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Symptom based Clinical Decision Support System using various Machine learning models

Symptom based Clinical Decision Support System using various...

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Rachit Kalia Ritik Kumar Rahul Kumar Shailendra Pratap Singh School of Computer Science Engineering Galgotias University Greater Noida

ISBN: (数字)9798350330861

ISBN: (纸本)9798350330878

the widespread adoption of computer-based technology in the healthcare industry has resulted in an influx of electronic data. Due to this substantial amount of data sometimes it is difficult for the doctor to accurately diagnose the disease in one go or in the early stages of that disease. the accurate and on-time analysis of any health-related problem is important for on-time accurate treatment. Developing a symptom based clinical decision support system using machine learning can help in prediction of most of the diseases that can help a doctor in discovering some things he did not anticipate and to support his decision while diagnosing a disease. the motive of the given project is to formulate a disease prediction or a clinical decision support system using multiple Machine Learning Algorithms. the dataset used has around 5000 rows in which there are around 130 different types of symptoms and currently 43 different types of prognosis or diseases to classify or predict from. the aim of our diagnosis model is to act as a doctor’s assistant which can help them analyze the patient better and help its prediction give some support to the doctor’s decision. it also increases the accuracy of predicting the disease day by day. A system based clinical support architecture with soul goal of improving the diagnosis report with our patient health information and other parameters to give a close approximation of what the person might be suffering from. A traditional system-based clinic decision is a database that has pre-existing knowledge of clinical procedures, symptoms- disease correlation and other parameters uses this software to match with patients’ condition and recommendation the list of most possible ailment the patient is suffering from.

关键词： Decision support systems Analytical models Medical services Predictive models Prediction algorithms software Prognostics and health management

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An Uncovered Neurons Information-Based Fuzzing Method for DNN

An Uncovered Neurons Information-Based Fuzzing Method for DN...

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International conference on computer Supported Cooperative Work in Design

作者： Kecheng Tang Jian-Tao Zhou Xu Guo College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot China Inner Mongolia Key Laboratory of Social Computing and Data Processing Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Hohhot China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Fuzzing is increasingly being utilized as a method to test the reliability of Deep Learning (DL) systems. In order to detect more errors in DL systems, exploring the internal logic of more DNNs has become the main objective of fuzzing. Despite advancements in the seed selection aspect of fuzzing, considerable opportunities still exist for improving testing efficiency. Current research has issues with the repeated consideration of neurons in the model that will be covered in the future by other seeds, leading to redundant seeds and lower testing efficiency. Additionally, there is a lack of a method to measure the potential of seeds to increase coverage, making it difficult to select the most worthy seeds for mutation in each iteration. We propose an uncovered neurons information based (UNIB) fuzzing method for DNN. UNIB uses clustering methods to organize the seed queue based on initial seed data, aiming to enhance the coverage rate improved in each iteration. It also integrates coverage information from the testing phase to identify the seeds with the greatest potential. the experimental results show that UNIB achieved a higher NC than the second-best method by 1.1% and 3% in LetNet-4 and LetNet-5, respectively. UNIB consistently required the fewest number of iterations to reach the same NC as other methods. For both LetNet-4 and LetNet-5, the adversarial test case sets generated by UNIB exhibited the highest diversity.

关键词： Deep learning Federated learning Current measurement Neurons Neural networks Fuzzing software

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Comparative Analysis of Deep Learning Models for Validating Use Case Diagrams

Comparative Analysis of Deep Learning Models for Validating ...

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International conference on Information Technology and Electrical engineering (ICITEE)

作者： Bella Dwi Mardiana Tiara Rahmania Hadiningrum Daniel Siahaan Informatics Department Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350375817

ISBN: (纸本)9798350375824

In the ever-evolving world of technology, the validation of use case diagrams is essential to ensure the reliability and consistency of software applications. An effective validation approach plays an important role in optimizing system quality. this study adopts a comparative approach to analyze the performance of three major models in object detection, namely Detectron2, YOLOv5, and YOLOv9, to validate use case diagrams. these three models were trained using an open repository dataset, Roboflow "Use Case Diagram Checker computer Vision Project". through a series of careful experiments, we evaluated and compared the three models based on relevant performance metrics, such as precision, recall, and AP50. Our analysis results show that YOLOv9 outperforms the other models with significant improvements in detecting objects in user case diagrams. Although YOLOv9 shows superior performance, we also consider other aspects such as model speed and complexity. this research not only provides deep insight into the relative performance of each model in the context of user case diagram validation but also provides valuable insights for practitioners and researchers in choosing the approach that best suits their needs. thus, the contribution of this research is highly relevant in the development of reliable and efficient software systems, as well as being a practical guide for the selection and implementation of object detection technologies in the context of user case diagram validation.

