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Gaussian Differential Privacy Integrated Machine Learning Model for Industrial Internet of Things

SN computer science 2023年第5期4卷 454页

作者： Lazar, Arokia Jesu Prabhu Soundararaj, Sivaprakash Sonthi, Vijaya Krishna Palanisamy, Vishnu Raja Subramaniyan, Vanithamani Sengan, Sudhakar Department of Computer Science and Engineering AI & ML CMR Institute of Technology Telangana Hyderabad 501401 India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore 632014 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram 522502 India Department of Computer Science and Engineering Kongu Engineering College Tamil Nadu Perundurai 638060 India Master of Computer Applications M. Kumarasamy College of Engineering Tamil Nadu Karur 638060 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627152 India

Agriculture, energy, mining, healthcare, and transportation are a few of the top industries transformed by the industrial internet of things (IIoT). Industry 4.0 mainly relies on machine learning (ML) to use the vast interconnectedness and large amounts of IIoT data that IIoT primarily drives. ML approaches are trained on confidential data generated by the IIoT environment often exposes privacy to adversarial assaults. Blockchain-based ML is established in the proposed secured IIoT research work to safeguard and enhance privacy. This research proposes a Gaussian differential privacy-integrated machine learning model (GDPIMLM), created for a scalable and controlled IIoT system, fully connected (FC) layer, implemented blockchain benefits to create a privacy-preserving mechanism while considering other limitations and reasonable time. A probability test using two altered Gaussian distributions (GD) forms the basis for defining Gaussian differential privacy (GDP). Due to a central restriction theorem for differential privacy, GDP is the primary privacy term within the family of Federal differential privacy (f-DP) policies. Ethereum is used to implement experimental evaluations. Data collections show that the recommended method enhances digital data privacy with industry-leading security without reducing performance. The performance analysis of the proposed model of secured industrial internet of things (IIoT) is studied using the accuracy of comparison with existing methods and for diverse distributed entities (DE). The proposed secured IIoT attains a minimal time consumption (TC) of 790 Sec—the highest accuracy of 92%, outperforming the existing method. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Blockchain Industry Industry 4.0 Learning Privacy Security Smart objects

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Predictive Analysis of Diabetes Mellitus Using Decision Tree Approach 2

Predictive Analysis of Diabetes Mellitus Using Decision Tree...

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2nd Asian Conference on Innovation in technology, ASIANCON 2022

作者： Thotad, Puneeth Bharamagoudar, Geeta R. Anami, Basavaraj S. Kle Institute of Technology Dept. of Master of Computer Applications Hubballi India Kle Institute of Technology Dept. of Computer Science & Engineering Hubballi India

ISBN: (数字)9781665468510

ISBN: (纸本)9781665468510

Diabetes Mellitus is one of the Non-Communicable Disease. It is characterized by hyperglycemia. It may be reason for numerous health difficulties. According to the statistics in coming years, in 2030, worldwide it reaches up to 643 million counts of diabetic patients. The various factors such as Bad Diet, High Blood Pressure, Obesity, Age, Lack of Exercise etc. can led to Diabetes Mellitus. The various diseases like nerve damage, heart problem, eye problem, kidney disease, stroke in many people caused by high risk of Diabetes Mellitus. Machine Learning plays vital role in healthcare sectors. Using various Machine Learning techniques, a study can be done on large datasets and discover unseen facts, unseen patterns to realize awareness from the data and predict results consequently. In this work, the PIMA Indian Diabetes dataset is considered for Predictive Analysis. The Decision Tree algorithm, namely ID3 (Iterative Dichotomize 3) is used for a predictive analysis. The major phases include preprocessing, feature extraction, classification, and prediction. The classification accuracies of 79.76% and 76.3% are obtained for balanced and unbalanced datasets respectively. The maximum specificity and sensitivity obtained are 79.75% and 79.78% respectively. The ROC curve gives 79.57% overall classification accuracy. The work assists physicians as a second opinion. It can help various health industries to predict the diabetes disease in a better way. © 2022 IEEE.

