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An Integrated Approach for Suicidal Tendency Detection

An Integrated Approach for Suicidal Tendency Detection

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： V. Baby Dama Sudheshna Desu Sarvani Sahithi Vesangi Saketh Reddy Regatte Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad India

Suicidal Tendency or the intention to kill oneself and end their life is a catastrophic situation which is mostly unknown by any person in the victim’s life. In many studies it is evident that, victims tend to kill themselves either to end their pain, pressure or to have a relief that they are not going to live in this world anymore. Current suicidal tendency detection methods include numerous machine learning and deep learning approaches using clinical data or online social media, provide a solution to this existing problem. This paper aims to present a multi-modal approach that could help the victim’s close ones to know if he/she is suffering from suicidal tendency. Providing a multi-faceted method that can detect this tendency and intimate the family, friends or the close ones beforehand can prove to be a boon for the invention. Three different approaches like Facial Gesture Recognition, Voice Pattern Recognition and Text Pattern Recognition are imbibed in the Android Mobile Application and used as modules to determine suicidal intent in a complementary manner. Finally, the limitations and future scope are extrapolated to provide an overview for the future research.

关键词： Deep learning Technological innovation Social networking (online) Pain Process control Gesture recognition Computer architecture

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A Systematic Study on methods of Spatiotemporal Hotspot Detection and Evaluation metrics

A Systematic Study on methods of Spatiotemporal Hotspot Dete...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rishabh Jain Aruna Bhat Department of Computer Science Delhi Technological University New Delhi India

A Spatial hotspot is a confined region/space inside a geographical area which has a higher concentration of activity points than outside the hotspot region within the study area. With technological breakthroughs in spatial data collection and increased computation capabilities, several research publications presented novel methods for Spatial Hotspot detection. The relationship between Spatial Hotspots and time (temporal effect) has also been investigated by various researchers, expanding the horizon of the spatial hotspot to spatiotemporal hotspot. The idea of Spatiotemporal hotspot detection gained considerable interest among researchers because of its practical utility in public health, public safety, traffic volume analysis, crime zone analysis, and other essential applications. However, it is also vital to analyze the research literature. Our research aims to organize the existing comprehensive literature into a well-organized hierarchical framework to comprehend better the various methodologies widely adopted around the world. We propose a framework that suitably categorizes the research effort of various scholars into several appropriate categories based on their differences in primary algorithmic approaches. Furthermore, we also present an extensive analysis of the widely utilized evaluation measures adopted in this research domain.

关键词： Systematics Gain measurement Extraterrestrial measurements Time measurement Spatial databases Spatiotemporal phenomena Safety

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A Study of LSTM in Stock Price Prediction

A Study of LSTM in Stock Price Prediction

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ishan Adhikari Bairagi A. Daniel Anchal Sharma School of Computer Science and Engineering Galgotias University Greater Noida India

Investing into stocks has become a trend in the new generation and everyone shows keen interest in putting their money and trying their luck into the stock game. But it requires deep analysis and thorough knowledge to get you through the risk and failures. There are multiple platforms which let you invest money into stocks nationally and internationally and with the boom in cryptocurrency, it has become evident that investments are an efficient way to earn and give us an upper hand over others as far as income parameters are concerned. This project aims to predict the price of the selected stock and give useful and near reliable insights so that investor can have an idea of how the stock may perform in the future. This can really help assess the performance of a stock and make calculated decisions. We intend to predict the stock using the LSTM neural network and then at a later stage provide the user with a dashboard using plotly dash for stock analysis. However stock prices are subject to market conditions and may change abruptly depending on multiple factors. Thus the project aims to give supposed prices to the investor on an approximate basis. The future of this project can be made in a mobile based app which plugged in with previous performance of stock data will help users take advantage of the prediction at their hands.

关键词： Neural networks Games Market research Cryptocurrency Reliability Investment

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Food Detection and Recognition Using Deep Learning – A Review

Food Detection and Recognition Using Deep Learning – A Revi...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Anushrie Banerjee Palak Bansal K.T. Thomas Department. of Data Science Christ University Pune Lavasa India

Studies show poor lifestyle choices and unhealthy eating patterns cause issues like obesity and other ongoing illnesses that raise the risk of heart attacks, such as hypertension, abnormal blood sugar levels, and diabetes. To improve this situation a lot of health apps have been built which use modern dietary monitoring systems that automatically evaluate dietary intake using machine learning and deep learning techniques rather. For these reasons indepth investigations on food detection, classification, and analysis have been conducted. Some of the top methods for automatic food recognition created have been discussed in this paper. We also propose an idea for detection of Indian food items using image classification. According to our findings of the papers we reviewed, convolutional neural networks (CNN) have been extensively been used in food detection as it has been giving better results compared to other models. We also observed that Vision transformers perform better in situations where the dataset is large and a hybrid model would give better accuracy. A review of potential applications for food image analysis, shortfalls in the area, and open issues concludes the paper.

