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Brain Tumor Detection and Segmentation Using RCNN

computers, Materials & Continua 2022年第6期71卷 5005-5020页

作者： Maham Khan Syed Adnan Shah Tenvir Ali Quratulain Aymen Khan Gyu Sang Choi Department of Computer Science University of Engineering&TechnologyTaxila47080Pakistan Faculty of Computing The Islamia University of BahawalpurBahawalpur63100Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongbuk38541Korea

Brain tumors are considered as most fatal *** reduce the risk of death,early identification of the disease is *** of the best available methods to evaluate brain tumors is Magnetic resonance Images(MRI).Brain tumor detection and segmentation are tough as brain tumors may vary in size,shape,and *** makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’.So an automated brain tumor detection and segmentation is *** work suggests a Region-based Convolution Neural Network(RCNN)approach for automated brain tumor identification and segmentation using MR images,which helps solve the difficulties of brain tumor identification efficiently and *** methodology is based on the accurate and efficient selection of tumorous *** reduces computational complexity and *** have validated the designed experimental setup on a standard dataset,BraTS *** used binary evaluation matrices based on Dice Similarity Coefficient(DSC)and Mean Average Precision(mAP).The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed *** suggested approach attained an averageDSC of 0.92 andmAP 0.92 for 10 patients,while on the whole dataset,the scores are DSC 0.89 and mAP *** following results clearly show the performance efficiency of the proposed methodology.

关键词： Brain tumor MRI preprocessing image segmentation brain tumor localization medical ML RCNN BraTS 2020 LGG HGG

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Novel Ransomware Hiding Model Using HEVC Steganography Approach

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computers, Materials & Continua 2022年第1期70卷 1209-1228页

作者： Iman Almomani Aala AlKhayer Walid El-Shafai Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Computer Science Department King Abdullah II School for Information TechnologyThe University of Jordan11942Jordan Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

Ransomware is considered one of the most threatening *** solutions have focused mainly on discriminating ransomware by analyzing the apps themselves,but they have overlooked possible ways of hiding ransomware apps and making them difficult to be detected and then ***,this paper proposes a novel ransomware hiding model by utilizing a block-based High-Efficiency Video Coding(HEVC)steganography *** main idea of the proposed steganography approach is the division of the secret ransomware data and cover HEVC frames into different *** that,the Least Significant Bit(LSB)based Hamming Distance(HD)calculation is performed amongst the secret data’s divided blocks and cover ***,the secret data bits are hidden into the marked bits of the cover HEVC frame-blocks based on the calculated HD *** main advantage of the suggested steganography approach is the minor impact on the cover HEVC frames after embedding the ransomware while preserving the histogram attributes of the cover video frame with a high *** is due to the utilization of an adaptive steganography cost function during the embedding *** proposed ransomware hiding approach was heavily examined using subjective and objective tests and applying different HEVC streams with diverse resolutions and different secret ransomware apps of various *** obtained results prove the efficiency of the proposed steganography approach by achieving high capacity and successful embedding process while ensuring the hidden ransomware’s undetectability within the video *** example,in terms of embedding quality,the proposed model achieved a high peak signal-to-noise ratio that reached 59.3 dB and a low mean-square-error of 0.07 for the examined HEVC ***,out of 65 antivirus engines,no engine could detect the existence of the embedded ransomware app.

关键词： Ransomware embedding steganography HEVC LSB hamming distance applications apk stego security confidentiality

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A Conceptual Framework for Multidisciplinary Data science Education Based on IoT Projects 6

A Conceptual Framework for Multidisciplinary Data Science Ed...

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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Marutschke, Daniel Moritz Kirjakovski, Atanas Bachtiar, Fitra A. Hananto, Valentinus Roby Kovacs, Mate Brockmann, Patricia Kryssanov, Victor V. Kyoto University of Advanced Science Faculty of Economics and Business Administration Kyoto Japan International Balkan University Department of Psychology Skopje Macedonia Universitas Brawijaya Faculty of Computer Science Malang Indonesia College of Information Science and Engineering Ritsumeikan University Osaka Japan Nuremberg Institute of Technology Faculty of Computer Science Nuremberg Germany

ISBN: (纸本)9798350378894

The necessity for professionals to make predictions using digital data has been recognized for many decades. With its roots in statistical analysis and later implemented as data analytics, Data science emerged as a widely accepted discipline. The data science life cycle begins by setting a goal and ends with reporting/reflecting on the gained results. It incorporates known analytical techniques, including modern artificial intelligence and machine learning techniques. While often considered an independent discipline, both engineering and liberal arts colleges have recognized the need for an understanding of data science in the general workforce. The literature still suggests a disconnect between the study fields and how the Internet of Things could be leveraged in Project-Based Learning (PBL) style data science education. In this paper, the authors propose a conceptual framework to teach multidisciplinary data science at university level to engineering and liberal arts students using data from real-world Internet of Things (IoT) projects. © 2024 IEEE.

