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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Vimal, S.P. Peter, John Benito Jesudasan Kavitha, U. Bhuvaneswari, G. Manikandan, G. Thamizhamuthu, R. Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Capital One Services Llc RichmondVA United States Vel Tech RangarajanDr.Sagunthala R&d Institute of Science and Technology School of Commerce Tamil Nadu Chennai India Saveetha Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Rmk Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers' physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS' use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system's excellent damage detection accuracy is due to CNNs' advanced image processing and pattern recognition skills and IoT devices' sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field. © 2024 IEEE.

关键词： Convolutional neural networks

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Approximate Message Passing with Rigorous Guarantees for Pooled data and Quantitative Group Testing

arXiv

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arXiv 2023年

作者： Tan, Nelvin Cobo, Pablo Pascual Scarlett, Jonathan Venkataramanan, Ramji Department of Engineering University of Cambridge United Kingdom Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore Singapore

In the pooled data problem, the goal is to identify the categories associated with a large collection of items via a sequence of pooled tests. Each pooled test reveals the number of items of each category within the pool. We study an approximate message passing (AMP) algorithm for estimating the categories and rigorously characterize its performance, in both the noiseless and noisy settings. For the noiseless setting, we show that the AMP algorithm is equivalent to one recently proposed by El Alaoui et al. Our results provide a rigorous version of their performance guarantees, previously obtained via non-rigorous techniques. For the case of pooled data with two categories, known as quantitative group testing (QGT), we use the AMP guarantees to compute precise limiting values of the false positive rate and the false negative rate. Though the pooled data problem and QGT are both instances of estimation in a linear model, existing AMP theory cannot be directly applied since the design matrices are binary valued. The key technical ingredient in our analysis is a rigorous asymptotic characterization of AMP for generalized linear models defined via generalized white noise design matrices. This result, established using a recent universality result of Wang et al., is of independent interest. Our theoretical results are validated by numerical simulations. For comparison, we propose estimators based on convex relaxation and iterative thresholding, without providing theoretical guarantees. The simulations indicate that AMP outperforms the convex estimator for noiseless pooled data and QGT, but the convex estimator performs slightly better for noisy pooled data with three categories when the number of observations is small. © 2023, CC BY.

关键词： Iterative methods

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Unraveling the Prediction of Fine Particulate Matter over Jaipur, India using Long Short-Term Memory Neural Network 22

Unraveling the Prediction of Fine Particulate Matter over Ja...

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Proceedings of the 4th International Conference on Information Management & Machine Intelligence

作者： Uday Pratap Singh Vivek Saxena Anil Kumar Purushottam Bhari Deepika Saxena Department of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology India Department of Computer Science & Engineering Poornima Institute of Engineering and Technology India Department of Computer Science & Engineering Poornima University India

ISBN: (纸本)9781450399937

Fine particulate matter (PM2.5) is a perilous air pollutant for human health, especially when present at high airborne concentrations. The national clean air program (NCAP) aims at a 40 % reduction in particulate matter by 2026. Therefore, the prediction of PM2.5 has great significance in order to cope with challenges and taking counter majors to cope with air quality issues. The annual change in PM2.5 in Jaipur from 2018 to 2019 shows a sharp decreasing trend but from 2019 to 2020 gradual increasing trend is evident. On the other hand, a sharp increasing trend from 2020 to 2021 is evident while it again starts to fall between 2021 to 2022. The air quality of Jaipur reached a good level during the lockdown period as in all other parts of the country. But now it is reaching toward Hazardous level. As most natural systems exhibit nonlinearity, the prediction of PM2.5 in this study is accomplished by utilizing the Long Short-Term Memory (LSTM) model. The model employed in this study uses several LSTM layers along with dropout and dense layers. The result of the study shows that the model used in the study captures the internal dynamics of the underlying dynamical system, which governs PM2.5.

关键词： Air Quality

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Time Series Forecasting in Agriculture: Explainable Deep Learning with Lagged Feature Selection 19th

Time Series Forecasting in Agriculture: Explainable Deep Le...

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19th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2024

作者： Troncoso-García, A.R. Jiménez-Navarro, M.J. Lourdes Linares-Barrera, M. Brito, I.S. Martínez-Álvarez, F. Martínez-Ballesteros, M. Department of Computer Languages and Systems University of Seville Seville Spain Data Science & Big Data Lab Pablo de Olavide University Seville41013 Spain Escola Superior de Tecnologia e Gestão Instituto Politécnico de Beja Beja7800-295 Portugal Caparica2829-516 Portugal

