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共 232 条记录，以下是101-110 订阅

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Using machine learning and AI to Find Homes for the Voiceless 2

Using Machine Learning and AI to Find Homes for the Voiceles...

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2nd international conference on Disruptive Technologies, ICDT 2024

作者： Jha, Neha Chaurasia, Anushka Chauhan, Pradyuman Rajput, Vansh G.L. Bajaj Institute of Technology and Management Department of Applied Computational Science and Engineering Greater Noida India G.L Bajaj Institute of Technology and Management. Department of Applied Computational Sciences and Engineering India

ISBN: (纸本)9798350371055

Adoption of stray animals from animal shelters has grown during the pandemic. Despite the fact that many people still choose to purchase pets from pet stores. Through this app, we encourage them to adopt rather than purchase. In India, this is typically done by purchasing dogs, cats, and many other animals from pet stores or from individuals whose animals have already given birth. The majority of adoption procedures nowadays are laborious and time- consuming. The project's primary goal is to create a platform that will streamline those procedures and give abandoned animals a fresh lease on *** the epidemic, everyone has learned the value of cellphones, and their use has expanded significantly, therefore we decided to make this programme a mobile app. As a result, it facilitates quickand effective transportation between the user and the animal shelter. Flutter technology is being used to develop the project. Firebase is used on the back end whereas flutter is used for the front end. This app's goal is to make it easier for stray animals to be adopted. To assist in automating the process of servingthe welfare of pets, the application offers a user-friendly interface. Because Flutter is Google's free, open-source software development kit (SDK) for developing cross-platform mobile applications, we are using it to create the app and Firebase as the database. It creates scalable, high-performance applications with appealing user interfaces. © 2024 IEEE.

关键词： Animal Rescue Current Anthropology Homeless Pet Participatory Action Research Social Movement

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Extreme Gradient Boosting Algorithm in employment prediction of college graduates 2

Extreme Gradient Boosting Algorithm in employment prediction...

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2nd international conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Deng, Xiaoyan Zhang, Xiaobin School of Medical Technology Chongqing Medical and Pharmaceutical College Chongqing400000 China Software Development Department Chongqing Rural Commercial Bank Chongqing400000 China

ISBN: (纸本)9781510689053

With the rapid development of science and technology, machine learning algorithms are applied to classification prediction and other fields by piping rice because of their automatic learning characteristics and powerful generalization ability. Aiming at the current problem of low efficiency of employment guidance for college graduates, the study proposes an extreme Gradient Boosting Algorithm. And for the problem of feature selection of college graduates, filtered and wrapped feature selection algorithms are introduced, and a hybrid feature selection algorithm is constructed by combining the two. Finally, the extreme Gradient Boosting Algorithm and the hybrid algorithm are combined to construct a college graduate employment prediction model based on them. The college graduate employment prediction model is analyzed for the comparison of prediction accuracy and recovery rate, and the results show that the average prediction accuracy and average recovery rate of the model are 97.26% and 96.7%, respectively, which are better than the comparison model. The above results show that the model proposed in the study has good performance and effectiveness, which helps to improve the efficiency of college graduate employment guidance. © 2025 SPIE.

关键词： Prediction models

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ML Assisted Sign Language to Speech Conversion Gloves for the Differently Abled 2

ML Assisted Sign Language to Speech Conversion Gloves for th...

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2nd IEEE international conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Hrithik, T.H. Rhethika, S. Akhil, K.H. Deepa, K. Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Bangalore India

ISBN: (纸本)9798350360523

In this rapidly changing world where everyone is vying for recognition, opportunities, success, and survival, people with disabilities frequently find themselves marginalized. They struggle to blend in with society and receive different treatment. The advancement of AI-assisted technologies has contributed to closing this social gap and given the disabled newfound hope. The "Sign-to-Speech"glove makes it possible to translate sign language into speech. Flex sensors that track finger movement are integrated into the glove, and an Arduino microcontroller is used to process the sensor data. The speech synthesis software is run on a Raspberry Pi, which is connected to the Arduino. The way in which the glove functions is by tracking finger and hand movements that create various sign language expressions. The Arduino receives data from the flex sensors, which track variations in resistance as the fingers move and acts as an Analog-to-Digital converter. The machine learning model is deployed in the Raspberry Pi, which is used to identify the sigh and convert it into speech. The speech output is routed through a speaker, hence making it possible for the listener to hear it. By providing pertinent data to the machine learning model for training, the device can be configured for various sign languages making the overall system versatile. © 2024 IEEE.

