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Recognition of Diseases on Horticultural Leaves Using Machine Learning Approach 3

Recognition of Diseases on Horticultural Leaves Using Machin...

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3rd IEEE Asian Conference on Innovation in technology, ASIANCON 2023

作者： Mehta, Pradnya Sapkal, Sarita Lichade, Tanush Kulkarni, Chinmay Patil, Yashowardhan Vishwakarma Institute of Information Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798350302288

In today's environment, Machine Learning has the potential to make huge strides toward making life better for everyone in the horticulture sector. Disease identification and prediction at the early stage of disease diagnosis will help the farmers get more yields and have a strong economic background. The proposed system uses a neural network algorithm to get insight into using machine learning technology for improving the nation's horticulture in areas like production, crop protection, and day-to-day field monitoring. Our work will also notify us about how various areas of machine learning may be brought together to create a unified result that will aid in the growth of the horticulture industry and, by extension, the nation's overall productivity. Our goal throughout this research was to provide a standardized method for evaluating the development and quality of produce. Because computer vision has so much promise to address issues in rural areas, this research drew on several prior publications for background material. © 2023 IEEE.

关键词： Horticulture

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A Comparative Study of Deep Learning Models for ECG Signal-based User Classification 12

A Comparative Study of Deep Learning Models for ECG Signal-b...

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12th IEEE International Conference on Advanced Computing, ICoAC 2023

作者： Pabitha, P. Praveen, R. Chandana, Kamma Cheruvu Jayaraja Ponlibarnaa, S. Aparnaa, A.S. Anna University MIT Campus Dept. of Computer Technology Chennai India

ISBN: (纸本)9798350318210

In recent years, Deep Learning (DL) has gained significant attention and has been extensively studied across various fields, including healthcare. In the context of healthcare, the timely detection of anomalies in Electrocardiogram (ECG) signals can play a crucial role in patient monitoring. Arrhythmia, characterized by irregular heart rate and abnormal heart rhythm, necessitates more than manual analysis of ECG signals for prompt identification of abnormalities. This paper presents a comprehensive review that focuses on the application of DL methods for the classification of ECG signals. Different DL techniques such as Convolutional Neural Network (CNN), INCEPTION, RESNET, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) have been considered in this study to detect five distinct types of arrhythmia. The results shows that the RESNET model surpasses all other existing DL models in terms of accuracy, achieving a remarkable 98.4% accuracy rate and identified that the ECG Signals can be used as a unique factor to identify user. © 2023 IEEE.

关键词： Classification (of information)

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Optimized Fog Colony Framework for Efficient Service Placement using Hybrid Genetic Algorithm

Optimized Fog Colony Framework for Efficient Service Placeme...

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2023 International Conference on Decision Aid Sciences and Applications, DASA 2023

作者： Verma, Nilesh Kumar Naik, K. Jairam National Institute of Technology Dept. of Computer Science & Engineering Raipur India

ISBN: (纸本)9798350342055

The transition from Cloud Computing to Fog Computing is being investigated as the preferred method for rapid data processing in Internet of Things (IoT) applications. Fog computing is a decentralized computing paradigm, harnesses the computational capabilities of IoT devices at the network edge to perform data processing and provide real-time services. However, effective service placement in fog environments remains a challenge. This research paper focuses on the lack of efficient techniques for organizing fog devices into optimal fog colonies that possess self-rule capabilities and require minimal energy for administration. We propose a hierarchical clustering-based fog colony architecture to fill this void, which will allow for in-dept. investigation and analysis of various colony configurations. The resulting dendrogram includes all feasible colony configurations, and a genetic algorithm is used for precise colony selection based on fitness and accuracy. The multi-objective hybrid genetic algorithm NSGA-II is used to assess how efficiently the suggested framework performs under a variety of conditions. The goal is to reduce the delay between an app's initial interaction with a user and the initial practice of the app. The results show improvements in reaching predefined goals when the proposed strategy is applied to different contexts. © 2023 IEEE.

关键词： Genetic algorithms

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Quote Detection: A New Task and Dataset for NLP 7

Quote Detection: A New Task and Dataset for NLP

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7th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, LaTeCH-CLfL 2023

作者： Tekir, Selma Güzel, Aybüke Tenekeci, Samet Haman, Bekir Ufuk Izmir Institute of Technology Dept. of Computer Engineering Izmir35430 Turkey

ISBN: (纸本)9781959429548

Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts. Finally, we provide a qualitative analysis to gain insight into the performance. © 2023 Association for Computational Linguistics.

