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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Dhanalakshmi, R. Vijayaraghavan, N. Narasimhan, S. Rajesh, R. School. of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India Kcg College of Technology Dept. of Mathematics Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350315127

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method. © 2023 IEEE.

关键词： Machine learning

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Fish Feeder System Using Internet of Things 7th

Fish Feeder System Using Internet of Things

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7th International Conference on Big Data and Cloud Computing Challenges, ICBCC 2022

作者： Malla, Jyotsna Lekkala, Preetham Raghu, Rishi Jayashree, J. Vijayashree, J. Diaz, Vicente Garcia School of Computer Science and Engineering Vellore Institute of Technology TN Katpadi India Department of Computer Science University of Oviedo Oviedo Spain

ISBN: (纸本)9789819910502

Pets have become quite common in almost every household. In the present busy world, it has become quite a hassle to take good care of the pets due to the lack of time and knowledge about their eating patterns and schedules. People having fish as pet need to devote more time and attention to them owing to their complex and timely food requirement. The goal of designing an IoT-based fish feeder is to eliminate manual labour involved in feeding the fish regularly at certain intervals of time. This proposed system can offer regular feeding without the interference of the owner. This system also helps to reduce costs by preventing the overspending on extra fish feed than required. This study is based on the concept that the fish will be fed the owner is not present at home. Wireless connectivity is used in the fish feeder system. The system can be built by programming a fish feeder to feed fish at a specific time, and then commanding it to deliver the food through stepper motors. It removes the existent need of a manual aquarium maintenance. The fish feeder system can be atomised and can be easily managed using a Graphical User Interface (GUI). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphical user interfaces

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Development of IoT based Smart Campus using STM32 15

Development of IoT based Smart Campus using STM32

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Raju, Leo Thirunavukkarasu, K. SSN College of Engineering Department of Electrical and Electronics Engineering Chennai India Thanthai Periyar Govt Institute of Technology Department of Computer Science and Engineering Vellore India

ISBN: (纸本)9798331300579

Over the past few decades, energy has been used mindlessly leading to many problems like reduction in supply, high power rates, increased carbon footprint, etc. Smart campuses, home automation, smart grid, etc. make living comfortable and economical. It optimizes energy consumption and is quite simple to operate too. This project proposes Internet of Things (IoT) based automation for smart car parking inside the campus, thus converting existing campus into smart campus. In this work, we have developed a prototype for smart campus using STM32 microcontroller. This microcontroller has better features compared to other contemporary microcontrollers. This prototype includes a smart car parking system which reduces traffic congestion and facilitates car parking inside the campus. © Grenze Scientific Society, 2024.

关键词： Traffic congestion

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. This research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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A Novel 18-Convolutional Layered Deep U-Net Architecture for COVID-19 Infection Diagnosis Through Object Detection on Lung CT Scan Segmentation 3rd

A Novel 18-Convolutional Layered Deep U-Net Architecture for...

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3rd International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications, ICMISC 2022

作者： Aruna, R. Devi, M. Shyamala Pandian, J. Arun Kanchanadevi, K. Kumar, P. Keshav Tarun, B. Sai Sai, N. Charan Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India School of Information Technology & Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9789811960871

With the advancement of digital technology, a large number of medical images are created in the field of digital medicine. Object detection is a critical field of study in medical image analysis, detection and processing. Object detection is the categorization of the pixels in an image that determines where the objects in the image are located. Image classification is the global classification of an image’s pixels. The objects are identified but not located from all of the pixels in the image. The intelligent identification process for the adjuvant diagnosis to assist medical doctors of various disciplines is in high demand. The need for the high accurate object detection method for the medical images remains a major challenge toward disease detection in the health care. With this summary overview, a novel model is proposed for lung CT scan image detection as 18-Convolutional Layered Deep U-Net architecture for diagnosis of COVID-19 detection through object detection. Model fitting is done with 18-Convolutional Layered Deep U-Net architecture, and the model design is formed with four contraction path and four expansive path layers. The customized model design is fine-tuned with the parameter optimization. The object detection is done, and the performance is analyzed. The dataset is also fitted with existing deep convolution neural network models like region-based, threshold, edge-based and clustering-based method, and the performance is analyzed and compared with proposed model with metrics like pixel accuracy, intersection over union and dice coefficient. Implementation results show that the proposed models have pixel accuracy of 98.32%, intersection over union of 48.7% and dice coefficient of 97.56% compared to existing object detection models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan Surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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CropBot: A Chatbot for Agriculture Advancement

CropBot: A Chatbot for Agriculture Advancement

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Banerjee, Shalini Akhila, Pujala Kanmani R, Sujithra Elakiya, E. Surendiran, B. 21MIA1022 - Scope Vellore Institute of Technology Tamilnadu Chennai India 21MIA1012 - Scope Vellore Institute of Technology Tamilnadu Chennai India Scope Vellore Institute of Technology Tamilnadu Chennai India NATIONAL INSTITUTE OF TECHNOLOGY PUDUCHERRY DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING KARAIKAL India

ISBN: (纸本)9798331540685

India is an agriculture-based country in that 82 percent of farmers are small scaled and less exposed to diverse knowledge. So, we developed chatbot which helps farmers in giving advises on what crop they have to grow according to the soil nutrients and environmental parameters. With the dataset 'crop recommendation' from Kaggle we created a chatbot with the help of machine learning and NLP to find which crop will be giving best yield according to the soil nutrients like nitrogen, potassium and phosphorous and environment factors like temperature, humidity, pH of soil and rainfall. With the help of NLP, we created a user-friendly chatbot which is developed to access and chat easily by a less knowledgeable person also. The chatbot is developed in such a way that the sentences are not needed to be well phrased and step by step our chatbot only will assist the user. Chatbot will give recommendation even the user doesn't have complete information with either soil nutrients or environment parameters the chatbot can give recommendations. This project aims to help farmers with accessible data driven solutions for crop's greater yield. © 2024 IEEE.

