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computer, Communication and Control (IC4), International Conference on

作者： More Praveen V. Prasanna Srinivasan V G Sivakumar P. Vijayan R Tharun R. Thamizhamuthu Department of Computer Science and Engineering Sreenidhi Institute of Science and Technology Hyderabad Telangana India Department of Information Technology R.M.D. Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad Telangana India Department of Artificial Intelligence and Data Science Saveetha Engineering College Chennai Tamil Nadu India Department of Civil Engineering Mother Theresa Institute of Engineering and Technology Palamaner Andhra Pradesh India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India

To accurately diagnose and treat sleep problems, which are major challenges to public health, more sophisticated evaluation methods are needed. Using Rapid Eye Movement (REM) craft Navigator’s Internet of Things (IoT) capabilities in conjunction with Random Forest algorithms, this research presents a novel method for comprehensive assessments of sleep disorders. The REMcraft Navigator IoT system incorporates a wide range of physiological and environmental factors into its comprehensive real-time data collection on sleep patterns. The research uses Random Forest algorithms’ ability to handle complicated, multi-dimensional datasets by using them to examine the gathered data. In detecting and categorizing sleep problems, the algorithm model outperforms the conventional diagnosis techniques. The comprehensive evaluation is provided by taking contextual elements like the surrounding surroundings and the sleep environment. The suggested method has promising use as a powerful tool for sleep problem diagnosis, and results from a large-scale clinical investigation confirm its efficacy. The system can improve assessment accuracy and personalized treatment plans. This paper provides a high-tech and realistic solution for doctors dealing with sleep disorders, which helps move the field forward in its diagnostic capabilities. The suggested system is an excellent option for home-based monitoring and extensive use in sleep clinics due to its scalability and flexibility, improving the treatment of sleep-related health problems in the long term.

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Comparative analysis of network forensic tools and network forensics processes 2

Comparative analysis of network forensic tools and network f...

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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Ghabban, Fahad M Alfadli, Ibrahim M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to examine networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools. © 2021 IEEE.

关键词： computer crime

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Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Accelerator

Deep Mapper: A Multi-Channel Single-Cycle Near-Sensor DNN Ac...

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IEEE International Conference on Rebooting computing (ICRC)

作者： Mehrdad Morsali Sepehr Tabrizchi Maximilian Liehr Nathaniel Cady Mohsen Imani Arman Roohi Shaahin Angizi Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA School of Computing University of Nebraska–Lincoln Lincoln NE USA College of Nanoscale Science and Engineering SUNY Polytechnic Institute Albany NY USA Department of Computer Science University of California Irvine Irvine CA USA

This work proposes the Deep Mapper, as a new near-sensor resistive accelerator architecture for Deep Neural Networks (DNN) inference that co-integrates the sensing and computing phases of resource-constrained edge devices. Deep Mapper is developed to intrinsically realize highly parallelized multi-channel processing of input frames supported by a new dense hardware-friendly mapping methodology. Our circuit-to-application simulation results on the DNN acceleration task show that Deep Mapper reaches an efficiency of 4.71 TOp/s/W outperforming state-of-the-art near-/in-sensor accelerators.

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Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

Prediction of COVID-19 Patients’ Emergency Room Revisit usi...

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IEEE International Conference on Healthcare informatics (ICHI)

作者： Yuelyu Ji Yuhe Gao Runxue Bao Qi Li Disheng Liu Yiming Sun Ye Ye Department of Information Science School of Computing and Information University of Pittsburgh Pittsburgh USA Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh USA Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh USA School of Business State University of New York at New Paltz New Paltz USA

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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Automated Tumor Detection using Convolution Neural Network

Automated Tumor Detection using Convolution Neural Network

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Global Conference for Advancement in Technology (GCAT)

作者： Ayush Raturi Kireet Joshi Paras Jain Vishan Kumar Gupta Avdhesh Gupta Jaishree Meena School of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India School of Computing Engineering and Artificial Intelligence VIT Bhopal University Bhopal MP India Amity School of Engineering & Technology Amity University Punjab Mohali Punjab India Department of CSE AKG Engineering College Ghaziabad UP India Amity School of Biological Sciences Amity School Punjab Mohali India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

