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检索条件"机构=Dept of Computer Engineering and Computer Science"

共 39335 条记录，以下是121-130 订阅

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Enhancing 5G Efficiency: Regression Analysis for Resource Allocation Optimization in Wireless Networks

Enhancing 5G Efficiency: Regression Analysis for Resource Al...

引用

2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Padmageetha, B.G. Kumar Naik, Pramod Patil, Mallanagouda Dayananda Sagar University Dept. of Computer Applications Bengaluru India Dayananda Sagar University Dept. of Computer Science and Engineering Bengaluru India RVITM Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350378092

The advent of 5G networks signifies a major breakthrough in mobile communications, providing better data rates, low latency, and improved connectivity. Efficient resource allocation within 5G networks is paramount to fully leverage these capabilities, ensuring optimal performance and user satisfaction. Traditional resource allocation methods often struggle to keep pace with the dynamic and complex nature of 5G environments. The potential benefits of machine learning (ML) is explored in this research to enhance resource allocation in 5G networks. We begin by examining the unique challenges posed by 5G networks, by ML approaches employed for resource allocation, including supervised learning such as Trees, SVM, Neural Networks. Our study we train the model using MATLAB simulator. Analysis on the response and residual plot is conducted for predicted response. Analysis and comparative study is made to find the best performance metric suitable for the predicting the model for resource allocation. The study shows the improvisation in the metrics of regression learner. Simulation results demonstrate significant improvements in network performance. Finally, we have done the comparison between the results of MATLAB simulation. This study gives better decisions to service providers to enhance the overall performance of network © 2024 IEEE.

关键词： MATLAB

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Reddy, Arra Keerthan Vardhan Rapeti, Harsha Reddy, Doduguru Amarnath Srinivas, M. Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems © 2025 IEEE.

关键词： Deep neural networks

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Spatial Dynamics for Identification of Individuals through Gait and Other Locomotion Activities

Spatial Dynamics for Identification of Individuals through G...

引用

2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Anusha, R. Sanshi, Shridhar Bapuji Institute of Engineering and Technology Dept. of Computer Science and Engineering Davanagere India National Institute of Technology Dept. of Computer Science and Engineering Karnataka Surathkal India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379525

Gait, the pattern of walking, has been extensively studied and various methods have been developed to use it as a biometric for individual recognition. Despite this, the potential to identify individuals through running videos has not been thoroughly explored. The paper introduces a novel method that expands the feature-based approach for identifying individuals based on their running style. This work focuses on extracting the mutual information and location specific metric from the key gait poses of subjects in the testing and training datasets. Later on, the assignment of a testing sample to the training sample is accomplished using the proposed classification method. The experiments are conducted on KTH, OU-ISIR A, and Weizmann database. The efficiency of this method is demonstrated by the obtained experimental results. © 2024 IEEE.

关键词： Feature extraction

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

引用

2025 International Conference on Artificial Intelligence and Data engineering, AIDE 2025

作者： Dinesh, Medisetty Sagar, Lingamaneni Moksha, Pallapu Baladhitya, Pappula Srinivas, M. Amrita Vishwa Vidyapeetham Computer Science Engineering Amaravati India Amrita Vishwa Vidyapeetham Dept. of Computer Science Amaravati India

ISBN: (纸本)9798331527518

Identifying handwritten words poses a complicated problem owing to the variances in handwriting styles and the possible noise and distortions present in the data. The performance of used deep learning and traditional machine learning handwriting recognition methods for recognizing handwritten words is studied in this paper and the advantages of using deep learning are discussed. In particular, neural network models such as CNN and 2DLSTM networks had a great work in this area. In contrast, we detect whole handwritten words without the need of segments into standalone characters. The CNNs are used for feature extraction, the Bidirectional LSTMs for sequence prediction and output is decoded via a CTC layer. The model was evaluated over the IAM words dataset from the IAM handwriting database, achieving 87% accuracy and a character error rate of 7.77%. In addition, we experimented with ML techniques, including SVM, RF has got an accuracy of 72% and 80% accuracies, and a Bayesian network of 61% accuracy, to provide a comparative analysis. © 2025 IEEE.

关键词： Support vector machines

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An Intelligent Self-Aware Financial Inclusion System for Digital Society 3

An Intelligent Self-Aware Financial Inclusion System for Dig...

引用

3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Rawat, Ruchira Goyal, Himanshu Rai Sharma, Sachin Kotiyal, Bina Graphic Era Deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331542375

The continuous advancement in technology has restructured industries, due to innovations in Artificial Intelligence (AI) the financial sector endorsing astonishing transformations. This research recognizes the potential of AI in fintech for digital society while focusing on the transition toward Industry 5.0, which is based on human-AI collaboration. AI technologies, such as Artificial Generative Intelligence (AGI) and Artificial Narrow Intelligence (ANI), are limited in handling intricate, vigorous financial environments. ANI has improved efficiency via credit risk assessment, customer service automation, and fraud detection, whereas AGI aims for more comprehensive decision-making capabilities. ANI and AGI are creating a gap in accomplishing a truly responsive, customer-centric system as it cannot fully adapt to subtle decision-making or erratic market shifts. This paper suggests a Super Artificial Intelligence (SAl) as a promising solution to overcome limitations, standing SAI-based design for a unified nifty digital financial system. Its forward-thinking decision-making proficiencies, self-learning, and flexibility are expected to transmute financial aligned with Industry 5.0's human-centered principles by providing adaptive services, enhancing financial inclusion and personalized. The study identifies key challenges in current AI applications, through a comprehensive review of literature and case studies, such as algorithmic errors and customer facility, and highlights how SAI could offer a more autonomous and ethical approach to financial decision-making. The paper concludes by underscoring the importance of continuous AI validation, ethical considerations, and human oversight to ensure sustainable, secure, and customer-focused financial systems. © 2025 IEEE.

