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

作者： Kuttalingam, U. Rajesh, M. Alone, vinod N. Ramakrishnan, Ruthra Rani, s. Maranan, Ramya V O.Chidambaram College Thoothukudi Department of Computer Science Tamil Nadu Thoothukudi628008 India Dr. Ambedkar Institute of Technology Department of Mechanical Engineering Karnataka Bangalore560056 India Vasantdada Patil Pratishthan's College of Engineering & Visual Arts Computer Engineering Maharashtra Mumbai400022 India St. Joseph's College of Engineering Omr Department of Civil Engineering Tamil Nadu Chennai600119 India Kalasalingam Academy of Research and Education Department of Commerce Tamil Nadu Krishnankoil626126 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350341485

In India, agriculture is of utmost importance because it provides the main means of support for a sizeable segment of the people, particularly in rural areas. It ensures food security, boosts the nation's economy, and aids in the country's general socioeconomic development. Irrigation is very important to agriculture and has several important benefits. Irrigation provides crops with a managed and consistent water supply, especially in regions with little or variable rainfall. In spite of how efficient controlled irrigation is, it also necessitates constant human monitoring, which in some situations may seem impossible. This study aims to overcome this limitation by developing a straightforward but effective circuit that can track multiple parameters and determine how much water the plant needs. Three different sensors are used to measure the vitals from the soil and send it to the Arduino board. The user is also allowed to enter the type of plant to pick the perfect value range of the parameters. The Arduino then analyses the values from the sensors and finalizes the amount of water that had to be sent to the plants. The sent data is also shared with the user via the cloud console of the Arduino. The user can also view the flow of the water through the console. This design is tested using three different circumstances for three plant types. The circumstances include normal range, dry range, and extremely dry. The performance of the system is then analyzed to find that the model is able to change the water flow according to the circumstance on its own. © 2023 IEEE.

关键词： Irrigation

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The Implementation of big Data With Cloud and Edge Computing in Enhancing the smart Grid Information Processes Through sem Model 2

The Implementation of Big Data With Cloud and Edge Computing...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Chahar, Ankit Christobel, Y. Angeline Ritika Adakane, Prashant K. sonal, Deepa Tripathi, vikas R.V. College of Engineering Student Department of Computer Science and Engineering Karnataka Bengaluru India School of Computational Studies Hindustan College of Arts & Science Chennai India G.L Bajaj Institute of Technology & Management It Department Greater Noida India G H Raisoni University Department of Computer Science and Engineering Amravati India Patna Women's College Department of Computer Science Bihar Patna800014 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

It is not possible to handle and huge amounts of data using present software programmes and personal computers owing to a lack of appropriate computing power. Manufacturing organisations must either employ Cloud computing technology or engage in more current techniques including Edge Computing technology if they are to compete in the global market. The sEM model is used to examine the smart Grid Information Processes in this article. Furthermore, it is proposed that the research contributes to the expansion of experience in the discipline of smart meters and Internet of Things acceptability, despite the fact that this is a very rare topic. © 2022 IEEE.

关键词： smart power grids

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An integrated machine learning and genetic algorithm approach for properties prediction of fly ash-based steel fiber-reinforced concrete

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Asian Journal of Civil engineering 2024年第3期26卷 1175-1191页

作者： Keote, Rashmi Keote, Minal balpande, Rupali s. Masram, bharati Dubey, Pragati Pinjarkar, Latika Muley, Manjushree Department of Electronics Engineering Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India Department of Computer Science and Applications Ramdeobaba University Maharashtra Nagpur 440013 India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed) University Maharashtra Pune 440008 India Department of Mathematics S. B. Jain Institute of Technology Management and Research Maharashtra Nagpur 441501 India Department of Electronics and Telecommunication Yeshwantrao Chavan College of Engineering Maharashtra Nagpur 441110 India

