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3d Printing: basic principles and applications

Materials Today: Proceedings 2022年 51卷 842-849页

作者： Chetan M. Thakar shailesh s. Parkhe Ankit Jain Khongdet Phasinam G. Murugesan Randy Joy Magno ventayen Department of Mechanical Engineering Government College of Engineering Karad Maharashtra India Department of Mechanical Engineering SNJB's Shri H H J B Polytechnic Chandwad (Nashik) India Department of Mechanical Engineering Amity University Jaipur Rajasthan India Faculty of Food and Agricultural Technology Pibulsongkram Rajabhat University Phitsanulok Thailand Computer Science and Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India University Director Department of Public Relations Publication and Information Office Pangasinan State University Philippines

Due to the economic growth and worldwide interference with today’s globalization and industrialization, the manufacturing sector has gained greater prominence in the last decade. Many fresh revolutionary tactics have been created, and revolution is an endless narrative. Recent popularity for 3D and additive printing concerns established norms worldwide. In contrast, the manufacture rate, quality and efficiency of existing systems are greatly enhanced. This recurrent technological transformation pushes firms to adopt these new technologies to their production methods. Most manufacturers are unlikely to fit into the new system because of a number of issues such as capital shortages, lack of funds, lack of knowledge, lack of professional organizations and so on. besides with these technical advances may also play a major part in influencing the industrial industry, both ethical and reputational. In addition to consideration of supply chain risks and resilience, the recent growth in the manufacturing industry focusing on sustainability changes the way manufacturers are thinking. Multi-perspective analyzes are necessary for surviving to endure in the production environment. Contrary to this, manufacturers and shareholders have been showing in the last several years that the financial benefits not only improve production but also bring considerable returns to the business in view of the ecological and social advantages. In recent years, manufacturers have had a huge push in combination with economic, environmental and social principles to look at technical advancement.

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IOT sensor-based pollution management control technique

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Measurement: sensors 2022年 24卷

作者： Rayabharapu, vijay Kumar Rampur, veeresh Jyothi, N.M. Tripathi, vikas bhaskar, Thupakula Glory, K.b. Department of Civil Engineering B V Raju Institute of Technology Medak Telangana State Narsapur502313 India Department of Electronics Government First Grade College Naubad Karnataka State Bidar585401 India Department of Computer Science Engineering KoneruLakshmaiah Education Foundation Andhra Pradesh State Vaddeswaram522502 India Department of Computer Science Engineering Graphic Era Deemed to Be University Uttarakhand Dehradun248002 India Department of Computer Engineering Sanjivani College of Engineering Kopargaon Tal.Kopargaon Maharashtra Ahmednagar423 603 India Department of Engineering English KoneruLakshmaiahEducation Foundation Andhra Pradesh State Vaddeswaram522302 India

Environmental pollution generates heart muscle difficulties, breathing difficulties, and other medical problems affecting human civilization. The purpose of this paper was to offer a comprehensive resolution to the issue of pollution in modern civilization, which will enable us to track and address significant issues with general health, societal issues, and environmental problems. The proposed methodology seems to be an IoT unit in dealing with all these concerns, which includes effectively using various sensors to gather predictions of different kinds of contaminants on the earth. Present information on a website uses various sensors to gather estimates of various types of toxins in the earth and present information on a website that is accessible to everyone without charge. The devices and sensors that are installed at various points around us provide the research information. After gathering information from sensors, a comparison of the information is made using the Centralized Pollution Control Committee (CPCC). The proposed approach aids in reducing the pollution in the modern atmosphere, and as a result, pollution can indeed be decreased by making significant progress in that direction. Our proposed approach makes it possible for everyone in civilization to periodically measure and evaluate the quantity of pollution in their surroundings, a practice that leads inevitably to healthcare, ecological, as well as other social and economic problems. © 2022 The Authors

关键词： Medical problems

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Enhancing House Price Prediction Accuracy using Deep Learning and Hyperparameter Tuning with AEDA

Enhancing House Price Prediction Accuracy using Deep Learnin...

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International Conference on Image Information Processing

作者： R.Padma Priya K. Raghuveer K sriniv.s.basi Reddy A P. J. shriidhar N shirisha Department of Electronics and Communication Engineering School of Engineering Avinashilingam Institute for Home Science and Higher Education for Women Coimbatore Tamilnadu India Department of Business Management V.R.Siddhartha Engineering College Kanuru Andhra Pradesh India Department of CSE(AI&ML) St. Martins Engineering College Dhulapally Secundrabad Telangana India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Department of CSE Vinayaka Mission's Kirupananda Variyar Engineering College Salem Tamilnadu India Department of CSE MLR Institute of Technology Dundigal Hyderabad Telangana India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

