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Archiving 4.0: dataset Generation and Facial recognition of DRC Political Figures Using machine learning 5th

Archiving 4.0: Dataset Generation and Facial Recognition of ...

引用

5th EAI international conference on Emerging Technologies for Developing Countries, AFRICATEK 2022

作者： Ngongo, Ferdinand Kahenga Bagula, Antoine Ajayi, Olasupo Ecole Supérieure d’Informatique SALAMA Lubumbashi Democratic Republic Congo University of Western Cape Cape Town South Africa

ISBN: (纸本)9783031358821

It is widely recognized that digital archiving represents many advantages compared to manually preserved documents. these include i) reduced risk of losing data, ii) eco-friendliness, iii) data security, iv) faster access to data, v) simple data management, vi) overall costs saving, and vii) potential for data recovery. However, in contrast to developed nations, which have experienced a steady maturity in the field, digital archiving is still in its infancy in developing countries, especially in Africa, where years of slavery, colonization, both economic and political issues, and wars have deprived nations of their history. Building around techniques of the fourth industrial revolution (4IR), this paper addresses this gap by proposing a digital archiving model called "Archiving 4.0". the model curates a dataset of images of political figures in the Democratic Republic of Congo (DRC) from different sources and classifies these images using machine learning techniques to achieve facial recognition that will help recognize historical people in photographs and videos. To the best of our knowledge, the proposed dataset constitutes the first attempt at digital archiving in the political space of the DRC and provides an example that can be emulated to spin out related works on the African continent where digital archiving is needed for political research studies and also for the preservation of the history of nations. through performance evaluation, accuracy, precision, recall, and loss;the paper reveals that Transfer learning outperforms traditional machine learning on different metrics of interest when using the generated dataset: 93% of validation accuracy. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Face recognition

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Computer-guided Design of Effective Guidance Platform for Music Appreciation based on Mixed-Frequency Signal recognition Algorithm 5

Computer-guided Design of Effective Guidance Platform for Mu...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： Zhang, Hong Sichuan Electromech Inst Vocat & Technol Sch Dept Management Engn Panzhihua 617000 Sichuan Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Computer-guided design of effective guidance platform for music appreciation based on mixed-frequency signal recognition algorithm is studied in this paper. Based on the small parameter stochastic resonance principle of bistable system, this paper uses the normalization transformation to detect and extract the weak signal mixed with large and small frequencies from the high noise background at the same time. By selecting the appropriate normalization transformation factor, the limitation of the frequency of the signal to be measured in the stochastic resonance system is eliminated, and the frequency signal that does not meet the conditions of stochastic resonance is converted into the qualified small parameter signal. With these theoretical basis, this study design the novel mixed-frequency signal recognition algorithm. then, the model is applied to the effective guidance platform for music appreciation. With the experiment analysis, the performance is then validated.

关键词： pattern recognition signal processing mixedfrequency music appreciation intelligent platform data mining

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AI-Based Digital Disease recognition Using Collaborative Conveyor machine for Poultry Farming 5

AI-Based Digital Disease Recognition Using Collaborative Con...

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5th IEEE international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Elango, M.K. Harini, A. Soundar, R. Suroopa, P. Electrical and Electronics Engineering K S Rangasamy College of Technology Tiruchengode India

ISBN: (纸本)9798350383522

this research presents a cutting-edge Al-driven digital disease identification system tailored to chicken husbandry. the system incorporates multiple sensors and enhanced object-detecting capabilities by leveraging collaborative conveyor machinery. the major goal is to improve disease detection precision in poultry habitats. the technology provides a complete method for recognizing potential health risks in chickens by painstakingly studying temperature changes and applying object detection algorithms using TensorFlow. the combination of artificial intelligence and sensor technology results in a potent tool for preventive disease control in the chicken business. this invention achieves some of the major sustainable development goals to kinetically monitor, protect and raise of chicks in day-to-day life, which are essentially named as Decent Work and Economic Growth, Industry Innovation and Infrastructure, Responsible Consumption and Production. the collaborative conveyor machine enables a coordinated and efficient procedure while collecting and analyzing data in real-time. this digital disease identification system marks a big step in optimizing chicken farming techniques due to the synergy of AI machine learning and sensor improvements. A collaborative conveyor machine with several sensors and item-detecting capabilities is used in this technique. the system is particularly designed to combat diseases such as fowl pox, avian paramyxovirus and coccidiosis. Using powerful AI algorithms, this system intends to increase illness detection accuracy and efficiency in chicken farming, resulting in better flock health management. the system promptly alerts farmers through GSM and IoT technologies, enhancing real-time monitoring and proactive management in poultry health. © 2024 IEEE.

