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Modeling of Cascade Regulation in Psoriasis: A Comprehensive Approach 1

Modeling of Cascade Regulation in Psoriasis: A Comprehensive...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Harishchander, A. Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India

ISBN: (纸本)9781607685395

The implication of a successful mathematical model in applied genomics requires mathematical modeling of regulatory cascades in a gene-regulated network in autoimmune disorders (AIDs) such as Psoriasis is a difficult job to complete in the current age of applied genomics. There were four computational approaches used to address the Challenge: (a) Gene Assisted Cascade Regulation, (b) Differential Gene Expression Cascade, (c) Gene Variant Cascade, and (d) Biologic Cascade to identify a potential biomarker for treating AIDS in the future, and finally, the reconstructed network concerning activation and deactivation loops that were subjected to mathematical modeling to understand and identify the mechanism of regulatory cascade in pathogenesis. © The Electrochemical Society

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Real-Time Human Action Recognition with Multimodal Dataset: A Study Review 5th

Real-Time Human Action Recognition with Multimodal Dataset: ...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Joshi, Kapil Rastogi, Ritesh Joshi, Pooja Anandaram, Harishchander Gupta, Ashulekha Mohialden, Yasmin Makki Department of CSE Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of IT Noida Institute of Engineering and Technology Greater Noida India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Dehradun India Computer Science Department Mustansiriyah University Baghdad Iraq

ISBN: (纸本)9789819906000

Due to difficulties including a cluttered background, partial occlusion, and variations on dimensions, angle, illumination, or look, identifying human endeavors using video clips or still images is a challenging process. A numerous mechanism for recognizing activity is necessary for numerous applications, such as robotics, human–computer interaction, and video surveillance for characterizing human behavior. We outline a classification of human endeavor approaches and go through their benefits and drawbacks. In specifically, we classify categorization of human activity approaches into the two broad categories based on whether or not they make use of information from several modalities. This study covered a depth motion map-based approach to human recognizing an action. A motion map in depth created by adding up to the fullest differences with respect to the two following projections maps for each projection view over the course of the entire depth video series. The suggested approach is demonstrated to be computationally effective, enabling real-time operation. Results of the recognition using the dataset for Microsoft Research Action3D show that our method outperforms other methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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A Study of Feature-Based and Pixel-Level Image Fusion Techniques 1

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3rd International Conference on Information Technology, InCITe 2023

作者： Kumar, Vivek Khanduja, Manisha Anandaram, Harishchander Joshi, Kapil Gupta, Ashulekha Diwakar, Manoj Department of Computer Science Uttaranchal Institute of Technology Uttaranchal University Dehradun India Uttaranchal School of Computing Sciences Uttaranchal University Dehradun India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India Department of Management Studies Uttarakhand Dehradun India Uttarakhand Dehradun India

ISBN: (数字)9789819959976

ISBN: (纸本)9789819959969

In order to offer a single, more precise representation of the display than any one of the original origin photographs, image fusion attempts to combine the original images of the same scenario. Symbol, feature, pixel and signal levels are only a few of the levels at which image fusion can be applied. The starting point for more image fusion approaches to the range of IF is pixel-level and feature based, and multi-scale decomposition multi-resolution image fusion is a significant area of image processing. In the research, we investigate different image fusion methods for the standard enhancement and performance evaluation of the merge image and get some relevant results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Discrete wavelet transforms

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Extraction of Fetal ECG Using ANFIS and the Undecimated-Wavelet Transform 3

Extraction of Fetal ECG Using ANFIS and the Undecimated-Wave...

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3rd IEEE Global Conference for Advancement in Technology, GCAT 2022

作者： Sharma, Avinash Dankan Gowda, V. Deenadhayalan, R. Madasamy Raja, G. Anandaram, Harishchander Department of Computer Science Engineering Mullana Haryan Ambala India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Paavai Engineering College Department of It Tamilnadu Namakkal India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

A trans-thoracic interpretation of the electrical activity of the heart throughout time collected and externally recorded by skin electrodes is electrocardiograph. An electrocardiographic device creates this recording, and it does not involve any invasive procedures. This research focuses on the extraction of fetal ECG from the maternal ECG using adaptive neuro fuzzy inference system (ANFIS) and undecimated wavelet transform (UWT), which combines the desirable qualities of ANFIS and UWT. The objective of this paper is to improve the accuracy of fetal ECG analysis. In a lot of applications, the network needs to imitate non-linear and time-varying functions, which means that the functions could change based on the incoming signal and other parameter alterations. This is necessary since non-linear functions are more complex than linear functions. In situations like these, a greater number of signals may be reproduced at random, and the approaches that are used were evaluated for their ability to accurately determine the fetal ECG as well as for their potential use in other diagnostic applications. This hybrid network was able to quickly tackle the difficult problem, making it superior to conventional adaptive approaches. © 2022 IEEE.

