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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Raji Gopinathan, N. Sherly, Elizabeth Cochin University of Science And Technology IIITM-Kerala Department of Computer Applications Trivandrum India Digital University Kerala School of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350394474

The global shift in the education paradigm demands technology-enabled learning platforms that monitor the performance and engagement of learners. In this paper, a new method of detecting learner engagement levels utilizing geometric angle features obtained from face images captured through a webcam attached to the learning device is proposed. Distinct ten angles resulting from lines joining seventeen landmark points serve as the feature set and employ machine learning algorithms for classification. The algorithms used for the classification purpose include Random forest, K-Nearest Neighbour, and Support Vector Machine. Random Forest outperforms the other classifiers in distinguishing between the two engagement levels being tested. It achieves the highest accuracy of 90% in distinguishing low and high engagement levels of students. The 3-Level classification model is also comprehensively compared with other state-of-the-art methods and found a notable accuracy than the deep learning models MobileNet and VGG16. This indicates that the proposed method can efficiently detect and output students' engagement levels which provides real-time support for teachers in classrooms and helps students while undergoing self-paced E-learning. © 2024 IEEE.

关键词： Students

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An Improved KNN Algorithm to Identify Parkinson's Disease on Voice Ailment

An Improved KNN Algorithm to Identify Parkinson's Disease on...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering Science, AMATHE 2024

作者： Sukanya, N.S. Shinty, P.K. New Horizon College Of Engineering Department Of Master Of Computer Applications Karnataka Bengaluru India School Of Science And Computer Studies Cmr University Karnataka Bengaluru India

ISBN: (纸本)9798350371567

In recent times, researchers have extended their research on Parkinson's Disease (PD) using Artificial Neural Network techniques. PD is an age related progressive neurological ailment that affects millions of older people worldwide. There is a need to identify PD accurately as early as possible. Identifying PD at an early stage is a challenging procedure. This paper aims to use machine learning models such as Artificial Neural Networks (ANN) and an enhanced k-Nearest Neighbour (kNN) techniques to identify PD at an early stage. To detect the PD, an audio data of older people is used in this study. An automatic process classifies an audio data into two categories such as PD patient and healthful individual. The proposed model uses the weights and the nearest neighbourhood concepts to predict the audio samples. The results of the improved kNN classifier produces optimal results by classifying the audio datasets into healthy and unhealthy categories. © 2024 IEEE.

关键词： Diagnosis

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Machine Learning Classifiers for Predicting the Risk of Cardiovascular Disease in Healthcare 4.0 3

Machine Learning Classifiers for Predicting the Risk of Card...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Kumari, Palak Kumar, Ashish Chatterjee, Kakali Master of Computer Applications Patna Women's College Patna India National Institute of Technology Patna Dept. of Computer Science and Engineering Patna India

ISBN: (纸本)9798331528201

Cardiovascular disease (CVD) generic phrase used to describe heart or blood vessel illness. Conditions that impact the body's, brain's, or heart's blood flow. Reduced blood flow can result from factors such as blood clots, leading to heart attack, angina, or stroke. Other potential meanings of CVD. These models offer key benefits like clear interpretability and the ability to analyze complex patterns, significantly enhancing the accuracy of cardiovascular disease risk predictions. The demerits of using SVM, LR, K-NN, and MLP for CVD prediction include inter-pretability issues, overfitting risks, data requirements, feature selection challenges, scalability concerns, algorithm complexity, and class imbalance. Collaboration between machine learning experts and healthcare professionals is essential for developing accurate and reliable CVD prediction models that enhance patient care. CVD is a significant health threat, often leading to fatal complications. Early detection is crucial for effective intervention. This study employs SVM (accuracy is 0.84%), LR (accuracy is 0.86%), K-NN (accuracy is 0.94%), and MLP (accuracy is 0.79%) machine learning techniques to enhance CVD detection by addressing outliers and null values, contributing to advancing automatic detection initiatives. © 2024 IEEE.

