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11th International Conference on Intelligent Computing and applications, ICRTC 2023

作者： Yadav, Poonam Satheesh Kumar, S. Muthukumaran, V. Sandeep Kumar, Mathivanan Radhika, E.K. Department of Computer Science School of Applied Sciences Reva University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India School of Computer Science and Engineering Galgotias University Uttar Pradesh Greater Noida203201 India Department of Computer Science and Applications Sindhi College Bangalore560024 India

ISBN: (纸本)9789819717231

In the polygram substitution, one set of characters is replaced by another set based on algorithm. The model is proposed for calculating and estimating secure online secret shares, where dynamic modification in any of the shares will be identified if number of the shares are more than specified threshold value. The security is based on the intractability of factoring as it uses Lagrange polynomial function. This is further enhanced by a graphical masking and unmasking technique;it includes two phases: encryption of the individual secrets and sharing of the data. The generation of the shares is done by masking using Polygram substitution, and the shares are regenerated and unmasking of the qualified shares is done to find original secret. Masking is done on original secret, then shares are generated. Each encrypted share contains information which is different than original secret. Masking by Polygram substitution gives additional protection of secret data on the Internet. Moreover, we achieve efficient recovery of the original secret data. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Polynomials

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A Deep Dive into Existing Lip Reading Technologies

A Deep Dive into Existing Lip Reading Technologies

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor applications, ICAIQSA 2024

作者： Khekare, Ganesh Majumder, Goutam Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science and Engineering Vellore Institute of Technology Vellore India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Lip reading, or Visual Speech Recognition (VSR), can revolutionize communication for those with hearing loss and in noisy environments. The researchers are trying to create advanced VSR systems using Deep learning models to overcome challenges. But still, several difficulties are there such as different shapes of lips during the video and different lightning conditions that can make deciphering or visualization difficult. Deep learning with the help of Convolutional Neural Networks (CNNs) helps to identify the exact shape and movement of a lip from a video frame. The continuous flow of lip movements in the video is handled by Recurrent Neural Networks (RNNs) which help in identifying words. However, RNNs are not good enough to remember the historical data. This issue is resolved by using Long Short Term Memory (LSTM) in the proposed research work. LSTMs have an additional internal memory that helps to store the history information for a longer period which helps to predict lip movement accurately even in rapid speech. The GRID and LRW datasets are used to generate the results. Additionally, VSR could improve video conferencing, allowing for natural communication even with compromised microphone quality. The impact of VSR extends beyond human-to-human and human-computer interaction. Frameworks like Streamlit offer a glimpse into the future, enabling the development of user-friendly lip reading applications. Deep learning unlocks the power of lip reading, paving the way for a future where communication go beyond the limitations of sound. © 2024 IEEE.

关键词： Video conferencing

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Predictive Maintenance of Power Grid Infrastructure using Long Short-Term Memory Networks

Predictive Maintenance of Power Grid Infrastructure using Lo...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Alsalami, Zaid Kumar, Rakesh Boob, Nandini Shirish Verma, Vikas Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Tamilnadu Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study examines the application of Long Short-Term Memory (LSTM) networks for the prescient upkeep of a control lattice framework. Leveraging verifiable information comprising sensor readings and support records, the LSTM demonstrate precisely expects potential disappointments or debasement within the control network, empowering proactive support techniques. Through broad experimentation, the LSTM demonstrates accomplishing momentous execution with an exactness of 90%, precision of 88%, review of 92%, F1-score of 90%, and an area beneath the ROC curve (AUC) of 0.95. Comparative examination against standard methods and related works within the writing illustrates the prevalence of the LSTM-based approach in prescient support of control network infrastructure. Optimization of hyperparameters and interpretability examination advance upgrades the model's execution and encourages decision-making in control network administration. This study sheds light on the potential of machine learning strategies to revolutionize control framework upkeep, guaranteeing a more solid, strong, and economic vitality supply. © 2024 IEEE.

关键词： Long short-term memory

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Efficient Load Balancing Algorithms for Edge Computing in IoT Environments

Efficient Load Balancing Algorithms for Edge Computing in Io...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Nainwal, Ankita Almusawi, Muntather Bansal, Saloni Michaelson, Jacob Kumar, Suresh Sangeeta, K. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This study investigates the accuracy of load balancing calculations in terms of edge computing for IoT. The paper compared four main calculations, namely Round Robin (RR), Weighted Round Robin (WRR), Least Connections (LC) and Ant Colony Optimization ratings in an reproduced edge computing atmosphere. As for the study, it focused on efficiency improvements of asset utilization as well as reducing inactivity and enhancing reasonableness. Weighted Round Robin showed that the predominant execution came about with an astonishing 25% decrease in idleness as compared to a depiction of noncapacity aware task load distribution. The Slightest Associations also showed extraordinary outcomes, achieving 20% decrease in idle period and demonstrated that it could be flexible with workloads. While flexible, Ant Colony Optimization demonstrated an inability to achieve steady variation improvements related with parameter tuning ranging from 15% toward 30%. The discoveries emphasize the significance of calculation choice based on the particular prerequisites of IoT edge situations. © 2024 IEEE.

