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4th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2022

作者： Mohapatra, Subhashree Pati, Girish Kumar Swarnkar, Tripti Siksha O Anusandhan University Department of Computer Science Engineering Bhubaneswar India Siksha O Anusandhan University Institute of Medical Science and SUM Hospital Department of Gastroenterology Bhubaneswar India Siksha O Anusandhan University Department of Computer Application Bhubaneswar India

ISBN: (纸本)9781665402392

Colonoscopy is a procedure which helps the gastroenterologists to examine the lower gastrointestinal (GI) region for any abnormal condition. Diagnosis of diseases in the lower GI area is challenging and complicated process which requires highly experienced professional for accurate examination. In recent times deep learning has gained popularity for faster and more accurate detection of diseases and can be an efficient assist to the gastroenterologists. In this work the effectiveness of pre-trained deep convolutional neural network (DCNN) models namely GoogleNet, ResNet-50 and ShuffleNet are analyzed using the concept of transfer learning for abnormality detection in lower GI. The DCNN architectures are trained and compared using the images related to lower GI region from HyperKvasir dataset. The work also demonstrates the effect of data augmentation in enhancing the performance of DCNN. The result shows that ResNet-50 architecture performs the best in classification of lower GI images as normal or abnormal with 94.08% of accuracy, MCC of 0.879, without data augmentation, as well as 95.93% of accuracy and MCC of 0.917 with data augmentation. This study can help researchers to further explore the strength of transfer learning and design their own deep networks. © 2022 IEEE.

关键词： Image classification

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The present situation and shifts observed in wetlands within the *** Seaway region of Canada,utilizing imagery from the Landsat archive and the cloud-based platform Google Earth Engine

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Big Earth Data 2025年第1期9卷 47-71页

作者： Meisam Amani Mohammad Kakooei Rebecca Warren Sahel Mahdavi Kevin Murnaghan Arsalan Ghorbanian Amin Naboureh Digital Environmental Group WSP Canada Inc.OttawaONCanada Department of Computer Science Chalmers University of TechnologyGothenburgSweden Digital Environmental Group WSP Canada Inc.EdmontonABCanada dIndependent Researcher Department of Photogrammetry and Remote Sensing Faculty of Geodesy and Geomatics EngineeringK.N.Toosi University of TechnologyTehranIran Research Center for Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengduChina

This study examined wetland trends in the *** Seaway(~500,000 km^(2))in Canada over the past four *** this end,historical Landsat data within the Google Earth Engine(GEE)big geo data platform were *** samples were scrutinized using the Continuous Change Detection and Classification(CCDC)algorithm to identify spectrally unchanged *** spectrally unchanged samples were subsequently employed as training data within an object-based Random Forest(RF)model to generate wetland maps from 1984 to ***,a change analysis was conducted to calculate the loss and gain of different wetland ***,it was observed that approximately 45%(184,434 km^(2))and 55%(220,778 km^(2))of the entire study area are covered by wetland and non-wetland categories,*** was also observed that 2.46%(12,495 km^(2))of the study area was changed during 40 ***,there was a decline in the Bog and Fen classes,while the Marsh,Swamp,Forest,Grassland/Shrubland,Cropland,and Barren classes had an ***,the wetland gain and loss were 6,793 km^(2)and 5,701 km^(2),*** study demonstrated that the use of Landsat data,along with advanced machine learning and GEE,could provide valuable assistance for wetland classification and change studies.

关键词： Remote sensing Google Earth Engine(GEE) cloud computing satellite change detection continuous change detection and classification(CCDC) wetlands

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Comparative Assessment of Brain Tumor Detection Techniques: A Comparative Study

Comparative Assessment of Brain Tumor Detection Techniques: ...

