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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： s. Gunasekaran Dhinesh M Ebinesh M Gowtham b Guruprasath C R Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Electronic voting systems, increasingly used in modern elections, offer several enhancements over traditional methods, including better accuracy, convenience, flexibility, privacy, verifiability, and mobility. However, these systems are not without their shortcomings. They can be time-consuming to manage and still require significant amounts of paperwork. High-level officials often have no direct involvement in their operation. Additionally, the machines are prone to damage if not properly maintained, and their software typically does not support the simultaneous updating and editing of multiple items, which can hinder their efficiency. Here proposes a novel approach that integrates encrypted face recognition and score voting. The system leverages Haar Cascade Classification for face detection, extracting and encrypting facial features during user registration, and subsequently comparing encrypted features during the authentication process. The encrypted feature storage ensures user privacy and protects sensitive biometric information. Due to identification and authentication users are permitted to cast votes in the system using a score voting system that introduces more flexibility to the procedure. score voting or also called range voting is the type of voting where voters have the liberty to assign different scores to the given candidates or options. With reference to other methods of voting that enable the voters to vote for only one candidate, the score-based methods are much better at providing a gradation of the views of the voters. This feature allows for the verification of every process of the voting exercise. However, this ID is well hidden and even the system administrators cannot track this ID thus ensuring that no anyone can identify any of the voters.

关键词： Privacy Face recognition Authentication Feature extraction software Cryptography servers Reliability Electronic voting Facial features

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Advanced Data Mining to Explore the Effects of brain Due to schizophrenia

Advanced Data Mining to Explore the Effects of Brain Due to ...

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IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： M. sangeetha Deepika s Rajeshwari G Priyadharshini b souparnika Cs Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

The paper investigates changes in microstate dynamics across various schizophrenia stages. Changes in microstate dynamics across schizophrenia stages can be detected through K-means clustering of EEG data. Machine learning models for classifying different stages of schizophrenia include random forest, gradient boosting, and XGboost. Results show that there are significant changes in microstate dynamics during schizophrenia phases and this shows changes in brain processing. Furthermore, it can be noted from the use of machine learning models that their accuracy levels are quite impressive when it came to distinguishing different phases of schizophrenia through patterns generated by activities taking place inside our brains which are obtained from electroencephalography analysis. Evidently, such a statement reflects how a combination between machine learning capabilities with electroencephalography research has been used so far for this purpose which may imply a better understanding regarding its neurological underpinnings or else creation of early diagnostic devices before treatment commences.

关键词： Machine learning algorithms Accuracy Heuristic algorithms Precision medicine schizophrenia brain modeling boosting Electroencephalography Classification algorithms Random forests

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smart Healthcare system Management Using IoT and Machine Learning Techniques 3rd

Smart Healthcare System Management Using IoT and Machine Lea...

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3rd International Conference on Machine Learning, Internet of Things and big Data, ICMIb 2023

作者： sekhar, P. sudam Jena, Gunamani Jena, shubhashish Jena, subhashree Department of Mathematis and Statistics Vignan’s Foundations for Science Technology and Research Guntur India Computer Science and Engineering B V C Engineering College AP Odalarevu India OUTR Bhubaneswar India CAS Marine Biology Annamalai University Cuddalore India

ISBN: (纸本)9789819939312

Traditional health care system need to be smarter because of the increasing complicacy in health of human kind. The new technology like IoT and Artificial intelligence system has a big role in making it success. IoT, Cloud Computing, and AI have made the traditional healthcare system smarter. Improving medical care with IoT and AI. Combining IoT and AI provides healthcare new options. In this approach, AI and IoT can assist intelligent healthcare systems detect illness. This article uses AI and IoT convergence to discover heart disease and diabetes. The given model includes data collection, preprocessing, classification, and parameter adjustment. Wearables and sensors make it easier to collect IoT data, which AI can use to diagnose illness. The suggested technique for detecting illnesses uses CsO-CLsTM, based on Crowd search Optimization. CsO fine-tunes the CLsTM model’s "weights" and "bias" to enhance medical data categorization. Outliers are removed using the isolation Forest (iForest) approach. CsO improves CLsTM’s diagnostics. Healthcare data proved CsO-validity. LsTM’s CNN2D now includes a new version of LsTM and a CsO features selection approach. Experiments utilising Heart and Diabetes data reveal that extension is correct. © The Author(s), under exclusive license to springer Nature singapore Pte Ltd. 2024.

关键词： Internet of things

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Decentralized Financing for Artistic Expression: A Crowdsourcing Model Powered by blockchain Technology

Decentralized Financing for Artistic Expression: A Crowdsour...