关键词： Electrocardiograms

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IoT Wearable Breast Temperature Assessment System 7

IoT Wearable Breast Temperature Assessment System

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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Ashreetha, B. Dankan Gowda, V. Anandaram, Harishchander Nithya, B.A. Gupta, Neeraj Verma, Basant Kumar Department of Electronics & Communication Engineering College Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bangalore India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamilnadu Coimbatore India Department of Computer Science School of Applied Sciences Reva University Karnataka Bengaluru India Department of Computer Science and Engineering SGT University Haryana Gurugram India

ISBN: (纸本)9781665464086

Cancer is an undesirable cell with odd characteristic varies from normal cell of the breast tissue. This will develop swiftly and infiltrate surrounding tissue and forms as tumor which happens in both men and women. After lung cancer, breast cancer has become the largest cause of malignancies in women which raise the mortality rate. In this article, Sensor enabled wearable device framework to detect breast temperature abnormalities is suggested. Early work on wearable sensors supported to monitor breast temperature is presented. The concept of a wearable garment with sensor-enabled patches is also considered. Detailed architecture of the proposed framework is explained, and implementation specifics are examined. Later the statistical highlights help to process the sensor-generated breast temperature data are explained. The performance of the proposed framework is examined by taking abnormal and normal patient temperature dataset and the findings are described using attractive figure representations. © 2023 IEEE.

关键词： Internet of things

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Plants Diseases Prediction Framework: A Image-Based System Using Deep Learning

Plants Diseases Prediction Framework: A Image-Based System U...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Kirola, Madhu Joshi, Kapil Chaudhary, Sumit Singh, Neha Anandaram, Harishchander Gupta, Ashulekha Uttaranchal University Dept. of Computer Engineering Dehradun India Indrashil University Dept. of Computer Engineering Kadi Rajpur India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

Plant diseases mostly harm the leaves, resulting in a loss in agricultural output's quality and quantity. Plant disease is the most common cause of large-scale crop mortality. India is a country where people's livelihoods are heavily reliant on agriculture. The disease has caused chaos in the agricultural industry. The human eye's perception is not quite as sharp as it needs to be to notice minute variations in the sick leaf region. It needs a complex process that requires both plant expertise and a large amount of processing time. As a result, plant diseases can be detected using machine learning. The disease detection method includes image acquisition, image pre-processing, image segmentation, feature extraction, and classification. To prevent crops at the initial stage from diseases, it is essential to develop an automatic system to diagnose plant diseases and identify its category. The goal of the proposed research is to examine several machine algorithms for plant disease prediction. The paper proposed a framework for disease and healthiness detection in plants and the classification of diseases based on symptoms appearing on a leaf. The diseases are grouped into three categories in the paper: bacterial, viral, and fungal. To conclude, the research paper investigates all of these factors and uses several machine learning(DL) techniques and deep learning(DL) techniques. The machine learning(ML) techniques used in the research work are SVM, KNN, RF(Random Forest), LR (Logistic Regression), and the deep learning(DL) technique used is-Convolutional Neural Network(CNN) for disease prediction in the plants. Following that, a comparison of machine learning and deep learning methodologies was conducted. The RF(Random forest) has the highest accuracy of 97.12 % among machine learning classifiers, however, in comparison to the deep learning model mentioned in the study, the CNN classifier has the highest accuracy of 98.43 % © 2022 IEEE.

关键词： Image segmentation

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IoT and WSN integration for Data Acquisition and Supervisory Control

IoT and WSN integration for Data Acquisition and Supervisory...

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2022 IEEE World Conference on Applied Intelligence and Computing, AIC 2022

作者： Shah, Sanjeev Kumar Joshi, Kapil Khantwal, Sandeep Bisht, Yashwant Singh Chander, Harish Gupta, Ashulekha Uttaranchal University Uttaranchal Institute of Technology Dehradun India Tula's Institute Dehradun India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Graphic Era University Dept. of Management Studies Dehradun India

ISBN: (数字)9781665479882

ISBN: (纸本)9781509050017

There is an increased use of WSN (Wireless Sensor Networks) in our daily lives with WSN finding application in different areas like maintaining health, better quality of life scenarios, production monitoring in industries, traffic control and various other fields. WSNs have a scope for being incorporated in IoT (Internet of Things). IoT is beneficial foe Web based applications having specific requirements of storage and computation. This paper gives a flexible and extensible architecture to integrate WSN and IoT. REST based internet services as used as a layer interoperating as an application layer which has a possibility of being integrated directly into the other domains of application to remotely monitor smart homes, VAN (Vehicular area networks) or healthcare services. © 2022 IEEE.

