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Comparative Impact Assessement of Energy Storage Systems on Frequency Regulation with Various Operating Strategies under ABT

Comparative Impact Assessement of Energy Storage Systems on ...

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Smart and Sustainable developments in Electrical engineering (SSdEE), IEEE International Conference on

作者： B.N. Suthar V.M. dholakiya Electrical Engineering Department L.E. College Morbi Gujarat India Electrical Engineering Department L. D. college of Engineering Gujarat India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper investigates the comparative impact assessment of energy storage systems on frequency regulation with various operating strategies under Availability Based Tariff in an isolated power system having generation from thermal, wind and wave energy sources along with various energy storage systems(ESS) — Superconducting Magnetic Energy Storage (SMES), Supercapacitor Energy Storage (SCES), Redox Flow Battery (RFB), and Flywheel Energy Storage — for mitigating frequency deviations. Simulation studies in MATlAB/Simulink were conducted to evaluate key performance under dynamic operating conditions. Results reveal that SMES and RFB exhibit relatively better response with minimal overshoot (≤ 0.001 Hz), negligible undershoot, and least instantaneous settling time, making them appropriate for rapid frequency stabilization. SCES also demonstrates effective performance, albeit with slight oscillations due to its slower discharge rate. The analysis emphasizes the critical role of ESS technologies in enhancing the frequency regulation under different operating strategies under ABT. This study provides insights into the preliminary selection and integration of ESS in modern power systems, contributing to the reliable and stable grid operations amidst increasing renewable energy penetration.

关键词： Wind energy generation Time-frequency analysis Wind energy Tariffs Power system stability Wind power generation Iron Frequency control Oscillators Energy storage

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Optimized Feature Selection for Brain Cancer detection

Optimized Feature Selection for Brain Cancer Detection

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2023 International Conference on Energy, Materials and Communication engineering, ICEMCE 2023

作者： Vinurajkumar, S. Sofia Bobby, J. Thiyam, deepa Beeta Rajasekar, M. Saveetha Engineering College Department of Medical Electronics Chennai India Jerusalem College of Engineering Department of Biomedical Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Department of Biomedical Engineering Tamilnadu India Saveetha University Department of Computer Science Engineering Tamilnadu India

ISBN: (纸本)9798350393378

A brain cancer is an unexpected growth of nerves within the brain that interferes with the brain's normal function. Numerous lives have been lost as a result of it. It will take time to protect individuals from this illness by prompt discovery and the appropriate treatment. The search for tumor-affected brain cells is a difficult and time-consuming process. However, detecting brain cancer with the precision and speed necessary is a significant hurdle in the field of image processing. This study suggests a brand-new, precise, and enhanced method for finding brain cancer. Preprocessing, segmentation, feature extraction, optimization, and identification are some of the processes the system uses. A skull scripping constitutes one of the first steps in the procedure of finding anomalies in the brain and is used in the initial processing method. discrete wavelet transform (dWT) is employed for feature extraction, while K-means algorithms are used for picture segmentation. In this section the optimised CNN method is used, which selects the best characteristics via dragon fly optimisation. The CNN classifier is used for identifying brain tumours. Utilising reliability, precision, and recall characteristics, this system evaluates its efficacy with that of another contemporary optimisation approach and declares that its work is superior. © 2023 IEEE.

关键词： Feature extraction

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Machine learning-Based Real-Time data Analysis Techniques: A Comprehensive Study 15

Machine Learning-Based Real-Time Data Analysis Techniques: A...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Narmadha, T. Mohammed, Mehmood Ali Kalpana, B. Karyakarte, Mandar S. Vishnoi, Manish dehankar, Pooja Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India University of The Cumberlands Department of Information Technology WilliamsburgKY United States Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Ajeenkya D. Y. Patil University School of Engineering Pune India

ISBN: (纸本)9798350370249

This paper gives a comprehensive take a look at of system learning-based real-time statistics analysis strategies. It discusses the various stages of the actual-time information analysis lifecycle, along with records collection, records preprocessing, feature engineering, model training, model assessment, and consequences interpretation. The paper offers an outline of dozens of machine getting to know strategies, along with supervised gaining knowledge of, unsupervised studying, semi-supervised mastering, and reinforcement studying strategies. tendencies within the development and application of gadget studying-primarily based real-time facts evaluation techniques are mentioned, with precise emphasis given to annular neural networks, deep mastering, and evolutionary-based totally algorithms. different subjects of hobby consist of the cloud-based totally deployment of gadget studying fashions and the future of real-time records analysis. Finally, the paper offers a brief précis of episode research on how actual-time statistics analysis has helped improve business choices. © 2024 IEEE.

