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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Chandrashekhar, Katha Leela, P. Madhavi, K. Reddy Avanija, J. Tangudu, Naresh Indur Institute of Engineering and Technology Computer Science and Engineering Siddipet India Mohan Babu University School of Computing Dept. of Data Science Tirupati India Mohan Babu University School of Computing Tirupati India Aditya Institute of Technology and Management Tekkali India

ISBN: (纸本)9789380544519

Accurate and efficient medical image analysis is required for modern healthcare applications, facilitating timely disease diagnosis and treatment. Considering the importance of optimization algorithms and deep learning methodologies, this work presents a novel approach, Grey Wolf Optimizer-Assisted Deep Neural Networks (GWO-DNN), for feature selection and classification in medical image analysis. The proposed framework utilizes the power of the Grey Wolf Optimizer to intelligently select the most relevant features from complex medical images. The selected features are then integrated into Deep Neural Networks, to create accurate models for medical image classification tasks. This integrated approach is designed to improve diagnostic accuracy and enhance patient care. The experimentation and evaluation, reveal that the proposed GWO-DNN based model for image analysis is more efficient than the existing methods. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Deep Neural Networks (DNN) Disease Classification Feature Selection Grey Wolf Optimization (GWO) Medical Image Analysis

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DQNdot: A Deep Learning Framework for Multi-Object Tracking in UAV-Enabled Aerial Surveillance

DQNdot: A Deep Learning Framework for Multi-Object Tracking ...

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2023 IEEE International Conference on Quantum Technologies, Communications, Computing, Hardware and Embedded Systems Security, iQ-CCHESS 2023

作者： Subash, Neethu Nithya, B. Martin Prabhu, S. National Institute of Technology Dept. of Computer Science and Engg. Tiruchirappalli India St.Joseph's College Department of Data Science. Tiruchirappalli India

ISBN: (纸本)9798350314946

Unmanned aerial vehicle (UAV) object tracking enables various applications in diverse domains, such as surveillance, traffic scrutiny, and search and rescue. However, it presents a formidable challenge that necessitates the proficiency to trail objects under arduous circumstances. In this paper, Deep Q Networks on dynamic object tracking (DQNdot) are presented, encompassing the ensuing components: You only look once (YOLOv8) that spotlights objects in video frames, UAV for Multi-Object Tracking (UAVMOT) traces objects in video frames employing the Kalman filter and Hungarian method. Subsequently, the prioritizing module is also advanced to prioritize things that are more liable to be seamlessly traced. This module harnesses a range of factors to appraise the probability of an object being efficaciously traced, such as its size, speed, and appearance. Deep Q network(DQN) endows the capability to trail resourcefully, learn and adapt to the environment. DQNdot has accomplished 92% accuracy of prophesying the results for pursuit. It transcends all the other frameworks as it can proficiently track as many targets as required. © 2023 IEEE.

关键词： Object detection

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Classification of Melanocytic Nevus Images using BigTransfer (BiT): A study on a novel transfer learning-based method to classify Melanocytic Nevus Images 3

Classification of Melanocytic Nevus Images using BigTransfer...

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3rd IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Sinha, Sanya Gupta, Nilay Birla Institute of Technology Mesra Patna Dept. of Electronics and Communications Engineering Patna India Sikkim Manipal Institute of Technology Dept. of Computer Science and Engineering Majitar India

ISBN: (纸本)9781665462006

Skin cancer is a fatal disease that takes a heavy toll over human lives annually. The colored skin images show a significant degree of resemblance between different skin lesions such as melanoma and nevus, making identification and diagnosis more challenging. Melanocytic nevi may mature to cause fatal melanoma. Therefore, the current management protocol involves the removal of those nevi that appear intimidating. However, this necessitates resilient classification paradigms for classifying benign and malignant melanocytic nevi. Early diagnosis necessitates a dependable automated system for melanocytic nevi classification to render diagnosis efficient, timely, and successful. An automated classification algorithm is proposed in the given research. A neural network previously-trained on a separate problem statement is leveraged in this technique for classifying melanocytic nevus images. The suggested method uses BigTransfer (BiT), a ResNet-based transfer learning approach for classifying melanocytic nevi as malignant or benign. The results obtained are compared to that of current techniques, and the new method's classification rate is proven to outperform that of existing methods. © 2022 IEEE.

