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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Ansari, Nadia Mustaqim Iqbal, Kashan Tariq, Talha Iqbal, Rizwan Dawood University of Engineering & Technology Department of Electronic Engineering Karachi Pakistan National University of Computer & Emerging Sciences Fast Department of Computer Science Karachi Pakistan Dawood University of Engineering & Technology Department of Telecommunication Engineering Karachi Pakistan

ISBN: (纸本)9798331534271

Agricultural cultivation is an essential requirement for the survival and well-being of the human species. Lack of care or incorrect assessment of various growth and productivity-related metrics leads to a significant loss of production. The wireless crop monitoring system needs a portable, user-friendly, accurate, and automatic data generator for various critical metrics. This paper presents the temperature, humidity, soil moisture, and air quality monitoring of soil conditions and atmosphere for efficient crop growth. The parameters listed above will be monitored using Wireless Sensor Networks (WSNs). This research resolves the issue of excessive water supply by assessing soil moisture. Monitoring temperature allows for the estimation of a plant's water requirements. Monitoring air quality allows for the maintenance of healthy air quality. © 2024 IEEE.

关键词： Soil moisture

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Integrating AI and computer Vision for Effective Foreign Object Detection on Airport Runways 3

Integrating AI and Computer Vision for Effective Foreign Obj...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Annie Silviya, S.H. Kingsley, S. Agitha, W. Sriman, B. Priyadarshan, V.J. Mohana, V. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India P.B. College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

The FOD-R Dataset is a collection of images that depict common types of foreign object debris (FOD) that can be found on runways or taxiways. The dataset has primarily been annotated using bounding boxes to facilitate object detection. However, the FOD-xR Dataset includes an additional feature - color codes - that provide information on the ease of removing the debris. Lightweight objects are marked in green, while heavy objects are marked in red. This centralized system simplifies coordination efforts, making it easier for organizations to manage FOD removal effectively. This, in turn, can have a positive impact on an organization's bottom line by minimizing delays and damage caused by FOD incidents. Overall, the FOD-R and FOD-xR Datasets are valuable resources for organizations looking to improve their FOD detection and removal processes. © 2023 IEEE.

关键词： Object detection

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Web based Payment Tracking and Accounting Application 7

Web based Payment Tracking and Accounting Application

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Baibhav, Jayesh Ghatak, Jyotishman Mayan, J. Albert Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350397253

The Payment Tracking System (PTS) is a sophisticated web-based platform designed for proficiently tracking and managing payments from various vendors. It serves as a centralized hub for consolidating payment requests from the accounts department to upper management, thereby ensuring timely delivery of supplier payments through the implementation of integrated best practices for managing operations and services. The system boasts advanced Transaction Management functionality such as Invoice Generation and Payment Reminders, providing unparalleled visibility, insight, isolation, and swift resolution of payment-related issues for any type of organization by delivering the necessary information in a timely manner to the user. In today's fast-paced business environment, managing invoicing and chasing payments from clients can be a challenging task. This innovative application offers a solution to this problem by providing businesses with the capability to track payments from clients and gain valuable insights into their earnings. The system not only facilitates the creation, sending, and receiving of invoices, but also generates automated follow-up reminders for overdue payments. By utilizing this application, clients can also avoid incurring late fees © 2023 IEEE.

关键词： Websites

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Classifying Bangla Book's Context: A Multi-Label Approach 26

Classifying Bangla Book's Context: A Multi-Label Approach

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26th International Conference on computer and Information technology, ICCIT 2023

作者： Ullah, Md Safaet Ali Al-Reza, Md. Rahman, Mohammad Shahidur Shahjalal University of Science & Technology Department of Computer Science & Engineering Sylhet Bangladesh

ISBN: (纸本)9798350359015

This paper thoroughly analyses the multi-label classification of Bangla book content. We introduce a novel dataset of the Bangla book's context, annotated with multiple labels, based on eight genres. Bangla is one of the most widely spoken languages and has an extensive literary tradition. However, the classification of book context data remained largely unexplored due to insufficient pertinent data. To overcome this limitation, we present Bangla Book Context, a large-scale dataset containing 10,211 samples categorized into eight main categories: fiction, nonfiction, mystery, tragedy, thriller, motivational, and romantic. For a proper evaluation, we employed three pre-trained models: bert-base-multilingual-case, BanglaBERT, BERT: Pre-training of Deep Bidirectional Transformers for Language Model and CNN-LSTM attention-based deep learning approach. However, the CNN-LSTM attention-based deep learning model consistently performs better than all the other models we have described. © 2023 IEEE.

