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detection of COVID-19 Using Convolutional Neural Networks

Detection of COVID-19 Using Convolutional Neural Networks

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international conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Arshita Srivastava Shubham Mishra Department of Computer Science and Engineering Rameshwaram institute of management and technology Dr. APJ Abdul Kalam Technical University Lucknow India

A highly contagious illness caused by the COVID-19 pandemic is proven to havoc with people's health and well-being all over the globe. Chest radiography is one of the most crucial databases for applying detection techniques. The respiratory system is contaminated by COVID-19, which also affects the alveoli and replicates itself. Conventional approaches such as RT - PCR tests, rapid antigen tests, serological tests, etc. are generally used for the detection of COVID came out to be costly and time-consuming. There have been several suggested artificial intelligence (AI)-based models for the detection of COVID-19 in contaminated individuals' using lung ultrasound images, voice patterns, chest sounds, etc. The proposed model shows how disease cases could be identified using features and variations in chest radiography images. A deep convolutional neural network (CNN) with ResNet50 modified configuration has been used for the identification of COVID-19 cases from the chest radiography image dataset. The suggested model also depicts the comparison of the proposed technique with the Resnet 101 model. Chest radiography dataset containing Covid infected, normal, and pneumonia-infected images. Additionally, the suggested model can identify covid-19 patients' current conditions in real-time by identifying coronavirus diseases from CT scan pictures. The database has the ability to monitor detected patients and keep their databases in order to improve the training and model's accuracy. The proposed model provides approximately 96.73% of accuracy and shows explicit competency with ResNet 101 and other existing models.

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Research on Power Stability Control technology of Light Source for Steel Ball Surface Defect detection

Research on Power Stability Control Technology of Light Sour...

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Power Electronics, Computer Applications (ICPECA), IEEE international conference on

作者： Dongdong Gao Guoping Li Meng Li University of Jinan Jinan China

When the semiconductor laser is used as the light source device to detect the surface defects of steel balls, because its output light power and light wavelength are greatly affected by injection current, ambient temperature and other factors, the output power of the light source is unstable, which will seriously affect the accuracy of steel ball surface defect detection. To solve this problem, this paper designs a power stability control system of small power semiconductor laser based on single-chip microcomputer, which can feedback control the output power and working temperature of the laser, and add an optical isolator in the laser to achieve automatic control of laser luminous power stability. After the simulation of the system, the 1310nm and 1550nm lasers are tested experimentally. The test results show that the circuit works reliably and the optical power control effect is good. The instability of the output power of the two lasers is 1.42% and 1.7% respectively. This design provides a relatively stable light source for the detection of steel ball surface defects.

关键词： Power lasers Surface emitting lasers Laser feedback Laser stability Stability analysis Steel Thermal stability

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Terminology Consistency of Computational Linguistics

Terminology Consistency of Computational Linguistics

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technology Enhanced Learning in Higher Education (TELE), international conference on

作者： Marina S. Matytcina Olga N. Prokhorova Igor V. Chekulai Vladislav A. Kuchmistyy Irina M. Chebotareva Olga N. Polshchykova Foreign languages department Lipetsk State Technical University Lipetsk Russia The Institute of Cross-cultural Communications and International Relations Belgorod State National Research University Belgorod Russia Belgorod State National Research University Belgorod Russia

The article is devoted to the analysis of the systematic nature of the terminology of computational linguistics. The purpose of the article is to establish system relations in the terminology of computational linguistics, identification of its relationships with other systems of special vocabulary, general scientific and interdisciplinary terms. The paper solves the problems of studying the sphere of functioning of the terms of computer linguistics, representation of its systemic connections in the form of a “core-periphery” model, analysis of the main types of interrelation of terms of computational linguistics with other systems of special vocabulary and common language. The study of terminological material is carried out on the basis of descriptive method and method of quantitative analysis. The results of the study showed that the terminology of computational linguistics functions as a system of special names in the scientific and applied field, focused on the use of computer tools, mathematical models and artificial intelligence in linguistics and related disciplines. system connections in the terminology of computational linguistics can be represented in the form of a core-periphery model.

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Multi-Frequency Damping Adaptive Control Strategy for Active Dampers Based on MultiHarmonic detection

Multi-Frequency Damping Adaptive Control Strategy for Active...