Enhancement of concrete strength is critically important for increasing construction materials’ lifespan and sustainability. Traditionally, concrete mixture optimization methods—especially those used for fly ash and steel fiber concretes—normally fail to accurately predict the strength due to the high degree of complexity and non-linearity involved in the interaction of their components. These limitations are overcome in this study, which uses advanced artificial intelligence techniques—The Multilayer Perceptron (MLP) Neural Networks, Gradient boosting Machines (GbM), and Convolutional Neural Networks (CNN) to optimize concrete mixtures for improved strength. Among these, the MLP neural network was selected for this work because of its ability to model highly complex, nonlinear relationships and hence will be able to capture the intricate interactions among fly ash, steel fibers, and other additives. For this reason, Gradient boosting Machine was chosen for its robustness against overfitting and high accuracy in handling linearity or nonlinearity in an optimization problem. Traditionally, CNN has been applied to image processing, but in this work, it had been uniquely adapted to include the spatial distribution of concrete mix components, hence giving a new dimension in strength prediction. In this study, every method was used with a comprehensive data set and the input variables were taken as the percentages of fly ash and steel fibers, the water-cement ratio, aggregate size distribution, and curing delays. The accuracies of prediction for the proposed models were improved significantly, with the Mean Absolute Error (MAE) for compressive strength by the MLP model and an R² value of 0.90–0.95 by the GbM model. It is interpreted from CNN that there could be a potential reduction in prediction error by 10–15% compared to traditional methods. The work provides a robust framework for concrete strength optimization with substantial improvements in the reliability and per

关键词： Concrete strength Fly ash Gradient boosting MLP neural networks steel fibers sustainable concrete

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Penetration of Deep Learning in Human Health Care and Pharmaceutical Industries;the Opportunities and Challenges 3

Penetration of Deep Learning in Human Health Care and Pharma...

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3rd International Conference on Innovative Practices in Technology and Management, ICIPTM 2023

作者： Raman, Ramakrishnan Radha, H.R. Inbamalar, T.M. subhahan, D. Abdus Kumar, Ashok bathrinath, s. sarkar, swagata Symbiosis Institute of Business Management Pune & Symbiosis International Maharashtra Pune India K. S. School of Engineering and Management Department of Chemistry Karnataka Bangalore India R.M.K. College of Engineering Department of Electronics and Communication Tamil Nadu Thiruvallur India B v Raju Institute of Technology Department of Computer Science Telangana Narsapur India Banasthali Vidyapith Department of Computer Science Rajasthan India Kalasalingam Academy of Research Department of Mechanical Engineering Tamil Nadu Krishnankoil India Sri Sai Ram Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu India

ISBN: (纸本)9798350336238

Computational medicine has emerged as a result of the advancement of medical technology, which has led to the emergence of the big data era in the biomedical area, which is supported by artificial intelligence technology. To advance the development of precision medicine, people must be able to extract the valuable information from this vast biomedical data. In the past, professionals in the field of feature engineering and domain knowledge were typically utilised to extract the features from the biological data using machine learning techniques, which took a lot of time and resources. Modern machine learning techniques like deep learning (DL) have an advantage over them in that they can automatically find strong, complex features from fresh data without the necessity for succeeding engineering. The study of DL's applications in the fields of genomics, drug development, electronic health records, and medical imaging suggests that deep learning has clear advantages in maximising the use of biomedical data. Deep learning is becoming increasingly important in the field of medicine and health due to its large range of potential applications. The lack of data, interpretability, data privacy, and heterogeneity are some of the limitations of deep learning in computational medical health. A resource for improving the use of deep learning in medical health is provided by the analysis and discussion of these difficulties. © 2023 IEEE.

关键词： Health care

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Ultra Low Power High speed DFT Implementation For AsIC soC 9

Ultra Low Power High Speed DFT Implementation For ASIC SoC

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Rao, K. Madhava sampath Kumar, Pattem Reddy, T. vasudeva Nilima, D. saikumar, K. Khlaif, Abbas Mahdi B V Raju Institute of Technology Dept of Ece Medak dt Telangana Narsapur502313 India Malla Reddy College of Engineering Department of Ece Maisammaguda Secunderabad Dhulapally500100 India Mlr Institute of Technology Department of Computer Science And Information Technology Telangana Hyderabad India Malla Reddy University School of Engineering Computer Science Dulapally Maisammaguda Telangana Hyderabad500100 India Al-Ayen University Scientific Research Center Thi-Qar64001 Iraq