关键词： Diseases

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AgriLLM:Harnessing Transformers for Framer Queries

arXiv

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arXiv 2024年

作者： Didwania, Krish Seth, Pratinav Kasliwal, Aditya Agarwal, Amit Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering India Department of Data Science & Computer Applications India Enterprise Analytics & Data Science Artificial Intelligence - Center of Excellence Wells Fargo International Solutions Private Limited Bangalore India

Agriculture, vital for global sustenance, necessitates innovative solutions due to a lack of organized domain experts, particularly in developing countries where many farmers are impoverished and cannot afford expert consulting. Initiatives like Farmers Helpline play a crucial role in such countries, yet challenges such as high operational costs persist. Automating query resolution can alleviate the burden on traditional call centers, providing farmers with immediate and contextually relevant information. The integration of Agriculture and Artificial Intelligence (AI) offers a transformative opportunity to empower farmers and bridge information gaps. Language models like transformers, the rising stars of AI, possess remarkable language understanding capabilities, making them ideal for addressing information gaps in agriculture. This work 1 explores and demonstrates the transformative potential of Large Language Models (LLMs) in automating query resolution for agricultural farmers, leveraging their expertise in deciphering natural language and understanding context. Using a subset of a vast dataset of real-world farmer queries collected in India, our study focuses on approximately 4 million queries from the state of Tamil Nadu, spanning various sectors, seasonal crops, and query types. Copyright © 2024, The Authors. All rights reserved.

关键词： Agriculture

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Physiological Signals based Student Grades Prediction using Machine Learning

Physiological Signals based Student Grades Prediction using ...

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Information technology (OCIT), OITS International Conference on

作者： Vanshikha Agarwal Naeem Ahmad Md Gulzarul Hasan Department of Data Science PSPH Manipal Academy of Higher Education Manipal India Department of Computer Applications National Institute of Technology Raipur India

Amidst today’s fast-paced world, the escalating levels of stress among students have become a significant concern, and it appears to be inversely related to their academic performance. There have been several types of research on the link between stress and grade prediction. Our study aims to leverage the available dataset about the stress level in such a way that can predict students’ grades to comment on the student’s performance. The dataset is collected using the wearable device during three exams containing electrodermal activity, skin temperature, and heart rate from 7680 student subjects. Our approach involves employing a variety of machine learning and deep learning algorithms, including Decision Trees Regressor, CNN Regressor, KNN Regressor, Random Forest Regressor, and Support Vector Regressor models. Through our investigation, we aim to demonstrate the potential of wearable devices in extracting meaningful insights from physiological signals to predict students’ academic outcomes. The challenges associated with wearable observations present unique opportunities for addressing and mitigating stress-related issues among students, and this study harnesses the power of a publicly available dataset to contribute to this important area of research.

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A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students

A Machine Learning Model for Predicting Internet Addiction i...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Geetika Johar Ravindra Patel Dept. of Computer Science & Engineering University Institute of Technology RGPV Bhopal India Dept. of Computer Applications University Institute of Technology RGPV Bhopal India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

Internet addiction is becoming one of the critical issues among teenagers and young university students. This habit not only negatively impacts the student’s learning performance, but also affects the student’s physical and mental health. This paper is focusing on designing a Machine Learning (ML) model for accurately identifying internet addiction. The model incorporates data preprocessing and feature selection techniques for reducing the computational complexity. Next, five different ML algorithms have been implemented and compared to identify appropriate ML algorithms. The identified ML model has been trained using the behavioral features of the students for predicting internet addiction. The experiments have been carried out and the performance of the models has been measured. The results demonstrate that a random forest classifier provides 83% accurate detection for university students and 96% accurate results are provided by an SVM-based classifier for teenager internet addiction detection. Therefore, by using the entire observation of experiments it is found that the proposed model can successfully identify internet addiction in students by analyzing the student's behavioral features.

关键词： Machine learning algorithms Accuracy Addiction Computational modeling Focusing Mental health Predictive models Prediction algorithms Internet Random forests

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Design of Six Element Antenna Array for IoT Application

Design of Six Element Antenna Array for IoT Application

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2nd International Conference on Signal Processing, Communication, Power and Embedded Systems, SCOPES 2024

作者： Satrusallya, Sarmistha Mahapatra, Shaktijeet Mohanty, Mihir Narayan Adarsh Rag, S. Department of Electronics and Communication Engineering Iter Bhubaneswar India Center for Internet of Things Iter Bhubaneswar India Manipal Institute of Technology Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798331506452

The concept of Internet-of -Things makes it possible to connect devices in large numbers. The IoT enabled devices with the advantage of sensing, monitoring and tracking are present in different application. For this reason antenna must be compact and multifunctional to be connected to high speed device. In this work, we propose a 6 element linear antenna array with microstrip feed for IoT application. The proposed array operates at 3.8 GHz with a band width of 200Mhz. A peak gain of 9dB makes it suitable for IoT and biomedical application. © 2024 IEEE.