Brain tumors are quite formidable primarily because they can occur at any place within the brain and may also exhibit dissimilar behavior patterning. The disease in its initial stage is not very dangerous, if it is not treated then the result is a lot of damage to the brain. To overcome these challenges, further research and development is needed and more specifically the implementation of new technologies. This paper seeks to contribute to these goals by developing a deep learning convolutional neural network with transfer learning approach, that can help in detection of brain tumors with high accuracy and efficiency. In the long run, it should help in the early diagnosis and timely appropriate treatment of patients which should somehow contribute to the development of neurooncology. This study proposes a model that involves MRI for detection of brain tumors through the application of deep learning. The high performance of this model is attributed to the transfer learning technique that has been applied on VGG16 trained on 3000 medical MRI images of the human brain. The proposed model provided quite satisfactory accuracy of 99%. 33% on this dataset. Using this automated detection model, it is possible for the healthcare professionals to be able to predict the presence of brain tumors quite well using this system.

关键词： Deep learning Accuracy Magnetic resonance imaging Transfer learning Brain tumors Brain modeling Data models Convolutional neural networks Medical diagnostic imaging Diseases

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5G-Vanet with Edge Technology: Cooperative Data Transmission

5G-Vanet with Edge Technology: Cooperative Data Transmission

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Mohammed Al-Farouni Sandeep Kumar Budhani Jatinder Kaur L. Suvonova Manish Kumar Jaishankar Bhatt Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq School of Computing Graphic Era Hill University Uttarakhand Department of Electrical and Communication Engineering Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Physics and Chemistry Tashkent Institute of Irrigation and Agricultural Mechanization Engineers" National Research University Tashkent Uzbekistan Scientific Researcher Western Caspian University Baku Azerbaijan Department of Electronics and Communication Engineering GLA University Mathura Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

It is well recognized that linked cars may further enhance in-car entertainment, road safety, and transportation intelligence. 5G-VANET, or 5G-enabled Vehicular Adhoc Networks, may be used to construct software-defined communication with very high accuracy, low latency, and large capacity. Despite being essential for many 5G-VANET enabling programs, mobile data transmission between cars remains a difficult problem due to the massive amount of data required and the high cost of 5G cellular network distribution. This work focuses on cooperative information sharing in 5G-VANET facilitated by edge computing. First, we provide a software-defined cooperation data sharing architecture for 5G-VANET, allowing cellular and targeted short-range communication to operate together effectively. Through cellular connections, OpenFlow-capable cars and the Controller may communicate and exchange contextual data. The data plane (DSRC) makes it easier for nearby cars to share data cooperatively. Secondly, we define the data sharing issue on the created conflict graph as the greatest weighted autonomously set problem, and we provide an effective graph-theoretic solution. In particular, we supply a balanced greedy method that may improve the material distribution balance by taking into consideration ongoing data sharing.

关键词： Greedy algorithms Costs 5G mobile communication Simulation Information sharing Transportation computer architecture Telecommunication computing Automobiles Edge computing

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AI-Powered Real-Time Big Data Analytics for ICU Resource Management

AI-Powered Real-Time Big Data Analytics for ICU Resource Man...

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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： S. Kalaichelvi N. Mohankumar C. Uthayakumar Barkathulla A K.S. Manojee S. Murugan Department of Computer Science and Applications Vivekanandha College of Arts and Sciences for Women (Autonomous) Tiruchengode Tamil Nadu India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Electronics and Communication Engineering Vivekanandha College of Engineering For Women Tiruchengode Namakkal Tamil Nadu India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Information Technology Kongunadu College of Engineering and Technology (Autonomous) Trichy Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Improving patient outcomes and operational efficiency in intensive care units (ICUs) relies heavily on efficient resource management. This work presents a novel LSTM-based methodology for real-time optimization of ICU resources, using big data to enhance decision-making. Patient vitals, equipment utilization, and staff activities are just a few examples of the real-time data gathered from the many ICU sensors and monitoring devices the system incorporates using LSTM networks. LSTM networks excel in processing time-series data, allowing the system to anticipate resource requirements, spot bottlenecks, and allocate resources optimally. The proposed system offers predicted insights together with practical suggestions by analyzing historical and real-time data streams. Simulations and case studies show that the system can increase operational efficiency and predict demand. Findings point to major gains in patient care, savings, and decision-making assistance in fast-paced ICUs. This method uses LSTM networks to tackle the intricacies of contemporary hospital environments, revolutionizing ICU management with innovative AI and big data analytics. LSTM networks improve real-time decision-making, resource management, and patient outcomes in ICUs.

