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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Aishwarya D Ponduru Sandeep Penmatsa V P S Akhilesh Varma Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

This study introduces a novel facial deconstruction method for face emotion identification. After identifying facial landmarks with the IntraFace algorithm, seven regions of interest (ROI) are extracted, representing the principal featuresof the face: the mouth, nose, an area between the eyebrows, right and left eyes, and eyebrows. Next, a variety of local descriptors, such as Local Binary Patterns, Completed Local Binary Patterns. Local Ternary Patterns, and Dynamic Local Ternary Patterns, are employed to capture distinctive features. Inputting the feature vector which represents a face image into a multi-class support vector machine completes the recognition process. The suggested method outperforms previous facial deconstruction techniques, according on experimental findings on two publicly available datasets.

关键词： Support vector machines Accuracy Reviews Face recognition Computational modeling Feature extraction Vectors

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Cardiovascular Disease Prediction Utilizing Machine Learning and Feature Selection with Clonal Selection Algorithm

Cardiovascular Disease Prediction Utilizing Machine Learning...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Wahidur Rahman Riasaad Haque Aneek Md. Moinuddin Md. Shadman Sakib Md. Sadiq Iqbal Mohammad Motiur Rahman Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of Computer Science and Engineering Z.H. Sikder University of Science and Technology Bhedarganj Shariatpur Dept. of Computer Science and Engineering University of Information Technology and Sciences Dhaka Bangladesh Dept. of Computer Science and Engineering Bangladesh University Adabor Bangladesh Dept. of Computer Science and Engineering Mawlana Bhashani Science and Technology University Tangail Bangladesh

A substantial and rising number of patients suffer from cardiovascular diseases, including heart attacks, heart failure, and other related illnesses. This case surge places increasing pressure on healthcare professionals and administrators while patients grapple with growing medical costs. To address these challenges, an automated system is necessary within the healthcare sector. In this paper, we present a cardiovascular health monitoring system that incorporates Machine Learning techniques. The study employed feature selection techniques on a secondary dataset. The study has used a nature-inspired technique, namely the Clonal Selection Algorithm (CSA) and Maximum Relevance Minimum Redundancy (mRMR) technique, to identify the most prominent feature in the detection of cardio-vascular illness. This approach was combined with a collection of Machine Learning classifiers. A group of experimental data has been listed to assess the suggested model's effectiveness. The research findings indicated a maximum accuracy rate of 100% when employing the proposed algorithms and orientations. The study also discusses the performance analysis for CSA and mRMR using a set of performance evaluation matrices. Based on the obtained results, it can be inferred that the suggested model will likely exhibit a high level of effectiveness in identifying cardiovascular diseases.

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Automated Grading of Bananas Using Advanced YOLO Models: A Machine Learning and computer Vision Approach for Ripeness Classification

Automated Grading of Bananas Using Advanced YOLO Models: A M...

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International Conference on computer and Information Technology (ICCIT)

作者： Md. Sariful Islam Robaaed Afred Tanik Noor Hossain Md Abdur Rahman Tamim Mohammed Imran Jubayer Ahmed Shawon Wahidur Rahman Samia Yasmin Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Among all fruits, bananas are consumed in high quantity around the world, and their health and industrial impact is enormous. The work presented here elaborates on how advanced machine learning and computer vision techniques can automate banana grading, particularly in its different versions of the YOLO model. In that respect, a dataset of 1899 images, representing five stages of ripeness, was manually created and pre-processed. Precision, recall, mean Average Precision at different IoU thresholds, and speed are applied to measure the performances of YOLOv8-n, YOLOv9-s, YOLOv10n, and YOLO11s. The experimental results demonstrated the high value of precision with 0.992 and a recall of 1.0 for the YOLOv8-n model, suitable for real-time applications, whereas YOLO11s attained the highest overall performance in various IoU thresholds with an mAP50-95 of 0.936. This work points out how automated grading systems can play an important role in improving both efficiency and the quality assessment of bananas, promoting consumer satisfaction while reducing post-harvest losses. Further research will be directed toward dataset enlargement, greater application of advanced imaging techniques, and overcoming practical challenges to implementation to improve the accuracy of grading and applicability in real situations, besides establishing a standardized framework for evaluation to gain more widespread acceptance of automated grading technologies in the agricultural sector.

