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Big Data & machine Learning (ICBDmL), IEEE International Conference on

作者： Gayathri S A. Jaya mabel Rani Anushalin P. S C.N. Ravi m. Jeyalaxmi V. Samuthira Pandi Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Biomedical Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai Department of Computer Science and Engineering CMR Engineering College Hyderabad Medchal Department of Physics Prince Shri Venkateshwara Padmavathy Engineering College Chennai Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350374100

ISBN: (纸本)9798350374117

medical records from a variety of sources must be integrated into healthcare data repositories for knowledge discovery to be possible. An essential area of study is how to keep records linked when merging medical data. Researchers have proposed many solutions to this issue, all of which are relevant to industrialized nations that use patient identification numbers such as social security numbers, health insurance numbers, universal patient identifiers (UPIs), etc., to keep track of electronic health records. Because of issues with missing data, unclear patient identification, and excessive noise in patient information, these systems are not suitable for record linking of health data in underdeveloped nations. There is a serious concern that patients' personally identifiable information stored in electronic health records poses a threat to their privacy and leaves health information systems open to intrusion. We have examined the real-world challenges of healthcare data collection and integration for a national health data warehouse in this article. To accommodate the limited health data of poor nations like Bangladesh, we have developed privacy-preserving secured record linkage architecture we call Secured Health Data Handler (SHDH). For the purpose of facilitating the knowledge discovery process, our approach may anonymize identifying private data of patients while preserving record linkage in integrated health repositories. Even without a standard ID, our suggested strategy was able to connect records with sufficient accuracy for noisy data, according to the experimental results.

关键词： Couplings Data privacy Sociology Data warehouses Knowledge discovery Security Electronic medical records

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Enhancing Text Input for motor Disabilities through IoT and machine Learning: A Focus on the Swipe-to-Type Algorithm

Enhancing Text Input for Motor Disabilities through IoT and ...

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

作者： m. Ahsan Shariff S P Vimal B. Gopi A. Anbarasi R Tharun C. Srinivasan M.Tech Computer Science and Engineering Sri Sai Ram Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Department of Electronics and Communication Engineering Muthayammal Engineering College (Autonomous) Rasipuram Tamil Nadu India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Civil Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

To improve text input for motor-disabled people, this research uses the Internet of Things (IoT) and machine learning. Swipe-to-Type, a popular touch-based input technique, is the study’s focus. User swipe motions and contextual data are collected in real-time using IoT devices. A machine learning framework trains the algorithm to adapt to disabled users’ motor skills and preferences. Addressing motor impairment problems, the suggested method improves text input efficiency and personalization. The Swipe-to-Type algorithm is constantly adjusted based on learned patterns to maximize text input speed and accuracy. Integration of IoT devices allows continuous monitoring and adaption, guaranteeing a responsive and user-centric solution. The study technique includes motor disability data gathering, machine learning model creation, and algorithm refining. Preliminary text input performance enhancements may improve communication and accessibility for motor-disabled people. By using IoT and machine learning to empower motor-disabled persons, this research advances assistive technology. The results show that adjusting input techniques to varied user demands is feasible and successful, encouraging digital inclusion.

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Digital twin and IOT technology for secure manufacturing systems

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measurement: Sensors 2023年 25卷

作者： Gopal, Lisa Singh, Harbaksh mounica, Panguluri mohankumar, N. Challa, Nagendra Panini Jayaraman, P. Computer Science and Engineering Graphic Era Hill University Dehradun M.Tech Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India BITS Pilani India Department of CSE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India Department of Mechanical Engineering Prathyusha Engineering College Aranvoyalkuppam Tiruvallur Tamil Nadu Chennai602025 India

The Digital Twin (DT) method offers new concepts for choosing the context of information technologies and smart production systems. This research focuses on low-price, extremely effective defect diagnosis methods, low-efficiency, high-cost devices to obtain timely feedback and accurate fault detection results, and secure manufacturing systems. The data structure, control plane, and output units are the three components of the manufacturing system that creates a data link between the virtual model using micro-Electro-mechanical (mEm) devices and the Zigbee wireless transmission system in the database layer. This study acquired DT information from the control plane using the Internet of Things (IoT) through sensors for secure manufacturing information. It separates and calls the pertinent data by the attribute processor and transfers it to the outcome units. To produce the classification and outcomes of work build features information, the evaluation method analysis the output nodes that split the test set and learning group using a dynamic database. The hybrid IoT with DT technology to examine the consequences of defects detected efficiently predicted and secures the manufacturing system. © 2023 The Authors

关键词： Internet of things

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Automated Email Notifications and Security monitoring for Cloud Storage Access using Cloud Function

Automated Email Notifications and Security Monitoring for Cl...

