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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Margret Sharmila, F. Shree Harini, R. Vityarubbasiri, M. Gopi Krishna, S. Sri Krishna College of Engineering and Technology Department of Computer Science and Business System Coimbatore641008 India

ISBN: (纸本)9798350394702

Early identification and diagnosis of cancerous cells will focus on the symptomatic patients, to give the best way of successful treatment. When the detection of cancer is delayed, then there is a lower chance of survival, high problems with treatment and higher cost estimates. Considering Pancreas Cancer growth, a type of cancer that is difficult to identify because there are less or no symptoms found at the early stage. So, we introduce Artificial Intelligence (AI) technology to identify pancreatic cancer cells. This is the first insight that is provided to detect cancerous cells at the early stage. With the use of technologies, anything is possible, so we use the process of medical imaging analysis, data processing and Machine Learning (ML) for the recognition. Data is collected through MRI and CT scan reports, biomarkers tests, liquid biopsies, endoscopic techniques, early symptom monitoring, and genetic testing which is then categorized into train and test sets. Every training data set is analyzed to provide a standard image to extract the features with the use of OpenCV. Building an AI model with the use of the Convolutional Neural Network (CNN) algorithm with an efficient set of filters. Training the model with the training data set helps to improve the accuracy of predicting results. When it is ready, the testing data set is used to check the accuracy of predictions. Finally, by assessing the model based on the performance metrics from both training and testing datasets, accuracy is achieved. After achieving a certain level of accuracy, and leveraging ML algorithms such as Hybrid models, the system can provide treatment recommendations consistent with a separate training dataset. This training dataset comprises predicted treatments corresponding to the achieved accuracy. After, the system is prepared for deployment. The envisioned process can track the patient's status for future use. © 2023 IEEE.

关键词： Forecasting

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Aging Analysis Framework of Windows-Based systems through Differential-Analysis of system Snapshots

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computers, Materials & Continua 2022年第12期73卷 5091-5102页

作者： Eun-Tae Jang Sung Hoon Baek Ki-Woong Park SysCore Lab Sejong UniversitySeoul05006Korea Department of Computer System Engineering Jungwon UniversityChungcheongbuk-do28024Korea

When aWindows-based system is used for an exceedingly long time,its performance degrades,and the error occurrence rate tends to *** is generally called system *** investigate the reasons for system aging,various studies have been conducted within the range of the operating system kernel to the user ***,finding an accurate reason for system performance degradation remains challenging research *** this study,system monitoring was conducted by dividing a system into‘before software installation,’‘after software installation,’and‘after software removal.’We confirmed that when a software installed in a system is removed,various system elements,such as storage and memory,are not restored to the level prior to the software ***,we established a hypothesis regarding the performance degradation of a computer system owing to repeated software installation/removal operations,investigated the correlation between system aging and repeated software installation/removal operations,and proposed a system aging analysis framework for analyzing the reason behind system *** the proposed system aging analysis framework,we aim to forcibly age a Windows-based system by repeating the software installation/removal operation by utilizing the system forced aging *** framework identifies the elements affecting system performance through a differential data analysis of the system time-series data extracted by the system performance extraction and system component snapshot ***,the aging analysis framework presented in this study is expected to be effectively utilized as an index for studying system aging.

关键词： system aging virtual machine system analysis

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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High-Performance Chip Design With Parallel Architecture for Magnetic Field Imaging system

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-13页

作者： Lin, Ming-Yi Hsieh, Sheng-Hsien Chen, Ching-Han Lin, Chun-Hung Yuan Ze University Department of Computer Science and Engineering Taoyuan320315 Taiwan National Pingtung University of Science and Technology Program of Artificial Intelligence and Mechatronics Pingtung912301 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan32001 Taiwan International Games System Company Ltd. New Taipei City248 Taiwan

To address the limitations of typical coil detection systems and enhance the performance of traditional magnetic field imaging (MFI) systems, we propose a MFI system that uses a 4×4 array of anisotropic magnetoresistance (AMR) sensors. The core controller of the proposed system was designed using a pipelined parallelization algorithm and implemented on a high-speed field-programmable gate array (FPGA) in a fully hardware-based architecture. The designed MFI system can achieve parallel acquisition of magnetic field components from the 16-channel AMR sensor array, parallel data processing, parallel data transmission, and parallel computations in a single cycle of only 4 ms. Compared with typical detection systems, the proposed solution not only exhibits superior performance but can also provide dynamic visual representations of sensed information. © 1963-2012 IEEE.

关键词： Parallel architectures

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Decision Support system For The Coaching Staff in the Process of Preliminary Selection of Players 18

Decision Support System For The Coaching Staff in the Proces...

