Department of Computer Applications BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India Department of Computer Science and Engineering BS Abdur Rahman Crescent Institute of Science and Technology GST Road vandalur Tamil Nadu Chennai India

ISBN: (数字)9781837242535

ISBN: (纸本)9781837242672

ECG signals are commonly used to diagnose various cardiac conditions, and the classification of these signals is an important task in Stress identification. With the advancement of ML techniques, several ML models have been developed to classify ECG signals with high accuracy. This paper aims to review recent studies that use ML models for ECG classification, equivalence to the working nature of different prototype, and discuss the challenges and limitations of these models. The study found that deep learning models, such as CNNs and RNNs, outperformed traditional ML models in terms of closeness and validity. The results also showed that the combination of multiple models could improve the classification performance. However, the study also highlighted the need for large annotated ECG datasets and the requirement of domain expertise for feature engineering. Overall, this review provides a complete analysis of the current state of ECG classification using ML models and identifies areas for future research and improvement. Even though the accuracy of ML models in ECG classification is not always perfect, they can still provide valuable information and support to healthcare professionals. In some cases, even a lower accuracy can still be useful, especially in the context of large-scale screening and monitoring programs, where the goal is to identify individuals who require further evaluation. In these cases, the high computational speed and scalability of ML models make them an attractive option, as they can quickly process large amounts of data and provide preliminary results that can be followed up with more comprehensive and accurate tests. Additionally, the results from ML models can also be used as a starting point for further analysis by healthcare professionals, who can use their domain knowledge to interpret the results and make informed decisions. Therefore, despite their limitations, ML models can still play an vital role in the field of Electrocardiogram cla

关键词： Convolutional neural networks

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Object Detection in Real-Time Surveillance Using Deep Learning-Based YOLO Framework

Object Detection in Real-Time Surveillance Using Deep Learni...

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Kukreti, Sanjeev Praveen, R.V.S. Bansal, Saloni Raju, Hemanth Singh, Navdeep Begum, Rowsonara Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India LTIMindtree Limited Telangana Hyderabad India GLA University Department of Computer Engineering and Applications Mathura India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India MLR Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

The continual growth of the computer vision and artificial intelligence fields overtime have provided the basis for more efficient and accurate surveillance. This paper examines the use of YOLO, a deep learning model that is used in the detection of objects, for use in real-time surveillance systems. The added benefits of identifying and labeling multiple objects in a single pass forward makes YOLO ideal for real-time surveillance. Unlike other approaches to object detection such as region-based methods or methods with multiple stages of detection, YOLO cast object detection as a regression problem where boxes and class probabilities are predicted jointly. This leads to a far improved performance when compared with their slower counterparts without the sacrifice of precision. In this work, the YOLO model is finetuned on a large database of surveillance videos to improve object detection at low lighting and in the presence of occlusion and crowding. The developed system has a high detection speed and accuracy;the application areas will include uses such as security inspection, traffic regulation, and anomaly detection. This is done to affirm that using YOLO is more efficient and faster than the other object detection algorithms in real-time surveillance tasks. The results indicate directions for further improvements to guarantee public safety and improve efficient surveillance systems. © 2025 IEEE.

关键词： Deep learning

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Spice Vision: Deep Learning Enhanced Spice Classification System 3

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Pujari, Laxmi Belavatgi, Meenaz Sajjan, Mahantesh M. Kamatar, Vishwanath Surasura, Pradeep Ammanagi, Nausheen K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580027 India K.L.E Institute of Technolog Dept. of Computer Science and Engineering Hubballi580027 India

ISBN: (纸本)9798350364569

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model. © 2024 IEEE.

关键词： Convolutional neural networks

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Blockchain-Enabled Secure and Fair Online Lottery Systems Using Smart Contracts

Blockchain-Enabled Secure and Fair Online Lottery Systems Us...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Datta, Suseta Mondal, Rituparna Roy, Rajdeep Banerjee, Sourav Biswas, Utpal Narula Institute of Technology Department of Computer Application West Bengal Kolkata India Techno India University Department of Computer Applications West Bengal Kolkata India University of Kalyani Department of Computer Science and Engineering West Bengal Kalyani India Kalyani Government Engineering College Department of Computer Science and Engineering West Bengal Kalyani India

