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Covid-19 detection using medical images

AIP Conference Proceedings 2023年第1期2591卷

作者： Sabah Abdulazeez Jebur Mohanad Ali Mohammed Alia Karim Abdulhassan 1Department of Computer Techniques Engineering Imam Al-Kadhum College Baghdad Iraq 2Computer Science Department University of Technology Baghdad Iraq

At the present time, COVID-19 considers the pandemic that has most dangerous, representing a great threat to both, Communities and individuals especially scientist and researchers. Determine the infection in the early time is the only way to avoid getting worst especially for those who have respiratory diseases. The researchers working daily without stopping to get the possible solutions to rescue patients or who have the Symptoms of the disease. CT-Scans and X-rays for the lungs consider one of the most popular methods used by the researchers, where analyzing this image to detect the infection by this pandemic. However, it demands long time to examine each test, beside a large number of radiology specialists will be *** data may be an interesting signal data samples that can be used to detect the COVID-19 via number of oxygen blood tests, in this research, detection of COVID-19 through some tests of oxygen signals is an essential goal. Tests like oxygen saturation (spO2), Pulse Oximetry (plusOX), Arterial blood gas (ABG) and Carboxyhaemoglobin (COHb) are the most common and important test for this purpose. In this research, a method based on deep neural network as well as machine learning methods using (stochastic gradient decision, decision tree, random forest, linear regression and k-nearest neighbour) was proposed for detecting COVID-19 through analysing the X-rays of patients which will look for visual indicators found in the chest radiography imaging of COVID-19 patients. Also proposed architecture can be applied to the oxygen data when it will be available (lack of well-structured data).

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A review analysis on load balancing as a crust of cloud computing

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AIP Conference Proceedings 2022年第1期2555卷

作者： Surinder Kaur Vishal Bharti Gurpreet Singh 1Computer Science and Engineering Chandigarh University Mohali Punjab India 2Computer Science and Engineering Chitkara University Rajpura Punjab India

Cloud Computing (CC) has gained a tremendous popularity in past few years. The concept of distributed systems in computing network has been reached to a large scale. To implement the cloud environment efficiently, several challenges are to be faced. This paper elaborates the concept of Cloud Computing and throws light on the various challenges in the way of successful implementation of Load Balancing (LB) in cloud computing environment. LB is the major concern in CC environment which imbalances the robustness of the cloud computing system. This paper illustrates the need of load balancing and explores the available techniques of load balancing. The paper also concentrates on the requirement of fault tolerance (FT) in CC and discusses various FT methods. In the last, comparative analysis between present load balancing techniques is represented.

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Multilevel feedback queue: Efficient scheduling and implementation by using dynamic quantum

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AIP Conference Proceedings 2023年第1期2554卷

作者： Muhammad Kashif Rab Nawaz Riries Rulaningtyas Shuaib Iqbal Aisha Zulfiqar 1Biomedical Engineering Faculty of Science and Technology Universitas Airlangga Surabaya Indonesia 2Computer Engineering Department College of Electrical and Mechanical Engineering National University of Sciences and Technology Rawalpindi Pakistan 3Computer Science Department Comsats University Islamabad Pakistan

A multitasking operating system allows many programs to run at the same time. We need to schedule our Central Processing Units (CPUs) efficiently to handle these services. CPU scheduling algorithms come in a score of forms and any strategy has advantages and disadvantages. We implemented a Multi-Level Feedback Queue (MLFQ) algorithm with static time quantum for the first queue, making an efficient scheduling algorithm of MLFQ dynamic time quantum used in the second queue. Other than that, we compared both results and observed which dynamic time quantum delivers the best result. In the first queue, we used the Round Robin (RR) algorithm with less quantum time for processes that have a short burst time. The process that had more burst time than the defined quantum in the first queue was allowed to move into the second queue. Here, we calculated the average burst time of all remaining processes and defined that burst time as a quantum for the second queue after the Shortest Job First (SJF) scheme was applied in this queue. As a result, 70% of the processes in the second queue was received CPU. Other processes moved into the lower queue (the last queue). Hence, this technique prevented the starvation problem for those processes which have large burst times.

