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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kumar, Gotte Ranjith Jeyasudha, J. Ravi, G. Jagadesh, T. Madhura, G.K. Sr University School of Computer Science & Artificial Intelligence Warangal India Srm Insitute of Science and Technology Department of Computational Intelligence Chennai India Sona College of Technology Dept of Ece Salem India Kpr Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Wireless Capsule Endoscopy (WCE) is an effective utensil with probe alimentary region diseases and executes effortless visualizing in intestine. Numerous enterprises produce its broad connection and adaptation challenging things comparable efficacity, forbearance, immunity, and consummation. Self-regulated study of the WCE handed datafile is of immense significance for discriminating peculiarity. Detecting a case's gastrointestinal region through WCE outcomes a huge datafile that involves a significant quantum moment with an exceptional competence put from the therapeutic knowledge. So many computers-backed, eyesight-grounded results can be recommended to determine this controversy, so far it don't give asked position in delicacy, farther advancements just the same as demanded. Combine with deep neural network to develop a proposed model called BIR (bleedy image recognizer) to classify images of bleeding detected by a WCE. The BIR model is combination of MobileNet and a custom-built BERT model. BIR utilizes the MobileNet model for original-position calculation for its lesser calculation energy demand and latterly the affair is fed to the BERT model for farther processing. utilized a datafiles consisting of 1650 pictures captured by WCE for the purpose of training and testing the BIR mode. © 2023 IEEE.

关键词： BERT models Bleedy image recognizer computer vision deep learning gastrointestinal tract infection Wireless capsule endoscopy

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Detection and Classification of Brain Tumor Using Machine Learning 3

Detection and Classification of Brain Tumor Using Machine Le...

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3rd International Conference on Smart Technologies and Systems for Next Generation Computing, ICSTSN 2024

作者： Kale, Prachi V. Gadicha, Ajay B. Dalvi, G.D. P R Pote College of Engineering & Management Department of Computer Science and Engineering Maharashtra Amravati India P R Pote College of Engineering & Management Department of AI Maharashtra Amravati India Allard College Department of EXTC Maharashtra Pune India

ISBN: (纸本)9798350391565

Diagnosing a brain tumor typically necessitates the expertise of a radiologist, whose skills and knowledge are essential to the process. Since the overall number of individuals has grown, so too has the quantity of medical data required to be managed, rendering outdated methods extremely costly and inefficient. Many investigators looked into different techniques that had been quick and effective in identifying and categorizing brain tumours. Currently, Machine learning (ml) techniques have gained popularity in the development of computerized systems that can quickly and effectively diagnose or segment brain tumours. The deep learning (DL) enables the application of ml frameworks for brain tumor identification. This paper proposed various ml models to analyze the MRI images. First, preprocessing and augmentation algorithms were applied to the MRI brain images. We evaluated the performance of several models, including LR, SVC, kNN, NB, NN, RF, and K-means clustering. The analysis focused on accuracy, precision, recall, F1-score, and AUC. LR (96%), NN (95%), and RF (96%) showed the highest performance across all metrics, effectively distinguishing between tumor and no-tumor images and providing reliable results. © 2024 IEEE.

关键词： K-means clustering

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Diabetic Retinopathy Severity Detection an Automated Tool 1

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World Conference on Information Systems for Business Management, ISBM 2023

作者： Rao, Supriya B. Shetty, S. Shailesh Padmanayana Thanmayee, S. Divya Department of AI&ML New Horizon College of Engineering Bangalore India Department of CS and Business System Srinivas Institute of Technology Mangalore India Department of CSE Srinivas Institute of Technology Mangalore India Department of Data Science Srinivas University Institute of Engineering and Technology Mangalore India Department of CSE AJ Institute of Engineering and Technology Mangalore India

