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共 18733 条记录，以下是51-60 订阅

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Increasing the RES Hosting Capacity of the Cyprus Distribution System

Increasing the RES Hosting Capacity of the Cyprus Distributi...

引用

CIRED 2024 Vienna Workshop

作者： Therapontos, Phivos Konstantinou, Rafail Charalambous, Charalambos A. Aristidou, Petros Electricity Authority of Cyprus Nicosia Cyprus University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Dept. of Electrical & Computer Engineering & Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9781837242672

The Renewable Energy Sources (RES) penetration in the power system of Cyprus has dramatically increased over the last years. As a result, the system is already facing significant challenges limiting the RES hosting capacity of the Distribution Network (DN). The major limitation factors are network congestion and voltage security. In this paper, alternative solutions for increasing the RES hosting capacity in DNs are reviewed, and a methodology is introduced to evaluate their effectiveness. More specifically, different inverter settings and an advanced centralised voltage control from power transformers are used to mitigate voltage-related issues, while network reinforcements and upgrading the operating voltage are considered for further increase in hosting capacity. The solutions and evaluation methodology are tested using a real MV network of the Cyprus distribution system and the strategic plan of the Cyprus Distribution System Operator (DSO) for maximizing RES hosting capacity is outlined. © The Institution of engineering & technology 2024.

关键词： Distribution transformers

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Intelligent Fall Detection and Alert System for the Elderly Using Yolov8 and Cloud-Based Analytics 5

Intelligent Fall Detection and Alert System for the Elderly ...

引用

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

作者： Papan, Vishal Maheswari, S. Vellore Institute of Technology Dept. of Computer Engineering Chennai India

ISBN: (纸本)9798350379945

As people age, the frequency and impact of falls among the elderly continue to escalate, presenting significant health and mobility risks. Efficient detection and immediate response are crucial in reducing the severity of injuries and potentially life-threatening complications associated with such falls. This research study introduces a real-time alert system for the elderly by utlilizing the cutting-edge capabilities of YOLO v8, the latest iteration in the You Only Look Once series of models known for its high speed and accuracy in object detection tasks. The system is designed to operate with inputs from a variety of sources, including direct video feeds, RTSP streams from existing CCTV infrastructure, static images, and data from wearable sensors. This multi-source approach ensures that the system is adaptable for use in a wide range of environments, from personal residences to care facilities, providing continuous monitoring with enhanced accuracy. Upon the detection of a fall, the system immediately activates an alert protocol via Amazon Web Services (AWS) Simple Notification Service (SNS), ensuring that caregivers or family members are promptly notified to respond swiftly to the incident. This data-driven approach not only enhances the accuracy and responsiveness of the system over time but also aids caregivers and healthcare providers in understanding fall patterns and potential risk factors associated with specific environments or individuals, effectively addressing the critical challenges in elderly fall prevention and care. © 2024 IEEE.

关键词： Web services

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eDermaCare: Enhancing Skin Disease Classification with ResNet50 18

eDermaCare: Enhancing Skin Disease Classification with ResNe...

引用

18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Karambele, Aakanksha Tamhane, Mrunal Jayanty, Pranati Patil, Rutuja Shirsat, Kavita Vidyalankar Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9789380544519

In today's world, skin diseases are caused by various factors and affect a person's mental and physical wellbeing. Particularly in private areas, there is considerable reluctance to disclose skin diseases, especially among female patients, which further intensifies the challenge of early detection and treatment. To tackle this issue, the study proposes the implementation of eDermaCare, a novel approach leveraging the advanced Residual Network-50 (ResNet-50) convolutional neural network architecture. Several convolutional neural network (CNN) architectures were explored, but unsatisfactory results were obtained. ResNet-50 has the ability to capture complex features from skin images. The goal is to improve skin disease classification using ResNet-50's deep learning capabilities. This model uses the rest of the course to explain training and retention issues. Block balances allow training deep networks without suffering from degradation in performance. As a result, the model achieves a classification accuracy of 76.07%. The proposed system wm help improve early detection and treatment of skin diseases, address societal challenges, and support effective healthcare. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： classification CNN Deep Learning Resnet50 Skin disease

