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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Rao, Chintala Nageswara Chatrapathy, K. Jainul Fathima, A. Sathish, Goski mukherjee, Saptarshi Reddy, Pundru Chandra Shaker CSE-AI&ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing&Information Technology REVA University Karnataka Bengaluru India Information Technology Francis Xavier Engineering College TamilNadu Tirunelveli India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology Uttarakhand India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350305258

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer's neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate. © 2023 IEEE.

关键词： Breast cancer Carcinoma Deep Learning KNN machine learning RNN SVm

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Challenges with reinforcement learning in prosthesis

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materials Today: Proceedings 2022年 49卷 3133-3136页

作者： Deepali Salwan Shri Kant Himanshu Pareek Roopali Sharma Pt. Deen Dayal Upadhyaya National Institute for Physically Handicapped New Delhi India Research and Technology Development Centre Department of Computer Science School of Engineering and Technology Sharda University Greater Noida India M.Tech IIT Hyderabad India M.Tech Amity University India

Reinforcement Learning has work wonders in games like Atari and AlphaZero. Recent advancement in Deep Reinforcement Learning showcase it’s ability in the active Prosthesis as well. RL is being used widely to solve problems where Learning of the Agent in its own environment is as necessary as training the model beforehand. However, model developed, and successful in the gaming environment could still need to be tuned to be effective with Real Time devices such as Prosthetic Limb and other Real-World devices. In this article, main challenges are presented which we face while working on a model Based and model Free Reinforcement Learning in real world environment and suggesting an approach which would work uniformly on most of the Real Time scenarios. We observed the performance and noticed that there are couple of factors which needs to be taken care of in Real Time Applications which are not much though about in games and other online applications. We also compared the algorithms such as Policy Proximal Optimization (PPO) and Deep Deterministic Policy Gradient (DDPG) vs model Based Policy search with Gaussian Processes and found out that a mix of model-Based and model-Free (mBmF) performed the best individually despite of all the challenges.

关键词： Reinforcement learning Artificial intelligence Deep learning Challenges model-based model-free Prosthesis OpenSim

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A Logical Design of Internet of Things Assisted Digital Health Record monitoring System using Deep Learning Principles

A Logical Design of Internet of Things Assisted Digital Heal...

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2023 IEEE Global Conference on Information technologies and Communications, GCITC 2023

作者： Gayathri, S. Vijayanandh Thirumoorthy, P. Valarmathy, A.S. Pandi, V. Samuthira Shobana, D. R.M.K. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350308167

Intelligent systems for monitoring health have been made possible by the IoT and the mental/physical health of an individual may be monitored using such a system. Determinants of numerous diseases, both physical and mental include tension, worry and high blood pressure. Problems associated with aging including anxiety, depression, stress and pressure require specialized care in this context. The prompt identification of health issues by monitoring of nervousness tension, stress, and heart rate is a key to avoiding irreversible harm. This will improve health outcomes;lessen the burden on caregivers, and save money. Find innovative technological answers for continuous monitoring of stress, anxiety, and blood pressure through the use of covert embedded sensors as well as deep learning methods. As a result of the suggested smart health monitoring system, health monitoring services may now be provided quickly, cheaply, and reliably from remote places, whereas this was not previously achievable with conventional health care systems. The implementation of a blockchain-based framework has increased the safety and privacy of patients' private information and reduced the likelihood of it being misused. Using Deep Learning and related technologies to evaluate health data for numerous goals has aided in the achievement of both preventative healthcare and fatality control. This has allowed for the early diagnosis of chronic illnesses, which was previously impossible. The combination of computing resources in the cloud and electronic storage has been introduced in order to render operations cheaper and instantaneously. This research provides a comprehensive overview of the state of health monitoring technology, including the most up-to-date developments and the most pressing problems. © 2023 IEEE.