ISBN: (数字)9789819986125

ISBN: (纸本)9789819986118

Diabetics start to occur when there is not enough insulin secretion in the body. Diabetes is the primary cause of diabetic retinopathy in patients, which leads to vision impairment. The main reason for vision impairment is that lesions get accumulated in the retina. Early detection of diabetic retinopathy using the fundus images surely helps in faster recovery of the patient. Manual detection of the severity of diabetic retinopathy requires time, which may cause a delay in cure. To automate the process of severity detection, a convolutional neural network (CNN) is developed and presented, which is capable to predict the severity of the diabetic retinopathy in the fundus images. A model developed can learn the features of the retinal images and predict either of the five stages of diabetic retinopathy that is non-proliferative diabetic retinopathy, mild non-proliferative diabetic retinopathy, moderate non-proliferative diabetic retinopathy, severe non-proliferative diabetic retinopathy, and proliferative diabetic retinopathy. Images were enhanced using various methods. After conducting tests on the model using random test data, the model proved to be reliable resulting in 95.5% specificity and 93.5% sensitivity. With this specificity and sensitivity, our model is reliable when deployed to the real world where the doctors in the medical field can rely on in detecting the presence of diabetic retinopathy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image enhancement

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SE-Resnet152 Model: Early Corn Leaf Disease Identification and Classification using Feature Based Transfer Learning Technique

SE-Resnet152 Model: Early Corn Leaf Disease Identification a...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Kalpana, Ponugoti Chanti, Yerrolla Ravi, G. Regan, D. Pareek, Piyush Kumar Avn Institute of Engineering & Technology Department of Cse Hyderabad India Sr University School of Computer Science & Artificial Intelligence Warangal India Sona College of Technology Dept of Ece Salem India Siddartha Institute of Science and Technology Department of Ece Puttur India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350313413

Various corn leaf diseases reduce the quantity and quality of corn crop production, so early detection and classification are important for preventing crop yield. However, the detection and classification of corn leaf disease are more difficult due to the regions of the leaf blur and noise effect. To solve the above-mentioned problems, a feature-based transfer learning approach called the Convolutional Deep Learning (CDL) model is proposed. First, the images of corn leaves taken from Plant Village are preprocessed with Green Channel Conversion (GCC) and Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithms to enhance low-contrast images. Then the important features are extracted through ResNet152, which contains two fully connected layers with a sigmoid function. Next, squeeze and excitation are combined with ResNet152 to gain better performance in detection and classification. The proposed SE-ResNet152 model outperforms with a Precision 98.71%, Recall 98.14%, F1-score 96.87% and accuracy 99.02% respectively compared to existing model like U-Net and ResNet50 models. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Green Channel Extraction Low-Contrast Images ResNet152

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FAT-HuBERT: Front-End Adaptive Training of Hidden-Unit BERT For Distortion-Invariant Robust Speech Recognition

FAT-HuBERT: Front-End Adaptive Training of Hidden-Unit BERT ...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Yang, Dongning Wang, Wei Qian, Yanmin Shanghai Jiao Tong University MoE Key Lab of Artificial Intelligence Ai Institute Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350306897

Advancements in monaural speech enhancement (SE) techniques have greatly improved the perceptual quality of speech. However, integrating these techniques into automatic speech recognition (ASR) systems has not yielded the expected performance gains, primarily due to the introduction of distortions during the SE process. In this paper, we propose a novel approach called FAT-HuBERT, which leverages distortion-invariant self-supervised learning (SSL) to enhance the robustness of ASR. To address the distortions introduced by the SE frontends, we introduce layer-wise fusion modules that incorporate features extracted from both observed noisy signals and enhanced signals. During training, the SE frontend is randomly selected from a pool of models. We evaluate the performance of FAT-HuBERT on simulated noisy speech generated from LIBRISPEECH as well as real-world noisy speech from the CHIME-4 1-channel dataset. The experimental results demonstrate a significant relative reduction in word error rate (WER). © 2023 IEEE.

