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Remote Monitoring solution with Predictive Analysis for Health Care Devices

Remote Monitoring Solution with Predictive Analysis for Heal...

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2020 International Conference on smart Electronics and Communication, ICOsEC 2020

作者： bharath Kumar, N. Nagarathna, N. B.M.S College of Engineering Department of Computer Science Engineering Bangalore India

ISBN: (纸本)9781728154619

Predictive analytics in machine learning can be used in conjunction with data mining to solve real-world use cases. These techniques are proven to be highly effective and with ensemble techniques, the accuracy of machine learning models can be further improved with minimal efforts. These techniques can be used to monitor health care devices remotely. Machine learning algorithms enable us to analyze different physical parameters which are collected from health care devices. These techniques enable us to predict future health status of medical devices and with accurate results, malfunctioning of health care devices can be avoided by monitoring them remotely. The health care devices connected to IoT, allows us to collect and store huge amount of data (Physical Parameter) in the cloud, which can be fetched later using polyglot persistence (NosQL and sQL) techniques to retrieve the data effectively and by using machine learning predictive techniques the collected data can be analyzed and the future health status of health care devices can be predicted.. © 2020 IEEE.

关键词： Predictive analytics

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Fusion-based Digital Twin Framework with Convolutional Neural Networks (CNN) for Real-Time Threat Detection and Image Enhancement in Low-Light IoT Environments

Fusion-Based Digital Twin Framework with Convolutional Neura...

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Integrated Intelligence and Communication systems (ICIICs), International Conference on

作者： K. Ashok Kumar Pareshwar Prasad barmola s. P. velmurugan Manjunath A C Umang soni Ramya Maranan Department of Physics B V Raju Institute of Technology Narsapur Medak India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishankoil India Department of Mechanical Engineering Brindavan college of Engineering Bangalore India Faculty of Engineering and Technology Parul institute of Engineering and Technology Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Image acquisition in low-light environments often suffers from poor contrast, high noise, and loss of fine details, making effective enhancement challenging. Conventional enhancement methods frequently fail to balance detail preservation and noise suppression. This paper introduces a novel fusion-based technique for enhancing images captured in low-light settings. The proposed method integrates multi-scale decomposition, adaptive histogram equalization, and wavelet transform to improve image quality. Multi-scale decomposition captures critical structural information, adaptive histogram equalization adjusts intensity levels for enhanced contrast, and wavelet transform reduces noise while refining image details. Evaluation on standard low-light image datasets demonstrates that this method outperforms current state-of-the-art techniques. The enhanced images show a peak signal-to-noise ratio (PsNR) of 32.45 db and a structural similarity index (ssIM) of 0.87, reflecting an 18% improvement in PsNR and a 12% gain in ssIM over traditional methods. Additionally, the technique reduces noise variance by 20% and enhances visual clarity, making it suitable for applications such as surveillance, medical imaging, and autonomous driving. In conclusion, this fusion-based approach provides an effective solution for low-light image enhancement, maintaining critical details and achieving improved visual quality through balanced contrast enhancement and noise reduction.

关键词： Wavelet transforms Histograms visualization PsNR Noise Noise reduction Adaptive equalizers Real-time systems Convolutional neural networks Image enhancement

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Growing Greener: Exploring the Creative Potential of Deep Learning in Agricultural Disease Control

Growing Greener: Exploring the Creative Potential of Deep Le...

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signal Processing, Computation, Electronics, Power and Telecommunication (IConsCEPT), International Conference on

作者： Ajmeera Kiran syed Ziaur Rahman P. sreelatha s. Dhanasekaran Ramesh Kumar Ayyasamy Devulapally swetha Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Faculty of Information Technology Majan University College Sultanate of Oman S.V. Arts college (TTD) Tirupati A.P India Department of Information Technology Kalasalingam Academy of Research and Education (Deemed to be University) Tamilnadu India Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman (UTAR) Kampar Malaysia Department of Computer Science and Engineering Vaagdevi Engineering College Bolli Kunta Telangana India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

