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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Kumar, Munish Gulhane, Monali Kumar, Sandeep Sharma, Harshita Verma, Rashmi Verma, Deepika Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Symbiosis Institute of Technology Nagpur Campus Symbiosis International Deemed University Department of Computer Science and Engineering Nagpur Pune India Dpg Institute of Technology and Management Department of Computer Science and Engineering Gurugram India Om Sterling Global University Department of Computer Science and Engineering Haryana Hisar India

ISBN: (纸本)9798350369861

This paper presents an improved approach to multi-face detection using ResNet50 in Python for real-world applications. The study focuses on enhancing the accuracy and efficiency of detecting multiple faces in a single frame, a challenge often encountered in real-world scenarios such as surveillance and social media platforms. The proposed method leverages the power of ResNet, a deep residual learning framework known for its superior performance in image recognition tasks. The paper discusses the implementation details of the ResNet model in Python and how it has been optimized for multi-face detection. Experimental results demonstrate that the improved ResNet model outperforms traditional face detection methods regarding accuracy and speed, making it a promising solution for real-world applications. The paper concludes with potential future work and improvements that could further enhance the model's performance. © 2023 IEEE.

关键词： Face recognition

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Enhanced hyperspectral image classification using multi-scale residual depthwise separable convolutional networks with advanced feature extraction and selection techniques

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International Journal of Information Technology (Singapore) 2025年 1-20页

作者： Medikonda, Venkata Reddy Reddy, Tatireddy Subba Yallamandaiah, S. Karthik, Sajja Anil, Chintalapudi Department of Computer Science and Engineering Marri Laxman Reddy Institute of Technology and Management Telangana Hyderabad 500043 India Department of Computer Science and Engineering B. V. Raju Institute of Technology Telangana Narsapur 502313 India Department of Electronics and Communication Engineering Vignan’s Nirula Institute of Technology & Science for Women Andhra Pradesh Guntur 522009 India Department of Computer Science and Engineering R.V.R & J.C College of Engineering Chowdavaram Andhra Pradesh Guntur 522019 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522302 India

Hyperspectral image classification faces significant challenges due to high-dimensional data and variability, often leading to inefficiencies in processing and reduced classification accuracy. Existing methods struggle with handling high-dimensional data, exhibit limited generalization capabilities, and lack efficiency in feature selection. To address these issues, this research proposes an advanced deep learning-based approach that integrates noise reduction, contrast enhancement, and min–max normalization for preprocessing. Feature extraction is performed using an Improved ResNet50 model, while optimal feature selection is achieved through the Improved Artificial Rabbits Optimization (IARO) algorithm, which effectively reduces dimensionality while preserving essential features. Classification is carried out using a Multi-Scale Residual Depthwise Separable Convolutional Network (MRDSCNN), leveraging residual learning and depthwise separable convolutions to enhance performance. The proposed approach significantly improves data quality and model accuracy, providing a robust and efficient solution for hyperspectral image classification. These innovations collectively address the limitations of traditional methods, offering a more reliable and effective classification system. © Bharati Vidyapeeth's Institute of computer Applications and management 2025.

关键词： Contrast enhancement model Feature extraction Hyperspectral imaging classification Improved artificial rabbits optimization algorithm Multi-scale residual depthwise separable convolutional neural network

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Dynamic Trust management in IoT Networks using Reputation-Based Systems

Dynamic Trust Management in IoT Networks using Reputation-Ba...

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

作者： Naidu, D J Samatha Jain, Upma Shruthi, S. Rao, K. B. V. Brahma Iqbal, Syed Mohammad Uzair Natrayan, L. Annamacharya Pg College of Computer Studies Department of Master of Computer Applications India Graphic Era Deemed to Be University Department of Computer Science & Engineering Dehradun India Bms Institute of Technology and Management Department of Artificial Intelligence and Machine Learning Bengaluru India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering India Symbiosis Law School Department of Law Pune India Simats Saveetha School of Engineering Department of Mechanical Engineering India

