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Attention-CoviNet: A Deep-Learning Approach to Classify Covid-19 Using Chest X-Rays 1

1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Karkera, Thejas Singh, Chandra Bekal, Anush Shetty, Shailesh Prajwal, P. Department of Electronics Communication Engineering Sahyadri College of Engineering and Management Mangalore India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Sahyadri College of Engineering and Management Mangalore India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

Covid-19 pandemic is spreading across the world at a breakneck pace. As of September 2022, JHU CSSE COVID-19 Data and Our World in Data have recorded up to 5.2 M deaths due to Covid-19. One of the essential steps of fighting this pandemic is to detect the disease early. Studies show some abnormalities in the chest X-rays of the Covid-19 patients, and these features help classify the covid-19 positive patients from the negative patients. In recent years in the field of medical x-rays, Deep Learning models have proven to have the ability to learn and identify features that a trained person can only identify. Due to the exponential increase in the covid-19 cases, the waiting time for covid results has increased, resulting in the late diagnosis of patients for Covid-19. Classifying a patient as Covid-19 positive for a genuinely covid positive patient is as essential as classifying a patient Covid-19 negative who is genuinely covid-negative. Here we present a detailed quantitative analysis on the performance of state-of-the-art models like ResNet-50, Dense-Net, Mobile-Net-V2, and MNAS-Net on classifying patients for Covid positive, Pneumonia positive, and Normal by evaluating Accuracy, Precision, Recall, and Jaccard Index. We propose a new deep neural network classification model for low-end devices that uses two Attention mechanisms. Figure 1. shows the attention map with confidence percentage for each classes. Our model uses fewer parameters and FLOPS than other state-of-the-art models and recorded a 2% increase in Accuracy and other evaluation parameters. The dataset used for implementation is a public dataset COVID-DATASET and NIH Chest X-ray from kaggle. It has over 1000 images of Covid Chest X-ray, Pneumothrax, Mass, Pneumonia, Cardiomegaly, Nodule, Effusion, Atelectasis and inflitration. All the model implementations are implemented on PyTorch. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： COVID-19

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Vacancy Modulated Analog Resistive Switching Memory Device Based on Bilayer of Zn@ZnO/ZnO for Neuromorphic Computing 30

Vacancy Modulated Analog Resistive Switching Memory Device B...

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30th IEEE International Conference on Electronics, Circuits and systems, ICECS 2023

作者： Khan, Muhammad Umair Mohammad, Baker Khalifa University System on Chip Center Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350326499

This work proposed a vacancy-modulated analog resistive random-access memory (ReRAM) for neuromorphic computing, utilizing a structure composed of Ag/Zn@ZnO/ZnO/FTO. The resistive switching behavior can be modulated by the movement of oxygen vacancies under positive or negative voltages. This characteristic leads to the desired resistive switching behavior for neuromorphic computing. The Ag/Zn@ZnO/ZnO/FTO based analog ReRAM exhibits several desirable features, including a low operation current, forming-free operation, and self-compliance. The conductance change rate of the device can be adjusted efficiently by modifying the amplitudes and pulse intervals of spike pulses. The device can be used for short-time memory (STM) and long-term memory (LTM) memory operations with good retention time. These characteristics align with the requirements for next-generation ReRAM. The introduction of the Ag/Zn@ZnO/ZnO/FTO-based device structure paves the way for the future development of neuromorphic computing memory applications. © 2023 IEEE.

关键词： Zinc oxide

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ANFIS-based Multilayered Algorithm for Botnet Detection

ANFIS-based Multilayered Algorithm for Botnet Detection

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2023 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2023

作者： Thamaraimanalan, T. Venkatesan, C. Ramkumar, M. Sivaramakrishnan, A. Marimuthu, M. Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India Hkbk College of Engineering Department of Electronics and Communication Engineering Bangalore India Knowledge Institute of Technology Department of Computer Science and Business System Salem India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra pradesh India Sona College of Technology Department of Computer Science and Engineering Salem India

ISBN: (纸本)9798350337426

In this study, a multilayered botnet detection system for network-based botnet detection based on ANFIS is suggested. Two layers make up the suggested system, one of which is utilized for feature extraction and the other for botnet detection. Using mean and variance, the feature extraction layer pulls features from network traffic data. The botnet detection layer employs ANFIS to identify botnet activity based on the collected features. Using the CICIDS2017 dataset, the suggested system's performance is assessed. The experimental findings demonstrate that the suggested algorithm works better than other current botnet detection techniques regarding accuracy, precision, and recall. The proposed system obtained 99.83% accuracy, 99.75% precision, and 99.91 % recall. The proposed multilayered ANFIS-based botnet detection system offers an excellent way to identify botnet activity in a network. It can be utilized in various network security applications, such as threat intelligence, malware detection, and intrusion detection. © 2023 IEEE.