关键词： Classification Deep-learning Multi-label Transfer-learning

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Optimized Machine Learning Model for Identifying and Predicting Attention Deficit Hyperactivity Disorder(ADHD) 14

Optimized Machine Learning Model for Identifying and Predict...

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

作者： Balasubramanian, Sriram Karthikeyan, S. Karthikeyan, M. SRM Institute of Science and Technology Department of Computer Science Engineering Chennai India

ISBN: (纸本)9798350335095

Attention Deficit/Hyperactivity Disorder (ADHD) a form of neurodevelopmental disorder often manifests in young age. As per a 2016 WHO Survey for 10 countries, the ubiquity percentage of adult ADHD was determined to be 2.8% globally, showing a high percentage in countries with above average wealth and a strong correlation with male gender [1]. Inattention, impulsivity, and hyperactivity are the three main symptoms of ADHD that children and adults with the disorder often exhibit. However, these symptoms can vary widely from person to person, and some people with ADHD are classified as having inattentional, hyperactive/impulsive, or combined subtypes of the disorder. Futures seem bright for the development of systems that can use phenotypic and neuroimaging data to anticipate a diagnosis. This paper suggests a system for categorizing ADHD issues using fMRI data. The usage of 3D convolutional neural networks (CNNs) in the processing of fMRI has made numerous significant advances. The 3D spatial data of the fMRI data may be retained thanks to these cutting-edge techniques. Additionally, 3D generative adversarial neural networks (GANs) have made considerable advancements in the creation of normal MRI data. In order to predict ADHD using fMRI, this study uses multi modal 3D CNNs and employing 3D GAN for data augmentation. A time distributed single modal 3D CNN model will be compared to the improved multi modal model utilizing MRI data for classification. © 2023 IEEE.

关键词： Magnetic resonance imaging

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On the Rique Number of Series-Parallel Graphs and Planar Bipartite Graphs 2nd

On the Rique Number of Series-Parallel Graphs and Planar ...

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2nd International Conference on Applied Algorithms, ICAA 2025

作者： Azgor, Sk Ruhul Rahman, Md. Saidur Graph Drawing and Information Visualization Laboratory Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh

ISBN: (纸本)9783031845420

The restricted input queue (rique) is a data structure which is recently introduced to study linear layout of graphs. The rique data structure is a special queue where insertions occur only at the head and removals occur at both the head and the tail. Considering a rique data structure the goal of a linear layout of a graph is to find a linear order of the vertices of the graph and a partition of its edges into pages such that the edges in each page follow the restriction of rique in the underlying order. The rique number of a graph is the smallest number of pages required for linear layout of the graph with rique data structure. A characterization of graphs for admitting a single page rique layout and some bounds on the rique number of complete and complete bipartite graphs are known. In this paper, we show that the rique number of series-parallel graphs as well as planar bipartite graphs is 2. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Directed graphs

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An IoT Based Framework for Monitoring Power Consumption in Smart Grids

An IoT Based Framework for Monitoring Power Consumption in S...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Raghavendra, R. Chauhan, Nidhi Mittal, Gaurav Department Of Computer Science And Information Technology Bangalore India Noida Institute Of Engineering And Technology Department Of Information Technology & M. Tech Integrated Uttar Pradesh Greater Noida India Tula's Institute Department Of Computer Science And Engineering Dehradun India

ISBN: (纸本)9798350366846

The electrical power system grid in India, commonly referred to as the power grid, has been in operation for an extensive duration. During this time period, there were no significant advancements or alterations documented in the electricity grid infrastructure. The need for electrical power consumption is seeing a significant increase, but the current grid infrastructure is unable to meet these growing demands. In order to meet the demands of future power load, it is imperative to develop an enhanced system that possesses the qualities of reliability, security, intelligence, and efficiency. This study develops a power management system and explains its automation and functionality by utilising smart grid and IoT technologies. The research suggests a method for keeping an eye on consumer power use and controlling the use of electrical devices by putting in place a system that can identify voltage, current, and weather conditions in addition to alerting users in advance of possible problems with power usage. Future system improvements that are region-specific and intended to reduce loads and maximise system performance are made possible by the data collected. © 2024 IEEE.

