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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Sonia, R. Jayadeva, Sujay Mugaloremutt Ragavendiran, S.D. Prabu Revathi, N. Arumugam, Jeevanantham Kumar, S Sandeep B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Tamil Nadu Vandalur600048 India Jss Academy of Higher Education and Research Department of Health System Management Studies Karnataka Mysuru570004 India Rvs Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore641402 India Panimalar Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Kongu Engineering College Department of Information Technology Tamil Nadu Perundurai638060 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9781665482714

India has the highest percentage of working women and mothers. It also has a record for the highest number of babies being born every single day. These two statements highly contradict each other. When a woman gives birth to a baby, she needs to spend almost a year or two taking care of the necessities of the baby. But when a need to leave the baby for a certain time arises regularly, it becomes tougher for them to do so. This study provides a solution to this problem. This research aims at the construction of a smart cradle. This cradle is des igned using various input elements, output elements, a controller module, and a mobile application. The input elements consist of a temperature sensor, a sound sensor or a microphone,a moisture sensor, and a music player. The output elements include the motor and The controller used in this study is the NodeMCU module. Cloud storage is also used and it is provided by the Arduino loT software. The sensors in the smart cradle begin recording relevant parameters such as temperature, humidity, and sound as soon as it is turned on. The information is then uploaded to the cloud The data is then processed in the cloud for analysis. If indeed the child's temperatures is over the safe range, the user receives a warning. Moisture levels above normal suggest that the infant has urinated The sound of the baby crying will be recorded in the microphone. The output elements are energized as a result of the sensor's data. If the baby has a high fever or has to go to the bathroom, the app will notify the parent. © 2022 IEEE.

关键词： Microphones

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Design and Implementation of a Smart Human Posture Monitoring Device Using a Wearable Vest 7

Design and Implementation of a Smart Human Posture Monitorin...

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7th International Conference on Advances in Biomedical Engineering, ICABME 2023

作者： Daou, Roy Abi Zeid Geha, Elio Aoun, Youssef Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Biomedical Engineering Department Furn El Chebbak Beirut Lebanon Mart Learning Education and Research Center Chananir Lebanon Lebanese German University Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Germany Universidad Politecnica de Madrid Lab of Bioinstrumentation and Nanomedicine Centro de Tecnologia Biomedica Spain

ISBN: (纸本)9798350325850

Chronic back pain can present a serious health concern, with symptoms that can significantly affect an individual's well-being, mobility, and overall quality of life over an extended period. While chronic back pain may manifest suddenly in some cases, it often develops gradually and persists for weeks, and in untreated cases, it can linger for years. Hence, the utilization of assistive devices such as wearable posture-monitoring vests can offer valuable assistance and guidance to users. This research paper is dedicated to the development of a system for detecting, diagnosing, and correcting poor posture, specifically leaning posture. The vest is designed to provide users with visual, auditory, and tactile cues to help them address this issue, thereby reducing the risk associated with leaning. Additionally, an integrated electrical box has been designed to consolidate all components directly onto the main board in a secure enclosure. This box also displays the daily count of instances where the user has leaned. This system is characterized by its electrical safety, portability, compactness, comfort, and affordability. A comprehensive analysis of the system's performance has been conducted with a meticulous evaluation of accuracy. Each component of the system has undergone successful testing, and the system as a whole is currently in the testing phase. The results of these tests have indicated a lack of faulty errors and have demonstrated outstanding accuracy and detection rates. Over 100 individuals of varying ages, genders, and BMI categories were involved in testing, with each person wearing the device for an average of six hours. The accuracy rate achieved was 98.85%, with an average of 54.35 instances of poor posture detected per participant. © 2023 IEEE.