ISBN: (纸本)9798350375442

The Internet of Things (IoT) enables intelligent data exchange across many kinds of interconnected devices in a diverse and unpredictable physical setting. The Internet of Things (IoT) network used in this study is a cluster-based sensor network that includes a base station and an application server that offers various services in a cloud setting. In this setting, assaults may occur in both device-to-device and user-to-device communication. For pervasive devices with limited resources, the use of conventional encryption techniques to address this problem is not practical. In addition, the majority of current trust-based security solutions either address the user level or the devices level. Therefore, this study aims to tackle environment-specific security concerns at the device and user levels in light of the environment's inherent dynamic and dispersed nature. This research explored the usage of a Trust Based Security Framework (TBSF) and Reputation Based Systems in the Internet of Things (IoT) to accurately describe the actions of devices and users. Fuzzy logic was used to calculate both the device trust and the user trust. A trust-based access control system, secure cluster head selection, and safe routing were all built using these trust values. At first, the security of device-to-device communication was addressed by the design and effective implementation of the Trust Aware Device Access Control System (TADACS). © 2024 IEEE.

关键词： Access control

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An Explainable AI Model for Student Engagement in Tablet Learning 6

An Explainable AI Model for Student Engagement in Tablet Lea...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Mohan, Akshay Mathew, Merin Malavika, K. Gutjahr, Georg Menon, Radhika Nedungadi, Prema Centre for Research in Analytics Technologies and Education Amrita Vishwa Vidhyapeetham Kollam India Department of Mathematics Amrita Vishwa Vidhyapeetham Kollam India Centre for Research in Analytics Technologies and Education Dept. of Computer Science Amrita Vishwa Vidhyapeetham Kollam India

ISBN: (纸本)9798350381689

This study examines the effectiveness of tabletintegrated teaching and learning in rural India using artificial intelligence (AI) models. Academic motivation was assessed using the Instructional Materials Motivation Survey (IMMS) in the afterschool programs of seven centers located in Tamil Nadu, Uttar Pradesh, Telangana, and Karnataka. The survey is based on the ARCS model for motivation, which postulates that the traits of academic motivation include Attention, Relevance, Confidence, and Satisfaction. The main research question is which of these components contributes the most to the successful participation of a student in the tablet-integrated teaching program, where participation is measured by tablet usage by the students. Our research utilized the AI model XGBoost to examine the trait of academic motivation to make predictions about the students' participation in the program. The SHAP (SHapley Additive exPlanations) algorithm enables the interpretation of the impact of traits on predictions, which is visually depicted using Waterfall and Beeswarm plots. These graphic explanations allow us to interpret the model's prediction. In that way, the explainable AI model helps to understand methods to improve confidence in students and to relate the connection between the relevance of any technologyintegrated academic intervention program and student attendance. © 2024 IEEE.

关键词： Explainable AI Instructional Materials Motivation Survey rural education SHAP analysis tablet learning XGboost

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Compared to The Threshold Dependent Segmentation Technique, Normal Type Lung Cancer Identification in CT Images Utilizing Structural Flood Segmentation Process to Enhance Efficiency 3

Compared to The Threshold Dependent Segmentation Technique, ...

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3rd International Conference on Advance Computing and Innovative technologies in Engineering, ICACITE 2023

作者： Yadav, Ashok Kumar Kashyap, Sneha Pund, Sachin S. Mahmood Salman, Hayder Ernest Ravindran, R.S. V. Sriramakrishnan, G. Galgotias University Department of Computer Applications India Arka Jain University Department of Computer Science India Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Al-Turath Universiy College Computer Science Department Iraq Koneru Lakshmaiah Education Foundation Department of Ece India Mohan Babu University Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350399264

Image segments are a method for analyzing digital pictures that divides one piece of the image with sufficient details to match other image data.. The aim of lung tumor detection in the input figure is to identify the affected images as malignant based on the existence of a tumor. The use of a morphological watershed transforms for segmenting benign type lung tumour affected regions in CT images is suggested in this study. The proposed work is compared to threshold dependent segmentation, another image segmentation technique, with a sample size of 30 for each group. Mean Squared Error (MSE) and Universal Index Quality Measure (UIQI) parameters are used to evaluate the output of an image segmentation algorithm. Better segmentation is indicated by high MSE and UIQI values. The image thresholding and the cutoff transformation, in that order, provide the average MSE rates, mean MSE values, and means. The effectiveness of the stream is tracked the decision is made through descriptive statistics employing a picking area T-test and actual results. © 2023 IEEE.

