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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair Tina Babu Kishore S K. Nithin Kumar R. Guna Vardhan Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte Deemed to be University Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Wind turbine blade design is a complex multi-objective optimization challenge, requiring a balance between aerodynamic efficiency, structural integrity, and cost minimization. This study proposes a hybrid optimization framework combining Genetic Algorithms (GA) for global exploration and Differential Evolution (DE) for local refinement. The hybrid GA-DE approach accelerates convergence and enhances solution quality by efficiently navigating the large design space. Experimental results show up to a 20% reduction in structural loads while maintaining high energy output, with a 15% faster convergence rate compared to traditional single-algorithm methods. The optimized blade designs achieved improved aerodynamic performance and reduced material costs, offering a more practical solution for next-generation wind energy systems. This research demonstrates the potential of hybrid evolutionary algorithms to drive innovation in renewable energy by delivering cost-effective and high-performance turbine blades, particularly for larger turbines requiring enhanced durability and power output.

关键词： Technological innovation Costs Wind energy Blades Evolutionary computation Aerodynamics Search problems Wind turbines Optimization Genetic algorithms

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NodeMCU-based Smart Car Parking System for Real-Time Monitoring and Efficient Space Utilization

NodeMCU-based Smart Car Parking System for Real-Time Monitor...

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Rekha R Nair S Kishore Tina Babu Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Applications Nitte Institute of Professional Education Nitte (Deemed to be University Mangalore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

The rapid increase in vehicle ownership has created a pressing demand for larger parking areas and efficient parking management solutions. This study proposes an innovative NodeMCU-based smart car parking system to address the challenges of efficient parking space management i n urban areas. The system utilizes NodeMCU technology, built on the ESP8266 micro-controller, to provide real-time monitoring and optimize parking space utilization. The architecture incorporates NodeMCU devices equipped with sensors to detect vehicle presence in individual parking spots, wirelessly transmitting data to a central server for processing and analysis. The server implements robust data processing and storage mechanisms, along with a user-friendly interface for easy retrieval of parking information. This approach aids in efficient parking space management and helps reduce illegal parking incidents. By providing real-time data and easy-to-use interfaces, the system helps drivers quickly locate available spots, potentially decreasing traffic congestion and parking-related frustration. The proposed smart parking solution aims to streamline parking management, improve urban mobility, and Improve the entire parking experience for drivers in increasingly crowded metropolitan locations. This research contributes to the development of smart city infrastructure, offering a practical solution to modern parking challenges.

关键词： Technological innovation Smart cities Transfer learning Pressing Automated parking Real-time systems Servers Traffic congestion Vehicles Residual neural networks

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Microwave-induced cooling in double quantum dots: Achieving millikelvin temperatures to reduce thermal noise around spin qubits

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Physical Review B 2025年第1期111卷 014305-014305页

作者： Daryoosh Vashaee Jahanfar Abouie Department of Materials Science and Engineering and Department of Electrical and Computer Engineering North Carolina State University Raleigh North Carolina 27606 USA Department of Physics Institute for Advanced Studies in Basic Sciences Zanjan 45137-66731 Iran

Spin qubits in gate-defined quantum dots are emerging as a leading technology due to their scalability and long coherence times. However, maintaining these qubits at ultralow temperatures typically requires complex cryogenic systems. This paper proposes a gate-defined double quantum dot (DQD) cooling system, where the DQDs act as refrigerants to reduce the local phonon environment around computational qubits. The cooling process occurs in two distinct stages: the first step involves microwave-induced state depopulation combined with fast cyclic detuning to transfer the DQD's population to the ground state, effectively lowering the DQD's temperature. In the second step, the cooled DQD interacts with and absorbs phonons resonant with the DQD spin energy, thereby filtering out these phonons that contribute to spin-lattice relaxation in the surrounding environment. This study focuses on the first step, presenting detailed calculations and numerical results that demonstrate the feasibility of achieving local DQD temperatures below 10mK at a bath temperature of 1K. The sensitivity of the cooling performance to detuning energy, magnetic field strength, spin-orbit couplings, and diabatic return time is analyzed, while the phonon filtering in the second step will require further investigation.

