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Feel Good AI: Voice-Enabled Emotion-based Music Recommendation system

Feel Good AI: Voice-Enabled Emotion-based Music Recommendati...

引用

Advances in Computing, communication and Applied Informatics (ACCAI), International Conference on

作者： Anjali Sharma Shubham Vishwakarma Liya T Mathew Dept. of Computer Science and Engg JSS Academy of Technical Education Bangalore India Dept. of Civil and Engg Thakur College of Engineering and Technology Mumbai India Dept. of Electronics and Communication Engg Cochin University of Science and Technology Kochi India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Feel Good AI is a novel effort committed to improving psychological well-being by means of methodically providing customized music suggestions that are in accordance with the user’s emotional state. By combining sophisticated recommendation algorithms with advanced technologies, most notably Convolutional Neural Networks (CNN) for facial emotion recognition, this project effortlessly develops innovative tech and empathetic emotional support. The users interact with an intuitive chatbot that generates unique music suggestions through the use of personalized conversations in which they are asked specific emotion-related inquiries. Beyond immediate relevance, incorporating AI-powered music curation to enhance positive emotional experiences for individuals, particularly those afflicted with dementia, is of greater significance. We live in a society where stress and mental health problems are prevalent. By combining problem-solving and emotional well-being, Feel Good AI offers evidence that technology can enhance the quality of life.

关键词： Emotion recognition Technological innovation Temperature Mood Refining Real-time systems Convolutional neural networks

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An Efficient way to optimize the FPGA routing Architecture 6

An Efficient way to optimize the FPGA routing Architecture

引用

6th IEEE International Conference on communication and Electronics systems, ICCES 2021

作者： K Ramesh, N V Kiran, K. Uday S V S Reshma, N K Kumar, K. Pavan Kumar Reddy, B. Naresh Koneru Lakshmaiah Education Foundation Dept. of Electronics and Communication Engineering Guntur India Koneru Lakshmaiah Education Foundation Dept. of Computer Science Engineering Guntur India Raghu Engineering College Dept. of Computer Science Engineering India Iit Delhi Department of Electrical Engineering Delhi India

ISBN: (纸本)9781665435871

Consumption of Low Energy and some switching region uses are two of the significant plan destinations in Field Programmable gate arrays worldwide directing engineering plan. In the current paper it is an improved model- based CAD stream that performs forceful enhancements, for example, geography and wire style advancements, to reduce the energy and the switching energy of FPGA worldwide directing structures. This analysis contrasted design with conventional cross section design, the streamlined FPGA directing models accomplish up to 9% to 16% energy investment funds and up to 20% switching energy that reserves the funds in normal for many circuits. © 2021 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Exploring User Experience and Challenges with Mobile Wallet Adoption in Emerging Markets

Exploring User Experience and Challenges with Mobile Wallet ...

引用

Technology Innovation and Its Applications (ICTIIA), International Conference on

作者： Priandito Ninda Lutfiani Natasya Aprila Yusuf Thomas Sumarsan Goh Muchlisina Madani Dept. of Informatics Engineering University of Raharja Tangerang Indonesia Dept. of Digital Business University of Raharja Tangerang Indonesia Dept. of Information Systems University of Raharja Tangerang Indonesia Dept. of Management Universitas Methodist Indonesia Medan Indonesia Dept. of Computer System University of Raharja Tangerang Indonesia

ISBN: (数字)9798350351613

ISBN: (纸本)9798350351620

This study explores user experiences and challenges in mobile wallet adoption in emerging markets using the UTAUT model. These markets face rapid technological growth alongside infrastructural and economic challenges. The research examines how factors like performance expectancy, effort expectancy, social influence, and facilitating conditions influence adoption. Data from 80 mobile wallet users was collected through an online survey and analyzed using SmartPLS 4.1.0.6. The results show that performance and effort expectancy predict behavioral intention, while facilitating conditions affect actual usage. The study highlights the role of infrastructure in supporting adoption and validates the UTAUT model's relevance in less developed regions. These findings provide insights into enhancing financial inclusion in emerging markets.

关键词： Surveys Technological innovation Soft sensors Decision making User interfaces User experience Security Sustainable development Socioeconomics Faces

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Development of a Dempster-Shafer Model for Diagnosing Mental Health Disorders on Social Media 7

Development of a Dempster-Shafer Model for Diagnosing Mental...