ISBN: (纸本)9783031750120

Reference evapotranspiration is a crucial metric in agricultural contexts, characterizing the evapotranspiration rate from a well-hydrated surface and serving as a fundamental benchmark for water management and crop irrigation, especially in arid regions. This study applied neural networks that integrates a Temporal Selection Layer to enhance the prediction of reference evapotranspiration levels through feature selection. This approach not only aims to refine the accuracy of time-series forecasting but also enhances the interpretability and efficiency of the model by identifying the most relevant features and periods. Additionally, the study incorporates the SHAP technique to determine and explain the contribution of individual features to the model outputs. The data for this research was collected from several meteorological stations of the Sistema Agrometeorológico para a Gestão da Rega no Alentejo in Portugal between 2012 and 2022. The results show that the Temporal Selection Layer effectively identifies important features in agricultural data, and by employing the SHAP technique, we enhance the understanding of how these features influence predictions to improve farm management decisions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： agriculture Feature selection neural networks time series forecasting XAI

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Toward Sustainable Development Goals (SDGs) with Statistical Modeling: Recursive Bivariate Binary Probit

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IAENG International Journal of Applied Mathematics 2024年第8期54卷 1515-1521页

作者： Ratnasari, Vita Utama, Syirrul Hadi Dani, Andrea Tri Rian Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia the Department of Statistics Faculty of Science and Data Analytics Sepuluh Nopember Institute of Technology Surabaya Indonesia Statistics Study Program Department of Mathematics Faculty of Mathematics and Natural Sciences Mulawarman University Samarinda Indonesia

Poverty is still a global problem that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. In 2021, West Papua province will have the 2nd most significant percentage of poor people after Papua province, with 21.84% of the poor population. Poor households in West Papua province are dominated by families, with the Head of Household (KRT) working in the agricultural sector at 65.10 %. In this research, joint modelling was carried out between the level of household welfare and the employment sector of the head of the household in the West Papua province. It is suspected that these two variables have endogeneity problems, where one of the response variables becomes a predictor variable in the other equation, so a recursive bivariate binary probit regression model is used. Recursive bivariate binary probit regression parameter estimation uses Maximum Likelihood Estimation (MLE), but the results are not closed form, so it is continued using the Newton-Raphson iteration method. The results of hypothesis testing show that partially, variables that significantly influence the level of household welfare include the variable marital status, KRT formal/informal workers, health complaints, asset ownership status, migration status, number of household members, classification of area of residence (Village/City), age of head of household, and employment sector of head of household. Meanwhile, variables that significantly influence the choice of working in the agricultural sector include the director of household education, classification of area of residence (rural/city), and the age of the head of household. © (2024), (International Association of Engineers). All rights reserved.

关键词： Maximum likelihood estimation

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IoT In Increasing Human to Machine Interactions in the Automobile Sector 1

IoT In Increasing Human to Machine Interactions in the Autom...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Mindarta, Erwin Komara Kavitha, D. Poongundran, Mahizharuvi Priyadarshini, R. Sethu Ram, M. Moldabayeva, Magira Universitas Negeri Automotive Engineering Education Program Malang Indonesia School of Computer Science and Engineering Vellore Institute of Technology India Sri Meenakshi Government Arts College for Women Pg & Research Department of Computer Science Tamil nadu India School of Computer Science and Engineering College Vit Sr.Grade Ii Department Chennai India School of Engineering & Technology Department of Computer Science & Engineering Tirupati India International Education Corporation Department of Linguistics Almaty Kazakhstan

ISBN: (纸本)9781665497107

IoT technologies can facilitate machine-to-machine as well as human-to-machine interactions. Use of an automotive human-machine interface can help in exchanging information between vehicles and passengers or drivers. Organizations in automobile sector are using these technologies for providing their customers with seamless customer-experience, which has helped not only in enhancing profitability of the organization;however also in enhancing customer base. An advanced speech-recognition technology is used in the cars for automation of different functions that ensures extraordinary experience for a driver. This requires adoption of an advanced industrial user-interface that connects the human with the machine actively. Present study has analyzed the ways IoT has played a significant role in making human-to-machine interaction possible in automobile industry. In literature-review, different concepts and theories support the impact of IoT in improving human-machine interactions in automobile industry. This chapter has further analyzed the challenges related to the use of this technology. In methodology section of current study, the ways data has been collected in the study has been analyzed. In findings-section, objectives and variables of the study have been considered for designing relevant themes for providing proper insight into the effectiveness of IoT technologies in improving human -machine interactions in automobile industry. The findings section reveals that IoT usage in this industry can enhance customer-experience by improving human-machine interactions especially when using IoT-enabled cars. It has provided proper suggestions regarding the best ways of utilizing IoT in this sector while ensuring reduction of possible risks. © 2022 IEEE.

关键词： Automotive industry

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Managing Vulnerabilities in Software Projects: the Case of NTT data

Managing Vulnerabilities in Software Projects: the Case of N...

