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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： Nisarg Doshi Sagar Bhavsar D Rajeswari R Srinivasan Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

Images photographed in foggy weather usually have poor visibility. To mitigate this problem researchers have come up with various image dehazing techniques. Now, more than ever, high-quality images that can be used to glean maximum information from autonomous systems are in high demand. This research work uses different Deep Learning (DL) architectures to draw out essential details from the picture and localize the information recovered to reduce the haze from the picture. The paper investigates to remove the hazes from the dehazed images using DL techniques. The first task of this proposed work attempts three pre-processing techniques namely, air light estimation, contextual regularization and boundary constraint. The second task of this work is to identify the suitable DL model to extract clear images from dehazed images. Evaluation metrics are PSNR value and SSIM value are used to estimate the values of dehazed images compared with clear images. Experimental results proves that AOD-Net outperforms good result with respect to PSNR value.

关键词： Deep learning Measurement Autonomous systems Atmospheric modeling Estimation Feature extraction Knowledge discovery

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Absolute Face Recognition System using Machine Learning Approach from Blurred Images

Absolute Face Recognition System using Machine Learning Appr...

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Advances in Computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： D. Hemavathi Utkarsh Yashwant Tambe Pao-Ann Hsiung Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur (Chennai) India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan R. O. C.

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

Face Recognition is an important task in many domains to obtain the exact image from the pool of images. In general, blurred images are extremely challenging for the sensitive areas like law and order, defense, etc. Many face recognition techniques work well in normal images with various dimensions. In sensitive domains, the blurred image act as key evidence for the entire scenario. So, it is very important to find the original and accurate image from the blurred images. Representation of face, extraction of features and classification are the important steps in face recognition process. In existing models, Linear Binary Pattern (LBP) methods are used to recognize accurate images. LBP with Histogram (LBPH) is used to improve the detection performance of original images. Image sorting is done using the Point Spread Function Estimation on the blurred region and it helped to recognize faces with more accuracy. Extended Uniform Linear Binary Pattern method is used to reduce the dimensions to concentrate more on center pixels, with the use of the Viola-Jones algorithm and K-nearest neighbor (KNN) classifier for *** proposed enhanced LBP approach assisted in achieving 94.7% accuracy in recognizing human faces from blurry images.

关键词： Image recognition Accuracy Machine learning algorithms Face recognition Nearest neighbor methods Prediction algorithms Feature extraction

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An FL-based Energy-Efficient Opportunistic Intelligent Node Selection Scheme for Healthcare IoV Applications

An FL-based Energy-Efficient Opportunistic Intelligent Node ...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Pallati Narsimhulu Rashmi Sahay Symbiosis Institute of Technology Hyderabad Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad India Department of Data Science and Artificial Intelligence Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

The complexity of managing the energy and operational needs in large-scale Wireless Sensor Networks (WSNs) emphasizes the importance of energy-aware routing. This paper proposes an energy-efficient routing protocol that uses an intelligent node selection scheme to address energy-aware routing in the healthcare Internet of Vehicles (IoV). The protocol identifies the most suitable cluster leader based on dynamic criteria. By building on a ranking system that considers factors like location and remaining energy, the protocol selects the next intelligent node to establish the most energy-efficient route within the Internet of Things (IoT) context. The energy-aware routing scheme is tested across various clustering systems to evaluate its impact on route discovery based on system size and time. Experimental results show that the model reduces energy consumption during broadcast operations. Performance metrics such as Jitter and Packet Loss Ratio (PLR) further illustrate the protocol’s energy optimization. Simulation results across diverse network conditions demonstrate the superiority of adaptive ranking and intelligent forwarder node selection over competing strategies. Overall, the proposed protocol presents a promising solution for achieving energy-efficient routing in IoV, offering insights into optimized strategies and their benefits for IoT applications.

