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Systematic Review of learning Models for Suicidal Ideation on Social Media 5th

Systematic Review of Learning Models for Suicidal Ideation o...

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5th International conference on Recent Innovations in computing, ICRIC 2022

作者： Sharma, Akshita Kaushik, Baijnath Shri Mata Vaishno Devi University Katra J&K Katra India

ISBN: (纸本)9789811998751

In present-day society, the major critical issues are mental health problems which eventually turn out to be suicidal ideation. Premature detection of suicidal thoughts among people is the solution to avoid suicide in the latter times. With each passing year, the growth rate of suicides is abruptly increasing. the social media platform is the key from which people around the world come across and share their feelings, emotions, and reaction to what they are going through in their lives. the data which is available on social media can be used for the identification and detection of suicidal ideation among people, on social media forums like Twitter, Reddit, Tumblr, Facebook, and Instagram. Natural Language Processing has come a long way in the research of finding the sentiment of individuals and checking the linguistic patterns of text shared by the people. Effective to carry out the research by using machine learning, deep learning, and transfer learning algorithms on the online forum data of people. Algorithms such as SVM, Logistic Regression, Naive Bayes, Convolution Neural Network, Recurrent Neural Network, Bidirectional Long Short-Term Memory, BERT, and RoBERTa, respectively, were enforced to classify or detect the suicides in the social media data. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： learning algorithms

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Data Management and Visual Information Processing using machine learning 5

Data Management and Visual Information Processing using Mach...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Kumar, Arhath Joshi, Shivani Akram, Shaik Vaseem Alanya-Beltran, Joel Ansari, Mohammed Saleh Al Tripathi, Vikas Department of MCA Karnataka India GL Bajaj Institute of Technology & Management Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Universidad Tecnológica Del Perú Department of Electronic Peru College of Engineering University of Bahrain Department of Chemical Engineering Bahrain Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

'machine learning (ML) '-based large-scale data insights are the foundation of many contemporary data-driven technologies. ML systems offer ways to define and carry out certain ML workloads effectively and flexibly. Because of information-driven application highlights, information-driven responsibility elements, and framework designs affected by customary information the executives draw near, data the board is at the centre of numerous ML frameworks. Moreover, a ton of present-day information-driven arrangements are based on AI (ML)- based on enormous data bits of knowledge. A few ML occupations can be characterized and executed effectively and deftly utilizing ML frameworks. Data the executives are at the underpinning of numerous ML frameworks are reasonable to information-driven application qualities, information-driven responsibilities attributes, and framework models affected by old-style information the board techniques. Various ML clients with different ML lifecycle obligations and ML abilities are available in many Calculations. Information researchers are ML clients who have specialized information in ML and examination to control the information and make ML models for the business. the potential issues with data management that might arise in settings where machine learning procedures are used in the real world. Because we have first-hand experience with such huge pipelines, we can home in on issues relating to the interpretation of training data as well as its validation, cleaning, and enrichment. the objective of this article is to draw attention to these issues, establish links to previous efforts in the database area, and identify research questions that have not yet been solved. © 2022 IEEE.

关键词： machine learning

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Epidemic Outbreak Prediction Using machine learning Model 5

Epidemic Outbreak Prediction Using Machine Learning Model

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5th IEEE International conference on Advances in Science and Technology, ICAST 2022

作者： Shinde, Soham Yadav, Seema Somvanshi, Ashelesha K.J Somaiya Institute of Engineering and Information Technology Information Technology Mumbai India

ISBN: (纸本)9781665492638

the intelligent models is used for prediction of diseases as well as creation of model that helps doctor to prevent spreading of disease globally is increased day by day. When a disease spreads rapidly in a short period of time in a specific area, it is called an epidemic outbreak. An outbreak might start in a single community or spread across multiple countries. It might last anywhere from a few days to several years. PHO (Public health organizations) are taking preventative efforts to stop the disease from spreading besides that they are highly benefited from accurate prediction of infectious disease. the emergence of big data in the sectors of health and biomedicine, precise data analysis aids early disease identification and better patient treatment. It is now increasingly viable to use massive computing power to predict and manage outbreaks. Our goal is to investigate and determine how outbreaks spread in villages and suburbs where medical care may be limited. A machine learning model is required to forecast epidemic dynamics and identify where the next outbreak is most likely to occur. Because these are important features that contribute subtly to the dynamics of the disease epidemic, our method considers the climate, geography, and distribution of population in impacted region. Our approach will assist health authorities in taking the necessary steps to guarantee that there are sufficient resources to fulfil demand and, if feasible, to prevent epidemics from arising. © 2022 IEEE.

