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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Prabu Selvam C. Gunasundari M. Tamilselvi Pinjari Haneef T. Suresh R. Jini School of Computing SRM Institute of Science & Technology Tiruchirapalli Campus Tiruchirapalli India Department of Computer Science & Engineering Roever Engineering College Tiruchirapalli India Department of Computer Science and Engineering (AIE) Amrita School of Computing Amrita Vishwa Vidyapeetham India Department of AIML K. Ramakrishnan College of Engineering Tiruchirapalli India Department of Information Technology Sri Muthukumaran Institute of Technology Chennai India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Alzheimer's Disease (AD) represents a significant health challenge in today's world. Applying deep learning across various disciplines has demonstrated remarkable potential and has garnered substantial interest. The Transformer model has been shown to have exceptional performance across various computer vision tasks, significantly enhancing its application in medical imaging. Compared to conventional CNN, RNNs, and traditional deep learning algorithms, the Transformer network has demonstrated encouraging results in numerous studies and tasks. This paper reviews multiple works for Mild Cognitive Impairment, early diagnosis, AD and related conditions in the Transformer network over the last half-decade. This paper also presents a comprehensive overview of the data sources, pre-processing, feature extraction, training protocols, and evaluation methods to serve as a resource for future research on AD. Additionally, this survey offers a performance comparison of Transformer models across different datasets, a concise examination of these challenges and suggests potential directions for future research.

关键词： Surveys Deep learning Wireless communication Training Protocols Reviews Soft sensors Transformers Feature extraction Alzheimer's disease

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MRI-Enhanced metastatic ovarian tumour detection: leveraging enhanced 3D CNN and Data augmentation for exceptional accuracy

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Australian Journal of Electrical and Electronics engineering 2024年

作者： Banumathi, S. Aruna, R. Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Metastatic Ovarian Tumour remains a challenging medical condition that occurs in the ovaries of female reproductive organs. This severe disease may reduce the life span of the affected person by causing serious effects like imbalanced hormones, improper digestive system, pelvic pain and fertility issues leading to depression. The proposed model employs a 3D CNN architecture, proven effective in image classification tasks, to analyse MRI scans. A few existing approaches have used 2D CNN to detect the tumour, but it is lacking in capturing spatial and temporal features of MRI, which leads to information loss. The MRI-based model achieved an impressive accuracy of 98.6%, showcasing its potential as a valuable tool for early diagnosis. This level of accuracy holds promise for clinical applications, allowing for timely interventions and improved patient life span in cases of ovarian cancer. ©, Engineers Australia.

关键词： Dynamic contrast enhanced MRI

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Mixture of Online and Offline Experts for Non-Stationary Time Series 39

Mixture of Online and Offline Experts for Non-Stationary Tim...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhao, Zhilin Cao, Longbing Wan, Yuanyu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computing Macquarie University Sydney Australia Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China School of Software Technology Zhejiang University Ningbo China

ISBN: (纸本)157735897X

We consider a general and realistic scenario involving nonstationary time series, consisting of several offline intervals with different distributions within a fixed offline time horizon, and an online interval that continuously receives new samples. For non-stationary time series, the data distribution in the current online interval may have appeared in previous offline intervals. We theoretically explore the feasibility of applying knowledge from offline intervals to the current online interval. To this end, we propose the Mixture of Online and Offline Experts (MOOE). MOOE learns static offline experts from offline intervals and maintains a dynamic online expert for the current online interval. It then adaptively combines the offline and online experts using a meta expert to make predictions for the samples received in the online interval. Specifically, we focus on theoretical analysis, deriving parameter convergence, regret bounds, and generalization error bounds to prove the effectiveness of the algorithm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Improvised Text Classification by Hybrid Machine Learning Techniques

Improvised Text Classification by Hybrid Machine Learning Te...

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2024 OPJU International technology Conference on Smart computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Anitha, Cuddapah Kapruwan, Akanksha Shilpa, M. Dange, Jyoti Vekariya, Vipul Maranan, Ramya Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati517102 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Rns First Grade College Department of Computer Application Bangalore Karnataka Channasandra560098 India Atharva College of Engineering Department of Information Technology Malad West Maharashtra Mumbai400095 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Post Limda Gujarat Waghodia391760 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350373783

To improve the performance of English-text classification, text classification systems aim to design appropriate algorithms for extracting features and auto-classifying texts with automatic semantic feature selection. Motivated by the efficiency of Attention mechanism (AM), the ways to use AMs at different levels in CNN and specific RNN organization are explored in this work and BLSTM based models to eliminate misinformation, obtain more accurate contextual information, and ultimately improve performance. Finally, all of the features are concatenated and fed into the softmax classifier. The BLSTM is primarily used to hold the sentence interdependencies, then CNN is selected to extract features for continuous modeling operations, and this model is evaluated by applying it to the ETC. Furthermore, the proposed integrated CNN and BLSTM with Attention mechanism (A-C-BLSTM) model achieves significant results through our evaluation and performs better than a wide range of baseline models. © 2024 IEEE.

