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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4173 条记录，以下是261-270 订阅

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A Comprehensive Review of Interpretability in AI and Its Implications for Trust in Critical applications 4

A Comprehensive Review of Interpretability in AI and Its Imp...

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4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Kathait, Ujjwal Singh Rana, Anamika Chauhan, Rahul Rawat, Ruchira Graphic Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798331540364

With the advent of deep learning (DL) and other advancements in machine learning, artificial intelligence has recently taken the globe by storm. the requirement for openness and interpretability has arisen as computing power has reached superhuman capability;its absence is a serious problem in industries like healthcare and finance, where trust is crucial because of its enormous impact on human lives. this necessitates more interpretability, which often means understanding the algorithmic system. Unfortunately, there are still a lot of unanswered questions regarding the DL. Our knowledge of machine decision-making is still incomplete. We provide an overview and categorize the interpretabilities provided by different studies. However, there is a significant disadvantage with AI: people would see it as a 'black box,' which would erode trust in its trustworthiness. In a field where decisions can literally mean the difference between life and death, this is a severe issue. Considering different application domains and activities, this study offers a thorough overview of the literature on the latest developments in XAI approaches and assessment metrics. © 2024 IEEE.

关键词： Adversarial machine learning

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A machine learning Approach to predict the Next Word in a Statement 4

A Machine Learning Approach to predict the Next Word in a St...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Rathee, Vishal Yede, Sakshi Shri Ramdeobaba College of Engineering and Management Department of Electronics Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350300093

Next-Word Prediction (NWP), an application of machine learning also known as language modeling, is one the area of natural language processing that could forecast the next word in a sentence. Employing recurrent neural network models some researchers have previously implemented this NWP. In this work, two approaches using long-term memory (LSTM) model and Bidirectional LSTM were tested, running for 50 epochs and their accuracy and loss were compared. Web scraping technique was used for acquiring the data through various channels. Libraries like tensor flow, keras, NumPy and matplotlib were imbibed in the code to obtain the results. Tensor flow was used to download the model in json format and then Google Colab was employed to code the tool. Medium-articles-dataset was used here which after preprocessing, was trained using LSTM (Long short-term memory) and BiLSTM models which were then used to predict the word that comes next in a statement. © 2023 IEEE.

关键词： Long short-term memory

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An application of Deep learning Techniques in Trajectory Planning of Autonomous Vehicles 4

An Application of Deep Learning Techniques in Trajectory Pla...

引用

4th international conference on Sustainable Expert Systems, ICSES 2024

作者： Podishetti, Jagadeeshwar Chintala, Sridhar Gude, Srihari School of Cs&ai Sr University Warangal India Kpit Technologies Ltd. Bangalore India

ISBN: (纸本)9798331540364

this research explores trajectory planning for Autonomous Vehicles, with a focus on optimizing routes for energy efficiency, safety, and regulatory compliance. Autonomous Vehicles utilize sensors, maps, and real-time traffic data to navigate dynamic and static obstacles, ensuring the safety of all road users. Collaborative planning, enabled by vehicle-to-vehicle and vehicle- to-infrastructure communication, is emerging as a crucial aspect of autonomous vehicles development, promising to enhance traffic flow and efficiency. the study examines various machine learning techniques, particularly deep learning and reinforcement learning, employed to improve the quality and safety of autonomous vehicles trajectory planning. these techniques enable autonomous vehicles to learn from human driving behaviors, adapt to complex environments, and optimize navigation strategies. Moreover, the study highlights the significant advancements and future potential of deep learning and reinforcement learning in creating more efficient, safe, and reliable autonomous driving systems. © 2024 IEEE.

关键词： Deep reinforcement learning

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Comparative Analysis of Deep learning, SVM, Random Forest, and XGBoost for Email Spam Detection: A Socio- Network Analysis Approach 4

Comparative Analysis of Deep Learning, SVM, Random Forest, a...

