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检索条件"机构=Computer Science with Artificial Intelligence and Machine Learning"

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Deep learning-Based Age and Gender Prediction through Feature Extraction from Facial Images using Convolutional Neural Networks 5

Deep Learning-Based Age and Gender Prediction through Featur...

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5th IEEE International Conference on Communication, Computing and Industry 6.0, C2I6 2024

作者： Manasa, S.M. Ramamoorthy, Raghu Velu, Anitha Angel Donny, F. Chaitrashree, S. Ramaiah Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350379884

In recent years, age estimation and gender classification have been focal points in the field of pattern recognition and computer vision. This paper introduces an automated approach for predicting age and gender by extracting features from human facial images. In contrast to conventional methods that operate on unfiltered facial images, our study demonstrates significant improvements in these tasks through the use of deep convolutional neural networks (CNNs). The employed feedforward neural network method enhances robustness for highly variable unconstrained recognition tasks, specifically targeting gender identification and age group estimation. The research methodology involves the analysis and validation of gender prediction and age estimation on two benchmark datasets: the Essex face dataset and the Adience benchmark dataset. Results indicate a substantial performance enhancement, showcasing the proposed approach's efficiency and state-of-the-art performance in both age and gender detection. The model achieves remarkable accuracy, positioning it as a notable advancement in the realm of age and gender prediction using deep learning techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Water Quality Parameters Modeling of Thamirabarani River

Water Quality Parameters Modeling of Thamirabarani River

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Sri Dhivya Krishnan, K. Prabhakaran, A. Danesh, K. Dinesh Kumar, E. Saai Srivathsan, R. Sachin M, P. Sri Sairam Engineering College Department of Computer Science and Engineering Artificial Intelligence and Machine Learning Chennai India Kumaraguru college of technology Department of Automobile Engineering Coimbatore India SRM Institute of science and technology Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

An extensive water quality analysis of the water quality of the Thamirabarani River located in the Tirunelveli and Thoothukudi districts of Tamil Nadu, India, is conducted. This investigation focuses on 28 exclusive water parameters divided into physical, chemical, and biological parameters, including ammonia, biological oxygen demand, pH, and significant indicators. The dataset utilized for this analysis is sourced from the Tamil Nadu Pollution Control Board. This study employs machine learning algorithms inclusive of prediction and classification algorithms. Multiple-Linear Regression, Polynomial Regression, Elastic Net regression, Support Vector machine, k-Nearest Neighbors and Random Forest are used to classify the 5 Designated Best Use classes. These algorithms iterate through various combinations of considered water parameters until convergence for providing accurate insights into their interrelationships. The random forest produces 77% accuracy which is high compared to other algorithms. The outcomes of this research hold significance for policymakers and environmentalists, offering valuable insights into the water quality of the Thamirabarani River. © 2024 IEEE.

关键词： Rivers

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Understanding the Impacts of ECG Signal Processing Techniques on Recognition of Cardiovascular Diseases 11

Understanding the Impacts of ECG Signal Processing Technique...

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11th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2023

作者： Agrawal, Vanita Mudhivarthi, Bhavesh Raju Kshirsagar, Ketki Jagtap, Jayant Department of Computer Science and Information Tech. Maharashtra Pune India Department of Electronics and Telecommunication Maharashtra Pune India Artificial Intelligence and Machine Learning Dept. Maharashtra Pune India

ISBN: (纸本)9798350326147

Cardiovascular diseases (CVD) are becoming the most common diseases that infect the heart's blood vessels. Building a fatty layer inside arteries causes more blood clots inside the heart, the most death-causing diseases from the past few decades. To sense heart condition and improve heart health, an Electrocardiogram (ECG) is used. ECG uses sensors and electronic signals for painless tests to find heart abnormalities and monitor heart health conditions. Digital signal processing (DSP) plays a crucial role in electrocardiograms in monitoring and detecting heart conditions. This paper describes the role of DSP in an ECG while detecting CVD kind congenital heart defects like aortic valve stenosis, ebstein's anomaly, ventricular and atrial septal defects, and valve stenosis of pulmonary with a detailed explanation of waves. It leads to information and analysis of existing works. There is a scope for detecting coarctation of the aorta, single ventricle defects, and patent ductus arteriosus using ECG. © 2023 IEEE.