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Advanced Computing & Communication Technologies (ICACCTech), International Conference on

作者： Pooja Dahiya Anshu Sharma Manisha Sharma Vijay Bharti Department of Computer Science and Engineering(Cyber Security) Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Science and Engineering Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Application Panipat Institute of Engineering and Technology Samalkha Affiliated by Kurukshetra University Panipat India Department of Computer Science and Engineering Apiit SD India Panipat Panipat India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

Medical picture segmentation refers to the process of categorizing the different parts of an image into anatomical components or defining the changes in structure and intensity in relation to the underlying functional process. The primary objective of this research is to analyze atherosclerotic lesions in living organisms using Magnetic Resonance Imaging (MRI). This research primarily aims to utilize computer technology to aid in the diagnosis and assessment of illness, with a particular emphasis on finding vulnerable plaques. MRI is a commonly used technique that helps to detect and forecast brain cancers in different neurological conditions and circumstances. Standard MRI sequences are frequently employed to distinguish various forms of brain tumors through visual examination of their characteristics and analysis of the contrast texture of the soft tissue. The brain tumor detection and classification system are developed using Discrete Wavelet Transform (DWT) and Support Vector Machines (SVMs). An efficient automated method for identifying a potential Region of Interest (ROI) where the existence of a tumor is certain. This study assesses the efficacy of the brain tumor extraction technique using symmetry analysis by calculating the mean and median values of the picture, specifically focusing on precision and sensitivity. Hence, the suggested technique is appropriate for rapid initial tumor extraction rather than precise segmentation.

关键词： Training Support vector machines Image segmentation Visualization Magnetic resonance imaging Brain cancer Transforms Discrete wavelet transforms Standards Tumors

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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images

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Journal of Advanced Research 2023年 48卷 191-211页

作者： Ukwuoma, Chiagoziem C. Qin, Zhiguang Belal Bin Heyat, Md Akhtar, Faijan Bamisile, Olusola Muaad, Abdullah Y. Addo, Daniel Al-antari, Mugahed A. School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China IoT Research Center College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen 518060 China Centre for VLSI and Embedded System Technologies International Institute of Information Technology Telangana Hyderabad 500032 India Department of Science and Engineering Novel Global Community Educational Foundation Hebersham 2770 NSW Australia School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu 611731 China Sichuan Industrial Internet Intelligent Monitoring and Application Engineering Technology Research Center Chengdu University of Technology Chengdu China Department of Studies in Computer Science University of Mysore Manasagangothri Mysore India Department of Artificial Intelligence College of Software & Convergence Technology Daeyang AI Center Sejong University Seoul 05006 South Korea

Introduction: Pneumonia is a microorganism infection that causes chronic inflammation of the human lung cells. Chest X-ray imaging is the most well-known screening approach used for detecting pneumonia in the early stages. While chest-Xray images are mostly blurry with low illumination, a strong feature extraction approach is required for promising identification performance. Objectives: A new hybrid explainable deep learning framework is proposed for accurate pneumonia disease identification using chest X-ray images. Methods: The proposed hybrid workflow is developed by fusing the capabilities of both ensemble convolutional networks and the Transformer Encoder mechanism. The ensemble learning backbone is used to extract strong features from the raw input X-ray images in two different scenarios: ensemble A (i.e., DenseNet201, VGG16, and GoogleNet) and ensemble B (i.e., DenseNet201, InceptionResNetV2, and Xception). Whereas, the Transformer Encoder is built based on the self-attention mechanism with multilayer perceptron (MLP) for accurate disease identification. The visual explainable saliency maps are derived to emphasize the crucial predicted regions on the input X-ray images. The end-to-end training process of the proposed deep learning models over all scenarios is performed for binary and multi-class classification scenarios. Results: The proposed hybrid deep learning model recorded 99.21% classification performance in terms of overall accuracy and F1-score for the binary classification task, while it achieved 98.19% accuracy and 97.29% F1-score for multi-classification task. For the ensemble binary identification scenario, ensemble A recorded 97.22% accuracy and 97.14% F1-score, while ensemble B achieved 96.44% for both accuracy and F1-score. For the ensemble multiclass identification scenario, ensemble A recorded 97.2% accuracy and 95.8% F1-score, while ensemble B recorded 96.4% accuracy and 94.9% F1-score. Conclusion: The proposed hybrid deep learning framewo

关键词： Chest X-ray imaging Explainable artificial intelligence (XAI) Pneumonia identification Self-attention network Transfer ensemble learning Transformer encoder (TE)

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Big data link stability-based path observation for network security

Lecture Notes on Data Engineering and Communications Technol...