关键词： YOLO computer vision Accuracy Computational modeling Customer satisfaction Imaging Machine learning Real-time systems Quality assessment Information technology

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Cost-efficient Blockchain-based e-KYC Platform using Biometric verification

Cost-efficient Blockchain-based e-KYC Platform using Biometr...

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International Conference on Information Networking

作者： Sahil Bhatia Lokendra Vishwakarma Debasis Das Computer Science & Engineering Dept. Indian Institute of Technology Jodhpur Rajasthan

ISBN: (数字)9798350330946

ISBN: (纸本)9798350330953

Know Your Customer (KYC) is the authentication and verification process of the user carried out in the financial sector by institutions such as banks, insurance companies, fintech companies, etc., and other sectors such as smart healthcare, real estate, telecommunication, online gaming and gambling companies before engaging in any financial activities. The KYC process is made mandatory for Financial Institutions (FIs) by governments worldwide to keep a check on terror financing and money laundering activities. However, the traditional physical KYC process has limitations such as high operational costs, large time consumption, privacy and security issues, repetitive processes across multiple institutions and user inconvenience. Hence, the FIs are looking for alternatives to the physical KYC. The digitization of the KYC process, known as e-KYC, was explored but had problems such as a single point of failure, repetition of KYC process across multiple FIs and lack of security. Blockchain-based e-KYC has become popular these days and caught the eye of FIs due to inherent properties of the blockchain such as decentralization, transparency, and immutable ledger. Moreover, blockchain-based solution is highly secure and cost-efficient. This paper proposes a blockchain-based e-KYC platform where users can register themselves and get their video KYC done after ether payment. Once successfully verified, the user will receive a KYC key. The user will submit this key to the banks or other FIs in which he wants to register. The FIs can verify whether the user has undergone the KYC process by looking in the blockchain using the KYC key. Our solution requires the user to submit his documents only at the e-KYC platform and not at different FIs for registration, thus preserving his privacy and, at the same time, ensuring the user’s authenticity.

关键词： Privacy Costs Government Insurance Companies Medical services Registers

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Densely Decoded Networks with Adaptive Deep Supervision for Medical Image Segmentation

arXiv

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arXiv 2024年

作者： Mishra, Suraj Chen, Danny Z. Dept. of Computer Science and Engineering University of Notre Dame United States

Medical image segmentation using deep neural networks has been highly successful. However, the effectiveness of these networks is often limited by inadequate dense prediction and inability to extract robust features. To achieve refined dense prediction, we propose densely decoded networks (DDN), by selectively introducing ‘crutch’ network connections. Such ‘crutch’ connections in each upsampling stage of the network decoder (1) enhance target localization by incorporating high resolution features from the encoder, and (2) improve segmentation by facilitating multi-stage contextual information flow. Further, we present a training strategy based on adaptive deep supervision (ADS), which exploits and adapts specific attributes of input dataset, for robust feature extraction. In particular, ADS strategically locates and deploys auxiliary supervision, by matching the average input object size with the layer-wise effective receptive fields (LERF) of a network, resulting in a class of DDNs. Such inclusion of ’companion objective’ from a specific hidden layer, helps the model pay close attention to some distinct input-dependent features, which the network might otherwise ‘ignore’ during training. Our new networks and training strategy are validated on 4 diverse datasets of different modalities, demonstrating their effectiveness. © 2024, CC BY.

关键词： Deep neural networks

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Advancing Multi-Agent Systems Integrating Federated Learning with Deep Reinforcement Learning: A Survey

Advancing Multi-Agent Systems Integrating Federated Learning...

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International Conference on Ubiquitous and Future Networks, ICUFN

作者： Jaemin Kim Gahyun Kim Seonghun Hong Sungrae Cho Dept. of Computer Science and Engineering Chung-Ang University Seoul Korea

ISBN: (数字)9798350385298

ISBN: (纸本)9798350385304

Recent advancements in the field of Deep Reinforcement Learning (DRL), such as the development of algorithms like Deep Q-Network, Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO), have been significant. However, these algorithms encounter data privacy issues in multi-agent environments. This paper investigates the latest research trends in applying federated learning to address data privacy issues in multi-agent systems within deep reinforcement learning. It delves into various approaches for implementing federated learning in these systems, examines the challenges faced, and explores potential solutions to enhance privacy while maintaining or improving the performance of DRL algorithms in multi-agent setting.