关键词： Wearable technology

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A More Efficient Way to Control Traffic Lights Through AI-Led Smart City management

A More Efficient Way to Control Traffic Lights Through AI-Le...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Shilpa Choudhary Shaik Shabbir Ali N. Ramesh Babu Harshita Sharma Bhawna Kaliraman Yash Dhankhar Department of Computer Science and Engineering (AIML) Neil Gogte Institute of Technology Hyderabad India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Mathematics Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India

Urban regions are nevertheless plagued by traffic congestion, which increases travel times, fuel use, and greenhouse gas emissions. To lessen traffic congestion and enhance the performance of traffic systems, effective traffic control is crucial. The ideal flow of traffic is not achieved by traditional traffic light control technologies, which are based on planned signal plans or fixed timings that do not adjust to real-time traffic circumstances. By dynamically changing signal timing based on real-time traffic data, artificial intelligence (AI) has emerged as a viable method to optimize traffic signal regulation. This paper suggests a real-time traffic signal timing optimization system that is powered by AI and leverages machine learning methods. This suggested work is a traffic light control system that uses artificial intelligence and offers a number of *** instance, a shorter trip distance, improved fuel economy, and lower pollutants. Additionally, the system is easily upgradeable with new data sources or algorithms and may be incorporated with current traffic management systems. It suggested using an artificial intelligence-based traffic light control system (TLCS) to greatly enhance traffic flow and relieve congestion in metropolitan areas. Real-time data and machine learning algorithms combined to enable dynamic and adaptive signal control, resulting in more effective traffic management and improved quality of life for city dwellers. This clarifies their potential to enhance urban transportation and lessen congestion.

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Comparative analysis of moral decision-making and trust dynamics: human reasoning vs. ChatGPT-3 narratives

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AI and Ethics 2024年 1-14页

作者： Rehman, Umair Shah, Muhammad Umair Iqbal, Farkhund Fatima, Ramsha Department of Computer Science Western University London Canada Department of Management Sciences Faculty of Engineering University of Waterloo London Canada College of Technological Innovation Zayed University Dubai United Arab Emirates Graduate School of Education University of Pennsylvania Philadelphia USA

As artificial intelligence (AI) becomes increasingly integrated into everyday decision-making, so does the influence of large language models like ChatGPT. While AI systems generate moral judgments, there may be distinct differences between how humans and AI approach moral dilemmas. This study explores these differences by conducting a comparative analysis of moral decision-making narratives produced by human participants and AI, specifically ChatGPT-3. Key evaluation metrics included causality, explicability, and overall satisfaction. Participants were presented with a complex moral dilemma and asked to provide justifications for their decisions, which were then compared with AI-generated responses. Surprisingly, the study found no significant difference in the quality of the explanations produced by ChatGPT-3 and human respondents. In the second phase, we examined the role of verification methods in fostering trust in these explanations. Participants evaluated explanations verified by humans, AI, or left unverified and assigned trust scores accordingly. The results demonstrate that human verification significantly enhances trust in explanations, while AI verification, though beneficial, had a smaller impact. This study underscores the importance of distinguishing between moral and ethical reasoning in AI systems and highlights the role of verification in trust-building.

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Full Robotics Automation: Approaches and Implementing Concerns

Full Robotics Automation: Approaches and Implementing Concer...

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Communication, Computing and Energy Efficient technologies (I3CEET), International Conference on

作者： A. Choriev B. Arun Kumar Abhishek Singh Baghel Varsha Grover Haider Alabdeli Gunjan Sharma Research University Tashkent Uzbekistan Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science & Engineering IES Institute of Technology and Management IES University Bhopal M.P. India Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Institute of Business Management GLA University Mathura India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

By using computer robotics that connect to systems via an operator interface to automate corporate activities, robotic process automation aims to reduce costs and boost productivity. However, if you make the wrong decision, a few important tasks, such as determining which procedures are suitable for her RPA robotics, might have a negative impact on your business. Thus, this study provides a way to investigate the advancement of RPA in corporate environments. To identify the essential RPA concepts that need to be investigated and considered in RPA case studies, this study offers a convincing analysis of the relevant literature. Apart from evaluating and applying the model based on his previous RPA research on case studies, a model that combines the fundamental ideas of RPA with research in design science is provided. The results of the research show that most of the core RPA ideas listed in the published review are not addressed by the selected RPA CS. RPA is a very young field, hence there isn't a lot of literature outlining its key concepts. This study attempts to bridge this gap by recognizing and including the main RPA topics and providing a model that integrates the key ideas. Researchers may use the model as a basis for their research and contribute RPA case examples.