关键词： Speech synthesis

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DeepJDOT: Deep joint distribution optimal transport in facial recognition 2

DeepJDOT: Deep joint distribution optimal transport in facia...

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2nd international conference on Mechatronics and Smart Systems, conf-MSS 2024

作者： Cai, Yuchen Wen, Chenglong Chen, Haoyuan Liu, Yijia Peng, Haofei Computer Science and Software Engineer Department Rose-Hulman Institute of Technology Terre Haute47803 United States Department of Physics University of Toronto TorontoM5S 1A1 Canada Department of Physics University of Washington Seattle98195 United States Department of Water Conservancy and Civil Engineering China Agricultural University Beijing100083 China Department of Information Engineering Xi'An Jiaotong University Xi'an710000 China

This paper explores the application of deep learning in domain adaptation, with a particular focus on using the Deep Joint Distribution Optimal Transportation (DeepJDOT) method for face recognition. We test DeepJDOT on a series of visual domain adaptation tasks and find that it exhibits high accuracy and competitive advantage, especially under smaller image sizes, compared to recent state-of-the-art competitors. This research not only highlights the potential of deep learning in domain adaptation but also underscores DeepJDOT as an effective approach for improving the performance of applications like face recognition. Furthermore, we introduce a domain adaptation-tailored loss function based on optimal transport to further enhance model performance. This study provides valuable insights and methods for research in visual domain adaptation and face recognition. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

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Image Forgery Detection Using MD5 & Open CV 2

Image Forgery Detection Using MD5 & Open CV

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Shaikh, Mohammad Shahnawaz Biswal, Aparajita Pandwal, Akruti Khodifad, Nilesh Vaghela, Bhavesh Parul University Parul Institute of Engineering and Technology Department of AI and AIDS Vadodara India Parul University Computer Science & Engineering Parul Institute of Engineering and Technology Vadodara India

ISBN: (纸本)9798331534950

The more the advanced image making tools become accessible, the more image forgery is proliferated in hardware and software across all these domains - forensics, journalism, authentication, etc. In this research paper, we investigate methods on how to detect forged images by using Python programing, MD5 hashing algorithm and OpenCV library. The first part of the study utilizes MD5 to do an integrity verification by generating a unique hash value for each original image. The digital fingerprint you get from hashing each step of the image is this hash, and is a powerful way to compare against suspected forged images. MD5 is not perfect, but it is useful for performing an initial check of manipulated site;while not perfect, it does the trick. As powerful image processing software, OpenCV is used to enhance the detection process. This library provides detailed analysis of pixel patterns, possibly strange color discrepancies or just other things that would hint a manipulation. The proposed method performs advanced anomaly detection upon the extracted features from both original and suspect images with the aim of discovering inconsistencies meant to imply forgery. The research then proves through a series of experimental validations that the use of MD5 coupled with OpenCV significantly increases detection accuracy, while reducing false positives and detecting both overt and subtle manipulation. This not only underlines the need for creating a multilayered approach to counter the challenges posed by image forgery but also introduces a hybrid framework called UEFRG that combines three different methods done specifically to address image forgery. As digital content evolves, this study indicates the necessity of reliable detection methods to ensure integrity of visual media. The algorithm is further refined and machine learning techniques are explored for incorporation for future work to further improve the detection capabilities to keep pace with emerging threats in digital

关键词： Image matching

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Unveiling Stress: A Comparative Analysis of Multimodal Sensor Fusion Techniques for Predictive Modeling 2

Unveiling Stress: A Comparative Analysis of Multimodal Senso...