关键词： Decision trees

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Enhanced Audio-Visual Speech Enhancement via Attention-based Sparse Graph Convolutional Neural Networks with Fire Hawk Optimization 3

Enhanced Audio-Visual Speech Enhancement via Attention-based...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Hebri, Dheeraj Joon, Rakesh Kumar Mohan, M. Ramapraba, P.S. Dhulipudi, Venkata Satish Narasimharao, Jonnadula Department of Master of Computer Applications Srinivas Institute of Technology Karnataka Mangalore574143 India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Haryana 124104 India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Tamil Nadu Chennai641407 India Department of Electrical and Electronics Engineering Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Electronics and Communication Engineering BVC Institute of Technology and Science Andhra Pradesh 533221 India Department of Computer Science and Engineering CMR Technical Campus Telangana Hyderabad501401 India

ISBN: (纸本)9798331532420

Audio-Visual Speech Enhancement (AVSE) has the vision to enhance speech quality and requite intelligibility by using both audio and video inputs, which is quite useful in noisy conditions. This approach acknowledges the fact that modalities such as lip movement may be equally informative of the auditory signals as the actual sound in cases where audio signals are distorted. In this research, an innovation approach to the AVSE is presented, which involves several state-of-the-art techniques. The data preprocessing step uses Z-score Min-Max Normalization to normalize the input data to help enhance the feature extraction process. Differentiable adaptive short-time Fourier transform (DA-STFT) is employed as the method of audio feature extraction that provides key temporal information. In the feature extraction process, a local directional structural pattern (LDSP) is utilized for the extraction of visual features since it examines the moving patterns on the face effectively. The fusion of data is done using a quadruplet depth-wise separable fusion convolutional neural network (QDSFCNN), which enhances the amalgamation of the audio and visual cues. Classification is done using an attention-based sparse graph convolutional neural network, known as ASGCNN, since the focus is on the relevant stipulations. Finally, to achieve the highest performance level, the fire hawk optimizer (FHO) is used for tuning parameters in the presented model. This combined approach presents advanced developments in AVSE, which provide improved speech quality in difficult conditions. © 2024 IEEE.

关键词： Convolutional neural networks

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A Design Study on Integrated Complex Coefficient Filters with Peripheral Circuits for Accurate Frequency Response Measurement

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2025年第1期145卷 16-24页

作者： Shouno, Kazuhiro Sato, Lina Fukagawa, Kanta Tanimoto, Hiroshi Muto, Cosy Division of Information Engineering University of Tsukuba 1-1-1 Tennoudai Ibaraki Tsukuba305-8573 Japan Graduate School of Science and Technology Degree Programs in Systems and Information Engineering Master’s Program in Computer Science University of Tsukuba Japan Kitami Institute of Technology 165 Koen-cho Hokkaido Kitami090-8507 Japan Faculty of Education Nagasaki University 1-14 Bunkyo-machi Nagasaki852-8521 Japan

This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square waves to an FUT (Filter Under Test) instead of sinusoidal waves or by superposing transfer characteristics obtained by applying a single sinusoidal wave to an FUT. In both of the above methods, the output signal connected to the external measurement equipment via a 50 Ω coaxial cable and so on. In this case, the output signal of the FUT should be amplified by internal or external amplifiers, which degrades the response. However, it is cancelled out by the proposed calibration circuitry with some analog switches. In addition to the above, for the RC polyphase filter, the source resistance and the load capacitance degrade the measurement results and their effect is discussed. The validity of the proposal is confirmed through computer simulation for a third-order RCPF whose passband is 8–40 MHz fabricated by Phenitec 0.6 µm CMOS process. It is shown that the measurement error is −1.10 dB for the square-wave method and −1.00 dB and −0.43 degrees for the superposition method at 40 MHz. © 2025 The institute of Electrical Engineers of Japan.

关键词： Capacitance

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Mental Health Tracker Using Machine Learning Approaches 4

Mental Health Tracker Using Machine Learning Approaches

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Thotad, Puneeth N. Kallur, Shanta Mundaragi, Laxmi Kadam, Swati H. Dept. of Master of Computer Applications K.L.E. Institute of Technology Hubballi580 027 India Dept. of Computer Science and Engineering K.L.E. Institute of Technology Hubballi580 027 India