关键词： Random processes

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Dual Auction Peer-to-Peer Control Scheme for Empowering Prosumers Participation in the Local Energy Markets 4

Dual Auction Peer-to-Peer Control Scheme for Empowering Pros...

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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Ikram, Muhammad Ahmed, Salman Khan Marwat, Safdar Nawaz University of Engineering and Technology Dept. of Computer Systems Engineering Peshawar Pakistan

ISBN: (纸本)9798350335316

The emerging trend of distributed and renewable energy sources encourages energy trading in the electricity market. Various forms of energy markets are evolving for successful energy trading. The local energy market is one of them in which various participants are actively coordinating for optimal utilization of power. Such type of local energy markets provides individual prosumers to trade their surplus energy in the neighbourhood in peer-to-peer topology. The complexity of such type of local energy market is increasing in terms of structure, control, coordination, and cost estimation. For the cooperative behaviour of the local energy markets, a dual auction coordination control scheme is important to encourage more prosumers for energy trading. We proposed a novel scheme that performs dual auctions for prosumers in a decentralized fashion. We assessed dual auction matching by coordinating prosumers and consumers to sell and purchase energy in the local energy market. The aggregator performed balancing in the proposed dual auction scheme as an autonomous entity. The results were simulated in MATLAB and JADE for the complex prosumer peer-to-peer network. Our proposed scheme shows each peer dual auction matching improves from 0.95 to 2.03 for optimal hierarchal energy trading in energy markets. For peer participation, the participation ratio increased from 0.684 to 1.74 and optimal aggregator coordination from 9.18% to 19% in the local energy markets. © 2023 IEEE.

关键词： Smart power grids

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Cross-Database Facial Expression Recognition using CNN with Attention Mechanism 14

Cross-Database Facial Expression Recognition using CNN with ...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Chandra, Jatin Annappa, B. Rashmi Adyapady, R. National Institute of Technology Dept. of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350335095

Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model's class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model's performance. The proposed model achieved better results and overcame the previous work's limitations. CDE's performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively. © 2023 IEEE.

关键词： Face recognition

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DEF-PIPE: Domain Specific Language Visualization for Big Data Pipelines

DEF-PIPE: Domain Specific Language Visualization for Big Dat...

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2023 International Conference on Computational Science and Computational Intelligence, CSCI 2023

作者： Kheirabadi, Amirhossein Layegh Mitrovic, Vlado Dinh, Khoa Chang, Yilin Matskin, Mihhail Royal Institute of Technology Kth Dept. of Computer Science Stockholm Sweden

ISBN: (纸本)9798350361513

The complexity of Big Data analysis requires a combination of different software components into a pipeline performing different analysis steps. Supporting such pipelines requires different expertise provided by different actors: domain experts and technical/computing experts. The main objective of this work is to support domain experts with a tool for pipeline description, which does not require deep technical knowledge about the deployment and execution of Big Data pipelines. We present a solution to visualize Big Data pipeline description using the DEF-PIPE tool. The solution shows that the process of pipeline description is simple and intuitive for users who are not experts in computing. At the same time, DEF-PIPE automatically generates a textual description of the designed data pipelines, which contains the necessary information for simulation, adaptation, deployment, and resource management of the pipeline. In this case, a separation of concerns between the design and run-time phases of the Big Data pipeline lifecycle is supported. This solution allows us to bridge the gap between domain and technical experts. Providing libraries of steps and pipelines also allows the reusing of previously developed solutions. © 2023 IEEE.