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2nd international conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Eldien, Noha A. Saad Ali, Raghda Essam Ezzeldin, Mustafa Zaher, Moamen Faculty of Computer Science Giza Egypt Helwan University Software Engineering Faculty of Computers and Artificial Intelligence Cairo Egypt

ISBN: (纸本)9798350362633

Stress in professional environments is a significant concern. Medical professionals are particularly vulnerable to stress, especially during emergencies. Nurses hold a vital position in delivering care within hospital settings. It's crucial to anticipate stress levels among nurses to help them perform their duties effectively and avoid the long-term impacts of stress. This study seeks to explore how body sensors and machine learning techniques can be employed to monitor physiological signs and identify stress levels among nurses. It utilizes a benchmark dataset collected from 15 different nurses, including signals such as heart rate (HR), Electrodermal Activity (EDA), and Skin Temperature alongside location data extracted by an accelerometer. This study explores several fusion strategies, such as data, model, and prediction fusion levels to improve the accuracy and reliability of stress prediction models. Through a comparative analysis, this paper highlights the strengths and limitations of diverse fusion techniques. shedding light on their efficacy in capturing the nuanced features of stress. The findings offer valuable insights into the optimization of multimodal sensor fusion for enhanced stress prediction, creating pathways for reliable frameworks in the healthcare domain. This research conducted a comparative study between 3 different levels: data-fusion, model-fusion, and prediction-fusion. Prediction-level fusion outperformed both model-level fusion by 1.97% and data-level fusion by 1.26%. © 2024 IEEE.

关键词： Nursing

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IRIS and Face-Based Multimodal Biometrics Systems 1

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2nd international conference on Data Analytics and learning, DAL 2022

作者： Kulkarni, Vaishnavi V. Hatture, Sanjeevakumar M. Karchi, Rashmi P. Saini, Rashmi Hiremath, Shantala S. Hiremath, Mrutyunjaya S. Alva’s Institute of Engineering and Technology Karnataka Moodbidri574225 India Nagarjuna College of Engineering Karnataka Bengaluru562164 India G. B. Pant Institute of Engineering and Technology Uttarakhand Pauri Garhwal246194 India Sony India Software Centre Pvt Ltd. Karnataka Bangalore560103 India eMath Technology Pvt. Ltd. Karnataka Bangalore560072 India

ISBN: (数字)9789819963461

ISBN: (纸本)9789819963454

Deep learning is a powerful visual model of machine learning tools. A multimodal biometrics system employing deep learning techniques with Face and Iris attributes improves human recognition’s security level and accuracy. The proposed work plans to develop a multimodal biometrics system using Face and Iris traits by identifying an optimal fusion level and efficient fusion rule for secure person authentication. In the proposed work, transfer learning methods, namely ResNet-50 and VGG16, are used for extracting features, and Softmax layer and Multi-class Support Vector machine are used for personal identification. The performance of the proposed system is evaluated using the VISA multimodal biometrics database. The accuracy of 93.334 and 96.735% are achieved for feature and score-level fusion. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Self-attention-Based Deep learning Approach for machine Translation of Low Resource Languages: A Case of Sanskrit-Hindi 1

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2nd international conference on Women Researchers in Electronics and Computing, WREC 2023

作者： Sethi, Nandini Dev, Amita Bansal, Poonam Indira Gandhi Delhi Technical University for Women New Delhi110006 India

ISBN: (数字)9789819970773

ISBN: (纸本)9789819970766

The automated translation of natural language text using software is known as machine translation (MT). Natural language’s intrinsic complexity and incompatibility have made machine translation (MT) a difficult process that is full of difficulties, especially in comparison to a physical conversion. The development of neural-based methods has brought machine translation outcomes closer to what people would expect. The seq-to-seq strategy, which uses encoder and decoder pairs, recurrent NN, complicated convolutions, and transformers, is one of the most modern deep learning techniques. This work proposes a self-attention deep learning architecture for MT that is built on a transformer with 8 layers of encoders and 8 layers of decoders and has all layers completely dedicated to multi-head attention. The important source and outcome of earlier layers that flow into the subsequent layer are weighted summarised to highlight the complete contribution of the proposed model. Comparing this process inputs that are not hybrid, the transformation produced is more exact. A parallel corpus for the Sanskrit-Hindi language pair is created in this study, and the model is assessed using the created corpus. The tests are carried out on Google Collab instances with GPUs, and the outcomes reveal an accuracy of 53.4 BLEU, which is an improvement over the earlier work. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural machine translation