关键词： Adversarial machine learning

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Soft kind of Computing Based least Resource data finding which is Used for Analysis purpose 4

Soft kind of Computing Based Least Resource Data finding whi...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Rani, l. Pattathu Surendran, S. Monica, d. Kumar, V. Senthil Rajaram, Gnanajeyaraman dwibedi, Rajat Kumar Simats University Saveetha School of Engineering Chennai India Tagore Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering India Aarupadai Veedu Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

deep learning (dl) approaches are utilized across three distinct contexts to showcase their applicability in commercial environments with restricted data for training. This study showcases the application of unsupervised dl with autoencoders that vary & illustrates how federation learning serves as a successful collaboration approach for machine learning (Ml) that safeguards confidentiality of data. © 2024 IEEE.

关键词： Federated learning

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Cancer Genomics: Identifying Biomarkers for Early detection and Treatment

Cancer Genomics: Identifying Biomarkers for Early Detection ...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Husain, Saif O. Kaur, Amandeep Chitra, S. Sharma, Vivek Aminovna, Marupova Manzura Rajesh, G. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Mohali India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Ies University Department of Information Technology M.P. Bhopal India Fergana Medical Institute of Public Health Fergana Uzbekistan Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350390759

Genomic sciences, specifically with regards to cancer, has provided optimism in early diagnosis, treatment options by pointing to the molecular basis of cancer. This work aims at defining biomarkers for cancer diagnosis and treatment - or in other words, discovering novel biomarkers for early detection and treatment of cancer using multi-omics data from TCGA and ICGC. Ing data obtained through genome, transcriptome, and proteome studies , we revealed novel biomarkers corresponding to different types of cancer. The aforementioned datasets were processed with the help of random forests and artificial neural networks because these machine learning techniques delivered high biomarker recognition rates. This will incorporate, biomarkers such as BRCA1 for breast cancer and EGFR for lung cancer, showing high sensitivity and specificity levels. It also tackles issues such as tumor heterogeneity and presents a cardiovascular treatment plan that involves multiple disciplines. Interestingly, the assessment of genomics for cancer diagnosis and treatment has outlined such possible avenues for further investigations and practical use in the future. It is positioned in the rapidly expanding field of personalized oncology;this research seeks to improve patients' lifestyles via the early and accurate identification of cancer. © 2024 IEEE.

关键词： lung cancer

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.M. Beevi, l. Sherin dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computers

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Forest Fire detection and Temperature Monitoring Alert using IoT and Machine learning Algorithm 5

Forest Fire Detection and Temperature Monitoring Alert using...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Venkataramanan, V. Kavitha, G. Joel, M. Robinson lenin, J. D.J Sanghvi College of Engineering Department of Electronics and Communication Engineering Maharashtra Mumbai India Department of Computer Science and Engineering Tamil Nadu Namakkal India Kings Engineering College Department of Information Technology Tamil Nadu Chennai India Al Musanna College of Technology Department of Information Technology Muladdah Muscat Oman

ISBN: (纸本)9781665474672

Forest fires are a prevalent hazard in forests that significantly damage wildlife and the environment. It may be averted if a comprehensive system is installed in forest regions to detect fires and inform firefighting authorities to take timely action. The goal of this project is to create an Internet of Things (IoT)-based real-time detection system that detects fires and sends emergency notices to authorities. A GSM/GPRS module interacts with an IoT server because network bandwidth is typically very poor or nonexistent in forest regions. As a result, a 2G network is ideal for communicating with the server. A real-time fire monitoring system that differentiates fire and smoke is used to identify an actual fire occurrence. The nano-based Atmega328 IoT gateway identifies the forest's fire as soon as possible and acts rapidly before it spreads across a large area. Here, this uses machine learning algorithms to detect the event of a fire in a large region. The system uses a flame sensor to detect the flame and a temperature sensor to measure the temperature in a particular area. By using the machine learning method, the system can easily give the result with actual detection and intimate the authority about the temperature condition. The data are then sent to the cloud-based application. In the event of an unusual rise in forest temperature, this will alert the forest authorities and sound the fire alarm. It can also predict future fires by using machine learning. This is accomplished using the fog computing method. due of the sensors' efficiency, this might potentially be applied in industrial settings. Any type of forest can make use of it. From this experiment, this research study deduced that it has a remarkable accuracy of 98% in predicting forest fires. As a result, the possibility of a false alarm is significantly decreased. © 2023 IEEE.

