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4th International Conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Sardar, Tanvir Habib Sarkar, Rashel Ahmed, Sheik Jamil Bandyopadhyay, Anjan Assistant Professor Department of CSE GITAM School of Technology GITAM University Bangalore India Associate Professor Department of Computer Science University of Science and Technology Meghalaya Ri-Bhoi India Assistant Professor School of Computer Science and Engineering & Information Science Presidency University Bangalore India Assistant Professor Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9789811994821

The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because external validation is usually not possible for clustering. This happens due to the unavailability of external proof. Although there are many methods developed and experimented with to validate the results obtained from clustering, it is always preferred to use more than a few cluster validity measures. The big data usually contain a high percentage of noise in it, making it necessary to use some additional techniques to incorporate while clustering big data. In this work, we have clustered document big data using fuzzy logic-based enhancement of traditional K-Means and K-Medoids. This work has suggested the Ensembling of seven different cluster quality validity measures to determine the best quality of fuzzy clusters. The Reuters standard document dataset is clustered using different cluster numbers, and the proposed ensemble methodology is proven to determine optimal numbers of clusters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Blockchain Technology for Secure Smart Grid Access Control

Blockchain Technology for Secure Smart Grid Access Control

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International Conference on Power engineering and Intelligent Systems, PEIS 2023

作者： Maurya, Vijendra Kumar Kothari, Rakshit Sachdev, Payal Rathore, Narendra Singh Associate Professor Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Dabok Udaipur India Assistant Professor Department of Computer Science and Engineering Geetanjali Institute of Technical Studies Dabok Udaipur India Campus Director Geetanjali Institute of Technical Studies Dabok Udaipur India

ISBN: (纸本)9789819972159

The use of smart grid technologies, in the energy sector has brought about upgrade, such as high efficiency, lower operating costs and better energy management. However, it has also established security aspects. Safeguarding the smart grid is of importance due to its role as essential infrastructure. The innovative potential of blockchain holds promise in enhancing the security of the grid. This paper aims to explore how blockchain technology can be utilized for protecting the grid. It delves into the security issues associated with grids and investigates how blockchain can address them. Additionally, it provides an analysis of studies on blockchain and its application, in grid protection while considering the pros and cons of utilizing blockchain based solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Smart power grids

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Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning 9th

Comparative Study of Future State Predictions of Unsteady Mu...

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9th International and 49th National Conference on Fluid Mechanics and Fluid Power, FMFP 2022

作者： Raj, Neil Ashwin Tafti, Danesh Muralidhar, Nikhil Karpatne, Anuj Department of Mechanical Engineering Virginia Tech Blacksburg United States Assistant Professor Department of Computer Science Stevens Institute of Technology Hoboken United States Department of Computer Science Virginia Tech Blacksburg United States

ISBN: (纸本)9789819971763

Flow across an array of solid obstructions is a common phenomenon observed in many applications such as multiphase flows, heat exchangers, and environmental flows. In this work, we aim to train deep learning models and to predict the time evolution of unsteady flow fields in a domain of randomly arranged 2D cylinders at Reynolds number 50. Two different approaches are used and compared in this paper, dynamic mode decomposition (DMD) which is a dimensionality-reduction algorithm based on singular value decomposition (SVD) and long short-term memory (LSTM) neural networks. In both cases, the model is trained on the first 165 time steps and then is tested on predicting the next 300 time steps. Two flow fields with different spectral characteristics are used to compare the performance of the two techniques. The LSTM architecture owing to its ability to learn nonlinear dynamics performs better than the DMD algorithm in the case with more temporal time scales present. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dynamic mode decomposition

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Intelligent Irrigation Mechanism to Enhance The Sensor Network Topology by Using Machine Learning And Artificial Intelligence 2

Intelligent Irrigation Mechanism to Enhance The Sensor Netwo...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Identification of Autism Spectrum Disorder from Functional MRI Using Deep Learning 4th

Identification of Autism Spectrum Disorder from Functional M...

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4th International Conference on Advances in Information Communication Technology and Computing, AICTC 2022

作者： Pavithra, R. Abirami, S.P. Krithika, S. Sabitha, S. Tharanidharan, P. Assistant Professor Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Coimbatore Institute of Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9789811998874

Autism spectrum disorder (ASD) is a collection of a way of continuing neurodevelopment illnesses identified by limited and repetitive behavioral patterns, as well as social and communication impairments. Despite the fact that symptoms are most common in childhood, diagnosis is sometimes delayed. Because the current ASD diagnostic technique is solely uncertain and questionnaire, requiring the physician to review the behavior and developmental history of a child. It's been suggested that behavioral symptoms in ASD are linked to brain findings of increased short-distance and diminished long-distance connections. The suggested approach makes use of brain imaging data from Autism Brain Imaging Data Exchange (ABIDE), a global multi-site database. For ASD identification, the proposed approach uses functional connectivity characteristics extracted from resting state functional MRI data. ASD patients’ brain connections could be disturbed. The presented method extracts time series from 122 regions of interest defined by the Bootstrap Analysis of Stable Clusters (BASC) and also extracts time series from 48 regions of interest defined by the Harvard Oxford (HO) brain atlas to create an efficient functional connectivity matrix for all individuals. Synthetic Minority Over-sampling Technique (SMOTE) an oversampling procedure employed for artificial data generation. The classification challenge is carried out using a Recurrent Convolutional Neural Network (RCNN) and Convolutional Neural Network (CNN) and results were compared. In both models, fivefold Cross-Validation is applied. The model performed better in the modified experiment, with RCNN achieving an accuracy of nearly 85%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Electronic data interchange