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The Role of Edge Computing in Ensuring Long-Term Sustainability of Smart Communities 2

The Role of Edge Computing in Ensuring Long-Term Sustainabil...

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2nd IEEE international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Jumaa, Amal Hasan Hussain, Ahmed Mohammed, Awwad Ahmed Sharif, Shukur Ramadan Hama Jaff, Nadhema Ahmed Al-Nisour University College Baghdad Iraq Al-Farahidi University Department of Medical Instruments Engineering Techniques Baghdad10011 Iraq Uruk University Department of Medical Laboratory Techniques Baghdad Iraq Al Kitab University Department: Medical Engineering Kirkuk Iraq Knowledge University College of Administrative and Financial Sciences Department of Business Administration Erbil Iraq

ISBN: (纸本)9798331534950

Intelligent communities can support high population growth thanks to their well-designed infrastructure. They should have round-the-clock access to utilities like electricity and water as well as better IoT system functionality. This article describes the smart screen, which was designed with edge computing in mind and is capable of carrying out several computations as well as interacting with different apps and tangible objects. The main concept is to store as much data as possible near the edge of the network instead of on distant servers or in the cloud. The compute for offloading on adjacent edge nodes can be managed by the suggested design. It can perform the extensive IoT data editing and analysis required to satisfy the anticipated rise in demand from smart cities. This article proposes Sustainable Development with Edge Computing (SD-EDC), a novel strategy for sustainable development. For smart city utility situations, the system is deemed robust and practical enough. The virtualization software for smart cities uses Intelligent Display Analytics, which is derived from the Internet of Things data gathered by the edge computing architecture of a network. To solve problems like forecasting methods, immediate fashion computing, edge device resource management, etc., machine learning looks at previously gathered data. Lastly, a lot of implementation factors were handled in the created design, and the planning method effectively controls management data. Both criteria are used when mapping software applications for edge devices. © 2024 IEEE.

关键词： Sustainable city

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machine learning Approach to Intrusion Detection: Performance Evaluation 2

Machine Learning Approach to Intrusion Detection: Performanc...

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2nd international conference on machine learning and Data engineering, ICMLDE 2023

作者： Giraddi, Vishal Giraddi, Shantala Narayan, D.G. Bidaragaddi, Anupama Kanakareddi, Suvarna G. School of Computer Science and Engineering KLE Technological University Hubli India

software-Defined Networking (SDN) simplifies network administration as well as reduces the need for manual configuration on individual devices. SDN is expected to remain a crucial technology in shaping the future of network infrastructure. However due to key vulnerabilities like Open Flow Protocol Weaknesses, Flow Table Poisoning, Weak authentication and authorization mechanisms, it is prone to intrusion attacks. In this paper, authors aim to build framework based on various machine learning models (ML) to capture anomaly packets. Four models Decision tree, Support Vector Classifier, Naïve Bayes Classifier and artificial neural networks are designed and trained to capture anomaly packets. While SVC achieved an impressive accuracy of 99%, Artificial Neural Networks (ANN) demonstrated satisfactory speed and efficiency, despite their accuracy being slightly lower than SVM's. ANN is then deployed in SDN simulated using Mininet. The model is able to detect all DOS traffic. Other types of attacks SQL Injection, U2R Attack, Buffer Overflow Attack, Probe Attack, Brute Force Attack could not be tested as there is provision to simulated these using Mininet. © 2024 Elsevier B.V.. All rights reserved.

关键词： Intrusion detection

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