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Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

作者： Sabato Nocera Simone Romano Rita Francese Riccardo Burlon Giuseppe Scanniello Department of Computer Science University of Salerno Fisciano Italy NTT Data Salerno Italy

Background: Software vulnerabilities are flaws in application source code that can be exploited to cause harm, hence companies must devise strategies to manage ***: We want to understand how software vulnerabilities are managed in a big IT (Information Technology) service and consulting company like NTT ***: We conducted a focus group involving six software professionals working at NTT data and analyzed the gathered data through a thematic analysis ***: We found that application security standards are defined based on the needs of the clients (i.e., companies that commissioned NTT data the software to be developed) and the projects’ nature (i.e., the development of greenfield projects vs. maintenance of existing ones). Also, to detect software vulnerabilities, SAST (Static Application Security Testing) tools are mainly used; among these, SonarLint and SonarQube appear to be the de-facto standards for NTT data. Finally, not all software vulnerabilities are fixed; for example, the presence of some software vulnerabilities is tolerated by the clients, who take on the responsibility of not removing these ***: It seems that developers and NTT data clients are not averse to securing their code. NTT data follows the application security standards established with their clients. To detect software vulnerabilities, SonarLint and SonarQube appear to be the de-facto standards, so explaining to some extent the increasing attention on these tools by the software engineering research community.

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MedSay-Tamil: A Pharmacological-Translator Mobile Application for the aid of Native Tamil Speakers

MedSay-Tamil: A Pharmacological-Translator Mobile Applicatio...

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2023 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2023

作者： Joshua Alfred, J. Sai Srivathsan, V. Sasithradevi, A. Mohamed Mansoor Roomi, S. Vellore Institute of Technology School of Electronics Engineering Chennai India Vellore Institute of Technology School of Computer Science and Engineering Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Madurai India

ISBN: (纸本)9798350312126

Medication error is one of the major causes of death, as victims intake the wrong medicine or dosage which can cause chronic effects. One such cause of medication error comes from the misinterpretation of information due to language barrier. There is a need for translation of information in medicinal strips to create awareness to the user. For this purpose, we propose an Innovative Mobile Application Using Flutter and Dart, empowering all native-Tamil speakers who find difficulty in acquiring information from tablet strips. The app is designed to revolutionize the way people obtain information from tablet strips such as general information, dosage, side effects, etc. with the help of web scraping. With its advanced Optical Character Recognition (OCR) engine, the app can accurately recognize text from images and convert it into editable text. The app's user-friendly interface with its state-of-art innovation mainly focusing native-Tamil speakers, built using the Flutter framework, allows users to quickly and easily scan and process strings. The application then translates and displays the information present in the Tamil language along with a text-to-speech feature. Upon testing, the application displayed a 90% accuracy in retrieving relevant information about the tablet present in images. © 2023 IEEE.

关键词： Optical character recognition

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Synergizing SVM Classifier with Hybrid CNN Architecture for Handwritten Recognition 6

Synergizing SVM Classifier with Hybrid CNN Architecture for ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Sujay, V. Lakshmi, B. Seetha Venkatesan, T. Ram, S Sakthiya Sangeetha, E. Satheeshkumar, S. Krishna University College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Machilipatnam India Sri Shanmugha College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamil Nadu Namakkal India R.M.D. Engineering College Department of Mathematics Tamil Nadu Thiruvallur India Bannari Amman Institute of Technology Department of ECE Tamil Nadu Erode India Department of ECE Tamil Nadu Namakkal India

ISBN: (纸本)9798350306613

The integration of Hybrid Convolutional Neural Network (CNN) architecture with a Support Vector Machine (SVM) classifier is proposed in the study as an innovative technique for Handwritten Recognition. The necessity for the research originates from existing systems' limitations in effectively understanding handwritten material due to their dependence on basic models. Existing system limitations included low accuracy, particularly in the presence of variances in writing styles and situations. The proposed system takes advantage of CNN's feature extraction and representation capabilities and SVM's discriminative power in classification. The hybrid design improves the model's capacity to grasp complicated patterns in handwritten input, overcoming existing system limitations. The results show a considerable improvement in recognition accuracy after extensive testing and analysis, confirming the effectiveness of the proposed system. The results demonstrate the system's durability in dealing with a wide range of handwritten inputs, making it a potential alternative for real-world applications requiring accuracy and flexibility. © 2024 IEEE.

关键词： Convolutional Neural Network (CNN) Feature Extraction Handwritten Recognition Hybrid Architecture Recognition Accuracy Support Vector Machine (SVM) Classifier Writing Style

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Application of Machine Learning in Mental Diseases Diagnosis

Application of Machine Learning in Mental Diseases Diagnosis

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Information Technology (ACIT)

作者： Rahaf Alsawi Raed Alazaidah Ghassan Samara Haneen Alzoui Omar Alidmat Esraa Abu Elsoud Mohammed Talat Khouj Anwar Katrawi Mo’Ath Alluwaici Department of Data Science and AI Faculty of Information Technology Zarqa University Zarqa Jordan Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia Department of Computer Science Faculty of IT Zarqa University Zarqa Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient’s way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person’s mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method.

关键词： Measurement Logistic regression Mood Machine learning Schizophrenia Feature extraction Depression Bayes methods Medical diagnosis Information technology

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