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International Conference on Innovative mechanisms for Industry Applications (ICImIA)

作者： N Renuka m Panneer Selvam P Ponmurugan S Kiruthika N Selvam N Kannan Department of Information Technology KSR Institute for Engineering and Technology Tiruchengode India Department of Electrical and Electronics Engineering Sona College of Technology Salem India Dean - IPR Meenakshi Academy of Higher Education & Research Deemed to be Univeristy Chennai India Department of M.Tech - Computer Science and Engineering Erode Sengunthar Engineering College Thudupathi Erode India Department of Electrical and Electronics Engineering M.Kumarasamy College of Engineering Karur India

To monitor a specified Google Cloud Storage bucket for any changes or accesses made to its contents. When an access or change is detected, the cloud function will automatically send an email notification to a specified email address with information about the access or change, including the time of access, the user who accessed the data, and the specific data that was accessed. The paper represents the Google Cloud Platform services, which includes Cloud Storage and Cloud Functions. The Cloud Function will be triggered by a Cloud Storage whenever an object is accessed. The Cloud Function will then parse the notification message and extract the relevant information, such as the object name and access time, and send an email notification. The automated email notification system will help cloud storage administrators monitor access to their storage buckets, identify potential security breaches or unauthorized access, and take appropriate action in a timely manner. It also provides a record of all access activity, which can be useful for audit purposes and compliance requirements.

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Enhancement of Channel Capacity in 5G Ultra Dense Network-UDN

Enhancement of Channel Capacity in 5G Ultra Dense Network-UD...

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International Conference on Edge Computing and Applications (ICECAA)

作者： Ritesh Rastogi T. Shanthi Pramod Kumar Naik m. Sahaya Sheela A.K mishra Nandhini T J Department of IT and CS M.Tech(Int) Noida Institute of Engineering and Technology Delhi India Department of ECE Paavai Engineering College Chennai India Departmeny of Computer Science Engineering Dayananda Sagar University Bangalore India Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Math Unit Foundation Program Dhofar University Salalah Oman Saveetha School of Engineering Saveetha Institute of Medical and Technical Science - (SIMATS) Chennai India

A 5G ultra dense network architecture makes use of a high density of micro cell base stations to provide increased coverage, capacity, and performance for 5G communication systems. This is accomplished by the utilization of a very densely packed network. A 5G ultradense network is another name for this particular category of network. Ultradense networks are built to overcome the limitations of ordinary cellular networks. This is accomplished by increasing the number of base stations that are located inside a given area. This is performed by reducing the coverage area provided by each base station, which ultimately leads to an increase in the capacity of the entire network. The maximum data rate or capacity that may be obtained through a certain wireless channel is referred to as the “channel capacity” in a 5G ultradense network, when the phrase “channel capacity” is used. One possibility is that this represents the maximum quantity of data that can be transmitted across the channel. These criteria, which represent the capabilities of the channel, can be used to evaluate the performance of channels and equipment on 5G networks. This article presents for further research a model for improving the channel capacity of the 5G ultradense network. The model can be found at the end of the article. This change will result in an increase in both the usefulness of the channel and the amount of bandwidth that it consumes; both of these aspects will improve. The fact that it uses a very small quantity of energy and power in compared to other methods is the major trait that sets it apart from other methods. This technology is superior to others in terms of both the amount of data it can communicate and the amount of electricity it consumes in the process.

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Cloud-based fault prediction using IoT in office automation for improvisation of the health of employees

Cloud-based fault prediction using IoT in office automation ...

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Advance Computing and Innovative technologies in engineering (ICACITE), International Conference on

作者： Nakul Shahdadpuri Somanchi Hari Krishna SathiJyothirmaye Reddy C. Ganesh Kirti Agarwal Sourabh Bhattacharya IPG Programme {(B.Tech + M.Tech (It)} IIITM Gwalior Department of Business Management Vignana Bharathi Institute of Technology Malkajigiri Telangana Department of Management Marwadi University Rajkot Gujarat Computer and Communication Engineering Sri Eshwar College of Engineering Anna University Chennai ITERC College Delhi (NCR) India Department of Management and Social Science Haldia Institute of Technology West Bengal

modern technology is the best thing to be used in every commercial sector. The devices are well equipped to understand the problems of the users. The offices are now been maintained by technology the devices for daily monitoring even the electrical appliances are been monitored and maintained by technology. In previous times the best method of this work is to make it via cloud computing, but now as technology has evolved and evolved new adding devices are making system malfunction. The fault of cloud computing needs to be identified, So the best method is to automate the office with the introduction of IoT. it is a platform equipped with all the facilities for monitoring and maintaining the devices inside the office, even the working capacity of employees is also measured via this method. The study is showing the effectiveness of the IoT to identify the faults of cloud computing related to maintaining the working method of the office. The data collected for this study is secondary data like articles, books, journals and many more. Then gathered data is analyzed by qualitative data analysis. The results are quite positive about the use of it to automate the office and its working procedure.

