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A Natural Language Processing System for Truth Detection and Text Summarization

A Natural Language Processing System for Truth Detection and...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： H P Rohith Kavitha Sooda Karunakara B Rai D B Srinivas Department of Information Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

In the futuristic world, differentiating between accurate information and false information would be challenging. To identify the fake news a truth detection system will be extremely useful. Different artificial intelligence models were compared, and the highest performing algorithm was chosen. This model was subsequently updated using a larger data-set in order to achieve better results in more real-world applications. The most essential assumptions are that the user will only utilize the subject field in which the model will work. Earlier works have implemented the process of fake news and truth detection separately from the process of summarizing the said news/information. No method was available for streamlining the process to detect whether given information was true or not and to then summarizes the given information automatically. This study has compared and contrasted the differences in accuracy and overall performance of each algorithm for fake news detection and summarization of text. Using smaller dataset, LSTM cells outperformed all other models with an accuracy of 99%. For the larger dataset, the modified LSTM has worked really well with 98.6 percent accuracy.

关键词： Industries Computational modeling Neurons Natural language processing Classification algorithms Fake news Artificial intelligence

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Transformer Neural Network for Throughput Improvement in Non-Terrestrial Networks

Transformer Neural Network for Throughput Improvement in Non...

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2023 IEEE International Conference on Network, Multimedia and Information technology, NMITCON 2023

作者： Giuliano, Romeo Innocenti, Eros Mazzenga, Franco Vizzarri, Alessandro Di Nunzio, Luca Divakarachari, Parameshachari Bidare Habib, Ibrahim Guglielmo Marconi University Department of Engineering Science Via Plinio 44 Rome00128 Italy University of Rome Tor Vergata viale del policlinico 1 Rome Italy Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering 6429 Nitte College Rd Bengaluru India The City College of New York Department of Computer Engineering 160 Convent Avenue New York CityNY United States

ISBN: (纸本)9798350300826

The integration of Non-Terrestrial Networks (NTNs) within the 6G framework provides full connectivity worldwide but some system procedures can be ineffective and should be modified for example in the timer thresholds, which may expire in NTN. Moreover, some procedures may also be affected by the different timeline of terrestrial networks and NTN. For example, the aging of the Channel State Information (CSI) collected by the User Equipment (UE) and sent to the gNode B (gNB) for the scheduler activities may affect its behavior reducing the user throughput, due to the high propagation latency in NTN and outdated data. In this paper we propose a strategy to overcome this problem by using a Transformer Neural Network (TNN) to predict the experienced Signal-to-Interference plus Noise Ratio (SINR) of a generic UE to the transmitting gNB. We assume a period in which the gNB receives frequently CSI/SINR reports by a given UE in order to train the TNN model. Next, the gNB uses data provided by the trained TNN to predict the UE channel behavior and then to properly select the best Modulation and Coding Scheme (MCS) for that UE, avoiding to transmit any CSI data. Results show that the throughput obtained with the TNN trained model is close to the highest CSI update rate (1 ms), while it is higher than the throughput obtained with a CSI update rate of 200 ms of about 5 Mbit/s in Sub-Urban environment for both always LOS and variable LOS/NLOS channel conditions, which corresponds to about a gain of 10 %. © 2023 IEEE.

关键词： Channel state information

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Model using GAN for Text to Image synthesis

Model using GAN for Text to Image synthesis

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North Karnataka Subsection Flagship International Conference (NKCon), IEEE

作者： Kumari Reshma Patil Vidyadevi G Biradar S.G. Mohan Dileep M R Clara Kanmani A Monalisa Pramanick Information Science Engineering Nitte Meenakshi Institute of Technology Bengaluru India Master of Computer Applications Nitte Meenakshi Institute of Technology Bengaluru India Information Science Engineering Presidency University Bengaluru India

Due to its remarkable data-generating capabilities, generative adversarial networks have gained significant relevance in unsupervised learning. Generative adversarial networks are very much useful in generating synthetic images from the given text description. The research work presented in this paper aims at developing a text-image synthesis model to generate high resolution synthetic images based on the scenario description given in input text. The paper proposes a novel model Df-GAN which is derived from VQGAN and CLIP using transfer learning. This model is evaluated on the COCO dataset. the experimental results show that the model can generate high resolution images. The model achieves FID score of 18.26 which is satisfactory.

