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Classifying Alzheimer's Disease Stages via Bootstrap Aggregation of CNN Architectures on MRI Scans 1

Classifying Alzheimer's Disease Stages via Bootstrap Aggrega...

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1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Neeharika, S. Rao, D. Rajeswara Nagaveni, V. Lalitha V.R. Siddhartha Engineering College Computer science Dept. Vijayawada India

ISBN: (纸本)9798350365016

Alzheimer's disease (AD) presents a pressing challenge in healthcare globally, necessitating timely and accurate diagnosis for effective management. Magnetic Resonance Imaging (MRI) holds promise for AD detection due to its ability to reveal structural brain changes. However, manual MRI analysis is labor-intensive and prone to human error. To tackle these issues, this study proposes a novel method for AD classification using ensemble learning with Convolutional Neural Network (CNN) architectures-specifically, CNN, Inception V3,DenseNet121, and MobileNet-leveraging bootstrap aggregation. This ensemble approach diversifies training data subsets, leading to enhanced accuracy. The proposed model achieved an impressive 84% accuracy with bootstrapping and 94 % without. Additionally, specificity values underscore the model's robustness, with 0.93 and 0.97 for bootstrapped and non-bootstrapped ensembles, respectively. This innovative approach not only improves accuracy but also addresses scalability and generalization challenges, offering promise for AD diagnosis and management based on neuroimaging data. © 2024 IEEE.

关键词： Convolutional neural networks

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Multi-Trajectory Drone Surveillance: Amplifying Monitoring Capabilities in Disaster Management

Multi-Trajectory Drone Surveillance: Amplifying Monitoring C...

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2024 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Mourya, Gojja Sree Jami, Abhinav Ajith, Abhinav Seikh, Ayan Habib Kumar, J. Sathish Motilal Nehru National Institute of Technology Dept. of Computer Science and Engineering Allahabad India

ISBN: (纸本)9798350348460

In the disaster-affected area, whether it's a search and rescue operation, mock drill, managing food distribution, issuing warnings or anything else, the key to successfully carrying out these activities is preparation. To ensure that plans are accurate, one needs to know what actually happened in the disaster-affected area and for this purpose thoroughly and quickly the entire area need to be studied. To address this issue, we use UAV or drone navigation autonomously in required trajectories. By using UAVs you can cover surveillance across the vast acres of land quickly and know what happened and plan rescue operations accordingly. In this paper, we presented our work on moving the drones in multiple trajectories and swarm, thus amplifying surveillance capabilities. Each trajectory is maneuvered autonomously and a corresponding trajectory-algorithm running at the back end is presented along with the simulation results. © 2024 IEEE.

关键词： Disaster management Drones Multi-Agent Systems Surveillance Swarm Trajectories Unmanned Aerial Vehicles (UAV)

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DriveQ: A Dedicated Vehicle-to-Vehicle Communication System for Enhancing Road Safety and Traffic Management

DriveQ: A Dedicated Vehicle-to-Vehicle Communication System ...

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2024 International Conference on E-Mobility, Power Control and Smart Systems, ICEMPS 2024

作者： Sunil, K.S. Biju, Alan Aiswarya Arun, G. Joy, Prince V. Rathul Krishnan, R. Government Engineering College Dept. of Computer Science & Engg. Kerala Idukki India

