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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Prabanand, S.C. Thanabal, M.S. Mahalakshmi, P. Solairaj, A. Durai Murugan, A. Gowsika Sree, S.M. Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Tamil Nadu Sathyamanagalam India Department of Computer Science and Engineering PSNA College of Engineering and Technology Tamil Nadu Dindigul India Department of Information Technology Kamaraj College of Engineering and Technology Tamil Nadu Virudhunagar India Department of Computer Science and Engineering Nadar Saraswathi College of Engineering and Technology Tamil Nadu Theni India Department of Computer Science and Business Systems M Kumarasamy College of Engineering Tamil Nadu Karur India

ISBN: (纸本)9798350305708

The current research provides a literature evaluation of works from sources such as IEEE and science Direct that explore testing and analysis methodologies and approaches for smart contracts. Smart contracts built on the blockchain have attracted a lot of attention from academics because of their unique properties. These include the immutability of transaction records, the automatic execution of contract instructions, and the decentralized building of trust. No one can deny blockchain's revolutionary potential across a number of sectors, but there are still technological hurdles to overcome before smart contracts gain widespread use. Solutions to these problems, as well as investigations into potential new uses for smart contracts outside of their present financial and logistics-related contexts, should guide future research efforts. In general, smart contracts built on blockchain technology have the power to revolutionize a number of sectors and open the door to a future that is more decentralized. © 2023 IEEE.

关键词： Blockchain concurrency Ethereum IoT scalability security smart contracts

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Interactive Framework for Cybersecurity Education and Future Workforce Development 54

Interactive Framework for Cybersecurity Education and Future...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Ghimire, Sujan Chowdhury, Muhtasim Alam Tsang, Ryan Yarnell, Richard Heckert, Emma Carpenter, Jaeden Lin, Yu-Zheng Mamun, Muntasir Demara, Ronald F. Rafatirad, Setareh Satam, Pratik Salehi, Soheil University of Arizona Department of Systems and Industrial Engineering TucsonAZ85721 United States University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States University of California Davis Department of Electrical and Computer Engineering DavisCA95616 United States University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32826 United States University of California Davis Department of Computer Science DavisCA95616 United States

ISBN: (纸本)9798350351507

This research-to-practice paper presents a novel pedagogical tool for hardware cybersecurity education and workforce development. The growing importance of hardware security has made it essential for individuals and organizations to understand hardware security principles and best practices. However, the current educational curriculum falls short of fulfilling these emerging demands due to the rapidly changing hardware security landscape and limited opportunities for hands-on training. To address these challenges, we propose and have developed the Interactive Hardware and Cybersecurity (I-HaC) Educational Framework, a pedagogical educational framework that supplements existing courses by leveraging generative AI for individualized instruction related to hardware and cybersecurity, data mining, and applied Machine Learning (ML), as well as data visualization to enhance cybersecurity education and workforce development. The framework is designed to be utilized by graduate and undergraduate Electrical and computer engineering (ECE) and computer science (CS) students for a comprehensive introduction to cybersecurity exploits and countermeasures in an interactive manner with hands-on components. Using I-HaC, we have developed tailored lab components for a diverse range of students and intend to release I-HaC as open-source for the benefit of the ECE and CS education community. © 2024 IEEE.

关键词： Students

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Map Building of Indoor Environment with Sensors using Neural Network

Map Building of Indoor Environment with Sensors using Neural...

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2023 Winter Summit on Smart Computing and Networks, WiSSCoN 2023

作者： Latha Mary, S Angel Ulagapriya, K. Poonguzhali, A. Menaha, R. David, Beaulah Priyadharshini, T.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India Vels Institute of Science Technology and Advanced Studies Department of Computer Science and Engineering Chennai India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bangalore India Sri Eshwar College of Engineering Department of Information Technology Coimbatore India Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India

ISBN: (纸本)9798350334050

The necessity of a blueprint of a building structure is a mandatory requirement for any reconnaissance or rescue operations. In our project, we build a modular system combining sensors related to sonar, laser, micro-wave to read sensory values and generate a 2D path of any building. The data is fetched and stored to feed to anOptimal Neural Network (ONN)-based computing system to create a 2D route with minimal discrepancies of error. Here the NN architecture is fine-tuned using Modified Dolphin Partner Optimization (MDPO) Exploration of unknown environments and space using autonomous vehicles has recently gained good attention in the field of Robotic Mapping. The recent advancements in the Internet of Things have enabled us to establish an ideal frame of reference for sonar and lidar-based systems. New effects are displayed by the sensors according to the physical characteristics of a room. The range data from sensors in various surroundings are interpreted by NNs. The distorted errors due to the material medium, particles, and moving objects present in the environment pose a threat to building a high-quality path map. The sensor fusion technique is applied to the rotatable modular array sensor to minimalize discrepancies caused by cloth materials during sonar readings, particle noise in an environment for Lidar reading, and moving human bodies present in the environment for path building and obstacle detection. © 2023 IEEE.