关键词： Adversarial machine learning

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning 1

Optimizing Inventory Management using Demand Forecasting bas...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Pavithra, V. Abbas, Haayder M. Manjula, B.M. Chidambaram, P.K. Jothilakshmi, S. Srm Institute of Science and Technology Faculty of Science and Humanities Department of Computer Science and Applications Ramapuram Chennai India The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering Chennai India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Information Technology Mamallapuram India

ISBN: (纸本)9798331529246

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF). © 2025 IEEE.

关键词： demand forecasting ensemble stacking model hyperparameter tuning optimized inventory management seasonality and trend decomposition

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Simulation model for power electronic conversion in stationary battery storage system 7

Simulation model for power electronic conversion in stationa...

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7th International Conference on Smart and Sustainable Technologies, SpliTech 2022

作者： Bilandzic, Josip Pelin, Denis Brandis, Andrej Topic, Danijel Knezcvic, Goran Simic, Zvonimir Technology Osijek Faculty of Electrical Engineering Computer Science and Information Department of Power Engineering Osijek Croatia

ISBN: (纸本)9789532901160

In this paper, battery charge and discharge simulation are shown for one battery energy storage system. For this purpose, the two-stage bidirectional power converter is used to achieve power conversion from battery storage to the grid in one, and from the grid to the battery storage in another case. Operation principles of a full bridge rectifier and full bridge three-phase AC/DC converter are explained, and the waveforms for both, battery charge and discharge modes are shown. The simulation model is done in MATLAB SIMULINK software, where simulation results are obtained and analyzed for both operation modes of the system. © 2022 University of Split, FESB.

关键词： MATLAB

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Coverless Image Steganography System Based on Maze Game Generation

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Intelligent Automation & Soft Computing 2023年第11期38卷 125-138页

作者： Al Hussien Seddik Saad Mohammed S.Reda GamalM.Behery Ahmed A.El-harby Mohammed Baz Mohamed Abouhawwash Ahmed Ismail Ebada Department of computer science Faculty of ScienceMinia UniversityShalabyMiniaEgypt Department of computer science Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt Department of Computer Engineering College of Computer and Information TechnologyTaif UniversityP.O.Box 11099Taif21994Saudi Arabia Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Department of Information Systems Faculty of Computers and Artificial IntelligenceDamietta UniversityNew DamiettaDamiettaEgypt

The trend of digital information transformation has become a topic of *** data are threatening;thus,protecting such data from attackers is considered an essential ***,a new methodology for data concealing has been suggested by researchers called coverless *** steganography can be accomplished either by building an image database to match its image subblocks with the secret message to obtain the stego image or by generating an *** paper proposes a coverless image steganography system based on pure image generation using secret message bits with a capacity higher than the other traditional *** system uses the secret message to generate the stego image in the form of one of the Intelligence Quotient(IQ)games,the ***,a full grid is generated with several specific rows and columns determined from the number of bits of the secret ***,these bits are fed to the full grid to form the maze game stego ***,the generated maze game stego image is sent to the *** experimental results,using the Bit Error Rate(BER),were conducted,and confirmed the strength of this system represented by a high capacity,perfect performance,robustness,and stronger hiding system compared with existing coverless steganography systems.

关键词： Coverless data hiding digital image steganography intelligence quotient games maze game

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Optimized Deep Learning Model for Colorectal Cancer Detection and Classification Model

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computers, Materials & Continua 2022年第6期71卷 5751-5764页

作者： Mahmoud Ragab Khalid Eljaaly Maha Farouk SSabir Ehab Bahaudien Ashary S.M.Abo-Dahab E.M.Khalil Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Center of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Systems Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Faculty of Computers and InformationLuxor University85951Egypt Mathematics Department Faculty of ScienceSouth Valley UniversityQena83523Egypt Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia

The recent developments in biological and information technologies have resulted in the generation of massive quantities of data it speeds up the process of knowledge discovery from biological *** to the advancements of medical imaging in healthcare decision making,significant attention has been paid by the computer vision and deep learning(DL)*** the same time,the detection and classification of colorectal cancer(CC)become essential to reduce the severity of the disease at an earlier *** existing methods are commonly based on the combination of textual features to examine the classifier results or machine learning(ML)to recognize the existence of *** this aspect,this study focuses on the design of intelligent DL based CC detection and classification(IDL-CCDC)model for bioinformatics *** proposed IDL-CCDC technique aims to detect and classify different classes of *** addition,the IDLCCDC technique involves fuzzy filtering technique for noise removal ***,water wave optimization(WWO)based EfficientNet model is employed for feature extraction ***,chaotic glowworm swarm optimization(CGSO)based variational auto encoder(VAE)is applied for the classification of CC into benign or *** design of WWO and CGSO algorithms helps to increase the overall classification *** performance validation of the IDL-CCDC technique takes place using benchmark Warwick-QU dataset and the results portrayed the supremacy of the IDL-CCDC technique over the recent approaches with the maximum accuracy of 0.969.