关键词： Internet of things

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Mobile-Based Classification and Detection of Diabetic Retinopathy Using TinyML 2

Mobile-Based Classification and Detection of Diabetic Retino...

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2nd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2023

作者： Kumar, Manish Mohan Kumar, H.P. Sultana, Husna Shweta, J.S. M S Ramaiah Institute of Technology Department of Master of Computer Applications Bangalore India Pes College of Engineering Dept of Master of Computer Applications Karnataka Mandya India Government First Grade College Department of Computer Science Karnataka Tumkur India

ISBN: (纸本)9798350314045

This paper presents a comprehensive approach to diabetic retinopathy (DR) classification and detection, encompassing the evaluation of ResNet, RegNet, and WideResNet models for DR severity assessment. Leveraging the Kaggle's APTOS 2019 dataset, we implement and analyze these models using PyTorch, highlighting their efficacy in categorizing DR severity levels. Additionally, we leverage Tiny Machine Learning (TinyML) techniques to develop a mobile-based solution, enabling real-time DR severity assessment on mobile devices. This mobile application showcases the potential of TinyML for early detection and intervention in DR cases, ultimately enhancing patient outcomes. By synergizing advanced deep learning models with TinyML, this study contributes to healthcare technology, enabling timely interventions and improved patient outcomes. © 2023 IEEE.

关键词： Equalizers

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WoEEE: a hybrid approach for enhancement of categorical data transformation

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International Journal of Data science and Analytics 2025年 1-29页

作者： M, Anitha Savarimuthu, Nickolas Bhanu, S. Mary Saira Department of Computer Applications National Institute of Technology- Tiruchirappalli Tiruchirappalli India Department of Computer Science and Engineering National Institute of Technology- Tiruchirappalli Tiruchirappalli India

Efficient encoding of categorical data is essential for enhancing the performance of machine learning (ML) models, statistical methods, particularly when handling high-cardinality features and complex relationships between variables. Traditional encoding methods have notable limitations. One-hot encoding (OHE) often results in high-dimensional feature spaces, reducing model efficiency and predictive accuracy; label encoding (LE) can introduce misleading ordinal relationships; binary encoding (BE) may create excessively large feature representations for high-cardinality categories; weight of evidence (WoE) is prone to overfitting when rare categories are present; and entity embedding (EE) is less interpretable, and may perform poorly on unseen categories. To address these challenges, this paper introduces weight of evidence–entity embedding encoding (WoEEE), a novel encoding technique that integrates the strengths of both WoE and EE. WoE, a statistical approach, encodes categorical variables based on their influence on the target variable, while EE, a DL-based technique, transforms categories into continuous embeddings to capture complex, nonlinear relationships. The proposed WoEEE encoding method first applies WoE to identify and encode significant categories, followed by EE to generate low-dimensional embeddings. Comprehensive empirical evaluations were conducted on various binary and multiclass classification datasets to assess the effectiveness of the proposed WoEEE encoding method. The results, obtained using classifiers such as random forest (RF), Decision Tree (DT), logistic regression (LR), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), demonstrate that WoEEE improves performance over the conventional encoding techniques as well as advanced methods including fuzzification-based encoding, deep-learned embedding, deep feature embedding, and regularized target encoding. On the Adult dataset, WoEEE achieves an F1-score of 0.9

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HealthPredictor: Accurate Health Insights at Your Fingertips 3

HealthPredictor: Accurate Health Insights at Your Fingertips

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

作者： Sajjan, Mahantesh M. Ammanagi, Nausheen Pujari, Laxmi Kamatar, Vishwanath S Belavatgi, Meenaz K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580 027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi580 027 India

ISBN: (纸本)9798350364569

The "HealthPredictor: Accurate Health Insights at Your Fingertips"system transforms healthcare by predicting diseases based on user symptoms. Utilizing advanced machine learning models such as Decision Tree, SVM, Random Forest, and Logistic Regression, it assesses the likelihood of various illnesses. The system compares symptoms to a comprehensive dataset to provide accurate predictions, aiding in early detection. Users can consult with specialist doctors through an integrated chat feature, ensuring quick access to medical advice. By continuously learning from historical data, the system enhances its predictive capabilities, identifying new health patterns and trends. This proactive approach empowers users with timely information and personalized consultations, leading to better health outcomes. © 2024 IEEE.

