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Enhancing Email Urgency Reply Prediction with ATAN-Transformer Fusion 1

Computing Conference

作者： Ayodele, Taiwo Oladipupo Solent Univ East Pk Terrace Southampton SO14 0YN Hants England

ISBN: (数字)9783031622816

ISBN: (纸本)9783031622830;9783031622816;9783031622809

In the contemporary landscape of burgeoning email communication, effective sorting and prioritization present formidable challenges. Precise prediction of email urgency is pivotal for efficient email management. Our study addresses this challenge by automating the critical process using advanced machine learning and deep learning algorithms. We introduce the Adaptive Temporal Attention Transformer Fusion (ATATFUSION), a novelmethodology meticulously crafted to cater to the delicate intricacies of email urgency prediction. By harnessing cutting-edge techniques, our approach overcomes prevailing limitations in the existing methodologies, offering a pioneering solution for enhanced email management.

关键词： Email urgency Reply prediction Urgency prediction Email management Email communication Innovative methodology Predictive email analytics Adaptive temporal attention networks Temporal attention Transformer fusion ATATFUSION Machine learning deep learning algorithms

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Applications of Artificial Intelligence in Ocular Oncology

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Advances in Ophthalmology and Optometry 2023年第1期8卷 111-122页

作者： Chawla, Bhavna Ganesh, Kusumitha B. Ocular Oncology Service RP Centre for Ophthalmic Sciences All India Institute of Medical Sciences New Delhi India

Artificial intelligence (AI) is becoming increasingly relevant in ocular oncology. Recently, several studies have used large clinical databases to develop robust artificial neural network based tumor screening and diagnostic systems. Some clinical applications of AI include the screening and early detection of retinoblastoma, and tumor surveillance after local control to provide recommendations for referral. In uveal melanoma, AI-based tools have been found to be useful in differentiating choroidal melanoma from nevus, assessment of metastatic risks, and predicting the prognosis. Thus, AI has the potential to significantly transform current disease diagnostic patterns and generate a significant clinical impact in the future.

关键词： Artificial intelligence Artificial neural networks (ANN) Choroidal melanoma Choroidal nevus deep learning algorithms Machine learning OSSN Retinoblastoma

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learning in Energy‐Efficient Neuromorphic Computing: Algorithm and Architecture Co‐Design 1

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丛书名： Wiley - IEEE

2019年

作者： Nan Zheng Pinaki Mazumder

ISBN: (数字)9781119507369

ISBN: (纸本)9781119507383

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic c

关键词： learning in energy-efficient neuromorphic computing: algorithm and architecture co-design neuromorphic cognitive systems approaches to neuromorphic computing memory centered approaches to neuromorphic computing guide to neuromorphic computing neuro-inspired computing computational intelligence neural computing neural network computing modern computer learning machine learning neural network hardware deep machine learning neural network learning capabilities building hardware neural networks building hardware neural networks for machine learning applications building algorithms for machine learning applications artificial neural networks (ANNs) spiking neural networks (SNN) low power circuit design communication networks machine learning designing for machine learning deep learning algorithms learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

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Dynamic MAC Protocol for Layered Data Aggregation in Underwater Wireless Sensor Networks 2

Dynamic MAC Protocol for Layered Data Aggregation in Underwa...

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2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Balakiruthig, B. Angayarkann, S. A. Shekhawat, Nikhil Singh Bathija, Nikhil Shrimali, Shubh K. Gupta, Neeharika SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

The material given describes an energy efficient routing protocol for industrial (WSNs) that makes use of deep learning algorithms like LSTM for movement prediction and a hybrid COOT-HOA optimization technique. Grid construction, base node selection based on distance and residual energy, and ESPRIT-based signal parameter estimation are all part of the protocol. Furthermore, the suggested technique uses a hybrid Adaboost and Random Forest machine learning methodology, incorporates GWO, and combines Hybrid PSO and ACO. Specifically designed for industrial Wireless Sensor Networks (WSNs), this study offers a novel energy efficient routing technique. The protocol uses deep learning methods like LSTM to predict future movements of mobile nodes and a hybrid COOT-HOA optimization algorithm to identify the best base node within each grid. In the experimental zone, grid creation is started, & node distance & residual energy are used to guide selection of base nodes. Finding active zones for data transmission is made easier with the use of ESPIRIT for signal parameter assessment. Notably, the protocol uses a hybrid Adaboost and Random Forest machine learning model, combines GWO characteristics, and offers a unique method by merging Hybrid PSO and ACO.

