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Checking the Truthfulness of News Channels using NLP Techniques

Checking the Truthfulness of News Channels using NLP Techniq...

2023 International Conference on the Confluence of Advancements in Robotics, Vision and Interdisciplinary technology Management, IC-RVITM 2023

作者： Vipin, C. Harshit, S. Sailesh Kumar, K.L. Vijay, K. Zaid, Mohamed Vellore Institute of Technology Department of Computer Science and Engineering Vellore India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350306088

Data analytics are difficult with unstructured data since it lacks pre-formatting, which makes up a large amount of information on social media. Research has looked into the potential of machine learning and natural language processing to interpret unstructured data and uncover significant information that could have gone unnoticed. We classify social media users using segmentation approaches on the basis of population, relationship status, and other criteria, allowing for the recognition of prominent people within particular categories. Behavior analysis assigns distinct roles to social media participants, such as users, recommenders, prospective users, and detractors, facilitating the tailoring of messages and reactions to address their concerns effectively. The study involves the accumulation of data from three news channels, data cleaning, and exploratory data analysis (EDA) to unearth patterns. Sentiment analysis is performed, and its results are compared for each of the news channels. Topic modeling is used to reveal different themes in the data. This study offers a solid basis for sentiment analysis and visualization, providing insightful information on the sentiment levels across various source kinds and features. It holds the potential to empower government agencies and businesses in harnessing the power of social media analytics for informed decision-making and improved communication strategies. © 2023 IEEE.

关键词： Visualization

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AttentionFractalCovNet Architecture for Breast Ultrasound Image Segmentation 21

AttentionFractalCovNet Architecture for Breast Ultrasound Im...

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21st OITS International Conference on Information technology, OCIT 2023

作者： Agarwalla, Rhythm Ray, Satyajeet Kumar Paul, Saurav Sikkim Manipal Institute of Technology Computer Science and Engineering Majitar India

ISBN: (纸本)9798350358230

Tumor in the breast is a significant cause of unhealthiness among women. It is primarily the malignant-natured lesions that are a big reason for death via cancer among females. Segmentation of the tumor is complex due to the hazy boundaries and non-identical shapes and sizes. In this paper, an attention-based architecture, called AttentionFractalCovNet(AttFCN) architecture is proposed for the accurate and automatic segmentation of tumors. It takes inspiration from FractalCovNet architecture that forms a W-shape rather than the U-shape of the UNet architecture. Convolution blocks along with Attention Gates(AG) in the second decoding layer are used for achieving better segmentation results. Breast Ultrasound (BUS) images are used as input to segment out the lesions. Various matrices like Accuracy, MSE, Dice Coefficient(DC), Recall, Intersection over Union(IoU), Precision, and F1 Score(F1) were calculated for all the implemented architectures. It achieves higher DC and IoU values as compared to other architectures like SegNet, UNet, Re-sUNet, AttentionUNet(AU), and FractalCovNet(FracCN). Thus, the model is more suitable for the segmentation of tumors and can give better results than the existing architectures mentioned above. © 2023 IEEE.

关键词： Tumors

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Survey on Methodological Model of IoT in Digital Forensic

Survey on Methodological Model of IoT in Digital Forensic

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2023 International Conference on Intelligent Systems, Advanced Computing and Communication, ISACC 2023

作者： Roy, Ritocheta Dheeba, J. School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350399387

This paper provides a synopsis of research openings in IoT-based forensic investigation. Usage of IoT in digital forensic investigation process is reviewed systematically and the methodology of IoT based forensic investigation is discussed. Problems arising from IoT-linked trials during all the forensic investigation stages are brought forward. The opportunities IoT fetches to forensic analysis are mentioned. Examples of IoT forensics applications are also shown. A thorough research outline is provided, containing data about the chief research paths and a brief overview of all the methods and models relevant to the papers. Finally, future scope and server models currently being researched are brought to light in this paper. © 2023 IEEE.

关键词： Digital forensics

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An Integrated Approach to Point Labeling Problem 14

An Integrated Approach to Point Labeling Problem

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Singh, Simranjeet Kumar, Varun Bhattacharjee, Subhasis Indian Institute of Technology Jammu Computer Science & Engineering Jammu India

ISBN: (纸本)9798350335095

The point feature labeling problem is a very wellknown problem where a set P = {p1, p2, •••, pn} of point sites are given along with their labels. The objective is to maximize the number of labels for points on the map so that no two placed labels are mutually overlapping. Here, we investigate a special class of the point site labeling problem where the label of a point pi touches the point either at one of its four corners (Corner model) or at the middle of one of its 4 edges (Middle Edge model). We assumed that only the height (but not the length) of the labels of all the points are same. This maximum point site labeling problem is known to be NP-hard. We first provided an O(n√ n) heuristic solution for point labeling in the Middle Edge model. On analysing the benchmarks, we felt the need of applying labeling solution simultaneously in Corner model and Middle Edge model for any problem instance. We thus provided an integrated approach for the point labeling problem in two models together. Experimental result show significant improvement (14%) in the number of points being labeled in our integrated approach compared to the existing Corner model of labeling. Our solution for Middle Edge model always take less time than the Corner model, and it also provides a nice trade-off between the solution quality and the runtime across two models. © 2023 IEEE.

