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8th International Conference on Frontiers of Signal Processing, ICFSP 2023

作者： Joorabchi, Marjan Ghorshi, Seyed Naderahmadian, Yashar Sharif University of Technology - International Campus School of Science and Engineering Kish Island Iran The University of Texas at Tyler Department of Electrical and Computer Engineering TylerTX United States University of Guilan Deparment of Electrical Engineering Rasht Iran

ISBN: (纸本)9798350308792

This paper presents a combinational method of Wavelet Transform and Adaptive Filter to reduce distortion that has been caused by interference noise. The procedure makes use of a Wiener filtering to improve and enhance the quality of the speech signal. The proposed method is tested for a couple of noisy speech signals with different SNRs in the range of -5 dB to 15 dB. In this method we aim to inspect the enhancement of noisy speech signals in more practical conditions. Thus, not only is the process employed on noisy speech signals distorted by Additive White Gaussian Noise (WGN) but also it is practiced on environmental noises such as CAR noise, etc. The results show improvement of noise reduction after applying Wiener filtering to the Wavelet Transform compared with utilizing Wavelet Transform only. © 2023 IEEE.

关键词： Wavelet transforms

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Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

Selecting Machine Learning Models for Student Performance Pr...

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2023 IEEE International Conference on Computing, ICOCO 2023

作者： Khan, Ijaz Zabil, Mohammed Hazli Mohamed Ahmad, Abdul Rahim Jabeur, Nafaa College of Computing and Informatics Univsiti Tenaga Nasional Department of Computing Kajang Malaysia Buraimi University College Information Technology Dept. Al-Buraimi Oman Deparment of Computing and Informatics' Kajang Malaysia German University of Technology Computer Science Dept. Muscat Oman

ISBN: (纸本)9798350302684

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models. © 2023 IEEE.

关键词： Students

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Web-based Application for Real-Time Chatting using Firebase

Web-based Application for Real-Time Chatting using Firebase

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Knowledge Engineering and Communication Systems (ICKECS), International Conference on

作者： Sri Nishant Reddy Lakkireddy Aaron A Thomas T.Shreya Shree Talakoti Mamatha Deparment of Computer Science Sreenidhi Institute of Science and Technology Hyderabad India

A chat application is a program in cyberspace that allows Internet users who are online or have access to the internet to communicate directly with one another. In other words, we can send or receive the messages from one client to another client. The current trend is to send and receive message through visiting application. Facebook, WhatsApp, QQ Mobile, WeChat are a broadly used application. The Chat Application is like an Instant Messaging forward is also called as platform which is used to maintain the communication. Hence, we plan on build a web-based chat application where user can gain remote access throughout the world. And in this we use ReactJ s has frontend, and firebase as the Backend. And this application is used in the Android and also is IOS.

关键词： Knowledge engineering Freeware Social networking (online) Operating systems Cyberspace Instant messaging Message services

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Detection and Classification of Rheumatoid Nodule using Deep Learning Models

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Procedia computer science 2023年 218卷 2401-2410页

作者： Uma N. Dulhare Azmath Mubeen Department of Computer Science & Artificial Intelligence Muffakham Jah College of Engineering & Technology Hyderabad500034 India Deparment of Computer Science and Engineering University College of Engineering Osmania University Hyderabad-500007 India

In the past few years there have been many cases in which the patient may get some form of nodule on the body and it becomes very difficult to identify the type of nodule and its relative disease. Sometimes the nodule may be a simple skin lesion or it may be cancerous cyst or it may be due to a severe condition of rheumatoid arthritis. This research work concentrates on classifying the nodules from a given data set of images and detecting the Rheumatoid Nodule (RN) and its risks. Many studies have been conducted for detection of different nodules but specific to rheumatoid nodule identification and its risks on other organs are minimum as per my cognizance. In imaging study, Deep Convolutional Neural Networks (DCNN) have shown efficient accomplishment, hence the model proposed uses dual three-dimensional Deep Convolutional Networks design for the detection of RN and an improved Res Net for classification, respectively. Rheumatoid Nodule detection was performed through Pure Deep Convolutional Networks (PDCNN) and then feature selection was done using Particle Swarm Optimization (PSO) and later classification was done using the Residual Network (Res-Net) on proficiently learned features from Particle Swarm Optimization. The Deep Learning (DL) model for Rheumatoid Nodule detection and classification, blended with medical aspects, aids in minimizing the errors and False Positive (FP) rate and help in early detection of rheumatoid nodule. The recommended system was assessed on dataset collected from different orthopaedic hospitals in the city and improved results were obtained in comparison to the prevailing approaches.

关键词： Classification Deep Learning PDCNN PSO Res-Net Rheumatoid Arthritis (RA) Rheumatoid Nodule

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Development of Charge Integrator Circuits with a Microcontroller for Cylindrical Capacitive Sensors in Paddy Moisture Balance Detectors

Development of Charge Integrator Circuits with a Microcontro...