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Current COVID-19 Analysis and Future Pandemics Prediction using Internet of Everything (IoE)

Current COVID-19 Analysis and Future Pandemics Prediction us...

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Advances in Computing and Future Communication technologies (ICACFCT), International Conference on

作者： Shivangi Sharma Ajay Kumar Singh M.Tech (CSE) Computer Science and Engineering Department Meerut Institute of Engineering and Technology Meerut India 250005 Dept. Of CSE Computer Science and Engineering Department Meerut Institute of Engineering and Technology Meerut India 250005

ISBN: (数字)9781665420761

ISBN: (纸本)9781665420778

Internet of the Everything (IoE) along with different technologies like Cloud and Arduino UNO is of great use throughout the disaster. We have used the mLX90614 Sensor to send the temperature of the person on the cloud with the help of Arduino UNO. This sensor can assist in discovering all of the humans who have come into touch with the infected people. This IoE generation is also useful in tracking sufferers. For those who are having a high probability of getting infected, their information can be supplied to the healthcare workforce to take further action. The COVID-19 epidemic can be controlled by IoE-stimulated frameworks. IoE answers at the side of far-flung health monitoring. If the disease is found in a suspected person then this information is passed through IoE to the health care team. IoE technology which is used here can be a fantastic way to control epidemics. Here we have maintained the privacy and protection issues in our project. IoE is very well analyzed with growing requirements and for multi-tasking practices.

关键词： COVID-19 Temperature sensors Privacy Cloud computing Pandemics medical services multitasking

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Retraction Notice: A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

Retraction Notice: A Machine Learning Method for Detecting S...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： V. Preetha P. Sathishkumar m. Janani B. manohari S. Jhansi Ida S. Suganyadevi Department of CSE Sri Ramakrishna Institute of Technology Coimbatore India Department of CSE Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Department of CSE KPR Institute of Engineering and Technology Coimbatore India Department of M.Tech Computer Science and Engineering Sri Krishna College of Engineering and Technology Coimbatore India Department of Computer Science and Business Systems R.M.K Engineering Colleg Kavaraipettai India Department of ECE KPR Institute of Engineering and Technology Coimbatore India

ISBN: (数字)9781665453424

ISBN: (纸本)9781665453431

The use of sarcasm as an expression is a common literary device that involves someone purposefully expressing the opposite of what is being meant. The accurate identification of sarcasm in a text may boost other natural language processing activities, particularly ones involving sentiment analysis, and make it easier to comprehend the genuine intents of the speakers in the text. It is difficult to determine if someone is being sarcastic only based on their written words since sarcasm is a kind of implicitly expressing one's feelings and sarcastic speakers purposefully confound their audiences. Existing methods based on machine learning and deep learning obtained an insufficient level of performance when processing sarcastic material that included complicated expressions or required a certain set of prior knowledge to comprehend. The purpose of this research is to examine how sarcasm may be detected in tweets that have a negative mood and include hyperboles. For the purpose of sarcasm identification, the hashtags #Chinesevirus, #Kungflu, #COVID19, #Hantavirus, and #Coronavirus were included in the collection of 6,600 pre-processed negative sentiment tweets. The three well-known machine learning algorithms known as Support Vector machine, Random Forest, and Random Forest with Bagging were utilised in order to conduct an analysis of the five hyperbole characteristics known as interjection, intensifier, capital letter, punctuation mark, and elongated word. The suggested model with the extended term attained an accuracy of 78.74% and an F-score of 71%, respectively, despite the inclusion of hyperbolic words in the tweets included in an unbiased dataset. It was discovered that intensifier was the exaggeration with the highest level of significance (p. 0001). The results of the experiments and analyses c

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A machine Learning method for Detecting Sarcasm in Negative Sentiment Tweets using Hyperboles

A Machine Learning Method for Detecting Sarcasm in Negative ...

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

关键词： Support vector machines Deep learning Sentiment analysis Analytical models machine learning algorithms mood Oral communication

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning

Intelligent Deep Learning Framework for Breast Cancer Predic...

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Global Conference for Advancement in technology (GCAT)

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

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.

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