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检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

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Pioneering Minimalist Speech Analysis through Optimized Spectral Features Machine Learning models 6

Pioneering Minimalist Speech Analysis Through Optimized Spec...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Harsha Vardhini, P.A. Shiva Prasad, S. Vishnu Sai, M.H.S. Santoshi, Ch Konduru, Dhanush Vignan Institute of Technology and Science Department of ECE Telangana Deshmukhi India Telangana India Matrusri Engineering College Department of CSE Telangana India Integrated Software Engineering VIT Department of CSE Tamilnadu Vellore India

ISBN: (纸本)9798350306613

In the contemporary landscape, speech processing stands as a cornerstone across diverse applications, with gender identification emerging as a pivotal task in numerous domains, including law enforcement for swift resolution of criminal cases. the discernment of gender from speech recordings, especially in languages such as Telugu, holds paramount importance given its widespread usage and cultural significance in South India. this paper addresses the imperative need for robust gender identification methodologies tailored specifically for Telugu speech. Leveraging the rich heritage of Telugu as one of the most historically significant and widely spoken Dravidian languages, our study delves into the application of sophisticated statistical models, namely Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM), to unravel the gender nuances embedded within Telugu speech patterns. To facilitate this identification task, we meticulously curated a bespoke dataset, drawing from diverse sources to ensure representativeness and reliability. Our methodology encompasses an array of advanced feature extraction techniques, including Mel Frequency Cepstral Coefficient (MFCC), augmented with first and second-order differentials, and Zero Crossing Rate (ZCR). Moreover, to streamline the analysis and optimize accuracy, we employ Principal Component Analysis (PCA) for feature reduction, thus enhancing computational efficiency without compromising on discernment accuracy. the efficacy of our approach is substantiated through rigorous evaluation and comparison analyses, wherein we meticulously assess the performance of both original and reduced feature sets. Our findings underscore the effectiveness of our methodology in achieving notable accuracy levels, thereby underscoring its potential for real-world applications in gender identification from Telugu speech data. © 2024 IEEE.

关键词： Machine Learning models MFCC PCA speech Analysis ZCR

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Development of a Prediction System for Criminality and Suicidal in Sri Lanka 5

Development of a Prediction System for Criminality and Suici...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Ranasinghe, D.P.D. Fernando, R.D.T.S. Girambe, D.G. Arambegama, D.M.H.K.D. Chathurika, Bhagyanie Jayasinghe, Dulani Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350358131

this research confronts the growing challenges of rising suicide rates and crime in Sri Lanka through a dual approach, harnessing advanced machine learning and time series analysis. the innovative methodology, merging 'Feature Importance Weighting and Ensemble of Diverse Trees with the Random Forest Regressor,' achieves impressive accuracy of 93% for factors and 94% for methods. Additionally, the accuracy rates of 94% for education and 93% for occupationbased predictions enhance this comprehensive approach, offering a profound understanding of suicidal incidents and enabling precise prevention strategies. Simultaneously this study employs an ensemble of AutoRegressive Integrated Moving Average (ARIMA) models for crime prediction. this approach consistently attains high accuracy, often surpassing 90%, such as 96% and 97%. Utilizing multiple models enhances predictive accuracy and hotspot identification, granting law enforcement and policymakers flexibility to address enduring yearly hotspots and evolving dynamic hotspots. this research bridges the comprehension of suicide and crime dynamics in Sri Lanka, providing invaluable insights for evidence-based policies and targeted interventions. © 2023 IEEE.