关键词： Semiconductor quantum dots

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Graph-based Clustering for Detecting Semantic Change Across Time and Languages

arXiv

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arXiv 2024年

作者： Ma, Xianghe Strube, Michael Zhao, Wei Faculty of Mathematics and Computer Science Heidelberg University Germany Heidelberg Institute for Theoretical Studies Germany Department of Computing Science University of Aberdeen United Kingdom

Despite the predominance of contextualized embeddings in NLP, approaches to detect semantic change relying on these embeddings and clustering methods underperform simpler counterparts based on static word embeddings. This stems from the poor quality of the clustering methods to produce sense clusters—which struggle to capture word senses, especially those with low frequency. This issue hinders the next step in examining how changes in word senses in one language influence another. To address this issue, we propose a graph-based clustering approach to capture nuanced changes in both high- and low-frequency word senses across time and languages, including the acquisition and loss of these senses over time. Our experimental results show that our approach substantially surpasses previous approaches in the SemEval2020 binary classification task across four languages. Moreover, we showcase the ability of our approach as a versatile visualization tool to detect semantic changes in both intra-language and inter-language setups. We make our code and data publicly available1 © 2024, CC BY-SA.

关键词： Semantics

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Boosting Cybersecurity Effectiveness through Machine Learning for Proactive Detection and Mitigation of New Threats 2

Boosting Cybersecurity Effectiveness through Machine Learnin...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Duraimutharasan, N. Rao, N. Venkatesvara Poongavanam, N. Kanimozhi, K.V. Manikandan, S.P. Faculty of Advanced Computing Sciences AMET UNIVERSITY Tamil Nadu Chennai India Department of Computer Science and Engineering J.N.N.INSTITUTE OF ENGINEERING Ushaa GardenKannigaipair Periyapalayam Highway Chennai India Saveetha University Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India School of Engineering and Technology CMR UNIVERSITY Karnataka Bengaluru India

ISBN: (纸本)9798350383867

Cybersecurity still remains an all-time issue before the technological age as the enterprises continue to deal with the evolving nature of the threats arising every day. Traditional cybersecurity approaches are becoming deficient in the situation of constantly improving, swiftly mutating threats, which leads to the revision and refocus of the current approaches to the preventative reactivity type. For that, machine learning (ML) has come into the arena as a strong tool for improving cybersecurity capacity through the ¨p roactivë recognition and the ¨mitigatioñ̈ of unknown threats. The focus of this paper is the function of ML in cyber security. Here is considered anomaly detection, malware classification, threat intelligence analysis, and security form adversarial attacks. We conduct literature search, detail a novel research methodology embracing data acquisition, preprocessing, ML model training, and deployment. We present experiments versus conventional security approaches from different ML domains used confirming the superiority in cybersecurity of the presented models. Moreover, resistance to chicanery such as adversarial attacks and the models understandability are discussed. Next, research vector and innovation directions in the ML cybersecurity are highlighted. As a whole, this study provides the example of how ML can strengthen previous alliance strategies to cybersecurity and prevent the risks associated with interchanging cyber threats. ©2024 IEEE.

关键词： Adversarial machine learning

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Bio-Inspired Optimization with Transfer Learning Based Crowd Density Detection on Sparse Environment 3

Bio-Inspired Optimization with Transfer Learning Based Crowd...

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3rd International Conference on Pervasive computing and Social Networking, ICPCSN 2023

作者： Poongodi, A. Kopperundevi, N. Poorani, S. Tiwari, Mohit Rao, B. Srinivasa Abilash, N. School of Computing Sciences Vels Institute of Science Technology & Advanced Studies Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Computer Technology-UG Kongu Engineering College Perundurai India Bharati Vidyapeeth's College of Engineering Computer Science and Engineering A-4 Rohtak Road Paschim Vihar Delhi110063 India Gokaraju Rangaraju Institute of Engineering & Technology Computer Science and Engineering Hyderabad Bachupally500090 India Annai Vailankanni College of Engineering Mechanical Engineering Kanyakumari District Pottalkulam India