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International Electrical Engineering Congress (iEECON)

作者： Sudarat Tokampang Nattapong Apirattanon Noppadon Sisuk Anucha Kaewpoonsuk Deparment of Physics Faculty of Science Naresuan University Phitsanulok Thailand Program of Computer Engineering Faculty of Industrial Technology Phibunsongkhram Rajabhat University Phitsanulok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

This article presents a method for indirectly measuring the moisture content of paddy using a cylindrical capacitive sensor combined with Charge Integrator Circuits. The moisture measurement device operates based on the principle of the dielectric constant of paddy, which changes according to the moisture content within them. The system consists of a moisture detection section utilizing a cylindrical capacitor, a signal generator operating at a frequency of 500 Hz with a microcontroller, a signal conditioning circuit converting the signal to voltage, and a processing unit. Testing was conducted with paddy at five different moisture levels, demonstrating the ability to accurately determine the moisture content of the rice. When compared to the commercially available PM-450TH moisture meter, the results showed a linear response across the measurement range, with an R 2 value of 0.9824 and an average error of 2.6%. This technology has the potential for further development and application to other types of seeds in the future.

关键词： Accuracy Moisture measurement Microcontrollers Circuits Measurement uncertainty Moisture Dielectric measurement Capacitive sensors Standards Charge measurement

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Deep Learning based Financial Management with Decision Support Systems for Adaptive Organization 7

Deep Learning based Financial Management with Decision Suppo...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Shanmuga Prabha, P. Rao, Kamarajugadda Tulasi Vigneswara Ravivarman, G. Prasad, K.V.S. Rajendiran, M. Maranan, Ramya Sathyabama Institute of Science And Technology Department of Computer Science And Engineering Tamil Nadu Chennai India Nicmar University School of Project Management Maharashtra Pune India Karpagam Academy of Higher Education Department of Electrical And Electronics Engineering Tamil Nadu Coimbatore India Gmr Institute of Technology Deparment of Basic Sciences And Humanities Andhra Pradesh Vizianagaram India Panimalar Engineering College Department of Computer Science And Engineering Chennai Tamil Nadu Poonamallee India Saveetha School of Engineering SIMATS Department of Research And Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350359299

The complexities of dynamic business platforms are overcome using the aid of strategic utilization of decision support systems (DSS) for financial management in adaptive organizations. Adaptive organizations are the ones that proactively respond to changes in their internal and external environments. Decision support systems are an important part of providing analytical tools with modelling strategies. This helps the financial managers to make informed decisions. The proposed system used deep learning with a genetic algorithm for decision support in financial management within adaptive organizations. They are used for various parameters such as resource allocation and risk assessment. The adaptability of genetic algorithms helps in obtaining optimum solutions with aligning perfectly with the dynamic structure of the adaptive organizations. The integration of genetic algorithms provides numerous advantages in financial management. The genetic algorithm discovers huge solution space which helps the organization for the identification of optimal financial strategies in dynamic environments. They play an important role in financial decision-making. The use of genetic algorithms in financial decision support systems improves the adaptability of the organization. The iterative phenomena of genetic algorithms help the system to adapt to the changing financial environment. Thus the proposed system helps in achieving optimum resource allocation with automatic decision-making techniques. © 2024 IEEE.

关键词： Risk assessment

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Contact Tracing over Uncertain Indoor Positioning Data (Extended Abstract)

Contact Tracing over Uncertain Indoor Positioning Data (Exte...

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International Conference on Data Engineering

作者： Tiantian Liu Huan Li Hua Lu Muhammad Aamir Cheema Harry Kai-Ho Chan Department of Computer Science Aalborg University Denmark College of Computer Science and Technology Zhejiang University China Deparment of People and Technology Roskilde University Denmark Faculty of Information Technology Monash University Australia Information School University of Sheffield United Kingdom

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Pandemics like COVID-19 often cause dramatic losses of human lives and societal impacts, urging efficient and effective contact tracing, especially in indoor venues where the risk of infection is higher. In this work, we formulate a novel query called Indoor Contact Query (ICQ) over raw, uncertain indoor positioning data that digitalizes people's indoor mobility. Given a query object $o$ , e.g., a virus-carrying person, an ICQ analyzes uncertain indoor positioning data to find objects that most likely had close contact with $o$ for a long period of time. To process ICQ, we propose a set of techniques. First, we design an enhanced indoor graph model to organize different types of data necessary for ICQ. Second, for indoor moving objects, we devise methods to determine uncertain regions and to derive positioning samples missing in the raw data. Third, we propose a query processing framework with a close contact determination method, a search algorithm, and multiple acceleration strategies. We conduct extensive experiments on synthetic and real datasets, which verify the efficiency and effectiveness of our proposals.