ISBN: (纸本)9798350394399

Modern urban transportation systems confront significant challenges, including escalating traffic congestion, accidents, and inefficient traffic management. One critical aspect contributing to these issues is the limited means of communication between vehicles, which represents a missed opportunity to en-hance road safety and traffic efficiency. To address these concerns and facilitate smarter transportation solutions, there's a need for an efficient, standardized, and secure Vehicle-to-Vehicle (V2V) communication system capable of seamlessly sharing real-time data between vehicles. DriveQ mainly focuses on implementing a dedicated V2V communication system that enhances road safety and traffic management. V2V communication technology is a revolutionary development that depends on Dedicated Short-Range Communication (DSRC), a specialized communication method enabling vehicles to exchange critical information such as location, speed, and status in real-time. In addition to providing exceptionally low latency communication, DSRC is essential for broadcasting messages that improve road safety by facilitating quick and easy communication between vehicles. The system allows users to view and share real-time data and allows us to connect with nearby vehicles within a specific range. With the help of this function, drivers can take preventative measures to keep themselves safe. DriveQ has a broad range of applications including Stop Sign Movement Assistance, Forward Collision Warning, Pre-Crash Warning, and Curve Speed Warning. © 2024 IEEE.

关键词： Vehicle to vehicle communications

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Bidirectional Communication System for Deaf-Blind Individuals 8

Bidirectional Communication System for Deaf-Blind Individual...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Saralaya, Sridevi Jijo, Jeevan V. Serrao, Lavisha Andri D'Souza, Flaivin Vaz, Jane Oveita St Joseph Engg. College Dept. of Computer Science & Engineering Mangalore India

ISBN: (纸本)9798350350593

In a world that thrives on connectivity and communication, we often take for granted the power of simple yet profound interactions. For individuals with sensory impairments, particularly the deaf-blind community, these fundamental human connections are often limited, making everyday life a challenge. In response to this challenge, we propose an innovative and transformative project that aims to bridge the communication gap for the deaf-blind community, enabling them to engage in text-based conversations with ease. While advancements in technology have greatly improved communication for many, those who are both deaf and blind remain largely excluded from the digital world. This exclusion hinders their ability to engage in meaningful conversations, access information, and connect with others, leading to social and informational isolation. While existing solutions often focused on either blindness or deafness, this project aimed to provide a holistic communication solution for individuals with both impairments. Through rigorous testing, the bidirectional communication system demonstrated its ability to accurately transcribe speech-to-text, translate text-to-braille, and facilitate seamless interaction between users and hardware peripherals. The project's novel solution for deaf-blind communication brings significant advantages namely cost saving, streamlined labor, efficient space utilization, and energy conservation. The integration of speech-to-text with real-time braille hardware interaction enhances user experience, reducing the need for extensive manual interpretation. © 2024 IEEE.

关键词： Ability testing

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Enhancing Athletic Performance:2D Human Pose Estimation Using Deep Neural Networks for Movement Analysis 7

Enhancing Athletic Performance:2D Human Pose Estimation Usin...

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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Devi, Pothreddypally Jhansi Sumani, Aleshwaram Chandra, Chenna Mukesh Thrisha, Battu Bhanu Vamshi, Arkatala Sai B V Raju Institute of Technology Dept. of Computer Science Engineering Telangana India

ISBN: (纸本)9798350372816

computer vision is a field that is used to build many neural network-based prototypes efficiently. It analyses images and videos effectively and processes them digitally. Human Pose estimation (HPE) is one of the computer vision applications which additionally uses neural networks for prediction. Human pose estimation is used widely in many fields which includes action recognition, fitness, gaming, and motion capture. HPE identifies and locates the key points on the body joints of a person's body. These body points are connected to form a Skeletal Structure. This skeletal representation helps to easily visualize the image in 2-D. Focusing on Sports domain, HPE plays major role in estimating athlete movements. It helps in analyzing the feedback on the posture which allows to make adjustments during training. It also helps viewers to analyse the insights of athlete performance. To achieve the model that contains all these features, requires involvement of few frameworks. OpenPose is utilised for fast processing of videos and can detect the multiple people in one frame. OpenPose has the capability of handling the occulsion. On the Other hand, the model also import the capabilities of HRNet, which is used for analyzing athlete movements and tracks the gestures. After detecting the keypoints on the body joints, these points must be integrated to form 2-D images. To achieve the spatial relationship between the bodypoints Joint Graph Convolutional Neural Networks (JGCN) framework is used. The proposed model is estimated to generate approximately (4- 5)percent more efficiency and accuracy in its output 2-D image compared with the previous models. © 2024 IEEE.

