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15th International Conference on Intelligent Computing and Networking, IC-ICN 2024

作者： Raparthi, Mohan Dodda, Sarath Babu Maruthi, Srihari Keshta, Ismail Agarwal, Abhishek Software Engineer Alphabet Life Science DallasTX75063 United States Software Engineer Central Michigan University Dallas Texas75009 United States Senior Technical Solutions Engineer University Of New Haven West Haven United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Vermont State University Vermont United States

ISBN: (纸本)9789819786305

Several real-time applications can benefit from wireless sensor networks (WSNs) because of their size, cost-effectiveness, and ease of distribution. It is possible for WSNs to change dynamically as a result of external or internal factors, requiring depreciating dispensable redesign. WSNs are traditionally explicitly programmed, making them hard to react dynamically. Q-learning based on K-Means can be used to avoid such scenarios. Q-learning is used to build a learning mechanism from past simulation data. In order to achieve minimal transmission delay, the work is estimated based on throughput, delay, network lifetime, and packet delivery ratio (PDR). The Q-learning-based mechanism is also investigated as an outcome of the simulation and rounds. Throughput, delay, network lifetime, and PDR analysis of the proposed mechanism are all validated by the comparative investigation against three existing models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

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Enhanced Federated RL with NEAT, Markov Chains, and Gaussian Processes

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Procedia computer science 2025年 260卷 742-751页

作者： Robert McMenemy Kallinatha H D Hamsaveni M Sachin D N Software Engineer Independent Researcher Glasgow Area Scotland UK Dept of Computer Science and Engineering Siddaganga Institute of Technology Tumakuru India Dept of Computer Science and Engineering Vidyavardhaka College of Engineering Mysuru India

Reinforcement Learning (RL) is a machine learning approach in which an agent learns to make decisions in an environment to maximize a cumulative reward. When combined with NeuroEvolution of Augmenting Topologies (NEAT), RL offers several advantages. NEAT is a genetic algorithm that optimizes the development of artificial neural networks by modifying both their structure and weights. When integrated with RL, NEAT can improve the learning process by merging evolutionary optimization with RL techniques. NEAT has demonstrated significant potential in evolving neural networks for RL tasks. However, traditional centralized training methods encounter scalability and data privacy issues. This paper investigates the integration of NEAT with Federated Learning (FL) and its enhancement with Markov Chains and Gaussian Processes to address certain issues. We propose a new framework that combines NEAT for neural network evolution with TensorFlow Federated (TFF) for decentralized training across multiple clients. Our approach is assessed using the BipedalWalker-v3 environment from OpenAI Gym. The experimental results show that our federated NEAT framework, augmented with Markov Chains and Gaussian Processes, achieves competitive performance while maintaining data privacy and reducing computational overhead on central servers. Additionally, we implement parallelization techniques using concurrent futures to enhance the efficiency of NEAT generations.

关键词： Federated Learning NEAT Reinforcement Learning TensorFlow Federated NeuroEvolution Privacy Decentralized Training Parallelization Markov Chains Gaussian Processes

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Social Media Multi-Emotion Detection 2

Social Media Multi-Emotion Detection

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Ibrahiem, Soha S. El-Sayed Mohammed, Bahy Ibrahim El Sayed Ahmed, Mohammed Ahmed Elbahnasy, Khaled Menofia University Computality R&D System Analyst & Nlp Researcher Faculty of Science Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Jeddah International College Computer Science and Information Technology Department Jeddah Saudi Arabia

ISBN: (纸本)9798331506995

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states;however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection. © 2025 IEEE.

关键词： Tweets

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Social Media Multi-Emotion Detection

Social Media Multi-Emotion Detection

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Soha S. Ibrahiem Bahy Ibrahim El-Sayed Mohammed Mohammed Ahmed El Sayed Ahmed Khaled ElBahnasy Computality R&D System Analyst & NLP Researcher Faculty of science Menofia university Menofia Egypt Web Developer Cairo Egypt Software Engineer Ekad Soft Cairo Egypt Computer Science and Information Technology Department Jeddah international College Jeddah KSA

ISBN: (数字)9798331506995

ISBN: (纸本)9798331507008

Natural language in social media is often ambiguous, carrying diverse perspectives that are critical for opinion analysis. With vast amounts of unstructured data, particularly on platforms like Twitter, it is impractical to manually analyse relevant insights. This rapid data growth has highlighted the need for intelligent filtering and mining techniques in decision support systems to help organizations build brand reputation, foster online communities, and enhance profitability. Multi-Emotion detection is key for capturing implicit user opinions and specific emotional states; however, challenges such as text bipolarity, limited representative feature vectors, and inadequate annotated lexicons often hinder accuracy. This research aims to improve multi-emotion detection accuracy by extracting emotion implied tokens and converting them into feature vectors that capture semantic relevance. Using annotated lexicons, word embeddings, unigrams, and term frequency, these vectors are trained across five machine learning models, which are Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), K- nearest neighbor (KNN), and Multi- layer Percepton (MLP). Finally, two ensemble techniques are applied to refine model outputs, achieving an improvement accuracy over previous work worked. The proposed system, evaluated on a real dataset, achieves a hamming score of 0.55 and an average F1 score of 0.68, demonstrating its potential for effective multi-label emotion detection.

