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Detecting the Political Bias in News Articles and Similarity Using Word Embeddings in American Journalism 1

Detecting the Political Bias in News Articles and Similarity...

引用

1st International Conference on Computational Intelligence for Information, Security and Communication Applications, CIISCA 2023

作者： Sinha, Apoorv Kumar Kumar Singh, Sanskriti Sanjay Sai, Shreyas School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350339727

Political polarity is on the rise in the world and machine learning tasks are adapting to the same. No longer is it sufficient to simply classify something as positive or negative, for a subject can be polarly opposite like positive or negative, but at the same time, can be switched entirely depending on the perspective. This is why the study of political bias makes up for a very interesting study since the multi-classification task of determining the alignment of a paper is a different take as to the usual understanding of sentiment analysis. Sentiment analysis in general is a natural language processing technique that helps to contextualize whether the natural text has any sort of bias to it. For the purposes of this paper, this polarity will be more aligned to whether a given news article in question is left-leaning, right-leaning or centrist. The outcome is doing so is to gain a knowledge of reporting in the USA as to see whether press organizations are truly neutral in their reporting and exactly how severe is the bias when it comes to particular words along with the other words they are naturally associated with. For the crux of this study, the proposed model will be utilizing three unsupervised document modelling algorithm that utilize document embeddings using paragraph vectors after utilizing Latent Dirichlet Allocation for fuzzy clustering. All three of these models will be used to make a prediction and while their accuracies will be evaluated separately, these three models will be combined towards a hard voting ensemble classifier so that a better classification score can be obtained and to reduce doubts since subjectivity is a large problem when dealing with natural text and especially, articles. © 2023 IEEE.

关键词： Sentiment analysis

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An approach for automatically solving handwritten equations using deep learning 2

An approach for automatically solving handwritten equations ...

引用

2nd International Conference on Trends in Electrical, Electronics and computer engineering, TEECCON 2023

作者： Khanduri, Arjun Mukherjee, Sourish Das, Sagnik Kartheek, G.C.R. CMR Institute of Technology Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350339949

The mathematical description of two things that are equal-one on each side of an "equals"sign-is called an equation. Equations help us address problems in our daily lives. Other fields that employ mathematical equations include medical, aerospace, traffic control, etc. In this paper, we have proposed a deep learning-based approach to solving handwritten equations in a digital system. We have only attempted to solve algebraic or arithmetic equations. The accuracy achieved while testing the application with expressions and equations was observed to be 90%. To solve equations received from the image scanned, we have proposed the use of a string extraction and manipulation method to convert the handwritten tedious equations into an expression that can be solved easily using a computer or digital system. © 2023 IEEE.

关键词： Deep learning

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FPAFS: Feature Selection Using the Flower Pollination Algorithm for Software Fault Detection System 4

FPAFS: Feature Selection Using the Flower Pollination Algori...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Rath, Pradeep Kumar Mahato, Susmita Singh, Niharika Gourisaria, Mahendra Kumar Das, Himansu School of Computer Science & Engineering Nist Institute of Science & Technology Berhampur India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

This paper suggests a suitable feature selection (FS) approach FSFPA using flower pollination algorithm (FPA). It is based on the concept of flower pollination, to choose a set of important features or variables from a vast set of characteristics present in a dataset. The objective is to choose the best suitable features for defect prediction. In this study FSFPA strategy is compared with other optimization methods such as ant colony optimization (ACO), particle swarm optimization (PSO) and evolution based algorithms differential evolution (DE), and genetic algorithm (GA). The experiment has been conducted using these five optimization strategies on nine datasets. To determine the most effective metrics for identifying error proneness, it uses five classifiers (NB, KNN, DT, LDA, QDA). It is observed that FSFPA was better considering the parameters training times and prediction accuracies as compared to the traditional GA, ACO, DE, and PSO methods. In conclusion, FPA produces better prediction outcomes in imbalanced data sets than the GA, ACO, DE, and PSO. It is necessary to correct the parameters that are responsible for software errors by utilizing optimization algorithms and appropriate classifiers. © 2024 IEEE.

