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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Routray, Sudhir K. Singh, Monika Samal, Laxmipriya Samal, Debashisa Sarangi, Susanta K. Cmr University Department of Computer Science and Engineering Bangalore India Cmr Institute of Technology Department of Electronics and Communication Engineering Bangalore India SoA University Department of Electronics and Communication Engineering Bhubaneswar India

ISBN: (纸本)9798350390759

This article explores the features and proximity of 5G advanced (well known as 5.5G) to 6G cellular communication system, emphasizing its speed, connectivity, and intelligence. Expected benefits of 5.5G include an enhanced user experience, industrial revolution, and support for the Internet of Things (IoT). The motivation behind 5.5G arises from addressing 5G limitations, accommodating IoT growth, enabling real-time applications, providing personalized experiences, and driving economic growth. Anticipated applications span from enhanced mobile broadband to ultra-reliable low latency communication and IoT-based massive machine-type communication, extending to personalized experiences, next-gen gaming, connected healthcare, and advanced education. Despite being in development, 5.5G holds immense significance for bridging the physical and digital world, fostering innovation, and unlocking the potential of the digital age. © 2024 IEEE.

关键词： 5G mobile communication systems

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Differentiating S1, S2 Noises from Abnormal Heart Sounds Generated in Closure of Atrioventricular and Semilunar Valves using MFCC and LSTM 1

Differentiating S1, S2 Noises from Abnormal Heart Sounds Gen...

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1st IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2022

作者： Prusty, Sashikanta Patnaik, Srikanta Dash, Sujit Kumar SOA University Department of Computer Science & Engineering Bhubaneswar751030 India SOA University Department of Electrical & Electronics Engineering Bhubaneswar751030 India

ISBN: (数字)9781665421492

ISBN: (纸本)9781665421492

Heart Sounds are generated by heartbeats in the form of noises. Each beat in a healthy human produces two normal heart sounds, which are sometimes referred to as a lub and a dub (or dup). For example, S1 and S2 heart sounds are produced by the closing of both atrioventricular and semilunar valves. This heart sound dataset has been collected in real-world circumstances and frequently incorporates various types of background noises. Differences in heart sounds that correspond to different cardiac symptoms might be quite subtle and difficult to distinguish. To overcome this type of situation, a deep learning-based classification model named long short-term memory (LSTM) with RNN (Recurrent Neural Network) technology has been proposed in this project. This approach featured with unique additive gradient structure that can differentiate the heart sounds i.e. S1 and S2 from abnormal noises. Along with Mel Frequency Cepstral Coefficient (MFCC) at the pre-processing step, ensures the patients' successful representation of heart sounds present in audio files into their respective frequency domain forms. Although, MFCC can move forward with four basic processes that include Fourier Transform (FT), mapping to Mel Scale, use of triangular windows, and log of each Mel frequency. Moreover, it may be difficult to solve problems like classifying heart sound that requires long-term memory due to having gradient problems present in RNN. Such problems can be overcome by applying both RNN and LSTM techniques as LSTM maintains the information in the memory cell for a longer period. To restrict the amount of data passed through the cell, this proposed model uses three sigmoid gates and one tanh layer. When information enters into the memory, when to output, and it is forgotten, that is controlled by a set of gates that allow for greater control of the gradient flow and maintenance of long-range dependencies. This RNN-LSTM model has been successfully implemented and differentiated heart s

关键词： Long short-term memory

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A Machine Learning Approach to Compare Causal Inference Modelling Strategies in the Digital Advertising Industry 2

A Machine Learning Approach to Compare Causal Inference Mode...

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2nd International Conference on Ambient Intelligence in Health Care, ICAIHC 2023

作者： Singh, Sanskriti Sanjay Kumar Kumar Sinha, Apoorv Pandey, Trilok Nath Acharya, Biswa Mohan School of Computer Science and Engineering Vellore Institute of Technology Chennai India SOA University ITER Dept. of Computer Application Bhubaneswar India