关键词： COVID-19 Pandemics Query processing Contact tracing Data engineering Data models Proposals

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Exploring the Feasibility of Transformer Based Models on Question Relatedness 24

Exploring the Feasibility of Transformer Based Models on Que...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Shu, Honglin Gao, Pei Yang, Ziwei Li, Chen Wu, Man Hong Kong Polytechnic University Department of Computing Hong Kong Nara Institute of Science and Technology Department of Information Science Nara Japan Kyushu Institute of Technology Department of Bioscience and Bioinformatics Iizuka Japan Keio University Deparment of Information and Computer Science Tokyo Japan

ISBN: (纸本)9798350319934

Professional question answering communities, such as Stack Overflow, are becoming a significant aspect of many intellectual endeavors. As a result, developing a strategy for swiftly locating relevant questions and answers can effectively assist experts in issue solving. Various detection models have been presented to address the problem of question-relatedness prediction, but all are sub-optimal since they cannot effectively capture the long-distance dependency of a long sequence. With the invention of the self-attention mechanism and transformer, a better approach to dealing with question-relatedness prediction has just emerged. The primary objective of this study is to investigate the feasibility of the transformer-based model for question-relatedness problems. We turn the question relatedness problem into a text classification problem and introduce a representative transformer-based model, namely Bidirectional Encoder Representation from Transformers (BERT), to deal with text classification. In our experiment, we show that BERT outperforms both SoftSVM and BiDotLSTM, implying that the transformer-based model has considerable potential to address the challenge in the question-relatedness problem. Furthermore, since we can re-formulate text classification problem into a link prediction problem, we discuss the possibility of incorporating the transformer-based approach and graph representation learning. © 2022 IEEE.

关键词： Forecasting

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Enhancing Toxic Comment Detection with BiLSTM-Based Deep Learning Model

Enhancing Toxic Comment Detection with BiLSTM-Based Deep Lea...

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International Conference on Information science and Communications Technologies (ICISCT)

作者： Naitik Dharm Raj D. Karthick Rajan Ajay Kumar Gupta Amrit Kumar Agrawal Kunchanapaalli Rama Krishna Department of Computer Science & Engineering Sharda School of Engineering & Technology Sharda University India Deparment of Computer Science & Engineering Chennai Institute of Technology Chennai India Department of Computer Science & Engineering IIMT College of Engineering Greater Noida India Koneru Lakshmaiah Education Foundation Vaddeswaram India

ISBN: (数字)9798331527921

ISBN: (纸本)9798331527938

Enhancing the ability to identify abusive comments is essential, as it addresses the growing issue of online toxicity while fostering healthier interactions. In this paper, a deep learning architecture is proposed using Bidirectional Long Short-Term Memory (BiLSTM) to enhance the identification of toxic comments with multiple labels. Due to its architectural characteristics, Bi-LSTM achieves high accuracy by thoroughly preprocessing data and effectively managing unknown words during FastText embeddings. It is especially effective at detecting subtle forms of intoxication, such as obscenity, insults, hate speech, and threats, by using contextual signals. Incorporating these techniques online may enable the automatic detection and deletion of toxic content, thereby promoting positive interactions. This model serves as a benchmark for improving the handling of classification problems with various labels through fine-tuning the neural network architecture.

关键词： Deep learning Information science Toxicology Accuracy Neural networks Hate speech Bidirectional long short term memory Benchmark testing Communications technology

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Distributed Multi-agent Coordination over Cellular Sheaves

arXiv

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arXiv 2025年

作者： Hanks, Tyler Riess, Hans Cohen, Samuel Gross, Trevor Hale, Matthew Fairbanks, James Department of Computer and Information Science and Engineering United States Department of Mechanical and Aerospace Engineering University of Florida United States Deparment of Electrical and Computer Engineering Georgia Institute of Technology United States

Techniques for coordination of multi-agent systems are vast and varied, often utilizing purpose-built solvers or controllers with tight coupling to the types of systems involved or the coordination goal. In this paper, we introduce a general unified framework for heterogeneous multi-agent coordination using the language of cellular sheaves and nonlinear sheaf Laplacians, which are generalizations of graphs and graph Laplacians. Specifically, we introduce the concept of a nonlinear homological program encompassing a choice of cellular sheaf on an undirected graph, nonlinear edge potential functions, and constrained convex node objectives, which constitutes a standard form for a wide class of coordination problems. We use the alternating direction method of multipliers to derive a distributed optimization algorithm for solving these nonlinear homological programs. To demonstrate the applicability of this framework, we show how heterogeneous coordination goals including combinations of consensus, formation, and flocking can be formulated as nonlinear homological programs and provide numerical simulations showing the efficacy of our distributed solution *** Codes 93A16, 93B45, 55N30 © 2025, CC BY.

关键词： Laplace transforms

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