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Secure De-Duplication Over Wireless Sensing Data Using Convergent Encryption 3

Secure De-Duplication Over Wireless Sensing Data Using Conve...

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

作者： Yuvaraj, D. Kumar, V. Pradeep Anandaram, Harishchander Samatha, Badugu Krishnamoorthy, R. Thiyagarajan, R. Cihan University Department of Computer Science Duhok Iraq B v Raju Institute of Technology Dept of Cse Narsapur India Centre for Excellence in Computational Engineering and Networking Amrita Vishwa India Vignan's Foundation for Science Technology and Research Department of Cse Guntur India Prathyusha Engineering College Department of Ece Chennai India Prathyusha Engineering College Department of It Chennai India

ISBN: (数字)9781665468558

ISBN: (纸本)9781665468534

The fast development of cloud computing technology has greatly accelerated the gathering and sharing of numerous types of data. Utilizing data deduplication technologies is crucial to decrease storage overhead and transmission costs. A significant quantity of processing and storage resources are made available for a variety of high-quality service applications using cloud computing, which is an online data center. In any case, cloud clients never again approach their information in a nearby information stockpiling foundation, making it challenging to really look at the respectability of information that has been re-appropriated. It has become crucial to assist the user in fully verifying the accuracy of the data that has been outsourced. This issue is settled utilizing safe encryption and de-duplication systems, and clients might use an outsider examiner to confirm the precision of information that has been re-appropriated and the encryption technique. Existing data deduplication systems still have efficiency and security issues. Information deduplication is achieved by the proposed procedures preceding the information being shipped off the distributed storage server, which brings down the expense of calculation and transmission. in particular, block-level deduplication with variable size. De-duplication strategies have been utilized to forestall information duplication by contrasting records in the client data set utilizing PHP;this further develops stock piling's memory proficiency and rates up handling. Clients encode information utilizing concurrent encryption techniques prior to re-appropriating it to the cloud to keep unapproved clients from getting to the information. © 2022 IEEE.

关键词： Electrocardiograms

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Analysis of Artificial Intelligence based Forecasting Techniques for Renewable Wind Power Generation 2

Analysis of Artificial Intelligence based Forecasting Techni...

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2nd International Conference on Smart Technologies, Communication and Robotics, STCR 2022

作者： Ranga, Jarabala Arun Srinivas, T. Kumar, Santosh Anandaram, Harishchander Kulkarni, Prakruti Amina Begum, M. Ramachandra College of Engineering Department of EEE AP Eluru India JP College of Engineering Department of EEE TN Thenkasi India School of Management Sciences Uttar Pradesh Varanasi India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking TN Coimbatore India Bharat Institute of Engineering and Technology Department of ECE Hyderabad India College of Engineering Thiruvannamalai Department of Oxford TN Chennai India

ISBN: (纸本)9781665460477

Wind is considered as the renewable energy resource which consists of high state of efficiency with low pollution. The accurate level of forecasting can reduce the minimal range of losses and risk in the unrealizable factor. High energy of wind powers are defined where it comprises with the challenges in the power systems and other variability in generating the power. One of the key factors of the electricity supply is wind forecasting. It implies with the several improvements where many literatures have initially developed new technologies to forecast the wind power. A different range of forecast are been developed and are been categorized according to the expected production. These are indicated using the power productivity potential over the state of time interval. In this research paper, an overview of the different technologies used in the wind power forecasting are been discussed. This paper mainly focuses upon the research work of different literature review and their principles with the practical development. Based upon the categories, the futuristic development of each wind forecasting are been directed accordingly. © 2022 IEEE.

关键词： Wind power

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Smart Online Oxygen Supply Management though Internet of Things (IoT)

Smart Online Oxygen Supply Management though Internet of Thi...

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

作者： Gehlot, Anita Rajat Mohan, Lakshmikanth Gupta, Anirudh Anandaram, Harishchander Alanya-Beltran, Joel Kalyan Chakravarthi, M. Uttaranchal University Uttaranchal Institute of Technology India Christ Deemed to Be University India Department of Civil Engineering IET Lucknow AKTU India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Universidad Tecnológica Del Perú Electronic Department Peru VIT-AP University School of Electronics Engineering Amaravathi522237 India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

We are surrounded by oxygen in the air we We cannot even exist without the ability to breathe. The need for oxygen has increased during the COVID19 pandemic, and although there is enough oxygen in our country, the main issue is getting it to hospitals or those in need on time. This is simply due to a significant communication gap between suppliers and hospitals, so we plan to implement an idea that will close this gap using real-time tracking as we can track the movement of oxygen tankers by gathering the requirements. We are using an ESP32 Wi-Fi module, a MEMS pressure sensor that enables the combination of precise sensors, potential processing, and wireless communication, such as Wi-Fi, Bluetooth, IFTTT, and MQTT protocols, to implement it successfully. The pressure sensor publishes the value of oxygen remaining from the location to the MQTT broker. © 2022 IEEE.

