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

作者： Puri, Shalini Soni, Akanksha Choudhary, Vishal Manipal University Department of Information Technology Rajasthan Jaipur India UIT-RGPV Department of Electronics Communication & Engineering Bhopal India Lovely Professional University Department of Computer Application Punjab India

ISBN: (纸本)9798350354218

Nowadays, a common hormonal condition that affects women of reproductive age and frequently results in infertility is Poly-Cystic ovary syndrome. Recognizing the Poly-Cystic ovary syndrome early is essential for symptom management and mitigating associated health risks. The traditional methods are used for diagnosis, encompassing high androgen levels, anovulation, and Poly-Cystic ovaries observed via ultra-sound. However, the challenging aspect of manually assessing Poly-Cystic ovaries observed via ultra-sound through follicle counting and thickness determination by medical professionals persists. In addition to addressing patient symptoms, clinicians must rely on a combination of biochemical and clinical indicators, lacking standardized diagnostic procedures. To alleviate these diagnostic challenges, this paper presents a hybrid Poly-Cystic Ovary Syndrome Detection model using a convolutional neural network comprising an ensemble inception model and a light gradient-boosting machine on ultra-sound Images. It significantly improves the detection of the Poly-Cystic ovaries observed and achieves an accuracy of 98.4%, surpassing InceptionV3's 96.3%. This study substantiates this performance gain through comprehensive quantitative effectiveness assessment metrics. This study proposes a promising model for accurate and efficient Poly-Cystic ovary syndrome diagnosis, aiding clinicians in providing timely and targeted care to affected individuals. © 2024 IEEE.

关键词： Deep neural networks

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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Automated Maternal Fetal Ultrasound Image Identification Using a Hybrid Vision Transformer Model 27th

Automated Maternal Fetal Ultrasound Image Identification Usi...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Krishna, Thunakala Bala Poreddy, Ajay Kumar Reddy Sindhu, Kolla Gnapika Kokil, Priyanka Lab Department of Electronics and Communication Engineering Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai600127 India Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Tamil Nadu Chennai India

ISBN: (纸本)9783031781940

Ultrasound (US) technology has revolutionized prenatal care by offering noninvasive, real-time visualization of maternal-fetal anatomy. The accurate classification of maternal-fetal US planes is a critical segment of effective prenatal diagnosis. However, the inherent inter-class variance among different fetal US images presents a significant hurdle, making fetal anatomy detection a laborious and time-consuming task, even for experienced sonographers. This paper proposes a novel approach using a Hybrid Vision Transformer (H-ViT) for automated fetal anatomical plane classification to address these challenges. The proposed method utilizes hierarchical features extracted from DenseNet-121, which are then inputted into the vision transformer to analyze complex spatial relationships and patterns within fetal US images. By incorporating both global and local features, the proposed method enhances feature discriminability, thus alleviating low inter-class variance. The effectiveness of the H-ViT is evaluated using the largest publicly available maternal-fetal US image dataset. The experimental results rigorously demonstrate the superiority of our approach, achieving an accuracy of 96.60% compared to other state-of-the-art techniques. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Chili Disease Detection and Classification using Various Machine Learning Techniques 1

Chili Disease Detection and Classification using Various Mac...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Sambrani, Yashodha Rajashekarappa Bhairannawar, Satish Computer Science & Engineering SDM College of Engineering and Technology Dharwad India Information Science & Engineering SDM College of Engineering and Technology Dharwad India Electronics & Communication Engineering SDM College of Engineering and Technology Dharwad India

ISBN: (纸本)9798350323795

For many developing countries like India the role of the agricultural sector is very significant. Chili has a very high economic value. India is ranked 5th in the production and 1st in exporting chili. Chili is one of the important cash crops of our country but they are vulnerable to various fungal, bacterial, and viral diseases. Among these diseases, the leaves of chili plants are more susceptible to harm, it's one main reason we have considered chili leaves for our study. This paper presents a detailed overview of various chili diseases and the work done in this field till now. Manual detection of diseases is a time consuming and strenuous task for the farmers. In this paper we are introducing various image processing and machine learning techniques for early and efficient detection of diseases in chili leaves. The diagnosis of chili disease by capturing leaf images is a very efficient and affordable system, especially for helping farmers to keep an eye on large plantations. © 2023 IEEE.