ISBN: (纸本)9798350394474

An ultra-low power DFT architecture suitable for MIMO-OFDM applications & it was introduced in this study. several radix such as 8/16/32 dependent optimization methods are performed to create variable lengths of 4k/8k points, a necessity in today's 4G and 5G communication technologies, which are plagued by a number of power and performance concerns with DFT processors. These technologies are widely used but have a higher energy footprint than others. Consequently, this work uses a novel approach called "sbOX"and the concept of parallelism to boost performance and reduce power consumption, all of which are essential for an advanced DFT architecture. Parallelism and the proposed sbOX model allow for fewer multiplications and faster processing times. Cadence-allegro-17.2 is used for the design and verification of the full implementation;this software is also used for simulation, synthesis, layout, and power analysis. The area required for 8-point s-box DFT is 105.15 um2, it requires 7mW of power, and it can achieve a throughput of 59.33GbPs. In addition, the throughput of 57.33GbPs may be achieved by 16-point s-box DFT with a footprint of 123.35um2, using 9mW of power. Also, the throughput of 32-point s-box DFT is 56.45GbPs on a chip that uses 10.24 mW and occupies 132.35um2. With MIMO in 5G technology, this layout becomes more practical for usage in telecommunications. The performance is enhanced over existing architectures thanks to the optimized radix 8 s-box DFT. © 2024 IEEE.

关键词： MIMO systems

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Residual U-Network for breast Tumor segmentation from Magnetic Resonance Images

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computers, Materials & Continua 2021年第6期67卷 3107-3127页

作者： Ishu Anand Himani Negi Deepika Kumar Mamta Mittal Tai-hoon Kim sudipta Roy Department of Computer Science&Engineering Bharati Vidyapeeth’s College of EngineeringNew Delhi110063India Department of Computer Science&Engineering G B Pant Govt.College of EngineeringNew Delhi110020India Glocal Campus Konkuk UniversityChungju-si Chungcheongbuk-do27478Korea Washington University in St.Louis MO63110USA

breast cancer positions as the most well-known threat and the main source of malignant growth-related morbidity and mortality throughout the *** is apical of all new cancer incidences analyzed among *** features substantially inuence the classication accuracy of malignancy and benignity in automated cancer *** are the precision of tumor segmentation and appropriateness of extracted attributes required for the *** this research,the authors have proposed a ResU-Net(Residual U-Network)model for breast tumor *** proposed methodology renders augmented,and precise identication of tumor regions and produces accurate breast tumor segmentation in contrast-enhanced MR ***,the proposed framework also encompasses the residual network technique,which subsequently enhances the performance and displays the improved training *** and above,the performance of ResU-Net has experimentally been analyzed with conventional U-Net,FCN8,*** performance is evaluated in the form of dice coefcient and MIoU(Mean Intersection of Union),accuracy,loss,sensitivity,specicity,*** results show that ResU-Net achieved validation accuracy&dice coefcient value of 73.22%&85.32%respectively on the Rider breast MRI dataset and outperformed as compared to the other algorithms used in experimentation.

关键词： UNet segmentation residual network breast cancer dice coefcient MRI

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A Deep Learning Model-based Neural Network Framework for Diabetic Foot Ulcer Classification

A Deep Learning Model-Based Neural Network Framework for Dia...

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Electronics and sustainable Communication systems (ICEsC), 2020 International Conference on

作者： P. shanmugam P. K. Manoj Kumar Mohammad Manzoor Hussain Revathi. R P. Deepa U. sakthivelu Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Computer Science Subbalakshmi Lakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Medak Telangana India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Computer Science Texcity Arts and Science College Coimbatore India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Diabetic Foot Ulcer (DFU) is a severe complication of diabetes, frequently resulting in amputation and having a significant impact on patients' quality of life. Early and accurate DFU classification is essential for prompt and efficient therapy. This study introduces an innovative neural network framework that utilizes deep learning techniques to categorize DFU based on thermograms. The proposed framework employs the pre-trained EfficientNet-b0 deep learning model, well-known for its exceptional performance in image classification tasks. The framework underwent training and assessment using a dataset sourced from Kaggle, consisting of 1866 colored images. Among them, 890 images depict plantar thermograms with negative DFU, whereas 976 images depict foot thermograms with positive DFU. The proposed framework demonstrated a higher accuracy of 98.71, outperforming previous approaches by a wide margin. The findings demonstrate the ability of deep learning to categorize DFU, paving the way for the development of automated testing techniques to assist healthcare practitioners in the timely identification and treatment of this incapacitating ailment.