关键词： Semantics

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A Topology-Aware Scheduling Strategy for Distributed Stream computing System 1

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12th International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2021

作者： Li, Bo Sun, Dawei Chau, Vinh Loi Buyya, Rajkumar School of Information Engineering China University of Geosciences Beijing100083 China School of Information Technology Deakin University GeelongVIC3216 Australia Laboratory School of Computing and Information Systems The University of Melbourne Parkville Australia

ISBN: (数字)9783030934798

ISBN: (纸本)9783030934781

Reducing latency has become the focus of task scheduling research in distributed big data stream computing systems. Currently, most task schedulers in big data stream computing systems mainly focus on tasks assignment and implicitly ignore task topology which can have significant impact on the latency and energy efficiency. This paper proposes a topology-aware scheduling strategy to reduce the processing latency of stream processing systems. We construct the data stream graph as a directed acyclic graph and then, divide it using the graph Laplace algorithm. On the divided graph, tasks will be assigned with a low-latency scheduling strategy. We also provide a computing node selection strategy, which enables the system to run tasks on the topology with the least number of computing nodes. Based on this scheduling strategy, the tasks of the data stream graph can be redistributed and the scheduling mechanism can be optimized to minimize the system latency. The experimental results demonstrate the efficiency and effectiveness of the proposed strategy. © 2022, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Big data

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Classifying Grape Leaf Disease using an Optimized YOLOv7 Deep Learning Model 4

Classifying Grape Leaf Disease using an Optimized YOLOv7 Dee...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Abraham, John T Hussain, Mohammad Manzoor Srividhya, R. Linda, P Edith Karthik, S. Nareshkumar, R. Bharata Mata College Department of Computer Science Kochi India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Dr G R Damodaran College of Science Department of Computer Science Coimbatore India Kristu Jayanti College Department of Computer Science Bengaluru India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798331515720

The prevalence of grape leaf diseases has had a profound influence on the worldwide grape industry, resulting in considerable financial setbacks. For effective disease management, it is essential to classify the disease quickly and accurately. This research presents an enhanced YOLOv7 deep-learning model for accurately classifying grape leaf diseases. The dataset is obtained from Kaggle and consists of four categories: black rot, leaf blight, black measles, and healthy. The Adam optimizer is utilized to optimize hyperparameters in order to enhance the performance of the model. The refined YOLOv7 model achieved a remarkable accuracy of 99.24% on the test set, showcasing its potential for detecting grape leaf diseases at an early stage. The model in precision agriculture can aid in the management of grape production, categorization of leaf diseases, and monitoring of operations. © 2024 IEEE.

关键词： Contrastive Learning

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Leveraging the Cuboidal Partitioning for Low Complexity CTU Structure Prediction in Versatile Video Coding

Leveraging the Cuboidal Partitioning for Low Complexity CTU ...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Md. Zahirul Islam Tanvir Ahmed Redoy Ashek Ahmmed Manoranjan Paul Manzur Murshed Department of CSE Rajshahi University of Engineering and Technology Bangladesh School of Computer Science University of Sydney Australia School of Computing and Mathematics Charles Sturt University Australia School of Info Technology Deakin University Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

As the capacity of communications systems is currently being outpaced by the increasing demand for access to audio-visual services, the versatile video coding (VVC) standard has emerged. It shows around 40% bit rate savings over the predecessor codec, the high efficiency video coding (HEVC) standard. However, this coding gain is obtained with a significant increase in the computational complexity associated with VVC. The cuboidal partitioning algorithm has shown capabilities in modeling both the global and local commonality of a video frame and discovers a set of non-overlapping rectangular regions, known as cuboids, over the current frame. Since cuboids are highly homogeneous and are computationally efficient, in this paper, it is proposed to employ their intrinsic homogeneity information in predicting the coding tree unit (CTU) structure of VVC. Experimental results show that up to 10.5% complexity reduction for intra coded 4K resolution video sequences, with an increase in bit rate by 1.4%.

关键词： Computational modeling Bit rate Video sequences Prediction algorithms Encoding Partitioning algorithms Computational efficiency Standards High efficiency video coding Periodic structures

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Enhancing Telecommunications Customer Retention: A Deep Learning Approach Using LSTM for Predictive Churn Analysis

Enhancing Telecommunications Customer Retention: A Deep Lear...