ISBN: (纸本)9798350344882

Cause and effect relationships are not extensively explored in machine learning but prior to creating a system that understands causal relationships, there needs to be a general ability to grasp and inculcate cause-and-effect statistically. The way of choice for the proposed model is causal inference where the causes are inferred from the data. The study of causality, unlike correlation, falls in the domain of prescriptive analytics, that is, the likelihood of an occurrence given that another occurrence has happened. As a result, the study of causality and predictions based on causal inference are highly important when it comes to the domain of marketing and sales, largely because many practices in the domain of advertising try to fundamentally mimic causal inference such as A/B testing, primarily used in e-commerce and the digital advertising industry. For the purposes of the study, the treatment will be exposure to multiple advertisements and the uplift modelling will be used to direct marketing efforts to the most agreeable customers. Uplift modelling is a causal learning approach that estimates the dataset's individual treatment effect and using the same, calculates the incremental impact of a direct treatment on the individual behavior of the customer, which is why the advertising use-case felt particularly suitable. The usual way causal inference is inculcated in machine learning models is that one attribute is taken as a treatment, that is, the intervention or policy that has been done, while the target label is the desired outcome while accounting for the confounders which are the rest of the attributes. However, for the crux of this study, the proposed model will be taking two treatment attributes so as to see how the causality of two outcomes improves or worsens the impact of a rudimentary machine learning model. At the same time, this comparative study will be in tandem with two other models featuring one causal relationship as well as no causal relations

关键词： Sales

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A Novel Transfer Learning Technique for Detecting Breast Cancer Mammograms using VGG16 Bottleneck Feature 1

A Novel Transfer Learning Technique for Detecting Breast Can...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Prusty, S. Dash, S.K. Patnaik, S. Department of Computer Science & Engineering SOA University Bhubaneswar751030 India Department of Electrical & Electronics Engineering SOA University Bhubaneswar751030 India

ISBN: (纸本)9781607685395

Breast cancer represents the highest percentage of cancers and the second most common cancer overall that affect women with 87,090 deaths approximately as reported by ICMR, 2018 in India (1). Breast tumours are classified into two types as a) benign, which is not very harmful and would not cause breast cancer and b) malignant, the tumours are extremely dangerous and would form an abnormal cell that may cause cancer. One transfer learning model called VGG16 (Visual Geometry Group16) has been implemented with a focus on breast cancer classification using mammography images taken from the MIAS dataset. As the name suggests, this VGG16 has taken 13 convolutional layers and 3 fully connected layers to train the dataset. In the training period, this model could predict whether the breast images contain any type of cancerous cells or not. And finally, the model has successfully implemented and produced Network's test score of 87.999 percent. © The Electrochemical Society

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Multiple occurrence point recognition method for unsafe traffic driving behavior based on YOLOv5 visual perception

Advances in Transportation Studies

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Advances in Transportation Studies 2023年第Special Issue期1卷 51-62页

作者： Guo, Y.R. Wu, R.N. Patnaik, S. School of Mathematics and Information Engineering Longdong University Qingyang745000 China Lanzhou Big Data Administration Bureau 122 Gannan Middle Road Lanzhou730000 China Department of Computer Science and Engineering SOA University Bhubaneswar751030 India

Reducing the risk behaviors of drivers during driving is of great significance to improve the level of road traffic safety. Using intelligent technology to monitor unsafe behaviors of drivers can effectively reduce and control the occurrence of unsafe events of drivers, and avoid and reduce casualties and property losses. In this paper, we propose a multiple occurrence point recognition method for traffic unsafe driving behavior based on YOLOv5 visual perception. This method uses media filtering to remove the noise of driving image, divides the image in YOLOv5 network structure, objectifies the image features;uses visual perception to obtain the true aspect ratio, calculates the fatigue coefficient;uses threshold value to judge unsafe driving behavior, and determines the frequent occurrence of unsafe driving through low function. The results show that the data SNR of this method can reach ***, the recall rate can reach 99.0%, and the recognition time is less than 8s, indicating that this method has stronger recognition performance. © 2023, Aracne Editrice. All rights reserved.