关键词： Colorectal cancer deep learning medical imaging bioinformatics metaheuristics parameter tuning

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Performance Assessment of Multi-Controller in software Defined Network 4

Performance Assessment of Multi-Controller in Software Defin...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Jaiswal, Ankita Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Barik, Lalbihari Pandey, Trilok Nath Mishra, Manoj Ranjan School of Computer Engineering KIlT Deemed to Be University Bhubaneswar India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Saudi Arabia School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350306118

In a network design, the control plane and the data plane are separated by the architectural concept of software defined networking (SDN). A centralised controller that serves as the only point of control for the whole network is introduced by the SDN architecture. The controller serves as a logically centralized entity in charge of communicating the appropriate rules to a switch's flow table. Using a single controller to handle a lot of flows becomes insufficient. A lot of large-scale networks have employed multi-controller SDN to meet their objectives. In this research, we use a multi-controller infinite buffer waiting queue model to analyze the system performance metrics. Both numerical and simulated assessments are used to illustrate the proposed model. © 2023 IEEE.

关键词： Controllers

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Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Method 12th

Anomaly Detection in Univariate Time Series: HOT SAX vesus L...

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12th EAI International Conference on Context-Aware Systems and Applications, ICCASA 2023

作者： Anh, Duong Tuan Hoai, Tran Long Department of Information Technology HCMC University of Foreign Languages and Information Technology Ho Chi Minh City Viet Nam Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology Ho Chi Minh City Viet Nam

ISBN: (纸本)9783031588778

Anomaly detection in time series has been an important and challenging research topic. There have been several methods proposed for detecting anomaly subsequences in a time series. The majority of these methods is classified into the window-based category, which applies a sliding window with a fixed length to extract subsequences before finding out anomaly subsequences. A well-known algorithm in this category is HOT SAX algorithm. Recently, deep neural network models, especially Long Short Term Memory (LSTM) network, are also applied for time series anomaly discovery. LSTM-based methods for time series anomaly detection belong to prediction-based category. So far, there has been no research work to compare the performance of LSTM-based method to that of any traditional window-based method in time series anomaly detection. The research question investigated in this paper is that whether the newly developed LSTM-based method for time series anomaly detection is superior to the traditional algorithms, such as HOT SAX or not. In this study, we give an empirical comparison between LSTM-based method and HOT SAX in time series anomaly detection. Extensive experiments on seven benchmark time series indicate that LSTM-based method is not superior to HOT SAX since it brings out the same detection accuracy as HOT SAX while it incurs much higher computational overhead. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Deep neural networks

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Deep learning and ensemble methods for anomaly detection in ICS security

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International Journal of information technology (Singapore) 2025年第3期17卷 1761-1775页

作者： Hossain, Md. Alamgir Hasan, Tahmid Karovic, jr, Vincent Abdeljaber, Hikmat A. M. Haque, Md. Alimul Ahmad, Sultan Zafar, Aasim Nazeer, Jabeen Mishra, B.K. Department of Computer Science and Engineering State University of Bangladesh South Purbachal Kanchan Dhaka 1461 Bangladesh Department of Computer Science and Engineering Prime University Dhaka Bangladesh Department of Information Systems Faculty of Management Comenius Bratislava Odbojárov 10 Bratislava 25 82005 Slovakia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman 11931 Jordan MEU Research Unit Middle East University Amman 11831 Lebanon Department of Computer Science Veer Kunwar Singh University Bihar Ara India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Computer Science Aligarh Muslim University U.P Aligarh 202002 India P.G. Department of Physics Veer Kunwar Singh University Bihar Ara India

This research addresses the escalating threats to industrial control systems by introducing a novel approach that combines deep learning for feature selection with a robust ensemble-based classification technique to enhance anomaly detection. Our method utilizes a tailored autoencoder architecture to efficiently select features, followed by a Random Forest classifier to ensure reliable and generalizable detection of anomalies. Evaluated on the HAI Security Dataset, the proposed approach demonstrates exceptional performance, with significant improvements in detecting anomalous activities in ICS. The results highlight the method’s potential to enhance ICS security by providing a scalable and adaptable framework that evolves with emerging threats. This study not only advances cybersecurity methodologies for industrial systems but also lays the groundwork for future research, emphasizing the need for innovative and effective security solutions in the face of evolving cyber challenges. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Anomaly detection in ICS Cyber-resilience in critical infrastructure Deep learning for feature selection Industrial control systems security Random forest classifier for ICS

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