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Innovation and Novelty in engineering and Technology (INNOVA), International Conference on

作者： Aniket Prabhu Taradale Suvarna Laxmikant Kattimani Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapura Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only Look Once (YOLO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOLO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% Fl-Score, 96.20% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method.

关键词： YOLO Training Image segmentation Technological innovation Accuracy Magnetic resonance imaging Resonance Real-time systems Medical diagnostic imaging Tumors

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High Harmonic Generation by bright Squeezed Vacuum

High Harmonic Generation by Bright Squeezed Vacuum

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CLeO: Fundamental science, CLeO:FS 2024 - Part of Conference on Lasers and electro-Optics, CLeO 2024

作者： Rasputnyi, Andrei birk, Michael Chen, Zhaopin Cohen, Oren Krüger, Michael Kaminer, Ido Chekhova, Maria Tani, Francesco Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Friedrich-Alexander Universität Erlangen-Nürnberg Staudtstraße 7/B2 Erlangen91058 Germany Physics Department and Solid-State Institute Technion - Israel Institute of Technology Haifa32000 Israel Department of Electrical and Computer Engineering Solid State Institute Technion Israel Institute of Technology Haifa32000 Israel

ISBN: (纸本)9781957171395

We present the first ob.ervation of non-perturbative high-harmonic generation driven by nonclassical light, namely bright squeezed vacuum. Compared to classical light, bright squeezed vacuum leads to higher efficiency, different power scaling, and unique photon-number distributions. © 2024 The Author(s) © Optica Publishing Group 2024.

关键词： Squeezed light

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A Vision Transformer Approach to efficient brain Tumor Detection and Segmentation in Medical Imaging

A Vision Transformer Approach to Efficient Brain Tumor Detec...

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Data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： ebinesh K Aashish D balaji Sougoumar Anand Karuppannan B.E. Electronics Engineering (VLSI Design and Technology) Chennai Institute of Technology Chennai India Department of Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

brain tumor detection and segmentation are important tasks in the field of medical imaging that have significant potential to enhance treatment planning and accurate diagnosis. The construction of a Vision Transformer (ViT) model for the purpose of identifying brain tumor from medical images specifically MRI scans has b.en covered in this work. Renowned for its outstanding performance recognition challenges, the Vision Transformer will be utilized to improve brain tumor detection accuracy and efficiency. The proposed system will be evaluated in terms of accuracy, performance, and inference time against conventional deep learning models. This research aims to improve real-time medical image processing with ViT, resulting in faster diagnosis and more efficient tumor identification in clinical practice.

关键词： computer vision Image segmentation Accuracy Hospitals Magnetic resonance imaging brain tumors Transformers brain modeling Real-time systems Planning

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Machine Learning-driven Quality Control Strategies for Industrial IoT Applications 4

Machine Learning-driven Quality Control Strategies for Indus...

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4th Ieee Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Dankan Gowda, V. Irshad, Muhammad Aashir Mary, P. Arockia Prasad, Y. Durga Prasad, K.D.V. Tarigonda, Hariprasad Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Northumbria University 110-114 Middlesex St LondonE17HT United Kingdom V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Mohan Babu University Department of Mechanical Engineering School of Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350354218

This paper introduces a new solution for improving Quality of Control in Industrial Internet of Things (IIoT) systems by using a machine learning approach. The planned system utilizes monitoring data originating from different sensors installed in the production line to detect faults as well as estimate maintenance requirements. besides, the proposed system uses the state-of-art machine learning techniques like Convolutional Neural Networks for defect detection and Recurrent Neural Networks for Predictive maintenance with high accuracy and efficiency. computer simulations indicate a high increase in the coverage of defects, predictive capability, and overall product quality. The combination of IoT and machine learning presents a convenient and evolutionary solution that becomes progressively enhanced as more data becomes availab.e, and give a solid framework for the sustained optimization of standards in industrial practices. Future work seeks to enhance the developed models and extend the implemented system's functionalities to other application domains and bigger data sets. © 2024 Ieee.

关键词： Smart manufacturing

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Hand Talk: Sign Language to Text Converter using CNN

Hand Talk: Sign Language to Text Converter using CNN

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： N. Shirisha D. b. V. Jagannadham Padma Parshapu Sivanaga Malleswara Rao Singu V. Sravani Kumari D. Abdus Subhahan Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of E.C.E G.V.P College of Engineering Visakhapatnam India Department of Computer Science and Engineering (DS) CVR College of Engineering Hyderabad Vensai Technologies Inc. Cumming GA USA Department of CSE Godavari Institute of Engineering & Technology Rajahmundry Department: CSE B V Raju Institute of Technology

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

This research presents a novel system for converting sign language gestures into text, bridging the communication gap between non-sign language speakers and the deaf and dumb community. The system utilizes advanced image processing and machine learning techniques to accurately interpret and translate sign language. The proposed system employs a Convolutional Neural Network (CNN) classifier to process captured hand gestures and convert them into text. Through rigorous training and testing, the SVM classifier achieved a 96.7% accuracy rate for one-minute intervals. This research contributes to the development of accessib.e and efficient sign language interpretation systems, facilitating communication and promoting inclusivity. by bridging the language barrier, this technology can enhance social interactions and improve the quality of life for individuals with hearing impairments.