关键词： Antenna arrays

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Development of a Calibration-Free Brain-computer Interface Utilizing Common Spatial Patterns and Artificial Neural Networks for EEG Signal Analysis 6

Development of a Calibration-Free Brain-Computer Interface U...

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6th International Congress on Human-computer Interaction, Optimization and Robotic applications, HORA 2024

作者： Cengiz, Korhan Shreyas, J. Udayaprasad, P.K. Gururaj, H.L. Kumar, Dilip S.M. College of Information Technology University of Fujairah United Arab Emirates Faculty of Organization and Informatics University of Zagreb Croatia Croatia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Artificial Intelligence & Machine Learning Global Academy of Technology Bengaluru India Dept. of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore India

ISBN: (纸本)9798350394634

A brain computer interface (BCI) system uses a technique known calibration, that takes 20 to 30 minutes to accomplish. For the objective of creating a reliable decoder, the calibration process is challenging and expensive. In order to address the drawbacks of the current system, a spectral-spatial technique has been suggested. The motor imagery (MI) data set, comprising 15 electroencephalography (EEG) signals and fourteen test subjects, is taken into consideration. The two modules are designed to extract characteristics and process data. An artificial neural network (ANN) is used to independently train and test the suggested spectral-spatial algorithm. Based on it, a variety of machine learning techniques, including random forest (RF), neural networks (NN), and XGboost, are used to classify the data, that is then sent to the hidden layer (Lth layer). The obtained results indicates 2% of improvement in comparison with existing methodology. © 2024 IEEE.

关键词： Electroencephalography

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Immersive Viewing of Dento-facial Medical data in Virtual Reality

Immersive Viewing of Dento-facial Medical Data in Virtual Re...

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International Conference on Computing and Networking technology (ICCNT)

作者： Shreenidhi H. Bhat Hareesh K. S. Abhay T. Kamath Adarsh Kudva Dept. Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Consultant-Oral and Maxillofacial Surgery KMC Hospitals Mangaluru India Dept. Oral and Maxillofacial Surgery Manipal College of Dental Sciences Manipal Academy of Higher Education Manipal India

Virtual reality (VR) requires dynamic and responsive rendering technologies to offer an immersive user experience. Failing, users may experience motion sickness due to motion lag in virtual space. This work aims to create a framework with a higher framerate to decrease the motion sickness in turn increasing the virtual world viewing quality. Essentially, an interactive environment is developed between existing volume rendering engines OpenGL and GPU-based ray casting techniques for rendering the dentofacial data volume. The developed visualization methods are incorporated into Head Mount Device(HMD) to offer immersive effects. The performance of the VR rendering application measured in terms of framerate was observed to be 125 frames per second, significantly higher than the current HMDs framerate. In addition, the adaptation of rendered image quality is activated by adjusting the sampling distance and jitter to complete the rendering process in the allotted time. The achieved result is expected to offer satisfactory comfort to the viewer with reduced motion sickness, particularly in pre-surgical planning for dentofacial surgery.

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Automatic Short Answer Grading Using a LSTM Based Approach

Automatic Short Answer Grading Using a LSTM Based Approach

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Udit Kr Chakraborty Anurag Mishra Head of Dept. of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Dept. of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

Short Answer Grading is an emerging application of Natural Language Processing and text processing. Automated Short Answer Grading (ASAG) is the process of evaluating student-written short responses using computer techniques like Machine learning. The ASAG task has been studied for a long time, but because of the difficulties in the research, it still attracts attention. One of the primary limitations of ASAG is the scarcity of domain-relevant training data. The job of ASAG can be approached using a variety of methods, which can be broadly divided between traditional methods using hand-crafted features and methods based on deep learning. Due to the growing popularity of this field, researchers have been using Deep Learning Approaches to address this challenge over the past five years. This paper explores the methods of creating an LSTM model, to test how close this approach will bring the machine score to that of the Human Score.

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