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A Text Classification in Tourism Reviews with the Hybrid Transformer-Attention Model in the Information Management of Smart Tourism 5th

A Text Classification in Tourism Reviews with the Hybrid Tra...

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5th International Conference on Computing, Communications, and Cyber Security, IC4S 2023

作者： Srinivasan, Jayanth Niranjanee, M. Nandhana, C. Azhagiri, M. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600 089 India

ISBN: (纸本)9789819773701

In the realm of smart tourism, understanding tourist feedback through sentiment analysis is pivotal for enhancing service quality and experience. This study introduces a novel Hybrid Transformer-Attention Model (HTAM), designed to advance sentiment classification accuracy, model interpretability, and linguistic adaptability in tourist reviews. By integrating the contextual insights of transformer models with a strategic attention mechanism, HTAM aims to provide superior performance over existing hybrid models like Convolutional Neural Networks—Bidirectional Long Short-Term Memory (CNN-BiLSTM) (Meng, The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things, 2024). With empirical evidence showcasing its capability to outperform conventional models in metrics with an accuracy of 96.74%, HTAM’s application promises to revolutionize smart tourism by enabling more responsive and tailored service improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Top-Performing Unifying Architecture for Network Intrusion Detection in SDN Using Fully Convolutional Network 5

Top-Performing Unifying Architecture for Network Intrusion D...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Roy, Bishwarup Acharya, Indranil Papalkar, Darshil Joseph, Mary Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350321425

Internet's omnipresent adoption over the course of many years leading up to the current era along with Wireless Sensor Networks' (WSN) limitations has only increased the security threats to these high-speed networks. As a result, it is critical to deploy Intrusion Detection Systems (IDS) to monitor malicious activities in the network framework. Software Defined Network (SDN) has served its purpose to a certain extent by enabling centralized control and management of WSN and providing ways to implement IDSs more efficiently. However, like any emerging technology, SDN also has its share of vulnerabilities as there exists a gap for contemporary architectures, which can exploit global network assets and balance network resource consumption while providing programmability in terms of security of the network. To tackle this problem, a model has been developed that uses a fitting feature selection method to cut down on the count of features extracted and minimize the data volume relayed over the routes. Conclusions drawn from the scheme's trials demonstrate the proposed IDS technology's potency and effectiveness. The openly accessible University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD) was used to evaluate the intrusion detection algorithm involving the Fully Convolutional Network (FCN) architecture and its effect using a statistical technique in detecting different types of attacks. © 2023 IEEE.

关键词： Deep neural networks

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Robust Prediction of Alcoholism from EEG Signals Using Auto-Encoder 14

Robust Prediction of Alcoholism from EEG Signals Using Auto-...

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

作者： Aarthi, B. Kulkarni, Raj Sanjay Chandra Kiran, K. Vinod, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The diagnosis of alcoholism (substance abuse) is crucial due to its impact on individuals, society, and healthcare systems worldwide. Electroencephalography (EEG) signals have been shown to contain valuable information about the effects of alcoholism on the brain, and can potentially be used as a non-invasive tool for early detection and monitoring. In this study, we proposed an auto-encoder model for predicting alcoholism from EEG signals, which achieved robust and accurate performance even in the presence of noise and variability in the data. We evaluated the model using multiple classification algorithms, including Logistic Regression, Random Forest Classifier, AdaBoost Classifier, and AutoEncoders. The auto-encoder model (consisting of Bi-LSTMs and ReLU, Sigmoid activation for Dense and output layers respectively) outperformed the other algorithms, achieving an accuracy of 65.5% on the test set, while Logistic Regression achieved an accuracy of 55%, Random Forest Classifier achieved 58%, and AdaBoost Classifier achieved 58.4%. These results demonstrate the effectiveness of auto-encoder models for biomedical signal processing and provide a promising avenue for future research in this area, including the investigation of more complex architectures and feature engineering techniques, and the use of larger and more diverse datasets for training and evaluation. This research contributes to the fields of biomedical signal processing, neurophysiology, and neuroimaging, offering insights into brain activity related to alcoholism and highlighting the importance of non-invasive diagnosis and early detection in tackling alcoholism disorders and their societal impact. © 2023 IEEE.

关键词： Long short-term memory

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Security Framework for Electronic Health Record in the Cloud Based on Public key Encryption 9

Security Framework for Electronic Health Record in the Cloud...

