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Exploiting Predictability of Random Vector Functional Link Networks in Forecasting Quality of Service (QoS) Parameters of IoT-Based Web Services Data

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International Journal of Sensors, Wireless Communications and Control 2023年第2期13卷 57-70页

作者： Lenka, Stitapragyan Pradhan, Sateesh Kumar Nayak, Samaleswari Prasad Nayak, Sarat Chandra Department of Computer Science Application Utkal University Odisha Bhubaneswar India Department of Computer Science and Engineering Silicon Institute of Technology Odisha Bhubaneswar India Department of Computer Science Yonsei University Seoul South Korea

Background: QoS parameters are volatile in nature and possess high nonlinearity, thus making the IoT-based service and recommendation process challenging. Methods: An efficient and accurate forecasting model is lacking in this area and needs to be explored. Though an artificial neural network is a prominent option for capturing such nonlinearities, its effi-ciency is limited by the structural complexity and iterative learning method. The random vector functional link network (RVFLN) significantly reduces the time complexity by randomly assigning input weights and biases without further modification. Only output layer weights are calculated iteratively by gradient methods or non-iteratively by least square methods. It is an efficient algorithm with low time complexity and can handle complex domain problems without compromising accuracy. Motivat-ed by these characteristics, this article develops an RVFLN-based model for forecasting QoS parameter sequences. Results: Two real-world IoT-enabled web service dataset series are used in developing and evaluating the effectiveness of RVFLN-based forecasts in terms of three performance metrics. Conclusion: Experimental results, comparative studies, and statistical tests are conducted to establish the superiority of the proposed approach over four other similar forecasting techniques. © 2023 Bentham science Publishers.

关键词： artificial neural network IoT QoS parameter forecasting random vector functional link network time series forecasting web service search

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Using Neural Network-Based Time Series Analysis in Wireless Sensor Networks 2

Using Neural Network-Based Time Series Analysis in Wireless ...

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2nd International Conference on Artificial Intelligence and Machine Learning applications, AIMLA 2024

作者： Goyal, Manish Kumar Patil, Nagaraj Bhagat, Akash Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India ARKA JAIN University Department of Computer Science & IT Jharkhand Jamshedpur India

ISBN: (纸本)9798350349221

Neural community-primarily based time series analysis (NNTSA) is an effective technique for research records collected from wireless sensor networks (WSNs). NNTSA uses models stimulated by means of biological neural networks to capture complicated time-various sensor statistics. It requires minimum training of the enter facts and offers a high degree of modelling accuracy. This method has turned out to be increasingly famous for WSN facts evaluation when you consider that it is miles appropriate for streaming statistics and seizing complicated patterns that might not be effortlessly detected with the aid of other techniques. NNTSA fashions are commonly carried out the use of supervised gaining knowledge of processes, together with lower back-propagation, wherein education styles are fed to a neural network at the same time as its parameters are adjusted the use of a fixed of desired output values, together with an average temperature over some time. After the NNTSA c section is complete, new patterns can be evaluated with the trained model to analyze their temporal conduct. NNTSA techniques have been applied in diverse areas, inclusive of fitness monitoring, site visitors management, and weather tracking, with suitable result. © 2024 IEEE.

关键词： Time series analysis

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Empirical Analysis of Digital Currency Herding and Contagion Effect Using Deep Learning Approaches 3

Empirical Analysis of Digital Currency Herding and Contagion...

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3rd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2024

作者： Tripathy, Nrusingha Hota, Sarbeswara Mishra, Debahuti Dash, Biswajit Nayak, Subrat Kumar Tripathy, Jogeswar Deemed to Be University Department of Computer Science and Engineering Siksha 'O' Anusandhan Odisha Bhubaneswar India Deemed to Be University Department of Computer Application Siksha 'O' Anusandhan Odisha Bhubaneswar India Raajdhani Engineering College Department of Computer Science and Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350354379

In recent years, the use of virtual or cryptocurrency-based money has grown. Blockchain, a digital accounting system, is the foundation of cryptocurrency. The markets for cryptocurrencies are eminent for their high volatility and quick price swings. People tend to be more likely to follow the lead of others at times of notable price fluctuations out of a concern of losing money or missing out on possible gains. In this work, we taking the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), the hybrid combinations of LSTM-Convolutional Neural Network (CNN), LSTM-Gated Recurrent Unit (GRU) and for Facebook Prophet (Fb-Prophet) model smoothing, triple exponential smoothing (Holt-Winters Method) is utilized, which adds seasonality into the forecasting model. In this work bitcoin exchanges providing real-time information casing the period from January 2012 to July 2020 is utilized. The price of cryptocurrencies fluctuates significantly throughout a range of time periods, from short-term intraday swings to long-term patterns that last for weeks or months. By altering the network layer design and the duration of the input sequences, LSTM-GRU models configured to adapt to various time horizons. In contrast to previous iterations, the hybrid LSTM-GRU performs well. When compared to other models, the model's Mean Absolute Error (MAE) score of 0.056 and Root Mean Square Error (RMSE) score of 0.073 are quite acceptable. Forecasting the value of cryptocurrencies will help traders and investors in making better-informed choices when purchasing, disposing of, and keeping assets. © 2024 IEEE.

