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Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA), International Conference on

作者： Sanjith Krishna S Premchand V H Shri Ram B Sakthi Satchidhanandan S Anitha N Hariharan S.V B.E. Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India B.E. Computer Science Engineering Kumaraguru College of Technology Coimbatore India B.E. Electrical and Electronics Engineering Kumaraguru College of Technology Coimbatore India Department of Mathematics Kumaraguru College of Technology Coimbatore India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

Battery Monitoring Systems (BMS) have played a pivotal role in our trajectory towards greener and sustainable living, especially in our commutation methods. The rise of electric cars and two-wheelers has put renewed focus on high density energy storage and their safety. Existing BMS’s often provide the bare minimum in terms of protection and regulation of energy, often falling short of certain safety standards or lack thereof. Within this research, we task ourselves to identify and provide an alternative, viable, cost-effective method to ensure safe monitoring and especially safe debugging of faulty battery packs. We employ off-the-shelf components and integrate them into existing BMS to provide an easier way for debugging via Internet of Things (IoT) to allow local technicians access to the same information as the Original Equipment Manufacturer (OEM) themselves. Therefore, our aim is to provide a platform for people to troubleshoot their BMS’ on electric vehicles and perform predictive maintenance via remote monitoring, providing the same flexibility of a regular combustion car.

关键词： Temperature measurement Buildings Debugging Programming Electric vehicles Batteries State of charge Internet of Things Remote monitoring Predictive maintenance

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IoT-Based Cost Saving Offloading System for Cellular Networks

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Tsinghua science and technology 2017年第4期22卷 379-388页

作者： Zhuojun Duan Mingyuan Yan Qilong Han Lijie Li Yingshu Li Department of Computer Science Georgia State University Department of Computer Science and Information Systems University of North Georgia the College of Computer Science and Technology Harbin Engineering University

Nowadays, with the new techniques available in hardware and software, data requests generated by applications of mobile devices have grown explosively. The large amount of data requests and their responses lead to heavy traffic in cellular networks. To alleviate the transmission workload, offloading techniques have been proposed, where a cellular network distributes some popular data items to other wireless networks, so that users can directly download these data items from the wireless network around them instead of the cellular *** this paper, we design a Cost Saving Offloading System（CoSOS）, where the Internet of Things（IoT） is used to undertake partial data traffic and save more bandwidth for the cellular network. Two types of algorithms are proposed to handle the popular data items distribution among users. The experimental results show that CoSOS is useful in saving bandwidth and decreasing the cost for cellular networks.

关键词： Internet of Things（IoT） cellular networks cost minimization

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Comparative Analysis of Machine Learning Techniques for Arabic Text Classification Using the SANAD Dataset

Comparative Analysis of Machine Learning Techniques for Arab...

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Mechatronics and Robotics Engineering (ICMRE), International Conference on

作者： Hani Attar Yousef Sharrab Saja Smadi Safa Jbaily Faculty of Engineering Zarqa University Zarqa Jordan College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Data Science and Artificial Intelligence Isra University Amman Jordan dept. Computer Science Yarmouk University Irbid Jordan

ISBN: (数字)9798331509293

ISBN: (纸本)9798331509309

Applying Machine Learning (ML) models for Arabic text classification is a growing research field that has recently raised a large number of research. Generally, classifying text involves selecting a category based on its content from a list of pre-established categories. Data classification is among the most popular Natural Language Processing (NLP) activities. Moreover, automatic article classifications, such as the Single-label Arabic News Articles Dataset (SANAD), are desperately needed to improve the retrieval of the requested data, which this paper investigates. The ML leverages large datasets to improve efficiency; indeed, by locating focal spots within the text, the learning mechanisms facilitate attention tools better suited for handling lengthy texts. The proposed work employs a dataset of 7,000 articles of separately classified Arab news items and then breaks them into seven categories of 1,000 news stories each: politics, education, sports, religion, culture, finance, and medicine. The investigated SANAD consists of three primary datasets from three news portals: Akhbarana, AlArabiva, and AlCtrl. Finally, to obtain trustable information, this study implements a variety of Conventional ML techniques, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machine (SVM), and Logistic Regression model to classify the investigated dataset. Our results show that the suggested solution outperformed the analytical search in the Arabic language in terms of accuracy, whereas the Logistic Regression model shows the best results among the seven utilized publicly available dataset classifications.