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9th International Conference on Signal Processing and Communication, ICSC 2023

作者： Aruna, A. Sellam, V. Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9798350383201

In the modern world, medical data is managed and stored online so that anybody can access it at any time. For maintaining medical records and sharing with other healthcare providers, the majority of hospitals already use EHR (Electronic Health Records) with cloud storage. Medical data confidentiality, authentication, integrity, and access control are just a few of the security and privacy challenges that have arisen as a result of the growth of healthcare technology and the shift to Cloud computing. In this article, a Medi-locker has been introduced to keep all of the patient's medical records where Both patient and medical practitioners can access healthcare documents at any time, from any location. To maintain the confidentiality and improve the security of the medical data Fully Homomorphic Encryption (FHE), which enables operations to be performed on data that has been encrypted without even decrypting it. The proposed scheme has a high resistance to brute-force attacks when it is compared to other cryptosystem. © 2023 IEEE.

关键词： Data Analytics

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An Optimized Hybrid Soil Texture Classification Model using Stacked Sparse Autoencoder 1

An Optimized Hybrid Soil Texture Classification Model using ...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Prabavathi, R. Chelliah, Balika J Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350359756

With effective soil management, which lowers the costs associated with soil erosion, improves water utilisation, and other factors, soil data analysis can offer vital information to raise agricultural yield and nutrient utilisation efficiency. An unsupervised method of data analysis called clustering finds similar groups of objects by analysing attribute values. The dynamic subject of soil classification starts with the model and moves on to explain the classes and, at the end, apply the knowledge gained from the field. In light of this, the study presents a novel clustering and classification-based hybrid soil texture classification model (HSTC). Soil data will be clustered and categorised into multiple classes by the proposed (HSTC) model. Hydrological soil groupings (HSG) of various kinds are used as classes to accomplish this. Based on characteristics like the proportion of clay, silt, and sand, the HGS can be categorised. The density-based clustering (DBSCAN) algorithm is used to group the soil texture data. The stacked sparse auto encoder (SSAE) model is used to categorise the clustered data. In addition, the parameter values of the SSAE technique are determined using a new barnacle mating optimizer (BMO). The HSTC model has achieved the best results, with accuracy of 95.66%. The experimental validation of the proposed HSTC model on soil texture dataset revealed that the suggested model outperformed previous techniques. © 2023 IEEE.

关键词： Deep learning

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Breast Cancer Detection and comparative analysis of Convolutional Neural Networks and VGG-16 3

Breast Cancer Detection and comparative analysis of Convolut...

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3rd IEEE International Conference on Intelligent Technologies, CONIT 2023

作者： Maniraj, S.P. Gupta, Ayush Mullamuri, Venkata Yaswanth Soumya, Prateek SRM Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350338607

Most common machine learning algorithms were designed to tackle particular problems in seclusion. Based on the business requirements, a machine learning algorithm was employed for a particular task and these algorithms were applied to train models separately on specific feature sets. Most of the machine learning models are trained on the assumption that the test and training sets have similar features while sharing the same underlying distributions. Convolutional Neural Networks (CNNs) have demonstrated remarkable potential for breast cancer detection using histopathology images. Moving forward with the latest research, we present VGG-16 architecture for breast cancer detection, which involves the use of pooling and convolutional layers to extract high-level accuracy from histopathology images. The proposed network consists of sixteen layers, each of which performs convolutional operations on the image to detect local features and pooling operations with the aim of simplifying the feature maps' dimensionality. The test results illustrate that the VGG-16 architecture secures superior classification accuracy when compared with the previous cutting-edge models. © 2023 IEEE.

关键词： Image classification

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Multi-Task Improve Domain Generalization in EEG-based Emotion Classification using Feature Fusion Learning Model 14

Multi-Task Improve Domain Generalization in EEG-based Emotio...

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

作者： Menaka, S. Joshi, Riddhi. S. Varun, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350335095

The use of Electroencephalography (EEG) signals for emotion identification tasks is common. However, the domain shift issue may cause EEG-based emotion detection models' performance to decline when used in new domains. In this article, we present a feature fusion learning model-based multi-task learning strategy to enhance domain generalization in EEG-based emotion classification. By simultaneously learning a shared feature extractor and numerous domain-specific classifiers, we train a single model to categorize emotions across various domains. Additionally, we present a feature fusion module that combines time-domain, frequency-domain, and wavelet-domain EEG characteristics. We run trials on the DEAP and SEED EEG emotion recognition datasets to assess our suggested methodology. The experimental findings show that our suggested strategy outperforms a number of cutting-edge techniques in terms of classification accuracy, both within- and across-domains. © 2023 IEEE.

