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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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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Timilsina, Sushil Khadka, Ashim Lin, Jianchu Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

ISBN: (纸本)9798350306293

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches. © 2023 IEEE.

关键词： Natural language processing systems

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Optimizing Railway Traffic Management Using Fuzzy Logic-Based Decision-Making Systems 2

Optimizing Railway Traffic Management Using Fuzzy Logic-Base...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kumari, Nalli Vinaya Pradeep Ghantasala, G.S. Sharma R, Rajesh Ananthanagu, U. Vidyullatha, Pellakuri Pandiya, Dileep Kumar Jntuh Malla Reddy Engineering College for Women Department of Computer Science and Engineering Hyderabad India Alliance University Alliance College of Engineering and Design Department of Computer Science and Engineering Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India Zoominfo Technologies Principl Software Engineer Boston United States

ISBN: (纸本)9798350364699

Railway web page traffic manipulate is a complex and dynamic method that includes making real-time picks based mostly on various uncertain and difficult to understand factors, collectively with fluctuating passenger demand, diverse train schedules, and unpredictable weather conditions. Traditional decision-making strategies often battle to cope with the ones uncertainties, foremost to inefficiencies and potential safety risks. This paper proposes the software program of fuzzy properly judgment-based completely decision-making systems to optimize railway web page traffic manipulate. Fuzzy properly judgment, with its potential to model uncertainty and control difficult to understand data, offers a robust framework for boosting decision-making approaches in railway operations. By simulating various conditions and integrating real-time data, the proposed gadget can dynamically regulate train schedules, manipulate delays, and enhance everyday operational efficiency. The paper may present case studies demonstrating the effectiveness of fuzzy properly judgment in dealing with railway web page traffic, reducing delays, and improving passenger satisfaction. The findings advice that incorporating fuzzy properly judgment into railway web page traffic manipulates systems can purpose greater adaptive and resilient railway operations, ultimately contributing to greater steady and additional reliable rail networks. © 2024 IEEE.

关键词： Efficiency

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive Learning

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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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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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Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits

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Multimedia Tools and Applications 2024年第33期83卷 80201-80223页

作者： Dutta, Ashit Kumar Raparthi, Mohan Alsaadi, Mahmood Bhatt, Mohammed Wasim Dodda, Sarath Babu Prashant, G.C. Sandhu, Mukta Patni, Jagdish Chandra Department of Computer Science and Information Systems College of Applied Sciences AlMaarefa University Ad Diriyah Riyadh13713 Saudi Arabia Alphabet Life Science DallasTX7506 United States Department of Computer Science Al-Maarif University College Al Anbar31001 Iraq Model Institute of Engineering and Technology J&K Jammu India Software Engineer Central Michigan University DallasTX75009 United States Department of Computer Science Texas Tech University Lubbock79409 United States Skill Faculty of Engineering & amp Technology Shri Vishwakarma Skill University Haryana Palwal India CSE School of advance Computing Alliance University Bengaluru India

The neurological condition epilepsy is demanding and even fatal. Electroencephalogram (EEG)-based epilepsy detection still faces various difficulties. EEG readings fluctuate, and different patients have various seizure activity patterns. EEG signal detection is time-consuming and labour-intensive, which puts a strain on medical staff and raises the possibility of erroneous detections. Usually, electrodes are inserted into the scalp or inside the brain for a brief period of time in order to obtain EEG data. It is essential to research efficient cross-patient automatic epilepsy detection techniques. The multi-head self-attention mechanism recognises long-distance dependencies with the same proficiency as it does temporal dynamic correlations between short-term temporal pattern characteristics and sequential relationships. The contextual representations are inputted into a bidirectional long short-term memory network (BiLSTM) so that information can be extracted in both directions. Classification and training are carried out utilising the log SoftMax algorithm. The experiments utilised scalp EEG data from the CHB-MIT database. Sensitivity, specificity, F1-score, and accuracy were computed to be 96.5 percent, 97.04 percent, 96.6 percent, and 96.2 percent, respectively. The results of the experiment show how well the technique works for detecting seizures in several patients utilising multi-channel EEG recordings. The results also demonstrate the method's improved generalisation capabilities and resilience and consistency in collecting seizure patterns. This is particularly critical for the tertiary diagnosis of epilepsy, and the findings indicate that the proposed method significantly improves the accuracy of detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electroencephalography

