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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Malathi, P. Sai Gayathri, B. Lakshmi Sreshta, G. Sahitya, Edarada Rupa, Jhade Sai Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9798350325188

Writing is a specific technique for communication that allows us to express our ideas. The two most common methods for recording information are typing and writing. The process involves using a pen or a finger to inscribe letters or words in a relaxed environment. With wearable devices capable of observing and comprehending our actions, gesture recognitionhas become a computational technique that strives to identify and comprehend human gestures using mathematical algorithms. Our project aims to exploit this technology to develop a motion- to-text converter, a software program for smart wearable devices designed for writing in the air. The application will utilize computer vision to track finger movement, with the resulting text having diverse applications, including text messaging and emailing. This technology will prove beneficial in facilitating communication for people with hearing impairments. © 2023 IEEE.

关键词： computer vision human gesture recognition motion-to-text converter

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data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

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Intelligent Automation & Soft Computing 2023年第1期35卷 839-851页

作者： B.Murugeshwari D.Selvaraj K.Sudharson S.Radhika Department of Computer Science and Engineering Velammal Engineering CollegeChennai600066India Department of Electronics and Communication Engineering Panimalar Engineering CollegeChennai600123India Department of Information Technology Velammal Insitutue of TechnologyChennai601204India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai600124India

Protecting the privacy of data in the multi-cloud is a crucial *** mining is a technique that protects the privacy of individual data while mining those *** most significant task entails obtaining data from numerous remote *** algorithms can obtain sensitive information once the data is in the data *** traditional algorithms/techniques promise to provide safe data transfer,storing,and retrieving over the cloud *** strategies are primarily concerned with protecting the privacy of user *** study aims to present data mining with privacy protection(DMPP)using precise elliptic curve cryptography(PECC),which builds upon that algebraic elliptic curve *** approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing ***,it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the *** approximation error,computational cost,anonymizing time,and data loss are considered performance *** suggested approach is practical and applicable in real-world situations according to the experimentalfindings.

关键词： data mining cryptography privacy preserving elliptic curve information security

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Smartphone based skin hydration level sensor and sunburn prediction 14

Smartphone based skin hydration level sensor and sunburn pre...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Pranav Adith, S. Guhan, M. Praveen Kumar, R. Hari, M. Sri Nalinadevi, K. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798350335095

Skin hydration prediction and sunburn detection are vital aspects of skin health monitoring, contributing to proactive skin care practices and prevention of potential skin damage. Sensors play a vital role in data capturing. Despite the availability of numerous sensors and smartphone applications that offer detailed information on skin health monitoring, these solutions are frequently associated with significant time consumption and high costs. As the requirement for predicting accurate results grows, the need for a larger number of records also increases, leading to computational expenses. To overcome these constraints, a streamlined approach was adopted by utilizing only two sensors: Galvanic Skin Response (GSR) for skin conductance and DHT11 for temperature and humidity. Additionally, the Dynamic Time Warping (DTW) algorithm was employed, which offers the advantage of not being dependent on the number of records. This combination of a minimal sensor setup and an efficient algorithm enables effective and cost-efficient skin health monitoring by providing real-time results without compromising on the accuracy. Furthermore, the system incorporates the prediction of sunburn and calculation of vitamin D intake, a crucial aspect for maintaining optimal skin health. By providing vitamin D intake in international units, this study introduces a simplified and efficient method for generating this valuable output. This research contributes to advancing skincare practices and dermatological interventions, enabling proactive measures to mitigate the risk of sunburn and promote optimal skin health. Finally the proposed methodology exhibits promising potential for widespread adoption and future enhancements in the field of skin health monitoring. © 2023 IEEE.

关键词： Forecasting

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Fetal Abnormality Detection: Exploring Trends Using Machine Learning and Explainable AI 4

Fetal Abnormality Detection: Exploring Trends Using Machine ...

