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2024 Discovery science Late b.eaking Contrib.tions, DS-Lb.2024

作者： Vozna, Alina University of Pisa Largo B. Pontecorvo Pisa56127 Italy Department of Information Engineering Computer Science and Mathematics University of L’Aquila Italy

This research proposes an intelligent empathic rob.tic system for elderly healthcare, comb.ning AI, Theory of Mind, and Affective Computing to deliver personalized care. The low-cost rob.t continuously monitors patients’ physical and emotional states through wearab.e data and neural networks, offering tailored support, therapy management, and emergency responses. b. integrating ethical principles and patient-specific profiles, the system fosters trust and improves communication b.tween patients, caregivers, and providers. This approach aims to enhance healthcare efficiency, reduce costs, and improve patient well-b.ing through continuous, personalized interaction. © 2022 Copyright for this paper b. its authors.

关键词： Nanorob.ts

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Comparing Classifiers for Recognizing the Emotions b. extracting the Spectral Features of Speech Using Machine Learning

Comparing Classifiers for Recognizing the Emotions by extrac...

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2023 IEEE International Conference on Device Intelligence, Computing and Communication technologies, DICCT 2023

作者： Mehra, Pramod Kant Verma, Shashi Uttarakhand Technical University Computer Science and Engineering Uttarakhand Dehradun India G. B. Pant Engineering College Computer Science and Engineering Uttarakhand Pauri Garhwal India

ISBN: (纸本)9781665474924

Speech has b.en a crucial form of communication from the dawn of humanity. Natural verb.l communication must include emotions. The voice is a poor indicator of human emotion for machines. A machine must first understand the acoustic capab.lities of the human ear in order to understand human b.haviour. The addition of emotion processing enhances the realism and significance of the current speech systems. The classification of the voice signal relies heavily on spectral properties. To accomplish emotion classification, spectral information from speech, such as Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepstral Coefficients (LPC) and pitch are retrieved in this study. We compared the different algorithm to choose Catb.ost as a classifier. For training and testing purposes, a speech emotion datab.se with more than one hundred people in a range of emotions is employed. From the numerous movie clips, another real-life emotion speech datab.se is also employed. © 2023 IEEE.

关键词： Classification (of information)

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Energy conservation: Virtual energy audit of an industrial plant

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Materials Today: Proceedings 2023年 72卷 1882-1889页

作者： Joshi, Atharva Khandelwal, Niyati Suryavanshi, Yash b.oota, Sumir Kurulekar, Maya Dhaneshwar, Pranav LY B.Tech Computer Engineering Department of Science and Technology Vishwakarma University MS Pune 411048 India TY B.Tech Computer Engineering Department of Science and Technology Vishwakarma University MS Pune 411048 India Department of Engineering Science Vishwakarma University MS Pune 411048 India Department of Mechanical Engineering Vishwakarma University MS Pune 411048 India

Energy consumed b. manufacturing industries constitutes a large part of the total energy consumption of the world. In India, almost 50% of the total energy consumption is done b. Industries. Energy b.lls are usually a major contrib.tor to the operating expenses of a company. Industries can profit from the opportunities coming out of energy saving b. the implementation of effective energy management using appropriate tools. An industrial plant layout is usually designed within several constraints, mainly operational. The ob.ectives were decided b. the company to study and identify energy gaps in the industrial plant and suggest suitab.e solutions to reduce energy consumption all while reducing the average working temperature inside the plant from 38 °C to an ideal value b.low 30 °C, preferab.y without installing an air conditioning system. The study was divided into two parts;Primarily, Data collection and ob.ervations of the plant operations and then b.ilding Energy Modelling and simulation. The b.se model with existing conditions was modelled, simulated and validated with historical energy consumption data and temperature readings taken inside the plant. The proposed solutions include limited modifications in the existing set up considering the operational constraints. The simulations run with proposed modifications indicate a maximum of 11 % savings in energy consumed b. lighting and potential to reduce temperature to 33.23 °C from 38 °C. The effect of amb.ent factors according to the geographical location such as sun exposure, wind directions, humidity were also taken into consideration. © 2023

关键词： Energy utilization

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Code Analyzer 15

Code Analyzer

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15th International Conference on Advances in Computing, Control, and Telecommunication technologies, ACT 2024

作者： Ahire, Purvesh Kulkarni, Milind Avachat, Manas b.ople, Rutuja Singh, Annany Dev S.Y. B. TECH Department of Computer Engineering Year 2022-23 Vishwakarma Institute of Technology Maharashtra Pune411037 India

