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7th International Conference on Mechanical engineering Research 2023, ICMER 2023

作者： Rajan, Dinesh Samykano, M. Suraparaju, S.K. Moorthy, K. Kadirgama, K. Ramasamy, D. Pandey, A.K. Faculty of Mechanical and Automotive Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Pahang26600 Malaysia Centre for Research in Advanced Fluid and Process University Malaysia Pahang Al-Sultan Abdullah Lebuhraya Tun Razak Pahang Kuantan Gambang26300 Malaysia Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Al-Sultan Abdullah Kuantan Malaysia School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan Petaling Jaya47500 Malaysia

The current study aims to bridge a crucial gap in existing research, potentially paving the way for a groundbreaking transformation in the development and application of PLA/Brass composites within diverse industries such as aerospace, automotive, consumer goods, and medical devices. The primary objective of this research is to assess the mechanical properties of a composite material made up of Polylactic Acid (PLA) and Brass, produced using Fused Deposition Modelling (FDM) 3D printing technology. Brass, renowned for its exceptional mechanical properties, has been integrated into PLA to form this composite material. The study employs various analytical techniques, including Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and Energy-Dispersive X-ray Spectroscopy (EDX), to scrutinize the chemical and physical characteristics of the PLA/Brass composite. This research revolves around exploring the impact of different printing parameters on the mechanical behavior of the printed specimens. The investigation delves into aspects such as tensile strength, compression resistance, bending properties, and impact resistance. To achieve this, test specimens with varying compositions have been produced using a Raise3D N2 Plus FDM 3D printer, with careful manipulation of printing parameters such as layer height and printing speed. The compositional variations range from 15% wt. to 80% wt., with layer height values spanning 0.25 mm, 0.30 mm, and 0.35 mm, and printing speeds ranging from 20 mm/s to 40 mm/s. The outcomes of this research have revealed the distinct influences of specific printing parameters on various mechanical properties. For example, in the context of tensile testing, it was observed that the combination of a layer height of 0.25 mm and a printing speed of 30 mm/s resulted in the highest elastic modulus. Similarly, the study provides crucial insights into optimizing PLA/Brass composite material properties through controlled addit

关键词： Scanning electron microscopy

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Robust Feature engineering Techniques for Designing Motor Imagery-Based Bci Systems

SSRN

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SSRN 2025年

作者： Gardezi Ahmed, Syed Saim Khan, Mahnoor Bukhari, Syeda Muneeba Khan, Rizwan Ahmed Jawed, Soyiba Department of Biomedical Engineering Faculty of Engineering Salim Habib University Karachi Pakistan Department of Computer Software Engineering College of Electrical and Mechanical Engineering NUST Islamabad Pakistan Department of Physics Faculty of Applied Sciences Riphah International University Islamabad Pakistan Department of Biomedical Engineering Faculty of Engineering NED University Karachi Pakistan Department of Computer Science Faculty of Information Technology Salim Habib University Karachi Pakistan

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors, Support Vector Machine, Decision Tree, and Naive Bayes models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provide an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation. © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Architecting Digital Twins for Intelligent Transportation Systems

arXiv

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arXiv 2025年

作者： Bhatt, Hiya Sahil Vaidhyanathan, Karthik Biju, Rahul Gangadharan, Deepak Trestian, Ramona Shah, Purav Software Engineering Research Centre IIIT Hyderabad India Computer Systems Group IIIT Hyderabad India Faculty of Science and Technology Middlesex University London United Kingdom

Modern transportation systems face growing challenges in managing traffic flow, ensuring safety, and maintaining operational efficiency amid dynamic traffic patterns. Addressing these challenges requires intelligent solutions capable of real-time monitoring, predictive analytics, and adaptive control. This paper proposes an architecture for DigIT, a Digital Twin (DT) platform for Intelligent Transportation Systems (ITS), designed to overcome the limitations of existing frameworks by offering a modular and scalable solution for traffic management. Built on a Domain Concept Model (DCM), the architecture systematically models key ITS components enabling seamless integration of predictive modeling and simulations. The architecture leverages machine learning models to forecast traffic patterns based on historical and real-time data. To adapt to evolving traffic patterns, the architecture incorporates adaptive Machine Learning Operations (MLOps), automating the deployment and lifecycle management of predictive models. Evaluation results highlight the effectiveness of the architecture in delivering accurate predictions and computational efficiency. © 2025, CC BY.

