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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Bharti, Dilip Kumar Singh, Sanjay Kumar Kumar, Mukesh Kanaujia, Vishal Kumar Aeri, Manisha Abes Engineering College Computer Science And Engineering-Data Science Ghaziabad India Kashi Institute Of Technology Department Of Computer Science & Applications Mirzamurad U.P Varanasi India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun248002 India Abes Engineering College Computer Science And Engineering Data Science Department Ghaziabad India

ISBN: (纸本)9798350366846

Diffusion tensor imaging (DTI) is a neuroimaging approach that lets in for the visualization and quantification of the structural integrity of white depend fibers in the brain. In latest years, DTI has come to be an essential tool for investigating the underlying neurobiological mechanisms of autism spectrum sickness (ASD), a neurodevelopmental disorder characterized via problems in social communication and repetitive behaviors. Through the usage of DTI, researchers have identified good sized styles of structural abnormalities in the brains of individuals with ASD. these abnormalities encompass changes in the microstructure of white remember fibers, along with decreased fiber density, changes in fiber directionality, and decreased connectivity between mind areas. Additionally, there may be evidence of bizarre mind development in early formative years, with a few research finding that these structural variations are present even in very younger children with ASD. One constant finding in DTI studies of ASD is reduced white rely integrity inside the corpus callosum, a large fiber package deal that connects the two hemispheres of the mind. This abnormality has been connected to deficits in social communiqué and language in people with ASD, as well as difficulties in integrating records from distinctive brain areas. The results showed extensive structural abnormalities in people with ASD in comparison to TD individuals. © 2024 IEEE.

关键词： Diffusion tensor imaging

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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A novel approach for image retrieval in remote sensing using vision-language-based image caption generation

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Multimedia Tools and Applications 2025年第6期84卷 2985-3014页

作者： Yadav, Prem Shanker Tyagi, Dinesh Kumar Vipparthi, Santosh Kumar Department of Computer Science and Engineering Malaviya National Institute of Technology Rajasthan Jaipur302017 India School of Artificial Intelligence and Data Engineering Indian Institute of Technology Ropar Punjab Rupnagar140001 India

Recent advancements in satellite technologies have resulted in the emergence of Remote Sensing (RS) images. Hence, the primary imperative research domain is designing a precise retrieval model for retrieving the most pertinent images based on the query. Present Remote Sensing Image Retrieval (RSIR) systems use visual descriptors to characterize the primitives (such as various land-cover types) that are visible in the images. However, the visual descriptors are inadequate for defining the complicated content of RS images. To solve this problem, a new model is devised for image retrieval based on image captions. The goal is to generate textual illustrations with captions to define relations amongst objects precisely. Here, image captioning is attained based on the vision-language pre-training model. The image captions are utilized for generating features like term frequency-inverse document frequency (TF-IDF), length of text, and Bag of Words. Meanwhile, query text is utilized wherein features like TF-IDF, text length, and Bag of Words are obtained. The similarity between query text features and the image captions features has been computed on the basis of a hybrid similarity measure wherein weights are tuned with the proposed Honey Badger Political Optimizer (HBPO) to retrieve the image. The proposed HBPO provided enhanced efficiency with elevated precision of 93.3%, recall of 93.7%, F1-score of 93.5%, and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) of 0.441. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Visual languages

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A Survey and Experimental Review on data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of computer science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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Polyp Detection Using U-Net Neural Network Based Algorithm 2

Polyp Detection Using U-Net Neural Network Based Algorithm

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Singh, Devansh Shukla, Archita Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Polyp is an earlier stage of cancer development in gastro-intestinal tract. Despite the fact that numerous techniques for automatic segmentation and detection of polyps have been developed, it still remains an open problem. Examining color images captured by the board camera in endoscopy is contemplated the most reliable method for detectingdeveloping polyps. To automate this process, in this paper we present a modified U-Net Neural Network based algorithm. This algorithm is a binary classifier which labels the pixels as part of the polyp or not. This model can analyze the video of GItract frame by frame and produce the collection of frames with possibility of presence of polyp thus reducing the time required to process the GI-tract. Detecting, localizing, and segmenting polyps using Kvasir-SEG, an open-access dataset of colonoscopy images, we benchmark various modern day approaches in this work, assessing bothmethod speed and accuracy. This is attributed to outstanding performance of image classification compared to preceding techniques. The automatically identifying polyps serves to aid gastroenterologists during colonoscopies. While existing literature contains publications addressing the challenge of polyp detection, many of these systems remain confined toresearch settings and lack implementation for clinical use. Hence, we present the inaugural fully open-source automated polyp detection system, which not only outperforms the best- performing system documented in the literature but is also prepared for immediate clinical application. © 2024 IEEE.