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4th IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Dhar, Akanksha Vedasree Anusha, K.O. Kataria, Arayan Khan, Mohd Ayan Computer Science and Engineering VIT Vellore Tamilnadu India

ISBN: (纸本)9798350306118

the surge in email usage has led to an upsurge in spam emails, posing threats to both security and user experience. In this era marked by ever-evolving cyber threats, the task of effectively detecting email spam remains critical. this research presents a comprehensive comparative analysis offour well-known machine learning techniques: Deep learning, Support Vector machine (SVM), Random Forest, and XGBoost, specifically aimed at detecting email spam. the approach goes beyond analyzing email content alone;it also considers the social network characteristics of emails to enhance overall detection accuracy. this involves examining factors such as sender-receiver relationships, temporal patterns, and the community structure within email networks. To ensure the robustness and real-world relevance of our analysis, the research employs a diverse and extensive dataset containing both legitimate and spam emails for training and evaluation. By bridging the gap between cutting-edge machine learning and email security, this study contributes to the ongoing efforts to combat the growing menace of email spam. © 2023 IEEE.

关键词： Electronic mail

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Utilizing machine learning to Aid in Type 2 Diabetes Diagnosis 4

Utilizing Machine Learning to Aid in Type 2 Diabetes Diagnos...

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4th IEEE international conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Pattnayak, Parthasarathi Mishra, Debaswapna Patnaik, Sanghamitra Mohanty, Arpeeta School of Computer Applications KIIT Deemed to Be University Bhubaneswar India OUAT Department of Computer Science and Application Bhubaneswar India School of Law KIIT Deemed to Be University Bhubaneswar India School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

High blood glucose levels are a defining characteristic of diabetes, with type 2 diabetes being the most prevalent form. Early detection and treatment can prevent or delay complications. To precisely anticipate type 2 diabetes risk, we applied machine learning techniques to identify unidentified correlations with a person's health condition. In order to find potential nonlinear correlations between the beginning of type 2 diabetes with an ensemble of health factors, this research used a binary classifier that was trained from scratch. three datasets - MIMIC-III, MIMIC-IV, and NHANES - were used. the equal number of people who had or did not have type 2 diabetes have been included in one data set that was created by combining the two sets of data. Reliability was the key metric, given the balanced dataset. the model achieved ROC AUC score of 0.934 with accuracy ratings up to 86%, which were positive results of this investigation. To increase the model's dependability, more research is required to take into account multiple readings taken throughout period for an identical person. © 2024 IEEE.

关键词： Adversarial machine learning

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Economic Load Dispatch Using machine learning 4

Economic Load Dispatch Using Machine Learning

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4th international conference for Emerging Technology, INCET 2023

作者： Andalu, Gopagoni Pranitha, Beeram Saivishal, Padmawar Saivinay, Mydam Vardhaman College of Engineering Dept. of EEE Hyderabad India

ISBN: (纸本)9798350335750

Economic Load Dispatch (ELD) is an important task in power system optimization, which aims to allocate the generation power of each unit in the power system to meet the demand at minimum cost. Accurate prediction of the load sharing and cost can assist in the effective planning and management of power systems. this paper presents a web application that predicts the load sharing and cost using machine *** this study, a novel approach that combines an Optimazation algorithm(OA) and machine learning (ML)considering Load requirements and Operating conditions to optimize data.A decision tree regressor algorithm is employed as ML and a Lagrangian Multiplier(LM) is used as an OA. the prediction in this technique involves 3 and 6 Generating Units. the web application is designed to be user-friendly and accessible, allowing users to input data and get predictions in real-time. the application can be used by power system planners and operators to optimize their systems and reduce costs. © 2023 IEEE.

