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2nd International Conference on computer Vision and Internet of Things, ICCVIoT 2024

作者： Kalpana, T. ThamilSelvan, R. Chitra, K. Rojaa, K.M. Santhiya, M. Sowmiyavarshan, A.D. Department of Computer Applications Kongu Engineering College Erode India Department of Computer Science and Engineering Kongu Engineering College Erode India

ISBN: (纸本)9781837242672

Alzheimer's disease is a well-known illness characterized by memory loss and cognitive decline. Since current treatments work best in the early stages, early detection is vital for effective management. The Magnetic Resonance Imaging (MRI) plays a key role in diagnosing Alzheimer's by providing detailed images of brain structures. Advances in deep learning have further improved MRI-based diagnosis through automated image ***2 and MobileNetV3 are lightweight convolutional neural networks (CNNs) known for their high performance and speed, making them ideal for resource-limited and real-time ***3 is a widely used CNN, known for its strong performance in image classification tasks. By training these deep learning models on complete MRI datasets, they offer the potential to aid doctors in making accurate and timely diagnoses. This approach couldlead to better outcomes and improved quality of life for *** research aims to enhance early detection and treatment methods for Alzheimer's disease, contributing to advancements in deep learning models. © The Institution of engineering & Technology 2024.

关键词： Convolutional neural networks

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A Critical Analysis on Early Stage of Lungs and Colon Cancer Detection Using CNN Algorithm 1

A Critical Analysis on Early Stage of Lungs and Colon Cancer...

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1st International Conference on Intelligent Technologies for Sustainable Electric and Communications Systems, iTech SECOM 2023

作者： Ramesh, K. Bhavadharani, C. Narmatha, V.S. Samraj, Andrews Nandhakumar, R. Department of Computer Science and Applications Namakkal India Pg and Research Department of Computer Science and Applications Namakkal India Mahendra Engineering College Department of Computer Science and Engineering Namakkal India

ISBN: (纸本)9798350371185

One of the most dangerous problems that people experience globally is lung and colon tumor that has spread rapidly to become a normal medical challenging problem. It is essential to achieve an accurate and preliminary detection in order to minimize the hike of death. The complexity of the task entirely relies on the histopathologists experience. The analysis of the histopathology images of lung and colon cancer is crucial because early and accurate diagnosis of cancer histology is actually needed and since the treatment of cancer is aimed on the kind of histology, genetic profile and stage of the illness. Histologists may even endanger the patient's life. In recent times popularity of deep learning increase and recognized in the diagnosis and treatment of medical imaging. In order to identify lung and colon cancer using histopathological image and more effective augmentation techniques, this research involves studying and modifying the current CNN model. In the past few years, CNN has reported amazing progress in the fields of CT, MRI, and ultrasound. A novel technique that combines the advantages of two imaging modalities, 3D CT scan and histopathological image processing - to produce results that are more accurate and dependable. They are trained on the LC25000 dataset. CNN are the most preferred approach of deep learning algorithm for the advanced detection of lung and colon cancer. The level of accuracy offered by CNN in this regard may help to reduce the frequency and improve evaluation and quality © 2023 IEEE.

关键词： computerized tomography

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Character Classification and Actor Recognition in Yakshagana Images Using Machine Learning Techniques and Facial Makeup Pattern Analysis 8

Character Classification and Actor Recognition in Yakshagana...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Deep Learning based Approach for Cassava Leaf Disease Detection 8

Deep Learning based Approach for Cassava Leaf Disease Detect...

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8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Kalpana, T. Thamilselvan, R. Chitra, K. Kaviya, S. Kiruthiga, M. Mahesh, C. Kongu Engineering College Department of Computer Applications Erode India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350367904

Cassava is a vital staple crop in numerous tropical countries, to enhance plant disease detection using deep learning architecture including ResNet, EfficientNet, Incetion V3 and simple CNN. By levering these advance model significant improve accuracy compared to previous methods. This approach addresses the pressing issue of food insecurity by efficiently identifying diseases in crops. Types of Cassava disease are Cassava Mosaic Disease (CMD), Cassava Green Mottle (CGM), Cassava Brown Streak Disease (CBSD), and Cassava Bacterial Blight (CBB), along with the presence of Whitefly. The proposed work uses Convolution Neural Network (CNN) to identify the leaf diseases to quickly identify the specific disease that is affecting the crop. CNN can automatically detect important features without any human supervision. A vast collection of labelled cassava leaf photos comprising multiple disease classes is utilized for training the CNN model. To advance deep learning architectures, our research integrates techniques for data augmentation and transfer learning to further enchance model performance. © 2024 IEEE.

