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Geo-Risk Conference 2023

作者： Yeo, Lit Yen Kueh, Pei Gee Choo, Chung Siung Lee, Sue Han Zhang, Dongming Faculty of Engineering Computing and Science Swinburne Univ. of Technology Sarawak Malaysia Key Laboratory of Geotechnical and Underground Engineering of Minister of Education Dept. of Geotechnical Engineering Tongji Univ. Shanghai China

ISBN: (数字)9780784484982

ISBN: (纸本)9780784484982

In pipe jacking operations, the prediction of jacking forces has been the subject of several studies. Many established jacking force models are based on the strength properties of the negotiated soils or rocks during jacking. The application of these models is useful during the planning for pipe jacking construction. However, such models have limited use during the construction stage when decisions are made based on pipe jacking operation parameters, that is, jacking speed, lubricant, slurry pressure, etc. The influence of pipe jacking operation parameters on jacking forces is not well quantified. This gap could present adverse risks to construction progress, particularly in the highly weathered "soft rocks" encountered in the Tuang Formation underlying the central business district of Kuching, Malaysia. To assist in modelling pipe jacking operation, deep learning approaches have been widely deployed due to its powerful capability of feature learning. Therefore, an interesting question is raised: are we able to absorb the knowledge gained from neural networks to help reduce the decision-making gap in pipe jacking operations between the construction and planning phases? To this end, this paper proposes the use of gated recurrent units (GRUs) with an attention-based mechanism for providing insight into the decisions made on operation parameters during pipe jacking. A case study will be presented to demonstrate the visualization of attention utilized by the deep learning model towards the operation parameters. The findings showed that the input features (i.e., operation parameters) shifted throughout the drive. At the initial sections of the drive, more attention was directed towards operation parameters related to the micro-tunnel boring machine (mTBM) face. Subsequently, the attention was shifted to the cumulative operation parameters with progressive pipe jacking. The findings from this paper have the potential to help pipe jacking specialists identify and quantify

关键词： Decision making

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HCMMA-Net: A Hybrid Convolutional Multi-Modal Attention Network for Human Activity Recognition in Smart Homes Using Wearable Sensor Data

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IEEE Access 2025年 13卷 104106-104119页

作者： Nazish Ashfaq Zeeshan Aziz Muhammad Hassan Khan Muhammad Adeel Nisar Adnan Khalid Faculty of Computing and Information Technology University of the Punjab Lahore Pakistan School of Science Engineering and Environment University of Salford Manchester U.K. Faculty of Computing Universiti Teknologi Malaysia Johor Bahru Malaysia

Human activity recognition (HAR) plays a pivotal role in applications such as healthcare monitoring, fitness tracking, and smart homes. Multi-modal sensor data from wearable devices offers diverse perspectives on human motion, enhancing recognition accuracy and robustness. However, integrating these modalities poses challenges due to sensor heterogeneity and variability in placement. This study examines the role of multi-modalities in HAR using a hybrid convolutional multi-modal attention network (HCMMA-Net), designed to exploit spatial and temporal dependencies in sensor data. We evaluate the model on two benchmark datasets Cogage, achieving an accuracy of 93.94%, and WISDM, with an accuracy of 99.29%, demonstrating its strong generalizability across varied sensor configurations. Additionally, we present a newly collected multi-modal dataset, HumcareV1.0, comprising different activities in smart-home-like scenarios. On this real-world dataset, HCMMA-Net attains an accuracy of 97.56%, highlighting its effectiveness in capturing subtle behavioral nuances in practical environments. The model exhibits robust generalization across complex activity patterns and sensor configurations, underscoring the significance of multi-modal integration in advancing HAR systems. These findings highlight the potential of our approach for deployment in real-time, context-aware smart environments.

关键词： Human activity recognition Feature extraction Accuracy Deep learning Wearable sensors Real-time systems Performance evaluation Data models Convolutional neural networks Smart phones

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Enhancing Data Quality in Image Pre-processing: A Case Study on Plant Disease Classification

Enhancing Data Quality in Image Pre-processing: A Case Study...