关键词： Convolutional neural networks

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IoT-Driven Precision Crop Recommendation System Using LSTM & TSC-NET for Sustainable Agriculture 1

IoT-Driven Precision Crop Recommendation System Using LSTM &...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Satya Sandeep, K. Lakshmi Sahithi, K. Shabana Azmi, Sd. Renuka Devi, J. Nirmala Jyothi, B. Vignan 'S Nirula Institute of Technology and Science for Women Dept. of Computer Science and Engineering Guntur India

ISBN: (数字)9798350384246

ISBN: (纸本)9798350384246

Andhra Pradesh is a well-known agricultural state with a major contribution to the production of rice, cotton, and chilies it is also known as the 'Rice Bowl of India'. Unpredictable weather events have been a concern to the state, it is proving to show a negative impact on the state's farming population, nevertheless the state still contributes materially to the country's cultivation production. on thorough research and historical analysis, it is stated that the food diffidence and conservationist problems arise from the established age-old agricultural methods, these methods are being followed without any systematic scientific pursuit. this study pursues to solve the mentioned problem by transitioning agricultural practices with the use of Internet of Things (IoT) technologies. A preferred multi-class classification model leveraging IoT data. the data primarily includes meteorological parameters such as soil NPK values, temperature, humidity, and more. The hypothesized model, a hybrid of Long Short-Term Memory (LSTM) and Time Series - Convolutional Neural Networks (TSC-NET), which recommends suitable crops for the cropland location, it is accomplished by integrating Time-space data. the food grain acreage in Andhra Pradesh is seen dwindling by 4.2 % in 2022-2023, underscoring the susceptibility of farmers to monsoon failures. In addition, macroeconomically speaking, the slowdown in agrarian development disrupts economy's advancement as a whole, advocating for agrarian development. The IoT -driven crop classification model provides the novel approach to enhance crop selection and optimize production potential. therefore, improving the precision and efficacy of crop forecasting while enabling farmers to make well-informed decisions accounting for soil conditions and climate data. It intends to tackle both macro-level economic concerns and micro-level farmer vulnerabilities, this research levels to improve the sustainability and efficiency of agriculture in Andhra Pr

关键词： Internet of things

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Modular Deep Reinforcement Learning: Enhancing Flexibility through Agent Splitting

Modular Deep Reinforcement Learning: Enhancing Flexibility t...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Tian, Chong Rawat, Danda B. Howard University Dept. of Electrical Engineering & Computer Science WashingtonDC United States

ISBN: (纸本)9798350362480

Deep reinforcement learning (DRL) has shown remarkable success in tackling complex tasks by learning representations directly from raw data. However, as DRL agents become increasingly sophisticated, they often face challenges in adapting to new tasks or environments due to their monolithic structure. In this paper, we prove that by decomposing the agent into modular components, we enable more efficient adaptation as well as more efficient than that of the singular DRL agent on tackling complex tasks. Overall, our work contributes to advancing the field of DRL by introducing a flexible and scalable framework for agent decomposition. By enabling the creation of modular agents, we empower DRL systems to efficiently adapt to changing environments, learn new tasks with minimal intervention, and achieve higher levels of performance and versatility. © 2024 IEEE.