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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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Artificial intelligence in the detection and classification of dental caries

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Journal of Prosthetic Dentistry 2025年第5期133卷 1326-1332页

作者： Ahmed, Walaa Magdy Azhari, Amr Ahmed Fawaz, Khaled Ahmed Ahmed, Hani M. Alsadah, Zainab M. Majumdar, Aritra Carvalho, Ricardo Marins Assistant Professor Department of Restorative Dentistry Faculty of Dentistry King Abdulaziz University Jeddah Saudi Arabia Associate Professor Department of Orthopedic Surgery Faculty of Medicine Cairo University Cairo Egypt Assistant Professor Department of Civil Engineering Faculty of Engineering King Abdulaziz University Jeddah Saudi Arabia Consultant in Restorative Dentistry Dental Department East Jeddah General Hospital Ministry of Health Jeddah Saudi Arabia Graduate student Department of Computer Science Computer Science and Applications Virginia Polytechnic Institute and State University Blacksburg VA United States Professor Department of Oral Biological and Medical Sciences Faculty of Dentistry University of British Columbia Vancouver BC Canada

Statement of problem: Automated detection of dental caries could enhance early detection, save clinician time, and enrich treatment decisions. However, a reliable system is lacking. Purpose: The purpose of this study was to train a deep learning model and to assess its ability to detect and classify dental caries. Material and methods: Bitewings radiographs with a 1876×1402-pixel resolution were collected, segmented, and anonymized with a radiographic image analysis software program and were identified and classified according to the modified King Abdulaziz University (KAU) classification for dental caries. The method was based on supervised learning algorithms trained on semantic segmentation tasks. Results: The mean score for the intersection-over-union of the model was 0.55 for proximal carious lesions on a 5-category segmentation assignment and a mean F1 score of 0.535 using 554 training samples. Conclusions: The study validated the high potential for developing an accurate caries detection model that will expedite caries identification, assess clinician decision-making, and improve the quality of patient care. © 2025 Editorial Council for The Journal of Prosthetic Dentistry

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GS-FFO Based Meta-Heuristic Clustering for Discovering Colossal Patterns in High-Dimensional Data 2nd

GS-FFO Based Meta-Heuristic Clustering for Discovering Colos...

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2nd Congress on Control, Robotics, and Mechatronics, CRM 2024

作者： Reddy, T. Sreenivasula Sathya, R. Nuka, Mallikharjuna Rao Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Assistant Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Annamalai Nagar Tamil Nadu Chidambaram608002 India Professor Department of Computer Applications Annamacharya Institute of Technology & Sciences YSR Kadapa Andhra Pradesh Rajampet516115 India

ISBN: (纸本)9789819768097

Colossal pattern mining, which involves deriving important relationships and insights from large and complex datasets, is an essential part of data analysis. Extracting association rules that satisfy preset minimum support and confidence criteria from a given database is the aim of association rule mining (ARM). Nevertheless, ARM often generates an unusually large number of association rules that are hard for end users to comprehend or validate, which lessens the significance of data mining discoveries. Based on the clustering methodology, this research study presents a new mining strategy. The process begins with a method called the Expressed Data Matrix (EDM), which transforms unstructured data into a structured format and enhances the quality and organisation of the information. Primary data cleaning processes are used to eliminate noise and inconsistencies from the data in order to further improve it. To address high-dimensionality issues and boost mining productivity, dimensionality reduction—which lowers the dataset to its most valuable features—is accomplished using Eigen-Principal Component Analysis (PCA). The core of this study is the hybridised clustering approach, or GS-FFO, which combines the Fruit Fly Optimisation technique (FOA) with Glow Swarm Optimisation (GSO). This technique improves the quality of clustering and reduces the probability of local optima convergence by integrating the benefits of both approaches. The suggested GS-FFO model outperforms with four datasets, including accident, pumbs*, retail, and yeast, with an accuracy of up to 98.5%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Dimensionality reduction

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Elicitation of Intracerebral Hemorrhage Using Deep Learning 2

Elicitation of Intracerebral Hemorrhage Using Deep Learning

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2nd International Conference on Signal and Information Processing, IConSIP 2022

作者： Patil, Saraswati Jaybhaye, Sangita M. Mamulwar, Ashwini Rambhad, Ayush Prajapati, Mihir Mante, Suresh Vishwakarma Institute of Technology Department of Computer Engineering Assistant Professor Pune411037 India

ISBN: (纸本)9781728168852

The paper presents modeling and prediction for Intracerebral hemorrhage. The elicitation of intracerebral bleeding from veins and arteries that damages the internal stability and mental control of an individual could help in life saving situations, thus reducing the risk of intracerebral hemorrhage to a great extent. Vascular bleeding involves a lot of fragilities, and therefore if not treated and diagnosed soon, this can cause life-threatening situations. Deep learning algorithms shows indubitable results for medical image recognition and classification in the healthcare sector. Neuro physicians face difficulties in diagnosing hemorrhage in the beginning stages of intracranial bleeding, thereby adding to possibilities of misdiagnosis. This challenge faced by Neuro physicians worldwide is considered for the research work in the present paper. It provides a solution model for elicitation of ICH within a CT scan with the support of CNN. An AI-based Web platform is developed to classify CT scans and predict ICH type using the model. © 2022 IEEE.

关键词： computerized tomography

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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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