关键词： Wi-Fi

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Emotional Detection Based on Bio Information and Data Analytics

Emotional Detection Based on Bio Information and Data Analyt...

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

作者： Alanya-Beltran, Joel Gehlot, Anita Gurav, Uma Valderrama-Zapata, Carlos Anandaram, Harishchander Jain, Amit Universidad Tecnológica Del Perú Electronic Department Peru Uttaranchal University Uttaranchal Institute of Technology India Kolhapur Institute of Technology's College of Engineering Kolhapur India Universidad San Ignacio de Loyola Peru Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Sir PadampatSinghania University Department of Computer Science and Engineering Udaipur India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The ability to recognise emotions is crucial in the healthcare industry. Effective sentiment analysis analysis aids in analysing a variety of factors, including psychosocial health, illness progress, and also more. Healthcare practitioners must also be able to recognise emotions. Emotion recognition monitoring often uses a variety of technologies, including artificial intelligence (AI), multimedia systems, pattern classification, digital logic, audiovisual evaluation, etc. The most sequences for inner detection is examined in the present research. In this research, 53 individuals from various UK health services participated in a poll data methods. According to the statistics, the best technology for automatically detecting emotions are ECG and electroencephalogram (EEG)-based integrative systems and pattern matching. The investigation demonstrates that affective recognition is crucial for healthcare practitioners, and that by examining clients' cognitive processes, it may increase the likelihood that they will recuperate.[1]. © 2022 IEEE.

关键词： Electrocardiograms

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Indoor Real-Time Location System for Efficient Location Tracking Using IoT

Indoor Real-Time Location System for Efficient Location Trac...

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

作者： Verma, Sunny Raj, Tej Joshi, Kapil Raturi, Preeti Anandaram, Harishchander Gupta, Ashulekha Dev Bhoomi Uttarakhand University Dept. of Electronics & Communication Engineering Dehradun India Uttaranchal University Dept. of Computer Science & Engineering Dehradun India Tula's Institute of Technology Dept. of Computer Science & Engineering 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

Researchers have broadened the focus of RFID technology development because to the growing need for low-cost edge devices to bridge the physical-digital gap. In addition to item identification, researchers have explored the use of RFID tags for low-power wireless sensing, localization, and activity inference. A security system utilized today is not powerful enough to provide a real-time alert after identifying a problem. Movement detection is a technology for detecting a change in the environment relative to an object. Sensor-based applications can be used to watch activity and receive alerts when movement is detected, which solves the issue and saves time and money. RFID technology is used to track the location of people or things in an interior setting in real time. A reader and several tags, each of which can house a number of sensors, make up an RFID system. In this research, we present the iLocate framework for the IoT, a recently developed real-time locating framework using dynamic RFID for resource the board in indoor settings. iLocate used ubidots to assist with a broad range RFID organization. Our exploratory findings and the actual project have both shown the prevalence. Location tracking indoors will be more effective using this iLocate framework. © 2022 IEEE.

关键词： Internet of things

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Mosquito Type Identification using Convolution Neural Network 3

Mosquito Type Identification using Convolution Neural Networ...

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

作者： Kumar, V Senthil Prasad, Veduri Veera Kunaisa, Khallik Swain, Mahendra Pratap Anandaram, Harishchander Kumar, Ashok Malla Reddy Institute of Technology and Science Department of Computer Science and Engineering Telangana Secunderabad500100 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem India Hkbk College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Department of Pharmaceutical Sciences and Technology Jharkhand Mesra Ranchi835215 India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore641112 India Banasthali Vidyapith Department of Computer Science Rajasthan Banasthali304022 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Mosquitoes are insects which are most commonly found in places with high water content. There are various types of mosquitos and each type has its own destructive characteristics. In order to completely eradicate mosquitoes from a particular place, people must know what type of mosquito it is. But this is not a task that can be done with naked eyes. To help this situation, this study develops a Deep Learning (DL) model which can predict the type of mosquito with great accuracy. For this purpose, a dataset of images of the Aedes and culex species of mosquito is collected from Kaggle. Following that, this dataset is split into three parts: training, validation, and testing. Then, pixel and image scaling is used to preprocess this dataset. A DL model is produced using the Convolutional Neural Network (CNN) technique. The model is then trained using the preprocessed dataset. The results of the training are also calculated and documented. The results are then put into a graph for easier examination. The highest accuracy of the model is produced during training and it is 99.2%. As for the loss values, the lowest loss value is 0.8% which is so small for a classification algorithm. Next, tests are conducted on the trained and validated models. A confusion matrix is then used to examine the model performance. The model is then assessed using the metrics such as accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), precision, and F1 score. These parameters' final values were also determined to be satisfactory. © 2022 IEEE.

