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AI Companion Robot Data Sharing: Preferences of an Online Cohort and Policy ImplicationsIntercambio de datos de robots complementarios de IA: preferencias de una cohorte en línea e implicaciones de política人工智能伴侣机器人数据共享：网络群体偏好与政策启示

Journal of Elder Policy

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Journal of Elder Policy 2023年第3期2卷 19-54页

作者： Clara Berridge PhD, MSW Yuanjin Zhou PhD, MA Julie M. Robillard PhD Jeffrey Kaye MD Associate Professor School of Social Work University of Washington Assistant Professor Steve Hicks School of Social Work University of Texas Austin Assistant Professor of Neurology Department of Medicine University of British Columbia Professor Departments of Neurology & Biomedical Engineering Oregon Center for Aging & Technology Oregon Health & Science University

Policymakers have recognized the urgent need to create AI data protections, yet the interests of older adults have thus far not been well represented. We report peoples' perspectives on small AI companion robots for older adults, along with attendant issues related to facial expression and conversation data collection and sharing. Data are from a cross-sectional survey of an online cohort of the Oregon Center for Aging & Technology at Oregon Health & science University, with a response rate of 45% and analytic sample of 825 (mean age: 63.9, rang: 25-88). Logistic regressions examined relationships between comfort and data sharing preferences with socio-demographic characteristics. Just over half (52.3%) were somewhat or very comfortable with an artificial companion robot during the pandemic and 45.2% were under normal circumstances. In adjusted models, being younger, male, and having lower formal education and greater confidence in computer use were associated with greater likelihood of being comfortable with a companion robot. Those who were at least somewhat comfortable with robots recording their conversations (15%) or reported that they would probably want their facial expressions read for emotion detection (52.8%) also selected with whom they want these data shared. Free text comments were thematically analyzed. Primary themes were that robot-based data collection constitutes over monitoring and invasion of privacy, with participants predicting data privacy, security, and use issues. These findings about the importance potential users place on data protection and transparency demonstrate a need for law and policy to act to enable trustworthy, desirable companion robots. Los formuladores de políticas han reconocido la necesidad urgente de crear protecciones de datos de IA, pero los intereses de los adultos mayores hasta ahora no han estado bien representados. Informamos las perspectivas de las personas sobre los pequeños robots acompañantes de IA para adultos ma

关键词： robotics artificial intelligence natural language processing emotion detection privacy robótica inteligencia artificial procesamiento de lenguaje natural detección de emociones privacidad 机器人人工智能自然语言处理情绪检测隐私

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Real Time Gas Alert Leakage System using Arduino and NodeMCU

Real Time Gas Alert Leakage System using Arduino and NodeMCU

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Gurupriya M P Umesh Raghav S Pavan Nikhith P Bhargav Reddy Rohit K C Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Associate Vice President Standard Chartered Global business Services Bengaluru India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Today, gas leakage has become a common problem in both industrial and household sectors as it can cause explosions, fires, and toxic exposures. The real time gas alert system ensures that gas leaks are not only detected but also transfer alerts so that risks are reduced and lives can be saved. The system involves an Arduino board that is already connected to a gas sensor. From there, the sensor information stays sent to a NodeMCU module which connected to the internet and sends alerts to users through the Blynk app and email once gas emission conforms to particular standards over a certain duration. Furthermore, if the intensity of the gas surpasses the threshold for a specific time period, the system activates the sending of the alarm through an automated procedure. The system presents a very good solution to gas leakage and gas positioning in a sight plan in regions where gas safety is of greater concern.

关键词： Machine learning algorithms Feature extraction Real-time systems Safety Reliability Leak detection Informatics Gas detectors Standards Mobile computing

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Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

Smart Water Taps Using IoT Sensors and Deep Reinforcement Le...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mageshkumar Naarayanasamy Varadarajan Viyyapu Lokeshwari Vinya R. Jenin Prabhu G. Swathi N. Mohankumar S. Velmurugan Lead Software Engineer Capital One Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Assistant Professor Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakundhala Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Professor Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Professor Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.