关键词： Feature extraction

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A Study Of Dynamic Thresholds Power Detection Spectrum Sensing Techniques In CRN 9

A Study Of Dynamic Thresholds Power Detection Spectrum Sensi...

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9th International Conference on Advanced Computing and Communication systems, ICACCS 2023

作者： Keerthika, J. AnandaKumar, H. Priya, V. Yuvalatha, S. Gayathri, N. Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Dr NGP Institute of Technology Department of Artificial Intelligence and Data Science Coimbatore India Bannari Amman Institute of Technology Department of Computer Science and Business System Sathyamangalam India

ISBN: (纸本)9798350397376

A Cognitive radio is the mobile communication technologies which help to utilize the efficient need of radio spectrum origin. The performance of CRN is significantly impacted by the spectrum sensing function. The problem of bandwidth has risen as a more specific one with breakthroughs in remote communications. By forgiving unlicensed users to usage the available spectrum in primary networks is the solution to the CR Technology. An innovative method for enhancing the electromagnetic spectrum's use efficiency, cognitive radio is a encouraging technology. Spectrum sensing has high spectral resolution capabilities and aids in the detection of the spectrum holes. The various methodologies are used to enhance the spectrum sensing essence. These techniques are distinguished by a sensing threshold, which is crucial to the performance of the sensing. The most important element in selecting the best threshold to optimize spectrum sensing performance is the dynamic threshold in the proposed system's spectrum detection algorithm. The energy earned from the licensed user over a precise time of observation, then the dynamic threshold is commutated between two levels. The estimation of the noise ambiguity factor forms the basis for threshold evaluations. These criteria are used to hike the likelihood of detection and contraction the possibility of false alarms. © 2023 IEEE.

关键词： Cognitive radio

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Text to Image Generation Using Attentional Generative Adversarial Network 1

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1st International Conference on Advanced Computing, Machine Learning, Robotics and Internet Technologies, AMRIT 2023

作者： Rao, Supriya B. Shetty, Shailesh S. Singh, Chandra M, Srinivas P. Bekal, Anush Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore Karnataka Mangaluru India Department of Computer Science and Business System Srinivas Institute of Technology Karnataka Mangaluru India Department of Electronics & Communication Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India Department of Computer Science Engineering Sahyadri College of Engineering & Management Karnataka Mangaluru India

ISBN: (数字)9783031472213

ISBN: (纸本)9783031472206

AttnGan – Attentional Generative Adversarial Network (GAN) is a type of GANs that is used for text to image generation. Unlike other art models, it focuses on fine-grained word level information and once the model is trained by mapping the images and the corresponding text in the dataset, the model will generate image part-by-part using the image description given by the user as an input to the model. Current paper, focuses on the conversion of text into images based on the description provided by the user. This enables them to translate their ideas in the form of text into images of their required choices. The model is initially trained by the description of the text and images and when the user provides the text as an input to the system, it is used as a testing data. By analyzing the text word-by-word, the respective images pertaining to the text is generated part-by-part by considering the text description into account. This can heavily help the users in a applications such as in editing aids, documental archives, etc. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Generative adversarial networks

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An Ensemble Learning Based Approach for Detecting and Tracking COVID19 Rumors

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computers, Materials & Continua 2022年第1期70卷 1721-1747页

作者： Sultan Noman Qasem Mohammed Al-Sarem Faisal Saeed Computer Science Department College of Computer and Information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia Computer Science Department Faculty of Applied ScienceTaiz UniversityTaiz6803Yemen College of Computer Science and Engineering Taibah UniversityMedinaSaudi Arabia Information System Department Saba’a Region UniversityMareebYemen

Rumors regarding epidemic diseases such as COVID 19,medicines and treatments,diagnostic methods and public emergencies can have harmful impacts on health and political,social and other aspects of people’s lives,especially during emergency situations and health *** huge amounts of content being posted to social media every second during these situations,it becomes very difficult to detect fake news(rumors)that poses threats to the stability and sustainability of the healthcare sector.A rumor is defined as a statement for which truthfulness has not been *** COVID 19,people found difficulty in obtaining the most truthful news easily because of the huge amount of unverified information on social *** methods have been applied for detecting rumors and tracking their sources for COVID 19-related ***,very few studies have been conducted for this purpose for the Arabic language,which has unique ***,this paper proposes a comprehensive approach which includes two phases:detection and *** the detection phase of the study carried out,several standalone and ensemble machine learning methods were applied on the Arcov-19 dataset.A new detection model was used which combined two models:The Genetic Algorithm Based Support Vector Machine(that works on users’and tweets’features)and the stacking ensemble method(that works on tweets’texts).In the tracking phase,several similarity-based techniques were used to obtain the top 1%of similar tweets to a target tweet/post,which helped to find the source of the *** experiments showed interesting results in terms of accuracy,precision,recall and F1-Score for rumor detection(the accuracy reached 92.63%),and showed interesting findings in the tracking phase,in terms of ROUGE L precision,recall and F1-Score for similarity techniques.