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Data Science and Network Security (ICDSNS), International Conference on

作者： M Ranjith Kumar Priyanga S Janani Srinivasan Anusha Veda Chatiyode Jerome Santiago Dharminder Chaudhary School of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Computer Science & Engg Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Dept. of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

The telecommunications industry has a significant challenge in customer churn, especially for companies aiming for sustainable growth in a competitive market. To overcome this issue, we suggested a method utilizing Long Short-Term Memory (LSTM) networks, a deep learning strategy that has revolutionized predictive modeling. We used 4250 customer profiles, each containing 19 features including consumption data and plan options, to train and test the LSTM model. Data preprocessing included encoding categorical variables, normalizing numerical features, and eliminating irrelevant variables to maintain data integrity and authenticity. The LSTM model was fine-tuned to recognize significant patterns rather than simply memorizing data. We also incorporated an additional layer to assess diverse information types, which improved the model's ability to process sequential data effectively. The proposed model achieved a prediction accuracy of 95.3% in identifying potential customer churn, demonstrating its effectiveness in predicting when customers might leave their telecommunications provider. These results demonstrate how LSTM networks can reduce customer attrition in the telecom industry, making them a valuable tool for companies aiming to retain customers and maintain growth in a competitive market. The study's findings pave the way for innovative applications of deep learning techniques in customer retention strategies across diverse industries. This breakthrough has the potential to transform business approaches to customer relationship management and loyalty programs.

关键词： Industries Deep learning Accuracy Data preprocessing Companies Predictive models Data models Telecommunications Numerical models Long short term memory

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GAMAN - GENETIC ALGORITHM IN MOBILE ADHOC NETWORKS FOR CREATING SYSTEMATIC QoS 1

GAMAN - GENETIC ALGORITHM IN MOBILE ADHOC NETWORKS FOR CREAT...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Tapas Bapu, B.R. Anitha, R. Soundararajan, S. Nagaraju, V. Nagappan, Partheeban Daniel, A. Department of Electronics and Communication Engineering S. A. Engineering College India S. A. Engineering College Chennai India Department of Computer Science and Engineering Velammal Institute of Technology India Department of Computer Science and Engineering Saveetha School of Engineering India School of Computing Science and Engineering Galgotias University India

ISBN: (纸本)9781607685395

MANET is a mobile ad-hoc network that is made of several mobile nodes that can communicate in multi-way without any fixed or regular infrastructure. Due to its special features such as its self-organization, easy deployment it has been preferred for many military and civil applications. MANET has also gained popularity in the multimedia field. MANET has certain levels of requirements such as QoS (Quality of Service), jitter and energy, bandwidth, and end-to-end delay. MANET's one of the basic requirements is having QoS and should have efficient routing to support other applications. In this research paper, a special Genetic Algorithm known as the GA algorithm based on routing on a Mobile Ad-hoc network is designed and termed as GAMAN. The proposed model uses a 2 QoS system for routing. The outcome of this paper showed that the GAMAN method is a significant one for QoS in MANET. © The Electrochemical Society

关键词： GAMAN MANET mobile nodes QoS Routing Model

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Recognition and Integration of AI with IoT for Innovative Decision Making Techniques in Cyber Physical Systems 2

Recognition and Integration of AI with IoT for Innovative De...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Nethani, Shivakumar Sivaranjani, Lingala Kumar, Marrapu Aswini Lal, Bechoo Tiwari, Mohit Hyderabad Institute of Technology and Management Department of Computer Science and Engineering Telangana Hyderabad India Mohan Babu University Rangampet School of Computing Andhra Pradesh Tirupati India Centurion University of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Vizianagaram India Department of Computer Science and Engineering Andhra Pradesh Guntur India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350325799

With the integration of Internet of Things (IoT) devices and the demand for effective decision-making methods, cyber-physical systems (CPS) are getting more complicated. In CPS, machine learning (ML) has become a potent technique for processing and analyzing massive volumes of data. The problem remains in optimizing ML algorithms to accomplish quick and accurate decision-making. To overcome this difficulty, we provide a novel strategy in this paper that integrates ML with Particle Swarm Optimisation (PSO) and IoT. The suggested framework optimizes ML algorithms for CPS using PSO, an optimization technique inspired by nature. To find the optimum solution in a multidimensional space, PSO imitates the behavior of a swarm of particles. Our goal is to improve the precision and effectiveness of decision-making processes in CPS by combining PSO with ML techniques. Additionally, by integrating IoT devices into CPS, a wealth of real-time data is made available, enabling the collection of diverse and dynamic data from numerous sources. Our method uses this data to analyze patterns, trends, and anomalies using ML algorithms, enabling wise decision-making in real time. The advantages of our suggested strategy are dual. First, PSO optimization increases the efficiency of ML algorithms and lessens the computational complexity related to large-scale datasets. Second, the framework's inclusion of IoT devices makes it possible to employ real-time data, leading to more precise and rapid decision-making. © 2023 IEEE.

关键词： Decision making

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