关键词： Deep Learning Health Record monitoring Healthcare Internet of Things IoT Sensors

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Utilizing Artificial Neural Networks and multivariate Patient Data for Anemia Detection using WEKA based Approach for Diagnosis

Utilizing Artificial Neural Networks and Multivariate Patien...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Parul Vaishnav manish Rai Professor School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India M.Tech Research Scholar MDS University Ajmer Department of Computer Science and Engineering (AI& ML) Manipal University Jaipur India

This research study describes a method that uses neural networks to figure out if a woman has anemia. An artificial neural network is a type of computer network that is based on the real neural networks that make up the structure of the human brain. In this paper, the WEKA tool is used for the classification process carried out using an ANN structure, which is referred to as one of the data mining approaches and is based on multiple perceptions. The network has seven inputs (RBC, HCT, mCV, mCH, mCHC, RDW, and WBC) and an output (decision). Simulations on 567 patients demonstrate that the Artificial Neural Network (ANN) accurately diagnose the disease. This study investigates the utilization of Artificial Neural Network (ANN)-supervised machine learning techniques for predicting anemia by using the data from Kaggle.

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A Conceptualized Study of mL Classifiers in Solid Waste management

A Conceptualized Study of ML Classifiers in Solid Waste Mana...

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Sustainable Computing and integrated Communication in Changing Landscape of AI (ICSCAI), International Conference on

作者： Prabha Shreeraj Nair Akanksha Srivastav mir Aadil Department of Information Technology & M. Tech Integrated Noida Institute Of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India

ISBN: (数字)9798350355338

ISBN: (纸本)9798350355345

The phenomenon of population development and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of municipal solid garbage. Consequently, scholars and experts have endeavoured to employ sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of machine learning (mL) algorithms into the field of municipal solid waste management (mSWm) with the aim of facilitating sustainable environmental growth. mL algorithms have demonstrated their effectiveness in modelling intricate nonlinear systems. This study involved the assessment and analysis of over 200 papers that were published throughout the timeframe of two decades, namely from 2016 to 2023. This study provides a comprehensive overview of the utilization of machine learning algorithms throughout the entirety of mSWm, encompassing trash creation, collection and transportation, and ultimate disposal stages. This thorough study aims to address the gaps and identify future directions in the application of mL in mSWm. The findings of this review offer both theoretical and practical guidelines for future research in this field.

关键词： Waste management Waste materials machine learning algorithms Reviews Urban areas Transportation Pressing machine learning Solids Nonlinear systems

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An IoT Based Framework for monitoring Power Consumption in Smart Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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Communication, computer sciences and engineering (IC3SE), International Conference on

作者： R Raghavendra Nidhi Chauhan Gaurav mittal Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore India Department of Information Technology & M. Tech Integrated Noida Institute of Engineering and Technology Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Tula's Institute Dehradun India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The electrical power system grid in India, commonly referred to as the power grid, has been in operation for an extensive duration. During this time period, there were no significant advancements or alterations documented in the electricity grid infrastructure. The need for electrical power consumption is seeing a significant increase, but the current grid infrastructure is unable to meet these growing demands. In order to meet the demands of future power load, it is imperative to develop an enhanced system that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power management system and explains its automation and functionality by utilising smart grid and IoT technologies. The research suggests a method for keeping an eye on consumer power use and controlling the use of electrical devices by putting in place a system that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problems with power usage. Future system improvements that are region-specific and intended to reduce loads and maximise system performance are made possible by the data collected.

关键词： Power demand System performance Power system management Voltage System improvement Smart grids Power system reliability

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A Detailed Observation on Association Rule mining 2

A Detailed Observation on Association Rule Mining

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2nd International Conference on Inventive Research in Computing Applications, ICIRCA 2020

作者： Sujatha, Pothula Elavarasan, G. Nithya, P. Kadhiravan, N. Gosh, Arindam Adhikary, Asmita Pondicherry University M.Tech-CSE Department of Computer Science and Engineering Kalapet Puducherry India

ISBN: (纸本)9781728153742

Association rule mining is utilized to take care of various types of area issues. These days' affiliation rule mining has been stretched out to huge information. As a result of the gigantic measure of information, a solitary way the sequential procedure isn't adequate subsequently. The most basic piece of ARm is discovering a potential relationship between things in an enormous exchange based dataset. The well-known algorithms in particular apriori and FP Growth has been identified implemented those algorithms using the WEKA (version 3.7.10) tool. © 2020 IEEE.

关键词： Association rules

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Brain Tumor Classification Using UNet Deep Neural Networks from 3D mRI Images

Brain Tumor Classification Using UNet Deep Neural Networks f...

