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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Nagendra Prabhu, S. Singh, Amarjeet Bhavya, B. Sreeja, K. Bhargavi, P. Srm Institute of Science Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai603203 India New Horizon College of Engineering Department of Computer Science and Engineering Department Bangalore India

ISBN: (纸本)9781665400527

Recently, Stress Prediction in every individual based on their profile and behaviour is a challenging task in the current sector. Current system is a manual process where it is difficult to identify the stress in the college students or employees. There is no automation for any stress prediction. System uses machine learning algorithms or AI algorithms to find out the stress levels, these technologies are used for application development. The purpose of this project is to reduce stress in students and employees. computer science has come a long way in the last few years. It is massive and multifaceted. It has been used in a variety of applications to meet the basic needs of human society. In the field of healthcare, machine learning has made significant progress. Stress is a deadly disease that kills a large number of people worldwide. How machine learning techniques can assist in reducing the risk of stress prediction, is examined in this study. © 2022 IEEE.

关键词： Personnel

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Transforming Diabetes Care with Predictive Machine Learning Techniques 4

Transforming Diabetes Care with Predictive Machine Learning ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Mariammal, G. Jasmine, R. Megiba Siva Rama Lingham, N. Poongothai, E. Prema, S. Sai Charan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Ponjesly College of Engineering Nagercoil India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India

ISBN: (纸本)9798331508845

Diabetes mellitus is a chronic metabolic disorder characterized by elevated blood sugar levels, affecting millions of individuals worldwide. Early detection and management of diabetes are crucial in preventing complications and improving patient outcomes. In recent years, machine learning techniques have shown promise in predicting diabetes risk based on various factors such as demographic information, medical history, and lifestyle habits. Verticaling through a not-too-well designed case study - This paper is an extensive examination and comparison of different ML techniques for diabetes prediction. It goes through the methodologies, datasets, features, performance metrics and challenges of current models that are based on machine learning. In addition, understand strengths and weaknesses of all the algorithms- SVM, Decision Trees, Random Forests, Logistic regression etc. Here the logistic regression algorithm is used to develop a predictive model for diabetes based on demographic and clinical features. After preprocessing the data, including handling missing values and scaling numerical features, trained a logistic regression model using a portion of the dataset. The model achieved a high accuracy of 90% on the testing set, indicating its effectiveness in distinguishing be- tween diabetic and non-diabetic individuals. © 2024 IEEE.

关键词： Logistic regression

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Approaches to the Development of an Effective Regulatory Ecosystem and Policies for Big Data Analytics in the Republic of Kazakhstan

Approaches to the Development of an Effective Regulatory Eco...

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2022 International Conference on Smart Information Systems and Technologies, SIST 2022

作者： Akatov, Kamil Yedilkhan, Didar Urnaliyev, Alibi Astana It University Department Of Computational And Data Science Nur-Sultan Kazakhstan Astana It University Department Of Computer Engineering Nur-Sultan Kazakhstan

ISBN: (数字)9781665467902

ISBN: (纸本)9781665467902

This paper studies the Big Data ecosystem and regulatory approaches for the technological issues used in Kazakhstan. Key points are considered in terms of the importance and necessity of managing data at various levels for decision-making and facilitating their introduction into economic circulation, including an analysis of international approaches and cases in the use of data by authorities and organizations. The main aim of this paper is to make analysis and prepare model for improving policies for the development of Big Data policies included analytics at national level. © 2022 IEEE.

关键词： Information management

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Streamlining Attendance with Fast Face Recognition Model 8

Streamlining Attendance with Fast Face Recognition Model

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Sundar, Koushik Ravikumar, S. Jeyalakshmi, J. Eugene Berna, I. Samuel, Prithi Citibank Na Jersey CityNJ United States Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science & Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidhyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Erode India School of Computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India

ISBN: (纸本)9798350376425

Facial recognition technology has become increasingly prevalent in today's world, with applications ranging from mobile phones to banking and workplace security. This research proposes a facial recognition-based solution to replace the manual attendance tracking process, streamlining the process and reducing human error. The proposed system integrates facial recognition, registration forms, and a Redis database to automate attendance tracking. By leveraging the ML search algorithm (similarity approach) and the Insight face Python module, the system can accurately identify individuals within a database. The system's implementation involves two phases: the initial phase focuses on integrating facial recognition, registration forms, and database access. The second phase involves utilizing software such as Streamlit and Dashboard to enhance user experience and visualization. This innovative solution offers a more efficient and accurate alternative to traditional manual attendance tracking methods, improving overall efficiency and reducing administrative burdens. By automating the process, the system contributes to a more streamlined and secure environment. © 2024 IEEE.