关键词： Plant diseases

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A Comprehensive Analysis of Knowledge Graph-based Social Recommendation System 1

A Comprehensive Analysis of Knowledge Graph-based Social Rec...

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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Navuluri, Madhavilatha Bhuyan, Hemanta Kumar RGUKT-IIIT Department of Computer Science and Engineering Ongole India Department of Information Technology and Computer Applications Guntur India

ISBN: (纸本)9798331529246

Recommendation systems are crucial due to their high relevance in terms of interpretability and performance. A Social Recommendation system explores how social relations influence user choices and how users select items. In this system, user-embeddings are learned from Knowledge graphs to enhance the recommendations. The social recommendation system can be categorized into three main areas: user recommendation, movie recommendation, and explainable recommendation. User recommendation focusses on providing users with personalized services, movie recommendation enhances the movie-watching experience, and explainable recommendation aims to provide the reasoning behind the suggested items. Data sparsity and the cold start problem are significant challenges faced by RS. The Social RS model addresses these issues by leveraging both user-item interactions and user-user social relationships, thereby improving the overall quality of social network-based recommendations. Methods such as Light Graph Convolutional Network (LGCN), Hyperbolic Recommender (HSR), and Knowledge Enhanced Graph Neural Networks (KEGNN) have been proposed to enhance social recommendation performance. Evaluation metrics such as recall @N, precision @N are commonly used to assess and estimate the effectiveness of recommendation systems. © 2025 IEEE.

关键词： Knowledge graph

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Efficient Deep Learning Model for Analyzing Muscle Activity Patterns in Biomechanical Simulations

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SN computer science 2025年第2期6卷 1-13页

作者： Dangi, Dharmendra Dixit, Dheeraj Kumar Bhagat, Amit Rao, Durgesh Gupta, Jeetendra Kumar Department of Information Technology IIIT Madhya Pradesh Bhopal India Department of Computer Science and Engineering MITS Madhya Pradesh Gwalior India Department of Computer Applications MANIT Madhya Pradesh Bhopal India Department of Computer Science & Engineering LNCT University Madhya Pradesh Bhopal India Department of Computer Science & Applications Atal Bihari Vajpayee Vishwavidyalaya Chhattisgarh Bilaspur India

Human motion detection is a critical application of biomechanics, leveraging computer vision to analyse and interpret human activities. This technology is essential across various fields, including sports training, where it aids in monitoring and optimizing athlete performance, and security, where it plays a role in tracking movements in sensitive areas. In this study, we employ two distinct datasets: the Movi dataset and the MoCap CMU dataset. To address the challenge of limited data, we utilized a Deep Convolutional Generative Adversarial Network (DCGAN) for data augmentation, generating new, synthetic images to enhance the diversity and quantity of the training data. Subsequent analysis was conducted using multiple deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), VGG16, AlexNet, and AlexNet with K-Fold Cross Validation. The application of these models yielded promising results, with accuracies of 87.5%, 89.9%, 90.2%, 91.2%, and 98.3%, respectively. The findings demonstrate the effectiveness of DCGAN in improving model performance through data augmentation, as well as the potential of these deep learning models to accurately detect and classify human motion. This research contributes to advancements in biomechanics and its applications in sports science and security domains. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Biomechanics DCGAN Deep learning Human motion detection K-Fold cross validation Transfer learning

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Optimized Autonomous Computing with Trusted Resilient Data Management for IoT Emerging Networks

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IEEE Sensors Journal 2025年第11期25卷 20778-20785页

作者： Shafique, Ayesha Cheng, Benmao Zhao, Bo Alwakid, Ghadah Naif Haq, Muhammad Inaam Ul Laboratory of Intelligent IOT Technology and Applications in Universities China Jouf University College of Computer and Information Sciences Department of Computer Science Al Jowf Sakaka72341 Saudi Arabia Quanzhou University of Information Engineering School of Computer Science and Software Engineering Quanzhou362000 China