关键词： Deep learning Support vector machines Performance evaluation Obesity Image recognition Computational modeling Benchmark testing

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Identification of Fake News Using Machine Learning Approach

Identification of Fake News Using Machine Learning Approach

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Gurupraksh Singh Brijendra Kumar Yadav Bhuvnesh Pratap Singh Shelja Sharama Galgotias College of Engineering And Technology Greater Noida UP India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida UP India

In today’s world everyone is using internet and Social media platforms like Instagram, Twitter, Facebook etc. In such scenario fake news spreads very rapidly and reaches to millions of user in a short span of time. Riots during election, riots between different religious groups are consequences of these fake news. Many political parties utilize false information to boost their vote totals. Machine learning is important for classification of data, but it has limitations. On the basis of the kaggle dataset, a model has been suggested to classify fake and authentic news in this project. Our technology will be programmed to discern between fake and legitimate news from various social media platforms. It also aims to distinguish genuine news from a variety of sources. Our research looks into several textual qualities that can be used to tell the difference between phoney and real content. We use machine learning algorithms, such as the Passive Aggressive classifier using TF-IDF, to train our model utilizing those properties.

关键词： Machine learning algorithms Social networking (online) Voting Multimedia Web sites Blogs Machine learning Partitioning algorithms

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House Price Prediction using regression techniques

House Price Prediction using regression techniques

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Rupam Dwivedi Rishabh Gupta Prashant Kumar Pal School of Computing Science and Engineering Galgotias University Greater Noida India

Real estate companies keep special care when they purchase or sell a new house. A significant amount of expertise and market awareness is required for making accurate price predictions of houses so as to turn it out into a profitable investment. Also, the setting of prices manually is quite difficult and tedious task and the accuracy of prediction done by the real estate experts is also not good in that case due to the probability of human errors. The primary goal of this project is to provide the best and most profitable house price predictions to real estate investors with the help of a machine learning model so that they can get best returns in their deals as per the market scenario. It can also provide a lot of convenience to buyers who want to purchase a house at best market prices depending upon the desired house features. It consists of a ML-based model which mainly involves the use of three machine learning algorithms namely linear regression, decision tree regression and random forest regression. Three individual models are created on the basis of each of these algorithms and the most suitable model with maximum accuracy is chosen. With the analysis of various features and specifications of the houses such as number of rooms, locality, population status, physical conditions etc., prices will be estimated.

关键词： Machine learning algorithms Computational modeling Sociology Linear regression Predictive models Prediction algorithms data models

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Application of machine learning algorithm for prediction of heart disease

Application of machine learning algorithm for prediction of ...

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advances in computing, Communication Control and Networking (ICAC3N), international conference on

作者： Ankit Kumar Upadhyay Sharzeel Saleem Adarsh Anubhav Shrddha Sagar Department of Computer Science and Engineering Galgotias University Greater Noida India

Healthcare is the one the domain in which every organization is working on it globally. It need to make it better and better via different researches and the innovations in this sector. Machine learning is the most used algorithm in every sector and organization. It not merely transforming the field of healthcare but also able to emulate the various human intelligence from the surrounding in order to solve various problems. Machine learning in health care sector has shown its various application in it. Disease prediction is one of the applications of it. This study helps to predict heart diseases with the help of its algorithm to determine the risk and treat the patient in the healthcare system. In this paper we have taken out the data from the kaggle and implemented it using the Jupiter notebook tool. The three algorithm we have implemented (naïve bayes, decision tree and random forest) out of which random forest is giving. Hence, with the help of the following algorithms it is possible for us to predict the heart disease with more accuracy. This research will able to predict various disease over its various symptoms more easily and accurately.

关键词： Heart Technological innovation Machine learning algorithms Jupiter Human intelligence Medical services Organizations

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Small Parts Recognition by Convolutional Neural Networks with Implementation to Virtual Reality Devices for Assisted Assembly Tasks 5th

Small Parts Recognition by Convolutional Neural Networks wit...