Traditionally, real estate agents and appraisers used their expertise and market knowledge to estimate house prices. However, due to the expansion of data sources and developments in data science, there was a growing interest in developing predictive models to automate and enhance the house price prediction process. This research introduces the Adaptive Exploratory Data Analysis (AEDA) tool, specifically the AEDA by frequency for categorical values, implemented in Python. The tool provides an API that maps usual AEDA tasks in statistical modeling for generating plots and statistics. It automatically generates insights and establishes standards and procedures. The frequency of users in various variables highlights the challenges in data processing pipelines for predicting house prices using Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also discusses the evaluation of the hyperparameter tuning model for house pricing in the subsequent chapter. Model building, in the context of ML, involves creating a model that can learn from data without explicit instructions. Regression analysis is utilized to observe and resolve prediction issues. The suggested house price prediction model combines a joint self-attention mechanism with a specific feature parameter representation. While buyers and sellers focus on external features like building area, location, and construction year, the proposed model emphasizes internal features. It emphasizes the importance of hyperparameter tuning in ML and DL algorithms to improve model accuracy by minimizing residual errors during validation.

关键词： Deep learning Pipelines buildings Predictive models Prediction algorithms Data models Task analysis

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Applying Deep Learning and Transfer Learning Techniques for Improving Animal Intrusion Detection in Agriculture Farms

Applying Deep Learning and Transfer Learning Techniques for ...

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Data science, Agents & Artificial Intelligence (ICDsAAI), International Conference on

作者： R. saraswathi G. shobarani A. subramani D. Tamilarasan Department of Master of Computer Applications Sreenivasa Institute of Technology and Management Studies Chittoor Andhra Pradesh India Department of Artificial Intelligence Islamiah Women’s Arts and Science College Vaniyambadi Thiruppatur Tamil Nadu India Department of Computer Science M.V.Muthiah Government Arts College for Women Dindigul Tamilnadu India Department of Master of Computer Applications Sri Venkateswara College of Engineering and Technology Chittoor Andhra Pradesh India

Due to the nature of farmlands or provincial landscapes, monitoring is required to keep unauthorized persons out of the region and to safeguard the district from wildlife. various techniques point straight towards perception, which is primarily for human gatecrashers, but we sometimes forget that the primary opponents of such farmers are the animals who trash the crops. Crop damage caused by monster attacks is one of the most serious dangers to collect yield. Crop striking is becoming one of the most distant human-natural life conflicts as created land expands into formerly untamed life domain. The principle is compelling and forceful checking of the wild creatures’ squarely at home. This research paper is focused to develop computational techniques for identifying the animals that swoop into the agricultural area. because there are so many various kinds of organisms, genuinely recognizing them may be a difficult task. In this paper, we have used a camera to capture routine images of the surrounding area and to observe the entire homestead at regular intervals. With the aid of further developed learning models, we can distinguish between different stages of a creature’s development and play appropriate noises to frighten them off. The various convolutional brain network libraries and ideas that went into creating the model are acknowledged in this study. We have worked on the path of creature interruption discovery in agribusiness areas using deep learning and move learning techniques.

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Next-Gen Human-computer Interaction: A Hybrid LsTM-CNN Model for superior Adaptive User Experience

Next-Gen Human-Computer Interaction: A Hybrid LSTM-CNN Model...

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Electrical, Electronics, Information and Communication Technologies (ICEEICT), International Conference on

作者： Ravindra Changala J. Manoranjini M. Misba shital Kakad s sai Deepthi Ponni valavan M Department of Computer Science and Engineering Guru Nanak Institutions Hyderabad India Department Of AI&ML Rajalakshmi Engineering College Chennai India Department CSE (AI&ML) Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of AI & DS Vishwakarma Institute of Technology Pune Maharashtra Department IT B V Raju Institute of Technology (Autonomous) Narsapur Hyderabad India Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369090

In the rapidly evolving landscape of human-computer interaction (HCI), the demand for personalized and adaptive user experiences has grown exponentially. To meet this demand, Research propose a groundbreaking approach leveraging the fusion of Long short-Term Memory (LsTM) and Convolutional Neural Network (CNN) architectures. This hybrid LsTM-CNN model is designed to enhance adaptability, responsiveness, and user engagement across various interactive platforms. Traditional HCI models often struggle to effectively capture the dynamic nature of user behavior and preferences. by integrating LsTM and CNN, This model achieves a synergistic blend of temporal and spatial feature extraction capabilities. The LsTM component excels in capturing sequential dependencies and long-term patterns, enabling the system to learn from past interactions and anticipate future user actions. Meanwhile, the CNN component efficiently processes spatial information, extracting meaningful features from multimedia inputs such as images, videos, and text. One of the key strengths of proposed model is its adaptability to diverse user contexts and preferences. Through continuous learning and adaptation, it dynamically adjusts its interface, content, and interaction patterns to match the evolving needs and preferences of individual users. Moreover, the hybrid architecture enables real-time processing of multimodal inputs, facilitating seamless interaction across a wide range of devices and platforms. In experimental evaluations, Hybrid LsTM-CNN model demonstrated superior performance compared to baseline methods in terms of user satisfaction, engagement, and task completion rates. Furthermore, it exhibited robustness and scalability, making it suitable for deployment in real-world applications across domains such as e-commerce, entertainment, education, and healthcare. In summary, The proposed approach represents a significant advancement in HCI research, paving the way for next-generation interactiv