According to estimates, by mid-2030, there will be 8.6 billion people on the planet. The human population has been growing exponentially. There will be a rise in food production and consumption due to the massive population growth. Crop disease is the primary danger to food security, which damages agricultural goods while growing. Due to the limited availability of infrastructure, it remains challenging to recognize crop-damaging diseases in various parts of the world. Deep learning methods and computer vision technologies are used in this work to help identify agricultural illnesses. The train set is used to train the model, and the data split is used for validation. The farmer can use the app (Identifying and Resolving Crop Diseases using Deep Learning) to take pictures of the crops and examine whether or not there are any infections, proving the solution's viability.

关键词： Deep learning Adaptation models Computational modeling sociology Crops Data models Convolutional neural networks

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IoT based smart Cradle for Neonatal Monitoring 6

IoT based Smart Cradle for Neonatal Monitoring

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： sonia, R. Jayadeva, sujay Mugaloremutt Ragavendiran, s.D. Prabu Revathi, N. Arumugam, Jeevanantham Kumar, s sandeep B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Tamil Nadu Vandalur600048 India Jss Academy of Higher Education and Research Department of Health System Management Studies Karnataka Mysuru570004 India Rvs Technical Campus Department of Computer Science and Engineering Tamil Nadu Coimbatore641402 India Panimalar Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai600123 India Kongu Engineering College Department of Information Technology Tamil Nadu Perundurai638060 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India

ISBN: (纸本)9781665482714

India has the highest percentage of working women and mothers. It also has a record for the highest number of babies being born every single day. These two statements highly contradict each other. When a woman giv.s.birth to a baby, she needs to spend almost a year or two taking care of the necessities of the baby. but when a need to leave the baby for a certain time arises regularly, it becomes tougher for them to do so. This study provides a solution to this problem. This research aims at the construction of a smart cradle. This cradle is des igned using various input elements, output elements, a controller module, and a mobile application. The input elements consist of a temperature sensor, a sound sensor or a microphone,a moisture sensor, and a music player. The output elements include the motor and The controller used in this study is the NodeMCU module. Cloud storage is also used and it is provided by the Arduino loT software. The sensors in the smart cradle begin recording relevant parameters such as temperature, humidity, and sound as soon as it is turned on. The information is then uploaded to the cloud The data is then processed in the cloud for analysis. If indeed the child's temperatures is over the safe range, the user receives a warning. Moisture levels above normal suggest that the infant has urinated The sound of the baby crying will be recorded in the microphone. The output elements are energized as a result of the sensor's data. If the baby has a high fever or has to go to the bathroom, the app will notify the parent. © 2022 IEEE.

关键词： Microphones

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Design and Implementation of smart Agriculture Management Intelligent Things Using Nb-IOT

Design and Implementation of Smart Agriculture Management In...

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smart Technologies in Computing, Electrical and Electronics (ICsTCEE), International Conference on

作者： s. senthil M.Rajesh Kumar R.sarath Kumar A.Kingsly Jabakumar K. Madhan Department of Mechanical Engineering Mailam Engineering College Department of Computer Science and Engineering B.S.Abdur Rahman Crescent Institute of Science and Technology Department of ECE Sri Krishna College of Engineering and Technology Coimbatore Department of ECE Christ the King Engineering College Coimbatore Department of CSE Mailam Engineering College

Agriculture is very important since our early daysand we can call India as Land of Agriculture. More than 60 to 70 % of Indian population are farmers. This field contributesover 20% of our Gross Domestic Product. so that we are making the agriculture to be smart rather than Hard using Internet of Things. The main objective of these smart devices using Nb-IOT makes this field not to be in backlogs. Hence we want to make this field to be more active by using some Internet of things and Machine Learning approaches. Hence we are designing an Intelligent Agriculture Management system using Narrow band IOT which helps farmers to improve their productivity with latest innovative reforms. Our Nb-IOT method uses multiple sensors and considers various environmental factors without wasting resources by using a innovative techniques called precision agriculture. All the data what we have gathered and theresults will be displayed as Graphs.

