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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Chitra Kiran, N. Maindola, Meenakshi Kumara, Bharath Kaliappan, S. Bhatt, Pooja Maranan, Ramya Alliance University Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering Post Limda India Saveetha School of Engineering Simats Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

The rapid proliferation of the Internet of Things (IoT) has led to a surge in cyber threats, demanding the development of robust security frameworks. This paper introduces an advanced threat detection model, leveraging Artificial Intelligence (AI) and Cyber Twin Technologies for enhanced IoT security. The proposed framework integrates a Cyber Twin-a digital replica of physical IoT devices-with real-time data analytics to detect, predict, and respond to sophisticated cyber-attacks. The Cyber Twin continuously monitors IoT networks, identifying abnormal behaviors and enabling the implementation of dynamic security measures through AI-driven intrusion detection systems (IDS). A hybrid deep learning approach combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) is utilized to enhance anomaly detection and threat classification accuracy. The system's ability to simulate various attack scenarios within a digital environment facilitates the development of effective countermeasures while minimizing the impact on actual IoT operations. Experimental results demonstrate significant improvements in detection accuracy, reduced false positives, and enhanced system resilience. The proposed AI and Cyber Twin-based threat detection model sets a new benchmark in safeguarding IoT infrastructures against emerging cyber threats. © 2024 IEEE.

关键词： Network intrusion

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Identification of Key Signaling Pathways and Novel Computational Drug Target for Depression and Coronary Artery Disease

Identification of Key Signaling Pathways and Novel Computati...

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2024 IEEE International Conference on Computing, Applications and systems, COMPAS 2024

作者： Hosen, Md. Faruk Basar, Md. Abul Yasmin, Mst. Farjana Morshed, Monir Uddin, Muhammad Shahin Daffodil International University Department of Computing and Information System Dhaka1216 Bangladesh Department of Computer Science and Engineering Dhaka1230 Bangladesh Mawlana Bhashani Science and Technology University Department of Information and Communication Technology Tangail1902 Bangladesh

ISBN: (纸本)9798331529765

Psychological disorders, such as anxiety, bipolar disorder, panic disorder, stress, depression, and schizophrenia, are increasingly prevalent worldwide. Among these conditions, depression is particularly notable as one of the most common and debilitating neuropsychiatric disorders. Individuals with depression may be at a higher risk of developing oronary Artery Disease (CAD). Depression can contribute to poor lifestyle choices, such as unhealthy eating, lack of exercise, and smoking, which are risk factors for CAD. The emotional stress and anxiety associated with depression can strain the heart and exacerbate CAD symptoms. The relationship is not one-sided. CAD itself can be a significant source of emotional distress, leading to symptoms of depression and anxiety in affected individuals. Managing both conditions in tandem can be complicated. Treating CAD may involve medications, lifestyle modifications, and potentially surgical interventions. Meanwhile, depression often requires therapy, counseling, and medication. Coordinating care and addressing both conditions simultaneously is crucial. In our study, we investigated the molecular connections between CAD and Depression using GSE98793 and GSE20681 microarray datasets. After preprocessing, we identified key hub genes, including CCT2, SVIL, REPS2, ASPH, and UBC, in the shared ProteinProtein Interaction network. KEGG pathways linked these DEGs to colorectal and cancer pathways. Our next steps involve exploring microRNAs, TFs, and GO analysis. These findings offer promising leads for potential therapies, uniting CAD and Depression under a common molecular framework, advancing our understanding of these conditions. © 2024 IEEE.

关键词： Blood vessels

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A cloud service composition method using a fuzzy-based particle swarm optimization algorithm

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Multimedia Tools and Applications 2024年第19期83卷 56275-56302页

作者： Nazif, Habibeh Nassr, Mohammad Al-Khafaji, Hamza Mohammed Ridha Jafari Navimipour, Nima Unal, Mehmet Department of Mathematics Payame Noor University Tehran Iran Communication Technology Engineering Department Tartous University Tartus Syria Dpartment of Mathematics and Natural Sciences Gulf University for Science and Technology Mishref Campus Mubarak Al-Abdullah Kuwait Biomedical Engineering Department College of Engineering and Technologies Al-Mustaqbal University Babil Hillah51001 Iraq Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Computer Engineering Nisantasi University Istanbul Turkey

In today's dynamic business landscape, organizations heavily rely on cloud computing to leverage the power of virtualization and resource sharing. Service composition plays a vital role in cloud computing, combining multiple cloud services to fulfill complex user requests. Service composition in cloud computing presents several challenges. These include service heterogeneity, dynamic service availability, QoS (Quality of Service) constraints, and scalability issues. Traditional approaches often struggle to handle these challenges efficiently, leading to suboptimal resource utilization and poor service performance. This work presents a fuzzy-based strategy for composing cloud services to overcome these obstacles. The fact that service composition is NP-hard has prompted the use of a range of metaheuristic algorithms in numerous papers. Therefore, Particle Swarm Optimization (PSO) has been applied in this paper to solve the problem. Implementing a fuzzy-based PSO for service composition requires defining the fuzzy membership functions and rules based on the specific service domain. Once the fuzzy logic components are established, they can be integrated into the PSO algorithm. The simulation results have shown the high efficiency of the proposed method in decreasing the latency, cost, and response time. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Fuzzy logic