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IEEE international conference on Industrial Electronics for Sustainable Energy systems (IESES)

作者： Hailong Gao Xuequan Xiao Yushan Zhou Xiao Zhang Yihang Zhao dept. of Xuzhou Power Supply Branch State Grid Jiangsu Electric Power Company Xuzhou China dept of Electrical and Power Engineering China University of Mining and Technology Xuzhou China

As the interface between new energy and the power grid, grid-connected converters are easy to cause multifrequency oscillation of the system. So, this paper proposes an active damper to dampen multiple harmonics by simulating virtual resistance. The basic idea of the active damper is that use the cascaded multiple Second-Order Generalized Integrator with the Frequency-Locked Loops (SOGI-FLL) to detect and extract the unknown multiple harmonics automatically. Then, for different content of resonance, the active damper adjusts the virtual resistance value adaptively to enhance the stability of the system. Finally, the simulations of MATLAB/Simulink are carried out and the results verify the accuracy and effectiveness of the proposed scheme.

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Impedance Severity Index Based Hybrid Islanding detection Approach for Zonal DC Microgrids

Impedance Severity Index Based Hybrid Islanding Detection Ap...

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Sustainable Energy and Future Electric Transportation (SEFET), international conference on

作者： Bhabani Kumari Choudhury Premalata Jena Department of Electrical Engineering Indian Institute of Technology Roorkee Roorkee India

A significant obstacle to the greater integration of renewable energy sources is the absence of an efficient protection system. Unintentional islanding can cause system instability and pose a threat to the safety of workers. However, DC microgrid islanding detection schemes have not been rigorously addressed in the literature yet. In order to overcome the above-mentioned issues, this research proposes a two stage hybrid islanding detection approach (IDA) for DC microgrids (DC MGs) to address the significant non detection zone (NDZ) and power quality degradation of prior IDAs. Using the proposed impedance emergency severity index ( $ESI_{z}$ ) based hybrid IDA, a disturbance is first recognized by calculating the magnitude of $ESI_{z}$ . In order to distinguish between islanding and other disruptions, an under over voltage (UOV) based current perturbation is then placed on top of the current. The proposed approach makes use of local measurement parameters of the distributed generation (DG) units. It does not impact the power quality while operating normally and has a nearly zero NDZ. On a 750 V zonal DC MG test system, numerous case studies are performed to demonstrate the nerformance of the proposed hybrid IDA.

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detection of Insider Threats Using Deep Learning

Detection of Insider Threats Using Deep Learning

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Pervasive Computing and Social Networking (ICPCSN), international conference on

作者： Anju A Nithya Kalyani M Shalini K Haritha Ravikumar Saranya P M Krishnamurthy Department of Information Technology KCG College of Technology Chennai Tamilnadu India Department of CSE KCG College of Technology Chennai Tamilnadu India

Information technology systems are more and more susceptible to many variety of security risks, the majority of risks are mostly started by internal users. Insider threats are a complicated and difficult problem to identify and avoid because they mostly have privileged access to the network and database of company details. Businesses, organisations, and governmental organisations have a serious cybersecurity risk as a result of insider attacks. Insider threat identification is challenging because of uneven data, scant ground truth, and potentially changing user behavior. This work provides an insider threat detection method based on anomaly detection using unsupervised learning. Computer network and system security are severely hampered by insider threats. Theft of intellectual property, sabotage, the release of sensitive data, and web application attacks are examples of malevolent actions committed by authorised users that have the potential to do serious harm. It is the responsibility of the organisation to protect all network layers and guard against intrusions. Using historical data, the extracted behavioral traits using the Deep Learning method. The project made advantage of the publicly accessible Computer Emergency Response Team (CERT) insider threats dataset.

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A Real-World Dataset “IDSIoT2024” for Machine Learning/Deep Learning Based Cyber Attack detection system for IoT Architecture

A Real-World Dataset “IDSIoT2024” for Machine Learning/Dee...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), international conference on

作者： Manasa Koppula L.M.I. Leo Joseph Dept. of ECE SR University Warangal India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

The Internet of Things (IoT) is an emerging technology evaluating its inception in all domains like industries, home automation, healthcare, agriculture, etc. The major challenge in IoT is securing the IoT devices and information associated with the IoT devices from hackers or unauthorized persons. These cyber security attacks can be detected early for timely interventions and reduce major risks. Machine Learning (ML)/Deep Learning (DL) has become an influential technique for automatic Attack or Intrusion detection systems (IDS), putting forward numerous advantages over traditional techniques. A well-structured, real-time IoT security dataset must be used to develop IDS using ML/DL approaches. The article proposes a Real-World dataset IDSIoT2024 consisting of 16,230,955 records to help researchers advance solutions for tackling cyber security in IoT networks. The records presented in the dataset were captured using the Wireshark tool in a real-world IoT architecture. The data collection has taken two months from the middle of June 2023 to August 2023. The proposed dataset can be employed in the field of Artificial intelligence (AI) to develop ML/DL approaches and detect cyber-attacks in IoT networks. A large amount of data needs to be trained to an ML algorithm to get precise detection of attacks. Therefore, the proposed dataset with a vast number of records with different attacks can accommodate the researchers for the development of Intrusion detection systems.