关键词： Electrocardiograms

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Human Genetic based Disease Identification 7

Human Genetic based Disease Identification

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bendigeri, Deepa Sakri, Leena Mural, Sakshi Hukkeri, Sakshi Tayannavar, Prithviraj Sdm College of Engineering And Technology Department of Information Science And Engineering Karnataka Dharwad India Sdm College of Engineering And Technology Department of Artificial Intelligence And Machine Learning Karnataka Dharwad India

ISBN: (纸本)9798350359299

Human genetic diseases come in a vast range, stemming from changes in the genetic makeup. They can manifest in various ways, affecting a person's physical, mental, and behavioral traits. It is crucial to identify the genetics behind these illnesses in order to create accurate diagnostic methods, comprehend their origins, and create personalized remedies. While every disease has a genetic component, the extent of its influence may differ. One example of a disease caused by a specific genetic mutation is cystic fibrosis. Fortunately, advancements in technology and techniques have greatly contributed to detecting detrimental genetic changes. Diseases may be fully analyzed using a graph-theoretic framework that portrays a network of diseases and disease genes, allowing us to build more focused and effective therapies. Cardiovascular disease is a pathological condition that is impacted by a combination of genetic and environmental factors. Genetics and epigenetics have a major influence in the genesis and expression of many diseases. Epigenetic modifications exert an impact on the expression of genetic material, without inducing alterations to its underlying nucleotide sequence. The discipline of epigenetics is an intriguing and continuously evolving domain within biology, which investigates the heritable modifications in gene expression that transpire without modifying the fundamental DNA sequence. Epigenetic modifications can exert an effect on the pathogenesis of cancer and several other diseases. Mutations, often known as pathogenic variations, can cause genetic conditions by altering gene material. DNA, the basic genetic material, contains genes, which determine distinct traits and instruct physiological activities. © 2024 IEEE.

关键词： Diseases

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IoT And IIoT Integrated Spruce Grid System

IoT And IIoT Integrated Spruce Grid System

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

作者： Gupta, Anand Kumar Raghavendra, R. Sikri, Alisha Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Department of Artificial Intelligence & Machine Learning Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

The Network of Things is a rapidly developing field of technology that provides creative solutions in many different areas, most notably essential infrastructures. Its revolutionary effect is especially seen in the field of standard electricity systems, where it makes the transition to a more intelligent and efficient energy grid easier. This analysis explores the architecture and features of Internet of Things (IoT)-enabled intelligent energy grid systems, with an emphasis on several IoT technologies (sensing, communication, and computation) and their respective standards within the smart energy grid context. This page also offers a thorough summary of previous studies on the use of IoT in systems with smart grids. A review of current studies and literature indicates a significant worry about the security flaws that IoT technologies and energy systems are known to have. As a result, we examine popular threat as attack models that are unique to IoT-enabled power plants and compile mitigation strategies designed to deal with these security issues. The article's conclusion highlights the use of cutting-edge technology, such as blockchain, artificial intelligence, and machine learning, to improve the security and resilience of IoT-enabled energy networks. These cutting-edge solutions help to improve the efficacy, stability, and dependability of smart energy systems by tackling contemporary issues including security flaws. This comprehensive investigation offers insightful information on the architecture of IoT-enabled clever energy systems, the security issues they raise, and how cutting-edge technology might improve their overall efficacy systems. © 2023 IEEE.