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18th IEEE International Conference on computer Science and Information Technologies, CSIT 2023

作者： Hovorushchenko, Tetiana Hnatchuk, Yaroslav Hnatchuk, Alina Fehyr, Oleksandr Computer Engineering and Information System Department Khmelnytskyi Ukraine Theory and Methods of Physical Education and Sports Department Khmelnytskyi Ukraine

ISBN: (纸本)9798350360462

Nowadays in Ukraine, there is a need to automate the routine work of coaches and sports managers in the preliminary selection of athletes. In the process of selecting athletes for competitions of various levels, the head coach and coaching staff need to process large amounts of information about the applicants. This is especially important at the stages of preliminary selection, when the number of players to be selected for preliminary training camps is quite large, and the amount of information about each individual athlete increases accordingly. Our research has shown that despite the rapid development and application of information technology in the sports industry, there is currently no intelligent sports-specific DSS in Ukraine that helps coaching staff and sports managers automate the routine and time-consuming process of preliminary selection of athletes. Taking into account the large amount and heterogeneity of data that must be processed for decision-making by the coaching staff during the preliminary selection of players, a decision support system for preliminary selection of players is proposed. The system is based on the method of supporting decision-making by the coaching staff in the process of preliminary selection of athletes. The result of the decision support system is a recommended list of 18 players who have been selected according to the specified criteria out of a total of 24 players. If the coaching staff is not satisfied with the recommended list, it is possible to set additional criteria and use the decision support method to obtain a more accurate result. The results can also be implemented in other team sports. © 2023 IEEE.

关键词： Decision support systems

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A Case Study of Big Data Processing in the Cloud Environments with Insights into Advantages, Tools, and Techniques 14

A Case Study of Big Data Processing in the Cloud Environment...

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14th International Conference on Cloud Computing, Data Science and engineering, Confluence 2024

作者： Chowdary, Pullela Harish Thapar, Puneet Alyas, Arsalan Ahmed Singh, Urvashi Lovely Professional University Department of Computer Science and Engineering Punjab Phagwara India Information System Technologies Wilmington University United States

ISBN: (纸本)9798350344837

Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data. © 2024 IEEE.

关键词： Trees (mathematics)

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Collaborative Deadline-sensitive Multi-task Offloading in Vehicular-Cloud Networks 17

Collaborative Deadline-sensitive Multi-task Offloading in Ve...

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17th International Conference on COMmunication systems and NETworkS, COMSNETS 2025

作者： Kumar, Prasanna Sushma, S.A. Chandrasekaran, K. Addya, Sourav Kanti National Institute of Technology Karnataka Cloud and Smart System Services Lab Department of Computer Science and Engineering Surathkal India Siddaganga Institute of Technology Cloud and Smart System Services Lab Tumakuru India

ISBN: (纸本)9798331531195

With the growing technological advancements in the Internet and advanced functionalities in vehicular networks, it becomes crucial to execute tasks quickly and efficiently. However, the limited onboard computational capacity and vehicle mobility make it challenging to accomplish latency-sensitive tasks efficiently. Task offloading provides a promising solution to overcome these challenges. Cloud data centers provide efficient solutions, but returning the results to the vehicles takes longer due to the large physical distance. Leveraging edge servers to execute latency-sensitive tasks provides a fast, interactive response and less transmission cost. However, in a dynamic network, vehicles will be in constant motion with varying speeds, resulting in frequent handoffs from one base station to another. Our proposed work aims to select the optimal nodes to perform binary offloading with minimum cost using the collaborative vehicular network. We use a greedy-based offloading approach to address these challenges and achieve better quality-of-service and quality-of-experience in a dynamic environment to minimize costs, delay reduction ratio, and satisfaction ratio. The proposed work outperforms the baseline by 60.44%, 53.43% in reducing total system cost, delay reduction ratio, and 36% improvement in the satisfaction ratio compared to baseline algorithms. © 2025 IEEE.

