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检索条件"机构=Artificial Intelligence and Machine Learning Engineering"

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Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction 3

Exploratory Analysis of Heart Attack and Breast Cancer Early...

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3rd International Conference on Trends in Electrical, Electronics and Computer engineering, TEECCON 2024

作者： Vandana, C.P. Kashyap, M Shivani Yashas, D. Singh, Neha New Horizon College of Engineering Dept of Information Science & Engg. Bengaluru India New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331505714

This paper involves the development of a web based platform with an integrated machine learning algorithm to address problems pertaining to heart disease and breast cancer. It enables users to select either the heart attack or breast cancer datasets and use machine learning models such as Logistic Regression, K-Nearest Neighbor (KNN), Support Vector machine (SVM), Random Forest and XG Boost. The website provides basic information about the datasets such as the total number of records in a dataset, number of classes in a dataset, and also allows users to generate graphs for better visualization of the data. The evaluation of the performance of each of the models is based on various metrics such as accuracy, F1 score, precision, recall and mean squared error. This way it is possible to assess the performance of the models in relation to each of the available data sets which makes it easy for users to pick the most effective model. There are also inputs for the users which would be their health indicators like age, sex, level of cholesterol, heart rate among others. Using these values as well as the picked model, the website estimates the possibility of the user developing heart disease or breast cancer. Apart from predicting health risks, there is an emphasis on the evaluation of health risks by different machine learning models and thus assists users in finding the most accurate prediction model regarding their healthcare problems. Overall, the website provides both predictive healthcare insights and a deeper understanding of how various algorithms perform in real-world scenarios. © 2024 IEEE.

关键词： K-Nearest Neighbor (KNN) Logistic Regression Predictive Analytics Random Forest Support Vector machine (SVM) XGBoost

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Classifying Parkinson's Disease Using Speech Features 2

Classifying Parkinson's Disease Using Speech Features

引用

2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Soliman, Hala A. Elshourbagi, Mohammed S. Dahy, Malak A. Osman, Mena M. Kamal, Bassant A. Elaziz, Mohamed Abd El-Rashidy, Nora Galala University Faculty of Computer Science and Engineering Suez435611 Egypt Intelligence-Kferelshikh University-kaferelshikh Machine learning and information retrieval-faculty of artificial Egypt

ISBN: (纸本)9798350362633

This scientific paper focuses on the detection of Parkinson's Disease in individuals using vocal/speech features. Specifically, the study records an individual's pronunciation of the short 'a' sound (\\ \\\ \\\ae) and applies various Signal Processing Algorithms to process the recorded data. To address the data's class imbalance, both oversampling and undersampling techniques are implemented, and their results are compared. Additionally, a feature selection algorithm is employed to eliminate noise from the dataset while retaining the relevant features necessary for the model. Logistic regression, Support Vector machines (SVM), decision trees, and Ensemble learning methods are utilized to determine the likelihood of an individual having Parkinson's Disease. Furthermore, SHAP (SHapley Additive exPlanations) analysis is conducted to gain a deeper understanding of the impact of features on class determination for each instance. The Random Forest model achieves an accuracy of approximately 95% when trained on the oversampled dataset, surpassing the accuracy obtained from the undersampled dataset. Additionally, a graphical user interface (GUI) is successfully developed to predict the diagnosis of Parkinson's disease. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Optimizing Energy Efficiency and Network Performance in Smart Street Lighting Systems Using LoRaWAN Technology 3

Optimizing Energy Efficiency and Network Performance in Smar...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Sivakumar, S. Jayanth, R. Mughesh Kumar, N.R. Srm Institute of Science and Technology Dept. of Comp. Sci. and Engineering Specialization in Artificial Intelligence and Machine Learning Tamil Nadu Tiruchirapalli India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361186

This paper presents a groundbreaking Smart Street Lighting System utilizing LoRaWAN technology to overcome the challenges of scalability, energy inefficiency, and communication limitations inherent in older smart street technologies. The system employs seamless, long-range communication for real-time monitoring, adapting to dynamic urban environments, and achieving a balance between energy conservation and public safety. Advanced components, including a Passive Infrared (PIR) sensor and microwave motion sensor, enhance object detection and urban safety. A unique feature of the system is its ability to automatically dim the street lights to 1/4th intensity when no object is detected, while instantly adjusting to full brightness upon object detection. Furthermore, the incorporation of a response factor ensures accurate detection and swift response to communication disruptions. Notably, the system achieves an outstanding 70% energy savings through optimized lighting and dynamic remote control, significantly contributing to the evolution of intelligent and energy-efficient street lighting solutions in smart city infrastructures. © 2024 IEEE.

