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Mutual Coupling Reduction in MIMO Antenna for 5G Application by Self-Decoupled Method 2

Mutual Coupling Reduction in MIMO Antenna for 5G Application...

2nd IEEE International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies, ViTECoN 2023

作者： Rashid, Aamir Shah, Syed Shahid Hussain, Irshad Jalal, Muhammad Ramalakshmi, R. Ramachandran, V. University of Engineering and Technology Faculty of Electrical and Computer Engineering Peshawar Pakistan College of Computer and Information Engineering King Saud University Department of Computer Engineering Riyadh Saudi Arabia Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India Gokaraju Rangaraju Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad500090 India

ISBN: (纸本)9798350347982

This paper shows a simple, compact and small 8×8 MIMO antenna. The ability of MIMO antennas is to broadcast signals in a various patterns and polarizations is extremely advantageous for contemporary communications systems. Excellent diversity gain (DG > 9.99dB), a low envelop correlation coefficient (ECC 0.002), and extremely low mutual coupling (S2l≥ 27) dB are all characteristics of the constructed MIMO antenna. S-boundaries, energy pattern characteristics, maximum gain, diversity gain, and envelop coefficient are used to evaluate the antenna performance. The mutual Coupling is the most basic fundamental parameter for the MIMO antenna, was likewise analyzed and it was observed that its value is under -27dB. self-decoupled MIMO antennas have a loop type ground antenna which reduces the requirement for extra decoupling structures or a postponed tuning method Antennas for ground radiation and loop antenna are magnetic-current and electric-current components, respectively. The suggested MIMO antennas feature a self-decoupled despite the antenna parts being near together, reduced profile and easy fabrication advantages the suggested, a revolutionary approach called MIMO antenna system is expected to offer excellent isolation and minimal mutual coupling, making it suitable for 5G applications. © 2023 IEEE.

关键词： MIMO systems

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Yoga Pose Detection Using Mediapipe and Cue Method

Yoga Pose Detection Using Mediapipe and Cue Method

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Gatikoppu, Sai Phanindra Pavan Kumar Vimal Subramanian, G. Thamizh Devi, C. Maruthupandi, V. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamil Nadu Virudhunagar India M.A.M College of Engineering and Technology Department of Informaion Technology Tamil Nadu Siruganur India

ISBN: (纸本)9798331528126

Popular exercise like yoga has numerous positive health effects. However, incorrectly executed yoga poses can be harmful and lessen the advantages of the practice. In recent years, yoga positions have been investigated and altered using computer vision techniques. This study introduces a novel Mediapipe-based cue-based computer vision method for improving yoga postures. By finding and recognizing the most crucial body aspects in yoga postures using deep learning algorithms, the suggested method is contrasted with proper alignment. The user is then given instructions on how to change the stance to attain the desired alignment right away by the computer. The proposed method is tested using the dataset of yoga poses, and the accuracy and real-time performance outcomes are promising. In yoga classes, online tutorials, and other situations, the suggested technique might be employed to maximize benefits while lowering risks of injury for practitioners. © 2024 IEEE.

关键词： Cue Method Mediapipe OpenCv OpenPose Pose Correction

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Secured Authentication Scheme for E-Healthcare in SIoT Using ECC and Improved Hash Function 4th

Secured Authentication Scheme for E-Healthcare in SIoT Using...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Arpitha, T. Chouhan, Dharamendra Shreyas, J. Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9789819726134

The convergence of Smart Internet of Things (IoT) and security is a new study topic. In this context, a new IoT trend known as Social IoT (SIoT) was examined for this study. The Social IoT has the advantage of connectivity demonstrated within the network of connected things via the IoT. In Social IoT, a wide range of IoT devices, such as wearables or monitoring sensors, are utilized in e-healthcare applications and the risk of identity theft and impersonation is a significant concern. To address this challenge, we propose a scheme that incorporates multi-factor authentication, elliptic curve cryptography (ECC), and hash functions scheme to provide a robust solution for secure and privacy-preserving authentication. Our system provides privacy protection along with mutual authentication and session key negotiation at a considerably low computational cost (of up to 71.04% compared to other existing techniques). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Internet of things