关键词： Ant colony optimization

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Detection and Estimation of Diameter of Retinal Vessels 7th

Detection and Estimation of Diameter of Retinal Vessels

引用

7th International Conference on computer, Communication and Signal Processing, ICCCSP 2023

作者： Jamwal, Abhinav Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

ISBN: (纸本)9783031398100

The change in retinal vessel diameter is a crucial measure of disorders, including hypertension, high blood pressure, and diabetes. Abnormalities in the retinal vasculatures can signify a spectrum of diseases. In retinal fundus images, providing an automatically estimated diameter of blood vessels may aid clinicians in diagnosing diseases like hypertensive retinopathy, which can even lead to blindness. Therefore, automated diagnosis of these diseases requires accurate measurements of vascular diameters. In this research work, this paper have investigated three methods for the estimation of the diameter of the retinal blood vessel. Two publicly available datasets such as Central Light Reflex Image Set (CLRIS) and Vascular Disease Image Set (VDIS) of The Retinal Vessel Image Set for Estimation of Width (REVIEW) database have been used for the comparison of estimated diameter of the retinal vessel. The performance of these three algorithms is compared against the available ground truth average diameter for both CLRIS and VDIS. It was found that the estimated diameter is closer to the available ground truth using morphological-based and center line-based, respectively. However, the estimated diameter using the line integral-based method is significantly different from the ground truth for both datasets. © IFIP International Federation for Information Processing 2023.

关键词： Image segmentation

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Deep Learning Architecture Based Skin Cancer Detection Using Deep Belief Network and Grey Wolf Optimization

Deep Learning Architecture Based Skin Cancer Detection Using...

引用

2023 International Conference on Innovations in engineering and technology, ICIET 2023

作者： Sainudeen, Jinu P Hemavathy, P. Sathyalakshmi, S. Hindustan Institute of Technology and Science IT Department Chennai India Hindustan Institute of Technology and Science Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350309980

Skin cancer is becoming more common every year, and this trend is seen in various nations. As a result of lifestyle modifications and sun-seeking behavior, skin cancer cases are increasing. Melanoma can be successfully treated when skin cancer is discovered early;as a result, curing and surviving melanoma directly depend on eliminating it when it is in its early stages. Unfortunately, traditional procedures for detecting skin cancer can be imprecise and frequently result in more testing. In this work, artificial intelligence (AI) techniques are used to create a special deep learning model that combines deep belief networks and grey wolf optimization. By integrating the advantages of two reliable methods, this network achieves maximum accuracy. The suggested method's performance is evaluated against current state-of-The-Art methods and a DBN. The effectiveness of the proposed technique is evaluated using the Human Against Machine (HAM10000) dataset. The proposed technology's accuracy and effectiveness allow for the early clinical detection of skin cancer by doctors and dermatologists. © 2023 IEEE.

关键词： Deep learning

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HyResPR: Hybridized Framework for Recommendation of Research Paper Using Semantically Driven Machine Learning Models

HyResPR: Hybridized Framework for Recommendation of Research...

引用

International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2022

作者： Maddineni, Saketh Deepak, Gerard Praveen, S.V. Department of Computer Science and Engineering National Institute of Technology Andhra Pradesh Tadepalligudem India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India

ISBN: (纸本)9789819946334

The obligation for research paper recommendation is high as scientific document recommendations like research paper recommendation frameworks are not many in number. The knowledge-centric semantically inclined framework for research paper recommendation HyResPR has been proposed. HyResPR is a hybridized research paper recommendation model which is implemented on the RARD II dataset. A hybridized research paper recommendation framework (HyResPR) is proposed. It uses user queries as input to obtain the query words after preprocessing, later these query words are induvial integrated with the domain ontologies. The dataset is subjected to preprocessing to create a synthesized knowledge map with the help of category term mapping and static domain ontology alignment. Features are extracted from the synthesized knowledge map and classified using logistic regression, the extracted query words are processed along with the top 75% of the instances classified. The experimentations are conducted on the RARD II dataset which is classified using the logistic regression classifier. The SemantoSim similarity measure and Cosine similarity measures are used to compute the similarity among the extracted instances from RARD II dataset. The query words obtained from input, and relevant research papers are recommended back to user depending on the value of the semantic similarity. Auxiliary knowledge is incorporated by using static domain ontology, topic modeling has been used experimentations have been computed for 1416 queries on the RARD II dataset. The proposed HyResPR framework achieved the highest average accuracy of 96.43%, recall of 97.05%, with a least FDR value of 0.05 observed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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NodeMCU Based Weather Monitoring System 56