关键词： Convolutional neural networks

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Modelling of Smart Sensing Device for Sensitive Nanocluster

Modelling of Smart Sensing Device for Sensitive Nanocluster

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

作者： Fakruddin Babavali, S.K. Gupta, Anirudh Bera, Prabin Kumar Abdullah Hamad, Abdulsattar Anandaram, Harishchander Vaseem Akram, Shaik V.R. Siddhartha Engineering College Department of Physics Andhra Pradesh Vijayawada07 India AKTU IET Lucknow Department of Civil Engineering India N. I. T. Durgapur Department of ECE West Bengal India Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10021 Iraq Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore India Uttaranchal University UIT Uttarakhand Dehradun India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

The report claims that nanocomposites have lately been used to create several smart gadgets with innovative characteristics. Because of their distinctive features, composites oxides have now been studied for many years as air gas sensors. Air testing relies heavily on biosensor. They meet the demand for portable analytical equipment with qualities like sensitivity and robustness, amongst many others, and provide considerable benefits over conventional approaches. The physical features of the capable of detecting materials employed in the manufacturing of nano sensing are essential for determining the effectiveness of the finished product. Trustable nano sensor nodes with important realistic necessities and practicability in the purview of increasing environmental detection and monitoring are expected to be developed. Current micro sensing have become more sophisticated and sensitive as a consequence of recent developments in the area of materials research. Often, they outperform conventional methods in terms of effectiveness. © 2022 IEEE.

关键词： Nanoclusters

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Deep Learning-based Automatic 3D Printer Anomaly Detection during the Printing Process 3

Deep Learning-based Automatic 3D Printer Anomaly Detection d...

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

作者： Lalitha, T. Anushkannan, N.K Shreepad, Sarange Sasireka, S. Anandaram, Harishchander Razia, Shaik Vellore Institute of Technology Department of Computer Science and Engineering Tamil Nadu Vellore632014 India Kathir College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641062 India Ajeenkya Dy Patil School of Engineering Department of Mechanical Maharashtra Pune412105 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Amrita VishwaVidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore641112 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

3D printing is a technology which is expected to be one of the most used technologies in the upcoming time. This technology allows to print out products that are designed using 3D modeling software. Though this is an effective technology, it also has its disadvantages. The disadvantages include anomalies. Anomaly is a defect that is often found when the printer finishes the printing process. Thus, it cannot be rectified when found during the process. To resolve this issue, this study aims in developing a deep learning model using the UNet algorithm. A dataset of pictures of various possible anomalies is gathered from Kaggle. The Kaggle data is then preprocessed using three different methods. The images are initially applied using the target format. The images are then multiplied and shrunk to keep the balance. The UNet method is employed to create a deep learning model. The preprocessed dataset is then used to train the model. To guarantee improved performance, the trained model is subsequently put to the test to assess the model's final accuracy and loss. In all three instances, the model's output is determined to be satisfactory. The model produced an accuracy of 98% during the testing and produced a loss value of 0.54%. This loss value is so small that it can be neglected. The model developed is found to be one of the best algorithms that can be used in anomaly detection. © 2022 IEEE.

关键词： Anomaly detection

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IoT Wearable Breast Temperature Assessment System

IoT Wearable Breast Temperature Assessment System

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International Conference on Computing Methodologies and Communication (ICCMC)

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

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.

关键词： Temperature sensors Performance evaluation Temperature measurement Temperature distribution Wearable computers Microprocessors Computer architecture

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Expression of Concern for: Prediction of Coronary Artery Disease by Adapting Hybrid Approach of Machine Learning Methods

Expression of Concern for: Prediction of Coronary Artery Dis...

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Smart Electronics and Communication (ICOSEC), International Conference on

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

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497657

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.

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