关键词： Forecasting

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Light-weight Sketch Recognition with Knowledge Distillation 6

Light-weight Sketch Recognition with Knowledge Distillation

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6th international conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Huynh, Viet-tham Nguyen, Tam.V Tran, Minh-Triet Software Engineering Laboratory Faculty of Information Technology University of Science VNU-HCM Viet Nam Vietnam National University Ho Chi Minh City Viet Nam University of Dayton Department of Computer Science United States

ISBN: (纸本)9798350327410

Recognizing hand-drawn sketches is a promising starting point for various applications, such as assisting artists in creating 3D environments for games or virtual environment scenes quickly and efficiently from concept arts. In addition, by understanding drawings, we can generate 3D models that can be used for further design and development. thus, in this paper, we aim to develop a novel lightweight network that can accurately recognize sketch drawings. We propose a lightweight-yet-efficient neural network based on MobileNetV2 for sketch recognition and employ knowledge distillation to train the proposed model from EfficientNet-B4. To evaluate the accuracy of the proposed method, we collect a dataset of sketches comprising 1800 drawings in 12 categories, ranging from furniture to animals. the experimental results show that our network model achieves an accuracy of 96.7%, with 96.9% precision, 96.7% recall, and 96.7% F1-score. these results demonstrate that the proposed approach has great potential for practical sketch recognition applications, such as interior design or VR scene generation. © 2023 IEEE.

关键词： Distillation

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Unmasking Illusion of Daily-used Deepfake Applications through Landmark Focused Image 6

Unmasking Illusion of Daily-used Deepfake Applications throu...

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6th international conference on Multimedia Analysis and Pattern Recognition, MAPR 2023

作者： Nguyen-Nhat, Minh-Khoi Huynh-Ngo, Trung-Truc Tran, Minh-Triet Do, Trong-Le University of Science VNU-HCM Faculty of Information Technology and Software Engineering Laboratory Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

the wide spread of deepfake technology has raised significant concerns regarding the authenticity and trustworthiness of visual media. In this paper, we present a novel approach for deepfake detection by incorporating the Facial Landmark Focusing Image (FAFI) technique. To evaluate the effectiveness of our method, we curated a comprehensive dataset specifically designed to capture deepfakes prevalent in daily-used applications. the dataset comprises diverse video clips obtained from different famous platforms, ensuring a realistic representation of deepfake scenarios encountered in everyday life. through extensive experiments and evaluation, we compare the performance of our proposed feature with a multi-scale retinex feature in baseline models using widely adopted evaluation metrics. the results showcase the superiority of the FAFI technique, highlighting its ability to enhance deepfake detection accuracy. Code and dataset will be made publicly available upon acceptance. © 2023 IEEE.

关键词： Face recognition

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AI-enabled Crop Health Monitoring and Nutrient Management in Smart Agriculture 6

AI-enabled Crop Health Monitoring and Nutrient Management in...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Swarnkar, Suman Kumar Dewangan, Leelkanth Dewangan, Omprakash Prajapati, Tamanna Manishkumar Rabbi, Fazle Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India Swangi Wardha India Kalinga University Department of CS & IT Chhattisgarh Raipur India Department of Computer Science Shri D. N. Institute of Computer Applications Gujarat India Australian Computer Society Australia

ISBN: (纸本)9798350304480

the rapid advancement of artificial intelligence (AI) has paved the way for transformative applications in various fields, and agriculture is no exception. In this research paper, we explore the integration of AI technologies in smart agriculture to enhance crop health monitoring and nutrient management. By leveraging AI algorithms and techniques, we aim to improve the efficiency and sustainability of farming practices. the paper delves into the utilization of AI for early detection of crop diseases and pests, as well as the development of predictive models for precise nutrient management. Remote sensing and imaging technologies play a crucial role in real-time monitoring, allowing for informed decision-making. through a series of case studies and evaluations, we demonstrate the effectiveness and potential benefits of AI-enabled crop health monitoring and nutrient management in modern agriculture. Despite the promising results, challenges and limitations are discussed, and future directions for research and application are suggested. © 2023 IEEE.

关键词： AI-enabled agriculture artificial intelligence crop diseases crop health monitoring machine learning nutrient management pest detection precision farming real-time monitoring remote sensing smart agriculture

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Human-AI Co-Creation of Art Based on the Personalization of Collective Memory 6

Human-AI Co-Creation of Art Based on the Personalization of ...