关键词： Nearest neighbor search

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A CNN Based Machine Learning Scheme to Detect Lung Cancer Detection from CT Scan Images

A CNN Based Machine Learning Scheme to Detect Lung Cancer De...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Saravanan, T.M. Jagadeesan, M. Selvaraj, P.A. Mubarak, M.Mohamed Kumar, S.Praveen Sanjay, S. Kongu Engineering College Department of Computer Applications Tamil Nadu Perundurai638 060 India

ISBN: (纸本)9798350348217

The major cause of mortality in today's globe is lung cancer. As a result, respiratory organ nodules, which are round or oval-shaped growths of scar tissues, must be identified properly and quickly in order to prevent lung cancer from developing. As a result of critical tasks, oxygen to the human body is provided by lungs while carbon dioxide is excreted;require early identification of malignant cells. In order to reduce the chance of mortality, it is critical to begin therapy by detecting cancer using a CT scan. The suggested technique involves changing the parameters, combinations that are dynamically pooled, dropout procedures that are added, and linear unit functions that are corrected (ReLu) to build a model for detecting lung cancer and categorizing tumors discovered in the lungs as malignant or benign. To forecast greater accuracy, this projected method is compared to the pre-trained model VGG16. This scheme will make it easier for doctors to give early treatment and reduce mortality. © 2023 IEEE.

关键词： Carbon dioxide

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Spine Disease Detection in Spinal X-Ray Images using Machine Learning 13

Spine Disease Detection in Spinal X-Ray Images using Machine...

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13th International Conference on System Modeling and Advancement in Research Trends, SMART 2024

作者： Sisodia, Ankur Vishnoi, Swati Khenwar, Medha Chauhan, Dushyant Sharma, Nandini Gupta, Priyanka Gla University Department of Computer Engineering and Applications Mathura India Banasthali Vidyapith Department of Computer Science Rajasthan India G.L. Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Sanskriti University Department of Computer Science and Engineering Mathura India

ISBN: (纸本)9798350380583

Millions of individuals globally experience spinal ailments. This stresses the importance of swift detection for achieving better treatment results. Traditional diagnosis techniques can be lengthy. They might lack precision. So here the aim is to design a powerful yet budget-friendly system that employs a deep neural network which has been trained on a vast X-ray image database. The system aims at carrying out X-ray scans of spinal vertebrae with improved accuracy. The method proposed utilizes a lite deep neural network to gather information. This is done from numerous levels to aid. In the detection of spinal health conditions, the autonomous system for finding spine illnesses consist crucial elements. These include photo filtering tools attribute excavation methods and efficient classification algorithms. Moreover, Clinical information and patient past records are added to the analyses. This augments the diagnostic precision and dependability of the system. © 2024 IEEE.

关键词： Budget control

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Image encryption using XOR-based continuous tone MSS and Cellular Automata 6

Image encryption using XOR-based continuous tone MSS and Cel...

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

作者： Akanksha Garg, Hitendra Shivani, Shivendra GLA University Dept. of Computer Engineering and Applications Mathura India Thapar Institute of Engineering and Technology Dept. of Computer Science Patiala India

ISBN: (纸本)9798350346961

Online communication has become quicker and simpler as a result of the growth of the internet and the advancement in technology. People are using multimedia data in almost all spheres of life. The amount of multimedia data transmitted has been increasing at a massive rate. Multimedia data delivered over open and unreliable networks are susceptible to unauthorized access. As a result, a cryptographic standard is needed to securely and efficiently transmit multimedia data such as images over the networks. This study uses cellular automata and an XOR-based continuous tone MSS approach to create an image encryption method. The suggested approach has eliminated VC's drawbacks, including secret/share quantity constraints, unpredictable share patterns, codebook requirements, and pixel extension in shares and recovered secret pictures. The recommended approach uses an (n,n) multi-secret sharing system that can send n secret photos simultaneously. At the time of encoding, the master share overcomes been made using the secret key, and it may be successfully retrieved when decoding using a comparable secret key. The exploratory results and security examination shows the proficiency of the given approach. The complexity of the proposed approach can be adjusted by using the different combinations of cellular automata rules.. © 2023 IEEE.