关键词： Analytical models Accuracy Decision making Predictive models Big Data Real-time systems Data models Resource management Long short term memory Load modeling

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DG-CNN:Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images

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Journal of computer Science & Technology 2022年第2期37卷 277-294页

作者： Xiao-Zheng Xie Jian-Wei Niu Senior Member Xue-Feng Liu Qing-Feng Li Yong Wang Jie Han Shaojie Tang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityHangzhou 310051China Department of Diagnostic Ultrasound National Cancer CenterChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100021China Naveen Jindal School of Management The University of Texas at DallasRichardsonTX 75080-3021U.S.A.

Although using convolutional neural networks(CNNs)for computer-aided diagnosis(CAD)has made tremendous progress in the last few years,the small medical datasets remain to be the major bottleneck in this *** address this problem,researchers start looking for information out of the medical *** efforts mainly leverage information from natural images via transfer *** recent research work focuses on integrating knowledge from medical practitioners,either letting networks resemble how practitioners are trained,how they view images,or using extra *** this paper,we propose a scheme named Domain Guided-CNN(DG-CNN)to incorporate the margin information,a feature described in the consensus for radiologists to diagnose cancer in breast ultrasound(BUS)*** DG-CNN,attention maps that highlight margin areas of tumors are first generated,and then incorporated via different approaches into the *** have tested the performance of DG-CNN on our own dataset(including 1485 ultrasound images)and on a public *** results show that DG-CNN can be applied to different network structures like VGG and ResNet to improve their *** example,experimental results on our dataset show that with a certain integrating mode,the improvement of using DG-CNN over a baseline network structure ResNet 18 is 2.17%in accuracy,1.69%in sensitivity,2.64%in specificity and 2.57%in AUC(Area Under Curve).To the best of our knowledge,this is the first time that the margin information is utilized to improve the performance of deep neural networks in diagnosing breast cancer in BUS images.

关键词： medical consensus domain knowledge breast cancer diagnosis margin map deep neural network

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Vader protocol based sentiment analysis technique using LSTM for Stock Trend prediction

Vader protocol based sentiment analysis technique using LSTM...

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International Conference on computing and Networking Technology (ICCNT)

作者： Amrita Ticku Niva Tripathy Pankaj Kumar Mishra Anurag Sinha Shivangi Jadon Aditya Raj Harsh Kulshrestha Vipin Rai Rejuwan Shamim Bharti Vidyapeeth’s College of Engineering India Driems Autonomous Engineering College Cuttack India Department of Electronics and Telecommunication Engg. Rungta College of Engg. & TechnologyR1 Bhilai CG India School of Computing and Information Science IGNOU New Delhi India Dept. of Computer Science and Engineering Galgotias University Noida India Department of Computer Science and Engineering Galgotias University Noida India Dept. of Computer Science and Engineering With Data Science Maharishi University of Information Technology Noida India

Stock prediction has always been a difficult task, and numerous strategies for predicting stock market behavior have been offered. Researchers have recently begun to investigate the use of sentimental analysis and deep learning models such as Long Short-Term Memory (LSTM) to predict stock prices. We offer a stock prediction model that integrates sentimental analysis and LSTM in this research. The model considers the mood of stock market news items and use LSTM to forecast future stock prices. The results of our suggested model's evaluation on the S& amp; P 500 dataset reveal that it outperforms traditional stock prediction approaches.

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