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Integration of Synergetic IoT Applications with Heterogeneous Format Data for Interoperability Using IBM ACE

SN computer Science 2024年第1期5卷 1-21页

作者： Sandeep, M. Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore 575025 India

Data interoperability is a crucial requirement in IoT to improve services and enhance business opportunities and innovation. Integrating synergetic applications with heterogeneous data formats is a critical issue that needs to be addressed to achieve interoperability. The use cases indicate IBM ACE is promising in resolving integration issues among on-premises and cloud applications. Further, many efforts are observed to address the interoperability issue apart from the IBM ACE approach. However, they are complex, restricted to few data formats, and use proprietary solutions. To address these above-mentioned issues, this paper proposes the Integration of Synergetic IoT applications with Heterogeneous format data for Interoperability using IBM ACE (ISHII). Further, an intelligence-based data recognition module in the proposed ISHII is trained with standard features defined in RFC 7111, 8259, 8996, JSON-LD of W3C, and Google’s Protobuf. Subsequently, recognized heterogeneous format data are integrated and translated to interoperable format using Data Format Description Language (DFDL) with Extended SQL codes on IBM ACE. Finally, the performance of ISHII has been evaluated with synthetically generated patient monitoring and room ambiance datasets with reference to accuracy, time required for integration, and translation efficiency. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： IBM ACE Interoperability IoT Machine learning

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia Detection

Tri-UnityNet: A Multifaceted Ensemble Model for Pneumonia De...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Dhivyaa, C.R. Nithya, K. Shanmugapriya, P. Deebika, R. Kumar, K. Sathis Sudhakar, R. School Of Computer Science And Engineering Vellore Institute Of Technology Vellore India Srm Institute Of Science And Technology Computer Science And Engineering Trichy India Computer Science And Engineering Erode India K.S.R College Of Engineering Computer Science And Engineering Tiruchengode India Bannari Amman Institute Of Technology Computer Science And Engineering Sathyamangalam India Nandha College Of Technology Computer Science And Engineering Erode India

ISBN: (纸本)9798350364828

Pneumonia is an infection often caused by several viral infections and prediction of pneumonia requires expertise from radiotherapists, posing challenges, especially in remote areas. Developing an automatic pneumonia detection system is crucial to detect and treat pneumonia disease. Hence, an innovative approach is proposed to detect pneumonia by utilizing the ensemble of deep learning models, specifically VGG16, ResNet50, and InceptionV3. The ensemble method integrates predictions from these various architectures to enhance overall classification performance. Rather than starting the training of each model from the beginning, transfer learning techniques are utilized to fine-tune Convolutional Neural Network (CNN) models using a pre-existing knowledge base. This involves leveraging pre-trained models and adapting them specifically for the disease diagnose on Chest X-ray image dataset. The features are extracted from VGG16, ResNet50, and InceptionV3 models and the classes of disease are predicted by using softmax layers. Finally, ensemble learning is employed to fuse the predictions of multiple neural network architectures within Tri-UnityNet, enhancing its accuracy and robustness in pneumonia detection. By leveraging the complementary strengths of diverse models, the ensemble approach achieves superior performance, contributing to more reliable diagnostic outcomes. © 2024 IEEE.

关键词： Forecasting

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An ocean water current-inspired Geoscience based optimization algorithm

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International Journal of Information technology (Singapore) 2024年第4期16卷 2619-2633页

作者： Mishra, Aishwarya Goel, Lavika Department of Computer Science and Engineering Malaviya National Institute of Technology (NIT) Rajasthan Jaipur India

A novel optimization technique is proposed based on ocean water currents. The combination of all-natural forces determines ocean current speed, further when resultant of all these forces nears to zero, stability is attained. Ocean water current optimization is the natural way to reach the stability point or the optimized point. The proposed evolutionary algorithm simulates the involved forces to generate Ocean Current Speed Index (OCSI), which further converges to the stability point. Compared to other nature-inspired algorithms, the proposed algorithm converges quickly due to an improved gradient descent algorithm. OWCO doesn’t get struck in local minima as the exploratory property of the algorithm uses highly mutating forces to generate new possible search spaces. Results with respect to CEC 2021 benchmark concludes that the proposed algorithm outperforms other contemporary algorithms in the context of application and empirical evaluations. This optimization algorithm is tested for D = 10 and D = 20 in CEC 2021 and found to be scalable with better exploration and convergence for single objective bound constraint problems. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Geoscience Nature Inspired Optimization Algorithms (NIOA’s) Ocean Current Speed Index (OCSI) Ocean Water Currents Optimization (OWCO) Optimization algorithms Stability point

