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Self Sustainable Artificial Intelligence Systems (ICSSAS), International Conference on

作者： C m Varun R. P. Anto Kumar E Gokila S. Gayathri m. Nalini R Siva Subramanian Dept of Computer Science and Business Systems R.M.K. Engineering College Kavaraipettai Dept: of CSE St. Xavier’s Catholic College of Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of science and technology and technology Dept of CSE S.A Engineering College Chennai Dept of CSE S.A Engineering College Dept of CSE RMK College of Engg and Tech

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

This research study aims to investigate the two rapidly growing fields of Augmented Reality and Virtual Reality from their technical aspect, possible uses, issues, and evolution. AR and VR are innovations that have experienced a massive advancement and are prevalent across the entertainment industry and the learning process, healthcare, industries, and more. The study defines the basic concepts and the physical components that form the basis of AR and VR. It then goes over specific examples, using AR in the context of interactive education and its social impact as well as in operations such as surgeries and in factory maintenance while demonstrating the applications of VR in gaming, education and as a therapy tool. A fundamental question of the examined technologies, which is primarily discussed in the paper, includes the distinct challenges it encountered the hardware constraints of the solutions, the problems related to UX and UI, and the privacy-related concerns. moreover, it covers the morality and social impact of the integration of AR and VR into society. Finally, examining the future directions of the survey, it is revealed that possible technologies and research areas for the next generation of AR and VR applications are discussed. Consequently, this overview seeks to present useful recommendations regarding the current status of four AR and VR technologies and their possible future developments.

关键词： Training Ethics technological innovation Entertainment industry Surgery Virtual environments market research Hardware Artificial intelligence Augmented reality

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Development of Recommender Systems for Better Services and Products using Data science

Development of Recommender Systems for Better Services and P...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Padmapriya Thamizhamuthu R S. Jagadeesh D. m. Kalai Selvi m. Ahsan Shariff Department of Artificial Intelligence and Data Science Velammal Institute of Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sridevi Women’s Engineering College Hyderabad Telangana India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India M. Tech - Computer Science and Engineering Sri Sairam Engineering College Chennai Tamil Nadu India

Online social networking and e-commerce are becoming increasingly popular. Recommender Systems (RS) let users find relevant information from several possibilities. Internet applications currently need RS. This technology uses huge data to provide customized suggestions to improve customer happiness. Concerns and ideas help customers choose items. Sentiment Analysis (SA) may increase RS recommendation accuracy by improving user behaviour, views, and responses. RS solves information overload in information retrieval, but data sparsity remains a big problem. SA is notable for reading text and expressing user preferences. It helps E-Commerce to monitor product feedback and to understand what client wants and their preferences. This research presents a hybrid recommendation approach to increase RS accuracy and correctness. The hybrid approach beats standard models in several assessment criteria. modern retailing businesses’ e-commerce operations are impossible without RSs. The content-based and context-aware techniques are hybridized for providing promising results. Content-based approaches connect consumers to new things based on prior ratings and activities. Create user profiles and classify it. Knowledge-based algorithms propose customized items with minimal use history. These systems use case-based recommendations or limitations to make recommendations. Finally, ensemble recommender systems combine data source prediction power.

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EvCenterNet: Uncertainty Estimation for Object Detection using Evidential Learning

arXiv

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arXiv 2023年

作者： Nallapareddy, monish R. Sirohi, Kshitij Drews-Jr, Paulo L.J. Burgard, Wolfram Cheng, Chih-Hong Valada, Abhinav Fraunhofer IKS Germany Department of Computer Science University of Freiburg Germany Department of Engineering University of Tech Nuremberg Germany Computational Science Center Federal University of Rio Grande Brazil TU München Germany

Uncertainty estimation is crucial in safety-critical settings such as automated driving as it provides valuable information for several downstream tasks including high-level decision making and path planning. In this work, we propose EvCenterNet, a novel uncertainty-aware 2D object detection framework using evidential learning to directly estimate both classification and regression uncertainties. To employ evidential learning for object detection, we devise a combination of evidential and focal loss functions for the sparse heatmap inputs. We introduce class-balanced weighting for regression and heatmap prediction to tackle the class imbalance encountered by evidential learning. moreover, we propose a learning scheme to actively utilize the predicted heatmap uncertainties to improve the detection performance by focusing on the most uncertain points. We train our model on the KITTI dataset and evaluate it on challenging out-of-distribution datasets including BDD100K and nuImages. Our experiments demonstrate that our approach improves the precision and minimizes the execution time loss in relation to the base model. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Optimizing machine Learning Algorithms for Heart Disease Prediction in Healthcare: A Comparative Study

Optimizing Machine Learning Algorithms for Heart Disease Pre...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： S. Subha C. Balakrishnan T.P. Anish Gokila. E Nalini. m Siva Subramanian R Department of Computer Science and Business Systems Rajalakshmi Institute of Technology Chennai Dept of CSE S.A Engineering College Dept of CSE R.M.K. College of Engineering and Technology Tiruvallur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