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A Model Predictive Current Control for a Three-Phase Modular Matrix Converter

A Model Predictive Current Control for a Three-Phase Modular...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Sarebanzadeh, Maryam Hosseinzadeh, Mohammad Ali Kennel, Ralph Babaei, Ebrahim Rivera, Marco Wheeler, Patrick Technical University of Munich High-Power Converters System Munich Germany University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran Research Group Department of Electrical and Electronic Engineering Faculty of Engineering Nottingham United Kingdom Vicerrectoría Academica Talca Chile

ISBN: (纸本)9798350376067

The modular matrix converter (MMC) integrates a multi-winding transformer with a three-phase-to-single-phase matrix converter for each phase, making it suitable for high-power, high-voltage applications. Predictive control is an advanced control technique widely applied to power converters due to its numerous advantages, including fast dynamic response, the elimination of the need for proportional gain, and straightforward implementation. This study proposes a model predictive control (MPC) strategy to regulate load currents, reactive power, and common-mode voltage in a three-phase modular matrix converter. The proposed MPC algorithm selects the optimal switching states from all valid options for the modular matrix converter to apply in the next control step. A cost function is then used to minimize the error between the predicted system variables and their reference values, ensuring that the best states are applied to the modular matrix converter. The proposed MPC technique effectively controls the load currents to follow the desired reference signals, maintains sinusoidal source current waveforms by regulating reactive power, and reduces the magnitude of the common-mode voltage. The performance of the proposed approach is demonstrated through simulation results obtained in the MATLAB/Simulink environment. © 2024 IEEE.

关键词： Predictive control systems

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An upgraded Multifunctional Radiology Program based on NSST that uses Optimization Technique and also the NLM Method for computer Vision 5

An upgraded Multifunctional Radiology Program based on NSST ...

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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Sinha, Deepak Kumar Sunil, M.P. Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350398267

Multifunctional health plasma imagery is an important component of several computer-assisted surgical treatments as well as appearance healthcare therapy and diagnostics. This study presents a- anti Shear let Transforming (NSST) + Gray Wolf optimized (GWO) technique-based advanced healthcare fused measurement device. Its GWO methodology as well as the ideal gain settings decide here on optimal sub band. Furthermore, a noise removal and improvement phase is applied to the whole method in order to increase the video appearance and also the degree of detail. Whenever the pictures are distorted by pollution, such classic merging methods' productivity is severely hindered. Therefore, it is necessary to create a transformation that can maintain specific data even though photos are damaged. The equal achievement of low noise plus improved texture is likewise difficult. The objective of the proposed research is to correlate advanced NSST fuse imagery with conventional geographical, translation, filter, etc artificial neural area merging approaches. © 2022 IEEE.

关键词： Diagnosis

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

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Multimedia Tools and Applications 2024年 1-22页

作者： Almujally, Nouf Abdullah Alzahrani, Abdulrahman Hakeem, Abeer M. Attiah, Afraa Umer, Muhammad Alsubai, Shtwai Polsinelli, Matteo Ashraf, Imran Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science and Engineering College of Computer Science and Engineering University of Hafr Al Batin Hafr Al Batin39524 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science The Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University P.O. Box 151 Al-Kharj11942 Saudi Arabia Department of Management & amp Innovation Systems University of Salerno Fisciano84084 Italy Department of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of

Ovarian cancer is a global health concern due to the unavailability of an effective screening strategy and is often diagnosed at a late stage with approximately 70% of the case which reduces the survival chances of patients. Initial diagnosis is challenging due to inconspicuous symptoms in its initial stages which complicate its timely diagnosis. Regular screenings, such as pelvic exams, ultrasounds, and blood tests targeting specific biomarkers can be helpful for early diagnosis. In addition, the use of machine learning models can help automate this process thereby assisting medical experts for accurate diagnosis. This study aims at timely and accurate detection of ovarian cancer using a transfer learning approach that uses the MobileNet model. Moreover, the Chi-square technique is used to extract the most impactful features for better accuracy. For experiments, the Soochow University ovarian cancer dataset is employed. Extensive experiments are performed using all features from the dataset, as well as, using Chi-square-based selective features. The best accuracy of 90.88% is achieved with the Xception model when all features are used. Experimental results show a substantial increase when selective features are used indicating a 98.49% accuracy using the MobileNet model with 20 most important features. In addition, precision, recall, and F1 scores of 99.13%, 99.27%, and 99.20% are obtained showing the model’s robustness and generalization. This study also employs Shapley additive explanations to explain the importance of various features toward the model’s output thereby providing transparency for the model’s decision-making process. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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Adaptive Score Normalization using Machine Learning for enhanced Fingerprint Recognition 2

Adaptive Score Normalization using Machine Learning for enha...

