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2023 International Conference on data science and Network Security, ICDSNS 2023

作者： Venkata Murari, L.M.A. Shashank, Marneni Mani Kumar, Sripathi Ravi Kumar, Parimi Saketh Ashwini, K. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350301595

Safety in India is broadly talked about everywhere these days. It has now turned into a significant issue. The crime percentage is on the spike. People are neither protected outside nor at home. Also, explorers from different nations are likewise in a questionable state while pondering coming to India. There are regulations, however there must be appropriate safety estimates which we need to be followed stringently to shield them from violence. A personal safety or SOS mobile application can be thought as the much-needed solution that can be used to aid personal safety. Such apps received increased prominence in the media after the 2012 Delhi gang rape case and consequent protests against 'brutal rapes, molestation and mistreatment of women'. Technology, including social and mobile tools, are playing an important role in improving overall personal safety, with technologies such as GPS, SMS, video, alerts, alarms, and more, allowing a user in distress to alert bystanders and close friends. In this paper, an android application to track people in need of help using the Global Positioning System is proposed and developed. The application can share their location with the nearest help-center and also to the nearby people along with voice recordings to analyze what is actually happening at the victim's place. The developed app will update the registered contacts on the user's location every few seconds. The GPS locations and recorded contacts are frequently stored in a database. The victim's location is communicated with greatest accuracy. © 2023 IEEE.

关键词： Global positioning system

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Scalable Web Crawling: Harnessing the Power of Hadoop MapReduce in a Distributed Framework

Scalable Web Crawling: Harnessing the Power of Hadoop MapRed...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Chennupati, Anjali Prahas, Bhamidipati Ghali, Bharadwaj Aaditya Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350308167

This paper demonstrates the implementation of Distributed Web Crawling using the Hadoop MapReduce framework on a distributed system where multiple Virtual Machines are connected through a master-slave relationship with the Hadoop MapReduce Framework as the underlay. Distributed web crawling optimizes data retrieval by involving multiple machines, leading to faster crawling, fault tolerance, and scalability. The proposed approach harnesses the strength of Hadoop MapReduce which is powerful in handling web data at scale for various applications, including search engine indexing, data mining, and large-scale analytics. Furthermore, this robust, scalable, and high-performance solution for gathering information from the vast World Wide Web enables deeper insights and broader applications within the realm of big data. A performance experiment demonstrates the model's efficiency compared to sequential crawling which reports a reduction in execution time ranging from 60 to 75%. © 2023 IEEE.

关键词： big data distributed architecture Hadoop MapReduce master - slave relationship scalable URLs Web Crawler

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Equation Detection in the Camera Captured Handwritten Document 2

Equation Detection in the Camera Captured Handwritten Docume...

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2nd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023

作者： Koushik, K.S. Mahale, Ankita Shobha Rani, N. Amrita School of Computing Mysuru Campus Department of Computer Science Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665456302

One of the most important tasks in the realm of document analysis and recognition is the detection of equations in documents that were acquired using a camera. The procedure includes several steps, including pre-processing of the images, segmentation, feature extraction, and classification. The suggested method comprises taking a user-provided input expression image and classifying it into one of three types of equations: simple, complex, and highly complex. By choosing a decision boundary set off from the initial hyperplane, the SVR algorithm encodes the image, producing a model that fits the data better. The result is then obtained by character-wise segmenting the image and comparing it with trained models. Two recurrent neural networks make up the RNN encoder-decoder that is used. One RNN creates a fixed-length vector representation from a sequence of symbols, and a different RNN decodes that representation into a different sequence of symbols. 1900 images containing various equations made up the dataset utilized for training, validating, and testing the SVR and RNN. The accuracy of the system was about 93.64%. © 2023 IEEE.

关键词： Recurrent neural networks

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Development of Cancer Drug Targets and Potential Anti-Cancer Agents using Deep Learning and Screening Processes 5

Development of Cancer Drug Targets and Potential Anti-Cancer...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Balamurugan, K.S. Ramesh, T. Sathish Kumar, P.J. Surendran, R. Tamilvizhi, T. Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350389609

Cancer is one of the global health issues with various complications that lead to fatal. A novel approach is initiated using deep learning techniques. They help in the analysis of complex biological structures which helps to provide potential anti-cancer drugs. They help in the screening of emergent compounds through therapeutic potential. The first stage in the proposed system involves the development of comprehensive datasets that include multi-omics data and protein-protein interaction network structures. They proceeded with preprocessing and feature extraction that led to the contribution of cancer progression stages. The deep learning model involves learning from cancer-associated genes and pathways. They help in the prediction of protein targets that are decisive for cancer development. In the subsequent stage, the deep learning model helps in employing a screen for the collection of emerging compounds from diverse platforms ranging from synthetic libraries. Another stage involves the validation process that leads to in vivo and in vitro experiments. They are proceeded with cellular assays. Compound efficacy is obtained through xenograft and mouse models followed by molecular biology techniques such as protein profiling and gene analysis. Thus the system provides a cumulative approach in obtaining anti-cancer drug targets. © 2024 IEEE.