关键词： Deep learning Machine learning algorithms Computer hacking Intrusion detection Telecommunication traffic Real-time systems Virtual machines Vectors Internet of Things Cyberattack

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Design of Commutator Cylindrical Foreign Object detection system Based on Improved YOLOv5

Design of Commutator Cylindrical Foreign Object Detection Sy...

引用

Signal Processing, Communications and Engineering Management conference (ISPCEM), international

作者： Huimin Li Guangzhi Zhang Yiluan Yue College of Mechanical Engineering Donghua University Shanghai China

In the automatic production line of a motor, after the commutator has been turned and other processes, there may be copper chips and other foreign objects falling into the insulation slot to affect the performance of the motor. This paper adopts machine vision technology to automatically detect foreign objects in the commutator insulation slot and designs a commutator column surface foreign object detection system based on the improved YOLOv5. The composite illumination design with multiple light source combinations is used on the illumination system to collect pictures with obvious features in the insulation slot. The detection algorithm uses a PP-LCNet lightweight network to replace the backbone structure in the YOLOv5 network, introduces the SE module at the end of the network, and replaces the Relu activation function with the H-Swish activation function to speed up the detection speed. The experimental results show that the improved YOLOv5 algorithm reduces 54 misjudgments of qualified products per day compared with the traditional image recognition algorithm, and the correct recognition rate reaches 97.54%; the single inspection time is reduced by 66.29% compared with the YOLOv5 algorithm, which meets the accuracy and speed requirements of realtime inspection in automated production.

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The Role of AI in Biochips for Early Disease detection

The Role of AI in Biochips for Early Disease Detection

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： C. Sukumaran K. Indhumathi P. Balamurugan R.P. Ambilwade P. Mohana Sunthari L. Natrayan Government Law College Thiruchirappalli Tamilnadu India Saveetha Engineering College India Department of Mathematics Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Computer science National Defence Academy Pune K S R Institute for Engineering and Technology India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

Improvements in illness diagnosis made possible by combining AI with biochip technology. It highlights the use of AI to improve the sensitivity and efficiency of biochips, allowing for faster and more precise illness diagnosis. Current healthcare uses of biochips are discussed, as well as artificial intelligence methods that may be used to the processing of biochip data. Through a variety of case studies, it shows how AI may be used for real-world tasks including early detection of cancer and other diseases through its capacity to analyze complicated data and perform predictive analysis. Data privacy, ethical considerations, and the need for extensive datasets to train AI are all discussed in the study as well as other obstacles to integrating AI with biochips. Furthermore, the legal implications of implementing new healthcare technology are covered. Future research options are suggested, with an emphasis on the importance of multidisciplinary cooperation and the possible effect of AI in biochip design.

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An Exploration of the Use Cases for Artificial intelligence in Data Access Optimization

An Exploration of the Use Cases for Artificial Intelligence ...

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Smart Generation Computing, Communication and Networking (SMART GENCON), international conference on

作者： Sharmila. P Snehal R. Rathi Manpreet Singh Preeti Naval Krishnan Batri Bhavesh Agnihotri Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India

This paper provides an exploration of the use cases for artificial intelligence (AI) in information access optimization. AI-based tactics to statistics optimization have the ability to enhance performance and reduce cost drastically. The paper discusses the characteristics of AI-pushed optimization techniques, together with fusion of heterogeneous facts assets, predictive modeling, forecasting, anomaly detection, and clustering. It also explores the numerous statistics assets that can be optimized using AI, which includes conventional database systems, disbursed information lakes, and streaming event statistics. Moreover, the paper considers the demanding situations related to AI-enabled facts get admission to optimization, such as loss of availability of labeled data and issue in imposing commercial enterprise good judgment. Finally, excellent practices and considerations for implementation are mentioned. Through exploring the use instances for AI in records get entry to optimization, this paper provides important insights for builders and businesses considering the usage of AI for data get entry to optimization.

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