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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Deekan, S. Manjula, S. Pown, M. Velu, C.M. Aadarsh, K.A.S. Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chennai India Sri Sai Ram Institute of Technology Department of Electronics and Communication Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Chennai India Chennai India

ISBN: (纸本)9798331508845

An advanced automated system for wheelchairs has been developed to improve the mobility and independence of individuals who have physical disabilities, including those who have been affected by paralysis, polio, or limb loss. The project has been designed to provide a user-friendly experience for people who have limited hand movements, and it incorporates voice recognition technology. The wheelchair system allows for smooth navigation in different directions based on voice commands, and it comes with interactive features to enhance functionality. The main aim of the proposed system is to promote voice recognition and to enable great communication between the user and the device. This means that users can easily access the wheelchair where they want to go within a specific location, such as a bedroom, and it will autonomously navigate to the designated area. The wheelchair system also integrates with the environment by allowing users to operate household fixtures such as fans and lights through voice commands. This interactive functionality is particularly useful for individuals with physical challenges, as it enhances their overall experience by adding a new level of convenience and accessibility. Overall, this innovative wheelchair system describes an advancement in assistive technology, providing a comprehensive solution to address the transportability needs of individuals with physical disabilities. The project makes use of an ESP32 Microcontroller, Voice recognition module, wheelchair mechanism, and Relay board, and it has been successfully implemented to benefit physically disabled individuals who are using the wheelchair. © 2024 IEEE.

关键词： Wheelchairs

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Analysis and Detection of Fake News Using Machine Learning 3

Analysis and Detection of Fake News Using Machine Learning

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sathish Kumar, P. Suthanthiradevi, P. Arul Stephen, C. Ebenezer Abishek, B. Sivakumar, S. Mathiyarasu, M. Computer and Communication Engineering Rajalakshmi institute of technology Chennai124 India Data Science and Business Systems School of Computing SRM institute of Science and Technology Kattankulathur Chennai603203 India Electronics and Communication Engineering Vels Institute of science Technology and Advanced studies Chennai117 India Electronics and Communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai600062 India Electronics and Communication Engineering BIHER Chennai73 India Computer and Communication Engineering Rajalakshmi institute of technology Chennai India

ISBN: (纸本)9798350372120

The Fake news becomes a more critical issue nowadays, it may reduce the trust of the public about the particular information. This project focuses on the advancement of fake news detection model using machine learning. The main goal of this project is to develop, analyze a model that can effectively classify the news articles based on their Subject or contents. Natural preprocessing Algorithm and features such as Stemming, Tokenization, Stop word and TF-IDF Vectorization are used to train the machine learning model. Then the Machine learning Algorithms likeLogistic Regression and Random Forest are used to classifying the dataset which gives 98% accuracy and 99% of accuracy score. For this Project we have taken a dataset of 22413 Real news and 23481 Fake News from the online resources. This model has a potential to reduce the impact of fake news by grant the promotion of accurate and reliable information in a society. © 2024 IEEE.

关键词： Machine learning

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Raga Yakshagana: Machine Learning for Yakshagana Bhagavatige's Raga Recognition

Raga Yakshagana: Machine Learning for Yakshagana Bhagavatige...

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and communications, COSMIC 2024

作者： Sadrusha Lisha, D.S. Chakshith, B.U. Vandana, C.H. Naik, Divya Gajanan Soorya Krishna, K. Mallya, Rakesh Naik, Nagalakshmi B Kannadaguli, Prashanth Srinivas Institute of Technology Affiliated to VTU Electronics and Communication Valachil Mangalore India Dhaarini Academy of Technical Education Senior Data Science Trainer Bengaluru India

ISBN: (纸本)9798331517892

The study titled 'Yakshagana Bhagavatige Raga Recognition Using Machine Learning' focuses on developing an innovative system capable of identifying distinct ragas within Yakshagana Bhagavatige performances based on audio input. Employing advanced machine learning techniques, specifically the Random Forest classifier, XGBoost, Gradient Boosting, and Logistic Regression, we meticulously train our model using a comprehensive dataset of relevant features. The primary focus of this paper is to bridge the gap between traditional Yakshagana artistry and contemporary technology. The outcome is a model that, upon receiving an audio file as input, accurately predicts the corresponding raga within the Yakshagana Bhagavatige, thus contributing significantly to the intersection of cultural heritage and machine learning. This paper details our methodology, dataset creation, training process, and the promising results achieved through the implementation of diverse classifiers, showcasing the potential for technology to enhance the understanding and preservation of cultural art forms. © 2024 IEEE.

