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Integrative Optimization Framework for Strategic Distributed Generation Placement and Sizing to Enhance Loss Minimization and Voltage Stability in Power Distribution Networks

Integrative Optimization Framework for Strategic Distributed...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： Kashi Sai Prasad Aravinda K Jay Singh Atul Singla Savita Kumari Sajjad Ziara Gotlur Karuna Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Electronics and Communication Engineering New Horizon College of Engineering Bangalore India Dept. of EEE GL Bajaj Institute of Technology and Management Gr. Noida U.P. India Lovely Professional University Phagwara India Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department Computer Science Engineering (AIML) Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The location and scale of distributed generation (DG) units are critical for minimizing losses and enhancing the performance of distribution networks. This research establishes the optimization approach for establishing the optimal location of the dg, especially solar PV and wind turbines using the IEEE 37-bus system as the test feeder. Having this in mind, this work applied a unified approach based on Pandapower, a versatile Python-based power systems tool, combining optimization and power flow computations. The algorithm analyzes a range of DG technologies and penetration levels to determine the effectiveness of decreasing loss, considering variability in renewable generation and load demand to increase realism. The single-tool forms presented in the paper show practical application potential and offer a better understanding of the best approaches to DG integration that would benefit grid stability and energy efficiency in the distribution networks of the future.

关键词： Renewable energy sources Electric potential Voltage fluctuations Uncertainty Distribution networks Power system stability Stability analysis Distributed power generation Wind turbines Optimization

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Musical Instrument Classification Using Deep Learning CNN Models

Musical Instrument Classification Using Deep Learning CNN Mo...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Sheeban E Tamanna Mohammed Ezhan R Mahesh Anupama Shetter B D Parameshachari D S Sunil Kumar Kiran Puttegowda Department of CSE (AI&ML) Vidyavardhaka College of Engineering Mysuru India Department of Software Engineer JSS Science and Technology University JPMorgan Chase & Co Bangalore India Department of CSE BGSIT Adichunchanagiri University BG Nagara Mandya India Department of ECE ATME College of Enginnering Mysuru India Department of ECE Nitte Meenakshi Institute of Technology Bengaluru India Scientist Anatta Innovations Bangalore India Department of ECE Vidyavardhaka College of Engineering Mysuru India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Musical instrument recognition is the act of counting on machine learning or the application of signal processing to isolate and classify various musical instruments in an audio track. Such capability makes it possible to better analyze musical pieces, which may be helpful whenever features need to be transcribed, songs recommended, or when they need to be composed automatically. In this work, we put forward a CNN based model for musical instruments classification using the FreeSound dataset. The model performs Mel-spectrograms on extracted audio inputs and make use of a highly effective data augmentation methodology known as CutMix. By using hyperparameter tuning and pruning approaches, the model configuration and structure is improved thereby enabling its effectiveness. Further, Class Activation Maps and an Ablation Study are employed to analyse interpretability and uncover the strength of the technique in instrument recognition. The accuracy was at 85%; the use of a richer database and convolutional neural networks may lift the accuracy of the classification. Using this algorithm means its application in a wider context of sound classification is possible and at some point, the algorithm can learn beyond a human's capability to differentiate between similar sounds (for example, distinguishing between viola and violin, which humans cannot do). Real world applications of musical instrument recognition are numerous. It can make the sorting and searching of enormous musical libraries possible with great efficiency, which would be valuable to streaming services since these offer their users relevant tune recommendations.

关键词： Accuracy Instruments Neurons Music Signal processing algorithms Signal processing Classification algorithms Convolutional neural networks Tuning Sorting

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A SPB based LB Algorithm in Order to Schedule the Sequence Task in CCS

A SPB based LB Algorithm in Order to Schedule the Sequence T...