关键词： Avian Paramyxovirus Catastrophic illnesses Coccidiosis Decent Work and Economic Growth Disease Identification Fowl pox IoT machine learning Object Detection TensorFlow

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Identification of Complaint Microblogs based on Multimodal and Bi-layer Fusion Framework 5

Identification of Complaint Microblogs based on Multimodal a...

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5th international conference on machine learning and Natural Language Processing (MLNLP)

作者： Chen, Feng Zhang, Yunjiao Xu, Yanhua Ludong Univ Sch Business Shandong Prov Big Data Dev & Innovat Lab Publ Res Yantai Shandong Peoples R China Ludong Univ Sch Business Yantai Shandong Peoples R China Ludong Univ Literature Sch Yantai Shandong Peoples R China

ISBN: (纸本)9781450399067

Monitoring and responding to complaints generated by consumers are critical for enterprises and governments. In this paper we propose a multimodal and bi-layer fusion framework to identify microblogs posted on SinaWeibo, the largest social media platform in China. We design an effective and simple method for feature fusion. Based bag-of words model, we represent text and images of a post of microblogs as a unified representation. Using multimodal features, we propose a bi-layer fusion framework. Experiments on two datasets of 5000 microblogs demonstrate our method performs better than text-based methods. the weighted F-measure reaches 0.904, and F-measure of complaint class is 37.07% higher than the baseline.

关键词： text mining complaint multimodal microblogs short text classification

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Spam Email Assessment Using machine learning and data mining Approach 5

Spam Email Assessment Using Machine Learning and Data Mining...

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5th international conference on Computational Intelligence and Communication Technologies, CCICT 2022

作者： Singh, Utkrisht Singh, Vinayak Gourisaria, Mahendra Kumar Das, Himansu Kiit Deemed to Be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (数字)9781665472241

ISBN: (纸本)9781665472241

Electronic mail is a crucial means of communication, used in both formal and informal ways. Unnecessary communications sent in bulk over the Internet or through any electronic messaging system are known as spam. Some illegal use of spam includes unethical behaviors, phishing, and various other scams. We found that spam emails constitute at least eighty percent of the mail received by the recipients. the key objective of this paper is to provide information for the detection of spam electronic mail. Popular machine learning techniques like K-Nearest Neighbor (KNN), Logistic regression (LR), Cat-Boost, Ada-Boost, Gradient Boosting, Extreme Gradient Boosting (XG-Boost) Light Gradient Boosting machine (Light GBM), Support Vector machine (SVM), Decision Tree, Gaussian Naïve Bayes, and Random Forest were implemented on selected features from the data set. Cat-Boost outperformed all the other machine learning algorithms and achieved an accuracy of 0.9699 and an Fl score of 0.97 for spam email classification. © 2022 IEEE.

关键词： Decision trees

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learning Rate Decay Scheduler Using the Loss Operator as Generalization

Learning Rate Decay Scheduler Using the Loss Operator as Gen...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Wenbo An Juntao Zhang Jianning Liu Kun Shao Kun Bian Jun Zhou Institute of System Engineering AMS Beijing China School of Electronic Engineering Xidian University Xi'an China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

learning rate decay scheduler is the commonly used method in training Deep Neural Networks. Traditional learning rate decay scheduler adjusts the learning rate of decrease based on the number of training iterations or epochs. However, when handling the dataset from different task types during training Large Language Models, the traditional method cannot adaptively adjust the learning rate to help the model converge better. In this paper, We propose to introduce training loss operator g t into the traditional time-based scheduler to adjust the rate of learning rate decay. Our experiments is conducted by using two large models, “Baichuan2” and “Qwen” on zero-shot and few-shot task datasets. Compared to primary cos learning rate decay methods, our method achieve state-of-the-art results on In-Context learning and zero-shot tasks. the code are publicly available at https://***/Lunamoon-flow/learning-rate-decay.

关键词： Training Adaptation models Codes Large language models machine learning Artificial neural networks data models pattern recognition

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Realization and Optimization of English Smart Teaching Environment based on Real-Time Action data Computer Acquisition Algorithm 5

Realization and Optimization of English Smart Teaching Envir...