关键词： Adversarial machine learning

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All-fiber and self-starting Yb-doped Mamyshev oscillator based on nonlinear polarization evolution

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Chinese Optics Letters 2024年第12期22卷 74-79页

作者： Zecong Li Kai Wang Zinan Huang Qianqian Huang YanlüLin Weixi Li Zhichao Luo Chengbo Mou Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data ScienceJoint International Research Laboratory of Specialty Fiber Optics and Advanced CommunicationShanghai UniversityShanghai 200444China Key Laboratory of Space Active Opto-Electronics Technology Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Laboratory of Nanophotonic Functional Materials and Devices School of Information and Optoelectronic Science and EngineeringSouth China Normal UniversityGuangzhou 510006China

We experimentally demonstrate a Yb-doped all-fiber mode-locked laser based on the Mamyshev *** entire experimental setup operates only by injecting pump powers and adjusting polarization controllers(PCs),which realizes *** types of pulse patterns are observed at different pump powers and polarization states,including single pulses and up to eight-pulse bound-state *** operating wavelength of single-pulse mode-locking switching between 1072.3and 1043.1 nm can be realized by increasing the pump power while keeping the PCs in a fixed *** design can provide an attractive experimental model for all-fiber and self-starting Mamyshev oscillators.

关键词： Mamyshev oscillator Yb-doped fiber laser pulse dynamic pattern nonlinear polarization evolution

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Intelligent Glasses for the Visually Impaired with Google Cloud API 3

Intelligent Glasses for the Visually Impaired with Google Cl...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Netish, V.P. Lakshmi, M.Vidhya Kumar, Puli Venkatesh Devi, K.Durga Kumar, Puli Nitish Srm Institute of Science and Technology Electronics and Communication Engineering Chennai India St.Ann's College of Engineering and Technology Computer Science Engineering-Data Science Chirala India

ISBN: (纸本)9798350322842

Visually challenged people struggle in their day to day life. People face difficulty in identifying objects, reading and navigation. Canes help them to detect obstacles during traveling. The Idea of this research work is to develop an auxiliary device with sensors that could be connected to ordinary glasses. The Camera provided with the device continuously captures images from the real time environment. The Images are processed with Machine learning algorithms in google vision API. Proposed model informs the user about the obstacles they face during navigation and the travel route. The device can determine the obstacle in front of the user and notify the user about the danger. It also helps the user to read out a text from a label or a document. The API provides a speech based Interface. The user can use voice to activate the device and process the information. The device also interacts with the user through voice. © 2023 IEEE.

关键词： Glass

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Expanding the Image Embedding Space for Language-Free Text-to-Face Image Generation 1

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46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024

作者： Jensen, Joakim Chanda, Sukalpa Krishnan, Narayanan C. Doermann, David Department of Computer Science and Communication Østfold University College Halden Norway Department of Data Science Indian Institute of Technology Palakkad Kanjikode India Department of Computer Science and Engineering University at Buffalo Buffalo United States

ISBN: (数字)9783031851872

ISBN: (纸本)9783031851865

Recent advancements in text-to-image (T2I) generation have revolutionized image synthesis, but conventional text-image paired training poses challenges when confronted with limited dataset size and narrow descriptive breadth. In particular, limited descriptive breadth can significantly impair a model’s ability to generate unmentioned image features. This issue is also evident in text-to-face image generation, where a method will underperform when rendering an image based on the text description of a specific facial feature not present in the training dataset. Language-free training emerges as a promising solution to this problem, leveraging latent spaces like CLIP to facilitate generalization from image embeddings to text embeddings. However, the modality gap remains a hurdle for language-free trained models. To address this, we propose a Gaussian perturbation-based technique that enhances perturbation coverage and robustness across varying modality gap sizes without the need to train a prior model. Our method achieves new state-of-the-art results on MM-CelebA-HQ in the language-free setting, presenting a novel solution to challenges in text-to-face image generation on limited datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Photointerpretation

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An Enhanced Deep Learning Technique for Crack Identification in Composite Materials

An Enhanced Deep Learning Technique for Crack Identificatio...

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International Conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2022

作者： Ramanathan, Saveeth Sankareswaran, Uma Maheswari Mohanraj, Prabhavathy Department of Computer Science Engineering Coimbatore Institute of Technology Coimbatore India Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore India Department of Artificial Intelligence and Data Science Coimbatore Institute of Technology Coimbatore India

ISBN: (纸本)9789819909803

Composite material consists of more than one substance, which inherit the superior properties of the constituent’s elements for light weight and tensile strength. The identification of cracks in composite material at an earlier stage reduces the cost and effort. Deep learning is a complex model in machine learning to extract significant features from a large dataset. The suitable construction of convolutional neural network topology for the application will give better results. Our proposed model uses Bayesian optimization for hyper-parameter tuning which updates the hyper-parameter value for each iteration to improvise the accuracy. In this work real time, composite material images were taken as dataset captured by Silicon Electron Microscope and Evolution VF camera. Applied proposed model on Silicon Electron Microscope dataset, obtained accuracy over 88% and on Evolution VF dataset obtained accuracy over 91%. The integration of optimization techniques with CNN reduces the computational time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electron microscopes