关键词： Optical radar

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Detection of Alzheimer's disease at Early Stage using Machine Learning

Detection of Alzheimer's disease at Early Stage using Machin...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Pavalarajan, S. Kumar, B.Arun Hammed, S.Shahul Haripriya, K. Preethi, C. Mohanraj, T. PSNA College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665495158

Identification of dementia is an important concern in medical image processing. Alzheimer is a common kind of dementia. Four machine learning models were designed for identifying this disease. This is classified as a classification problem, and the classification algorithms tested include logistic regression, support vector classifier, decision tree, and random forest classifier. The models are fine tuned by choosing optimal values for parameters that influences the accuracy of the model. The optimal parameters are found using a K-fold cross validation score, and the models are generated using that. The dataset used in the model is longitudinal cross sectional data from OASIS. It has been inferred from the results that random forest classifier performs well than the other models. © 2022 IEEE.

关键词： Decision trees

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Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in Distributed Learning Environments 29

Weighted Sampled Split Learning (WSSL): Balancing Privacy, R...

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29th IEEE International Conference on Parallel and Distributed systems, ICPADS 2023

作者： Osti, Manish Thakuri, Aashray Qolomany, Basheer Mulahuwaish, Aos Saginaw Valley State University University Center Department of Computer Science and Information Systems MI United States University of Michigan - Dearborn College of Engineering and Computer Science Dearborn United States University of Nebraska at Kearney Cyber Systems Department Kearney United States

ISBN: (纸本)9798350330717

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning. © 2023 IEEE.

关键词： and Distributed Learning Privacy-Preserving Machine Learning Robust Learning Unbiased Learning Weighted Sampled Split Learning (WSSL)

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EsECC_SDN:Attack Detection and Classification Model for MANET

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computers, Materials & Continua 2023年第3期74卷 6665-6688页

作者： Veera Ankalu Vuyyuru Youseef Alotaibi Neenavath Veeraiah Saleh Alghamdi Korimilli Sirisha Department of Computer Science and Engineering Koneru Lakshmaiah Education FoundationVaddeswaram522502A.PIndia Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communications DVR&DHS MIC Engineering CollegeKanchikacharla521180A.PIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications BVC Institute of Technology&ScienceAmalapuram533221A.PIndia

Mobile Ad Hoc Networks(MANET)is the framework for social networking with a realistic *** theMANETenvironment,based on the query,information is transmitted between the sender and *** the MANET network,the nodes within the communication range are involved in data *** the nodes that lie outside of the communication range are involved in the transmission of relay ***,due to the openness and frequent mobility of nodes,they are subjected to the vast range of security threats ***,it is necessary to develop an appropriate security mechanism for the dataMANET environment for data *** paper proposed a security framework for the MANET network signature escrow *** proposed framework uses the centralised Software Defined Network(SDN)with an ECC cryptographic *** developed security framework is stated as Escrow Elliptical Curve Cryptography SDN(EsECC_SDN)for attack detection and *** developed EsECC-SDN was adopted in two stages for attack classification and detection:(1)to perform secure data transmission between nodes SDN performs encryption and decryption of the data;and(2)to detect and classifies the attack in theMANET hyper alert based HiddenMarkovModel Transductive Deep ***,the EsECC_SDN is involved in the assignment of labels in the transmitted data in the database(DB).The escrow handles these processes,and attacks are evaluated using the hyper *** labels are assigned based on the k-medoids attack clustering through label assignment through a transductive deep learning *** proposed model uses the CICIDS dataset for attack detection and *** developed framework EsECC_SDN’s performance is compared to that of other classifiers such as AdaBoost,Regression,and Decision *** performance of the proposed EsECC_SDN exhibits∼3%improved performance compared with conventional techniques.

关键词： MANET security classifier cryptography attack escrow ECC

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KDTL: knowledge-distilled transfer learning framework for diagnosing mental disorders using EEG spectrograms

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Neural Computing and Applications 2024年第30期36卷 18919-18934页

作者： Singh, Shreyash Jadli, Harshit Padma Priya, R. Surya Prasath, V.B. Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Vellore India Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati College of Medicine CincinnatiOH45257 United States Department of Biomedical Informatics College of Medicine University of Cincinnati CincinnatiOH45267 United States Department of Computer Science University of Cincinnati CincinnatiOH45221 United States

Electroencephalography (EEG) is a well-known modality in neuroscience and is widely used in identifying and classifying neurological disorders. This paper investigates how EEG data can be used along with knowledge distillation-based deep learning models to detect mental disorders like epilepsy and sleep disorders. The EEG signals are converted into time–frequency plots using short-time Fourier transforms. Further, we propose a novel methodology for using knowledge distillation-based transfer learning (KDTL). Knowledge distillation is becoming quite prevalent in the machine learning field and is associated with various applications in our work;we propose its use in the detection of mental disorders from EEG spectrograms. We convert the EEGs using short-term Fourier transform to obtain time–frequency representation and apply teacher-student by first training a large teacher model and use knowledge distillation to train a student model. In our experiments, we found that ConvNext teacher and MobileNet student combination obtained better results. Our proposed KDTL approach is tested on two datasets with multiple cases, namely the Bonn and ISRUC datasets and obtain 98% and 95% accuracies, respectively. Further experimental results show that the overall KDTL methodology can obtain high classification accuracy across both datasets in binary and multiclass classifications and proves to be better than multiple prior works. Further, our KDTL approach provides a way to train lightweight models which have a smaller number of trainable parameters and thereby constitute lower training time overall. Our proposed KDTL-based approach obtained accurate results in diagnosing mental disorders from EEG spectrograms. Compared to other related methods, KDTL outperformed across tasks with obtained good results in both binary and multiclass classifications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Electroencephalography