ISBN: (纸本)9798350305258

Mental illness is a common condition in the 21st century. Despite the significant number of people who experience mental illness, many continue to experience stress without comprehending its damaging effects on their brain. Python and machine learning tools are adopted to create the mental health tracker system. According to the experimentation, the work focuses on developing a psychological well-being tracker. The prediction uses various models such as logistic regression, random forest, decision tree, and k-nearest neighbor. In the proposed system, the patient must answer the questions to predict their mental illness condition. Based on the responses given by the patient, the system will predict whether they need treatment or not. The system will attempt to gain an understanding of a user's mental state, determine whether they are suffering, and then suggest steps they can take to improve their mental health situation. Mental health prediction might benefit psychological departments looking to enhance their counseling and diagnosis abilities. The model was developed by collecting the question-answer set as options related to mental health by consulting specialized doctors in the hospital. The model is trained using different ML algorithms, finding their accuracy and predicting the results. The analysis was made by considering the raw data and normalized data. Models performance was improved with normalization with the following accuracies. 71.4%, 73.5%, 80.6%, and 81.2% accuracies are obtained for Logistic regression, K-Nearest Neighbor, Decision Tree, and Random Forest, respectively. © 2023 IEEE.

关键词： Decision Tree Digital Mental Health K-Nearest Neighbor Logistic Regression Machine Learning Random Forests

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Personalized Secure E-Identity Card Using Block Chain

Personalized Secure E-Identity Card Using Block Chain

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Burugari, Vijay Kumar Kanmani, P. Selvaraj, Prabha Potluri, Varun Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering K L Deemed to Be University Vaddeeswaram Andhra Pradesh India SRM Institute of Science and Technology Kattankulathur Department of DSBS Chengalpattu India VIT-AP University Amaravati School of Computer Science and Engineering Andhra Pradesh India University of Texas Dallas Richardson Master of Science in Computer Science TX United States

ISBN: (纸本)9798350348217

Blockchain technology is a new technology in digital platforms for making transactions in a decentralized manner. Blockchain is used for last some year for bitcoin. The preceding block's cryptographic hash, a timestamp, and a transaction are all included in every block in the blockchain. As a result, it is noticeable for security on an identity card. Implementation of blockchain technology for digital identity card maintains a secure network for the user. Identity card has personal information and some important information included. Individual person identity cards are stored in file with encryption method which will hacked by hackers. So a system using blockchain technology for identity card is proposed. In distribute file system, data security for information exchange and storage is provided using blockchain and IPFS (Interplanetary File System) for storing and communicating data securely. © 2023 IEEE.

关键词： Blockchain

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Bharatanatyam hasta mudra categorization using deep learning approaches 2

Bharatanatyam hasta mudra categorization using deep learning...

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

作者： Nandeppanavar, Anupama S. Kallur, Shanta S. Thotad, Puneeth Sankannavar, Vaishnavi A. K. L. E. Institute of Technology Department of Master of Computer Applications Hubballi India Affiliated to Visvesvaraya Technological University Belagavi India K. L. E. Institute of Technology Department of Computer Science and Engineering Hubballi India K. L. E. Institute of Technology Vtu Department of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350314045

The present study explores the precise identification of Bharatanatyam hasta mudras (hand gestures) by applying advanced convolutional neural networks and deep learning methodologies. Specifically, the article employs the Convolutional Sequential Neural Network, VGG-19, and, prominently, ResNet50V2 architectures to analyze a meticulously labeled dataset comprising Bharatanatyam hasta mudra images. With accuracy, recall, precision, and F1-score, key performance metrics are systematically employed for comprehensive performance evaluation, revealing an impressive accuracy rate of approximately 96.44%. Notably, the projected system is a valuable resource for Bharatanatyam practitioners, offering a sophisticated tool for discovering and mastering diverse hasta mudras. Its potential impact extends to dance schools and academies, where the system can be instrumental in teaching and integrating various hasta mudras into choreographic works. Beyond dance education, the article's contribution resonates in the broader context of preserving and promoting Indian classical dance forms. © 2023 IEEE.

关键词： Convolutional neural networks

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Leveraging Machine Learning for Early Detection of Cardiovascular Diseases

Leveraging Machine Learning for Early Detection of Cardiovas...

