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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Bhandurge, Sonam Sambrekar, Kuldeep Allagi, Shridhar Rajpurohit, Vijay S. Sannakki, Sanjeev Angadi Institute of Technology and Management Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Gogte Institute of Technology Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering Karnataka Belagavi India Kle Institute of Technology Hubli Visvesvaraya Technological University Belagavi Dept. Computer Science and Engineering India

ISBN: (纸本)9798350354171

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient's death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of 100% by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness. © 2024 IEEE.

关键词： Support vector regression

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Comprehensive Analysis of Pose Estimation and Machine Learning Classifiers for Precise Yoga Pose Detection and Classification 3

Comprehensive Analysis of Pose Estimation and Machine Learni...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Meghana, J.H. Chethan, H.K. Santhosh Kumar, K.S. Prakash, S.P. Shiva Department of Studies in Computer Science University of Mysore Mysuru Karnataka570006 India Department of Computer Science and Engineering Maharaja Institute of Technology Thandavapura Mysuru571302 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India

Yoga contributes to mental and physical well-being by improving flexibility, strength, balance, and emotional stability when integrated into daily routines. This ancient practice can become more accessible and adaptable to a wider audience when combined with modern artificial intelligence (AI). This study introduces a comprehensive system for detecting and classifying yoga poses using computer vision and machine learning techniques. Central to this work is the application of posture estimation algorithms, such as MediaPipe, PoseNet, and OpenPose, to identify key points on the human body within a single image or video frame. These key points are analyzed in both two-dimensional (2D) and three-dimensional (3D) spaces to construct a skeletal representation of the body, enabling accurate classification of yoga poses. The study focuses on five distinct yoga poses: Downdog, Goddess, Plank, Tree, and Warrior II. To categorize these poses, machine learning classifiers including Support Vector Machines (SVM), Random Forest, K-Nearest Neighbors (KNN), and Naive Bayes utilize the key points extracted from the pose estimation models. This research is distinctive in its thorough evaluation of various conventional classifiers across multiple yoga positions. A comprehensive comparative analysis is essential for identifying the most effective classifiers for posture detection and classification. The dataset used in this study has been carefully curated to encompass a wide array of yoga poses and is divided into training and testing sets at various ratios (90:10, 80:20, 70:30, and 60:40) to ensure robust validation. Results indicate the system's effectiveness, with SVM and KNN consistently achieving high values for AccuracyPE, PrecisionPE, RecallPE, and F1-S corePE across all yoga poses. Notably, Random Forest attains up to 100% AccuracyPE in detecting and classifying certain poses, demonstrating its robustness and reliability. This research highlights the potential of integrating p

关键词： Deep learning

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A Hybrid Model to Predict the Breast Cancer using Stacking and Bagging Model 3

A Hybrid Model to Predict the Breast Cancer using Stacking a...

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Yuvalatha, S. Nithyapriya, S. Prabhavathy, S. Priyadharshini, R. Savitha, S. Kayathri, S. Bannari Amman Institute of Technology Department of Computer Science and Business Systems Sathyamangalam India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Christ the King Engineering College Department of Electronics and Communication Engineering Coimbatore India Sri Ranganathar Institute of Engineering Department of Computer Science and Engineering Coimbatore India PSR Engineering College Department of Computer and Science and Engineering Sivakasi India

ISBN: (纸本)9798350317022

Breast cancer is a malignant tumor that develops in the cells of the breast tissue. Breast cancer is one of the major causes of death for women globally. In the examination of medical data, breast cancer prediction is a difficult task. To make decisions and accurately distinguish between benign and malignant tumors, physicians and pathologists need certain automated technologies. In this paper, hybrid ensemble technique (Bagging and Stacking) is used to predict the breast tumors as benign and malignant tumors. In the proposed work, the subset of data is created from the initial Wisconsin (Diagnostic) Data Set by bootstrapping technique. Each bootstrap dataset is used to train the weak learner. The weak learners are K-Nearest Neighbors (KNN) Random Forest (RF), Decision Tree (DT) and Support Vector Machine (SVM). The Logistic Regression (LR) is used as the Meta Learner. The Meta Learner uses the predictions of weak learners as its training data. The proposed hybrid ensemble model obtains an accuracy 98.7%, Precision 98.83%, Recall 98.54%, F1 Score 98.68% and 0.012% error. © 2023 IEEE.