The healthcare business faces significant challenges in successfully maintaining and evaluating large amounts of patient data. Data analytics has emerged as a viable tool for issue resolution and decision-making as a result of technological advancements in healthcare. This automated procedure improves the resilience of healthcare systems by collecting and analyzing data to provide more effective and cost-effective treatments. One critical use of technology, primarily machine learning, involves the identification and diagnosis of diseases, notably heart disease, which contributes significantly to global mortality. This research study analyzes how machine learning methods (NB, RF, SVm, and LR) can predict the existence of heart disease. Given that nearly 90% of cardiac diseases are avoidable, using machine learning for prediction is critical. This research study assesses the performance of different algorithms using performance metrics such as accuracy, precision, area under the curve (AUC), and F1-Score. The experimental results reveal Random Forest (RF) as the most reliable predictor of heart disease with an accuracy of 83.52% compared to other supervised machine learning algorithms. The Random Forest classifier achieves F1-Score, AUC, and accuracy values of 84.21 %, 88.24%, and 88.89%, respectively. This study highlights the potential of mL, notably the Random Forest algorithm, in improving the predictive capacity of healthcare systems for more effective heart disease prevention and treatment.

关键词： Heart measurement Accuracy machine learning algorithms medical services Prediction algorithms Reliability Random forests Diseases Classification tree analysis

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A Deep Learning Approach for Automated Skin Disease Classification and Diagnosis

A Deep Learning Approach for Automated Skin Disease Classifi...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Helina Rajini Suresh m. Amutha A. Tamizharasi A. P. Jaya Krishna S. Lalitha P Nithya Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Chennai Tamil Nadu India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamilnadu India Department of Computer Science and Engineering Arunai Engineering College Tiruvannamalai Tamil Nadu India

Everyone is ware that a wide range of problems affect skin structure, function, and appearance. Dermatological issues or dermatoses are common names used to represent the skin related illness. Early detection of skill llness is possible by employing Artificial Intellengence. A wide range of artificial intelligence techniques have been devised to promptly detect and diagnose skin problems. In this article we are proposing mobilenetv2 and LSTm algorithms to identify skin disorders at an early stage. The combination of mobileNetV2 and LSTm (Long Short-Term memory) for skin disease detection involves leveraging a convolutional neural network (CNN) for image feature extraction and a recurrent neural network (RNN) with LSTm units for sequence modeling, this architecture aims to capture both spatial and temporal dependencies in skin image data, making it well-suited for skin disease detection tasks. The method shown in this study can identify five distinct diseases with ease, and with accuracy rates of 99.2%.

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AI algorithms for Autonomous Robotics Navigation

AI algorithms for Autonomous Robotics Navigation

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International Conference on Computational and Characterization techniques in engineering & sciences (CCTES)

作者： Komala C R J. Raja Sarange Shreepad marotrao Nalla Neelima R. Sujatha K. Chidambarathanu Department of Information Science and Engineering East Point College of Engineering and Technology Bengaluru Karnataka India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Mechanical Engineering Ajeenkya D Y Patil School of Engineering Pune Maharashtra India Department of Information Technology R.V.R & J.C. College of Engineering Chowdavaram Andhra Pradesh India Department of Computer Science Subbalakshmi Lakshmipathy College of Science Madurai Tamil Nadu India Department of Computer Science and Business Systems R.M.K. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Robotic systems depend much on autonomous navigation since it allows them to operate on their own in environments constantly changing and lacking clarity. The most evolved artificial intelligence (AI) techniques applied in self-driving robot navigation are briefly reviewed in this work. These approaches call for classical methodologies, machine learning, and deep learning as well. We examine the advantages and drawbacks of numerous approaches applied in route planning, location finding, map creation, obstacle avoidance. We investigate how neural networks, sensor fusion, and reinforcement learning may be applied to provide increased dependability and flexibility for guiding systems. Case examples illustrating the advantages and drawbacks of applying artificial intelligence (AI) in practical contexts including service robots, drones, and self-driving cars abound. Combining modern AI algorithms with conventional approaches indicates that it is feasible to create self-driving robots more dependable and efficient based on the increased tracking performance.

关键词： Deep learning Navigation Service robots Neural networks Reinforcement learning Sensor fusion Robot sensing systems Planning Artificial intelligence Drones

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Cancer Cell Detection and Classification from Digital Whole Slide Image 5th

Cancer Cell Detection and Classification from Digital Whole ...