关键词： Convolutional neural networks

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A Unified Framework for Image Caption Generation Using Deep Reinforcement Learning 15

A Unified Framework for Image Caption Generation Using Deep ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Shree, Ritu Narmadha, T. Joseph, Vishakha Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Arka Jain University Department of Management Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

This paper introduces a unified framework for picture caption generation with the use of deep reinforcement mastering (RL) that combines convolution neural networks (CNNs) with RL. The paper first introduces a country representation as a remark for RL by means of combining CNN-primarily based photo representation and natural language processing (NLP) natural language representation. Then, the paper describes an RL-based reinforcement mastering (RL-RL) set of rules which uses the statement to generate captions. Eventually, the paper shows the experimental consequences of the proposed RL-RL set of rules on real-world datasets and shows that the RL-RL set of rules outperforms the present RL-NLP and CNN-RL algorithms. The paper additionally demonstrates the very best overall performance on the benchmark dataset COCO with a BLEU score of 0.645 and a METEOR score of 0.348. The proposed framework affords a sensible technique to generate interesting and affordable captions for photographs. © 2024 IEEE.

关键词： Benchmark Consequences Convolution Photographs Reinforcement Representation

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Exploring the Benefits of Interactive Communication Media Algorithms for Networking applications 3

Exploring the Benefits of Interactive Communication Media Al...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Ahmed, Zahid Beemkumar, N. Ghosh, Debanjan Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India Arka Jain University Department of Computer Science & It Jharkhand Jamshedpur India

ISBN: (纸本)9798350381931

Interactive communication media algorithms are useful tools for creating efficient and effective networking applications. Such algorithms enable the application to accurately predict user behaviour and preferences, use natural language processing to automate conversations, and integrate with other systems. This paper investigates the benefits of using interactive communication media algorithms for developing networking applications, including improved user experience, scalability, and quicker response times. Additionally, this paper will address potential limitations, such as the need for significant data collection and availability of relevant computing resources. Finally, this paper will conclude with a discussion of the potential implications of further investigations into interactive communication media algorithms for networking applications. © 2024 IEEE.

关键词： Behavioral research

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Deep Learning Techniques for Identification of Pneumonia: A CNN Approach 2

Deep Learning Techniques for Identification of Pneumonia: A ...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Das, Ruchika Nayak, Debasish Swapnesh Kumar Rout, Chinmayee Priyadarshini Jena, Lambodar Swarnkar, Tripti Siksha 'o' Anusandhan University Department of Computer Science and Engineering Bhubaneswar India Nalanda Institute of Technology Department of Computer Application Bhubaneswar India Siksha 'O Anusandhan University Department of Data Science Bhubaneswar India Siksha 'o' Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9798350370188

Pneumonia is an inflammatory lung disease that mostly affects the tiny air sacs known as alveoli in human bodies. Pneumonia may be caused by bacteria, viruses, or fungi. Mild to severe symptoms may include a mucus-producing cough and a fever, appals, and difficulty to breath. The approach most usually used to diagnose pneumonia is chest X-ray (CXR) imaging. Images from a CXR screening can be used as an alternative or confirmation method because they are simple and affordable to acquire. Examining the chest X-rays however is a hard task that might be vulnerable to subjective variation. We used a deep learning technique in this investigation. Convolutional neural networks (CNNs) for deep learning were employed in this paper. In his work, the proposed model is applied to the original CXR image dataset and the augmented image dataset. We generated the augmented images from the existing CXR image dataset and deployed VGG-19, a component of the CNN model to create an autonomous Pneumonia detection system. The suggested techniques that have been employed include scaling the image, adding extra information, deep learning using Keras, batch normalization, and finally using weights from the pre-trained Keras model (VGG19). Finding pneumonia sickness is difficult and takes time. In this study, we address the issue and demonstrate how to detect pneumonia disease more quickly and accurately. Our proposed model shows an accuracy of 95% on the augmented CXR image dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Classification of Online Sentiment Data tweets using Various...

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2023 International Conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Rawat, Vandana Bisht, Paritosh Suryakant Kothiyal, Shruti Singh, Neelam Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Department of Computer Application Dehradun India Department of Computer Science and Engineering France

ISBN: (纸本)9798350323917

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy. © 2023 IEEE.

关键词： Sentiment analysis

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PD-YOLO:Colon Polyp Detection Model Based on Enhanced Small-Target Feature Extraction

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computers, Materials & Continua 2025年第1期82卷 913-928页

作者： Yicong Yu Kaixin Lin Jiajun Hong Rong-Guei Tsai Yuanzhi Huang New Engineering Industry College Putian UniversityPutian351100China Engineering Research Center of Big Data Application in Private Health Medicine(Putian University) Fujian Province UniversityPutian351100China Computer College Guangdong University of Science&TechnologyDongguan523668China