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Multi-Sensor data Fusion-based Parallel Manipulator with IoT Monitoring Employing Machine Learning

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SN Computer science 2023年第2期4卷 165页

作者： Shreyanth, S. Department of Electronics and Communication Engineering Anna University Tamil Nadu Chennai 600025 India Data Science and Engineering Birla Institute of Technology and Science Rajasthan Pilani 333031 India

A robotic parallel manipulator is implemented by employing embedded systems integrated with a set of sensors. More than one type of sensor are implemented together with the control input data from a human limb. Initially, a data set is collected on the map to certain equivalent actuations at the manipulator, and then using an appropriate machine learning algorithm, the control data value for the continuous position of the actuator is generated. A substantial amount of work is done on mapping the position of the limb to the actuator position by creating a three-dimensional model conventional 3D conversion, which is used on the boundary values of the input and output matched with a certain level of intermediate values, a proper training dataset for a machine learning algorithm can be created. The position of the manipulator is monitored by an IoT system and a set of sensors installed at the end. In addition, applied sciences transmits the possession date of the equator, and this information can be viewed remotely from any device connected to the internet. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Convolution neural networks Image processing Intelligent systems Internet of things Machine learning Multi-sensor data fusion Robotics Sensor fusion Signal processing

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Intelligence Non-Invasive Blood Group Prediction using Thermography

Intelligence Non-Invasive Blood Group Prediction using Therm...

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2023 International Conference on data science, Agents and Artificial Intelligence, ICDSAAI 2023

作者： Babu, M.C. Muthusundar, S.K. Kirubashankar, K. Chennai Institute of Technology Department of Artificial Intelligence and Data Science Tamilnadu Chennai India Chennai Institute of Technology Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350348910

Blood bunch forecast is pivotal in bonding medication, as it guarantees protected and compelling blood bondings. Predicting a person's blood group has traditionally been done through serological methods, which look at whether or not particular antigens are present on the surface of red blood cells. Be that as it may, these techniques are tedious and require specific hardware and prepared faculty. Thermography is a method that uses infrared imaging to detect temperature differences [1] on the surface of an object or organism. As a result, there is a need for methods that are both non-invasive and quick to predict blood groups. This method has been utilized in different clinical applications, including bosom disease recognition and skin temperature checking. On account of blood bunch expectation, thermography can be utilized to recognize temperature contrasts brought about by the presence or nonattendance of explicit antigens on the outer layer of red platelets [2]. To use thermography for blood group prediction, a thermograph image of a person it can be taken using a thermal camera. The image can then be analyzed to detect temperature differences on the surface of the it. These temperature differences can be compared to a database of known blood group patterns to predict the person's blood group [3]. © 2023 IEEE.

关键词： Antigens

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Automatic type Identification of Sub - Axial Vertebral Column Fractures using Deep learning Technique 2

Automatic type Identification of Sub - Axial Vertebral Colum...

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2nd International Conference on Artificial Intelligence, Computational Electronics and communication System, AICECS 2023

作者： Sindhura, D.N. Pai, Radhika M Bhat, Shyamasunder N Manohara Pai, M.M. Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Data Science and Department of Orthopaeadics Kasturba Medical College Manipal Manipal Academy of Higher Education Manipal India Department of Data Science Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Traumas like falls, sports injuries, or vehicle accidents are the main causes of Vertebral Column Fracture(VCFs) or spine fracture. The symptoms of VCF include back pain, swelling, numbness, and height decrease. VCF causes extreme pain, paralysis, difficulty in movement, etc. CT scans are inexpensive and effective in providing precise and quick VCF type detection. The determination of type VCF, however subject to inter-observer variability. To address this limitation this work introduces an automatic system for the detection of VCF type based on an ensemble of deep fine tuned models. This can assist the orthopaedicians in type identification of VCF which decreases the image interpretation time and increases the patient care time. For type identification, a total of two fine-tuned CNN architectures are used. Then, to further enhance the type identification performance, we developed an ensemble model of fine tuned VGG16 and ResNet50 deep learning models, which average the outputs of the models during final prediction. We used a dataset of 2820 CT images from eight different VCF types that were collected from Kasturba Medical college, Manipal Academy of Higher education, Manipal. The results shows that the ensemble model performs well compare to individual fine-tuned models with accuracy of 82%, precision of 0.84, recall of 0.82 and F1-score of 0.81 for VCF type identification from CT scans. This study shows that type identification of VCF in CT scans may be successfully accomplished using an ensemble deep transfer learning system with various fine tuned CNN architectures. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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