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Advancement in Electronics & Communication engineering (AECE), International Conference on

作者： R. saranya sangavi C sarni K J Dehavi D santhiyadevi s Department of Computer Science and Engineering V.S.B. Engineering College Karur India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

The blockchain with its decentralization, security, and transparency has disrupted the contemporary society. Another donor-based technique, crowdfunding, started as a way to fund projects with limited donations over the Internet and has also changed. Present scenario is that, currently people can put their money to work in startup ventures through brokers or digital wallets. Current crowdfunding sites have little control over where the money goes, and donors often do not have guarantees. In this paper, we present a potential solution as a blockchain and decentralized crowdfunding system.

关键词： Crowdfunding smart contracts Companies Tokenization blockchains safety security

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Anodyne for Piracy by Hue Anatomization 2

Anodyne for Piracy by Hue Anatomization

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2nd IEEE International Conference on Data, Decision and systems, ICDDs 2023

作者： srinivasan, K N Pranav sujatha, D.N. New Horizon College of Engineering Dept. of Information Science and Engineering Bangalore India B M S College of Engineering Dept. of Computer Applications Bangalore India

ISBN: (纸本)9798350314168

Piracy is a malignant and deleterious parasite in the world of digital content. Here we are specific to piracy that occurs in movies through cam recorders and mobiles. Our solution aims to find the source of piracy among screening theaters to remediate the film industry and expunge piracy altogether. The method we use to solve this problem is hue anatomization, which uses hue and its properties to locate a theater distinctively from other theaters. The proposed method is studied with four progressions, Further with structural similarity Index Measure (ssIM), the results are accurate only if the window size it searches for is correct, this needs to be rechecked manually. © 2023 IEEE.

关键词： Theaters

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Optimized Image Compression Using Multiple Compressed sensing Techniques

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sN computer science 2025年第4期6卷 1-18页

作者： Puttegowda, Kiran Mohan, b.A. veeraprathap, v. vijay, C.P. sudheesh, K.v. Kumar, D. s. sunil Department of ECE Vidyavardhaka College of Engineering Karnataka Mysuru India Department of ISE B.M.S. Institute of Technology and Management Yelahanka Karnataka Bengaluru India Department of ECE ATME college of Engineering Karnataka Mysuru India Department of Computer Science Engineering (AI & ML) Vidyavardhaka College of Engineering Mysuru Karnataka India Department of Computer Applications East Point College of Higher Education Banglore India

In the digital era, the demand for efficient image compression techniques is ever-increasing due to the exponential growth of visual data. Compressed sensing (Cs), a groundbreaking theory in signal processing, offers a promising solution by exploiting the inherent sparsity of images. The proposed work introduces a novel approach to image compression, leveraging compressed sensing techniques to achieve optimized results. Our method works by expressing image data in a higher-order non-raw and non-dense format hence dramatically decreasing the size of the entire data. This significantly reduces storage requirements while preserving image quality. by combining advanced signal processing with innovative image analysis techniques, our approach maintains low computational complexity, enabling real-time applications without sacrificing the fidelity of reconstructed images, even after compression. This makes our approach especially valuable for applications where storage space is limited but image integrity remains crucial. We present experimental results demonstrating the effectiveness of our technique across various image types and compression ratios. Our findings underscore the potential of compressed sensing as a transformative paradigm in the realm of image compression, paving the way for more efficient storage and transmission of visual content in diverse applications. © The Author(s), under exclusive licence to springer Nature singapore Pte Ltd. 2025.

关键词： Compressed sensing Compression Compression ratio Images Reconstruction

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Implementation and Prediction of Accurate Data Forecasting Detection with Different Approaches 4

Implementation and Prediction of Accurate Data Forecasting D...

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4th International Conference on smart Electronics and Communication, ICOsEC 2023

作者： Maheswari, b. Uma Kirubakaran, s. saravanan, P. Jeyalaxmi, M. Ramesh, Alabazar vidhya, R.G. St. Joseph's College of Engineering Department of Computer Science and Engineering Chennai India CMR College of Engineering and Technology Department of Computer Science Engineering Telangana Kandlakoya India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai44 India Prince Shri Venkateshwara Padmavathy Engineering College Physics Chennai India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Narsapur India Hkbk College of Engineering Department of ECE Bangalore India