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Improving Healthcare with Machine Learning and Deep Learning 4

Improving Healthcare with Machine Learning and Deep Learning

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Kothai, G. George Princess, T. Subramani, Nandhagopal Balakrishnan, D. Ravi, P. Amutha, S. Coimbatore India Kpr institute of Engineering and Technology Coimbatore India Coimbatore India KGiSL institute of Technology Coimbatore India Kalasalingam Academy of Research and Education Dept of Computer Science and Engineering Krishnankoil India

ISBN: (纸本)9798331540364

Due to the evolving lifestyle choices and the state of the environment, people nowadays suffer from a wide range of ailments. Consequently, early illness prediction becomes a critical obligation. For doctors, however, making a meaningful prognosis based only on symptoms becomes too difficult. Predicting illness correctly is the hardest problem. ML is crucial in helping to anticipate illnesses and find treatments in order to solve this issue. Using disease data, MRIs, chest X-rays, and diabetes records, machine learning (ML) assists in diagnosing and treating various conditions. Given the patient's symptoms, we utilize techniques such as SVM, Decision Trees, Random Forest Classifier, Gaussian Bayes and Multinomial Bayes, and Gradient Boost to predict illnesses. Given an X-ray image, we employ CNN models such as VGG for image classification with transfer learning to predict if a person has tuberculosis, pneumonia, or is disease-free. Brain MRIs (Magnetic Resonance Imaging) can be used to predict brain tumors. With the aid of Support Vector Machines, we are able to forecast if the patient has a pituitary tumor, meningioma, glioma, or no tumor at all (SVM). Diabetes is predicted using a number of important variables, including age and body mass index (BMI). Random forest classifiers are what we use to determine if a person has diabetes or not. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Smart Grid Security with XMSS-Based Blockchain technology 1

Enhancing Smart Grid Security with XMSS-Based Blockchain Tec...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Kathrine, G. Jaspher W. Krittikka, P. Johnraja, Immanuel Kirubakaran, Stewart Salaja, S. Arunkumar, K. Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Cse Coimbatore India

ISBN: (纸本)9798350359756

Smart grids are electricity network that can help improve the efficiency, reliability, and security of the power grid. Smart grid uses digital and other advanced technologies to monitor and manage the flow of electricity from generation to consumption It is a technological advancement in the management and delivery of electrical power. But the combination of a variety of IoT gadgets and there are serious security problems with the integration of communication networks into the smart grid infrastructure. The Traditional security methods can easily be broken and it will not be sufficient to safeguard against the modern evolving threats. Quantum resistant smart grids Blockchain is an emerging technology that has the potential to completely change how smart grids function. This study investigates the utilization of the extended Merkle Signature Scheme (XMSS) in combination with using blockchain technology to improve the smart grid's security. We talk about the benefits of XMSS and how to incorporate it into a blockchain-based design to safeguard smart grid systems from a range of security risks. © 2023 IEEE.

关键词： Blockchain

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Enhancing Lung Disease Identification with Multimodal Data Fusion and Deep Learning CNN Approach 5

Enhancing Lung Disease Identification with Multimodal Data F...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Grace Shalini, T. Shiny, G. Susan Saranya, R. Suresh Babu, P. Kavitha, R. Atheeswaran, Athiraja Srm Institute of Science and Technology School of Computing Department of Computational Intelligence Chengalpattu Kattankulathur India Sethu Institute of Technology Department of information technology Kariapatti India Velammal college of engineering and technology Department of Eee Madurai India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil India Bannari Amman Institute Of Technology Department Of Computer Science And Engineering Erode Sathyamangalam India Hyderabad India