关键词： Decision trees

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Blockchain and machine learning Empowered Internet of things applications: Current Issues, Challenges and Future Research Opportunities 4

Blockchain and Machine Learning Empowered Internet of Things...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Gomathi, L. Mishra, Anand Kumar Tyagi, Amit Kumar School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India Cse Niit University Neemrana India National Institute of Fashion Technology Department of Fashion Technology New Delhi India

ISBN: (纸本)9798350300888

Two advanced technologies that have the potential to revolutionise the Internet of things (IoT) ecosystem are blockchain and machine learning, as they address a wide range of issues, including data security, interoperability, and scalability. However, there are a number of barriers to integrating these technologies, such as issues with trust, reliability, and governance. this research study summarises the current state of the BML-IoT applications and identifies the challenges and issues encountered in developing these applications. It also discusses possible directions for future research on BML-IoT applications for smart cities, energy management, smart homes, smart agriculture, smart education, and smart healthcare. Overall, this study contributes to a better understanding of the endless possibilities of BML-IoT applications and the research challenges that need to be addressed to fully realise their potential. © 2023 IEEE.

关键词： Blockchain

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application of Artificial Intelligence Technology in the Field of Digital Currency Security 4

Application of Artificial Intelligence Technology in the Fie...

引用

4th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2023

作者： Mu, Jie Liu, Dongbing School of Management Wuhan Technology and Business University Hubei Wuhan China College of Mathematics and Computer Panzhihua University Sichuan Panzhihua China

this paper explores the application of artificial intelligence technology in ensuring the security of digital currencies. With the increasing popularity of digital currencies, ensuring their security has become a crucial concern. AI techniques, such as machine learning and deep learning, can be employed to identify and prevent security threats in digital currency systems. Additionally, it investigates how AI enhances security measures and risk control on digital currency trading platforms, offering insights into real-time risk alert systems. therefore, through empirical research in four distinct sections, this paper continuously refines and revises the theoretical model proposed in this project. the ultimate goal is to provide new theoretical tools for the management of financial stability in the process of Digital Currency replacing M0. this project gives the research content of the four parts, which include public perception, delivery path, underlying technology and virtual currency bubble, we hope that this research can offer valuable insights to relevant field managers. © 2024 the Authors.

关键词： Adversarial machine learning

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2023 IEEE 4th international conference on Pattern Recognition and machine learning, PRML 2023

2023 IEEE 4th International Conference on Pattern Recognitio...

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4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

ISBN: (纸本)9798350324303

the proceedings contain 98 papers. the topics discussed include: self-supervised learning for point clouds through multi-crop mutual prediction;assessment and prediction of corrosion rate of marine railway bridges based on ridge regression model;Chunk-BERT: boosted keyword extraction for long scientific literature via BERT with chunking capabilities;chunking of Setswana noun and verb phrase;RarKGQA: multi-hop question and answering method based on knowledge graph embedding;diagnosis and identification of citrus canker growth rate using machine learning;joint spatial similarity-based attention for retina vessel segmentation with super resolution encoding;mixed attention interleaved execution cascade architecture for fittings instance segmentation in overhead transmission line images;and RFBiCF: a relation-first bidirectional cascade framework for relational triple extraction.

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the Possibility of Applying the theory of Multioperations to Build Self-learning Artificial Intelligence Systems with the Explainability Property 4

The Possibility of Applying the Theory of Multioperations to...

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4th international conference on Neural Networks and Neurotechnologies, NeuroNT 2023

作者： Todikov, Sergey I. Saint Petersburg Electrotechnical University 'Leti' Department of Computer Science and Engineering Saint Petersburg Russia

ISBN: (纸本)9798350301526

When developing artificial intelligence systems, one of the important aspects is a more accurate description of the considered events and their uncertainties. At the moment, most of the modern solution's mathematical apparatus have a significant drawback-the data is considered only between two logical elements-True and false. In this regard, it is impossible to give detailed description of the considered events and their uncertainties. Another important aspect of Artificial Intelligence systems is the explainability property, since making key decisions, it is necessary to explain of how the AI system came to its answer. One of the possible solutions is to apply the theory of multioperations. the paper suggests a methodology for building self-learning systems of explainable artificial intelligence based on the theory of multioperations and presents an abstract example of the application of the developed methodology for building an AI system of a medical field of study. © 2023 IEEE.

关键词： learning systems

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