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4th International Conference on Communication, Computing and Industry 6.0, C216 2023

作者： Gaddam, Thanmai Maheswari, B. Uma Chennupalle, Divya Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350327328

Monitoring fetal health and classifying it into different classes is crucial to prevent adverse situations. Healthy fetus leads to healthy new generations. Early detection and treatment of any medical condition is supreme for the well-being and safety of both mother and newborn. This work presents a novel approach to fetal health classification using machine learning models. Three classes-Normal, Suspect, and Pathological-are created by selecting pertinent features from the Cardiotocography (CTG) dataset. Predictive models are created using machine learning (ML) techniques, such as XGBoost, Voting Classifier, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR). XGBoost model has performed well and attained an accuracy-94%. RF model gave the highest precision and recall for classifying normal and suspect classes. Furthermore, this work also uses Explainable AI (XAI) tool called SHAP (SHapley Additive exPlanations) to know/interpret how RF model produces the outcome for test data. ML embedded with XAI supports healthcare providers to trust the model as well as timely intervention of doctors leads to better pregnancy outcomes and maternal-infant health. © 2023 IEEE.

关键词： Decision trees

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Machine Learning Integrated Software Defined Networking Architecture for Congestion Control 2

Machine Learning Integrated Software Defined Networking Arch...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Sha, Akhbar Madhan, S. Neemkar, Sandeep Varma, Vanapala Bharath Chandra Nair, Lekha S. Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350347456

data center networks rely upon the excellent throughput of the network to ensure data transfer happens at incredible speeds. Many factors can determine the network's throughput, such as the design, routing mechanism, and congestion control model. The introduction of the cloud made it more challenging as high traffic can happen and disrupt the data transfer for the cloud applications;this is a challenge in hosting cloud services and applications. The introduction of Software Defined Networking (SDN) made it possible to program the networks and thus provide scope for enhancing the congestion control mechanisms. Separating the control plane from the data plane helps in independence and extracting and processing the data from different perspectives. This paper proposes a Machine Learning approach implementable in an SDN context that helps reduce congestion control by learning data transfer patterns. We propose a modified version of SDN in which an additional layer is implemented above the control plane called ML plane. ML plane takes care of the trend learning in the network that guides the control plane for better decisions. In this paper, we exhibit the architecture of ML plane for the purpose of congestion control in busy networks. © 2023 IEEE.

关键词： Software defined networking

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An Effective Approach for Reducing Time and Memory Requirements for Sign Language Detection 12

An Effective Approach for Reducing Time and Memory Requireme...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Pravin Sabari Bala, S.S. Sriram, R. Arumugam Arun, R.A. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350318210

Sign language serves as a crucial means of communication for individuals who are hard of hearing or deaf, but it is often misunderstood or unrecognized by the hearing population, leading to communication barriers and social exclusion. To address this issue, this research work introduces an approach that utilizes Mediapipe data and neural networks to accurately detect individual sign language gestures. By preprocessing the data, the size is significantly reduced by focusing solely on landmark points rather than entire videos. Furthermore, the preprocessing techniques decrease the training data size by approximately four times. The proposed method employs simple fully connected layers, avoiding computationally intensive CNN or LSTM architectures typically used in existing sign language models. Consequently, the training time is significantly reduced, from 7 hours to just 22 minutes, making the process more efficient and accessible. The effectiveness of the approach is evaluated across a range of 10 to 250 signs, surpassing the limited number of signs explored in most existing research. The achieved accuracies range from 84% for 10 signs to 73% for 250 signs. These results show the efficacy of the proposed approach in facilitating daily communication for individuals with hearing impairments. By employing Mediapipe data and leveraging neural networks with simplified architectures, this approach offers a promising solution for accurate sign language gesture detection, providing an avenue for improved understanding and inclusion between the deaf and hearing communities. © 2023 IEEE.

关键词： computer vision

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QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

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Intelligent Automation & Soft Computing 2023年第8期37卷 1499-1512页

作者： Wenlong Ma Youhong Xu Jianwei Zheng Sadaqat ur Rehman School of Information Engineering Quzhou College of TechnologyQuzhou324000China School of Computer Science and Technology Zhejiang University of TechnologyHangzhou310023China Department of Natural and Computing Science University of AberdeenScotlandAberdeenAB243FXUK

In a cloud manufacturing environment with abundant functionally equivalent cloud services,users naturally desire the highest-quality service(s).Thus,a comprehensive measurement of quality of service(QoS)is ***-mizing the plethora of cloud services has thus become a top *** ser-vice optimization is negatively affected by untrusted QoS data,which are inevitably provided by some *** resolve these problems,this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantiﬁcation *** data are assessed by an information correction *** weights discovered by the variable precision Rough Set,which mined the evaluation indicators from historical data,providing a comprehensive performance ranking of service *** manufacturing cloud service optimization algorithm thus provides a quantitative reference for service *** experimental simulations,this method recommended the optimal services that met users’needs,and effectively reduced the impact of dis-honest users on the selection results.