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Text recognition using improved dual attention based on textual double embedding network with aquila optimization algorithm

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International Journal of Information technology (Singapore) 2024年 1-15页

作者： Singhdev, Harsiddhi Gupta, Shruti Srivastava, Vivek Saxena, Apoorva Department of Computer Science and Engineering ABES Engineering College Uttar Pradesh Ghaziabad India Department of Computer Science and Engineering Axis Institute of Technology & amp Management Uttar Pradesh Kanpur India

Text recognition is the process that changes an image of text into a system readable text format. Different approaches were suggested related to text detection but the existing methods accuracy is low and error rate is high. Therefore, a text recognition using Improved Dual-attention based on Textual Double Embedding (IDTDE) with aquila optimization algorithm is proposed in this manuscript for effective text recognition. In this method the input image is taken from several datasets. Usually, the images have some kind of noise in it, and to eliminate that, the Structural interval gradient filtering preprocessing technique is used. Then, the ternary pattern and discrete wavelet technique is use to extract best features. Next, residual-based temporal attention convolutional neural network is utilized for the text classification and character identification are effectively attained by and IDTDE network. The Aquila optimization algorithm is used to optimize the network for best extracted text as output. Experimental outcomes demonstrate that the introduced approach accomplishes the high accuracy rates of 98.14%, 98.89%, and 90.47% on the IIIT5K, ICDAR 2013, and ICDAR 2015 datasets, respectively, surpassing the performance of existing frameworks. © Bharati Vidyapeeth's institute of computer Applications and Management 2024.

关键词： Aquila optimizer Character recognition Gradient filtering Improved self-attention Text recognition

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Optimizing CNN Performance for Heart Attack Detection through Grey Wolf Algorithm

Optimizing CNN Performance for Heart Attack Detection throug...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Aditya Kumar, G. Katiyar, Aditi Kannan, A. Vellore Institute of Technology Computer Science Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350361537

Better patient outcomes and prompt care depend on early detection of heart attacks. In this current work, we use the infamous MIT-BIH Arrhythmia Dataset, a reference resource for cardiac abnormality recognition, to train and validate our models. We propose an improved method for heart attack diagnosis and detection which includes a CNN model that is optimized with the help of the Grey Wolf Optimization (GWO) technique. This novel integration aims to enhance feature extraction and classification processes by tackling the drawbacks of traditional deep learning methods. A comprehensive testing process has been conducted on the proposed CNN-GWO model versus well-known deep learning models, such as Artificial Neural Networks also known as ANN, as well as many state-of-the-art machine learning models used for heart attack detection such as SVM, Decision Tree, and other ensemble learning models such as XG Boost. Our findings demonstrate a significant increase in detection accuracy and efficiency when comparing the CNN-GWO model to its rivals. This work not only demonstrates the effectiveness of combining CNN with GWO, but also demonstrates how well the proposed model performs in detecting heart attacks. This work contributes to the advancement of medical diagnostic technology by offering a more reliable and accurate diagnostic instrument. It also opens new options for algorithmic advancements in the future that forecast heart attacks. © 2024 IEEE.

关键词： Diseases

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An Efficient Approach to Reduce Energy Consumption in a Fog Computing Environment Using a Moth Flame Optimization Algorithm

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SN computer Science 2024年第6期5卷 1-24页

作者： Asgarnezhad, Razieh Department of Computer Engineering Aghigh Institute of Higher Education

After decades of growth in the computer computing field, cyber-physical systems (CPS), a combination of physical and tangible hardware and virtual and supernatural tools and concepts, distributed. Today, fog and cloud computing are the most complex cyber-physical systems available. Theretofore, the cloud data centers developed to provide the resources needed by users, home, and industrial businesses. Cloud computing has provided the possibility of providing services near the occurrence of requests with processing in proximity (PP). However, edge or fog computing will supply a possible solution to improve the quality of service delivery compared to using cloud computing. Applications in cyber-physical fog systems utilize different services provided by diverse resources in fog colonies based on criteria and restriction rules. Since internet of things (IoT) applications executed in real-time and sensitive to time, the problem of delay in providing service to application requests in cloud computing distributed resources is very challenging. Fog computing supply an ideal platform for CPSs with fully geographically distributed features. At first, by placing services on resources are located in the edge layer, the cloud decrease the volume of requests sent to the cloud. Moreover, the response and the average delay time be solved in the proposed method. As a result, it makes the problem of placing services in cloud computing more complicated than in other areas like cloud computing and standard distributed systems. In addition, to balance the load and ensure the quality of services, requested services in the fog system can be freely processed by any of the resources (nodes) available in the fog computing. According to the characteristics of the geographic distribution of fog nodes, the complexity of placing services to provide services to reduce energy consumption will be very high. In this study, we offer a solution based on the meta-heuristic algorithm of moth flame optim

