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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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An Efficient CNN-Based Automated Diagnosis Framework from COVID-19 CT Images

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computers, Materials & Continua 2021年第10期69卷 1323-1341页

作者： Walid El-Shafai Noha A.El-Hag Ghada M.El-Banby Ashraf A.M.Khalaf Naglaa F.Soliman Abeer D.Algarni Fathi E.Abd El-Samie Department Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electronics and Electrical Communications Faculty of EngineeringMinia UniversityEgypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the *** has dramatically affected daily lives,public health,and the world *** paper presents a segmentation and classification framework of COVID-19 images based on deep ***,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT ***,the resulting segmented images are used to identify the infected region,whether COVID-19 or *** proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and *** maxpooling layers are employed with a stride of 2 and a 2×2 *** classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT *** acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.

关键词： Classification segmentation COVID-19 CNN deep learning diagnosis applications

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Towards Instance Segmentation-Based Litter Collection with Multi-Rotor Aerial Vehicle

Towards Instance Segmentation-Based Litter Collection with M...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Filip Zoric Antonio Franchi Matko Orsag Zdenko Kovacic Chiara Gabellieri Laboratory for Robotics and Intelligent Control Systems (LARICS) at the Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Robotics and Mechatronics Group from the University of Twente Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

This paper presents a novel aerial robotics application of instance segmentation-based floating litter collection with a multi-rotor aerial vehicle (MRAV). In the scope of the paper, we present a review of the available datasets for litter detection and segmentation. The reviewed datasets are used to train a Mask-RCNN neural network for instance segmentation. The neural network is off-board deployed on an edge computing device and used for litter position estimation. Based on the estimated litter position, we plan a path based on a quadratic Bezier curve for the litter pickup. We compare different trajectory generation methods for the object pickup. The system is verified in a laboratory environment. Eventually, we present practical considerations and improvements necessary to enable autonomous litter collection with MRAV.

关键词： Instance segmentation Reviews Neural networks Laboratories Estimation Trajectory Aircraft

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Health Care DNS Tunnelling Detection Method via Spiking Neural Network 3rd

Health Care DNS Tunnelling Detection Method via Spiking Neur...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kumar, Narendra Surendiran, R. Samuel, G. K. Jabash Bhavana, N. Shirgire, Anil Malar, A. Jasmine Gnana Qalid, Aznul Faculty of Science and Technology NIET NIMS University Rajasthan Jaipur303121 India School of Information Science Annai College of Arts and Science Tamilnadu Kumbakonam612503 India Department of Electrical and Electronics Engineering Rohini College of Engineering and Technology Kanyakumari India School of Computing and Information Technology REVA University Karnataka Bengaluru560064 India Department of Civil Engineering Dr D Y Patil Institute of Technology Maharashtra Pimpri Pune411018 India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627152 India Department of Artificial Intelligence Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9789819986453

A common DNS exploit that has been used for several decades to steal data is DNS tunneling. To avoid being detected as an intrusion from healthcare domain, the stolen health information is encoded and contained within DNS requests. Machine learning techniques for detection frequently make use of characteristics like DNS activity and healthcare network activity. The majority of features, such as time–frequency-related information, can only be obtained when data exfiltration occurs. Identifying a malicious query from a single DNS suggestion is key to preventing data exfiltration based on DNS tunneling. We can perceive DNS tunnelling before healthcare data exfiltration by not using internet traffic properties or DNS behavior information. In this paper, we describe a detection method based on deep learning that uses DNS query payloads as predictive variables in the models. Using the Spiking Neural network (SNN) in healthcare DNS Tunnelling Detection Method a DNS Tunnelling attack or not? Specificity, sensitivity, f1 score, accuracy, and Matthew's correlation coefficient (MCC) are the factors taken into account when evaluating the efficiency of the suggested model. In comparison to the Proposed Spiking neural network, the Existing Deep Neural Network (DNN), Mobile Net, and Recurrent Neural Network (RNN) technique improves the performance of a DNS tunneling detection method in a real-world network system. Results indicate that the suggested strategy is far more dependable than existing methods and achieves a 99.84% accuracy rate. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Recurrent neural networks

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Liver Tumor Decision Support System on Human Magnetic Resonance Images:A Comparative Study

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computer Systems Science & engineering 2023年第8期46卷 1653-1671页