关键词： Colonoscopy ColonSegNet Colorectal cancer Kvasir-SEG Medical image segmentation Polyp Detection U-net

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Diabetic Retinopathy Detection using Deep Learning Model ResNet15 2

Diabetic Retinopathy Detection using Deep Learning Model Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Ojha, Harsh Srivastava, Ishani Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering- Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Diabetic Retinopathy stands out as one of the common retinal diseases which has a significant threat to vision and can lead to blindness. Several problems occurred due to DR can be stopped by controlling blood glucose and timely treatment, but the manual assessment of retinal images is often a lengthy process and can prevail human errors. This paper attempts in the direction of finding an automated way to classify the set of retinal images. The study starts with the collection of retinal images and pre- processing them, followed by training of a CNN model, Utilizing Deep Learning methodologies, CNN classifies the degree of diabetic retinopathy (DR) into four categories: 0 (no DR) to 4 (proliferative DR). We evaluate our model's performance using a diverse dataset. We use Convolutional Neural Networks (CNNs) strength to DR detection, which involves three major difficulties: Classification, Segmentation and Detection. We work towards developing a GUI based system to store and maintain a series of predictions with the patient's id and name. Twilio API have been used to make SMS connectivity to patients possible in case they are not accessible. Many research activities in the previous years have many limitations and disadvantages like models used other than Resnet have low accuracy, not capturing intricate features and patterns in images, Vanishing Gradient Descent and low performance for image classification. Resnet models that are previously used in DR detection with Resnet152, we can conclude that it has the lowest Training loss, lowest Test loss and it has the lowest loss Difference. We have worked on a RESNET-152 pre-trained deep learning model, which consists of thousands of layers and allows for the capturing of intricate features, high performance of image classification, and high accuracy, in order to overcome the issues and limitations of earlier developed models. © 2024 IEEE.

关键词： Deep Learning Diabetic Retinopathy (DR) Fundus images Ophthalmologist Proliferative DR Resnet152

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A Framework for Video Based Sign Language Interpretation Using Machine Learning and Statistical Methods 4

A Framework for Video Based Sign Language Interpretation Usi...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Swathi, D. Susmetha, K. Sreeji, S. Mangairkarasi, S. B.E Computer Science and Engineering with Data Science Sathyabama Institute of Science and Technology Semmancheri TamilNadu Chennai600119 India Sathyabama Institute of Science and Technology Semmancheri Department of Computer Science and Engineering TamilNadu Chennai6001109 India

ISBN: (纸本)9798331523923

The Sign Language Recognition System has been designed to capture video input, process it to detect hand gestures, and translate these gestures into readable text. The project consists of several key components and steps: Video Processing: Using OpenCV, the system captures frames from the video input. MediaPipe processes these frames to detect and track hand landmarks in real time. OpenCV capabilities allow for efficient frame extraction and basic image processing tasks such as resizing and normalization. Hand Detection and Tracking: MediaPipe pre-trained models identify and track hand movements within the video frames. The accurate detection and tracking of the hand movements are critical for the subsequent recognition of the sign language gestures. Sign Language Recognition: The core system is the deep learning model, trained using the TensorFlow and Keras on a dataset of sign language gestures. The model learns to classify the detected hand movements into corresponding sign language characters or words. Convolutional Neural Networks (CNNs) are typically used for task due to their effectiveness in image recognition tasks. Text Display: Once the system recognizes the signs, it converts them into text and displays the output. This can be done through a console output or a graphical user interface (GUI) built with Tkinter. The GUI provides a user friendly experience, allowing users to see the translated text in real-time. © 2025 IEEE.

关键词： Convolutional neural networks

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Adaptive Treatment Planning via Multi-class Segmentation of GI Tract Tumours 6th

Adaptive Treatment Planning via Multi-class Segmentation of ...

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Jain, Samyak Pal, Aarush Andrew, J. Maheshwari, Saatvik Department of Computer Science and Engineering and Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India

ISBN: (纸本)9789819774258

This study addresses gastrointestinal cancer radiation therapy challenges by implementing advanced deep learning techniques. We focus on automating manual segmentation tasks during treatment planning to enhance efficiency and patient tolerance. Our methodology uses a diverse 3D MRI scan dataset to employ image analysis, Convolutional Neural Networks (CNNs), and Image Segmentation. The primary architectural choices involve the U-Net and InceptionResNetv2 models, contributing to developing a predictive segmentation model. This substantial result underscores the effectiveness of our approach in achieving accurate segmentations in the clinical setting. The overarching objective of our project is to automate the segmentation of the stomach and intestines in MRI scans, thereby streamlining radiation therapy planning for improved efficiency and patient care. In our evaluations, the implemented model demonstrates a commendable dice coefficient of 0.7, as indicated by metrics such as dice loss. Anticipated outcomes encompass reduced treatment times, enhanced patient comfort, and optimised tumour radiation doses while minimising healthy tissue exposure. The successful implementation of this advanced model holds the potential to revolutionise current radiation therapy practices, significantly elevating cancer treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Secure multimedia communication: advanced asymmetric key authentication with grayscale visual cryptography