The innovative city network integrates numerous computational and physical components to develop real-time systems. These systems can capture sensor data and distribute it to end stations. Most solutions have been presented based on the centralized computing paradigm, which effectively and systematically increases data flow;however, distributed wireless technologies and heterogeneous network services continue to raise significant research problems. These challenges lower the optimization criteria and reflect communication structure around the network edges. In this research, we proposed a sustainable development for smart networks using efficient big data management with collaborative decisions for network devices. It differs from most existing work in the mentioned aspect. It applies computational lightweight intelligence for forwarding collected data using mobile collectors and reduces the congestion flow between devices on the limited bandwidth of wireless links. Moreover, the energy load is efficiently managed with edge-driven methods, and the incorporation of trusted devices ensures the integrity of the smart network. It also tackles potential communication threats smartly. Based on the experiments conducted in Network Simulator (NS-3), the proposed model enhances the efficacy of smart networks for performance metrics with reliability and effective management of resources in Internet of Things (IoT) network. © 2025 IEEE.

关键词： Mathematical models Computational modeling Intelligent sensors Data aggregation Internet of Things Security Routing Load modeling Silicon Data models

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Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

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Light(science & applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

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ETiSeg-Net: edge-aware self attention to enhance tissue segmentation in histopathological images

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Multimedia Tools and applications 2024年 1-21页

作者： Rashmi, R. Girisha, S. Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India

Digital pathology employing Whole Slide Images (WSIs) plays a pivotal role in cancer detection. Nevertheless, the manual examination of WSIs for the identification of various tissue regions presents formidable challenges due to its labor-intensive nature and subjective interpretation. Convolutional Neural Network (CNN) based semantic segmentation algorithms have emerged as valuable tools for assisting in this task by automating ROI delineation. The incorporation of attention modules and carefully designed loss functions has shown promise in further augmenting the performance of these algorithms. However, there exists a notable gap in research regarding the utilization of attention modules specifically for tissue segmentation, thereby constraining our comprehension and application of these modules in this context. This study introduces ETiSeg-Net (Edge-aware self attention to enhance Tissue Segmentation), a CNN-based semantic segmentation model that uses a novel edge-based attention module to achieve effective delineation of class boundaries. In addition, an innovative iterative training strategy is devised to efficiently optimize the model parameters. The study also conducts a comprehensive investigation into the impact of attention modules and loss functions on the efficacy of semantic segmentation models. Qualitative and quantitative evaluations of these semantic segmentation models are conducted using publicly available datasets. The findings underscore the potential of attention modules in enhancing the accuracy and effectiveness of tissue semantic segmentation. © The Author(s) 2024.

关键词： Semantic Segmentation

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Machine Learning and Artificial Intelligence in Cybersecurity: Innovations and Challenges 2

Machine Learning and Artificial Intelligence in Cybersecurit...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Khatri, Vinayak Agarwal, Gaurav Gupta, Amit Kumar Sanghi, Akash Invertis University Department of Computer Science and Engineering Bareilly India KIET Group of Institutions Department of Computer Applications Ghaziabad India Invertis University Department of Computer Applications Bareilly India

ISBN: (纸本)9798331519056

Rapid developments in machine learning (ML) and artificial intelligence (AI) have had a big impact on cyber security by providing creative answers to challenging security problems. This analysis examines how AI and ML are integrated into cyber security, emphasizing the uses, advantages, and drawbacks of each technology. To improve threat detection, malware categorization, phishing prevention, and network security, this study analyses many AI and ML methods, such as reinforcement learning, deep learning, supervised learning, and unsupervised learning. According to recent study findings, AI-driven intrusion detection systems can detect intrusions with up to 99% accuracy, which greatly lowers the number of false positives. Comparatively, compared to conventional techniques, machine learning algorithms for malware detection have increased detection rates by 20-30%. However, several technological obstacles stand in the way of AI's smooth integration with cyber security. These obstacles include problems with data quality, adversarial assaults, interpretability of models, and the computing demands of implementing AI at scale. There are other major obstacles, such as privacy and ethical considerations. Nonetheless, issues including adversarial attacks, ethical dilemmas, and poor data quality persist. This paper illustrates the usefulness of AI and ML technologies in cyber security through real-world case studies. Lastly, it emphasizes how AI and ML have the ability to revolutionize cyber security procedures by highlighting upcoming trends and research paths. This review attempts to provide useful insights for researchers, practitioners, and policymakers to harness AI and ML for a safer digital environment by providing a thorough overview and highlighting important discoveries. © 2024 IEEE.

关键词： Phishing

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