ISBN: (纸本)9798331540821

In the capricious digital dominion, conventional online lottery systems come across consequential impediment regarding transparency, fairness, and security. This paper presents an inventive solution by merging Blockchain technology and smart contracts into online lotteries to take forward these challenges. The preferred disperse implementation capitalizes on Blockchain's decentralized category to covenant the translucency and immutability of lottery transactions, thereby undoing the requisite for trusted interceders. Smart contracts are immersed to automate the entire lottery process-from ticket provisioning to winner selection and payout-certifying agreement to preconceived rules and reinforcing contender trust. Through meticulous perusal and enactment, illustration of how Blockchain technology can transform online lottery systems, providing users with a secure, transparent, and fair platform have been performed. Furthermore, the suggested system's resistance to recurrent deceit and its prospective impression on the online gambling industry are talked about, foregrounding the welfares of a Blockchain-drove technique to online lotteries. © 2024 IEEE.

关键词： Smart contract

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A Music Recommender System to Identify the Emotional State of a Person Using Machine Learning Algorithms 4

A Music Recommender System to Identify the Emotional State o...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Muruganantham, A. Malarvizhi, C. Divya Vahini, S. Saranya, D. Uthiramoorthy, A. Karnataka Bengaluru India Psg Institute of Advanced Studies Department of Computer Science Coimbatore India Dr.Mahalingam College of Engineering and Technology Department of Computer Applications Tamil Nadu Pollachi India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Rathinam College of Arts and Science Department of Computer Science India

ISBN: (纸本)9798331523923

In today's digital world, customized recommendation algorithms can improve user experiences in many different areas. Within the sphere of intelligent human-technology interaction, this paper specifically examines a music recommender system driven by machine learning algorithms. It figures out what mood users are in and can provide an appropriate response in real time. The system being propounded in this essay uses advanced technology in facial expression recognition. With the help of a webcam, it aims to accurately detect user emotions, including joy, sadness, and anger. Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Support Vector Machines (SVM) have been employed to effectively classify people's mood and recommend an appropriate song. Our extensive tests and examinations show that CNN, RNN, and SVM models are all very good at guessing users' moods - with high precision, recall, F1-score, and accuracy percentages. Furthermore, in-depth analyses like confusion matrixes illuminate how well each model serves as an emotion recognizer. This study's findings are representative of future prospects in affective computing around the globe with major implications for intelligent recommendation systems to which it's important that the models are sensitive. In conclusion, the suggested Music Recommender System is something new for taking a more personal view: We can be as deeply entertained as the web allows us to be - make tailored entertainment paths in the digital era. © 2025 IEEE.

关键词： Convolutional neural networks

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Quantum Inspired Zeroshot Classification For Adverse Drug Reactions Detection From Social Media Reviews

Quantum Inspired Zeroshot Classification For Adverse Drug Re...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Dey, Arijit Shrivastava, Jitendra Nath B. P. Poddar Institute of Management and Technology Dept. of Computer Applications WB Kolkata India Invertis University Dept. of Computer Science and Engineering UP Bareilly India

ISBN: (纸本)9798350379716

A deep learning model enhanced with quantum transformer has been introduced in detection of adverse reactions of drugs within the reviews collected from social media written in English language. This proposed model enhanced the human language understanding for detecting adverse reactions of drug. Quantum transformers are used for position embedding and attention purpose. Variational quantum circuit encoded the human language and identify the relevant components of the input reviews as class feature vectors to develop a relationship network for each drug correspond to the condition and review comments on which probability distribution functions applied to detect ADRs found in a medicinal product or not. The correctness of advanced model architecture built on quantum transformer is quite encouraging, and it performs better than other cutting-edge learning models. Achieved an accuracy of 93% with a loss of 0.0393 with this proposed model. Though it is too early to declare that quantum computing has completely replaced. This proposed quantum enhanced transformer helps to show the direction towards quantum enhanced hybrid model design in research. © 2024 IEEE.

关键词： Distribution functions

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The λNF Problem 2nd

The λNF Problem

引用

2nd International Conference on Cybersecurity and Evolutionary Data engineering, ICCEDE 2022

作者： Dutta, Anurag Munjal, DeepKiran Department of Computer Science and Engineering Government College of Engineering and Textile Technology Serampore India Department of Computer Applications G L Bajaj Institute of Technology and Management Greater Noida India

ISBN: (纸本)9789819950799

The Dutch National Flag problem—DNF Problem, a Computational Problem proposed by the Dutch computer Scientist—Edsger Wybe Dijkstra—brought a new edge to the domains of Sorting. In this work, we would extend Dijkstra’s Problem to a generalized version, where we would be considering flags with variable numbers of colours and will try to devise an algorithm to arrange them in a monotonic order by its magnitude. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Computational complexity Dnf sort Partitioning Sorting algorithms