关键词： Wireless sensor networks Simulation Packet loss Medical services Jitter Routing Routing protocols Energy efficiency Vehicle dynamics Optimization

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Efficient IOT based COVID patient healthcare control system using arduino with mobile app

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Measurement: Sensors 2024年 33卷

作者： sudha, K. Mercy Theresa, M. Vinayagam, P. Suganthi, R. Sripriya, T. uthayakumar, G.S. Computer Science and Business System R. M. D Engineering College India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of ECE Saveetha Engineering College India Department of ECE Panimalar Engineering College India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamilnadu Chennai India Department of ECE St.Joseph's Institute of Technology India

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. Sensors, an information acquisition unit, a microcontroller (e.g., Arduino), and a product modification were used to construct a mobile phone-based remote healthcare monitoring framework that may provide continuous web-based data about a patient's physiological states (i.e., JAVA). The framework checks, displays, and stores the patient's temperature, heart rate, and EEG data before sending it out on the specialist's portable device with the application. In this way, an IoT-based patient monitoring framework may effectively monitor a patient's health state and save their life on time. © 2024

关键词： data acquisition

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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IoT Based Fish Pond Monitoring System to Enhance Its Productivity 3

IoT Based Fish Pond Monitoring System to Enhance Its Product...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Kumar, S M Ravi Mohammed, Thayyaba Khatoon Rao, Seeram Srinivasa Dinesh Anton Raja, P. Bakhare, Ruhi Kumar, Ashok Sarkar, Swagata G. Pullaiah College of Engineering and Technology Department of Mechanical Andhra Pradesh India School of Engineering Malla Reddy University Telangana India Koneru Lakshmaiah Education Foundation Department of Mechanical Engineering Andhra Pradesh India R.M.K. Engineering College Department of Electronics & Communication Chennai India Dr. Ambedkar Institute of Management Studies & Research Department of Master of Business Administration Maharashtra India Banasthali Vidyapith Department of Computer Science Rajasthan India Sri Sai Ram Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350336238

Modernizing fish ponds for agricultural production face major challenges in terms of capital expenditure and operational expenses. Fish farming of particular types of fish species necessitates the fulfilment of several requirements because, like several other living organisms, fish have a precise limit for an assortment of environmental criteria. For the good health and development of the organisms, big farms typically have certain kinds of water surveillance and replacement mechanization systems. To preserve the ecosystems for living fish, the people who work in the fish farming ponds must be active throughout the day. Local farmers who operate on relatively small ponds couldn't afford to compensate employees to manage daily tasks, which typically include keeping an eye on water levels, temperature, and pH levels. As a result, the prime motive for this paper is to monitor and take steps to keep the habitat's eco-friendly environment for specific species of fish, which will decrease the time required for some basic actions. This article puts forth a smart Internet of Things (IoT) based fish pond water monitoring system. Such a smart system consists of several real-time sensors that monitor and send inputs to a microcontroller and the data is stored in a real-time database. The user can track these values through a phone app that is assimilated with the cloud by having them transmitted to the cloud at periodic intervals and this helps the fish pond owners to take required action quickly and effectively when needed. As embedding devices are typically made up of an Arduino board, internet and relay frames, and a computer interface, a farmer could easily source these parts. With the help of this integration system, farmers can reduce operating costs and boost overall effectiveness by avoiding the need to hire employees for their location. © 2023 IEEE.

关键词： Fish

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Hybrid Visual Verification

Hybrid Visual Verification

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International Conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： S.Nikkath Bushra A. Syed Ismail P. Sasigresa Edwin Singh C Department of Computing Technologies SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department of Data Science and Business Systems SRM Institute of Scienece and Technology KattankulathurChengalpattu India Department: Computer Science and Engineering Veltech University Department: Computer Science and Engineering Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

data security is becoming increasingly important as cloud computing advances. data security is the fundamental problem of all distributed computing systems. Cloud computing enables access to distributed applications and services belonging to various organizations spread across different locations. Cloud computing raises security concerns, because it shares data with distributed users via network in an open terrain. In this system, some of the most important security services like image-based security are handled by the cloud computing system. The proposed scheme takes care of several attacks like botnets, malware and guessing attacks. This scheme is contrasted withseveral established schemesand a new form of authentication is used for the purpose of storing files in a secure manner.