关键词： Viruses

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Detecting Android Malware and Sensitive Data Flows Using machine learning Techniques 5

Detecting Android Malware and Sensitive Data Flows Using Mac...

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5th International conference on Contemporary computing and Informatics, IC3I 2022

作者： Raman, Ramakrishnan Nirmal, Khyati R. Gehlot, Anita Trivedi, Snehal Sain, Dimple Ponnusamy, R. Maharashtra Pune India SNJB'S Late Sau. KBJ College of Engineering Department of Computer Engineering Chandwad Dist. Nashik India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India Parul University Department of Mech Gujarat India Department of Operations & SCM India Chennai Institute of Technology Department of Computer Science & Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350398267

Currently, Android is considered to be the finest Smartphone operating system. According to statistics, Android malware has sharply expanded, and in current years, theft of personal information has grown to be a significant attack vector. Nevertheless, the majority of detection techniques for sensitive data thefts have specific drawbacks, like computational expense and false positives. this investigation suggests an Android malware detection method that offers very precise categorization and effective analysis of the flow of sensitive data. In order to identify Android malware, the research uses a machine learning technique that makes utilization of data-flow application program interfaces (APIs) as classifying features. 1,160 benign as well as 1,050 malicious data are used to assess the suggested strategy. the technology can detect unidentified Android malware with an accuracy ratio of up to 97.66%, according to the findings. the static data-flow study trial demonstrates that greater than 85% of sensitive data flow channels can be identified employing the revised API subset, while assessment time is reduced by about 40%. © 2022 IEEE.

关键词： Android malware

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Novel machine-learning-Based Decision Support System for Fraud Prevention 5th

Novel Machine-Learning-Based Decision Support System for Fra...

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5th EAI International conference on Big Data Innovation for Sustainable Cognitive computing, BDCC 2022

作者： Bereczki, Norman Simon, Vilmos Wiandt, Bernat Department of Networked Systems and Services Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9783031283239

Due to the popularity of cloud services, it is unavoidable, that not just legitimate, but fraudulent registrations will happen. For a service with good reputation, it is essential to prevent fraud users. A common way is to filter these cases during the registration process by analysts. this chapter presents a novel decision support system that can recognize anomalous behavioral patterns and classify accounts based on the available data thus implementing an automated fraud prevention system. the process uses both supervised and unsupervised approaches, thus avoiding errors due to inaccurate labeling. As a supervised machine learning algorithm, random forest classifier and logistic regression are used, and as an unsupervised, auto encoder is used. the developed flow gives a recommendation to the analyst whether a new user is potentially fraud or not and provides feedback on the accuracy of analysts’ work based on the results of the unsupervised approach. the newly developed process is able to supervise the decisions made by analysts thus improving the labeling process. the main goal of this chapter is to present a new, more deterministic labeling workflow with the ability to provide feedback so it can improve the correctness of the training data set. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anomaly detection

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Subverting machines, fluctuating identities: Re-learning human categorization 22

Subverting machines, fluctuating identities: Re-learning hum...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Lu, Christina Kay, Jackie McKee, Kevin DeepMind London United Kingdom

ISBN: (纸本)9781450393522

Most machine learning systems that interact with humans construct some notion of a person's "identity, "yet the default paradigm in AI research envisions identity with essential attributes that are discrete and static. In stark contrast, strands of thought within critical theory present a conception of identity as malleable and constructed entirely through interaction;a doing rather than a being. In this work, we distill some of these ideas for machine learning practitioners and introduce a theory of identity as autopoiesis, circular processes of formation and function. We argue that the default paradigm of identity used by the field immobilizes existing identity categories and the power differentials that co-occur, due to the absence of iterative feedback to our models. this includes a critique of emergent AI fairness practices that continue to impose the default paradigm. Finally, we apply our theory to sketch approaches to autopoietic identity through multilevel optimization and relational learning. While these ideas raise many open questions, we imagine the possibilities of machines that are capable of expressing human identity as a relationship perpetually in flux. © 2022 Owner/Author.

关键词： Iterative methods

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High Frequency Forecasting on Stock Market using machine learning 3

High Frequency Forecasting on Stock Market using Machine Lea...