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

作者： Shrivastava, Anurag Maindola, Meenakshi Kumar, Rakesh Thethi, H. Pal Sreeja, S.P. Sirisha, N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India GLA University Department of Computer Engineering & Applications Mathura India Lovely Professional University Phagwara India New Horizon College of Engineering Master of Computer Application Bangalore India MLR Institute of Technology Department of Computer Science & Engineering Telangana Hyderabad India

ISBN: (纸本)9798331512965

In order to enhance patient outcomes, rapid and precise detection approaches are needed for cardiovascular diseases (CVDs), which are among the top causes of death globally. By examining a wide range of demographic and clinical data, this study investigates how machine learning approaches could improve the early diagnosis of CVDs. We explore how many different machine learning methods could identify different heart diseases. Decision trees, support vector machines and more, are included in these algorithms. By using feature selection and optimization methods we want to strengthen these models' prediction abilities and make them more resilient. The dataset used by this research includes all of the medical information of patients as they include all the pertinent biomarkers such as age, blood pressure, cholesterol levels and etc. The results suggest that machine learning models, especially ensemble methods like Random Forest and gradient boosting, are indeed able to predict the risk of CVD better than conventional diagnostic strategies. Results suggest that early risk assessment using machine learning embedded within healthcare processes is a reliable and non-invasive approach. This study has found machine learning to have promise for change and further research into the practical use of these models to revolutionize treatment of cardiovascular diseases. © 2025 IEEE.

关键词： Risk assessment

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Prediction of Traffic Accidents Using Deep Learning Ensemble Model

Prediction of Traffic Accidents Using Deep Learning Ensemble...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sekhar, G Soma Mahadik, Shubhangi Ramana, Attili Venkata Chandra Shaker Reddy, Pundru Pughazendi, N. Asif, S. Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Bharati Vidyapeeth's Institute of Management & Information Technology Master of Computer Applications Navi Mumbai India Hyderabad India SR University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Computer Science and Engineering Panimalar Engineering College Tamil Nadu Chennai India Marri Laxman Reddy Institute of Technology and Management CSE-Data Science Telangana Hyderabad India

ISBN: (纸本)9798350319200

Because of the widespread negative effects that traffic accidents can have on people's lives, the study of how to assess and anticipate them using cutting-edge technology is an important area of study. The research of traffic accidents has traditionally relied on data made public by traffic-entities and road-police forces. However, with the advent of social-media, researchers now have access to detailed, real-time data on road accidents occurring across a region, allowing for more comprehensive analyses that take into account previously unreported incidents. This paper uses an ensemble-Deep-Learning(EDL) strategy made up of Gated-Recurrent Units(GRUs) and Convolutional-Neural-Networks(CNNs) to offer a model to forecast traffic accidents utilizing data mined from social-media and the open web. The acquired results are compared to those of published baseline methods and other researchers. The outcomes are encouraging, showing that the proposed EDL approach outperforms the conventional models and other DL approaches published in the literature when applied to the problem setting. Authorities in charge of preventing accidents on the roads can use the data generated by the program. © 2023 IEEE.

关键词： DL ML risk prediction traffic accident traffic accidents

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Early Prediction of Parkinson's Disease with Machine Learning: A KNN Approach

Early Prediction of Parkinson's Disease with Machine Learnin...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Aruna S Saraswathi R Shanthy R Navaneethan S Geethosh G. V Gokulakrishnan M Department of Master of Computer Applications Sona College of Technology Salem India Department of Master of Computer Applications Srinivasa Institute of Technology and Management Studies Chitoor AP Department of Computer Science Sona College of Arts and Science Salem India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Parkinson's disease is a degenerative condition of the nervous system that significantly affects the quality of life of many people. Most of these disorders impact human motor functions. This research study employs machine learning (ML) to predict Parkinson's disease (PD), utilizing the Parkinson’s dataset. The dataset encompasses biomedical voice measurements from 31 individuals, distinguishing between healthy and Parkinson’s Disease subjects. Their predictive performance was evaluated using a variety of machine learning methods, such as Decision Tree, K-Nearest Neighbors (KNN), Random Forest, Naïve Bayes, Support Vector Machines (SVM), and Logistic Regression. Among these, the KNN model exhibited remarkable accuracy, exceeding 98%. This robust performance underscores its potential for accurate Parkinson’s disease prediction. We improved accessibility to diagnosis by utilizing the top-performing KNN model to create an intuitive web application with the Streamlit open-source framework. This research is significant because it could help with early intervention, which would improve patient care. The implementation involves predicting Parkinson’s disease with an accuracy score of 98% using the KNN model and deploying it in a web app. This innovative approach ensures wider accessibility and encourages patient self-management. In summary, this machine learning-driven research promises to improve patient outcomes by encouraging early intervention and offering an efficient Parkinson's disease prediction tool.

关键词： Support vector machines Pervasive computing Accuracy Parkinson's disease Social networking (online) Nearest neighbor methods Predictive models User interfaces Decision trees Random forests

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