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Predicting Schizophrenia from fMRI Using Deep Learning 1

3rd International Conference on Innovations in Computational Intelligence and computer Vision, ICICV 2022

作者： Kardani, Shail Sharma, Raghav Sharma, Abhishek Deparment of Computer Science Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India Deparment of Electronics and Communication Engineering The LNM Institute of Information Technology Rajasthan Jaipur302031 India

ISBN: (数字)9789819926022

ISBN: (纸本)9789819926015

Schizophrenia is a complex and multifaceted disorder that is still not fully understood. However, many processes are known to drive its onset, relapse, symptomatology, and therapy. One possible explanation for Schizophrenia is that the person has difficulty using proper analytic methods to deal with the complexity and variety of the condition. Many AI algorithms have been designed and developed over time specifically for Neuroinformatics. These algorithms have been a go-to for many researchers and scientists, even for those in the medical field. Because of these AI-powered algorithms, Scientists are able to detect various diseases, whether they are related to brain activity or any other organs. Deep Learning is yet another field of Machine Learning which is made by the inspiration of how neural networks are connected inside our brain. These are more flexible than the Scikit-Learn-based Machine Learning models as these Deep Neural Network models don’t often require data preprocessing at initial stages. Not only this fact, but also they are used in various places, for example, processing of unstructured data, including texts and images, feature extraction, etc. In this study, we have attempted to detect Schizophrenia from resting-state functional magnetic resonance images using trained Deep Neural Networks as well as Scikit-Learn-based state-of-the-art Machine Learning models. For the dataset, we have utilized the public COBRE dataset available in the nilearn library, along with some public datasets from OpenfMRI. The Deep Learning models detected Schizophrenia with a reliable performance compared to Scikit-Learn-based Machine Learning models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Magnetic resonance

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Development of an Air Pollution Monitoring System

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Procedia computer science 2025年 260卷 645-654页

作者： Rekha Kaushik Eshan Kelkar Aryan Gupta Arpita Bhargava Deparment of Information Technology Indian Institute of Information Technology Bhopal 462003 India Deparment of Computer Science and Engineering Indian Institute of Information Technology Bhopal 462003 India

Clean air is a fundamental requirement for the proper functioning of the human body. Increasing population, urbanization and industrialization has led to a stark degradation in air quality. Bad air quality has severe effects on humans, wildlife, vegetation and man-made architectures. Hence, forecasting future air quality and monitoring current air quality is of utmost importance. This paper presents a comparative analysis of various machine learning techniques for air quality forecasting and an analysis of various sensors for air quality monitoring. In addition, a sensing architecture for monitoring air pollutant concentration on a wide scale has been delineated. This work would help in detecting the factors involved in degradation of air and plan measures to improve the air quality.

关键词： Air Quality Pollution Air Quality Index Time Series Forecasting Air Quality Monitoring Sensors IoT

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IoT-Enhanced Gesture Recognition System for Healthcare Applications

IoT-Enhanced Gesture Recognition System for Healthcare Appli...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Krithika, L.B. Nallakaruppan, M.K. Baza, Mohamed Badr, Mahmoud Deora, Yuvraj Singh School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India College of Charleston Deparment of Computer Science CharlestonSC United States Polytechnic Institute Department of Networks and Computer Security UticaNY12201 United States

ISBN: (纸本)9798350351408

In response to the prevalent language barriers in medical settings, this paper proposes an innovative solution leveraging gesture recognition technology. Through the integration of deep learning models and Internet of Things (IoT) tools, the system aims to facilitate seamless communication between patients and healthcare providers. Initially, a deep learning model is developed using a personalized dataset containing various medical gestures, allowing for the recognition of nuanced patient expressions such as 'I have Fever' or 'Shoulder pain.' Additionally, the system incorporates IoT sensors to monitor vital signs like body temperature and pulse in real-time, ensuring comprehensive patient assessment. With the capability to continuously evolve and adapt to new gestures, this solution not only enhances accessibility but also improves the quality of healthcare delivery, particularly in underserved communities. By bridging the gap between language barriers and medical communication, this innovative approach holds promise for fostering inclusive and effective healthcare practices worldwide. In addressing the challenges posed by language barriers in medical interactions, this paper presents a comprehensive solution that combines gesture recognition technology with deep learning models and IoT devices. By capturing and interpreting patient gestures, such as indicating specific symptoms or discomfort, the system facilitates clear communication between patients and doctors. Furthermore, the integration of IoT sensors enables continuous monitoring of vital signs, allowing for timely interventions and personalized care. With its ability to adapt and expand to accommodate new gestures and medical scenarios, this solution represents a significant advancement in healthcare accessibility and quality. By leveraging cutting-edge technology, this approach has the potential to revolutionize healthcare delivery, particularly in areas with limited access to linguistic resources or medi