关键词： Advanced Metering Infrastructure (AMI) cybersecurity energy system optimization fog computing and SCADA technologies Internet of Things (IoT) technologies IoT-enabled smart energy grid renewable energy integration self-healing mechanisms smart grid architecture smart meters

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Multi-View Clustering by Inter-cluster Connectivity Guided Reward 41

Multi-View Clustering by Inter-cluster Connectivity Guided R...

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41st International Conference on machine learning, ICML 2024

作者： Dai, Hao Liu, Yang Su, Peng Cai, Hecheng Huang, Shudong Lv, Jiancheng College of Computer Science Sichuan University Chengdu610065 China Engineering Research Center of Machine Learning and Industry Intelligence Ministry of Education Chengdu610065 China

Multi-view clustering has been widely explored for its effectiveness in harmonizing heterogeneity along with consistency in different views of data. Despite the significant progress made by recent works, the performance of most existing methods is heavily reliant on strong priori information regarding the true number of clusters k, which is rarely feasible in real-world scenarios. In this paper, we propose a novel graph-based multi-view clustering algorithm to infer unknown k through a graph consistency reward mechanism. To be specific, we evaluate the cluster indicator matrix during each iteration with respect to diverse k. We formulate the inference process of unknown k as a parsimonious reinforcement learning paradigm, where the reward is measured by inter-cluster connectivity. As a result, our approach is capable of independently producing the final clustering result, free from the input of a predefined cluster number. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed approach in comparison to existing state-of-the-art methods. Copyright 2024 by the author(s)

关键词： Contrastive learning

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BEYOND WEISFEILER-LEHMAN: A QUANTITATIVE FRAMEWORK FOR GNN EXPRESSIVENESS 12

BEYOND WEISFEILER-LEHMAN: A QUANTITATIVE FRAMEWORK FOR GNN E...

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12th International Conference on learning Representations, ICLR 2024

作者： Zhang, Bohang Gai, Jingchu Du, Yiheng Ye, Qiwei He, Di Wang, Liwei National Key Laboratory of General Artificial Intelligence SIST Peking University China School of Mathematical Science Peking University China Yuanpei College Peking University China Beijing Academy of Artificial Intelligence China Center for Machine Learning Research Peking University China

Designing expressive Graph Neural Networks (GNNs) is a fundamental topic in the graph learning community. So far, GNN expressiveness has been primarily assessed via the Weisfeiler-Lehman (WL) hierarchy. However, such an expressivity measure has notable limitations: it is inherently coarse, qualitative, and may not well reflect practical requirements (e.g., the ability to encode substructures). In this paper, we introduce a novel framework for quantitatively studying the expressiveness of GNN architectures, addressing all the above limitations. Specifically, we identify a fundamental expressivity measure termed homomorphism expressivity, which quantifies the ability of GNN models to count graphs under homomorphism. Homomorphism expressivity offers a complete and practical assessment tool: the completeness enables direct expressivity comparisons between GNN models, while the practicality allows for understanding concrete GNN abilities such as subgraph counting. By examining four classes of prominent GNNs as case studies, we derive simple, unified, and elegant descriptions of their homomorphism expressivity for both invariant and equivariant settings. Our results provide novel insights into a series of previous work, unify the landscape of different subareas in the community, and settle several open questions. Empirically, extensive experiments on both synthetic and real-world tasks verify our theory, showing that the practical performance of GNN models aligns well with the proposed metric. © 2024 12th International Conference on learning Representations, ICLR 2024. All rights reserved.

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Sample Complexity of Posted Pricing for a Single Item 38

Sample Complexity of Posted Pricing for a Single Item

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Jin, Billy Kesselheim, Thomas Ma, Will Singla, Sahil Cornell University School of Operations Research and Information Engineering United States University of Bonn Institute of Computer Science Lamarr Institute for Machine Learning and Artificial Intelligence Germany Columbia University Graduate School of Business Data Science Institute United States Georgia Tech School of Computer Science United States

Selling a single item to n self-interested buyers is a fundamental problem in economics, where the two objectives typically considered are welfare maximization and revenue maximization. Since the optimal mechanisms are often impractical and do not work for sequential buyers, posted pricing mechanisms, where fixed prices are set for the item for different buyers, have emerged as a practical and effective alternative. This paper investigates how many samples are needed from buyers' value distributions to find near-optimal posted prices, considering both independent and correlated buyer distributions, and welfare versus revenue maximization. We obtain matching upper and lower bounds (up to logarithmic factors) on the sample complexity for all these settings. © 2024 Neural information processing systems foundation. All rights reserved.