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： G B Hima Bindu L. Manjula Anil Tellur V.K Vincy Devi Najlaa Nasrulaah Omar Muhsin Department of CSE School of Technology The Apollo University Chittoor Department of Computer Science and Engineering The Oxford College of Engineering Bangalore Department of CSE (AI & ML) M S Ramaiah Institute of Technology Bengaluru Karnataka India Department of computer applications Adi shankara institute of engineering&Technology kalady Faris Collage of Nursing National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

The increasing pervasiveness of computers in almost every aspect of our everyday lives has led to a commensurate rise in the need for computing resources, including bandwidth, processing power, and storage space. Originally, computer networks were created to address this difficulty, which helped ideas and paradigms like distributed computing, grid computing, and cluster computing to develop. But providing such services calls for advanced methods to effectively handle workloads and quickly attend to customer demands. One of the major problems with cloud computing is load balancing, which is a vital procedure that distributes work among virtual computers to avoid excess or underuse. In order to support the increasing number of users, the algorithms controlling this work assignment need to balance robustness with efficient use of resources. The related costs and hazards increase, especially for larger organisations, which emphasises how crucial it is to evaluate the risks involved in deploying load balancing algorithms in order to achieve desired service levels. Assessing the algorithms, applications, and rules in a virtualized setting is a useful method for risk assessments. With the use of simulation tools, one may practically mimic the intricacies of actual cloud settings and gain insights into possible problems and performance. As a powerful simulation toolset, CloudSim is intended to fully represent and simulate various cloud environments. It makes experimentation and optimisation easier by providing the ability to design and test different load balancing algorithms and cloud broker policies.

关键词： computers Cloud computing Schedules Load management Grid computing Robustness Hazards

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Pre-trained adversarial perturbations 22

Pre-trained adversarial perturbations

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Yuanhao Ban Yinpeng Dong Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and Department of Electronic Engineering Tsinghua University Department of Computer Science & Technology Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and RealAI

ISBN: (纸本)9781713871088

Self-supervised pre-training has drawn increasing attention in recent years due to its superior performance on numerous downstream tasks after fine-tuning. However, it is well-known that deep learning models lack the robustness to adversarial examples, which can also invoke security issues to pre-trained models, despite being less explored. In this paper, we delve into the robustness of pre-trained models by introducing Pre-trained Adversarial Perturbations (PAPs), which are universal perturbations crafted for the pre-trained models to maintain the effectiveness when attacking fine-tuned ones without any knowledge of the downstream tasks. To this end, we propose a Low-Level Layer Lifting Attack (L4A) method to generate effective PAPs by lifting the neuron activations of low-level layers of the pre-trained models. Equipped with an enhanced noise augmentation strategy, L4A is effective at generating more transferable PAPs against fine-tuned models. Extensive experiments on typical pre-trained vision models and ten downstream tasks demonstrate that our method improves the attack success rate by a large margin compared with state-of-the-art methods.

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Deep Learning Based Abstractive Text Summarization: A Survey

Deep Learning Based Abstractive Text Summarization: A Survey

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engineering and Technology (PICET), Parul International Conference on

作者： G Uday Kiran Ramakrishna Gandi M. Lavanya Girish Bhagwant Desale Ch. Upendar Rao B. Veerasekhar Reddy Department of CSE(AI & ML) B V Raju Institute of Technology Narsapur India Department of Computer Science & Engineering Madanapalle Institute of Technology & Science India Department of CSE(Data Science) Vignana Bharathi Institute of Technology Hyderabad India Department of Computer Science & IT JET'S Z. B. Patil College DHULE M.S. Department of Information Technology MLR Institute of Technology Hyderabad India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

The explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either “extractive” or “abstractive” depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based A TS in this work. This paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. The study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers.

关键词： Deep learning Surveys Measurement Training Text summarization Linguistics Transformers Natural language processing Long short term memory Postal services

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LookCursorai: Machine Learning-Enhanced Eye-Powered Interaction

LookCursorAI: Machine Learning-Enhanced Eye-Powered Interact...