关键词： Fires

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Federated learning for Secure and Privacy-Preserving Medical Image Analysis in decentralized Healthcare Systems 5

Federated Learning for Secure and Privacy-Preserving Medical...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Muthalakshmi, M. Jeyapal, Karthik Vinoth, M. dinesh, P.S. Murugan, N. Senthil Sheela, K.Santha Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology India Wipro India Ifet College of Engineering Autonomous Department of Chemistry Villupuram India Bannari Amman Institute of Technology Computer Science and Engineering Alathukombai Sathyamangalam India J.P.College of Engineering Department of Information Technology Tenkasi India Velammal College of Engineering and Technology Department of Cse Madurai India

ISBN: (纸本)9798350379945

The need for safe and effective ways to manage private patient information has grown in tandem with deep learning algorithms in medical image analysis. Preserving patient privacy while utilizing collective intelligence for accurate diagnostic models is difficult in decentralized healthcare systems when medical data is disseminated across many organizations and locations. This study suggests a federated learning architecture to analyze medical images in decentralized healthcare systems safely and privately. The suggested method uses federated learning concepts to enable several healthcare organizations to work together in training a deep learning model, all while keeping patient data private. Regarding data privacy and security, each institution handles things on its own. Medical image analysis can benefit from developing strong and precise deep learning models through federated learning's decentralized model training approach. The federated learning framework uses differential privacy measures, secure communication protocols, and advanced encryption techniques to guarantee privacy and security. during the training of the model, these safeguards protect sensitive medical data. The study also investigates how the privacy-preserving features of the suggested system might be improved by using federated averaging and homomorphic encryption. Various healthcare institutions' real-world medical imaging datasets are used to evaluate the federated learning system experimentally. The results show that the method can achieve centralized model performance levels while keeping data private and secure with 98.6% accuracy. Model convergence, communication overhead, and computational efficiency are other consequences of federated learning examined in the paper. © 2024 IEEE.

关键词： decentralized Healthcare Systems deep learning Federated learning Medical Image Analysis Privacy-Preserving

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deep learning-based Classification for Melanoma detection using XceptionNet and denseNet

Deep Learning-based Classification for Melanoma Detection us...

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Pervasive Computing and Social Networking (ICPCSN), International Conference on

作者： Sudha V Pareddy Virupakshappa Department of Computer Science & Engineering P D A College of Engineering Kalaburagi India Department of Computer Science & Engineering Sharnbasva University Kalaburagi India

ISBN: (数字)9798331535193

ISBN: (纸本)9798331535209

Melanoma is among the deadliest tumours, according to clinical trials and epidemiological research. It is now known that this condition is caused by the uncontrolled proliferation of melanocytes. General learning models and deep learning models are two more examples that have been used to reevaluate this paradigm. Experimental studies of melanoma also need visual data, such as dermatological scans or standard camera photos. It is more evidence that a trustworthy method for melanoma diagnosis is urgently needed. Some models that have been successful in melanoma diagnosis include Resnet, denseNet, Inception, and VGG. Regardless, data is often only labelled as benign or malignant. Our study's data set consists of seven lesions: melanoma, dermatofibroma, basal cell carcinoma, actinic keratosis, vasculopathy, and blepharocytic nevus. We used a variety of deep learning models using the HAM10000 dataset. Confidence is bolstered by positive outcomes. Cancers of the skin account for at least 40% of all cancers and are among the most prevalent tumour forms globally. In 2018, melanoma was predicted to be the sixteenth most common cancer in humans, with 300,000 new cases. The recommended strategy outperforms current state-of-the-art methods to skin cancer detection, according to results from a simulated comparison.

关键词： deep learning Training Visualization Accuracy Social networking (online) Computational modeling Melanoma Skin lesions Standards

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IOT and Sensor Based Technique to Track and Monitor the Work of the Street Sweepers 4

IOT and Sensor Based Technique to Track and Monitor the Work...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Jayaudhaya, J. Srinivas, Amgoth Srinivasan, A. Vijayalakshmi, G. Karthikeyan, M. Kumar, A. R. M. D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Narasimha Reddy Engineering College Department of Electronics and Communication Engineering Maisammaguda Telangana India Dhanalakshmi College of Engineering Department of Ai and Ds Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chennai India Dhanalakshmi College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350300888

To inform the public about environmental issues, scientists are developing portable sensors. However, data collection alone is insufficient for effective environmental action. This study emphasizes the proposed innovative environmental sensing technology, which is currently being utilized on street sweepers in an American metropolis. The proposed product was informed by industry executives' requirements and refined as an environmental action project through consultation. This article examines the environmental action landscape qualitatively to better equip innovators to design efficient technological solutions. Current environmental initiatives, the environmental effects of street cleansing, and related research on environmental sensing technologies are examined. The proposed research has implications for design fieldwork, sensor system performance evaluation, and ecological consciousness. Proposed innovation paves the path for environmental action by bridging the divide between observation and response. © 2023 IEEE.

关键词： Internet of things

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