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Lips and Tongue Cancer Classification Using Deep Learning Neural Network 6

Lips and Tongue Cancer Classification Using Deep Learning Ne...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Bansal, Satish Jadon, Rakesh S. Gupta, Sanjay Kumar Prestige Institute of Management and Research Gwalior India Mits Department of Computer Engineering Gwalior India Sos in Computer Science and Applications Jiwaji University Gwalior India

ISBN: (纸本)9798350346961

One of the major diseases in developing countries is Oral Cancer, caused by alcohol, tobacco product and smoking which creates uncontrolled and abnormal cells in parts of human body. Recent Convolution Neural Network (CNN) has helped in medical industry to used medical images for finding the different types of diseases. The objective of research to build new CNN model which use for analysis the oral cancer images and determine the cancerous and noncancerous image. In this paper, CNN technique and image processing are used to categorize cancer or non-cancer lips and tongue image dataset. Deep Learning approaches is used to develop and check the performance of proposed CNN model. For experiment small Kaggle dataset that contains cancerous and non-cancerous lips and tongue images and apply proposed CNN model (Oral_Cancer_Detection). The result of Oral_Cancer_Detection model is very effective and accurate with low complexity. The Oral_Cancer_Detection model found 94% accuracy in our experiment and achieved better accuracy results in less time.. © 2023 IEEE.

关键词： Neural network models

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A Genetic-Muted Leader Scheme with CNN-Based IDS for Industrial Internet of Things Networks 1

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9th International Conference on Cyber Security, Privacy in Communication Networks, ICCS 2023

作者： Reddy, Boyella Mala Konda Abdul Azeez Khan, A. Sathick, K. Javubar Raj, L. Arun Department of Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Applications B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9789819739738

ISBN: (纸本)9789819739721

The industrial industry has adopted web and cloud-based technologies widely, which has accelerated the IoT ecosystem’s expansion. Industrial Internet of Things (IIoT) has established a large network due to the sheer volume of information and linked equipment. IIoT networks are made to be vulnerable in terms of cybersecurity. So as to ensure the security of the IIoT networks, intrusion detection systems (IDS) must be developed. This study suggests an IDS-based on a Genetic-Muted Leader Scheme (GMLS)-CNN for usage in IIoT networks. The data collection and preprocessing steps improve the detection accuracy. The next step is to classify data using convolutional neural networks (CNNs), with GMLS used to fine-tune the parameters for the most accurate results. The two datasets such as UNSW-NB15 and X-IIoTID and named as TD were used to distinguish normal and problematic data, which allowed the study to evaluate several categorization techniques. Among the other schemes, the GMLS-CNN model had the maximum accuracy rate for IDSis 98.7% (X-IIoTID) and 95.21% (UNSW-NB15). Additionally, the model’s success in correctly detecting attacks based on the various types of attacks reported in the datasets was assessed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Network intrusion

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Stacked Bi-LSTM for Advanced Toxicity Detection in Comment Classification 6

Stacked Bi-LSTM for Advanced Toxicity Detection in Comment C...

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6th IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2024

作者： Kumar, N. Naveen Abraham, Prabhakaran Kaliba, Syed Ibrahim Diniesh, V.C. Maruthamuthu, R. Balamurugan Seshu Kumar, P. Vekkudu, Naveen Madanapalle Institute of Technology & Science Department of Computer Applications Andhra Pradesh Madanapalle India Mai-nefhi College of Engineering and Technology Department of Computer Science and Engineering Mainefhi Eritrea Kongu Engineering College Department of Electronics & Communication Engineering Tamil Nadu Erode India Jain University Department of Information Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350389692

The surge in Internet usage has revolutionized online forums, providing dynamic forums for active participation and meaningful debates. However, this has also exposed users to the risk of harassment. Efforts to address toxic comments in online forums have faced challenges, with current solutions unveiling unreliability. Advances in hardware, cloud computing, and natural language processing (NLP) enable the development of more robust approaches, predominantly NLP-based deep learning models. Pre-trained transformer models are gradually utilized for accurate toxic comment classification, bigger traditional methods. Stacked LSTM models, emphasizing hierarchical feature learning and improved contextual understanding, outperform single layer counterparts, offering an effective solution for online content moderation and sentiment analysis. This research indicates a significant stride towards more dependable and sophisticated toxic comment detection, addressing the challenges of social media effectively. © 2024 IEEE.

关键词： Sentiment analysis

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