关键词： Adaptive control systems

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The research of human interaction recognition based on fusion features of key frame feature library

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International Journal of Information and Communication technology 2021年第1期18卷 57-69页

作者： Zhang, Haiyan Gao, Shangbing Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an China Nanjing Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

Some issues such as computational complexity and low recognition accuracy still exist in human interaction recognition. In order to solve the problem, the paper has proposed innovative and effective method based on fixed features of key frame feature library. Firstly, GIST feature and HOG feature were extracted from the pre-processed videos. Secondly, training videos with different kinds of actions were clustered by the K-means algorithm respectively to get key frame feature of each action for constructing key frame feature library. And similarity measure was utilised to calculate the frequency of different key frames in every interactive video, and statistical histogram representation of interactive videos were obtained. Finally, the decision level fusion was achieved by using SVM classifier based on histogram intersection kernel to obtain impressive results on UT-interaction dataset. The correct recognition rate of the proposed algorithm is 85%, which indicates that the proposed algorithm is simple and effective than others. © 2021 Inderscience Enterprises Ltd.

关键词： K-means clustering

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Unraveling user perceptions and biases: A comparative study of ML and DL models for exploring twitter sentiments towards ChatGPT

Journal of Engineering Research (Kuwait)

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Journal of engineering Research (Kuwait) 2024年

作者： Baker, Mohammed Rashad Utku, Anil Software Department College of Computer Science and Information Technology University of Kirkuk Kirkuk 36001 Iraq Department of Computer Engineering Faculty of Engineering Munzur University Tunceli 62100 Turkey

ChatGPT is a powerful chatbot that used to generate human-like text. Chat GPT, developed by OpenAI, can answer many questions honestly, like a personal teacher who knows almost everything. It can perform various tasks such as question and answer, solving mathematical equations, writing text, debugging, and translating between languages. Compared to traditional chatbots, ChatGPT has been adopted by many users since its introduction. Some users feel that ChatGPT will override most content creation professions. This study analyzed users’ feelings about ChatGPT by analyzing tweets shared about ChatGPT. For this purpose, a hybrid Deep Learning (DL) model was developed using Convolutional Neural Networks (CNN) and Bi- Long Short-Term Memory (Bi-LSTM) models. The study has been compared with a number of DL and Machine Learning algorithms, LSTM, Bi-LSTM, CNN, Gated Recurrent Unit, Random Forests and Support Vector Machines. The experimental outcomes demonstrated that, the FastText-trained CNN-Bi-LSTM model exceeded the other models in terms of accuracy, reaching 96.59%. © 2024 The Authors

关键词： Bi-LSTM ChatGPT CNN Deep Learning Sentiment Analysis

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Properties of Nonlinear Power System Models that are Symmetrically Configured in the Narrow Sense

Properties of Nonlinear Power System Models that are Symmetr...

引用

IEEE General Meeting Power& Energy Society

作者： Elior Segev Ron Ofir Yoash Levron Juri Belikov Faculty of Electrical & Computer Engineering Technion—Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia

Symmetry is a fundamental property of three-phase circuits and is widely used in the analysis of large-scale power systems. However, when the system is nonlinear or time-varying, classical symmetry generally does not provide sufficient information. Therefore, in this paper, we explore properties of three-phase circuits with dynamic models that are “simple” in some stronger sense. We define a circuit to be simple if its state and input matrices in the dq0 domain are constant, that is, independent of the reference angle, and show that this property leads to dynamic models which are linear and time-invariant, or at least suitable for low-complexity numerical analysis. We formally define circuits with this property to be Symmetrically Configured in the Narrow Sense (SCNS), and explore their properties. We show, for instance, that SCNS circuits must be symmetrically configured and that their state-space models have several interesting properties and structures. The overall aim is to develop a small toolbox, which may help identify and analyze SCNS circuits in the context of large-scale power systems. We believe this may contribute to the understanding of dynamic phenomena in large-scale systems which are too complex for direct simulation.

