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Developing Multi-Scale Patch Representations for Low Energy Data Aggregation Using Unit Block Building Model

Developing Multi-Scale Patch Representations for Low Energy ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bhuvana, J. Kumar, Ritesh Kharbas, Vikas Kumar Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

Developing Multi-Scale Patch Representations using Low energy information Aggregation is a paper examining the development of multi-scale representations from massive aggregated datasets for pc vision duties. The paper presents the low-strength records aggregation (LEDA) framework, an aggregate of electricity-green processes to extract multi-scale representations from big datasets. This paper provides a unique method for developing multi-scale patch representations for image analysis and using low-energy data aggregation. The technique utilizes records aggregation to create texture capabilities from which neighbourhood patch representations are constructed. These representations are then used to assemble more than one scale of features for analysis in pc vision structures. The proposed approach is evaluated for texture segmentation, and the consequences endorse that the patch representations received from the proposed approach outperform those acquired from conventional strategies. The paper additionally discusses the consequences of using different levels of information aggregation for generating patch representations. Subsequently, the paper explores the want for further studies to assess the overall performance gains acquired from the proposed method. © 2024 IEEE.

关键词： aggregation datasets minimizing performance processes

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Correlating Time Series Analysis with Radiotherapy for the Early Detection of Hodkin Lymphoma

Correlating Time Series Analysis with Radiotherapy for the E...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Varun Somanna, M.N. Yadav, Rakesh Kumar Inzimam Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

This study aimed to quantify the temporal capabilities of radiotherapy facts from sufferers with Hodgkin lymphoma (HL) and correlate those functions with time collection evaluation (TSA) measures to perceive potential temporal relationships with early analysis of HL. Excessive-decision 3-dimensional computed tomography (CT) scans of HL sufferers have been analyzed using batch-clever TSA methods to derive the temporal collection that characterizes the radiotherapy facts. In the end, a set of temporal features from the measured temporal series have been extracted and correlated with HL's medical and laboratory analysis. The outcomes showed that the derived temporal capabilities had undoubtedly correlated with the analysis, indicating a capability for using radiotherapy information to diagnose HL early. The mean and general deviation of the temporal collection in the radiotherapy curves from HL sufferers has been notably one-of-a-kind from wholesome patients. Furthermore, the temporal series of radiotherapy statistics from HL sufferers showed notably better peaks in the temporal capabilities than healthful patients, indicating an elevated stage of radiotherapy for HL sufferers. The effects of this observation recommend that TSA may be a viable tool for early analysis of HL, presenting a more comprehensive view of the temporal traits of radiotherapy data for HL sufferers. © 2024 IEEE.

关键词： capabilities evaluation radiotherapy relationships tomography

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Optimized Sensor Network Design for Real-Time Environmental Monitoring

Optimized Sensor Network Design for Real-Time Environmental ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Harron, Syed Shukla, Gaurav Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

A sensor community is a set of interconnected sensors that can degree, monitor, and report phenomena within the environment. Sensor networks have many packages consisting of actual-time environmental tracking. A good way to effectively use these networks for monitoring, an optimized network layout needs to be employed to ensure that the network can meet the necessities of its operational demands. This paper proposes a fashionable-motive sensor network design framework for real-time environmental tracking. The proposed framework gives a step-by-step technique to optimize a sensor network design for the project at hand. It begins with a version of the surroundings that the sensor community will screen, which is used to generate sensor node configurations and topologies. These configurations and topologies are then used to lay out the sensor community such that its deployment is optimized for the surroundings. The design framework makes use of a community optimization set of rules to attain a configuration that maximizes the network's performance and is capable of meeting its operational requirements. The set of rules operates with the aid of looking through the distance of all feasible sensor community topologies, looking for those that have the first-rate value-benefit ratio. The algorithm can be further progressed by way of the usage of the device, gaining knowledge of strategies, which makes it capable of managing extra complex eventualities. © 2024 IEEE.

