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Enhancing the Efficacy Of Virtual Study Spaces In the Age Of Online Learning 4

Enhancing The Efficacy Of Virtual Study Spaces In The Age Of...

引用

4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Panda, Akshyat Kumar Dash, Anushka Johari, Kali Das, Swati Dey, Raghunath Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

In response to the evolving technology, the field of education and remote learning is undergoing a significant shift to an online mode, connecting students and educators worldwide at the press of a button. A novel virtual study room platform has been developed to efficiently handle this shift and eliminate the existing barriers. After thorough research and reviewing feedback from students during the COVID19 pandemic, the platform aims to include seamless video-audio interface fostering a sense of community, collaborative document sharing, and robust security protocols. the Pomodoro technique for effective study sessions, dedicated and secure private study rooms with identity and password systems, document and resource sharing, and real-time video and audio communication are just a few of the crucial features that this platform incorporates to overcome existing limitations. With more ideas for development, the platform has a promising future to bring about a change in the era of online learning by providing a more engaging, efficient, and collaborative space for academic interaction. © 2024 ieee.

关键词： Students

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Enhancing Security in 5G Vehicular Networks through Machine Learning-Based Attack Detection 4

Enhancing Security in 5G Vehicular Networks Through Machine ...

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4th ieee international conference on Applied Electromagnetics, Signal processing, and communication, AESPC 2024

作者： Verma, Aarzoo Bhol, Seema Gupta Pattnaik, Prasant Kumar Panda, Bishnupriya School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

the vehicular networks along with 5G technology is promising and has a potential to revolutionize transportation sector, making it both smarter and safer. By analyzing data from connected vehicles, ML can improve traffic flow, predict accidents, and optimize route planning. However, challenges such as accurately predicting vehicle movements, managing computational and network resources, and ensuring data security in the cloud persist. the proposed study is presenting a comprehensive approach that combines various ML techniques with a focus on edge computing and MLbased attack detection. Edge computing improves the performance, as data is being processed closer to its source, enabling real-time decision-making. Furthermore, incorporating ML-based attack detection mechanisms strengthens network security by identifying and responding to cyber threats promptly. this integrated strategy aims to refine classification algorithms used in traffic management, resulting in more accurate predictions and efficient resource use. By harnessing the advantages of both edge and cloud computing while implementing robust security measures, this study aspires to design an efficient and more secured transportation system powered by 5G technology. the ultimate goal is to develop a system that not only improves traffic flow and reduces accidents but also ensures data security and optimal resource management, paving the way for the future of smart mobility. © 2024 ieee.

关键词： 5G mobile communication systems

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Crop Intelligence: Optimizing Agriculture through Machine Learning-guided Recommendations 4

Crop Intelligence: Optimizing Agriculture through Machine Le...

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4th international conference on Multimedia processing, communication and Information Technology, MPCIT 2024

作者： Ankitha, K. Patil, Shruti Dept. of Artificial Intelligence and Data Science Nitte Karkala India SJEC Dept. of Intelligent Computing and Business System Vamanjur Mangalore India

ISBN: (纸本)9798350375466

Crop Intelligence, powered by Machine Learning (ML) and data analytics, is a transformative paradigm poised to revolutionize modern agriculture. this approach involves harnessing the capabilities of Machine Learning (ML) to provide tailored recommendations and insights to farmers, enabling data-driven decision-making for optimizing crop yield, resource utilization, and sustainability. this paper explores the potential and advancements in crop intelligence, focusing on how ML-guided recommendations can significantly enhance agricultural practices. Key challenges such as data quality, local adaptability, and user acceptance are identified and discussed, providing a holistic view of the landscape. Additionally, the paper highlights future directions, including personalized recommendations, integration of IoT and remote sensing, and collaborative platforms, as crucial steps toward realizing the full potential of crop intelligence. Ultimately, crop intelligence stands as a promising avenue to bolster global food security, sustainable farming, and the livelihoods of farmers through the fusion of agricultural knowledge with cutting-edge technology. © 2024 ieee.

关键词： Artificial Intelligence Crop prediction KNN Machine Learning

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DFGR: Deep Feature Graph Representation for Predicting Knee Osteoarthritis 4

DFGR: Deep Feature Graph Representation for Predicting Knee ...