关键词： Quality of service

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Fractal Dimension Analysis Demonstrates Overestimation and Underestimation of Time in EEG Signal 1

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13th National Technical Symposium on Unmanned system Technology, NUSYS 2023

作者： Azari, Maryam Mollazadeh Sarbaz, Yashar Koohestani, Behrooz Farzamnia, Ali Modeling Biological System’s Laboratory Department of Biomedical Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Faculty of Engineering Universiti Malaysia Sabah Sabah Kota Kinabalu Malaysia School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom

ISBN: (数字)9789819720279

ISBN: (纸本)9789819720262

Keeping track of time is regarded as an essential human behavior. The question of how the brain deals with temporal information remains a subject of scholarly debate. The current investigation aims to explore the mechanism underlying time perception by extracting fractal dimension from an electroencephalogram (EEG) signal and its frequency bands. To accomplish this, Higuchi’s fractal dimension was calculated for 42 healthy subjects’ electroencephalogram (EEG) signal and its sub-bands during the time perception task. The EEG signal was recorded from 19 channels. Subsequently, a statistical analysis was conducted to compare participants who underestimated versus those who overestimated the elapsed time and significantly different channels were presented. The findings suggest an elevated level of fractal dimensionality in persons who displayed a tendency to overestimate time. The EEG signal and Gamma rhythm emerged as the most distinguishing signals between the two cohorts. The contrast in fractal dimension between the two groups was predominantly apparent in the parietal and central channels. To summarize, an increased level of complexity is discernible in the EEG signal and high-frequency rhythms when there is an overestimation of temporal duration. It can be asserted that the employment of FD yields presents an exceptional approach to comprehending cerebral functionalities, notably temporal perception. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Electroencephalography

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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

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computers, Materials & Continua 2023年第2期74卷 3117-3131页

作者： Saeed Masoud Alshahrani Saud S.Alotaibi Shaha Al-Otaibi Mohamed Mousa Anwer Mustafa Hilal Amgad Atta Abdelmageed Abdelwahed Motwakel Mohamed I.Eldesouki Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Electrical Engineering Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlKharjSaudi Arabia

Object detection(OD)in remote sensing images(RSI)acts as a vital part in numerous civilian and military application areas,like urban planning,geographic information system(GIS),and search and rescue *** recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition *** latest advancements in deep learning(DL)approaches permit the design of effectual OD *** study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection(AEODCNN-VD)model on Remote Sensing *** proposed AEODCNN-VD model focuses on the identification of vehicles accurately and *** detect vehicles,the presented AEODCNN-VD model employs single shot detector(SSD)with Inception network as a baseline *** addition,Multiway Feature Pyramid Network(MFPN)is used for handling objects of varying sizes in *** features from the Inception model are passed into theMFPNformultiway andmultiscale feature ***,the fused features are passed into bounding box and class prediction *** enhancing the detection efficiency of the AEODCNN-VD approach,AEO based hyperparameter optimizer is used,which is stimulated by the energy transfer strategies such as production,consumption,and decomposition in an *** performance validation of the presentedmethod on benchmark datasets showed promising performance over recent DL models.

关键词： Object detection remote sensing vehicle detection artificial ecosystem optimizer convolutional neural network

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Controller Design And Modelling of UAV Quadcopter 4

Controller Design And Modelling of UAV Quadcopter

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4th International Multidisciplinary Information Technology and engineering Conference, IMITEC 2024

作者： Maijama'a, Ladan Jiya, Jibril Danladi Anene, Ejike C. Miya, Hassan Sabo School of Engineering Technology Federal Polytechnic Department of Computer Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Electrical and Electronics Engineering Bauchi Nigeria Abubakar Tafawa Balewa University Department of Mechatronics and System Engineering Bauchi Nigeria

ISBN: (纸本)9798350387988

The dynamics of a quadcopter unmanned aerial vehicle (UAV) and proportional integral derivative (PID) control based on disturbance rejection are mathematically modelled in this work. The model presented is a nonlinear in nature and modelled with respect to the two fundamental frames of reference and physical mathematical laws. The objective is to create the most accurate replica of the vehicle. The created model is utilized to develop a PID controller method that will precisely and steadily operate the quadrotor system. The development of a controller is to obtain stability in the quadcopter system. First, the nonlinear and couple model quadcopter were established, and control described the principles of the ability of decoupling rejection control then the model was decoupled. The system contains four input forces, which are basically the thrust provided by each propeller coupled to each rotor with a fixed angle. Pitch angle must change in order to maintain forward (backward) motion, which is accomplished by simultaneously increasing (decreasing) front (rear) rotor speed and decreasing (increasing) rear (front) rotor speed. The same method is used to change the roll angle for left and right motion. The motors in the front and rear spin counter-clockwise, while the motors in other motor positions rotate clockwise. This causes the yaw command to be generated by either raising or reducing the speed of the counter-clockwise motors while reducing or increasing the speed of the clockwise motors. The achieved goal here is the derivation of a model that properly reflects the physical properties enabling realistic simulations and is simple enough to be applied to controller design in the future. The system and controller's mathematical models were converted to matching Simulink models in order to make system simulations and investigations easier. The results show the accuracy and significant disturbance rejection of the nonlinear model. © 2024 IEEE.

关键词： Three term control systems

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