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EAI Endorsed Transactions on Energy Web 2021年第35期8卷 1-7页

作者： Arun, Manicka Raja M Sumitha, T. Maria, Michael Visuwasam L Rejin, Paul N R Assistant Professor Department of Computer Science and Engineering R.M.K College of Engineering and Technology Chennai India Assistant Professor Department of Computer Science and Engineering R.M.K Engineering College Chennai India Assistant Professor Department of Computer Science and Engineering Rajalakshmi Institute of Technology Chennai India

Data analytics is played a vital role in Information Technology and Information Technology essential services ITeS for making effective decisions. The demand for tourism and prediction of tourist arrivals are important for tourism organisation. In this paper, we analyse tourist and extract information using data analytics process. The web data are processed using travellers' details and applying an aggregate function to calculate the searching index. Here, we use the autoregression analytics method for accurate prediction. The tourist information is recorded and creates a system log for processing and extracting information. The interaction between each record and their logs are used for data processing and analytics model. This paper uses a recommendation system for the data analytics process and compares the results with existing models. Our proposed method provides good and accurate results for tourism organisation.___________________________________________ © 2021. Arun Manicka Raja M et al.,. All Rights Reserved.

关键词： Data Analytics

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Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model.

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Procedia computer science 2025年 259卷 306-315页

作者： Geethu S Kumar Jo Cheriyan Aparna N Swathy J Research Scholar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai- 600 119 Tamilnadu India Assistant Professor Department of Information Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India Assistant Professor Department of Computer Science and Engineering Saintgits College of Engineering Kottayam- 686 532 Kerala India Assistant Professor Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions.

关键词： Stress PPG Conv-XGBoost deep learning mental health

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Survey of Various Machine Learning Techniques for Analyzing IoMT-Based Remote Patient Monitoring System 1

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International Conference on Advances in Data science and Computing Technologies, ASDC 2022

作者： Johar, Sayyed Manjula, G.R. Assistant Professor Department of Artificial Intelligence and Machine Learning Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India Professor Department of Computer Science and Engineering Jawaharlal Nehru New College of Engineering Karnataka Shivamogga India

ISBN: (数字)9789819936564

ISBN: (纸本)9789819936557

Internet of Medical Things (IoMT) consists of several medical devices used to capture patient health information. The recorded details are transmitted to the healthcare centers via computer networks. This transmitted information is stored in the cloud environment for making further clinical analysis. The continuous recordings of patient information help predict the patient’s chronic disease and help to initiate the respective solutions. This recorded information is processed by applying various machine learning and deep learning techniques to predict patient health conditions. Therefore, here the general discussion of IoMT and the impact of patient health monitoring systems are discussed. Along with this, various methodology’s respective pitfalls and advantages are analyzed to improve the healthcare system’s remote patient health monitoring process. The major goals of this study were to comprehend the role played by IoMT in remote patient monitoring and the effects of intelligent approaches on data analysis. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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WOA-DNN for Intelligent Intrusion Detection and Classification in MANET Services

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Intelligent Automation & Soft Computing 2023年第2期35卷 1737-1751页

作者： C.Edwin Singh S.Maria Celestin Vigila Department of Computer Science and Engineering Noorul Islam Centre for Higher EducationNagercoil629180India Department of Information Technology Associate ProfessorNoorul Islam Centre for Higher EducationNagercoil629180India

Mobile ad-hoc networks(MANET)are garnering a lot of attention because of their potential to provide low-cost solutions to real-world *** are more vulnerable to security *** in nodes,band-width limits,and centralized control and management are some of the ***(Intrusion Detection System)are the aid for detection,deter-mination,and identiﬁcation of illegal system activity such as use,copying,mod-iﬁcation,and destruction of *** address the identiﬁed issues,academics have begun to concentrate on building IDS-based machine learning *** learning is a type of machine learning that can produce exceptional *** study proposes that WOA-DNN be used to detect and classify incursions in MANET(Whale Optimized Deep Neural Network Model)WOA(Whale Opti-mization Algorithm)and DNN(Deep Neural Network)are used to optimize the preprocessed data to construct a system for classifying and predicting unantici-pated cyber-attacks that are both effective and efﬁ*** a result,secure data transport to other nodes is provided,preventing intruder *** invaders are found using the(Machine Learning)ML-IDS and WOA-DNN *** data is reduced in dimensionality using Principal Component Analysis(PCA),which improves the accuracy of the outputs.A classiﬁer is used in forward propagation to predict whether a result is normal or *** compare the traditional and proposed models’effectiveness,the accuracy of classiﬁcation,detection of the attack rate,precision rate,and F-Measure,Recall are *** proposed WOA-DNN model has higher assessment metrics and a 99.1%accuracy ***-DNN also has a greater assault detection rate than others,resulting in fewer false *** classiﬁcation accuracy of the proposed WOA-DNN model is 99.1%.