关键词： Gesture recognition

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Development of Virtual Reality Platform through Human computer Interaction using Artificial Intelligence 3

Development of Virtual Reality Platform through Human Comput...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Saranya, S. Channarayapriya, B. Harshavardhini, U. Nandhini, A.Sunitha Revathi, J. Venkatesan, R. New Horizon College of Engineering Department of Computer Science and Engineering Bangalore India Vel Tech Multi Tech Dr.Rangarajan Dr Sakunthala Engineering College Deparment of Information Technology Chennai India Psg Institute of Technology and Applied Research Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Mathematics Erode India Bannari Amman Institute of Technology Deparment of Information Technology Erode India

ISBN: (纸本)9798350375190

The development of a virtual reality platform through human-computer interaction (HCI) is obtained using deep learning with a genetic algorithm. The human-computer interaction forms a bridge between users and the VR system. This helps in providing a natural interaction experience to the users. Deep learning techniques help in obtaining the realistic nature of the physical world in the virtual platform. The personalized experiences are obtained using the integration of deep learning with VR platforms. The optimization algorithm helps in achieving user interaction in a real-time platform. The adaptive learning helps in increasing the overall immersion with a user-friendly interface. The VR development process is done using a genetic algorithm. This helps to evolve and optimize themselves within the period of time. This helps the VR platform to iteratively improve its performance and complete design through adopting successful combinations of various parameters. The proposed approach helps the VR system to evolve towards an optimal condition. This helps to satisfy the various needs and preferences of the users. Thus the proposed system helps in obtaining adaptive and intelligent virtual environments. This helps in overcoming various challenges in designing immersive VR experiences with personalized digital environments. © 2024 IEEE.

关键词： Virtual reality

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Mapping of Land Use and Land Cover(LULC)Using EuroSAT and Transfer Learning

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Revue Internationale de Géomatique 2024年第1期33卷 1-13页

作者： Suman Kunwar Jannatul Ferdush Faculty of Computer Science Selinus University of Sciences and LiteratureRagusaItaly Deparment of Computer Science and Engineering Jashore University of Science and TechnologyJashoreBangladesh

As the global population continues to expand,the demand for natural resources ***,human activities account for 23%of greenhouse gas *** a positive note,remote sensing technologies have emerged as a valuable tool in managing our *** technologies allow us to monitor land use,plan urban areas,and drive advancements in areas such as agriculture,climate changemitigation,disaster recovery,and *** advances in Artificial Intelligence(AI),computer vision,and earth observation data have enabled unprecedented accuracy in land use *** using transfer learning and fine-tuning with red-green-blue(RGB)bands,we achieved an impressive 99.19%accuracy in land use *** findings can be used to inform conservation and urban planning policies.

关键词： Land use land cover EuroSAT transfer learning convolutional neural networks vision transformers

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Numerical Modeling of Dissipative Cavity Quantum Electrodynamics without Markovian Approximation

Numerical Modeling of Dissipative Cavity Quantum Electrodyna...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Na, Dong-Yeop Chew, Weng Cho Deparment of Electrical Engineering Pohang University of Science and Technology Pohang Korea Republic of Elmore Family School of Electrical and Computer Engineering Purdue University West Lafayette United States

ISBN: (纸本)9798350364774

We develop a numerical framework to analyze the dynamics of a dissipative cavity quantum electrodynamics without the Markovian approximation. For the correct quantization of electromagnetic fields in the presence of lossy dielectric objects in the free space, we employ the so-called BAMA formulation [1] which takes into account fluctuations to both radiation and medium losses. With the coarse-graining strategy, the BAMA fields (or modes) can be evaluated by using conventional computational electromagnetics numerical modes, e.g., FEM, FDTD, or MoM. We then incorporate the BAMA modes into the multimode Jaynes-Cummings model and solve the time-dependent Schrödinger equation to find probability amplitudes. © 2024 IEEE.

关键词： Schrodinger equation

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CPW-fed Monopole Antenna with 3.5/5.5 GHz Band-Notched Characteristics for Ultrawideband Applications

CPW-fed Monopole Antenna with 3.5/5.5 GHz Band-Notched Chara...