关键词： Integrated control

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Mathematical Model and AI Integration for COVID-19: Improving Forecasting and Policy-Making

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SN computer science 2024年第2期5卷 1-19页

作者： Panda, Dev Sourav Dixit, Rahul Dixit, Anuja Dwaracherla, Harshitha Shukla, Anupam Bosch Digital Department Bosch Global Software Technologies Private Limited Karnataka Bangalore India Department of Artificial Intelligence Sardar Vallabhbhai National Institute of Technology Gujarat Surat India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Pune India Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat India

In this work, a new susceptible–exposed–infectious–recovered (SEIR) compartmental model is proposed which has additional media influence for precise quantization of the coronavirus disease 2019 (COVID-19). In the proposed model, first-order ordinary differential equations (ODEs) are used for the formulation of basic reproduction number, whereas genetic algorithm (GA) is used for its estimation. The inclusion of climatic parameters, governmental impact, and human behavioral response toward the disease provides an upper hand in determining the dynamics of its transmissibility, thereby indicating their significance in precising the outcomes. In addition, the future trends for the new normalized confirmed cases of COVID-19 are predicted using the long short-term memory (LSTM) model which helps in evaluating and modifying the current preventive actions taken to improve the situation. The robustness of the proposed model is measured by five different error functions which are tested in five different countries. According to the experimental results, this is observed that the proposed model has a smaller prediction deviation as well and the proposed scheme outperforms state-of-the-art models of COVID-19. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Genetic algorithm LSTM model Reproduction number SEIR-type compartmental model Social media Transmission dynamics

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Impact of nutrients in food quality and safety by machine learning classifier using internet of things

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International Journal of Information technology (Singapore) 2024年第5期16卷 2803-2812页

作者： Balamurugan, S. Gurumoorthi, E. Devi, P.P. Maruthamuthu, R. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & amp Department of IT CMR College of Engineering and Technology Telangana Hyderabad India Department of Computer Applications Madanapalle Institute of Technology and Science AP Madanapalle India

The traditional Food Supply Chain (FSC) is challenged by a number of issues, including uncertainty, security, expense, complexity, and quality worries. A precise supply chain is required to address these problems. Managing the supply chain to distribute high-quality goods is a difficult task in the modern food sector. The efficient Tree Augmented Naive Bayes linked with FSC utilizing Internet of Things (IoT) is proposed in this paper to enable tracking, tracing, and managing the full food supply chain process, including supplier, exporter, and consumers. This paper's goals are to evaluate the food safety and create the best possible chronological data for the analysis of potential future assumptions. It also intends to connect the producer and the buyer by delivering high-quality food goods while using IoT technology to transport food from the producers to the customers. IoT-based food supply chain traceability is used to successfully transact data with ambiguous, unreliable, and inadequate data. In order to provide secure backdrop and food safety for FSC process utilizing IoT technology, the suggested effective Tree Augmented Naive Bayes algorithm would be able to overcome the challenges of conventional supply chain. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Bayes classifier algorithm Food safety and IoT FSC Traceability Tree augmented Naive Bayes

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Automated Test Case Generation Using T5 and GPT-3 9

Automated Test Case Generation Using T5 and GPT-3

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Mathur, Alok Pradhan, Shreyaan Soni, Prasoon Patel, Dhruvil Regunathan, Rajeshkannan Vellore Institute of Technology School of Computer Science Engineering Vellore India

ISBN: (纸本)9798350397376

Test case generation for a given topic can be a challenging and time-consuming task, particularly when the conversation about the topic includes boundary conditions and requirements. This process requires a complete understanding of the system and its scenarios to cover all possible examples, both good and bad. This paper proposes an automated solution for generating test cases using natural language processing techniques. The proposed methodology uses T5 and GPT-3 models to extract the context and topic of the conversation and generate test cases. The model identifies relevant keywords and generates test cases based on them, providing context-based meanings of specific words and defining new terms used in the conversation. The proposed approach eliminates the need for manual test case generation and allows for the testing of more complex systems with a larger number of scenarios. This will reduce the dependency on the expertise of the test designer, resulting in complete coverage of all scenarios. Additionally, this automation will save time and resources, making the process of test case generation more manageable. By utilizing NLP techniques, it will help to ensure that all scenarios are taken into account and that the test cases generated are comprehensive and accurate. © 2023 IEEE.