关键词： Aspect ratio

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An Intelligent Completion Time Aware Task Scheduling (CTATS) Algorithm for Fog Computing Environment for Delay Sensitive IoT Applications

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Procedia computer science 2025年 258卷 1856-1864页

作者： Ranjit Kumar Behera Manjula Gururaj H Diptendu Sinha Roy Siba Tripathy Computer Science and Engineering Silicon University Bhubaneswar India Information Science and Engineering NMAM Institute of Technology NITTE University Mangalore Karnataka Computer Science and Engineering NIT Meghalaya Shilong India Computer Science and Engineering SOA University Bhubaneswar India

Fog computing also known as Edge computing is emerging as an attention gaining computing technology for providing Cloud services to IoT applications which requires instantaneous and real-time responses. In absence of an efficient and systematic task scheduling algorithm, Fog with limited computing power and resources may fail to handle high traffic and hence may result in delays. Although many studies have been carried out and many task scheduling models has been proposed by researchers pertaining to different application areas, many of these models have some inherent drawbacks like increase in response time and execution time, as well as migration delays which hinders these models from being the most competent ones particularly the case of delay sensitive IoT applications. Hence, being mindful of the mentioned issues and challenges, in this paper, an efficient Completion Time Aware Task Scheduling (CTATS) algorithm has been proposed at Fog Computing layer. We have implemented reinforcement learning (RL), a machine learning strategy that is environment-adaptive, to enhance performance with respect to predict the suitable VM for a delay sensitive task and for this we have used our own dataset. To check the efficacy of the proposed model, the simulation has been carried out using CloudSim simulator and the obtained results is compared with the existing models for the comparison of average response time and overall completion time. Obtained results shows the superior efficacy of CTATS over the QBT and Prioritized Scheduled Mechanism.

关键词： Fog Computing Task Scheduling Virtual Machine Reinforcement Learning (RL) CloudSim

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A Deep Learning Based Translation Performance Analysis of International Languages

A Deep Learning Based Translation Performance Analysis of In...

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Electrical Power engineering, Communication and Computing Technology(ODICON), Odisha International Conference on

作者： Subhashree Satpathy Smita Prava Mishra Ajit Ku. Nayak Computer Science & Engineering SOA Deemed to be University Bhubaneswar India Computer Science & Information Technology SOA Deemed to be University Bhubaneswar India

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

Machine Translation (MT) is a specialized domain within the broader field of Natural Language Processing (NLP) that utilizes machine learning algorithms to translate speech or text from one language to another language without human participation. The goal of such system construction is to find relatively high accuracy, cost effective and low error in the outcome. Numerous MT systems have been developed and deployed for widespread use with many individuals, particularly learners encountering difficulties while utilizing them. In recent times Recurrent Neural Networks (RNNs) and Long Short Term Memory (LSTM) techniques have shown promising results for machine translation as compared to other existing approaches of Neural Machine Translation (NMT). The current work compares performances of NMT and RNN methods in different international datasets (FRENCH-ENGLISH, RUSSIAN-ENGLISH, and ITALIANENGLISH). Their translation errors have been calculated by using Sparse Categorical Crossentropy loss function. We have observed the effect of size of the data in specified settings and found that there is no significant change in loss function after 50k training data.

关键词： Power engineering Recurrent neural networks Translation Computational modeling Training data Neural machine translation Data models Machine translation Speech processing Long short term memory

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Prediction of Software Bug Fixing Time using Intuitionistic Fuzzy Similarity Measure on Bug Informative Terms 22

Prediction of Software Bug Fixing Time using Intuitionistic ...

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22nd OITS International Conference on Information Technology, OCIT 2024

作者： Panda, Rama Ranjan Nagwani, Naresh Kumar Center for Cyber Security Department of Computer Science and Engineering ITER SOA Deemed to be University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Raipur Raipur India

ISBN: (纸本)9798331510404

Nowadays, industries are more concerned with the timely delivery of quality software. The release of software totally depends upon the time required to fix the software bugs. The project team has to estimate the average time required to fix the bugs in accordance with the project schedule and available resources. The severity and priority of the software bug play a significant role in estimating the average time required to fix the bugs. However, the presence of multiple classes of severity and priority poses a greater challenge for the project team in terms of bug fixing time estimation. To address the aforementioned problem in this paper, an intuitionistic fuzzy similarity (IFS) measure-based bug time estimation technique is developed using the priority and severity of the software bug. The informative terms of severity and priority are used to determine the IFS measure of a new bug to multiple classes of severity and priority. The average bug fixing time for each severity and priority groups is utilized to predict the bug fixing time of a newly reported bug. The proposed bug estimation techniques provide a better MAE, RMSE, and R² score over various software bug repositories compared to other bug fixing time estimation techniques. © 2024 IEEE.