关键词： Bagging Bootstrapping Ensemble Malignant Meta Learner Stacking

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READS: Resource efficient attack detection system for drones

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Multimedia Tools and Applications 2024年 1-20页

作者： Vishnu, Aditya Arora, Sumedha Kerala University of Digital Sciences Innovation and Technology Kerala Thiruvananthapuram India Department of Computer Science and Engineering Institute of Technology Nirma University Gujarat Ahemdabad India

Unmanned Aerial Vehicles (UAVs), commonly known as aerial drones, have gained popularity due to their versatility and applications in various industries. However, their increasing use has raised concerns about cybersecurity and the vulnerability of networked UAVs to malicious attacks. A deep learning-based Attack Detection System (ADS) is essential for mitigating cyber threats to UAVs. It can provide proactive protection even against unknown attacks. Also, for resource constraints of UAVs, ADS must be resource-efficient to optimize the deep learning model for embedded hardware, making a low code footprint. Therefore, this work focuses on developing a Resource Efficient Attack Detection System (READS) for drones that uses a lite deep learning-based network activity classification algorithm for detecting cyber threats on UAVs. The entire system has been implemented in three phases, where the first phase includes dataset training. The second phase involves running multiple conventional models to choose the best conventional model for intrusion detection using accuracy, precision, and recall. This is followed by phase three, which converts the selected conventional Dense Neural Network (DNN) model into a lite weight (Tiny) model that works on UAVs. The selected Lite DNN model is evaluated on a real-time testbed. The optimized model could lower the storage space and generates 97.69% accuracy. The proposed system consumes 30% of the original power. Hence, it allows efficient threat detection while minimizing the impact on UAV resources. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Drones

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Deep Learning Approaches for Cataract Detection Using Fundus and Real-Time Naked Eye Imaging

Deep Learning Approaches for Cataract Detection Using Fundus...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Shetty, Chinmai Fernandes, Shainy Bagchi, Susama Malathi, S.Y. Nayak, Subrahmanya Rajesh Debnath, Sanjoy Kumar Department of Information Science and Engineering Nitte India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Kle Institute of Technology Dept of Computer Science and Engineering Hubballi India

ISBN: (纸本)9798350388602

Cataracts are the most common eye disease and the leading cause of blindness worldwide. The eye ailment known as cataracts results in clouded lenses, leading to vision that appears foggy or frosted. People with cataracts often struggle with tasks like reading, driving, or recognizing faces due to the impaired clarity of their vision. Effective treatment and prevention of vision loss depend on the early detection of cataracts. The proposed system is a deep learning-based approach to detect cataracts from the naked eye and fundus images that can help ophthalmologists diagnose cataracts earlier, more accurately, and efficiently. Both fundus and naked eye images are processed by the system to detect cataracts. The system offers a dependable and automated solution for cataract detection, enabling timely diagnosis and treatment. Its performance is on par with that of ophthalmologists, highlighting its potential as an effective and trustworthy tool for automated cataract detection. The proposed model employs two distinct deep learning approaches. For identifying cataracts using naked eye images, a standard CNN architecture was utilized, achieving an accuracy of 94.26%. For detecting cataracts in fundus eye images, a Deep Convolutional Neural Network (DCNN) with the VGG19 architecture was implemented, demonstrating a notable accuracy of 96.78%. This model holds substantial promise, offering a non-invasive and accessible method for cataract screening. It is particularly helpful in environments with limited resources, where access to expert eye care is frequently limited. © 2024 IEEE.