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4th international conference on Neural Networks, Information and communication Engineering, NNICE 2024

作者： Xu, Tao Dai, Dawei Wang, Yuqi Li, Gen Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing Chongqing China Shanghai Jiaotong University School of Medicine Department of Orthopedics Ruijin Hospital Shanghai China

ISBN: (纸本)9798350394375

Knee osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is evaluated by observing the X-ray images of the knee joint, which is highly dependent on the subjective experience of the doctor. In this study, we developed a novel deep CNN model that integrates structural information processing to predict KOA severity automatically. Specifically, (1) the knee joint regions of the original X-ray images are segmented using automatic detection;(2) We employed a pre-trained deep CNN models to extract deep features of key regions, and construct their corresponding graph representations;(3) A graph attention network (GAT) was designed as a fine-tuning module to build a KOA prediction model. Experiments demonstrate that our method significantly improves the performance in multiple aspects. In addition, our method demonstrates good interpretability. Codes are available at https://***/ddw2AIGROUP2CQUPT/DFGR © 2024 ieee.

关键词： Forecasting

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A Transfer Learning and Feature Ranking-based Feature Extraction Approach for Enhancing Skin Lesion Classification 4

A Transfer Learning and Feature Ranking-based Feature Extrac...

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4th international conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Mohanty, Niharika Pradhan, Manaswini Mallick, Pradeep Kumar Routray, Ashanta Ranjan Fakir Mohan University Department of Information and Communication Technology Odisha Balasore India Deemed to Be University School of Computer Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

this study investigates skin lesion classification through feature fusion, focusing on transfer learning-based extraction for improved model discernment. Utilizing VGG16, ResNet, and EfficientNet B0, the research ranks features using methods like model weights, RFE, correlation-based techniques, LASSO regression, gradient boosting, and variance thresholding. the approach aims to enhance diagnostic precision by combining diverse features essential for accurate skin lesion classification. Assessing HAM 10000 and BCN 20000 datasets, the study evaluates the impact of ranking on model performance. Results consistently demonstrate that ranked feature sets outperform initial sets across classifiers (KNN, SVM, CNN) for both datasets. Notably, CNN excels with ranked feature sets from RFE applied to fused features from transfer learning networks. © 2024 ieee.

关键词： Extraction

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Pneumonia Detection Using Hybrid CNN Model 4

Pneumonia Detection Using Hybrid CNN Model

引用

4th ieee international conference on Computing, communication, and intelligent Systems, ICCCIS 2023

作者： Chaitanya Krishna, C. Rajendran, P. Naga Chaitanya, C.A. Mohamed Iabal, M. School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India CMR Institute of Technology Computer Science and Engineering Hyderabad501401 India

ISBN: (纸本)9798350306118

this medical research introduces a hybrid model using transfer learning from DenseNet201, InceptionResNetV2, and ResNet50 for superior pneumonia detection. Emphasizing the crucial value of early identification, we combine features from these architectures to enhance accuracy. Rigorous experiments on a sizable chest X-ray dataset showcase our model's superiority, achieving 95.67% accuracy, surpassing individual architectures and conventional methods. the model's improved generalization to new cases is attributed to the synergy of transfer learning and diverse architectures. Furthermore, we explore the interpretability of predictions for potential clinical insights. By leveraging these advanced techniques, our project contributes to more effective early-stage pneumonia detection, promising better health care outcomes. © 2023 ieee.

关键词： Deep learning

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An Effective Wireless communication Architecture Using Ultra Lightweight Automatic Modulation Classification Model over Cortex M4 Based Edge Device 4

An Effective Wireless Communication Architecture Using Ultra...

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4th international conference on Electrical, computer, communications and Mechatronics Engineering, ICECCME 2024

作者： Desai, Dev Gupta, Riya Shah, Shweta Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (纸本)9798350391183

the future advancement in computation, IoT and ultra-edge computing devices will require support of efficient communication methods. the presented research focuses on two parts. the first one focuses on a novel automatic modulation classification (AMC) technique developed using 1D-Convolutional Neural Network useful for STM32L4 series. the approach effectively balances performance with the stringent energy and computational constraints of the ultra-edge devices. the approached has shown 95.42% reduction in Memory requirement while achieving high classification accuracy of 79.01% along with having 99.63% at high SNR (10dB to 20dB), 92.90% at medium SNR (0dB to 10dB), and 44.78% at low SNR (-10dB to 0dB). the second part highlights a combination of multiple modulation schemes in a sequential manner, which is improvising the security of wireless communication. the model also demonstrates robust performance against noise and other channel impairments. this research provides a scalable, efficient AMC solution for ultra-edge devices, enhancing the practicality and security of intelligent communication systems. By optimising for the STM32L4 platform, this work represents a significant advancement in deploying advanced signal processing techniques on resource constrained devices. © 2024 ieee.

