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Email notifier using named entity extraction

AIP Conference Proceedings 2023年第1期2523卷

作者： K. Anand M. Vijay Anand R. Saravanan 1Department Information Technology Center for Artificial Intelligence and Research Chennai Institute of Technology Chennai Tamil Nadu India 2Department of Computer Science and Engineering Saveetha Engineering College (Autonomous) Chennai Tamil Nadu India 3Department Information Technology Center for Artificial Intelligence and Research Chennai Institute of Technology Chennai Tamil Nadu India

Email plays an essential role in communication and its flexibility, simplicity, diversified types, and low cost of information in the modern world. Therefore handling out a large volume of emails takes are mark able amount of human power and time. To quickly extract the named entities from an email requires an automated solution. So we proposed a tool to extract named entities from emails. This solution incorporates the latest technologies like NLP (Natural Language Processing) and IR (Information Retrieval). This would perform named entities extraction of email automatically organizes and notifies the user. The proposed mechanism can be used in many organizations. Named Entity Recognition (NER) will be a vital language processing tool for information retrieval from texts like newspapers, blogs, and emails. NER performs the classification of words and sensing the expression of text from unstructured data. Spa Cy may be a free, open-source python library for advanced language Processing. It is well known for production use and helps build the system that performs understanding of a text. It helps in information extraction, understanding the plans, and preprocesses the text for deep learning.

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Application of machine learning techniques for an evaluation of hazardous chemical assessment in firework industry

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AIP Conference Proceedings 2022年第1期2520卷

作者： N. Indumathi R. Ramalakshmi N. Selvapalam 1Department of Computer Applications Kalasalingam Academy of Research and Education Tamil Nadu India 2Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu India 3Department of Chemistry Kalasalingam Academy of Research and Education Tamil Nadu India

Occupational Health Hazard is prevalently on the rise as industrialization increases in the global world. In the fireworks industry, manufacturing procedures for fireworks utilize hazardous chemicals for producing light, color, and sound. Fireworks manufacturing often necessitates human handling of dangerous chemicals, for all manufacturing processes where employees come into close contact with dangerous substances that cause illness to heath. It produces health implications that could result in diseases and sicknesses. Machine learning supervised algorithms were a dominating approach in the field of data mining. Recently, predictions on the health effects associated with hazardous chemicals have proven that these strategies may be applied. Each algorithm's experimental outcomes on the dataset have been examined. The purpose of this study is to discover major trends between different types of supervised machine learning algorithms and their performance and use to prevent ailment risk based on the work process. In comparison between the Support Vector Machine with Random Forest algorithm predicts that the severity level of disease would have the highest accuracy (90.7%), Sensitivity (88.8%) and specificity (92.6%).

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IoT based solid waste management system: A conceptual approach with an architectural solution as a smart city application

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AIP Conference Proceedings 2022年第1期2519卷

作者： A. Narasima Venkatesh G. Manimala P. Kiran Kumar Reddy S. S. Arumugam 1Department of MBA-Human Resource Management R.V. Institute of Management Bangalore Karnataka India 2Department of Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India 3Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Secunderabad Telangana India 4Department of Computer Science and Engineering Chennai Institute of Technology Malayambakkam Tamil Nadu India

The Internet of Things (IoT), which provides open access to specified data subsets for the creation of a wide range of digital services, will be able to include many distinct and heterogeneous end systems transparently and seamlessly. One of our key environmental issues is the management of solid waste, with harmful impacts on society, besides upsetting the equilibrium of the ecosystem. One of the key issues of the contemporary period is identification, monitoring and management of garbage. A complex and burdensome procedure uses older methods for manually monitoring garbage in waste bins that use more human work, time and costs that are not compatible in any manner with current technology. This is a sophisticated way of automating trash management. A really interesting technology that will assist to keep cities clean is this IoT garbage monitoring system project. This system monitors the waste containers and provides information on the waste collected in the waste containers via a website. All information is also sent through this website to cars collecting waste.