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Tangthong, Nipont Moeikham, Pichet Sangpet, Patchadaporn Jamnien, Atthakorn Akkaraekthalin, Prayoot Chudpooti, Nonchanutt Deparment of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Deparment of Electrical Engineering Faculty of Engineering Rajamangala University of Technology Lanna Chiang Rai Chiang Rai Thailand KMUTNB Techno Park King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Deparment of Industrial Physics and Medical Instrumentation Faculty of Applied Science King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9798350364774

This research presents a printed ultrawideband monopole antenna designed with notches at the 3.5 GHz and 5.5 GHz frequency bands. The antenna incorporates a 50-Ω Coplanar Waveguide (CPW) feed, featuring a gradual central line and ground plane. To notch unwanted frequencies at 3.5 GHz and 5.5 GHz, an inverted triangle slot and an inverted U-shaped slot are integrated into the monopole patch. Operating across a frequency range of 2.62-12 GHz, the proposed antenna supports the UWB system while excluding the 3.22-3.62 GHz and 5.14-5.62 GHz bands. The lengths of the inverted triangle and U-shaped slots correspond to quarter and half wavelengths at 3.5 GHz and 5.5 GHz, respectively. The prototype has been fully developed and tested, showing its effectiveness in reducing Electromagnetic Interference (EMI) in WLAN/WiMAX bands. © 2024 IEEE.

关键词： Monopole antennas

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Comparative Analysis of Machine Learning Models to Predict Depression, Anxiety and Stress 11

Comparative Analysis of Machine Learning Models to Predict D...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Srishti Gupta, Harsh Singh, Prashant Agrawal, Arun Prakash School of Engineering and Technology Sharda University Greater Noida Deparment of Computer Science and Engineering India

ISBN: (纸本)9781665487320

Mental Health Issues are a hidden pandemic which will emerge in the upcoming years. As the world witnessed COVID-19 pandemic and went into lockdown, the cases of Depression, Anxiety and Stress skyrocketed than ever before. This has given rise to the need for exploring the interdisciplinary field of Artficial Intelligence and Psychometry. In this paper, we propose compare various machine learning and ensemble learning methods, on the survey dataset comprising of the DASS-42 Psychometric Test Results and Demographic information. Random Forest, Decision Tree, Support Vector Machine (SVM), AdaBoost, CatBoost, and Extreme Gradient Boosting (XGBoost) are used to classify the level of Depression, Anxiety and Stress into normal, mild, moderate, severe and extremely severe categories. In our experiments on the dataset, Support Vector Machine outperformed and reached a final F1-measure of 94%, 95% and 91% in the prediction of Depression, Anxiety and Stress, respectively. © 2022 IEEE.

关键词： Support vector machines

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Does Image Anonymization Impact computer Vision Training?

Does Image Anonymization Impact Computer Vision Training?

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Håkon Hukkelås Frank Lindseth Deparment of Computer Science Norwegian University of Science and Technology

Image anonymization is widely adapted in practice to comply with privacy regulations in many regions. However, anonymization often degrades the quality of the data, reducing its utility for computer vision development. In this paper, we investigate the impact of image anonymization for training computer vision models on key computer vision tasks (detection, instance segmentation, and pose estimation). Specifically, we benchmark the recognition drop on common detection datasets, where we evaluate both traditional and realistic anonymization for faces and full bodies. Our comprehensive experiments reflect that traditional image anonymization substantially impacts final model performance, particularly when anonymizing the full body. Furthermore, we find that realistic anonymization can mitigate this decrease in performance, where our experiments reflect a minimal performance drop for face anonymization. Our study demonstrates that realistic anonymization can enable privacy-preserving computer vision development with minimal performance degradation across a range of important computer vision benchmarks.

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Artificial Intelligence Based Approach to Identify Plastic Bottle for Environment Sustainability 3

Artificial Intelligence Based Approach to Identify Plastic B...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Gautam, Vinay Tiwari, Raj Gaurang Trivedi, Naresh Kumar Jain, Anuj Kumar Sharma, Vikrant Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Deparment of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350302288

Several classification problems have been resolved with computer vision and deep learning technology. Despite this, no dedicated work has been carried out to classify plastic bottles from non-bottle objects. Therefore, this paper is concerned to resolve issue to classify waste plastic bottle from non-bottle objects. Here a dataset of 52000 images of bottle and non-bottle has been used to classify different objects based on their size. Each image contains a single bottle and non-bottle object. In this paper, various pre-trained models have been employed to classify these objects based on their size. The pretrained models have been trained with bottle dataset and attained 97%, 92% and 91 % accuracy. The comparison shows that the ResNet-18 outperforms. © 2023 IEEE.

关键词： Image classification

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