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Exploring the Integration of Reinforcement Learning for Enhancing Game Performance: A Comprehensive Review

Exploring the Integration of Reinforcement Learning for Enha...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Munugapati Bhavana K Vijay Krupa Vatsal Yerra Anjali Satyavathi Navyatha Ravi J. Kavitha V. Thrimurthulu Department of CSE MLR Institute of Technology Affiliated to JNTUH Hyderabad Department of CSE Malla Reddy University Hyderabad Department of Data Science MLR Institute of Technology Hyderabad Department of CSE Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Department of Computer Science & Engineering MLR Institute of Technology Hyderabad

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Reinforcement learning, a subset of machine learning, encompasses the process by which an agent learns through trial-and-error feedback to anticipate its subsequent actions. Its versatility extends across various domains, with gaming being a prominent application. Success in gaming often pivots on the formulation of effective strategies, a task that necessitates repetitive game play, consuming valuable time, energy, and resources. This study aims tointegrate a reinforcement learning agent into gaming scenarios, replicating game dynamics and progressively refining outcomes with each iteration. By mimicking the agent’s performance, human players can augment their likelihood of success. This document highlights the utilization of reinforcement learning techniques, focusing on the application of Value-based methods (Q-learning, Deep Q Network (DQN)), and Model-based methods(State-Action-Reward-State-Action (SARSA)). Q-learning from Value-based methodis designed as an off-policy algorithm, whereas SARSA from Model-based methods functions as *** outcomes presented encompass insights from previous research on Q-learning and SARSA across diverse test fields or settings. Furthermore, the proposed reinforcement learning methodologies address key facets such as data comprehension, problem categorization, algorithm identification, and algorithm implementation.

关键词： Q-learning Machine learning algorithms Reviews Heuristic algorithms Refining Focusing Games Generative adversarial networks Deep reinforcement learning Real-time systems

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Structuring Clinical Notes of Italian ST-elevation Myocardial Infarction Patients 1

Structuring Clinical Notes of Italian ST-elevation Myocardia...

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1st Workshop on Patient-Oriented Language Processing, CL4Health 2024

作者： Torri, Vittorio Mazzucato, Sara Dalmiani, Stefano Paradossi, Umberto Passino, Claudio Moccia, Sara Micera, Silvestro Ieva, Francesca MOX - Modelling and Scientific Computing Lab Department of Mathematics Politecnico di Milano Italy Biorobotics Institute Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Italy Fondazione Toscana Gabriele Monasterio Italy Health Science Interdisciplinary Center Scuola Superiore Sant’Anna Italy Bertarelli Foundation Chair in Translational Neural Engineering Center for Neuroprosthetics Institute of Bioengineering Ecole Polytechnique Federale de Lausanne Switzerland HDS - Health Data Science Centre Human Technopole Italy

ISBN: (纸本)9782493814258

In recent years, it has become common for patients to get full access to their Electronic Health Records (EHRs), thanks to the advancements in the EHRs systems of many healthcare providers. While this access empowers patients and doctors with comprehensive and real-time health information, it also introduces new challenges, in particular due to the unstructured nature of much of the information within EHRs. To address this, we propose a pipeline to structure anamneses, providing patients with a clear and concise overview of their health data and its longitudinal evolution, also allowing clinicians to focus more on patient care during consultations. In this paper, we present preliminary results on extracting structured information from EHRs of patients diagnosed with ST-Elevation Myocardial Infarction from an Italian hospital. Our pipeline exploits text classification models to extract relevant clinical variables, comparing rule-based, recurrent neural network and BERT-based models. While various approaches utilized ontologies or knowledge graphs for Italian data, our work represents the first attempt to develop this type of pipeline. The results for the extraction of most variables are satisfactory (f1-score > 0.80), with the exception of the most rare values of certain variables, for which we propose future research directions to investigate. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Recurrent neural networks

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Monolithic 50 GHz Tunable Microwave Signal Generation Based on Erbium-Doped Lithium Niobate Dual Lasers

Monolithic 50 GHz Tunable Microwave Signal Generation Based ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Minglu Cai Xujia Zhang Tianyi Li Hao Shi Tieying Li Xianyi Cao Zekun Cui Jianping Chen Kan Wu Department of Electronic Engineering State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Institute for Advanced Communication and Data Science Shanghai Jiao Tong University Shanghai China

The monolithic erbium-doped lithium niobate lasers can achieve a tunable wavelength range of 56.25 GHz and generate a 50 GHz microwave signal with a side-mode suppression ratio of 35 dB by dual lasers.

ISBN: (纸本)9798350369311

关键词： Lithium niobate Masers Lasers and electrooptics Electro-optical waveguides

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