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Reduction in voltage drop of distribution feeders using bifurcation technique - A case study of 11kV feeder of 220kV subdivision

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AIP Conference Proceedings 2022年第1期2463卷

作者： Puneet Chawla T. R. Mudgal Sanjay Dahiya 1Department of Electrical Engineering CDL State Institute of Engineering & Technology Sirsa Haryana (India) 2Department of Physics CDL State Institute of Engineering & Technology Sirsa Haryana (India) 3Department of Computer Sci. & Engg CDL State Institute of Engineering & Technology Sirsa Haryana (India)

State Electricity Boards are main components in Distribution Sector to reduce voltage regulation and voltage drop at load points. It has been observed countrywide that distribution companies/feeders are running in overload conditions because, this system is extended without any proper planning. Now, energy requirement and consumption rates are increasing day-by-day and thus distribution sectors are under tremendous pressure. As a result service conditions and quality of supply to consumers are affecting adversely. These conditions require proper planning of pressured networks for accommodation of future needs as well to reduce above components and the technical losses. There are many methods adopted by researchers with a focus on to improve voltage profile and made suggestions for distribution sector, Deregulation power systems, HVDS system, Distribution Generation, power loss reduction by various optimization techniques. In present study, we have made an attempt to e implement bifurcation technique in feeder for solution in power sectors. By using bifurcation technique a feeder splits up into smaller sections. Object of this technique is to minimize voltage drop. In present work bifurcation technique is implemented at an 11KV distribution feeder in a 220KV subdivision of Punjab State.

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Emotion Equalization App: A First Study and Results

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Proceedings of Meetings on Acoustics 2022年第1期50卷

作者： Man Hei Law Andrew Brian Horner Hoi Ting Leung 1Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong HONG KONG mhlawchris@*** horner@cse.ust.hk htleungak@connect.ust.hk

In recent years, there is an increasing trend of mental health issues in society. It is important to identify mental first aid strategies that can be applied at an early age, whether emotional issues have been diagnosed or have yet to be found. Music has tremendous potential impact on changing emotional states since it can distract listeners from rumination on negative thoughts and engage them in a moment of musical *** paper presents a new emotion equalization app that incorporates validated diagnosis tests (PHQ-9 and GAD-7) and an emotion measuring tool (SAM) for establishing a personalized therapy treatment using emotion rebalancing methods. By determining the emotional state of the user, songs are chosen and sequenced in a playlist using one of three proposed methods (consoling, relaxing, and uplifting) with a baseline method (random). With this systematic generation of playlists, the app can be used for personalized therapeutic treatment even for users without music background. In our experiment, the results showed positive changes in listeners’ valence levels while there was no significant change in arousal. Further, the relaxing and uplifting methods showed a significant effect on moving listeners from negative to more positive emotional states.

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Theoretical analysis of medical healthcare chatbot system

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AIP Conference Proceedings 2022年第1期2446卷

作者： M. Suguna D. Prakash A. Amritha M. Saumiya A. Devi Prabha 1Department of Information Science and Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India 2Electrical Engineer ThinkAside Technology Coimbatore Tamilnadu India 3Department of Computer Science and Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India

A Chatbot is a computer program which enables us to make communication through our text or voice and get reply through Artificial Intelligence (AI) technology. A chatbot can be used in different applications like gaming, customer service and e-commerce site. Chatbots are developed to interact with received messages automatically presenting the experience of human-to-human interaction in digital mode. Chatbots provide responses to messages containing keywords that are matched using machine learning technique to adapt the responses to fit the respective situations. A developing number of hospitals, private health centers, and medical clinics currently utilize Chatbots on their internet sites. These chatbots connect with the potential patients to visit the location of the medical centers, to help themselves discovering the right specialists for their treatment, to book appointments with the doctors, and to get access to the right treatment for cure. Using AI in a domain where human lives have the chances of becoming a question brings up and act as replacement to the issues of the need to assign a human staff to a task. This theoretical analysis AI based healthcare chatbot system will help hospitals to offer healthcare online support 24 x 7, answering intense as well as general queries appropriately.