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2023 International Conference for Technological engineering and its Applications in Sustainable Development, ICTEASD 2023

作者： Ong, Ge Ye Abdullah, Nurzulaikha Ridzuan, Fakhitah Mustafa, Wan Azani Alzubaidi, Laith H. INTI International College Penang School of Engineering and Technology Penang Malaysia Universiti Malaysia Kelantan Faculty of Data Science and Computing Kota Bharu Malaysia Universiti Malaysia Perlis Faculty of Electrical Engineering & Technology Perlis Arau02600 Malaysia The Islamic University Najaf Faculty of Engineering Iraq

ISBN: (纸本)9798350336474

Data quality plays a vital role in image pre-processing for machine learning applications. The effectiveness of building accurate and reliable models lies in the high-quality of data. Prioritizing data quality in the initial stages of the image pre-processing pipeline lays a strong foundation for subsequent machine learning stages. Therefore, this research aims to identify the appropriate steps for image pre-processing and compare the performance of machine learning model based on different pre-processing approaches. In this study, we utilized a plant diseases dataset sourced from Kaggle, comprising approximately 87,000 RGB images of both healthy and diseased crop leaves, categorized into 38 distinct classes. To significantly enhance the image quality, we implemented a range of transformation techniques, including resizing, normalization, data augmentation, and cropping. The analysis clearly indicates that implementing comprehensive pre-processing techniques enhances data quality and improves the machine learning model's classification performance. © 2023 IEEE.

关键词： Machine learning

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Real-time Water Logging Monitoring and Forewarning System Facilitated by Vision and IoT

Real-time Water Logging Monitoring and Forewarning System Fa...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Md Shoreef Uddin Mabrouka Abuhmida Faculty of Computing Engineering and Science University of South Wales Wales United Kingdom

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

Water logging is a significant problem for developing nations, especially those with high average rainfall or those closer to the sea. In addition to traffic congestion, water logging in cities can also result in financial hardship and, in rare situations, even fatalities. We are plagued by this water logging problem year after year because of ourselves. In addition to natural causes, water logging can also be carried on by man-made factors, including excessive plastic usage, a lack of effective waste management systems, the slow intervention of authorities, and a lack of real-time alarm systems. However, if the likelihood of water logging can be predicted beforehand and a warning can be provided to the authorities at the same time, the situation may be resolved by taking the appropriate action. In this study, we proposed a cost-effective IoT Based embedded system that can predict water logging and send alert notifications to the Authority. Early flood alarms will be sent to the authorities by installing the system in various city locations, especially in those that are prone to water logging. This will assist the authorities to take the appropriate action. The development of real-time notification and remote system monitoring capabilities are the main focuses of this study. To get the desired result, In this study, we use Esp 32 for data processing and transmission, the system also consists of a solar panel, camera, rain sensor, Humidity and temperature sensor, water flow sensor, and Ultrasonic sensor for collecting real-time data from the environment.

关键词： Waste management Temperature sensors Technological innovation Rain Urban areas Real-time systems Solar panels Plastics Monitoring Traffic congestion

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Barber Optimization Algorithm:A New Human-Based Approach for Solving Optimization Problems

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Computers, Materials & Continua 2025年第5期83卷 2677-2718页

作者： Tareq Hamadneh Belal Batiha Omar Alsayyed Widi Aribowo Zeinab Montazeri Mohammad Dehghani Frank Werner Haider Ali Riyadh Kareem Jawad Ibraheem Kasim Ibraheem Kei Eguchi Department of Mathematics Al Zaytoonah University of JordanAmman11733Jordan Department of Mathematics Faculty of Science and Information TechnologyJadara UniversityIrbid21110Jordan Department of Mathematics Faculty of ScienceThe Hashemite UniversityP.O.Box 330127Zarqa13133Jordan Department of Electrical Engineering Faculty of Vocational StudiesUniversitas Negeri SurabayaSurabaya60231Indonesia Department of Electrical and Electronics Engineering Shiraz University of TechnologyShiraz***Iran Faculty of Mathematics Otto-von-Guericke UniversityP.O.Box 4120Magdeburg39016Germany Department of Cybersecurity and Cloud computing Technical EngineeringUruk UniversityBaghdad10001Iraq Department of Medical Instrumentations Techniques Engineering Al-Rasheed University CollegeBaghdad10001Iraq Department of Electrical Engineering College of EngineeringUniversity of BaghdadBaghdad10001Iraq Department of Information Electronics Fukuoka Institute of TechnologyFukuoka8110295Japan