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Deception technology companions with IOT-a platform of connected world and bluff

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AIP Conference Proceedings 2022年第1期2555卷

作者： Jalaj Pateria Laxmi Ahuja Kirti Seth 1Capgemini India 2Computer Science and Engineering Amity Institute of Information Technology Noida India 3Computer Science and Engineering School of Computer and Information Technology INHA University in Tashkent Uzbekistan

Internet of Things (IoT) networks securityis one of the most crucial issue what informationtechnology (IT) community faces. As IoT ecosystem continues to accelerate, so do the cyber security threats associated with connected devices. Any IOT device compromised becomes a backdoor into the organization network. IOT devices are new exploitable agents due to its weaknesses such as less secured firmware, missing with high-grade authentication. Hence, intruders can take advantage of weaker level of security and management of the devices is much labour intensive and are considered as soft targets. Deception which is an amalgamation of analytics and Machine Learning can act as a silver harpoon and adds extra layer of security of IOT device where deception platform is introduced into set of devices deceiving attackers into thinking they are authentic. This paper describes the limitations of Internet of Things End Points and how Deception Technology can be used to create a layer of confusion against intruder. We have done experiments on raw logs to interpret IOT and NON-IOT devices, Type of IOT device, device anomalous behaviour and what type of anomaly device is facing by using multiple machine learning algorithms and predicting possible attacks by performing effective feature selection and classification. Our results showed comparison statistics of multiple models based on various metrics and derive deceptive strategy the defender adopt to keep attackers success rate low.

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Detection of cyber attacks using machine learning

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AIP Conference Proceedings 2022年第1期2405卷

作者： N. Vadivelan K. Bhargavi Sarangam Kodati M. Nalini 1Department of Computer Science Teegala Krishna Reddy Engineering College Hyderabad 500097 India 2Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

Cyber security professionals pay greater regard to risk evaluation and propose techniques for mitigating. Throughout the area of cyber defense, designing successful strategies was a plan set. Machine learning also increasingly become an important concern in data protection although machine learning is successful in cyber defense. The rapid expansion in Cloud Computing, networking and evolutionary computation has been the result of unprecedented developments in computing, storage and computational technology. The planet is rapidly being digitizested - there is a growing want of comprehensive and sophisticated information security and privacy issues And Strategies to fight security threats, which are becoming more complicated. Cyber terrorism is spreading worldwide using all kinds of computer weakness. Machine learning algorithms were used to address global computer security threats such as malware detection, ransom ware recognition, fraud detection and spoofing identification. It research analyzes how cyber training is used in defense as well as offence, providing details about cyber threats on machine learning techniques and The much more popular kinds of cyber security risks are evaluated using machine learning algorithms which describe how machine learning is used for computer defence such as the identification and avoidance of attacks, vulnerability scanning and recognition and public internet risk assessment.

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Secure internet of battlefield from malicious software using deep eigenspace learning

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AIP Conference Proceedings 2022年第1期2405卷

作者： K. Bhargavi N. Vadivelan Sarangam Kodati M. Nalini 1CSE Teegala Krishna Reddy Engineering College Hyderabad India 2Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

In military cases, the Internet of Things (IoT) is normally made of a variety and nodes for the Internet (e.g. Portable military jackets as well as diagnostic equipment). This IoT system and network is an important target for malicious, especially for individuals funded by the state or indeed the political entity. Malware usage is a popular access point. This paper introduces a method of huge information to install malicious on the Internet of Battlefield Items (IoBT) or the internet of military things(IoMT) through the OpCode sequence of both the system. They transform OpCodes in a vector field and implement a profound methodology for studying to distinguish hazardous and successful software. The specificity of certain suggested malware analysis strategy and the sustainable development towards injection of garbage software threats are indeed demonstrated. Finally, we have our Github malicious analysis, that they believe will help ongoing studies (e.g. to promote assessment of potential contributor to malware detection).

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Machine learning IoT based framework for analysing heart disease prediction

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AIP Conference Proceedings 2023年第1期2523卷

作者： A. K. Velmurugan K. Padmanaban A. M. Senthil Kumar H. Azath Murugan Subbiah 1Computer Science and Engineering KoneruLakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh-522502 India 2Computer Science and Engineering Vel Tech RangarajanDrSagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 3School of Computing Science and Engineering VIT Bhopal University India 4Vee Eee Technologies Chennai Tamil Nadu India

Coronary illness forecast is exceptionally fundamental in the present climate. Different explores have, as of now, been done to anticipate coronary illness from the huge dataset. IoT environment essentially produces information from various sensors and predicts the illness plausibility accordingly. Different synthetic dataset content diverse body boundaries extricated by explicit sensor esteem, the significant pretended by machine learning (ML) algorithm. This paper proposes coronary illness prediction with the mix of IoT and hybrid ML approach, which integrates the ANN with RNN. This examination has a sufficient degree for progress due to most boundaries, for example, increment record, blood vessel firmness, increment pressure, and so on. The proposed RNN gives better arrangement and infection expectation precision over the other ML calculations. This framework considered synthetic data, which is essentially used to anticipate coronary illness probability and prediction. The trial investigation shows the viability of the proposed methodology over other existing methods.