关键词： Fintech

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Dynamic Real-Time Scheduling on Distributed Hierarchical Fog Networks 31

Dynamic Real-Time Scheduling on Distributed Hierarchical Fog...

引用

31st IEEE International Conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

作者： Sharma, Amit Auluck, Nitin Dept. of Computer Science & Engineering IIT Ropar India

ISBN: (纸本)9798331509118

We present a novel approach for efficient task scheduling on hierarchical fog nodes, catering to real-time (RT) and non-real-time (NRT) tasks with varying sizes and deadline constraints. Leveraging machine learning (ML) techniques, our proposed solution autonomously allocates tasks across fog nodes, dynamically adapting to changing workload patterns and system conditions. Our novel methodology integrates supervised learning algorithms to predict workload patterns and resource availability, enabling intelligent decision-making in task assignment. Our findings contribute to advancing the state-of-the-art in fog computing by offering a scalable and adaptive solution for dynamic task scheduling in hierarchical heterogeneous environments. © 2024 IEEE

关键词： Fog computing

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FFDM - GNN:A Financial Fraud Detection Model using Graph Neural Network 1

FFDM - GNN:A Financial Fraud Detection Model using Graph Neu...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Kesharwani, Abhishek Shukla, Prashant United University Dept of Computer Science and Engineering Prayagraj India

ISBN: (纸本)9798350353648

Financial Fraud Detection Model (FFDM) is used to develop an advanced detection framework utilizing Graph Neural Networks (GNNs) to accurately identify fraudulent transactions within the transactions. Traditional fraud detection systems often struggle with the dynamic and intricate nature of fraudulent activities. In this paper, we introduce an innovative fraud de-tection model leveraging the power of Graph Neural Networks (GNN) to address these challenges. Our model synergistically combines the strengths of graph-based learning with deep neural networks to effectively capture the complex relationships and patterns inherent in financial transactions. By utilizing a multi-layered approach, our GNN model not only identifies anomalous patterns indicative of fraud but also adapts to evolving fraudulent tactics.A key feature of our model is the integration of node and edge features, which enhances the representation of transaction networks. © 2024 IEEE.

关键词： Decentralized finance

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An Automatic Prediction of Elevator Capacity Using ML to Customize the Total Capacity 5

An Automatic Prediction of Elevator Capacity Using ML to Cus...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Talwandi, Navjot Singh Tripathi, Anubhav Khare, Shanu Chandigarh University Dept of Computer Science Engineering Punjab India

ISBN: (纸本)9798350383522

Elevators serve as vital components in modern buildings, yet optimizing passengers' waiting time remains a crucial challenge. This study proposes a machine learning-based approach to enhance the efficiency of elevator systems by minimizing the waiting time experienced by passengers. By leveraging historical data and predictive models, this research aims to optimize elevator operations, considering factors such as traffic patterns, peak hours, and user demand. The implementation of machine learning algorithms enables the system to dynamically adjust elevator scheduling strategies, ultimately reducing waiting times and enhancing user experience. The findings highlight the potential of machine learning techniques in revolutionizing elevator management for improved passenger satisfaction and operational efficiency. © 2024 IEEE.

关键词： Arduino Elevator optimization kNN Machine learning Operational efficiency Passenger waiting time Predictive modeling Traffic patterns User demand

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Comparative Analysis of Machine Learning Algorithms for Binary Classification of Tea Leaf Diseases 5

Comparative Analysis of Machine Learning Algorithms for Bina...

引用

5th IEEE International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Shekhar, Nidhi Megha, J.S. Varsha, J.S. Revathi, G.P. Pesu - Isfcr Dept. of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350356236

India stands as one of the world's largest tea exporters. This requires for an implementation of an efficient and robust system for disease detection and prevention. The most important challenge faced by the tea industry is tea leaf diseases due to continuous pathogen infestation. These diseases can cause full-size crop losses, adversely affecting the economic system and the livelihood of tea farmers. The ability to detect these diseases early is essential. Early detection can cause timely intervention that can notably reduce harm to tea plants and keep them at their best possible quality. Unfortunately, traditional diagnostic techniques often depend on visible inspection and are hence inefficient as those methods can be gradual, labour in-depth and vulnerable to human errors, reducing the effectiveness in larger fields. This research paper aims to classify whether a leaf is diseased or healthy through a two-step process involving feature extraction using a pretrained Convolutional Neural Network (CNN) and classification using four machine learning models - LightGBM, CatBoost, Autoencoders and Self Organized Maps (SOMs). This paper utilizes a pre-trained VGG16 model, which is trained on the ImageNet dataset. This model is capable of extracting high-level features from images. © 2025 IEEE.

关键词： Self organizing maps

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SubQUBO Annealing Based on Efficient Binary Variable Selection for Combinatorial Optimization Problems with One-Hot Constraints

SubQUBO Annealing Based on Efficient Binary Variable Selecti...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Noguchi, Tatsuya Fukada, Keisuke Bao, Siya Togawa, Nozomu Dept. of Computer Science and Communications Engineering Waseda University Japan

ISBN: (纸本)9798331521165

In combinatorial optimization problems with one-hot constraints, the number of binary variables that have the value of one in the feasible solution becomes constant and small, so it is important to always consider them to escalate the solution searching process and obtain a more converged solution in annealing. In this paper, we propose a method to preferably select binary variables with the value of one in the subQUBO annealing process using Ising machines such as quantum annealers. Evaluation experiments show that the proposed method obtains a solution with lower energy than a conventional subQUBO method and a full QUBO method. © 2025 IEEE.

关键词： Combinatorial optimization

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