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Intelligent Technologies (CONIT), International Conference on

作者： A Prabagaran Vinay Dwivedi Neelam Jyoti Rani J Angelin Blessy P Padmanabhan Gupshup Technology India Pvt Ltd Bengaluru India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Dept of Computer Application School of Computing Science and Engineering Galgotias Univeristy India Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Alzheimer’s, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals’ independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer’s disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer’s disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD.

关键词： Surveys Accuracy Magnetic resonance imaging Predictive models Convolutional neural networks Alzheimer's disease Kernel

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities 7

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Nasreen, A. Kathija Shenbagapriya, M. Seeni, Senthil Kumar Veda, Parthiban Meenakshi, B. Murugan, S. Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science And Technology Centre For Yoga College of Engineering And Technology Chenagalpattu Chennai Tamil Nadu Kattankulathur India Sri Sairam Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. This research proposes an advanced robotics, Internet of Things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. The proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. The RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. This dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. The device also warns cleaning personnel when certain areas need quick attention. This novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. This study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage. © 2024 IEEE.

关键词： Internet of things

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring

DL-Based Somnolence Detection for Improved Driver Safety and...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Peddinti. Neeraja R G Kumar M. Sunil Kumar Kazi Kutubuddin Sayyad Liyakat M. Sowmya Vani Department of computer Application School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur AP Department of computer science and engineering School of Computing Mohan Babu University (erstwhile SreeVidyanikethan Engineering College) Tirupathi AP India Department of computer Science and Engineering Brahmdevdada Mane Institute of Technology Solapur Maharashtra India

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads.

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RIS-Assisted Indoor Visible Light Positioning Based on Sparse Bayesian Learning 3

RIS-Assisted Indoor Visible Light Positioning Based on Spars...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Wang, Yuhao Wu, Sifan Yu, Lisu Xu, Chuan Wang, Zhenghai Cai, Xiangxiang School of Information Engineering Nanchang University Jiangxi Nanchang330031 China Digital Technology Application Industry College Shangrao Normal University Jiangxi Shangrao334001 China School of Mathematics and Computer Science Nanchang University Jiangxi Nanchang330031 China

ISBN: (纸本)9798350308327

Visible light communication (VLC) relies heavily on the line of sight (LoS). Once the LoS link is blocked, the communication efficiency will be greatly reduced. The indoor environment is complex, and it is inevitable that the LoS link will be blocked. In order to solve such problem, we have integrated the reconfigurable intelligence surface (RIS) into the indoor visible light positioning (VLP) system to enhance the positioning accuracy when the LoS link is blocked. We have proposed a sparse Bayesian learning (SBL)-Based algorithm to solve the formulated joint-optimization problem of estimated position, noise variance, and sparsity. This algorithm uses Bayesian reconstruction theory and conditional probability expressions to calculate the mean and covariance matrix of the formula, and further obtain the updated noise variance value and sparsity value, thereby finally attain the accurate position. From the simulation results, the positioning accuracy can reach 0.41m when the cascade channel length is 10m. © 2023 IEEE.

关键词： Covariance matrix

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Multimodal Depression Detection Using Audio Visual Cues

Multimodal Depression Detection Using Audio Visual Cues

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computer science and Emerging Technologies (CSET), International Conference on

作者： Gyanendra Tiwary Shivani Chauhan Krishan Kumar Goyal Computer Science and Engineering Bhagwant University Ajmer India Computer Application Department Raja Balwant Singh Management Technical Campus Agra India

Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches.

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Analytical Evaluation of Web Performance Testing Tools: Apache JMeter and SoapUI

Analytical Evaluation of Web Performance Testing Tools: Apac...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Vatsya Tiwari Sachin Upadhyay Jayati Krishna Goswami Sanjiv Agrawal Department of Computer Science & Applications BSA College of Engineering & Technology Mathura Deparmtnent of Computer Engineering & Application GLAUniv. Mathura Computer Sciecnce & Engineering G L BAJAJ Group of Institutions Mathura

Computing on the Web is based on distributed system applications that use the client-server architecture. Since web-based applications have been beta-tested, the quality criteria cannot be fixed. Whenever a website is launched, the question is always raised as to how many HTTP requests of users can the website handle at once. As part of this paper, various tools for performance testing will be used, including Apache SoapUI, JMeter, etc., and selection tools for load testing will be discussed.

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry

A Machine Learning Approach to Compare Causal Inference Mode...

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Ambient Intelligence in Health Care (ICAIHC), International Conference on

作者： Sanskriti Sanjay Kumar Singh Apoorv Kumar Sinha Trilok Nath Pandey Biswa Mohan Acharya School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Application ITER SOA University Bhubaneswar India

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

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Bengali language-based Disease diagnosis system for rural people using Bidirectional Encoder Representations from Transformers

Bengali language-based Disease diagnosis system for rural pe...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Kailash Pati Mandal Prasenjit Mukherjee Baisakhi Chakraborty Souvik Ganguly Computer Science and Engineering National Institute of Technology Durgapur India Bachelor of Computer Application Dr. B.C. Roy Engineering College Durgapur India

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual’s illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual’s query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system’s performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively.

关键词： computer science Bit error rate Predictive models Linguistics Transformers Medical diagnosis Sustainable development

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Visualization and visual analysis of vessel trajectory data: A survey

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Visual Informatics 2021年第4期5卷 1-10页

作者： Haiyan Liu Xiaohui Chen Yidi Wang Bing Zhang Yunpeng Chen Ying Zhao Fangfang Zhou Institute of Data and Target Engineering Information Engineering UniversityZhengzhouChina School of Computer Science and Engineering Central South UniversityChangshaChina Rail Data Research and Application Key Laboratory of Hunan Province ChangshaChina

Maritime transports play a critical role in international trade and *** vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel *** and understanding these patterns are valuable for maritime traffic surveillance and *** essential techniques in complex data analysis and understanding,visualization and visual analysis have been widely used in vessel trajectory data *** paper presents a literature review on the visualization and visual analysis of vessel trajectory ***,we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data ***,we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in ***,we expound on the prospects of the remaining challenges and directions for future research.

关键词： Maritime traffic Vessel trajectory data Automatic identification system Visualization and visual analysis

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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Computational Intelligence and Sustainable engineering Solutions (CISES), International Conference on

作者： Vandana Rawat Paritosh Bisht Suryakant Shruti Kothiyal Neelam Singh Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Application Graphic Era Deemed to be University Dehradun India Department of Computer Science and Engineering IFP Energies Nouvelles (IFPEN) France

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy.

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