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5th International Conference on Smart technologies in Data science and Communication, SmART-DSC 2022

作者： Gavade, Anil B. Nerli, Rajendra B. Ghagane, Shridhar Gavade, Priyanka A. Bhagavatula, Venkata Siva Prasad Department of E&C KLS Gogte Institute of Technology Karnataka Belagavi India Karnataka Belagavi India Department of Biotechnology KAHER’s Dr. Prabhakar Kore Basic Science Research Center V. K. Institute of Dental Sciences Campus Karnataka Belagavi India Department of Computer Science and Engineering KLE Tech University Dr. M. S. Sheshgiri College of Engineering and Technology Karnataka Belagavi India Hyderabad Medtronic India

ISBN: (纸本)9789811968792

The World Health Organisation has identified cancer as one of the foremost causes of death globally which reports that nearly one in six deaths is due to cancer. Hence, an early and correct diagnosis is required to assist doctors in selecting the accurate and best treatment option for the patient. Pathological data have huge tumour information that can be used to diagnose cancer. Digitizing pathological data into images and its analysis using Deep learning applications will be a significant contribution to clinical testing. Due to advancements in technology, artificial intelligence (AI) and digital pathology can now be combined allowing for image-based diagnosis. This study uses residual networks (ResNet-50) and convolutional neural network (CNN), which is pre-trained on ImageNet dataset to train and categories lung histopathology images into non-cancerous, lung adenocarcinoma, and lung squamous cell carcinoma delivering an accuracy of 98.9%. Experimentation results show that the ResNet-50 model delivers finer classification results when compared to state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Artificial intelligence Classification Convolutional neural network Deep learning Graphic processing unit Histopathological image Residual networks

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Intelligent Deep Learning Framework for Breast Cancer Prediction using Feature Ensemble Learning

Intelligent Deep Learning Framework for Breast Cancer Predic...

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Global Conference for Advancement in technology (GCAT)

作者： Chintala Nageswara Rao K Chatrapathy A Jainul Fathima Goski Sathish Saptarshi mukherjee Pundru Chandra Shaker Reddy CSE-AI & ML Geethanjali College of Engineering and Technology Hyderabad India School of Computing & Information Technology REVA University Bengaluru Karnataka India Information Technology Francis Xavier Engineering College Tirunelveli TamilNadu India Information Technology St. Martins Engineering College Dhullapally Hyderabad India M.Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

The mortality and morbidity rates associated with breast cancer are extremely high. In the fight against breast cancer (BC), a correct diagnosis is crucial. The diagnostic process can benefit from the application of machine-learning (mL) and deep-learning (DL) strategies. In this paper, a deep-recurrent-neural-network (DRNN) technique optimized for breast cancer analysis using RNN and the Keras-Tuner optimization technique is proposed. The input layer, five hidden layers, five dropout layers, and the output layer make up the optimized DRNN. It fine-tuned the dropout layer’s neurologist count and firing rate for each successive hidden layer. The most crucial data points have been elected utilizing three different feature-selection methods. The optimized DRNN was contrasted to five standard mL strategies: decision-tree (DT), support-vector-machine (SVm), random-forest (RF), Naïve-Bayes (NB), and the K-nearest neighbor algorithm (KNN). The elected features have been applied to both standard mL models and optimized DRNN. The outcomes showed that, compared to the other models, the cross-validation (CV) and testing outcomes were best achieved (99.16%) by the optimized DRNN with the features picked by univariate.

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EvCenterNet: Uncertainty Estimation for Object Detection Using Evidential Learning

EvCenterNet: Uncertainty Estimation for Object Detection Usi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： monish R. Nallapareddy Kshitij Sirohi Paulo L. J. Drews Wolfram Burgard Chih-Hong Cheng Abhinav Valada Fraunhofer IKS Germany Department of Computer Science University of Freiburg Germany Computational Science Center Federal University of Rio Grande Brazil Department of Engineering University of Tech. Nuremberg Germany TU München Germany

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Enhanced Lung Cancer Detection Using CNNs for Accurate and Efficient CT Image Classification

Enhanced Lung Cancer Detection Using CNNs for Accurate and E...

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Computing, sciences and Communications (ICCSC), International Conference on

作者： mohammad Bdair E D Kanmani Ruby Prachi Juyal Rakheeba Taseen A. Abirami A Soujanya School of Architecture Computing & Engineering University of East London London UK Department of ECE Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Allied Science (Mathematics) Graphic Era (Deemed to be University) Dehradun Uttarakhand India School of computer science and Engineering Presidency university Bangalore India Department of Mathematics Sona College of Technology Salem Tamil Nadu India Department of Computer Science and Engineering CVR College of Engineering Ibrahimpatnam (M) Telangana India

ISBN: (数字)9798350353648

ISBN: (纸本)9798350353655

Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are prone to errors, which produces uneven outcomes. The research provides a unique deep learning-based method for classifying lung nodules from CT images using convolutional neural networks (CNNs). By automating feature extraction and categorization, the suggested technique greatly increases diagnostic precision. The result shows superior performance over existing systems is demonstrated by evaluation measures such as AUC-ROC, accuracy, sensitivity, and specificity. With a training duration of 12 hours, an AUC-ROC of 0.95, accuracy of 92%, sensitivity of 94%, specificity of 90%, and inference speed of 0.5 seconds per scan, the results demonstrate notable improvements in diagnostic efficiency and accuracy. Deep learning has the potential to revolutionize the diagnosis of lung cancer and enhance patient care, as demonstrated by the study.

关键词： Training Accuracy Lungs Computed tomography Lung cancer Sensitivity and specificity Feature extraction Data models Stability analysis Convolutional neural networks

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