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2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Bhargava, Neeraj Rathore, Pramod Singh Goswami, Ankur Bhowmick, Apoorva Singh, Jatin Mds University School of Engineering & System Sciences Ajmer India Manipal University Department of Computer and Communication Engineering Jaipur India Govt. Engineering College Department of Computer Applications Bikaner India United Nations Secretariate New YourkNY10017 United States Baghdad Iraq Mds University Ajmer India

ISBN: (纸本)9798350368413

Fingerprint identification systems play a vital role in biometric security but are often influenced by fluctuations in scanning settings and environmental variables. The conventional normalizing techniques such as Min-Max, Z-Score, and Tanh normalization provide fixed solutions that do not adjust to these variations, therefore degrading the accuracy and resilience of the results. This study presents a machine learning-based adaptive normalization system that seamlessly adapts to present circumstances by integrating contextual information, such as ambient variables and scanner-specific features, with raw fingerprint scores. The procedure includes the gathering of data, doing preprocessing, training models using decision trees, random forests, and neural networks, and implementing dynamic normalization. Comprehensive testing conducted on several scanners and settings has shown the efficacy of this method, demonstrating substantial improvements in both recognition accuracy and resilience. Assessment measures such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R2 Score validate the higher efficacy of this adaptive approach in comparison to static methods, thereby improving operational adaptability and establishing a basis for wider biometric identification applications. © 2024 IEEE.

关键词： Random forests

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A Performance Study of Selected Machine Learning Techniques for Predicting Heart Diseases 2nd

A Performance Study of Selected Machine Learning Techniques ...

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2nd International Conference on Security and Information Technologies with AI, Internet Computing and Big-data Applications, SITAIBA 2023

作者： Olorunfemi, Blessing Oluwatobi Ogunde, Adewale Opeoluwa Adeniyi, Abidemi Emmanuel Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Li, Chun-Ta Faculty of Natural Sciences Department of Computer Science Redeemer’s University Osun Nigeria Department of Computer Science Bowen University Iwo Nigeria Faculty of Information and Communication Sciences Department of Computer Science University of Ilorin Ilorin240003 Nigeria Faculty of Engineering Department of Electrical and Electronics Engineering University of Lagos Lagos Akoka100213 Nigeria Bachelor’s Program of Artificial Intelligence and Information Security Fu Jen Catholic University New Taipei City24206 Taiwan

ISBN: (纸本)9789819777853

Heart Disease remains a leading cause of mortality worldwide. It alarmingly rises at a quick rate, making early heart disease prediction crucial for effective prevention and timely intervention. Heart disease diagnosis is a difficult process that requires technical skills and accuracy to complete. With improvements in technology, computing has lent its voice to simplify the diagnosis of various health problems. Machine learning uses past or existing history to predict future results. Various machine learning techniques have been developed over the years and used in predicting heart diseases with various levels of performance. Identifying the best-suited machine learning technique to use for prediction purposes can be a challenging task. This research work analyses the performance of seven (7) machine learning techniques, comprising AdaBoost Algorithm, KNN, Logistic Regression, Naïve Bayes Classifier, Random Forest, SVM, and XGBoost. The heart disease dataset was downloaded from the UCI repository and analysed using Python programming language in the Jupyter Notebook environment. A comparative analysis of the seven (7) techniques was performed based on Accuracy, Precision, and Recall. From the results obtained, KNN, Random Forest, and XGBoost showed superior performance over the others with an accuracy of 100%, AdaBoost Algorithm followed with an accuracy of 92.2%, SVM followed with an accuracy of 91.71%, Naïve Bayes Classifier followed with an accuracy of 88.29% while Logistic Regression has the least accuracy of 86.34%. KNN, RF, and XGBoost outperformed AdaBoost, SVN, and LR. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Cramer-Rao Bound Investigation of the Double-Nested Arc Array Virtual Extension and Butterfly Antenna Configuration 3rd

Cramer-Rao Bound Investigation of the Double-Nested Arc Arra...

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3rd World Conference on Internet of Things: Applications and Future, ITAF 2023

作者： El-Rahman, Tarek Abd Salama, Amgad A. Saad, Walid M. El-Shafey, Mohamed H. Faculty of Engineering Computer and System Engineering Department Ain Shams University Cairo11517 Egypt The Egyptian Technical Research and Development Centre Cairo11618 Egypt

ISBN: (纸本)9789819947638

This paper exploits a sparse array representation, a novel double-nested arc array to generate a multiple virtual nested arc array that forms the virtual non-uniform circular array which has been presented and investigated. The nested arc array has been developed using higher-order statistics, namely using the fourth-order cumulants (FOC), to increase the array aperture. The extended array contains a hybrid of physical and virtual sensors which is distributed in a butterfly shape. The enhanced degrees of freedom (DOFs) in the resultant virtual array make it appropriate for over-and under-determined direction-of-arrival (DOA) estimation. The Cramer-Rao bound (CRB), the number of nested arc arrays, the structure of the double-nested arc related to each other, and the overlap between sensors for such an array are developed and investigated for various arrays. The proposed array performance is superior to that of the uniform one. Further, the proposed array is appropriate for two-dimensional (2D) DOA estimation problems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Direction of arrival

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