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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Singh, Vinit Kumar Patel, Anoop Kumar National Institute of Technology Kurukshetra Computer Engineering Department Kurukshetra India

ISBN: (纸本)9798350364088

At present Lung Cancer is the second largest death threat to the world following heart attack. Lung cancer develops a tumor in the lung and the tumor spreads with time if it is malignant. This tumor can be detected in computed tomography(CT) Scan imagery as it shows spots in the lung region. Sometimes it is difficult to detect these spots with human eye as we have some limitation as to how much we can differentiate finer details and most of the time it is detected at later stages. Using Convolution Neural Network we can however detect much finer spots in the lung region and thus can go for further examination. CT-scan images has been used in this paper. The preprocessing operations like median filter for noise reduction and Otsu's method for thresholding has been used to make the images more prudent for classification. Three pretrained Convolutional Neural Network(CNN) models VGG16, ResNet50 and InceptionV3 have been used with modifications. Classification layers of these models have been removed and reconstructed to train on the specific dataset. Images from the preprocessing operations will be fed into the convolutional layers of these models and then output from these will be concatenated and will act as initial layer for the newly constructed dense layer having output nodes of three classes normal, benign and malignant. On the testing dataset this model provides an excellent accuracy of 98%. © 2024 IEEE.

关键词： Tumors

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Human Activity Recognition in Construction Industry Using Machine Learning Pose Estimation Technique

Human Activity Recognition in Construction Industry Using Ma...

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International Conference on Interdisciplinary Approaches in Civil engineering for Sustainable Development, IACESD 2023

作者： Manoj Kumar, M. Hegde, Bhuvaneshwari Veda Murthy, S.P. Akhila, M.K. Bhoomika, A.S. Department of Computer Science and Engineering Jyothy Institute of Technology Bengaluru India

ISBN: (纸本)9789819996094

In many applications, including measuring physical activity, understanding sign language, and controlling full-body gestures, human position estimation from video is essential. This has the potential to be utilized for activity recognition in civil work. By accurately tracking human body posture from video, the technology can identify and classify different tasks and actions being performed by workers in construction, manufacturing, or other industries. This information can be used to monitor worker productivity, optimize workflow, and identify potential safety hazards. The proposed project is a machine learning (ML) solution for high-fidelity body posture tracking, employing current open source research that also drives the Machine Learning Pose Detection Application programming interface to infer predefined 3D landmarks and background segmentation mask on the entire body from RGB (Red, Green, Blue) video frames. The suggested method in this project achieves real-time performance on the majority of modern mobile phones, desktops/laptops, Python, and even the web, in contrast to current state-of-the-art methodologies, which rely mostly on strong desktop environments for inference. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Flame Frenzy Fighter

Flame Frenzy Fighter

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2024 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2024

作者： Unpane, Nikita Jewani, Gunjan Umarvaishya, Manish Kokatey, Akshay Gupta, Ashutosh Nikhare, Amita Jhulelal Institute of Technology Computer Science and Engineering Department Nagpur India

ISBN: (纸本)9798350348460

Our research project aims to develop and implement a revolutionary fire extinguishing robot carefully adapted for the detection and autonomous extinguishing of fires in enclosed spaces. The lack of immediate human intervention in fire alarm systems often causes significant damage, highlighting the need for automated fire protection solutions. Our robot, equipped with flame detection and control sensors, has the ability to quickly detect fire sources and approach them autonomously with unmatched precision. This remotecontrolled marvel ensures a safe and effective response to fires, eliminating the need for human intervention in dangerous situations. This research represents a significant step forward in improving safety, preventing disasters, and saving lives from home fires. This is an important milestone in the field of autonomous robotics and could revolutionize our approach to residential fire safety and disaster management in private homes as well as commercial establishment. © 2024 IEEE.

关键词： Arduino IOT real time robot Sensors snapshot

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Comparative Performance Analysis of Differential Evolution Variants on engineering Design Problems

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Journal of Bionic engineering 2022年第4期19卷 1140-1160页

作者： Sanjoy Chakraborty Apu Kumar Saha Sushmita Sharma Saroj Kumar Sahoo Gautam Pal Department of Computer Science and Engineering National Institute of Technology AgartalaAgartalaTripura 799046India Department of Computer Science and Engineering Iswar Chandra Vidyasagar CollegeBeloniaTripura 799155India Department of Mathematics National Institute of Technology AgartalaAgartalaTripura 799046India Department of Computer Science and Engineering Tripura Institute of TechnologyNarsingarhTripura 799015India