关键词： Deep learning Heating systems Training Accuracy Neural networks Wounds Diabetes Foot Testing sorting

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An ensemble framework of decision trees for class imbalance using partitioning

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International Journal of Innovative Computing and Applications 2024年第1期15卷 1-13页

作者： Kadappa, vijayakumar Guggari, shankru veerabhaskar, Rajeshwari Devi Doddapoojari Department of Computer Applications B.M.S. College of Engineering Bengaluru560019 India School of Computer Science and Engineering KLE Technological University Hubballi580031 India Department of Electronics and Communication Engineering Global Academy of Technology Bengaluru560098 India

Decision tree classifiers are widely used in machine learning and data mining due to their intuitiveness. However, they do not perform well for class-imbalanced data due to bias creation towards the majority class. Therefore, handling class-imbalanced data is an active research area in many applications of machine learning. Feature set partitioning paradigm is proven to be effective for classification by many researchers. In this paper, we extend the ideas of partitioning to propose a decision tree ensemble to overcome class-imbalance issues. The framework consists of balancing the data, partitioning the feature set, building local decision trees, then combining decisions. some instances of the proposed framework (e.g., Ferrer diagram-based approach) are used to validate the performance of the framework. An empirical study is carried out using University of California Irvine (UCI), Knowledge Extraction Evolutionary Learning (KEEL), and other datasets. The performance of the instances of the proposed framework demonstrates improved performance compared to other benchmark machine learning approaches. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Adversarial machine learning

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bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management

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IEEE Transactions on Cognitive Communications and Networking 2023年第5期9卷 1282-1295页

作者： Zhang, Yunchuan simeone, Osvaldo Jose, sharu Theresa Maggi, Lorenzo valcarce, Alvaro King's College London Department of Engineering LondonWC2R 2LS United Kingdom University of Birmingham Department of Computer Science BirminghamB15 2TT United Kingdom Nokia Bell Labs France Department of Radio Systems and Ai Nozay91620 France

Optimal resource allocation in modern communication networks calls for the optimization of objective functions that are only accessible via costly separate evaluations for each candidate solution. The conventional approach carries out the optimization of resource-allocation parameters for each system configuration, characterized, e.g., by topology and traffic statistics, using global search methods such as bayesian optimization (bO). These methods tend to require a large number of iterations, and hence a large number of key performance indicator (KPI) evaluations. In this paper, we propose the use of meta-learning to transfer knowledge from data collected from related, but distinct, configurations in order to speed up optimization on new network configurations. specifically, we combine meta-learning with bO, as well as with multi-armed bandit (MAb) optimization, with the latter having the potential advantage of operating directly on a discrete search space. Furthermore, we introduce novel contextual meta-bO and meta-MAb algorithms, in which transfer of knowledge across configurations occurs at the level of a mapping from graph-based contextual information to resource-allocation parameters. Experiments for the problem of open loop power control (OLPC) parameter optimization for the uplink of multi-cell multi-antenna systems provide insights into the potential benefits of meta-learning and contextual optimization. © 2015 IEEE.

关键词： Network topology

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An extensive critique on advances in digital control techniques for smart grid and micro grid operation

An extensive critique on advances in digital control techniq...

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IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： sarathkumar D K. bashkaran Murugesan Manivel T. Jayakumar R. sivakumar Anandhakumar C Department of Electrical and Electronics Engineering Kongu Engineering College Tamil Nadu India Department of Biomedical Engineering Kongunadu College of Engineering and Technology Thottiam Tamil Nadu Department of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore Tamilnadu India Department of Electrical and Electronics Engineering Nandha Engineering College Tamil Nadu India Department of Electrical and Electronics Engineering V.S.B Engineering College Tamil Nadu India Department of Electrical and Electronic Engineering Sri Ramakrishna Institute of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The integration of digital control techniques within smart grids and microgrids represents a pivotal advancement in the optimization, reliability, and efficiency of modern electrical networks. This paper explores the latest developments in digital control strategies, focusing on their application to the operation of smart grids and microgrids. Key areas of discussion include the implementation of advanced algorithms for real-time monitoring and control, the role of artificial intelligence and machine learning in predictive maintenance and decision-making, and the impact of distributed control systems on grid stability and resilience. Furthermore, the paper examines the challenges and solutions associated with integrating renewable energy sources and energy storage systems into the grid, emphasizing the importance of adaptive and scalable control frameworks. Through a comprehensive review of recent research and case studies, this paper provides insights into the future trends and potential breakthroughs in digital control technologies, highlighting their critical role in the evolution of smart grid and microgrid infrastructures. The findings suggest that continued innovation in this field will significantly enhance the operational capabilities of electrical grids, paving the way for more sustainable and resilient energy systems.

关键词： Digital control Renewable energy sources Microgrids Machine learning Power system stability Real-time systems smart grids Reliability Optimization Resilience

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