关键词： Algorithm Community Eventualities Interconnected Requirements Sensor Strategies Value-Benefit

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Exploiting Convolutional Neural Networks for Automated Pathology Image Classification

Exploiting Convolutional Neural Networks for Automated Patho...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Sharma, Sunil Menaka, C. Singh, Akhilendra Pratap Vivekananda Global University Department of Computer Science & Application Jaipur India Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350383348

Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. Automatic picture evaluation has become increasingly crucial to exploit those photographs for clinical selections. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized, and their capability applicability in pathology picture classification is discussed. This overview also introduces the maximum hit CNN methods that have been proposed and have proven promising consequences in pathology photograph category duties. The paper concludes with a dialogue of future research directions in this area. Convolutional neural networks (CNNs) are a deep mastering method for the computerized pathology photograph category. With the improvement of superior imaging techniques, the quantity of virtual and representable pathology pix has increased notably. To exploit those photographs for clinical selections, automatic picture evaluation has grown to be an increasingly number of crucial. CNNs have attracted huge interest and were established to be very powerful for automated photograph classification and object detection. The improvement of deep getting-to-know based on Convolutional Neural Networks (CNNs) for computerized pathology photo classes has been a focal point of research in current years. This paper critiques the recent advances in CNNs in automatic pathology image type. The key architectures and algorithms for CNNs are summarized,

关键词： classification Convolutional development discussion pathology

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Hierarchical Representation Learning for Improved Diagnosis of Prostate Cancer on MRI Scans

Hierarchical Representation Learning for Improved Diagnosis ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Prabhu, A. Pandeya, Megha Kaushik, Harshita Department of Computer Science and Information Technology Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350383348

It focuses on using hierarchical illustration mastering (HRL) for the progressed prognosis of most prostate cancers on MRI scans. HRL is a gadget getting-to-know technique using a hierarchy of function vectors to encode record sets, allowing extra complicated non-linear styles to be recognized and applied. In this examination, HRL is compared to a fashionable convolution neural network (CNN) classifier for the challenge of prostate cancer analysis on MRI scans. The effects show that HRL outperforms the CNN classifier, providing a higher accuracy fee and universal predictive performance. Furthermore, HRL results in fewer fake positives and extra correct type accuracy. Its improved overall performance suggests that utilizing HRL in medical decision-making can offer a more correct, practical analysis of most prostate cancers. © 2024 IEEE.

关键词： convolutional improved performance predictive representation

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Enhancing Software Quality with Fault Detection and Prediction based on AI-Driven Model 1

Enhancing Software Quality with Fault Detection and Predicti...

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

作者： Bishukarma, Ramesh Mathur, Sumeet Matapurkar, Puneet Gupta, Sandeep University of Waikato Nz - Joint Institute Zhejiang University Hangzhou China Bundelkhand University Department of Mathematical Sciences & Computer Application Jhansi India Sati Department of Computer Science Engineering Vidisha India

ISBN: (纸本)9798331529246

Early defect detection and prediction are essential in software engineering to reduce costs and improve quality. This study presents an AI-driven approach for fault detection and prediction employing ML techniques on the CM1 dataset from NASA's software metrics program. The data preprocessing steps include normalisation, feature selection, and the use of the ADASYN algorithm to enhance minority class detection. Faults are predicted using ML classifiers: Extra Trees (ET), Support Vector Machine (SVM), KNN, RF, MLP, and NB. F1 score, recall, accuracy, and precision are a metrics utilised to assess a models. When it comes to defect prediction, the Extra Trees Classifier (ETC) model outperforms all of the others with an accuracy91.51%, precision91.76%, and recall99%. Although the Naive Bayes model shows lower accuracy (77.61%) and efficiency, it offers a simpler approach for fault detection. Future research in this area should be guided by these findings, which highlight the promise of AI-driven approaches to enhance frameworks for software defect prediction and quality assurance. © 2025 IEEE.

关键词： Software quality

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An Efficient Approach to Deal with Cyber Bullying using Machine Learning: A Systematic Review 2

An Efficient Approach to Deal with Cyber Bullying using Mach...

