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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Ranjbarzadeh, Ramin Anari, Shokofeh Crane, Martin Bendechache, Malika School of Computing Faculty of Engineering and Computing Dublin City University Dublin Ireland Department of Accounting Economic and Financial Sciences Islamic Azad University South Tehran Branch Tehran Iran ADAPT Research Centre School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9789819638628

This research presents an advanced neural network architecture for the segmentation of brain tumors. Our suggested model for brain tumor segmentation incorporates the Vision Transformer (ViT) as a 3D encoder alongside a modified UNet architecture to effectively capture both global and local information in MRI data. The model contains a dual-encoder architecture: a 3D ViT encoder captures global contextual information throughout the MRI volume, while a concurrent convolutional encoder emphasizes localized features. The encoder routes are enhanced by skip connections, facilitating efficient feature transmission between the ViT and UNet layers. In the bottleneck, features from both encoders are integrated via a multi-scale fusion block, augmenting the model's capacity to discern intricate spatial hierarchies and tumor changes. The decoder pathway employs a hierarchical upsampling and multi-scale fusion framework, meticulously rebuilding the final segmentation map with a focus on spatial accuracy. This hybrid model integrates the ViT's capacity to capture long-range relationships with the UNet's proficiency in fine-grained spatial localization, yielding a strong segmentation output on the BraTS 2020 dataset that is adept at addressing the complex and diverse architecture of brain tumors in MRI. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Signal encoding

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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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Estimating Chlorophyll Content from Hyperspectral Data Using Gradient Features 23rd

Estimating Chlorophyll Content from Hyperspectral Data Usin...

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23rd International Conference on Computational Science, ICCS 2023

作者： Ruszczak, Bogdan Wijata, Agata M. Nalepa, Jakub Faculty of Electrical Engineering Automatic Control and Informatics Department of Informatics Opole University of Technology Opole Poland Faculty of Biomedical Engineering Silesian University of Technology Zabrze Poland Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Silesian University of Technology Gliwice Poland KP Labs Gliwice Poland

ISBN: (纸本)9783031360206

Non-invasive estimation of chlorophyll content in plants plays an important role in precision agriculture. This task may be tackled using hyperspectral imaging that acquires numerous narrow bands of the electromagnetic spectrum, which may reflect subtle features of the plant, and inherently offers spatial scalability. Such imagery is, however, high-dimensional, therefore it is challenging to transfer from the imaging device, store and investigate. We propose a machine learning pipeline for estimating chlorophyll content from hyperspectral data. It benefits from the Savitzky-Golay filtering to smooth the (potentially noisy) spectral curves, and from gradient-based features extracted from such a smoothed signal. The experiments revealed that our approach significantly outperforms the state of the art according to the widely-established estimation quality metrics obtained for four chlorophyll-related parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Enhancing a Random Forest Model Based on Single Rule Reduction for Tax Evasion Depends on the Values of K in K-Fold Validation Technique 1

Enhancing a Random Forest Model Based on Single Rule Reducti...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Abdullah, Abeer Ba-Alwi, Fadl Mutaher Zahary, Ammar T. Al-Gaphari, Ghaleb Al-Badani, Abdulkader M. Alsabry, Ayman Shujaaddeen Computer Science Faculty of Computer and Information Technology Sana’a University Sana’a Yemen Faculty of Computer and Information Technology Sana’a University Sana’a Yemen International University of Technology Twintech Malaysia Department of Computer Science Faculty of Computing and Information Technology University of Science and Technology Sana’a University Sana’a Yemen Faculty of Science and Engineering Department of Computers Aljazeera University Ibb Yemen Department of Computer Science International University of Technology Twintech Sana’a Yemen