In this study,a completely different approach to optimization is introduced through the development of a novel metaheuristic algorithm called the Barber Optimization Algorithm(BaOA).Inspired by the human interactions between barbers and customers,BaOA captures two key processes:the customer’s selection of a hairstyle and the detailed refinement during the *** processes are translated into a mathematical framework that forms the foundation of BaOA,consisting of two critical phases:exploration,representing the creative selection process,and exploitation,which focuses on refining details for *** performance of BaOA is evaluated using 52 standard benchmark functions,including unimodal,high-dimensional multimodal,fixed-dimensional multimodal,and the Congress on Evolutionary Computation(CEC)2017 test *** comprehensive assessment highlights BaOA’s ability to balance exploration and exploitation effectively,resulting in high-quality solutions.A comparative analysis against twelve widely known metaheuristic algorithms further demonstrates BaOA’s superior performance,as it consistently delivers better results across most benchmark *** validate its real-world applicability,BaOA is tested on four engineering design problems,illustrating its capability to address practical challenges with remarkable *** results confirm BaOA’s versatility and reliability as an optimization *** study not only introduces an innovative algorithm but also establishes its effectiveness in solving complex problems,providing a foundation for future research and applications in diverse scientific and engineering domains.

关键词： Optimization metaheuristic barber hairstyle human-based algorithm exploration exploitation

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Deep Learning for Plant Disease Detection and Crop Yield Prediction based on NPP-WPF Analysis in Smart Agriculture 7

Deep Learning for Plant Disease Detection and Crop Yield Pre...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Amudha, S. Bindu, G. Sankar, Sasi Rekha Poongothai, E. VijayaLakshmi, K. Sarveshwaran, Velliangiri Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computational Intelligence Kattankulathur Chennai602203 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9798350341485

Despite a sharp rise in population, agriculture still provides food for all people and acts as a backbone of the country. Nowadays, no proper guidance given to farmers and they are not able to analyze the nature of soil, disease of plant and its requirements. This leads to the depletion of farming in future. Crop diseases must be identified and prevented in order to increase the productivity. To overcome this, the nature of the soil is analyzed through various parameters like Nitrogen-Potassium-Phosphorus Content in soil, Humidity, Temperature, pH value of the soil, Rainfall using sensors, plants disease using cameras in real-time environment. These parameters are obtained using various IoT Sensors, cameras and existing Data, are analyzed through ML Algorithms. The proposed system consists of two modules in crop yield prediction system using the features NPP-WPF whereas NPP stands for Nitrogen-Potassium-Phosphorus and WPF stands for Water Content-Pesticide-Fertilizer, the Farmers can be guided with the details of the crop that is suitable for the soil and about Fertilizer, Pesticide, Water requirements. The second module called as automatic disease detection system uses CNN, ResNet, VGGNet, and Mobile net models to recognize and diagnose plant diseases from their leaves at the early stage. The proposed model is implemented with the dataset of 14 different species of plants and with the categorical classes of 38 classes. The result analysis shows that the suggested model provides accuracy of 98.2% when compared to the existing state-of-the-art methodologies. © 2023 IEEE.

关键词： Phosphorus

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A Review on the Efficiency Enhancement Techniques of Solar Cell

A Review on the Efficiency Enhancement Techniques of Solar C...

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Sustainable Power and Energy Conference (iSPEC)

作者： Kelly Li Zhen Jee Nur Syukrina Binti Shuardi Bhagya Priyadarshani Kumari Wijesuriya Sathira Yasendra Wimalaratna Bassam Sherif Mousa Hadi Nabipour Afrouzi Hong Siang Chua Hui En Lee Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (数字)9798350395075

ISBN: (纸本)9798350395082

The efficiency of silicon-based solar cells, averaging approximately 20%, requires improvements for maximum performance. This review highlights various potential techniques for enhancing the structural efficiency of solar cells from different studies conducted by researchers worldwide. The techniques evaluated include utilising a cooling system, metamaterial cell, antireflection coating, black silicon surface texturisation, multijunction cell, heterojunction cell, perovskite cell, and TOPCon, with a comparative analysis performed. Metamaterial solar cells emerge as a promising approach, demonstrating an impressive efficiency of 90% due to their ability to absorb a broad spectrum (UV to NIR). Among the surface-approach techniques, ARC is a better option in increasing efficiency by 3.3% using MgF 2 and CaF 2 , but the small improvement gain is a concern. From an economic perspective, ARC and TOPCon are the techniques that are low-cost implementations with surface-based and structural architecture manipulation respectively. TOPCon also proves itself to be the best option among the commercially available techniques, with an efficiency of 24.58% and passivation performance that reduces shading and resistance losses. A suggestion is given on further improving efficiency by combining the techniques. The surface-based techniques are applied together with the structure-based techniques such as ARC with multijunction to bring the efficiency of around 50% combined, giving a new area to explore. In essence, this review highlights potential advanced techniques such as metamaterial cells and the collaborative integration of different techniques to significantly enhance the efficiency of a conventional solar cell which maximises its potential.