ISBN: (纸本)9798331504403

Lung diseases, encompassing a wide spectrum from chronic conditions to acute infections, present a significant burden on global healthcare systems, necessitating precise and timely diagnostic methodologies. In this study, we propose a pioneering approach leveraging multimodal data fusion and deep learning Convolutional Neural Network (CNN) algorithms for enhanced identification of lung diseases. Our methodology integrates diverse data sources including medical images, clinical reports, and patient demographics to capture a comprehensive representation of lung pathology. We employ a state-of-the-art CNN architecture for medical image analysis, trained on a large-scale dataset encompassing various lung diseases and healthy lung tissue. Through extensive experimentation and validation, the approach achieved remarkable performance, with an accuracy of 94%, sensitivity of 92%, specificity of 93%, and an F1-score of 93% in accurately identifying and classifying lung abnormalities. Furthermore, we demonstrate the clinical utility of our method in providing valuable insights to healthcare professionals, aiding in timely diagnosis, prognosis, and treatment planning. Our findings highlight the potential of multimodal data fusion coupled with deep learning CNN algorithms in advancing the field of lung disease identification, offering a promising avenue for improving healthcare practices and patient outcomes in respiratory medicine. © 2024 IEEE.

关键词： Lung cancer

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Digital Twin-Based Automated Fault Diagnosis in Industrial IoT Applications

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computers, Materials & Continua 2023年第4期75卷 183-196页

作者： Samah Alshathri Ezz El-Din Hemdan Walid El-Shafai Amged Sayed Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Computer Science Department Security Engineering LabPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Electrical Energy Engineering College of Engineering&TechnologyArab Academy for Science Technology&Maritime TransportSmart Village CampusEgypt

In recent years, Digital Twin (DT) has gained significant interestfrom academia and industry due to the advanced in information technology,communication systems, Artificial Intelligence (AI), Cloud Computing (CC),and Industrial Internet of Things (IIoT). The main concept of the DT isto provide a comprehensive tangible, and operational explanation of anyelement, asset, or system. However, it is an extremely dynamic taxonomydeveloping in complexity during the life cycle that produces a massive amountof engendered data and information. Likewise, with the development of AI,digital twins can be redefined and could be a crucial approach to aid theInternet of Things (IoT)-based DT applications for transferring the data andvalue onto the Internet with better decision-making. Therefore, this paperintroduces an efficient DT-based fault diagnosis model based on machinelearning (ML) tools. In this framework, the DT model of the machine isconstructed by creating the simulation model. In the proposed framework,the Genetic algorithm (GA) is used for the optimization task to improvethe classification accuracy. Furthermore, we evaluate the proposed faultdiagnosis framework using performance metrics such as precision, accuracy,F-measure, and recall. The proposed framework is comprehensively examinedusing the triplex pump fault diagnosis. The experimental results demonstratedthat the hybrid GA-ML method gives outstanding results compared to MLmethods like LogisticRegression (LR), Na飗e Bayes (NB), and SupportVectorMachine (SVM). The suggested framework achieves the highest accuracyof 95% for the employed hybrid GA-SVM. The proposed framework willeffectively help industrial operators make an appropriate decision concerningthe fault analysis for IIoT applications in the context of Industry 4.0.

关键词： Automated fault diagnosis control system ML AI CC IIoT digital twins genetic algorithm GA-ML technique

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Adaptive solar power generation forecasting using enhanced neural network with weather modulation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10955-10968页

作者： Sujeeth, T. Ramesh, C. Palwe, Sushila Ramu, Gandikota Basha, Shaik Johny Upadhyay, Deepak Chanthirasekaran, K. Sivasankari, K. Rajaram, A. Department of CSE Siddartha Educational Academy Group of Institutions Andhra Pradesh Tirupati India Department of Mechanical Engineering M. Kumarasamy College of Engineering Karur India Department of Computer Engineering and Technology MIT-WPU Maharashtra India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Telangana Hyderabad India Andhra Pradesh Mylavaram India Department of Computer Science and Engineering Graphic Era hill univeristy Dehradun India Department of Electronics and Communication Engineering Saveetha Engineering College Chennai India Department of Electronics and Communication Engineering Akshaya College of Engineering and Technology Coimbatore India Department of Electronics and Communication Engineering E.G.S. Pillay Engineering College Nagapattinam India