关键词： Rumor detection rumor tracking similarity techniques COVID-19 social media analytics

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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Application of Recursive Query on Structured Query Language Server

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Journal of Donghua University(English Edition) 2023年第1期40卷 68-73页

作者：荀雪莲 ABHIJIT Sen 姚志强 Library North China Institute of Aerospace EngineeringLangfang 065000China Department of Computer Science&Information System Kwantlen Polytechnic UniversitySurreyCanada School of Computer Science and Engineering North China Institute of Aerospace EngineeringLangfang 065000China

The advantage of recursive programming is that it is very easy to write and it only requires very few lines of code if done *** query language(SQL)is a database language and is used to manipulate *** Microsoft SQL Server 2000,recursive queries are implemented to retrieve data which is presented in a hierarchical format,but this way has its *** table expression(CTE)construction introduced in Microsoft SQL Server 2005 provides the significant advantage of being able to reference itself to create a recursive *** data structures,organizational charts and other parent-child table relationship reports can easily benefit from the use of recursive *** recursive query is illustrated and implemented on some simple hierarchical *** addition,one business case study is brought forward and the solution using recursive query based on CTE is *** the same time,stored procedures are programmed to do the recursion in *** results show that recursive queries based on CTEs bring us the chance to create much more complex queries while retaining a much simpler syntax.

关键词： structured query language(SQL)server common table expression(CTE) recursive query stored procedure hierarchical data

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Receding-Horizon Control of Constrained Switched systems with Neural Networks as Parametric Function Approximators

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SN computer science 2023年第1期4卷 62页

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Department of Electrical Engineering and Computer Science University of Kassel Wilhelmshöher Allee 71-73 Kassel 34121 Germany

This work studies receding-horizon control of discrete-time switched linear systems subject to polytopic constraints for the continuous states and inputs. The objective is to approximate the optimal receding-horizon control strategy for cases in which the online computation is intractable due to the necessity of solving mixed-integer quadratic programs in each discrete time instant. The proposed approach builds upon an approximated optimal finite-horizon control law in closed-loop form with guaranteed constraint satisfaction. The paper derives the properties of recursive feasibility and asymptotic stability for the proposed approach. A numerical example is provided for illustration and evaluation of the approach. © 2022, The Author(s).

关键词： Model approximation Neural networks Online control Optimization Reinforcement learning Stability

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Advancing Smartphone Sensor-Based Keystroke Dynamics for Implicit and Active Authentication: Addressing Challenges and Enhancing Usability Control 11th

Advancing Smartphone Sensor-Based Keystroke Dynamics for Imp...

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11th International Symposium on Applied Computing for Software and Smart systems, ACSS 2024

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science Bagnan College Khalore Bagnan Howrah711303 India Department of Computer and System Sciences VisvaBharati Santiniketan731235 India Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector-III Kolkata Saltlake City700106 India

ISBN: (纸本)9789819797615

Smartphone sensors play a pivotal role in delineating user behaviour, particularly in the context of keystroke dynamics-based implicit and active/continuous authentication, as well as liveness detection. The acquisition of multifaceted sensory data during typing serves as a fundamental source of behavioural attributes, yet the field faces several intricate challenges requiring careful consideration to recalibrate the delicate balance between security and usability. Notable challenges encompass the capricious nature of anomaly detectors when confronted with unavoidable influencing factors, the substantial battery drainage associated with prolonged sensor operation, the computational inefficiency intrinsic to smartphones, and the imperative need for usability control in frequently switching applications and balancing security in both device and application levels, especially in scenarios with varying security requirements. This study endeavours to mitigate these challenges through the implementation of cutting-edge anomaly detection techniques, incorporating four distinct schemes—namely, column medians template formation, synthetic samples, soft biometrics and score-level fusion. The study further leverages advanced sensory patterns, including gyroscope, accelerometer and rotational information, captured during abbreviated typing intervals at a low sampling rate to mitigate battery consumption. In the pursuit of enhanced usability control, a Watchman score is assigned to each running application, dictating security adjustments based on genuineness evaluations of micro-behaviours within predefined temporal windows. A novel rule-based mechanism is introduced to govern device and application security within a continuous authentication mode. This empirical investigation, supported by robust statistical evidence, aspires to yield more precise, energy-efficient, and user-friendly implicit and active user identity verification mechanisms. The findings present an avenue for f

关键词： Smartphones

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