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6th Asian conference on Artificial Intelligence Technology, ACAIT 2022

作者： Wu, Zhuohao Li, Yanni Ji, Danwen Wu, Dingming Shidujaman, Mohammad Zhang, Yuan Zhang, Chenfan Communication University of China School of Animation and Digital Arts Beijing China Tongji University College of Design and Innovation Shanghai China Shenzhen University College of Computer Science and Software Engineering Shenzhen China American International University-Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh School of Design Politecnico di Milano Milan Italy

ISBN: (纸本)9781665453110

Artificial intelligence (AI) is trained with data, especially texts, numbers, images, videos and music on Internet. these data all together across time and space make a collective memory of the world. the latest large-scale AI models give people a chance to create out of a large pool of this collective memory, which they won't be able to access before, and communicate with AI in both human natural language and the unique machine supported ways. As demonstrated and discussed in this paper, effective and efficient workflows can be built up for human and AI to co-create meaningful results based on both the collective memory of the world and the personalized ideas and tastes. this kind of human-AI co-creation has a new force with great potential, and expect a new strategy and philosophy to guide human-AI collaboration. © 2022 IEEE.

关键词： Artificial intelligence

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Sizing an Off-Grid Photovoltaic System for a Regular East African Residence 6

Sizing an Off-Grid Photovoltaic System for a Regular East Af...

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6th international conference on Electrical Information and Communication Technology, EICT 2023

作者： Fahim, Khairul Eahsun Amin, Md Hasnatul Islam, Md. Rakibul Rahman, Maria Das, Adri Shankar Al Jonayed, Khondaker Labib Shihab, Nahid Ahmed ZNRF University of Management Sciences Dhaka Bangladesh Brac University Department of Electrical & Electronic Engineering Dhaka Bangladesh

ISBN: (纸本)9798350370836

Power has emerged as one of the most important factors in improving East African economies and living conditions. Kenya is highly dependent on fossil fuels that cause pollution and high electricity. In this investigation, we proposed a method for calculating the average residential energy consumption. We compared the use of an independent photovoltaic (PV) system to a typical grid connection between them. It demonstrates the financial advantage of using PV-generated electricity, which reduces the energy cost from the grid and enables them to realize savings from the system over time. Consequently, this study measures the optimal size of solar panels on rooftops for home power systems that will meet all requirements for powering electrical appliances at an affordable price. A cost-based comparison of the two energy systems has been performed using the Hybrid Optimization of Multiple Energy Renewable (HOMER) software. In addition to the HOMER simulation, a manual, simple-to-calculate method is provided for sizing individual systems without the help of commercial software. © 2023 IEEE.

关键词： Solar panels

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Assessing the Circular Transformation of Warehouse Operations through Simulation 6

Assessing the Circular Transformation of Warehouse Operation...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Alasmari, Loloah Packianather, Michael Tuthill, Peter Liu, Ying Guo, Xiao School of Engineering Cardiff University Queen's Buildings The Parade CardiffCF24 3AA United Kingdom DSV-Global Transport and Logistics Scandinavia House Refinery Road Parkeston Essex HarwichCO12 4QG United Kingdom

Companies across industries increasingly prioritise environmental responsibility, driving initiatives to minimise environmental impact. the logistics industry generates substantial material waste, with cardboard being the primary packaging material. Applying Circular Economy (CE) principles to control usage is crucial for enhancing sustainability. However, there is a significant lack of studies on transforming warehouses into more sustainable operations. this paper focuses on studying the ability to transform the linear supply chain of distribution warehouses into a circular supply chain by applying lean manufacturing principles to eliminate cardboard waste. A framework outlining the project's methodology is presented to illustrate the steps taken to apply the concept of CE. the paper also tests the capability to simulate warehouse operations with engineering software using limited available data to generate multiple scenarios. A comparative evaluation is shown to demonstrate the effectiveness of each alternative and determine the optimum solution. Results indicate that models that do not consider handling the recycling process inside the warehouse demonstrate increased flexibility and improved efficiency. Furthermore, suggestions for future improvements are presented, assisting that on-site actions are grounded in a simulation that reflects reality. © 2025 the Authors.

关键词： Circular economy

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Uncertainty Quantification for Digital Twins in Smart Manufacturing and Robotics: A Review 6

Uncertainty Quantification for Digital Twins in Smart Manufa...