关键词： Human computer interaction Adaptation models scalability streaming media Feature extraction User experience Robustness Convolutional neural networks Long short term memory videos

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Towards Refining Developer Questions using LLM-based Named Entity Recognition for Developer Chatroom Conversations

arXiv

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arXiv 2025年

作者： Fathollahzadeh, Pouya Mezouar, Mariam El Li, Hao Zou, Ying Hassan, Ahmed E. Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada School of Computing Queen’s University KingstonONK7L 3N6 Canada Department of Mathematics and Computer Science Royal Military College of Canada KingstonONK7K 7B4 Canada

In software engineering chatrooms, communication is often hindered by imprecise questions that cannot be answered. Recognizing key entities (e.g., programming languages and libraries) and user intent (e.g., learning or requesting a review) can be essential for improving question clarity and facilitating better exchange. However, existing research using natural language processing techniques often overlooks these software-specific nuances. In this paper, we introduce softwarE-specific Named entity recognition, Intent detection, and Resolution classification (sENIR), a labelling approach that leverages a Large Language Model to annotate entities, intents, and resolution status in developer chatroom conversations. To offer quantitative guidance for improving question clarity and resolvability, we build a resolution prediction model that leverages sENIR’s entity and intent labels along with additional predictive features. We evaluate sENIR on the DIsCO dataset using a subset of annotated chatroom dialogues. sENIR achieves an 86% F-score for entity recognition, a 71% F-score for intent detection, and an 89% F-score for resolution status classification. Furthermore, our resolution prediction model, tested with various sampling strategies (random undersampling and oversampling with sMOTE) and evaluation methods (5-fold cross-validation, 10-fold cross-validation, and bootstrapping), demonstrates AUC values ranging from 0.7 to 0.8. Key factors influencing resolution include positive sentiment and entities such as Programming Language and User variable across multiple intents, while diagnostic entities (e.g., Error Name) are more relevant in error-related questions. Moreover, resolution rates vary significantly by intent: questions about API Usage and API Change achieve higher resolution rates, whereas Discrepancy and Review have lower resolution rates. A Chi-square analysis confirms the statistical significance of these differences. © 2025, CC bY.

关键词： Classification (of information)

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AC to DC Converter Execution using EMI Filtration system

AC to DC Converter Execution using EMI Filtration System

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Integrated Circuits and Communication systems (ICICACs), IEEE International Conference on

作者： Kathir I venkatasubramanian R sivakumar s Muthuraj b shelly Garg siranthini b Department of EEE V. S. B. Engineering College Karur India Department of EEE New Prince ShriBhavani college of Engineering and Technology Chennai India Department of ECE Bharath institute of higher education and Research Chennai India Department of EEE Panimalar Engineering College Chennai India Department of ECE SGT University Gurugram Haryana India Department of EEE SRM Institute of Science and Technology (Ramarapuram Campus) Chennai India

In applications requiring controlled power, such as those for mobile phones, computers, communication systems, and many other electronic devices, ADC- DC converters are among the most extensively used and simplest converters. Devices called DC-DC converters change one voltage level to another, which may have a larger or lower magnitude. They are categorized as boost and buck converters as a result. On the latter or buck converter, we are now working. because of their excellent efficiency and single stage conversion, they are utilized everywhere. These converters are nothing more than PWM- based high frequency switching devices. All switching-mode power conversion systems produce electromagnetic interference (EMI) noise that is conducted. In this research, we suggest a design technique for the EMI filter with DC-DC power converter and put them into practise to lessen the EMI noise produced, and so increase the power converter's general efficiency.

关键词： Integrated circuits Power filters buck converters Power demand Communication systems Electromagnetic interference switches

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Human Resource Management based Economic analysis using Data Mining 3

Human Resource Management based Economic analysis using Data...