NodeMCU Based Weather Monitoring System

引用

56th International Carnahan Conference on Security technology, ICCST 2023

作者： Bindal, Rohi Yadav, Aniruddha Dewanzan, Prajjwal Shinde, Aditya Salaria, Yuvraj Deokar, Shubhangi Department of Computer Science and Engineering Symbiosis Institute of Technology Pune India

A Weather Monitoring Station being an efficient application of Internet of Things (loT) which measures the surrounding temperature, pressure, humidity and rainfall. To capture these measurements various sensors, like DHT11, BMP180, LDR and rain sensor are utilized in proposed research.. These sensors interface with the Blynk website and app to provide a platform for establishing control of the ESP-8266 microcontroller through an Android application. With Blynk user interface users can easily access real time data. The esp8266 acts as the systems mainboard by consolidating data from all sensors and transmitting it to the Blynk app platform via a laptop. © 2023 IEEE.

关键词： Rain

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Securing Fault-Detectable CNN Hardware Accelerator Against False Claim of IP Ownership Using Embedded Fingerprint as Countermeasure 9

Securing Fault-Detectable CNN Hardware Accelerator Against F...

引用

9th IEEE International Symposium on Smart Electronic Systems, iSES 2023

作者： Sengupta, Anirban Chaurasia, Rahul Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798350383249

This paper presents a novel methodology to design the secure fault-detectable hardware accelerator for convolutional neural network (CNN) during high-level synthesis (HLS). The security of fault-detectable CNN hardware accelerator design has been ensured against false claim of intellectual property (IP) ownership using the embedded fingerprint of the original IP vendor as a countermeasure. The proposed approach first generates a fault-detectable schedule for CNN. Subsequently, the unique fingerprint of the original IP vendor in the form of encoded hardware security constraints is covertly embedded into the design during the register allocation module of HLS, which acts as digital evidence to nullify the false claim of IP ownership by an adversary that may be present in an untrustworthy design house (such as foundry) and/or in the system on chip (SoC) integration house. The proposed approach achieves the following: i) secure fault-detectable hardware accelerator for CNN with integrated unique fingerprint of original IP vendor at zero design cost overhead ii) more definitive proof of IP ownership for a genuine IP vendor (indicating lower value of false positive) using proposed fingerprint embedding and iii) stronger robustness against brute force attack (indicating extreme difficulty in removing/tampering) using proposed fingerprint embedding than the related approaches. © 2023 IEEE.

关键词： System-on-chip

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Comparative Study of Popular Word Embeddings and Deep Learning Methods for Tweets Classification 5

Comparative Study of Popular Word Embeddings and Deep Learni...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sethumadhavan, Praneeth Alluru, Kavya School of Computer Science & Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350305449

This study aims to find the best deep learning models and word embedding methods that can be used for the classification of tweets as positive or negative. Data was preprocessed and lemmatized and then tokenization of tweets was done to be made suitable for training. Four different word embedding methods Word2Vec, GloVe, FastText, and SpaCy were used as embedding layers for deep learning models. Six different neural networks, simple NN, CNN, LSTM, GRU, Bi-LSTM, and BiGRU, were trained and tested. Bi-LSTM along with Word2Vec word embedding layer produced the highest accuracy of 84.28%. © 2023 IEEE.

关键词： Embeddings

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Unveiling Disparities in Polycystic Ovary Syndrome Detection: A Complex Comparative Study Integrating Deep Neural Networks and Cutting-edge Data Visualization Modalities

Unveiling Disparities in Polycystic Ovary Syndrome Detection...

引用

2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Srivastav, Advaith Chandra Krishnamoorthy, Parthiban School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350319088

The objective of the Polycystic Ovary Syndrome (PCOS) Detection and Analysis project is to find a solution to the ongoing problem of PCOS by employing data visualization in the form of graphs and machine learning algorithms to diagnose and investigate the condition. Polycystic ovarian syndrome (PCOS) is a multifaceted medical illness that affects the female reproductive system and has the potential to result in congenital anomalies and several other health concerns. It is difficult to diagnose and treat PCOS because its underlying cause is still unknown, even though there are medications available to treat the condition. By employing graphs and algorithms for machine learning, the project will analyze data to recognize patterns, with the goal of enhancing our understanding of PCOS and reducing the risk of further injury to those who are afflicted. The initiative aims to provide an effective solution for the diagnosis and treatment of polycystic ovarian syndrome (PCOS). © 2023 IEEE.

关键词： Data visualization

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