关键词： Convolutional neural networks

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Polyp Detection Using U-Net Neural Network Based Algorithm 2

Polyp Detection Using U-Net Neural Network Based Algorithm

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Singh, Devansh Shukla, Archita Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Polyp is an earlier stage of cancer development in gastro-intestinal tract. Despite the fact that numerous techniques for automatic segmentation and detection of polyps have been developed, it still remains an open problem. Examining color images captured by the board camera in endoscopy is contemplated the most reliable method for detectingdeveloping polyps. To automate this process, in this paper we present a modified U-Net Neural Network based algorithm. This algorithm is a binary classifier which labels the pixels as part of the polyp or not. This model can analyze the video of GItract frame by frame and produce the collection of frames with possibility of presence of polyp thus reducing the time required to process the GI-tract. Detecting, localizing, and segmenting polyps using Kvasir-SEG, an open-access dataset of colonoscopy images, we benchmark various modern day approaches in this work, assessing bothmethod speed and accuracy. This is attributed to outstanding performance of image classification compared to preceding techniques. The automatically identifying polyps serves to aid gastroenterologists during colonoscopies. While existing literature contains publications addressing the challenge of polyp detection, many of these systems remain confined toresearch settings and lack implementation for clinical use. Hence, we present the inaugural fully open-source automated polyp detection system, which not only outperforms the best- performing system documented in the literature but is also prepared for immediate clinical application. © 2024 IEEE.

关键词： Colonoscopy ColonSegNet Colorectal cancer Kvasir-SEG Medical image segmentation Polyp Detection U-net

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A new method for routing protocols using hybrid fuzzy clustering in UWSNs

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Journal of engineering and Applied science 2025年第1期72卷 1-19页

作者： Wang, Xiaoju Meng, Bin Yuan, Xuexu Zhao, Junwei College of Computer North China Institute of Science and Technology Langfang065201 China Internal Network Operations Department Ministry of Emergency Management Big Data Center Beijing100013 China College of Safety Engineering North China Institute of Science and Technology Langfang065201 China

In the last couple of decades, the functional area of underwater wireless sensor networks (UWSNs) has widened, though these networks are burdened with unique problems such as a lack of GPS, high-energy usage, long propagation delay, and limited bandwidth. UWSNs use acoustic communication, which requires higher-power consumption compared to terrestrial networks. This research proposes a new energy-efficient routing strategy using the hybrid fuzzy clustering technique in UWSNs. The recommended methodology applies fuzzy logic to dynamically choose cluster heads with various parameters like residual energy, node distance, and data load to enhance load distribution among nodes and minimize energy expenditure. Simulation results prove that the protocol outperforms the DABC and IDACB algorithms by reducing routing packets by 29% and enhancing the packet delivery ratio by 19%. Besides, the new scheme improved energy variance by 40% at nodes to prolong the network lifetime and maintain a higher throughput in conditions of variable network load. This fuzzy clustering technique stands apart because it tries to overcome UWSN-specific challenges using adaptive data aggregation along with multistep communication and hence is more resistant and energy-efficient compared to the conventional protocols. © The Author(s) 2025.

关键词： Routing protocols

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Radioactive Metal Detection in Dustbin using Metaheuristic with Augmented Reality for Enhanced Safety and Security 6

Radioactive Metal Detection in Dustbin using Metaheuristic w...

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6th International Conference on Recent Trends in Advance Computing, ICRTAC 2023

作者： Preethi, C. Hammed, S. Shahul Haripriya, K. Pavalarajan, S. Saran, V. S.Shree Meikannan, R. Pranesh Raj Psna College of Engineering and Technology Department of Computer Science and Business Systems Dindigul India

ISBN: (纸本)9798350394702

Enabling everyday objects to collect, exchange, and analyze data, the Internet of Things (IoT) is a technology paradigm that has the potential to revolutionize industries through data-driven decision-making, automation, and efficiency. IoT's fundamental principles and applications are succinctly summarized in this abstract, with emphasis on its role as a catalyst for the Fourth Industrial Revolution. Its ubiquity in various domains, such as smart homes and industrial automation, highlights its importance in shaping our interconnected world. With this in mind, we are encouraged to explore the vast opportunities and challenges presented by the IoT ecosystem. In industrial, medical, and research environments utilizing radioactive materials, ensuring radiation safety and security is essential. An important measure in preventing harm to staff and surroundings is identifying and segregating radioactive metal objects. Introducing an innovative camera-based approach for augmenting the detection of such objects is the aim of this *** order to develop a reliable and real-time detection system, our suggested Radioactive Metal Detection Bin (RMDB) integrates cutting-edge camera systems with powerful image processing *** acquire pictures of items moving across the detecting region, the RMDB system places high-resolution cameras strategically. These cameras have specific sensors and filters that are made to find gamma radiation coming from radioactive metals. © 2023 IEEE.