关键词： Natural language processing systems

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Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 642-656页

作者： Madhavi, Macharapu Kolikipogu, Ramakrishna Prabakar, S. Banerjee, Sudipta Maguluri, Lakshmana Phaneendra Raj, G. Bhupal Balaram, Allam Department of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Andhra Pradesh Vijayawada India Department of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India School of Commerce and Management Studies Dayananda Sagar University Bangalore India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune Campus Maharashtra Pune India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India School of Agriculture SR University Warangal India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Climate change is one of the most pressing global challenges of our time, with far-reaching impacts on ecosystems, economies, and human societies. Accurate prediction of climate change patterns is crucial for developing effective mitigation and adaptation strategies. Remote sensing data, with its ability to provide comprehensive and continuous observations of the Earth’s surface, plays a vital role in monitoring and predicting these changes. However, the complexity and high dimensionality of remote sensing data present significant challenges for traditional predictive models. In this study, we present an Enhanced Deep Learning Strategy for climate change prediction using remote sensing data, integrating a Cascaded Inception-LGBM model. The proposed model combines the feature extraction capabilities of the Inception module with the predictive power of the Light Gradient Boosting Machine (LGBM). The methodology was evaluated on various climate variables, including temperature, precipitation, and CO₂ levels, achieving an accuracy of 97.22%. Comparative analysis with state-of-the-art models demonstrated the superior performance of our approach, particularly in terms of RMSE, MAE, and R² metrics. Robustness tests further confirmed the model’s generalization capabilities under different data conditions. This study underscores the potential of advanced deep learning techniques in enhancing climate change prediction accuracy and offers insights into the key drivers of climate variability. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Climate change prediction Deep learning Environmental monitoring Feature extraction Inception module Light Gradient Boosting Machine Model accuracy NDVI Remote sensing Spectral indices

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Design Optimization of Leakage Based PUF Circuit targeting at Ultra-Low Voltage Operation

Design Optimization of Leakage Based PUF Circuit targeting a...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Hata, Shunkichi Usami, Kimiyoshi Graduate School of Engineering and Science Japan Shibaura Institute of Technology Department of Computer Science and Engineering Japan

ISBN: (纸本)9798350326413

PUF (Physically Unclonable Function) utilizing the physical characteristics of semiconductors has been attracting an attention as the LSI individual identification technology. This technology is expected to make it possible to authenticate genuine products and prevent the distribution of counterfeit products. On the other hand, IoT devices used in sensor nodes, are required to operate for a long period of time with limited power, so low power consumption is important. LRPUF (Leak Racing PUF) was proposed a leakage based PUF composed of digital circuits, but there is a problem of large power consumption. It has also been reported that PUF performance, such as uniqueness, uniformity and reliability, degrades significantly when operated at ultra-low voltages to reduce power consumption. In this paper, in order to improve the performance of LRPUF under ultra-low voltage, we propose an optimal LRPUF circuit for ultra-low voltage operation. By using design-of-experiments, we identified the PMOS gate width and body bias as high-impact factors on performance. Based an this finding, we conducted circuit optimization. Evaluation through simulation demonstrated that the proposed PUF achieved the uniqueness of 50.0%, the uniformity of 50.4%, and the reliability of 98.9% at the supply voltage 0.4V. © 2023 IEEE.

关键词： Electric power utilization

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Gender Classification using MLP-based Speech Emotion Detection 14

Gender Classification using MLP-based Speech Emotion Detecti...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Kavya, S.A. Kusuma, K. Srinivasa, K. Department of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India

Speech Emotion Detection (SED) is the extraction of the speaker's emotional state from his or her speech signal. There are a few universal emotions, such as Neutral, Anger, Happiness, and Sadness, that can be trained by any intelligent system with limited computational resources to identify as needed. In this work a gender classification model is proposed using an Multi-Layer Perceptron (MLP) based SED. Because spectral features contain emotional information, they are used in this work. The prosodic features that are used to model different emotions are fundamental frequency, loudness, pitch, speech intensity, and glottal parameters. Each utterance is analysed to extract potential features for the computational mapping of emotions and speech patterns. Classification of Gender is done by detecting pitch using the selected features. Here we used RAVDESS dataset which is trained and tested using a Convolution Neural Network. The most notable features like weight connectivity, local connectivity, and polling results are thoroughly trained and have an accuracy of 52.43 percent. To improve the model accuracy, we used the MLP classifier and feature extraction techniques namely, Mel-Frequency Cepstral Coefficients (MFCC) and Mel spectrogram, which resulted in an accuracy of 68.43 percent. © Grenze Scientific Society, 2023.

关键词： Classification (of information)

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