关键词： Underwater acoustic sensor networks Media Access Protocol data collection Energy efficient routing protocols Industrial Wireless Sensor Networks deep learning algorithms Movement prediction Grid construction Base node selection Residual energy

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Vocational Education Development Model Based on Intelligent Management System 11

Vocational Education Development Model Based on Intelligent ...

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11th International Conference on Educational and Information Technology (ICEIT)

作者： Ni, Li Huang, Qiang Ye, Jingyi Hu, Bin Zhang, Ni Chang, Xiangqian Su, Zhihao Guangzhou Coll Appl Sci & Technol Guangzhou Peoples R China Guangzhou Huali Coll Guangzhou Peoples R China Natl Univ Malaysia Fac Informat Sci & Technol Kuala Lumpur Malaysia

ISBN: (纸本)9781665445467

Intelligent management system centered on artificial intelligence algorithms, cloud computing technology, mobile Internet technology, and big data applications have made remarkable achievements in various industries. The intelligent management system designed in this project is to apply these technologies to explore a vocational education development model that adapts to the current Internet society. Vocational education needs to be oriented to the professional needs of the society, it needs to be oriented to students' vocational choices, and it needs to be oriented to the skill range of teachers. This project collects students' learning data and teaches students in accordance with their aptitude in the course of students' daily classes, matches students with appropriate vocational and technical elective courses and teachers, and records students' feedback during their studies, and in the future career development of students, to help students make better career choices. At the same time, the needs of students, companies, and teachers in vocational education will be collected, and artificial intelligence algorithms will be used to process student data and corporate recruitment data, automatically matching the most suitable combination of students and companies. The biggest core innovation of this system is the latest deep learning algorithm, and according to the actual situation of vocational education, the residual neural network is improved to create an improved residual neural network suitable for students' professional development recommendations. The final result of this algorithm, the auxiliary role in the process of student vocational education and development is far greater than the traditional vocational education management system, and in the C University student education and professional development database, there is about 90% satisfaction rate and the 85% occupational matching accuracy rate is far higher than the performance of the traditional vocation

关键词： intelligence management system deep learning algorithms recommendation system

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ReUnite AI: Harnessing Face Detection and Age Progression for missing person identification 2

ReUnite AI: Harnessing Face Detection and Age Progression fo...

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2nd IEEE World Conference on Communication and Computing (WCONF)

作者： Galphat, Yugchhaya Amamani, Gaurav Bajaj, Chetaniya Mulchandani, Kaplesh Repale, Jayesh Vivekanand Educ Soc Inst Technol Dept Comp Engn Chembur India

ISBN: (纸本)9798350395334;9798350395327

ReUnite AI is a system that has been engineered to address the extensive issue of missing individuals, by leveraging advanced face detection and age progression *** offers a comprehensive solution catering to both the general public and law enforcement agencies. deep learning algorithms are employed in the system to analyze and match facial features from input images against a database of missing individuals. ReUnite AI utilizes Principal Component Analysis (PCA) and Generative Adversarial Networks (GANs) to achieve accuracy and efficiency. System is integrated with age-progression model that generates realistic images depicting how a missing person might appear after a period of time thereby enhancing the identification process. This paper introduces a robust system and provides detailed implementation strategies, showcasing significant potential in locating missing persons.

关键词： Missing persons Facial recognition Age progression Reunite AI project deep learning algorithms Societal challenges

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5G MEC+AI Pathology "Anti-Cancer Guardian": Design of Intelligent Gastric Cancer Auxiliary Diagnosis and Warning Platform for Smart Hospital System 15th

5G MEC+AI Pathology "Anti-Cancer Guardian": Design of Intell...

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15th International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management (DHM)

作者： Sun, Xin Shu, Xin Hu, Juncheng Mo, Chunbao Guilin Med Univ Guilin 541199 Peoples R China Zhonglin Sci Tech Dev Grp Co Ltd Nanning 530201 Peoples R China