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GAA-Enhanced UNet++ Model for Brain Tumor Segmentation 2

GAA-Enhanced UNet++ Model for Brain Tumor Segmentation

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Manigandan, S.K. Ramya, D. Vishnukumar, A. Velmurugan, J. Muthu, V. Vigneshwaran, K. Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Information Technology Chennai India Rajalakshmi Engineering College Department of Computer Science and Business Systems Chennai India Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of AIML Chennai India

ISBN: (纸本)9798350365269

Advanced techniques in medical image segmentation often leverage deep learning methodologies. UNet and its variants plays a major role in image segmentation of medical images. UNet based networks have so many limitations that there needs an improvement in the segmentation performance. The proposed UNet++ architecture integrates dense skip connections and nested convolutions to enhance feature representation and improve accuracy of segmentation. Its superior performance and versatility make it a precious tool for assisting clinicians in detection of diseases and planning for treatment, ultimately improving patient outcomes in neuro-oncology. In this paper, the new method called GAAUNET++ (Gated Axial Attention UNet++) have been proposed with enhancements like 1. The inclusion of the Axial Attention gate aims to enhance performance while also capturing global features of the image. 2. Different metrics like precision, dice coefficient, recall and IoU have been used. The results shows that the proposed approach achieves finer performance in segmentation and accuracy also has been improved. © 2024 IEEE.

关键词： Image segmentation

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A Passive Grid Impedance Estimation Method in Exploiting Variable Operating Conditions

A Passive Grid Impedance Estimation Method in Exploiting Var...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Di Nezio, Giulia Marini, Giovanni D'Arco, Salvatore Tedeschi, Elisabetta Lidozzi, Alessandro Roma Tre University Department of Civil Computer Science and Aeronautical Technologies Engineering Rome Italy Sintef Energy Research Department of Energy Systems Trondheim Norway Department of Electric Energy Trondheim Norway

ISBN: (纸本)9798350376067

The integration of Distributed Generation (DG) units, especially from renewable sources, is transforming low-voltage grids from passive to active systems. Accurate grid impedance information is vital for the reliable operation of grid-connected inverters, enabling stability analysis, fault detection, short-circuit current calculation, and islanding detection. This paper proposes a passive grid impedance estimation method leveraging on the harmonics introduced by variations in the operating conditions. The method records voltages and currents and uses these data when a current variation is detected. Moreover, it performs the parametric estimation using two approaches in parallel to achieve redundancy and it validates the impedance estimates to ensure a Desired Reliability Rate (DRR). Simulation parameters and validation results show a very small error in estimating resistive and inductive components. Hardware-In-the-Loop (HIL) emulation using a real-time FPGA-based platform confirms the algorithm's effectiveness in a controlled environment. © 2024 IEEE.

关键词： Hardware-in-the-loop simulation

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An energy-aware routing scheme based on a virtual relay tunnel in flying ad hoc networks

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Alexandria engineering Journal 2024年 91卷 249-260页

作者： Hosseinzadeh, Mehdi Ali, Saqib Mohammed, Adil Hussein Lansky, Jan Mildeova, Stanislava Yousefpoor, Mohammad Sadegh Yousefpoor, Efat Hassan Ahmed, Omed Rahmani, Amir Masoud Mehmood, Asif Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Information Systems College of Economics and Political Science Sultan Qaboos University Al Khoudh Muscat Oman Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Department of Computer Science and Mathematics Faculty of Economic Studies University of Finance and Administration Prague Czech Republic Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan Department of Biomedical Engineering Gachon University Gyeonggi-do Seongnam-si13120 Korea Republic of

Flying ad hoc networks (FANETs) tackle diverse challenges, for example, dynamic topological structure, high mobility of nodes, low density, and energy restrictions. These challenges make problems in designing reliable and stable routing in FANETs. Hence, the design of a routing algorithm is essential for transferring data between drones. In this paper, an energy-aware routing scheme based on a virtual relay tunnel (EARVRT) is proposed in flying ad hoc networks. EARVRT includes a virtual relay tunnel (VRT) that manages relay nodes in the route discovery process and lowers network overhead. Moreover, three metrics, namely route energy, hop counts, and a new metric called path correlation are regarded in the path selection operation. Note that correlation is defined with regard to two scales, including the cosine similarity of two nodes and their distance. EARVRT is evaluated according to delay, network longevity, consumed energy, and packet delivery rate. Then, our scheme is compared with three methods, namely LoCaL, O-LAR, and PSO-GLFR. These results show the superiority and success of EARVRT in comparison with other algorithms. © 2024 The Author(s)

关键词： Drones

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