关键词： Developing countries

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Convolutional Neural Network-based image tamper detection with Error Level Analysis 2

Convolutional Neural Network-based image tamper detection wi...

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2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and electronics, ICIITCEE 2024

作者： Manjunatha, S. Swetha, M.D. Rashmi, S. Subramanian, Ananda Kumar Vinoth Kumar, V. Mallikarjuna Swamy, S. Global Academy of Technology Department of Information Science and Engineering Bengaluru India BNM Institute of Technology Department of Computer Science and Engineering Bengaluru India Bengaluru India Vit - University School of Computer Science Engineering Dept of Computational Intelligence Vellore India Vit - University School of Computer Science Engineering and Information Systems Vellore India Jss Academy of Technical Education Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350306415

Photography is the most important, powerful, and reliable means of expression. Today, digital images not only provide disinformation but also act as agents for secret communication. Users and editing professionals work with digital images for a variety of purposes. Images are often regarded as facts or proof of reality, so they are misleading and fake news or publications of any form that use images manipulated in a highly misleading way. To recognize image tampering needs multiple image data and a model that can handle all the pixels in the image. Furthermore, training the data more efficiently and needed flexibility support everyday use. Models based on Deep learning such as Convolutional Neural Networks with error level analysis (ELA) are the perfect solution. © 2024 IEEE.

关键词： Convolutional Neural Network Deep Learning Efficiency Error Level Analysis Image Forgery Detection

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IoT based Women Safety Bag

IoT based Women Safety Bag

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2023 IEEE International Conference on Innovative Data communication Technologies and Application, ICIDCA 2023

作者： Kumar, M. Ashok Pradeepa, P. Muthu, A. Essaki Krishnan, R. Santhana Saravanan, K. Narayanan, K. Lakshmi SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations. © 2023 IEEE.

关键词： Crime

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A Post-Processing Algorithm for Boosting Contrast of MRI Images

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computers, Materials & Continua 2022年第8期72卷 2749-2763页

作者： B.Priestly Shan O.Jeba Shiney Sharzeel Saleem V.Rajinikanth Atef Zaguia Dilbag Singh Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Electrical Electronics&Communication EngineeringGalgotias UniversityGreater Noida201310India Department of Electronics and Instrumentation Engineering St.Joseph’s College of EngineeringChennai600119Tamil NaduIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju61005Korea

Low contrast of Magnetic Resonance(MR)images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated ***-of-the-art contrast boosting techniques intolerably alter inherent features of MR *** changes in brightness features,induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic *** overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as *** method termed as Power-law and Logarithmic Modification-based Histogram Equalization(PLMHE)partitions the histogram of the image into two sub histograms after a power-law transformation and a log *** a modification intended for improving the dispersion of the sub-histograms and subsequent normalization,cumulative histograms are *** grey level values are computed from the resultant cumulative *** performance of the PLMHE algorithm is comparedwith traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression,a significant change in mean brightness,and contrast-overshoot.

关键词： Contrast enhancement histogram equalisation image quality magnetic resonance imaging medical image analysis post-processing

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Edge Computing for Distributed and Cooperative Media Algorithms in Connected Networks 15

Edge Computing for Distributed and Cooperative Media Algorit...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Grace, V. Lilly Gupta, Anchal Giri, Rakesh Kumar Dhope, Tanuja Satish Vittapu, Mani Sarma Dhabliya, Dharmesh G. Pullaiah College of Engineering and Technology Department of Computer Science and Engineering Andhra Pradesh Kurnool India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura India Sunrise University Computer Science & Engineering Alwar India College of Engineering Electronics and Communication Maharashtra Pune India Mallareddy Engineering College and Management Sciences Kistapur Village Computer Science and Engineering Medchal Telangana Hyderabad India Vishwakarma Institute of Information Technology Department of Information Technology Maharashtra Pune India

ISBN: (纸本)9798350370249

Side Computing is a rising technique used for connected networks' allotted and cooperative media algorithms. Part Computing refers to processing operations being carried out near the source of the facts, generally close to the edge of the network, in place in a centralized cloud. It allows information to be processed quickly, securely, and reliably in remote or low-connectivity locations, allowing dispensed computing operations inside connected networks. This distributed machine enables latency when working with significant facts sets and enhances security and reliability. Cooperating media algorithms, including laptop vision, audio processing, or natural language processing, are frequently optimized using aspect Computing in related networks. The advantages of facet Computing allow for progressed scalability, green helpful resource utilization, and better latency performance, mainly in areas that suffer from restrained or negative networking situations and bandwidth. Moreover, aspect computing enables enhanced privacy, protection, and personal revel when handling sensitive information and data stored within the cloud. © 2024 IEEE.