关键词： Image segmentation

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Cloud-Based Wireless Network management System Using COPRAS Method

Cloud-Based Wireless Network Management System Using COPRAS ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Ravindran, D. Raja, S. Murugannatham, A. Deepak, Stephen Shanthi, K. Sivaraj, V. School of Management Kristu Jayanti College Autonomous Bengaluru India School of Management Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Bengaluru India Shri Krishnaswamy College for Women Department of Computer Science Tamilnadu Chennai600096 India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India

ISBN: (纸本)9798331509828

This research discusses the assessment and ranking of cloud-based wireless network solutions in a manufacturing environment and examines ways to deal with the major troubles and improve performance and reliability as well as safety. The research uses the COPRAS method which in turn is applied to evaluate a wide range of criteria including technical specifications, performance metrics, security features or scalability. Two different network solutions are assessed, named Solutions A and B which has superior composite performance according to our method. As opposed to Solution A, Solution B has advanced scalability options, superior performance metrics, strong security features, and improved cost performance. Implementation of Solution B is expected to make it easier for factories to deal with fluctuations in production volume, resulting in gains in efficiency and productivity. The study makes the point that Manufacturing operations must be able to assess the benefits and drawbacks of moving on various network solutions. However, to ensure that the solution adopted by all concerned will remain effective for some time to come, this kind vigilance is necessary. Since listening alone will do no good, there is a need for continuous surveillance and appraisal to ensure the system of Solution B maintains effectiveness in line with industry needs and standards. The study's findings are useful deeper insights into the evaluation and ranking of cloud-based wireless network solutions in the manufacturing industry making a contribution to its network infrastructure and the optimization of the operational process in manufacturing. © 2025 IEEE.

关键词： Scalability

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Metagenomic Face: Applying Machine Learning to Facial Microbiome Data for Pattern Recognition and Disease Classification 4

Metagenomic Face: Applying Machine Learning to Facial Microb...

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4th International Conference on Intelligent technologies, CONIT 2024

作者： Guru Prasad, M.S. Devadas, Raghavendra M Hiremani, Vani Jahan, Afsar Praveen Gujjar, J. Dahiya, Aryan Department of Computer Science and Engg Dehradun India Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India University Pune India Department of Computer Applications Dehradun India Faculty of Management Studies Bengaluru India

ISBN: (纸本)9798350349900

This innovative study utilizes machine learning techniques to analyze facial microbiome data, drawing inspiration from the metaphorical representation of microbial information through facial features. Leveraging the Olivetti Faces dataset as a model for facial microbiome data, the research introduces the Metagenomic Face framework. This framework facilitates the translation of microbial information into facial features, enabling the application of machine learning algorithms such as Random Forests or Neural Networks for pattern recognition. The investigation focuses on identifying correlations between facial features and underlying microbial compositions, aiming to assess the feasibility of classifying health and disease states based on these patterns. Preliminary results demonstrate promising outcomes, with precision, recall, and accuracy metrics reaching 0.94, 0.91, and 0.91, respectively, and support for the model encompassing 80 instances. These findings underscore the potential of Metagenomic Face as an innovative approach to microbiome analysis, shedding light on the intricate relationship between facial features and microbial communities. Furthermore, a comparative analysis involving Support Vector Machine, Logistic Regression, and Random Forest classifiers reveals compelling performance metrics. The Support Vector Machine Classifier attained an accuracy rate of 85%, the Logistic Regression Classifier showed an accuracy of 91%, and the Random Forest Classifier displayed a matching accuracy of 91%. These additional insights contribute to a comprehensive understanding of the Metagenomic Face framework's efficacy compared to alternative machine learning approaches, emphasizing its potential in advancing precision medicine through microbiome analysis. © 2024 IEEE.

关键词： Adversarial machine learning

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Iot monitoring lathe machine performance

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Materials Today: Proceedings 2023年 80卷 3570-3574页

作者： Sharma, Rupak Sharma, Rajeev Prabha, Y.B. Rema Devi, S. Saxena, Pankaj Rajasanthosh kumar, T. Department of Computer Applications Faculty of Science& Humanities SRM Institute of Science & Technology NCR Campus Delhi NCR Campus Meerut-Delhi Road Modinagar UP Ghaziabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Vaddeswaram India Department of English School of Liberal Arts and Special Education Kalasalingam Academy of Research and Education Krishnankoil Virudhunagar District India R.B.S. Management Technical Campus Agra India Department of Mechanical Engineering Oriental Institute of Science & Technology Bhopal India