关键词： Bug Fix Time Fuzzy Similarity Informative Term Intuitionistic Fuzzy Set Machine Learning Software Maintenance

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WU-Net++: A novel enhanced Weighted U-Net++ model for brain tumor detection and segmentation from multi-parametric magnetic resonance scans

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Multimedia Tools and Applications 2024年第28期83卷 71885-71908页

作者： Das, Suchismita Dubey, Rajni Jena, Biswajit Tsai, Lung-Wen Saxena, Sanjay Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India Division of Cardiology Department of Internal Medicine Taipei Medical University Hospital Taipei111031 Taiwan Department of Computer Science and Engineering Institute of Technical Education and Research SOA Deemed to Be University Bhubaneswar India Department of Medicine Research Taipei Medical University Hospital Taipei111031 Taiwan Department of Information Technology Office Taipei Medical University Hospital Taipei11031 Taiwan Graduate Institute of Data Science College of Management Taipei Medical University Taipei11031 Taiwan

Brain tumor detection and segmentation from multi-parametric magnetic resonance (MR) scans are crucial for the prognosis and treatment planning of brain tumor patients in current clinical practice. With recent technological advancements, artificial intelligence-based deep learning has proven its indispensable image analysis capability in the most challenging tasks. This study proposes an automated WU-Net + + deep learning model for brain tumor segmentation using multiparametric structural MR scans obtained from the BraTS 2018 dataset. The model was validated through cross-dataset testing for intracranial hemorrhage (ICH) classification (ATLAS V2.1 dataset) and multi-organ segmentation from abdominal images of TCIA and BTCV datasets. The WU-Net++ model, a novel version of U-Net, was developed by adjusting its pooling operation as a weighted function of max and average pooling for brain tumor segmentation. The proposed model (WU-Net + +) achieved an F1 score of 0.94 ± 0.124, a dice score of 0.91 ± 0.132, and an AUC value of 0.915 for whole tumor segmentation. The model also achieved a high accuracy of 0.9949 ± 0.121 in ICH classification and dice scores of 0.912 ± 0.21, 0.844 ± 0.25, and 0.893 ± 0.17 for spleen, esophagus, and portal and splenic vein segmentation, respectively. Our study revealed that WU-Net + + has significant potential to improve the accuracy of segmentation and could be an effective method in the era of precision medicine. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Magnetic resonance imaging

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Designing a Pipeline for Predicting Hypothyroidism with Different Machine Learning Classifiers 2

Designing a Pipeline for Predicting Hypothyroidism with Diff...

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2nd Odisha International Conference on Electrical Power engineering, Communication and Computing Technology, ODICON 2022

作者： Prusty, Sashikanta Patnaik, Srikanta Dash, Sujit Kumar Prusty, Sushree Gayatri Priyadarsini SOA University Department of Computer Science & Engineering Bhubaneswar751030 India SOA University Department of Electrical & Electronics Engineering Bhubaneswar751030 India Sudhananda Group of Institutions Department of Computer Science & Engineering Balianta Bhubaneswar752115 India

ISBN: (纸本)9781665478397

Thyroid cancer is caused by abnormal development of thyroid tissue near the thyroid gland's edge. The clinical symptoms of thyroid illness occur when the thyroid gland produces abnormal levels of hormones i.e. hypothyroid (insufficient hormones) and hyperthyroid (excessive hormones). These hormones have a greater impact on the regulation of critical systems including body temperature and heart rate. Thus, the need of thyroid function test (TFT) is required that can examine the blood levels for thyroid-stimulating hormone (TSH) and thyroxine (T4). In this work, the prediction for hypothyroidism disease using different Machine Learning (ML) techniques such as K-NN, XGB, CatBoost, Decision Tree, Naive Bayes, and Neural Network have been implemented and compared to find the best predictive model. Furthermore, a distinguished process has been carried out for finding hypothyroidism, if present in the thyroid gland. After successfully implementing all models, the K-NN achieves the highest accuracy of 98.44%, while detecting hypothyroid disease. © 2022 IEEE.

关键词： Hormones

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