关键词： Cellular automata

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Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

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computer Systems Science & engineering 2023年第8期46卷 1415-1429页

作者： R.Sheeba R.Sharmila Ahmed Alkhayyat Rami Q.Malik Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Applications Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India College of Technical Engineering The Islamic UniversityNajafIraq Medical Instrumentation Techniques Engineering Department Al-Mustaqbal University CollegeBabylonIraq

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and *** address these shortcomings,big data analytics is the most superior technology that has to be *** though big data and IoT could make human life more convenient,those benefits come at the expense of *** manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT *** cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security *** study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)*** the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big *** MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature ***,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL *** comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

关键词： Big data analytics internet of things security intrusion detection deep learning

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MACPGANA: design of a highly efficient multimodal agriculture commodity price prediction model via generative adversarial networks & autoencoders

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2355-2368页

作者： kambo, Rubi Pillai, Jyothi Soni, Sunita Urkude, Giridhar Department of Computer Science and Engineering Bhilai Institute Technology Chhattisgarh Durg India Department of Computer Science and Engineering Rungta College of Engineering and Technology Chhattisgarh Bhilai India Symbiosis Institute of Technology Nagpur Campus Symbiosis International(Deemed University) Pune India Department of Computer Applications Bhilai Institute of Technology Durg Chhattisgarh India

This paper presents MACPGANA, a novel multimodal agriculture commodity price prediction model leveraging Generative Adversarial Networks (GAN) and Autoencoders (AEs). The model integrates multimodal data to extract and represent features efficiently, employs GAN for feature selection, and uses predictive autoencoders to estimate inter-day price movements. A Q-Learning-based feedback loop ensures continuous optimization of prediction performance. MACPGANA achieves high accuracy, precision, and recall while maintaining low computational complexity, making it suitable for real-time and large-scale applications. The model’s effectiveness is demonstrated across diverse commodities, highlighting its potential for scalable and efficient agricultural price prediction. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Agriculture commodity price prediction Autoencoders (AE) Generative adversarial networks (GAN) Multimodal feature extraction Q-learning optimization Real-time price forecasting

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Secure medical data storage in DPOS-hyper ledger fabric block chain using PM-ECC and L2-DWT

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International Journal of computers and applications 2023年第7-8期45卷 516-522页

作者： Shinzeer, C.K. Bhagat, Avinash Kushwaha, Ajay Shriram School of Computer Applications Lovely Professional University Punjab India Sharda School of Engineering & Technology Department of CSA Sharda University UP Greater Noida India

Governments and individuals have taken extraordinary measures to protect the health of the people during the COVID pandemic. Stored medical data remains the main target for hackers, and hence it needs to be stored securely. To achieve this objective, this paper proposes a novel model using Delegated Proof of Stake-Hyper ledger Fabric Block Chain (DPOS-HFBC). Primarily, by employing LL Subbandeigen Value decomposition employed Discrete Wavelet Transform (L2-DWT), the patient’s Lung Computed Tomography (CT) image data are collected and embedded. For embedding, the patient’s name and ID are taken. In embedding, a Pseudorandom number generator using the Mersenne twister algorithm employed in Elliptic Curve Cryptography (PM-ECC) is applied for key encryption. It covered the image that was embedded with the original and then stored in DPOS-HFBC. Likewise, for authorization, every patient’s biometric ID was hashed and stored in DPOS-HFBC. Data requesters request data in the Interplanetary File System (IPFS) of DPOS-HFBC, and the attributes from the request are extracted and sent to the authority for verification. After verifying, the authority shares their biometric ID with the requester and this gets hashed and then verified in DPOS-HFBC. To show the model’s supremacy, the proposed method was evaluated and compared with existing methods. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Public key cryptography

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Comparative Study of Detection and Analysis of Different Malware with the Help of Different Algorithm

Comparative Study of Detection and Analysis of Different Mal...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Selvaraj, P.A. Jagadeesan, M. Saravanan, T.M. Kumar, Aniket Kumar, Anshu Singh, Mayank Kumar Kongu Engineering College Department of Computer Applications Tamil Nadu Perundurai638 060 India

ISBN: (纸本)9798350348217

The motive of this paper is to detect the malware from computer systems in order to protect the confidential data, information, documents etc. from being accessing. The detection of malware is necessary because it steals the data from that system which is affected by malware. There are different malware detection techniques (cloud-based, signature-based, Iot-based, heuristic based etc.) and different malware detection tools (static, dynamic) area used in this paper to detect new generation malware. It is necessary to detect malware because the attacks of malware badly affect our economy and no one sector is untouched by it. The detection of malware is compulsory because it exploits goal devices vulnerabilities, along with a Trojan horse in valid software e.g. browser that may be hijacked. There are also different tools used for detection of malware like static or dynamic that we see in this paper. We also see different methods of detection of malware in android. © 2023 IEEE.

关键词： Machine learning

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