ISBN: (纸本)9798350322842

Crowd density estimation is a major importance for applications including crowd control, public space planning, autonomous driving, visual surveillance, and warning visually distrait drivers previous accident. With strong scale, reflective, and translational symmetry, techniques to estimate the density of the crowd yield a promising outcome. But dynamic scenes with constantly evolving spatial and temporal domains and perspective distortion yet have difficulties. The dynamic nature of scenes and the complexity of demonstrating and integrating the feature space of objects of different magnitudes as predictive prototypes are the primary reason for this. This manuscript presents a Red Fox Algorithm with Transfer Learning based Crowd Density Detection (RFOTL-CDD) technique in Sparse Environment. The purpose of the RFOTL-CDD system lies in the effectual identification and classification of distinct types of crowds in a sparse environment. To achieve this, the presented RFOTL-CDD method uses a ResNet prototype for feature vector generation. For the identification and classification of a crowd, the RFOTL-CDD technique applies Naive Bayes (NB) classifiers. In this work, the RFO algorithm is utilized for boosting the performance of the ResNet method. The stimulation outcomes of the RFOTL-CDD technique can be well studied on a crowd dataset and the outcomes confirmed the supremacy of the RFOTL-CDD technique on crowd detection. © 2023 IEEE.

关键词： Deep learning

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An Approach to Radiotherapy Treatment Planning based on Machine Learning Algorithms

An Approach to Radiotherapy Treatment Planning based on Mach...

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Expert Clouds and Applications (ICOECA), International Conference on

作者： Komati Divya C H Bhargavi M. Abirami Ponnaganti Arjun Pabbathi Padmaja Batini Dhanwanth Department of Computer Science and Engineering Panimalar Institute of Technology Chennai India Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai India Department of Computer Science and Engineering St.Peter's Institute Of Higher Education And Research Chennai India

ISBN: (数字)9798350385793

ISBN: (纸本)9798350385809

Customizing radiation treatments for each patient is a formidable obstacle in the fight against cancer. Because they rely on human intervention and generalization, traditional methods often provide less-than-ideal results and unwanted side effects. Utilizing patient-specific anatomical and tumor-specific data, this research analyzes the revolutionary possibilities of machine learning for planning radiation therapy. The goal of this research work is to forecast radiation doses, identify tumor boundaries, and reduce the exposure to healthy tissue using Deep Learning (DL), Support Vector Machine (SVM) and Random Forest (RF) models. Radiation planners must prioritize data heterogeneity, model interpretability, and clinical validation if machine learning has to be widely used. Improved patient outcomes and quality of life may be achieved via the use of Machine Learning (ML) to improve the radiation efficacy and accuracy, as shown in this research study that synthesizes existing research.

关键词： Deep learning Support vector machines Accuracy Cancer treatment Vectors Planning Radiation therapy

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A novel Approach for optimizing the crop yield by considering climatic and soil features through fusion of Machine Learning and Deep Learning algorithms

A novel Approach for optimizing the crop yield by considerin...

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International Conference on computing and Networking Technology (ICCNT)

作者： K. Suganthavalli C. Meenakshi K Parvathy Department of Computer Science Vels Institute of Science Technology and Advanced Studies(VISTAS) Pallavaram Chennai Tamil Nadu India Department of Computer Application Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The agricultural sector is the scientific and artistic cultivation of plants and animals. Agriculture has an important role in India’s economy, which covers a significant 60.45% of the country’s land and stands as the world’s second-largest agricultural sector, driving economic growth through its diverse produce and associated industries. Soil composition really matters for growing stuff. Elements like Nitrogen, Phosphorous, & Potassium play a big role. The way you rotate crops, soil moisture, and even temperature all affect how well things grow. Rainfall is important too. Research has shown that using machine learning can help figure out the best crops to grow. They utilize methods that include Random Forest (RF), Decision Tree (DT), and Artificial Neural Network (ANN) for this. The study also looks into deep learning methods to make the model even better. In addition to predicting crop yields, it also provides detailed information on the required amounts of soil elements and their associated costs. The new model offers superior precision compared to the current one. The system examines the provided data and assists farmers in forecasting a crop, which subsequently aids in achieving financial gains. The prediction of a suitable yield takes into account the meteorological and soil parameters of the area. This Python-based system utilizes advanced algorithms to optimize decision-making, forecasting the most lucrative outcomes in various situations while streamlining costs and boosting productivity. The study employs Support Vector Machines (SVM) as an example of Machine Learning (ML) method, while Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) are used as Deep Learning (DL) techniques.