关键词： Electronic health record

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Convolutional Neural Networks for Intrusion Detection in IoT Networks with Limited Resources

Convolutional Neural Networks for Intrusion Detection in IoT...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Mittal, Mudit Al-Fatlawy, Ramy Riad Bansal, Saloni Michaelson, Jacob Bhalla, Lalit Veena, Ch. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering And Applications Mathura India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Chennai India Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

In this research, we explore the adequacy of Convolutional Neural Systems (CNNs) for interruption location in resource-constrained IoT systems. Leveraging two freely accessible datasets, we compare the execution of CNNs with conventional machine learning calculations such as Support Vector Machines (SVM) and Random Forests (RF). Our tests uncover that CNNs accomplish competitive location exactness, beating SVM and RF models over both datasets. Particularly, on Dataset A, CNNs accomplish an accuracy of 0.92, a review of 0.88, and an F1-score of 0.90, whereas, on Dataset B, they accomplish an accuracy of 0.86, a review of 0.84, and an F1-score of 0.85. Also, CNNs illustrate prevalent computational proficiency, with shorter preparing times and lower memory impressions compared to conventional calculations. This inquiry contributes to progressing the state-of-the-art in IoT interruption discovery by highlighting the viability of profound learning methods in tending to cybersecurity challenges in IoT situations. Moving forward, the selection of CNNs can upgrade the versatility and security of IoT systems, clearing the way for more strong and effective interruption location frameworks. © 2024 IEEE.

关键词： Convolutional neural networks

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Deep Learning Techniques for Human Resource Management Optimization

Deep Learning Techniques for Human Resource Management Optim...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Ranawat, Pratyaksha Kumar, Manish Karthik, A. Asha, V. Nagpal, Amandeep Shaik, Bazani Jecrc University Computer Science Engineering India Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Computer Applications Bangalore India Lovely Professional University Phagwara India Ramachandra College Of Engineering Department Of Mechanical Engineering Andhra Pradesh Eluru India

ISBN: (纸本)9798350366846

Because deep learning and machine learning (ML) techniques have the potential to totally change how firms manage their human capital, there has been a lot of interest in developing HRM procedures that use these techniques. Strong DSS (Decision Support System) technology must be incorporated into the HRM (Human Resource Management) profession in order to make judgements that are effective in today's competitive environment. This study focuses on the problem of prediction, decision making, prediction, and testing assistance in a HRM system. The paper discusses a creative decision support system for HR procedures. Machine learning and deep learning techniques have been offered as fundamental instruments for tracking various HR indicators in the systems developed and implemented analytical process. The description of the suggested methodology and a discussion of a outcomes from the experiments are included in the paper. © 2024 IEEE.

关键词： Decision making

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To Detect Spammer from Image-Based Spam E-mail through Adjacent-Duplicate Technique

To Detect Spammer from Image-Based Spam E-mail through Adjac...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Patel, Kavita Kumar, Sachin Singh, Vijendra Pratap Singh, Sandeep Kumar Saha, Tapash Kumar Jaiswal, Priya Ashoka Institute of Technology and Management Computer Science and Engineering Department UP Varanasi India Computer Science and Applications Veer Bahadur Singh Purvanchal University UP Jaunpur India Computer Science and Applications Mahatama Gandhi Kashi Vidyapeeth UP Varanasi India Computer Science & Engineering Kashi Institute of Technology UP Varanasi India Computer Science and Applications Kashi Institute of Technology UP Varanasi India

ISBN: (纸本)9781665456371

An Email facility proves to be efficient and more convenient communication way. Now a day, an internet service grows continuously, so information available to user is transformed from text to enriched multimedia. Where, spammer uses a new approach in which text is embedded in images. Widely known image detection algorithm such as optical character recognition (OCR) algorithm is defeated by spam image building techniques where randomization method is used to make sure the entire image is look different to each other. Spam image e-mails are mostly forwarded in a group that have visual similarity but dissimilar due to randomization technique used. The proposed system uses adjacent-duplicate detection technique to identify image-based spam. The system uses conventional spam detection technique to identify image-based spam and then further, use of numerous image-based spam filter to identify all the images which have similarity among them. The system is implemented to gain high identification rate where other side have false positive rate (FPR) is less than 0.001%. © 2022 IEEE.

关键词： Data mining

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