引用

7th International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2024

作者： Nasyuha, Ayahri Hadi Hadi Ilyas, Ananda Andriyani, Widyastuti Subagyo, Aloysius Agus Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Information System Yogyakarta Indonesia Universitas Dharmawangsa Faculty Of Engineering And Computer Science Dept. of Information System Medan Indonesia Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Master of Information Technology Yogyakarta Indonesia Universitas Teknologi Digital Indonesia Faculty of Information Technology Dept. of Accounting Information System Yogyakarta Indonesia

ISBN: (纸本)9798331519643

The rapid proliferation of social media has been paralleled by an increase in mental health concerns, often reflected in users' online behaviors and text posts. Traditional methods for diagnosing mental health disorders do not effectively leverage this vast amount of data, nor do they address the ambiguities and uncertainties inherent in natural language processing of social media content. A novel diagnostic model based on the Dempster-Shafer evidence theory is developed specifically for analyzing social media text. The model aims to enhance the precision of mental health disorder diagnoses by incorporating probabilistic reasoning and handling uncertainty in the data more effectively than traditional binary classification methods. It integrates linguistic cues, sentiment analysis, and behavioral indicators extracted from user posts to assess the likelihood of various mental health conditions. We collected a dataset comprising social media posts tagged with mental health keywords, verified by clinical annotations. The Dempster-Shafer model was trained with features derived from text analytics, including sentiment scores, thematic elements, and engagement metrics. The model's performance was evaluated against standard classification models through metrics such as accuracy, sensitivity, and specificity. The Dempster-Shafer model demonstrated superior capability in handling the uncertainty and imprecision of social media data, showing an improvement in diagnostic accuracy by 15% over traditional models. The findings suggest that applying Dempster-Shafer theory in mental health diagnostics can effectively utilize the nuanced and sparse data available on social media, offering a promising tool for early identification and intervention in mental health issues. © 2024 IEEE.

关键词： Tweets

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Mouse Cursor Control Using Dynamic Hand Gesture Recognition

Mouse Cursor Control Using Dynamic Hand Gesture Recognition

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communication, Computing and Signal Processing (IICCCS), IEEE International Conference on

作者： Darryll Fonseca Tanmoy Majumder Banita Mitra Arpita Das Biswajit Das Anirban Bhowmik School of Computer Engineering Vellore Institute of Technology Chennai India School of Electronics Engineering Vellore Institute of Technology Chennai India Dept. of Electronics and Communication Engineering Tripura Institute of Technology Agartala

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

Hand gestures are the most common way of interaction among humans in the physical world. Gestures are used in everyday life to interact with objects around us. Hand gesture is becoming the most comfortable alternative technique to replace the conventional mouse. Herein, we report the development of a dynamic hand gesture user interface that tracks and recognizes in real time based on color segmentation and recognition. Operation such as file dragging, right and left clicks and scrolling up and down has been studied. The colors are calibrated in order to make sure that no other similar shades of color from the background interfere during the process.

关键词： Heuristic algorithms Memory management Signal processing algorithms Color Gesture recognition User interfaces Signal processing Real-time systems Telecommunication computing

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Fusion of Images to Detect Brain Tumors Using DWT, IDWT and VGG19 Algorithms

Fusion of Images to Detect Brain Tumors Using DWT, IDWT and ...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Noorul Julaiha A G B. Vasudevan Ruhit Krishnan M Suriya M S Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India Dept. of Electronics & Communication Engineering St. Joseph's College of Engineering Chennai

Detecting brain tumors from medical images is a complex task in medical image analysis and is crucial for accurate diagnosis and treatment planning. Over the past few years, there has been significant development in the field of medical image processing. One such technique that has been employed is the multi-scale decomposition of brain images, which is achieved through the discrete wavelet transform (DWT). DWT decomposes an image into two different components, structural and textural information, without compressing the image. As a result, it provides high accuracy details and it preserves edge and texture details when reconstructing the image from its original frequency, and reduces problems like blocking and ringing artifacts. The low frequency sub-band coefficients are fused by selecting the coefficient with the maximum spatial frequency, indicating the overall active level of an image, while the high frequency subband coefficients are fused by selecting the coefficient with the maximum code value. Brain tumor detection using wavelet transform involves the use of both CT scan and MRI scan images. CT scan uses X-ray images taken from different angles of a specific part of the human body to provide detailed information about its internal structure, while MRI scan employs strong magnetic fields, radio waves, and field gradients to generate images of the inner human body. Finally, the fused image is reconstructed by taking the inverse Inverse Discrete Wavelet Transformation (IDWT) of two varied frequency sub-bands. The methods such as Visual Geometry Group (VGG19) which is a sub class of Convolutional Neural Network (CNN), watershed algorithm, Procrustes Analysis Algorithm, data argumentation are used in registration, fusion and segmentation process to detect brain tumor. Overall, the application of DWT and the fusion of multi-scale components of brain images has significant potential in improving the accuracy and quality of medical image processing, especially in t

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Development of a Dempster-Shafer Model for Diagnosing Mental Health Disorders on Social Media

Development of a Dempster-Shafer Model for Diagnosing Mental...