In recent years,the number of patientswith colon disease has increased *** polyps are the precursor lesions of colon *** not diagnosed in time,they can easily develop into colon cancer,posing a serious threat to patients’lives and health.A colonoscopy is an important means of detecting colon ***,in polyp imaging,due to the large differences and diverse types of polyps in size,shape,color,etc.,traditional detection methods face the problem of high false positive rates,which creates problems for doctors during the diagnosis *** order to improve the accuracy and efficiency of colon polyp detection,this question proposes a network model suitable for colon polyp detection(PD-YOLO).This method introduces the self-attention mechanism CBAM(Convolutional Block Attention Module)in the backbone layer based on YOLOv7,allowing themodel to adaptively focus on key information and ignore the unimportant *** help themodel do a better job of polyp localization and bounding box regression,add the SPD-Conv(Symmetric Positive Definite Convolution)module to the neck layer and use deconvolution instead of *** results indicate that the PD-YOLO algorithm demonstrates strong robustness in colon polyp *** to the original YOLOv7,on the Kvasir-SEG dataset,PD-YOLO has shown an increase of 5.44 percentage points in AP@0.5,showcasing significant advantages over other mainstream methods.

关键词： Polyp detection YOLOv7 SPD-Conv CBAM deconvolution

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Balancing data imbalance in biomedical datasets using a stacked augmentation approach with STDA, DAGAN, and pufferfish optimization to reveal AI's transformative impact

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International Journal of Information Technology (Singapore) 2025年第1期17卷 455-480页

作者： Veedhi, Bhaskar Kumar Das, Kaberi Mishra, Debahuti Mishra, Sashikala Behera, Mandakini Priyadarshani Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Application Siksha ‘O’ Anusandhan (Deemed to Be) University Odisha Bhubaneswar India Department of Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed to Be) University Maharashtra Pune India School of Computer Engineering KIIT (Deemed to Be) University Odisha Bhubaneswar India

This study systematically assesses the effectiveness of data augmentation techniques, focusing particularly on the stacked approach, across a diverse range of biomedical datasets. Advanced data augmentation methods, including style transfer data augmentation (STDA) and data augmentation generative adversarial network (DAGAN), offer promising solutions in this context. STDA leverages style transfer techniques from computer vision to create varied and realistic synthetic samples while preserving semantic content, thereby enhancing dataset variability and improving model generalization. Conversely, DAGAN utilizes generative adversarial networks (GANs) to learn the underlying data distribution, resulting in the generation of high-quality synthetic samples. Subsequently, STDA and DAGAN techniques are applied to the selected data, followed by convolution and pooling operations on the augmented samples. The pooled samples are then stacked, and the Cosine similarity is calculated to measure the resemblance to the original dataset. Parameters of STDA and DAGAN are fine-tuned to maximize the Cosine similarity. Additionally, a metaheuristic optimization algorithm called Pufferfish optimization algorithm (POA) is employed to optimize decision variables—learning rate, number of hidden units (common for both STDA and DAGAN), number of attention heads for STDA, and latent space dimensionality of DAGAN. The classification performance of the proposed POA-stacked STDA-DAGAN data augmentation methodology, along with variants using genetic algorithm (GA) and particle swarm optimization (PSO), is compared with original datasets and SMOTE balanced datasets using various classifiers and the observed accuracies, sensitivity, specificity and F-score are ranging from 91.83% to 99.11%, 91.38% to100%, 92.50% to 100% and 92.95% to 98.32% respective for all ten biomedical datasets used in this research. Additionally, the class distribution graphs, learning curves, statistical validation, and exe

关键词： Biomedical data Data augmentation Data augmentation generative adversarial network (DAGAN) Pufferfish optimization algorithm (POA) Style transfer data augmentation (STDA)

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Accelerating local SGD for non-IID data using variance reduction

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Frontiers of computer science 2023年第2期17卷 73-89页

作者： Xianfeng LIANG Shuheng SHEN Enhong CHEN Jinchang LIU Qi LIU Yifei CHENG Zhen PAN Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of ChinaHefei 230027China Ant Financial Services Group Hangzhou 310000China Department of Computer Science and Engineering Hong Kong University of Science and TechnologyHong Kong 999077China

Distributed stochastic gradient descent and its variants have been widely adopted in the training of machine learning models,which apply multiple workers in *** them,local-based algorithms,including Local SGD and FedAvg,have gained much attention due to their superior properties,such as low communication cost and ***,when the data distribution on workers is non-identical,local-based algorithms would encounter a significant degradation in the convergence *** this paper,we propose Variance Reduced Local SGD(VRL-SGD)to deal with the heterogeneous *** extra communication cost,VRL-SGD can reduce the gradient variance among workers caused by the heterogeneous data,and thus it prevents local-based algorithms from slow convergence ***,we present VRL-SGD-W with an effectivewarm-up mechanism for the scenarios,where the data among workers are quite *** from eliminating the impact of such heterogeneous data,we theoretically prove that VRL-SGD achieves a linear iteration speedup with lower communication complexity even if workers access non-identical *** conduct experiments on three machine learning *** experimental results demonstrate that VRL-SGD performs impressively better than Local SGD for the heterogeneous data and VRL-SGD-W is much robust under high data variance among workers.

关键词： distributed optimization variance reduction local SGD federated learning non-IID data

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