关键词： Image resolution Image synthesis Transfer learning Generative adversarial networks Unsupervised learning

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Certain Electroenchaphalography Signal Analysis for Artifact Removal Using Multiwavelet Transform 2

Certain Electroenchaphalography Signal Analysis for Artifact...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication engineering, ICATIECE 2022

作者： Basavesha, D.B. Madhumala, B.M.R. Venkatareddy, Prashanth Kadakadiyavar, Sapna Pareek, Piyush Kumar Surabhi, R.S.K. Shridevi Institute of Engineering & Technology Department of Computer Science & Engineering Bengaluru India Dayananda Sagar Academy of Technology and Management Department of ISE Bangalore India Nitte Meenakshi Institute of Technology Department of Electrical & Electronics Engineering Bengaluru India Sambhram Institute of Technology Department of Electronics and Communication Bangalore India

ISBN: (纸本)9781665493963

This work presents an efficient strategy for eliminating mixed artifacts that are present in electroencephalogram recordings. All three categories of recordings are suitable candidates for use of the approach. The presence of the noise source makes it more challenging to do an analysis of the EEG and get clinical information. This is because the EEG is being measured in a noisy environment. EEG signals are biological signals that take place in the temporal domain and are multidimensional, non-stationary, and irreproducible. EEG signals are also known as electroencephalogram signals. Electroencephalogram signals are sometimes often referred to as EEG signals. This suggests that the statistical qualities of these people do not remain the same over time as shown by the fact that this is the case. It is anticipated that it will offer information on the activities that are taking place at the level of the ensemble of excitatory pyramidal neurons, with a temporal resolution scale of milliseconds. This prediction is based on the fact that the scale measures time in milliseconds. It is highly difficult to derive any useful information from scalp EEG since it includes a substantial amount of background noise and artifacts, and the particular area from which it originates cannot be determined. As a result of these factors, it is quite difficult to analyze scalp EEG. This is because the background noise and artifacts might make it difficult to determine the source of the signal. As a result of this, it is of the utmost importance to develop specific methods for reducing the occurrence of artifacts in EEG recordings, such as the one in question. These methodologies need to be created as quickly as time permits. In this piece of research, we suggest making use of the Multiwavelet transform to clean up the EEG data and get rid of any artifacts that could be present. Following the use of the GHM algorithm, which cleans up the noisy signal produced by the Multiwavelet transform, th

关键词： Electroencephalography

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Preserve the Medical Data using Secure Partially Homomorphic Encryption with Blockchain technology in Smart Healthcare

Preserve the Medical Data using Secure Partially Homomorphic...

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Data science and Information System (ICDSIS), IEEE International Conference on

作者： Kavitha S B S Pavithra A B Shashikala B N Jagadeesh P Uma Mageswari Dileep Marichi Department of MCA Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering East West Institute of Technology Bengaluru India Department of Information Science and Engineering RNS Institute of Technology Bengaluru India Department of Computer Applications Presidency college Bangalore India

ISBN: (数字)9798350381665

ISBN: (纸本)9798350381672

Healthcare information is vital for both individuals and service providers, necessitating secure sharing and maintenance of electronic healthcare records (EHR). Traditional EHR systems have relied on centralized architectures, such as the cloud, which may expose sensitive information to undisclosed intelligence. However, these systems have struggled to address the diverse needs of stakeholders while ensuring separation, security, and compliance with legal requirements. Blockchain technology offers a decentralized and distributed ledger system, providing secure, verifiable, and immutable data transfer capabilities. By employing cryptographic techniques like hashes, blockchain eliminates the need for a central authority, making data exploitation challenging and evident. This study proposes a blockchainbased architecture utilizing the Proof of Stake (POS) consensus mechanism to authenticate user identities and facilitate secure EHR exchange across multiple healthcare systems. Additionally, to enhance data security, Secure Partially Homomorphic Encryption (SPHE) is recommended for outsourced data, enabling users to perform operations on encrypted data. The proposed technique surpasses alternatives in terms of energy efficiency, authenticity, and protection of medical data.

关键词： Cloud computing Proof of stake Distributed ledger Law computer architecture Maintenance Stakeholders

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Smart Modular Few-Shot Learning Framework Changes Classification of Signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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

作者： Spoorthi, T. Tabassum, Husna Devika, S.V. Rao, K. B. V. Brahma Ramachandra, A.C. Gst Gitam University Bengaluru Campus Dept. of Cse Bengaluru India Hkbk College of Engineering Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques. © 2023 IEEE.

关键词： Learning systems

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Advancing Music Genre Identification Through Deep Learning Techniques

Advancing Music Genre Identification Through Deep Learning T...

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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： Chinmai Shetty Sanjoy Kumar Debnath Adithya Malden Joel Falleiro Sarojadevi H Ramya Srikanteswara Dept of Information Science and Engineering NMAM Institute of Technology-Affiliated to Nitte(Deemed to be University) India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Information Science and Engineering NMAM Institute of Technology-Affiliated to Nitte (Deemed to be University) India Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore

Music plays a huge role in human's lives, inspiring a wide range of feelings including enthusiasm and nostalgia. 97 million songs have been recorded globally; an incredible figure that is only increasing exponentially as more people use music applications. Many people love particular musical genres, such as classical, hip-hop, or disco, and want for an easy way to arrange their music in accordance with their preferences. This gave rise to the idea of “music genre classification”. In the current environment, where there are many music recordings available both online and offline, proper genre categorization has become essential. Several machine learning methods may be used to categorize the different types of music. This model's emphasis is on using deep learning methods to identify the musical genre. The Mel-frequency cepstral coefficients (MFCC), a technique that works exceptionally well for music files, are used to extract key elements. The proposed model is excellent at categorizing musical subgenres and provides default settings as well as interactive options depending on user preferences. The simple web interface built using Flask allows users to submit audio files and view the anticipated genre. The system also has the capability of recommending new genres to the user.