Machines have to operate effectively and free from faults in order to enhance productivity, increase quality, and decrease costs. With automated and flexible action, this is far more likely. An accurate identification of the underlying cause Causes of failure that need repairs and the time for restoration include: A lot of Various monitoring methods are available to track the experimentally-recorded machine health. Maintaining instruments and sensors. The vibration monitoring methods in this list. It is the right fit, and it has shown itself over time. Measuring vibrations in this job three different locations resembling the stock of a gun: headstock, tailstock. When conducting turning operation with mild steel, the tool post of the lathe machine is made of metal. Data that is gathered through sensors is sent straight to the cloud in this case, such as Ubidots IoT platform. via way of nodeMCU. Whether a task is impacted or a tool is impacted, mission failure has occurred. Thus, in either instance, early warning is provided by a signature, thus the development does not progress as quickly machining. If the tail-stock seems to be broken, and this appears to be a consistent problem, then the whole batch must be inspected for errors to be inspected closely is anything that has been created under such circumstances. An optimal decision-making procedure light may be thrown on CM so processes may be machine-wise instituted in a shop there. Tools while talking about lathes that are large, medium, or tiny. Overall it is a method which is easy but yet capable of benefitting people at all levels, from the absolute beginner to experts Floor productivity is aided by training elements that are almost unnecessary. © 2021

关键词： Lathes

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Crop and Fertilizer Recommendation System Applying Machine Learning Classifiers 1

Crop and Fertilizer Recommendation System Applying Machine L...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Iniyan, S. Raja, M. Senthil Srinivasan, R. Santhanakrishnan, C. Srinivasan, Aravindan Srm Institute of Science and Technology School of Computing Department of Computing Technologies Tamilnadu Kattankulathur India Lakshmaiah Education Foundation Department of Computer Science and Engineering Andrapradesh Vaddeswaram India

ISBN: (纸本)9798350359756

In today's agriculture, increasing crop yields while minimizing resource use is critical for sustainable and profitable agriculture. The program addresses the urgent need for personalized crop and fertilizer recommendations in various agricultural regions of India. Using machine learning techniques such as linear regression, random forests, k-nearest neighbors and outlier detection, our model aims to provide farmers with clear guidance based on various environmental factors and soil. Linear regression algorithm is used to establish the relationship between input and product yield, K-Neighbors adds recommendations by identifying the similarity between farm features and neighboring farm features. This holistic approach is designed to increase the credibility of our proposal, given the unique challenges posed by India's diversity and climate. The model accepts soil characteristics, climate, crop history and fertilizer use patterns. We worked to create a variety of useful and flexible products that can be more efficient and practical and provide good results to farmers in different regions. The need of the project is an easy-to-use website that provides farmers with information about crop selection and planting. By leveraging the power of machine learning, the program helps modern agriculture, promotes sustainable practices and increases the overall profitability of Indian agriculture. © 2023 IEEE.

关键词： Crops

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HDL Environment for the Synthesis of 2-Dimensional and 3- Dimensional Network on Chip Mesh Router Architecture

HDL Environment for the Synthesis of 2-Dimensional and 3- Di...

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

作者： Kumari, Shweta Rajput, Kapil Singh, Gurwinder Jain, Arpit Sachi, Savya Manwal, Manika Shri Fanishwar Nath Renu Engineering College Department Of Computer Science & Engineering Bihar Araria India Graphic Era Hill University Computer Science And Engineering Uttarakhand Dehradun India Chandigarh University Department Of Aitcse Punjab Mohali India Koneru Lakshmaiah Education Foundation Department Of Computer Science & Engineering AP Vaddeswaram India L N Mishra College Of Business Management Department Of Information Technology Bihar Muzaffarpur India Graphic Era Deemed To Be University Computer Science And Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350366846

Application specific Network on Chip (NoC) designs are quickly becoming the technology of choice for solving the problem of multiprocessor system architecture. Broadband, interposes communication, deadlock avoidance, port bandwidth, router architecture, and bespoke NoCs may all be improved by modelling and simulation of multilayer network structures and synthesis. Topology-based organization of 2D and 3D routers allows for the construction and laDyout of NoCs. The articles compare and contrast the design and analysis of two-dimensional and three-dimensional network-on-chip routers for setting up mesh topological nodes in a network. Xilinx 14.2 is used to execute the code. The most recent version of modalism, 10.0 student edition, is used to functionally mimic the program and modules. The experimental work and thorough testing on the Virtex-5 FPGA confirm the chip design. © 2024 IEEE.

关键词： Network-on-chip

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