关键词： Database systems

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Hybrid2 Neural ODE Causal Modeling and an Application to Glycemic Response 41

Hybrid2 Neural ODE Causal Modeling and an Application to Gly...

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41st International Conference on Machine Learning, ICML 2024

作者： Zou, Bob Junyi Levine, Matthew E. Zaharieva, Dessi P. Johari, Ramesh Fox, Emily B. Institute for Computational and Mathematical Engineering Stanford University United States Broad Institute of MIT and Harvard United States Department of Pediatrics Stanford University United States Department of Management Science and Engineering Stanford University United States Department of Statistics Department of Computer Science Stanford University United States Chan Zuckerberg Biohub San Francisco United States

Hybrid models composing mechanistic ODE-based dynamics with flexible and expressive neural network components have grown rapidly in popularity, especially in scientific domains where such ODE-based modeling offers important interpretability and validated causal grounding (e.g., for counterfactual reasoning).The incorporation of mechanistic models also provides inductive bias in standard blackbox modeling approaches, critical when learning from small datasets or partially observed, complex ***, as the hybrid models become more flexible, the causal grounding provided by the mechanistic model can quickly be *** address this problem by leveraging another common source of domain knowledge: ranking of treatment effects for a set of interventions, even if the precise treatment effect is *** encode this information in a causal loss that we combine with the standard predictive loss to arrive at a hybrid loss that biases our learning towards causally valid hybrid *** demonstrate our ability to achieve a win-win, state-of-the-art predictive performance and causal validity, in the challenging task of modeling glucose dynamics post-exercise in individuals with type 1 diabetes. Copyright 2024 by the author(s)

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Hypothesizing the Transmission of Smells: Six Methods of Chemical Travel

arXiv

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arXiv 2024年

作者： Ahmad, Usman School of Computer Science and Communication Engineering Jiangsu University China Department of Computational Science The University of Faisalabad Pakistan

In this paper, we propose a hypothesis regarding the travel and movement of chemicals between locations. We introduce six distinct methods to explain this process. The chemicals referred to in this article are those that we detect by the nose and can travel through the air. These are generally known as odorants or volatile compounds. Method 1 involves decomposing chemicals and transmitting their formulas digitally. Method 2 embeds chemicals in water, freezes it into ice, and transports it to the receiver, where the chemicals are extracted upon melting. Method 3 transfers chemicals via water logistics, with the receiver extracting them from the water. Method 4 uses airtight pipelines to transport chemicals in water, which are then extracted by the receiver. Method 5 projects chemical odors into the air, where the receiver captures them using suction. Method 6 draws from optical fiber technology to transmit chemical odors through a cable, ensuring the chemical composition remains intact. Each method ensures the receiver experiences the same scent as the sender. © 2024, CC BY.

关键词： Volatile organic compounds

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Design and Performance Analysis of a Solar Powered Dough Maker 7

Design and Performance Analysis of a Solar Powered Dough Mak...

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7th International Conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Periyasamy, Muthuvel Pitchaimanickam, B. Dheepanchakkravarthy, Azhagesan Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnan Koil626126 India Srm Institute of Science and Technology Department of Computational Intelligence Tamilnadu Kattankolathur603203 India Bannari Amman Institute of Technology Sathi Department of Electrical and Electronics Engineering Erode India

ISBN: (纸本)9798350397284

All the domestic loads in the kitchen are powered by AC supply. Usages of domestic loads are drastically increasing each day and the power needs are also increasing, but generation of power is not sufficient to meet the demand. Because of the non-availability of the fuel and coal, the world focuses on generating power from renewable energy resources. Renewable energy in general and Photovoltaic (PV) in particular are generating DC supply. Hence, PV cannot be directly used, because our domestic loads are AC. DC to AC convertion causes losses and the efficiency is reduced. This system is proposed which comprises a PV generator, a Permanent Magnet Direct Current motor for driving a dough maker smart system for automated motion which can be controlled by any Smart phone. The main part of our system architecture has a micro-controller Atmega328P which drives the various directions of the dc motor for forward and reverse motion of the dough maker. Also, the proposed system is analyzed to obtain the performance ratio and average efficiencies for total system. Further, obtaining the optimum PV array size based on the Life Cycle Cost analysis for the dough maker is presented. The complete operation and implementation of this scheme is also shown and steady state results are presented. © 2023 IEEE.