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Two-Dimensional Multi-Deep CNNs for Accurate and Robust Software Defect Detection: A Performance-Driven Approach 5

Two-Dimensional Multi-Deep CNNs for Accurate and Robust Soft...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Veeramachaneni, Vinod Mallick, Pradeep Kumar Rout, Subhashree Pareek, Piyush Kumar Colorado Technical University Department of Information Technology United States Kiit Deemed to be University School of Computer Engineering India Nalanda Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Yelahanka Karnataka Bengaluru560064 India

ISBN: (纸本)9798331522100

Software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models. © 2025 IEEE.

关键词： Search engines

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Secure Smart Healthcare Surveillance Framework Using Fuzzy C-Means Clustering with Effective Ant Colony Optimization in Internet of Things

Secure Smart Healthcare Surveillance Framework Using Fuzzy C...

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2022 IEEE International Conference on Knowledge engineering and Communication Systems, ICKES 2022

作者： Karuppaiah, Jayakumar Aarthi, C. Girirajan Jananai, M. Parameshachari, D.B. Biomedical Engineering Kalaigar Karunanidhi Institute of Technology Coimbatore India Sengunthar Engineering College Thiruchengode India Sr University Department of ECE Warangal India Kpr Instiute of Engineering and Technology Computer Science and Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665456371

The emergence of IoT device networks is not limited to a specific industry, application, or environment;instead, they are assisting in the development of the country in a number of sectors, including industries, cities, agriculture, and defense. But IoT device networks have some restrictions, including those related to bandwidth, energy resources, and other resources, as well as restrictions based on the environment. The fast growth of the Internet of Things (IoT) has brought to light significant privacy protection problems. This has a tremendous impact on the large-scale IoT applications. In this study, a ground-breaking IOT computing platform for secure and knowledgeable healthcare monitoring services is introduced. Within an IoT architecture, fully homomorphism encryption (FHE), which protects data privacy, is handled and kept. For the proposed IoT framework, a distributed method for clustering-based approaches is created with the scalability to gather and independently analyse massive amounts of hetrogeneous data in distributed IoT devices before it is transferred to the cloud. In this study, the Cluster Heads (CHs) were chosen from the available IoT devices using a brand-new Efficient Ant Colony Optimization (EACO) technique. For healthcare surveillance, fuzzy C-Means clustering (FCMC) is employed. FCM allows us to recognise regular (typical) bio signal patterns. © 2022 IEEE.

关键词： Internet of things

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COCOs Nucifera Tree Disease Prediction Using Faster Region-Convolutional Neural Network with ResNet-50

COCOs Nucifera Tree Disease Prediction Using Faster Region-C...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： M G Kavitha M L Raviprakash Zaid Alsalami Y. Nagendar A C Ramachandra Department of Computer Science and Engineering Kalpataru Institute of Technology Tiptur India Department of Computers Techniques Engineering College of technical engineering The Islamic University Najaf Iraq School of Computer Science and Engineering SR University Warangal India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Coconut diseases were predicted based on various categories such as color, shape, time frame, and changes in growth. Deep learning (DL) was applied for this purpose. However, the image dataset used was unlabelled, leading to challenges such as overfitting and complexity in computational time for prediction. The proposed method is the Faster Region-Convolutional Neural Network (R-CNN) model for detecting and classifying coconut clusters, aiming to minimize overfitting in the identification of coconut nucifera diseases. The coconut disease dataset involved performing image classification, distinguishing between healthy and unhealthy coconuts. Normalization, feature extraction, and classification techniques were utilized for this task. For feature extraction, the VGG-16 model was involved, which includes maximum pooling layers to extract features at different levels of the network enhancing the quality of feature maps and providing better region identification. Classification was performed using the R-CNN with ResNet-50. The dataset contained images of both healthy and unhealthy coconuts, and the regions were predicted to identify and classify the different diseases within the images. ResNet-50 captures high-level features and helps mitigate the vanishing gradient problem during training, making it easier to connect with the R-CNN method. The proposed Faster R-CNN with ResNet-50 achieved a performance accuracy of 98.40%. This performance was compared to existing methods such as Convolutional Neural Network (CNN), Deep-CNN (DCNN) and Long Short-Term Memory (LSTM).

关键词： Training Deep learning Shape Computational modeling Neural networks Feature extraction Convolutional neural networks

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