关键词： YOLO Measurement Electrical engineering Analytical models Accuracy Computational modeling software systems software reliability Object recognition Information technology

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Context-Adaptation Bugs in Micro-Clones

Context-Adaptation Bugs in Micro-Clones

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Asia-Pacific conference on software engineering

作者： Sayeedi Mottakin Maliha Bintay Zaman Manishankar Mondal Atanu Shome Computer Science and Engineering Discipline Khulna University Bangladesh

Whenever we copy a code fragment from one place of a code-base and paste it to another place, the pasted fragment might appear to be a buggy fragment if it is not properly adapted to its surrounding code. In such a situation, the bug that is contained in the pasted fragment because of not adapting it to its context is known as a context-adaptation bug (simply, context bug). In this research, we investigate the context adaptation bugs in micro-clones (code clones of at most 4LOC) through analyzing their evolutionary history from thousands of revisions of our subject systems. An existing study has investigated such bugs in regular code clones (code clones of at least 5LOC). However, context bugs in micro-clones have never been studied. If microclones also contain context bugs, automatic support for repairing such bugs in micro-clones is important as well. We automatically identify patterns that indicate fixes of context bugs in microclones, and then analyze and compare the intensity of such bugs in regular and micro-clones. We also identify the vulnerable coding patterns that introduce context-bugs in micro-clones. According to our study on thousands of revisions of five subject systems written in three different programming languages, micro-clones generally have a higher possibility of containing context bugs during evolution compared to regular clones. Making microclones through copy/pasting across different files has a significantly higher tendency of introducing context bugs compared to cloning within the same file. We also realize that Type 1 and Type 3 micro-clones are generally more vulnerable than Type 2 micro-clones. Our findings are important for devising automatic mechanisms for fixing context bugs. We have also identified risky cloning patterns so that programmers can avoid those patterns during coding to minimize context-bugs in micro-clones.

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Flexible C-RAN: Radio technology for 5G

Flexible C-RAN: Radio technology for 5G

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International conference on Telecommunications in Modern Satellite, Cable and Broadcasting Service (TELSIKS)

作者： Liljana Gavrilovska Valentin Rakovic Aleksandar Ichkov Davor Todorovski Simona Marinova Faculty of Electrical Engineering and Information Technologies Ss Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9781538618011

Conventional radio access networks are designed to utilize static configuration and are statically deployed. As results they are inefficient in handling any spatio-temporal fluctuations of the underlying traffic demand. Cloud-RAN represents a novel centralized technology based on softwareization and virtualization technology that can efficiently address the problems of legacy networks. Cloud-RAN facilitates high data rates as well as high network and radio adaptability across the software defined wireless networks. this paper elaborates the CRAN concept providing overview of current status, relevant features and research challenges related to it. Moreover, the paper presents a developed demo platform capable of efficient virtualization of different types of wireless technologies that belong to a heterogeneous wireless environment. It utilizes commercial and open-source solutions, such as OpenStack and SDR software. the performance analysis highlights the behavior of GSM and LTE technologies, with respect to cloud resource allocation. the paper also discusses some aspects and technical requirements for a C-RAN based dynamical resource allocation.

关键词： Cloud computing Virtualization Wireless communication computer architecture Resource management Mobile communication

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Adaptive control of a grid-connected SPV system with DSTATCOM capabilities

Adaptive control of a grid-connected SPV system with DSTATCO...

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IEEE UP Section conference on Electrical computer and Electronics (UPCON)

作者： Bakul Kandpal K. P. Tomar Ikhlaq Hussain Bhim Singh Department of Electrical and Electronics Manav Rachna International University Faridabad Haryana India Department of Electrical Engineering Indian Institute of Technology Delhi New Delhi India

ISBN: (纸本)9781538630051

this paper applies frequency domain block LMS (FBLMS) algorithm for implementation of a three phase grid-connected solar photovoltaic (SPV) system. this system provides power factor correction (PFC), voltage regulation and maintains an IEEE-519 standard by keeping the harmonic distortion of grid currents below 5%. To successfully extract the maximum power from the SPV array at varying environmental conditions, a single stage system with perturb and observe MPPT method is used, which is integrated with adaptive control for the estimation of reference grid currents. Modelling of the system is done through MATLAB software and the results of simulation are shown under various operating conditions.

关键词： Voltage control Adaptive control Power harmonic filters Mathematical model Algorithm design and analysis Control systems Load modeling

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Energy efficient network data transport through adaptive compression using the DEEP platforms

Energy efficient network data transport through adaptive com...