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3rd International conference for Emerging Technology, INCET 2022

作者： Khot, Amruta Bhat, Rashmi Urunkar, Abhijeet Mudegol, Nandinee Walchand College of Engineering Sangli India St. John College of Engineering and Management Palghar India

ISBN: (纸本)9781665494991

Intraday stock trading has become a popular trend in US, Europe, and Indian markets and forecasting these rapid market movements have become an important topic in finance. With the emergence of technology and computing power machine learning and neural network, methodologies are becoming a reality and efficient way to solve problems. the trend of intraday trading has made the researchers and developers explore neural networks approach to forecast the high frequency changing data. We focused on gathering minute to minute changing data of 80 stocks belonging to 8 different sectors from S&P 500 and developed a custom double layer neural network which efficiently captures the dependencies in the stock market and allows us to predict the returns of a specific stock at the end of next 5th minute time interval. © 2022 IEEE.

关键词： Data mining

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A Systematic Study of Various Approaches and Problem Areas of Named Entity Recognition 5th

A Systematic Study of Various Approaches and Problem Areas o...

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5th International conference on Recent Innovations in computing , ICRIC 2022

作者： Madan, Monica Rani, Ashima Bhateja, Neha Amity University Haryana Gurgaon India

ISBN: (纸本)9789819906000

Named entity recognition (NER) is an essential part of the natural language processing (NLP) pipeline for the extraction of information as it finds application in many downstream applications. Many different rule-based, statistical, probabilistic, or hybrid approaches have been used to address it, the recent ones being machine or deep learning. Due to fast-changing technologies, many new developments have emerged in NER as well. this paper aims to provide a brief introduction, an overview of the new advancements, the addressed problem areas and approaches for the duration of five years from 2017 to 2021. this period has seen advances in deep learning, and some major developments are seen such as contextual embedding. the study explores different problem areas identified such as performance, handling noise or robustness, nested entities, low resources in new languages, scalability, fine-grained entities, and others being worked upon using supervised, unsupervised, reinforcement transfer learning working at character, morphological, word or sentence level, or by adding dictionaries for including knowledge. the paper can help provide a high-level input, to give an idea, of the areas that are currently being worked upon in NER along with the latest approaches with their advantages and future directions. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reinforcement learning

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Analysis of Text Classification Using machine learning and Deep learning 5th

Analysis of Text Classification Using Machine Learning and D...

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5th International conference on Emerging Technologies in Computer Engineering: Cognitive computing and Intelligent IoT, ICETCE 2022

作者： Desai, Chitra Department of Computer Science National Defence Academy Pune India

ISBN: (纸本)9783031070112

this paper analyzes the performance of text classification using machine learning and deep learning models. the data is preprocessed, followed by text preprocessing. Natural language processing requires the conversion of text data to numerical vectors before they are passed to machine learning or deep learning models. Bag of words and Term Frequency and Inverse Document Frequency (TFIDF) techniques are used for converting text to a numeric vector. the machine learning models demonstrated are Naïve Bayes Classifier and Logistic Regression. Here, the Bag of words is used with Naïve Bayes Classifier, and TFIDF is used with the logistic regression model. the deep learning models demonstrated here are Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). the data set used consists of 1786 instances. Train test ratio used is 80:20. the performance of text classification models depends upon the dataset and how well its features are preprocessed as well as the selection of train test data. In this paper, four different algorithms are applied to the same dataset to analyze the performance of text classification. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Text processing

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A Novel Recommendation System for Mutual Management on Both OTT Video Streaming and Advertising Using Incremental learning Approach 5th

A Novel Recommendation System for Mutual Management on Both ...

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5th International conference on Recent Innovations in computing, ICRIC 2022

作者： Samraj, Jasmine Menaka, N. Tamil Nadu Chennai India

ISBN: (纸本)9789811998751

In today’s trend OTT video service provides a comfortable and luxurious lifestyle and users are easily adapting to it. Advertisements played in middle of the video are the main way of promoting the products or video services. the paper represents a novel approach "Mutual Management over OTT video streaming and Advertisements" (MMOA) based on the incremental learning of user perspective and OTT providers views. the proposed system uses a recommendation system and deep learning to analyze the user and provide service according to their requirements. Here the advertisement influencing is the main target for OTT providers, where the advertisements are chosen to be promoted in exact and minimal time validating the user choice of video streaming. Further, the paper uses machine learning (ML) producing recommendation engines to filter the most viewed video streaming and produces recommendation of ads/videos to user accordingly. Hereby, by the process implementation the OTT platform services and advertisements promotions are improved. An efficient Quality of Experience (QOE) for subscribers in OTT platform is ensured guaranteeing a full-fledged entertainment. An experimental analysis is made from collecting the Kaggle dataset using Github Repository to evaluate the user preference and the effectiveness of the proposed system is produced. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Recommender systems

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