ISBN: (纸本)9798350300888

The ability to recognize anomalies and make accurate forecasts is essential to the practice of many disciplines, including finance, economics, meteorology, and anomaly detection. The installation and prediction of reliable forecasting detection using various methodologies are the primary objectives of this abstract's discussion. The accuracy of forecasting and detection models has been improved through the development of a variety of strategies, methodologies, and approaches. Among these are the more common statistical methods, algorithmic approaches to machine learning, and more recent deep learning methods. Each method has advantages and disadvantages, and the degree to which it is successful is highly dependent on the nature of the data as well as the specific application area. In order to put accurate forecasting detection into action, the data must first be preprocessed, then appropriate features must be identified, and finally, models must be trained utilizing the method that was selected. After then, the models are judged according to how well they can forecast the outcomes of future occurrences or identify deviations from the norm. The purpose of this abstract is to investigate and evaluate the effectiveness of several methodologies regarding accurate forecasting detection. This article addresses the benefits and drawbacks of each technique, as well as the difficulties associated with putting them into practice and their capacity to make accurate forecasts. In addition, the abstract investigates how the accuracy of predicted detection is affected by a variety of aspects including data quality, model complexity, feature selection, and training approaches. This demonstrates the significance of utilizing dependable metrics of evaluation and processes of validation in order to produce accurate and trustworthy predictions. The facts and results that are discussed in this abstract help to a greater knowledge of the many methods that can be utilized for accurate fo

关键词： Forecasting

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LeNet with bidirectional Long short-Term Memory for signal Detection in Underwater Optical Communication 1

LeNet with Bidirectional Long Short-Term Memory for Signal D...

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1st International Conference on software, systems and Information Technology, ssITCON 2024

作者： subhashini, s. Al-Farouni, Mohammed Kumar, b. satish sharmiladevi, D. Anita sofia Liz, D.R. B S Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Department of Computers Techniques Engineering College of Technical Engineering Najaf Iraq Maisammaguda Department of Electronics and Communication Engineering Hyderabad India Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

Underwater optical signal detection performance suffers from occlusion and turbidity in degraded environments. Due to complex underwater environment the high directionality of light beam and vibration transceiver incident optical power usually spans a very wide dynamic range. This paper, proposed LeNet with bidirectional Long short-Term Memory (biLsTM) technique performed efficiently in the signal detection in under water optical communication by effectively extracting spatial features and capturing temporal dependencies. The data collected for this research includes data bits of 72 vectors of transmitted signals through the Gulf seawater. The LeNet approach process the received optical communication signals and extract spatial features from convolutional layer, converting them into relevant signal to enhance the communication of underwater. The combination of LeNet feature extraction capabilities and bi-LsTM sequential learning the signal then enhances the model ability leading to achieve better performance in signal detection. The performance of proposed method is evaluated with various metrics such as accuracy, precision, recall, f1-score and bit Error Rate (bER) metrics based on sea water. © 2024 IEEE.

关键词： Error statistics

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DesU-NetAM: optimized DenseU-Net with attention mechanism for hyperspectral image classification

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International Journal of Information Technology (singapore) 2023年第7期15卷 3761-3777页

作者： balaji, K. Nirosha, veeramachaneni Yallamandaiah, s. Karthik, sajja Prasad, v. siva Prathyusha, G. Department of Computer Science and Engineering B V Raju Institute of Technology Vishnupur Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology and Science for Women Peda Palakaluru Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R. V. R & J. C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Sree Vidyanikethan Engineering College Sree Sainath Nagar Andhra Pradesh Tirupati 517102 India

The utilization of hyperspectral images (HsI) is expanding rapidly with the advancement of remote sensing technology. Accurately categorizing ground features using HsI is a crucial research topic that has garnered considerable interest. The high dimensional space, numerous spectral bands, and lack of labeled training data make categorizing hyperspectral images difficult. We provide a novel hyperspectral image categorization approach based on DenseU-Net to address these issues. The data is first normalized by separating the maximum value of the entire set of data by the average intensity of each pixel. Then we also propose an attention mechanism network. because spectral and spatial data are extracted independently, there may be less interference between the two types of features in this network. The retrieved spectral and spatial data are integrated to categorize the data. Finally, DenseU-Net method parameters are adjusted using the Tuna swarm Optimization (TsO) algorithm, which has a global search capability. This optimized DenseU-Net is then utilized to manage a hyperspectral image categorization approach effectively. Three standard HsI datasets utilized for experiments were acquired by various sensors at different acquisition times and used for classification studies. The comparison findings show that the proposed approach outperforms other deep learning methods models in classification effectiveness. © 2023, The Author(s), under exclusive licence to bharati vidyapeeth's Institute of computer Applications and Management.

关键词： Attention mechanism DenseU-Net Hyperspectral images (HsI) spectral and spatial data Tuna swarm optimization (TsO) algorithm

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DLMNN based Heart Disease Prediction with PD-ss Optimization Algorithm

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Intelligent Automation & soft Computing 2023年第2期35卷 1353-1368页

作者： s.Raghavendra vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby singh D.Lakshmi s.velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-smell sensing(PD-ss)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDss achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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