关键词： Signal to noise ratio

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Handwritten Text Recognition Using Machine Learning 4

Handwritten Text Recognition Using Machine Learning

引用

4th international conference on intelligent Technologies, CONIT 2024

作者： Kausalya Nandan, T.P. Ankitha, Gandla Narutham, Karnee Bharadhwaz, Maduguri Anusha, M. Chinnaiah, M.C. B.V Raju Institute of Technology Department of Electronics and Communication Engineering Medak India

ISBN: (纸本)9798350349900

computer vision tasks include image-based sequence recognition for offline handwriting recognition. Conventional methods depend on extensive linguistics knowledge, intricate feature extraction methods, and segmentation. In this paper, a novel method for handwriting identification using Connectionist Temporal Classification in conjunction with Convolutional Recurrent Neural Network is presented. the goal of this work is to categorize a single handwritten word so that handwritten material can be converted to digital format. Since each character's occurrence is contingent upon the existence of a few preceding characters, the challenge involves both sequence and classification modeling. To create a model that can segment characters and reassemble each word, we look into different neural network topologies. Our model will be trained and tested using the IAM handwriting database. Additionally, the model may be trained globally and across many languages due to the symbolic and character-independent nature of the employed approaches. © 2024 ieee.

关键词： Recurrent neural networks

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EEG-Based Classification for Autism Spectrum Disorder and Emotional State Recognition - A Critical Evaluation 4

EEG-Based Classification for Autism Spectrum Disorder and Em...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Sarvalingam, Parameswaran Vajravelu, Ashok Asick Ali, M. Bin Abdul Jamil, Muhammad Mahadi Ahammed, Syed Riyaz Selvam, Janani Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Malaysia K S R College of Engineering Department of Mathematics Tiruchengode India Nitte University Mangalore India Nitte University Department of Electronics and Communication Engineering Mangalore India Lincoln University College Faculty of Civil and Built Environment Engineering Malaysia

ISBN: (纸本)9798331529635

EEG signal processing has seen rapid advances in recent years due to innovations in machine learning, deep learning, and data preprocessing techniques. this review examines major studies from 2020 to 2024, exploring preprocessing methods, sampling rates, data samples, and model accuracy metrics across various EEG applications. through a year-wise analysis, highlight the progression in signal processing methods, the impact of higher sampling rates, and the rise of sophisticated architectures like hybrid neural networks and graph-based models. Identify notable trends, including improved accuracy rates and efficiency gains with adaptive algorithms and the integration of automated artifact removal in preprocessing pipelines. this review also contrasts the advantages and limitations of recent technologies, providing a comprehensive outlook on current capabilities and future directions in EEG signal processing. © 2024 ieee.

关键词： Adaptive algorithms

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Enhancing DFT Performance: the Power of Scan Compression and Lockup Latches 4

Enhancing DFT Performance: The Power of Scan Compression and...

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4th international conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Rani, V. Rajitha Samson, Mamatha Jntu Department of Electronics and Communication Engineering Hyderabad India Nmrec Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331529635

With the miniaturization of transistors and the exponential growth in transistor count within integrated circuits, the challenges of controlling and observing internal nodes have become increasingly pronounced, particularly in sequential circuits. this has led to the complexities associated with fault localization and debugging. Design for Testability (DFT) offers a valuable solution by enabling efficient fault detection within the circuit under test, reducing simulation time and minimizing area overhead. Various DFT techniques have been proposed for pattern simulation, and this paper focuses on comparing two prominent approaches: scan compression and internal scan. the paper discusses the inclusion of lockup latches, which enhance performance by improving test coverage, reducing test data volume, and minimizing test time. Experiments conducted on a range of benchmark circuits demonstrate that both scan compression and lockup latch significantly reduce simulation time, and test data volume while achieving high fault coverage, with a modest increase in area overhead. © 2024 ieee.

关键词： Design for testability

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