关键词： Deep learning Support vector machines Recurrent neural networks Machine learning algorithms Costs Soil moisture Prediction algorithms Forecasting Long short term memory Crop yield

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Chaotic Map based Compression Approach to Classification

arXiv

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arXiv 2025年

作者： Harikrishnan, N.B. Vats, Anuja Nagaraj, Nithin Pedersen, Marius Department of Computer Science and Information Systems BITS Pilani K K Birla Goa Campus Goa 403726 India Adjunct Faculty Consciousness Studies Programme National Institute of Advanced Studies Indian Institute of Science Campus Bengaluru560012 India Department of Computer Science Norwegian Institute of Science and Technology Gjøvik Norway Complex Systems Programme National Institute of Advanced Studies Indian Institute of Science Campus Karnataka Bengaluru560012 India

Modern machine learning approaches often prioritize performance at the cost of increased complexity, computational demands, and reduced interpretability. This paper introduces a novel framework that challenges this trend by reinterpreting learning from an information-theoretic perspective, viewing it as a search for encoding schemes that capture intrinsic data structures through compact representations. Rather than following the conventional approach of fitting data to complex models, we propose a fundamentally different method that maps data to intervals of initial conditions in a dynamical system. Our GLS (Generalized Lüroth Series) coding compression classifier employs skew tent maps - a class of chaotic maps - both for encoding data into initial conditions and for subsequent recovery. The effectiveness of this simple framework is noteworthy, with performance closely approaching that of well-established machine learning methods. On the breast cancer dataset, our approach achieves 92.98% accuracy, comparable to Naive Bayes at 94.74%. While these results do not exceed state-of-the-art performance, the significance of our contribution lies not in outperforming existing methods but in demonstrating that a fundamentally simpler, more interpretable approach can achieve competitive results. Copyright © 2025, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Early Retinal Degeneration Detection with Deep Faster CNNs

Early Retinal Degeneration Detection with Deep Faster CNNs

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International Conference on Advances in Information Technology (ICAIT)

作者： K. Priyadharshini Sindhusaranya Balraj JV Rama Kumar Deepak A. Vidhate Thilakavathy. P P. Vanathi Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai India Department of Computer Science and Engineering Sona College of Technonolgy Salem India Computer Science and Engineering SRKR Engineering College Bhimavaram Andhra Pradesh Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra Computer science and Engineering Vels Institute of Science Technology and Advanced Studies Chennai Department of Information Technology Adhiyamaan College of Engineering Hosur

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

The issue of retinogenesis probably constitutes a major public health threat which gives the reason to carry out its early detection for intervention purposes to be efficient. Throughout this investigation, we suggest a new technique which uses the faster CNNs for early detection of retina degradation. We set up a diverse data collection where OCT(optical coherence tomography), fundus photography and angiography modality were included. This collection covered the various stages of degeneration. The dataset was prepared for instance with resizing, normalizing, and data augmentation were used to improve share quality and diversity. Deeper and faster networks that were developed and trained using the backpropagation technique performed well and further optimized using Adam, with high accuracy of 94 in the test dataset. 7%. The strength of our results can be seen through evaluation metrics in detail like confusion matrices and classification reports. The model can distinguish various cases of retinal degeneration accurately which proves to have high precision and recall for mild and normal degeneration. Interpolation analysis of the model using Grad-CAM and occlusion sensitivity, giving the clue about the model's decision making process, setting a precedent for clinical interpretation. The enhanced cross-validated model on an independent dataset also arrays the model in a representational where it will be able to generalize in a diverse population and in different imaging conditions. The systematic approach which proved to be more accurate and resilient than the other techniques was the one which was compared to the mentioned ones. In conclusion, our study shows the possibility for application of the Deep Faster CNNs in identifying the early signs of retinal degeneration; the tool therefore becomes an important tool for enhancing patient outcomes and prevention of vision loss in the world at large.

关键词： Training Analytical models Technological innovation Accuracy Systematics Coherence Tomography Retina Data models Diseases

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