关键词： Fog computing IoT MFO algorithm Node Physical cyber Service placement

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Sentiment Analysis of Movie Reviews Using Machine Learning

Sentiment Analysis of Movie Reviews Using Machine Learning

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Yadav, Avantika Garg, Payal Jain, Tarun Manipal University Department of Computer Science & Engineering Jaipur India Gl Bajaj Institute of Technology and Management Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798331508685

This paper delves into the critical role that machine learning plays in the accurate classification of sentiments expressed in movie reviews. Throughout the study we research and apply NLP using deep learning and machine learning algorithms to train and test our IMDb 50K movie reviews dataset on different models and feature embedding to compare their accuracies and choose the most optimal model for sentimental analysis. Ultimately, using deep neural network model BERT neural gives us the best accuracy of 94.2% compared to lexicon-based approaches due to the limitations in feature extractions like Bag of Words and TF-IDF. © 2025 IEEE.

关键词： Bag of Words BERT Deep Learning IMDb Dataset Machine Learning Natural Language Processin Sentiment Analysis TF-IDF Training and Testing

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Hybrid Filtering-Based Product Recommendation System Integrating GRU and BFGS Optimization

Hybrid Filtering-Based Product Recommendation System Integra...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Suresh, A. Kumar, R.G. Nagaraju, D. Mohana Sundaram, K.D. Anandan, B. Siddharth Institute of Engineering and Technology Department of Computer Science and Engineering Puttur India Siddharth Institute of Engineering and Technology Department of ECE Puttur India

ISBN: (纸本)9798331522988

It is more crucial than ever to handle crucial problems including data sparsity, cold-start problems, and the requirement for extremely precise forecasts in today's ever changing recommendation system field. This work presents a hybrid filtering product recommendation system that integrates Gated Recurrent Units (GRU) with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization method to significantly enhance recommendation accuracy and performance. Standard techniques such content-based filtering and collaborative filtering can fail to offer reliable recommendations for large and dynamic datasets. Our hybrid model uses BFGS optimization to increase model accuracy and efficiency while utilizing GRU-based collaborative filtering to capture sequential user-item interactions. The GRU-BFGS model outperforms matrix factorization (MF) techniques in numerous areas, achieving an astounding accuracy of up to 92% on the Amazon customer review dataset. It also exhibits excellent recall, precision, and notable decreases in error metrics (MAE, MSE, and RMSE), which makes it a very successful method for providing individualized, accurate suggestions. © 2024 IEEE.

关键词： Collaborative filtering

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Exploring the Impact of Financial News Sentiment on Stock Price Forecasting: A Comparative Deep Learning Approach 1

Exploring the Impact of Financial News Sentiment on Stock Pr...

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1st IEEE International Conference on Communications and computer Science, InCCCS 2024

作者： Rajhans, Abhishek Das, Trinanjan Kumar, Anish Sinha, Bam Bahadur Computer Science and Engineering Indian Institute of Information Technology Ranchi India Computer Science and Engineering National Institute of Technology Sikkim India

ISBN: (纸本)9798350358858

The application of Artificial Intelligence (AI) in stock price prediction has demonstrated significant advancements, with Machine Learning and Deep Learning techniques proving highly efficient in this domain. Two widely adopted architectures for stock price prediction are the Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) models. Recognizing the potential impact of financial news sentiment on forecasting, this study investigates whether incorporating such sentiment yields superior results compared to relying solely on historical stock prices. Accurate predictions in the financial market are challenging due to its inherent high volatility and non-linear nature. To contribute insights into the effectiveness of various deep learning architectures, a comparative analysis was conducted on Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and the Prophet model. The evaluation focused on three companies listed on the National Stock Exchange (NSE). The study employs the Mean Squared Error (MSE) metric to rigorously assess and compare the models’ performance. The results of the comparative analysis provide valuable implications for precision in stock price prediction within the context of market volatility and non-linearity. The study adds to the growing body of research on the application of AI in financial forecasting, emphasizing the importance of considering sentiment analysis alongside traditional approaches for enhanced predictive accuracy. © 2024 IEEE.

关键词： Long short-term memory

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