关键词： Intrusion detection system whale optimization algorithm deep neural network mobile ad-hoc networks forward and back propagation

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Quantum-Accelerated Hyperparameter Tuning for Dynamic NLP Models

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Procedia computer science 2024年 235卷 506-519页

作者： Ravikumar S Arockia Raj Y R. Babu Vijay K R. Ramani Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India 600062 Assistant Professor Department of Computer Science and Engineering PSNA College of Engineering and Technology Dindigul India Assistant Professor Department of Computational Intelligence Faculty of College of Engineering and Technology SRM Institute of Science and Technology Chennai Tamilnadu India 603203 Assistant Professor Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai Tamilnadu India 602117 Associate Professor Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamilnadu India 626140

In the rapidly evolving field of natural language processing (NLP), performance optimization of large-scale NLP models is crucial. Through the application of Quantum-Accelerated Hyperparameter Tuning (QAHT), this abstract introduces a novel approach to addressing this issue. Our proposed framework leverages quantum computing capabilities to dynamically optimize NLP model hyperparameters in real-time, catering to the ever-changing character of textual data streams. Traditional hyperparameter optimisation methods usually rely on laborious grid searches or random exploration, which may not be suitable for dynamic NLP jobs. Contrarily, QAHT uses Quantum Neural Network (QNN) architectures that have been specially designed for hyperparameter optimisation. These QNNs improve performance and efficacy by dynamically modifying and improving model configurations. This abstract discusses the key elements of the QAHT architecture, including real-time model deployment, adaptive learning, and continuous data stream processing. In addition to speeding up the hyperparameter optimisation process, QAHT makes sure that NLP models are still flexible and responsive to shifts in the sentiment of the data and its distribution. This method has applications beyond NLP since it provides a foundation for effectively optimising machine learning models in complex, real-time situations. As quantum computing develops, QAHT represents a promising future in machine learning, where quantum-enhanced capabilities satisfy the needs of contemporary data-driven applications.

关键词： Hyperparameter optimization Quantum computing Natural language processing (NLP) Quantum Neural Networks (QNN) Real-time processing Adaptive learning

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Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 389-398页

作者： Vaithianathan, V. Rebecca, B. Begum, M. Fathima Sundarraj, Antony Allwyn Kumar, M. Prema Kiran, Ajmeera Department of Electronics and Communication Engineering Sri Sivasubramaniya Nadar College of Engineering Kalavakkam Chennai 603110 India Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Hyderabad Telangana Dundigal 500043 India Assistant Professor Department of Computer Science and Engineering (Cyber Security) Madanapalle Institute of Technology and Science Andhra Pradesh-517325 Madanapalle India Department of Food Technology Sri Shakthi Institute of Engineering and Technology Coimbatore Tamilnadu Coimbatore India Dept. of ECE Shri Vishnu Engineering College for Women (A) Andhra Pradesh Bhimavaram 534202 India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

The primary sector of our Indian economy is agriculture. Today’s world’s most important and vital industries are agriculture and irrigation. Information and communication technology must be used in our agriculture sectors in order to support farmers and agriculturalists in improving their practices across the entire crop-growing and post-harvest processes. It is still challenging to define processed food, even in light of mounting research that suggests consuming more ultra-processed food has negative health effects. This research proposes novel technique in smart agriculture model based on cloud machine learning in cloud machine learning and healthcare system. By leveraging IoT and cloud-based data to create data-driven product decisions, cloud computing in agriculture can assist farmers with smart agriculture practices. Here, the input has been collected as food quality based on smart agriculture using a cloud model as well as processed for noise removal and normalisation. Then, based on this data, the healthcare security system has been analysed using reinforcement adversarial stacked encoder with Markov fuzzy algorithm. The experimental analysis has been carried out for various food quality datasets in terms of training accuracy, random precision, normalised correlation ratio, F-1 score, and AUC. The proposed technique achieved training accuracy of 96%, F-1 score of 94%, random precision of 97%, normalised correlation ratio of 93%, and AUC of 92%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial stacked encoder Cloud machine learning Cloud machine learning Healthcare system Smart agriculture

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Streamlined Lung Abnormality Identification with Explainable AI: A Unified Framework for Chest CT and X-Ray Analysis

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Procedia computer science 2025年 258卷 467-475页

作者： R. Renugadevi Associate Professor Department of Computer Science and Engineering Saveetha Engineering College Saveetha Nagar Thandalam Chennai 602105 India

Based on CT and chest X-ray images, a new automatic classification method for lung anomaly identification is introduced. The issue of an unbalanced and limited available dataset is addressed by data augmentation. Various preprocessing methods are implemented to eliminate noise. Original CCT model serves as a foundation of this model and it is undergoing an ablation study with a 32 x 32 image CT scan dataset for best configuration determination. The model is then trained on an X-ray dataset for testing its performance in different modalities and performance is compared with six pre-trained models. Classic models showed mediocre outcomes with test accuracies between 40% and 78% for CT and 50% to 75% for X-ray with a large number of training cycles. On the other hand, the proposed model showed outstanding performance with test accuracies of 99.70% for CT and 95.30% for X-ray. By progressively shrinking the dataset of training images, the robustness of the model is assured, demonstrating that the model will still be able to perform well despite the reduction in data. The decisions of the model are explained through the explainable AI method Grad-CAM, which creates color visualizations to enable medical practitioners to make swift and certain judgments. This paper solves issues with training time and computational complexity by integrating deep learning methods with image preprocessing to identify lung abnormalities.