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3rd International Conference on Intelligent engineering and Management, ICIEM 2022

作者： sindhura, K. sabarirajan, A. Kumar siddamallappa, U. Narang, Pravin bhanushali, Mahesh Manohar Turai, Anjaneya Krishna Studies University of Madras Department of Management Tamilnadu Chennai India PSNA College of Engineering & Technology Department of Management Studies Tamilnadu Dindigul India Devangere University DOS in Computer Science Karnataka Davanagere India VPM's Dr. V. N. Bedekar Institute of Management Studies Management Studies Maharashtra Thane India Data Science Student Data Science Symbiosis Skill and Professional University Maharashtra Mumbai India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Researchers in this field are looking at how data mining and HRM are intertwined to manage human resources better. An Ensemble Classifier-Decision Tree (EC-DT) approach may analyse Human Resource Management (HRM) data. C4.5, Random Tree, J48, and simple Cart are the single decision tree algorithms employed. based on the algorithm design, algorithms for evaluation and talent recommendation are then assessed in an HRM system. The algorithm under development is put through its last paces in terms of comparisons and testing. The EC-DT method has a classification accuracy of 79.97 %, whereas C4.5 has a classification accuracy of 76.69 %. DMRM's accuracy and recall were 35.2 % and 41.6 % better than CRM's and CFR's combined, proving that data mining may be a powerful tool for recommending products and services (CFRM). Thus, the data mining-based human resources management system may help organisations expand due to quantitative analyses. In this way, the data mining-based HRM system may encourage and direct businesses to grow under the outcomes of quantitative assessments. A starting point for further research on data mining-based HRM systems may be found in the abovementioned findings © 2022 IEEE.

关键词： Data mining

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Hybrid Learning based visual Facial Emotion Recognition in speech videos

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Annals of Data science 2025年

作者： Rochlani, Yogesh Raut, A.b. Department of Computer Science and Engineering H. V. P. M’s College of Engineering & Technology Maharashtra Amravati India

Facial Emotion Recognition (FER) plays a crucial role in understanding human behavior and interactions, with applications in human-computer interaction, healthcare, surveillance, and multimedia content analysis. Emotion recognition from speech videos is particularly challenging as it requires capturing and analyzing subtle variations in facial expressions along with dynamic speech changes. In this paper, a hybrid learning approach is presented that integrates machine learning and deep learning models to enhance the accuracy and robustness of visual facial emotion recognition in speech videos. The proposed approach leverages a pre-trained ResNet-101 model for feature extraction and evaluates three widely used machine learning classifiers, Random Decision Forest (RDF), Decision Tree boosting (DTb), and support vector Machine (svM) for emotion prediction. Experiments conducted on the RAvDEss dataset demonstrate that our hybrid model, combining Deep CNN with RDF classification, achieves a high accuracy of 92.50%, outperforming existing state-of-the-art models. This work highlights the potential of integrating deep learning and machine learning for FER, offering significant benefits for applications in healthcare, education, and human-computer interaction. © The Author(s), under exclusive licence to springer-verlag GmbH Germany, part of springer Nature 2025.

关键词： Convolutional neural networks

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IoT and Cloud based Face detection application design for surveillance systems using Deep Learning 22

IoT and Cloud based Face detection application design for Su...

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6th International Conference on Cloud and big Data Computing, ICCbDC 2022

作者： Patil, Gouri bethu, srikanth bhavani, Y.v.K. Durga Harikrishna, b. vijayalakshmi, Adluri b sankara babu Department of It Muffakham Jah College of Engg and Tech Telangana Hyderabad500034 India Department of Cse - Aiml and Iot Vnrvjiet Telangana Hyderabad500090 India Department of Cse National Institute of Technology Telangana Warangal506004 India Department of It Vijaya Institute of Technology for Women VijayawadaAP-521108 India Department of Cse Mallareddy Engineering College Telangana Hyderabad500100 India Department of Cse B v Raju Institute of Technology Telangana Hyderabad502313 India Department of Computer Science and Engineering Griet Telangana Hyderabad500090 India

ISBN: (纸本)9781450396578

semantic Index, Human Action Detection, and Event Detection are video surveillance packages that assist automate surveillance tasks. video surveillance structures have entered the generation of virtual surveillance structures in which virtual video is used to traverse the course of different virtual facts. Advances in storage, telecommunications, and facts compression have enabled the increase of more than one technology in virtual surveillance structures. Using more than one video surveillance fashion in risky conditions extends the competencies of rule implementation organizations. In addition, video surveillance enables the form of a motion to carry out in one-of-a-kind conditions. In addition, spotting a particular man or woman in a video is essential for added security, multimedia, and multimedia packages, including offline, seek, and online monitoring of involved humans within the video. The proposed IoT- Cloud based Face detection application is designed to find a person from a huge size dataset and that can generate more accurate results. This application uses Deep Learning methods to find exactness when classifying the images. IoT here is used for liveness detection by comparing all the images present in cloud systems. For results comparison, we used Haar Cascade and DenseNet architectures. © 2022 ACM.

关键词： Face recognition

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