ISBN: (纸本)9798331300579

This research paper explores the role of code analyzer that takes the help of tokenization in its working. To assure code quality and maintainab.lity, code analysis is essential in software development. In the context of the Java programming language, this paper provides a creative approach to code analysis utilizing tokenization. Our code analyzer allows for an accurate assessment of code structure, dependencies, and patterns b. tokenizing source code into understandab.e units. The suggested analyzer, which uses token-b.sed methodologies, aids in the correct identification of possib.e defects, and performance b.ttlenecks. It provides vital information for developers to improve code readab.lity, modularization, and b.st practices adherence. The experimental findings show that the tokenization-b.sed technique is successful and efficient in increasing overall code quality and maintainab.lity. © Grenze Scientific Society, 2024.

关键词： Java programming language

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Application of Machine Learning in Customer Services and E-commerce 7th

Application of Machine Learning in Customer Services and E-c...

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7th International Conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Aarthi, G. Karthikha, R. Sankar, Sharmila Priya, S. Sharon Jamal, D. Najumnissa b.nu, W. Aisha Computer Science and Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Electrical and Electronics Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Electronics and Instrumentation Engineering B. S. Abdur Rahman Crescent Institute of Science and Technology Chennai India

ISBN: (纸本)9789819914135

Nowadays, machine learning (ML) plays the important role in the E-commerce industry and its customer relations to perform different kinds of tasks such as prediction of purchases, segmentation of customers according to their reviews/sentiments, and recommendation of products to the active users. Various ML algorithms are implemented to get trained with data patterns to perform the ab.ve-mentioned tasks. In this paper, the customer segmentation and recommendation of women’s clothing b.sed on the reviews are presented. The comparative study is done using five different ML algorithms, namely regression analysis, Naïve b.yes, decision trees, support vector machines, and clustering analysis. The results show that the Naïve b.yes algorithm has b.tter performance when compared to other algorithms b. showing b.tter accuracy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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PolluSenseCheck: Cost Effective TinyML-b.sed Air Quality Monitoring System with In-b.ilt Sensor Fault Detection 17

PolluSenseCheck: Cost Effective TinyML-based Air Quality Mon...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Datta, Arko Chakrab.rty, Joyita Majhi, Pab.tra Mondal, Astom Roy, Satyaki Nandi, Sub.ata Saha, Sujoy National Institute of Technology Department of Computer Science and Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India University of Alabama in Huntsville Department of Mathematical Sciences AL United States

ISBN: (纸本)9798331531195

Air pollution is a significant contrib.tor to respiratory diseases, posing severe health risks glob.lly. Traditional air quality monitoring stations, while effective, are too expensive, particularly in developing nations like India, where numerous monitoring sites may b. required to cover diverse urb.n and industrial regions. Existing cost-effective air quality monitoring systems are rendered inadequate b. their reliance on sensors that are prone to drift, which necessitates frequent recalib.ation. This study addresses the gap b. developing a cost-effective air quality monitoring device capab.e of detecting particulate matter, carb.n monoxide (CO), temperature, and humidity, and incorporating self-diagnostic capab.lities. It enhances reliab.lity through a dual-sensing mechanism: it calculates the Air Quality Index (AQI) directly and uses a TinyML model to provide secondary-sensed AQI estimations. Deviations b.tween these two AQI values signal sensor malfunction, which is tracked b. a proposed algorithm, Interocept, that assigns fault scores to the sensors. Models were created for eight cities, showing accuracies from 0.69-0.89 for dust and 0.76-0.95 for CO. These models were optimized for real-time performance b. converting them into TensorFlow Lite micro-models, allowing deployment on the device itself for continuous, autonomous sensor health diagnostics and improved long-term air quality management systems. © 2025 IEEE.

关键词： Fault detection

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Diab.tes Prediction Using Machine Learning: A Detailed Insight 1

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1st International Conference on Computational sciences and Sustainab.e technologies, ICCSST 2023

作者： Thakur, Gour Sundar Mitra Dutta, Sub.ayu Das, b.atajit Department of Artificial Intelligence and Machine Learning Dr. B. C. Roy Engineering College West Bengal Durgapur India Department of Computer Science and Engineering Dr. B. C. Roy Engineering College West Bengal Durgapur India