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6th international conference on Advancements in Materials and Manufacturing, ICAAMM 2024

作者： Battula, S. Alla, S.N. Ramana, E.V. Kiran Kumar, N. Bhanu Murthy, S. Department of Mechanical Engineering VNR Vignana Jyothi Institute of Engineering and Technology Vignana Jyothi Nagar Pragathi Nagar Nizampet Telangana Hyderabad500090 India

this paper elaborates on the large number of Uncertainty Quantification (UQ) techniques that have been proposed to enhance the reliability and the fidelity of Digital Twins that are increasingly finding applications in domains like Robotics and Smart Manufacturing. Digital twins are virtual duplicates or virtual models of a physical asset;they use advanced techniques such as data analytics and simulation-driven methods. However, the development and use of these advanced systems are plagued by a host of uncertainties, which are mainly introduced from sensor noise, intermittent connectivity, biases from data processing, and model abstractions and simulation stochasticity. Such uncertainties can be quantified by methods such as frequentist statistics, interval analysis, Bayesian inference, and random sampling. the mapping is important in gaining insights into these UQ methods and their associated advantages and limitations and the mitigation guidelines are to be used throughout the Digital Twin pipeline. UQ at its core involves real-time adaptive control in dynamically changing environments that leverage state awareness towards responsive action within predictive control models and feedback systems. In addition, machine learning algorithms support the ability to make better decisions from the identification of patterns in historical data to make plans for responsive trajectories of robots. UQ further allows the collaboration of human and machine, giving early warnings on anomalies and risks that enhance visibility which further fosters coordination and communication during disruptive situations. Robust development of digital twins for robotics and manufacturing relies on integrated UQ practices. the current review provides best practices, insights, and guidelines on the application of UQ across modeling, control strategies, and collaborative workflows aimed at delivering actionable and reliable insights from digital twin simulations, analytics, and decision support. ©

关键词： Smart manufacturing

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Alzheimer Disease Progression Forecasting: Empowering models through hybrid of CNN and LSTM with PSO Op-Timization 6

Alzheimer Disease Progression Forecasting: Empowering Models...

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6th IEEE international conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Deshpande, Pallavi Dhabliya, Ritika Khubalkar, Deepti Upadhye, Pawan Arunkumar Wagh, Kapil Adhar Khetani, Vinit EnTC VIIT Pune India Yashika Journal Publications Pvt. Limited Maharashtra Wardha India Pune India Bharati Vidyapeeth College of Engineering Department of Electronics and Telecommunication Maharashtra Pune India MS Pune India Cybrix Technologies Maharashtra Nagpur India

ISBN: (纸本)9798350306613

A common neurodegenerative disease, Alzheimer Disease (AD) affects society. Early intervention and personalized care require accurate condition prediction. A hybrid model using Convolutional Neural Networks (CNN) and Long Short-Term Memory networks (LSTM) and Particle Swarm Optimization (PSO) is developed in this study to optimize performance. this research uses a large MRI dataset with important neuroimaging data. this study uses a dataset to train and validate our models, enabling a data-centric approach to AD progression. Forecasting involves predicting future events or outcomes using available data. AD causes cognitive decline and memory loss, making healthcare more complicated. Timely prognosis is essential for prompt interventions and personalized patient care. Conventional forecasting models like CNN and LSTM are good at predicting disease progression. CNN excels at capturing spatial dependencies in datasets, while LSTM excels at temporal sequences. We proposed a novel hybrid model to take advantage of both architectures. this paper uses Particle Swarm Optimization (PSO), an effective optimization algorithm, to fine-Tune hybrid model parameters. the goal is to improve model forecasting accuracy. In this study, the hybrid CNN-LSTM model with and without PSO accurately predicted AD progression. Our analysis includes accuracy, precision, recall, F1-Score, and ROC AUC to assess model efficacy. this study advances predictive analytics in healthcare and offers new ways to improve AD patient outcomes. © 2024 IEEE.

关键词： Long short-term memory

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