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3rd International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2021

作者： Yadao, Shivani Babu, A. Vinaya Janarthanan, Midhunchakkaravarthy Bhaumik, Amiya Lincoln University College Dept. of Computer Science and Engineering Selangor Malaysia Stanley College of Engineering and Technology for Women Dept. of Computer Science and Engineering Hyderabad India Lincoln University College Dept. of Computer Science and Multimedia Selangor Malaysia Lincoln University College Selangor Malaysia

ISBN: (纸本)9780738111834

Web mining is emerging as one of the most demanding streams for researchers as it deals with the mining of information on the internet. It is especially useful in the application domains of e-commerce. In order to understand and better serve the needs of Web-based applications, web use mining (WUM) is used as the application of data mining techniques to discover use mining. Other techniques like web content mine (WCM), web structure mine (WCM) are also discussed in the paper. It has seen a brisk extension in interest from both the assessment and practice of organizational data patterns. In dept., this paper discusses each of these steps. Web use mining has seen a quick expansion in interest from both the examination and practice perspective in networks, considering its application potential. This paper provides a comprehensive taxonomy, counting research endeavors of the work in the same field. An up-to-date survey is also given on the ongoing work. © 2021 IEEE.

关键词： Data mining

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Simulating Federated Transfer Learning for Lung Segmentation using Modified UNet Model

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Procedia computer science 2023年 218卷 1485-1496页

作者： Sateesh Ambesange B Annappa Shashidhar G Koolagudi Computer Science Engineering Dept National Institute of Technology Karnataka Surathkal Mangalore Karnataka state India-575025.

Lung segmentation helps doctors in analyzing and diagnosing lung diseases effectively. Covid -19 pandemic highlighted the need for such artificial intelligence (AI) model to segment Lung X-ray images and diagnose patient covid conditions, in a short time, which was not possible due to huge number of patient influx at hospitals with the limited radiologist to diagnose based on test report in short time. AI models developed to assist doctors to diagnose faster, faces another challenge of data privacy. Such AI Models, for better performance, need huge data collected from multiple hospitals/diagnostic centres across the globe into single place to train the AI models. Federated Learning (FL) framework, using transfer learning approach addresses these concerns as FL framework doesn't need data to be shared to outside hospital ecosystem, as AI model get trained on local system and AI model get trained on distributed data. FL with Transfer learning doesn't need the parallel training of the model at all participants nodes like other FL. Paper simulates Federated Transfer learning for Image segmentation using transfer learning technique with few participating nodes and each nodes having different size dataset. The proposed method also leverages other healthcare data available at local system to train the proposed model to overcome lack of more data. Paper uses pre-trained weights of U-net Segmentation Model trained for MRI image segmentation to lung segmentation model. Paper demonstrates using such similar healthcare data available at local system helps improving the performance of the model. The paper uses Explainable AI approach to explain the result. Using above three techniques, Lung segmentation AI model gets near perfect segmentation accuracy.

关键词： Federated Learning Federated Transfer Learning Lung image segmentation MRI image segmentation Transfer Learning U-net Architecture X-ray Image segmentation data privacy

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Speech Steganography based on Double Approximation of LSFs Parameters in AMR Coding 7

Speech Steganography based on Double Approximation of LSFs P...

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7th International Conference on Image and Signal Processing and their Applications, ISPA 2022

作者： Kheddar, Hamza Mazari, Ahmed Cherif Ilk, Gokhan Hakki University of Medea Lsea Lab Dept. Electrical Engineering Faculty of Technology Medea26000 Algeria University of Medea Lsea Lab Dept. of Mathematics and Computer Science Medea26000 Algeria Ankara University Dept. of Electrical Engineering Ankara Turkey

ISBN: (纸本)9781665480420

A steganographic method for VoIP applications based on line spectral frequency (LSF) modification, is proposed in this paper. The aim of this research is to securely transmit secret binary data into AMR cover bitstream. The proposed scheme is Newton-based interpolation of the LSF coefficients of the four sub-frames, alongside with the original linear interpolation. Newton interpolation gives a perfect similarity to linear interpolation when there is no secret information is being transmitted. Besides that, Newton interpolation provides a high flexibility, which doesn't exist in linear interpolation, to modify the eight LSF coefficients from within ten, excluding the first and the tenth LSFs. The results show that after a slight modification of ±0.01 (D) during LSF interpolation using our proposed scheme, the intelligibility is perceived, the maximum steganographic quality change is 0.08 MOS. The proposed scheme provides a high capacity of 1.05 kbps, a high undetectability, and security level against statistical steganalysis. Hence, our proposed steganographic method is effective, secure and, avoid rising the suspicious to eavesdropper who is listening to the channel. © 2022 IEEE.