关键词： Data acquisition

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Prediction of Coronary Artery Disease by Adapting Hybrid Approach of Machine Learning Methods 3

Prediction of Coronary Artery Disease by Adapting Hybrid App...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Parthasarthy, Vijay Pallavi, L. Anandaram, Harishchander Praveen, M. Arun, S. Krishnamoorthy, R. University of Petroleum and Energy Studies Chemical Engineering - Energy Cluster School of Engineering Dehradun India B v Raju Institute of Technology Department of Cse India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking India Saveetha Engineering College Department of Ece Chennai India Jerusalem College of Engineering Department of Ece Chennai India Chennai Institute of Technology Centre for Nonlinear Systems Chennai India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Recently, there have been many implementations of AI and its application tactics in the field of medicine, and it is commonly mentioned as a substantial rich data. One of the major causes of death from one end of the world to the other is coronary artery disease, which can be prevented with early diagnosis. The goal of this work is to use reliable clinical data to predict coronary course infection. Expecting Coronary Artery Disease (CAD) is a very challenging and challenging undertaking in the clinical profession. One of the virtuosi in the clinical field is the early forecast, especially in the cardiovascular region.. The earlier studies on the creation of the early forecast model encouraged an understanding of the new approaches to find the variation in clinical imaging. An eating plan graph prepared by the concerned doctor following early anticipation might satisfy the cardiovascular counteraction. Our exam paper includes a forecast based on a suggested computation created using a pooling region bend AI technology. This data-based ID is a crucial element for accurate expectation. Despite the weak pixels around it, this extensive methodology has a respectable impact on deciding variety in clinical images. With the help of vein halting and vein plaque, this pooling region development in our AI calculation is storing contracting veins and tissues. The new flexible picture-based grouping strategies are presented in this investigation piece, which also contrasts the current characterization techniques with anticipated CAD previous for a higher exact worth. This suggested method uses any prior cardiac ailment as evidence to draw a conclusion. In our suggested calculation, the decision-production of grouped yield yields more precise results. © 2022 IEEE.

关键词： Electrocardiograms

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Automatic Hatching System by designing IoT-based Egg Incubator 3

Automatic Hatching System by designing IoT-based Egg Incubat...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Jaichandran, R. Shobana, R. Mohamed Tharick, K. Raja, L. Anandaram, Harishchander Vijaipriya, K. AarupadaiVeedu Institute of Technology Department of Computer Science and Engineering Tamil Nadu Paiyanoor603104 India Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India Centre for Excellence in Computational Engineering and Networking Amrita VishwaVidyapeetham Tamil Nadu Coimbatore641112 India Sri SaiRanganathan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Thondamuthur641109 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Poultry is the kind of bird farming in which birds like geese, ducks, chickens, and turkeys are domesticated to produce eggs and meat for the food. Of these birds, chickens are the most consumed bird by the people. The chickens which are grown up for eggs are called layers and chickens for meat are known as broilers. Hatching eggs naturally by the hens is considered a big problem for poultry farmers. Hatching the eggs fast by using the technology can produce more chicks. To hatch the eggs by technology, a hatching incubator is implemented by using the Internet of Things (IoT), temperature sensor, humidity sensor, NodeMCU, servo motor, and Blynk cloud. Egg hatching manually by hens takes approximately 20 to 21 days. An automatic hatching system will reduce the number of days required for hatching. The cloud displays the data of the incubator like temperature, degree of rotation of egg tray, humidity, and the ON or OFF conditions of the light, fan, and humidifier. © 2022 IEEE.

关键词： Birds

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Application of Neural Network in the Prediction Models of Machine Learning Based Design

Application of Neural Network in the Prediction Models of Ma...

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Gehlot, Anita Ansari, Bushra Khatoon Arora, Deepika Anandaram, Harishchander Singh, Bhagwant Arias-Gonzáles, José Luis Uttaranchal University Uttaranchal Institute of Technology India Institute of Engineering and Technology AKTU Department of Civil Engineering Lucknow India Lloyd Institute of Engineering & Technology Computer Science & Engineering Centre for Excellence in Computational Engineering and Networking Amrita Vishwa Vidyapeetham Tamil Nadu Coimbatore India SS Informatics Cluster School of Computer Science Uttarakhand Dehradun248007 India Pontificia Universidad Católica Del Peru Peru

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

This research examines way to pass, among the most dynamic and quickly-evolving technology fields in the telecommunication sector. Data may be sent between two locations wirelessly without the usage of digital downloads like wires and cables. Business telecom companies have advanced as a consequence and joined the big data age. Among the most potential machine learning methods for interpreting this deluge of information is computer science, which is employed in a range of business and scholarly research situations. This paper gives a broad overview of technology advancements and big data analytics, as well as possible uses for them in succeeding cellular networks. Apply sophisticated analytics to determine what mobile users' demands are, and then utilise that information to raise the effectiveness of "social wireless channels of communication."The huge, integrated, information machine educational approach it offers, which consists of extracting the features, simulation models, and forecasting enhancements, is particularly noteworthy. The key benefit of the proposed methodology is the logic of robust mathematical methods, issue conceptualization within the context of cellular networks, and reliance on a vast amount of data that represents both the bandwidth as well as other stringent user needs. and the ability to create a procedure. In a communication network, data is normally sent from the transmitter to the receiver across a distance. Using communication systems, the transmitter may be positioned somewhere within a few metres. We will examine existing strategies, their advantages and disadvantages, and potential developments in underwater sensors right now. We had discovered a fresh method of investigation. © 2022 IEEE.

关键词： Information management

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