关键词： Smart city

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Anti-Accident Mechanism to Detect Driver Drowsiness Integrated with Alerting Mechanism: An IoT-based Model

Anti-Accident Mechanism to Detect Driver Drowsiness Integrat...

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2023 International Conference on Computer Communication and Informatics, ICCCI 2023

作者： Katukam, Srujan Potluri, Sai Vinay Abburi, Sunell Dutt Cherukuri, Rahul Sreeka Parinishta, C. Peddineni, Mohith Ram Kumar, R.P. Polepaka, Sanjeeva Gokaraju Rangaraju Institute of Engineering and Technology Department of AI & ML Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Engineering Hyderabad India

ISBN: (纸本)9798350348217

According to the report released by the National Safety Council (NSC), about 1,00,000 accidents, 71,000 injuries, and 1,550 deaths are caused due to Drowsiness while driving each year. According to a study by the AAA Foundation for Road Safety, Drowsiness is responsible for up to 9.5% of accidents and 10.8% of those involving airbag deployment, injury, or significant property damage. The paper aims to build a real-time system to prevent accidents caused due to Drowsiness. It implements the features of Computer Vision, Deep learning, and Internet of Things (IoT) tools to detect Drowsiness and alert the Driver. The various alerting techniques adopted to alert the Driver includes Alarming, Vibration, and Water Sprinkler are implemented using IoT. © 2023 IEEE.

关键词： Internet of things

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The Future of Driving: A Review on the Combination of artificial intelligence and Autonomous Vehicles 2

The Future of Driving: A Review on the Combination of Artifi...

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2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Zade, Nikita Gawande, Mayur Verma, Prateek Gundewar, Swapnil Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The convergence of artificial intelligence (AI) and Autonomous Vehicles (AV) are poised to revolutionize the automotive industry, offering safer, more efficient, and sustainable transportation solutions. This review article explores the current state of AVs, role of AI in AV, challenges and hurdles, Real world applications & development, its critical technological components, and their impact on society. This paper presents a wide array of applications of AI in AV, elaborating on the way complex sensor fusion, computer vision, and machine learning (ML) algorithms makes AVs perceive their surroundings, form critical opinions, and carry out accurate motions. AI is making real time interpretations of great quantities of data which are paving the way for safer and more effective navigation under a diversity of traffic circumstances, from sensor fusion to decision-making algorithms. The next section of this paper elaborates on the role of AI in the field of AV in perceiving, planning, and navigating through real world environment, while also talking about issues of integration and ethics. This paper further identifies its social, economic, and environmental consequences, resulting from mass use of AV. This article discusses the latest developments and opportunities in the regulatory environment, gives an overview of how AV define transportation in the near future. This paper also explores the levels of autonomy of AVs as defined by the Society of Automotive Engineers (SAE) and defines the process from driver assistance to full automation. It examines the ethical and technical considerations arising at each instant and emphasizes the key role AI continues to play in providing more autonomy while preserving security and dependability. © 2024 IEEE.

关键词： Automotive industry

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CampusSight—College Management Automation Using Full Stack and RPA 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, machine learning and artificial intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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Review of machine learning and Deep learning algorithms for Personality traits classification 2

Review of Machine Learning and Deep Learning algorithms for ...