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Cement Strength Prediction using Regression Techniques 10

Cement Strength Prediction using Regression Techniques

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Dewan, Ritu Polishetty, Rohith Sharan, Bhagwati Goyal, Mayank Kumar Balde, Amadou Mouctar Greynoon, Ameen Mohamad Ahmad Galgotias College of Engineering and Technology Department of Computer Science and Engineering Greater Noida India Sr. Software Engineer Migo India Limited Hyderabad India Srm University-AP School of Engineering and Sciences Department of Computer Science and Engineering Amaravati India Sharda University School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India School of Engineering and Technology Sharda University Department of Computer Science and Applications Greater Noida India

ISBN: (纸本)9789380544472

One of the primary structural materials in the development business is concrete. Compressive strength is the most frequently utilized metric when assessing the strength and nature of cement. This paper clarifies how to make use of technical regression (RT) methodology to decide the legitimate dose of the concrete mixture. Water, Cement, Fly Ash, Blast Furnace, Superplasticizer, Coarse, and Fine Aggregate, were used as input parameters, on the other hand, compressive strength was used as an output parameter. The produced models were evaluated using statistical parameters: Linear Regression, Decision Tree Regression, and Random Forest Regression. In contrast with other applicable models, it was found that the Gaussian process regression model based on the function of the nucleus of radial bases delivered relatively better results. The recommended models should get a good deal on saving money on materials, labor, and time while also improving accuracy. The proposed concrete should be more durable and accordingly more cost-effective. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Cements

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A Framework Design of ML Classifier Algorithm for Retrieve the Information about the Drugs and its Quality 4

A Framework Design of ML Classifier Algorithm for Retrieve t...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Meenakshi, M. Ruban, S. Nandhini, T.J. Loganayagi, S. Sayed, Raad Muhammed Hassan, Enas Al-Saidi, Imad Al-Din Hussein Saveetha University Saveetha School of Engineering Chennai Thandalam602117 India St Aloysius Deemed to Be University Department of Software Technology India Saveetha University Saveetha School of Engineering-SIMATS Department of Computer Science and Engineering chennai India Department of Computer Science and Engineering chennai tamil nadu thandalam602105 India Collage of Nursing National University of Science and Tqechnology dhi qar64001 Iraq Al-Farahidi University Baghdad Iraq Al-kunooze University College Computer Engineer Department basra Iraq

ISBN: (纸本)9798350360165

Every day, pharmaceutical firms have to do extensive product analyses. The same product is frequently registered more than once in several systems, each using unique properties. For these businesses, precise and high-quality information is crucial, especially considering the nature of the pharmaceuticals these goods contain. The hypothesis of this research project is that machine learning provides a workable way to effectively combine different data sources and match records pertaining to the same product. It is no longer feasible to match records by hand because of the extraordinarily large volume that has to be processed. In a large data context, this paper presents a framework for matching pharmacological records using machine learning approaches. The framework trains machine learning by utilising specified criteria for record matching. These models are then tested by forecasting matches between records that deviate from these preset guidelines. Lastly, by creating a huge variety of record combinations and forecasting matches based on them, the system mimics the production environment. The findings show that although the training datasets produce good results - the top model has an average accuracy of about 85% - the production environment has its own set of difficulties. In spite of this, matches that depart from established norms are effectively predicted by the framework. These results highlight how machine learning can be used to perform record matching tasks, as manual processing at this size is impossible. © 2024 IEEE.

关键词： Adversarial machine learning

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