关键词： Productivity Intelligent automation Costs Error analysis Computational modeling Energy efficiency Telecommunication computing Robots Business Systematic literature review

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Text Mining for the Identification and Synopsization of Trusted Ratings 2

Text Mining for the Identification and Synopsization of Trus...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Kalra, Simran Jeyalaxmi, M. Chaudhary, Anisha Ghosal, Suparna Nalawade, Dattatraya Babanrao Singh, K. Ranjith Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Quantum University Research Center Uttarakhand Roorkee247667 India Adjunct Faculty School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India

ISBN: (纸本)9798350388916

Online reviews have become increasingly valuable in the realm of purchasing and business decision-making. They not only contribute to brand loyalty but also aid customers in understanding product experiences. Unfortunately, the rise of fraudulent reviews, where authors are rewarded for generating fake evaluations to manipulate readers' perceptions, is a growing concern. This research paper aims to address this issue by developing a product review summarizer specifically designed to generate summaries based on genuine Amazon product reviews. The summarizer utilizes text analysis techniques to assess the linguistic characteristics of sentences and determine their value. The paper provides a comprehensive overview of existing and previous studies on sentiment analysis and text description. To enhance classification accuracy, a new hybrid classification system is proposed, combining multiple classification methods using arcing classifiers. The quality of these classifiers is thoroughly evaluated. In this study, supervised machine learning algorithms are compared with SVD dimensionality reduction, deep learning, and a text mining approach for summarization purposes. The model is built using a labeled Amazon review dataset. The key contribution of this paper lies in its innovative approach of generating text summaries for Amazon reviews while effectively filtering out fraudulent content. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimization of Neural Networks using Swarm Intelligence Techniques for Achieving Energy Efficiency in Smart Building Architecture

Optimization of Neural Networks using Swarm Intelligence Tec...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： M. Karthick Raja Makhbuba Shermatova Mohammed Saleh Al Ansari Shokhjakhon Abdufattokhov Vuda Sreenivasa Rao I. Infant Raj Department of Computer Science and Engineering Sri Eshwar College of Engineering Coimbatore Civil Engineering and Architecture Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan College of Engineering Department of Chemical Engineering University of Bahrain Bahrain Automatic Control and Computer Engineering Dept Turin Polytechnic University Dept of Information Technologies Tashkent Intl. University of Education Tashkent Uzbekistan Department of Computer Science and Engineering Koneru Lakshmaiah Education Fou. Green Fields Vaddeswaram A.P India Department of Computer Science and Engineering K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

The increasing prevalence of smart building architectures, driven by the integration of Internet of Things (IoT) devices and automation systems, has led to a surge in energy consumption. This research explores the application of swarm intelligence techniques as an innovative approach to optimize neural networks, aiming to strike a balance between maintaining the desired performance levels and minimizing energy consumption. The study investigates the integration of swarm-based optimization algorithms, such as Particle Swarm Optimization (PSO) into the training and operation of neural networks. These algorithms enable the networks to dynamically adapt and optimize their parameters in response to changing environmental conditions and user requirements. The research focuses on developing a comprehensive framework that considers the specific challenges posed by smart building architectures, including real-time data processing, sensor integration, and adaptive control. The proposed approach aims to achieve optimal neural network configurations that minimize energy consumption while ensuring reliable and responsive operation of smart building systems. The results demonstrate the potential of swarm intelligence to significantly improve the energy efficiency of neural network-enabled smart building architectures, providing a promising avenue for sustainable and intelligent infrastructure. The proposed model has an accuracy of 98.23% which is 7.64% higher than that of the traditional approaches.

关键词： Training Energy consumption Smart buildings Architecture Heuristic algorithms Neural networks Energy efficiency

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Analysis of Wordle Game Mechanism Based on Multi-task Model 4

Analysis of Wordle Game Mechanism Based on Multi-task Model

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4th International Conference on computers, Information Processing and Advanced education, CIPAE 2023

作者： Liang, Renzhao He, Zijun Zhang, Zhuo He, Haidong Gao, Bo Wuhan University School of Physical Science and Technology Wuhan China Sichuan University School of Aeronautics and Astronautics Chengdu China Dalian Jiaotong University School of Economics and Management Dalian China Department of Physical Education Qingdao China Dalian Jiaotong University School of Computer and Communication Engineering Dalian China