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Optimizing Kidney Stone Detection with Vision Transformers and CNNs: A Transfer Learning Strategy

Optimizing Kidney Stone Detection with Vision Transformers a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Suhaas Rao, S. Chopra, Tushar Kumar Yeshwanth, S.P. Bhanushree, K.J. Suneetha, K.R. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Kidney stones are a common and impactful urological condition, with timely and accurate diagnosis being critical to prevent complications such as renal damage and avoid invasive treatments. This study explores the use of advanced deep learning models, including Vision Transformers (ViT), Swin Transformer and Convolutional Neural Networks (CNNs) - specifically XResNet-50, VGG-16, MobileNetV2 and AlexNet - to enhance kidney stone detection from CT scan images. Leveraging a robust dataset of annotated CT scans in JPG format, the approach employs transfer learning which leverages features learnt from the ImageNet dataset to optimize model performance, focusing on classifying cases as either "Kidney Stone Present"or "Kidney Stone Absent". To further improve diagnostic reliability, the Grad-CAM (Gradient-weighted Class Activation Mapping) identifies and localizes kidney stones within CT images, enhancing understanding of the model's decision-making and supporting interpretability. Addressing challenges related to dataset imbalance, model interpretability, and generalizability across healthcare settings. By refining these models for kidney stone detection, the main objective of this work is to build a reliable diagnostic tool that aids in clinical judgement and treatment planning, ultimately enhancing patient outcomes and encouraging broader clinical adoption. © 2025 IEEE.

关键词： Convolutional neural networks

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Voice Analysis for Detecting Artificial Speech and Predicting Gender

Voice Analysis for Detecting Artificial Speech and Predictin...

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bharadwaj, Rakhi Dugad, Raj Gile, Akshata Patil, Gyaneshwari Hatyalikar, Shrinivas Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350373783

This research paper presents a comprehensive voice analysis system capable of detecting artificial speech and accurately predicting the gender and age of the speaker. Leveraging machine learning, acoustic features, and spectral analysis, the system distinguishes between natural human speech and artificial synthesis with high accuracy. It also excels in predicting the gender and age of the speaker, offering versatile applications in marketing, human-computer interaction, and demographic analysis. The study not only introduces an innovative solution to these challenges but also emphasizes its practical applicability across various domains, underscoring the relevance of voice analysis in today's technology landscape. © 2024 IEEE.

关键词： Prediction models

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

引用

2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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Automail AI: Intelligent Voice-Controlled Email Generator 3

Automail AI: Intelligent Voice-Controlled Email Generator

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

作者： Jayaram, Kavitha Mandagere, Samarth Gopisetti, Sampriti Sahit, S. Bnm Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331515911

The complexity of contemporary communication further emphasizes the need to automate monotonous work to increase efficiency and effectiveness. This paper introduces a new advance, voice-controlled Automail AI, in the form of an email generation tool that automatically sends an email according to end-user-specified keywords. Automail AI utilizes NLP, ML, and Speech Recognition to differentiate formal emails from informal ones and draft appropriate responses as the situation demands. It has been tuned with Naive Bayes Classifier that yields a better level of accuracy as compared to models such as Decision Tree, SVM, and Logistic Regression. Setting the benchmark for Automated Communication tools, Automail AI combines diverse technologies like SMTP to guarantee secure Email delivery, Google Text-to-Speech or GTTS for auditory feedback, and a module providing an interface for speech recognition that offers the possibility to use the tool with no hands. © 2025 IEEE.