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Intent Classification of Nepali Factoid Questions

Intent Classification of Nepali Factoid Questions

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Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Sushil Timilsina Ashim Khadka Jianchu Lin Faculty of Science and Technology Gandaki College of Engineering and Science Pokhara Nepal Faculty of Science and Technology Nepal College of Information Technology Lalitpur Nepal Faculty of Computer and Software Engineer Huaiyin Institute of Technology Jiangsu China

Question Classification plays a vital role in identifying the accurate answer to any question, and is considered as a core component of Question Answering Systems. In Nepali Natural Language Processing, the area of Question Answering has not even been initiated due to a lack of dataset. The dataset of Nepali factual questions has been created by collecting questions from general knowledge books and labelling them with developed taxonomy. The problem of Nepali factoid question classification has been studied with a Support Vector Machine, which works as a baseline classification algorithm and obtained a 0.74 weighted F1-score in the imbalance dataset. The experimental results of the proposed model show the effectiveness of the approach in classifying factoid questions accurately compared with the state-of-the-art approaches.

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Essential proteins identification based on weak consensus model and neighborhood aggregation centrality

Essential proteins identification based on weak consensus mo...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Hu, Zhipeng Li, Gaoshi Wu, Jingli Luo, Xinlong Liu, Jiafei Peng, Wei Zhu, Xiaoshu Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-Source Information Mining & Security Guilin541004 China Guangxi Normal University College of Computer Science and Engineering Guilin541004 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Guilin University of Electronic Science and Technology School of Computer and Information Security & School of Software Engineering Guilin China

ISBN: (纸本)9798350337488

Essential proteins play an essential role in cell survival and replication. Currently, more and more computational methods are developed to identify essential proteins, which overcome the time-consuming, costly and inefficient shortcomings with biological experimental methods. In order to improve the recognition rate, some new methods by fusing multiple features are developed, but they seldom consider the connection among features. After analyzing a large number of methods based on multi-feature fusion, a weak consensus model to fuse features is proposed in this paper. Then, this paper uses the weak consensus model to fuse protein-protein interaction network, gene expression data, and orthologous data, thus proposing a new method, WOL. Then experiments are performed on one *** dataset. The results show that compared with WDC, PeC, ION, JDC, NCCO and E_POC, WOL has a higher recognition rate. © 2023 IEEE.

关键词： Essential proteins orthologous data protein-protein interaction weak consensus model

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Novel Approach for Enhancing Mental Well-Being Through Machine Learning Techniques

Novel Approach for Enhancing Mental Well-Being Through Machi...

引用

Advancements in Computing (ICAC), International Conference on

作者： O.V.D.E Roshani H.M.K.J.J Ayuwardhana P.H.M.S Rodrigo R.U Hewageegana Harinda Fernando Dilshan I. de Silva Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Frequently, individuals undergo specific episodes of mental health challenges throughout their lifetime. But the COVID pandemic has triggered a surge in mental health disorders arising from isolation, monotonous routines, demanding workloads, financial disparities, and disruptions to daily schedules. Furthermore, the global pandemic has induced constant anxiety and stress. Beyond the pandemic, the competition and intense pressure of the modern world impact mental health. Access to advanced mental health solutions and the necessary familiarity remain limited for most of the population. Given the integration of technology into daily life, diverse remedies, including mobile and web applications, have emerged to tackle the escalating challenge of mental health disorders. This study proposes an accessible and cost-effective approach that employs machine learning to detect stress levels and discern user emotions from journal entries and facial expressions while integrating self-journaling, video recommendations, and visual content generation to stimulate positive emotions and relieve stress.

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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