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Applied Intelligence and Computing (aiC), IEEE World Conference on

作者： V. S. Saranya U Ganesh Naidu ParvathananiRajendra Kumar E Elamathi JayavarapuKarthik AjithSundaram Department of Computing Technologies SRM Institute of Science and Technology India Department of CSBS B V Raju Institute of Technology Narsapur Medak Telanagana India Department of CSE (AI&ML) NRI Institute of Technology Agiripalli Vijayawada India Department of AIDS K Ramakrishnan College of Engineering Trichy Tamilnadu India Department of CSE Koneru Lakshmaiah Education Foundation India Amrita School of Business Ernakulam India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

This project presents the development and implementation of a hands-free computer interaction system leveraging facial recognition technology. aimed at enhancing accessibility for individuals with physical disabilities, the system enables users to control their computers exclusively through facial expressions and movements. By harnessing real-time detection and tracking of facial landmarks using sophisticated algorithms, the system interprets a diverse range of facial gestures, from subtle cues like blinking to more pronounced actions such as mouth movements. Through customizable thresholds and intuitive interfaces, users can seamlessly navigate interfaces, manipulate content, and communicate with others without the need for traditional input devices. The system's robust performance and user-friendly design underscore its potential to revolutionize the way individuals with disabilities engage with computing devices, fostering independence and empowerment in the digital realm. This project not only exemplifies a paradigm shift in human-computer interaction but also embodies a commitment to inclusivity and accessibility in technology design.

关键词： Human computer interaction Performance evaluation Visualization Technological innovation Tracking Face recognition Process control Real-time systems Robustness Usability

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Big Data Analytical Engine for Exploring the Power in Modern Business

Big Data Analytical Engine for Exploring the Power in Modern...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Shreya Patchala Enquero Manoj Kumar Padhi Nagendar Yamsani M. Anitha Revanasiddappa Revanasiddappa Global-A Genpact Company Bangalore India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India School of Computer Science and Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Hyderabad India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

Most current applications of Big Data focus on examining consumer preferences and feedback expressed through social media and legitimate websites. Data is generated by all parties involved in the company, including mobile device location data and data created by machines with high-tech sensors, automated controls, and relevant software. We are currently living through an Internet revolution fueled by Big Data following the Industrial and Internet revolutions. Therefore, the implementation of Big Data platforms represents a significant opportunity for enterprises today. This article presents three case studies that highlight the potential of Big Data in the cellular phone business, online retail, and insurance sales over the Internet. To help companies locate, gather, and analyze huge amounts of data, a Big Data Analytical Engine has been proposed. From the analysis, it clearly shows that proposed model achieves better results in terms of accuracy (0.92), precision (0.93), recall (0.91) and f1-score (0.92) respectively.

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Enhancing Social Media Safety with Automated Toxicity Classification using Semantic Topic Models

Enhancing Social Media Safety with Automated Toxicity Classi...

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Shreya Patchala Enquero Jayapal Lande M. Syamala Padhyala Priyadarsan Sunil Kumar V Global-A Genpact Company Bangalore India School of Computer Science & Artificial Intelligence SR University Warangal India IT Department Malla Reddy Engineering College for Women Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaadeswaram India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India

In social media platforms, there is a growing interest in developing automated solutions for toxicity classification using machine learning algorithms. This paper presents a novel approach to automated toxicity classification using semantic topic models, achieving high accuracy and transparency. The report emphasizes the importance of detecting and adjusting for biases to ensure fair categorization. Moreover, the report highlights the challenges of natural language processing tasks, importance of understanding and addressing potential biases in machine learning models. Datasets are produced by the Origami project at Google, labeled by human labelers with adjectives such as "toxic," "very poisonous," "accusation," "danger," "obscene," and "identity hatred." From the analysis, it shows that proposed method achieved better results on toxicity classification in terms of accuracy (0.85), precision (0.87), recall (0.84) and f1-score (0.85) respectively. For non-toxicity classification, the proposed method achieves accuracy (0.92), precision (0.89), recall (0.88), f1-score (0.88) and AUC-ROC (0.96) respectively.