ISBN: (纸本)9798331533557

This paper developed a new model called Single Rule Random Forest (SrRF) that enhances the performance of the Fandom Forest technique(RF)and reduces its rules, then compared the performance of this model with a set of ML models as follows (DT, RF, GPT) of the datasets provided by the Tax Authority of Yemen which consists of 1083 records using two values for k in K-Fold Cross Validation). The results showed that by using the value k=10, the GPT Classifier gave the highest result of 100 % but this result will cause overfitting, then the SRI-RF Classifier gave the best result with 99.89%, while RF gave the worst result. By using the value k=5, the GPT Classifier also gave the highest result of 100 % but this result also caused overfitting, then the SrRF Classifier gave the best result, while RF gave the worst result. From the above, the researchers also note that training the data using K-fold cross-validation with the value K=10 gave better results. On the other side, our proposed model SRIRF with the value of k=10 gave the best result. © 2024 IEEE.

关键词： Decision trees

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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IP SafeGuard-An AI-Driven Malicious IP Detection Framework

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IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

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QoS Based Cloud Security Evaluation Using Neuro Fuzzy Model

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computers, Materials & Continua 2022年第1期70卷 1127-1140页

作者： Nadia Tabassum Tahir Alyas Muhammad Hamid Muhammad Saleem Saadia Malik Syeda Binish Zahra Department of Computer Science Virtual University of PakistanLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Department of Statistics and Computer science University of veterinary and animal sciencesLahore54000Pakistan Department of Industrial Engineering Faculty of EngineeringRabighKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology-RabighKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cloud systems are tools and software for cloud computing that are deployed on the Internet or a cloud computing network,and users can use them at any *** assessing and choosing cloud providers,however,customers confront the variety and difficulty of quality of service(QoS).To increase customer retention and engagement success rates,it is critical to research and develops an accurate and objective evaluation *** is the emerging environment for distributed services at various *** to the benefits of this environment,globally cloud is being taken as a standard environment for individuals as well as for the corporate sector as it reduces capital expenditure and provides secure,accessible,and manageable services to all stakeholders but Cloud computing has security challenges,including vulnerability for clients and association acknowledgment,that delay the rapid adoption of computing *** of resources in the Cloud is difficult because resources provide numerous measures of quality of *** this paper,the proposed resource allocation approach is based on attribute QoS Scoring that takes into account parameters the reputation of the asset,task completion time,task completion ratio,and resource *** article is focused on the cloud service’s security,cloud reliability,and could *** this paper,the machine learning algorithm neuro-fuzzy has been used to address the cloud security issues to measure the parameter security and privacy,trust *** findings reveal that the ANFIS-dependent parameters are primarily designed to discern anomalies in cloud security and features output normally yields better results and guarantees data consistency and computational power.

关键词： Cloud computing performance quality of service machine learning neuro-fuzzy

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Advancements in Sustainable Charging Infrastructure: Integrating Solar Energy and IoT for Smart E-Vehicle Charging Stations

Advancements in Sustainable Charging Infrastructure: Integra...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Sivasankar, C. Saravanan, G. Pradeepa, H. Arun, V. Ganesh, E.N. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Thiagarajar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Madurai India School of Computing Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350313987

An effective and environmentally friendly charging infrastructure is essential due to the rising popularity of Electric Vehicles (EVs). The proposed system offers an efficient approach to this problem by suggesting a solar charging station for EV incorporating the Internet of Things (IoT). In order to encourage the use of renewable energy and lessen the environmental impact, the system uses solar energy to power EV charging stations. IoT technologies improve the charging station's performance by allowing realtime monitoring and control, remote access and data analytics. Considerations for solar panel layout optimization and energy storage are central to this study's emphasis on the design and execution of a solar-powered EV charging station. In addition, the station can easily respond to changes in demand and grid circumstances thanks to dynamic load control made possible by IoT. This research proves the feasibility and efficiency of the proposed Solar with IoT Charging Station for Electric Vehicles via practical assessment and simulations. The results provide a scalable and long-term answer that promotes a cleaner and greener transportation environment by aiding the development of electric mobility and integrating renewable energy. © 2023 IEEE.

关键词： Charging Station Dynamic Load Management Electric Vehicle Internet of Things Solar Energy

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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