Because of their superior problem-solving ability,nature-inspired optimization algorithms are being regularly used in solving complex real-world optimization *** academics have recently focused on meta-heuristic algorithms to solve various optimization *** the state-of-the-art algorithms,Differential Evolution(DE)is one of the most successful algorithms and is frequently used to solve various industrial *** the previous 2 decades,DE has been heavily modified to improve its *** DE variations secured positions in IEEE CEC competitions,establishing their ***,to our knowledge,there has never been a comparison of performance across various CEC-winning DE versions,which could aid in determining which is the most *** this study,the performance of DE and its eight other IEEE CEC competition-winning variants are ***,the algorithms have evaluated IEEE CEC 2019 and 2020 bound-constrained functions,and the performances have been *** unconstrained problem from IEEE CEC 2011 problem suite and five other constrained mechanical engineering design problems,out of which four issues have been taken from IEEE CEC 2020 non-convex constrained optimization suite,have been solved to compare the *** analyses like Friedman's test and Wilcoxon's test are executed to verify the algorithm’s ability *** analysis exposes that none of the DE variants can solve all the problems *** of SHADE and ELSHADE-SPACMA are considerable among the methods used for comparison to solve such mechanical design problems.

关键词： Differential evolution Metaheuristics IEEE CEC Mechanical design problem

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BlasT: Blood Clot Classification Model Using Transfer Learning Based Convolutional Neural Network 16

BlasT: Blood Clot Classification Model Using Transfer Learni...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Mishra, Shruti Pal, Sujata Dey, Sweta Indian Institute of Technology Department of Computer Science & Engineering Ropar India

ISBN: (纸本)9798350383119

Blood cells contain a plethora of clotting factors vital for sustaining human life. Blood coagulation manages excessive blood loss from the blood vessels of an injured individual. Blood clots can appear due to various medical reasons in humans, such as platelet aggregation and converting fibrinogen into fibrin. Consequently, blood clots generate multiple disorders and even elevate the risk of death when not promptly diagnosed. Therefore, the prompt identification and treatment of blood clotting are paramount. In this study, we introduced an analytical approach for detecting blood clots. Also, we introduced a blood clot classification model: Blood Clot Classification Model using Transfer Learning and Convolutional Neural Network (BlasT). Our evaluation results demonstrate an impressive accuracy rate of 93.99% for the proposed model: BlasT. The primary objective of this study is to establish an early clot classification model using transfer learning in conjunction with a Convolutional Neural Network (CNN) layer. Our proposed BlasT model can identify blood clots swiftly and accurately. Furthermore, it allows timely notifications to medical professionals. © 2024 IEEE.

关键词： Blood-clot CNN Machine Learning (ML) Red Blood Cells (RBCs) White Blood Cells (WBCs)

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Optimal trained ensemble of classification model for satellite image classification

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Multimedia Tools and Applications 2025年第7期84卷 3745-3766页

作者： Singh, Sarabjeet Kundra, Harish Kundra, Sheetal Pratima, P. Venkata Devi, M. V. Anjana Kumar, Sushil Hassan, Mariya Department of Computer Science and Engineering Chandigarh College of Engineering and Technology Chandigarh160019 India Department of Computer Science and Engineering Guru Nanak Institutions Technical Campus Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Artificial Intelligence and Data Science Guru Nanak Institute of Technology Ranga Reddy District Telangana Ibrahimpatnam501506 India Department of Computer Science and Engineering Central University of Haryana Jant-Pali Mahendergarh123031 India

Satellite image classification is the most significant remote sensing method for computerized analysis and pattern detection of satellite data. This method relies on the image's diversity structures and necessitates a thorough validation of the training data using the chosen classification algorithm. The classifying of remotely sensed images refers to the process of classifying pixels into a fixed set of distinct classes based on their data values. The pixel is categorized into that class if it complies with a specific set of requirements. A new SIC scheme is suggested in this work, where, Contrast limited adaptive histogram equalization (CLAHE) is deployed for pre-processing the image. Further, statistical features, Vegetation Indices (VI) and Improved Hybrid Vegetation Index (IHVI) are extracted from the preprocessed image. Then, we deploy 2 phase classification process for classifying the image. In the first phase, Bi-Directional Gated Recurrent Unit (BI-GRU), Convolutional Neural Network (CNN) and Deep Max Out (DMO) are used. In 2nd phase, optimized RNN is used for determining the final classification result. Also, the training of the model is done by the new Chaotic Opposition Behavior Improvised Coot Algorithm (COBI-CA) optimization via tuning the optimal weights. The COOT method has been enhanced and is now the suggested algorithm. Lastly, we conduct an analysis to compare the suggested Satellite Image Classification (SIC) model's performance to the traditional approaches in terms of statistical analysis, precision, accuracy, and error measure. In comparison to other conventional methods, the accuracy of the suggested model is 97%. © This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply 2024.