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Genetic based algorithm to improve performance of mobile ad-hoc network

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AIP Conference Proceedings 2022年第1期2393卷

作者： Hamela K. M. Narayanan 1Department of Computer Science Government First Grade College Malur Karnataka India 2Dept. of CSE St. Martin's Engineering College (Autonomous) Secunderabad Telangana India

Mobile Ad-hoc Network (MANET) does not require any fixed infrastructure and it is a decentralized network that required a strong dynamic routing protocol. The process of finding the nodes between the source and destination is known as routing. Routing is the basic functionality of any communication network. One type of proactive routing protocol in MANET is Optimized Link State Protocol (OLSR). The fundamental optimization of OLSR is to reduce the control traffic by selecting a small number of nodes, known as multipoint relays (MPR) which is an improved flooding mechanism for topological information. In this paper, we would like to bring a genetic based algorithm, namely Ant Colony Optimization (ALO) in OLSR to the performance of Mobile Ad-hoc Network.

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Risk based pricing using k-means clustering

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AIP Conference Proceedings 2022年第1期2516卷

作者： N. B. Sanjana M. Ishwarya N. Balaji E. P. Siva 1Department of Computer Science (Software) Engineering SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India 2Department of Mathematics SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India

Credit Risk Analysis is a process adapted by loan associations to evaluate the risk -based pricing for sanctioning loans. The chances of approving loans depend on the customer’s credit score, which includes annual income, banking history, loan, insurance and different elements of risks such as default risk, credit correlation risk, collateral risk, credit concentration risk, rating migration risk and recovery risk. Results of the calculation allow lenders and customers to assess credit profile characteristics. Data Science allows discovering of new patterns within a complex data set which can be an efficient method to compute risk percentage by means of non – hierarchical clustering. K-means is an iterative algorithm - the idea is to use small unstructured groups of data of fixed size, assigned to clusters to be stored in the memory depending on the previous locations of the cluster centroids. It is validated that the suggested technique predicts better accuracy, is a faster and targeted approach than existing methods.

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A global to local double embedding method for multi-person pose estimation

arXiv

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arXiv 2021年

作者： Xu, Yiming Li, Jiaxin Peng, Yiheng Ding, Yan Wei, Hualiang Yingcai Honors College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China School of Computer Science and Technology Donghua University Shanghai China Department of Automatic Control and Systems Engineering University of Shefield ShefieldS1 3JD United Kingdom

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method. Copyright © 2021, The Authors. All rights reserved.

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Unattended detection in neural OCT image sequences of disease markers

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AIP Conference Proceedings 2022年第1期2519卷

作者： V. Anbarasu Sharanabasappa C. Gandage Z. Vakil V. Antony Asir Daniel 1Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Tamil Nadu India 2Department of Computer Science and Engineering Poojya Doddappa Appa College of Engineering Hyderabad Karnataka India 3Department of Electronics and Communication Engineering Malla Reddy Institute of Engineering and Technology Hyderabad Telangana India 4Francis Xavier Engineering College Tirunelveli Tamil Nadu India

In treatment, initial diagnosis and the treatment of illness, detection, and quantitative analysis of markers in medical images are important. Supervised computer education allows results to be identified and exploited a priori after the samples of instruction by experts are annotated. But due to the number of required examples of instruction and the restriction of the marker language to recognized individuals, monitoring does not scale well. In this proof of concept research, we suggest unattended recognition of anomalies as marker applicants in retinal optical coherent imagery without a prior description restriction. We classify and categorize marker applicants that often appear in the data and prove that these markers provide a predictive value for the disease detection role. A cautious qualitative study of the established data control indicators shows, in patients with early and late-age macular degeneration, how their quantifiable frequency fits our present knowledge of the diseases. A deep denoise auto encoder is trained in multi-scaled images, and any anomalies are detected in new data with the one-class support vector machine. The anomaly cluster distinguishes stable groups. With these markers, the precision of 81,40% is measured as safe, early AMD, and late AMD cases. The model obtained a region under the Receiver operator of hackney in a second binary classification experiment with publicly accessible safe and mid-Dexter datasets.

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