关键词： Computing Distributed Handling Networking Performance Scalability

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Accurate Breast Tumor Identification Using Cutting-Edge Deep Learning

Accurate Breast Tumor Identification Using Cutting-Edge Deep...

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2023 International Conference on Recent Advances in Science and engineering Technology, ICRASET 2023

作者： Acharjya, Kalyan Manikandan, M. Adlin Jebakumari, S. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350306927

Millions of people throughout the world are diagnosed with breast cancer each year, and it can be fatal in its later stages. Better patient outcomes and lower death rates depend on rapid and precise diagnosis. In this study, we provide a new method that makes use of cutting-edge deep learning algorithms to accurately detect breast cancers. To evaluate mammographic pictures with high precision, the suggested technique makes use of deep neural networks, in particular convolutional neural networks (CNNs). The main contribution is the creation of a CNN architecture that is both powerful and highly specialized for recognizing breast tumors. By autonomously learning and extracting complex information from mammograms, this architecture can distinguish between benign and malignant cancers with exceptional accuracy. A more sophisticated approach to patient management is possible thanks to the model's capacity to generate likelihood ratings for tumor malignancy. This study solves the pressing problem of lack of interpretability in deep learning models used in healthcare. The model includes an attention mechanism for seeing relevant parts of mammograms that are used to make a final diagnostic. This not only makes radiologists more confident in the model's predictions, but it also makes it easier for them to interpret and validate those predictions. In this study, we provide a deep learning-based method that greatly improves the accuracy and reliability of tumor detection, opening up a promising new path for advancing breast cancer diagnostics. This approach has the potential to significantly improve breast cancer screening and diagnosis, which might ultimately lead to earlier identification, more effective treatment, and lower breast cancer death rates. A major advance in the combat against breast cancer has been made through the integration of modern deep learning technologies into the field of medical imaging. © 2023 IEEE.

关键词： Diseases

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Resource Allocation Optimization in 5G Networks Using Variational Quantum Regressor 1

Resource Allocation Optimization in 5G Networks Using Variat...

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1st International Conference on Quantum communications, Networking, and Computing, QCNC 2024

作者： Pathak, Param Oad, Vidhi Prajapati, Aditya Innan, Nouhaila Sardar Vallabhbhai Patel Institute of Technology Dept. of Computer Engineering India Vishwakarma Government Engineering College Dept. of Electronics and Communication India Dhirubhai Ambani Institute of Information and Communication Technology Dept. of Information and Communication Technology India Quantum Physics and Magnetism Team LPMC Faculty of Sciences Ben M'sick Hassan II University of Casablanca Morocco

ISBN: (纸本)9798350366778

Quantum Machine Learning (QML) is a powerful tool for addressing complex telecommunications challenges. As 5G technology continues to evolve, our research provides a roadmap for integrating QML into the resource allocation framework, potentially improving the efficiency and performance of 5G networks. Through the application of the Variational Quantum Regressor (VQR), our study demonstrates the potential for quantum models to enhance predictive accuracy in resource allocation scenarios. Our VQR accurately aligns predicted quantum states with their intended targets, thereby reducing discrepancies. Such precision in regression analysis, particularly in the context of 5G networks, represents a significant breakthrough. A key highlight of our algorithm is its ability to substantially reduce the mean squared error to an impressively low value of 0.0081, indicating near-perfect prediction accuracy. This innovative approach is set against the backdrop of the evolving landscape of communication networks and computational systems, where the challenge of resource allocation is increasingly pronounced due to the rising demand for bandwidth-intensive and computation-heavy services. © 2024 IEEE.

关键词： Mean square error

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