关键词： Automation

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Facial Emotion Recognition Using Swarm Optimized Multi-Dimensional DeepNets with Losses Calculated by Cross Entropy Function

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computer systems science & engineering 2023年第9期46卷 3285-3301页

作者： A.N.Arun P.Maheswaravenkatesh T.Jayasankar Department of Computer Science&Engineering Sri Venkateswara Institute of Science and TechnologyTiruvallurTamilnaduIndia Department of Electronics and Communication Engineering Anna UniversityTrichyTamilnaduIndia

The human face forms a canvas wherein various non-verbal expressions are *** expressional cues and verbal communication represent the accurate perception of the actual *** many cases,a person may present an outward expression that might differ fromthe genuine emotion or the feeling that the person *** when people try to hide these emotions,the real emotions that are internally felt might reflect as facial expressions in the form of micro *** micro expressions cannot be masked and reflect the actual emotional state of a person under *** expressions are on display for a tiny time frame,making it difficult for a typical person to spot and recognize *** necessitates a place for Machine Learning,where machines can be trained to look for these micro expressions and categorize them once they are on *** study’s primary purpose is to spot and correctly classify these micro expressions,which are very difficult for a casual observer to *** research improves upon the accuracy of the recognition by using a novel learning technique that not only captures and recognizes multimodal facial micro expressions but also has features for aligning,cropping,and superimposing these feature frames to produce highly accurate and consistent results.A modified variant of the deep learning architecture of Convolutional Neural Networks combined with the swarm-based optimality technique of the Artificial Bee Colony Algorithm is proposed to effectively get an accuracy of more than 85%in identifying and classifying these micro expressions in contrast to other algorithms that have relatively less *** of the main aspects of processing these expressions from video or live feeds is aligning the frames homographically and identifying these concise bursts of micro expressions,which significantly increases the accuracy of the *** proposed swarm-based technique handles this in the research to precisely alig

关键词： Facial micro expression recognition deep learning CNN artificial bee colony

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Identification and Acknowledgment of Programmed Traffic Sign Utilizing Profound Convolutional Neural Organization

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Intelligent Automation & Soft Computing 2023年第2期35卷 1527-1543页

作者： P.Vigneshwaran N.Prasath M.Islabudeen A.Arunand A.K.Sampath Department of Networking and Communications SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering School of EngineeringPresidency UniversityBengaluru560064India

Trafﬁc signs are basic security workplaces making the rounds,which expects a huge part in coordinating busy time gridlock direct,ensuring the pros-perity of the road and dealing with the smooth segment of vehicles and indivi-duals by walking,*** a segment of the clever transportation structure,the acknowledgment of trafﬁc signs is basic for the driving assistance system,trafﬁc sign upkeep,self-administering driving,and various *** are different assessments turns out achieved for trafﬁc sign acknowledgment in the ***,most of the works are only for explicit arrangements of trafﬁc signs,for example,beyond what many would consider a possible ***ﬁc sign recognizable proof is generally seen as trying on account of various complexities,for example,extended establishments of trafﬁc sign *** critical issues exist during the time spent identiﬁcation(ID)and afﬁrmation of trafﬁc *** signs are occasionally blocked not entirely by various vehicles and various articles are accessible in busy time gridlock scenes which make the signed acknowledgment hard and walkers,various vehicles,constructions,and loads up may frustrate the ID structure by plans like that of road *** concealing information from trafﬁc scene pictures is affected by moving light achieved by environment conditions,time(day-night),and ***ﬁc sign revelation and afﬁrmation structure has two guideline sorts out:The essential stage incorpo-rates the trafﬁc sign limitation and the resulting stage portrays the perceived trafﬁc signs into a particular class.

关键词： Trafﬁc sign classiﬁer convolution neural network image vehicle

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