作者： Hiam Alquran Yazan Al-Issa Mohammed Alslatie Isam Abu-Qasmieh Amin Alqudah Wan Azani Mustafa Yasmin Mohd Yacob Department of Biomedical Systems and Informatics Engineering Yarmouk University 556Irbid21163Jordan Department of Biomedical Engineering Jordan University of Science and TechnologyIrbid22110Jordan Department of Computer Engineering Yarmouk University 556Irbid21163Jordan The of Biomedical Technology King Hussein Medical CenterRoyal Jordanian Medical ServiceAmman11855Jordan Faculty of Electrical Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Faculty of Electronic Engineering&Technology Campus Pauh PutraUniversiti Malaysia PerlisArau02600PerlisMalaysia Advanced Computing Centre of Excellence(CoE)Universiti Malaysia PerlisArau02600PerlisMalaysia

Liver cancer is the second leading cause of cancer death *** tumor detection may help identify suitable treatment and increase the survival *** imaging is a non-invasive tool that can help uncover abnormalities in human *** Resonance Imaging(MRI),in particular,uses magnetic fields and radio waves to differentiate internal human organs ***,the interpretation of medical images requires the subjective expertise of a radiologist and ***,building an automated diagnosis computer-based system can help specialists reduce incorrect *** paper proposes a hybrid automated system to compare the performance of 3D features and 2D features in classifying magnetic resonance liver tumor *** paper proposed two models;the first one employed the 3D features while the second exploited the 2D *** first system uses 3D texture attributes,3D shape features,and 3D graphical deep descriptors beside an ensemble classifier to differentiate between four 3D tumor *** top of that,the proposed method is applied to 2D slices for comparison *** proposed approach attained 100%accuracy in discriminating between all types of tumors,100%Area Under the Curve(AUC),100%sensitivity,and 100%specificity and precision as well in 3D liver *** the other hand,the performance is lower in 2D *** maximum accuracy reached 96.4%for two classes and 92.1%for four *** top-class performance of the proposed system can be attributed to the exploitation of various types of feature selection methods besides utilizing the ReliefF features selection technique to choose the most relevant features associated with different *** novelty of this work appeared in building a highly accurate system under specific circumstances without any processing for the images and human input,besides comparing the performance between 2D and 3D *** the future,the presented work can be extended to be

关键词： Liver tumors ensemble classifier 3D shape features 3D cooccurrence matrix ResNet101

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Application of Serverless computing in Blockchain Distributed Technology

Application of Serverless Computing in Blockchain Distribute...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Chellasamy, Natarajan Akram, Faiz Rajesh, Nichenametla Umamaheswari, R. Assosa University Department of Electrical and Computer Engineering Assosa Ethiopia Jimma University Jimma University Faculty of Computing and Informatics Jimma Ethiopia Department of Cse Andhra Pradesh Guntur India Gnanamani College of Technology Department of Cse Tamil Nadu Namakkal India

ISBN: (纸本)9798350323887

Serverless is a peer-to-peer system that is associated with the cloud architecture that aims to reduce the efforts required by the cloud manager to provide an efficient service. Blockchain enable collaborative distributed mechanism to enable a decentralized management of resource allocation. The resource allocation is been a major concern when blockchain is enabled in the serverless computing. In this paper, we develop a serverless computing model that is embedded with blockchain manner in a distributed fashion. The formulation is made in such a way that if fulfils the task allocation in a better way with increased makespan and reduced latency while a resource is allocated for required operation. © 2023 IEEE.

关键词： Cloud computing architecture

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Robust Frequency Regulation for Dual-Area Interconnected Grids Using Hybrid controller: A Comparative Study 3

Robust Frequency Regulation for Dual-Area Interconnected Gri...

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3rd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah A. Alobaidi, Abdulraheem H. Altaf, Aatif Kumar, Anil Kumar, Mahendra Alghamdi, Sultan King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah Saudi Arabia Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar India

ISBN: (纸本)9798350304732

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance. © 2023 IEEE.

关键词： Fuzzy logic controller integral controller load frequency control optimization technique parameter estimation

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IoT-Based Magnetic Field Strength Monitoring for Industrial Applications 2