关键词： Lung Abnormality CT scan X ray CNN Artificial Intelligence

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Paving the Way for Economically Accepted and Technically Pronounced Smart Radio Environment

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China Communications 2022年第8期19卷 247-266页

作者： Mostafa Salah Andreas Pitsillides Ahmed S.Mubarak Department of Electrical Engineering Sohag UniversitySohagEgypt Department of Computer Science University of CyprusNicosiaCyprus Visiting Professor University of JohannesburgDepartment of Electrical and Electronic Engineering ScienceJohannesburgSouth Africa Department of Electrical Engineering Aswan UniversityAswanEgypt

Recently,Reconfigurable Intelligent Surfaces(RISs)have been introduced to provide the necessary flexibility for the design of the Smart Radio Environment(SRE),which can be optimally shaped to facilitate efficient signal *** line with the concept of smart cities,SRE needs to be carefully designed with respect to the city infrastructure and utilization of *** this paper,we provide our vision on RIS integration into future Smart Cities by highlighting the potential technical,environmental,and economic motivations of RIS deployment in harmony with various ecosystems at a city level such as buildings *** this end,we are pointing out some scenarios for mitigating the conflict between RIS realization and the existing building facade’s ecosystems such as advertising display and solar cells on building ***,in this fashion,the proposed vision supports a win-win relationship between all stakeholders of different *** study presents guidelines for not only enabling seamless economically accepted RIS widespread utilization but,also more technically sounding SRE by supporting enhanced RIS features and more advanced applications that cannot be attained by traditional passive ***,based on the current research directions,we offer promising insights for cost-effective mass pro-duction through motivating two scenarios of“all on silicon”and“all as metasurface”fabrication *** this study,we aim to encourage the metasurface researchers,that for a broad deployment of the technical solution,economic,environmental,and other commercial requirements should be planned together,early on in the design phase.

关键词： smart radio environment(SRE) reconfigurable intelligent surface(RIS) 6G passive/active metasurface

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Fine-Tuning DenseNet121 for Effective Waste Categorization: Insights into Automated Sorting Systems

Fine-Tuning DenseNet121 for Effective Waste Categorization: ...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Kant, Vishnu Mishra, Amit Kumar Chandhar, K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Dehradun Adjunct Professor Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India

ISBN: (纸本)9798331517953

The rapid expansion of industrialization and urbanization has resulted in a notable increase in garbage generation, so effective waste management systems become ever more important. Automated solutions are especially important as conventional garbage sorting techniques are labour-intensive and prone to human error. This work investigates the application of convolutional neural network architecture DenseNet121 to categorize waste into six groups: cardboard, glass, metal, paper, plastic, and rubbish. We seek to maximize classification accuracy by using DenseNet121's dense connection and thereby minimize model parameters. We trained DenseNet121 for the task using a dataset taken from Kaggle and obtained an accuracy of 88%. The outcomes show the possibilities of the model in automating waste classification, so supporting sustainable cities, resource economy, environmental preservation, responsible manufacturing and consumption habits, and enhanced recycling procedures. We then address the design, training approach, and performance criteria, stressing the advantages and difficulties of including such systems in major waste management projects. © 2024 IEEE.

关键词： Convolutional neural networks

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An Extensive Overview of anamoly Detection Analysis in FL-Based IDS

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Procedia computer science 2025年 259卷 326-335页

作者： Himani Tyagi Abhilasha Singh Research Scholar Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad Associate Professor Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi- NCR Campus Ghaziabad

In this paper, we present a Federated Learning-based Intrusion Detection System (FL-IDS) aimed at enhancing security in Connected Autonomous Vehicles (CAVs) within IoT environments. The novelty lies in the dual utilization of Support Vector Machine (SVM) and Convolutional Neural Networks (CNN) for anomaly detection, leveraging the Flower framework for privacy-preserving, decentralized model training. Unlike traditional centralized approaches, FL-IDS ensures data confidentiality by sharing only model updates. Using the NSL-KDD dataset, our evaluation highlights superior accuracy and loss metrics, validating the efficiency of both machine learning and deep learning methods for detecting DDoS, Probe, and U2R attacks. This work contributes to the advancement of secure and efficient vehicular communication systems, providing scalable solutions for real-world IoT deployments.

关键词： Federated Learning Deep Learning Machine learning CAV IDS Edge Computing

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