关键词： Mammals

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Social Media Comment Classification and Crime Forecasting 3

Social Media Comment Classification and Crime Forecasting

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3rd IEEE Asian Conference on Innovation in Technology, ASIANCON 2023

作者： Divya Udayan, J. Ram Mohan Reddy, S. Sravan Ganesh, Sai Vidyacharan Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350302288

The increasing use of social media has led to a vast amount of user-generated content, including comments, which can provide valuable insights for crime forecasting. However, the sheer volume of data makes it difficult to manually analyze and categorize each comment. Once comments are classified, they can be used for crime forecasting. For example, patterns in social media comments related to criminal activity can be analyzed to identify potential hotspots for criminal activity. Additionally, social media comments can provide early warning signals for criminal activity, allowing law enforcement agencies to take preemptive action. Therefore, this paper proposes a social media comment classification model that uses machine learning techniques to identify and classify comments related to criminal activity. The proposed model combines supervised and unsupervised learning algorithms to classify comments into different categories, such as potential criminal activity, crime reporting, and general discussion. Additionally, the paper explores the potential of using the classified comments for crime forecasting by analyzing patterns and trends in the data. The results demonstrate the effectiveness of the proposed model in accurately classifying comments, and the potential for using social media data for crime forecasting. In summary, social media comment classification and crime forecasting is an emerging field of research that has the potential to revolutionize the way law enforcement agencies prevent and respond to criminal activity. By using machine learning algorithms to automatically classify social media comments, law enforcement agencies can gain valuable insights into criminal activity and take proactive measures to prevent it. © 2023 IEEE.

关键词： Forecasting

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The Application of Artificial Intelligence in Alzheimer’s Research

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Tsinghua science and Technology 2024年第1期29卷 13-33页

作者： Qing Zhao Hanrui Xu Jianqiang Li Faheem Akhtar Rajput Liyan Qiao Faculty of Information Technology Beijing University of TechnologyBeijing 100124China School of Medicine Tsinghua UniversityBeijing 100084China Department of Computer Science Sukkur IBA UniversitySukkur 65200Pakistan Department of Neurology Tsinghua University Yuquan HospitalBeijing 100040China

Alzheimer's disease(AD)is an irreversible and neurodegenerative disease that slowly impairs memory and neurocognitive function,but the etiology of AD is still *** the explosive growth of electronic health data,the application of artificial inteligence(Al)in the healthcare setting provides excellent potential for exploring etiology and personalized treatment approaches,and improving the disease's diagnostic and prognostic *** paper first briefly introduces Al technologies and applications in medicine,and then presents a comprehensive review of Al in *** simple,it includes etiology discovery based on genetic data,computer-aided diagnosis(CAD),computer-aided prognosis(CAP)of AD using multi-modality data(genetic,neuroimaging and linguistic data),and pharmacological or non-pharmacological approaches for treating ***,some popular publicly available AD datasets are introduced,which are important for advancing Al technologies in AD ***,core research challenges and future research directions are discussed.

关键词： Alzheimer's disease artificial intelligence etiology discovery computer-aided diagnosis computer-aided prognosis treatment

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Study of Bitcoin Price Prediction Based on Sentiment Scores of Influencer Tweets with Experimental Validation 14th

Study of Bitcoin Price Prediction Based on Sentiment Scores ...

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12th International Advanced Computing Conference, IACC 2022

作者： Gandhi, Abhishek Shinde, Mukund Kulkarni, Rajendra Chavan, Shubham Sayyed, Arshadali Yedurkar, Dhanalekshmi Phansalkar, Shraddha Department of Computer Science and Engineering MIT School of Engineering MIT ADT University Pune India

ISBN: (纸本)9783031356438

Although Bitcoin is the most popular cryptocurrency, it is very unstable even today. As the number of newsfeeds, articles, comments, trends, and opinions of influencers on popular social media platforms like Twitter is exponentially growing, they have a significant impact on the price of Bitcoin. People are often influenced by people who inspire them, events that impact their lives, or products and services they use. Therefore, an analysis of sentiments of people or investors triggered by social events becomes important for predicting the future of Bitcoin and its value. This paper forecasts Bitcoin prices by analyzing the sentiment scores of tweets and newsfeeds and their history. A large number of opinions on Twitter and other social media have drawn researchers to study public sentiment. We applied sentiment analysis to extract tweets from Twitter and analyzed the news blogs and historical bitcoin price data to improve our results’ accuracy. The presented work have achieved 82.87% accuracy to predict the sentiment score. © 2023, Springer Nature Switzerland AG.