IoT-Based Magnetic Field Strength Monitoring for Industrial ...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Raman, Ramakrishnan Meenakshi, R. Ramya, R. Kaul, Meenakshi Jayaprakash, S. Srinivasan, C. Maharashtra Pune India Department of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu Chennai India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai India Symbiosis Law School Uttar Pradesh Noida India Maharashtra Pune India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Monitoring and controlling magnetic field intensity is essential in industrial settings for the reliable and secure functioning of equipment, systems, and other components. The Internet of Things (IoT) is advancing technology solutions for industrial monitoring in magnetic fields. A Raspberry Pi works as the main processing unit of the system and is equipped with a Wi-Fi module for wireless communications. Connecting a Hall Effect sensor to the Raspberry Pi through the proper interfaces may take magnetic field measurements in real-time. The Hall Effect sensor sends its readings to the Raspberry Pi, which then processes and analyzes them. A connected display then shows the processed data, giving the users or operators rapid visual feedback. Wi-Fi allows uninterrupted data transfer to be sent to the cloud or other remote monitoring systems. Magnetic field strength measurements may be taken with great precision using the Hall Effect sensor. Regarding processing and analyzing data, the Raspberry Pi proves to be a flexible and adaptable platform. The data on the magnetic field may be seen in real-time and monitored on the screen. Wi-Fi allows users to connect from afar, share information, and use cloud-based features and storage. By facilitating continuous data gathering, remote access, and enhanced analytics, an IoT-based method improves monitoring capabilities. The technology can identify irregular magnetic field patterns, sending real-time alarms or messages to facilitate preventative maintenance and safety checks. The suggested approach provides a scalable and reasonably priced means of tracking magnetic fields in manufacturing settings. It improves manufacturing safety, efficiency, and productivity by providing users with real-time information and enabling data-driven decision-making. © 2023 IEEE.

关键词： Hall Effect sensor IoT Magnetic field monitoring Raspberry Pi Real-time

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Watermarking and Encryption for Electronic Medical Records (EMR) Security Based on mCloud 2

Watermarking and Encryption for Electronic Medical Records (...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Abdulshaheed, Haider Rasheed Abbas, Yaseen Saadi Tawfeq, Jamal F. Ateeq, Karamath Alnawayseh, Saif. E. A. Ghazal, Taher M. Baghdad College Of Economic Sciences University Computer Engineering Techniques Department Baghdad Iraq College Of Engineering Al-Nahrain University Electronic And Communications Engineering Department Baghdad Iraq Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering Mutah University Department Of Electrical Engineering Al-Karak Jordan Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

Providing quality healthcare services and promoting collaborative clinical research are easier and more efficient with electronic medical record (EMR) systems. All aspects of care are included in electronic health records. Data characterized by volume, variety, and velocity is what IBM defines as big data. Therefore, we evidently have a significant problem managing 'big EMR data' from a volume, accuracy, and velocity perspective. Data misuse and patient safety are significant risks associated with poorly implemented EMR-based systems. Storage, access, and retrieval of big data are all aspects of security to consider. Location is another important aspect of secure data use, in addition to strong access control mechanisms. Researchers are investigating secure mobile data usage based on location after recent reports of illegal trade and theft of patient data over mobile devices. To improve work flow dynamics and efficiency, healthcare facilities are increasingly using mobile devices. In spite of this, mobile devices are becoming more and more common. A new technique for preventing EMR infiltration and manipulation by hackers using the cloud is presented in this article. © 2024 IEEE.

关键词： Authentication

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From Federated Learning to Quantum Federated Learning for Space-Air-Ground Integrated Networks

From Federated Learning to Quantum Federated Learning for Sp...

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2024 IEEE Conference on Standards for Communications and Networking, CSCN 2024

作者： Quy, Vu Khanh Quy, Nguyen Minh Hoai, Tran Thi Shaon, Shaba Uddin, Md Raihan Nguyen, Tien Nguyen, Dinh C. Kaushik, Aryan Chatzimisios, Periklis Hung Yen University of Technology and Education Faculty of Information Technology Hung Yen Viet Nam University of Tasmania College of Sciences and Engineering Sandy Bay HobartTAS7001 Australia University of Alabama in Huntsville Department of Electrical and Computer Engineering United States Lac Hong University Department of Electrical and Electronics Engineering Viet Nam Manchester Metropolitan University Department of Computing & Mathematics United Kingdom International Hellenic University Thessaloniki Greece University of New Mexico AlbuquerqueNM United States

ISBN: (纸本)9798331507428

6G wireless networks are expected to provide seamless and data-based connections that cover space-air-ground and underwater networks. As a core partition of future 6G networks, Space-Air-Ground Integrated Networks (SAGIN) have been envisioned to provide countless real-time intelligent applications. To realize this, promoting AI techniques into SAGIN is an inevitable trend. Due to the distributed and heterogeneous architecture of SAGIN, federated learning (FL) and then quantum FL are emerging AI model training techniques for enabling future privacy-enhanced and computation-efficient SAGINs. In this work, we explore the vision of using FL/QFL in SAGINs. We present a few representative applications enabled by the integration of FL and QFL in SAGINs. A case study of QFL over UAV networks is also given, showing the merit of quantum-enabled training approach over the conventional FL benchmark. Research challenges along with standardization for QFL adoption in future SAGINs are also highlighted. © 2024 IEEE.

关键词： Federated learning

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