关键词： Big data

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Photoplethysmography Signal Quality Assessment Using Neighbour Edge Restricted Horizontal Visibility Graph and Machine Learning Classifiers 16

Photoplethysmography Signal Quality Assessment Using Neighbo...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Khan, Zahir Manikandan, M. Sabarimalai Mahmud, Mufti Indian Institute of Technology Palakkad Dept. of Computer Science and Engineering Kerala Palakkad678623 India Indian Institute of Technology Palakkad Dept. of Electrical Engineering Kerala Palakkad678623 India Nottingham Trent University Dept. of Computer Science Nottingham Clifton Lane United Kingdom

ISBN: (纸本)9798350371154

Photoplethysmography (PPG) signals are vital for monitoring pulse rate, blood pressure, and more, but they are prone to motion artefacts and noise, leading to unreliable data. Assessing PPG signal quality is crucial for reliable healthcare and accurate medical diagnoses. By transferring the PPG time domain signal to a Horizontal Visibility Graph (HVG) network and combining extracted features from HVG with machine learning algorithms, we can classify the PPG signal into clean (or high quality) and noisy. We have proposed a new version of HVG called Neighbour Edge Restricted Horizontal Visibility Graph (NERHVG) by invoking some extra conditions for joining edges in HVG for PPG signal quality assessment (SQA). We have used the average degree (AD) of graphs extracted from HVG, and NERHVG algorithms as features in 3 different machine learning classifiers such as Random Forest (RF), Gaussian Naive Bayes (GNB), Decision Tree (DT) to classify 4 standard untrained PPG datasets (DS). The classifier models DT, RF, GNB associated with graph feature AD of HVG, NERHVG algorithms are named as: DT-HVG, DT-NERHVG, RF-HVG, RF-HVG, RF-NERHVG, GNB-HVG and GNB-NERHVG. After all the performance of HVG and NERHVG algorithms using AD feature are compared over the mentioned classifier models. It is observed that the NERHVG algorithm outperformed the HVG algorithm with the AD feature in all 4 datasets with a maximum accuracy of: 99.09%, 95.03%, 96.56 % and 84.63% using the GNB classifier. © 2024 IEEE.

关键词： Decision trees

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Generating Textual Video Summaries using Modified Bi-Modal Transformer and Whisper Model 3

Generating Textual Video Summaries using Modified Bi-Modal T...

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3rd International Conference on Advanced Computing Technologies and Applications, ICACTA 2023

作者： Madkaikar, Siddhesh Belhekar, Vipul Dharmadhikari, Yash Tripathy, Amiya Kumar Don Bosco Institute of Technology Dept. of Computer Engineering Mumbai India

ISBN: (纸本)9798350348347

Online video material availability has grown significantly over the past few years, making it challenging for viewers to consume and process information effectively. Video summaries written in text form have shown to be a helpful technique for solving this problem. The key aspects of a video can be accurately and briefly summarised by utilizing methods like video summarising and natural language processing. The purpose of the model is to display a short textual summary of the video to the user before the user watches the complete video. It is frequently difficult and time-consuming for students to locate a suitable educational video. It is occasionally seen on video streaming platforms such as YouTube that the title and the content of the video are not precisely the same. So the model will generate a textual summary of the video and the user can read the summary and get to know about the content of the video. Also, this approach to summarizing videos is helpful in other tasks like video content moderation and could also enable visually-impaired people to consume video material and improve their quality of life. © 2023 IEEE.

关键词： Video recording

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Alumni Management and Networking System 2

Alumni Management and Networking System

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2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023

作者： Rajini, S. Hari, Prasad B Upendrasingh, A. Dept. of Computer Science and Engineering Kumaraguru College of Technology Coimbatore India

ISBN: (纸本)9798350306811

The Alumni Management and Networking System aims to address the challenges of alumni disconnection and facilitate effective communication and engagement between educational institutions and their alumni. The system provides a platform for institutes to create profiles, connect with fellow alumni and offers opportunities for alumni to contribute to the institution's success through mentorship, volunteering, and other forms of support. This paper reviews existing research on alumni management systems, highlighting their strengths, limitations, and gaps. It evaluates different approaches such as centralized systems, data collection methods, interactive questionnaires, secure messaging, and tag recommendation systems. The proposed Alumni Network Management System incorporates content-based tagging, utilizing a fine-tuned BERT model trained on a dataset of 10,000 Twitter posts. This approach generates relevant tags for new social media posts, enhancing content visibility and discoverability. Additionally, it employs end-to-end encryption for secure messaging, ensuring privacy and confidentiality. The system's implementation has been tested successfully, showcasing its user-friendliness and effectiveness in managing alumni databases. The results indicate that the system provides a convenient platform for alumni to update their information, stay connected, and engage with their alma mater. © 2023 IEEE.

关键词： Cryptography

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