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Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed

Virtual Drive: Immersive VR-Controlled Car with Real-Time In...

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2024 International Conference on Computational intelligence and Computing Applications, ICCICA 2024

作者： Jain, Shweta Gandhi, Anju Bhandari Mehla, Stuti Aggarwal, Rishit Kwatra, Tanu Panipat Institute Of Engineering And Technology Department Of Artificial Intelligence And Machine Learning Panipat India Panipat Institute Of Engineering And Technology Department Of Computer Science And Engineering Panipat India Infosys Limited Pune India D.A.V Centenary College Department Of Computer Science And Applications Faridabad India

ISBN: (纸本)9798350306446

This project investigates the creation and application of a revolutionary virtual drive experience that blends immersive virtual reality (VR) technology with a real-world automotive simulator. The technology promises to deliver an unrivaled driving experience by allowing users to manage a virtual car in real time and view live video feeds from cameras mounted on the real car. Motion controllers, an excellent VR headset with motion tracking, and a customizable driving simulator with haptic feedback are examples of hardware components. The virtual experience is improved by the cameras and sensors in the actual automobile, which capture ambient data and real-time video feeds. Software development includes both realistic virtual reality surroundings and a sophisticated control mechanism that translates human motions into virtual activity. Real-time interaction features and the incorporation of live video streams boost user engagement. Network connectivity ensures smooth interaction and video streaming. Easy-to-use interfaces facilitate information access and control. Several tests and optimizations ensure performance and safety. The deployment process includes continuing adjustments based on user feedback and integration with VR platforms. The process of creating a unique virtual drive experience is described in this paper, along with examples of hardware-software integration that open up new possibilities for VR-controlled applications in the future. © 2024 IEEE.

关键词： Virtual reality

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AI -Driven Detection of Gastrointestinal Disease based on Advanced Deep learning Techniques 4

AI -Driven Detection of Gastrointestinal Disease based on Ad...

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4th International Conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Izankar, Sakshi V. Puri, Chetan Burle, Rushikesh Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Maharashtra Wardha India

ISBN: (纸本)9798331523923

Esophageal, gastric, and colorectal cancers (CRC) are some of the gastrointestinal (GI) cancers threatening global health with CRC being particularly concerning due to its high fatality rate. While early diagnosis is crucial in improving treatment efficiency, barriers such as image noise and visual subjective assessments during diagnostics such as colonoscopies often render it inconceivable. For better diagnostic accuracy in detecting gastrointestinal disease via endoscopic images, the research explores whether highly advanced artificial intelligence (AI) models such as Vision Transformers (ViTs) and pre-trained DenseNet201 can be effective. We compare these models to popular deep learning techniques such as Support Vector machines (SVM), ResNet50, VGG19, and GoogleNet. At a training accuracy of 99.9% and a validation accuracy of 98.8%, experiments on the KVASIR dataset indicate that Vision Transformer is superior to other models since it can learn long-range dependencies. The robustness of deep learning models for identifying GI disease is exemplified by the superior performance of DenseNet201, ResNet50, and InceptionV3, all of which possess high accuracy, precision, and recall values. The paper illustrates the potential of Vision Transformers, when combined with transfer learning and pre-trained CNN models, to revolutionize endoscopic diagnoses and provide a valuable resource to help physicians manage gastrointestinal conditions more effectively. © 2025 IEEE.

关键词： Endoscopy

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