关键词： Training Support vector machines Sign language Text recognition Auditory system Machine learning Vectors Convolutional neural networks Research and development Testing

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Decentralized Federated Learning: A Survey on Security and Privacy

arXiv

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arXiv 2024年

作者： Hallaji, ehsan Razavi-Far, Roozbeh Saif, Mehrdad Wang, boyu Yang, Qiang The Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada The Faculty of Computer Science University of New Brunswick FrederictonNBE3B 5A3 Canada The Department of Computer Science The Brain Mind Institute University of Western Ontario LondonONN6A 5B7 Canada The Department of Computer Science and Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The Department of AI WeBank Gaungdong Province China

Federated learning has b.en rapidly evolving and gaining popularity in recent years due to its privacy-preserving features, among other advantages. Nevertheless, the exchange of model updates and gradients in this architecture provides new attack surfaces for malicious users of the network which may jeopardize the model performance and user and data privacy. For this reason, one of the main motivations for decentralized federated learning is to eliminate server-related threats by removing the server from the network and compensating for it through technologies such as blockchain. However, this advantage comes at the cost of challenging the system with new privacy threats. Thus, performing a thorough security analysis in this new paradigm is necessary. This survey studies possib.e variations of threats and adversaries in decentralized federated learning and overviews the potential defense mechanisms. Trustability and verifiability of decentralized federated learning are also considered in this study. © 2024, CC bY.

关键词： blockchain

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Retraction Note: Design of precoder for a MIMO–NOMA system using Gaussian mixture modelling

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Markkandan, S Aggarwal, Kapil Ashok, K. Selvakumarasamy, K Kaushal, Rajanish Kumar Jadhav, Makarand Mohan School of Electronics Engineering (SENSE) Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electronics and Communication Engineering Saveetha School of Engineering (SIMATS) Chennai India Department of Electrical Engineering Chandigarh University Mohali India Department of E & T C N B N Sinhgad Technical Institutes Campus Pune India

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Disease Detection In Pomegranate Plant Using Image Processing Techniques

Disease Detection In Pomegranate Plant Using Image Processin...

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Ieee International Conference on electronics, Computing and Communication Technologies (CONeCCT)

作者： Sahebgouda M Sumana Dept. of Computer Science and Engineering B.L.D.E.AsV.P.Dr.P.G.Halakatti College of Engineering and Technology Vijayapur INDIA Dept. of Information Science and Engineering MSRIT Bangalore INDIA

The majority of the people in India is dependent on agriculture. every crop the farmers cultivate has some sort of illness. The most successfully adopted subtropical fruit crop in India is the pomegranate. Pomegranates have the highest economic value of all the fruits. Pomegranate has very little immunity to infection during cultivation, especially in the early stages. As a result, it is particularly susceptib.e to the sickness. The detection of plant diseases is a beneficial use of image processing. Picture acquisition, picture pre-processing, Image segmentation, feature extraction, ob.ect recognition, and classification are all steps within the infection discovery handle.

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A Lightweight Convolutional Neural Network with Representation Self-challenge for Fingerprint Liveness Detection

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computers, Materials & Continua 2022年第10期73卷 719-733页

作者： Jie Chen Chengsheng Yuan Chen Cui Zhihua Xia Xingming Sun Thangarajah Akilan School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Key Laboratory of Public Security Information Application Based on Big-Data Architecture Ministry of Public SecurityZhejiang Police CollegeHangzhou310053China Jiangsu Yuchi Blockchain Research Institute Nanjing210044China Department of Software Engineering Lakehead UniversityThunder BayON P7B 5E1Canada

Fingerprint identification systems have b.en widely deployed in many occasions of our daily ***,together with many advantages,they are still vulnerab.e to the presentation attack(PA)by some counterfeit *** address challenges from PA,fingerprint liveness detection(FLD)technology has b.en proposed and gradually attracted people’s *** vast majority of the FLD methods directly employ convolutional neural network(CNN),and rarely pay attention to the prob.em of overparameterization and over-fitting of models,resulting in large calculation force of model deployment and poor model *** at filling this gap,this paper designs a lightweight multi-scale convolutional neural network method,and further proposes a novel hybrid spatial pyramid pooling block to extract abundant features,so that the number of model parameters is greatly reduced,and support multi-scale true/fake fingerprint ***,the representation self-challenge(RSC)method is used to train the model,and the attention mechanism is also adopted for optimization during execution,which alleviates the prob.em of model over-fitting and enhances generalization of detection ***,experimental results on two publicly benchmarks:LivDet2011 and LivDet2013 sets,show that our method achieves outstanding detection results for blind materials and *** size of the model parameters is only 548 Kb,and the average detection error of cross-sensors and cross-materials are 15.22 and 1 respectively,reaching the highest level currently availab.e.

关键词： FLD lightweight multi-scale RSC blind materials

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Federated Continual Learning: Concepts, Challenges, and Solutions

arXiv

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arXiv 2025年

作者： Hamedi, Parisa Razavi-Far, Roozbeh Hallaji, ehsan Faculty of Computer Science University of New Brunswick 550 Windsor Street FrederictonNBE3B 5A3 Canada Department of Electrical and Computer Engineering University of Windsor 401 Sunset Ave WindsorONN9B 3P4 Canada

Federated Continual Learning (FCL) has emerged as a robust solution for collaborative model training in dynamic environments, where data samples are continuously generated and distributed across multiple devices. This survey provides a comprehensive review of FCL, focusing on key challenges such as heterogeneity, model stability, communication overhead, and privacy preservation. We explore various forms of heterogeneity and their impact on model performance. Solutions to non-IID data, resource-constrained platforms, and personalized learning are reviewed in an effort to show the complexities of handling heterogeneous data distributions. Next, we review techniques for ensuring model stability and avoiding catastrophic forgetting, which are critical in non-stationary environments. Privacy-preserving techniques are another aspect of FCL that have b.en reviewed in this work. This survey has integrated insights from federated learning and continual learning to present strategies for improving the efficacy and scalability of FCL systems, making it applicab.e to a wide range of real-world scenarios. © 2025, CC bY.

关键词： Federated learning

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