关键词： Support vector machines Logistic regression Adaptation models Accuracy Text categorization Artificial neural networks Nearest neighbor methods Natural language processing Robots Sports

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A Deep Transfer Learning Model for Classifying Alzheimer's Disease Using MRI Images

A Deep Transfer Learning Model for Classifying Alzheimer's D...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

作者： Anitha Thavasimuthu P. Jesu Jayarain Narmatha C. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Faculty of Computers and Information Technology University of Tabuk Saudi Arabia

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Alzheimer's disease (AD) affects the cells of the human brain and leads to the development of dementia. The progressive brain cells degeneration results in the inability to carry out everyday duties. The therapy for this condition is currently insufficiently developed. Nevertheless, detecting the AD at an early stage can help control its transmission. To detect AD at an early stage using brain Magnetic Resonance Imaging (MRI), it is necessary to create an automated system that can identify and categorize individuals with dementia. In this research, a deep learning (DL) model is developed for the classification of AD based on MRI pictures. The research model was created by applying a pre-trained transfer learning model known as ResNeXt-101. The OASIS-3 dataset of MRI images is initially gathered and utilized for both training the model and evaluating its performance. The dataset comprises 2842 MRI pictures pertaining to AD and its different phases. As part of the preprocessing stage, all the images are resized and cropped to a uniform size of 224×224 pixels. Furthermore, they are enhanced utilizing the contrast enhancement technique. The ResU-Net segmentation method is utilized following the preprocessing phase. The ResNeXt-101 model is employed for the purpose of classification the segmented images. The MRI pictures are divided into training and testing sets, with 75% allotted to train the model and 25% allocated to evaluate the model. The classification is conducted using a binary approach, categorizing the data as either the presence or absence of AD. The research model's performance was evaluated using metrics such as accuracy, specificity, sensitivity, f1-score, and precision. The research model achieved a training accuracy of 99.67%, sensitivity of 99.54%, precision of 99.72%, specificity of 99.41%, and a f1-score of 99.60%. The model achieved an accuracy of 99.38%, a sensitivity of 98.29%, a precision of 99.40%, a specificity of 99.24%, and a f1-score of 99.36

关键词： Training Performance evaluation Image segmentation Sensitivity Accuracy Magnetic resonance imaging Computational modeling Transfer learning Alzheimer's disease Testing

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Lightweight Facial Attractiveness Prediction Using Dual Label Distribution

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Liu, Shu Huang, Enquan Zhou, Ziyu Xu, Yan Kui, Xiaoyan Lei, Tao Meng, Hongying Central South University School of Computer Science and Engineering Hunan410083 China Shaanxi University of Science and Technology School of Electronic Information and Artificial Intelligence Shaanxi Xi'an710021 China Brunel University London Department of Electronic and Electrical Engineering UxbridgeUB8 3PH United Kingdom

Facial attractiveness prediction (FAP) aims to assess facial attractiveness automatically based on human aesthetic perception. Previous methods using deep convolutional neural networks have improved the performance, but their large-scale models have led to a deficiency in efficiency. In addition, most methods fail to take full advantage of the dataset. In this paper, we present a novel end-to-end FAP approach that integrates dual label distribution and lightweight design. The manual ratings, attractiveness score, and standard deviation are aggregated explicitly to construct a dual-label distribution to make the best use of the dataset, including the attractiveness distribution and the rating distribution. Such distributions, as well as the attractiveness score, are optimized under a joint learning framework based on the label distribution learning (LDL) paradigm. The data processing is simplified to a minimum for a lightweight design, and MobileNetV2 is selected as our backbone. Extensive experiments are conducted on two benchmark datasets, where our approach achieves promising results and succeeds in balancing performance and efficiency. Ablation studies demonstrate that our delicately designed learning modules are indispensable and correlated. Additionally, the visualization indicates that our approach can perceive facial attractiveness and capture attractive facial regions to facilitate semantic predictions. The code is available at https://***/enquan/2D-FAP. © 2016 IEEE.