Solar power generation forecasting plays a vital role in optimizing grid management and stability, particularly in renewable energy-integrated power systems. This research paper presents a comprehensive study on solar power generation forecasting, evaluating traditional and advanced machine learning methods, including ARIMA, Exponential Smoothing, Support Vector Regression, Random Forest, Gradient Boosting, and Physics-based Models. Moreover, we propose an innovative Enhanced Artificial Neural Network (ANN) model, which incorporates Weather Modulation and Leveraging Prior Forecasts to enhance prediction accuracy. The proposed model is evaluated using real-world solar power generation data, and the results demonstrate its superior performance compared to traditional methods and other machine learning approaches. The Enhanced ANN model achieves an impressive Root Mean Square Error (RMSE) of 0.116 and a Mean Absolute Percentage Error (MAPE) of 36.26%. The integration of Weather Modulation allows the model to adapt to changing weather conditions, ensuring reliable forecasts even during adverse scenarios. Leveraging Prior Forecasts enables the model to capture short-term trends, reducing forecasting errors arising from abrupt weather changes. The proposed Enhanced ANN model showcases its potential as a promising tool for precise and reliable solar power generation forecasting, contributing to the efficient integration of solar energy into the power grid and advancing sustainable energy practices. © 2024 – IOS Press.

关键词： Solar power generation

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Comparison of machine learning and deep learning methods for detection of liver abnormality 4th

Comparison of machine learning and deep learning methods for...

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4th Annual International Conference on Data science, Machine Learning and Blockchain technology, AICDMB 2023

作者： Vaidehi Nayantara, P. Kamath, Surekha Manjunath, K.N. Kadavigere, Rajagopal Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Kasturba Medical College Manipal Academy of Higher Education Manipal India

ISBN: (纸本)9781032426853

Liver is a vital organ that performs various essential functions. Liver cancer can have severe implications on human health, including death. To reduce the mortality rate, early detection of hepatic cancer is imperative. For rapid diagnosis and treatment planning, developing a computerized system that can aid in the diagnosis is essential. In this paper, machine learning and deep learning methods were compared for identifying abnormal liver from computed tomography images. The liver was segmented using DeepLabv3 + network. The machine learning method used, histogram and gray level co-occurrence matrix for feature extraction and support vector machine for classification. The deep learning methods investigated were ResNet, ShuffleNet and EfficientNet. Accuracy, sensitivity and specificity were used for performance evaluation. The results of our experiments showed that the deep learning methods outperformed the machine learning methods. Among the deep learning models, EfficientNet gave the highest accuracy of 87%. As future work, the abnormal liver can be further classified into benign and malignant classes. © 2024 The Author(s).

关键词： Support vector machines

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Determining human-coronavirus protein-protein interaction using machine intelligence

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Medicine in Novel technology and Devices 2023年第2期18卷 123-142页

作者： Arijit Chakraborty Sajal Mitra Mainak Bhattacharjee Debashis De Anindya J.Pal Bachelor of Computer Application Department The Heritage AcademyKolkataIndia Department of Computer Science and Engineering Heritage Institute of TechnologyKolkataIndia Department of Biotechnology Heritage Institute of TechnologyKolkataIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataIndia University of Burdwan BurdwanIndia

The Severe Acute Respiratory Syndrome CoronaVirus 2(SARS-CoV-2)virus spread the novel CoronaVirus−19(nCoV-19)pandemic,resulting in millions of fatalities *** research demonstrated that the Protein-Protein Interaction(PPI)between SARS-CoV-2 and human proteins is accountable for viral ***,many of these PPIs are poorly understood and unexplored,necessitating a more in-depth investigation to find latent yet critical *** article elucidates the host-viral PPI through Machine Learning(ML)lenses and validates the biological significance of the same using web-based *** classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins,namely Amino Acid Composition,Pseudo Amino Acid Composition,Conjoint Triad,Dipeptide Composition,and Normalized Auto Correlation.A majority voting rule-based ensemble method composed of the Random Forest Model(RFM),AdaBoost,and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this *** proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor≥70%,validated by utilizing Gene Ontology(GO)and KEGG pathway enrichment ***,this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

关键词： Coronavirus Ensemble learning Machine intelligence Protein-protein interaction

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