关键词： Support vector machines Technological innovation Accuracy Social networking (online) Smart cities Profitability Semantics Lexicon Feature extraction Vectors

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Collaborative Framework for circRNA-Disease Associations Prediction using Dual Variational Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Changchun Wang, Lei Zhao, Bowei Wei, Mengmeng Li, Yang Lu, Mianshuo Wei, Yu Liang, Sizhe The School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Key Lab of Human-machine Interaction and Intelligent Decision Guangxi Acad emy of Science Nanning530007 China The School of Computer Science and Information Engineer ing Hefei University of Technology Hefei230601 China The College of Computer and Information Science School of Software University of Chinese Academy of Sciences Urumqi830011 China

Many experiments have shown that circular RNA (circRNA) can act as biomarkers for complex diseases and play significant regulatory roles in multiple pathological processes. However, most circRNA-disease associations remain unknown, and discovering these associations through biological experimental approach is expensive and time-consuming. Taking into account the shortcomings of current methods, we introduce a new collaborative framework that utilizes multi-heterogeneous graphs, along with variational graph auto-encoders (VGAE) to predict associations between circRNA and diseases. First, we build multi-similarity networks using various biological attributes of circRNA and diseases, and integrate these similarity networks. Two subnetworks are constructed from association matrix and combined similarity network, which included a circRNA-based network and a disease-based network. We then employ random walk with restart and Singular Value Decomposition, for feature extraction from the similarity matrix. Finally, we use collaborative framework to predict the circRNA-disease association scores based on the two subnetworks. We integrate the two score matrices to obtain a final prediction scoring matrix. Using 5-fold cross-validation on the CircR2Disease dataset, our model achieved an AUC score of 0.9828 and an AUPR score of 0.9820. Additionally, among the top 30 highest-scoring circRNA-disease association pairs, 26 associations have already been validated. Our model shows strong performance and can accurately predict associations between circRNA and diseases, according to experimental results. © 2015 IEEE. All rights reserved.

关键词： Variational techniques

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Natural Language Processing for Conversational AI: Chatbots and Virtual Assistants

Natural Language Processing for Conversational AI: Chatbots ...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Neeraj Shrivastava Pushpa Tewari S. Sujatha Srinivasa Rao Bogireddy Neeraj Varshney Vinod Sharma Dept. of Engineering Medicaps University Indore India Dept. of Vocational Studies Guru Nanak College Dhanbad India Dept. of Civil Engineering K. Ramakrishnan College of Technology Trichy India Dept. of Software Engineer Horizon Systems Inc Phoenix Arizona Dept. of CSE GLA University Mathura India Dept. of Computer Science Jiwaji University Gwalior India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

The frontline in developing the capabilities of chatbots and virtual assistants involves the use of NLP, enabling these machines to evolve into truly advanced, interactive machines that are able to comprehend and respond to human language with an almost unimaginable degree of accuracy. The paper will analyze some of the major NLP techniques and methodologies used for such systems: machine learning algorithms, neural networks, and semantic analysis. This would also mean that, with the help of big data and continuous learning, the work of chatbots and virtual assistants will range from answering queries to personalized recommendations. Language understanding, generation, and context management are discussed in detail, underlining how NLP improves user experience by making interactions more natural and intuitive. The challenges of ambiguous language processing, conversational context, and ensuring data privacy and security are discussed as well. Case studies and applications in real life show the impact of NLP on different sectors: customer service, healthcare, and e-commerce. The findings bring out the potential of NLP to change human-computer interaction and allow for more responsive and intelligent digital assistants.