关键词： Sentiment analysis

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LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSIS 11

LEARNING DOMAIN-AGNOSTIC REPRESENTATION FOR DISEASE DIAGNOSI...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Churan Li, Jing Sun, Xinwei Zhang, Fandong Yu, Yizhou Wang, Yizhou School of Computer Science Peking University China CFCS School of CS Inst. for AI Peking University China China Nat'l Eng. Research Center of Visual Technology China AI lab Deepwise Beijing China Department of Computer Science The University of Hong Kong Hong Kong School of Data Science Fudan University China

In clinical environments, image-based diagnosis is desired to achieve robustness on multi-center samples. Toward this goal, a natural way is to capture only clinically disease-related features. However, such disease-related features are often entangled with center-effect, disabling robust transferring to unseen centers/domains. To disentangle disease-related features, we first leverage structural causal modeling to explicitly model disease-related and center-effects that are provable to be disentangled from each other. Guided by this, we propose a novel Domain Agnostic Representation Model (DarMo) based on variational Auto-Encoder. To facilitate disentanglement, we design domain-agnostic and domain-aware encoders to respectively capture disease-related features and varied center effects by incorporating a domain-aware batch normalization layer. Besides, we constrain the disease-related features to well predict the disease label as well as clinical attributes, by leveraging Graph Convolutional Network (GCN) into our decoder. The effectiveness and utility of our method are demonstrated by the superior performance over others on both public datasets and in-house datasets. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Diagnosis

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Assessing Social Media's Impact on Mental Health: A Gaussian Naive Bayes Approach 4

Assessing Social Media's Impact on Mental Health: A Gaussian...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Nanditha, S. Joy, Helen K. Kounte, Manjunnth R. Sridevi, R. Jayarani, A. Electa Alice Vanusha, D. Christ University Department of Computer Science Bangalore560029 India School of Electronics and Communication Engineering Reva University Bangalore India Ks Institute of Technology Department of Electronics and Communication Engineering Bangalore India Srm Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387490

In recent times, social media has gained immense popularity. However, there is a growing concern among individuals regarding its impact on mental well-being. Extensive research has been conducted to unravel the intricate connection between the use of social media and mental health. This study thoroughly examines the existing literature by incorporating various studies that cover topics such as machine learning for analysing depression-related sentiments and the effects of social media on specific demographic groups. The aim is to develop a comprehensive understanding of the relationships between these factors. In order to gain a deeper understanding of mental health indicators, a dataset consisting of 481 records was analysed using various techniques including pre-processing, correlation analysis, data visualisation, and Gaussian Naive Bayes predictive modelling. Based on our findings, it appears that individuals who engage with social media may experience negative impacts on their mental well-being. During our investigation, we conducted tests on different machine learning models and discovered that Gaussian Naive Bayes yielded the highest level of accuracy. The presence of multicollinearity among independent variables emphasises the importance of being cautious when interpreting research findings. This study makes a valuable contribution to our understanding of the complexities involved in using social media platforms while managing mental health issues. By focusing on the intersection of technology and psychiatric illnesses, it lays the groundwork for future research and interventions in this important area. © 2024 IEEE.

关键词： Adversarial machine learning

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Sweep Spot: Empowering Communities for Sustainable Waste Management in Urban Areas

Sweep Spot: Empowering Communities for Sustainable Waste Man...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Venkata Krishna Vivek, Y. Sai, Pendry Pavan Sashank, Ratnala Abhishek, S. Anjali, T. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (纸本)9798350325188

Rapid urbanization has led to a surge in garbage production, causing severe environmental and public health concerns. A trash management system has been designed using mobile technologies and geolocation services to address this problem. Users can take, and post automatically geotagged photos of trash. The system incorporates an API to offer information on the closest municipality in charge of waste management. Based on user feedback, analysis and action are made more accessible by a backend database. An algorithm has been put in place to assess how close the user's position is to the nearest municipality. By streamlining garbage reporting and promoting cleaner, more sustainable urban environments, the system hopes to improve interaction with local government agencies. © 2023 IEEE.

关键词： Application program Interface Backend database Distance Calculations Geolocation retrieval Mobile Technology Municipality

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