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Electronics, Computing, Communication and Control technology (ICECCC), International Conference on

作者： mohammad Khaja Shaik D Kalpana U Sesadri Saptarshi mukherjee C Dastagiraiah Pundru Chandra Shaker Reddy Electronics and Communication Engineering St. Ann's College of Engineering and Technology Chirala Andhra Pradesh India Departmet of CSE-Data Science Malla Reddy Engineering College (Autonomous) India Computer Science and Engineering Vardhaman College of Engineering Hyderabad Telanagana India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computer Science and Engineering Anurag University Hyderabad Telangana India School of Computer Science and Artificial Intelligence SR University Warangal Telangana India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

A patient's therapy options and prognosis are both improved by detecting and diagnosing a brain tumor early. Brain tumors can be located and diagnosed with the help of magnetic resonance imaging (mRI). In real practice, however, medical personnel rely entirely on their own time and expertise when manually identifying brain tumors from a vast number of mRI scans. These days, medical diagnosis and treatment recommendation systems that use computer-aided expertise are all the rage. Brain tumor detection makes use of a plethora of frameworks based on machine-learning(mL) &deep-learning(DL). The purpose of this research is to present a DL-based approach for effective brain tumor segmentation and classification. The research uses convolutional-neural-networks(CNNs) for tumor classification after volumetric segmentation of mRI-images using the 3D-UNet model. To demonstrate the models' validity, loss and precision plots are provided. We compare the outcomes of several methods described in the literature with the measured performance of the models we offer. When compared to the most recent cutting-edge methods, the proposed work comes out on top.

关键词： Training Image segmentation Solid modeling Three-dimensional displays magnetic resonance imaging Computational modeling Artificial neural networks

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IoT-Enabled Sleep monitoring Wearables: Advancements in Tracking and Analysis

IoT-Enabled Sleep Monitoring Wearables: Advancements in Trac...

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computer, Communication and Control (IC4), International Conference on

作者： K. Thinakaran V. Vijaya Baskar m. Ahsan Shariff manduva Chervith G. Raghavendra S. Velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Electronics and Communication Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh India Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

The importance of sleep to a person’s health and well-being cannot be overstated. Sleep monitoring and analysis control has been transformed by the combination of Internet of Things (IoT) technologies and wearable devices in recent years. This system analyzes the improvements achieved in sleep monitoring wearable enabled by IoT, providing a thorough and discreet method of evaluating sleep activities. Accelerometers and heart rate monitors are at the heart of these wearables, which also have complex processing units and connectivity modules. Insightful sleep quality analysis, precise sleep stage separation, and real-time data collecting are all made possible by this complementary relationship. Wearables may track sleep patterns, interruptions, and effectiveness by recording motion, heart rate variability, and brain activity through various sensors. Smart cloud interfaces that provide sleep metrics and trends improve the user experience and allow a deeper analysis of a person’s sleeping habits. In addition, these wearables may easily connect to smart home systems, enhancing sleep quality by modifying the house’s lighting and audio settings depending on the individual’s sleeping habits. The data gathered by these wearables has wider uses beyond personal sleep monitoring, such as helping the system better understand and treat sleep conditions.

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Reliable and Efficient Routing for Water Quality monitoring in Underwater WSN

Reliable and Efficient Routing for Water Quality Monitoring ...

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computer, Communication and Control (IC4), International Conference on

作者： J. Bennilo Fernandes B. Gopi m. Ahsan Shariff mohammed Rehaman Pasha S. Srinivasan S.P. maniraj Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Tamil Nadu India

monitoring water quality has become crucial to provide clean and safe water. The traditional monitoring procedures are useless because they take a lot of function, require a long time, and cannot give results instantly to promote proactive water contamination reactions. This paper introduces a Reliable and efficient routing for Water Quality monitoring (REWQ) in Underwater Wireless Sensor Networks (UWSNs). This mechanism utilizes the Cuckoo search (CS) algorithm to select the next hop to transmit the data from the sender to the surface region sink. The CS algorithm computes the fitness function by node energy usage, depth value and loss ratio. The proposed CS algorithm aims to improve reliability and successful data forwarding. This model is implemented, and its performances are evaluated by network simulator-NS-3. From the simulation results, the REWQ mechanism minimizes the average deliverance time and improves the residual energy.

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