关键词： Solar energy

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Secure E-Voting System using Deep Learning Techniques 2

Secure E-Voting System using Deep Learning Techniques

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2nd International Conference on Innovative Sustainable computational Technologies, CISCT 2022

作者： Kanchana, M. Gopirajan, P.V. Ramar, Kaladevi Hariharan, Shanmugasundaram School of Computing Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu India Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chennai India Department of Computer Science and Engineering Chennai India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9781665474160

Democracy is vital in many emerging economies because it places power in control of the general public, who choose government via the electoral procedure. Elections rely heavily on polling places and people. Numerous changes have happened in electoral processes since the year of the republic till now. Subsequently, Ballot sheets were implemented as a voting medium, but this was later replaced by electronic voting machines, in which voters must select from a list of buttons, each symbolizing a symbol. This paper proposes techniques for ensuring voter identification and privacy through fingerprint scanners and other biometric identification techniques-based machine learning algorithms. As a result, we offered a new approach to speed up the voting process and eliminate similar issues. For face identification, we employed Convolutional Neural Network (CNN) architectures such as Alex Net, Visual Geometry Group (VGG-16), and Random Forest Algorithms in this study. The accuracy provided by Alex Net, VGG-16, and Random Forest (R.F.) Algorithms was 94.16%, 98.45%, and 92%, respectively. VGG-16 architecture outperformed all other algorithms with a 98.45% accuracy. We utilized an ATmega328-based Arduino for finger detection. © 2022 IEEE.

关键词： Biometrics

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Enhancing Processor Performance using Bidirectional LSTM Memory Prefetching

Enhancing Processor Performance using Bidirectional LSTM Mem...

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International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT)

作者： Rachana Gade Aditya Bornare Saurabh Jog Amit Joshi Suraj Sawant Department of Computer Science and Engineering School of Computational Sciences COEP Technological University Pune India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533144

Memory prefetching plays a vital role in enhancing processor performance, with modern processors using various prefetching methods to manage data access patterns efficiently. Traditional prefetchers work well with predetermined patterns but struggle with the unpredictability of modern applications. Advanced techniques, especially those employing machine learning, aim to predict and prefetch data based on complex spatiotemporal patterns, yet they face significant challenges like the class explosion problem and labeling issues. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM) deep learning model to dynamically adapt to intricate access patterns, addressing these limitations by leveraging features, localization, and semantic locality to improve prediction accuracy and adaptability. By integrating insights from traditional and machine learning-based approaches, this model seeks to optimize data prefetching, advancing memory system efficiency and ultimately enhancing overall processor performance. These contributions provide a robust framework for designing adaptive and efficient processors that can meet the demands of modern computing environments.

关键词： Location awareness Adaptation models Technological innovation Prefetching Memory management Semantics Bidirectional long short term memory Predictive models Spatiotemporal phenomena Labeling

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Soft Measures for Extracting Causal Collective Intelligence 1

Soft Measures for Extracting Causal Collective Intelligence

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1st Workshop on NLP for science, NLP4science 2024

作者： Berijanian, Maryam Dork, Spencer Singh, Kuldeep Millikan, Michael Riley Riggs, Ashlin Swaminathan, Aadarsh Gibbs, Sarah L. Friedman, Scott E. Brugnone, Nathan Department of Computational Mathematics Science & Engineering Michigan State University United States Department of Computer Science Michigan State University United States Complex & Social Systems Lab Two Six Technologies United States University of South Alabama United States SIFT United States

ISBN: (纸本)9798891761858

Understanding and modeling collective intelligence is essential for addressing complex social systems. Directed graphs called fuzzy cognitive maps (FCMs) offer a powerful tool for encoding causal mental models, but extracting high-integrity FCMs from text is challenging. This study presents an approach using large language models (LLMs) to automate FCM extraction. We introduce novel graph-based similarity measures and evaluate them by correlating their outputs with human judgments through the Elo rating system. Results show positive correlations with human evaluations, but even the best-performing measure exhibits limitations in capturing FCM nuances. Fine-tuning LLMs improves performance, but existing measures still fall short. This study highlights the need for soft similarity measures tailored to FCM extraction, advancing collective intelligence modeling with NLP. © 2024 Association for computational Linguistics.

关键词： computational linguistics

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