关键词： Steganography

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Crude Oil Price Forecasting using Multi-Feed Data: A Comparative Study

Crude Oil Price Forecasting using Multi-Feed Data: A Compara...

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Intelligent Systems (ETIS), Emerging Technologies for

作者： Sruthi N Paul Anu Mary Chaco Biplab Bhattacharjee Dept. of Computer Science and Engineering National Institute of Technology Calicut India Jindal Global Business School O.P. Jindal Global University Sonepat India

ISBN: (数字)9798331507541

ISBN: (纸本)9798331507558

Crude oil price forecasting has a significant role in advancing the Sustainable Development Goals by enabling more informed decision-making in energy management and resource allocation. The accurate forecasting of crude oil prices is always a challenging task due to the inherently volatile nature of the price data. Additionally, the non-linearity and non-stationarity of the data make the price forecasting further complicated. In such cases, machine learning models can outperform the traditional statistical models. The machine learning models have the potential to analyse complex, non-linear, and non-stationary time series data and generate more precise forecasting results. In this work, we implemented the machine learning-based crude oil price forecasting model using multi-feed data. Along with the historic crude oil price data, we used financial markets' data and Google Trends data, which give additional information about the price fluctuations. We can analyse user sentiment information using Google Trends data, which also influences crude oil prices. We developed three forecasting models using Random Forest, XGBoost, and Linear Regression models. The comparative study reveals that the Random Forest model outperforms the other two models in terms of accuracy.

关键词： Accuracy Biological system modeling Oils Predictive models Market research Data models Internet Forecasting Sustainable development Random forests

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Advanced Algorithm for Cardiac Arrhythmia Classification and Efficient Detection of Abnormalities in ECG Signals 4

Advanced Algorithm for Cardiac Arrhythmia Classification and...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Amhia, Hemant Sarkar, Vikas Pandey, L.N. Shakunthala, M. Anudeep, Sadula Singh, Shreya Jabalpur Engineering College Electrical Engineering Department MP Jabalpur India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal462044 India Gyan Ganga Institute of Technology & Sciences Dept of Cse MP Jabalpur India R.M.D. Engineering College Department of Ece Kavaraipettai TN Thiruvallur India Mlr Institute of Technology Department of Cse Dundigal Hyderabad India Ggits MP India

ISBN: (纸本)9798331518981

High-Performance Algorithm for Cardiac Arrhythmias Classification and ECG Abnormality Detection Aspecific heart rhythms that are not found upon initial examinations, often lead to heart failure and stroke. Arrhythmias have to be diagnosed early through precise classification of ECG signals. Conventional methods employ ineffective and tedious manual intervention in interpreting the time series and the filtering of noise. To boot, we apply intricate deep learning and the Superlet Transform (SLT) for detection's automation and enhancement. A superior time-frequency analysis tool, the SLT, transforms 1D ECG signals into 2D spectrograms to identify transient oscillation phenomena such as arrhythmias with higher resolution than STFT and CWT. SLT spectrograms are classified by deep Convolutional Neural Networks. This duty is accomplished by some advanced CNN models such as AlexNet, GoogLeNet, *** to the flexibility of information flow and gradient propagation in DenseNet-201, it achieves a high accuracy of classification. There are three classes of ECG signals, namely, healthy heart, atrial fibrillation, and ventricular fibrillation. The model is tested and demonstrated in various ECG database such as MIT-BIH arrhythmia and AF termination challenge. This method simplifies the hardware by the removal of various noise reduction processes and enables real-time monitoring of heart rates. The utilization of SLT and deep CNN models for early detection of cardiac arrhythmia is current trends that enhance clinical diagnosis and accurate results. © 2024 IEEE.