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Predictive Modeling of Crop Yield in Precision Agriculture Using Machine Learning Techniques

Predictive Modeling of Crop Yield in Precision Agriculture U...

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Advances in Computing, Communication and Applied Informatics (ACCai), International Conference on

作者： G. Bhupal Raj Eswararao Boddepalli Ch Veena Manjunatha Atul Singla JoshuvaArockia Dhanraj School of Agriculture SRUniversity Warangla Bradely Department of Electrical and Computer Engineering Virginia Tech United Status of America Institute of Aeronautical Engineering Dundigal Hyderabad Department of Mechanical Engineering New Horizon College of Engineering Bangalore Lovely Professional University Phagwara India Department of Computer Science and Engineering (AI & ML) School of Engineering Dayananda Sagar University Devarakaggalahalli Harohalli Kanakapura Road Ramanagara District Bengaluru Karnataka India

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The purpose of this work is to investigate the operation of machine learning (ml) methods for prophetic modeling of crop yield in production agriculture. In the context of ultramodern agricultural techniques, perfection husbandry provides the implicit opportunity to maximize the utilization of resources, boost productivity, and reduce the negative impact on the environment. One of the most important aspects of perfect husbandry is the capacity to directly forecast crop yields, which makes it easier for growers and other stakeholders to make decisions based on accurate information. Using machine learning algorithms such as decision trees, arbitrary timbers, support vector machines, and neural networks, the purpose of this investigation is to develop predictive models that are capable of predicting crop yields based on a variety of input variables. These variables include soil characteristics, rainfall data, crop operation practices, and remote seeing imagery. This project aims to contribute to the improvement of ideal husbandry techniques by utilizing the power of machine learning-driven prophetic modeling. This will allow growers to make opinions based on facts, which will improve the sustainability and profitability of agriculture.

关键词： Support vector machines Rain Profitability Computational modeling Machine learning Predictive models Soil

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The Intersection of ai and IoT for Clinical Digitalization in Healthcare Domain

The Intersection of AI and IoT for Clinical Digitalization i...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： K. Kavitha R. Sangeethapriya V. Malathi T. Mylsami Riyanka Hazra R. Mohankumar Department of AI and ML School of Computing Mohan Babu University Tirupati India Department of Information Technology Sona College of Technology Salem Tamil Nadu India Department of Computer Science With Cyber Security Dr. N.G.P Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science Dr. G.R. Damodaran College of Science Coimbatore Tamil Nadu India Department of Computational Science Brainware University Barasat West Bengal India Department of Computer Science and Engineering Paavai Engineering College Pachal Namakkal Tamilnadu India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

People in modern world suffer from a multitude of diseases, the most common of which is diabetes, which affects most senior citizens. Diabetes is associated with kidney problems, cancer, and heart disease. Combining Internet of Things (IoT) with Artificial intelligence (ai) to improve the functionality of remote systems for monitoring health for doctors, patients and patient attendants is one of the most significant technological advancements to date. The integration of ai and IoT also improves clinical workflow efficiency. Intelligent hospital management systems use ai-powered IoT networks to increase resource allocation, shorten wait times, and improve outcomes for patients. Predictive analytics helps to estimate the number of patients admitted and manage hospital inventories effectively. This paper explores the development of an innovative patient monitoring system that focuses on analyzing patient diabetes to facilitate timely medical interventions. In this research work applied three types of Learning methods and subsequently some main parameters were utilized to assess the efficiency of methods. The different machine learning and Deep learning algorithms are compared with one another and created new model with IoT advantages.

关键词： Learning systems Accuracy Hospitals Neural networks Prediction algorithms Diabetes Classification algorithms Internet of Things Medical diagnostic imaging Wearable sensors

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