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Deepq: Residue analysis of localization images in large scale solid state physical environments

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AIP Conference Proceedings 2022年第1期2393卷

作者： S. Manikandan K. S. R. Radhika M. P. Thiruvenkatasuresh G. Sivakumar 1Department of Information Technology E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India 2Computer Science and Engineering SRKR Engineering College Bhmavaram Andhra Pradesh India 3Dept. of Computer Science and Engineering Erode Sengunthar Engineering College ErodeTamilnadu India

Deep Learning is the process to led machine learning, natural language processing and neural networks. The various deep learning models, computer vision systems and artificial intelligence services are used to study of various real time applications. Due to lack of computing resource the conventional neural network are produces delay in progress and reduce the GPUs performance and throughput. In this paper we review difference deep learning approaches with increases GPUs performance and apply various image processing classification and localization techniques. The high availability and GPUs performance can be verified by state-of-arts results using conventional deep learning methods.

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Hybrid data mining technique based breast cancer prediction

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AIP Conference Proceedings 2023年第1期2523卷

作者： K. Padmanaban A. M. Senthil Kumar H. Azath A. K. Velmurugan Murugan Subbiah 1Computer Science and Engineering KoneruLakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh 522502 India 2Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science And Technology Chennai Tamil Nadu India 3School of Computing Science and Engineering VIT Bhopal University India 4Embedded System Vee Eee Technologies Chennai Tamil Nadu India

In recent days, Breast malignancy has been considered the main real for death in ladies when contrasted with any remaining diseases. Bosom malignant growth has turned into the most unsafe sort of disease among ladies on the planet. Early discovery of bosom malignant change is fundamental in diminishing life misfortunes. This paper intends to present a hybrid data mining strategy that incorporates K-Nearest Neighbour (KNN) and Support Vector Machine SVM to build up a precise arrangement model for Breast malignant growth forecast, to utilize the important data in clinical information, particularly which is typically overlooked by a large portion of the current techniques when they focus on high expectation exactness’s We have done trials on WBC information. Examination results show that the proposed hybrid information mining strategy has higher expectation exactness than those techniques. Various strategies for bosom disease location are investigated, and their exactness is thought about. We induce that the SVM-based hybrid methods are more appropriate in addressing bosom disease expectations with these outcomes. We suggest utilizing these methodologies with incomparable arrangement issues.

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Ant based routing algorithm for balanced the load and optimized the AMNET lifetime

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AIP Conference Proceedings 2023年第1期2523卷

作者： H. Azath A. K. Velmurugan K. Padmanaban A. M. Senthil Kumar Murugan Subbiah 1School of Computing Science and Engineering VIT Bhopal University India. 2Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh-522502 India. 3Computer Science and Engineering Koneru Lakshmaiah Education Foundation Green Field Vaddeswaram Guntur Dist. Vijayawada Andhra Pradesh 522502 India 4Computer Science and Engineering Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India 5Embedded System Vee Eee Technologies Chennai Tamil Nadu India

Mobile ad hoc networks (MANETs) are perfect for positions here lacking preset structure. Nowadays, may experience from partitioning network. This restriction builds MANETs incompatible for applications for example, crisis management and battleground communications. To deal with this weakness, an Autonomous Mobile Mesh Network (AMMNET) is used. AMMNET introduce a disseminated tracking the client clarification to handle with the active nature of client movement, and this approach for dynamic topology alteration in accordance with the movement sample of the clients. However, the significant problems in AMMNET are to utilize an energy-efficient routing approach to extend the network lifetime. This approach introduced Ant Based Routing Algorithm for balanced the load and Optimized the AMNET lifetime (ARAB). It utilizes a well-organized heuristic update technique to optimize the path concern. This algorithm using pheromone trail revises to balance the utilization of energy. As a result, minimize the energy utilization induced with the control overhead. This approach balances the energy between clusters and evades preliminary network partitioning. Our simulation outcomes specify that ARAB is healthy against partitioning and accomplished of offering high forwarder throughput for the mobile clients.

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