关键词： Speech enhancement

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Convolutional Neural Network Based Glaucoma Detection and Classification using Optic Disc Localization 2

Convolutional Neural Network Based Glaucoma Detection and Cl...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Neelagar, Mahesh B Manjunath, T.C. Patra, Raj Kumar Jagadish, Sripelli Mazumder, Debarshi Visvesvaraya Technological University Dayanand College of Engineering Bengaluru Department of ECE Karnataka Belgavi India CMR Technical Campus Department of Computer Science and Engineering Hyderabad India SR University School of Computer Science and Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of AI and DS Bengaluru India

ISBN: (纸本)9798350317558

Glaucoma in the retina is considered a significant cause of irreparable vision loss, and automated glaucoma detection on fundus images has seen the development of several approaches recently. Although early diagnosis and treatment can prevent possible blindness, manually segmenting the optic disc depends on the availability of a large number of labeled samples for the training phase. The proposed method utilizes U-Net for segmentation based on labeled and unlabeled data for prediction and data enhancement with a large number of feature channels in up sampling. This approach can improve overall performance by considering pixel-level and image-level labels during network training and encoding the data train, differentiating U-Net from Convolutional Neural Network (CNN). Classification is performed using CNN for diagnosing glaucoma, evaluated on a large training dataset. The obtained results demonstrate that the proposed U-Net model achieves better accuracy, reaching 0.99 on the Drishti dataset. These results ensure accurate segmentation and classification performance compared to other existing methods such as CNN and Multi-layer Neural Network. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Deep Neural Network Glaucoma Detection ResNet-50 and U-Net

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Real-time Gesture Recognition for Sign Language Communication with CNN

Real-time Gesture Recognition for Sign Language Communicatio...

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2024 International Conference on Distributed Systems, computer Networks and Cybersecurity, ICDSCNC 2024

作者： Pandiyan, K. Kamal Raj, T. Monikarchana, Y. Jayashree, A. Judith Varshini, J.C. Shenbaga Valli, S. Srm Arts and Science College Kattankulathur Department of Cs with Ai Chennai India RajaRajeswari College of Engineering Department of Cse Banglore India Mohan Babu University Department of Mathematics India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala EngineeringCollege Avadi Department of Computer Science and Business Systems Chennai India Aurobindo Pre University College Department of Computer Science Bangalore India J.P. College of Engineering Department of Computer Science and Engineering Tenkasi India

ISBN: (纸本)9798350375442

Communicating with hearing-impaired people is difficult. Meeting society's requirements requires developing systems that can recognize indications and notify people. Deaf-mute people now have better support thanks to sign language. Sign language-using hearing-impaired persons can enhance their communication and social skills with real-time gesture detection devices. The author presents a full CNN-based, real-time sign language recognition approach in this research. This system includes data collecting, preprocessing, architectural design, training, and real-time deployment. CNNs are trained on a vast and diverse sign language motion dataset. To increase model generalizability across signals and contexts, data preparation generally includes normalization, scaling, and augmentation. For optimal picture recognition, the CNN architecture is thoroughly trained and verified. A real-time video processing pipeline captures and prepares frames for CNN-based recognition. The model uses quantization and hardware acceleration for real-time performance to enhance efficiency. User-friendly interfaces make the system accessible to hearing-impaired people. Testing and assessment have shown the system's accuracy and resilience, permitting its actual deployment. Continuous improvement involves monitoring system performance and altering the model to fix issues and improve usability. © 2024 IEEE.

关键词： Gesture recognition

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Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

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Research in Astronomy and Astrophysics 2024年第1期24卷 51-58页

作者： Atila Poro Ehsan Paki ailar Alizadehsabegh Mehdi Khodadadilori Selda Ranjbar Salehian Mahya Hedayatjoo Fatemeh Hashemi Yasaman Dashti Fatemeh Mohammadizadeh Astronomy Department of the Raderon AI Lab. BurnabyBCCanada Faculty of Electrical and Computer Engineering Department of PhotonicsUniversity of TabrizTabriz 51664Iran Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Astronomy Students'Scientific Association University of TabrizTabriz 51664Iran Department of Physics Iran University of Science and TechnologyTehranIran Institute for the Intellectual Development(Kanoon) Astronomy GroupBojnordIran Department of Mechanical Engineering Amirkabir University of TechnologyTehranIran Aerospace Engineering Department Science and Research Branch of Islamic Azad UniversityTehranIran

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary *** this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere *** sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 *** reviewed previous studies on 2D relationships and updated six parameter ***,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were *** selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic *** results show that the systems are in good agreement with the results of this study.