关键词： Surveys Data privacy Privacy Federated learning Deep reinforcement learning Market research Optimization

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

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Pioneering Developments in computer science & Digital Technologies (IC2SDT), International Conference on

作者： Subhasish Sarkar Gautam Kumar Dept. of Computer Science and Engineering National Institute of Technology Delhi India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer.

关键词： computer vision Plant diseases Accuracy Crops Production Transformers Nutrients

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Meta-Adaptation Goals: Leveraging Feedback Loop Requirements for Effective Self-Adaptation

Meta-Adaptation Goals: Leveraging Feedback Loop Requirements...

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Autonomic Computing and Self-Organizing Systems Companion (ACSOS-C), IEEE International Conference on

作者： Raffaela Groner Ricardo Diniz Caldas Rebekka Wohlrab Dept. of Computer Science and Engineering Chalmers | University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798350389760

ISBN: (纸本)9798350389777

Self-* systems focus on optimizing systems regarding adaptation goals, e.g. availability, that relate to properties of the managed system. While these adaptation goals are important, the feedback loop must also be considered. It is problematic if systems adapt too often, make too time-consuming changes that involve insecure transition states, or react too slowly when planning adaptations. Current approaches do not allow to enforce requirements on the adaptation loop—including sensitivity, sta-bility, and transition states. When planning adaptations, those meta-adaptation goals need to be considered and potentially traded off against traditional adaptation goals. Such a tradeoff might entail, for instance, that it may be worth it to adapt a system less frequently, although the overall availability of the system could be higher with more adaptations. We propose to explicitly consider feedback loop requirements for self-adaptive systems. Concretely, we propose using machine learning to predict quality measures based on the state of the feedback loop and the managed system. This information is used to plan for better adaptations that consider the adaptation goals and the meta-adaptation goals of the feedback loop. The explicit representation of meta-adaptation goals and adaptation goals enables the optimization of adaptation strategies, tradeoff analysis, and evaluation of adaptations.

关键词： Feedback loop Adaptation models Sensitivity Machine learning Planning Stakeholders Requirements engineering Distributed computing Optimization Monitoring

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A Photovoltaic Interleaved Boost Integrated CL Resonant Converter for Magnetron Application

A Photovoltaic Interleaved Boost Integrated CL Resonant Conv...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Aref Molaei Kajanan Kanathipan John Lam Dept. of Electrical Engineering and Computer Science York University Toronto Canada

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance.

关键词： Photovoltaic systems Maximum power point trackers Legged locomotion Magnetic resonance Magnetic devices Resonant converters Microwave devices High-voltage techniques Energy conversion Microwave theory and techniques

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Stock Index Probability Prediction using the FB Prophet Model 1

Stock Index Probability Prediction using the FB Prophet Mode...

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1st International Conference on Futuristic Technologies, INCOFT 2022

作者： Harish, Neha Likith, H. Yashwanth, Gundala Krishnaswamy, Narayan Eshanya Sunil, G.L. Sai Vidya Institute of Technology Dept. of Computer Science and Engineering Bengaluru India Sai Vidya Institute of Technology Dept. of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9781665450461

Given how unpredictable the stock market has recently become, it is now crucial to be able to accurately predict the future trend of equities. Because of the financial crisis, it is now essential to estimate stock values accurately in order to make money. Non-linear signal forecasting requires sophisticated statistical models and machine learning methods. We must identify the stock's purchasing and selling points for a specific stock market history in order to make a profit. The Dow Theory, MACD, the Relative Strength Index (RSI), the Exponential Moving Average, and others have all been used to forecast stock market performance. It could be challenging to make a specific judgement because these models could provide a variety of indications. The aforementioned issue was attempted to be solved by a wave of machine learning algorithms in artificial intelligence. Some strategies have yet to provide promising results, while others have not reacted as well on the stock market exchange, due to the non-linear structure of the stock market signals. We intend to employ the Prophet model, which is reliable and generates findings with minimal data. The Prophet refers to the method of forecasting time series data based on an additive model with non-linear trends suited with holiday impacts, daily seasonality, weekly, and yearly. Strong seasonal impacts in time-series data are ideal for it. The prophet is resilient to missing data and adept.at handling outliers. Facebook has made available the prophet, an open-source Python package. Predictions of stock prices are shown by indicators like RSI, MACD, Super Trend and others. © 2022 IEEE.

关键词： Machine learning

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