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Lecture Notes on Data engineering and Communications Technologies 2021年 66卷 459-468页

作者： Arappali, Nedumaran Mekonnen, Melaku Tamene Tefera, Wondatir Teka Sundaram, B. Barani Karthika, P. Department of Electrical and Computer Engineering Wollo University Kombolcha Institute of Technology Kombolcha Ethiopia Department of Computer Science College of Informatics Bule Hora University Bule Hora Ethiopia Department of Computer Application Kalasalingam Univerity Viruthunagar Tamilnadu India

Wireless ad hoc network [WANET] systems are creating multihop communication structure between the mobiles to transfer the data groups. The remarkable qualities of remote frameworks cause the correspondence to interface among the conflicting mobiles. To manage high convey ability and biological blocks, most physical directing shows will not believe the stable associations during pack communication, which prompts the elevated delay and bundle reducing in the master-mind. The proposed research work recommends a way perception support physical steering convention that specifies POPR for WANET. The anticipated guiding show merges the relative partition, course and mid-expand forwarder center point with transfer thickness to propel data toward the objective in order to recover physical sending among the connection point. Multiplication results illustrate the projected directing convention, which performs superior to the existing arrangements. © The Author(s).

关键词： Routing protocols

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch

Stage Wise Classification and prediction of Alzheimer diseas...

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Intelligent Technologies (CONIT), International Conference on

作者： A Prabagaran Vinay Dwivedi Neelam Jyoti Rani J Angelin Blessy P Padmanabhan Gupshup Technology India Pvt Ltd Bengaluru India School of Computer Science and Engineering Galgotias University Greater Noida Uttar Pradesh India Department of Computer Science and Engineering Noida Institute of Engineering & Technology Greater Noida Uttar Pradesh India Dept of Computer Application School of Computing Science and Engineering Galgotias Univeristy India Department of Computer Science and Engineering Greater Noida Institute of Technology Greater Noida Uttar Pradesh India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Alzheimer’s, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals’ independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer’s disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer’s disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD.

关键词： Surveys Accuracy Magnetic resonance imaging Predictive models Convolutional neural networks Alzheimer's disease Kernel

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DL-Based Somnolence Detection for Improved Driver Safety and Alertness Monitoring

DL-Based Somnolence Detection for Improved Driver Safety and...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Peddinti. Neeraja R G Kumar M. Sunil Kumar Kazi Kutubuddin Sayyad Liyakat M. Sowmya Vani Department of computer Application School of Computing Mohan Babu University (erstwhile Sree Vidyanikethan Engineering College) Tirupathi AP India Computer Science and Engineering Siddharth Institute of Engineering & Technology Puttur AP Department of computer science and engineering School of Computing Mohan Babu University (erstwhile SreeVidyanikethan Engineering College) Tirupathi AP India Department of computer Science and Engineering Brahmdevdada Mane Institute of Technology Solapur Maharashtra India

This abstract explores the utilization of deep learning for detecting driver somnolence, aiming to enhance driver safety and alertness monitoring. It investigates the integration of computer vision, physiological signals, and machine learning algorithms. Key considerations include real-time detection, accuracy, scalability, and driver intervention mechanisms. By leveraging deep learning techniques, effective driver somnolence detection systems can contribute to preventing accidents and promoting safer roads.

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Robotic Restroom Hygiene Solutions with IoT and Recurrent Neural Networks for Clean Facilities 7

Robotic Restroom Hygiene Solutions with IoT and Recurrent Ne...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Nasreen, A. Kathija Shenbagapriya, M. Seeni, Senthil Kumar Veda, Parthiban Meenakshi, B. Murugan, S. Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Department of Biomedical Engineering Tamil Nadu Namakkal India Cognizant Technology Solutions Department of Mobile Application Development Buffalo GroveIL United States Srm Institute of Science And Technology Centre For Yoga College of Engineering And Technology Chenagalpattu Chennai Tamil Nadu Kattankulathur India Sri Sairam Engineering College Department of Electrical And Electronics Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