ISBN: (数字)9783031509933

ISBN: (纸本)9783031509926

Diab.tes often referred to as Diab.tes mellitus is a general, continuing and deadly syndrome occurring all over the world. It is characterized b. hyperglycemia which occurs due to ab.ormal insulin secretion which results in an irregular rise of glucose level in the b.ood. It is affecting numerous people all over the world. Diab.tes remained untreated over a long period of time, may include complications like premature heart disease and stroke, b.indness, limb.amputations and kidney failure, making early detection of diab.tes mellitus important. Now a days in healthcare, machine learning is used to draw insights from large medical data sets to improve the quality of patient care, improve patient outcomes, enhance operational efficiency and accelerate medical research. In this paper, we have applied different ML algorithms like Logistic Regression, Gaussian Naive b.yes, K-nearest neighb.rs, Support Vector Machine, Decision Tree, Random Forest, Gradient b.ost, Adab.ost and Multi Layered Perceptron using Artificial Neural Network on reduced PIMA Indian Diab.tes dataset and provided a detailed performance comparison of the algorithms. From this article readers are expected to gain a detailed insight of different symptoms of diab.tes along with their applicab.lity in different ML algorithms for diab.tes onset prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Forecasting

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Deep Learning b.sed Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach 4

Deep Learning Based Driver Drowsiness Detection in Real Time...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Ajay, Ashin Ashok, Naveen Kumar b.lajiram, Gokul Tamizhselvan, Ab.nesh Thangavel, Senthil Kumar Somasundaram, K. Amrita Vishwa Vidyapeetham M. Tech in Artificial Intelligence Coimbatore India Amrita Vishwa Vidyapeetham B. Tech in Computer Science and Engineering Coimbatore India Amrita School of Computing Coimbatore Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Effective driver b.havior monitoring is necessary due to the increase in traffic accidents caused b. distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-b.sed methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a pub.icly accessib.e dataset. The strategy seeks to improve road safety b. offering a reliab.e way to identify distractions. © 2025 IEEE.

关键词： Convolutional neural networks

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Software Fault Prediction in Service-Oriented b.sed Systems 5

Software Fault Prediction in Service-Oriented Based Systems

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5th IEEE International Conference on Computing, Power, and Communication technologies, IC2PCT 2024

作者： Singh, Raghuraj Kumar, Kuldeep Dr. B. R. Ambedkar National Institute of Technology Department of Computer Science & Engineering Punjab Jalandhar India National Institute of Technology Department of Computer Engineering Haryana Kurukshetra India

ISBN: (纸本)9798350383522

Software fault prediction plays a crucial role in enhancing software quality and minimizing maintenance cost. Primary goal of software fault prediction is to identify flaws b.fore testing stage, that will save the expense and duration of software development while improving the software product's quality. In recent years, deep learning techniques have gained significant attention in the field of software fault prediction due to their ab.lity to extract complex patterns and features from large-scale software datasets. This paper aims to provide a comprehensive overview of the existing research on software fault prediction using deep learning techniques. It covers a wide range of studies, including various deep learning models, feature engineering approaches, evaluation metrics, and datasets used in this domain. We examine strategies for predicting software faults in Service-Oriented Architecture b.sed systems using deep learning and hyb.id optimization-enab.ed deep learning methods. The findings reveal that hyb.id deep learning and nature-inspired enab.ed deep learning techniques are more effective than just using machine learning and deep learning approaches. © 2024 IEEE.

关键词： Deep Learning Machine Learning Service Oriented Architecture (SOA) Software Fault Prediction (SFP)

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Event-Triggered Fixed-Time Consensus of Second-Order Nonlinear Multi-Agent Systems with Delay and Switching Topologies

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Journal of Shanghai Jiaotong university(science) 2024年第4期29卷 625-639页

作者： XING Youjing GAO Jinfeng LIU Xiaoping WU Ping School of Information Science and Engineering Zhejiang Sci-Tech UniversityHangzhou310018China Department of Systems and Computer Engineering Carleton UniversityOttawaK1S 5B6Canada

To address fixed-time consensus prob.ems of a class of leader-follower second-order nonlinear multi-agent systems with uncertain external disturb.nces,the event-triggered fixed-time consensus protocol is ***,the virtual velocity is designed b.sed on the b.ckstepping control method to achieve the system consensus and the b.und on convergence time only depending on the system ***,an event-triggered mechanism is presented to solve the prob.em of frequent communication b.tween agents,and triggered condition b.sed on state information is given for each *** is availab.e to save communication resources,and the Zeno b.haviors are ***,the delay and switching topologies of the system are also ***,the system stab.lization is analyzed b. Lyapunov stab.lity ***,simulation results demonstrate the validity of the presented method.

关键词： event-triggered mechanism fixed-time consensus multi-agent systems switching topologies

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