关键词： Water heating Water conservation User interfaces Deep reinforcement learning Real-time systems Sensor systems Internet of Things

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Machine Learning Techniques with LowDimensional Feature Extraction for Improving the Generalizability of Cardiac Arrhythmia

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2021年第2期48卷 1-10页

作者： Nurmaini, Siti Tutuko, Bambang Rachmatullah, Muhammad Naufal Darmawahyuni, Annisa Firdaus, Firdaus A Professor of Computer Engineering Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia A lecturer of Computer Engineering Department Faculty of Computer Science Universitas Sriwijaya Palembang30139 Indonesia A research assistant of Intelligent System Research Group Universitas Sriwijaya Palembang30139 Indonesia

Automatic heartbeat classification is an important stage in identifying cardiac arrhythmia. Several machine learning (ML) techniques have been proposed to perform this, but they produce an accuracy result of below 99%. In this study, a deep neural network (DNN) structure is applied to improve ML performance. The feature selection method is based on the combination of discrete wavelet transform (DWT) and principal component analysis (PCA). To avoid computational complexity, the components of PCA are derived by low-dimensional DWT coefficients. The results show that the proposed ML model achieves good performance, producing 99.76% accuracy, 91.80% sensitivity, 99.78% specificity, 93.02% precision, and 92.31% F1-score. To benchmark the proposed model, the support vector machine (SVM) and random forest (RF) techniques are used as the baseline models. The DNNs are 2.3% more sensitive than SVM, while the RF fails to classify the ECG heartbeat. Four datasets are used to analyze the robustness and generalization performance of the proposed model: MIT-BIH, SVDB, MITDB, and IncartDB. All testing results produce satisfying performance. The proposed ML model offers a potential solution to improve the generalizability of a DNN-based model in different cardiac datasets for classifying tasks. © 2021. All Rights Reserved.

关键词： Principal component analysis

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A Novel Intelligent AI-Based Security to Enhance the Data Communication: An Empirical Review

A Novel Intelligent AI-Based Security to Enhance the Data Co...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Shaik, Amjan Hung, Bui Thanh Chakrabarti, Prasun Siva Shankar, S. Parveen, Nikhat Department of Computer Science and Engineering Dean R&D St. Peter’s Engineering College Maisammaguda Telangana Hyderabad India Data Science Laboratory Faculty of Information Technology Industrial University of Ho Chi Minh Ho Chi Minh Viet Nam Department of Computer Science and Engineering Sir Padampat Singhania University Rajasthan Udaipur India Adjunct Distinguished Professor ITM SLS Baroda University Gujarat Vadodara India CSE K G Reddy College of Engineering and Technology Moinabad India CSE Koneru Lakshmaiah Education Foundation AP Guntur Vaddeswaram India

ISBN: (纸本)9789819947164

The transmission of digital data between two devices is termed data communication. As the world nowadays relies on digital communication, the transferring of data among various devices is vitally important, and hence, the security of the data is a concern. Several works are carried out to secure the data transmission between the devices in various applications like mobile communication, wireless communication, vehicular communication, and more. Incorporated with security, the data classification is also performed to ignore the unwanted or hackers’ data. In this work, we have discussed several secure data communication-related papers from various applications. Works including machine learning-based approaches such as decision tree, linear regression, logistic regression, KNN, K-means, and deep learning-based approaches such as a convolutional neural network, recurrent neural network, and long short-term memory approaches were discussed and listed with their limitations along with merits. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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An Automated Security Approach of Image and Video Steganography 2nd

An Automated Security Approach of Image and Video Steganogra...