关键词： adaboost agriculture chatbot cropbot knn nlp prediction models random forest regex

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An effective Image-based Improved Deep Neural Network Framework for Coal and Gangue Detection 3

An effective Image-based Improved Deep Neural Network Framew...

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

作者： Praveena, M. Afsana, A. Devi, T. Durga Sudharsan, S. Arulkumar, V. Dr Sns Rajalakshmi College of Arts and Science Department of Computer Science Coimbatore641049 India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India

ISBN: (纸本)9798350322842

In coal power plants, gangue is a by-product that is not wanted, even though coal is the energy source utilized the most worldwide. As a consequence of this, the removal of gangue from coal during the pre-processing stage of the mining technique is an essential step. With the development of new computer technologies, a method may be discovered that will allow for an improvement in the efficiency of gangue separation. This study presents a method for identifying gangue using improved deep neural networks. The method involves building a coal-gangue filtration system based on the distinctions between gangue and coal in terms of its rough surface and grayscale feature. In addition, this tactic will make it possible for robots, rather than people, to identify the gangue. Suppose the technology described in this article is applied in the coal industry. In that case, it will lead to the computerization of the extraction of coal gangue, an increase in the raw coal processing efficiency, and an improvement in the quality of the coal. According to the findings of experiments, the strategy enhances the accuracy results of coal and gangue materials by as much as 98.87% compared to the currently used approaches. © 2023 IEEE.

关键词： Coal

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A Deep Dive into Existing Lip Reading Technologies

A Deep Dive into Existing Lip Reading Technologies

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Khekare, Ganesh Majumder, Goutam Shelke, Nilesh Sakarkar, Gopal Buchade, Siddhant School of Computer Science and Engineering Vellore Institute of Technology Vellore India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Pune India Dr. Vishwanath Karad MIT World Peace University Department of Computer Science and Applications Pune India

ISBN: (纸本)9798331517953

Lip reading, or Visual Speech Recognition (VSR), can revolutionize communication for those with hearing loss and in noisy environments. The researchers are trying to create advanced VSR systems using Deep learning models to overcome challenges. But still, several difficulties are there such as different shapes of lips during the video and different lightning conditions that can make deciphering or visualization difficult. Deep learning with the help of Convolutional Neural Networks (CNNs) helps to identify the exact shape and movement of a lip from a video frame. The continuous flow of lip movements in the video is handled by Recurrent Neural Networks (RNNs) which help in identifying words. However, RNNs are not good enough to remember the historical data. This issue is resolved by using Long Short Term Memory (LSTM) in the proposed research work. LSTMs have an additional internal memory that helps to store the history information for a longer period which helps to predict lip movement accurately even in rapid speech. The GRID and LRW datasets are used to generate the results. Additionally, VSR could improve video conferencing, allowing for natural communication even with compromised microphone quality. The impact of VSR extends beyond human-to-human and human-computer interaction. Frameworks like Streamlit offer a glimpse into the future, enabling the development of user-friendly lip reading applications. Deep learning unlocks the power of lip reading, paving the way for a future where communication go beyond the limitations of sound. © 2024 IEEE.

关键词： Video conferencing

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Smart Watch with Machine Learning Based Fall Detection System

Smart Watch with Machine Learning Based Fall Detection Syste...

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2024 IEEE Region 10 Symposium, TENSYMP 2024

作者： Nandini, Budigi Poorna Reddy, P.Satyananda Mary, Gerardine Immaculate Julian, Anitha Ramyadevi, R. Rele, Mayur School of Electronics Engineering Vellore Institute of Technolgy Tamilnadu Vellore India Saveetha Engineering College Computer Science and Engineering Tamilnadu Chennai India Department of Information Technology Parachute Health NJ United States

ISBN: (纸本)9798350364866

The goal of the proposed work is to develop a tool in the form of a smart watch, for accurately and quickly detecting falls and alerting emergency contacts, potentially saving lives, and enhancing the quality of life for seniors and people with mobility issues. The smart watch designed can transmit real-time notifications to the user's smart phone and to their emergency contacts in the event of a fall, enabling quick actions. The tool can be modified to add new features like heart rate monitoring and prescription reminders, which could have significant use in the healthcare industry. The hardware components of the device include GPS and GSM modules. To increase the fall detection algorithm's accuracy and investigate prospective system extensions and upgrades, the system also involves the collection and analysis of real-world data. The overall goal is to create a useful smart watch with a fall detection system that can offer critical assistance to those in need and their emergency contacts. The system also involves creating a cloud-based infrastructure to store and process sensor data from the smart watch, enabling remote data analysis and monitoring. The research also investigates additional potential uses for the smart watch, such as activity tracking and health monitoring, in addition, to fall detection. Through this initiative, it is hoped to enhance the lives of people with mobility challenges, their careers, and the expanding field of wearable technology. © 2024 IEEE.

关键词： Fall detection

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