关键词： Pixels

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Mindful Meadows: A Mental Health App 8

Mindful Meadows: A Mental Health App

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Gaikwad, Aryan Nimbolkar, Gayatri Keswani, Roshani Kolhe, Vaishnavi Navale, Geeta.S. Sinhgad Institute of Technology and Science Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

This paper presents 'Mindful Meadows,' a Flutter-based mobile application designed as an all-inclusive platform for mental health support and wellness. The app uniquely combines various mental health functionalities into a singular, user-friendly interface, eliminating the need for multiple disparate applications. It addresses the diverse needs of individuals seeking mental well-being support. Firstly, it offers specialized tests for different mental conditions, aiding users in self-assessment and awareness. These tests are complemented by a history tracking feature for monitoring mental health progress over time. Another significant component is the inclusion of a serene and soothing music library, curated to promote relaxation and stress relief. Additionally, the app introduces a mental yoga section, providing guided practices aimed at enhancing mental resilience and mindfulness. For community support and interaction, "Mindful Meadows"incorporates a community chat feature, enabling users to connect with others experiencing similar mental health challenges. The app also integrates a responsive chatbot for immediate assistance and guidance. In its commitment to comprehensive care, "Mindful Meadows"offers a unique feature for consulting with therapists. This includes a seamless appointment booking system, facilitating easy access to professional help. Overall, "Mindful Meadow"emerges as a pioneering solution in mental health technology, intertwining professional care, community support, and cutting-edge technology to promote optimal mental wellness. © 2024 IEEE.

关键词： Stress relief

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Designing a Heuristic Based Hybrid CNN with Attention Mechanism for the Effective Classification of Fish Species 5

Designing a Heuristic Based Hybrid CNN with Attention Mechan...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Bhanumathi, M. Arthi, B. Srm Institute of Science and Technology Department of Computer Science and Engineering India Department of Computer Science and Engineering India College of Engineering and Technology Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350321425

In recent days, the population of fish species is enormously increased. The measurement of the total population of the fish species is also a complex task. The population of fishes can be easily identified by its classification of variants. But, the categorization of fishes is an annoying task for marine ecologists and biologists. The fishery activities can be easily managed by calculating the total population and size. Several methods are developed for classifying the fish species, but they take more computational time and manpower. These limitations are overthrown by automatic methods. The automatic method monitors the activities of fish and their path. The major advantages of this automatic System are to reduce labor requirement and time. This research study suggests a new fish species categorization model with the help of a Hybrid Convolution Neural Network(CNN) with an Attention Mechanism (HCNN-AM). In the initial phase, the images are aggregated from the standard benchmark dataset. Then, the images are forwarded to the hybrid CNN with an attention mechanism for effectively classifying the fish species, in which the attributes are optimized with the aid of a hybrid Fitness-based Hybrid Rat Swarm with Billiards-inspired Optimization (FH-RSBO). Therefore, the planned method is an efficient substitute for strenuous and time-consuming methods of physical recognition by marine experts. Thus, it becomes the most advantageous task to monitor the fish biodiversity in their natural habitats. © 2023 IEEE.

关键词： Optimization

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SimRank on Data Streams 31

SimRank on Data Streams

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31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Yajnik, Dhyan Ansari, Huzefa Aiyub Nijhara, Prajjwal Banerjee, Dip Sankar Department of Computer Science and Engineering Indian Institute of Technology Jodhpur India

ISBN: (纸本)9798331509118

This paper introduces a novel approach that integrates machine learning techniques to optimize SimRank convergence on streaming data. Our approach utilizes the structural properties of the graph, such as edge features, to train a linear regression model that predicts SimRank values for newly added nodes. These predictions serve as effective initializations, ensuring faster convergence of the SimRank algorithm. By avoiding full recomputation and providing a new direction for research, our method offers a promising solution for similarity computation in evolving networks. © 2024 IEEE

关键词： Data streams

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Improving Students' Understanding of Education Using Generative Adversarial Networks: Bridging Text and Visuals 8

Improving Students' Understanding of Education Using Generat...

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8th IEEE International Conference on Computational System and Information technology for Sustainable Solutions, CSITSS 2024

作者： Shidhaganti, Ganeshayya I. Kavyashree, K.N. Vishwachetan, D. Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331505462

Utilizing technology in the classroom is essential in today's environment to enhance students' learning. The background problem revolves around the necessity to adapt traditional educational methods to meet the evolving needs of students in today's digital age. With rapid technological advancements, classrooms face the challenge of effectively integrating technology to enhance learning experiences and outcomes. This includes addressing issues such as access to technology, digital literacy among students and educators, and the effective utilization of technology to facilitate interactive and engaging learning environments. The need to employ Generative Model to link school students descriptive 'Poem' statements with relevant visual imagery, accommodating diverse learning preferences. Through pre-trained VQGAN and CLIP models, this research work seamlessly integrates textual description with visually captivating images, employing iterative optimization methods to align image representations with textual descriptions. By incorporating advanced technique such as cutout augmentation the project ensures the quality and variety of generated images, catering to the diverse learning needs of school students. The outcome is successfully produced and an image is generated matching the 'Poem' description, where the average approximate percentage of accuracy is approximately 77.80% and the average approximate percentage of loss is approximately 22.89%. Additionally, a video documenting the iterative image synthesis process was created, providing insights into the image's evolution and highlighting the effective integration of technology to enhance educational experiences. © 2024 IEEE.

关键词： Generative adversarial networks

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