引用

2nd DMIHER International Conference on artificial intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bhadane, Satish N. Verma, Prateek Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Sawangi Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Sawangi Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

Nowadays, people deal with major psychological issues on a daily basis, often in the absence of a desired support system. In the field of psychology, focus is on the thoughts, feelings, behaviors, goals, and interests of individuals. The psychologist conventionally considers various types of personality traits, like the Big 5 personality traits, the Myers-Briggs Type Indicator (MBTI), the OCEAN Model (Openness, Conscientiousness, Agreeableness, Extraversion, Neuroticism), and other methods also considered for measuring personality. To forecast personality traits like extraversion, agreeableness, openness, conscientiousness, and neuroticism, consider the name "Big Five personality traits"It is a universal model to forecast these personality traits. Studies on personality prediction that identify personality disorders from user content on social media sites like Facebook, Twitter, and WhatsApp primarily rely on user behavior on these platforms. A number of popular Deep learning (DL) techniques have advanced recently, including Deep Belief Networks (DBNs), Convolution Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Examine these methods in terms of single and multiple modalities, including psychological, audio, video, text, and image information. Researchers currently focus on predicting personality traits using single-modality text features, static facial images, audio, and video clips, including interview recordings and YouTube videos. Therefore, the current study highlights that personality prediction mostly relies on written text, audio and video datasets used in machine learning (ML) and DL models. Additional investigation is needed to resolve ethical concerns and justify the use of psychological datasets and DL models to evaluate personality traits. By combining personality traits with a psychological dataset, this study imagines a time when people everyday struggles can be effectively addressed. The integration of ML and DL in the development of pe

关键词： Adversarial machine learning

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Edge Computing: An Overview 14

Edge Computing: An Overview

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Shettar, Girish B. Devangavi, Anil D. Basaveshwar Engineering College Department of Information Science and Engineering Bagalkote India Basaveshwar Engineering College Department of Artificial Intelligence & Machine Learning Bagalkote India

In current era, the Edge computing archetype has captured substantial recognition in various educational universities and IT sector. Edge computing assists as an opportunity and act as a key empowering influence for different technological innovation like Augmented Reality (AR), 5th Generation Mobile Network (5G), communications between vehicles (V2V) by interfacing amenities or services at the cloud, Internet of Things (IoT) and providing resources to end clients. The Edge computing architype anticipates to reduce latency, faster digital experiences, location mindfulness backing to time-delicate applications such as voice over IP, live streaming videos, multimedia teleconferencing. This paper comprehensively presents the concepts of the edge computing and in contrast to the cloud computing. Then succinctly describe the edge computing, challenges faced in edge computing, and finally windup with the solutions and various edge computing applications. © Grenze Scientific Society, 2023.

关键词： Internet of things

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Accurate Classification of Tessellated Retinal Disease with Deep learning Model 6

Accurate Classification of Tessellated Retinal Disease with ...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Anvesh, Kachi Reshmi, Bharati M Basaveshwar Engineering College Department of Computer Science and Engineeering Bagalkote India Vardhaman College of Engineering Hyderabad India Basaveshwar Engineering College Department of Artificial Intelligence and Machine Learning Bagalkote India

ISBN: (纸本)9798350306613

We provide a novel Tessellated Eye Classifier (TEC) model designed for accurate identification of retinal diseases, with a focus on Tessellation Disease (TSL). Our trials were carried out on the AWS cloud platform, utilizing cutting-edge technology such as optimized RAM and high-performance GPU computers. The TEC model is based on the VGG-16 architecture and has extra Convolutional layers, global average pooling, and a customized output layer designed for the categorization of normal circumstances and retinal disorders, such as TSL. To improve the performance of the model, training and validation were carried out using a variety of fundus images with consistent color channels and resolutions. After undergoing repeated training, the customized TEC model successfully classified retinal disorders with a notable test set accuracy of roughly 79%. This study provides insights into the complexity of the neural network by analyzing parameter distribution in both our proprietary TEC model and the VGG-16 base model in depth. These results highlight the importance of the TEC model for patient care and early diagnosis;more research is being done to optimize the model further to increase its accuracy and therapeutic usefulness. © 2024 IEEE.

关键词： AWS Cloud Platform Deep learning Model Model Optimization Optometry Retinal Disease Classification Tessellated Eye Classifier VGG-16 Architecture

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A Review of machine learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of machine learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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