ISBN: (纸本)9783031610622;9783031610639

Purpose: Investigate the technical means of intelligent Gastric Cancer (GC) auxiliary diagnosis and warning, providing treatment guidance for physicians. Methods: The product is developed from a technical perspective, involving algorithm design, software development, and application design. Based on the UNet3+ digital pathology slices auxiliary diagnosis method, it enables precise segmentation of breast cancer pathology slices across the entire field of view, addressing issues such as excessive network complexity, high false positives, and inadequate capture of multi-scale information in existing algorithms. Additionally, utilizing a convolutional neural network-based cancer cell image classification algorithm enables rapid generation of predictive results for pathology slices. Key algorithms in 5G Mobile Edge Computing (MEC) for smart healthcare applications facilitate specific applications of 5G technology in integrated data exchange and device integration within medical consortia, achieving offloading of computational tasks and storage content to MEC nodes. This implementation includes patient physiological indicator warning mechanisms and device management techniques based on MEC nodes. The platform assists physicians in diagnosing more efficiently, aiding patients in earlier recovery, and providing robust support for treatment. Results: The new algorithm accurately segments and predicts entire cancer pathology slices, identifying cancerous regions. Conclusion: The "AI Pathology Anti-Cancer Guardian" intelligent GC auxiliary diagnosis and warning platform can analyze gastroscopy results more quickly and accurately, aiding physicians in diagnosis and treatment.

关键词： Gastric Cancer Auxiliary Diagnosis Early Warning deep learning algorithms

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Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python

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2018年

作者： Iffat Zafar Giounona Tzanidou Richard Burton Nimesh Patel

ISBN: (数字)9781789132823

ISBN: (纸本)9781789130331

Learn how to apply TensorFlow to a wide range of deep learning and Machine learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges. Key Features • Learn the fundamentals of Convolutional Neural Networks • Harness Python and Tensorflow to train CNNs • Build scalable deep learning models that can process millions of items Book Description Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images. What you will learn • Train machine learning models with TensorFlow • Create systems that can evolve and scale during their life cycle • Use CNNs in image recognition and classification • Use TensorFlow for building deep learning models • Train popular deep learning models • Fine-tune a neural network to improve the quality of results with transfer learning • Build TensorFlow models that can scale to large da

关键词： deep learning Artificial Intelligence Machine learning Python DL Frameworks Python deep learning algorithms deep learning Techniques AI Methods Intelligent Programs Intelligent algorithms Computational Intelligence deep learning for Vision

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Automatic segmentation and classification of the Thamnophilidae's family of the western andean region of Colombia 22

Automatic segmentation and classification of the Thamnophili...

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23rd Symposium on Image, Signal Processing and Artificial Vision (STSIVA)

作者： Virginia Martinez-Ortega, Sara Sarria-Paja, Milton Univ Santiago Cali Engn Fac Cali Colombia

ISBN: (纸本)9781665416696

Colombia has the greatest diversity of birds in the world, approximately 1900 species, and therefore it is important to promote their protection and conservation. Monitoring the vocalization of the birds allows a sampling that does not affect the species or the habitat, helps to monitor migrations and estimate the populations present in the determined area, in addition to strengthening different fields of research. In the Thamnophilidae family, their vocalization is key to find differences in speciation. This paper propose a preprocessing process where the files first are separate from the environment noise with a RPCA technique and then is apply a segmentation process, when is calculated the Hilbert enveloping that find the importance segments in the audio. These segments contain the syllables that are saved separately to feed the classification system. Different techniques of Machine learning and deep learning are applied and were obtained high accuracies, like the support vector machine that obtained a 96,96 +/- 0,64.

关键词： deep learning algorithms Mel Frequency Cepstral Coefficients machine learning spectrograms Thamnophilidae family

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Workload Forecasting Model using Adaptive Sliding Window in the Cloud Environment 2

Workload Forecasting Model using Adaptive Sliding Window in ...

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2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS)

作者： Lavanya, V Saravanan, M. SRM Inst Sci & Technol Dept Networking & Commun Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

Efficient management of resources in a cloud computing environment heavily relies on workload forecasting. Accurately predicting the workload is critical to ensure optimal utilization of resources. User demands are dynamic and unpredictable, necessitating accurate workload forecasting to allocate resources optimally, ensure service-level agreements, and control costs. Current prediction techniques rely on fixed-size observation windows, which cannot identify local patterns in the most recent data and hence cannot yield reliable findings. Thus, the methods trained with relatively few observations on large, fixed sliding windows lead to wrong estimates or accepting wrong estimates because small windows on changing trends quickly worsen calculations. This research study proposes a proactive workload forecasting model using a Bi-LSTM algorithm with an adaptive sliding window to handle the dynamic and unpredictable nature of user requests (workloads). A proactive workload forecasting model needs to predict incoming future workloads for effective resource provisioning to satisfy service level agreements and handle power consumption to avoid the greenhouse effect. Additionally, the performance evaluation of the proposed method is performed using Bitbrains workload traces. The experimental findings demonstrate that the proposed model achieves better accuracy and lower errors.

关键词： Cloud workload Workload forecasting Adaptive Sliding window Prediction deep learning algorithms

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