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IEEE International conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Digvijay Singh William J. Kaiser Department of Electrical Engineering University of California Los Angeles USA

Direct measurement of event and component resolved energy dissipation in computing systems is critical for energy optimization of computing and networking applications. Prior research focuses on development of energy consumption models and custom-built energy measurement systems, but suffers from critical drawbacks. this paper addresses these limitations and presents solutions using DEEP (Decision-support for Energy Efficient Processing). DEEP is a rapidly-deployed open-source energy measurement platform architecture. the platform provides an unprecedented ability to non-intrusively measure the energy consumption associated with execution of software application code. DEEP is implemented as both an online and offline version. Evaluation demonstrates processing and energy overheads less than 1% for offline and about 5% for the online implementation. the DEEP implementation investigates the impact of data compression on network data transport. An intelligent data compression and transport algorithm is developed using the decision-support capabilities of DEEP. the algorithm creates significant energy savings (38%) in network data transport using dynamic selection of compression schemes to adapt to varying system and wireless network conditions.

关键词： Energy measurement Synchronization Hardware software computer architecture Energy consumption Data acquisition

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Key Technologies and Future Trends of 5G Wireless Network Applications

Key Technologies and Future Trends of 5G Wireless Network Ap...

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IEEE International conference on Signal and Image Processing (ICSIP)

作者： Vincent Yong Kai Loung Razali Ngah Joseph Owusu Chua Tien Han Samuel Tweneboah-Koduah Jafri Din Wireless Communication Centre School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia Computer & Electrical Eng. Universiti of Energy and Natural Resource Sunyani Ghana

ISBN: (纸本)9781665446006

5G systems are important telecommunication infrastructure for worldwide wireless networks. High throughput data transmission and low latency are two key requirements in present-day applications. 5G networks also provide wider capacity and bandwidth to support billions of devices and users which are reliable for long-distance, critical communications, and mobile broadband services. this article gives an overview of 5G RAN, Open RAN Hybrid Management, software-defined Networking SoftAir, and Service-Based architectures, including core network, deployment options, their key technologies, future trends of 5G applications, and benefits for subscribers and businesses towards the next generation.

关键词： 5G mobile communication Wireless networks Bandwidth Streaming media Network architecture Market research throughput

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A Two-stage P2P Botnet Detection Method Based on Statistical Features

A Two-stage P2P Botnet Detection Method Based on Statistical...

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IEEE International conference on software engineering and Service Sciences (ICSESS)

作者： Bin Zhou Jie He Mingtao Tan School of Computer and Electrical Engineering Hunan University of Arts and Science Changde Hunan Province China Officers College of PAP Chengdu Sichuan Province China

ISBN: (数字)9781728165790

ISBN: (纸本)9781728165806

P2P botnet has become one of the most serious threats to today's network security. It can be used to launch kinds of malicious activities, ranging from spamming to distributed denial of service attack. However, the detection of P2P botnet is always challenging because of its decentralized architecture. In this paper, we propose a two-stage P2P botnet detection method which only relies on several traffic statistical features. this method first detects P2P hosts based on three statistical features, and then distinguishes P2P bots from benign P2P hosts by means of another two statistical features. Experimental evaluations on real-world traffic datasets shows that our method is able to detect hidden P2P bots with a detection accuracy of 99.7% and a false positive rate of only 0.3% within 5 minutes.

关键词： Botnet Unsolicited e-mail Feature extraction Real-time systems Monitoring software engineering Payloads

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Exploring the use of Synthetic Training Data for the Classification of Electronic Components in Artificial Intelligence systems

Exploring the use of Synthetic Training Data for the Classif...

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conference on Human System Interactions, HSI

作者： Bernardus C. Bothma Nicolaas Luwes Department of Electrical Electronic and Computer Engineering Central University of Technology Free State Bloemfontein South Africa Department of Electrical Electronic and Computer Engineering Centre for Sustainable SMART Cities Central University of Technology Central University of Technology Bloemfontein Free State South Africa

ISBN: (数字)9798350362916

ISBN: (纸本)9798350362923

Artificial Intelligence (AI) is only as good as its training data. Large training sets with variants on the same classifier improve AI performance and accuracy, especially in image processing systems. Obtaining these large amounts of training data required for training AI and deep neural networks, is laborintensive, expensive and in some cases not possible. this article explores creating a synthetic image dataset of basic electronic components by using the Blender 3D software package to automatically generate large amounts of synthetic images and image augmentation to expand the synthetic dataset. A YOLOv5 classifier model was trained on the resulting synthetic data, and the performance of the model was evaluated using a set of real-world and synthetic testing images. the results show that good-quality synthetic data that accurately represent real-world electronic components can be used to successfully train a deep learning classifier, leading to cost and time savings in the data acquisition process. However, it also shows that synthetic data that does not accurately represent real-world electronic components is of no use and will reduce the overall performance of the classifier.

关键词： Training YOLO three-dimensional displays software packages Electronic components Training data Data models

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