关键词： smart agriculture Productivity Protocols sociology Machine learning Environmental factors Internet of Things

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Exploring the security of blockchain Applications: A Review of Current solutions and Open Challenges

Exploring the Security of Blockchain Applications: A Review ...

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International Conference on I-sMAC (IoT in social, Mobile, Analytics and Cloud) (I-sMAC)

作者： Milind shah Mayur Chauhan vaishnavi solanki bhargav bharvad Priyanka Jadav Manav shah Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Gujarat Power Engineering & Research Institute(GPERI) A Constitute College of Gujarat Technological University Mehsana Gujarat India IoT Security e-Infochips Pvt Ltd Ahmedabad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

blockchain technology has gained extensive recognition across a range of industries and is currently considered as one of the most essential technologies in the computing industry. The incorporation of private computing into blockchain technology is very necessary because of the huge challenges that blockchain technology faces in terms of preserving the privacy of consumers while simultaneously ensuring the immutability and transparency of data. This technology enhances the protection of users' privacy and the safety of their data within blockchain networks, while enhancing transparency and accountability. Additionally, it has the capability of enabling blockchain applications to carry out actions that require complex cooperation and smart contracts while yet maintaining the confidentiality of user information. The review article offers an introductory overview of the security of blockchain-based applications, encompassing the fundamental principles of blockchain security, the associated challenges, and prospective solutions. The article introduces research questions related to these topics and incorporates an extensive literature analysis of more than 15 academic articles. In addition, it discusses the use of blockchain technology in secure systems, current developments in blockchain security, and presents case examples from previous research that focus on protecting blockchain applications. In conclusion, the research highlights the challenges and unexplored research domains in the field of blockchain security, concluding in a brief summary of its findings.

关键词： Industries Data privacy Reviews smart contracts blockchains security Protection Faces

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Combining the AIML Model with Acoustic signal to Evaluate the bowel sound

Combining the AIML Model with Acoustic Signal to Evaluate th...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTEs)

作者： s.Pavan Kumar Reddy N. sreevani Revathi v samuel babu Navdeep singh Ali Kareem Department of Artificial Intelligence and Data Science(AI&DS) B V Raju Institute of Technology Narsapur Campus Hyderabad Department of CSE(Cyber Security) Institute of Aeronautical Engineering Hyderabad India Department of Applied Sciences New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Automated methods for assessing bowel sounds (bs) were well developed by the early 2000s. several teams, using diverse analytical techniques, achieved high levels of accuracy. bs has shown potential in doing non-invasive research on irritable bowel syndrome, studying gastrointestinal motility, and performing surgical procedures. This article introduces hybrid convolution and recursive neural networks that are used for bs analysis. It was among the first deep-learning approaches to be extensively studied. An experimental workflow and a novel dataset from a contact microphone device were used to assess the results. Data were obtained at night, the most neuro gastroenterological intriguing time. Previous studies solely preserved daytime information, ignoring this period. The programme accurately detects bowel noises. In addition, they acquired good diagnostic specificity. Medical professionals confirmed the data, supporting the clinical diagnosis. Medical professionals may submit patient recordings and have them analyzed online using the client-server solution. Technologies are established, but bs research needs a consistent methodology, a worldwide venue for debate, & a free platform for data interchange, hence it is not widely employed. A common bs research framework may start with the server.

关键词： Wireless sensor networks Intestines Accuracy Working environment noise Noise Neural networks Motors Gastrointestinal tract Recording Medical diagnostic imaging

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brain Tumor Detection Using MRI Image Processing Techniques

Brain Tumor Detection Using MRI Image Processing Techniques

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Innovation and Novelty in engineering and Technology (INNOvA), International Conference on

作者： Ambaji. s. Jadhav Rashmi Pawar sunilkumar. M. Hattaraki shwetha bagali Rajeshwari s. Patil bhagyashree Patil Department of Elecrtical and Electronics Engineering B.L.D.E. As.V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Electronics and Communication Engineering B.L.D.E. As. V.P. Dr. P.G.H. College of Engg. and Tech. Vijayaapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image pre-processing is done using CLAHE and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MsD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection.