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5th EAI international conference on Management of Manufacturing Systems, MMS 2020

作者： Židek, Kamil Piteľ, Ján Pavlenko, Ivan Lazorík, Peter Hošovský, Alexander Department of Industrial Engineering and Informatics Faculty of Manufacturing Technologies with the Seat in Prešov Technical University of Košice Prešov Slovakia Department of General Mechanics and Machine Dynamics Faculty of Technical Systems and Energy Efficient Technologies Sumy State University Sumy Ukraine

ISBN: (纸本)9783030672409

The paper presents a new approach to recognize small parts from high-resolution 4K images grabbed by the embedded camera with trained convolutional network models. The main principle is a combination of standard machine vision techniques (OpenCV library) to identify blobs in the high-resolution image and parse to a suitable set of regions of interest, which are later recognized by trained convolutional neural network model (Tensorflow framework). The convolutional neural network model with single-shot detection was selected to identify the position of the object. 3D virtual models of parts with autogenerated orientations are used for simpler input data generation. Two embedded devices were selected for use in the experimental system. The first is NVidia Xavier dev board with 4K camera and integrated APU and the second is Raspberry PI 4 with external APU Intel Neural Compute Stick 2. The last part of the paper shows the implementation of the presented principle to the virtual device (HTC Vive Pro) for assisted assembly tasks to train and check workers during the teaching process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolutional neural networks

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Analysis of Intrusion Detection Using Machine Learning Techniques

Analysis of Intrusion Detection Using Machine Learning Techn...

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Advanced technologies in Intelligent Control, Environment, computing & Communication Engineering (ICATIECE), international conference on

作者： G.Janani Pandeeswari S. Jeyanthi Department of Computer Science and Engineering RVS Educational Trust's Group of Institutions Dindigul India Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India

ISBN: (纸本)9781665493970

The Internet of Things relates to many sensors and devices connected via the Internet. With IoT, data is easily accessible and can be exchanged with each other. Technological developments raise security problems. As these IoT devices are inter linked to sensors that are coupled to large cloud servers, smart city network traffic through IoT systems is increasing rapidly, creating new cyber security challenges. The increasing network traffic reflects in the task of identifying IoT attacks and detect malicious data at an early stage. The proliferation of IoT systems comes with the hazard of network attacks such as denial of service (DoS) and spoofing. Lot of experimenter have worked on IDS development using machine learning approaches to rectify the problems outlined above. With the accuracy of computer drills, the non-uniform data can be spotted automatically. Even unknown threats can be detected by machine learning systems due to their generalizability. Although several studies have addressed the application of machine learning (ML) solutions to detect attacks in recent years, little attention has been paid to detecting attacks on IoT networks. This paper contributes in this area by analysing varying machine learning methods to discover network attacks quick and perfectly. In this study, ADFA dataset is used for intrusion detection, intelligent anomaly detection based on the Random Forest machine learning algorithm, to address IoT cyber security and compare it with the literature study, and the new features have proven useful.

关键词： Training Machine learning algorithms Smart cities Intrusion detection Telecommunication traffic Feature extraction Internet of Things

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DRSAL: Deep Reinforcement Skin Cancer Diagnosis with Active Learning Technique

DRSAL: Deep Reinforcement Skin Cancer Diagnosis with Active ...

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Smart technologies in computing, Electrical and Electronics (ICSTCEE), international conference on

作者： Renith G A. Senthilselvi Dept of CSE SRM Institute of Science and Technology Chennai India

Malignancy is the most dangerous disease that causes higher fatality rate among humans. Albeit, different malignancy found in recent times, skin cancer is considered as highly contagious and affects human life in a slower pace. Current development of skin imaging modality such as Dermoscopy, clinicians can able to diagnose the disease quickly. With less availability of experts around the world, automating the disease diagnosis helps to start the treatment earlier. Advancement of deep learning techniques helps this automation procedure for disease diagnosis. Main problem behind this algorithm to get good accuracy, is that it needs more amount of labelled input data. Labelling huge input data is a time-consuming process and the chance for getting experts in medical field to label the input data is very less. We propose a novel algorithm which can train themselves with less data available and predicts and annotate the remaining dataset available with less intervention of human experts. This is possible with the improvement of deep reinforcement learning algorithm which smartly analyses the given data and diagnose the disease by providing a smaller number of datasets with active learning technique. Multi-Agent in the reinforcement algorithm helps to learn the important features from the given class and based on the similarity it aligns the input data of individual class accordingly. Each agent shares the gained information to other agent in order to improvise the learning method in a quicker way and start labelling or predicting the remaining unknown dataset. The proposed method Deep Reinforcement Skin Cancer Diagnosis with Active Learning Technique (DRSAL) achieves higher accuracy of 94.2%in diagnosing the disease compared with other popular deep learning and active learning algorithms.

关键词： Deep learning Industries Training data Reinforcement learning Prediction algorithms Skin Medical diagnosis

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