关键词： Cloud manufacturing quality of service optimization algorithm rough set

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Decentralized Security in IoT: Lightweight Blockchain with Optimized CA-LSTM for Improved Performance and Privacy 5

Decentralized Security in IoT: Lightweight Blockchain with O...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Selvakumarasamy, K. Kumar, T. Rajesh Meenambika, A. Balamanigandan, R. Mahaveerakannan, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Computer Science and Business Systems India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

An existing infrastructure can be linked to billions of devices, or "things,"through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. Massive volumes of data are being generated due to the proliferation of devices globally, which permeates every facet of everyday life. Thus, new problems are emerging as a result of the development and use of existing technologies, and these problems pertain to new applications, regulations, cloud computing, security, and privacy. With its decentralized nature, the blockchain has the potential to protect users' and data's privacy. This study introduces a novel method that makes use of lightweight blockchain technology to significantly lessen the computing load usually seen in traditional blockchain systems. Implementation time and computational complexity can be significantly reduced by connecting this lightweight blockchain with IoT devices. Improved safety is achieved by the use of CA-LSTM (channel attention long short-term memory) technology for attack detection. In order to improve the CA-LSTM algorithm's solution, the suggested system uses the RMSSO algorithm as a hyperparameter tuning technique. Similarly, to found very little power consumption and physical memory utilization;for example, Raspberry Pi devices used 0.2 GB of memory and NVIDIA Jetson devices used 0.42 GB. With the deployed model, power consumption increased by an average of 15% per device. This technology makes it easier to build certain types of business models and gives decentralized apps the ability to securely compute on encrypted data without compromising user privacy. The findings confirm that the proposed system is very secure, on par with conventional blockchain implementations. © 2024 IEEE.

关键词： Differential privacy

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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Facial Expression Recognition Using Multi-Block Deep CNN 5

Facial Expression Recognition Using Multi-Block Deep CNN

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Priyadarshini, V. Srinivasulu Reddy, U. Reddy Chirra, Venkata Rami Mrudula, M. Suneetha, M. National Institute of Technology Dept. of Computer Science & Engineering Andhra Pradesh India Machine Learning & Data Analytics Lab National Institute of Technology Dept. of Computer Applications Tiruchirappalli India School of Computer Science & Engineering VIT-AP University Amaravathi India Malla Reddy College of Engineering Department of CSE Telangana India Independent Researcher Amaravati India

ISBN: (纸本)9798331528539

Emotions play a crucial role in decision-making, moral judgments, and other cognitive processes. The goal of this work is to identify people's facial emotions from face images. Facial expression recognition (FER), a highly diverse field with many commonalities between classes, is a challenging problem. The capabilities of current FER systems are limited in this regard. In this work, a multi-block Deep Convolutional Neural Networks (DCNN) model was proposed to identify facial expressions. Five blocks with different computational components were defined in a multi-block DCNN to extract the unique characteristics from input images. The second model (DCNN-SVM) is developed with an ensemble of SVM classifier to improve predictability and stability. The third model (DCNN-MV) is based on the majority voting with 4 classifiers SVM, KNN, Random Forest, and Decision Tree. The CK+ dataset was utilized and expanded via image data augmentation to enhance model performance and generalization. By adjusting hyperparameters, the suggested DCNN model's accuracy was investigated. The DCNN model reports an accuracy score of 97.71%. Both DCNN-SVM and DCNN-MV models achieved 97.79% accuracy. On the CK+ dataset, the proposed models demonstrated good accuracy in identifying facial expressions. When compared to existing models, the proposed model outperformed existing models in terms of accuracy. © 2024 IEEE.

关键词： Convolutional neural networks

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