关键词： Electrocardiography

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Level-Crossing Acquisition and Deep Convolutional Neural Network based Classification of Arrhythmia for Mobile Healthcare

Research Square

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Research Square 2024年

作者： Hussain, Syed Fawad Qaisar, Saeed Mian Sherjeel, Muhammad MDS Lab Faculty of Computer Science and Engineering G.I.K Institute Topi23640 Pakistan School of Computer Science Univeristy of Birmingham United Kingdom Dept. of Electrical and Computer Engineering Effat University Jeddah22332 Saudi Arabia

Mobile healthcare is an evolving area that typically employs cloud-connected wireless biomedical gadgets for diagnosis, monitoring, and prognosis. In such environment, data compression, transmission, and processing are key issues. We propose a new method for the automated diagnosis of arrhythmia in an efficient and effective manner. The goal is to reach an efficient method by achieving a real-time data size reduction, computationally efficient signal preconditioning and a lower latency accurate classification. The Electrocardiogram (ECG) signal is sampled by using the level-crossing concept. The segmentation and adaptive-rate denoising with lower tap filters are realized. The denoised segments, without any handcrafted features extraction, are classified with one dimensional (1-D) deep Convolutional Neural Network (CNN). Five clinically important classes of arrhythmias, collected from the Beth Israel Hospital (MIT-BIH) dataset, are used to examine its applicability. Our experimental results show a 4.2-times reduction in the count of acquired samples on average compared to fix-rate counterparts. Similarly, data dimension reduction results in a more than 7.2 fold computational improvement of the post denoising stage over the conventional counterparts. Moreover, classification latency is also significantly reduced while still achieving an accuracy rate of 99%. © 2024, CC BY.

关键词： Convolutional neural networks

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Comparing Transfer Learning and Rollout for Policy Adaptation in a Changing Network Environment

Comparing Transfer Learning and Rollout for Policy Adaptatio...

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IEEE Symposium on Network Operations and Management

作者： Forough Shahab Samani Hannes Larsson Simon Damberg Andreas Johnsson Rolf Stadler Dept. of Computer Science KTH Royal Institute of Technology Sweden Ericsson Research Sweden Department of Information Technology Uppsala University Sweden

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Dynamic resource allocation for network services is pivotal for achieving end-to-end management objectives. Previous research has demonstrated that Reinforcement Learning (RL) is a promising approach to resource allocation in networks, allowing to obtain near-optimal control policies for non-trivial system configurations. Current RL approaches however have the drawback that a change in the system or the management objective necessitates expensive retraining of the RL agent. To tackle this challenge, practical solutions including offline retraining, transfer learning, and model-based rollout have been proposed. In this work, we study these methods and present comparative results that shed light on their respective performance and benefits. Our study finds that rollout achieves faster adaptation than transfer learning, yet its effectiveness highly depends on the accuracy of the system model.

关键词： Adaptation models Accuracy Transfer learning Reinforcement learning Dynamic scheduling Resource management

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Fine Tuning GANs for Image Colorization: Effects of Training Data and Epochs

Fine Tuning GANs for Image Colorization: Effects of Training...

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International Conference on Computing Communication Control and Automation (ICCUBEA)

作者： Rahul Patil Pramod Patil Rohit Joshi Atharv Joshi Ninad Hastak Akshay Joshi Dept. of Computer Engineering Pimpri Chinchwad College of Engineering Pune India Dept. of Computer Engineering Dr. D. Y. Patil Institute of Technology Pune India

Ever since the advent of Deep Learning, there has been a lot of experimentation and research being carried out in image processing. This gave rise to the conversion of black and white i.e. grayscale images into colorized images. Image colorization is a popular image-to-image translation problem. It finds its application in various domains like image restoration, movie colorization, colorization of old aerial surveillance shots, medical image colorization, etc. Earlier this was done manually by artists and photographers but recent advancements in deep learning have enabled automatic image colorization using techniques like Convolutional Neural Networks and Generative Adversarial Networks. CNN is a neural network that has layers that are essential for extracting features from images and can learn the complex mapping between grayscale images and their corresponding ground truth images. On the other hand, GANs is a relatively new technique that consists of two components: generator and discriminator. This research paper aims to present the results obtained by using both the above-mentioned methodologies for image colorization. Many examples of generated images and their respective accuracies and structure according to the technique used are discussed in the later part.