ISBN: (纸本)9798350342710

Wordle is a popular word-guessing game on Twitter, which can stimulate people's interest in word structure and has pedagogical significance. In this paper, we analyze the game mechanics through a large amount of game data deposited in the backend of Wordle, and provide corresponding decision support for game managers. In this paper, a multi-task model (MMCL) is firstly established to combine MASK masking with contrast learning to encode words as a matrix with deep word construction features by using letters as the smallest unit;secondly, a specialized decoder is trained to realize the mapping between encoded words and difficulty coefficients by using difficulty histogram as supervision;finally, in this paper, through a systematic clustering algorithm, the words are mapped by Finally, this paper classifies the words into difficult, medium and easy levels through the difficulty coefficients mapped out by a systematic clustering algorithm. Based on this model, this paper well completes our research on word structure patterns and the psychological logic of human word construction. © 2023 IEEE.

关键词： K-means clustering

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A Survey in Using Machine Learning (Ml) Approaches for the Diagnosis of Personal Stress 2

A Survey in Using Machine Learning (Ml) Approaches for the D...

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2nd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2022

作者： Kumar, Brajesh Reddy, Tangirala Ruthvika Sharma, Deepti Dewani, Safura Narayana, M Srinivasa Tripathi, Vikas Computer Science & Engineering Madhyanchal Professional University Bhopal India Tamil Nadu Thanjavur India Uttaranchal University Department of Management Dehradun India Government Medical College Srinagar Department of Physiology India Kl Business School K L University Koneru Lakshmaiah Education Foundation Vijayawada India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India

ISBN: (纸本)9781665437899

The present research work depicts the in-depth analysis of physiological data through machine learning technology. The use of machine learning is helpful in deriving the data regarding patients with personal stress. The approaches of machine learning are also useful for the researcher in gathering the accurate, reliable, and viable results from the research. As for this, both the primary data collection and secondary data collection method is used by the research in obtaining the data and information from both the sources. Hence, using both the sources in the research the researcher has increased the reliability of the research since simultaneous use of primary as well as secondary method could enable the research in collection of correct data and information about the stress diagnosed stressed people of the UK. © 2022 IEEE.

关键词： Machine learning

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Performance Analysis Of Machine Learning And Deep Learning Models For Text Classification 2

Performance Analysis Of Machine Learning And Deep Learning M...

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2nd IEEE Global Conference on Communications and Information technologies, GCCIT 2024

作者： Rajarajeswari, S. Goyal, Samaksh Srinivasa Rao, K.S. Dongre, Y.V. Singh, K. Ranjith Singh, Jaskirat Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Quantum University Quantum University Research Center Uttarakhand Roorkee247667 India School of Mangement - Pg Management Karnataka Bangalore India Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India

ISBN: (纸本)9798350388916

Information retrieval and data fields that concentrate on automated lengthy text categorization are becoming more popular due to the quick development of modern science and technology. However, machine learning-based text categorization learning must be thoroughly examined and assessed from an algorithmic, model, assessment, and other perspective. This article classifies texts using the deep learning approach. First, the traditional text classification procedure is explained, which includes pre-processing data, segmenting the text, creating features, summarizing the classification algorithm, and so on. Second, text categorization is done using CNN with multiple convolution kernels. The word vector model takes the role of the conventional bag of words model, and text characteristics are extracted using a multi-dimensional convolution kernel. Deep learning models - primarily convolutional neural networks and multilayer perceptrons - have been more and more popular recently for use with these kinds of datasets. Since there isn't any such integrated study, the purpose of this research is to evaluate how well deep learning models perform on various cancer gene expression datasets. Three deep learning models, two feature selection techniques, and four cancer gene expression datasets have been taken into consideration for this aim. The experiment in this study showed that CNN models performed promisingly in text document classification, but it also raised the following questions that need more, in-depth investigation: 1) How to compare the performance of the suggested models more effectively. Which text-based categorization tasks are best suited for each model? 2) How to better comprehend the facts. Specifically, what was learned by the deep learning model? 3) Adjusting hyperparameters. The process of hyper-parameter tuning is one that requires expertise. 4) Label imbalance: how to modify the sample weight in a loss akin to the bootstrap approach when the data samples are

关键词： Contrastive Learning

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