关键词： Reviews Photovoltaic cells Microprocessors Surface resistance Computer architecture Heterojunctions Metamaterials Silicon Perovskites Passivation

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Ultrasonic Sensors in Companion Robots: Navigational Challenges and Opportunities 22nd

Ultrasonic Sensors in Companion Robots: Navigational Challen...

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22nd Asia Simulation Conference, AsiaSim 2023

作者： Asante, Isaac Theng, Lau Bee Tsun, Mark Tee Kit Chin, Zhan Hung Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Kuching Malaysia

ISBN: (纸本)9789819972425

This paper investigates the integral role of ultrasonic sensors in developing multi-sensor companion robots. It focuses on environment mapping, landmark-based localization, and path reconstruction. Despite the significant advancements in the field, the research identifies notable gaps that hinder the full potential of ultrasonic sensors. Addressing these gaps could revolutionize companion robots’ functionality, reliability, and efficiency, particularly in real-world applications such as healthcare, elderly care, and home assistance. The research emphasizes the importance of sensor fusion techniques and the potential improvements in environment mapping and landmark-based localization, which could significantly enhance the navigation capabilities of companion robots. Furthermore, advancements in path reconstruction could transform how companion robots retrace their steps, increasing their operational efficiency. The findings of this research contribute significantly to the field of robotics and pave the way for future studies, highlighting the transformative potential of advanced environment mapping techniques, sensor fusion, and path reconstruction in propelling advancements in robotic navigation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Ultrasonic sensors

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MuLAD: Multimodal Aggression Detection from Social Media Memes Exploiting Visual and Textual Features 27th

MuLAD: Multimodal Aggression Detection from Social Media Me...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Hasan, Md. Maruf Ahsan, Shawly Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada

ISBN: (纸本)9783031781186

Aggression detection from memes is challenging due to their region-specific interpretation and multimodal nature. Detecting or classifying aggressive memes is complicated in low-resource languages (including Bengali) because benchmark datasets and primary language processing software are needed. This paper proposes an innovative meme classification technique that harnesses deep learning (DL) approaches to leverage memes’ visual and textual features in Bengali. Various DL frameworks, such as VGG16, VGG19, ResNet50, CNN, BiLSTM, and BiLSTM+CNN, extract visual and textual features from memes. A novel corpus named the Bengali Meme Dataset (AMemD) is also introduced, comprising a substantial amount of multimodal data, including text and image components. Experimental results on AMemD demonstrate the effectiveness of the proposed approach. The CNN combined with VGG16 obtained the highest f1-score of 0.738 among all multimodal techniques tested. This pioneering research offers valuable insights into the complex task of aggression detection from memes in Bengali and provides a foundation for future studies in this area. The dataset is available at https://***/Maruf089/Multimodal-Aggression-Detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Comparative Analysis of Machine Learning Algorithms on Mental Health Dataset 7th

Comparative Analysis of Machine Learning Algorithms on Menta...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Lakshmi Prasanna Mehrotra, Shashi Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur India Faculty of Engineering College of Computing Science and Technology Teerthanker Mahaver University Moradabad India

ISBN: (纸本)9789819937578

Maintaining good health for the well-being of a person is very important. Due to prolonged working hours and hectic schedules, IT and non-IT professionals are prone to stress. Companies provide health incentives to employees to address this issue, which is insufficient to alleviate stress. This paper compiles data from Open Source Mental Illness (OSMI) on the working conditions of both IT and non-IT employees. The data was further analyzed to find the essential attributes affecting a person’s health. The paper explored the Decision tree, Bayesian classification and Logistic Regression techniques for predicting employee stress. Experimental results show the Decision tree classification achieves a better result than the Bayesian classification and Logistic Regression. With these findings, industries can now focus on reducing stress and create a more pleasant work environment for their employees. The primary objective of this study is to help the medical community by providing a novel method for generating relevant stress regarding rules and predicting a patient's stress level using machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Decision trees

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