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2nd International Conference on Emerging Trends and Advances in Electrical engineering and Renewable Energy, ETAEERE 2020

作者： Lavanya, P. Anusha, P. Kaveri, V. Vijeya Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Professor CSE Sri Krishna College of Engineering and Technology Coimbatore India

ISBN: (纸本)9789811575037

The steganography utilizes the craft of covered up;its principle point is to pass unnoticed information in information. There are numerous kinds of information utilized in steganography, for example, message, image, and video. Encryption is the job of securing data by shuffling it into a provision that cannot be used effectively and is called as the figure content. This mysterious figure or message content should be used by people using an anonymous key and able to decode the note into basic form. To match mystery information or single point data to the targeted alternative to various applications we use steganography. In cryptography, normally the matter in the mysterious note is mixed whereas in steganography the mysterious note is installed from diffuse standard. The proposed framework encodes a book or image within a cover image. Videos and images are exceptionally regular decisions to hide information. It is significant for the powerful and effective installation procedure to choose the video outlines which contains proper picture view that can be used to store mystery information. The audiovisual-built steganography is used due to memory prerequisites along with huge scope. LSB inclusion system, for concealing data we change LSB of video document with the data bits. This paper will concentrate on concealing data in an explicit video envelope and in an explicit advantage situation by replacing LSB. A verified copy is anticipated and created through joining less important bit and advanced encryption standard. Here we use AES for encryption plus LSB method for steganography. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Steganography

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A Novel Automatic Text Document Classification Using Learning based Text Classification(LbTC) Approach

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Procedia computer science 2025年 258卷 4279-4290页

作者： Avinash N J Krishnaraj Rao Rama Moorthy H Raviprakash B Raghunadan K R Vasudeva Venkatadri M Department of Electronics and Communication Engineering Mangalore Institute of Technology and Engineering Moodabidre 574225 Karnataka India Department of Information Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University) Mangalore 575007 Karnataka India Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala 574110 Karnataka India Associate Dean MPSTME Shirpur Campus NMIMS (Deemed-to-be-University) Shirpur Maharashtra India

There are practical uses for text document classification. In actuality, it is crucial to categorize and parse documents with natural language. Utilizing applications like bogus news identification, query tagging, sentiment classification, and spam filtering needs this sort of study. However, because of their ambiguity, open-ended nature, and vastness, text documents provide a difficult Classification challenge. Machine learning (ML) methods became valuable alongside the growth of artificial intelligence (AI) due to their data-driven learning techniques. These methods are seen as highly effective at handling and analyzing vast data sets in depth. Topic segmentation, text categorization, entity identification, machine translation, and text summarization, to name a few, are just a few of the issues that may be resolved with ML approaches. In this study, we introduced an automatic Text Classification system (ATCF), a system that uses shallow and deep neural networks to classify text documents. To implement our system, we proposed an approach called Learning based Text Classification (LbTC). We investigated the performance of several models in comparison. Based on the training provided to ML models, the suggested framework assists in categorizing any type of document. It is compatible with practical applications where the categorization of documents is essential.

关键词： Machine learning shallow learning deep learning text classification document Classification

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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

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Procedia computer science 2025年 252卷 53-62页

作者： Milind Shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real Time Networking Multimedia Challenges Innovations Intelligent Networking

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An investigation on feature and text extraction from images using image recognition in Android

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Materials Today: Proceedings 2022年 51卷 798-802页

作者： Brijeshkumar Y. Panchal Gaurang Chauhan Sandipkumar R. Panchal Urvashi M. Chaudhari Department of Computer Science and Engineering DEPSTAR CHARUSAT Charotar University of Science and Technology (CHARUSAT) CHARUSAT campus Changa 388421 India ITsouls Vadodara India Associate Professor & I/C Principal Knowledge Institute of Technology & Engineering Gujarat India Department of Computer Engineering Sardar Patel College of Engineering Bakrol-Anand Gujarat India

In today’s era, there is a huge rise in demand for the data and information available in a printed medium. To resume time, cost, and energy, we have been developing numerous technology. One of them, convert JPG, JPEG, or PDF softcopy into text, this process calls Text extraction. Identification of characters and detection of words and texts is again an upfront challenging task. This paper is focused on an investigation of feature and text extraction from images using image recognition in Android. In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia

Texture-based Feature Extraction and Machine Learning Model ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Rangini M Sumit Pundir Manzoore Elahi M. Soudagar Daxa Vekariya Harshal Patil S. Rajkumar Assistant Professor Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable.

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