关键词： Image segmentation Technological innovation Accuracy Thresholding (Imaging) Magnetic resonance imaging Noise brain tumors Training data X-rays Robustness

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boosting EFL Learners' speaking skills and Motivation Through Dual Graph Neural Networks

Boosting EFL Learners' Speaking Skills and Motivation Throug...

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Distributed systems, computer Networks and Cybersecurity (ICDsCNC), International Conference on

作者： sasirekha P K. M. Devendraiah sree vidhya s C. Raghavendra Reddy Riyaz Mohammad Angela Jean Mary E Department of English Dr. M.G.R Educational and Research Institute Chennai India V. V. Sangha’s Independent P U College Ballari India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of English Mohan Babu University Tirupati India Department of English and Foreign Languages S.R.K.R. Engineering College Chinaamiram India Department of Humanities St. Claret College Autonomous Bangalore India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Effective communication and the capacity to express oneself precisely are central to English as a Foreign Language (EFL) courses. successful language acquisition is now often measured by the ability to communicate effectively orally. Previous studies have shown that insufficient teaching methods and a lack of motivation are the main causes of EFL students' low speaking proficiency. There is limited opportunity for teachers to innovate or offer fresh viewpoints to their teachings due to the course’s standardized curriculum and the traditional, inflexible, and tedious teaching methods employed in speaking classes. As a result, pupils lack interest in the classes. Included in the suggested method are the steps of preprocessing, feature extraction, and model training. The normalization of the signal should precede the addition of frames to the speech signal during preprocessing. Mel-Frequency Cepstral Coefficients (MFCCs) were employed for feature extraction. All through the course, sTDGNN was always in use. This novel approach outperforms CNN and GNN with an average accuracy of 91.63 percent.

关键词： Training Knowledge engineering Accuracy semantics Oral communication Feature extraction Graph neural networks Grammar Pupils Mel frequency cepstral coefficient

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Automatic speed Measurement and License Plate Reader for Motor vehicles using CNN

Automatic Speed Measurement and License Plate Reader for Mot...

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International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： D. Poornima N. Gopinath L Harish Manju santosh Kumar C. Tamilselvi Department of Computer and Science Engineering Sathyabama Institute of Science and Technology Tamil Nadu India Department of Computer Science Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu India Department of Information Science and Engineering CMR Institute of Technology Karnataka India Department of Electrical Engineering Government Engineering College Bihar India Department of Electrical and Electronics Engineering Gaya Colleg of Engineering Bihar India Department of Information Technology Dr. MGR Educational and Research Institute Tamil Nadu India

Assessing traffic surveillance data is a prevalent utilization of deep learning methodologies. video processing techniques were employed to develop the system aimed at determining the velocity of vehicles. The analysis of video speed is conducted without the requirement of camera calibration. Frame subtraction and masking techniques are employed to effectively segregate moving vehicles. The suggested system comprises four primary components. Object detection techniques are employed in the initial phase to identify and locate moving vehicles within a given footage. Real-time vehicle recognition leverages the utilization of R-CNN and deep learning techniques. In the subsequent section, algorithms are employed to observe the movement of objects within individual video frames. Optical flow methods are employed in the third segment to ascertain the velocities of the respective vehicles. In the preceding part, the proposed work have utilized image processing techniques in conjunction with deep learning methodologies to accurately decipher the license plates of the automobiles under our surveillance. The experimental evaluation involving the utilization of traffic film demonstrated a system performance of 97% in precisely quantifying speed while achieving a license plate identification accuracy of 95%. The incorporation of state-of-the-art traffic management systems has facilitated the implementation of real-time speed monitoring and license plate reading capabilities. This facilitates the deterrence of automobile theft and the implementation of traffic regulations. Additionally, the methodology has the capability to identify traffic patterns and pinpoint regions of congestion, thus assisting in the effective management of traffic.

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