关键词： Electroencephalography

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Outlier detection in maize field using Isolation Forest: A one-class classifier 1

Outlier detection in maize field using Isolation Forest: A o...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Michael, J Justina Thenmozhi, M. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350336009

In agriculture, manual weed identification remains a challenge for farmers and agriculturalists. Recent technologies address the problem of weeds through several methods among which outlier detection is one powerful technique. Outlier detection is an effective weed identification method because it builds the model only by training the positive samples, hence known as one-class classification (OCC). In the context of agriculture, all crops except the target crop are considered to be weeds. This makes OCC a suitable method to identify weeds. In this work, we build OCC-IF-WD (One-Class Classification using Isolation Forest for Weed Detection) model that treats the target class as an inlier and all other classes as outliers. To train this model, an original dataset from Kaggle is used. Isolation Forest, which is a one-class classifier was used to build the model. OCC-IFWD recognizes maize as an inlier and all its associated weeds as outliers. The model was trained with 1082 images. The model was tested against various categories of outliers to examine its performance level. The results show the highest accuracy of 90.2% when the model is tested to differentiate between maize (inlier) and blackgrass (outlier). Also, a precision of 87%, recall of 94%, and Fl-score of 91% were recorded from the model. © 2023 IEEE.

关键词： Crops

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Optimized Machine Learning Model for Identifying and Predicting Attention Deficit Hyperactivity Disorder(ADHD) 14

Optimized Machine Learning Model for Identifying and Predict...

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

作者： Balasubramanian, Sriram Karthikeyan, S. Karthikeyan, M. SRM Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Attention Deficit/Hyperactivity Disorder (ADHD) a form of neurodevelopmental disorder often manifests in young age. As per a 2016 WHO Survey for 10 countries, the ubiquity percentage of adult ADHD was determined to be 2.8% globally, showing a high percentage in countries with above average wealth and a strong correlation with male gender [1]. Inattention, impulsivity, and hyperactivity are the three main symptoms of ADHD that children and adults with the disorder often exhibit. However, these symptoms can vary widely from person to person, and some people with ADHD are classified as having inattentional, hyperactive/impulsive, or combined subtypes of the disorder. Futures seem bright for the development of systems that can use phenotypic and neuroimaging data to anticipate a diagnosis. This paper suggests a system for categorizing ADHD issues using fMRI data. The usage of 3D convolutional neural networks (CNNs) in the processing of fMRI has made numerous significant advances. The 3D spatial data of the fMRI data may be retained thanks to these cutting-edge techniques. Additionally, 3D generative adversarial neural networks (GANs) have made considerable advancements in the creation of normal MRI data. In order to predict ADHD using fMRI, this study uses multi modal 3D CNNs and employing 3D GAN for data augmentation. A time distributed single modal 3D CNN model will be compared to the improved multi modal model utilizing MRI data for classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Analyzing the Prediction Accuracy of Corn Leaf Diseases Using A Pre-Trained Network Model

Analyzing the Prediction Accuracy of Corn Leaf Diseases Usin...

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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Ashwini, C. Sellam, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350396485

During corn's exploration and manufacturing phases, farmers have a complicated issue in accurately diagnosing corn crop infections. To solve this issue, this work provides a method for specific position three prevalent diseases of corn leaves: grey spot, leaf blight, and rusty depending on grouping and an upgraded deep learning method. Deep learning advancements paved the path for enhancing prediction accuracy as its significance is adopted over various research fields. Disease prediction and classification is time-consuming due to the lesser foreground and background intensity information. To identify three illnesses, first cluster reference images using the clustering technique, then input them into the enhanced deep network. The influences of various 'k' values on corn diagnostic techniques are investigated in this research. The trial findings show that the approach has the most significant identification impact on observations with the analytical prediction of corn disease, rust, and grey spot disease. Here, VGG-16 and ResNet18 likewise produce the best diagnostic findings, with an average diagnostic accuracy, respectively. The technique presented in this study has a diagnostic performance of 95% for the three corn diseases. It has a more substantial diagnostic impact than another four techniques and can be used to safeguard crops in the agricultural area. The simulation is done in the MATLAB 2020a environment, and various performance metrics like accuracy, precision, F1-score, recall and some other statistical measures are evaluated and compared with existing approaches. © 2023 IEEE.

关键词： Forecasting

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