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International Seminar on Research of Information Technology and Intelligent systems (ISRITI)

作者： Ayahri Hadi Nasyuha Ananda Hadi Ilyas Widyastuti Andriyani Aloysius Agus Subagyo Dept. of Information System Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Dept. of Information System Faculty Of Engineering And Computer Science Universitas Dharmawangsa Medan Indonesia Dept. of Master of Information Technology Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia Dept. of Accounting Information System Faculty of Information Technology Universitas Teknologi Digital Indonesia Yogyakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

The rapid proliferation of social media has been paralleled by an increase in mental health concerns, often reflected in users' online behaviors and text posts. Traditional methods for diagnosing mental health disorders do not effectively leverage this vast amount of data, nor do they address the ambiguities and uncertainties inherent in natural language processing of social media content. A novel diagnostic model based on the Dempster-Shafer evidence theory is developed specifically for analyzing social media text. The model aims to enhance the precision of mental health disorder diagnoses by incorporating probabilistic reasoning and handling uncertainty in the data more effectively than traditional binary classification methods. It integrates linguistic cues, sentiment analysis, and behavioral indicators extracted from user posts to assess the likelihood of various mental health conditions. We collected a dataset comprising social media posts tagged with mental health keywords, verified by clinical annotations. The Dempster-Shafer model was trained with features derived from text analytics, including sentiment scores, thematic elements, and engagement metrics. The model’s performance was evaluated against standard classification models through metrics such as accuracy, sensitivity, and specificity. The Dempster-Shafer model demonstrated superior capability in handling the uncertainty and imprecision of social media data, showing an improvement in diagnostic accuracy by 15% over traditional models. The findings suggest that applying Dempster-Shafer theory in mental health diagnostics can effectively utilize the nuanced and sparse data available on social media, offering a promising tool for early identification and intervention in mental health issues.

关键词： Measurement Seminars Analytical models Sentiment analysis Accuracy Social networking (online) Mental health Sensitivity and specificity Data models Standards

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Convolutional Neural Networks Model for Screening Tuberculosis Disease

Convolutional Neural Networks Model for Screening Tuberculos...

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2021 International Conference of Technology, Science and Administration, ICTSA 2021

作者： Alawi, Abdulfattah E. Ba Al-Basser, Amer Sallam, Amer Al-Sabaeei, Amr Al-Khateeb, Hesham Taiz University Software Engineering Dept. Taiz Yemen Taiz University Mechatronics and Robotics Engineering Dept. Taiz Yemen Taiz University Computer Networks and Distributed System Dept. Taiz Yemen

ISBN: (纸本)9781665428620

Tuberculosis disease has a big concern and it is spreading quickly across the world. The secret for managing the condition is an accurate diagnosis. Acid quick staining, conventional approaches such as tuberculin skin test (TST), yield findings are unreliable or require more time to detect. This paper presents an automated solution that uses chest radiographs to diagnose tuberculosis. Chest radiographic images are used for tuberculosis diagnosis. Tuberculosis in chest radiographs is difficult to investigate under the current system of cavity identification, ribs, and diaphragm removal. By using a CNN-based model, the lung area is separated to resolve the problems. The proposed technique can classify chest x-ray (CXR) images as Tuberculosis (TB) infected or not. We analyzed 3500 CXR cases and 3500 normal cases with exposure to tuberculosis. Then, we built and trained our own CNN and found that the features map or heat-map generated from this network performed a slightly better job. The implementation was done in Tensorflow and Keras library. An accuracy of 98.71%, a sensitivity of 98.86%, and a specificity of 98.57% were achieved. © 2021 IEEE.

关键词： Convolutional neural networks

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AI Based Monitoring system for Social engineering 3

AI Based Monitoring System for Social Engineering

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Yapa, K. Udara, S.W.I. Wijayawardane, U.P.B. Kularatne, K.N.P. Navaratne, N.M.P.P. Dharmaphriya, W. G. V. U Sri Lanka Institute of Information Technology Dept. of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Social media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets;and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits. © 2021 IEEE.

关键词： Social networking (online)

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Empirical Characterization of Doppler in Industrial Wireless Channels

Empirical Characterization of Doppler in Industrial Wireless...

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European Conference on Antennas and Propagation, EuCAP

作者： Dreyelian Morejón Jon Montalbán Eneko Iradier Mohamed Kashef Hany Richard Candell Pablo Angueira Dept. Communication Engineering UPV/EHU Bilbao Spain Dept. Electronics Technology UPV/EHU Bilbao Spain Dept. Computer Languages and Systems UPV/EHU Bilbao Spain NIST: National Institute of Standards and Technology Gaithersburg USA

This paper analyzes the temporal variation of the radio propagation channel in industrial environments. The results have been obtained by processing empirical data from the National Institute of Standards and Technology (NIST) in a field measurement campaign, at different industrial premises, with various dimensions and clutter densities. The paper analyzes the shape of the Doppler spectra and related parameters such as Doppler bandwidth and coherence time. The results are supplemented with maximum Delay Times and RMS Delay Spread data. The influence of factors such as the operational frequency band, environment, and polarization is also analyzed.

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