关键词： Automail AI email generation GTTS machine learning Naive Bayes natural language processing SMTP speech recognition

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Enhancing Doodling Experience Using Generative Adversarial Networks 2

Enhancing Doodling Experience Using Generative Adversarial N...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Ashtagi, Rashmi Rathi, Devanshu Handoo, Ripul Purohit, Rishi Madhavaswala, Abbas Nogamawala, Mohammad Vishwakarma Institute of Technology Dept. Of Computer Engineering Pune India

ISBN: (纸本)9798350388916

Interacting with AI systems solely through textual prompts often encounters limitations due to the challenges of expressing complex visual ideas in text. This paper introduces an advanced AI system that interprets and visualizes users' sketches by employing state-of-the-art Generative Adversarial Networks (GANs), such as pix2pix, SketchGAN, DCGAN, and ESRGAN. This system not only significantly enhances real-time interactions but also shows exceptional proficiency in image editing tasks, thus facilitating more intuitive communication between humans and AI. By enabling rapid and accurate visualizations, this technology substantially streamlines and improves design workflows in the architecture and fashion industries. The advancements fostered by this system contribute significantly towards achieving greater capabilities akin to Artificial General Intelligence, enhancing creative processes and production efficiencies. © 2024 IEEE.

关键词： Generative adversarial networks

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Towards Vision Based Bi-Cycles Detection and An Autonomous Decision Making System for Driving Situation Awareness 13

Towards Vision Based Bi-Cycles Detection and An Autonomous D...

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13th International Symposium on computer Science and Educational technology, ISCSET 2024

作者： Nine, Julkar Mehta, Ayushkumar Rajkumar, Sai Rohit Hardt, Wolfram Chemnitz University of Technology Dept. of Computer Engineering Chemnitz Germany

ISBN: (纸本)9798350390803

In response to the growing global emphasis on healthy living and environmental consciousness, the popularity of eco-friendly transportation options like bicycles has surged. However, cyclist safety remains a critical concern, particularly in interactions with automobiles, where accidents often result in severe consequences. This project aims to enhance cyclist safety through machine learning-based bicycle detection and situational awareness techniques, aiming to mitigate vehicle-bicycle collisions. By integrating OpenCV lane detection and YOLOv8-based vehicle/bicycle detection with distance estimation, the system can analyze real-Time road conditions and make informed decisions, including speed reduction, overtaking safety assessment, and maintaining safe distances from cyclists. Testing validates the system's accurate detection, precise analysis, and effective decision-making capabilities, ensuring vehicles respond appropriately to encountered road scenarios. Ultimately, this project addresses the pressing need for cyclist safety in vehicle-rich environments, leveraging machine learning, real-Time analysis, and advanced detection technologies to foster safer roads. © 2024 IEEE.

关键词： Bicycles

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Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN and Hybrid Models with Ultrasound Imaging

Advancing Pediatric Appendicitis Diagnosis: Harnessing CNN a...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Kulkarni, Supritha Sharma, Sanchit Manavi, J. Vachana, U. Bhargavi, M.S. Bangalore Institute of Technology Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798331508685

Pediatric appendicitis, an acute inflammatory disease, arises from the obstruction of the appendix, often due to inflammation or a fecalith. This common abdominal emergency in children presents diagnostic challenges due to its varied clinical manifestations, which can lead to misdiagnosis or higher perforation rates stemming from atypical symptoms. This research investigates the use of deep learning, more especially Convolutional Neural Networks (CNNs), by utilizing ultrasonic imaging data, which is less expensive, non-invasive, and quicker than the other approaches now in use. Several variants are evaluated including a basic CNN and more advanced hybrid models such as CNN-Support Vector Regression, CNN-Transformer, and Hierarchical Deep CNN, to identify the most optimal model to significantly enhance diagnostic accuracy, for young patients facing this condition. The study's main goal is to develop a highly accurate and dependable diagnostic tool for evaluating the severity of pediatric appendicitis by differentiating between simple and complex cases and identifying whether surgery is required to inform treatment plans. Various challenges that need to be tackled are managing significant dataset imbalances, enhancing interpretability and transparency for real-world clinical settings, and ensuring the robustness and adaptability of all these models across various healthcare environments. © 2025 IEEE.

关键词： Pediatrics

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