关键词： Internet of things

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A Comprehensive Comparison of Machine Learning Algorithms for Breast Cancer Prediction 9

A Comprehensive Comparison of Machine Learning Algorithms fo...

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9th International Conference on Advanced Computing and Communication Systems, ICACCS 2023

作者： Duraisamy, Premkumar Natarajan, Yuvaraj Ebin, N.L. Jawahar Raja, P. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350397376

The root cause of death for women is breast cancer. Early detection is essential for rectifying cancer. In this study, we explored the use of machine learning models for predicting breast cancer. We evaluated logistic regression, K-neighbors classifier, support vector machine, decision trees, random forest, and voting classifier using a dataset of breast cancer patients and clinical and demographic factors. Our results showed that the voting classifier had the highest accuracy, followed by the random forest and support vector machine. Age, family history, and breast density were essential factors influencing the development of breast cancer. These findings suggest that machine learning can be helpful in early detection and diagnosis and highlight the importance of considering these risk factors in screeningprograms. © 2023 IEEE.

关键词： Classification (of information)

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A deep learning model framework for diabetic retinopathy detection

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International Journal of Networking and Virtual Organisations 2022年第2期27卷 107-124页

作者： Padmapriya, M. Pasupathy, S. Sumathi, R. Punitha, V. Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Tamilnadu Annamalai Nagar India Department of Information Technology Saranathan College of Engineering Tamilnadu India

Diabetic retinopathy (DR) is the typical diabetic eye issue and a main reason of blindness around the world. As per the International Diabetes Federation (IDF), the rates of diabetes would rise to 552 million by 2034. Breakthroughs in computer science techniques inclusive of artificial intelligence (AI) and deep learning (DL) have multiplied opportunities for early detection of DR. This indicates that the likelihood of a patient's healing will improve, and the risk of eyesight loss could be minimised in due course. A deep learning model (ResNet) for medical DR detection was examined in this article. The dataset of Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 was used to train and test the DL model. To demonstrate the vitality of the chosen ResNet model, performance measures and testing accuracy like recall, precision, and F1 score were determined. The modified ResNet model attained a testing accuracy of around 84% even with only a few dataset images. Also, training time and computational complexity were reduced with this simpler model. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Statistical tests

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Bangla to English Translation Using Sequence to Sequence Learning Model Based Recurrent Neural Networks 1st

Bangla to English Translation Using Sequence to Sequence L...

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1st International Conference on Machine Intelligence and Emerging Technologies, MIET 2022

作者： Islam, Rafiqul Hasan, Mehedi Rashid, Mamunur Khatun, Rabea Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh

ISBN: (纸本)9783031346187

Translation is a way to convert one language to another for effective communication. Researchers have discovered many machine translation methods to translate the human language. Natural Language Processing (NLP) is a breakthrough innovation to process human language. Since the Bangla language holds complex grammatical rules and an enormous vocabulary, it appears quite challenging to translate the Bangla language into another. In this paper, an efficient machine translator technique is proposed to translate the Bangla language to English. Instead of the traditional rule-based method, we develop a deep learning-based model for a successful machine translator. A sequence-to-sequence (Seq2Seq) learning model with attention layer-based recurrent neural networks (RNN) is utilized in the deep neural network model. Since the Seq2Seq-based learning for Bangla to English conversion is a recent model, a Seq2Seq-based Bangla to English database is developed for evaluating the algorithm. Experimental analysis shows the effectiveness in terms of the accuracy and precision of the algorithm. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Recurrent neural networks

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