关键词： (stars:)binaries(including multiple):close stars:fundamental parameters methods:data analysis

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Enhancing the Security in Group Message Communication Using Multi-Party Key Distribution Technique

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Journal of Mobile Multimedia 2024年第5期20卷 961-996页

作者： Haldar, Bilas Sinha, Rohit Paul, Pranam Department of Computer Science and Engineering The Neotia University Sarisha Diamond Harbour Road West Bengal South 24 Parganas 743368 India AI Labs Academy Pvt. Ltd. West Bengal Kolkata 700091 India Department of Computer Science and Engineering Global Institute of Engineering and Management Palpara More NH-34 Krishnanagar West Bengal Nadia 741102 India

Authentication and authorization techniques are essential for any kind of secure transaction over the Internet. It enables and empowers mechanisms to implement highly secure systems such as digital financial transition systems, hospitality management systems, and defence organizations. Most of the work on these techniques has been done based on single-user authorization. It is less secure than the multi-user authorization technique. Multi-party settings require the authorization of the majority user pool, the work is still in its nascent stage. Hence the presented work includes a multi-user authentication technique based on the approach of arithmetic greatest common divisor. This method aims to significantly enhance the security and reliability of the authorization process. Additionally, the proposed work also presented innovative encryption and decryption techniques for the security of data transformation using a modified N prime RSA algorithm. Furthermore, this work also discussed the application of the proposed methodology in the hospitality industry to ensure the security of customer data. The results of this work indicate that the suggested techniques enhance the security of data transformation and improve the time efficiency of key generation, encryption, and decryption techniques. © 2024 River Publishers.

关键词： authentication decryption encryption hospitality industry Key generation N prime RSA security analysis

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Cancer Diseases Diagnosis Using Deep Transfer Learning Architectures 1st

Cancer Diseases Diagnosis Using Deep Transfer Learning Archi...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Promy, Tania Ferdousey Joya, Nadia Islam Turna, Tasfia Haque Sukhi, Zinia Nawrin Ashraf, Faisal Bin Uddin, Jia Department of Computer Science and Engineering School of Data Science Brac University Dhaka Bangladesh AI and Big Data Department Endicott College Woosong University Daejeon Korea Republic of

ISBN: (纸本)9783031346187

Cancer is a lethal disease among the diseases in the world. It is clinically known as ‘Malignant Neoplasm’ which is a vast group of diseases that encompasses unmonitored cell expansion. It can begin anywhere in the body such as the breast, skin, liver, lungs, brain, and so on. As reported by the National Institutes of Health (NIH), the projected growth of new cancer cases is forecast at 29.5 million and cancer-related deaths at 16.4 million through 2040. There are many medical procedures to identify the cancer cell, such as mammography, MRI, CT scan, which are common methods for cancer diagnosis. The methods used above have been found to be ineffective and necessitate the development of new and smarter cancer diagnostic technologies. Persuaded by the phenomena of medical image classification using deep learning, our recommended initiative targets to analyze the performance of different deep transfer learning models for cancer cell diagnosis. In this paper, we have used VGG16, Inception V3 and MobileNet V2 deep architectures to diagnosis the breast cancer (KAU-BCMD dataset), lung cancer (IQ-OTH/NCCD dataset) and skin cancerHam10000). Experimental results demonstrate that VGG16 architecture shows comparatively higher accuracy by exhibiting 98.5% of accuracy for breast cancer, 99.90% for lung cancer and 93% for skin cancer dataset. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Diseases

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