关键词： dual label distribution Facial attractiveness prediction label distribution learning lightweight

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Sensitivity and optimization study on heat transfer enhancement of MHD hybrid nanofluid radiative transient flow past a spinning surface with quadratic Boussinesq approximation and variable suction

Hybrid Advances

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Hybrid Advances 2025年 8卷

作者： Thumma, Thirupathi Ontela, Surender Dharanikota, Rupalaksmi Department of Computer Science and Engineering Vardhaman College of Engineering Kacharam Shamshabad Telangana Hyderabad 501218 India Department of Mathematics National Institute of Technology Mizoram Aizawl 796012 India

This paper presents an advanced numerical investigation of the heat transfer enhancement in a magnetohydrodynamic (MHD) hybrid nanofluid radiative transient flow past a spinning surface, incorporating the complex effects of quadratic Boussinesq approximation, variable suction, and Hall currents. Employing the Finite Element Method (FEM) with the Galerkin weighted residual approach and linear approximation functions, the study explores the intricate interactions between these factors. To optimize and analyze the heat transfer rate, Response Surface Methodology (RSM) is combined with a Central Composite Design (CCD) model, providing a robust framework for both sensitivity and optimization analysis. The results reveal that, as the thermal Grashof number, nonlinear convection parameter, and nanofluid volume fraction grow, both the primary and secondary velocities within the boundary layer experience an upward trend. Similarly, higher values of heat source intensity, nanoparticle volume fraction, or thermal radiation contribute to a noticeable increase in temperature profiles. The optimization process pinpoints the ideal conditions for maximizing heat transfer efficiency. From the results, it is observed that the rise in parameters of the heat source and radiation together significantly improves response function while the same trend is observed when both radiation and volume fraction rise from lower to higher values, the heat transfer rate improves notably. On the other hand, sensitivity analysis identifies the most influential parameters on the response function. It is shown from the results that heat transmission has the highest value of sensitivity (10.40125) towards volume fraction and the lowest value (−0.131640) of sensitivity towards thermal radiation. This investigation offers a deeper understanding of the mechanisms driving heat transfer in MHD hybrid nanofluid systems, paving the way for innovative designs in advanced thermal management applications. © 2024 Th

关键词： Finite Element Method (FEM) Galerkin weighted residual approach (GWRA) Quadratic Boussinesq approximation Response surface methodology Thermal radiation Variable suction

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Classification of Alzheimer's Disease using Attention UNet-MobileNet Model

Classification of Alzheimer's Disease using Attention UNet-M...

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Computing and information technology (ICCIT-1441), 2020 International Conference on

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Alzheimer's disease (ALD) is a degenerative and incurable neurological condition that impacts many individuals globally. Early identification and precise diagnosis of ALD are essential for efficient therapy and control of the condition. This research presents a transfer learning-based method for diagnosing ALD using magnetic resonance image (MRI) data. This research introduced a deep learning (DL) model known as MobileNet-v2 to accurately classify ALD using MRI images. The classification task involves the use of MIRIAD, a dataset consisting of MRI images specifically related to ALD. The collection comprises 708 MRI pictures pertaining to ALD and its different phases. The MRI pictures undergo preprocessing and are then split into training and testing sets, with 70% allocated for training and 30% for testing. Following the preprocessing stage, the segmentation procedure is carried out with the Attention UNet architecture. The segmented images are inputted into the MobileNet-v2 model for ALD classification. The classification is conducted as a binary classification, categorizing the data as either the presence or absence of ALD. The research model's performances are evaluated based on metrics including accuracy, specificity, sensitivity, f1-scores, and precision. The test results were compared to the existing models for validation. The research model has demonstrated superior performance when compared to all other models.

关键词： Training Image segmentation Sensitivity Accuracy Magnetic resonance imaging Computational modeling Transfer learning Data models Alzheimer's disease Testing

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FP-MOS: Frame-to-Frame Prediction for Dynamic Object Segmentation with LiDAR Data

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IEEE Internet of Things Journal 2025年

作者： Du, Nan Xie, Liangbo Zhou, Mu Ma, Yong Gao, Wei Chen, Zhipeng Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China Tangshan Normal University Department of Computer Science and Technology Tangshan063000 China Chongqing University of Posts and Telecommunications School of Optoelectronic Engineering Chongqing400065 China

As unmanned robotic operations become prevalent in various fields, the presence of moving objects in complex scenes poses challenges to key technologies such as environment mapping, obstacle avoidance, and trajectory prediction. In this paper, we propose FP-MOS, a LiDAR-based dynamic object segmentation framework that integrates prediction information to continuously capture dynamic object information under constrained data conditions. Firstly, a Prediction Module (PM) is employed to obtain predicted range images, enhancing the continuity of temporal information in the point cloud within the segmentation network. This improved continuity helps mitigate misjudgment issues caused by dynamic object turns and retrieves previously missed moving objects. Additionally, a motion attention weight guidance module is introduced, which accurately captures dynamic point cloud features through the transmission of point cloud weights between adjacent frames. Finally, we apply an improved Adaptive Local Outlier Factor (ALOF) method to filter out outliers in the segmentation results. Experimental results on the SemanticKITTI-MOS and Apollo datasets show that our method achieves leading Intersection over Union (IoU) scores of 79.3% and 79.0%, demonstrating the effectiveness of the FP-MOS method in network design and data optimization. © 2014 IEEE.