Public restrooms needs to be frequently cleaned to maintain public health and hygiene. Traditional bathroom cleaning techniques may not solve current cleanliness issues. This research proposes an advanced robotics, Internet of Things (IoT), and Recurrent Neural Networks (RNNs) solution to these problems. The proposed system uses autonomous robots with IoT sensors and cameras. Robots identify cleaning and maintenance needs in restrooms. A central system receives data from IoT sensors on cleanliness indicators, including toilet paper, soap, and foot movement. Recurrent Neural Networks (RNN) processes this data to predict and prioritize cleaning requirements. The RNN monitors toilet conditions in real time and reacts to changing use and cleaning needs. This dynamic technique optimizes cleaning resource allocation and maintains facility cleanliness. The device also warns cleaning personnel when certain areas need quick attention. This novel technique makes restroom care more cost-effective, responsive, and environmentally friendly by combining robots, IoT, and RNNs. This study advances smart facility management, which uses technology to improve public space cleanliness, user experience, and resource usage. © 2024 IEEE.

关键词： Internet of things

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Multimodal Depression Detection Using Audio Visual Cues

Multimodal Depression Detection Using Audio Visual Cues

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computer science and Emerging Technologies (CSET), International Conference on

作者： Gyanendra Tiwary Shivani Chauhan Krishan Kumar Goyal Computer Science and Engineering Bhagwant University Ajmer India Computer Application Department Raja Balwant Singh Management Technical Campus Agra India

Millions of people all around the world suffer from depression, which is a common mental health issue. Effective intervention and therapy depend on early and precise identification of depression. Multimodal techniques that incorporate audio and video data have recently demonstrated promising outcomes in the detection of depression. In this article, authors suggest a convolutional neural network (CNN) model for multimodal depression identification based on audio and video. The proposed model makes use of audio and visual elements to gather comprehensive and complementary data about depression. Pitch, intensity, and spectral information, among others, are derived from voice recordings for the audio modality. The video modality uses face landmarks, optical flow, and pose estimation techniques to combine facial expressions, head movements, and body gestures. The parallel branches of the CNN architecture process the audio and visual inputs individually. To learn discriminative audio representations, the audio branch applies three convolutional layers, followed by pooling and dense layers. To extract video-specific information, the video branch uses five convolutional layers with different filter widths and depths, followed by fully connected and pooling layers. Further a late fusion technique has been adopted for multimodal fusion, concatenating learned features from the two modalities and passing them through additional thick layers for depression prediction. Authors also used regularization strategies, including dropout and batch normalization, during training to alleviate the overfitting problem. Authors evaluated the proposed multimodal CNN model on a DAIC-WOZ dataset consisting of audio and video recordings of individuals with and without depression. The proposed model achieved 77% accuracy and hence demonstrate that the proposed multimodal CNN model achieves superior performance compared to unimodal approaches.

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Bengali language-based Disease diagnosis system for rural people using Bidirectional Encoder Representations from Transformers

Bengali language-based Disease diagnosis system for rural pe...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Kailash Pati Mandal Prasenjit Mukherjee Baisakhi Chakraborty Souvik Ganguly Computer Science and Engineering National Institute of Technology Durgapur India Bachelor of Computer Application Dr. B.C. Roy Engineering College Durgapur India

Machine intelligence is an essential branch of computer science that imitates human behavior. A human recognizes every real-world object and incident by their distinguishable features. If a computerized system incorporates human cognitive abilities, the system will be able to classify many real-world phenomena accurately. Therefore, an automated system that detects the disease based on symptoms has been addressed. Bidirectional Encoder Representations from Transformers (BERT) has been used to design this system. The BERT model has been developed based on the Transformers concept, forecasting a token by looking at every token in the series. The proposed system recognizes each and every individual’s illness in the Bengali language. Punctuation marks are stripped from the user-supplied query which is in form of disease symptoms. Tokens are obtained from the individual’s query. Tokens that have been filtered are sent to the proposed system. The proposed method employs filtered tokens to diagnose the disorder. The BERT model is trained using a dataset of diseases and their symptoms obtained from Kaggle. Precision, recall, fl-score, root mean squared error (RMSE), mean squared error (MSE), and mean absolute error (MAE) have been used to describe the proposed system’s performance. The accuracy of the proposed system is 98.12% whereas RMSE, MSE, and MAE exhibit 1.8618, 1.0852, and 1.0826 respectively.

关键词： computer science Bit error rate Predictive models Linguistics Transformers Medical diagnosis Sustainable development

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