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Mathematical Biosciences and engineering 2024年第3期21卷 4762-4778页

作者： Liu, Tao Vairagar, Shubhangi Adagale, Sushadevi Karthick, T. Karunya, Catherine Esther Blesswin, A. John Mary, G. Selva Tianjin Sino-German University of Applied Sciences Tianjin300350 China Department of Artificial Intelligence and Data Science Dr. D. Y. Patil Institute of Technology Pimpri Pune411018 India Department of Computer Engineering KJEI's Trinity Academy of Engineering Pune411048 India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattankulathur603203 India Directorate of Learning and Development SRM Institute of Science and Technology Kattankulathur603203 India

The secure authentication of user data is crucial in various sectors, including digital banking, medical applications and e-governance, especially for images. Secure communication protects against data tampering and forgery, thereby bolstering the foundation for informed decision-making, whether managing traffic, enhancing public safety, or monitoring environmental conditions. Conventional visual cryptographic protocols offer solutions, particularly for color images, though they grapple with challenges such as high computational demands and reliance on multiple cover images. Additionally, they often require third-party authorization to verify the image integrity. On the other hand, visual cryptography offers a streamlined approach. It divides images into shares, where each pixel represented uniquely, thus allowing visual decryption without complex computations. The optimized multi-tiered authentication protocol (OMTAP), which is integrated with the visual sharing scheme (VSS), takes secure image sharing to the next level. It reduces share count, prioritizes image fidelity and transmission security, and introduces the self-verification of decrypted image integrity through asymmetric key matrix generators, thus eliminating external validation. Rigorous testing has confirmed OMTAP's robustness and broad applicability, thereby ensuring that decrypted images maintain their quality with a peak signal-to-noise ratio (PSNR) of 40 dB and full integrity at the receiver's end. © 2024 American institute of Mathematical sciences. All rights reserved.

关键词： Medical applications

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Detecting the symptoms of COVID-19 during pandemic environment using smart spectacle thermal images and deep capsule networks

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Multimedia Tools and Applications 2025年第8期84卷 4475-4501页

作者： Dwarakanath, B. Pandimurugan, V. Mohandas, R. Sambath, M. Baiju, B.V. Chinnasamy, A. SRM Institute of Science and Technology Ramapuram Chennai India Networking and Communications School of Computing SRMIST kattankulathur Chennai India Computational Intelligence School of Computing SRMIST kattankulathur Chennai India Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of Data Science and Business Systems School of Computing SRMIST kattankulathur Chennai India

The pandemic creates a more complicated providence of medical assistance and diagnosis procedures. In the world, Covid-19, Severe Acute Respiratory Syndrome Coronavirus-2 (SARS Cov-2), and plague are widely known pandemic disease desperations. Due to the recent COVID-19 pandemic tragedies, various medical diagnosis models and intelligent computing solutions are proposed for medical applications. In this era of computer-based medical environment, conventional clinical solutions are surpassed by many Machine Learning and Deep Learning-based COVID-19 diagnosis models. Anyhow, many existing models are developing lab-based diagnosis environments. Notably, the Gated Recurrent Unit-based Respiratory data Analysis (GRU-RE), Intelligent Unmanned Aerial Vehicle-based Covid data Analysis (Thermal Images) (I-UVAC), and Convolutional Neural Network-based computer Tomography Image Analysis (CNN-CT) are enriched with lightweight image data analysis techniques for obtaining mass pandemic data at real-time conditions. However, the existing models directly deal with bulk images (thermal data and respiratory data) to diagnose the symptoms of COVID-19. Against these works, the proposed spectacle thermal image data analysis model creates an easy and effective way of disease diagnosis deployment strategies. Particularly, the mass detection of disease symptoms needs a more lightweight equipment setup. In this proposed model, each patient's thermal data is collected via the spectacles of medical staff, and the data are analyzed with the help of a complex set of capsule network functions. Comparatively, the conventional capsule network functions are enriched in this proposed model using adequate sampling and data reduction solutions. In this way, the proposed model works effectively for mass thermal data diagnosis applications. In the experimental platform, the proposed and existing models are analyzed in various dimensions (metrics). The comparative results obtained in the experiments just

关键词： COVID-19

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