关键词： Image enhancement

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Internet of Things-Based Smart Precision Farming in Soilless Agriculture: Opportunities and Challenges for Global Food Security

arXiv

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arXiv 2025年

作者： Dutta, Monica Gupta, Deepali Tharewal, Sumegh Goyal, Deepam Sandhu, Jasminder Kaur Kaur, Manjit Alzubi, Ahmad Ali Alanazi, Jazem Mutared Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Bolzano39100 Italy School of Advance Computing DBS Global University Uttarakhand Dehradun248011 India School of Computer Science and Engineering IILM University Greater Noida201306 India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Department of Computer Science Community College King Saud University Riyadh11451 Saudi Arabia

The rapid growth of the global population and the continuous decline in cultivable land pose significant threats to food security. This challenge worsens as climate change further reduces the availability of farmland. Soilless agriculture, such as hydroponics, aeroponics, and aquaponics, offers a sustainable solution by enabling efficient crop cultivation in controlled environments. The integration of the Internet of Things (IoT) with smart precision farming improves resource efficiency, automates environmental control, and ensures stable and high-yield crop production. IoT-enabled smart farming systems utilize real-time monitoring, data-driven decision-making, and automation to optimize water and nutrient usage while minimizing human intervention. This paper aims to explore the opportunities and challenges of IoT-based soilless farming. It also highlights its role in sustainable agriculture, urban farming, and global food security. These advanced farming methods ensure greater productivity, resource conservation, and year-round cultivation. However, these methods also face challenges such as high initial investment, technological dependency, and energy consumption. Through a comprehensive study, bibliometric analysis, and comparative analysis, this research highlights current trends and research gaps. It also outlines future directions for researchers, policymakers, and industry stakeholders to drive innovation and scalability in IoT-driven soilless agriculture. By highlighting the benefits of vertical farming and Controlled Environment Agriculture (CEA)-enabled soilless techniques, this paper supports informed decision-making to address food security challenges and promote sustainable agricultural innovations. © 2025, CC BY.

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Bioelectric Signal Acquisition and Design of Portable ECG for Medical Internet of Things Applications 1st

Bioelectric Signal Acquisition and Design of Portable ECG fo...

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1st International Conference on Computing and Emerging Technologies, ICCET 2023

作者： Ashraf, Abrar Sahar, Sadia Iqbal, Muhammad Waseem Irfan, Danish Hamza, M. Ameer Joseph, Sylvester Iqbal, Muhammad Tassawar Department of Computer Science Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Information Technology Superior University Lahore Pakistan Trondheim Norway COMSATS University Islamabad Wah Campus Islamabad Pakistan

ISBN: (纸本)9783031776199

In contemporary times, heart diseases have emerged as the leading cause of untimely deaths. To tackle this alarming situation, engineers have developed a range of medical devices for diagnosing and scrutinizing various diseases. Numerous patients suffer from heart problems that pose critical threats to their lives, thereby necessitating continuous monitoring via traditional monitoring systems such as Electrocardiography (ECG). However, this technique is available only in hospitals, making it prohibitively expensive and inaccessible to remote patients. The advent of wireless technologies has paved the way for building a network of connected devices through the internet. To address this issue, a proposed ECG monitoring system comprises an AD8382 ECG sensor for reading patient data, an Arduino Uno, an ESP32 Wi-Fi module, and an IoT Blynk application. Implementing this healthcare system would enable doctors to remotely monitor patients using the IoT Blynk application installed on their smartphones, which processes and visualizes the patient's ECG signal. This monitoring process can be carried out at any time and from any location, without requiring patients to visit the hospital. To conclude, the compact and integrated system design could revolutionize the healthcare industry by making it more accessible and affordable to patients. By providing real-time monitoring, this system could help doctors diagnose and treat heart diseases more effectively. Therefore, it is imperative to promote such innovative technologies to improve healthcare services worldwide. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electrocardiography

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