关键词： Deep reinforcement learning

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Machine Learning based Real Estate Price Prediction 8

Machine Learning based Real Estate Price Prediction

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Mukherjee, Anupam Nambiar, Rtwik Choudhury, Deepjyoti Manipal University Jaipur School of Computer Science and Engineering Dept. of Aiml Jaipur India

ISBN: (纸本)9798350386578

The dynamic nature of real estate markets gives rise to huge challenges for buyers and sellers in accurately predicting property prices. This paper presents a detailed study on real estate price predictions, focusing on using machine learning techniques to forecast property prices effectively. Using a dataset from Bengaluru, as a case study, it explored the intricacies of Real Estate markets and developed a predictive model to estimate the property prices. Our studies consist of data preprocessing, featuring engineering, model selection, and evaluations, giving insights into the factors that affect property prices and the performances of various regression algorithms. After performing multiple experiments, we showcase the efficiency of our approaches in predicting property prices. Existing techniques that have tried to predict house prices have often struggled to deal with nonlinear relationships present among the factors. Recent techniques such as Linear regression, Ridge and Lasso Regression and Support Vector Machines(SVM) have come up short of being perfect when it comes to dealing with computational intensity and these techniques require extensive hyperparameter tuning. However, our predictive model is able to deal with these challenges by leveraging the strengths of Random Forest regression. This is done with high accuracy and the implications for real estate stakeholders are discussed, subsequently. This paper also highlights the various avenues through which the performance of real estate estimation models can be improved in the future. © 2024 IEEE.

关键词： Support vector regression

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NutriSpy: Investigate Your Food's Health Quotient 3

NutriSpy: Investigate Your Food's Health Quotient

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Thanay Kumar, Y.R. Joshi, Sudhanva S Sujan, A.T. Ujwal Karanth, K.V. Asha Rani, K.P. Gowrishankar, S. Dr. Ambedkar Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350364569

NutriSpy is a comprehensive web and mobile application designed to enhance users' health and wellbeing through personalized food and exercise recommendations. At its core, NutriSpy integrates three powerful features: a tailored workout recommendation system, an advanced food identification tool, and a robust diet advisory service. By leveraging state-of-the-art machine learning algorithms, NutriSpy accurately identifies food items, assesses their nutritional and caloric content, and generates customized dietary guidance. The food identification system employs image recognition and natural language processing to provide precise nutritional information from photographs or descriptions of meals. This is combined with a diet advice system that considers individual health profiles, dietary restrictions, and fitness goals to offer personalized nutrition plans. Additionally, NutriSpy's workout recommendation system aligns exercise routines with users' dietary needs and fitness aspirations, creating a harmonious balance between food intake and physical activity. NutriSpy's sophisticated technology facilitates real-time adjustments and recommendations, making it a dynamic tool for promoting healthier lifestyles. By analyzing user data and integrating current health trends, NutriSpy not only aids in immediate dietary and fitness decisions but also contributes to long-term health improvements. This study explores the development and functionality of NutriSpy, detailing its components, underlying technologies, and potential impact on fostering healthier living habits. © 2024 IEEE.

关键词： Diet Recommendation system Exercise Recommendation System Food Recognition System

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HAR-CLET: A Novel Conv-LSTM with Extra Tree Classifier for Human Activity Recognition

HAR-CLET: A Novel Conv-LSTM with Extra Tree Classifier for H...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Choudhury, Nurul Amin Soni, Badal National Institute of Technology Silchar Dept. of Computer Science and Engineering Assam Silchar India

ISBN: (纸本)9798350350821

In this paper, we propose a novel ensemble of Convolutional Neural Network-Long Short-Term Memory with an Extra Tree Classifier for automatic feature engineering in the spatiotemporal domain and classification of different human activities collected using inbuilt smartphone sensors. Prominent sensor-based HAR systems are trained and validated in extracted features of datasets collected in a controlled and simulated environment, which can adversely impact the reliability in real-world scenarios. Our approach utilizes unsimulated data from diverse users collected using inbuilt smartphone sensors in real world environment. Also, the proposed model was effectively trained and tested with multiple standard public datasets for validation and benchmarking with diverse evaluation metrics. With effective parameter tuning and lightweight feature extraction architecture, the proposed model achieved an average performance accuracy of 99 % with their own generated dataset, mHealth and MotionSense dataset, in comparatively optimized computational time compared to the benchmark models. © 2024 IEEE.

关键词： Convolutional neural networks

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