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5th international conference on Inventive Computation Technologies (ICICT)

作者： Xue, Fei Yantai Nanshan Univ Yantai 265713 Shandong Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

Realization and optimization of English smart teaching environment based on real -time action data computer acquisition algorithm is studied in the paper. data preprocessing is needed to improve the proportion of useful data before action recognition. In this paper, the method of looking for standardized modulus is used to reduce the deviation caused by absolute data. the vector extracted from the data of human inherent limb structure can be used as a standard vector because it is less affected by the change of human action. the core idea of the algorithm is: firstly initialize the weights of each training sample in the sample set, then train and update the weights to obtain different weak classifiers, and then arrange the weak classifiers according to their classification ability from low to high. With this integration, the designed model is then implemented through the analysis of the smart systems. through the systematci test and verification, the performance is validated.

关键词： Systematic optimization smart teaching environment real-time data computer acquisition action recognition pattern mining

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Integrated Leaf Disease recognition Across Diverse Crops through Transfer learning 5

Integrated Leaf Disease Recognition Across Diverse Crops thr...

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5th international conference on Innovative data Communication Technologies and Application, ICIDCA 2024

作者： Tambe, Utkarsh Yashwant Shanthini, A. Hsiung, Pao-Ann Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan

Global food security is seriously threatened by plant diseases and can cause severe economic losses to farmers. Automated detection of plant diseases using computer vision and machine learning techniques has become a popular research area due to its potential to provide faster and more accurate results than traditional methods. In this research, we propose a plant disease detection model based on transfer learning using the MobileNetV2. We evaluate the suggested method on a dataset consisting of 38 various kinds of diseases across 14 different plants. Our experimental findings indicate that the proposed method has a typical accuracy of 91.98% and outperforms additional cutting-edge CNN models for plant disease detection. the experimental findings support the suggested approach's validity and show that it effectively detects plant diseases. We also have put forth the evaluation metrics to investigate the model. the suggested method has potential applications in real-world scenarios and can help farmers detect diseases in their crops at an early stage, allowing them to take timely action to minimize crop losses. © 2024 the Authors. Published by Elsevier B.V.

关键词： Losses

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A Real-Time Face recognition Attendance Using machine learning 1

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5th international conference on Applied machine learning and data Analytics, AMLDA 2022

作者： Adeline, Umurerwa Marie Gaspard, Harerimana Innocent, Kabandana College of Science and Technology University of Rwanda Kigali Rwanda College of Science and Technology University of Rwanda Kigali Rwanda

ISBN: (数字)9783031342226

ISBN: (纸本)9783031342219

Systems for managing attendance are crucial for all organizations. One of the most often utilized biometrics for verifying human identity is the face. Most businesses in Rwanda utilize logbooks, cards, or fingerprints to track employees’ attendance at work. However, because of COVID-19, which is an infectious disease, attendance has been discontinued due to concerns over its spread. this study describes real-time Face recognition attendance using machine learning, an Internet of things (IoT)-based biometric face recognition solution. To capture the live streaming video, a high-quality camera with a Sony IMX477 sensor and a 16mm 10MP Telephoto lens connected to a Raspberry Pi 4 Model B, can send frames at a time to the cloud. the pre-trained FaceNet model is employed by the system to extract features from a face image after using MTCNN (Multi-Task Cascaded Convolutional Neural Networks) to recognize facial landmarks on images. Real-time image processing is done in the cloud, and attendance is recorded on a dashboard that is accessible from anywhere. the system sends the email to the employee using SMTP Protocol in case of arriving late /absent without permission. the result reveals that the system is safe, dependable, trustworthy, and does not require physical touch. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Design of music data analysis platform based on visualization technology 23

Design of music data analysis platform based on visualizatio...

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5th Asia Pacific Information Technology conference, APIT 2023

作者： Fan, Qiuyue Heihe University China

ISBN: (纸本)9781450399500

With the advent of the Internet era, online music is booming, and the number of digital music and users is also showing an explosive growth trend. A data analysis platform based on Netease Cloud Music is designed. the main modules include data collection, data storage, data analysis and data visualization. Combined with the massive users and music resources of Netease Cloud Music, machine learning and deep learning algorithms are used to analyze their data from roughly three directions: user influence analysis, using PageRank algorithm and Triangle Count algorithm;the MLPC multilayer perceptron classifier algorithm in depth learning is mainly used for song style classification prediction;Comment data topic mining uses the BGLL algorithm of community detection based on modularity gain. It is recommended by various creators with music aesthetic experience and lovers with independent music taste, taking the song list as the main mode, including social function and interactive function. In this era of advocating personalization, NetEase Cloud Music has broken the popular recommendation of traditional music and provided music recommendation services for thousands of individuals. therefore, it has won a large number of user resources and music resources. © 2023 ACM.

关键词： Music

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