关键词： Ethics Technological innovation Sentiment analysis Virtual assistants Semantics Speech recognition Chatbots Natural language processing User experience Text processing

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A global collaboration to deploy help to China

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Communications of the ACM 2010年第12期53卷 142-149页

作者： Morelli, Ralph De Silva, Chamindra De Lanerolle, Trishan Curzon, Rebecca Mao, Xin Sheng Department of Computer Science Trinity College Hartford CT United States Computer Science Department Trinity College Hartford CT United States IBM Corporate Citizenship United States Distinguished Engineer in IBM Software Beijing China

A firsthand account of an international team effort to install the Sahana disaster-management system in Chengdu, Sichuan after an earthquake. On Monday May 12, 2008, an earthquake measuring 7.9 on the Richter scale struck in Sichuan Province in southwestern China, destroying homes, schools, hospitals, roads, and vital power and communication infrastructure. More than 45 million people were affected-tens of thousands were killed, hundreds of thousands injured, millions of people were evacuated and left homeless, and millions of buildings were destroyed. When the earthquake hit, several members of what became an international, volunteer, disaster-management IT team were attending a workshop in Washington, D.C. The workshop was organized by the IBM Office of Corporate Citizenship and Corporate Affairs department to train IBM personnel and others in the use and deployment of Sahana, a free and open source software (FOSS) disaster management system. Sahana, which means relief in Sinhalese, is a Web-based collaboration tool that helps manage information resources during a disaster recovery effort. It supports a wide range of relief efforts from finding missing persons, to managing volunteers, tracking resources, and coordinating refugee camps. Sahana enables government groups, non-governmental organizations (NGOs) and the victims themselves to work together during a disaster recovery effort. Over the next several weeks, the team members, distributed among several cities (Beijing and Chengdu in China, Hartford and New York in the U.S., and Colombo in Sri Lanka), worked together over global communication channels to configure and deploy Sahana in Chengdu, in order to support the disaster recovery effort there. The organizations involved in the collaboration included: The Lanka software Foundation (LSF), developers of the Sahana system. Three LSF members, led by the second author, were conducting the training workshop. Various departments of IBM, including Business Continuit

关键词： Earthquakes

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Ocean Barrier: A Floating Intrusion Detection Ocean Sensor Networks 12

Ocean Barrier: A Floating Intrusion Detection Ocean Sensor N...

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12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016

作者： Luo, Hanjiang Wu, Kaishun Hong, Feng Department of Information Engineer Zibo Vocational Institute China College of Computer Science and Software Engineering Shenzhen University China Department of Computer Science Ocean University of China China

ISBN: (纸本)9781509056965

Ocean sensor networks have many applications ranging from oceanographic data collection, pollution monitoring, disasters prevention to tactical surveillance. However, due to the harsh ocean environment, it is difficult to deploy three-dimensional sensor networks underwater for sustainable monitoring tasks. In this paper, we propose a novel floating three-dimensional sensor networks for ocean monitoring and surveillance applications, which leverage nodes' restricted movement to enlarge the monitoring area. The networks are deployed on both the surface of the ocean and underwater, with many chains of nodes attached to mooring lines. Compared with deployments requiring self-Adjusted nodes, our scheme is simpler and more cost-efficient, and the sustainable monitoring cost is low. We design deployment strategies, and provide in-depth mathematical analysis for network coverage. Then we derive the minimum number of sensors to achieve full surface coverage. Evaluated by simulations of a few underwater scenarios, the effectiveness and efficiency of the proposed scheme is proved. © 2016 IEEE.

关键词： Monitoring

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Research on USB driver for data acquisition

Research on USB driver for data acquisition

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2010 2nd International Conference on Future computer and Communication, ICFCC 2010

作者： Li, Guo Li, Mingli Zhao, Gensen Zang, Jinmei College of Computer Science and Technology Civil Aviation University of China Tianjin China Department of Software Engineer ShanDong College of Information Technology Weifang China

ISBN: (纸本)9781424458226

Drivers play an important role in developing devices and they are an interface for the end user to communicate with external devices. In this paper, we present a Windows Driver Model (WDM)-based USB device driver for data acquisition, as well as analyzing the USB communication model and rationale of WDM under Windows2000/XP. Finally we test the driver through application programming. In order to reduce development difficulty, we exploit DriverStudio as the development tool, rather than Driver Development Kit (DDK). The experimental results show that this driver is of practical interest in applications. ©2010 IEEE.

关键词： Data acquisition

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Fake view analytics in online video services

Fake view analytics in online video services

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24th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2014

作者： Chen, Liang Zhou, Yipeng Chiu, Dah Ming College of Computer Science and Software Engineer Shenzhen University China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450327060

Online video-on-demand (VoD) services invariably maintain a view count for each video they serve, and it has become an important currency for various stakeholders, from viewers, to content owners, advertizers, and the online service providers themselves. There is often significant financial incentive to use a robot (or a botnet) to artificially create fake views. How can we detect the fake views? Can we detect them (and stop them) efficiently? What is the extent of fake views with current VoD service providers? These are the questions we study in this paper. We develop some algorithms and show their effectiveness for this problem. © 2014 ACM.

关键词： Video on demand

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