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Enhancing Healthcare Delivery Through IoT Applications in Remote Patient Monitoring and Telemedicine 3

Enhancing Healthcare Delivery Through IoT Applications in Re...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Yogi, Kottala Sri Gowda V, Dankan Manoj Kumar, S.B. Nithya, R. Begum, A.Yasmine Suganthi.n Symbiosis Institute of Business Management Hyderabad Symbiosis International University Department of Operations Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Bgsit Adichunchanagiri University Faculty of Engineering Management & Technology Dept. of Ece Karnataka 571448 India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tamilnadu Namakkal India Mohan Babu University School of Engineering Department of Electronics and Communication Engineering Tirupati India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India

ISBN: (纸本)9798350364569

This paper presents how IoT plays a vital role in improving delivery of healthcare services especially through remote patient monitoring and telemedicine. The proposed system also employs IoT devices aimed at passing various health indicators like;pulse rate, blood glucose levels, blood pressure, and physical activity to continually and objectively provide real-time data in the body. This particular study, the participants with chronic diseases, normal population and elderly people also participated in the test of the system. Some of the findings include;it is possible to measure the daily steps corresponding to the a calorie expenditure, the sleep score ranging between 60-80 and oxygen saturation percentages ranging from 96%-99% in various postures. The system also proved the correlation between the percentage of variation in ambient temperature and the percentage of variation in the pulse rate which was quite sensitive to the changes in temperatures. Based on these observations, it is possible to conclude that IoT-based monitoring can improve patient's conditions as long as underlying issues will be detected at an early stage and necessary actions will be taken in time. The study also also demonstrates the application of telemedicine in making healthcare more accessible especially for the people from rural settings or in developing countries. The results show a good direction regarding the future of the system;however, security issues, privacy relating to data as well as integration are key hurdles in this connection. © 2024 IEEE.

关键词： Blood pressure

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Technical Job Distribution at BSD SHARP Service Center Using Combination of Naïve Bayes and K-Nearest Neighbour

Technical Job Distribution at BSD SHARP Service Center Using...

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2022 IEEE Symposium on Wireless technology and Applications, ISWTA 2022

作者： Pebrianti, Dwi Ariawan, Angga Bayuaji, Luhur Mahdiana, Deni Rusdah, Rusdah Universitas Budi Luhur Magister of Computer Science Faculty of Information Technology Jakarta Indonesia International Islamic University Malaysia Faculty of Engineering Dept. of Mechanical Engineering Gombak Malaysia Universitas MNC Faculty of Computer Science Department of Computer and Media Jakarta Indonesia Universitas Budi Luhur Faculty of Information Technology Department of Information System Jakarta Indonesia

ISBN: (数字)9781665484824

ISBN: (纸本)9781665484824

Works distribution is a routine carried out every day by the head of the branch in the SHARP Service Center. The accuracy of the labor division is very important to get customer satisfaction. Inappropriate work distribution can increase complaints from customers. Currently, works distribution in SHARP Service Center is carried out manually, where the works received on the selected system is then shared through the document provided. Time taken for this process is about 1.42 minutes on average for each damage reports. Speed of Service also depends on the Head of Department's expertise and experience. In this study, an automatic system based on Machine Learning will be designed for the technicians work distribution by using a combination of k Nearest Neighbor (k-NN) and Naïve Bayes. Naïve Bayes algorithm is used to improve the feature extraction accuracy by considering the feature below the average (α). Meanwhile, k-NN algorithm is used to classify the experimental data. From the study, it is found that the best of k value for k-NN algorithm is 15. It is known that a high number of accuracy values, the labor distribution can be more accurate. The validation of the proposed method is conducted by using a confusion matrix with a composition of 80% training data and 20% test data. The single Classifier test with the Naïve Bayes algorithm produces the highest accuracy value of 72.7%, while using k-NN algorithm is 81.5%. With a combination of Naive Bayes and k-NN algorithms, the accuracy value is increasing to 86%. This result shows that the proposed method improves the accuracy by 13.3% on single Naive Bayes algorithms and 4% on a single k-NN algorithm. The results obtained show that in the manual process, the average time per job is 1.42 minutes, while by using the proposed method, the average processing time is around 0.03 seconds per job. An increase of 2480 times faster is found and confirmed during the implementation of the proposed method. © 2022 IEEE.

关键词： Customer satisfaction

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