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Naveen Kumar, M.R. Vishwachetan, D. Mushtaq Ahmed, D.M. Dayananda, K.J. Manasa, K.J. Nanda, K.V. Vidyavardhaka College of Engineering Information Science and Engineering Mysuru India Ramaiah Institute of Technology Information Science and Engineering Bengaluru India Gsss Ssfgc Dept. of Computer Application Mysuru India MUSoE Dept. of Aiml Manasagangothri Mysuru India Sri Siddhartha Institute of Technology Compter Application Tumkur India

ISBN: (纸本)9798350328202

The prominence of social media platforms like Facebook, WhatsApp, and Instagram have given rise to Cyber crime, notably in the form of Cyber bullying. This unique manifestation of harassment involves individuals exploiting social networks to engage in harmful behaviors such as posting toxic comments or sharing embarrassing content, often causing severe reputation damage to victims, particularly among teenagers. This Paper gives a comprehensive survey of technical approach to tackle this escalating issue, employing advanced machine learning algorithms such as Support Vector Machines (SVM), Convolution Neural Networks (CNN), and J48. The objective is to review models capable of accurately detecting Cyber bullying, thereby contributing to the creation of robust systems that promote a safer online environment, focusing on safeguarding vulnerable demographics like teenagers and also possible future direction. © 2024 IEEE.

关键词： Support vector machines

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An Enhanced Power Energy Environment and Intelligent Control System for Solar Energy Harvesting System

An Enhanced Power Energy Environment and Intelligent Control...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Saraswat, Birendra Kumar Mani, Chandra Chaudhary, Akshita Tiwari, Devesh Sagar, Pramod Kumar Raj Kumar Goel Institute of Technology Computer Science & Engineering UP Ghaziabad India Srm Institute of Science and Technology Department of Computer Application UP Modinagar India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350357769

This paper affords a stronger energy power environment and wise management gadget (PEECS) for a solar strength harvesting device. The proposed gadget leverages statistics amassed from numerous assets inside the surroundings and utilizes intelligent algorithms to autonomously alter the photovoltaic (PV) panel and optimize the solar energy harvesting overall performance. Via deciding on the perfect actuators, the PEECS enables a solar strength harvesting gadget to function in a huge variety of PV panels with diverse specifications and environmental situations. The proposed machine video display units the strength generated on the PV panel degree detects faults inside the operation of the PV panel and actively adjusts its tilt and orientation to maximize the power harvested. The machine can autonomously hit upon the pleasant working configuration for the PV panel underneath converting environmental situations. This paper offers an in-intensity description of the hardware setup and associated algorithms used for operation. A laboratory-scale PV device with three one of a kind PV panel kinds changed into used to validate the system's overall performance and solidify the layout of the smart control algorithms. Outcomes show that the proposed system outperforms a static solar strength harvesting gadget and achieves 12.2% development in strength harvested. © 2023 IEEE.

关键词： Configuration Environmental Management Photovoltaic Strength

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Investigating the Impact of Public-Key Infrastructure in Information Security Models for Networked applications 5th

Investigating the Impact of Public-Key Infrastructure in Inf...

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5th International Conference on Data science, Machine Learning and applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Exploring Deep Learning Techniques for Automated Cataract Detection in Digital Camera Imagery

Exploring Deep Learning Techniques for Automated Cataract De...

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2024 International Conference on Advances in Modern Age Technologies for Health and engineering science, AMATHE 2024

作者： Kamepalli, Sujatha Veeranjaneyulu, N. Bodapati, Jyostna Devi Vignan's Foundation For Science Technology And Research Dept. Department Of Information Technology And Computer Application Guntur India Vignan's Foundation For Science Technology And Research Dept. Department Of Advnced Computer Science And Engineering Guntur India

ISBN: (纸本)9798350371567

Cataracts, a prevalent eye condition impacting vision, demand accurate and prompt identification for effective risk reduction and blindness prevention. Traditional methods of cataract diagnosis rely heavily on manual examination by ophthalmologists, which can be time-consuming and subject to human error. With the advent of digital imaging technology, there is an opportunity to enhance the efficiency of cataract detection. The deep neural network models VGG19 and ResNet-50 are recommended in this paper for automatic cataract identification on digital camera images. To optimize network performance, adjustments to loss and activation functions were made, incorporating minimal kernels, reduced training parameters, and layers. This resulted in a ResNet-50 model with significantly decreased computational cost and average operating time compared to the cutting-edge models. The proposed ResNet 50 model achieved an average accuracy of 93.41%, outperforming the VGG 19 model, which had an accuracy of 91.13%. This research contributes to the evolution of cataract detection methodologies, emphasizing the potential of deep learning models, particularly ResNet-50, in improving diagnostic accuracy and operational efficiency for enhanced eye health outcomes. © 2024 IEEE.

关键词： Digital cameras

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